[ { "id": "ENF.CONT.COEN.ATDR", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Alternative dispute resolution (0-3) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The alternative dispute resolution evaluates two aspects: (i) whether domestic commercial arbitration is regulated by law, all disputes can be submitted to arbitration and valid arbitration clauses are usually enforced by courts; and (ii) whether voluntary mediation and/or conciliation are a recognized way of resolving commercial disputes, they are regulated by law and there are financial incentives for parties to attempt mediation of conciliation. The index is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.ATFE.PR", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Attorney fees (% of claim)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The attorney fees are the fees that plaintiff must advance to a local attorney in the standardized case, regardless of final reimbursement." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.COST.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Cost (% of claim)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The cost to enforce contracts is recorded as a percentage of the claim value, assumed to be equivalent to 200% of income per capita or $5,000, whichever is greater. Three types of costs are recorded: average attorney fees, court costs and enforcement costs. Bribes are not taken into account." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.COST.ZS.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Score-Cost (% of claim)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for cost to enforce contracts benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.CSMG", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Case management (0-6) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The case management evaluates six aspects of the court case management system: (i) whether there are regulations setting time standards for key court events; (ii) whether there are regulations on adjournments and continuances; (iii) whether performance measurement reports are publicly available; (iv) whether a pretrial conference is available; (v) whether an electronic case management system for judges is available; and (vi) whether an electronic case management system for lawyers is available. The index is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.CTAU", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Court automation (0-4) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The court automation evaluates four aspects: (i) whether the initial complaint can be filed electronically; (ii) whether the initial complaint can be served electronically; (iii) whether court fees can be paid electronically; and (iv) whether judgments rendered in commercial matters are made available to the public. The index is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.CTFE.PR", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Court fees (% of claim)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The court fees include all costs that plaintiff must advance to the court, regardless of the final cost borne by plaintiff. Court costs include the fees that the parties must pay to obtain an expert opinion, regardless of whether they are paid to the court or to the expert directly." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.CTSP.DB1719", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Court structure and proceedings (0-5) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The court structure and proceedings evaluates five aspects of the court system: (i) whether a specialized commercial court, division or section is available; (ii) whether a small claims court and/or simplified procedure for small claims is available; (iii) whether pretrial attachment of defendant's movable assets is available; (iv) whether new cases are assigned randomly and through an automated system to judges; and (v) whether a woman’s testimony in court carries the same evidentiary weight as that of a man's. The index is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.DB0415.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Score-Enforcing contracts (DB04-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for enforcing contracts is the simple average of the scores for each of the component indicators: the procedures, time and cost for resolving a commercial dispute through a local first-instance court. The score is computed based on the methodology in the DB04-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.DB1719.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Score-Enforcing contracts (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for enforcing contracts is the simple average of the scores for each of the component indicators: the time and cost for resolving a commercial dispute through a local first-instance court, as well as the quality of judicial processes that promotes quality and efficiency in the court system. The score is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.ENFE.PR", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Enforcement fees (% of claim)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The enforcement fees are all costs that plaintiff must advance to enforce the judgment through a public sale of defendant’s movable assets, regardless of the final cost borne by plaintiff." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.ENJU.DY", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Enforcement of judgment (days)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time for enforcement of judgment captures the time from the moment the time to appeal has elapsed until the money is recovered by the winning party." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.FLSR.DY", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Filing and service (days)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time for filing and service captures the time from the moment plaintiff decides to sue until defendant is served." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.PROC.NO", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Procedures (number) (DB04-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The procedures to enforce contracts records the list of procedural steps compiled for each economy that traces the chronology of a commercial dispute before the relevant court. A procedure is defined as any interaction, required by law or commonly carried out in practice, between the parties or between them and the judge or court officer. The component indicator is computed based on the methodology in the DB04-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.PROC.NO.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Score-Procedures (number) (DB04-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for procedures to enforce contracts benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB04-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.QUJP.DB16.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Score-Quality of judicial processes index (0-18) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for quality of judicial processes index benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.QUJP.XD", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Quality of judicial processes index (0-18) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The quality of judicial processes index is the sum of the court structure and proceedings, case management, court automation and alternative dispute resolution. The index is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.RK.DB19", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Rank-Enforcing contracts" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The ranking of economies on the ease of enforcing contracts is determined by sorting their scores for enforcing contracts." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year. The country ranking is only available for the latest year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.COEN.TRJU.DY", "metatype": [ { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Trial and judgment (days)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time for trial and judgment captures the time from the moment defendant is served until the time to appeal has elapsed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Enforcing contracts" } ], "source_id": "1" }, { "id": "ENF.CONT.DURS.DY", "metatype": [ { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." } ], "source_id": "1" }, { "id": "ENF.CONT.DURS.DY.DFRN", "metatype": [ { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." } ], "source_id": "1" }, { "id": "IC.BUS.EASE.DFRN.DB1014", "metatype": [ { "id": "Developmentrelevance", "value": "Good rules create an environment where new entrants with drive and innovative ideas can get started in business and where productive firms can invest, expand and create new jobs. The role of government policy in the daily operations of small and medium-size domestic firms is a central focus of the Doing Business data. The objective is to encourage regulation that is efficient, transparent and easy to implement so that businesses can thrive and promote economic freedom and social progress. Doing Business data focus on the 10 areas of regulation affecting small and medium-size domestic firms in the largest business city of an economy. The project uses standardized case studies to provide objective, quantitative measures that can be compared across 190 economies." }, { "id": "IndicatorName", "value": "Ease of doing business score (DB10-14 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The ease of doing business score is the simple average of the scores for each of the Doing Business topics: starting a business, dealing with construction permits, getting electricity, registering property, getting credit, protecting minority investors, paying taxes, trading across borders, enforcing contracts and resolving insolvency. The score is computed based on the methodology in the DB10-14 studies for topics that underwent methodology updates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Ease of doing business" } ], "source_id": "1" }, { "id": "IC.BUS.EASE.DFRN.DB16", "metatype": [ { "id": "Developmentrelevance", "value": "Good rules create an environment where new entrants with drive and innovative ideas can get started in business and where productive firms can invest, expand and create new jobs. The role of government policy in the daily operations of small and medium-size domestic firms is a central focus of the Doing Business data. The objective is to encourage regulation that is efficient, transparent and easy to implement so that businesses can thrive and promote economic freedom and social progress. Doing Business data focus on the 10 areas of regulation affecting small and medium-size domestic firms in the largest business city of an economy. The project uses standardized case studies to provide objective, quantitative measures that can be compared across 190 economies." }, { "id": "IndicatorName", "value": "Ease of doing business score (DB15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The ease of doing business score is the simple average of the scores for each of the Doing Business topics: starting a business, dealing with construction permits, getting electricity, registering property, getting credit, protecting minority investors, paying taxes, trading across borders, enforcing contracts and resolving insolvency. The score is computed based on the methodology in the DB15 studies for topics that underwent methodology updates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Ease of doing business" } ], "source_id": "1" }, { "id": "IC.BUS.EASE.DFRN.XQ.DB1719", "metatype": [ { "id": "Developmentrelevance", "value": "Good rules create an environment where new entrants with drive and innovative ideas can get started in business and where productive firms can invest, expand and create new jobs. The role of government policy in the daily operations of small and medium-size domestic firms is a central focus of the Doing Business data. The objective is to encourage regulation that is efficient, transparent and easy to implement so that businesses can thrive and promote economic freedom and social progress. Doing Business data focus on the 10 areas of regulation affecting small and medium-size domestic firms in the largest business city of an economy. The project uses standardized case studies to provide objective, quantitative measures that can be compared across 190 economies." }, { "id": "IndicatorName", "value": "Ease of doing business score (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The ease of doing business score is the simple average of the scores for each of the Doing Business topics: starting a business, dealing with construction permits, getting electricity, registering property, getting credit, protecting minority investors, paying taxes, trading across borders, enforcing contracts and resolving insolvency. The score is computed based on the methodology in the DB17-20 studies for topics that underwent methodology updates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Ease of doing business" } ], "source_id": "1" }, { "id": "IC.BUS.EASE.XQ", "metatype": [ { "id": "Developmentrelevance", "value": "Good rules create an environment where new entrants with drive and innovative ideas can get started in business and where productive firms can invest, expand and create new jobs. The role of government policy in the daily operations of small and medium-size domestic firms is a central focus of the Doing Business data. The objective is to encourage regulation that is efficient, transparent and easy to implement so that businesses can thrive and promote economic freedom and social progress. Doing Business data focus on the 10 areas of regulation affecting small and medium-size domestic firms in the largest business city of an economy. The project uses standardized case studies to provide objective, quantitative measures that can be compared across 190 economies." }, { "id": "IndicatorName", "value": "Ease of Doing Business rank" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The ease of doing business ranking ranges from 1 to 190. The ranking of economies is determined by sorting the aggregate ease of doing business scores." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year. The country ranking is only available for the latest year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Ease of doing business" } ], "source_id": "1" }, { "id": "IC.CNST.LIR.XD.02.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "IndicatorName", "value": "Liability and insurance regimes index (0-2) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The liability and insurance regimes index evaluates the stringency of latent defect liability and insurance regimes. It has two components: (i) whether any parties involved in the construction process are held legally liable for latent defects such as structural flaws or problems in the building once it is in use; (ii) whether any party involved in the construction process is legally required to obtain a latent defect liability or decennial (10 years) liability insurance policy to cover possible structural flaws or problems in the building once it is in use. The index is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Dealing with construction permits" } ], "source_id": "1" }, { "id": "IC.CNST.PC.XD.04.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "IndicatorName", "value": "Professional certifications index (0-4) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The professional certifications index evaluates professional certi?cation requirements. It has two components: (i) the qualification requirements of the professional responsible for verifying that the architectural plans or drawings are in compliance with the building regulations; and (ii) the qualification requirements of the professional who conducts the technical inspections during construction. The index is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Dealing with construction permits" } ], "source_id": "1" }, { "id": "IC.CNST.PRMT.BQCI.015.DB1619.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "IndicatorName", "value": "Score-Building quality control index (0-15) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the building quality control index benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Dealing with construction permits" } ], "source_id": "1" }, { "id": "IC.CNST.PRMT.COST.WRH.VAL", "metatype": [ { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "IndicatorName", "value": "Cost (% of Warehouse value)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The cost records all official costs associated with completing the procedures to legally build a warehouse, including the costs associated with obtaining land use approvals and preconstruction design clearances; receiving inspections before, during and after construction; obtaining utility connections; and registering the warehouse at the property registry. It is calculated as a percentage of the warehouse value. Nonrecurring taxes required for the completion of the warehouse project are also recorded. Sales taxes (such as value added tax) or capital gains taxes are not recorded. Nor are deposits that must be paid up front and are later refunded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Dealing with construction permits" } ], "source_id": "1" }, { "id": "IC.CNST.PRMT.COST.WRH.VAL.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "IndicatorName", "value": "Score-Cost (% of Warehouse value)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for cost benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Dealing with construction permits" } ], "source_id": "1" }, { "id": "IC.CNST.PRMT.DFRN.DB0615", "metatype": [ { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "IndicatorName", "value": "Score-Dealing with construction permits (DB06-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for dealing with construction permits is the simple average of the scores for each of the component indicators: the procedures, time, cost to deal with construction permits, as well as the building quality control index that evaluate the quality of building regulations, the strength of quality control and safety mechanisms, liability and insurance regimes and professional certification requirements. The score is computed based on the methodology in the DB06-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Dealing with construction permits" } ], "source_id": "1" }, { "id": "IC.CNST.PRMT.DFRN.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "IndicatorName", "value": "Score-Dealing with construction permits (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for dealing with construction permits is the simple average of the scores for each of the component indicators: the procedures, time, cost to deal with construction permits, as well as the building quality control index that evaluate the quality of building regulations, the strength of quality control and safety mechanisms, liability and insurance regimes and professional certification requirements. The score is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Dealing with construction permits" } ], "source_id": "1" }, { "id": "IC.CNST.PRMT.PROC.NO", "metatype": [ { "id": "Developmentrelevance", "value": "Registered property rights are necessary to support investment, productivity and growth. Cadasters or surveys, together with land registries, are tools used around the world to map, prove and secure property and use rights. Keeping an up-to-date land information system is crucial as land and buildings account for between half and three-quarters of the wealth in most economies. Implementing an effective property registration system makes local owners more likely to invest in the economy. Formal property markets also increase domestic stability and decrease the likelihood of evictions in poor, urban areas, which is beneficial for employment and productivity." }, { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "IndicatorName", "value": "Procedures (number)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The number of procedures records all interactions of the building company’s employees or any party acting on its behalf with external parties, including government agencies, notaries, the land registry, the cadastre, utility companies and public inspectors. Procedures that the company undergoes to connect the warehouse to water and sewerage are included. All procedures that are legally required and that are done in practice by a majority of companies are counted, even if they may be avoided in exceptional cases." }, { "id": "Longdefinition", "value": "The number of procedures records all the procedures necessary for a business (the buyer) to purchase a property from another business (the seller) and to transfer the property title to the buyer’s name so that the buyer can use the property for expanding its business, use the property as collateral in taking new loans or, if necessary, sell the property to another business. A procedure is defined as any interaction that is legally or in practice required between the buyer, the seller or their agents (if required) and external parties, including government agencies, inspectors, notaries and lawyers." }, { "id": "Longdefinition", "value": "The number of procedures records all the procedures required in practice for businesses to obtain a new electrical connection. A procedure is defined as any interaction of the company’s employees or its main electrician or electrical engineer with external parties, such as the electricity distribution utility, electricity supply utilities, government agencies, electrical contractors and firms. Interactions between company employees and steps related to the internal electrical wiring, such as the design and execution of the internal electrical installation plans, are not counted as procedures. However, internal wiring inspections and certifications that are prerequisites to obtain a new connection are counted as procedures." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" }, { "id": "Topic", "value": "Registering property" }, { "id": "Topic", "value": "Dealing with construction permits" } ], "source_id": "1" }, { "id": "IC.CNST.PRMT.PROC.NO.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Registered property rights are necessary to support investment, productivity and growth. Cadasters or surveys, together with land registries, are tools used around the world to map, prove and secure property and use rights. Keeping an up-to-date land information system is crucial as land and buildings account for between half and three-quarters of the wealth in most economies. Implementing an effective property registration system makes local owners more likely to invest in the economy. Formal property markets also increase domestic stability and decrease the likelihood of evictions in poor, urban areas, which is beneficial for employment and productivity." }, { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "IndicatorName", "value": "Score-Procedures (number)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the number procedures benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Longdefinition", "value": "The score for the number of procedures benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" }, { "id": "Topic", "value": "Registering property" }, { "id": "Topic", "value": "Dealing with construction permits" } ], "source_id": "1" }, { "id": "IC.CNST.PRMT.QBR.XD.02.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "IndicatorName", "value": "Quality of building regulations index (0-2) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The quality of building regulations index evaluates the accessibility and transparency of building regulations. It has two components: (i) whether building regulations are easily accessible; and (ii) whether the requirements for obtaining a building permit are clearly specified. The index is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Dealing with construction permits" } ], "source_id": "1" }, { "id": "IC.CNST.PRMT.QCAC.XD.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "IndicatorName", "value": "Quality control after construction index (0-3) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The quality control after construction index evaluates quality control after the construction process. It has two components: (i) whether a final inspection is mandated by law to verify that the building was built in accordance with the approved plans and existing building regulations; and (ii) whether the final inspection is implemented in practice. The index is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Dealing with construction permits" } ], "source_id": "1" }, { "id": "IC.CNST.PRMT.QCBC.XD.01.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "IndicatorName", "value": "Quality control before construction index (0-1) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The quality control before construction index evaluates quality control in the review of the building plans. The index measures whether by law, a licensed architect or engineer is part of the committee or team that reviews and approves building permit applications and whether that person has the authority to refuse an application if the plans are not in conformity with regulations. The index is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Dealing with construction permits" } ], "source_id": "1" }, { "id": "IC.CNST.PRMT.QCDC.XD.03.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "IndicatorName", "value": "Quality control during construction index (0-3) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The quality control during construction index evaluates quality control during the construction process. It has two components: (i) whether inspections are mandated by law during the construction process; (ii) whether inspections during construction are implemented in practice. The index is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Dealing with construction permits" } ], "source_id": "1" }, { "id": "IC.CNST.PRMT.RK", "metatype": [ { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "IndicatorName", "value": "Rank-Dealing with construction permits" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The ranking of economies on the ease of dealing with construction permits is determined by sorting their scores for dealing with construction permits." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year. The country ranking is only available for the latest year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Dealing with construction permits" } ], "source_id": "1" }, { "id": "IC.CNST.PRMT.TM.DY", "metatype": [ { "id": "Developmentrelevance", "value": "Registered property rights are necessary to support investment, productivity and growth. Cadasters or surveys, together with land registries, are tools used around the world to map, prove and secure property and use rights. Keeping an up-to-date land information system is crucial as land and buildings account for between half and three-quarters of the wealth in most economies. Implementing an effective property registration system makes local owners more likely to invest in the economy. Formal property markets also increase domestic stability and decrease the likelihood of evictions in poor, urban areas, which is beneficial for employment and productivity." }, { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Time (days)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time captures the median duration that local experts indicate is necessary to complete a procedure in practice. It is calculated in calendar days. The time estimates of all procedures are added to calculate the total time required to complete each procedure, taking into account simultaneity of processes. It is assumed that the minimum time required for each procedure is one day, except for procedures that can be fully completed online, for which the time required is recorded as half a day." }, { "id": "Longdefinition", "value": "The time captures the median duration that property lawyers, notaries or registry officials indicate is necessary to complete a procedure. It is calculated in calendar days. The time estimates of all procedures are added to calculate the total time required to obtain transfer the property title, taking into account simultaneity of processes. It is assumed that the minimum time required for each procedure is one day, except for proce­dures that can be fully completed online, for which the time required is recorded as half a day." }, { "id": "Longdefinition", "value": "The time captures the median duration that the electricity utility and private sector electricity experts indicate is necessary in practice to complete all procedures required to obtain a new electricity connection with minimum follow-up and no extra payments. It is calculated in calendar days. The time estimates of all procedures are added to calculate the total time required to obtain a new electrical connection, taking into account simultaneity of processes. It is assumed that the minimum time required for each procedure is one day, except for procedures that can be fully completed online, for which the time required is recorded as half a day." }, { "id": "Longdefinition", "value": "The time to enforce contracts is recorded in calendar days, counted from the moment plaintiff decides to file the lawsuit in court until payment. The average duration of the following three different stages of dispute resolution is recorded: (i) filing and service, (ii) trial and judgment, and (iii) enforcement." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Dealing with construction permits" }, { "id": "Topic", "value": "Enforcing contracts" }, { "id": "Topic", "value": "Registering property" }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.CNST.PRMT.TM.DY.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "Developmentrelevance", "value": "Registered property rights are necessary to support investment, productivity and growth. Cadasters or surveys, together with land registries, are tools used around the world to map, prove and secure property and use rights. Keeping an up-to-date land information system is crucial as land and buildings account for between half and three-quarters of the wealth in most economies. Implementing an effective property registration system makes local owners more likely to invest in the economy. Formal property markets also increase domestic stability and decrease the likelihood of evictions in poor, urban areas, which is beneficial for employment and productivity." }, { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "Developmentrelevance", "value": "Efficient contract enforcement is essential to economic development and sustained growth. Economic and social progress cannot be achieved without respect for the rule of law and effective protection of rights, both of which require a well-functioning judiciary that resolves cases in a reasonable time and is predictable and accessible to the public. Economies with a more efficient judiciary, in which courts can effectively enforce contractual obligations, have more developed credit markets and a higher level of development overall. A stronger judiciary is also associated with more rapid growth of small firms. Overall, enhancing the efficiency of the judicial system can improve the business climate, foster innovation, attract foreign direct investment and secure tax revenues." }, { "id": "IndicatorName", "value": "Score-Time (days)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for time to enforce contracts benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Longdefinition", "value": "The score for time benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" }, { "id": "Topic", "value": "Registering property" }, { "id": "Topic", "value": "Enforcing contracts" }, { "id": "Topic", "value": "Dealing with construction permits" } ], "source_id": "1" }, { "id": "IC.CRED.ACC.ACES.DB0514", "metatype": [ { "id": "Developmentrelevance", "value": "The getting credit indicator set covers two aspects of access to finance—the strength of credit reporting systems and the effectiveness of collateral and bankruptcy laws in facilitating lending. These institutions and legal rights matter. The inability of lenders to accurately assess the creditworthiness of borrowers contributes to higher default rates and smaller loan portfolios. Lenders are also unwilling to provide credit if there is no guarantee that they will be able to enforce their rights and collect a debt or repossess collateral through a timely and inexpensive process." }, { "id": "IndicatorName", "value": "Getting Credit total score (DB05-14 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The total score for getting credit is the sum of the strength of legal rights index and the depth of credit information index, based on the methodology in the DB05-14 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting credit" } ], "source_id": "1" }, { "id": "IC.CRED.ACC.ACES.DB1519", "metatype": [ { "id": "Developmentrelevance", "value": "The getting credit indicator set covers two aspects of access to finance—the strength of credit reporting systems and the effectiveness of collateral and bankruptcy laws in facilitating lending. These institutions and legal rights matter. The inability of lenders to accurately assess the creditworthiness of borrowers contributes to higher default rates and smaller loan portfolios. Lenders are also unwilling to provide credit if there is no guarantee that they will be able to enforce their rights and collect a debt or repossess collateral through a timely and inexpensive process." }, { "id": "IndicatorName", "value": "Getting Credit total score (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The total score for getting credit is the sum of the strength of legal rights index and the depth of credit information index, based on the methodology in the DB15-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting credit" } ], "source_id": "1" }, { "id": "IC.CRED.ACC.CRD.DB0514.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "The getting credit indicator set covers two aspects of access to finance—the strength of credit reporting systems and the effectiveness of collateral and bankruptcy laws in facilitating lending. These institutions and legal rights matter. The inability of lenders to accurately assess the creditworthiness of borrowers contributes to higher default rates and smaller loan portfolios. Lenders are also unwilling to provide credit if there is no guarantee that they will be able to enforce their rights and collect a debt or repossess collateral through a timely and inexpensive process." }, { "id": "IndicatorName", "value": "Score-Getting credit (DB05-14 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for getting credit benchmarks economies with respect to the regulatory best practice on the indicator set. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB05-14 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting credit" } ], "source_id": "1" }, { "id": "IC.CRED.ACC.CRD.DB1519.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "The getting credit indicator set covers two aspects of access to finance—the strength of credit reporting systems and the effectiveness of collateral and bankruptcy laws in facilitating lending. These institutions and legal rights matter. The inability of lenders to accurately assess the creditworthiness of borrowers contributes to higher default rates and smaller loan portfolios. Lenders are also unwilling to provide credit if there is no guarantee that they will be able to enforce their rights and collect a debt or repossess collateral through a timely and inexpensive process." }, { "id": "IndicatorName", "value": "Score-Getting credit (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for getting credit benchmarks economies with respect to the regulatory best practice on the indicator set. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB15-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting credit" } ], "source_id": "1" }, { "id": "IC.CRED.ACC.CRD.RK", "metatype": [ { "id": "Developmentrelevance", "value": "The getting credit indicator set covers two aspects of access to finance—the strength of credit reporting systems and the effectiveness of collateral and bankruptcy laws in facilitating lending. These institutions and legal rights matter. The inability of lenders to accurately assess the creditworthiness of borrowers contributes to higher default rates and smaller loan portfolios. Lenders are also unwilling to provide credit if there is no guarantee that they will be able to enforce their rights and collect a debt or repossess collateral through a timely and inexpensive process." }, { "id": "IndicatorName", "value": "Rank-Getting credit" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The ranking of economies on the ease of getting credit is determined by sorting their scores for getting credit." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year. The country ranking is only available for the latest year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting credit" } ], "source_id": "1" }, { "id": "IC.CRED.ACC.DPTH.CISI.XD.06.DB0514", "metatype": [ { "id": "Developmentrelevance", "value": "Globally, 27% of firms identify access to finance as a major constraint while around 70% of formal SMEs in developing economies are estimated to be either unserved or underserved by the formal financial sector. Credit bureaus and registries are essential elements of the financial infrastructure that helps to address the issue of access to financial services, including credit. By sharing credit information, they help to reduce information asymmetries, increase access to credit for small firms, lower interest rates, improve borrower discipline and support bank supervision and credit risk monitoring." }, { "id": "IndicatorName", "value": "Depth of credit information index (0-6) (DB05-14 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The depth of credit information index measures the coverage, scope and accessibility of credit information available through credit reporting service providers such as credit bureaus or credit registries. The index ranges from 0 to 8 based on the methodology in the DB05-14 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting credit" } ], "source_id": "1" }, { "id": "IC.CRED.ACC.DPTH.CISI.XD.06.DB0514.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Globally, 27% of firms identify access to finance as a major constraint while around 70% of formal SMEs in developing economies are estimated to be either unserved or underserved by the formal financial sector. Credit bureaus and registries are essential elements of the financial infrastructure that helps to address the issue of access to financial services, including credit. By sharing credit information, they help to reduce information asymmetries, increase access to credit for small firms, lower interest rates, improve borrower discipline and support bank supervision and credit risk monitoring." }, { "id": "IndicatorName", "value": "Score-Depth of credit information index (0-6) (DB05-14 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the depth of credit information benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB05-14 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting credit" } ], "source_id": "1" }, { "id": "IC.CRED.ACC.DPTH.CISI.XD.08.DB1519", "metatype": [ { "id": "Developmentrelevance", "value": "Globally, 27% of firms identify access to finance as a major constraint while around 70% of formal SMEs in developing economies are estimated to be either unserved or underserved by the formal financial sector. Credit bureaus and registries are essential elements of the financial infrastructure that helps to address the issue of access to financial services, including credit. By sharing credit information, they help to reduce information asymmetries, increase access to credit for small firms, lower interest rates, improve borrower discipline and support bank supervision and credit risk monitoring." }, { "id": "IndicatorName", "value": "Depth of credit information index (0-8) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The depth of credit information index measures the coverage, scope and accessibility of credit information available through credit reporting service providers such as credit bureaus or credit registries. The index ranges from 0 to 8 based on the methodology in the DB15-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting credit" } ], "source_id": "1" }, { "id": "IC.CRED.ACC.DPTH.CISI.XD.08.DB1519.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Globally, 27% of firms identify access to finance as a major constraint while around 70% of formal SMEs in developing economies are estimated to be either unserved or underserved by the formal financial sector. Credit bureaus and registries are essential elements of the financial infrastructure that helps to address the issue of access to financial services, including credit. By sharing credit information, they help to reduce information asymmetries, increase access to credit for small firms, lower interest rates, improve borrower discipline and support bank supervision and credit risk monitoring." }, { "id": "IndicatorName", "value": "Score-Depth of credit information index (0-8) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the depth of credit information benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB15-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting credit" } ], "source_id": "1" }, { "id": "IC.CRED.ACC.LGL.RGHT.010.XD.DB0514.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Movable assets, rather than land or buildings, often comprise most of the capital stock of private firms; this is especially true for SMEs. In economies with a modern secured transactions system, these assets can easily be used as collateral. However, in many developing economies lenders may consider movable property to be an unacceptable form of collateral—either because the law does not recognize nonpossessory interests in movable collateral or because it does not provide sufficient protection for those lenders accepting it. This constraint matters: research shows that in developed economies borrowers with collateral obtain nine times as much credit as those without it. These borrowers also benefit from repayment periods 11 times as long and up to 50% lower interest rates." }, { "id": "IndicatorName", "value": "Score-Strength of legal rights index (0-10) (DB05-14 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the strength of legal rights benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB05-14 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting credit" } ], "source_id": "1" }, { "id": "IC.CRED.ACC.LGL.RGHT.012.XD.DB1519.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Movable assets, rather than land or buildings, often comprise most of the capital stock of private firms; this is especially true for SMEs. In economies with a modern secured transactions system, these assets can easily be used as collateral. However, in many developing economies lenders may consider movable property to be an unacceptable form of collateral—either because the law does not recognize nonpossessory interests in movable collateral or because it does not provide sufficient protection for those lenders accepting it. This constraint matters: research shows that in developed economies borrowers with collateral obtain nine times as much credit as those without it. These borrowers also benefit from repayment periods 11 times as long and up to 50% lower interest rates." }, { "id": "IndicatorName", "value": "Score-Strength of legal rights index (0-12) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the strength of legal rights benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB15-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting credit" } ], "source_id": "1" }, { "id": "IC.CRED.ACC.LGL.RGHT.XD.010.DB0514", "metatype": [ { "id": "Developmentrelevance", "value": "Movable assets, rather than land or buildings, often comprise most of the capital stock of private firms; this is especially true for SMEs. In economies with a modern secured transactions system, these assets can easily be used as collateral. However, in many developing economies lenders may consider movable property to be an unacceptable form of collateral—either because the law does not recognize nonpossessory interests in movable collateral or because it does not provide sufficient protection for those lenders accepting it. This constraint matters: research shows that in developed economies borrowers with collateral obtain nine times as much credit as those without it. These borrowers also benefit from repayment periods 11 times as long and up to 50% lower interest rates." }, { "id": "IndicatorName", "value": "Strength of legal rights index (0-10) (DB05-14 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The strength of legal rights index measures whether certain features that facilitate lending exist within the appli­cable collateral and bankruptcy laws. The index ranges from 0 to 10 based on the methodology in the DB05-14 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting credit" } ], "source_id": "1" }, { "id": "IC.CRED.ACC.LGL.RGHT.XD.012.DB1519", "metatype": [ { "id": "Developmentrelevance", "value": "Movable assets, rather than land or buildings, often comprise most of the capital stock of private firms; this is especially true for SMEs. In economies with a modern secured transactions system, these assets can easily be used as collateral. However, in many developing economies lenders may consider movable property to be an unacceptable form of collateral—either because the law does not recognize nonpossessory interests in movable collateral or because it does not provide sufficient protection for those lenders accepting it. This constraint matters: research shows that in developed economies borrowers with collateral obtain nine times as much credit as those without it. These borrowers also benefit from repayment periods 11 times as long and up to 50% lower interest rates." }, { "id": "IndicatorName", "value": "Strength of legal rights index (0-12) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The strength of legal rights index measures whether certain features that facilitate lending exist within the appli­cable collateral and bankruptcy laws. The index ranges from 0 to 12 based on the methodology in the DB15-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting credit" } ], "source_id": "1" }, { "id": "IC.CRED.ACC.PRVT.CRD.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Globally, 27% of firms identify access to finance as a major constraint while around 70% of formal SMEs in developing economies are estimated to be either unserved or underserved by the formal financial sector. Credit bureaus and registries are essential elements of the financial infrastructure that helps to address the issue of access to financial services, including credit. By sharing credit information, they help to reduce information asymmetries, increase access to credit for small firms, lower interest rates, improve borrower discipline and support bank supervision and credit risk monitoring." }, { "id": "IndicatorName", "value": "Credit bureau coverage (% of adults)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The credit bureau coverage reports the number of individuals and firms listed in a credit bureau’s database as of January 1 with information on their borrowing history from the past five years, and the number of individuals and firms that have had no borrowing history in the past five years but for which a lender requested a credit report from the bureau in the previous calendar year. The number is expressed as a percentage of the adult population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting credit" } ], "source_id": "1" }, { "id": "IC.CRED.ACC.PUBL.CRD.REG.COVR.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Globally, 27% of firms identify access to finance as a major constraint while around 70% of formal SMEs in developing economies are estimated to be either unserved or underserved by the formal financial sector. Credit bureaus and registries are essential elements of the financial infrastructure that helps to address the issue of access to financial services, including credit. By sharing credit information, they help to reduce information asymmetries, increase access to credit for small firms, lower interest rates, improve borrower discipline and support bank supervision and credit risk monitoring." }, { "id": "IndicatorName", "value": "Credit registry coverage (% of adults)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The credit registry coverage reports the number of individuals and firms listed in a credit registry’s database as of January 1 with information on their borrowing history from the past five years, and the number of individuals and firms that have had no borrowing history in the past five years but for which a lender requested a credit report from the registry in the previous calendar year. The number is expressed as a percentage of the adult population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting credit" } ], "source_id": "1" }, { "id": "IC.DCP.BQC.XD.015.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Good construction regulation matters for public safety since sound regulation of construction helps protect the public from faulty building practices. Efficient construction permitting and inspection systems can indeed strengthen property rights and contribute to the process of capital formation. If procedures are too complicated or too costly, builders are more likely to proceed without a permit, especially in developing economies. And because the construction permitting process generally involves licensing requirements from several different agencies, those seeking permits are exposed to different bureaucracies, which creates opportunities for rent-seeking. Overly complicated or costly construction rules can also increase opportunities for corruption." }, { "id": "IndicatorName", "value": "Building quality control index (0-15) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The building quality control index is the sum of the following six indices: quality of building regulations, quality control before construction, quality control during construction, quality control after construction, liability and insurance regimes and professional certifications. The component indicator is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Dealing with construction permits" } ], "source_id": "1" }, { "id": "IC.ELC.ACES.DFRN.DB1015", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "IndicatorName", "value": "Score-Getting electricity (DB10-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for getting electricity is the simple average of the scores for each of the component indicators: the procedures, time, cost for a business to obtain a permanent electricity connection and supply for a standardized warehouse, as well as the reliability of supply and transparency of tariffs index. The score is computed based on the methodology in the DB10-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.ELC.ACES.DFRN.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "IndicatorName", "value": "Score-Getting electricity (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for getting electricity is the simple average of the scores for each of the component indicators: the procedures, time, cost for a business to obtain a permanent electricity connection and supply for a standardized warehouse, as well as the reliability of supply and transparency of tariffs index. The score is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.ELC.ACES.RK.DB19", "metatype": [ { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year. The country ranking is only available for the latest year." } ], "source_id": "1" }, { "id": "IC.ELC.ACS.COST", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "IndicatorName", "value": "Cost (% of income per capita)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The cost is the total median cost associated with completing the procedures to connect a warehouse to electricity. It is calculated as a percentage of income per capita. All the fees and costs associated with completing the procedures to connect a warehouse to electricity are recorded, including those related to obtaining clearances from government agencies, applying for the connection, receiving inspections of both the site and the internal wiring, purchasing material, getting the actual connection works and paying a security deposit. Bribes are not included." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.ELC.ACS.COST.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "IndicatorName", "value": "Score-Cost (% of income per capita)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for cost benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.ELC.COMM.TRFF.CG.01.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "IndicatorName", "value": "Communication of tariffs and tariff changes (0-1) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The communication of tariffs and tariff changes index evaluates whether electricity tariffs are transparent. A score of 1 is assigned if effective tariffs are available online and customers are notified of a change in tariff a full billing cycle (that is, one month) ahead of time. The index is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.ELC.LMTG.OUTG.01.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "IndicatorName", "value": "Financial deterrents aimed at limiting outages (0-1) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "Th financial deterrents index evaluates whether financial deterrents exist to limit outages. A score of 1 is assigned if the utility compensates customers when outages exceed a certain cap, if the utility is fined by the regulator when outages exceed a certain cap or if both these conditions are met. The index is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.ELC.MONT.OUTG.01.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "IndicatorName", "value": "Mechanisms for monitoring outages (0-1) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The mechanisms for monitoring outages index evaluates what tools are used by the distribution utility to monitor power outages. A score of 1 is assigned if the utility uses automated tools, such as an Outage/Incident Management System (OMS/IMS) or Supervisory Control and Data Acquisition (SCADA) system. The index is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.ELC.OUTG.FREQ.DURS.03.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "IndicatorName", "value": "Total duration and frequency of outages per customer a year (0-3) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The total duration and frequency of outages per customer a year index evaluates the quality of the power supply. If SAIDI and SAIFI are 12 (equivalent to an outage of one hour each month) or below, a score of 1 is assigned. If SAIDI and SAIFI are 4 (equivalent to an outage of one hour each quarter) or below, 1 additional point is assigned. If SAIDI and SAIFI are 1 (equivalent to an outage of one hour per year) or below, 1 more point is assigned. The index is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.ELC.OUTG.MN.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "IndicatorName", "value": "Minimum outage time (in minutes) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The minimum outage time is the minimum time, in minutes, considered for the calculation of the SAIDI and SAIFI indices. If the minimum outage time exceeds 5 minutes, an economy is not eligible to obtain a score on the Reliability of supply and transparency of tariff index component. The index is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.ELC.PRI.KH.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "IndicatorName", "value": "Price of electricity (US cents per kWh) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The price of electricity is measured in U.S. cents per kWh. A monthly electricity consumption is assumed, for which a bill is then computed for a warehouse based in the largest business city of the economy for the month of March. The bill is then expressed back as a unit of kWh. The index is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.ELC.PROC.NO", "metatype": [ { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." } ], "source_id": "1" }, { "id": "IC.ELC.PROC.NO.DFRN", "metatype": [ { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." } ], "source_id": "1" }, { "id": "IC.ELC.REGU.MONT.01.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "IndicatorName", "value": "Regulatory monitoring (0-1) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The regulatory monitoring index evaluates whether a regulator—that is, an entity separate from the utility—monitors the utility’s performance on reliability of supply. A score of 1 is assigned if the regulator performs periodic or real-time reviews. The index is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.ELC.RSTOR.01.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "IndicatorName", "value": "Mechanisms for restoring service (0-1) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The mechanisms for restoring service index evaluates what tools are used by the distribution utility to restore power supply. A score of 1 is assigned if the utility uses automated tools, such as an OMS/IMS or SCADA system. The index is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.ELC.RSTT.XD.08.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "IndicatorName", "value": "Reliability of supply and transparency of tariff index (0-8) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The reliability of supply and transparency of tariff index is calculated on the basis of the following six components: (i) duration and frequency of power outages, (ii) tools to monitor power outages, (iii) tools to restore power supply, (iv) regulatory monitoring of utilities’ performance, (v) financial deterrents aimed at limiting outages, and (vi) transparency and accessibility of tariffs. An economy is eligible to obtain a score on the reliability of supply and transparency of tariffs index only if (i) the utility collects data on all types of outages (average total duration of outages per customer and the average number of outages per customer), including planned and unplanned outages, as well as load shedding, with the minimum outage time of not more than 5 minutes; and (ii) the SAIDI value is below a threshold of 100 hours and the SAIFI value is under 100 outages. The component indicator is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.ELC.RSTT.XD.08.DFRN.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "IndicatorName", "value": "Score-Reliability of supply and transparency of tariff index (0-8) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the reliability of supply and transparency of tariff index benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.ELC.SAID.XD.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "IndicatorName", "value": "System average interruption duration index (SAIDI) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The system average interruption duration index is the average total duration of outages (in hours) experienced by a customer in a year. The index is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.ELC.SAIF.XD.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Infrastructure services are a major concern for businesses around the world. Getting electricity services in particular is known to be a substantial obstacle to entrepreneurial activity. Power outages can severely hamper business activity as well as the economy as a whole. For example, power outages can lead to sale losses and decreases in productivity. Research also shows that capital tends to go to economies that can offer a reliable and competitively priced supply of electricity. That is why, by providing insight into the complex regulatory environment concerning electricity connections and measuring how regulations and institutions affect a firm's ability to get a new connection, Doing Business can thus help identify bottlenecks and prompt policy makers to introduce more conducive regulations." }, { "id": "IndicatorName", "value": "System average interruption frequency index (SAIFI) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The system average interruption frequency index (SAIFI) is the average number of service interruptions experienced by a customer in a year. The index is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Getting electricity" } ], "source_id": "1" }, { "id": "IC.ELC.TIME", "metatype": [ { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." } ], "source_id": "1" }, { "id": "IC.ELC.TIME.DFRN", "metatype": [ { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." } ], "source_id": "1" }, { "id": "IC.REG.COST.PC.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Where the rules are excessively burdensome, resource-constrained entrepreneurs might not have the opportunity to turn their ideas into a business. More generally, making it difficult to start a business may prevent an economy and its private sector from reaping the benefits of business formalization. In fact, a growing body of empirical research shows a positive correlation between business entry regulations and social and economic outcomes. Economies with high levels of formal entrepreneurship also benefit from higher levels of employment and enhanced economic growth. Moreover, as more businesses formalize, the tax base expands, which enables governments to spend on productivity-enhancing areas and pursue other social and economic policy objectives. Conversely, economies with overly cumbersome regulations for starting a business are associated with higher levels of informality and corruption as well as a smaller tax base." }, { "id": "IndicatorName", "value": "Cost - Women (% of income per capita)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The cost for women is the total cost required for five female married entrepreneurs to complete the procedures to incorporate and operate a business. It is calculated as a percentage of income per capita. All the fees and costs associated with completing the procedures to start a business are recorded, including all official fees and fees for legal and professional services, if such services are required by law or commonly used in practice. Only incorporation costs are counted, which excludes value added taxes and bribes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Starting a business" } ], "source_id": "1" }, { "id": "IC.REG.COST.PC.FE.ZS.DRFN", "metatype": [ { "id": "Developmentrelevance", "value": "Where the rules are excessively burdensome, resource-constrained entrepreneurs might not have the opportunity to turn their ideas into a business. More generally, making it difficult to start a business may prevent an economy and its private sector from reaping the benefits of business formalization. In fact, a growing body of empirical research shows a positive correlation between business entry regulations and social and economic outcomes. Economies with high levels of formal entrepreneurship also benefit from higher levels of employment and enhanced economic growth. Moreover, as more businesses formalize, the tax base expands, which enables governments to spend on productivity-enhancing areas and pursue other social and economic policy objectives. Conversely, economies with overly cumbersome regulations for starting a business are associated with higher levels of informality and corruption as well as a smaller tax base." }, { "id": "IndicatorName", "value": "Score-Cost - Women (% of income per capita)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the cost for women benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Starting a business" } ], "source_id": "1" }, { "id": "IC.REG.COST.PC.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Where the rules are excessively burdensome, resource-constrained entrepreneurs might not have the opportunity to turn their ideas into a business. More generally, making it difficult to start a business may prevent an economy and its private sector from reaping the benefits of business formalization. In fact, a growing body of empirical research shows a positive correlation between business entry regulations and social and economic outcomes. Economies with high levels of formal entrepreneurship also benefit from higher levels of employment and enhanced economic growth. Moreover, as more businesses formalize, the tax base expands, which enables governments to spend on productivity-enhancing areas and pursue other social and economic policy objectives. Conversely, economies with overly cumbersome regulations for starting a business are associated with higher levels of informality and corruption as well as a smaller tax base." }, { "id": "IndicatorName", "value": "Cost - Men (% of income per capita)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The cost for men is the total cost required for five male married entrepreneurs to complete the procedures to incorporate and operate a business. It is calculated as a percentage of income per capita. All the fees and costs associated with completing the procedures to start a business are recorded, including all official fees and fees for legal and professional services, if such services are required by law or commonly used in practice. Only incorporation costs are counted, which excludes value added taxes and bribes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Starting a business" } ], "source_id": "1" }, { "id": "IC.REG.COST.PC.MA.ZS.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Where the rules are excessively burdensome, resource-constrained entrepreneurs might not have the opportunity to turn their ideas into a business. More generally, making it difficult to start a business may prevent an economy and its private sector from reaping the benefits of business formalization. In fact, a growing body of empirical research shows a positive correlation between business entry regulations and social and economic outcomes. Economies with high levels of formal entrepreneurship also benefit from higher levels of employment and enhanced economic growth. Moreover, as more businesses formalize, the tax base expands, which enables governments to spend on productivity-enhancing areas and pursue other social and economic policy objectives. Conversely, economies with overly cumbersome regulations for starting a business are associated with higher levels of informality and corruption as well as a smaller tax base." }, { "id": "IndicatorName", "value": "Score-Cost - Men (% of income per capita)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the cost for men benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Starting a business" } ], "source_id": "1" }, { "id": "IC.REG.DFRN.PC.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Where the rules are excessively burdensome, resource-constrained entrepreneurs might not have the opportunity to turn their ideas into a business. More generally, making it difficult to start a business may prevent an economy and its private sector from reaping the benefits of business formalization. In fact, a growing body of empirical research shows a positive correlation between business entry regulations and social and economic outcomes. Economies with high levels of formal entrepreneurship also benefit from higher levels of employment and enhanced economic growth. Moreover, as more businesses formalize, the tax base expands, which enables governments to spend on productivity-enhancing areas and pursue other social and economic policy objectives. Conversely, economies with overly cumbersome regulations for starting a business are associated with higher levels of informality and corruption as well as a smaller tax base." }, { "id": "IndicatorName", "value": "Score-Paid-in Minimum capital (% of income per capita)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the paid-in minimum capital requirement benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Starting a business" } ], "source_id": "1" }, { "id": "IC.REG.DURS.FE.DY", "metatype": [ { "id": "Developmentrelevance", "value": "Where the rules are excessively burdensome, resource-constrained entrepreneurs might not have the opportunity to turn their ideas into a business. More generally, making it difficult to start a business may prevent an economy and its private sector from reaping the benefits of business formalization. In fact, a growing body of empirical research shows a positive correlation between business entry regulations and social and economic outcomes. Economies with high levels of formal entrepreneurship also benefit from higher levels of employment and enhanced economic growth. Moreover, as more businesses formalize, the tax base expands, which enables governments to spend on productivity-enhancing areas and pursue other social and economic policy objectives. Conversely, economies with overly cumbersome regulations for starting a business are associated with higher levels of informality and corruption as well as a smaller tax base." }, { "id": "IndicatorName", "value": "Time - Women (days)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time for women captures the median duration that business incorporation experts indicate is necessary for five female married entrepreneurs to complete all procedures required to start and operate a business with minimum follow-up and no extra payments. It is calulared in calendar days. The time estimates of all procedures are added to calculate the total time required to start and operate a business, taking into account simultaneity of processes. It is assumed that the minimum time required for each procedure is one day, except for procedures that can be fully completed online, for which the time required is recorded as half a day." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Starting a business" } ], "source_id": "1" }, { "id": "IC.REG.DURS.FE.DY.DRFN", "metatype": [ { "id": "Developmentrelevance", "value": "Where the rules are excessively burdensome, resource-constrained entrepreneurs might not have the opportunity to turn their ideas into a business. More generally, making it difficult to start a business may prevent an economy and its private sector from reaping the benefits of business formalization. In fact, a growing body of empirical research shows a positive correlation between business entry regulations and social and economic outcomes. Economies with high levels of formal entrepreneurship also benefit from higher levels of employment and enhanced economic growth. Moreover, as more businesses formalize, the tax base expands, which enables governments to spend on productivity-enhancing areas and pursue other social and economic policy objectives. Conversely, economies with overly cumbersome regulations for starting a business are associated with higher levels of informality and corruption as well as a smaller tax base." }, { "id": "IndicatorName", "value": "Score-Time - Women (days)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the time for women benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Starting a business" } ], "source_id": "1" }, { "id": "IC.REG.DURS.MA.DY", "metatype": [ { "id": "Developmentrelevance", "value": "Where the rules are excessively burdensome, resource-constrained entrepreneurs might not have the opportunity to turn their ideas into a business. More generally, making it difficult to start a business may prevent an economy and its private sector from reaping the benefits of business formalization. In fact, a growing body of empirical research shows a positive correlation between business entry regulations and social and economic outcomes. Economies with high levels of formal entrepreneurship also benefit from higher levels of employment and enhanced economic growth. Moreover, as more businesses formalize, the tax base expands, which enables governments to spend on productivity-enhancing areas and pursue other social and economic policy objectives. Conversely, economies with overly cumbersome regulations for starting a business are associated with higher levels of informality and corruption as well as a smaller tax base." }, { "id": "IndicatorName", "value": "Time - Men (days)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time for men captures the median duration that business incorporation experts indicate is necessary for five male married entrepreneurs to complete all procedures required to start and operate a business with minimum follow-up and no extra payments. It is calulared in calendar days. The time estimates of all procedures are added to calculate the total time required to start and operate a business, taking into account simultaneity of processes. It is assumed that the minimum time required for each procedure is one day, except for procedures that can be fully completed online, for which the time required is recorded as half a day." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Starting a business" } ], "source_id": "1" }, { "id": "IC.REG.DURS.MA.DY.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Where the rules are excessively burdensome, resource-constrained entrepreneurs might not have the opportunity to turn their ideas into a business. More generally, making it difficult to start a business may prevent an economy and its private sector from reaping the benefits of business formalization. In fact, a growing body of empirical research shows a positive correlation between business entry regulations and social and economic outcomes. Economies with high levels of formal entrepreneurship also benefit from higher levels of employment and enhanced economic growth. Moreover, as more businesses formalize, the tax base expands, which enables governments to spend on productivity-enhancing areas and pursue other social and economic policy objectives. Conversely, economies with overly cumbersome regulations for starting a business are associated with higher levels of informality and corruption as well as a smaller tax base." }, { "id": "IndicatorName", "value": "Score-Time - Men (days)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the time for men benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Starting a business" } ], "source_id": "1" }, { "id": "IC.REG.MIN.CAP", "metatype": [ { "id": "Developmentrelevance", "value": "Where the rules are excessively burdensome, resource-constrained entrepreneurs might not have the opportunity to turn their ideas into a business. More generally, making it difficult to start a business may prevent an economy and its private sector from reaping the benefits of business formalization. In fact, a growing body of empirical research shows a positive correlation between business entry regulations and social and economic outcomes. Economies with high levels of formal entrepreneurship also benefit from higher levels of employment and enhanced economic growth. Moreover, as more businesses formalize, the tax base expands, which enables governments to spend on productivity-enhancing areas and pursue other social and economic policy objectives. Conversely, economies with overly cumbersome regulations for starting a business are associated with higher levels of informality and corruption as well as a smaller tax base." }, { "id": "IndicatorName", "value": "Paid-in Minimum capital (% of income per capita)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The paid-in minimum capital requirement re?ects the amount that the entrepreneur needs to deposit in a bank or with a third-party before registration or up to three months after incorporation. It is calculated as percentage of income per capita. Any legal limitation of the company’s operations or decisions related to the payment of the minimum capital requirement is recorded. In case the legal minimum capital is provided per share, it is assumed 5 shareholders own the company and the legal minimum capital is multiplied by 5 shares. If an economy requires a minimum capital but allows businesses to pay only a part of it before registration, only this part is recorded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Starting a business" } ], "source_id": "1" }, { "id": "IC.REG.PROC.FE.NO", "metatype": [ { "id": "Developmentrelevance", "value": "Where the rules are excessively burdensome, resource-constrained entrepreneurs might not have the opportunity to turn their ideas into a business. More generally, making it difficult to start a business may prevent an economy and its private sector from reaping the benefits of business formalization. In fact, a growing body of empirical research shows a positive correlation between business entry regulations and social and economic outcomes. Economies with high levels of formal entrepreneurship also benefit from higher levels of employment and enhanced economic growth. Moreover, as more businesses formalize, the tax base expands, which enables governments to spend on productivity-enhancing areas and pursue other social and economic policy objectives. Conversely, economies with overly cumbersome regulations for starting a business are associated with higher levels of informality and corruption as well as a smaller tax base." }, { "id": "IndicatorName", "value": "Procedures - Women (number)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The number of procedures for women records all the procedures required in practice for five female married entrepreneurs to start and operate a local limited liability company. A procedure is de?ned as any interaction of the company founders with external parties or spouses (if legally required). Both pre- and post-incorporation procedures that are officially required or commonly done in practice are recorded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Starting a business" } ], "source_id": "1" }, { "id": "IC.REG.PROC.FE.NO.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Where the rules are excessively burdensome, resource-constrained entrepreneurs might not have the opportunity to turn their ideas into a business. More generally, making it difficult to start a business may prevent an economy and its private sector from reaping the benefits of business formalization. In fact, a growing body of empirical research shows a positive correlation between business entry regulations and social and economic outcomes. Economies with high levels of formal entrepreneurship also benefit from higher levels of employment and enhanced economic growth. Moreover, as more businesses formalize, the tax base expands, which enables governments to spend on productivity-enhancing areas and pursue other social and economic policy objectives. Conversely, economies with overly cumbersome regulations for starting a business are associated with higher levels of informality and corruption as well as a smaller tax base." }, { "id": "IndicatorName", "value": "Score-Procedures - Women (number)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the number of procedures for women benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Starting a business" } ], "source_id": "1" }, { "id": "IC.REG.PROC.MA.NO", "metatype": [ { "id": "Developmentrelevance", "value": "Where the rules are excessively burdensome, resource-constrained entrepreneurs might not have the opportunity to turn their ideas into a business. More generally, making it difficult to start a business may prevent an economy and its private sector from reaping the benefits of business formalization. In fact, a growing body of empirical research shows a positive correlation between business entry regulations and social and economic outcomes. Economies with high levels of formal entrepreneurship also benefit from higher levels of employment and enhanced economic growth. Moreover, as more businesses formalize, the tax base expands, which enables governments to spend on productivity-enhancing areas and pursue other social and economic policy objectives. Conversely, economies with overly cumbersome regulations for starting a business are associated with higher levels of informality and corruption as well as a smaller tax base." }, { "id": "IndicatorName", "value": "Procedures - Men (number)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The number of procedures for men records all the procedures required in practice for five male married entrepreneurs to start and operate a local limited liability company. A procedure is de?ned as any interaction of the company founders with external parties. Both pre- and post-incorporation procedures that are officially required or commonly done in practice are recorded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Starting a business" } ], "source_id": "1" }, { "id": "IC.REG.PROC.MA.NO.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Where the rules are excessively burdensome, resource-constrained entrepreneurs might not have the opportunity to turn their ideas into a business. More generally, making it difficult to start a business may prevent an economy and its private sector from reaping the benefits of business formalization. In fact, a growing body of empirical research shows a positive correlation between business entry regulations and social and economic outcomes. Economies with high levels of formal entrepreneurship also benefit from higher levels of employment and enhanced economic growth. Moreover, as more businesses formalize, the tax base expands, which enables governments to spend on productivity-enhancing areas and pursue other social and economic policy objectives. Conversely, economies with overly cumbersome regulations for starting a business are associated with higher levels of informality and corruption as well as a smaller tax base." }, { "id": "IndicatorName", "value": "Score-Procedures - Men (number)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the number of procedures for men benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Starting a business" } ], "source_id": "1" }, { "id": "IC.REG.PRRT.COST.PRT.VAL", "metatype": [ { "id": "Developmentrelevance", "value": "Registered property rights are necessary to support investment, productivity and growth. Cadasters or surveys, together with land registries, are tools used around the world to map, prove and secure property and use rights. Keeping an up-to-date land information system is crucial as land and buildings account for between half and three-quarters of the wealth in most economies. Implementing an effective property registration system makes local owners more likely to invest in the economy. Formal property markets also increase domestic stability and decrease the likelihood of evictions in poor, urban areas, which is beneficial for employment and productivity." }, { "id": "IndicatorName", "value": "Cost (% of property value)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The cost is the total of official costs associated with completing the procedures to transfer the property, expressed as a percentage of the property value, assumed to be equiva­lent to 50 times income per capita. It is calculted as a percentage of the property value. Only official costs required by law are recorded, including fees, transfer taxes, stamp duties and any other payment to the property registry, notaries, public agencies or lawyers. Other taxes, such as capital gains tax or value added tax, are excluded from the cost measure. Both costs borne by the buyer and the seller are included. If cost estimates differ among sources, the median reported value is used." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Registering property" } ], "source_id": "1" }, { "id": "IC.REG.PRRT.COST.PRT.VAL.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Registered property rights are necessary to support investment, productivity and growth. Cadasters or surveys, together with land registries, are tools used around the world to map, prove and secure property and use rights. Keeping an up-to-date land information system is crucial as land and buildings account for between half and three-quarters of the wealth in most economies. Implementing an effective property registration system makes local owners more likely to invest in the economy. Formal property markets also increase domestic stability and decrease the likelihood of evictions in poor, urban areas, which is beneficial for employment and productivity." }, { "id": "IndicatorName", "value": "Score-Cost (% of property value)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for cost benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Registering property" } ], "source_id": "1" }, { "id": "IC.REG.PRRT.DFRN.DB0515", "metatype": [ { "id": "Developmentrelevance", "value": "Registered property rights are necessary to support investment, productivity and growth. Cadasters or surveys, together with land registries, are tools used around the world to map, prove and secure property and use rights. Keeping an up-to-date land information system is crucial as land and buildings account for between half and three-quarters of the wealth in most economies. Implementing an effective property registration system makes local owners more likely to invest in the economy. Formal property markets also increase domestic stability and decrease the likelihood of evictions in poor, urban areas, which is beneficial for employment and productivity." }, { "id": "IndicatorName", "value": "Score-Registering property (DB05-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for registering property is the simple average of the scores for each of the component indicators: the procedures, time, cost to transfer property between two local companies, as well as the quality of land administration index that evaluates the reliability of infrastructure, transparency of information, geographic coverage, land dispute resolution and equal access to property rights. The score is computed based on the methodology in the DB05-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Registering property" } ], "source_id": "1" }, { "id": "IC.REG.PRRT.DFRN.DB1719", "metatype": [ { "id": "Developmentrelevance", "value": "Registered property rights are necessary to support investment, productivity and growth. Cadasters or surveys, together with land registries, are tools used around the world to map, prove and secure property and use rights. Keeping an up-to-date land information system is crucial as land and buildings account for between half and three-quarters of the wealth in most economies. Implementing an effective property registration system makes local owners more likely to invest in the economy. Formal property markets also increase domestic stability and decrease the likelihood of evictions in poor, urban areas, which is beneficial for employment and productivity." }, { "id": "IndicatorName", "value": "Score-Registering property (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for registering property is the simple average of the scores for each of the component indicators: the procedures, time, cost to transfer property between two local companies, as well as the quality of land administration index that evaluates the reliability of infrastructure, transparency of information, geographic coverage, land dispute resolution and equal access to property rights. The score is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Registering property" } ], "source_id": "1" }, { "id": "IC.REG.PRRT.DURS.TM", "metatype": [ { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." } ], "source_id": "1" }, { "id": "IC.REG.PRRT.DURS.TM.DRFN", "metatype": [ { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." } ], "source_id": "1" }, { "id": "IC.REG.PRRT.EQACCS.XD.08.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Registered property rights are necessary to support investment, productivity and growth. Cadasters or surveys, together with land registries, are tools used around the world to map, prove and secure property and use rights. Keeping an up-to-date land information system is crucial as land and buildings account for between half and three-quarters of the wealth in most economies. Implementing an effective property registration system makes local owners more likely to invest in the economy. Formal property markets also increase domestic stability and decrease the likelihood of evictions in poor, urban areas, which is beneficial for employment and productivity." }, { "id": "IndicatorName", "value": "Equal access to property rights index (-2-0) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The equal access to property rights index evaluates whether married or unmarried women have equal access to property rights. It has two components: (i) whether unmarried men and unmar­ried women have equal ownership rights to property; and (ii) whether married men and married women have equal ownership rights to property. The index is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Registering property" } ], "source_id": "1" }, { "id": "IC.REG.PRRT.GEO.COVR.XD.08.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Registered property rights are necessary to support investment, productivity and growth. Cadasters or surveys, together with land registries, are tools used around the world to map, prove and secure property and use rights. Keeping an up-to-date land information system is crucial as land and buildings account for between half and three-quarters of the wealth in most economies. Implementing an effective property registration system makes local owners more likely to invest in the economy. Formal property markets also increase domestic stability and decrease the likelihood of evictions in poor, urban areas, which is beneficial for employment and productivity." }, { "id": "IndicatorName", "value": "Geographic coverage index (0-8) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The geographic coverage index assesses the extent to which the land registry and cadastre provide complete geographic coverage of privately held land parcels. It has four components: (i) how complete the coverage of the land registry is at the level of the largest business city; (ii) how complete the coverage of the land registry is at the level of the economy; (iii) how complete the coverage of the mapping agency is at the level of the largest business city; and (iv) how complete the coverage of the mapping agency is at the level of the economy. The index is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Registering property" } ], "source_id": "1" }, { "id": "IC.REG.PRRT.LAND.DISP.XD.08.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Registered property rights are necessary to support investment, productivity and growth. Cadasters or surveys, together with land registries, are tools used around the world to map, prove and secure property and use rights. Keeping an up-to-date land information system is crucial as land and buildings account for between half and three-quarters of the wealth in most economies. Implementing an effective property registration system makes local owners more likely to invest in the economy. Formal property markets also increase domestic stability and decrease the likelihood of evictions in poor, urban areas, which is beneficial for employment and productivity." }, { "id": "IndicatorName", "value": "Land dispute resolution index (0-8) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The land dispute resolution index measures the accessibility of conflict resolution mechanisms and the extent of liability for entities or agents recording land transactions. It has eight components: (i) whether the law requires that all property sale transactions be regis­tered at the immovable property registry to make them opposable to third parties; (ii) whether the formal system of immovable property registration is subject to a guarantee; (iii) whether there is a specific, out-of-court compensation mechanism to cover for losses incurred by parties who engaged in good faith in a prop­erty transaction based on erroneous information certified by the immov­able property registry; (iv) whether the legal system requires verification of the legal validity of the documents (such as the sales, transfer or conveyance deed) necessary for a property transaction; (v) whether the legal system requires verification of the identity of the parties to a property transaction; (vi) whether there is a national database to verify the accuracy of identity documents; (vii) how much time it takes to obtain a decision from a court of first instance (without an appeal) in a standard land dispute between two local busi­nesses over tenure rights worth 50 times income per capita and located in the largest business city; and (viii) whether there are publicly avail­able statistics on the number of land disputes in the first instance. The index is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Registering property" } ], "source_id": "1" }, { "id": "IC.REG.PRRT.PROC.NO", "metatype": [ { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." } ], "source_id": "1" }, { "id": "IC.REG.PRRT.PROC.NO.DFRN", "metatype": [ { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." } ], "source_id": "1" }, { "id": "IC.REG.PRRT.QUAL.LNDADM.XD.030.DB16", "metatype": [ { "id": "Developmentrelevance", "value": "Registered property rights are necessary to support investment, productivity and growth. Cadasters or surveys, together with land registries, are tools used around the world to map, prove and secure property and use rights. Keeping an up-to-date land information system is crucial as land and buildings account for between half and three-quarters of the wealth in most economies. Implementing an effective property registration system makes local owners more likely to invest in the economy. Formal property markets also increase domestic stability and decrease the likelihood of evictions in poor, urban areas, which is beneficial for employment and productivity." }, { "id": "IndicatorName", "value": "Quality of land administration index (0-30) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The quality of land administration index is the sum of the reliability of infrastructure, transparency of information, geographic coverage, land dispute resolution and equal access to property rights. The score is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Registering property" } ], "source_id": "1" }, { "id": "IC.REG.PRRT.QUAL.LNDADM.XD.030.DB16.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Registered property rights are necessary to support investment, productivity and growth. Cadasters or surveys, together with land registries, are tools used around the world to map, prove and secure property and use rights. Keeping an up-to-date land information system is crucial as land and buildings account for between half and three-quarters of the wealth in most economies. Implementing an effective property registration system makes local owners more likely to invest in the economy. Formal property markets also increase domestic stability and decrease the likelihood of evictions in poor, urban areas, which is beneficial for employment and productivity." }, { "id": "IndicatorName", "value": "Score-Quality of land administration index (0-30) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the quality of land administration index benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Registering property" } ], "source_id": "1" }, { "id": "IC.REG.PRRT.REG.RK.DB19", "metatype": [ { "id": "Developmentrelevance", "value": "Registered property rights are necessary to support investment, productivity and growth. Cadasters or surveys, together with land registries, are tools used around the world to map, prove and secure property and use rights. Keeping an up-to-date land information system is crucial as land and buildings account for between half and three-quarters of the wealth in most economies. Implementing an effective property registration system makes local owners more likely to invest in the economy. Formal property markets also increase domestic stability and decrease the likelihood of evictions in poor, urban areas, which is beneficial for employment and productivity." }, { "id": "IndicatorName", "value": "Rank-Registering property" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The ranking of economies on the ease of registering property is determined by sorting their scores for registering property." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year. The country ranking is only available for the latest year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Registering property" } ], "source_id": "1" }, { "id": "IC.REG.PRRT.RELI.INFR.XD.09.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Registered property rights are necessary to support investment, productivity and growth. Cadasters or surveys, together with land registries, are tools used around the world to map, prove and secure property and use rights. Keeping an up-to-date land information system is crucial as land and buildings account for between half and three-quarters of the wealth in most economies. Implementing an effective property registration system makes local owners more likely to invest in the economy. Formal property markets also increase domestic stability and decrease the likelihood of evictions in poor, urban areas, which is beneficial for employment and productivity." }, { "id": "IndicatorName", "value": "Reliability of infrastructure index (0-8) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The reliability of infrastructure index assesses whether the land registry and cadaster have adequate infrastructure to guarantee high standards and reduce the risk of errors. It has six components: (i) how land titles are kept at the registry of the largest business city of the economy; (ii) whether there is an electronic database for checking for encumbrances; (iii) how maps of land plots are kept at the mapping agency of the largest business city of the economy; (iv) whether there is a geographic information system—an electronic database for recording boundaries, checking plans and providing cadastral information; (v) how the land ownership registry and mapping agency are linked; and (vi) how immovable property is identified. The index is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Registering property" } ], "source_id": "1" }, { "id": "IC.REG.PRRT.TRAP.INFO.XD.06.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Registered property rights are necessary to support investment, productivity and growth. Cadasters or surveys, together with land registries, are tools used around the world to map, prove and secure property and use rights. Keeping an up-to-date land information system is crucial as land and buildings account for between half and three-quarters of the wealth in most economies. Implementing an effective property registration system makes local owners more likely to invest in the economy. Formal property markets also increase domestic stability and decrease the likelihood of evictions in poor, urban areas, which is beneficial for employment and productivity." }, { "id": "IndicatorName", "value": "Transparency of information index (0-6) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The transparency of information index evaluates whether the land administration system makes land-related information publicly available. It has ten components: (i) whether information on land ownership is made publicly available; (ii) whether the list of documents required for completing the registra­tion of property transactions is made publicly available; (iii) whether the fee schedule for completing the registration of prop­erty transactions is made publicly available; (iv) whether the agency in charge of immovable property registration commits to a specific time frame for delivering a legally binding document that proves property ownership; (v) whether there is a specific and independent mechanism for filing complaints about a problem that occurred at the agency in charge of immovable property registration; (vi) whether there are publicly available official statistics tracking the number of transactions at the immovable property registration agency; (vii) whether maps of land plots are made publicly available; (viii) whether the fee schedule for accessing maps is made publicly available; and (ix) whether the mapping agency commits to a specific time frame for delivering an updated map; and (x) whether there is a specific and independent mechanism for filing complaints about a problem that occurred at the mapping agency. The index is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Registering property" } ], "source_id": "1" }, { "id": "IC.REG.STRT.BUS.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Where the rules are excessively burdensome, resource-constrained entrepreneurs might not have the opportunity to turn their ideas into a business. More generally, making it difficult to start a business may prevent an economy and its private sector from reaping the benefits of business formalization. In fact, a growing body of empirical research shows a positive correlation between business entry regulations and social and economic outcomes. Economies with high levels of formal entrepreneurship also benefit from higher levels of employment and enhanced economic growth. Moreover, as more businesses formalize, the tax base expands, which enables governments to spend on productivity-enhancing areas and pursue other social and economic policy objectives. Conversely, economies with overly cumbersome regulations for starting a business are associated with higher levels of informality and corruption as well as a smaller tax base." }, { "id": "IndicatorName", "value": "Score-Starting a business" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for starting a business is the simple average of the scores for each of the component indicators: the procedures, time and cost for an entrepreneur to start and formally operate a business, as well as the paid-in minimum capital requirement." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Starting a business" } ], "source_id": "1" }, { "id": "IC.REG.STRT.BUS.RK.DB19", "metatype": [ { "id": "Developmentrelevance", "value": "Where the rules are excessively burdensome, resource-constrained entrepreneurs might not have the opportunity to turn their ideas into a business. More generally, making it difficult to start a business may prevent an economy and its private sector from reaping the benefits of business formalization. In fact, a growing body of empirical research shows a positive correlation between business entry regulations and social and economic outcomes. Economies with high levels of formal entrepreneurship also benefit from higher levels of employment and enhanced economic growth. Moreover, as more businesses formalize, the tax base expands, which enables governments to spend on productivity-enhancing areas and pursue other social and economic policy objectives. Conversely, economies with overly cumbersome regulations for starting a business are associated with higher levels of informality and corruption as well as a smaller tax base." }, { "id": "IndicatorName", "value": "Rank-Starting a business" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The ranking of economies on the ease of starting a business is determined by sorting their scores for starting a business." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year. The country ranking is only available for the latest year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Starting a business" } ], "source_id": "1" }, { "id": "OTHR.TAX.PAID.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Other taxes (% of profit)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The other taxes measures all other taxes and fees that are borne by the business in the second year of operation, expressed as a share of commercial pro?t. This includes property taxes, turnover taxes and other taxes (such as municipal fees and vehicle taxes)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.COIT.AU.HRS.DB1719", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Time to comply with a corporate income tax correction (hours) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time to comply with a corporate income tax correction measures the time spent preparing and submitting the correction, and the time spent preparing information for the tax officers, if, in 25% or more of cases, a company that voluntarily reports an error in its CIT return and an underpayment of the tax due would be selected for additional review. The component indicator is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.COIT.AU.HRS.TM.DB1719.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Score-Time to comply with a corporate income tax correction (hours) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for time to comply with a corporate income tax correction benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.COIT.AU.WKS.DB1719", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Time to complete a corporate income tax correction (weeks) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "Time to complete a corporate income tax correction (weeks) (DB17-20 methodology) measures the time to complete a review by the tax authority including a formal tax audit if in 25% or more of cases, a company that voluntarily reports an error in its corporate income tax return and an underpayment of the tax due would be selected for additional review. The component indicator is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.COIT.WKS.TM.DB1719.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Score-Time to complete a corporate income tax correction (weeks) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for time to complete with a corporate income tax correction benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.DB0616.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Score-Paying taxes (DB06-16 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for paying taxes is the simple average of the scores for each of the component indicators, the payments, time and total tax and contribution rate for a company to comply with tax laws in an economy. The score is computed based on the methodology in the DB06-16 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.DB1719.DRFN", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Score-Paying taxes (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for paying taxes is the simple average of the scores for each of the component indicators, the payments, time and total tax and contribution rate for a company to comply with tax laws in an economy, as well as the postfiling procedures to request and process a VAT refund claim and to comply with and complete a corporate income tax correction. The score is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.LABR.TAX.CONTR.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Labor tax and contributions (% of profit)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The labor tax and contributions measures all government mandated labor contributions that are borne by the business in the second year of operation, expressed as a share of commercial pro?t." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.POST.FIL.XD.0100.DB1719.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Score-Postfiling index (0-100) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for postfiling index benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.PRFT.CP.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Profit tax (% of profit)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The profit tax measures the amount of income taxes borne by the business in the second year of operation, expressed as a share of commercial pro?t." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.PYMT.FREQ.NO", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Payments (number per year)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The tax payments capture the total number of taxes and contributions paid, the method of payment, the frequency of payment, and the frequency of ?ling. It includes taxes withheld by the company, such as sales tax, VAT and employee-borne labor taxes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.PYMT.NO.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Score-Payments (number per year)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for tax payments benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.RK.DB19", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Rank-Paying taxes" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The ranking of economies on the ease of paying taxes is determined by sorting their scores for paying taxes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year. The country ranking is only available for the latest year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.TM", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Time (hours per year)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time to comply with tax laws measures the time taken to prepare, ?le and pay three major types of taxes and contributions: the corporate income tax, value added or sales tax and labor taxes, including payroll taxes and social contributions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.TM.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Score-Time (hours per year)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for time to comply with tax laws benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.TOT.TAX.RT.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Total tax and contribution rate (% of profit)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The total tax and contribution rate measures the amount of taxes and mandatory contributions borne by the business in the second year of operation, expressed as a share of commercial pro?t. The total amount of taxes and contributions borne is the sum of all the different taxes and contributions payable after accounting for allowable deductions and exemptions. The taxes withheld (such as personal income tax) or collected by the company and remitted to the tax authorities (such as VAT, sales tax or goods and service tax) but not borne by the company are excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.TOT.TAX.RT.ZS.DRFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Score-Total tax and contribution rate (% of profit)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for total tax and contribution rate benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.VAT.REF.OBT.WKS.TM.DB1719", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Time to obtain VAT refund (weeks) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time to obtain VAT refund measures the time from purchase of the machine to the date of submission of the refund claim (this is equal to half the filing period), the length of any mandatory period that the excess output VAT must be carried forward before a claim can be made, and the time from the submission of the VAT refund claim to the date the refund is received. If a company that requests a VAT cash refund arising from a capital purchase would be selected for additional review in 50% or more of cases, the duration of the review is included in the time to obtain a VAT refund. The component indicator is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.VAT.REF.OBT.WKS.TM.DB1719.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Score-Time to obtain VAT refund (weeks) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for time to obtain VAT refund benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.VAT.REFU.COMP.HRS.TM.DB1719", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Time to comply with VAT refund (hours) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time to comply with VAT refund measures the time spent preparing and submitting the refund claim and the time spent preparing information for the tax officers, if, in 50% or more of cases, a company that requests a VAT cash refund arising from a capital purchase would be selected for an additional review. The component indicator is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PAY.TAX.VAT.REFU.COMP.HRS.TM.DB1719.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Governments need sustainable sources of funding for social programs and public investments to foster economic growth and development. The amount of the tax cost for businesses matters for investment and growth. Keeping tax rates at a reasonable level can encourage the development of the private sector and the formalization of businesses. Efficient tax administration can help encourage businesses to become formally registered, thereby expanding the tax base and increasing tax revenues." }, { "id": "IndicatorName", "value": "Score-Time to comply with VAT refund (hours) (DB17-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for time to comply with VAT refund benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB17-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Paying taxes" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.DFRN.DB0614", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Score-Protecting minority investors (DB06-14 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for protecting minority investors benchmarks economies with respect to the regulatory best practice on the indicator set. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB06-14 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.DFRN.DB1519", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Score-Protecting minority investors (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for protecting minority investors benchmarks economies with respect to the regulatory best practice on the indicator set. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB15-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.EASE.SHARE.LGL.XD.010.DB0614", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Ease of shareholder suits index (0-10) (DB06-14 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The ease of shareholder suits index measures how likely plaintiffs are to access internal corporate evidence. It has six components: (i) whether shareholders owning 10% of the company’s share capital have the right to inspect the Buyer-Seller transaction documents before filing a suit; (ii) whether shareholders owning 10% of the company’s share capital can request that a government inspector investigate the Buyer-Seller transaction without filing a suit; (iii) what range of documents is available to the shareholder plaintiff from the defendant and witnesses during trial; (iv) whether the plaintiff can obtain cate­gories of relevant documents from the defendant without identifying each document specifically; (v) whether the plaintiff can directly examine the defendant and witnesses during trial (0-2); and (vi) whether the standard of proof for civil suits is lower than that for criminal cases. The index is computed based on the methodology in the DB06-14 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.EASE.SHARE.LGL.XD.010.DB1519", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Ease of shareholder suits index (0-10) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The ease of shareholder suits index measures how likely plaintiffs are to access internal corporate evidence and recover legal expenses. It has six components: (i) whether shareholders owning 10% of the company’s share capital have the right to inspect the Buyer-Seller transaction documents before filing a suit. Alternatively, whether they can request that a government inspector investigate the Buyer-Seller transaction without filing a suit; (ii) what range of documents is available to the shareholder plaintiff from the defendant and witnesses during trial; (iii) whether the plaintiff can obtain cate­gories of relevant documents from the defendant without identifying each document specifically; (iv) whether the plaintiff can directly examine the defendant and witnesses during trial; (v) whether the standard of proof for civil suits is lower than that for criminal cases; and (vi) whether shareholder plaintiffs can recover their legal expenses from the company. The index is computed based on the methodology in the DB15-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.EASE.SSI.XD.0010.DB0614.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Score-Ease of shareholder suits index (0-10) (DB06-14 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for ease of shareholder suits index benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB06-14 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.EASE.SSI.XD.0010.DB1519.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Score-Ease of shareholder suits index (0-10) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for ease of shareholder suits index benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB15-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.EXT.BUS.DISC.010.XD", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Extent of disclosure index (0-10)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The extent of disclosure index measures the approval and disclosure requirements of related-party transactions. It has five components: (i) whether it is the managing director alone, the board of directors, or the general meeting of shareholders the corporate body who can provide legally sufficient approval for the transaction (points are assigned depending on whether interested directors are permitted to vote or not); (ii) whether an external body (an independent auditor, for example) must review the transaction before it takes place; (iii) whether disclosure by Mr. James to the board of directors or the supervisory board is required; (iv) whether immediate disclosure of the transaction to the public, the regulator or the shareholders is required; and (v) whether disclosure in periodic filings (for example, annual reports) is required." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.EXT.CORP.TRANP.XD.0010.DB1519", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Extent of corporate transparency index (0-7) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The extent of corporate transparency index measures the level of information that companies must share regarding their board members, senior executives, annual meetings and audits. This index has seven components: (i) whether Buyer must disclose direct and indirect beneficial ownership stakes representing 5%; (ii) whether Buyer must disclose information about board members’ primary employment and director­ships in other companies; (iii) whether Buyer must disclose the compensation of individual managers; (iv) whether a detailed notice of general meeting must be sent 21 calendar days before the meeting; (v) whether shareholders representing 5% of Buyer’s share capital can put items on the general meeting agenda; (vi) whether Buyer’s annual financial statements must be audited by an external auditor; (vii) whether Buyer must disclose its audit reports to the public. The index is computed based on the methodology in the DB15-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.EXT.CORP.TRANS.XD.010.DB1519.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Score-Extent of corporate transparency index (0-7) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for extent of corporate transparency index benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB15-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.EXT.DIR.LBL.010.XD.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Score-Extent of director liability index (0-10)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for extent of director liability index benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.EXT.DISC.010.XD.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Score-Extent of disclosure index (0-10)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for extent of disclosure index benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.EXT.OWNR.CONT.XD.0100.DB1519", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Extent of ownership and control index (0-7) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The extent of ownership and control index measures the rules governing the structure and change in control of companies. This index has seven components: (i) whether the same individual cannot be appointed CEO and chairperson of the board of directors; (ii) whether the board of directors must include independent nonexecutive board members; (iii) whether shareholder can remove members of the board of directors without cause before the end of their term; (iv) whether the board of directors must have an audit committee; (v) whether a potential acquirer must make a tender offer to all shareholders upon acquiring 50% of Buyer; (vi) whether Buyer must pay declared dividends within a maximum period set by law; (vii) whether a subsidiary cannot acquire shares issued by its parent company. The index is computed based on the methodology in the DB15-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.EXT.OWNR.CONTL.010.XD.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Score-Extent of ownership and control index (0-7) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for extent of ownership and control index benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB15-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.EXT.SHARE.RTS.XD.010.DB1519", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Extent of shareholder rights index (0-6) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The extent of shareholder rights index measures the role of shareholders in key corporate decisions. It has six components: (i) whether the sale of 51% of Buyer’s assets requires shareholder approval; (ii) whether shareholders representing 10% of Buyer’s share capital have the right to call for a meeting of shareholders; (iii) whether Buyer must obtain its share­holders’ approval every time it issues new shares; (iv) whether shareholders automatically receive preemption rights when Buyer issues new shares; (v) whether shareholders elect and dismiss the external auditor; (vi) whether changes to the rights of a class of shares are only possible if the holders of the affected shares approve. The index is computed based on the methodology in the DB15-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.EXT.SHRHLD.RGT.XD.0010.DB1519.DRFN", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Score-Extent of shareholder rights index (0-6) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for extent of shareholder rights index benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB15-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.IC.PRIN.EXT.DIR.LGL.010.XD", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Extent of director liability index (0-10)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The extent of director liability index measures when board members can be held liable for harm caused by related-party transactions and what sanctions are available. It has seven components: (i) whether shareholders can sue directly or derivatively for the damage the transaction causes to the company; (ii) whether a shareholder plaintiff can hold Mr. James liable for the damage the Buyer-Seller transaction causes to the company; (iii) whether a shareholder plaintiff can hold other executives and directors (the CEO, members of the board of directors or members of the supervisory board) liable for the damage the transaction causes to the company; (iv) whether Mr. James pays damages for the harm caused to the company upon a successful claim by the shareholder plaintiff; (v) whether Mr. James repays profits made from the transaction upon a successful claim by the shareholder plaintiff; (vi) whether Mr. James is disqualified upon a successful claim by the shareholder plaintiff; and (vii) whether a court can void the trans­action upon a successful claim by a shareholder plaintiff." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.IC.PRIN.MINOR.RK", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Rank-Protecting minority investors" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The ranking of economies on the strength of minority shareholder protections is determined by sorting their scores for protecting minority investors." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year. The country ranking is only available for the latest year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.STRENG.INV.PROT.XD.010.DB0614", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Strength of investor protection index (0-30) (DB06-14 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The strength of investor protection index is the sum of the extent of disclosure index, extent of director liability index and ease of shareholder suits index. The index is computed based on the methodology in the DB06-14 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "PROT.MINOR.INV.STRENG.MIN.INV.PROT.XD.010.DB0614", "metatype": [ { "id": "Developmentrelevance", "value": "Minority investors protection plays a crucial part in addressing many corporate governance issues. One of the most important problems in corporate governance is self-dealing—the use of corporate assets by company insiders for personal gain. Empirical research shows that stricter regulation of self-dealing is associated with greater equity investment and lower concentration of ownership. Corporate governance standards on board composition and independence, firm transparency and disclosure, and shareholders' rights relative to the board of directors and management can minimize the agency problem between majority and minority shareholders as well as that between minority shareholders and the board of directors and management." }, { "id": "IndicatorName", "value": "Strength of minority investor protection index (0-50) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The strength of minority investor protection index is the sum of the extent of disclosure index, extent of director liability index, ease of shareholder suits index, extent of shareholder rights index, extent of ownership and control index and extent of corporate transparency index. The index is computed based on the methodology in the DB15-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Protecting minority investors" } ], "source_id": "1" }, { "id": "RESLV.ISV.COPR.03.XD.DB1519", "metatype": [ { "id": "Developmentrelevance", "value": "Keeping viable businesses operating is among the most important goals of insolvency systems. A good insolvency regime should inhibit the premature liquidation of sustainable businesses. It should also discourage lenders from issuing high-risk loans, and managers and shareholders from taking imprudent loans and making other reckless financial decisions. A firm suffering from poor management choices or a temporary economic downturn can still be turned around. When this happens, all stakeholders benefit. Creditors can recover a larger part of their investment, more employees keep their jobs and the network of suppliers and customers is preserved." }, { "id": "IndicatorName", "value": "Commencement of proceedings index (0-3) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The commencement of proceedings index has three components: (i) whether debtors can initiate both liquidation and reorganization proceedings; (ii) whether creditors can initiate both liquidation and reorganization proceedings; and (iii) what standard is used for commence­ment of insolvency proceedings." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Resolving insolvency" } ], "source_id": "1" }, { "id": "RESLV.ISV.COST.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Keeping viable businesses operating is among the most important goals of insolvency systems. A good insolvency regime should inhibit the premature liquidation of sustainable businesses. It should also discourage lenders from issuing high-risk loans, and managers and shareholders from taking imprudent loans and making other reckless financial decisions. A firm suffering from poor management choices or a temporary economic downturn can still be turned around. When this happens, all stakeholders benefit. Creditors can recover a larger part of their investment, more employees keep their jobs and the network of suppliers and customers is preserved." }, { "id": "IndicatorName", "value": "Cost (% of estate)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The cost to resolve insolvency is the cost of the proceedings is recorded as a percentage of the value of the debtor’s estate, including court fees and government levies, fees of insolvency administrators, auctioneers, assessors and lawyers, and all other fees and costs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Resolving insolvency" } ], "source_id": "1" }, { "id": "RESLV.ISV.CPI.04.XD.DB1519", "metatype": [ { "id": "Developmentrelevance", "value": "Keeping viable businesses operating is among the most important goals of insolvency systems. A good insolvency regime should inhibit the premature liquidation of sustainable businesses. It should also discourage lenders from issuing high-risk loans, and managers and shareholders from taking imprudent loans and making other reckless financial decisions. A firm suffering from poor management choices or a temporary economic downturn can still be turned around. When this happens, all stakeholders benefit. Creditors can recover a larger part of their investment, more employees keep their jobs and the network of suppliers and customers is preserved." }, { "id": "IndicatorName", "value": "Creditor participation index (0-4) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The creditor participation index has four components: (i) whether creditors appoint the insolvency representative or approve, ratify or reject the appointment of the insolvency representative; (ii) Whether creditors are required to approve the sale of substantial assets of the debtor in the course of insol­vency proceedings; (iii) Whether an individual creditor has the right to access financial information about the debtor during insolvency proceedings; and (iv) Whether an individual creditor can object to a decision of the court or of the insolvency representative to approve or reject claims against the debtor brought by the creditor itself and by other creditors." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Resolving insolvency" } ], "source_id": "1" }, { "id": "RESLV.ISV.DB1519.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Keeping viable businesses operating is among the most important goals of insolvency systems. A good insolvency regime should inhibit the premature liquidation of sustainable businesses. It should also discourage lenders from issuing high-risk loans, and managers and shareholders from taking imprudent loans and making other reckless financial decisions. A firm suffering from poor management choices or a temporary economic downturn can still be turned around. When this happens, all stakeholders benefit. Creditors can recover a larger part of their investment, more employees keep their jobs and the network of suppliers and customers is preserved." }, { "id": "IndicatorName", "value": "Score-Resolving insolvency" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for resolving insolvency is the simple average of the scores for each of the component indicators: the recovery rate of insolvency proceedings involving domestic entities, as well as the strength of the legal framework applicable to judicial liquidation and reorganization proceedings." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Resolving insolvency" } ], "source_id": "1" }, { "id": "RESLV.ISV.DFRN.RCOV.RT", "metatype": [ { "id": "Developmentrelevance", "value": "Keeping viable businesses operating is among the most important goals of insolvency systems. A good insolvency regime should inhibit the premature liquidation of sustainable businesses. It should also discourage lenders from issuing high-risk loans, and managers and shareholders from taking imprudent loans and making other reckless financial decisions. A firm suffering from poor management choices or a temporary economic downturn can still be turned around. When this happens, all stakeholders benefit. Creditors can recover a larger part of their investment, more employees keep their jobs and the network of suppliers and customers is preserved." }, { "id": "IndicatorName", "value": "Score-Recovery rate (cents on the dollar)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for recovery rate benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Resolving insolvency" } ], "source_id": "1" }, { "id": "RESLV.ISV.DURS.YR", "metatype": [ { "id": "Developmentrelevance", "value": "Keeping viable businesses operating is among the most important goals of insolvency systems. A good insolvency regime should inhibit the premature liquidation of sustainable businesses. It should also discourage lenders from issuing high-risk loans, and managers and shareholders from taking imprudent loans and making other reckless financial decisions. A firm suffering from poor management choices or a temporary economic downturn can still be turned around. When this happens, all stakeholders benefit. Creditors can recover a larger part of their investment, more employees keep their jobs and the network of suppliers and customers is preserved." }, { "id": "IndicatorName", "value": "Time (years)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time to resolve insolvency captures the time for creditors to recover their credit and is recorded in calendar years. Potential delay tactics by the parties, such as the filing of dilatory appeals or requests for extension, are taken into consideration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Resolving insolvency" } ], "source_id": "1" }, { "id": "RESLV.ISV.MGDA.XD.DB1519", "metatype": [ { "id": "Developmentrelevance", "value": "Keeping viable businesses operating is among the most important goals of insolvency systems. A good insolvency regime should inhibit the premature liquidation of sustainable businesses. It should also discourage lenders from issuing high-risk loans, and managers and shareholders from taking imprudent loans and making other reckless financial decisions. A firm suffering from poor management choices or a temporary economic downturn can still be turned around. When this happens, all stakeholders benefit. Creditors can recover a larger part of their investment, more employees keep their jobs and the network of suppliers and customers is preserved." }, { "id": "IndicatorName", "value": "Management of debtor's assets index (0-6) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The management of debtor's assets index has six components: (i) whether the debtor (or an insolvency representative on its behalf) can continue performing contracts essential to the debtor’s survival; (ii) whether the debtor (or an insolvency representative on its behalf) can reject overly burdensome contracts; (iii) whether undervalued transactions entered into before commencement of insolvency proceedings can be avoided after proceedings are initiated; (iv) whether transactions entered into before commencement of insolvency proceedings that give preference to one or several creditors can be avoided after proceedings are initiated; (v) whether the insolvency framework includes specific provisions that allow the debtor (or an insolvency representa­tive on its behalf), after commencement of insolvency proceedings, to obtain financing necessary to function during the proceedings; and (vi) whether post-commencement finance receives priority over ordinary unse­cured creditors during distribution of assets." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Resolving insolvency" } ], "source_id": "1" }, { "id": "RESLV.ISV.OTCM", "metatype": [ { "id": "Developmentrelevance", "value": "Keeping viable businesses operating is among the most important goals of insolvency systems. A good insolvency regime should inhibit the premature liquidation of sustainable businesses. It should also discourage lenders from issuing high-risk loans, and managers and shareholders from taking imprudent loans and making other reckless financial decisions. A firm suffering from poor management choices or a temporary economic downturn can still be turned around. When this happens, all stakeholders benefit. Creditors can recover a larger part of their investment, more employees keep their jobs and the network of suppliers and customers is preserved." }, { "id": "IndicatorName", "value": "Outcome (0 as piecemeal sale and 1 as going concern)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "Outcome records whether the case study company emerges from the proceedings as a going concern (1) or its assets are sold piecemeal (0). If the business continues operating, 100% of the company value is preserved. If the assets are sold piecemeal, the maximum amount that can be recovered is 70% of the value of the company." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Resolving insolvency" } ], "source_id": "1" }, { "id": "RESLV.ISV.RCOV.RT", "metatype": [ { "id": "Developmentrelevance", "value": "Keeping viable businesses operating is among the most important goals of insolvency systems. A good insolvency regime should inhibit the premature liquidation of sustainable businesses. It should also discourage lenders from issuing high-risk loans, and managers and shareholders from taking imprudent loans and making other reckless financial decisions. A firm suffering from poor management choices or a temporary economic downturn can still be turned around. When this happens, all stakeholders benefit. Creditors can recover a larger part of their investment, more employees keep their jobs and the network of suppliers and customers is preserved." }, { "id": "IndicatorName", "value": "Recovery rate (cents on the dollar)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The recovery rate is recorded as cents on the dollar recovered by secured creditors through judicial reorganization, liquidation or debt enforcement (foreclosure or receivership) proceedings. The calculation takes into account the outcome: whether the business emerges from the proceedings as a going concern or the assets are sold piecemeal." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Resolving insolvency" } ], "source_id": "1" }, { "id": "RESLV.ISV.RCOV.RT.016.DB1519.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Keeping viable businesses operating is among the most important goals of insolvency systems. A good insolvency regime should inhibit the premature liquidation of sustainable businesses. It should also discourage lenders from issuing high-risk loans, and managers and shareholders from taking imprudent loans and making other reckless financial decisions. A firm suffering from poor management choices or a temporary economic downturn can still be turned around. When this happens, all stakeholders benefit. Creditors can recover a larger part of their investment, more employees keep their jobs and the network of suppliers and customers is preserved." }, { "id": "IndicatorName", "value": "Score-Strength of insolvency framework index (0-16)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for strength of insolvency framework index benchmarks economies with respect to the regulatory best practice on the indicator. The score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Resolving insolvency" } ], "source_id": "1" }, { "id": "RESLV.ISV.RK.DB19", "metatype": [ { "id": "Developmentrelevance", "value": "Keeping viable businesses operating is among the most important goals of insolvency systems. A good insolvency regime should inhibit the premature liquidation of sustainable businesses. It should also discourage lenders from issuing high-risk loans, and managers and shareholders from taking imprudent loans and making other reckless financial decisions. A firm suffering from poor management choices or a temporary economic downturn can still be turned around. When this happens, all stakeholders benefit. Creditors can recover a larger part of their investment, more employees keep their jobs and the network of suppliers and customers is preserved." }, { "id": "IndicatorName", "value": "Rank-Resolving insolvency" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The ranking of economies on the ease of resolving insolvency is determined by sorting their scores for resolving insolvency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year. The country ranking is only available for the latest year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Resolving insolvency" } ], "source_id": "1" }, { "id": "RESLV.ISV.ROPC.03.XD.DB1519", "metatype": [ { "id": "Developmentrelevance", "value": "Keeping viable businesses operating is among the most important goals of insolvency systems. A good insolvency regime should inhibit the premature liquidation of sustainable businesses. It should also discourage lenders from issuing high-risk loans, and managers and shareholders from taking imprudent loans and making other reckless financial decisions. A firm suffering from poor management choices or a temporary economic downturn can still be turned around. When this happens, all stakeholders benefit. Creditors can recover a larger part of their investment, more employees keep their jobs and the network of suppliers and customers is preserved." }, { "id": "IndicatorName", "value": "Reorganization proceedings index (0-3) (DB15-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The reorganization proceedings index has three components: (i) whether the reorganization plan is voted on only by the creditors whose rights are modified or affected by the plan; (ii) whether creditors entitled to vote on the plan are divided into classes, each class votes separately and the creditors within each class are treated equally; and (iii) whether the insolvency framework requires that dissenting creditors receive as much under the reorganization plan as they would have received in liquidation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Resolving insolvency" } ], "source_id": "1" }, { "id": "RESLV.ISV.SOIF.06.DB1519", "metatype": [ { "id": "Developmentrelevance", "value": "Keeping viable businesses operating is among the most important goals of insolvency systems. A good insolvency regime should inhibit the premature liquidation of sustainable businesses. It should also discourage lenders from issuing high-risk loans, and managers and shareholders from taking imprudent loans and making other reckless financial decisions. A firm suffering from poor management choices or a temporary economic downturn can still be turned around. When this happens, all stakeholders benefit. Creditors can recover a larger part of their investment, more employees keep their jobs and the network of suppliers and customers is preserved." }, { "id": "IndicatorName", "value": "Strength of insolvency framework index (0-16)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The strength of insolvency framework index measures the legal framework applicable to judicial liquidation and reorganization proceedings and the extent to which best insolvency practices have been implemented in each economy covered by the Doing Business. This index ranges has four components, the commencement of proceedings index, management of debtor’s assets index, reorganization proceedings index and creditor participation index." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Resolving insolvency" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.DB0615.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Score-Trading across borders (DB06-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "Doing Business measures the time and cost associated with exporting and importing a standardized cargo of goods by sea transport. The time and cost necessary to complete 4 predefined stages (document preparation; customs clearance and inspections; inland transport and handling; and port and terminal handling) for exporting and importing the goods are recorded. All documents needed by the trader to export or import the goods across the border are also recorded. The process of exporting goods ranges from packing the goods into the container at the warehouse to their departure from the port of exit. The process of importing goods ranges from the vessel’s arrival at the port of entry to the cargo’s delivery at the warehouse. For landlocked economies, since the seaport is located in the transit economy, the time, cost and documents associated with the processes at the inland border are also included. The score for trading across borders is a simple average of the cost to export and import, time to export and import, and the number documents to export and import. It is computed based on the methodology in the DB06-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.DB1619.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Score-Trading across borders (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "Doing Business measures the time and cost associated with three sets of procedures of exporting and importing goods —documentary compliance, border compliance and domestic transport—within the overall process of exporting or importing a shipment of goods. The score for trading across borders is the simple average of the scores for the time and cost for documentary compliance and border compliance to export and import. It is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.DOC.COMP.HR.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Time to export: Documentary compliance (hours) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time for documentary compliance to export records the time associated with compliance with the export documentary requirements of all government agencies of the origin economy, the destination economy and any transit economies. It is calculated in hours. It includes the time for obtaining documents; preparing documents; processing documents; presenting documents; and submitting documents. All electronic or paper submissions of information requested by any government agency in connection with the shipment are considered to be documents obtained, prepared and submitted during the export process. All documents prepared by the freight forwarder or customs broker for the product and partner pair assumed in the case study are included regardless of whether they are required by law or in practice. The component indicator is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.DOCS.EXPT.NO.DB0615", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Documents to export (number) (DB06-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The number of documents to export records the number of documents required by law or common practice by relevant agencies per export shipment. All documents required by law or common practice by relevant agencies—including government ministries, customs authorities, port authorities and other control agencies—per export shipment are taken into account. For landlocked economies, documents required by authorities in the transit economy are also included. Since payment is by letter of credit, all documents required by banks for the issuance or securing of a letter of credit are also taken into account. Documents that are requested at the time of clearance but that are valid for a year or longer or do not require renewal per shipment (for example, an annual tax clearance certificate) are not included. Documents that are required by customs authorities purely for purposes of preferential treatment but are not required for any other purpose by any of the authorities in the process of trading are not included. The component indicator is computed based on the methodology in the DB06-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.DOCS.EXPT.NO.DB0615.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Score-Documents to export (number) (DB06-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the number of documents to export benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB06-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.DOCS.IMP.NO.DB0615", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Documents to import (number) (DB06-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The number of documents to import records the number of documents required by law or common practice by relevant agencies per import shipment. All documents required by law or common practice by relevant agencies—including government ministries, customs authorities, port authorities and other control agencies—per import shipment are taken into account. For landlocked economies, documents required by authorities in the transit economy are also included. Since payment is by letter of credit, all documents required by banks for the issuance or securing of a letter of credit are also taken into account. Documents that are requested at the time of clearance but that are valid for a year or longer or do not require renewal per shipment (for example, an annual tax clearance certificate) are not included. Documents that are required by customs authorities purely for purposes of preferential treatment but are not required for any other purpose by any of the authorities in the process of trading are not included. The component indicator is computed based on the methodology in the DB06-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.DOCS.IMP.NO.DB0615.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Score-Documents to import (number) (DB06-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the number of documents to import benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB06-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.EXPT.BRDR.COMP.HR.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Time to export: Border compliance (hours) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time for border compliance to export records the time associated with compliance with the economy’s customs regulations and with regulations relating to other inspections that are mandatory in order for the export shipment to cross the economy’s border, as well as the time and cost for handling that takes place at its port or border. It is calulcated in hours. The time for this segment includes time for customs clearance and inspection procedures conducted by other agencies. If all customs clearance and other inspections take place at the port or border at the same time, the time estimate for border compliance takes this simultaneity into account. The component indicator is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.EXPT.COST.BRDR.COMP.CD.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Cost to export: Border compliance (USD) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The cost for border compliance to export records the cost associated with compliance with the economy’s customs regulations and with regulations relating to other inspections that are mandatory in order for the export shipment to cross the economy’s border, as well as the time and cost for handling that takes place at its port or border. It is calculated in US dollars. The cost for this segment include the cost for customs clearance and inspection procedures conducted by other agencies. For example, the cost for conducting a phytosanitary inspection would be included here. Informal payments for which no receipt is issued are excluded from the costs recorded. The component indicator is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.EXPT.COST.BRDR.COMP.CD.DB1619.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Score-Cost to export: Border compliance (USD) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the cost for border compliance to export benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.EXPT.COST.CD.DB0615", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Cost to export (US$ per container deflated) (DB06-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The cost to export records the cost associated with exporting a standardized cargo of goods by sea transport through 4 predefined stages: document preparation; customs clearance and inspections; inland transport and handling; and port and terminal handling. It is calculated in US dollars per container deflated. Cost measures the fees levied on the export of goods in a 20-foot container, in US dollars. All fees charged by government agencies and the private sector to a trader in the process of exporting and importing the goods are taken into account. These include but are not limited to costs for documents, administrative fees for customs clearance and inspections, customs broker fees, port-related charges and inland transport costs. Only official costs are recorded. The component indicator is computed based on the methodology in the DB06-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.EXPT.COST.CD.DB0615.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Score-Cost to export (US$ per container deflated) (DB06-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the cost to export benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB06-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.EXPT.COST.DOC.COMP.CD.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Cost to export: Documentary compliance (USD) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The cost for documentary compliance to export records the cost associated with compliance with the export documentary requirements of all government agencies of the origin economy, the destination economy and any transit economies. It is calculated in UD dollars. The cost for documentary compliance includes the cost for obtaining, preparing, processing, presenting and submitting documents. Insurance cost and informal payments for which no receipt is issued are excluded from the costs recorded. The component indicator is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.EXPT.COST.DOC.COMP.CD.DB1619.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Score-Cost to export: Documentary compliance (USD) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the cost for documentary compliance to export benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.EXPT.DURS.DY.DB0615", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Time to export (days) (DB06-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time to export records the time associated with exporting a standardized cargo of goods by sea transport through 4 predefined stages: document preparation; customs clearance and inspections; inland transport and handling; and port and terminal handling. It is calculated in calendar days. The time calculation for each of the 4 predefined stages starts from the moment the stage is initiated and runs until it is completed. Fast-track procedures applying only to firms located in an export processing zone, or only to certain accredited firms under authorized economic operator programs, are not taken into account because they are not available to all trading companies. Sea transport time is not included. It is assumed that neither the exporter nor the importer wastes time and that each commits to completing the process without delay. It is assumed that document preparation, inland transport and handling, customs clearance and inspections, and port and terminal handling require a minimum time of 1 day each and cannot take place simultaneously. The component indicator is computed based on the methodology in the DB06-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.EXPT.TM.BRDR.COMP.HR.DB1619.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Score-Time to export: Border compliance (hours) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the time for border compliance to export benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.EXPT.TM.DOC.COMP.HR.DB1619.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Score-Time to export: Documentary compliance (hours) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the time for documentary compliance to export benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.EXPT.TM.DY.DB0615.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Score-Time to export (days) (DB06-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the time to export benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB06-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.IMP.BRDR.COMP.HR.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Time to import: Border compliance (hours) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time for border compliance to import records the time associated with compliance with the economy’s customs regulations and with regulations relating to other inspections that are mandatory in order for the import shipment to cross the economy’s border, as well as the time and cost for handling that takes place at its port or border. It is calculate in hours. The time for this segment includes time for customs clearance and inspection procedures conducted by other agencies. If all customs clearance and other inspections take place at the port or border at the same time, the time estimate for border compliance takes this simultaneity into account. The component indicator is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.IMP.COST.BRDR.COMP.CD.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Cost to import: Border compliance (USD) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The cost for border compliance to import records the cost associated with compliance with the economy’s customs regulations and with regulations relating to other inspections that are mandatory in order for the import shipment to cross the economy’s border, as well as the time and cost for handling that takes place at its port or border. It is calculated in US dollars. The cost for this segment include the cost for customs clearance and inspection procedures conducted by other agencies. For example, the cost for conducting a technical standard inspection would be included here. Informal payments for which no receipt is issued are excluded from the costs recorded. The component indicator is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.IMP.COST.BRDR.COMP.CD.DB1619.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Score-Cost to import: Border compliance (USD) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the cost for border compliance to import benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.IMP.COST.CD.DB0615", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Cost to import (US$ per container deflated) (DB06-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The cost to import records the cost associated with importing a standardized cargo of goods by sea transport through 4 predefined stages: document preparation; customs clearance and inspections; inland transport and handling; and port and terminal handling. It is calculated in US dollars per container deflated. Cost measures the fees levied on import of goods in a 20-foot container, in US dollars. All fees charged by government agencies and the private sector to a trader in the process of exporting and importing the goods are taken into account. These include but are not limited to costs for documents, administrative fees for customs clearance and inspections, customs broker fees, port-related charges and inland transport costs. Only official costs are recorded. The component indicator is computed based on the methodology in the DB06-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.IMP.COST.CD.DB0615.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Score-Cost to import (US$ per container deflated) (DB06-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the cost to import benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB06-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.IMP.COST.DOC.COMP.CD.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Cost to import: Documentary compliance (USD) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The cost for documentary compliance to import records the cost associated with compliance with the import documentary requirements of all government agencies of the origin economy, the destination economy and any transit economies. It is calculated in US dollars. The cost for documentary compliance includes the cost for obtaining, preparing, processing, presenting and submitting documents. Insurance cost and informal payments for which no receipt is issued are excluded from the costs recorded. The component indicator is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.IMP.COST.DOC.COMP.CD.DB1619.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Score-Cost to import: Documentary compliance (USD) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the cost for documentary compliance to import benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.IMP.DOC.COMP.HR.DB1619", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Time to import: Documentary compliance (hours) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time for documentary compliance to import records the time associated with compliance with the import documentary requirements of all government agencies of the origin economy, the destination economy and any transit economies. It is calculated in hours. It includes the time for obtaining documents, preparing documents, processing documents, presenting documents and submitting documents. All electronic or paper submissions of information requested by any government agency in connection with the shipment are considered to be documents obtained, prepared and submitted during the import process. All documents prepared by the freight forwarder or customs broker for the product and partner pair assumed in the case study are included regardless of whether they are required by law or in practice. The component indicator is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.IMP.DURS.DY.DB0615", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Time to import (days) (DB06-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The time to import records the time associated with importing a standardized cargo of goods by sea transport through 4 predefined stages: document preparation; customs clearance and inspections; inland transport and handling; and port and terminal handling. It is calulcated in calendar days. The time calculation for each of the 4 predefined stages starts from the moment the stage is initiated and runs until it is completed. Fast-track procedures applying only to certain accredited firms under authorized economic operator programs are not taken into account because they are not available to all trading companies. Sea transport time is not included. It is assumed that neither the exporter nor the importer wastes time and that each commits to completing the process without delay. It is assumed that document preparation, inland transport and handling, customs clearance and inspections, and port and terminal handling require a minimum time of 1 day each and cannot take place simultaneously. The component indicator is computed based on the methodology in the DB06-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.IMP.TM.BRDR.COMP.HR.DB1619.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Score-Time to import: Border compliance (hours) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the time for border compliance to import benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.IMP.TM.DOC.COMP.HR.DB1619.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Score-Time to import: Documentary compliance (hours) (DB16-20 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the time for documentary compliance to import benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB16-20 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.IMP.TM.DY.DB0615.DFRN", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Score-Time to import (days) (DB06-15 methodology)" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The score for the time to import benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB06-15 studies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "TRD.ACRS.BRDR.RK.DB19", "metatype": [ { "id": "Developmentrelevance", "value": "Access to international markets plays an important role in an economy’s development. Logistics and freight expenses, customs administrative fees and border costs have become important for small traders. While the significance of small and medium-size enterprises (SMEs) in the overall economy is widely recognized, until recently SMEs were largely absent from trade debates. Along with fixed entry costs, cumbersome border procedures and standards are major hurdles for SMEs. Given that SMEs account for the majority of firms and the vast majority of employment worldwide, encouraging government policies aimed at facilitating the participation of SMEs in trade is essential." }, { "id": "IndicatorName", "value": "Rank-Trading across borders" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has five limitations that should be considered when interpreting the data. First, for most economies the collected data refer to businesses in the largest business city and may not be representative of regulation in other parts of the economy. Second, the data often focus on a specific business form—generally a limited liability company (or its legal equivalent) of a specified size—and may not be representative of the regulation on other businesses. Third, transactions described in a standardized case scenario refer to a specific set of issues and may not represent the full set of issues that a business encounters. Fourth, the measures of time involve an element of judgment by the expert respondents. When sources indicate different estimates, the time indicators reported in Doing Business represent the median values of several responses given under the assumptions of the standardized case. Finally, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. In practice, completing a procedure may take longer if the business lacks information or is unable to follow up promptly. Alternatively, the business may choose to disregard some burdensome procedures. For both reasons the time delays reported in Doing Business would differ from the recollection of entrepreneurs reported in the World Bank Group Enterprise questionnaires or other firm-level questionnaires." }, { "id": "Longdefinition", "value": "The ranking of economies on the ease of trading across borders is determined by sorting their scores for trading across borders." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "Data are presented for the survey year instead of publication year. The country ranking is only available for the latest year." }, { "id": "Source", "value": "World Bank Group, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group with a standardized questionnaire that uses a simple business case to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location and nature of its operation. Questionnaires are administered to more than 13,800 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials and other professionals routinely administering or advising on legal and regulatory requirements.\n\nThe Doing Business data are based on a detailed reading of domestic laws, regulations and administrative requirements as well as their implementation in practice as experienced by private firms. The report covers 190 economies—including some of the smallest and poorest economies, for which little or no data are available from other sources. The data are collected through several rounds of communication with expert respondents (both private sector practitioners and government officials), through responses to questionnaires, conference calls, written correspondence and visits by the team. Doing Business relies on four main sources of information: the relevant laws and regulations, Doing Business respondents, the governments of the economies covered and the World Bank Group regional staff." }, { "id": "Topic", "value": "Trading across borders" } ], "source_id": "1" }, { "id": "AG.CON.FERT.PT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Factors, such as the green revolution, have led to impressive progress in increasing crop yields over the last few decades. This progress, however, is not equal across all regions. Continued progress depends on maintaining agricultural research and education. The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth.\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\nIn many developed countries, excessive nitrogen fertilizer applications have sometimes led to pest problems by increasing the birth rate, longevity and overall fitness of certain agricultural pests, such as aphids. Further, excessive use of fertilizers emits significant quantities of greenhouse gases into the atmosphere. Over-fertilization of a vital nutrient can be detrimental, as \"fertilizer burn\" can occur when too much fertilizer is applied, resulting in drying out of the leaves and damage or even death of the plant. In many industrialized countries, overuse of fertilizers has resulted in contamination of surface water and groundwater.\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Fertilizer consumption (% of fertilizer production)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The FAO has revised the time series for fertilizer consumption and irrigation for 2002 onward. FAO collects fertilizer statistics for production, imports, exports, and consumption through the new FAO fertilizer resources questionnaire. In the previous release, the data were based on total consumption of fertilizers, but the data in the recent release are based on the nutrients in fertilizers. Some countries compile fertilizer data on a calendar year basis, while others compile on a crop year basis (July-June). Previous editions of this indicator, Fertilizer consumption (100 grams per hectare of arable land), reported data on a crop year basis, but this edition uses the calendar year, as adopted by the FAO. Caution should thus be used when comparing data over time.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Longdefinition", "value": "Fertilizer consumption measures the quantity of plant nutrients and is calculated as production plus imports minus exports. Fertilizer products cover nitrogenous, potash, and phosphate fertilizers (including ground rock phosphate). Traditional nutrients--animal and plant manures--are not included. Because some chemical compounds used for fertilizers have other industrial applications, the consumption data may overstate the quantity available for crops. Fertilizer consumption as a share of production shows the agriculture sector's vulnerability to import and energy price fluctuation. Most fertilizers that are commonly used in agriculture contain the three basic plant nutrients-nitrogen, phosphorus, and potassium. Some fertilizers also contain certain micronutrients such as zinc and other metals that are necessary for plant growth. Materials that are applied to the land primarily to enhance soil characteristics (rather than as plant food) are commonly referred to as soil amendments." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Fertilizer consumption measures the quantity of plant nutrients, and is calculated as production plus imports minus exports. Because some chemical compounds used for fertilizers have other industrial applications, the consumption data may overstate the quantity available for crops. Fertilizer consumption as a share of production shows the agriculture sector's vulnerability to import and energy price fluctuation.\n\n\n\nFor the purpose of data dissemination, FAO has adopted the concept of a calendar year (January to December). Some countries compile fertilizer data on a calendar year basis, while others are on a split-year basis.\n\n\n\nFAO has revised the time series for fertilizer consumption and irrigation from 2002 onward. FAO collects fertilizer statistics for production, imports, exports, and consumption through the new FAO fertilizer resources questionnaire. In the previous release, the data were based on the total consumption of fertilizers, but the data in the recent release are based on the nutrients in fertilizers. Some countries compile fertilizer data on a calendar year basis, while others compile on a crop year basis (July-June). Previous editions of this indicator, Fertilizer consumption (100 g per ha of arable land), reported data on a crop year basis, but this edition uses the calendar year, as adopted by the FAO. Caution should thus be used when comparing data over time. The data are collected by FAO through annual questionnaires. FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.\n\n\n\nMost fertilizers that are commonly used in agriculture contain the three basic plant nutrients - nitrogen, phosphorus, and potassium. Some fertilizers also contain certain \"micronutrients,\" such as zinc and other metals that are necessary for plant growth. Materials that are applied to the land primarily to enhance soil characteristics (rather than as plant food) are commonly referred to as soil amendments. Fertilizers and soil amendments are largely derived from raw material, composts and other organic matter, and wastes, such as sewage sludge and certain industrial wastes." }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "% (ratio)" } ], "source_id": "2" }, { "id": "AG.CON.FERT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Factors, such as the green revolution, have led to impressive progress in increasing crop yields over the last few decades. This progress, however, is not equal across all regions. Continued progress depends on maintaining agricultural research and education. The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many developed countries, excessive nitrogen fertilizer applications have sometime lead to pest problems by increasing the birth rate, longevity and overall fitness of certain agricultural pests, such as aphids. Further, excessive use of fertilizers emits significant quantities of greenhouse gas into the atmosphere. Over-fertilization of a vital nutrient can be detrimental, as \"fertilizer burn\" can occur when too much fertilizer is applied, resulting in drying out of the leaves and damage or even death of the plant. In many industrialized countries, overuse of fertilizers has resulted in contamination of surface water and groundwater.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Fertilizer consumption (kilograms per hectare of arable land)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The FAO has revised the time series for fertilizer consumption and irrigation for 2002 onward. FAO collects fertilizer statistics for production, imports, exports, and consumption through the new FAO fertilizer resources questionnaire. In the previous release, the data were based on total consumption of fertilizers, but the data in the recent release are based on the nutrients in fertilizers. Some countries compile fertilizer data on a calendar year basis, while others compile on a crop year basis (July-June). Previous editions of this indicator, Fertilizer consumption (100 grams per hectare of arable land), reported data on a crop year basis, but this edition uses the calendar year, as adopted by the FAO. Caution should thus be used when comparing data over time.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Longdefinition", "value": "Fertilizer consumption measures the quantity of plant nutrients used per unit of arable land. Fertilizer products cover nitrogenous, potash, and phosphate fertilizers (including ground rock phosphate). Traditional nutrients--animal and plant manures--are not included. For the purpose of data dissemination, FAO has adopted the concept of a calendar year (January to December). Some countries compile fertilizer data on a calendar year basis, while others are on a split-year basis. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Fertilizer consumption measures the quantity of plant nutrients, and is calculated as production plus imports minus exports. Because some chemical compounds used for fertilizers have other industrial applications, the consumption data may overstate the quantity available for crops. Fertilizer consumption as a share of production shows the agriculture sector's vulnerability to import and energy price fluctuation.\n\n\n\nMost fertilizers that are commonly used in agriculture contain the three basic plant nutrients - nitrogen, phosphorus, and potassium. Some fertilizers also contain certain \"micronutrients,\" such as zinc and other metals that are necessary for plant growth. Materials that are applied to the land primarily to enhance soil characteristics (rather than as plant food) are commonly referred to as soil amendments. Fertilizers and soil amendments are largely derived from raw material, composts and other organic matter, and wastes, such as sewage sludge and certain industrial wastes.\n\n\n\nFAO defines arable land as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow; land abandoned as a result of shifting cultivation is excluded." }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "kg per hectare of arable land" } ], "source_id": "2" }, { "id": "AG.LND.AGRI.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Agricultural land covers more than one-third of the world's land area. In many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFAO's agricultural land data contains a wide range of information on variables that are significant for understanding the structure of a country's agricultural sector; making economic plans and policies for food security; and deriving environmental indicators, including those related to investment in agriculture and data on gross crop area and net crop area which are useful for policy formulation and monitoring.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Agricultural land (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Longdefinition", "value": "Agricultural land refers to the land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies. Three components of the agricultural land are a) arable land - land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow, b) permanent pasture - land used for five or more years for forage, including natural and cultivated crops, and c) and under permanent crops - land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber; land under flowering shrubs, fruit trees, nut trees, and vines is included, but land under trees grown for wood or timber is not.\n\n\n\nAgricultural land is also sometimes classified as irrigated and non-irrigated land. In arid and semi-arid countries agriculture is often confined to irrigated land, with very little farming possible in non-irrigated areas. Land abandoned as a result of shifting cultivation is excluded from arable land.\n\n\n\nData on agricultural land are valuable for conducting studies on various perspectives concerning agricultural production, food security and for deriving cropping intensity among other uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers (sq. km)" } ], "source_id": "2" }, { "id": "AG.LND.AGRI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Agricultural land covers more than one-third of the world's land area, with arable land representing less than one-third of agricultural land (about 10 percent of the world's land area). Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFAO's agricultural land data contains a wide range of information on variables that are significant for: understanding the structure of a country's agricultural sector; making economic plans and policies for food security; deriving environmental indicators, including those related to investment in agriculture and data on gross crop area and net crop area which are useful for policy formulation and monitoring.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Agricultural land (% of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through annual questionnaires and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage." }, { "id": "Longdefinition", "value": "Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Agriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgricultural land is also sometimes classified as irrigated and non-irrigated land. In arid and semi-arid countries agriculture is often confined to irrigated land, with very little farming possible in non-irrigated areas. Land abandoned as a result of shifting cultivation is excluded from Arable land.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "2" }, { "id": "AG.LND.ARBL.HA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Agricultural land covers more than one-third of the world's land area. Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is significant geographic variation in the availability of land considered suitable for agriculture. Increasing population and demand from other sectors place growing pressure on available resources. According to FAO, the world's cultivated area has grown by 12 percent over the last 50 years. The global irrigated area has doubled over the same period, accounting for most of the net increase in cultivated land. Agriculture already uses 11 percent of the world's land surface for crop production. It also makes use of 70 percent of all water withdrawn from aquifers, streams and lakes. Agricultural policies have primarily benefitted farmers with productive land and access to water, bypassing the majority of small-scale producers who are still locked in a poverty trap of high vulnerability, land degradation and climatic uncertainty.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLand resources are central to agriculture and rural development, and are intrinsically linked to global challenges of food insecurity and poverty, climate change adaptation and mitigation, as well as degradation and depletion of natural resources that affect the livelihoods of millions of rural people across the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land." }, { "id": "IndicatorName", "value": "Arable land (hectares)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Food and Agriculture Organization (FAO) tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. Data on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTrue comparability of the data is limited, however, by variations in definitions, statistical methods, and quality of data. Countries use different definitions land use. The Food and Agriculture Organization of the United Nations (FAO), the primary compiler of the data, occasionally adjusts its definitions of land use categories and revises earlier data. Because the data reflect changes in reporting procedures as well as actual changes in land use, apparent trends should be interpreted cautiously.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSatellite images show land use that differs from that of ground-based measures in area under cultivation and type of land use. Moreover, land use data in some countries (India is an example) are based on reporting systems designed for collecting tax revenue. With land taxes no longer a major source of government revenue, the quality and coverage of land use data have declined." }, { "id": "Longdefinition", "value": "Arable land (in hectares) includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Temporary fallow land refers to land left fallow for less than five years. The abandoned land resulting from shifting cultivation is not included in this category. Data for \"Arable land\" are not meant to indicate the amount of land that is potentially cultivable." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "hectares" } ], "source_id": "2" }, { "id": "AG.LND.ARBL.HA.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Agricultural land covers about one-third of the world's land area, with arable land representing less than one-third of agricultural land (about 10 percent of the world's land area). Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is significant geographic variation in the availability of land considered suitable for agriculture. Increasing population and demand from other sectors place growing pressure on available resources. According to FAO, the world's cultivated area has grown by 12 percent over the last 50 years. The global irrigated area has doubled over the same period, accounting for most of the net increase in cultivated land. Agriculture already uses 11 percent of the world's land surface for crop production. It also makes use of 70 percent of all water withdrawn from aquifers, streams and lakes. Agricultural policies have primarily benefitted farmers with productive land and access to water, bypassing the majority of small-scale producers who are still locked in a poverty trap of high vulnerability, land degradation and climatic uncertainty.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLand resources are central to agriculture and rural development, and are intrinsically linked to global challenges of food insecurity and poverty, climate change adaptation and mitigation, as well as degradation and depletion of natural resources that affect the livelihoods of millions of rural people across the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land." }, { "id": "IndicatorName", "value": "Arable land (hectares per person)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Food and Agriculture Organization (FAO) tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTrue comparability of the data is limited, by variations in definitions, statistical methods, and quality of data. Countries use different definitions land use. The Food and Agriculture Organization of the United Nations (FAO), the primary compiler of the data, occasionally adjusts its definitions of land use categories and revises earlier data. Because the data reflect changes in reporting procedures as well as actual changes in land use, apparent trends should be interpreted cautiously.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSatellite images show land use that differs from that of ground-based measures in area under cultivation and type of land use. Moreover, land use data in some countries (India is an example) are based on reporting systems designed for collecting tax revenue. With land taxes no longer a major source of government revenue, the quality and coverage of land use data have declined." }, { "id": "Longdefinition", "value": "Arable land (hectares per person) includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Temporary fallow land refers to land left fallow for less than five years. The abandoned land resulting from shifting cultivation is not included in this category. Data for \"Arable land\" are not meant to indicate the amount of land that is potentially cultivable. The data collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "hectares per person" } ], "source_id": "2" }, { "id": "AG.LND.ARBL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Agricultural land covers more than one-third of the world's land area. Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is significant geographic variation in the availability of land considered suitable for agriculture. Increasing population and demand from other sectors place growing pressure on available resources. According to FAO, the world's cultivated area has grown by 12 percent over the last 50 years. The global irrigated area has doubled over the same period, accounting for most of the net increase in cultivated land. Agriculture already uses 11 percent of the world's land surface for crop production. It also makes use of 70 percent of all water withdrawn from aquifers, streams and lakes. Agricultural policies have primarily benefitted farmers with productive land and access to water, bypassing the majority of small-scale producers who are still locked in a poverty trap of high vulnerability, land degradation and climatic uncertainty.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLand resources are central to agriculture and rural development, and are intrinsically linked to global challenges of food insecurity and poverty, climate change adaptation and mitigation, as well as degradation and depletion of natural resources that affect the livelihoods of millions of rural people across the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFAO's agricultural land data contains a wide range of information on variables that are significant for: understanding the structure of a country's agricultural sector; making economic plans and policies for food security; deriving environmental indicators, including those related to investment in agriculture and data on gross crop area and net crop area which are useful for policy formulation and monitoring." }, { "id": "IndicatorName", "value": "Arable land (% of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Food and Agriculture Organization (FAO) tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\nThe data collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. Data on agricultural land are valuable for conducting studies on various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\nTrue comparability of the data is limited, by variations in definitions, statistical methods, and quality of data. Countries use different definitions of land use. The Food and Agriculture Organization of the United Nations (FAO), the primary compiler of the data, occasionally adjusts its definitions of land use categories and revises earlier data. Because the data reflect changes in reporting procedures as well as actual changes in land use, apparent trends should be interpreted cautiously.\n\nSatellite images show land use that differs from that of ground-based measures in area under cultivation and type of land use. Moreover, land use data in some countries (India is an example) are based on reporting systems designed for collecting tax revenue. With land taxes no longer a major source of government revenue, the quality and coverage of land use data have declined." }, { "id": "Longdefinition", "value": "Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and website, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Temporary fallow land refers to land left fallow for less than five years. The abandoned land resulting from shifting cultivation is not included in this category. Data for \"Arable land\" are not meant to indicate the amount of land that is potentially cultivable. Total land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area. The data collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "2" }, { "id": "AG.LND.CREL.HA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality od seeds, and the techniques applied to promote growth.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn developed countries, cereal crops are universally machine-harvested, typically using a combine harvester, which cuts, threshes, and winnows the grain during a single pass across the field. In many industrialized countries, particularly in the United States and Canada, farmers commonly deliver their newly harvested grain to a grain elevator or a storage facility that consolidates the crops of many farmers. In developing countries, a variety of harvesting methods are used in cereal cultivation, depending on the cost of labor, from small combines to hand tools such as the scythe or cradle.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production systems have evolved rapidly over the past century and have resulted in significantly increased crop yields, but have also created undesirable environmental side-effects such as soil degradation and erosion, pollution from chemical fertilizers and agrochemicals and a loss of bio-diversity. Factors such as the green revolution, has led to impressive progress in increasing cereals yields over the last few decades. This progress, however, is not equal across all regions. Continued progress depends on maintaining agricultural research and education. The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Land under cereal production (hectares)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. They are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses." }, { "id": "Longdefinition", "value": "Land under cereal production refers to harvested area, although some countries report only sown or cultivated area. Cereals include wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/QCL, note: Item code F1717, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cereals production includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nA cereal is a grass cultivated for the edible components of their grain, composed of the endosperm, germ, and bran. Cereal grains are grown in greater quantities and provide more food energy worldwide than any other type of crop; cereal crops therefore can also be called staple crops." }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "hectares" } ], "source_id": "2" }, { "id": "AG.LND.CROP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Agricultural land covers more than one-third of the world's land area. Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrops are divided into temporary and permanent crops. Permanent crops are sown or planted once, and then occupy the land for some years and need not be replanted after each annual harvest, such as cocoa, coffee and rubber. This category includes flowering shrubs, fruit trees, nut trees and vines, but excludes trees grown for wood or timber. Temporary crops are those which are both sown and harvested during the same agricultural year, sometimes more than once. Temporary crop land is used for crops with a less than one-year growing cycle and which must be newly sown or planted for further production after the harvest.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is significant geographic variation in the availability of land considered suitable for agriculture. Increasing population and demand from other sectors place growing pressure on available resources. According to FAO, the world's cultivated area has grown by 12 percent over the last 50 years. The global irrigated area has doubled over the same period, accounting for most of the net increase in cultivated land. Agriculture already uses 11 percent of the world's land surface for crop production. It also makes use of 70 percent of all water withdrawn from aquifers, streams and lakes. Agricultural policies have primarily benefitted farmers with productive land and access to water, bypassing the majority of small-scale producers who are still locked in a poverty trap of high vulnerability, land degradation and climatic uncertainty.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLand resources are central to agriculture and rural development, and are intrinsically linked to global challenges of food insecurity and poverty, climate change adaptation and mitigation, as well as degradation and depletion of natural resources that affect the livelihoods of millions of rural people across the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land." }, { "id": "IndicatorName", "value": "Permanent cropland (% of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Food and Agriculture Organization (FAO) tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\nTrue comparability of the data is limited by variations in definitions, statistical methods, and quality of data. Countries use different definitions of land use. The Food and Agriculture Organization of the United Nations (FAO), the primary compiler of the data, occasionally adjusts its definitions of land use categories and revises earlier data. Because the data reflect changes in reporting procedures as well as actual changes in land use, apparent trends should be interpreted cautiously.\n\nSatellite images show land use that differs from that of ground-based measures in area under cultivation and type of land use. Moreover, land use data in some countries (India is an example) are based on reporting systems designed for collecting tax revenue. With land taxes no longer a major source of government revenue, the quality and coverage of land use data have declined." }, { "id": "Longdefinition", "value": "Permanent cropland is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data on Permanent cropland and land area are collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through a questionnaire and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "2" }, { "id": "AG.LND.EL5M.RU.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Scientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems." }, { "id": "IndicatorName", "value": "Rural land area where elevation is below 5 meters (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is “unequivocal” and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20–86 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5°C by 2100 (Kattenberg and others 1996) and increase of “killer” heat waves during the warm season (Karl and others 1997)." }, { "id": "Longdefinition", "value": "Rural land area below 5m is the total rural land area in square kilometers where the elevation is 5 meters or less." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev3-3.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Elevation data used to generate the low elevation coastal zones come from the SRTM3 Enhanced Global Map developed by ISCIENCES. The ISCIENCES digital elevation model was created using NASA’s Jet Propulsion Laboratory Shuttle Radar Topography Mission data processed to 3 arc-seconds (SRTM3).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers" } ], "source_id": "2" }, { "id": "AG.LND.EL5M.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Scientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems." }, { "id": "IndicatorName", "value": "Rural land area where elevation is below 5 meters (% of total land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is “unequivocal” and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20–86 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5°C by 2100 (Kattenberg and others 1996) and increase of “killer” heat waves during the warm season (Karl and others 1997)." }, { "id": "Longdefinition", "value": "Rural land area below 5m is the percentage of total land where the rural land elevation is 5 meters or less." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev3-3.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Elevation data used to generate the low elevation coastal zones come from the SRTM3 Enhanced Global Map developed by ISCIENCES. The ISCIENCES digital elevation model was created using NASA’s Jet Propulsion Laboratory Shuttle Radar Topography Mission data processed to 3 arc-seconds (SRTM3).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "2" }, { "id": "AG.LND.EL5M.UR.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Scientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems." }, { "id": "IndicatorName", "value": "Urban land area where elevation is below 5 meters (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Urban land area below 5m is the total urban land area in square kilometers where the elevation is 5 meters or less." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev3-3.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Elevation data used to generate the low elevation coastal zones come from the SRTM3 Enhanced Global Map developed by ISCIENCES. The ISCIENCES digital elevation model was created using NASA’s Jet Propulsion Laboratory Shuttle Radar Topography Mission data processed to 3 arc-seconds (SRTM3).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers" } ], "source_id": "2" }, { "id": "AG.LND.EL5M.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Scientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems." }, { "id": "IndicatorName", "value": "Urban land area where elevation is below 5 meters (% of total land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Urban land area below 5m is the percentage of total land where the urban land elevation is 5 meters or less." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev3-3.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Elevation data used to generate the low elevation coastal zones come from the SRTM3 Enhanced Global Map developed by ISCIENCES. The ISCIENCES digital elevation model was created using NASA’s Jet Propulsion Laboratory Shuttle Radar Topography Mission data processed to 3 arc-seconds (SRTM3).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "2" }, { "id": "AG.LND.EL5M.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Scientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems." }, { "id": "IndicatorName", "value": "Land area where elevation is below 5 meters (% of total land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is “unequivocal” and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20–86 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5°C by 2100 (Kattenberg and others 1996) and increase of “killer” heat waves during the warm season (Karl and others 1997)." }, { "id": "Longdefinition", "value": "Land area below 5m is the percentage of total land where the elevation is 5 meters or less." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev3-3.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Elevation data used to generate the low elevation coastal zones come from the SRTM3 Enhanced Global Map developed by ISCIENCES. The ISCIENCES digital elevation model was created using NASA’s Jet Propulsion Laboratory Shuttle Radar Topography Mission data processed to 3 arc-seconds (SRTM3).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "2" }, { "id": "AG.LND.FRST.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOn a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems. The decrease of primary forest area, 0.4 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNational parks, game reserves, wilderness areas and other legally established protected areas cover more than 10 percent of the total forest area in most countries and regions. FAO estimates that around 10 million people are employed in forest management and conservation - but many more are directly dependent on forests for their livelihoods. Also, 80 about percent of the world's forests are publicly owned, but ownership and management of forests by communities, individuals and private companies is on the rise.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClose to 1.2 billion hectares of forest are managed primarily for the production of wood and non-wood forest products. An additional 25 percent of forest area is designated for multiple uses - in most cases including the production of wood and non-wood forest products. The area designated primarily for productive purposes has decreased by more than 50 million hectares since 1990 as forests have been designated for other purposes." }, { "id": "IndicatorName", "value": "Forest area (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Food and Agricultural Organization (FAO) has been collecting and analyzing data on forest area since 1946. This is done at intervals of 5-10 years as part of the Global Forest Resources Assessment (FRA). FAO reports data for 229 countries and territories; for the remaining 56 small island states and territories where no information is provided, a report is prepared by FAO using existing information and a literature search. The data are aggregated at sub-regional, regional and global levels by the FRA team at FAO, and estimates are produced by straight summation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe lag between the reference year and the actual production of data series as well as the frequency of data production varies between countries. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData includes areas with bamboo and palms; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters; plantations primarily used for forestry or protective purposes, such as rubber-wood plantations and cork oak stands. Data excludes tree stands in agricultural production systems, such as fruit plantations and agroforestry systems. Forest area also excludes trees in urban parks and gardens. The proportion of forest area to total land area is calculated and changes in the proportion are computed to identify trends." }, { "id": "Longdefinition", "value": "Forest area is land under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "FAOSTAT, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/RL, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 meters are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFAO provides detail information on forest cover, and adjusted estimates of forest cover. The current survey uses a uniform definition of forest. Although FAO provides a breakdown of forest cover between natural forest and plantation for developing countries, this indictor data does not reflect that breakdown. Thus the deforestation data may underestimate the rate at which natural forest is disappearing in some countries.\nStatistical concept(s): Forest - Forests are lands of more than 0.5 hectares, with a tree canopy cover of more than 10 percent, which are not primarily under agricultural or urban land use. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters in situ. Areas under reforestation which have yet to reach a crown density of 10 percent or tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, that are expected to regenerate. The term specifically includes: forest nurseries and seed orchards that constitute an integral part of the forest; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks and shelterbelts of trees with an area of more than 0.5 ha and width of more than 20 m; plantations primarily used for forestry purposes, including rubberwood plantations and cork oak stands. The term specifically excludes trees planted primarily for agricultural production, for example in fruit plantations and agroforestry systems." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers" } ], "source_id": "2" }, { "id": "AG.LND.FRST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOn a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems. The decrease of forest area, .11 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDestruction of rainforests remains a significant environmental problem Much of what remains of the world's rainforests is in the Amazon basin, where the Amazon Rainforest covers approximately 4 million square kilometers. The regions with the highest tropical deforestation rate are in Central America and tropical Asia. FAO estimates that the decrease of primary forest area, 0.4 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions. Large-scale planting of trees is significantly reducing the net loss of forest area globally, and afforestation and natural expansion of forests in some countries and regions have reduced the net loss of forest area significantly at the global level.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nForests cover about 31 percent of total land area of the world; the world's total forest area is just over 4 billion hectares. On a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNational parks, game reserves, wilderness areas and other legally established protected areas cover more than 10 percent of the total forest area in most countries and regions. FAO estimates that around 10 million people are employed in forest management and conservation - but many more are directly dependent on forests for their livelihoods. Close to 1.2 billion hectares of forest are managed primarily for the production of wood and non-wood forest products. An additional 25 percent of forest area is designated for multiple uses - in most cases including the production of wood and non-wood forest products. The area designated primarily for productive purposes has decreased by more than 50 million hectares since 1990 as forests have been designated for other purposes." }, { "id": "IndicatorName", "value": "Forest area (% of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "FAO has been collecting and analyzing data on forest area since 1946. This is done at intervals of 5-10 years as part of the Global Forest Resources Assessment (FRA). FAO reports data for 229 countries and territories; for the remaining 56 small island states and territories where no information is provided, a report is prepared by FAO using existing information and a literature search. The data are aggregated at sub-regional, regional and global levels by the FRA team at FAO, and estimates are produced by straight summation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe lag between the reference year and the actual production of data series as well as the frequency of data production varies between countries. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData includes areas with bamboo and palms; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters; plantations primarily used for forestry or protective purposes, such as rubber-wood plantations and cork oak stands. Data excludes tree stands in agricultural production systems, such as fruit plantations and agroforestry systems. Forest area also excludes trees in urban parks and gardens. The proportion of forest area to total land area is calculated and changes in the proportion are computed to identify trends." }, { "id": "Longdefinition", "value": "Forest area (% of land area) is the share of total land area that is under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "FAOSTAT, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/RL, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 meters are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Food and Agriculture Organization (FAO) provides detail information on forest cover, and adjusted estimates of forest cover. The survey uses a uniform definition of forest. Although FAO provides a breakdown of forest cover between natural forest and plantation for developing countries, forest data used to derive this indictor data does not reflect that breakdown. Total land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe indictor is derived by dividing total area under forest of a country by country's total land area, and multiplying by 100.\nStatistical concept(s): Forest - Forests are lands of more than 0.5 hectares, with a tree canopy cover of more than 10 percent, which are not primarily under agricultural or urban land use. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters in situ. Areas under reforestation which have yet to reach a crown density of 10 percent or tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, that are expected to regenerate. The term specifically includes: forest nurseries and seed orchards that constitute an integral part of the forest; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks and shelterbelts of trees with an area of more than 0.5 ha and width of more than 20 m; plantations primarily used for forestry purposes, including rubberwood plantations and cork oak stands. The term specifically excludes trees planted primarily for agricultural production, for example in fruit plantations and agroforestry systems." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "2" }, { "id": "AG.LND.IRIG.AG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Worldwide, irrigated agriculture accounts for about four-fifths of global water withdrawals. The share of irrigated land ranges widely, from 4 percent of the total area cropped in Africa to 42 percent in South Asia. The leading countries are India and China with about 30 percent and 52 percent of all cropland irrigated, respectively. Without irrigation and drainage, much of the increases in agricultural output that has fed the world's growing population and stabilized food production would not have been possible.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn the dry sub-humid countries, irrigation is critical for crop production. Due to highly variable rainfall, long dry seasons, and recurrent droughts, dry spells and floods, water management is a key determinant for agricultural production in these regions and is increasingly becoming more important with climate change. World Bank estimates that rainfed agriculture is most significant in Sub-Saharan Africa where it accounts for about 96 percent of the cropland.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIrrigation and drainage continue to be an important source of productivity growth, especially in Sub-Saharan Africa and parts of Latin America that still have large untapped water resources for agriculture. In other regions where the scope for further expanding irrigated agriculture is limited, more efforts are needed to enhance the policy, technical, and governance aspects of agricultural water use.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgricultural land covers more than one-third of the world's land area. In many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land. Data on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Agricultural irrigated land (% of total agricultural land)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through annual questionnaires and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries." }, { "id": "Longdefinition", "value": "Agricultural irrigated land refers to agricultural areas purposely provided with water, including land irrigated by controlled flooding." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/RL, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Irrigated agricultural area refers to area equipped to provide water (via artificial means of irrigation such as by diverting streams, flooding, or spraying) to the crops. In non-irrigated agricultural areas, production of crops is dependent on rain-fed irrigation. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies. Agricultural land can also be classified as irrigated and non-irrigated land. In arid and semi-arid countries agriculture is often confined to irrigated land, with very little farming possible in non-irrigated areas." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of total agricultural land" } ], "source_id": "2" }, { "id": "AG.LND.PRCP.MM", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The agriculture sector is the most water-intensive sector, and water delivery in agriculture is increasingly important. Data on irrigated agricultural land and data on average precipitation illustrate how countries obtain water for agricultural use." }, { "id": "IndicatorName", "value": "Average precipitation in depth (mm per year)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible." }, { "id": "Longdefinition", "value": "Average precipitation is the long-term average in depth (over space and time) of annual precipitation in the country. Precipitation is defined as any kind of water that falls from clouds as a liquid or a solid." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2022" }, { "id": "Source", "value": "FAOSTAT, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimation of Areal Precipitation\n\n\n\n\n\nA single point precipitation measurement is quite often not representative of the volume of precipitation falling over a given catchment area. A dense network of point measurements and/or radar estimates can provide a better representation of the true volume over a given area. A network of precipitation measurements is converted to areal estimates using the arithmentic mean. This technique calculates areal precipitation using the arithmetic mean of all the point or areal measurements considered in the analysis.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "milimeter (mm) per year" } ], "source_id": "2" }, { "id": "AG.LND.TOTL.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Land area is particularly important for understanding an economy's agricultural capacity and the environmental effects of human activity. Innovations in satellite mapping and computer databases have resulted in more precise measurements of land and water areas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPopulation, land area, income, and output are basic measures of the size of an economy. They also provide a broad indication of actual and potential resources. Land area is therefore used as one of the major indicator to normalize other indicators." }, { "id": "IndicatorName", "value": "Land area (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization (FAO) of the United Nations through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data collected from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Longdefinition", "value": "Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAOSTAT, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/RL, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Total land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area. Including areas of former states; for example, the areas of the Union of Soviet Socialist Republics (USSR) are counted in Russian Federation and other successor states." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers" } ], "source_id": "2" }, { "id": "AG.LND.TOTL.RU.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Rural land area (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is “unequivocal” and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20–86 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5°C by 2100 (Kattenberg and others 1996) and increase of “killer” heat waves during the warm season (Karl and others 1997)." }, { "id": "Longdefinition", "value": "Rural land area in square kilometers, derived from urban extent grids which distinguish urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev2-2.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2013" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) urban extent grid distinguishes urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights . Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT)\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers" } ], "source_id": "2" }, { "id": "AG.LND.TOTL.UR.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Urban land area (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Urban land area in square kilometers, based on a combination of population counts (persons), settlement points, and the presence of nighttime lights. Areas are defined as urban where contiguous lighted cells from the nighttime lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev2-2.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2013" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) urban extent grid distinguishes urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights . Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT)" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers" } ], "source_id": "2" }, { "id": "AG.PRD.CREL.MT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Food and Agriculture Organization (FAO) estimates that cereals supply 51 percent of Calories and 47 percent of protein in the average diet. The total annual cereal production globally is about 2,500 million tons.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFAO estimates that maize (corn), wheat and rice together account for more than three-fourths of all grain production worldwide. In developed countries, cereal crops are universally machine-harvested, typically using a combine harvester, which cuts, threshes, and winnows the grain during a single pass across the field. In many industrialized countries, particularly in the United States and Canada, farmers commonly deliver their newly harvested grain to a grain elevator or a storage facility that consolidates the crops of many farmers. In developing countries, a variety of harvesting methods are used in cereal cultivation, depending on the cost of labor, from small combines to hand tools such as the scythe or cradle.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production systems have evolved rapidly over the past century and have resulted in significantly increased crop yields, but have also created undesirable environmental side-effects such as soil degradation and erosion, pollution from chemical fertilizers and agrochemicals and a loss of bio-diversity. Factors such as the green revolution, has led to impressive progress in increasing cereals yields over the last few decades. This progress, however, is not equal across all regions. Continued progress depends on maintaining agricultural research and education. The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth." }, { "id": "IndicatorName", "value": "Cereal production (metric tons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on cereal production may be affected by a variety of reporting and timing differences. Millet and sorghum, which are grown as feed for livestock and poultry in Europe and North America, are used as food in Africa, Asia, and countries of the former Soviet Union. So some cereal crops are excluded from the data for some countries and included elsewhere, depending on their use.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are collected by the Food and Agriculture Organization (FAO) of the United Nations through annual questionnaires and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data collected from official national sources." }, { "id": "Longdefinition", "value": "Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/QCL, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A cereal is a grass cultivated for the edible components of their grain, composed of the endosperm, germ, and bran. Cereal grains are grown in greater quantities and provide more food energy worldwide than any other type of crop; cereal crops therefore can also be called staple crops. Cereals production data relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. The Food and Agriculture Organization (FAO) allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year." }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "metric tons" } ], "source_id": "2" }, { "id": "AG.PRD.CROP.XD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. The category of food production includes commodities that are considered edible and that contain nutrients. Accordingly, coffee and tea are excluded along with inedible commodities because, although edible, they have practically no nutritive value.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption)." }, { "id": "IndicatorName", "value": "Crop production index (2014-2016 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The FAO indices may differ from those produced by the countries themselves because of differences in concepts of production, coverage, time periods, weights, time reference of data, methods of calculation, and use of international prices.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgricultural data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through annual questionnaires and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage." }, { "id": "Longdefinition", "value": "Crop production index shows agricultural production for each year relative to the base period 2014-2016. It includes all crops except fodder crops. Regional and income group aggregates for the FAO's production indexes are calculated from the underlying values in international dollars, normalized to the base period 2014-2016." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2022" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The agricultural production index is prepared by the Food and Agriculture Organization of the United Nations (FAO). The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 2014-2016. They are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. All the indices at the country, regional and world levels are calculated by the Laspeyres formula*. Production quantities of each commodity are weighted by 2014-2016 average international commodity prices and summed for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period 2014-2016. Since the FAO indices are based on the concept of agriculture as a single enterprise, amounts of seed and feed are subtracted from the production data to avoid double counting, once in the production data and once with the crops or livestock produced from them. Deductions for seed (in the case of eggs, for hatching) and for livestock and poultry feed apply to both domestically produced and imported commodities. They cover only primary agricultural products destined to animal feed (e.g. maize, potatoes, milk, etc.). Processed and semi-processed feed items such as bran, oilcakes, meals and molasses have been completely excluded from the calculations at all stages. It should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed. Indices which take into account deductions for feed and seed are referred to as ''net''. Indices calculated without any deductions for feed and seed are referred to as ''gross\". The \"international commodity prices\" are used in order to avoid the use of exchange rates for obtaining continental and world aggregates, and also to improve and facilitate international comparative analysis of productivity at the national level. These\" international prices,\" expressed in so-called \"international dollars,\" are derived using a Geary-Khamis formula** for the agricultural sector. This method assigns a single \"price\" to each commodity. For example, one metric ton of wheat has the same price regardless of the country where it was produced. The currency unit in which the prices are expressed has no influence on the indices published. The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. \n* A Laspeyres Index is known as a \"base-weighted\" or \"fixed-weighted\" index because the price increases are weighted by the quantities in the base period. The Consumer Price Index is an example of a Laspeyres Index. http://www.usna.edu/Users/econ/rbrady/312%20Materials/LaspeyresCalc.pdf\n** Geary-Khamis formula is an aggregation method in which category \"international prices\" (reflecting relative category values) and country purchasing power parities (PPPs), (depicting relative country price levels) are estimated simultaneously from a system of linear equations. http://stats.oecd.org/glossary/detail.asp?ID=5528" }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "index (2014-2016=100)" } ], "source_id": "2" }, { "id": "AG.PRD.FOOD.XD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. The category of food production includes commodities that are considered edible and that contain nutrients. Accordingly, coffee and tea are excluded along with inedible commodities because, although edible, they have practically no nutritive value.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption)." }, { "id": "IndicatorName", "value": "Food production index (2014-2016 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Agricultural data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through the questionnaire and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage." }, { "id": "Longdefinition", "value": "Food production index covers food crops that are considered edible and that contain nutrients. Coffee and tea are excluded because, although edible, they have no nutritive value." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2022" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The agricultural production index is prepared by the Food and Agriculture Organization of the United Nations (FAO). The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 2014-2016. They are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. All the indices at the country, regional and world levels are calculated by the Laspeyres formula*. Production quantities of each commodity are weighted by 2014-2016 average international commodity prices and summed for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period 2014-2016. Since the FAO indices are based on the concept of agriculture as a single enterprise, amounts of seed and feed are subtracted from the production data to avoid double counting, once in the production data and once with the crops or livestock produced from them. Deductions for seed (in the case of eggs, for hatching) and for livestock and poultry feed apply to both domestically produced and imported commodities. They cover only primary agricultural products destined to animal feed (e.g. maize, potatoes, milk, etc.). Processed and semi-processed feed items such as bran, oilcakes, meals and molasses have been completely excluded from the calculations at all stages. It should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed. Indices which take into account deductions for feed and seed are referred to as ''net''. Indices calculated without any deductions for feed and seed are referred to as ''gross\". The \"international commodity prices\" are used in order to avoid the use of exchange rates for obtaining continental and world aggregates, and also to improve and facilitate international comparative analysis of productivity at the national level. These\" international prices,\" expressed in so-called \"international dollars,\" are derived using a Geary-Khamis formula** for the agricultural sector. This method assigns a single \"price\" to each commodity. For example, one metric ton of wheat has the same price regardless of the country where it was produced. The currency unit in which the prices are expressed has no influence on the indices published. The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. \n* A Laspeyres Index is known as a \"base-weighted\" or \"fixed-weighted\" index because the price increases are weighted by the quantities in the base period. The Consumer Price Index is an example of a Laspeyres Index. http://www.usna.edu/Users/econ/rbrady/312%20Materials/LaspeyresCalc.pdf\n** Geary-Khamis formula is an aggregation method in which category \"international prices\" (reflecting relative category values) and country purchasing power parities (PPPs), (depicting relative country price levels) are estimated simultaneously from a system of linear equations. http://stats.oecd.org/glossary/detail.asp?ID=5528" }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "index (2014-2016=100)" } ], "source_id": "2" }, { "id": "AG.PRD.LVSK.XD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. The category of food production includes commodities that are considered edible and that contain nutrients. Accordingly, coffee and tea are excluded along with inedible commodities because, although edible, they have practically no nutritive value.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption)." }, { "id": "IndicatorName", "value": "Livestock production index (2014-2016 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Agricultural data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through the questionnaire and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage." }, { "id": "Longdefinition", "value": "Livestock production index includes meat and milk from all sources, dairy products such as cheese, and eggs, honey, raw silk, wool, and hides and skins." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2022" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The agricultural production index is prepared by the Food and Agriculture Organization of the United Nations (FAO). The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 2014-2016. They are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. All the indices at the country, regional and world levels are calculated by the Laspeyres formula*. Production quantities of each commodity are weighted by 2014-2016 average international commodity prices and summed for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period 2014-2016. Since the FAO indices are based on the concept of agriculture as a single enterprise, amounts of seed and feed are subtracted from the production data to avoid double counting, once in the production data and once with the crops or livestock produced from them. Deductions for seed (in the case of eggs, for hatching) and for livestock and poultry feed apply to both domestically produced and imported commodities. They cover only primary agricultural products destined to animal feed (e.g. maize, potatoes, milk, etc.). Processed and semi-processed feed items such as bran, oilcakes, meals and molasses have been completely excluded from the calculations at all stages. It should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed. Indices which take into account deductions for feed and seed are referred to as ''net''. Indices calculated without any deductions for feed and seed are referred to as ''gross\". The \"international commodity prices\" are used in order to avoid the use of exchange rates for obtaining continental and world aggregates, and also to improve and facilitate international comparative analysis of productivity at the national level. These\" international prices,\" expressed in so-called \"international dollars,\" are derived using a Geary-Khamis formula** for the agricultural sector. This method assigns a single \"price\" to each commodity. For example, one metric ton of wheat has the same price regardless of the country where it was produced. The currency unit in which the prices are expressed has no influence on the indices published. The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. \n* A Laspeyres Index is known as a \"base-weighted\" or \"fixed-weighted\" index because the price increases are weighted by the quantities in the base period. The Consumer Price Index is an example of a Laspeyres Index. http://www.usna.edu/Users/econ/rbrady/312%20Materials/LaspeyresCalc.pdf\n** Geary-Khamis formula is an aggregation method in which category \"international prices\" (reflecting relative category values) and country purchasing power parities (PPPs), (depicting relative country price levels) are estimated simultaneously from a system of linear equations. http://stats.oecd.org/glossary/detail.asp?ID=5528" }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "index (2014-2016=100)" } ], "source_id": "2" }, { "id": "AG.SRF.TOTL.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Total surface area is particularly important for understanding an economy's agricultural capacity and the environmental effects of human activity. Innovations in satellite mapping and computer databases have resulted in more precise measurements of land and water areas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPopulation, surface area, income, and output are basic measures of the size of an economy. They also provide a broad indication of actual and potential resources. Land area is therefore used as one of the major indicator to normalize other indicators." }, { "id": "IndicatorName", "value": "Surface area (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization (FAO) of the United Nations through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data collected from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Longdefinition", "value": "Surface area is a country's total area, including areas under inland bodies of water and some coastal waterways." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Total land area includes inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area. Including areas of former states; for example, the areas of the Union of Soviet Socialist Republics (USSR) are counted in Russian Federationand other successor states." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers" } ], "source_id": "2" }, { "id": "AG.YLD.CREL.KG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In developed countries, cereal crops are universally machine-harvested, typically using a combine harvester, which cuts, threshes, and winnows the grain during a single pass across the field. In many industrialized countries, particularly in the United States and Canada, farmers commonly deliver their newly harvested grain to a grain elevator or a storage facility that consolidates the crops of many farmers. In developing countries, a variety of harvesting methods are used in cereal cultivation, depending on the cost of labor, from small combines to hand tools such as the scythe or cradle.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production systems have evolved rapidly over the past century and have resulted in significantly increased crop yields, but have also created undesirable environmental side-effects such as soil degradation and erosion, pollution from chemical fertilizers and agrochemicals and a loss of bio-diversity. Factors such as the green revolution, has led to impressive progress in increasing cereals yields over the last few decades. This progress, however, is not equal across all regions. Continued progress depends on maintaining agricultural research and education. The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth." }, { "id": "IndicatorName", "value": "Cereal yield (kg per hectare)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Cereals production data relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. The FAO allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on cereal yield may be affected by a variety of reporting and timing differences. Millet and sorghum, which are grown as feed for livestock and poultry in Europe and North America, are used as food in Africa, Asia, and countries of the former Soviet Union. So some cereal crops are excluded from the data for some countries and included elsewhere, depending on their use.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data collected from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Longdefinition", "value": "Cereal yield, measured as kilograms per hectare of harvested land, includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. The FAO allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/QCL, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A cereal is a grass cultivated for the edible components of their grain, composed of the endosperm, germ, and bran. Cereal yield is measured as kilograms per hectare of harvested land. Cereal grains are grown in greater quantities and provide more food energy worldwide than any other type of crop; cereal crops therefore can also be called staple crops Cereals production includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded." }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "kg per hectare" } ], "source_id": "2" }, { "id": "BG.GSR.NFSV.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Trade in services (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total trade in services includes services provided by residents to non-residents plus services provided by non-residents to residents. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "BM.GSR.CMCP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Communications, computer, etc. (% of service imports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Communications, computer, information, and other services cover international telecommunications; computer data; news-related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; personal, cultural, and recreational services; manufacturing services on physical inputs owned by others; and maintenance and repair services and government services not included elsewhere. This indicator is expressed as a percentage of service imports which are services provided by non-residents to residents." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "BM.GSR.FCTY.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Primary income payments (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Primary income payments refer to employee compensation paid to nonresident workers and investment income (payments on direct investment, portfolio investment, other investments).This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BM.GSR.GNFS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Imports of goods and services (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Discrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded." }, { "id": "Longdefinition", "value": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BM.GSR.INSF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Insurance and financial services (% of service imports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Insurance and financial services cover various types of insurance provided to nonresidents by resident insurance enterprises and vice versa, and financial intermediary and auxiliary services (except those of insurance enterprises and pension funds) exchanged between residents and nonresidents. This indicator is expressed as a percentage of service imports which are services provided by non-residents to residents." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "BM.GSR.MRCH.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Goods imports (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Imports of goods occur when there are changes in the economic ownership of goods from non-residents to residents of the compiling economy, irrespective of physical movement of goods across national borders. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BM.GSR.NFSV.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Service imports (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Imports of services are services provided by non-residents to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. Manufacturing services on physical inputs owned by others (goods for processing in BPM5) and maintenance and repair services n.i.e. are reclassified from goods to services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BM.GSR.ROYL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Charges for the use of intellectual property, payments (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Charges for the use of proprietary rights (such as patents, trademarks, copyrights, industrial processes and designs including trade secrets, franchises), and charges for licenses to reproduce or distribute (or both) intellectual property embodied in produced originals or prototypes (such as copyrights on books and manuscripts, computer software, cinematographic works, and sound recordings) and related rights (such as for live performances and television, cable, or satellite broadcast). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BM.GSR.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Imports of goods, services and primary income (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. Primary income represents the return that accrues to institutional units for their contribution to the production process or for the provision of financial assets and renting natural resources to other institutional units. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BM.GSR.TRAN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Transport services (% of service imports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Transport services covers the process of carriage of people and objects from one location to another as well as related supporting and auxiliary services. Also included are postal and courier services. This indicator is expressed as a percentage of service imports which are services provided by non-residents to residents." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "BM.GSR.TRVL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Travel services (% of service imports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Travel services cover goods and services for own use or to give away acquired from an economy by nonresidents during visits to that economy, or acquired from other economies by residents during visits to these other economies. This indicator is expressed as a percentage of service imports which are services provided by non-residents to residents." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "BM.KLT.DINV.CD.WD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits." }, { "id": "IndicatorName", "value": "Foreign direct investment, net outflows (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intra-unit flows of goods and services.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available." }, { "id": "Longdefinition", "value": "Foreign direct investment refers to direct investment equity flows in an economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. This series shows net outflows of investment from the reporting economy to the rest of the world. Data are in current U.S. dollars." }, { "id": "Othernotes", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Balance of Payments database, International Monetary Fund (IMF), note: International Monetary Fund, Balance of Payments database, supplemented by data from the United Nations Conference on Trade and Development and official national sources.;\nUN Conference on Trade and Development (UNCTAD);\nOfficial national sources" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Foreign direct investment (FDI) data are supplemented by the World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources.\n\nThe internationally accepted definition of FDI (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results in a direct investment relationship, based on control and influence. Distinguished from other kinds of international investment, FDI is made to establish a lasting interest in or effective management control over an enterprise in another country. A lasting interest in an investment enterprise typically involves establishing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. BoP refers to Balance of Payments." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "2" }, { "id": "BM.KLT.DINV.WD.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits." }, { "id": "IndicatorName", "value": "Foreign direct investment, net outflows (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intra-unit flows of goods and services.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available." }, { "id": "Longdefinition", "value": "Foreign direct investment refers to direct investment equity flows in an economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. This series shows net outflows of investment from the reporting economy to the rest of the world, and is divided by GDP." }, { "id": "Othernotes", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Balance of Payments database, International Monetary Fund (IMF), note: International Monetary Fund, Balance of Payments database, supplemented by data from the United Nations Conference on Trade and Development and official national sources.;\nUN Conference on Trade and Development (UNCTAD);\nOfficial national sources" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Foreign direct investment (FDI) data are supplemented by the World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources.\n\nThe internationally accepted definition of FDI (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results in a direct investment relationship, based on control and influence. Distinguished from other kinds of international investment, FDI is made to establish a lasting interest in or effective management control over an enterprise in another country. A lasting interest in an investment enterprise typically involves establishing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. BoP refers to Balance of Payments." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "2" }, { "id": "BM.TRF.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies." }, { "id": "IndicatorName", "value": "Secondary income, other sectors, payments (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Secondary income refers to transfers recorded in the balance of payments whenever an economy provides or receives goods, services, income, or financial items without a quid pro quo. All transfers not considered to be capital are current. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BM.TRF.PWKR.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\nIn most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers. Globally, the number of refugees at end 2010 was 10.55 million, including 597,300 people considered by the United Nations High Commissioner for Refugees (UNHCR) to be in a refugee-like situation; developing countries hosted 8.5 million refugees, or 80 percent of the global refugee population.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights." }, { "id": "IndicatorName", "value": "Personal remittances, paid (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Remittance transactions have grown in importance over the past decade. In a number of developing economies, receipts of remittances have become an important and stable source of funds that exceeds receipts from exports of goods and services or from financial inflows on foreign direct investment. But the quality of statistical remittance data is not high. Remittances are a challenge to measure because of their nature. They are heterogeneous with numerous small transactions conducted by individuals through a wide variety of channels: formal channels, such as electronic wire, or through informal channels, such as cash or goods carried across borders. The large number of remittance transactions and the multitude of channels pose challenges to the compilation of comprehensive statistics. The small size of individual transactions means that they often go undetected by typical data source systems, although the aggregate level of transactions may be substantial.\n\nBecause of difficulties in obtaining data on informal remittance transactions, the remittance transactions undertaken through informal channels are sometimes not well covered in current balance of payments data. As a result, even though direct measurement of remittances - through transactions reporting or surveys - may be considered preferable if feasible, some countries instead combine different sources and estimation methods to achieve better coverage, by using direct measurements where practical and supplemented estimates where they are not. Model-based approaches are used in some countries as they are flexible. Compilers can design models to fill gaps in data sources or to provide global totals.\n\nHowever, only reliable input data can lead to sound estimates, regardless of the sophistication of an estimation method or econometric model. Indirect data are converted to remittance estimates using a set of assumptions. These assumptions should be plausible, but it is often not possible to test or verify these assumptions and also the results in practice." }, { "id": "Longdefinition", "value": "Personal remittances comprise personal transfers and compensation of employees. Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual: personal transfers and compensation of employees. Data are in current U.S. dollars." }, { "id": "Othernotes", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1966-2024" }, { "id": "Source", "value": "IMF balance of payments data, International Monetary Fund (IMF);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The two main components of personal remittances, \"personal transfers\" and \"compensation of employees\", are items in the balance of payments (BPM6) framework. Both of these standard components are recorded in the current account. \n\"Personal transfers,\" a new item in the Balance of Payments (BPM6) represents a broader definition of worker remittances. Personal transfers include all current transfers in cash or in kind between resident and nonresident individuals, independent of the source of income of the sender (irrespective of whether the sender receives income from labor, entrepreneurial or property income, social benefits, and any other types of transfers; or disposes assets) and the relationship between the households (irrespective of whether they are related or unrelated individuals).\n\nCompensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Compensation of employees represents remuneration in return for the labor input to the production process contributed by an individual in an employer-employee relationship with the enterprise. Compensation of employees is recorded gross and includes amounts paid by the employee as taxes or for other purposes in the economy where the work is performed. Compensation of employees has three main components: wages and salaries in cash, wages and salaries in kind, and employers' social contributions." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "2" }, { "id": "BN.CAB.XOKA.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the capital account of the balance of payments. The capital account records acquisitions and disposals of nonproduced nonfinancial assets, such as sales of leases and licenses, crypto assets without a corresponding liability designed as a medium of exchange, as well as capital transfers. These transactions can have a profound impact on a country's economy and are an essential part of understanding the overall balance of payments." }, { "id": "IndicatorName", "value": "Current account balance (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Discrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded." }, { "id": "Longdefinition", "value": "Balance of current transactions (transactions in goods and services, earned income and transfer income) between residents and non-residents. The term current account balance is used in the external accounts and is expressed from the perspective of resident units. The term current external balance is used in the national accounts and is expressed from the perspective of the non-resident units, and therefore with the opposite sign. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Balances" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BN.CAB.XOKA.GD.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the capital account of the balance of payments. The capital account records acquisitions and disposals of nonproduced nonfinancial assets, such as sales of leases and licenses, crypto assets without a corresponding liability designed as a medium of exchange, as well as capital transfers. These transactions can have a profound impact on a country's economy and are an essential part of understanding the overall balance of payments." }, { "id": "IndicatorName", "value": "Current account balance (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Balance of current transactions (transactions in goods and services, earned income and transfer income) between residents and non-residents. The term current account balance is used in the external accounts and is expressed from the perspective of resident units. The term current external balance is used in the national accounts and is expressed from the perspective of the non-resident units, and therefore with the opposite sign. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Balances" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "BN.FIN.TOTL.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the financial account of the balance of payments, which shows net acquisition and disposal of financial assets and liabilities. It is crucial for assessing, among other things (i) capital mobility, reflecting the degree of international capital mobility and the country's integration into the global financial system; (ii) investor confidence and country's creditworthiness; (iii) pressures on a country's currency and central bank's actions in the foreign exchange market; (iv) effectiveness of a country's economic policies, including interest rate and exchange rate policies; or (v) country's vulnerability to external economic shocks and its ability to finance current account deficits. Overall, the financial transactions section is a critical source of information for central banks to formulate and adjust monetary policy to achieve objectives like price stability, full employment, and economic growth." }, { "id": "IndicatorName", "value": "Net financial account (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The net financial account shows net acquisition and disposal of financial assets and liabilities. It measures how net lending to or borrowing from nonresidents is financed, and is conceptually equal to the sum of the balances on the current and capital accounts. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. In BPM6, the headings of the financial account have been changed from credits and debits to net acquisition of financial assets and net incurrence of liabilities; i.e., all changes due to credit and debit entries are recorded on a net basis separately for financial assets and liabilities. Financial account balances are calculated as the change in assets minus the change in liabilities; signs are reversed from previous editions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BN.GSR.FCTY.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Net primary income (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Discrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded." }, { "id": "Longdefinition", "value": "Net primary income includes the net labor income and net property and entrepreneurial income components of the SNA. Labor income covers compensation of employees paid to nonresident workers. Property and entrepreneurial income covers investment income from the ownership of foreign financial claims (interest, dividends, rent, etc.) and nonfinancial property income (patents, copyrights, etc.). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BN.GSR.GNFS.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Net trade in goods and services (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The balance of international trade in goods and services is the difference between the exports and imports of goods and services. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Balances" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BN.GSR.MRCH.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Net trade in goods (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The balance of international trade in goods is the difference between the exports and imports of goods. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Balances" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BN.KAC.EOMS.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth." }, { "id": "IndicatorName", "value": "Net errors and omissions (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net errors and omissions constitute a residual category needed to ensure that accounts in the balance of payments statement sum to zero. Net errors and omissions are derived as the balance on the financial account minus the balances on the current and capital accounts. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BN.KLT.DINV.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth." }, { "id": "IndicatorName", "value": "Foreign direct investment, net (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Foreign direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the voting power is evidence of a direct investment relationship. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. In BPM6, the headings of the financial account have been changed from credits and debits to net acquisition of financial assets and net incurrence of liabilities; i.e., all changes due to credit and debit entries are recorded on a net basis separately for financial assets and liabilities. Financial account balances are calculated as the change in assets minus the change in liabilities; signs are reversed from previous editions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BN.KLT.PTXL.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth." }, { "id": "IndicatorName", "value": "Portfolio investment, net (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Portfolio investment includes cross-border flows and positions involving debt or equity securities, other than those included in direct investment or reserve assets. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. In BPM6, the headings of the financial account have been changed from credits and debits to net acquisition of financial assets and net incurrence of liabilities; i.e., all changes due to credit and debit entries are recorded on a net basis separately for financial assets and liabilities. Financial account balances are calculated as the change in assets minus the change in liabilities; signs are reversed from previous editions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BN.RES.INCL.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth." }, { "id": "IndicatorName", "value": "Reserves and related items (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Reserves and related items is the net change in a country's holdings of international reserves resulting from transactions on the current, capital, and financial accounts. Reserve assets are external assets, including monetary gold, that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency.Also included are net credit and loans from the IMF (excluding reserve position) and total exceptional financing. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. In BPM6, the headings of the financial account have been changed from credits and debits to net acquisition of financial assets and net incurrence of liabilities; i.e., all changes due to credit and debit entries are recorded on a net basis separately for financial assets and liabilities. Financial account balances are calculated as the change in assets minus the change in liabilities; signs are reversed from previous editions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BN.TRF.CURR.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies." }, { "id": "IndicatorName", "value": "Net secondary income (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Discrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded." }, { "id": "Longdefinition", "value": "Net secondary income (from abroad) comprises transfers of income between residents of the reporting country and the rest of the world that carry no provisions for repayment. Net secondary income is equal to the unrequited transfers of income from nonresidents to residents minus the unrequited transfers from residents to nonresidents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BN.TRF.KOGT.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies." }, { "id": "IndicatorName", "value": "Net capital account (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net capital account records acquisitions and disposals of nonproduced nonfinancial assets, such as land sold to embassies and sales of leases and licenses, as well as capital transfers, including government debt forgiveness. The use of the term capital account in this context is designed to be consistent with the System of National Accounts, which distinguishes between capital transactions and financial transactions. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BX.GRT.EXTA.CD.WD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The OECD’s aid statistics are completely transparent and publicly available to users. They seek to do the following: 1) Inform taxpayers in OECD members and other countries about what is being spent on aid overseas which enables the public to see what governments are doing with their money, 2) Allow users to understand the key characteristics of ODA to help inform the policies and programs of development co-operation providers in low- and middle-income countries, 3) Provides a comprehensive perspective on what ODA is doing worldwide, 4) Hold governments accountable for their international commitments and legal obligations on where and how to spend their aid to maximize results or benefit the neediest countries, 5) Showcase the contributions of providers outside of the members of the Development Assistance Committee (DAC) where many bilateral providers and multilateral agencies outside of the DAC have been reporting their statistics to the OECD on a voluntary basis, and 6) improve OECD’s ability to provide a comprehensive perspective on development finance flows to partner countries." }, { "id": "IndicatorName", "value": "Grants, excluding technical cooperation (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Grants are transfers made in cash, goods or services for which no repayment is required. For ODA reporting purposes, they also include forgiveness of non-military debt, support to non-governmental organisations, certain interest subsidies, and certain costs incurred in the implementation of aid. Grants to multilateral agencies intended to soften the terms of the latter’s lending are a direct resource outflow and should also be recorded as ODA grants. For OOF reporting purposes, grants for commercial purposes such as subsidies to national private investors, and grants to forgive military debt, are also included. Grant-like flows are assimilated to grants. They comprise a) loans for which the service payments are to be made into an account in the borrowing country and used in the borrowing country for its own benefit, and b) provision of commodities for sale in the recipient’s currency the proceeds of which are used in the recipient country for its own benefit. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "DAC2A: Aid (ODA) disbursements to countries and regions, Organisation for Economic Co-operation and Development (OECD), uri: DSD_DAC2@DF_DAC2A, note: Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows, Development Co-operation Report, and OECD Data Explorer database. Data are available online at: https://data-explorer.oecd.org/., publisher: Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Grants are transfers in cash or in kind for which no legal debt is incurred by the recipient. For ODA reporting purposes, they also include forgiveness of non-military debt, support to non-governmental organisations, certain interest subsidies, and certain costs incurred in the implementation of aid. Grants to multilateral agencies intended to soften the terms of the latter’s lending are a direct resource outflow and should also be recorded as ODA grants. For OOF reporting purposes, grants for commercial purposes such as subsidies to national private investors, and grants to forgive military debt, are also included. Grant-like flows are assimilated to grants. They comprise a) loans for which the service payments are to be made into an account in the borrowing country and used in the borrowing country for its own benefit, and b) provision of commodities for sale in the recipient’s currency the proceeds of which are used in the recipient country for its own benefit. \n\nData are in current U.S. dollars.\n\nFor more information, please refer to the Converged Statistical Reporting Directives for the Creditor Reporting System (CRS) and the Annual DAC Questionnaire at https://one.oecd.org/document/DCD/DAC/STAT(2023)9/FINAL/en/pdf.\n\nStatistical concept(s): Grants are wholly concessional by definition. All grants are reported as flows from the sector providing the funds for development or relief purposes. For their grant equivalents to be counted as official development assistance (ODA), the loans must be concessional i.e. bear a grant element of at least:\n\n• 45 per cent in the case of bilateral loans to the official sector of least developed countries (LDCs) and other low-income countries (LICs).\n• 15 per cent in the case of bilateral loans to the official sector of lower middle-income countries (LMICs).\n• 10 per cent in the case of bilateral loans to the official sector of upper middle-income countries (UMICs), loans to multilateral institutions and loans to international non-governmental organizations (INGOs).\n\nLoans whose terms are not consistent with the IMF Debt Limits Policy and/or the World Bank’s Non-Concessional Borrowing Policy/Sustainable Development Finance Policy are not reportable as ODA.\n\nLoans committed before 2018, qualifying under the rules valid at the time (25% threshold calculated at a 10% discount rate) but not qualifying under the new rules (new concessionality thresholds calculated using the new discount rates) is reportable as ODA. Loans committed before 2018, not qualifying under the rules valid at the time but qualifying under the new rules are reportable as OOF over their life time. Future reporting on repayments of those loans are recorded as OOF too, not as ODA." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "2" }, { "id": "BX.GRT.TECH.CD.WD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The OECD’s aid statistics are completely transparent and publicly available to users. They seek to do the following: 1) Inform taxpayers in OECD members and other countries about what is being spent on aid overseas which enables the public to see what governments are doing with their money, 2) Allow users to understand the key characteristics of ODA to help inform the policies and programs of development co-operation providers in low- and middle-income countries, 3) Provides a comprehensive perspective on what ODA is doing worldwide, 4) Hold governments accountable for their international commitments and legal obligations on where and how to spend their aid to maximize results or benefit the neediest countries, 5) Showcase the contributions of providers outside of the members of the Development Assistance Committee (DAC) where many bilateral providers and multilateral agencies outside of the DAC have been reporting their statistics to the OECD on a voluntary basis, and 6) improve OECD’s ability to provide a comprehensive perspective on development finance flows to partner countries." }, { "id": "IndicatorName", "value": "Technical cooperation grants (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Technical cooperation grants include free-standing technical cooperation grants, which are intended to finance the transfer of technical and managerial skills or of technology for the purpose of building up general national capacity without reference to any specific investment projects; and investment-related technical cooperation grants, which are provided to strengthen the capacity to execute specific investment projects. Data are in current U.S. dollars." }, { "id": "Othernotes", "value": "For more information, please refer to the Converged Statistical Reporting Directives for the Creditor Reporting System (CRS) and the Annual DAC Questionnaire at https://one.oecd.org/document/DCD/DAC/STAT(2023)9/FINAL/en/pdf.\nStatistical concept(s): Grants are wholly concessional by definition. All grants are reported as flows from the sector providing the funds for development or relief purposes. For their grant equivalents to be counted as official development assistance (ODA), the loans must be concessional i.e. bear a grant element of at least:\n\n• 45 per cent in the case of bilateral loans to the official sector of least developed countries (LDCs) and other low-income countries (LICs).\n• 15 per cent in the case of bilateral loans to the official sector of lower middle-income countries (LMICs).\n• 10 per cent in the case of bilateral loans to the official sector of upper middle-income countries (UMICs), loans to multilateral institutions and loans to international non-governmental organizations (INGOs).\n\nLoans whose terms are not consistent with the IMF Debt Limits Policy and/or the World Bank’s Non-Concessional Borrowing Policy/Sustainable Development Finance Policy are not reportable as ODA.\n\nLoans committed before 2018, qualifying under the rules valid at the time (25% threshold calculated at a 10% discount rate) but not qualifying under the new rules (new concessionality thresholds calculated using the new discount rates) is reportable as ODA. Loans committed before 2018, not qualifying under the rules valid at the time but qualifying under the new rules are reportable as OOF over their life time. Future reporting on repayments of those loans are recorded as OOF too, not as ODA." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "DAC2A: Aid (ODA) disbursements to countries and regions, Organisation for Economic Co-operation and Development (OECD), uri: DSD_DAC2@DF_DAC2A, note: Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows, Development Co-operation Report, and OECD Data Explorer database. Data are available online at: https://data-explorer.oecd.org/." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The term technical co-operation covers a large variety of aid activities. Some technical co-operation is extended in the form of projects.\n\nNon-project technical co-operation comprises activities such as the supply of volunteers or experts, other technical assistance, provision of scholarships and imputed student costs. Many of these activities are funded through specific TC budget lines, which may or may not be administered by the main aid agency. The exact use of funds is seldom known at the commitment stage. Consequently, data on the sectoral and geographical breakdown of such programmes are often collected on a disbursement basis only. As disbursement data can be very detailed (one “activity” corresponding to one individual expert or student), aggregation by recipient and sector (purpose code) is recommended prior to reporting to the CRS++. Grants of technical co-operation to individual countries include the actual or imputed costs of tuition in the reporting country of nationals of developing countries concerned. \n\nFree-standing technical co-operation comprises activities financed by a donor country whose primary purpose is to augment the level of knowledge, skills, technical know-how or productive aptitudes of the population of developing countries, i.e. increasing their stock of human intellectual capital, or their capacity for more effective use of their existing factor endowment. This relates essentially to activities that either enhance or supply human resources. It includes financing of students and trainees who are nationals of developing countries; experts, teachers, and volunteers; equipment and materials for training; research; development-oriented social and cultural programmes, etc. Associated supplies are also classified as technical co-operation.\n\nGrants are transfers in cash or in kind for which no legal debt is incurred by the recipient. For ODA reporting purposes, they also include forgiveness of non-military debt, support to non-governmental organisations, certain interest subsidies, and certain costs incurred in the implementation of aid. Grants to multilateral agencies intended to soften the terms of the latter’s lending are a direct resource outflow and should also be recorded as ODA grants. For OOF reporting purposes, grants for commercial purposes such as subsidies to national private investors, and grants to forgive military debt, are also included. Grant-like flows are assimilated to grants. They comprise a) loans for which the service payments are to be made into an account in the borrowing country and used in the borrowing country for its own benefit, and b) provision of commodities for sale in the recipient’s currency the proceeds of which are used in the recipient country for its own benefit. \n\nData are in current U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "2" }, { "id": "BX.GSR.CCIS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "ICT service exports (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Information and communication technology service exports include computer and communications services (telecommunications and postal and courier services) and information services (computer data and news-related service transactions). Data are in current U.S. dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The balance of payments (BoP) is a double-entry accounting system that shows all flows of goods and services into and out of an economy; all transfers that are the counterpart of real resources or financial claims provided to or by the rest of the world without a quid pro quo, such as donations and grants; and all changes in residents' claims on and liabilities to nonresidents that arise from economic transactions. All transactions are recorded twice - once as a credit and once as a debit. In principle the net balance should be zero, but in practice the accounts often do not balance, requiring inclusion of a balancing item, net errors and omissions.\nThe concepts and definitions underlying the data are based on the sixth edition of the International Monetary Fund's (IMF) Balance of Payments Manual." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "2" }, { "id": "BX.GSR.CCIS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The balance of payments records an economy's transactions with the rest of the world. Balance of payments accounts are divided into two groups: the current account, which records transactions in goods, services, income, and current transfers, and the capital and financial account, which records capital transfers, acquisition or disposal of non-produced, nonfinancial assets, and transactions in financial assets and liabilities." }, { "id": "IndicatorName", "value": "ICT service exports (% of service exports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Discrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded." }, { "id": "Longdefinition", "value": "Information and communication technology service exports include computer and communications services (telecommunications and postal and courier services) and information services (computer data and news-related service transactions)." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The balance of payments (BoP) is a double-entry accounting system that shows all flows of goods and services into and out of an economy; all transfers that are the counterpart of real resources or financial claims provided to or by the rest of the world without a quid pro quo, such as donations and grants; and all changes in residents' claims on and liabilities to nonresidents that arise from economic transactions. All transactions are recorded twice - once as a credit and once as a debit. In principle the net balance should be zero, but in practice the accounts often do not balance, requiring inclusion of a balancing item, net errors and omissions.\n\nThe concepts and definitions underlying the data are based on the sixth edition of the International Monetary Fund's (IMF) Balance of Payments Manual." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "2" }, { "id": "BX.GSR.CMCP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Communications, computer, etc. (% of service exports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Communications, computer, information, and other services cover international telecommunications; computer data; news-related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; personal, cultural, and recreational services; manufacturing services on physical inputs owned by others; and maintenance and repair services and government services not included elsewhere. This indicator is expressed as a percentage of service exports which are services provided by residents to non-residents." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "BX.GSR.FCTY.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Primary income receipts (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Primary income receipts refer to employee compensation paid to resident workers working abroad and investment income (receipts on direct investment, portfolio investment, other investments, and receipts on reserve assets). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BX.GSR.GNFS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Exports of goods and services (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Discrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded." }, { "id": "Longdefinition", "value": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BX.GSR.INSF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Insurance and financial services (% of service exports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Insurance and financial services cover various types of insurance provided to nonresidents by resident insurance enterprises and vice versa, and financial intermediary and auxiliary services (except those of insurance enterprises and pension funds) exchanged between residents and nonresidents. This indicator is expressed as a percentage of service exports which are services provided by residents to non-residents." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "BX.GSR.MRCH.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Goods exports (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Exports of goods occur when there are changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. Merchanting is reclassified from services to goods." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BX.GSR.NFSV.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Service exports (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Exports of services are services provided by residents to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. Manufacturing services on physical inputs owned by others (goods for processing in BPM5) and maintenance and repair services n.i.e. are reclassified from goods to services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BX.GSR.ROYL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Charges for the use of intellectual property, receipts (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Charges for the use of proprietary rights (such as patents, trademarks, copyrights, industrial processes and designs including trade secrets, franchises), and charges for licenses to reproduce or distribute (or both) intellectual property embodied in produced originals or prototypes (such as copyrights on books and manuscripts, computer software, cinematographic works, and sound recordings) and related rights (such as for live performances and television, cable, or satellite broadcast). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BX.GSR.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Exports of goods, services and primary income (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. Primary income represents the return that accrues to institutional units for their contribution to the production process or for the provision of financial assets and renting natural resources to other institutional units. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BX.GSR.TRAN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Transport services (% of service exports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Transport services covers the process of carriage of people and objects from one location to another as well as related supporting and auxiliary services. Also included are postal and courier services. This indicator is expressed as a percentage of service exports which are services provided by residents to non-residents." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "BX.GSR.TRVL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Travel services (% of service exports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Travel services cover goods and services for own use or to give away acquired from an economy by nonresidents during visits to that economy, or acquired from other economies by residents during visits to these other economies. This indicator is expressed as a percentage of service exports which are services provided by residents to non-residents." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "BX.KLT.DINV.CD.WD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits." }, { "id": "IndicatorName", "value": "Foreign direct investment, net inflows (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intra-unit flows of goods and services.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available." }, { "id": "Longdefinition", "value": "Foreign direct investment refers to direct investment equity flows in the reporting economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. Data are in current U.S. dollars." }, { "id": "Othernotes", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Balance of Payments database, International Monetary Fund (IMF), note: International Monetary Fund, Balance of Payments database, supplemented by data from the United Nations Conference on Trade and Development and official national sources.;\nUN Conference on Trade and Development (UNCTAD);\nOfficial national sources" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Foreign direct investment (FDI) data are supplemented by the World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources.\n\nThe internationally accepted definition of FDI (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results in a direct investment relationship, based on control and influence. Distinguished from other kinds of international investment, FDI is made to establish a lasting interest in or effective management control over an enterprise in another country. A lasting interest in an investment enterprise typically involves establishing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. BoP refers to Balance of Payments." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "2" }, { "id": "BX.KLT.DINV.WD.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits." }, { "id": "IndicatorName", "value": "Foreign direct investment, net inflows (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intra-unit flows of goods and services.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available." }, { "id": "Longdefinition", "value": "Foreign direct investment are the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows net inflows (new investment inflows less disinvestment) in the reporting economy from foreign investors, and is divided by GDP." }, { "id": "Othernotes", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Financial Statistics and Balance of Payments databases, International Monetary Fund (IMF);\nInternational Debt Statistics, World Bank (WB);\nWorld Bank GDP estimates, World Bank (WB);\nOECD GDP estimates, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Foreign direct investment (FDI) data are supplemented by the World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources.\n\nThe internationally accepted definition of FDI (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results in a direct investment relationship, based on control and influence. Distinguished from other kinds of international investment, FDI is made to establish a lasting interest in or effective management control over an enterprise in another country. A lasting interest in an investment enterprise typically involves establishing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. BoP refers to Balance of Payments." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "2" }, { "id": "BX.PEF.TOTL.CD.WD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits." }, { "id": "IndicatorName", "value": "Portfolio equity, net inflows (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Portfolio investors typically have less of a role in the decision making of the enterprise with potentially important implications for future flows and for the volatility of the price and volume of positions. Portfolio investment differs from other investment in that it provides a direct way to access financial markets, and thus it can provide liquidity and flexibility. It is associated with financial markets and with their specialized service providers, such as exchanges, dealers, and regulators. The nature of financial derivatives as instruments through which risk is traded in its own right in financial markets sets them apart from other types of investment. Whereas other instruments may also have risk transfer elements, these other instruments also provide financial or other resources.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available." }, { "id": "Longdefinition", "value": "Portfolio equity includes net inflows from equity securities other than those recorded as direct investment and including shares, stocks, depository receipts (American or global), and direct purchases of shares in local stock markets by foreign investors. Data are in current U.S. dollars." }, { "id": "Othernotes", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments database, International Monetary Fund (IMF);\nInternational Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF).\n\nPortfolio equity investment is defined as cross-border transactions and positions involving equity securities, other than those included in direct investment or reserve assets. Equity securities are equity instruments that are negotiable and designed to be traded, usually on organized exchanges or \"over the counter.\" The negotiability of securities facilitates trading, allowing securities to be held by different parties during their lives. Negotiability allows investors to diversify their portfolios and to withdraw their investment readily. Included in portfolio investment are investment fund shares or units (that is, those issued by investment funds) that are evidenced by securities and that are not reserve assets or direct investment. Although they are negotiable instruments, exchange-traded financial derivatives are not included in portfolio investment because they are in their own category." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "2" }, { "id": "BX.TRF.CURR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies." }, { "id": "IndicatorName", "value": "Secondary income receipts (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Secondary income refers to transfers recorded in the balance of payments whenever an economy provides or receives goods, services, income, or financial items without a quid pro quo. All transfers not considered to be capital are current. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BX.TRF.PWKR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies." }, { "id": "IndicatorName", "value": "Personal transfers, receipts (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Personal transfers are current transfers, in cash or in kind, received by resident households from non-resident households. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1968-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "BX.TRF.PWKR.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\nIn most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers. Globally, the number of refugees at end 2010 was 10.55 million, including 597,300 people considered by the United Nations High Commissioner for Refugees (UNHCR) to be in a refugee-like situation; developing countries hosted 8.5 million refugees, or 80 percent of the global refugee population.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights." }, { "id": "IndicatorName", "value": "Personal remittances, received (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Remittance transactions have grown in importance over the past decade. In a number of developing economies, receipts of remittances have become an important and stable source of funds that exceeds receipts from exports of goods and services or from financial inflows on foreign direct investment. But the quality of statistical remittance data is not high. Remittances are a challenge to measure because of their nature. They are heterogeneous with numerous small transactions conducted by individuals through a wide variety of channels: formal channels, such as electronic wire, or through informal channels, such as cash or goods carried across borders. The large number of remittance transactions and the multitude of channels pose challenges to the compilation of comprehensive statistics. The small size of individual transactions means that they often go undetected by typical data source systems, although the aggregate level of transactions may be substantial.\n\nBecause of difficulties in obtaining data on informal remittance transactions, the remittance transactions undertaken through informal channels are sometimes not well covered in current balance of payments data. As a result, even though direct measurement of remittances - through transactions reporting or surveys - may be considered preferable if feasible, some countries instead combine different sources and estimation methods to achieve better coverage, by using direct measurements where practical and supplemented estimates where they are not. Model-based approaches are used in some countries as they are flexible. Compilers can design models to fill gaps in data sources or to provide global totals.\n\nHowever, only reliable input data can lead to sound estimates, regardless of the sophistication of an estimation method or econometric model. Indirect data are converted to remittance estimates using a set of assumptions. These assumptions should be plausible, but it is often not possible to test or verify these assumptions and also the results in practice." }, { "id": "Longdefinition", "value": "Personal remittances comprise personal transfers and compensation of employees. Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual: personal transfers and compensation of employees. Data are in current U.S. dollars." }, { "id": "Othernotes", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "IMF balance of payments data, International Monetary Fund (IMF);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The two main components of personal remittances, \"personal transfers\" and \"compensation of employees\", are items in the balance of payments (BPM6) framework. Both of these standard components are recorded in the current account. \n\"Personal transfers\", a new item in the Balance of Payments (BPM6) represents a broader definition of worker remittances. Personal transfers include all current transfers in cash or in kind between resident and nonresident individuals, independent of the source of income of the sender (irrespective of whether the sender receives income from labor, entrepreneurial or property income, social benefits, and any other types of transfers; or disposes assets) and the relationship between the households (irrespective of whether they are related or unrelated individuals).\n\nCompensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Compensation of employees represents remuneration in return for the labor input to the production process contributed by an individual in an employer-employee relationship with the enterprise. Compensation of employees is recorded gross and includes amounts paid by the employee as taxes or for other purposes in the economy where the work is performed. Compensation of employees has three main components: wages and salaries in cash, wages and salaries in kind, and employers' social contributions." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "2" }, { "id": "BX.TRF.PWKR.DT.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Personal remittances, received (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Personal remittances comprise personal transfers and compensation of employees. Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual: personal transfers and compensation of employees." }, { "id": "Othernotes", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nIMF balance of payments data, International Monetary Fund (IMF);\nWorld Bank GDP estimates, World Bank (WB);\nOECD GDP estimates, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Balance of Payments (BOP) from the International Monetary Fund (IMF) serves as the primary source of information for personal transfers, which are categorized under secondary income, and for the compensation of employees, classified as primary income of the current account. Depending on data availability, references may be made to quarterly or annual figures.\n\nInformation from government agencies such as central banks and national statistical offices further complements the BOP data. When countries have missing data for certain years, this is addressed using methods like Last Observation Carried Forward (LOCF) and Next Observation Carried Backward (NOCB). If disaggregated data is unavailable, estimates are created based on historical ratios and trends.\n\nThe data is presented as a percentage of \"GDP (current US$)\" (NY.GDP.MKTP.CD), which is sourced from the World Bank's national accounts data and the OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "2" }, { "id": "CM.MKT.INDX.ZG", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information. This lowers transaction costs, which in turn improves resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks. Open economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets." }, { "id": "IndicatorName", "value": "S&P Global Equity Indices (annual % change)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Markets included in Standard & Poor's emerging markets category vary widely in level of development. Thus, it is best to look at the entire category to identify the most significant market trends. It is also useful to remember that stock market trends may be distorted by currency conversions, especially when a currency has registered a significant devaluation.\nIndex methodology details are available on the S&P Global website: https://www.spglobal.com/spdji/en/indices/equity/sp-global-bmi/#overview" }, { "id": "Longdefinition", "value": "S&P Global Equity Indices measure the U.S. dollar price change in the stock markets covered by the S&P BMI country indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2022" }, { "id": "Source", "value": "S&P Global BMI, S&P Dow Jones Indices, uri: https://www.spglobal.com/spdji/en/index-family/equity/global-equity/sp-global-bmi/#overview" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The S&P Global Broad Market Index (BMI) series is a global index suite with a transparent, modular structure that has been fully float-adjusted since 1989. It includes more than 14,000 stocks from 25 developed and 24 emerging markets. It is a rules-based index that measures global stock market performance. The index covers all publicly listed equities with float-adjusted market values of US$ 100 million or more and that meet minimum liquidity criteria measured by median daily value traded figures. The S&P Global BMI is made up of the S&P Developed BMI and S&P Emerging BMI. Additional information about methodology can be found on the S&P Global website: https://www.spglobal.com/spdji/en/documents/methodologies/methodology-sp-global-bmi-sp-ifci-indices.pdf\n\nThe percentage changes for France, Germany, Japan, the United Kingdom, and the United States refer to local stock market prices: CAC40, DAX, Nikkei, FTSE, S&P500.\nStatistical concept(s): Ratios of end-of-period levels in U.S. dollars over previous end-of-period values in U.S. dollars times 100." }, { "id": "Topic", "value": "Financial Sector: Capital markets" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "CM.MKT.LCAP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Stock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations." }, { "id": "IndicatorName", "value": "Market capitalization of listed domestic companies (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards." }, { "id": "Longdefinition", "value": "Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates." }, { "id": "Othernotes", "value": "Stock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1975-2025" }, { "id": "Source", "value": "World Federation of Exchanges database, World Federation of Exchanges (WFE)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Market capitalization figures include: shares of listed domestic companies; shares of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange); common and preferred shares of domestic companies; and shares without voting rights. Market capitalization figures exclude: collective investment funds ; rights, warrants, ETFs, convertible instruments ; options, futures ; foreign listed shares other than exclusively listed ones; companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status; and companies admitted to trading (i.e., companies whose shares are traded at the exchange but not listed at the exchange).\nStatistical concept(s): Market capitalization figures include: shares of listed domestic companies; shares of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange); common and preferred shares of domestic companies; and shares without voting rights. Market capitalization figures exclude: collective investment funds ; rights, warrants, ETFs, convertible instruments ; options, futures ; foreign listed shares other than exclusively listed ones; companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status; and companies admitted to trading (i.e., companies whose shares are traded at the exchange but not listed at the exchange)." }, { "id": "Topic", "value": "Financial Sector: Capital markets" }, { "id": "Unitofmeasure", "value": "stocks, bonds, options contracts, futures contracts and commodities." } ], "source_id": "2" }, { "id": "CM.MKT.LCAP.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Stock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations." }, { "id": "IndicatorName", "value": "Market capitalization of listed domestic companies (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards." }, { "id": "Longdefinition", "value": "Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values." }, { "id": "Othernotes", "value": "Stock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1975-2024" }, { "id": "Source", "value": "World Federation of Exchanges database, World Federation of Exchanges (WFE)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Market capitalization figures include: shares of listed domestic companies; shares of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange); common and preferred shares of domestic companies; and shares without voting rights. Market capitalization figures exclude: collective investment funds ; rights, warrants, ETFs, convertible instruments ; options, futures ; foreign listed shares other than exclusively listed ones; companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status; and companies admitted to trading (i.e., companies whose shares are traded at the exchange but not listed at the exchange).\nStatistical concept(s): Market capitalization figures include: shares of listed domestic companies; shares of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange); common and preferred shares of domestic companies; and shares without voting rights. Market capitalization figures exclude: collective investment funds ; rights, warrants, ETFs, convertible instruments ; options, futures ; foreign listed shares other than exclusively listed ones; companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status; and companies admitted to trading (i.e., companies whose shares are traded at the exchange but not listed at the exchange)." }, { "id": "Topic", "value": "Financial Sector: Capital markets" }, { "id": "Unitofmeasure", "value": "End-of-year total market capitalization of listed domestic companies, divided by gross domestic product, expressed as a percentage." } ], "source_id": "2" }, { "id": "CM.MKT.LDOM.NO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Stock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations." }, { "id": "IndicatorName", "value": "Listed domestic companies, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards." }, { "id": "Longdefinition", "value": "Listed domestic companies, including foreign companies which are exclusively listed, are those which have shares listed on an exchange at the end of the year. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status, are excluded. A company with several classes of shares is counted once. Only companies admitted to listing on the exchange are included." }, { "id": "Othernotes", "value": "Stock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1975-2025" }, { "id": "Source", "value": "World Federation of Exchanges database, World Federation of Exchanges (WFE)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A company is considered domestic when it is incorporated in the same country as where the exchange is located. The only exception is the case of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange as defined in the domestic market capitalization definition).\nStatistical concept(s): A company is considered domestic when it is incorporated in the same country as where the exchange is located. The only exception is the case of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange as defined in the domestic market capitalization definition)." }, { "id": "Topic", "value": "Financial Sector: Capital markets" }, { "id": "Unitofmeasure", "value": "End-of-year total market capitalization of listed domestic companies, divided by gross domestic product, expressed as a percentage. (percentile)" } ], "source_id": "2" }, { "id": "CM.MKT.TRAD.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Stock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations." }, { "id": "IndicatorName", "value": "Stocks traded, total value (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards. Only EOB trades are included in the total value of shares traded." }, { "id": "Longdefinition", "value": "The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates." }, { "id": "Othernotes", "value": "Stock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1975-2025" }, { "id": "Source", "value": "World Federation of Exchanges database, World Federation of Exchanges (WFE)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The value of shares traded represent the transfer of ownership effected automatically through the exchange's electronic order book (EOB), where orders placed by trading members are usually exposed to all market users and automatically matched according to precise rules set up by the exchange, generally on a price/time priority basis. For data before 2001, the WFE used two different approaches for the collection of trading data, depending on the individual stock exchange's market organization and rules. The first approach is the Trading System View (TSV). Stock exchanges adopting this view count only those transactions which pass through their trading system or trading floor. The TSV is generally adopted by exchanges which operate a centralized order book (order-driven market). Trades done by their members off the exchange are not included. The second approach is the Regulated Environment View (REV). Stock exchanges in this category include all transactions subject to supervision by the market authority, including transactions made by members, and sometimes non-members, on outside trading systems and transactions into foreign markets. Figures reported under the REV approach will be higher than those reported under the TSV approach.\nStatistical concept(s): The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates. The value of shares traded represent the transfer of ownership effected automatically through the exchange's electronic order book (EOB), where orders placed by trading members are usually exposed to all market users and automatically matched according to precise rules set up by the exchange, generally on a price/time priority basis. https://data.worldbank.org/indicator/CM.MKT.TRAD.CD" }, { "id": "Topic", "value": "Financial Sector: Capital markets" }, { "id": "Unitofmeasure", "value": "stocks, bonds, options contracts, futures contracts and commodities" } ], "source_id": "2" }, { "id": "CM.MKT.TRAD.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Stock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations." }, { "id": "IndicatorName", "value": "Stocks traded, total value (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards. Only EOB trades are included in the total value of shares traded." }, { "id": "Longdefinition", "value": "The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values." }, { "id": "Othernotes", "value": "Stock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1975-2024" }, { "id": "Source", "value": "World Federation of Exchanges database, World Federation of Exchanges (WFE)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The value of shares traded represent the transfer of ownership effected automatically through the exchange's electronic order book (EOB), where orders placed by trading members are usually exposed to all market users and automatically matched according to precise rules set up by the exchange, generally on a price/time priority basis. For data before 2001, the WFE used two different approaches for the collection of trading data, depending on the individual stock exchange's market organization and rules. The first approach is the Trading System View (TSV). Stock exchanges adopting this view count only those transactions which pass through their trading system or trading floor. The TSV is generally adopted by exchanges which operate a centralized order book (order-driven market). Trades done by their members off the exchange are not included. The second approach is the Regulated Environment View (REV). Stock exchanges in this category include all transactions subject to supervision by the market authority, including transactions made by members, and sometimes non-members, on outside trading systems and transactions into foreign markets. Figures reported under the REV approach will be higher than those reported under the TSV approach.\nStatistical concept(s): The value of shares traded represent the transfer of ownership effected automatically through the exchange's electronic order book (EOB), where orders placed by trading members are usually exposed to all market users and automatically matched according to precise rules set up by the exchange, generally on a price/time priority basis. For data before 2001, the WFE used two different approaches for the collection of trading data, depending on the individual stock exchange's market organization and rules. The first approach is the Trading System View (TSV). Stock exchanges adopting this view count only those transactions which pass through their trading system or trading floor. The TSV is generally adopted by exchanges which operate a centralized order book (order-driven market). Trades done by their members off the exchange are not included. The second approach is the Regulated Environment View (REV). Stock exchanges in this category include all transactions subject to supervision by the market authority, including transactions made by members, and sometimes non-members, on outside trading systems and transactions into foreign markets. Figures reported under the REV approach will be higher than those reported under the TSV approach." }, { "id": "Topic", "value": "Financial Sector: Capital markets" }, { "id": "Unitofmeasure", "value": "stocks, bonds, options contracts, futures contracts and commodities. (total number of shares traded, both domestic and foreign, multiplied by respective prices)" } ], "source_id": "2" }, { "id": "CM.MKT.TRNR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Stock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations." }, { "id": "IndicatorName", "value": "Stocks traded, turnover ratio of domestic shares (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards. Only domestic shares are used in order to be consistent with domestic market capitalization." }, { "id": "Longdefinition", "value": "Turnover ratio is the value of domestic shares traded divided by their market capitalization. The value is annualized by multiplying the monthly average by 12." }, { "id": "Othernotes", "value": "Stock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1975-2024" }, { "id": "Source", "value": "World Federation of Exchanges database, World Federation of Exchanges (WFE)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Turnover ratio is the value of electronic order book (EOB) domestic shares traded divided by their market capitalization. The value is annualized by multiplying the monthly average by 12, according to the following formula: (Monthly EOB domestic shares traded / Month-end domestic market capitalization) x 12.\nStatistical concept(s): Statistical Concept and Methodology: Turnover ratio is the value of electronic order book (EOB) domestic shares traded divided by their market capitalization. The value is annualized by multiplying the monthly average by 12, according to the following formula: (Monthly EOB domestic shares traded / Month-end domestic market capitalization) x 12." }, { "id": "Topic", "value": "Financial Sector: Capital markets" } ], "source_id": "2" }, { "id": "DC.DAC.AUSL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Australia (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1965-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.AUTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Austria (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.BELL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Belgium (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.CANL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Canada (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.CECL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, European Union institutions (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.CHEL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Switzerland (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.CZEL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Czech Republic (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.DEUL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Germany (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.DNKL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Denmark (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.ESPL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Spain (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.ESTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Estonia (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Lithuania, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 32 members - 31 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.FINL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Finland (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.FRAL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, France (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.GBRL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, United Kingdom (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.GRCL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Greece (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.HUNL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Hungary (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.IRLL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Ireland (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1974-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.ISLL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Iceland (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.ITAL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Italy (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.JPNL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Japan (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.KORL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Korea, Rep. (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.LTUL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Lithuania (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Lithuania, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 32 members - 31 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.LUXL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Luxembourg (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.NLDL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Netherlands (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.NORL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Norway (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.NZLL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, New Zealand (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.POLL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Poland (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.PRTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Portugal (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.SVKL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Slovak Republic (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.SVNL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Slovenia (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.SWEL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Sweden (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Total (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.DAC.USAL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, United States (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.ODA.TLDC.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net ODA provided, to the least developed countries (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. The list of least developed countries (LDCs) has been agreed by the General Assembly, on the recommendation of the Committee for Development Policy, Economic and Social Council." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.ODA.TLDC.GN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net ODA provided to the least developed countries (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. The list of least developed countries (LDCs) has been agreed by the General Assembly, on the recommendation of the Committee for Development Policy, Economic and Social Council. Series is shown as a share of donors' GNI." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is presented as a percentage of Gross National Income (GNI)." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.ODA.TOTL.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net ODA provided, total (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.ODA.TOTL.GN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "Official development assistance (ODA): Frequently asked questions" }, { "id": "IndicatorName", "value": "Net ODA provided, total (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. It is shown as a share of donors' GNI." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2017" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is presented as a percentage of Gross National Income (GNI)." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DC.ODA.TOTL.KD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net ODA provided, total (constant 2023 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. Data are in constant 2023 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in constant U.S. dollar prices to account for inflation in the donor's currency and the changes in exchange rates with the U.S. dollar." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.DOD.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels." }, { "id": "IndicatorName", "value": "External debt stocks, total (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\nStatistical concept(s): Disbursed and outstanding debt definition (??)\nDebt stock definition (??)" }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "DT.DOD.DECT.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "External debt stocks (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total external debt stocks to gross national income. Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "2" }, { "id": "DT.DOD.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels." }, { "id": "IndicatorName", "value": "Use of IMF credit (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Use of IMF Credit: Data related to the operations of the IMF are provided by the IMF Treasurer’s Department. They are converted from special drawing rights into dollars using end-of-period exchange rates for stocks and average-over-the-period exchange rates for flows. IMF trust fund operations under the Enhanced Structural Adjustment Facility, Extended Fund Facility, Poverty Reduction and Growth Facility, and Structural Adjustment Facility (Enhanced Structural Adjustment Facility in 1999) are presented together with all of the IMF’s special facilities (buffer stock, supplemental reserve, compensatory and contingency facilities, oil facilities, and other facilities). SDR allocations are also included in this category. According to the BPM6, SDR allocations are recorded as the incurrence of a debt liability of the member receiving them (because of a requirement to repay the allocation in certain circumstances, and also because interest accrues). This debt item is introduced for the first time this year with historical data starting in 1999." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data related to the operations of the IMF come from the IMF Treasurer's Department and are converted from special drawing rights (SDRs) into dollars using end-of-period exchange rates for stocks and average over the period exchange rates for converting flows. DOD refers to disbursed and outstanding debt; data are in current U.S. dollars.\n\nData on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "2" }, { "id": "DT.DOD.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels." }, { "id": "IndicatorName", "value": "External debt stocks, long-term (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Long-term debt is debt that has an original or extended maturity of more than one year. It has three components: public, publicly guaranteed, and private nonguaranteed debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "2" }, { "id": "DT.DOD.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels." }, { "id": "IndicatorName", "value": "External debt stocks, private nonguaranteed (PNG) (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt comprises long-term external obligations of private debtors that are not guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "2" }, { "id": "DT.DOD.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels." }, { "id": "IndicatorName", "value": "External debt stocks, public and publicly guaranteed (PPG) (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt comprises long-term external obligations of public debtors, including the national government, Public Corporations, State Owned Enterprises, Development Banks and Other Mixed Enterprises, political subdivisions (or an agency of either), autonomous public bodies, and external obligations of private debtors that are guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "2" }, { "id": "DT.DOD.DSTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels." }, { "id": "IndicatorName", "value": "External debt stocks, short-term (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "2" }, { "id": "DT.DOD.DSTC.IR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "Short-term debt (% of total reserves)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The DRS encourages debtor countries to voluntarily provide information on their short-term external obligations. By its nature, short-term external debt is difficult to monitor: loan-by-loan registration is normally impractical, and monitoring systems typically rely on information requested periodically by the central bank from the banking sector. The World Bank regards the debtor country as the authoritative source of information on its short-term debt. Where such information is not available from the debtor country, data are derived from BIS data on international bank lending based on time remaining to original maturity. The data are reported based on residual maturity, but an estimate of short-term external liabilities by original maturity can be derived by deducting from claims due in one year those that have a maturity of between one and two years. However, BIS data include liabilities reported only by banks within the BIS reporting area. The results should thus be interpreted with caution. Because short-term debt poses an immediate burden and is particularly important for monitoring vulnerability, it is compared with total debt and foreign exchange reserves, which are instrumental in providing coverage for such obligations.\n\nA country's external debt burden, both debt outstanding and debt service, affects its creditworthiness and vulnerability. While data related to public and publicly guaranteed debt are reported to the DRS on a loan-by-loan basis, aggregate data on long-term private nonguaranteed debt are reported annually and are reported by the country or estimated by World Bank staff for countries where this type of external debt is known to be significant. Estimates are based on national data from the World Bank's Quarterly External Debt Statistics." }, { "id": "Longdefinition", "value": "Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Total reserves includes gold." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "World Development Indicators, World Bank (WB);\nInternational Monetary Fund (IMF), type: Balance of Payments Statistics Yearbook and data files;\nWorld Bank (WB), type: GDP estimates;\nOrganisation for Economic Co-operation and Development (OECD), type: GDP estimates" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "2" }, { "id": "DT.DOD.DSTC.XP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "Short-term debt (% of exports of goods, services and primary income)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Exports of goods, services and primary income is the sum of goods (merchandise) exports, exports of (nonfactor) services and income (factor) receipts." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value. \n\nThe data is presented as a percentage of \"Exports of goods, services and primary income (BoP, current US$)\" (BX.GSR.TOTL.CD), sourced from the International Monetary Fund's Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "2" }, { "id": "DT.DOD.DSTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "Short-term debt (% of total external debt)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "2" }, { "id": "DT.DOD.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PPG, IBRD (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "2" }, { "id": "DT.DOD.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PPG, IDA (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "2" }, { "id": "DT.DOD.MWBG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "IBRD loans and IDA credits (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "IBRD loans and IDA credits are public and publicly guaranteed debt extended by the World Bank Group. The International Bank for Reconstruction and Development (IBRD) lends at market rates. Credits from the International Development Association (IDA) are at concessional rates. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "2" }, { "id": "DT.DOD.PVLX.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nPresent value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. The PV of external debt is a better measure for debt burden's of countries that have access to concessional financing. The IMF/World Bank's Low-Income Countries (LICs) Debt Sustainability Framework (DSF) uses the present value of external debt as a means to assess a country's risk of external and overall debt distress.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "Present value of external debt (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Present value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "DT.DOD.PVLX.EX.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nPresent value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. The PV of external debt is a better measure for debt burden's of countries that have access to concessional financing. The IMF/World Bank's Low-Income Countries (LICs) Debt Sustainability Framework (DSF) uses the present value of external debt as a means to assess a country's risk of external and overall debt distress.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "Present value of external debt (% of exports of goods, services and income)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Present value of external debt to exports of goods, services and income. Present value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan. Exports of goods, services and primary income is the sum of goods (merchandise) exports, exports of (nonfactor) services and income (factor) receipts. The exports denominator is a three-year average." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "DT.DOD.PVLX.GN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nPresent value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. The PV of external debt is a better measure for debt burden's of countries that have access to concessional financing. The IMF/World Bank's Low-Income Countries (LICs) Debt Sustainability Framework (DSF) uses the present value of external debt as a means to assess a country's risk of external and overall debt distress.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "Present value of external debt (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Present value of external debt to gross national income. Present value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. The GNI denominator is a three-year average." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "DT.NFL.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net financial flows, bilateral (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data show concessional and nonconcessional financial flows from official bilateral sources. The Organisation for Economic Co-operation and Development's (OECD) Development Assistance Committee (DAC) defines concessional flows from bilateral donors as flows with a grant element of at least 25 percent; they are evaluated assuming a 10 percent nominal discount rate." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "2" }, { "id": "DT.NFL.BOND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels." }, { "id": "IndicatorName", "value": "Portfolio investment, bonds (PPG + PNG) (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The DRS encourages debtor countries to voluntarily provide information on their short-term external obligations. By its nature, short-term external debt is difficult to monitor: loan-by-loan registration is normally impractical, and monitoring systems typically rely on information requested periodically by the central bank from the banking sector. The World Bank regards the debtor country as the authoritative source of information on its short-term debt. Where such information is not available from the debtor country, data are derived from BIS data on international bank lending based on time remaining to original maturity. The data are reported based on residual maturity, but an estimate of short-term external liabilities by original maturity can be derived by deducting from claims due in one year those that have a maturity of between one and two years. However, BIS data include liabilities reported only by banks within the BIS reporting area. The results should thus be interpreted with caution. Because short-term debt poses an immediate burden and is particularly important for monitoring vulnerability, it is compared with total debt and foreign exchange reserves, which are instrumental in providing coverage for such obligations.\n\nA country's external debt burden, both debt outstanding and debt service, affects its creditworthiness and vulnerability. While data related to public and publicly guaranteed debt are reported to the DRS on a loan-by-loan basis, aggregate data on long-term private nonguaranteed debt are reported annually and are reported by the country or estimated by World Bank staff for countries where this type of external debt is known to be significant. Estimates are based on national data from the World Bank's Quarterly External Debt Statistics." }, { "id": "Longdefinition", "value": "Bonds are securities issued with a fixed rate of interest for a period of more than one year. They include net flows through cross-border public and publicly guaranteed and private nonguaranteed bond issues. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Bonds are debt instruments issued by public and publicly guaranteed or private debtors with durations of one year or longer. Bonds usually give the holder the unconditional right to fixed money income or contractually determined, variable money income.\n\nData on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "2" }, { "id": "DT.NFL.CERF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Official development assistance (ODA) is defined as government aid that promotes and specifically targets the economic development and welfare of developing countries. The DAC adopted ODA as the “gold standard” of foreign aid in 1969 and it remains the main source of financing for development aid. ODA data is collected, verified and made publicly available by the OECD. The DAC has measured resource flows to developing countries since 1961. Special attention has been given to the official and concessional part of this flow, defined as “official development assistance” (ODA). The DAC first defined ODA in 1969, and tightened the definition in 1972. ODA is the key measure used in practically all aid targets and assessments of aid performance." }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, CERF (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2017-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "DT.NFL.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator tells us the net flows (disbursements minus principal payments) of private debtor's external debt that is not guaranteed for repayment by a public entity." }, { "id": "IndicatorName", "value": "Net flows on external debt, private nonguaranteed (PNG) (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Countries that report to the World Bank’s Debtor Reporting System (DRS) submit their private nonguaranteed (PNG) on an aggregate basis. The Debt Data Team then compiles this information to produce this indicator." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" }, { "id": "Unitofmeasure", "value": "current US$" } ], "source_id": "2" }, { "id": "DT.NFL.FAOG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, FAO (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2013-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.IAEA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, IAEA (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.IFAD.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, IFAD (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1979-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.ILOG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, ILO (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2012-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.IMFC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net financial flows, IMF concessional (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IMF is the International Monetary Fund, which provides concessional lending through the Poverty Reduction and Growth Facility and the IMF Trust Fund. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Monetary Fund (IMF) makes concessional funds available through its Extended Credit Facility (which replaced the Poverty Reduction and Growth Facility in 2010), the Standby Credit Facility, and the Rapid Credit Facility. Eligibility is based principally on a country's per capita income and eligibility under IDA." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "2" }, { "id": "DT.NFL.IMFN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net financial flows, IMF nonconcessional (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IMF is the International Monetary Fund, which provides nonconcessional lending through the credit it provides to its members, mainly to meet balance of payments needs. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Nonconcessional lending from the IMF is provided mainly through Stand-by Arrangements, the Flexible Credit Line, and the Extended Fund Facility." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "2" }, { "id": "DT.NFL.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net financial flows, IBRD (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IBRD is the International Bank for Reconstruction and Development, the founding and largest member of the World Bank Group. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank's International Bank for Reconstruction and Development (IBRD) lends to creditworthy countries at a variable base rate of six-month LIBOR plus a spread, either variable or fixed, for the life of the loan. The rate is reset every six months and applies to the interest period beginning on that date." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "2" }, { "id": "DT.NFL.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net financial flows, IDA (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IDA is the International Development Association, the concessional loan window of the World Bank Group. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: World Bank concessional lending is done by the International Development Association (IDA) based on gross national income (GNI) per capita and performance standards assessed by World Bank staff." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "2" }, { "id": "DT.NFL.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net financial flows, multilateral (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data show concessional and nonconcessional financial flows from international financial institutions. International financial institutions fund nonconcessional lending operations primarily by selling low-interest, highly rated bonds backed by prudent lending and financial policies and the strong financial support of their members. Funds are then on-lent to developing countries at slightly higher interest rates with 15- to 20-year maturities. Lending terms vary with market conditions and institutional policies. Concessional flows from international financial institutions are credits provided through concessional lending facilities. Subsidies from donors or other resources reduce the cost of these loans. Grants are not included in net flows." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "2" }, { "id": "DT.NFL.MOTH.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net financial flows, others (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Others is a residual category in the World Bank's Debtor Reporting System. It includes such institutions as the Caribbean Development Fund, Council of Europe, European Development Fund, Islamic Development Bank, Nordic Development Fund, and the like. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "2" }, { "id": "DT.NFL.NIFC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "IFC, private nonguaranteed (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt privately placed from the International Finance Corporation (IFC). Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "2" }, { "id": "DT.NFL.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PPG, official creditors (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "2" }, { "id": "DT.NFL.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PPG, bonds (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "2" }, { "id": "DT.NFL.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PPG, commercial banks (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "2" }, { "id": "DT.NFL.PCBO.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nProposed: External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the governments, corporations or private households. The debt includes money owed to governments or public agencies (bilateral), to international organizations (multilateral) or to exporters, commercial banks or other financial institutions.\nExternal indebtedness affects a country's creditworthiness and investor perceptions. When used effectively, a reasonable level of external debt can help a country finance productive investments, such as building infrastructure and investing in education and health, that can increase growth." }, { "id": "IndicatorName", "value": "Commercial banks and other lending (PPG + PNG) (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Longdefinition", "value": "Commercial bank and other lending includes net commercial bank lending (public and publicly guaranteed and private non- guaranteed) and other private credits. Data are in current U.S. dollars.\n\nProposed: Commercial bank and other lending includes net commercial bank and other private creditors lending excluding bonds (public and publicly guaranteed + private nonguaranteed)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Commercial banks include all commercial banks, whether or not publicly owned, that provide loans and other financial services. Private creditors include commercial banks, bondholders, and other private creditors. This indicator includes only publicly guaranteed creditors. Nonguaranteed private creditors are shown separately. Bonds include publicly issued or privately placed bonds. Commercial bank loans are loans from private banks and other private financial institutions. Credits of other private creditors include credits from manufacturers, exporters, and other suppliers of goods, plus bank credits covered by a guarantee of an export credit agency.\n\nChanged to:\nCountries that report to the World Bank’s Debtor Reporting System (DRS) submit their 1- public and publicly guaranteed debt (PPG) and private debt with a public guarantee on a loan-by-loan basis and 2- private nonguaranteed (PNG) debt on an aggregate basis. The World Bank Debt Data Team then compiles this information to produce this indicator.\n\n\nStatistical concept(s): Commercial banks include all commercial banks, whether or not publicly owned, that provide loans and other financial services. Private creditors include commercial banks, bondholders, and other private creditors. This indicator includes only publicly guaranteed creditors. Nonguaranteed private creditors are shown separately. Bonds include publicly issued or privately placed bonds. Commercial bank loans are loans from private banks and other private financial institutions. Credits of other private creditors include credits from manufacturers, exporters, and other suppliers of goods, plus bank credits covered by a guarantee of an export credit agency.\n\nproposed: Commercial banks include all commercial banks, whether or not publicly owned, that provide loans and other financial services. Credits of other private creditors include credits from manufacturers, exporters, and other suppliers of goods, plus bank credits covered by a guarantee of an export credit agency." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "DT.NFL.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PNG, bonds (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics, note: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Countries that report to the World Bank’s Debtor Reporting System (DRS) submit their private nonguaranteed (PNG) on an aggregate basis. The World Bank Debt Data Team then compiles this information to produce this indicator." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "DT.NFL.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics, note: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Countries that report to the World Bank’s Debtor Reporting System (DRS) submit their private nonguaranteed (PNG) on an aggregate basis. The World Bank Debt Data Team then compiles this information to produce this indicator." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "DT.NFL.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PPG, other private creditors (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "2" }, { "id": "DT.NFL.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PPG, private creditors (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "2" }, { "id": "DT.NFL.RDBC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Concessional finance is below market rate finance provided by major financial institutions, such as development banks and multilateral funds, to developing countries to accelerate development objectives. The term concessional finance does not represent a single mechanism or type of financial support but comprises a range of below market rate products used to accelerate development objective.\n\nConcessional finance often targets high-impact projects responding to globally significant development challenges – from climate change mitigation and resilience to vaccine deployment, water sanitation and education - that otherwise could not go ahead without specialized financial support.\n\nNet financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal." }, { "id": "IndicatorName", "value": "Net financial flows, RDB concessional (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Concessional financial flows cover disbursements made through concessional lending facilities. Regional development banks are the African Development Bank, in Tunis, Tunisia, which serves all of Africa, including North Africa; the Asian Development Bank, in Manila, Philippines, which serves South and Central Asia and East Asia and Pacific; the European Bank for Reconstruction and Development, in London, United Kingdom, which serves Europe and Central Asia; and the Inter-American Development Bank, in Washington, D.C., which serves the Americas. Aggregates include amounts for economies not specified elsewhere. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), note: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Regional Development Banks (RDB) share their lending information on a loan by loan basis with the World Bank through the World Bank's Debtor Reporting System (DRS). The Debt Data Team then compiles this information to produce this indicator.\nStatistical concept(s): Regional development banks also maintain concessional windows. Their loans are recorded according to each institution's classification and not according to the Organisation for Economic Co-operation and Development's (OECD) Development Assistance Committee (DAC) definition." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "DT.NFL.RDBN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The financial flows from regional development banks (RDB) that are not made through concessional lending facilities. Concessional finance is below market rate finance provided by major financial institutions." }, { "id": "IndicatorName", "value": "Net financial flows, RDB nonconcessional (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Nonconcessional financial flows cover all disbursements except those made through concessional lending facilities. Regional development banks are the African Development Bank, in Tunis, Tunisia, which serves all of Africa, including North Africa; the Asian Development Bank, in Manila, Philippines, which serves South and Central Asia and East Asia and Pacific; the European Bank for Reconstruction and Development, in London, United Kingdom, which serves Europe and Central Asia; and the Inter-American Development Bank, in Washington, D.C., which serves the Americas. Aggregates include amounts for economies not specified elsewhere. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Regional Development Banks (RDB) share their lending information on a loan by loan basis with the World Bank through the World Bank's Debtor Reporting System (DRS). The Debt Data Team then compiles this information to produce this indicator.\nStatistical concept(s): Regional development banks also maintain concessional windows. Their loans are recorded according to each institution's classification and not according to the Organisation for Economic Co-operation and Development's (OECD) Development Assistance Committee (DAC) definition." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "DT.NFL.SDGF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, SDGFUND (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2021-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.SPRP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, SPRP (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2021-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNAI.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNAIDS (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNCD.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNCDF (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2020-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNCF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNICEF (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNCR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNHCR (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1969-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNCTAD.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNCTAD (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2020-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNCV.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNCOVID (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2021-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNDP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNDP (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1968-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNEC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNECE (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Othernotes", "value": "Data for net official flows from UNECE at present are reported at the regional level only. A more detailed breakdown by recipient country will be available in the future." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2008-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNEP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNEP (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2015-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNFP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNFPA (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1977-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNID.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNIDIR (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNIDO.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNIDO (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2020-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNPB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNPBF (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2007-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNRW.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNRWA (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1969-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNTA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNTA (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1969-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNWN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNWOMEN (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2021-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.UNWT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNWTO (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2016-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.WFPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, WFP (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1969-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.WHOL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, WHO (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2009-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.NFL.WITC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, WTO-ITC (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2020-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.ODA.ALLD.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official development assistance and official aid received (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions). \n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nNet official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.ODA.ALLD.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official development assistance and official aid received (constant 2023 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in constant 2023 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions). \n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in constant U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nNet official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.ODA.OATL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "DAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about US $130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net official aid received (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe nominal values may overstate the real value of aid to recipients. Changes in international prices and exchange rates can reduce the purchasing power of aid. Tying aid, still prevalent though declining in importance, also tends to reduce its purchasing power. Tying requires recipients to purchase goods and services from the donor country or from a specified group of countries. Such arrangements prevent a recipient from misappropriating or mismanaging aid receipts, but they may also be motivated by a desire to benefit donor country suppliers.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions). \n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nNet official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Data are in current U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.ODA.OATL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official aid received (constant 2023 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in constant 2023 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in constant U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.ODA.ODAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "DAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about USD 130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net official development assistance received (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe nominal values may overstate the real value of aid to recipients. Changes in international prices and exchange rates can reduce the purchasing power of aid. Tying aid, still prevalent though declining in importance, also tends to reduce its purchasing power. Tying requires recipients to purchase goods and services from the donor country or from a specified group of countries. Such arrangements prevent a recipient from misappropriating or mismanaging aid receipts, but they may also be motivated by a desire to benefit donor country suppliers.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions). \n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.ODA.ODAT.GI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The ratio of aid to gross capital formation provides a measure of recipient country's dependency on aid. Ratios of aid are generally much higher in Sub-Saharan Africa than in other regions, particularly in the 1980s. High ratios are due only in part to aid flows. Many African countries saw severe erosion in their terms of trade in the 1980s, along with weak policies, falling incomes, imports, and investment. Thus the increase in aid dependency ratios reflects events affecting both the numerator (aid) and the denominator (gross capital formation).\n\nDAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about US $130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net ODA received (% of gross capital formation)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nRatio of aid to gross capital formation provides measures of recipient country's dependency on aid. But care must be taken in drawing policy conclusions. For foreign policy reasons some countries have traditionally received large amounts of aid. Thus aid dependency ratio may reveal as much about a donor's interests as about a recipient's needs. The quality of data on government fixed capital formation depends on the quality of government accounting systems which tend to be weak in developing countries. Measures of fixed capital formation by households and corporations - particularly capital outlays by small, unincorporated enterprises - are usually unreliable. Estimates of changes in inventories are rarely complete but usually include the most important activities of commodities.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nWorld Bank gross capital formation estimates, World Bank (WB), note: World Bank gross capital formation estimates are used for the denominator" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).Gross capital formation consists of outlays on additions to the conomy's fixed assets plus net changes in the level of inventories. It is generally obtained from industry reports of acquisitions and distinguishes only the broad categories of capital formation. Data on capital formation may be estimated from direct surveys of enterprises and administrative records or based on the commodity flow methods using data from production, trade and construction activities.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Net ODA received as a percent of gross capital formation is calculated using values in U.S. dollars converted at official exchange rates." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.ODA.ODAT.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The ratio of aid to GNI provides a measure of recipient country's dependency on aid. Ratios of aid are generally much higher in Sub-Saharan Africa than in other regions, and they increased in the 1980s. High ratios are due only in part to aid flows. Many African countries saw severe erosion in their terms of trade in the 1980s, along with weak policies, falling incomes, imports, and investment. Thus the increase in aid dependency ratios reflects events affecting both the numerator (aid) and the denominator (GNI).\n\nDAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about US $130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net ODA received (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nRatio of aid to gross national income (GNI) provides measures of recipient country's dependency on aid. But care must be taken in drawing policy conclusions. For foreign policy reasons some countries have traditionally received large amounts of aid. Thus aid dependency ratio may reveal as much about a donor's interests as about a recipient's needs. \n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nWorld Bank GNI estimates, World Bank (WB), note: World Bank GNI estimates are used for the denominator" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Net ODA received as a percent of GNI is calculated using values in U.S. dollars converted at official exchange rates." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.ODA.ODAT.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official development assistance received (constant 2023 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in constant 2023 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions). \n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in constant U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.ODA.ODAT.MP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The ratio of aid to imports of goods and services provides a measure of recipient country's dependency on aid. Ratios of aid are generally much higher in Sub-Saharan Africa than in other regions, and they increased in the 1980s. High ratios are due only in part to aid flows. Many African countries saw severe erosion in their terms of trade in the 1980s, along with weak policies, falling incomes, imports, and investment. Thus the increase in aid dependency ratios reflects events affecting both the numerator (aid) and the denominator (imports of goods and services).\n\nDAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about US $130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net ODA received (% of imports of goods, services and primary income)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nRatio of aid to imports of goods and services provides measures of recipient country's dependency on aid. But care must be taken in drawing policy conclusions. For foreign policy reasons some countries have traditionally received large amounts of aid. Thus aid dependency ratio may reveal as much about a donor's interests as about a recipient's needs. Data on imports are compiled from customs reports and balance of payments data. Although data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or correspond to the change of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in league but unreported shuttle trade may further distort trade statistics.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nWorld Bank imports of good and services estimates, World Bank (WB), note: World Bank imports of good and services estimates are used for the denominator." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\nData on imports are compiled from customs reports and balance of payments data. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services. They exclude compensation of employees and investment income (factor services in the 1969 SNA) and transfer payments.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Net ODA received as a percent of imports of goods and services is calculated using values in U.S. dollars converted at official exchange rates." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.ODA.ODAT.PC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The ratio of aid per capita provides a measure of recipient country's dependency on aid. DAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about USD 130 billion. This demonstrates how effective aid pledges can be when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net ODA received per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients; and is calculated by dividing net ODA received by the midyear population estimate. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nWorld Bank population estimates, World Bank (WB), note: World Bank population estimates are used for the denominator" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\nTotal population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship - except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. The values shown are midyear estimates. Net official development assistance per capita is net ODA divided by midyear population.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThis definition excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.ODA.ODAT.XP.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ratio of aid to central government expense provides measures of recipient country's dependency on aid. Ratios of aid are generally much higher in Sub-Saharan Africa than in other regions, and they increased in the 1980s. High ratios are due only in part to aid flows. Many African countries saw severe erosion in their terms of trade in the 1980salong with weak policies, falling incomes, imports, and investment. Thus the increase in aid dependency ratios reflects events affecting both the numerator (aid) and the denominator (central government expense).\n\nDAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about US $130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net ODA received (% of central government expense)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nRatio of aid to central government expense provides measures of recipient country's dependency on aid. But care must be taken in drawing policy conclusions. For foreign policy reasons some countries have traditionally received large amounts of aid. Thus aid dependency ratio may reveal as much about a donor's interests as about a recipient's needs.\n\nThe nominal values used here may overstate the real value of aid to recipients. Changes in international prices and exchange rates can reduce the purchasing power of aid. Tying aid, still prevalent though declining in importance, also tends to reduce its purchasing power. Tying requires recipients to purchase goods and services from the donor country or from a specified group of countries. Such arrangements prevent a recipient from misappropriating or mismanaging aid receipts, but they may also be motivated by a desire to benefit donor country suppliers.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nIMF central government expense estimates, International Monetary Fund (IMF), note: IMF central government expense estimates are used for the denominator" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\nCentral government expense is cash payments for operating activities of the government in providing goods and services. It includes compensation of employees (such as wages and salaries), interest and subsidies, grants, social benefits, and other expenses such as rent and dividends.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Net ODA received as a percent of central government expense is calculated using values in U.S. dollars converted using the DEC alternative conversion factor which is the underlying annual exchange rate used for the World Bank Atlas method." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "2" }, { "id": "DT.TDS.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the governments, corporations or private households. The debt includes money owed to governments or public agencies (bilateral), to international organizations (multilateral) or to exporters, commercial banks or other financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. When used effectively, a reasonable level of external debt can help a country finance productive investments, such as building infrastructure and investing in education and health, that can increase growth." }, { "id": "IndicatorName", "value": "Debt service on external debt, total (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "DT.TDS.DECT.EX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "Total debt service (% of exports of goods, services and primary income)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total debt service to exports of goods, services and primary income. Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF." }, { "id": "Othernotes", "value": "The denominator for this indicator in previous versions of Global Development Finance included workers' remittances. Workers' remittances are no longer included." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "2" }, { "id": "DT.TDS.DECT.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Total debt service (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "2" }, { "id": "DT.TDS.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "IMF repurchases and charges (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. IMF charges cover interest payments with respect to all uses of IMF resources, excluding those resulting from drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "2" }, { "id": "DT.TDS.DPPF.XP.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Debt service to exports (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Debt service, the sum of principal repayments and interest actually paid in currency, goods, or services, is expressed as a percentage of exports of goods and services--all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, net exports of goods under merchanting, nonmonetary gold, and services. This series differs from the standard debt to exports series in that it covers only long-term public and publicly guaranteed debt and repayments (repurchases and charges) to the IMF." }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "2" }, { "id": "DT.TDS.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the governments, corporations or private households. The debt includes money owed to governments or public agencies (bilateral), to international organizations (multilateral) or to exporters, commercial banks or other financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. When used effectively, a reasonable level of external debt can help a country finance productive investments, such as building infrastructure and investing in education and health, that can increase growth." }, { "id": "IndicatorName", "value": "Debt service on external debt, public and publicly guaranteed (PPG) (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Countries that report to the World Bank’s Debtor Reporting System (DRS) submit their public and publicly guaranteed debt (PPG) and private debt with a public guarantee on a loan-by-loan basis. The World Bank Debt Data Team then compiles this information to produce this indicator." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "DT.TDS.DPPG.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Public and publicly guaranteed debt service (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt service to gross national income. Public and publicly guaranteed debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Countries that report to the World Bank’s Debtor Reporting System (DRS) submit their public and publicly guaranteed debt (PPG) and private debt with a public guarantee on a loan-by-loan basis. The World Bank Debt Data Team then compiles this information to produce this indicator." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "DT.TDS.DPPG.XP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Public and publicly guaranteed debt service (% of exports of goods, services and primary income)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt service to exports of goods, services, and income. Public and publicly guaranteed debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Exports of goods, services and primary income is the sum of goods (merchandise) exports, exports of (nonfactor) services and income (factor) receipts." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Countries that report to the World Bank’s Debtor Reporting System (DRS) submit their public and publicly guaranteed debt (PPG) and private debt with a public guarantee on a loan-by-loan basis. The World Bank Debt Data Team then compiles this information to produce this indicator." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "DT.TDS.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Multilateral debt service (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "2" }, { "id": "DT.TDS.MLAT.PG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Multilateral debt service (% of public and publicly guaranteed debt service)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Multilateral debt service is the repayment of principal and interest to the World Bank, regional development banks, and other multilateral agencies. public and publicly guaranteed debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "2" }, { "id": "EG.CFT.ACCS.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Access to clean fuels and technologies for cooking, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to clean fuels and technologies for cooking, rural is the proportion of rural population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Tracking SDG 7: The Energy Progress Report, International Energy Agency (IEA), note: License: Creative Commons Attribution—NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO);\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data for access to clean fuels and technologies for cooking are based on the World Health Organization’s (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Trends in the proportion of the population using each fuel type are estimated using a single multivariate hierarchical model, with urban and rural disaggregation. Estimates for overall ‘polluting’ fuels (unprocessed biomass, charcoal, coal, and kerosene) and ‘clean’ fuels (gaseous fuels, electricity, as well as an aggregation of any other clean fuels like alcohol) are produced by aggregating estimates of relevant fuel types. The model was used to derive clean fuel use estimates for 191 countries (ref. Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (2020), Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. J. R. Stat. Soc. C, 69: 815-839). Countries classified by the World Bank as high income (57 countries) in the 2022 fiscal year are assumed to have universal access to clean fuels and technologies for cooking." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of rural population" } ], "source_id": "2" }, { "id": "EG.CFT.ACCS.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Access to clean fuels and technologies for cooking, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to clean fuels and technologies for cooking, urban is the proportion of urban population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Tracking SDG 7: The Energy Progress Report, International Energy Agency (IEA), note: License: Creative Commons Attribution—NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO);\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data for access to clean fuels and technologies for cooking are based on the World Health Organization’s (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Trends in the proportion of the population using each fuel type are estimated using a single multivariate hierarchical model, with urban and rural disaggregation. Estimates for overall ‘polluting’ fuels (unprocessed biomass, charcoal, coal, and kerosene) and ‘clean’ fuels (gaseous fuels, electricity, as well as an aggregation of any other clean fuels like alcohol) are produced by aggregating estimates of relevant fuel types. The model was used to derive clean fuel use estimates for 191 countries (ref. Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (2020), Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. J. R. Stat. Soc. C, 69: 815-839). Countries classified by the World Bank as high income (57 countries) in the 2022 fiscal year are assumed to have universal access to clean fuels and technologies for cooking." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of urban population" } ], "source_id": "2" }, { "id": "EG.CFT.ACCS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Access to clean fuels and technologies for cooking (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to clean fuels and technologies for cooking is the proportion of total population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Tracking SDG 7: The Energy Progress Report, International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistical Division (UNSD), World Bank, World Health Organization (WHO), uri: https://trackingsdg7.esmap.org/, note: License: Creative Commons Attribution—NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO), publisher: International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistical Division (UNSD), World Bank, World Health Organization (WHO), date published: 2025-06;\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data for access to clean fuels and technologies for cooking are based on the World Health Organization’s (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Trends in the proportion of the population using each fuel type are estimated using a single multivariate hierarchical model, with urban and rural disaggregation. Estimates for overall ‘polluting’ fuels (unprocessed biomass, charcoal, coal, and kerosene) and ‘clean’ fuels (gaseous fuels, electricity, as well as an aggregation of any other clean fuels like alcohol) are produced by aggregating estimates of relevant fuel types. The model was used to derive clean fuel use estimates for 191 countries (ref. Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (2020), Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. J. R. Stat. Soc. C, 69: 815-839). Countries classified by the World Bank as high income are assumed to have universal access to clean fuels and technologies for cooking." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "2" }, { "id": "EG.EGY.PRIM.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Energy intensity level of primary energy (MJ/$2021 PPP GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Energy intensity level is only an imperfect proxy to energy efficiency indicator and it can be affected by a number of factors not necessarily linked to pure efficiency such as climate." }, { "id": "Longdefinition", "value": "Energy intensity level of primary energy is the ratio between energy supply and gross domestic product measured at purchasing power parity. Energy intensity is an indication of how much energy is used to produce one unit of economic output. Lower ratio indicates that less energy is used to produce one unit of output." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Tracking SDG 7: The Energy Progress Report, International Energy Agency (IEA), note: License: Creative Commons Attribution—NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO);\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is obtained by dividing total primary energy supply over gross domestic product measured in constant 2021 US dollars at purchasing power parity." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "MJ per 2021 USD PPP GDP" } ], "source_id": "2" }, { "id": "EG.ELC.ACCS.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnergy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indication of a country's energy poverty status.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nElectricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas." }, { "id": "IndicatorName", "value": "Access to electricity, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to electricity, rural is the percentage of rural population with access to electricity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "SDG 7.1.1 Electrification Dataset, World Bank (WB), uri: https://trackingsdg7.esmap.org/downloads, note: Data is downloaded from ESMAP website. Data is released when a new Tracking SDG7 report is released., publisher: World Bank (WB), date accessed: 2024-05-16, date published: 2023" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank’s Global Electrification Database (GED) compiles nationally representative household survey data, and occasionally census data, from sources going back as far as 1990. The database also incorporates data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), Middle East and North Africa Poverty Database (MNAPOV) and the Europe and Central Asia Poverty Database (ECAPOV), which are based on similar surveys. At the time of this analysis, the GED contained 1,375 surveys for 149 countries in 1990-2021.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of rural population" } ], "source_id": "2" }, { "id": "EG.ELC.ACCS.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnergy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indication of a country's energy poverty status.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nElectricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas." }, { "id": "IndicatorName", "value": "Access to electricity, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to electricity, urban is the percentage of urban population with access to electricity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "SDG 7.1.1 Electrification Dataset, World Bank (WB), uri: https://trackingsdg7.esmap.org/downloads, note: Data is downloaded from ESMAP website. Data is released when a new Tracking SDG7 report is released., publisher: World Bank (WB), date accessed: 2024-05-16, date published: 2023" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank’s Global Electrification Database (GED) compiles nationally representative household survey data, and occasionally census data, from sources going back as far as 1990. The database also incorporates data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), Middle East and North Africa Poverty Database (MNAPOV) and the Europe and Central Asia Poverty Database (ECAPOV), which are based on similar surveys. At the time of this analysis, the GED contained 1,375 surveys for 149 countries in 1990-2021.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of urban population" } ], "source_id": "2" }, { "id": "EG.ELC.ACCS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnergy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indication of a country's energy poverty status.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nElectricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas." }, { "id": "IndicatorName", "value": "Access to electricity (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to electricity is the percentage of population with access to electricity. Electrification data are collected from industry, national surveys and international sources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "SDG 7.1.1 Electrification Dataset, World Bank (WB), uri: https://trackingsdg7.esmap.org/downloads, note: Data is downloaded from ESMAP website. Data is released when a new Tracking SDG7 report is released., publisher: World Bank (WB), date accessed: 2024-05-16, date published: 2023" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank’s Global Electrification Database (GED) compiles nationally representative household survey data, and occasionally census data, from sources going back as far as 1990. The database also incorporates data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), Middle East and North Africa Poverty Database (MNAPOV) and the Europe and Central Asia Poverty Database (ECAPOV), which are based on similar surveys. At the time of this analysis, the GED contained 1,375 surveys for 149 countries in 1990-2021." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "2" }, { "id": "EG.ELC.COAL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Since the beginning of the 21st century, coal has been the fastest-growing global energy source; it currently provides about 40 percent of the world's electricity needs. Coal is the second source of primary energy in the world after oil, and the first source of electricity generation.. The last decade's growth in coal use has been driven by the economic growth of developing economies, mainly China. Irrespective of its economic benefits for the countries, the environmental impact of coal use, especially that coming from carbon dioxide emissions, is significant, and efforts are underway globally to build more efficient plants, to retrofit old plants and to decommission the oldest and least efficient coal plants.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "IndicatorName", "value": "Electricity production from coal sources (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "The share of electricity production from coal sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Coal refers to all coal and brown coal, both primary (including hard coal and lignite-brown coal) and derived fuels (including patent fuel, coke oven coke, gas coke, coke oven gas, and blast furnace gas). Peat is also included in this category." }, { "id": "Othernotes", "value": "Electricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production is total number of kilowatt-hours (kWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity production" } ], "source_id": "2" }, { "id": "EG.ELC.FOSL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Oil, gas and coal, which are fossil fuels, account for well over 70% of the World's electricity generation. Irrespective of its economic benefits for the countries, the environmental impact of fossil fuels use, especially that coming from carbon dioxide emissions, is significant, and efforts are underway globally to build more efficient plants, to retrofit old plants and to decommission the oldest and least efficient plants as well as to transition to renewable energy sources.\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "IndicatorName", "value": "Electricity production from oil, gas and coal sources (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The share of electricity production from oil. gas and coal sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Oil refers to crude oil and petroleum products. Gas refers to natural gas but excludes natural gas liquids. Coal refers to all coal and brown coal, both primary (including hard coal and lignite-brown coal) and derived fuels (including patent fuel, coke oven coke, gas coke, coke oven gas, and blast furnace gas). Peat is also included in this category." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production from oil, gas and coal sources (% of total) is the share of electricity produced by oil and petroleum products, natural gas, which is natural gas but not natural gas liquids, and coal in total electricity production which is the total number of Gigawattt-hours (GWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity production" } ], "source_id": "2" }, { "id": "EG.ELC.HYRO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Electrical energy from hydropower is derived from turbines being driven by flowing water in rivers, with or without man-made dams forming reservoirs. Presently, hydropower is the world's largest source of renewable electricity. Hydropower represents the largest share of renewable electricity production. It was second only to wind power for new-built capacities between 2005 and 2010. IEA estimates that hydropower could produce up to 6,000 terawatt-hours in 2050, roughly twice as much as today.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHydropower's storage capacity and fast response characteristics are especially valuable to meet sudden fluctuations in electricity demand and to match supply from less flexible electricity sources and variable renewable sources, such as solar photovoltaic (PV) and wind power.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "IndicatorName", "value": "Electricity production from hydroelectric sources (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "The share of electricity production from hydroelectric sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Hydropower refers to electricity produced by hydroelectric power plants." }, { "id": "Othernotes", "value": "Electricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production is total number of kilowatt-hours (kWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity production" } ], "source_id": "2" }, { "id": "EG.ELC.LOSS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "An economy's production and consumption of electricity are basic indicators of its size and level of development. Although a few countries export electric power, most production is for domestic consumption. Expanding the supply of electricity to meet the growing demand of increasingly urbanized and industrialized economies without incurring unacceptable social, economic, and environmental costs is one of the great challenges facing developing countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nModern societies are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies. Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions." }, { "id": "IndicatorName", "value": "Electric power transmission and distribution losses (% of output)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Electricity consumption is equivalent to production less power plants' own use and transmission, distribution, and transformation losses less exports plus imports. It includes consumption by auxiliary stations, losses in transformers that are considered integral parts of those stations, and electricity produced by pumping installations. Where data are available, it covers electricity generated by primary sources of energy - coal, oil, gas, nuclear, hydro, geothermal, wind, tide and wave, and combustible renewables. Neither production nor consumption data capture the reliability of supplies, including breakdowns, load factors, and frequency of outages." }, { "id": "Longdefinition", "value": "Electric power transmission and distribution losses include losses in transmission between sources of supply and points of distribution and in the distribution to consumers, including pilferage. The losses are expressed as a share of the total output." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on electric power production and consumption are collected from national energy agencies by the International Energy Agency (IEA) and adjusted by the IEA to meet international definitions. Electric power transmission and distribution losses percentage of output is the share of electric power transmission and distribution losses to electricity production which is the total number of GWh generated by power plants separated into electricity plants and combined heat and power (CHP) plants." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity output" } ], "source_id": "2" }, { "id": "EG.ELC.NGAS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Natural gas is considered a good source of electricity supply for a number of economic, operational and environmental reasons, such as:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n1) it is technically and financially of low-risk;\n\n\n\n\n\n\n\n\n\n2) lower carbon relative to other fossil fuels;\n\n\n\n\n\n\n\n\n\n3) gas plants can be built relatively quickly in around two years, unlike nuclear facilities, which can take much longer.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAlso, gas plants are flexible both in technical and economic terms, so they can react quickly to demand peaks, and are ideally twinned with intermittent renewable options such as wind power.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "IndicatorName", "value": "Electricity production from natural gas sources (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "The share of electricity production from natural gas sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Gas refers to natural gas but excludes natural gas liquids." }, { "id": "Othernotes", "value": "Electricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production from natural gas sources (% of total) is the share of natural gas, which is natural gas but not natural gas liquids, in total electricity production which is the total number of Gigawattt-hours (GWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity production" } ], "source_id": "2" }, { "id": "EG.ELC.NUCL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The generation of electricity using nuclear energy was first demonstrated in the 1950s, and the first commercial nuclear power plants entered operation in the early 1960s. Nuclear capacity grew rapidly in the 1970s and 1980s as countries sought to reduce dependence on fossil fuels, especially after the oil crises of the 1970s. There was a renewed interest in nuclear energy from 2000, and 60 new countries expressed interest in launching a nuclear program to the International Atomic Energy Agency (IAEA). However, after the earthquake and tsunami devastation of the Pacific coast of northern Japan, most nuclear countries announced safety reviews of their nuclear reactors (stress tests) and the revision/improvement of their plans to address similar emergency situations; countries such as Germany and Italy decided to eventually phase out nuclear power or to abandon their nuclear plant projects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "IndicatorName", "value": "Electricity production from nuclear sources (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "The share of electricity production from nuclear sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Nuclear power refers to electricity produced by nuclear power plants." }, { "id": "Othernotes", "value": "Electricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production from nuclear sources (% of total) is the share of electricity produced by nuclear power plants in total electricity production which is the total number of Gigawattt-hours (GWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity production" } ], "source_id": "2" }, { "id": "EG.ELC.PETR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Oil includes crude oil, condensates, natural gas liquids, refinery feedstocks and additives, other hydrocarbons (including emulsified oils, synthetic crude oil, mineral oils extracted from bituminous minerals such as oil shale, and bituminous sand) and petroleum products (refinery gas, ethane, LPG, aviation gasoline, motor gasoline, jet fuels, kerosene, gas/diesel oil, heavy fuel oil, naphtha, white spirit, lubricants, bitumen, paraffin waxes and petroleum coke).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "IndicatorName", "value": "Electricity production from oil sources (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on access to electricity are collected by the IEA from industry, national surveys, and international sources." }, { "id": "Longdefinition", "value": "The share of electricity production from oil sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Oil refers to crude oil and petroleum products." }, { "id": "Othernotes", "value": "Electricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production from oil sources (% of total) is the share of electricity produced by oil and petroleum products in total electricity production which is the total number of Gigawattt-hours (GWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity production" } ], "source_id": "2" }, { "id": "EG.ELC.RNEW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Renewable energy sources are essential for reducing greenhouse gas emissions and combating climate change. They help decrease dependence on fossil fuels, enhancing energy security and price stability. The sector also drives economic growth by creating jobs and attracting investment. Technological advancements in renewables support innovation in storage, smart grids, and sustainable infrastructure. Additionally, they improve energy access in remote areas, promoting social and economic development worldwide." }, { "id": "IndicatorName", "value": "Renewable electricity output (% of total electricity output)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Renewable electricity is the share of electrity generated by renewable power plants in total electricity generated by all types of plants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of renewable electricity output is calculated using the formula:\n\n\n\n\n\nRenewable Electricity Share(%)=(Electricity from Renewable Sources (MWh) / Total Electricity Output (MWh))×100\n\n\n\n\n\nWhere:\n\n\n\n\n\nElectricity from Renewable Sources = Total electricity generated from hydropower, wind, solar, biomass, geothermal, and ocean energy.\n\n\n\n\n\nTotal Electricity Output = Sum of electricity generated from all sources, including fossil fuels, nuclear, and renewables." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of electricity output" } ], "source_id": "2" }, { "id": "EG.ELC.RNWX.KH", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Renewable energy sources are essential for reducing greenhouse gas emissions and combating climate change. They help decrease dependence on fossil fuels, enhancing energy security and price stability. The sector also drives economic growth by creating jobs and attracting investment. Technological advancements in renewables support innovation in storage, smart grids, and sustainable infrastructure. Additionally, they improve energy access in remote areas, promoting social and economic development worldwide." }, { "id": "IndicatorName", "value": "Electricity production from renewable sources, excluding hydroelectric (kWh)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Electricity production from renewable sources in kilowatt-hour (kWh), excluding hydroelectric, includes geothermal, solar, tides, wind, biomass, and biofuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production from renewable sources in kilowatt-hour (kWh) is the amount of electricity produced by geothermal, solar photovoltaic, solar thermal, tide, wind, industrial waste, municipal waste, primary solid biofuels, biogases, biogasoline, biodiesels, other liquid biofuels, nonspecified primary biofuels and waste, and charcoal in total electricity production which is the total number of GWh generated by power plants separated into electricity plants and combined heat power (CHP) plants. Hydropower is excluded. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "kilowatt-hour (kWh)" } ], "source_id": "2" }, { "id": "EG.ELC.RNWX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Renewable energy sources are essential for reducing greenhouse gas emissions and combating climate change. They help decrease dependence on fossil fuels, enhancing energy security and price stability. The sector also drives economic growth by creating jobs and attracting investment. Technological advancements in renewables support innovation in storage, smart grids, and sustainable infrastructure. Additionally, they improve energy access in remote areas, promoting social and economic development worldwide." }, { "id": "IndicatorName", "value": "Electricity production from renewable sources, excluding hydroelectric (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "The share of electricity production from renewable sources of total electricity production. Electricity production from renewable sources, excluding hydroelectric, includes geothermal, solar, tides, wind, biomass, and biofuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production from renewable sources (% of total) is the share of electricity produced by geothermal, solar photovoltaic, solar thermal, tide, wind, industrial waste, municipal waste, primary solid biofuels, biogases, biogasoline, biodiesels, other liquid biofuels, nonspecified primary biofuels and waste, and charcoal in total electricity production which is the total number of Gigawattt-hours (GWh) generated by power plants separated into electricity plants and CHP plants. Hydropower is excluded. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity production" } ], "source_id": "2" }, { "id": "EG.FEC.RNEW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Renewable energy consumption (% of total final energy consumption)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Renewable energy consumption is the share of renewables energy in total final energy consumption." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2022" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The numerator includes the direct consumption of renewable energy sources plus the final consumption of gross electricity and heat estimated to have come from renewable sources, while the denominator is the total final energy consumption of all energy products.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of final energy consumption" } ], "source_id": "2" }, { "id": "EG.GDP.PUSE.KO.PP", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis." }, { "id": "IndicatorName", "value": "GDP per unit of energy use (PPP $ per kg of oil equivalent)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GDP per unit of energy use is the PPP GDP per kilogram of oil equivalent of energy use. PPP GDP is gross domestic product converted to current international dollars using purchasing power parity rates based on the 2017 ICP round. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: GDP per unit of energy use, measured as PPP $ per kg of oil equivalent, is calculated by dividing the gross domestic product (PPP) by the total energy consumption, expressed in kilograms of oil equivalent." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "PPP $ per kg of oil equivalent" } ], "source_id": "2" }, { "id": "EG.GDP.PUSE.KO.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFossil fuels are non-renewable resources because they take millions of years to form, and reserves are being depleted much faster than new ones are being made. In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis." }, { "id": "IndicatorName", "value": "GDP per unit of energy use (constant 2021 PPP $ per kg of oil equivalent)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "GDP per unit of energy use is the PPP GDP per kilogram of oil equivalent of energy use. PPP GDP is gross domestic product converted to 2021 constant international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The ratio of gross domestic product (GDP) to energy use indicates energy efficiency. To produce comparable and consistent estimates of real GDP across economies relative to physical inputs to GDP - that is, units of energy use - GDP is converted to 2021 international dollars using purchasing power parity (PPP) rates. Differences in this ratio over time and across economies reflect structural changes in an economy, changes in sectoral energy efficiency, and differences in fuel mixes. Total energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. GDP data are from World Bank's national accounts files." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "constant 2021 PPP $ per kg of oil equivalent" } ], "source_id": "2" }, { "id": "EG.IMP.CONS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Modern energy services are crucial to a country's economic development. Access to modern energy is essential for the provision of clean water, sanitation and healthcare and for the provision of reliable and efficient lighting, heating, cooking, mechanical power, and transport and telecommunications services.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions." }, { "id": "IndicatorName", "value": "Energy imports, net (% of energy use)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Net energy imports are estimated as gross imports less gross exports, both measured in tons of oil equivalents (toe). A negative value indicates that the country is a net exporter. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nA negative value in energy imports indicates that the country is a net exporter. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of energy use" } ], "source_id": "2" }, { "id": "EG.USE.COMM.CL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Alternative energy is produced without the undesirable consequences of the burning of fossil fuels, such as high carbon dioxide emissions, which is considered to be the major contributing factor of global warming.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPast few decade have seen a rise in global investment in renewable energy, led by wind and solar. In transport, major car companies are adding hybrid and full-electric vehicles to their product lines and many governments have launched plans to encourage consumers to buy these vehicles Fossil fuels continue to outpace alternative and renewable energy growth. Coal has been the fastest-growing global energy source, meeting about one-half of new electricity demand.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions." }, { "id": "IndicatorName", "value": "Alternative and nuclear energy (% of total energy use)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Clean energy is noncarbohydrate energy that does not produce carbon dioxide when generated. It includes hydropower and nuclear, geothermal, and solar power, among others. This is the share of total energy supply that is non-fossil." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of energy use" } ], "source_id": "2" }, { "id": "EG.USE.COMM.FO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Fossil fuels are non-renewable resources because they take millions of years to form, and reserves are being depleted much faster than new ones are being made. In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity." }, { "id": "IndicatorName", "value": "Fossil fuel energy consumption (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Fossil fuel comprises coal, oil, petroleum, and natural gas products." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences. All forms of energy - primary energy and primary electricity - are converted into oil equivalents. A notional thermal efficiency of 33 percent is assumed for converting nuclear electricity into oil equivalents and 100 percent efficiency for converting hydroelectric power." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of energy consumption" } ], "source_id": "2" }, { "id": "EG.USE.COMM.GD.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "\"In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\nFossil fuels are non-renewable resources because they take millions of years to form, and reserves are being depleted much faster than new ones are being made. In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\"" }, { "id": "IndicatorName", "value": "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2021 PPP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Energy use per PPP GDP is the kilogram of oil equivalent of energy use per constant PPP GDP. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport. PPP GDP is gross domestic product converted to 2021 constant international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by dividing the total energy use (in kg of oil equivalent) by the total GDP (in constant 2021 PPP dollars) and then multiplying by 1000, to express the energy use per $1,000 of GDP." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "kg of oil equivalent per $1,000 GDP constant 2021 PPP" } ], "source_id": "2" }, { "id": "EG.USE.CRNW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Total energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRenewable energy is derived from natural processes (e.g. sunlight and wind) that are replenished at a higher rate than they are consumed. Solar, wind, geothermal, hydro, and biomass are common sources of renewable energy. Majority of renewable energy in the world is from solid biofuels and hydroelectricity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRenewable sources of energy have been the driver of much of the growth in the global clean energy sector in the past few decades. Recent years have seen a major scale-up of wind and solar photovoltaic (PV) technologies. Other renewable technologies - including hydropower, geothermal and biomass - continued to grow from a strong established base, adding hundreds of gigawatts of new capacity worldwide.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions." }, { "id": "IndicatorName", "value": "Combustible renewables and waste (% of total energy)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Combustible renewables and waste comprise solid biomass, liquid biomass, biogas, industrial waste, and municipal waste, measured as a percentage of total energy use. The indicator expresses the share of total energy supply." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\n\n\n\n\n\n\n\nThe indicator is calculated as the share of biofuels and waste in the total energy supply of the country.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences. All forms of energy - primary energy and primary electricity - are converted into oil equivalents. A notional thermal efficiency of 33 percent is assumed for converting nuclear electricity into oil equivalents and 100 percent efficiency for converting hydroelectric power." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total energy" } ], "source_id": "2" }, { "id": "EG.USE.ELEC.KH.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "An economy's production and consumption of electricity are basic indicators of its size and level of development. Although a few countries export electric power, most production is for domestic consumption. Expanding the supply of electricity to meet the growing demand of increasingly urbanized and industrialized economies without incurring unacceptable social, economic, and environmental costs is one of the great challenges facing developing countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nModern societies are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies. Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions." }, { "id": "IndicatorName", "value": "Electric power consumption (kWh per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on electric power production and consumption are collected from national energy agencies by the International Energy Agency (IEA) and adjusted by the IEA to meet international definitions. Data are reported as net consumption as opposed to gross consumption. Net consumption excludes the energy consumed by the generating units. For all countries except the United States, total electric power consumption is equal total net electricity generation plus electricity imports minus electricity exports minus electricity distribution losses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Electric power consumption measures the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electric power consumption per capita (kWh ) is the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants, divided by midyear population. Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. Electricity consumption is equivalent to production less power plants' own use and transmission, distribution, and transformation losses less exports plus imports. It includes consumption by auxiliary stations, losses in transformers that are considered integral parts of those stations, and electricity produced by pumping installations. Where data are available, it covers electricity generated by primary sources of energy - coal, oil, gas, nuclear, hydro, geothermal, wind, tide and wave, and combustible renewables. Neither production nor consumption data capture the reliability of supplies, including breakdowns, load factors, and frequency of outages." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "kilowatt-hour (kWh) per capita" } ], "source_id": "2" }, { "id": "EG.USE.PCAP.KG.OE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions." }, { "id": "IndicatorName", "value": "Energy use (kg of oil equivalent per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Total energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. World Bank population estimates are used to calculate per capita data.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnergy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences. All forms of energy - primary energy and primary electricity - are converted into oil equivalents. A notional thermal efficiency of 33 percent is assumed for converting nuclear electricity into oil equivalents and 100 percent efficiency for converting hydroelectric power." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "kg of oil equivalent per capita" } ], "source_id": "2" }, { "id": "EN.ATM.PM25.MC.M3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly." }, { "id": "IndicatorName", "value": "PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Population-weighted exposure to ambient PM2.5 pollution is defined as the average level of exposure of a nation's population to concentrations of suspended particles measuring less than 2.5 microns in aerodynamic diameter, which are capable of penetrating deep into the respiratory tract and causing severe health damage. Exposure is calculated by weighting mean annual concentrations of PM2.5 by population in both urban and rural areas." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2020" }, { "id": "Source", "value": "Global Burden of Disease Study 2023 (GBD 2023) Air Pollution Exposure Estimates and Risk Curves 1990-2023, Global Burden of Disease Collaborative Network, uri: https://ghdx.healthdata.org/record/ihme-data/gbd-2023-air-pollution-exposure-estimates-1990-2023, note: Need to create account to retrieve data., publisher: Institute for Health Metrics and Evaluation (IHME), date accessed: 2026-04-03, date published: 2026-01-23" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population exposure to ambient PM2.5 air pollution is estimated using an integrated modeling approach developed for the Global Burden of Disease (GBD) study by the Institute for Health Metrics and Evaluation (IHME). Annual mean concentrations of fine particulate matter (PM2.5) are derived by combining satellite-based aerosol optical depth measurements, chemical transport models, and available ground-level air quality monitoring data. These data sources are fused using geophysical–statistical models to generate globally consistent, high-resolution gridded estimates of PM2.5 concentrations, including areas without direct monitoring.\n\nPopulation exposure is calculated by weighting annual mean PM2.5 concentrations by the spatial distribution of population in both urban and rural areas. National estimates represent the population-weighted average annual concentration of PM2.5 to which a country’s population is exposed. Estimates are produced annually using a consistent methodology to allow comparison across countries and over time. Values represent modeled exposure levels and are intended for comparative risk assessment rather than regulatory compliance monitoring.\nStatistical concept(s): Fine particulate matter (PM2.5) refers to airborne particles with an aerodynamic diameter of 2.5 micrometers or less, which are small enough to penetrate deeply into the human respiratory system. Exposure to PM2.5 is associated with adverse health outcomes, including cardiovascular and respiratory diseases and premature mortality." }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "microgram per cubic meter" } ], "source_id": "2" }, { "id": "EN.ATM.PM25.MC.T1.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly. Three interim targets were defined for PM2.5 and have been shown to be achievable with successive and sustained abatement measures. Countries may find these interim targets particularly helpful in gauging progress over time in the difficult process of steadily reducing population exporsure to PM. IT-1 level corresponds to the highest mean concentrations reported in studies of long-term effects, and may also reflect higher but unknown historical concentrations that may have been contributed to observed health effects. IT-1 level has been shown to be associated with significant mortality in the developed world." }, { "id": "IndicatorName", "value": "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-1 value (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 1 (IT-1) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 35 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2017" }, { "id": "Source", "value": "Global Burden of Disease Study 2017 (GBD 2017), Institute for Health Metrics and Evaluation (IHME), uri: https://ghdx.healthdata.org/gbd-2017, publisher: Institute for Health Metrics and Evaluation (IHME), date published: 202112" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762–3772;GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Prüss-Üstün A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population." }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "2" }, { "id": "EN.ATM.PM25.MC.T2.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly. Three interim targets were defined for PM2.5 and have been shown to be achievable with successive and sustained abatement measures. Countries may find these interim targets particularly helpful in gauging progress over time in the difficult process of steadily reducing population exporsure to PM. IT-2 level is greater than the mean concentration at which effects have been observed in studies of long-term exposure and mortality and is likely to be associated with significant health impacts from both long-term and daily exposures to PM2.5. Attainment of IT-2 value would reduce the health risks of long-term exposure by about 6% relative to the IT-1 value." }, { "id": "IndicatorName", "value": "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-2 value (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 2 (IT-2) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 25 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2017" }, { "id": "Source", "value": "Brauer, M. et al. 2017, for the Global Burden of Disease Study 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762–3772; GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Prüss-Üstün A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "2" }, { "id": "EN.ATM.PM25.MC.T3.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly. Three interim targets were defined for PM2.5 and have been shown to be achievable with successive and sustained abatement measures. Countries may find these interim targets particularly helpful in gauging progress over time in the difficult process of steadily reducing population exporsure to PM. IT-3 level places greater weight than IT-2 on the likelihood of signifcant effects associated with long-term exposures. IT-3 value is close to the mean concentrations that are reported in studies of long-term exposure and provides an additional 6% reduction in mortality risk relative to the IT-2 value." }, { "id": "IndicatorName", "value": "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-3 value (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 3 (IT-3) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 15 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2017" }, { "id": "Source", "value": "Global Burden of Disease Study 2017 (GBD 2017), Institute for Health Metrics and Evaluation (IHME), uri: https://ghdx.healthdata.org/gbd-2017, publisher: Institute for Health Metrics and Evaluation (IHME), date published: 202112" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762–3772; GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Prüss-Üstün A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population." }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "2" }, { "id": "EN.ATM.PM25.MC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly." }, { "id": "IndicatorName", "value": "PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the WHO guideline value is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 10 micrograms per cubic meter, the guideline value recommended by the World Health Organization as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2017" }, { "id": "Source", "value": "Global Burden of Disease Study 2017 (GBD 2017), Institute for Health Metrics and Evaluation (IHME), uri: https://ghdx.healthdata.org/gbd-2017, publisher: Institute for Health Metrics and Evaluation (IHME), date published: 202112" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762–3772; GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Prüss-Üstün A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "2" }, { "id": "EN.BIR.THRD.NO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. The Red List Index for the world's birds shows that there has been a steady and continuing deterioration in the threat status of the world's birds since 1988, when the first complete global assessment was carried out.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Threatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN guides conservation activities of governments, NGOs and scientific institutions. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGlobally, threatened birds occur worldwide - nearly all countries support one or more threatened bird species. Small islands hold disproportionately high numbers of Globally Threatened Birds, supporting over half of threatened species. Threatened seabirds are found throughout the world's oceans. The most important threats to the world's birds are the spread of agriculture and an ever increasing human use of biological resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future." }, { "id": "IndicatorName", "value": "Bird species, threatened" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List." }, { "id": "Longdefinition", "value": "Birds are listed for countries included within their breeding or wintering ranges. Threatened species are the number of species classified by the IUCN as endangered, vulnerable, rare, indeterminate, out of danger, or insufficiently known." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2017-2022" }, { "id": "Source", "value": "The IUCN Red List of Threatened Species, UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.iucnredlist.org/;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.iucnredlist.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threated species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" }, { "id": "Unitofmeasure", "value": "species" } ], "source_id": "2" }, { "id": "EN.CLC.DRSK.XQ", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Hyogo Framework's goal is to substantially reduce disaster losses by 2015 - in lives, and in the social, economic, and environmental assets of communities and countries. The Hyogo Framework offers guiding principles, priorities for action, and practical means for achieving disaster resilience for vulnerable communities. Governments around the world have committed to take action to reduce disaster risk, and have adopted a guideline to reduce vulnerabilities to natural hazards, called the Hyogo Framework for Action (HFA). The HFA assists the efforts of nations and communities to become more resilient to, and cope better with the hazards that threaten their development gains.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nScientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems." }, { "id": "IndicatorName", "value": "Disaster risk reduction progress score (1-5 scale; 5=best)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Hyogo Framework for Action (FHA) national progress reports assess strategic priorities in the implementation of disaster risk reduction actions and establish baselines on levels of progress achieved in implementing the HFA's five priorities for action. National reporting processes are led by officially designated HFA focal institutions in country, and regional reporting by regional intergovernmental organizations.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHFA's five priorities are:\n\n\n\n\n\n\n\n1. Making disaster risk reduction a policy priority, institutional strengthening\n\n\n\n\n\n\n\n2. Risk assessment and early warning systems\n\n\n\n\n\n\n\n3. Education, information and public awareness\n\n\n\n\n\n\n\n4. Reducing underlying risk factors\n\n\n\n\n\n\n\n5. Preparedness for effective response" }, { "id": "Longdefinition", "value": "Disaster risk reduction progress score is an average of self-assessment scores, ranging from 1 to 5, submitted by countries under Priority 1 of the Hyogo Framework National Progress Reports. The Hyogo Framework is a global blueprint for disaster risk reduction efforts that was adopted by 168 countries in 2005. Assessments of \"Priority 1\" include four indicators that reflect the degree to which countries have prioritized disaster risk reduction and the strengthening of relevant institutions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2011-2011" }, { "id": "Source", "value": "2009-2011 Progress Reports, UN Office for Disaster Risk Reduction (UNDRR), uri: http://www.preventionweb.net/english/hyogo" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Resilience is measured by the disaster risk reduction progress score, an average of self-assessment scores submitted by countries under Priority 1 of the Hyogo Framework National Progress Reports. The Hyogo Framework is a global blueprint for disaster risk reduction efforts that was adopted by 168 countries in 2005. Assessments of Priority 1 include four indicators that reflect the degree to which countries have prioritized disaster risk reduction and the strengthening of relevant institutions.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "index (1-5)" } ], "source_id": "2" }, { "id": "EN.CLC.MDAT.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Scientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nA drought can lead to losses in agriculture, affect inland navigation and hydropower plants, reduce access to drinking water, and cause famines. A flood is a significant rise of water level in a stream, lake, reservoir, or coastal region. Extreme temperature events are either cold waves or heat waves. A cold wave can be both a prolonged period of excessively cold weather and the sudden invasion of very cold air over a large area. Accompanied by frost, it can damage agriculture, infrastructure, and property. A heat wave is a prolonged period of excessively hot and sometimes humid weather. Population affected by these natural disasters is the number of people injured, left homeless, or requiring immediate assistance and can include displaced or evacuated people." }, { "id": "IndicatorName", "value": "Droughts, floods, extreme temperatures (% of population, average 1990-2009)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is \"unequivocal\" and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20-86 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5°C by 2100 (Kattenberg and others 1996) and increase of \"killer\" heat waves during the warm season (Karl and others 1997)." }, { "id": "Longdefinition", "value": "Droughts, floods and extreme temperatures is the annual average percentage of the population that is affected by natural disasters classified as either droughts, floods, or extreme temperature events. A drought is an extended period of time characterized by a deficiency in a region's water supply that is the result of constantly below average precipitation. A drought can lead to losses to agriculture, affect inland navigation and hydropower plants, and cause a lack of drinking water and famine. A flood is a significant rise of water level in a stream, lake, reservoir or coastal region. Extreme temperature events are either cold waves or heat waves. A cold wave can be both a prolonged period of excessively cold weather and the sudden invasion of very cold air over a large area. Along with frost it can cause damage to agriculture, infrastructure, and property. A heat wave is a prolonged period of excessively hot and sometimes also humid weather relative to normal climate patterns of a certain region. Population affected is the number of people injured, left homeless or requiring immediate assistance during a period of emergency resulting from a natural disaster; it can also include displaced or evacuated people. Average percentage of population affected is calculated by dividing the sum of total affected for the period stated by the sum of the annual population figures for the period stated." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2009-2009" }, { "id": "Source", "value": "EM-DAT The International Disaster Database, Centre for Research on the Epidemiology of Disasters (CRED) - Université Catholique de Louvain, uri: https://www.emdat.be/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures vulnerability of population affected by droughts, floods, and extreme temperature. A drought is an extended period of deficiency in a region's water supply as a result of below average precipitation." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of population, average 1990-2009" } ], "source_id": "2" }, { "id": "EN.FSH.THRD.NO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. The Red List Index for the world's birds shows that there has been a steady and continuing deterioration in the threat status of the world's birds since 1988, when the first complete global assessment was carried out.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThreatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN guides conservation activities of governments, NGOs and scientific institutions. The introduction in 1994 of a scientifically rigorous approach to determine risks of extinction that is applicable to all species, has become a world standard. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe freshwater system represents the most threatened of all ecosystems, and many freshwater species have a very high livelihood value for local human communities. IUCN's freshwater focus is on the following taxonomic groups: fish; molluscs; crabs and crayfish; and dragonflies. Global assessment of these groups is being pursued through a series of regional projects, such as one for Africa that is currently being implemented.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe marine realm is poorly covered in the IUCN Red List, comprising less than 5 percent of the species included. IUCN has identified priority taxonomic groups of marine fish, invertebrates, plants (mangroves and seagrasses) and macro-algae (seaweeds). If these priority groups can be assessed, the number of marine species on the IUCN Red List will be increased more than six-fold.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future." }, { "id": "IndicatorName", "value": "Fish species, threatened" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List." }, { "id": "Longdefinition", "value": "Fish species are based on Froese, R. and Pauly, D. (eds). 2008. Threatened species are the number of species classified by the IUCN as endangered, vulnerable, rare, indeterminate, out of danger, or insufficiently known." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2017-2022" }, { "id": "Source", "value": "FishBase database, Froese, R. and Pauly, D. (eds)., uri: https://www.fishbase.org/, date published: 2008" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threated species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" }, { "id": "Unitofmeasure", "value": "species" } ], "source_id": "2" }, { "id": "EN.GHG.ALL.LU.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Total greenhouse gas emissions including LULUCF (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of the six greenhouse gases (GHG) covered by the Kyoto Protocol (carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphurhexafluoride (SF6)) from the energy, industry, waste, agriculture, and land use, land use changes, and forestry (LULUCF) sectors, standardized to carbon dioxide equivalent values. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nEuropean Forest Observatory, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.ALL.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Total greenhouse gas emissions excluding LULUCF (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of the six greenhouse gases (GHG) covered by the Kyoto Protocol (carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphurhexafluoride (SF6)) from the energy, industry, waste, and agriculture sectors, standardized to carbon dioxide equivalent values. This measure excludes GHG fluxes caused by Land Use Change Land Use and Forestry (LULUCF), as these fluxes have larger uncertainties. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.ALL.PC.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using population as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Total greenhouse gas emissions per capita excluding LULUCF (t CO2e/capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "Total annual emissions of the six greenhouse gases (GHG) covered by the Kyoto Protocol (carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphurhexafluoride (SF6)) from the energy, industry, waste, and agriculture sectors, standardized to carbon dioxide equivalent values divided by the economy's population. This measure excludes GHG fluxes caused by Land Use Change Land Use and Forestry (LULUCF), as these fluxes have larger uncertainties." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n?? =??×????\n\nWhere:\n\nE = Total emissions (kg, tons, or CO2-equivalent)\nA = Activity data (e.g., fuel consumption, production levels)\nEF = Emission factor (kg of pollutant per unit of activity)\n\nDepending on the pollutant, different tiers of complexity are used:\n\nTier 1 – Default IPCC emission factors (simple estimation)\nTier 2 – Country/region-specific emission factors\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "t CO2e/capita" } ], "source_id": "2" }, { "id": "EN.GHG.CH4.AG.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions from Agriculture (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the agricultural sector. This includes emissions from livestock (IPCC 2006 codes 3.A.1 (enteric fermentation, 3.a.2 (manure management) and crops (IPCC 2006 codes 3.C.1 Emissions from biomass burning, 3.C.2 Liming, 3.C.3 Urea application, 3.C.4 Direct N2O Emissions from managed soils, 3.C.5 Indirect N2O Emissions from managed soils, 3.C.6 Indirect N2O Emissions from manure management, 3.C.7 Rice cultivations). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CH4.BU.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions from Building (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the building sector (subsector of the energy sector) including IPCC 2006 codes 1.A.4 Residential and other sectors, 1.A.5 Non-Specified. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CH4.FE.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions from Fugitive Emissions (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from fugitive emissions (subsector of the energy sector) including IPCC 2006 codes 1.A.1.bc Petroleum Refining - Manufacture of Solid Fuels and Other Energy Industries, 1.B.1 Solid Fuels, 1.B.2 Oil and Natural Gas, 5.B. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CH4.IC.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions from Industrial Combustion (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from industrial combustion (subsector of the energy sector) including IPCC 2006 code 1.A.2 Manufacturing Industries and Construction. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CH4.IP.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions from Industrial Processes (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from industrial processes including IPCC 2006 codes 2.A.1 Cement production, 2.A.2 Lime production, 2.A.3 Glass Production, 2.A.4 Other Process Uses of Carbonates, 2.B Chemical Industry, 2.C Metal Industry, 2.D Non-Energy Products from Fuels and Solvent Use, 2.E Electronics Industry, 2.F Product Uses as Substitutes for Ozone Depleting Substances, 2.G Other Product Manufacture and Use and 5.A Indirect N2O emissions from the atmospheric deposition of nitrogen in NOx and NH3). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CH4.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions (total) excluding LULUCF (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CH4.PI.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions from Power Industry (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from electricity and heat generation (subsector of the energy sector) including IPCC 2006 code 1.A.1.a. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CH4.TR.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions from Transport (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the transportation sector (subsector of the energy sector) including IPCC 2006 codes 1.A.3.a Civil Aviation, 1.A.3.b_noRES Road Transportation no resuspension, 1.A.3.c Railways, 1.A.3.d Water-borne Navigation, 1.A.3.e Other Transportation. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CH4.WA.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions from Waste (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the waste sector. This includes emissions from solid waste (IPCC 2006 codes 4.A Solid Waste Disposal, 4.B Biological Treatment of Solid Waste, 4.C Incineration and Open Burning of Waste) and wastewater treatment (IPCC 2006 code 4.D Wastewater Treatment and Discharge). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CH4.ZG.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using 1990 emission levels as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions (total) excluding LULUCF (% change from 1990)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "Change of emissions (as %) of current year with respect to emissions in baseline year 1990 emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5). Negative values indicate that the emission level for that year is lower than the emissions level in 1990." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.AG.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions from Agriculture (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agricultural sector. This includes emissions from livestock (IPCC 2006 codes 3.A.1 (enteric fermentation, 3.a.2 (manure management) and crops (IPCC 2006 codes 3.C.1 Emissions from biomass burning, 3.C.2 Liming, 3.C.3 Urea application, 3.C.4 Direct N2O Emissions from managed soils, 3.C.5 Indirect N2O Emissions from managed soils, 3.C.6 Indirect N2O Emissions from manure management, 3.C.7 Rice cultivations). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.BU.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions from Building (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the building sector (subsector of the energy sector) including IPCC 2006 codes 1.A.4 Residential and other sectors, 1.A.5 Non-Specified. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.FE.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions from Fugitive Emissions (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from fugitive emissions (subsector of the energy sector) including IPCC 2006 codes 1.A.1.bc Petroleum Refining - Manufacture of Solid Fuels and Other Energy Industries, 1.B.1 Solid Fuels, 1.B.2 Oil and Natural Gas, 5.B. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.IC.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions from Industrial Combustion (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from industrial combustion (subsector of the energy sector) including IPCC 2006 code 1.A.2 Manufacturing Industries and Construction. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.IP.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions from Industrial Processes (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from industrial processes including IPCC 2006 codes 2.A.1 Cement production, 2.A.2 Lime production, 2.A.3 Glass Production, 2.A.4 Other Process Uses of Carbonates, 2.B Chemical Industry, 2.C Metal Industry, 2.D Non-Energy Products from Fuels and Solvent Use, 2.E Electronics Industry, 2.F Product Uses as Substitutes for Ozone Depleting Substances, 2.G Other Product Manufacture and Use and 5.A Indirect N2O emissions from the atmospheric deposition of nitrogen in NOx and NH3). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.LU.DF.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) net fluxes from LULUCF - Deforestation (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net flux of carbon dioxide (CO2) in the category \"Deforestation\"." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "Carbon fluxes from land 2000–2020: bringing clarity on countries’ reporting, uri: https://doi.org/10.5194/essd-14-4643-2022, note: Data available from https://doi.org/10.5281/zenodo.7190605, publisher: Earth System Science Data (ESSD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.LU.FL.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) net fluxes from LULUCF - Forest Land (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net flux of carbon dioxide (CO2) in the category \"Forest land\"." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "Carbon fluxes from land 2000–2020: bringing clarity on countries’ reporting, uri: https://doi.org/10.5194/essd-14-4643-2022, note: Data available from https://doi.org/10.5281/zenodo.7190605, publisher: Earth System Science Data (ESSD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.LU.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) net fluxes from LULUCF - Total excluding non-tropical fires (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net flux of carbon dioxide (CO2) from Land Use, Land Use Change and Forestry LULUCF, excluding non-ropical fires at the country level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Carbon fluxes from land 2000–2020: bringing clarity on countries’ reporting, uri: https://doi.org/10.5194/essd-14-4643-2022, note: Data available from https://doi.org/10.5281/zenodo.7190605, publisher: Earth System Science Data (ESSD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.LU.OL.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) net fluxes from LULUCF - Other Land (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net flux of carbon dioxide (CO2) in the category \"Other land\"." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "Carbon fluxes from land 2000–2020: bringing clarity on countries’ reporting, uri: https://doi.org/10.5194/essd-14-4643-2022, note: Data available from https://doi.org/10.5281/zenodo.7190605, publisher: Earth System Science Data (ESSD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.LU.OS.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) net fluxes from LULUCF - Organic Soil (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net flux of carbon dioxide (CO2) in the category \"Organic soil\"." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "Carbon fluxes from land 2000–2020: bringing clarity on countries’ reporting, uri: https://doi.org/10.5194/essd-14-4643-2022, note: Data available from https://doi.org/10.5281/zenodo.7190605, publisher: Earth System Science Data (ESSD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions (total) excluding LULUCF (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: International Energy Agency (IEA), date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.PC.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using population as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions excluding LULUCF per capita (t CO2e/capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "Total annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF, standardized to carbon dioxide equivalent values divided by the economy's population. This measure excludes GHG fluxes caused by Land Use Change Land Use and Forestry (LULUCF), as these fluxes have larger uncertainties." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "t CO2e/capita" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.PI.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions from Power Industry (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from electricity and heat generation (subsector of the energy sector) including IPCC 2006 code 1.A.1.a. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.RT.GDP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using GDP as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon intensity of GDP (kg CO2e per constant 2021 US$ of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "Annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF divided by the GDP in constant 2021 US$." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "kg CO2e per 2021 constant US$ of GDP" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.RT.GDP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using GDP as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon intensity of GDP (kg CO2e per 2021 PPP $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "Annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF divided by the GDP in 2021 PPP $." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "kg CO2e per 2021 PPP $" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.TR.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions from Transport (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the transportation sector (subsector of the energy sector) including IPCC 2006 codes 1.A.3.a Civil Aviation, 1.A.3.b_noRES Road Transportation no resuspension, 1.A.3.c Railways, 1.A.3.d Water-borne Navigation, 1.A.3.e Other Transportation. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.WA.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions from Waste (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the waste sector. This includes emissions from solid waste (IPCC 2006 codes 4.A Solid Waste Disposal, 4.B Biological Treatment of Solid Waste, 4.C Incineration and Open Burning of Waste) and wastewater treatment (IPCC 2006 code 4.D Wastewater Treatment and Discharge). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.CO2.ZG.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using 1990 emission levels as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions (total) excluding LULUCF (% change from 1990)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "Change of emissions (as %) of current year with respect to emissions in baseline year 1990 emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5). Negative values indicate that the emission level for that year is lower than the emissions level in 1990." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "EN.GHG.FGAS.IP.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Fluorinated greenhouse gases (F-gases) emissions from Industrial Processes (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of fluorinated gases (hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphurhexafluoride (SF6)), from industrial processes including IPCC 2006 codes 2.B Chemical Industry, 2.C Metal Industry, 2.E Electronics Industry, 2.F Product Uses as Substitutes for Ozone Depleting Substances, 2.G Other Product Manufacture and Use The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.N2O.AG.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions from Agriculture (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the agricultural sector. This includes emissions from livestock (IPCC 2006 codes 3.A.1 (enteric fermentation, 3.a.2 (manure management) and crops (IPCC 2006 codes 3.C.1 Emissions from biomass burning, 3.C.2 Liming, 3.C.3 Urea application, 3.C.4 Direct N2O Emissions from managed soils, 3.C.5 Indirect N2O Emissions from managed soils, 3.C.6 Indirect N2O Emissions from manure management, 3.C.7 Rice cultivations). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.N2O.BU.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions from Building (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the building sector (subsector of the energy sector) including IPCC 2006 codes 1.A.4 Residential and other sectors, 1.A.5 Non-Specified. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.N2O.FE.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions from Fugitive Emissions (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from fugitive emissions (subsector of the energy sector) including IPCC 2006 codes 1.A.1.bc Petroleum Refining - Manufacture of Solid Fuels and Other Energy Industries, 1.B.1 Solid Fuels, 1.B.2 Oil and Natural Gas, 5.B. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.N2O.IC.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions from Industrial Combustion (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from industrial combustion (subsector of the energy sector) including IPCC 2006 code 1.A.2 Manufacturing Industries and Construction. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.N2O.IP.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions from Industrial Processes (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from industrial processes including IPCC 2006 codes 2.A.1 Cement production, 2.A.2 Lime production, 2.A.3 Glass Production, 2.A.4 Other Process Uses of Carbonates, 2.B Chemical Industry, 2.C Metal Industry, 2.D Non-Energy Products from Fuels and Solvent Use, 2.E Electronics Industry, 2.F Product Uses as Substitutes for Ozone Depleting Substances, 2.G Other Product Manufacture and Use and 5.A Indirect N2O emissions from the atmospheric deposition of nitrogen in NOx and NH3). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.N2O.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions (total) excluding LULUCF (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.N2O.PI.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions from Power Industry (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from electricity and heat generation (subsector of the energy sector) including IPCC 2006 code 1.A.1.a. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.N2O.TR.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions from Transport (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the transportation sector (subsector of the energy sector) including IPCC 2006 codes 1.A.3.a Civil Aviation, 1.A.3.b_noRES Road Transportation no resuspension, 1.A.3.c Railways, 1.A.3.d Water-borne Navigation, 1.A.3.e Other Transportation. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.N2O.WA.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions from Waste (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the waste sector. This includes emissions from solid waste (IPCC 2006 codes 4.A Solid Waste Disposal, 4.B Biological Treatment of Solid Waste, 4.C Incineration and Open Burning of Waste) and wastewater treatment (IPCC 2006 code 4.D Wastewater Treatment and Discharge). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "2" }, { "id": "EN.GHG.N2O.ZG.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using 1990 emission levels as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions (total) excluding LULUCF (% change from 1990)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "Change of emissions (as %) of current year with respect to emissions in baseline year 1990 emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5). Negative values indicate that the emission level for that year is lower than the emissions level in 1990." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nEDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, International Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "% change from 1990" } ], "source_id": "2" }, { "id": "EN.GHG.TOT.ZG.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using 1990 emission levels as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Total greenhouse gas emissions excluding LULUCF (% change from 1990)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "Change of emissions (as %) of current year with respect to emissions in baseline year 1990 emissions of the six greenhouse gases (GHG) covered by the Kyoto Protocol (carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphurhexafluoride (SF6)) from the energy, industry, waste, and agriculture sectors, standardized to carbon dioxide equivalent values. This measure excludes GHG fluxes caused by Land Use Change Land Use and Forestry (LULUCF), as these fluxes have larger uncertainties. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5) to combine different GHGs. Negative values indicate that the emission level for that year is lower than the emissions level in 1990." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\nTier 3 – Uses detailed modeling, facility-level data, or continuous emissions monitoring\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "% change from 1990" } ], "source_id": "2" }, { "id": "EN.HPT.THRD.NO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThreatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN guides conservation activities of governments, NGOs and scientific institutions. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe plants and animals assessed for the IUCN Red List are the bearers of genetic diversity and the building blocks of ecosystems, and information on their conservation status and distribution provides the foundation for making informed decisions about conserving biodiversity from local to global levels. Only a small number of the world's plant and animal species have been assessed. In addition to the many thousands of species which have not yet been assessed so far, other species not included on the IUCN Red List are those that went extinct before 1500 AD and the \"Least Concern\" (plants that have been evaluated to have a low risk of extinction) species that have not yet been data based.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future." }, { "id": "IndicatorName", "value": "Plant species (higher), threatened" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAlthough there are over 12,000 plant species on the IUCN Red List, fewer than one thousand of these are properly documented. To help address this gap, IUCN is pursuing global assessments of plant species of value to people including species of high economic value. The conifer and cycad species already on the IUCN Red List need to be fully documented. IUCN is also developing a tool to assist with preliminary assessments of plant species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List." }, { "id": "Longdefinition", "value": "Higher plants are native vascular plant species. Threatened species are the number of species classified by the IUCN as endangered, vulnerable, rare, indeterminate, out of danger, or insufficiently known." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2017-2022" }, { "id": "Source", "value": "The IUCN Red List of Threatened Species, UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.iucnredlist.org/;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.iucnredlist.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threated species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" }, { "id": "Unitofmeasure", "value": "species" } ], "source_id": "2" }, { "id": "EN.MAM.THRD.NO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. The number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThreatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nthe IUCN Red List covers a comprehensive assessment of the conservation status of the world's 5,488 mammal species, including global summary statistics, individual species accounts/threat category, range map, ecology information, and some other data. Mammal species are found spread across the globe, with the exception of the land mass of Antarctica. Nearly one-quarter of the world's mammal species are known to be globally threatened or extinct, 63 percent are known to not be threatened, and 15 percent have insufficient data to determine their threat status. Habitat loss, affecting over 2,000 mammal species, is the greatest threat globally. The second greatest threat is utilization which is affecting over 900 mammal species, mainly those in Asia.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future." }, { "id": "IndicatorName", "value": "Mammal species, threatened" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSome parts of the world, such as the Andes, Central and West Africa, Angola, parts of South and Southeast Asia, and Melanesia, still have sparse information available of their mammal faunas. In addition, many species' names, especially in the tropics, actually represent complexes of several species that have not yet been resolved. The information on the relative importance of different threatening processes to mammal species is incomplete. IUCN codes all threats that appear to have an important impact, but not their relative importance for each species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List." }, { "id": "Longdefinition", "value": "Mammal species are mammals excluding whales and porpoises. Threatened species are the number of species classified by the IUCN as endangered, vulnerable, rare, indeterminate, out of danger, or insufficiently known." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2017-2022" }, { "id": "Source", "value": "The IUCN Red List of Threatened Species, UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.iucnredlist.org/;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.iucnredlist.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threated species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" }, { "id": "Unitofmeasure", "value": "species" } ], "source_id": "2" }, { "id": "EN.POP.DNST", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries; in developing countries errors may be substantial because of limits in the transport, communications, and other resources required conducting and analyzing a full census.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPopulation density is a measure of the intensity of land-use, and can be calculated for a block, city, county, state, country, continent or the entire world. Considering that over half of the Earth's land mass consists of areas inhospitable to human inhabitation, such as deserts and high mountains, and that population tends to cluster around seaports and fresh water sources, a simple number of population density by itself does not give any meaningful measurement of human population density.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSeveral of the most densely populated territories in the world are city-states, microstates, or dependencies.[6][7] These territories share a relatively small area and a high urbanization level, with an economically specialized city population drawing also on rural resources outside the area, illustrating the difference between high population density and overpopulation." }, { "id": "IndicatorName", "value": "Population density (people per sq. km of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data are provided by the United Nations Population Division and other agencies. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data." }, { "id": "Longdefinition", "value": "Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO population estimates, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO);\nWorld Bank population estimates, World Bank (WB), publisher: World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population density is midyear population divided by land area in square kilometers. This ratio can be calculated for any territorial unit for any point in time, depending on the source of the population data. Populationestimates are prepared by World Bank staff from variety of sources. They are based on the de facto definition of population and include all residents regardless of legal status or citizenship, within the physical boundaries of a country and under the jurisdiction of that country's political control. Refugees not permanently settled in the country of asylum are considered part of the population of their country of origin. Population numbers are either current census data or historical census data extrapolated through demographic methods. The count also excludes visitors from overseas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPopulation density is calculated by dividing midyear population by land area in a country. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship - except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "people per square kilometer of land area" } ], "source_id": "2" }, { "id": "EN.POP.EL5M.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Rural population living in areas where elevation is below 5 meters (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Rural population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112, date published: 202112;\nCUNY Institute for Demographic Research (CIDR) - City University of New York, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population counts in low elevation zones in the year 1990 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2000 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2010 derived from the application of United Nations 2000-2010 national growth rates to year 2000 population data from GRUMPv1 ( see documentation for full description of methodologies ).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "2" }, { "id": "EN.POP.EL5M.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Urban population living in areas where elevation is below 5 meters (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Urban population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112, date published: 202112;\nCUNY Institute for Demographic Research (CIDR) - City University of New York, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population counts in low elevation zones in the year 1990 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2000 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2010 derived from the application of United Nations 2000-2010 national growth rates to year 2000 population data from GRUMPv1 ( see documentation for full description of methodologies ).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "2" }, { "id": "EN.POP.EL5M.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Scientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems." }, { "id": "IndicatorName", "value": "Population living in areas where elevation is below 5 meters (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is “unequivocal” and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20–86 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5°C by 2100 (Kattenberg and others 1996) and increase of “killer” heat waves during the warm season (Karl and others 1997)." }, { "id": "Longdefinition", "value": "Population below 5m is the percentage of the total population living in areas where the elevation is 5 meters or less." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112, date published: 202112;\nCUNY Institute for Demographic Research (CIDR) - City University of New York, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population counts in low elevation zones in the year 1990 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2000 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2010 derived from the application of United Nations 2000-2010 national growth rates to year 2000 population data from GRUMPv1 (see documentation for full description of methodologies).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "2" }, { "id": "EN.POP.SLUM.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Population living in slums (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, housing durability, and security of tenure, as adopted in the Millennium Development Goal Target 7.D. The successor, the Sustainable Development Goal 11.1.1, considers inadequate housing (housing affordability) to complement the above definition of slums/informal settlements." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Urban Indicators Database, UN Human Settlements Programme (UN-Habitat), uri: https://data.unhabitat.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, housing durability, and security of tenure, as adopted in the Millennium Development Goal Target 7.D. The successor, the Sustainable Development Goal 11.1.1, considers inadequate housing (housing affordability) to complement the above definition of slums/informal settlements." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "% (share) of urban population" } ], "source_id": "2" }, { "id": "EN.URB.LCTY", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A metropolitan area includes the urban area, and its satellite cities plus intervening rural land that is socio-economically connected to the urban core city, typically by employment ties through commuting, with the urban core city being the primary labor market. According to the United Nations' definition, a metropolitan area includes both the contiguous territory inhabited at urban levels of residential density and additional surrounding areas of lower settlement density that are also under the direct influence of the city (e.g., through frequent transport, road linkages, commuting facilities etc.).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nExplosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service. For the first time ever, the majority of the world's population lives in a city, and this proportion continues to grow. One hundred years ago, 2 out of every 10 people lived in an urban area. By 1990, less than 40 percent of the global population lived in a city, but as of early 2010s, more than half of all people live in an urban area. By 2030, 6 out of every 10 people will live in a city, and by 2050, this proportion will increase to 7 out of 10 people. About half of all urban dwellers live in cities with between 100,000-500,000 people, and fewer than 10% of urban dwellers live in megacities (a city with a population of more than 10 million, as defined by UN HABITAT). Currently, the number of urban residents is growing by nearly 60 million every year.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBy the middle of the 21st century, the urban population will almost double, reaching 6.4 billion in 2050. Almost all urban population growth in the next 30 years will occur in cities of developing countries. By the middle of the 21st century, it is estimated that the urban population of developing counties will more than double, reaching almost 5.2 billion in 2050. In high-income countries, the urban population is expected to remain largely unchanged over the next two decades, reaching to just over 1 billion by 2025. In these countries, immigration (legal and illegal) will account for more than two-thirds of urban growth. Without immigration, the urban population in these countries would most likely decline or remain static.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment. Poverty is growing faster in urban than in rural areas. According to UN one billion people live in urban slums, which are typically overcrowded, polluted and dangerous, and lack basic services such as clean water and sanitation." }, { "id": "IndicatorName", "value": "Population in largest city" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Most countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries. For example, in Botswana, agglomeration of 5,000 or more inhabitants where 75 per cent of the economic activity is non-agricultural is considered \"urban\" while in Iceland localities of 200 or more inhabitants, and in Peru population centers with 100 or more dwellings, are considered \"urban.\" In the United States places of 2,500 or more inhabitants, generally having population densities of 1,000 persons per square mile or more are considered \"urban\".\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. According to China's State Statistical Bureau, by the end of 1996 urban residents accounted for about 43 percent of China's population, more than double the 20 percent considered urban in 1994. In addition to the continuous migration of people from rural to urban areas, one of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as cities in recent years.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Population in largest city is the urban population living in the country's largest metropolitan area." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Urbanization Prospects 2018, United Nations (UN), uri: https://population.un.org/wup/, publisher: UN Population Division, date published: 2018" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. The United Nations Population Division and other agencies provide current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\" Typically, a community or settlement with a population of 2,000 or more is considered urban, but national definitions are most commonly based on size of locality. Eurostat defines urban areas as clusters of contiguous grid cells of 1 km2 with a density of at least 300 inhabitants per km2 and a minimum population of 5,000. Further it defines high-density cluster as contiguous grid cells of 1 km2 with a density of at least 1,500 inhabitants per km2 and a minimum population of 50,000.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe population of a city or metropolitan area depends on the boundaries chosen. For example, in 1990 Beijing, China, contained 2.3 million people in 87 square kilometers of \"inner city\" and 5.4 million in 158 square kilometers of \"core city.\" The population of \"inner city and inner suburban districts\" was 6.3 million and that of \"inner city, inner and outer suburban districts, and inner and outer counties\" was 10.8 million. (Most countries use the last definition.)" }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "people" } ], "source_id": "2" }, { "id": "EN.URB.LCTY.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A metropolitan area includes the urban area, and its satellite cities plus intervening rural land that is socio-economically connected to the urban core city, typically by employment ties through commuting, with the urban core city being the primary labor market. According to the United Nations' definition, a metropolitan area includes both the contiguous territory inhabited at urban levels of residential density and additional surrounding areas of lower settlement density that are also under the direct influence of the city (e.g., through frequent transport, road linkages, commuting facilities etc.).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nExplosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service. For the first time ever, the majority of the world's population lives in a city, and this proportion continues to grow. One hundred years ago, 2 out of every 10 people lived in an urban area. By 1990, less than 40 percent of the global population lived in a city, but as of early 2010s, more than half of all people live in an urban area. By 2030, 6 out of every 10 people will live in a city, and by 2050, this proportion will increase to 7 out of 10 people. About half of all urban dwellers live in cities with between 100,000-500,000 people, and fewer than 10% of urban dwellers live in megacities (a city with a population of more than 10 million, as defined by UN HABITAT). Currently, the number of urban residents is growing by nearly 60 million every year.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBy the middle of the 21st century, the urban population will almost double, reaching 6.4 billion in 2050. Almost all urban population growth in the next 30 years will occur in cities of developing countries. By the middle of the 21st century, it is estimated that the urban population of developing counties will more than double, reaching almost 5.2 billion in 2050. In high-income countries, the urban population is expected to remain largely unchanged over the next two decades, reaching to just over 1 billion by 2025. In these countries, immigration (legal and illegal) will account for more than two-thirds of urban growth. Without immigration, the urban population in these countries would most likely decline or remain static.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment. Poverty is growing faster in urban than in rural areas. According to UN one billion people live in urban slums, which are typically overcrowded, polluted and dangerous, and lack basic services such as clean water and sanitation." }, { "id": "IndicatorName", "value": "Population in the largest city (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Most countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries. For example, in Botswana, agglomeration of 5,000 or more inhabitants where 75 per cent of the economic activity is non-agricultural is considered \"urban\" while in Iceland localities of 200 or more inhabitants, and in Peru population centers with 100 or more dwellings, are considered \"urban.\" In the United States places of 2,500 or more inhabitants, generally having population densities of 1,000 persons per square mile or more are considered \"urban\".\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. According to China's State Statistical Bureau, by the end of 1996 urban residents accounted for about 43 percent of China's population, more than double the 20 percent considered urban in 1994. In addition to the continuous migration of people from rural to urban areas, one of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as cities in recent years.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Urbanization Prospects 2018, United Nations (UN), uri: https://population.un.org/wup/, publisher: UN Population Division, date published: 2018" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. The United Nations Population Division and other agencies provide current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\" Typically, a community or settlement with a population of 2,000 or more is considered urban, but national definitions are most commonly based on size of locality. Eurostat defines urban areas as clusters of contiguous grid cells of 1 km2 with a density of at least 300 inhabitants per km2 and a minimum population of 5,000. Further it defines high-density cluster as contiguous grid cells of 1 km2 with a density of at least 1,500 inhabitants per km2 and a minimum population of 50,000.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe population of a city or metropolitan area depends on the boundaries chosen. For example, in 1990 Beijing, China, contained 2.3 million people in 87 square kilometers of \"inner city\" and 5.4 million in 158 square kilometers of \"core city.\" The population of \"inner city and inner suburban districts\" was 6.3 million and that of \"inner city, inner and outer suburban districts, and inner and outer counties\" was 10.8 million. (Most countries use the last definition.)" }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "% (share) of urban population" } ], "source_id": "2" }, { "id": "EN.URB.MCTY", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "According to the United Nations, an Urban Agglomeration refers to the de facto population contained within the contours of a contiguous territory inhabited at urban density levels without regard to administrative boundaries. It usually incorporates the population in a city or town plus that in the sub-urban areas lying outside of but being adjacent to the city boundaries. In general, an urban agglomeration is an extended city or town area comprising the built-up area of a central place and any suburbs linked by continuous urban area. INSEE, the French Statistical Institute, uses the term unité urbaine, which means continuous urbanized area. There are differences in definitions of what does and does not constitute an \"agglomeration\", as well as differenced in statistical and geographical methodology. Some of the well-known urban agglomerations of the world are Tokyo, New York City, Mexico City, New Delhi, and Seoul.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nA metropolitan area includes the urban area, and its satellite cities plus intervening rural land that is socio-economically connected to the urban core city, typically by employment ties through commuting, with the urban core city being the primary labor market. According to the United Nations' definition, a metropolitan area includes both the contiguous territory inhabited at urban levels of residential density and additional surrounding areas of lower settlement density that are also under the direct influence of the city (e.g., through frequent transport, road linkages, commuting facilities etc.).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nExplosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service. For the first time ever, the majority of the world's population lives in a city, and this proportion continues to grow. One hundred years ago, 2 out of every 10 people lived in an urban area. By 1990, less than 40 percent of the global population lived in a city, but as of early 2010s, more than half of all people live in an urban area. By 2030, 6 out of every 10 people will live in a city, and by 2050, this proportion will increase to 7 out of 10 people. About half of all urban dwellers live in cities with between 100,000-500,000 people, and fewer than 10% of urban dwellers live in megacities (a city with a population of more than 10 million, as defined by UN HABITAT). Currently, the number of urban residents is growing by nearly 60 million every year.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBy the middle of the 21st century, the urban population will almost double, reaching 6.4 billion in 2050. Almost all urban population growth in the next 30 years will occur in cities of developing countries. By the middle of the 21st century, it is estimated that the urban population of developing counties will more than double, reaching almost 5.2 billion in 2050. In high-income countries, the urban population is expected to remain largely unchanged over the next two decades, reaching to just over 1 billion by 2025. In these countries, immigration (legal and illegal) will account for more than two-thirds of urban growth. Without immigration, the urban population in these countries would most likely decline or remain static.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment. Poverty is growing faster in urban than in rural areas. According to UN one billion people live in urban slums, which are typically overcrowded, polluted and dangerous, and lack basic services such as clean water and sanitation." }, { "id": "IndicatorName", "value": "Population in urban agglomerations of more than 1 million" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to varying definitions, it is not possible to compare different agglomerations around the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Most countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries. For example, in Botswana, agglomeration of 5,000 or more inhabitants where 75 per cent of the economic activity is non-agricultural is considered \"urban\" while in Iceland localities of 200 or more inhabitants, and in Peru population centers with 100 or more dwellings, are considered \"urban.\" In the United States places of 2,500 or more inhabitants, generally having population densities of 1,000 persons per square mile or more are considered \"urban\".\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. According to China's State Statistical Bureau, by the end of 1996 urban residents accounted for about 43 percent of China's population, more than double the 20 percent considered urban in 1994. In addition to the continuous migration of people from rural to urban areas, one of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as cities in recent years.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Population in urban agglomerations of more than one million is the country's population living in metropolitan areas that in 2018 had a population of more than one million people." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Urbanization Prospects 2018, United Nations (UN), uri: https://population.un.org/wup/, publisher: UN Population Division, date published: 2018" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. The United Nations Population Division and other agencies provide current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\" Typically, a community or settlement with a population of 2,000 or more is considered urban, but national definitions are most commonly based on size of locality. Eurostat defines urban areas as clusters of contiguous grid cells of 1 km2 with a density of at least 300 inhabitants per km2 and a minimum population of 5,000. Further it defines high-density cluster as contiguous grid cells of 1 km2 with a density of at least 1,500 inhabitants per km2 and a minimum population of 50,000.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe population of a city or metropolitan area depends on the boundaries chosen. For example, in 1990 Beijing, China, contained 2.3 million people in 87 square kilometers of \"inner city\" and 5.4 million in 158 square kilometers of \"core city.\" The population of \"inner city and inner suburban districts\" was 6.3 million and that of \"inner city, inner and outer suburban districts, and inner and outer counties\" was 10.8 million. (Most countries use the last definition.)\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "people" } ], "source_id": "2" }, { "id": "EN.URB.MCTY.TL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "According to the United Nations, an Urban Agglomeration refers to the de facto population contained within the contours of a contiguous territory inhabited at urban density levels without regard to administrative boundaries. It usually incorporates the population in a city or town plus that in the sub-urban areas lying outside of but being adjacent to the city boundaries. In general, an urban agglomeration is an extended city or town area comprising the built-up area of a central place and any suburbs linked by continuous urban area. INSEE, the French Statistical Institute, uses the term unité urbaine, which means continuous urbanized area. There are differences in definitions of what does and does not constitute an \"agglomeration\", as well as differenced in statistical and geographical methodology. Some of the well-known urban agglomerations of the world are Tokyo, New York City, Mexico City, New Delhi, and Seoul.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nA metropolitan area includes the urban area, and its satellite cities plus intervening rural land that is socio-economically connected to the urban core city, typically by employment ties through commuting, with the urban core city being the primary labor market. According to the United Nations' definition, a metropolitan area includes both the contiguous territory inhabited at urban levels of residential density and additional surrounding areas of lower settlement density that are also under the direct influence of the city (e.g., through frequent transport, road linkages, commuting facilities etc.).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nExplosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service. For the first time ever, the majority of the world's population lives in a city, and this proportion continues to grow. One hundred years ago, 2 out of every 10 people lived in an urban area. By 1990, less than 40 percent of the global population lived in a city, but as of early 2010s, more than half of all people live in an urban area. By 2030, 6 out of every 10 people will live in a city, and by 2050, this proportion will increase to 7 out of 10 people. About half of all urban dwellers live in cities with between 100,000-500,000 people, and fewer than 10% of urban dwellers live in megacities (a city with a population of more than 10 million, as defined by UN HABITAT). Currently, the number of urban residents is growing by nearly 60 million every year.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBy the middle of the 21st century, the urban population will almost double, reaching 6.4 billion in 2050. Almost all urban population growth in the next 30 years will occur in cities of developing countries. By the middle of the 21st century, it is estimated that the urban population of developing counties will more than double, reaching almost 5.2 billion in 2050. In high-income countries, the urban population is expected to remain largely unchanged over the next two decades, reaching to just over 1 billion by 2025. In these countries, immigration (legal and illegal) will account for more than two-thirds of urban growth. Without immigration, the urban population in these countries would most likely decline or remain static.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment. Poverty is growing faster in urban than in rural areas. According to UN one billion people live in urban slums, which are typically overcrowded, polluted and dangerous, and lack basic services such as clean water and sanitation." }, { "id": "IndicatorName", "value": "Population in urban agglomerations of more than 1 million (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to varying definitions, it is not possible to compare different agglomerations around the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Most countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries. For example, in Botswana, agglomeration of 5,000 or more inhabitants where 75 per cent of the economic activity is non-agricultural is considered \"urban\" while in Iceland localities of 200 or more inhabitants, and in Peru population centers with 100 or more dwellings, are considered \"urban.\" In the United States places of 2,500 or more inhabitants, generally having population densities of 1,000 persons per square mile or more are considered \"urban\".\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. According to China's State Statistical Bureau, by the end of 1996 urban residents accounted for about 43 percent of China's population, more than double the 20 percent considered urban in 1994. In addition to the continuous migration of people from rural to urban areas, one of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as cities in recent years.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Population in urban agglomerations of more than one million is the percentage of a country's population living in metropolitan areas that in 2018 had a population of more than one million people." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Urbanization Prospects 2018, United Nations (UN), uri: https://population.un.org/wup/, publisher: UN Population Division, date published: 2018" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. The United Nations Population Division and other agencies provide current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\" Typically, a community or settlement with a population of 2,000 or more is considered urban, but national definitions are most commonly based on size of locality. Eurostat defines urban areas as clusters of contiguous grid cells of 1 km2 with a density of at least 300 inhabitants per km2 and a minimum population of 5,000. Further it defines high-density cluster as contiguous grid cells of 1 km2 with a density of at least 1,500 inhabitants per km2 and a minimum population of 50,000.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe population of a city or metropolitan area depends on the boundaries chosen. For example, in 1990 Beijing, China, contained 2.3 million people in 87 square kilometers of \"inner city\" and 5.4 million in 158 square kilometers of \"core city.\" The population of \"inner city and inner suburban districts\" was 6.3 million and that of \"inner city, inner and outer suburban districts, and inner and outer counties\" was 10.8 million. (Most countries use the last definition.)" }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "2" }, { "id": "ER.FSH.AQUA.MT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Aquaculture is understood to mean the farming of aquatic organisms including fish, molluscs, crustaceans and aquatic plants. Farming implies some form of intervention in the rearing process to enhance production, such as regular stocking, feeding, protection from predators, etc. Farming also implies individual or corporate ownership of the stock being cultivated. For statistical purposes, aquatic organisms which are harvested by an individual of corporate body which has owned them throughout their rearing period contribute to aquaculture while aquatic organisms which are exploitable by public as a common property resource, with or without appropriate licences, are the harvest of fisheries." }, { "id": "IndicatorName", "value": "Aquaculture production (metric tons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Aquaculture is understood to mean the farming of aquatic organisms including fish, molluscs, crustaceans and aquatic plants. Aquaculture production specifically refers to output from aquaculture activities, which are designated for final harvest for consumption." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Food and Agriculture Organization., Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Aquaculture production specifically refers to output from aquaculture activities, which are designated for final harvest for consumption. At this time, harvest for ornamental purposes is not included." }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "metric tons" } ], "source_id": "2" }, { "id": "ER.FSH.CAPT.MT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Capture fisheries production (metric tons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Capture fisheries production measures the volume of fish catches landed by a country for all commercial, industrial, recreational and subsistence purposes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Food and Agriculture Organization., Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Information on capture production is collected annually from relevant national offices concerned with fishery statistics, by means of a system of standardized forms, which list for each country the relative species items and fishing areas breakdown.\n\n\n\n\n\n\n\n\nIn the case of some \"aquatic products\", data are also obtained from trade associations or other specialized international organizations to which data are also submitted. In this way the statistics are reviewed by subject matter specialists.\n\n\n\n\n\n\n\n\nData concerning the nominal catch of certain major groups are generally reviewed in collaboration with the regional agency concerned. For example, for ISSCAAP group 36 (Tunas, bonitos and billfishes) data provided by the national correspondents are often replaced by the \"best scientific estimates\" produced by regional bodies collecting tuna catch statistics (i.e. ICCAT, IOTC, SPC and IATTC.)\n\n\n\n\n\n\n\n\nSee https://www.fao.org/fishery/en/collection/capture" }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "metric tons" } ], "source_id": "2" }, { "id": "ER.FSH.PROD.MT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Total fisheries production (metric tons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total fisheries production measures the volume of aquatic species caught by a country for all commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is also included." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Food and Agriculture Organization., Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Information on capture production is collected annually from relevant national offices concerned with fishery statistics, by means of a system of standardized forms, which list for each country the relative species items and fishing areas breakdown.\n\n\n\n\n\n\n\n\nIn the case of some \"aquatic products\", data are also obtained from trade associations or other specialized international organizations to which data are also submitted. In this way the statistics are reviewed by subject matter specialists.\n\n\n\n\n\n\n\n\nData concerning the nominal catch of certain major groups are generally reviewed in collaboration with the regional agency concerned. For example, for ISSCAAP group 36 (Tunas, bonitos and billfishes) data provided by the national correspondents are often replaced by the \"best scientific estimates\" produced by regional bodies collecting tuna catch statistics (i.e. ICCAT, IOTC, SPC and IATTC.)\n\n\n\n\n\n\n\n\nSee https://www.fao.org/fishery/en/collection/capture" }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "metric tons" } ], "source_id": "2" }, { "id": "ER.GDP.FWTL.M3.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectoral planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain." }, { "id": "IndicatorName", "value": "Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Water productivity is calculated as GDP in constant prices divided by annual total water withdrawal." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO);\nWorld Bank GDP estimates, World Bank (WB), publisher: World Bank (WB);\nOECD GDP estimates, Organisation for Economic Co-operation and Development (OECD), publisher: Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Water productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. GDP data are from World Bank's national accounts files.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "constant 2015 US$ GDP per cubic meter of total freshwater withdrawal" } ], "source_id": "2" }, { "id": "ER.H2O.FWAG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectoral planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times)." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, agriculture (% of total freshwater withdrawal)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for agriculture are total withdrawals for irrigation and livestock production. Data are for the most recent year available for 1987-2002." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1965-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the pressure on the renewable water resources of a country caused by irrigation. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "% (share) of total freshwater withdrawal" } ], "source_id": "2" }, { "id": "ER.H2O.FWDM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "UNESCO estimates that in developing countries in Asia, Africa and Latin America, public water withdrawal represents just 50-100 liters (13 to 26 gallons) per person per day. In regions with insufficient water resources, this figure may be as low as 20-60 (5 to 15 gallons) liters per day. People in developed countries on average consume about 10 times more water daily than those in developing countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, domestic (% of total freshwater withdrawal)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes. Data are for the most recent year available for 1987-2002." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1965-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Domestic water withdrawal, sometimes used interchangeably with municipal water withdrawal, focuses on human needs (drinking, cooking, cleaning, and sanitation). Data includes renewable freshwater resources, potential over-abstraction of renewable groundwater, withdrawal of fossil groundwater, and the potential use of desalinated water or treated wastewater. It is usually computed as the total water withdrawn by the public distribution network, and includes that part of the industries, which is connected to the municipal network. The ratio between the net consumption and the water withdrawn can vary from 5 to 15 percent in urban areas and from 10 to 50 percent in rural areas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "% (share) of total freshwater withdrawal" } ], "source_id": "2" }, { "id": "ER.H2O.FWIN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture. UNESCO estimates that Industrial uses account for about 20 percent of global freshwater withdrawals. Of this, 57-69 percent is used for hydropower and nuclear power generation, 30-40 percent for industrial processes, and 0.5-3 percent for thermal power generation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, industry (% of total freshwater withdrawal)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for industry are total withdrawals for direct industrial use (including withdrawals for cooling thermoelectric plants). Data are for the most recent year available for 1987-2002." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1965-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Annual industrial freshwater withdrawals include renewable water resources as well as potential over-abstraction of renewable groundwater or potential use of desalinated water or treated wastewater. It includes water for the cooling of thermoelectric plants, but it does not include hydropower.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for industry are total withdrawals for direct industrial use (including withdrawals for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "% (share) of total freshwater withdrawal" } ], "source_id": "2" }, { "id": "ER.H2O.FWST.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The level of water stress can show the degree to which water resources are being exploited to meet the country's water demand. It measures a country's pressure on its water resources and therefore the challenge on the sustainability of its water use. It tracks progress in regard to “withdrawals and supply of freshwater to address water scarcity”, i.e. the environmental component of target 6.4. It also shows to what extent water resources are already used, and signals the importance of effective supply and demand management policies. It indicates the likelihood of increasing competition and conflict between different water uses and users in a situation of increasing water scarcity. Increased water stress, shown by an increase in the value of the indicator, has potentially negative effects on the sustainability of the natural resources and on economic development. On the other hand, low values of water stress indicate that water does not represent a particular challenge for economic development and sustainability." }, { "id": "IndicatorName", "value": "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Water withdrawal as a percentage of water resources is a good indicator of pressure on limited water resources, one of the most important natural resources. However, it only partially addresses the issues related to sustainable water management. Supplementary indicators that capture the multiple dimensions of water management would combine data on water demand management, behavioural changes with regard to water use and the availability of appropriate infrastructure, and measure progress in increasing the efficiency and sustainability of water use, in particular in relation to population and economic growth. They would also recognize the different climatic environments that affect water use in countries, in particular in agriculture, which is the main user of water. Sustainability assessment is also linked to the critical thresholds fixed for this indicator and there is no universal consensus on such threshold.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTrends in water withdrawal show relatively slow patterns of change. Usually, three-five years are a minimum frequency to be able to detect significant changes, as it is unlikely that the indicator would show meaningful variations from one year to the other. Estimation of water withdrawal by sector is the main limitation to the computation of the indicator. Few countries actually publish water use data on a regular basis by sector. Renewable water resources include all surface water and groundwater resources that are available on a yearly basis without consideration of the capacity to harvest and use this resource. Exploitable water resources, which refer to the volume of surface water or groundwater that is available with an occurrence of 90% of the time, are considerably less than renewable water resources, but no universal method exists to assess such exploitable water resources. There is no universally agreed method for the computation of incoming freshwater flows originating outside of a country's borders. Nor is there any standard method to account for return flows, the part of the water withdrawn from its source and which flows back to the river system after use. In countries where return flow represents a substantial part of water withdrawal, the indicator tends to underestimate available water and therefore overestimate the level of water stress.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOther limitations that affect the interpretation of the water stress indicator include: difficulty to obtain accurate, complete and up-to-date data; potentially large variation of sub-national data; lack of account of seasonal variations in water resources; lack of consideration to the distribution among water uses; lack of consideration of water quality and its suitability for use; and the indicator can be higher than 100 per cent when water withdrawal includes secondary freshwater (water withdrawn previously and returned to the system), non-renewable water (fossil groundwater), when annual groundwater withdrawal is higher than annual replenishment (over-abstraction) or when water withdrawal includes part or all of the water set aside for environmental water requirements. Some of these issues can be solved through disaggregation of the index at the level of hydrological units and by distinguishing between different use sectors. However, due to the complexity of water flows, both within a country and between countries, care should be taken not to double-count." }, { "id": "Longdefinition", "value": "The level of water stress: freshwater withdrawal as a proportion of available freshwater resources is the ratio between total freshwater withdrawn by all major sectors and total renewable freshwater resources, after taking into account environmental water requirements. Main sectors, as defined by ISIC standards, include agriculture; forestry and fishing; manufacturing; electricity industry; and services. This indicator is also known as water withdrawal intensity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Proportion of total renewable water resources withdrawn is the total volume of groundwater and surface water withdrawn from their sources for human use (in the agricultural, municipal and industrial sectors), expressed as a percentage of the total actual renewable water resources. The terms water resources and water withdrawal are understood as freshwater resources and freshwater withdrawal. Water withdrawal is estimated for the following three main sectors: agriculture, municipalities (including domestic water withdrawal) and industries, at country level and expressed in km3/year. The total actual renewable water resources for a country or region are defined as the sum of internal renewable water resources and the external renewable water resources, also expressed in km3/year. The indicator is computed by dividing total water withdrawal by total actual renewable water resources minus environmental requirements and expressed in percentage points.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal freshwater withdrawal is the volume of freshwater extracted from its source (rivers, lakes, aquifers) for agriculture, industries and municipalities. It is estimated at the country level for the following three main sectors: agriculture, municipalities (including domestic water withdrawal) and industries. Freshwater withdrawal includes primary freshwater (not withdrawn before), secondary freshwater (previously withdrawn and returned to rivers and groundwater, such as discharged wastewater and agricultural drainage water) and fossil groundwater. It does not include non-conventional water, i.e. direct use of treated wastewater, direct use of agricultural drainage water and desalinated water. Total freshwater withdrawal is in general calculated as being the sum of total water withdrawal by sector minus direct use of wastewater, direct use of agricultural drainage water and use of desalinated water.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal renewable freshwater resources are expressed as the sum of internal and external renewable water resources. The terms “water resources” and “water withdrawal” are understood here as freshwater resources and freshwater withdrawal. Internal renewable water resources are defined as the long-term average annual flow of rivers and recharge of groundwater for a given country generated from endogenous precipitation. External renewable water resources refer to the flows of water entering the country, taking into consideration the quantity of flows reserved to upstream and downstream countries through agreements or treaties.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnvironmental water requirements (Env.) are the quantities of water required to sustain freshwater and estuarine ecosystems. Water quality and also the resulting ecosystem services are excluded from this formulation which is confined to water volumes. This does not imply that quality and the support to societies which are dependent on environmental flows are not important and should not be taken care of. Methods of computation of Env. are extremely variable and range from global estimates to comprehensive assessments for river reaches. Water volumes can be expressed in the same units as the total freshwater withdrawal, and then as percentages of the available water resources." }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "% (ratio)" } ], "source_id": "2" }, { "id": "ER.H2O.FWTL.K3", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times)." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, total (billion cubic meters)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including withdrawals for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Annual freshwater withdrawals are total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Water withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "billion cubic meters" } ], "source_id": "2" }, { "id": "ER.H2O.FWTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times)." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, total (% of internal resources)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including withdrawals for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes. Data are for the most recent year available for 1987-2002." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Annual freshwater withdrawals are total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "% (share) of internal resources" } ], "source_id": "2" }, { "id": "ER.H2O.INTR.K3", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "UNESCO estimates that in developing countries in Asia, Africa and Latin America, public water withdrawal represents just 50-100 liters (13 to 26 gallons) per person per day. In regions with insufficient water resources, this figure may be as low as 20-60 (5 to 15 gallons) liters per day. People in developed countries on average consume about 10 times more water daily than those in developing countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations." }, { "id": "IndicatorName", "value": "Renewable internal freshwater resources, total (billion cubic meters)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Renewable internal freshwater resources flows refer to internal renewable resources (internal river flows and groundwater from rainfall) in the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. Renewable water resources (internal and external) include average annual flow of rivers and recharge of aquifers generated from endogenous precipitation, and those water resources that are not generated in the country, such as inflows from upstream countries (groundwater and surface water), and part of the water of border lakes and/or rivers. Non-renewable water includes groundwater bodies (deep aquifers) that have a negligible rate of recharge on the human time-scale. While renewable water resources are expressed in flows, non-renewable water resources have to be expressed in quantity (stock). Runoff from glaciers where the mass balance is negative is considered non-renewable.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal actual renewable water resources correspond to the maximum theoretical yearly amount of water actually available for a country at a given moment. The unit of calculation is km3/year or 109 m3/year. Calculation Criteria is [Water resources: total renewable (actual)] = [Surface water: total renewable (actual)] + [Groundwater: total renewable (actual)] - [Overlap between surface water and groundwater].*\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFresh water is naturally occurring water on the Earth's surface. It is a renewable but limited natural resource. Fresh water can only be renewed through the process of the water cycle, where water from seas, lakes, forests, land, rivers, and dams evaporates, forms clouds, and returns as precipitation. However, if more fresh water is consumed through human activities than is restored by nature, the result is that the quantity of fresh water available in lakes, rivers, dams and underground waters can be reduced which can cause serious damage to the surrounding environment.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n* http://www.fao.org/nr/water/aquastat/data/glossary/search.html?termId=4188&submitBtn=s&cls=yes\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "billion cubic meters" } ], "source_id": "2" }, { "id": "ER.H2O.INTR.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "UNESCO estimates that in developing countries in Asia, Africa and Latin America, public water withdrawal represents just 50-100 liters (13 to 26 gallons) per person per day. In regions with insufficient water resources, this figure may be as low as 20-60 (5 to 15 gallons) liters per day. People in developed countries on average consume about 10 times more water daily than those in developing countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations." }, { "id": "IndicatorName", "value": "Renewable internal freshwater resources per capita (cubic meters)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Renewable internal freshwater resources flows refer to internal renewable resources (internal river flows and groundwater from rainfall) in the country. Renewable internal freshwater resources per capita are calculated using the World Bank's population estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Renewable water resources (internal and external) include average annual flow of rivers and recharge of aquifers generated from endogenous precipitation, and those water resources that are not generated in the country, such as inflows from upstream countries (groundwater and surface water), and part of the water of border lakes and/or rivers. Non-renewable water includes groundwater bodies (deep aquifers) that have a negligible rate of recharge on the human time-scale. While renewable water resources are expressed in flows, non-renewable water resources have to be expressed in quantity (stock). Runoff from glaciers where the mass balance is negative is considered non-renewable. Renewable internal freshwater resources per capita are calculated using the World Bank's population estimates. The unit of calculation is m3/year per inhabitant. Internal renewable freshwater resources per capita are calculated using the World Bank's population estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal actual renewable water resources correspond to the maximum theoretical yearly amount of water actually available for a country at a given moment. The unit of calculation is km3/year or 109 m3/year. Calculation Criteria is [Water resources: total renewable (actual)] = [Surface water: total renewable (actual)] + [Groundwater: total renewable (actual)] - [Overlap between surface water and groundwater].*\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFresh water is naturally occurring water on the Earth's surface. It is a renewable but limited natural resource. Fresh water can only be renewed through the process of the water cycle, where water from seas, lakes, forests, land, rivers, and dams evaporates, forms clouds, and returns as precipitation. However, if more fresh water is consumed through human activities than is restored by nature, the result is that the quantity of fresh water available in lakes, rivers, dams and underground waters can be reduced which can cause serious damage to the surrounding environment.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n* http://www.fao.org/nr/water/aquastat/data/glossary/search.html?termId=4188&submitBtn=s&cls=yes\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "cubic meters" } ], "source_id": "2" }, { "id": "ER.LND.PTLD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The International Union for Conservation of Nature (IUCN) defines a protected area as \"a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values.\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTerrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Nationally protected terrestrial are terrestrial areas as a percentage of total territorial area, where all nationally designated protected areas with known location and extent are included.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nProtected areas remain the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. They provide the core of efforts to protect the world's threatened species and are increasingly recognized as essential providers of ecosystem services and biological resources. Some sites are owned and managed by governments, others by private individuals, companies, communities and faith groups.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Sustainable Development Goals (SDGs) address concerns common to all economies. In recognition of the vulnerability of animal and plant species, SDGs include targets 14 and 15 to highlight the importance of marine and terrestorial protected areas. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity." }, { "id": "IndicatorName", "value": "Terrestrial protected areas (% of total land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data source for this indicator is the World Database on Protected Areas (WDPA), the most comprehensive global dataset on marine and terrestrial protected areas available. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe extent to which the land areas, including inland waters, and territorial waters of a country/territory are protected is useful for planning purpose to protect biodiversity. However, it is neither an indication of how well managed the terrestrial and marine protected areas are, nor confirmation that protection measures are effectively enforced. Further, the indicator does not provide information on non-designated or internationally designated protected areas that may also be important for conserving biodiversity. There are known data and knowledge gaps for some countries/regions due to difficulties in reporting national protected area data to the WDPA and/or determining whether a site conforms to the IUCN definition of a protected area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGaps and/or time lags in reporting national protected area data to the WDPA can however result in discrepancies, which are resolved in communication with data providers. The World Conservation Monitoring Centre (WCMC) compiles data on protected areas, numbers of certain species, and numbers of those species under threat from various sources. Because of differences in definitions, reporting practices, and reporting periods, cross-country comparability is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas." }, { "id": "Longdefinition", "value": "Terrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Marine areas, unclassified areas, littoral (intertidal) areas, and sites protected under local or provincial law are excluded." }, { "id": "Othernotes", "value": "Restricted use: Please contact the Protected Planet for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2013-2025" }, { "id": "Source", "value": "Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM), UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516, date published: 202405;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon). \n\nDesignating an area as protected does not mean that protection is in force. And for small countries that have only protected areas smaller than 1,000 hectares, the size limit in the definition leads to an underestimate of protected areas. Nationally protected areas are defined using the six IUCN management categories for areas of at least 1,000 hectares: scientific reserves and strict nature reserves with limited public access; national parks of national or international significance and not materially affected by human activity; natural monuments and natural landscapes with unique aspects; managed nature reserves and wildlife sanctuaries; protected landscapes (which may include cultural landscapes); and areas managed mainly for the sustainable use of natural systems to ensure long-term protection and maintenance of biological diversity. \n\nA GIS analysis is used to calculate terrestrial and marine protection. For this a global protected area layer is created by combining the polygons and points recorded in the WDPA. Circular buffers are created around points based on the known extent of protected areas for which no polygon is available. Annual protected area layers are created by dissolving the global protected area layer by the known year of establishment of protected areas recorded in the WDPA. The annual protected area layers are overlaid with country/territory boundaries, coastlines and buffered coastlines (delineating the territorial waters) to obtain the absolute coverage (in square kilometers) of protected areas by country/territory. The total area of a country's/territory's terrestrial protected areas and marine protected areas in territorial waters is divided by the total area of its land areas (including inland waters) and territorial waters to obtain the relative coverage (percentage) of protected areas.\n\nThe data reported for a given year reflects all protected areas reported until January of the succeeding yer. For example, the value for 2025 is calculated based on the January 2026 version of the WDPA." }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "2" }, { "id": "ER.MRN.PTMR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The International Union for Conservation of Nature (IUCN) defines a protected area as \"a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values.\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMarine protected areas are areas of intertidal or subtidal terrain - and overlying water and associated flora and fauna and historical and cultural features - that have been reserved by law or other effective means to protect part or the entire enclosed environment. Sites protected under local or provincial law are excluded.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nProtected areas remain the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. They provide the core of efforts to protect the world's threatened species and are increasingly recognized as essential providers of ecosystem services and biological resources. Some sites are owned and managed by governments, others by private individuals, companies, communities and faith groups.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Sustainable Development Goals (SDGs) address concerns common to all economies. In recognition of the vulnerability of animal and plant species, SDGs include targets 14 and 15 to highlight the importance of marine and terrestorial protected areas. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity." }, { "id": "IndicatorName", "value": "Marine protected areas (% of territorial waters)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data source for this indicator is the World Database on Protected Areas (WDPA), the most comprehensive global dataset on marine and terrestrial protected areas available. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe extent to which the land areas, including inland waters, and territorial waters of a country/territory are protected is useful for planning purpose to protect biodiversity. However, it is neither an indication of how well managed the terrestrial and marine protected areas are, nor confirmation that protection measures are effectively enforced. Further, the indicator does not provide information on non-designated or internationally designated protected areas that may also be important for conserving biodiversity. There are known data and knowledge gaps for some countries/regions due to difficulties in reporting national protected area data to the WDPA and/or determining whether a site conforms to the IUCN definition of a protected area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGaps and/or time lags in reporting national protected area data to the WDPA can however result in discrepancies, which are resolved in communication with data providers. The World Conservation Monitoring Centre (WCMC) compiles data on protected areas, numbers of certain species, and numbers of those species under threat from various sources. Because of differences in definitions, reporting practices, and reporting periods, cross-country comparability is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas." }, { "id": "Longdefinition", "value": "Marine protected areas are areas of intertidal or subtidal terrain--and overlying water and associated flora and fauna and historical and cultural features--that have been reserved by law or other effective means to protect part or all of the enclosed environment." }, { "id": "Othernotes", "value": "Restricted use: Please contact the Protected Planet for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2013-2025" }, { "id": "Source", "value": "Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM), UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.protectedplanet.net/en, note: Only the latest data can be retrieved from the Protected Planet website. The time series are provided directly to WDI by Protected Planet., publisher: Protected Planet, date accessed: 20240516, date published: 202405;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon). \n\nDesignating an area as protected does not mean that protection is in force. And for small countries that have only protected areas smaller than 1,000 hectares, the size limit in the definition leads to an underestimate of protected areas. Nationally protected areas are defined using the six IUCN management categories for areas of at least 1,000 hectares: scientific reserves and strict nature reserves with limited public access; national parks of national or international significance and not materially affected by human activity; natural monuments and natural landscapes with unique aspects; managed nature reserves and wildlife sanctuaries; protected landscapes (which may include cultural landscapes); and areas managed mainly for the sustainable use of natural systems to ensure long-term protection and maintenance of biological diversity.\n\nA GIS analysis is used to calculate terrestrial and marine protection. For this a global protected area layer is created by combining the polygons and points recorded in the WDPA. Circular buffers are created around points based on the known extent of protected areas for which no polygon is available. Annual protected area layers are created by dissolving the global protected area layer by the known year of establishment of protected areas recorded in the WDPA. The annual protected area layers are overlaid with country/territory boundaries, coastlines and buffered coastlines (delineating the territorial waters) to obtain the absolute coverage (in square kilometers) of protected areas by country/territory per year from 1990 to present. The total area of a country's/territory's terrestrial protected areas and marine protected areas in territorial waters is divided by the total area of its land areas (including inland waters) and territorial waters to obtain the relative coverage (percentage) of protected areas.\n\nThe data reported for a given year reflects all protected areas reported until January of the succeeding year. For example, the value for 2025 is calculated based on the January 2026 version of the WDPA." }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" }, { "id": "Unitofmeasure", "value": "% (share) of territorial waters" } ], "source_id": "2" }, { "id": "ER.PTD.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The International Union for Conservation of Nature (IUCN) defines a protected area as \"a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values.\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTerrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMarine protected areas are areas of intertidal or subtidal terrain - and overlying water and associated flora and fauna and historical and cultural features - that have been reserved by law or other effective means to protect part or the entire enclosed environment. Sites protected under local or provincial law are excluded.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nProtected areas remain the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. They provide the core of efforts to protect the world's threatened species and are increasingly recognized as essential providers of ecosystem services and biological resources. Some sites are owned and managed by governments, others by private individuals, companies, communities and faith groups.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Sustainable Development Goals (SDGs) address concerns common to all economies. In recognition of the vulnerability of animal and plant species, SDGs include targets 14 and 15 to highlight the importance of marine and terrestorial protected areas. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity." }, { "id": "IndicatorName", "value": "Terrestrial and marine protected areas (% of total territorial area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data source for this indicator is the World Database on Protected Areas (WDPA), the most comprehensive global dataset on marine and terrestrial protected areas available. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe extent to which the land areas, including inland waters, and territorial waters of a country/territory are protected is useful for planning purpose to protect biodiversity. However, it is neither an indication of how well managed the terrestrial and marine protected areas are, nor confirmation that protection measures are effectively enforced. Further, the indicator does not provide information on non-designated or internationally designated protected areas that may also be important for conserving biodiversity. There are known data and knowledge gaps for some countries/regions due to difficulties in reporting national protected area data to the WDPA and/or determining whether a site conforms to the IUCN definition of a protected area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGaps and/or time lags in reporting national protected area data to the WDPA can however result in discrepancies, which are resolved in communication with data providers. The World Conservation Monitoring Centre (WCMC) compiles data on protected areas, numbers of certain species, and numbers of those species under threat from various sources. Because of differences in definitions, reporting practices, and reporting periods, cross-country comparability is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas." }, { "id": "Longdefinition", "value": "Terrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Marine protected areas are areas of intertidal or subtidal terrain--and overlying water and associated flora and fauna and historical and cultural features--that have been reserved by law or other effective means to protect part or all of the enclosed environment. Sites protected under local or provincial law are excluded." }, { "id": "Othernotes", "value": "Restricted use: Please contact the Protected Planet for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2013-2025" }, { "id": "Source", "value": "Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM), UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516, date published: 202405;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon).\n\nA GIS analysis is used to calculate terrestrial and marine protection. For this a global protected area layer is created by combining the polygons and points recorded in the WDPA. Circular buffers are created around points based on the known extent of protected areas for which no polygon is available. Annual protected area layers are created by dissolving the global protected area layer by the known year of establishment of protected areas recorded in the WDPA. The annual protected area layers are overlaid with country/territory boundaries, coastlines and buffered coastlines (delineating the territorial waters) to obtain the absolute coverage (in square kilometers) of protected areas by country/territory per year from 1990 to present. The total area of a country's/territory's terrestrial protected areas and marine protected areas in territorial waters is divided by the total area of its land areas (including inland waters) and territorial waters to obtain the relative coverage (percentage) of protected areas.\n\nThe data reported for a given year reflects all protected areas reported until January of the succeeding yer. For example, the value for 2025 is calculated based on the January 2026 version of the WDPA." }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" }, { "id": "Unitofmeasure", "value": "% (share) of total territorial area" } ], "source_id": "2" }, { "id": "FB.AST.NPER.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Ratio of non-performing/total loan portfolio" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator serves as a key measure of asset quality within the banking sector. A higher ratio indicates a greater share of impaired loans, which can signal deteriorating credit risk and financial health of lending institutions. Conversely, a lower ratio reflects better asset quality and lower credit risk. Importantly, total gross loans include the full book value of loans before provisioning and do not net out collateral or guarantees. This indicator helps assess potential vulnerabilities in a financial system, especially when tracked over time or compared across institutions and jurisdictions." }, { "id": "IndicatorName", "value": "Bank nonperforming loans to total gross loans (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting countries compile the data using different methodologies, which may also vary for different points in time for the same country. Users are advised to consult the accompanying metadata on the IMF FSI website (data.imf.org) to conduct more meaningful cross-country comparisons or to assess the evolution of the indicator for any of the countries." }, { "id": "Longdefinition", "value": "The indicator measures the proportion of a deposit taker’s loan portfolio that is impaired or at risk of default. It is calculated as the ratio of non-performing loans (NPLs) to total gross loans, where NPLs are defined as loans that are past due by 90 days or more or are otherwise considered unlikely to be repaid in full without the realization of collateral. Both non-performing loans and total gross loans should be reported at their gross book value, without deducting for loan-loss provisions or collateral. This indicator provides a key measure of asset quality and potential credit risk in the banking system." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Financial Soundness Indicators, International Monetary Fund (IMF), uri: https://data.imf.org/en/datasets/IMF:EXTERNAL_DATASET_CARDS/IMF.STA:LFSI" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The ratio is calculated by dividing the value of non-performing loans (NPLs) by the total value of gross loans, expressed as a percentage. Both NPLs and total gross loans should be reported at their gross book value, meaning before any deductions for provisions, write-offs, or collateral. Gross loans include all outstanding loans on the balance sheet, including those classified as non-performing. The classification of NPLs should be based on either quantitative criteria (e.g., days past due) or qualitative assessments (e.g., evidence of financial difficulties), following supervisory standards aligned with Basel guidance. Data should be compiled using a consistent consolidation basis, typically the cross-border, cross-sector, domestically incorporated consolidation approach (CBCSDI), to ensure comparability across reporting entities and jurisdictions. \nStatistical concept(s): The Non-performing Loans (NPLs) to Total Gross Loans ratio is a core indicator of asset quality within the banking sector. It measures the proportion of a deposit taker’s loan portfolio that is impaired or at significant risk of default. A loan is classified as non-performing when payments of principal or interest are overdue by 90 days or more, or when it is assessed that full repayment is unlikely without the realization of collateral, even if the loan is not yet past due. Data are submitted by national authorities to the IMF following the Financial Soundness Indicators (FSI) Compilation Guide." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "FB.ATM.TOTL.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion acts as a powerful driver not only of economic growth but, more importantly, of inclusive growth. It ensures that diverse segments of society—especially low-income households and small enterprises—have access to and can effectively use financial services, allowing them to share in the benefits of economic development. By promoting savings and investment, stabilizing consumption, and reducing the financial vulnerability of individuals and businesses, financial inclusion supports broader economic expansion. Accessible and affordable financial tools—such as savings accounts, credit, and insurance—enable people, particularly those traditionally underserved or excluded, to invest in their futures, manage spending more effectively, and mitigate financial risks. These benefits can translate into higher income levels and contribute to reducing poverty and inequality. Ultimately, financial inclusion aims to expand economic opportunity and participation, helping to build a more equitable and prosperous society." }, { "id": "IndicatorName", "value": "Automated teller machines (ATMs) (per 100,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Population-based ratios of the number of branches and ATMs assume a uniform distribution of bank outlets within a country's area and across its population, while in most countries bank branches and ATMs are concentrated in urban centers of the country and are accessible only to some individuals." }, { "id": "Longdefinition", "value": "Automated teller machines (ATMs) are electromechanical devices which enable customers of financial institutions to perform financial transactions such as cash withdrawals, balance inquiries, deposits, transfer of funds, and obtaining account information, using an electronic card." }, { "id": "Othernotes", "value": "Country-specific metadata can be found on the IMF’s FAS website (data.imf.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2023" }, { "id": "Source", "value": "Financial Access Survey, International Monetary Fund (IMF), uri: https://data.imf.org/?sk=E5DCAB7E-A5CA-4892-A6EA-598B5463A34C" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are shown as the total number of ATMs for every 100,000 adults in the reporting country. Calculated as (number of ATMs)*100,000/adult population in the reporting country.\nStatistical concept(s): Data are shown as the total number of ATMs for every 100,000 adults in the reporting country. Calculated as (number of ATMs)*100,000/adult population in the reporting country." }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "(number of ATMs)*100,000/adult population" } ], "source_id": "2" }, { "id": "FB.BNK.CAPA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy. \n\nTotal assets in this context refer to the gross value of all assets on the balance sheet, without deducting for provisions or reserves. By calculating the ratio of Tier 1 capital to total assets, this indicator provides a non-risk-weighted measure of leverage, offering insight into the overall capital buffer relative to the size of a deposit taker’s balance sheet. A higher ratio indicates a stronger capital position and lower leverage, while a lower ratio may suggest vulnerability in times of financial stress. This metric is useful for analyzing systemic stability and comparing institutions that may have different levels of risk exposure." }, { "id": "IndicatorName", "value": "Bank capital to assets ratio (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting countries compile the data using different methodologies, which may also vary for different points in time for the same country. Users are advised to consult the accompanying metadata on the IMF FSI website (data.imf.org) to conduct more meaningful cross-country comparisons or to assess the evolution of the indicator for any of the countries." }, { "id": "Longdefinition", "value": "The indicator is a measure of capital adequacy that evaluates the financial strength of deposit takers by comparing Tier 1 capital to total assets. Tier 1 capital, often referred to as core capital, includes the most stable and readily available forms of capital, such as common equity, disclosed reserves, retained earnings, and certain other instruments that meet regulatory requirements under the Basel framework. This capital is considered the highest quality because it is fully available to cover losses and does not need to be repaid." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Financial Soundness Indicators, International Monetary Fund (IMF), uri: https://data.imf.org/en/datasets/IMF:EXTERNAL_DATASET_CARDS/IMF.STA:LFSI" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The ratio is calculated by dividing Tier 1 capital by total assets, expressed as a percentage. Tier 1 capital should be measured in accordance with the Basel regulatory framework as adopted by national supervisory authorities, ensuring consistency with international standards. Total assets are recorded at gross book value and include all financial and non-financial assets, without deduction for provisions, write-downs, or collateral. The data are compiled on a consolidated basis, typically using the cross-border, cross-sector, domestically incorporated consolidation approach (CBCSDI), which includes all relevant financial entities under common control, regardless of location or legal form.\nStatistical concept(s): The Tier 1 Capital to Assets ratio is a core financial soundness indicator that assesses the capital adequacy and financial resilience of deposit takers by measuring the proportion of high-quality, loss-absorbing capital relative to their total balance sheet size. Tier 1 capital, often referred to as core capital, comprises components such as common equity, retained earnings, and other instruments that meet Basel criteria for being fully available to cover losses." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "FB.CBK.BRCH.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion acts as a powerful driver not only of economic growth but, more importantly, of inclusive growth. It ensures that diverse segments of society—especially low-income households and small enterprises—have access to and can effectively use financial services, allowing them to share in the benefits of economic development. By promoting savings and investment, stabilizing consumption, and reducing the financial vulnerability of individuals and businesses, financial inclusion supports broader economic expansion. Accessible and affordable financial tools—such as savings accounts, credit, and insurance—enable people, particularly those traditionally underserved or excluded, to invest in their futures, manage spending more effectively, and mitigate financial risks. These benefits can translate into higher income levels and contribute to reducing poverty and inequality. Ultimately, financial inclusion aims to expand economic opportunity and participation, helping to build a more equitable and prosperous society." }, { "id": "IndicatorName", "value": "Commercial bank branches (per 100,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Population-based ratios of the number of branches and ATMs assume a uniform distribution of bank outlets within a country's area and across its population, while in most countries bank branches and ATMs are concentrated in urban centers of the country and are accessible only to some individuals." }, { "id": "Longdefinition", "value": "Commercial bank branches are retail locations of resident commercial banks and other resident banks that function as commercial banks that provide financial services to customers and are physically separated from the main office but not organized as legally separated subsidiaries." }, { "id": "Othernotes", "value": "Country-specific metadata can be found on the IMF’s FAS website (data.imf.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2023" }, { "id": "Source", "value": "Financial Access Survey, International Monetary Fund (IMF), uri: https://data.imf.org/en/datasets/IMF.STA:FAS" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Number of branches refers to all units in the country that are physically separated from the main offices, but not incorporated as separate subsidiaries. The number of main offices is excluded. Typically, a branch provides a wide range of services to its customers including cash withdrawals, deposits in an account with a teller, financial advice, safe deposit box rentals, and currency exchange. Units, with or without human staff, which offer only automated services for cash withdrawal and deposits, or computer terminals for online banking and check depositing machines should also be counted as branches.\nStatistical concept(s): Data are shown as the number of branches of commercial banks for every 100,000 adults in the reporting country. It is calculated as (number of institutions + number of branches)*100,000/adult population in the reporting country." }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "(number of branches)*100,000/adult population" } ], "source_id": "2" }, { "id": "FB.CBK.BRWR.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion acts as a powerful driver not only of economic growth but, more importantly, of inclusive growth. It ensures that diverse segments of society—especially low-income households and small enterprises—have access to and can effectively use financial services, allowing them to share in the benefits of economic development. By promoting savings and investment, stabilizing consumption, and reducing the financial vulnerability of individuals and businesses, financial inclusion supports broader economic expansion. Accessible and affordable financial tools—such as savings accounts, credit, and insurance—enable people, particularly those traditionally underserved or excluded, to invest in their futures, manage spending more effectively, and mitigate financial risks. These benefits can translate into higher income levels and contribute to reducing poverty and inequality. Ultimately, financial inclusion aims to expand economic opportunity and participation, helping to build a more equitable and prosperous society." }, { "id": "IndicatorName", "value": "Borrowers from commercial banks (per 1,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For many countries data cover the total number of loan accounts due to lack of information on loan account holders. For several countries, data cover all borrowers including commercial banks, credit unions and financial cooperatives, deposit taking microfinance institutions, and other deposit takers. These include all resident financial corporations and quasi-corporations (except the central bank) that are mainly engaged in financial intermediation and that issue liabilities included in the national definition of broad money. These institutions have varying names in different countries, such as savings and loan associations, building societies, rural banks and agricultural banks, post office giro institutions, post office savings banks, savings banks, and money market funds." }, { "id": "Longdefinition", "value": "Borrowers from commercial banks are the reported number of resident customers that are nonfinancial corporations (public and private) and households who obtained loans from commercial banks and other banks functioning as commercial banks." }, { "id": "Othernotes", "value": "Country-specific metadata can be found on the IMF’s FAS website (data.imf.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2023" }, { "id": "Source", "value": "Financial Access Survey, International Monetary Fund (IMF), uri: https://data.imf.org/en/datasets/IMF.STA:FAS" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Number of borrowers refers to the number of resident nonfinancial corporations (public and private) and individuals in the household sector that have obtained credit (loans) from each type of financial institution.\n\n• A corporate entity must be counted as one borrower, irrespective of the number of loans extended to that corporate borrower.\n• An individual from the household sector must be counted as one borrower, irrespective of the number of loan accounts held.\n• If a loan is extended to a group of borrowers, all borrowers must be counted individually rather than as one borrower.\n\nStatistical concept(s): Borrowers from commercial banks denotes the total number of resident customers that are nonfinancial corporations (public and private) and households who obtained loans from commercial banks for every 1,000 adults in the reporting country. It is calculated as (number of borrowers)*1,000/adult population in the reporting country." }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "(number of borrowers)*1,000/adult population" } ], "source_id": "2" }, { "id": "FB.CBK.DPTR.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion acts as a powerful driver not only of economic growth but, more importantly, of inclusive growth. It ensures that diverse segments of society—especially low-income households and small enterprises—have access to and can effectively use financial services, allowing them to share in the benefits of economic development. By promoting savings and investment, stabilizing consumption, and reducing the financial vulnerability of individuals and businesses, financial inclusion supports broader economic expansion. Accessible and affordable financial tools—such as savings accounts, credit, and insurance—enable people, particularly those traditionally underserved or excluded, to invest in their futures, manage spending more effectively, and mitigate financial risks. These benefits can translate into higher income levels and contribute to reducing poverty and inequality. Ultimately, financial inclusion aims to expand economic opportunity and participation, helping to build a more equitable and prosperous society." }, { "id": "IndicatorName", "value": "Depositors with commercial banks (per 1,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For many countries data cover the total number of deposit accounts due to lack of information on account holders." }, { "id": "Longdefinition", "value": "Depositors with commercial banks are the reported number of deposit account holders, including both resident non-financial corporations (both public and private) and individuals from the household sector, at commercial banks within the reporting jurisdiction for every 1,000 adults. The major types of deposits are checking accounts, savings accounts, and time deposits." }, { "id": "Othernotes", "value": "Country-specific metadata can be found on the IMF’s FAS website (data.imf.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2023" }, { "id": "Source", "value": "Financial Access Survey, International Monetary Fund (IMF), uri: https://data.imf.org/en/datasets/IMF.STA:FAS" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: While calculating the number of depositors at each type of financial institution, the following are recommended: (a) Corporate accounts are counted as only one depositor, irrespective of the number of deposit accounts (checking, demand, saving, time deposits, etc.) held; (b) Individual accounts are counted as only one depositor, irrespective of the number of deposit accounts (checking, demand, saving, time deposits, etc.) held; (c) For joint accounts, all depositors are counted individually rather than as one depositor.\nStatistical concept(s): Depositors with commercial banks are deposit account holders at commercial banks and other resident banks functioning as commercial banks that are resident nonfinancial corporations (public and private) and households. It is calculated as (number of depositors)*1,000/adult population in the reporting country. The major types of deposits are checking accounts, savings accounts, and time deposits." }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "(number of depositors)*1,000/adult population" } ], "source_id": "2" }, { "id": "FD.AST.PRVT.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Domestic credit to private sector by banks (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Credit to the private sector may sometimes include credit to state-owned or partially state-owned enterprises." }, { "id": "Longdefinition", "value": "Domestic credit to private sector by banks refers to financial resources provided to the private sector by other depository corporations (deposit taking corporations except central banks), such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FD.RES.LIQU.AS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Bank liquid reserves to bank assets ratio (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Ratio of bank liquid reserves to bank assets is the ratio of domestic currency holdings and deposits with the monetary authorities to claims on other governments, nonfinancial public enterprises, the private sector, and other banking institutions. This indicator is expressed as a percentage (a÷b)*100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2001-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FI.RES.TOTL.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Total reserves, including gold (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Discrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded." }, { "id": "Longdefinition", "value": "Reserve assets are external assets, including monetary gold, that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "FI.RES.TOTL.DT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Total reserves (% of total external debt)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Reserve assets are external assets, including monetary gold, that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency. This indicator is expressed as a percentage of total external debt which are all liabilities that require payment(s) of interest and/or principal by the debtor at some point(s) in the future and that are owed to non-residents by residents of an economy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1971-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FI.RES.TOTL.MO", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Total reserves in months of imports" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Reserve assets are external assets, including monetary gold, that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency. This item is expressed in terms of the number of months of imports of goods and services they could pay for [X/(Imports/12)]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" }, { "id": "Unitofmeasure", "value": "months" } ], "source_id": "2" }, { "id": "FI.RES.XGLD.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Total reserves, excluding gold (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This series includes external assets (excluding monetary gold) that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "FM.AST.CGOV.ZG.M3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Claims on central government (annual growth as % of broad money)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Monetary accounts are derived from the balance sheets of financial institutions - the central bank, commercial banks, and nonbank financial intermediaries. Although these balance sheets are usually reliable, they are subject to errors of classification, valuation, and timing and to differences in accounting practices. For example, whether interest income is recorded on an accrual or a cash basis can make a substantial difference, as can the treatment of nonperforming assets. Valuation errors typically arise for foreign exchange transactions, particularly in countries with flexible exchange rates or in countries that have undergone currency devaluation during the reporting period. The valuation of financial derivatives and the net liabilities of the banking system can also be difficult. The quality of commercial bank reporting also may be adversely affected by delays in reports from bank branches, especially in countries where branch accounts are not computerized. Thus the data in the balance sheets of commercial banks may be based on preliminary estimates subject to constant revision. This problem is likely to be even more serious for nonbank financial intermediaries." }, { "id": "Longdefinition", "value": "Claims on central government include loans to central government institutions net of deposits. Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator represents the annual percentage growth in the ratio of claims to broad money." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FM.AST.DOMO.ZG.M3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Claims on other sectors of the domestic economy (annual growth as % of broad money)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Monetary accounts are derived from the balance sheets of financial institutions - the central bank, commercial banks, and nonbank financial intermediaries. Although these balance sheets are usually reliable, they are subject to errors of classification, valuation, and timing and to differences in accounting practices. For example, whether interest income is recorded on an accrual or a cash basis can make a substantial difference, as can the treatment of nonperforming assets. Valuation errors typically arise for foreign exchange transactions, particularly in countries with flexible exchange rates or in countries that have undergone currency devaluation during the reporting period. The valuation of financial derivatives and the net liabilities of the banking system can also be difficult. The quality of commercial bank reporting also may be adversely affected by delays in reports from bank branches, especially in countries where branch accounts are not computerized. Thus the data in the balance sheets of commercial banks may be based on preliminary estimates subject to constant revision. This problem is likely to be even more serious for nonbank financial intermediaries." }, { "id": "Longdefinition", "value": "Claims on other sectors of the domestic economy include gross credit from the financial system to households, nonprofit institutions serving households, nonfinancial corporations, state and local governments, and social security funds. Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator represents the annual percentage growth in the ratio of claims to broad money." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FM.AST.DOMS.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Net domestic credit (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net domestic credit is the sum of net claims on the central government and claims on other sectors of the domestic economy. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "FM.AST.NFRG.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Net foreign assets (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net foreign assets are the sum of foreign assets held by monetary authorities and deposit money banks, less their foreign liabilities. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "FM.AST.PRVT.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Monetary sector credit to private sector (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Domestic credit to private sector refers to financial resources provided to the private sector, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FM.AST.PRVT.ZG.M3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Claims on private sector (annual growth as % of broad money)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Claims on private sector include gross credit from the financial system to individuals, enterprises, nonfinancial public entities not included under net domestic credit, and financial institutions not included elsewhere. Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator represents the annual percentage growth in the ratio of claims to broad money." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FM.LBL.BMNY.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Broad money (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "FM.LBL.BMNY.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Broad money (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Othernotes", "value": "The derivation of this indicator was simplified in September 2012 to be current-year broad money divided by current-year GDP times 100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FM.LBL.BMNY.IR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Broad money to total reserves ratio" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. Reserve assets are external assets, including monetary gold, that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency. This indicator is expressed as a ratio (a÷b)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" }, { "id": "Unitofmeasure", "value": "ratio" } ], "source_id": "2" }, { "id": "FM.LBL.BMNY.ZG", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Broad money (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Monetary accounts are derived from the balance sheets of financial institutions - the central bank, commercial banks, and nonbank financial intermediaries. Although these balance sheets are usually reliable, they are subject to errors of classification, valuation, and timing and to differences in accounting practices. For example, whether interest income is recorded on an accrual or a cash basis can make a substantial difference, as can the treatment of nonperforming assets. Valuation errors typically arise for foreign exchange transactions, particularly in countries with flexible exchange rates or in countries that have undergone currency devaluation during the reporting period. The valuation of financial derivatives and the net liabilities of the banking system can also be difficult. The quality of commercial bank reporting also may be adversely affected by delays in reports from bank branches, especially in countries where branch accounts are not computerized. Thus the data in the balance sheets of commercial banks may be based on preliminary estimates subject to constant revision. This problem is likely to be even more serious for nonbank financial intermediaries." }, { "id": "Longdefinition", "value": "Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FP.CPI.TOTL", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A general and continuing increase in an economy’s price level is called inflation. The increase in the average prices of goods and services in the economy should be distinguished from a change in the relative prices of individual goods and services. Generally accompanying an overall increase in the price level is a change in the structure of relative prices, but it is only the average increase, not the relative price changes, that constitutes inflation. A commonly used measure of inflation is the consumer price index, which measures the prices of a representative basket of goods and services purchased by a typical household. The consumer price index is usually calculated on the basis of periodic surveys of consumer prices. Other price indices are derived implicitly from indexes of current and constant price series." }, { "id": "IndicatorName", "value": "Consumer price index (2010 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Consumer price indexes should be interpreted with caution. The definition of a household, the basket of goods, and the geographic (urban or rural) and income group coverage of consumer price surveys can vary widely by country. In addition, weights are derived from household expenditure surveys, which, for budgetary reasons, tend to be conducted infrequently in developing countries, impairing comparability over time. Although useful for measuring consumer price inflation within a country, consumer price indexes are of less value in comparing countries." }, { "id": "Longdefinition", "value": "Index of the prices of consumption goods and services, as compared to a certain reference period (2010=100)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Consumer Prices Indices are compiled in accordance with international standards: Consumer Price Index Manual, 2020 or 2004 version. Specific information on how countries compile their CPI statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of a consumer price index series is to measure the rate at which prices of consumption goods and services are changing from one period to another." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "index (2010 = 100)" } ], "source_id": "2" }, { "id": "FP.CPI.TOTL.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A general and continuing increase in an economy’s price level is called inflation. The increase in the average prices of goods and services in the economy should be distinguished from a change in the relative prices of individual goods and services. Generally accompanying an overall increase in the price level is a change in the structure of relative prices, but it is only the average increase, not the relative price changes, that constitutes inflation. A commonly used measure of inflation is the consumer price index, which measures the prices of a representative basket of goods and services purchased by a typical household. The consumer price index is usually calculated on the basis of periodic surveys of consumer prices. Other price indices are derived implicitly from indexes of current and constant price series." }, { "id": "IndicatorName", "value": "Inflation, consumer prices (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Consumer Prices Indices are compiled in accordance with international standards: Consumer Price Index Manual, 2020 or 2004 version. Specific information on how countries compile their CPI statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of a consumer price index series is to measure the rate at which prices of consumption goods and services are changing from one period to another." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FP.WPI.TOTL", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A general and continuing increase in an economy’s price level is called inflation. The increase in the average prices of goods and services in the economy should be distinguished from a change in the relative prices of individual goods and services. Generally accompanying an overall increase in the price level is a change in the structure of relative prices, but it is only the average increase, not the relative price changes, that constitutes inflation." }, { "id": "IndicatorName", "value": "Wholesale price index (2010 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Index of prices of a mix of agricultural and industrial goods at various stages of production and distribution, including import duties, as compared to a certain reference period (2010=100)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Wholesale Prices Indices are compiled in accordance with international standards: Producer Price Index Manual, 2004 version. Specific information on how countries compile their WPI statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of a wholesale price index series is to measure the rate at which prices of goods and services are changing from one period to another, for products that flow from a wholesaler to a retailer." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "index (2010 = 100)" } ], "source_id": "2" }, { "id": "FR.INR.DPST", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Both banking and financial systems enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient. The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy." }, { "id": "IndicatorName", "value": "Deposit interest rate (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Countries use a variety of reporting formats, sample designs, interest compounding formulas, averaging methods, and data presentations for indices and other data series on interest rates. The IMF's Monetary and Financial Statistics Manual does not provide guidelines beyond the general recommendation that such data should reflect market prices and effective (rather than nominal) interest rates and should be representative of the financial assets and markets to be covered. For more information, please see http://www.imf.org/external/pubs/ft/mfs/manual/index.htm." }, { "id": "Longdefinition", "value": "Deposit interest rate is the rate paid by commercial or similar banks for demand, time, or savings deposits. The terms and conditions attached to these rates differ by country, however, limiting their comparability. This indicator is expressed as a percentage (a÷b)*100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Interest rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FR.INR.LEND", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Both banking and financial systems enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient. The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy." }, { "id": "IndicatorName", "value": "Lending interest rate (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Countries use a variety of reporting formats, sample designs, interest compounding formulas, averaging methods, and data presentations for indices and other data series on interest rates. The IMF's Monetary and Financial Statistics Manual does not provide guidelines beyond the general recommendation that such data should reflect market prices and effective (rather than nominal) interest rates and should be representative of the financial assets and markets to be covered. For more information, please see http://www.imf.org/external/pubs/ft/mfs/manual/index.htm." }, { "id": "Longdefinition", "value": "Lending rate is the bank rate that usually meets the short- and medium-term financing needs of the private sector. This rate is normally differentiated according to creditworthiness of borrowers and objectives of financing. The terms and conditions attached to these rates differ by country, however, limiting their comparability. This indicator is expressed as a percentage (a÷b)*100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Interest rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FR.INR.LNDP", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Both banking and financial systems enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient. The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy." }, { "id": "IndicatorName", "value": "Interest rate spread (lending rate minus deposit rate, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Countries use a variety of reporting formats, sample designs, interest compounding formulas, averaging methods, and data presentations for indices and other data series on interest rates. The IMF's Monetary and Financial Statistics Manual does not provide guidelines beyond the general recommendation that such data should reflect market prices and effective (rather than nominal) interest rates and should be representative of the financial assets and markets to be covered. For more information, please see http://www.imf.org/external/pubs/ft/mfs/manual/index.htm." }, { "id": "Longdefinition", "value": "Interest rate spread is the interest rate charged by banks on loans to private sector customers minus the interest rate paid by commercial or similar banks for demand, time, or savings deposits. The terms and conditions attached to these rates differ by country, however, limiting their comparability. This indicator is expressed as a percentage (a÷b)*100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1967-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Interest rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FR.INR.RINR", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Both banking and financial systems enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient. The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy." }, { "id": "IndicatorName", "value": "Real interest rate (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "An interest rate is the amount charged, expressed as a percentage of the principal over a period of time, by the owners of certain kinds of financial assets for putting the financial assets at the disposal of another institutional unit. The real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator. The terms and conditions attached to lending rates differ by country, however, limiting their comparability. This indicator is expressed as a percentage (a÷b)*100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Interest rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FR.INR.RISK", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Both banking and financial systems enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient. The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy." }, { "id": "IndicatorName", "value": "Risk premium on lending (lending rate minus treasury bill rate, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Countries use a variety of reporting formats, sample designs, interest compounding formulas, averaging methods, and data presentations for indices and other data series on interest rates. The IMF's Monetary and Financial Statistics Manual does not provide guidelines beyond the general recommendation that such data should reflect market prices and effective (rather than nominal) interest rates and should be representative of the financial assets and markets to be covered. For more information, please see http://www.imf.org/external/pubs/ft/mfs/manual/index.htm." }, { "id": "Longdefinition", "value": "Risk premium on lending is the interest rate charged by banks on loans to private sector customers minus the \"risk free\" treasury bill interest rate at which short-term government securities are issued or traded in the market. In some countries this spread may be negative, indicating that the market considers its best corporate clients to be lower risk than the government. The terms and conditions attached to lending rates differ by country, however, limiting their comparability. This indicator is expressed as a percentage (a÷b)*100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Interest rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FS.AST.CGOV.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Claims on central government, etc. (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Claims on central government include loans to central government institutions net of deposits. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FS.AST.DOMO.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Claims on other sectors of the domestic economy (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Claims on other sectors of the domestic economy include gross credit from the financial system to households, nonprofit institutions serving households, nonfinancial corporations, state and local governments, and social security funds. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2001-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FS.AST.DOMS.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Domestic credit provided by financial sector (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In a few countries governments may hold international reserves as deposits in the banking system rather than in the central bank. Since claims on the central government are a net item (claims on the central government minus central government deposits), the figure may be negative, resulting in a negative figure for domestic credit provided by the banking sector." }, { "id": "Longdefinition", "value": "Domestic credit provided by the financial sector includes all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net. The financial sector includes monetary authorities and deposit money banks, as well as other financial corporations where data are available (including corporations that do not accept transferable deposits but do incur such liabilities as time and savings deposits). Examples of other financial corporations are finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FS.AST.PRVT.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Domestic credit to private sector (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Credit to the private sector may sometimes include credit to state-owned or partially state-owned enterprises." }, { "id": "Longdefinition", "value": "Domestic credit to private sector refers to financial resources provided to the private sector by financial corporations, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises. The financial corporations include monetary authorities and deposit money banks, as well as other financial corporations where data are available (including corporations that do not accept transferable deposits but do incur such liabilities as time and savings deposits). Examples of other financial corporations are finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "FX.OWN.TOTL.40.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families. financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). https://www.sciencedirect.com/science/article/pii/S2110701724000027" }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, poorest 40% (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (poorest 40%, share of population ages 15+)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (poorest 40%, share of population ages 15+).\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (poorest 40%, share of population ages 15+)." }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "Sum" } ], "source_id": "2" }, { "id": "FX.OWN.TOTL.60.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda(GPFI, 2011).\n\n Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families (see, for example, Agarwal, 2010; Hannig and Stefan, 2010; Sarma and Pais, 2011; Kumar, 2013; Ghosh and Dixit, 2014; Talledo, 2015; Aparicio et al., 2016; Schmied and Marr, 2016; among others). \n\nFinancial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination. https://www.sciencedirect.com/science/article/pii/S2110701724000027" }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, richest 60% (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (richest 60%, share of population ages 15+)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (richest 60%, share of population ages 15+).\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (richest 60%, share of population ages 15+)." }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "Weighted average" } ], "source_id": "2" }, { "id": "FX.OWN.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families.\n\nIn addition, financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination. https://www.sciencedirect.com/science/article/pii/S2110701724000027" }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (female, % age 15+)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (female, % age 15+). Assessment of this occurs triennial\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (female, % age 15+). Assessment of this occurs triennial" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "2" }, { "id": "FX.OWN.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination.\n\nFinancial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families.\nhttps://www.sciencedirect.com/science/article/pii/S2110701724000027" }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, male (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (male, % age 15+)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (male, % age 15+). Assessment of this occurs triennial.\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (male, % age 15+). Assessment of this occurs triennial." }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "Weighted average" } ], "source_id": "2" }, { "id": "FX.OWN.TOTL.OL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families \n\nfinancial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination. https://www.sciencedirect.com/science/article/pii/S2110701724000027" }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, older adults (% of population ages 25+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (older adults, % of population ages 25+)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (older adults, % of population ages 25+). This assessment occurs triennially\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (older adults, % of population ages 25+). This assessment occurs triennially" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "2" }, { "id": "FX.OWN.TOTL.PL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families (see, for example, Agarwal, 2010; Hannig and Stefan, 2010; Sarma and Pais, 2011; Kumar, 2013; Ghosh and Dixit, 2014; Talledo, 2015; Aparicio et al., 2016; Schmied and Marr, 2016; among others). In addition, nowadays, financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that it offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination." }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, primary education or less (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (primary education or less, % of population ages 15+)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (primary education or less, % of population ages 15+). This assessment is conducted triennially \nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (primary education or less, % of population ages 15+). This assessment is conducted triennially" }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "Weighted average" } ], "source_id": "2" }, { "id": "FX.OWN.TOTL.SO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families (see, for example, Agarwal, 2010; Hannig and Stefan, 2010; Sarma and Pais, 2011; Kumar, 2013; Ghosh and Dixit, 2014; Talledo, 2015; Aparicio et al., 2016; Schmied and Marr, 2016; among others). In addition, nowadays, financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination. https://www.sciencedirect.com/science/article/pii/S2110701724000027" }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, secondary education or more (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (secondary education or more, % of population ages 15+)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (secondary education or more, % of population ages 15+). This assessment is conducted triennially\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (secondary education or more, % of population ages 15+). This assessment is conducted triennially" }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "Weighted average" } ], "source_id": "2" }, { "id": "FX.OWN.TOTL.YG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families (see, for example, Agarwal, 2010; Hannig and Stefan, 2010; Sarma and Pais, 2011; Kumar, 2013; Ghosh and Dixit, 2014; Talledo, 2015; Aparicio et al., 2016; Schmied and Marr, 2016; among others). In addition, nowadays, financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination (Levine et al., 2000; Honohan, 2004; DFID, 2004; Levine, 2004; Andrianova and Demetriades, 2008). https://www.sciencedirect.com/science/article/pii/S2110701724000027" }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, young adults (% of population ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (young adults, % of population ages 15-24)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (young adults, % of population ages 15-24). This assessment is conducted triennially.\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (young adults, % of population ages 15-24). This assessment is conducted triennially." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "2" }, { "id": "FX.OWN.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families (see, for example, Agarwal, 2010; Hannig and Stefan, 2010; Sarma and Pais, 2011; Kumar, 2013; Ghosh and Dixit, 2014; Talledo, 2015; Aparicio et al., 2016; Schmied and Marr, 2016; among others). In addition, nowadays, financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination (Levine et al., 2000; Honohan, 2004; DFID, 2004; Levine, 2004; Andrianova and Demetriades, 2008). https://www.sciencedirect.com/science/article/pii/S2110701724000027" }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (% age 15+)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (% age 15+). Assessment conducted triennially\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (% age 15+). Assessment conducted triennially" }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "Weighted average" } ], "source_id": "2" }, { "id": "GB.XPD.RSDV.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Science, technology, and innovation constitute pivotal elements for achieving sustainable growth. Sustainable Development Goal (SDG) target 9.5 is dedicated to the enhancement of scientific research and the advancement of technological capabilities within industrial sectors, with a particular focus on low- and middle-income countries. Furthermore, this target encompasses the objective of augmenting the cadre of research and development personnel, as well as escalating expenditures in research." }, { "id": "IndicatorName", "value": "Research and development expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers. \n\n\n\n\n\n\n\nR&D totals from SNA accounts may differ from these estimates, due in part to the different treatments of software R&D in the totals." }, { "id": "Longdefinition", "value": "Gross domestic expenditures on research and development (R&D), expressed as a percent of GDP. They include both capital and current expenditures in the four main sectors: Business enterprise, Government, Higher education and Private non-profit. R&D covers basic research, applied research, and experimental development." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2024" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources/bulk, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-03-26, date published: 2025-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by taking the number of researchers in a specified year, dividing it by the total population—referencing the mid-year population figure—and then multiplying the result by one million.\n\n\n\n\n\n\n\n\n\nThe calculation of this indicator is performed by dividing the total domestic intramural expenditure on research and development (R&D) for a specified year by the gross domestic product (GDP)—which is the aggregate of gross value added by all resident producers in the economy, inclusive of distributive trades and transport, along with product taxes and less any subsidies not included in product values—and then multiplying the quotient by 100. \n\n\n\n\n\n\n\n\n\nData are collected through national research and experimental development (R&D) surveys, either by the national statistical office or a line ministry (such as the Ministry for Science and Technology). The data compilers are the UNESCO Institute for Statistics (UIS), Organisation for Economic Co-operation and Development (OECD), Eurostat (Statistical Office of the European Union) and the Network on Science and Technology Indicators – Ibero-American and Inter-American (RICYT), African Science, Technology and Innovation (STI) Indicators Initiative (ASTII) of the African Union Development Agency-NEPAD (AUDA-NEPAD).\nStatistical concept(s): The gross domestic expenditure on R&D indicator consists of the total expenditure (current and capital) on R&D by all resident companies, research institutes, university and government laboratories, etc. It excludes R&D expenditures financed by domestic firms but performed abroad. \n\n\n\n\n\n\n\n\n\nThe OECD's Frascati Manual defines research and experimental development as \"creative work undertaken on a systemic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications.\" R&D covers basic research, applied research, and experimental development.\n\n\n\n\n\n\n\n\n\n(1) Basic research - Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view\n\n\n\n\n\n\n\n\n\n(2) Applied research - Applied research is also original investigation undertaken in order to acquire new knowledge; it is, however, directed primarily towards a specific practical aim or objective.\n\n\n\n\n\n\n\n\n\n(3) Experimental development - Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed.\n\n\n\n\n\n\n\n\n\nThe fields of science and technology used to classify R&D according to the Revised Fields of Science and Technology Classification are:\n\n\n\n\n1. Natural sciences;\n\n\n\n\n2. Engineering and technology;\n\n\n\n\n3. Medical and health sciences;\n\n\n\n\n4. Agricultural sciences;\n\n\n\n\n5. Social sciences;\n\n\n\n\n6. Humanities and the arts.\n\n\n\n\n\n\n\n\n\nThe data are obtained through statistical surveys which are regularly conducted at national level covering R&D performing entities in the private and public sectors." }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "2" }, { "id": "GC.AST.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the transactions of the general government in financial assets. Data on transactions of the general government in financial assets provide information on how the government manages its investments and cash flow. These data help in assessing the government's fiscal strength, its ability to manage public debt, and the overall health of the economy. They are also used by policymakers to make informed decisions about monetary policy, budgeting, and economic planning. Additionally, these statistics are crucial for maintaining transparency and accountability in government operations, as they allow the public and investors to see how public funds are being utilized and managed. This can influence investor confidence and a country's credit rating, which in turn affects borrowing costs and investment levels. Overall, these data are essential for a comprehensive understanding of the government's financial position and for ensuring responsible financial governance." }, { "id": "IndicatorName", "value": "Net acquisition of financial assets (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net acquisition of government financial assets includes domestic and foreign financial claims, SDRs, and gold bullion held by monetary authorities as a reserve asset. The net acquisition of financial assets should be offset by the net incurrence of liabilities. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.AST.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the transactions of the general government in financial assets. Data on transactions of the general government in financial assets provide information on how the government manages its investments and cash flow. These data help in assessing the government's fiscal strength, its ability to manage public debt, and the overall health of the economy. They are also used by policymakers to make informed decisions about monetary policy, budgeting, and economic planning. Additionally, these statistics are crucial for maintaining transparency and accountability in government operations, as they allow the public and investors to see how public funds are being utilized and managed. This can influence investor confidence and a country's credit rating, which in turn affects borrowing costs and investment levels. Overall, these data are essential for a comprehensive understanding of the government's financial position and for ensuring responsible financial governance." }, { "id": "IndicatorName", "value": "Net acquisition of financial assets (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net acquisition of government financial assets includes domestic and foreign financial claims, SDRs, and gold bullion held by monetary authorities as a reserve asset. The net acquisition of financial assets should be offset by the net incurrence of liabilities. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.DOD.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the government debt. Statistics on government debt provide essential information for economic planning, as they can influence a country's fiscal policies and spending. Government debt levels are also a key indicator for investors, who use this information to assess the risk of investing in a country's bonds. High debt levels can lead to lower investor confidence and higher interest rates. Furthermore, debt statistics are crucial for evaluating the sustainability of a government's fiscal policy. They help determine whether adjustments are needed to avoid potential default. These statistics also enable international comparisons, allowing for benchmarking against other countries and identifying potential issues. Accurate debt statistics are vital for the formulation of monetary and fiscal policies, including decisions on taxation and government spending. They also promote public awareness by providing transparency and accountability in how public funds are managed. Lastly, credit rating agencies use government debt statistics to assign credit ratings, which affect a country's borrowing costs and its ability to attract investment. Overall, government debt statistics are a key component of a country's economic analysis and are essential for informed decision-making by policymakers, investors, and the public." }, { "id": "IndicatorName", "value": "Central government debt, total (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Debt is the entire stock of direct government fixed-term contractual obligations to others outstanding on a particular date. It includes domestic and foreign liabilities such as currency and money deposits, securities other than shares, and loans. It is the gross amount of government liabilities reduced by the amount of equity and financial derivatives held by the government. Because debt is a stock rather than a flow, it is measured as of a given date, usually the last day of the fiscal year. Central government is the part of general government that includes all administrative departments of the national executive, legislative, and judicial functions, other central agencies and those non-market producers controlled by the central government, whose competence extends normally over the whole economic territory. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.DOD.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the government debt. Statistics on government debt provide essential information for economic planning, as they can influence a country's fiscal policies and spending. Government debt levels are also a key indicator for investors, who use this information to assess the risk of investing in a country's bonds. High debt levels can lead to lower investor confidence and higher interest rates. Furthermore, debt statistics are crucial for evaluating the sustainability of a government's fiscal policy. They help determine whether adjustments are needed to avoid potential default. These statistics also enable international comparisons, allowing for benchmarking against other countries and identifying potential issues. Accurate debt statistics are vital for the formulation of monetary and fiscal policies, including decisions on taxation and government spending. They also promote public awareness by providing transparency and accountability in how public funds are managed. Lastly, credit rating agencies use government debt statistics to assign credit ratings, which affect a country's borrowing costs and its ability to attract investment. Overall, government debt statistics are a key component of a country's economic analysis and are essential for informed decision-making by policymakers, investors, and the public." }, { "id": "IndicatorName", "value": "Central government debt, total (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Debt is the entire stock of direct government fixed-term contractual obligations to others outstanding on a particular date. It includes domestic and foreign liabilities such as currency and money deposits, securities other than shares, and loans. It is the gross amount of government liabilities reduced by the amount of equity and financial derivatives held by the government. Because debt is a stock rather than a flow, it is measured as of a given date, usually the last day of the fiscal year. Central government is the part of general government that includes all administrative departments of the national executive, legislative, and judicial functions, other central agencies and those non-market producers controlled by the central government, whose competence extends normally over the whole economic territory. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.LBL.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the transactions of the general government in financial assets. Data on transactions of the general government in financial assets provide information on how the government manages its investments and cash flow. These data help in assessing the government's fiscal strength, its ability to manage public debt, and the overall health of the economy. They are also used by policymakers to make informed decisions about monetary policy, budgeting, and economic planning. Additionally, these statistics are crucial for maintaining transparency and accountability in government operations, as they allow the public and investors to see how public funds are being utilized and managed. This can influence investor confidence and a country's credit rating, which in turn affects borrowing costs and investment levels. Overall, these data are essential for a comprehensive understanding of the government's financial position and for ensuring responsible financial governance." }, { "id": "IndicatorName", "value": "Net incurrence of liabilities, total (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net incurrence of government liabilities includes foreign financing (obtained from nonresidents) and domestic financing (obtained from residents), or the means by which a government provides financial resources to cover a budget deficit or allocates financial resources arising from a budget surplus. The net incurrence of liabilities should be offset by the net acquisition of financial assets. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.LBL.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the transactions of the general government in financial assets. Data on transactions of the general government in financial assets provide information on how the government manages its investments and cash flow. These data help in assessing the government's fiscal strength, its ability to manage public debt, and the overall health of the economy. They are also used by policymakers to make informed decisions about monetary policy, budgeting, and economic planning. Additionally, these statistics are crucial for maintaining transparency and accountability in government operations, as they allow the public and investors to see how public funds are being utilized and managed. This can influence investor confidence and a country's credit rating, which in turn affects borrowing costs and investment levels. Overall, these data are essential for a comprehensive understanding of the government's financial position and for ensuring responsible financial governance." }, { "id": "IndicatorName", "value": "Net incurrence of liabilities, total (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net incurrence of government liabilities includes foreign financing (obtained from nonresidents) and domestic financing (obtained from residents), or the means by which a government provides financial resources to cover a budget deficit or allocates financial resources arising from a budget surplus. The net incurrence of liabilities should be offset by the net acquisition of financial assets. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.NFN.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance." }, { "id": "IndicatorName", "value": "Net investment in nonfinancial assets (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net investment in government nonfinancial assets includes fixed assets, inventories, valuables, and nonproduced assets. Nonfinancial assets are stores of value and provide benefits either through their use in the production of goods and services or in the form of property income and holding gains. Net investment in nonfinancial assets also includes consumption of fixed capital. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.NFN.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance." }, { "id": "IndicatorName", "value": "Net investment in nonfinancial assets (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net investment in government nonfinancial assets includes fixed assets, inventories, valuables, and nonproduced assets. Nonfinancial assets are stores of value and provide benefits either through their use in the production of goods and services or in the form of property income and holding gains. Net investment in nonfinancial assets also includes consumption of fixed capital. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.NLD.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is the balance of the government statistics. Balance items represent the difference between the total revenue and total expenditure of a government. If this balance is positive, it indicates net lending, meaning the government has a surplus and can potentially lend or invest the excess funds. Conversely, if the balance is negative, it indicates net borrowing, meaning the government has a deficit and may need to borrow funds to cover the shortfall. This figure is important because it provides a clear indicator of a government's fiscal health and its ability to finance its operations without resorting to additional borrowing. It is also a key indicator used by policymakers to make informed decisions about fiscal policy, taxation, and public spending. Furthermore, it is an essential metric for international organizations, investors, and credit rating agencies to assess a country's economic stability and creditworthiness." }, { "id": "IndicatorName", "value": "Net lending (+) / net borrowing (-) (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net lending (+) / net borrowing (–) equals government revenue minus expense, minus net investment in nonfinancial assets. It is also equal to the net result of transactions in financial assets and liabilities. Net lending/net borrowing is a summary measure indicating the extent to which government is either putting financial resources at the disposal of other sectors in the economy or abroad, or utilizing the financial resources generated by other sectors in the economy or from abroad. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.NLD.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is the balance of the government statistics. Balance items represent the difference between the total revenue and total expenditure of a government. If this balance is positive, it indicates net lending, meaning the government has a surplus and can potentially lend or invest the excess funds. Conversely, if the balance is negative, it indicates net borrowing, meaning the government has a deficit and may need to borrow funds to cover the shortfall. This figure is important because it provides a clear indicator of a government's fiscal health and its ability to finance its operations without resorting to additional borrowing. It is also a key indicator used by policymakers to make informed decisions about fiscal policy, taxation, and public spending. Furthermore, it is an essential metric for international organizations, investors, and credit rating agencies to assess a country's economic stability and creditworthiness." }, { "id": "IndicatorName", "value": "Net lending (+) / net borrowing (-) (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net lending (+) / net borrowing (–) equals government revenue minus expense, minus net investment in nonfinancial assets. It is also equal to the net result of transactions in financial assets and liabilities. Net lending/net borrowing is a summary measure indicating the extent to which government is either putting financial resources at the disposal of other sectors in the economy or abroad, or utilizing the financial resources generated by other sectors in the economy or from abroad. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.REV.GOTR.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Grants and other revenue (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Grants are transfers receivable by government units, from other resident or nonresident government units or international organizations, that do not meet the defi nition of a tax, subsidy, or social contribution. Other revenue is all revenue receivable excluding taxes, social contributions, and grants. This category of revenue includes property income, sales of goods and services, and miscellaneous other types of revenue. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.REV.GOTR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Grants and other revenue (% of revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Grants are transfers receivable by government units, from other resident or nonresident government units or international organizations, that do not meet the defi nition of a tax, subsidy, or social contribution. Other revenue is all revenue receivable excluding taxes, social contributions, and grants. This category of revenue includes property income, sales of goods and services, and miscellaneous other types of revenue. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.REV.SOCL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Social contributions (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Social contributions are actual or imputed contributions payable to social insurance schemes to\n\n\nmake provisions for social benefits to be paid. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.REV.SOCL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Social contributions (% of revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Social contributions are actual or imputed contributions payable to social insurance schemes to\n\n\nmake provisions for social benefits to be paid. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.REV.XGRT.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Revenue, excluding grants (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Revenue is an increase in net worth resulting from a transaction. Grants are excluded from this figure. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.REV.XGRT.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Revenue, excluding grants (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Revenue is an increase in net worth resulting from a transaction. Grants are excluded from this figure. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.TAX.EXPT.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on exports (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Export taxes are taxes on goods or services that become payable to government when the goods leave the economic territory or when the services are delivered to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.TAX.EXPT.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on exports (% of tax revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Export taxes are taxes on goods or services that become payable to government when the goods leave the economic territory or when the services are delivered to non-residents. This indicator is expressed as a percentage of tax revenue which includes compulsory, unrequited payments, in cash or in kind, made by institutional units to government units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.TAX.GSRV.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on goods and services (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "General taxes on goods and services are taxes levied on the production, leasing, delivery, sale, purchase or other change of ownership of a wide range of goods and the provision of a wide range of services. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.TAX.GSRV.RV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on goods and services (% of revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "General taxes on goods and services are taxes levied on the production, leasing, delivery, sale, purchase or other change of ownership of a wide range of goods and the provision of a wide range of services. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.TAX.GSRV.VA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on goods and services (% of industry and services value added)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "General taxes on goods and services are taxes levied on the production, leasing, delivery, sale, purchase or other change of ownership of a wide range of goods and the provision of a wide range of services. This indicator is expressed as a percentage of value added in industry and services which is the contribution to the economy by industries in ISIC (Rev. 3) divisions 05-43 and 50-99." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.TAX.IMPT.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Customs and other import duties (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes and duties on imports are taxes on goods and services that become payable at the moment when goods enter the economic territory or when services are delivered by non-resident producers to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.TAX.IMPT.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Customs and other import duties (% of tax revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes and duties on imports are taxes on goods and services that become payable at the moment when goods enter the economic territory or when services are delivered by non-resident producers to residents. This indicator is expressed as a percentage of tax revenue which includes compulsory, unrequited payments, in cash or in kind, made by institutional units to government units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.TAX.INTT.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on international trade (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes on international trade are taxes that become payable when goods cross the national or customs frontiers of the economic territory or when transactions in services exchange between residents and non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.TAX.INTT.RV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on international trade (% of revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes on international trade are taxes that become payable when goods cross the national or customs frontiers of the economic territory or when transactions in services exchange between residents and non-residents. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.TAX.OTHR.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Other taxes (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Other taxes include employer payroll or labor taxes, taxes on property, and taxes not allocable to other categories, such as penalties for late payment or nonpayment of taxes. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.TAX.OTHR.RV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Other taxes (% of revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Other taxes include employer payroll or labor taxes, taxes on property, and taxes not allocable to other categories, such as penalties for late payment or nonpayment of taxes. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.TAX.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Tax revenue (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes are compulsory, unrequited payments, in cash or in kind, made by institutional\n\n\nunits to government units. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.TAX.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Tax revenue (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes are compulsory, unrequited payments, in cash or in kind, made by institutional\n\n\nunits to government units. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.TAX.YPKG.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on income, profits and capital gains (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes on income, profits, and capital gains are taxes payable on the actual or presumed incomes, profits and capital gains. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.TAX.YPKG.RV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on income, profits and capital gains (% of revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes on income, profits, and capital gains are taxes payable on the actual or presumed incomes, profits and capital gains. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.TAX.YPKG.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on income, profits and capital gains (% of total taxes)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes on income, profits, and capital gains are taxes payable on the actual or presumed incomes, profits and capital gains. This indicator is expressed as a percentage of total taxes which includes all compulsory, unrequited payments, in cash or in kind, made by institutional units to government units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.XPN.COMP.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Compensation of employees (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Compensation of employees is defined as the total remuneration, in cash or in kind, payable by an enterprise to an employee in return for work done by the latter during the accounting period. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.XPN.COMP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Compensation of employees (% of expense)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Compensation of employees is defined as the total remuneration, in cash or in kind, payable by an enterprise to an employee in return for work done by the latter during the accounting period. This indicator is expressed as percentage of total expenses which is any decrease in net worth resulting from a transaction." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.XPN.GSRV.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Goods and services expense (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Goods and services include all government payments in exchange for goods and services used for the production of market and nonmarket goods and services. Use of goods and services for account capital formation is excluded. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.XPN.GSRV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Goods and services expense (% of expense)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Goods and services include all government payments in exchange for goods and services used for the production of market and nonmarket goods and services. Use of goods and services for own account capital formation is excluded. This indicator is expressed as percentage of total expenses which is any decrease in net worth resulting from a transaction." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.XPN.INTP.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Interest payments (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Interest payments include interest payments on government debt (including long-term bonds, long-term loans, and other debt instruments) to domestic and foreign residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.XPN.INTP.RV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Interest payments (% of revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Interest payments include interest payments on government debt (including long-term bonds, long-term loans, and other debt instruments) to domestic and foreign residents. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.XPN.INTP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Interest payments (% of expense)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Interest payments include interest payments on government debt (including long-term bonds, long-term loans, and other debt instruments) to domestic and foreign residents. This indicator is expressed as percentage of total expenses which is any decrease in net worth resulting from a transaction." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.XPN.OTHR.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Other expense (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Other expense is spending on dividends, rent, and other miscellaneous expenses, including provision for consumption of fixed capital. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.XPN.OTHR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Other expense (% of expense)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Other expense is spending on dividends, rent, and other miscellaneous expenses, including provision for consumption of fixed capital. This indicator is expressed as percentage of total expenses which is any decrease in net worth resulting from a transaction." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.XPN.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Expense (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Expense is a decrease in net worth resulting from a transaction. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.XPN.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Expense (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Expense is a decrease in net worth resulting from a transaction. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF);\nWorld Development Indicators, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GC.XPN.TRFT.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Subsidies and other transfers (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Subsidies are current unrequited payments that government units, including nonresident government units, make to enterprises on the basis of the levels of their production activities or the quantities or values of the goods or services that they produce, sell, export or import. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "GC.XPN.TRFT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Subsidies and other transfers (% of expense)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Subsidies are current unrequited payments that government units, including nonresident government units, make to enterprises on the basis of the levels of their production activities or the quantities or values of the goods or services that they produce, sell, export or import. This indicator is expressed as percentage of total expenses which is any decrease in net worth resulting from a transaction." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "GD_WBL_OVL_ENF", "metatype": [ { "id": "Aggregationmethod", "value": "Simple average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions Index (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The index is a composite measure (0–100) that summarizes how enforcement perceptions shape women’s economic opportunities across ten topics: Safety, Mobility, Work, Pay, Marriage, Parenthood, Childcare, Entrepreneurship, Assets, and Pension. It is calculated as the unweighted average of the ten topic scores, with 100 representing the highest possible score and 0 the lowest. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "Referenceperiod", "value": "2025-2025" }, { "id": "Source", "value": "Women, Business and the Law, World Bank (WB), uri: https://wbl.worldbank.org/en/wbl-data, date accessed: 2026-03-24, date published: 2026-02-24" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Gender: Public life & decision making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "2" }, { "id": "GD_WBL_OVL_LAW", "metatype": [ { "id": "Aggregationmethod", "value": "Simple average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "IndicatorName", "value": "WBL: Legal Framework Index (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The index refers to the composite measure (0–100) that summarizes how legal frameworks shape women’s economic opportunities across ten topics: Safety, Mobility, Work, Pay, Marriage, Parenthood, Childcare, Entrepreneurship, Assets, and Pension. It is calculated by taking the unweighted average of the ten topic scores, with 100 representing the highest possible score and 0 the lowest. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "Referenceperiod", "value": "2025-2025" }, { "id": "Source", "value": "Women, Business and the Law, World Bank (WB), uri: https://wbl.worldbank.org/en/wbl-data, date accessed: 2026-03-24, date published: 2026-02-24" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Gender: Public life & decision making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "2" }, { "id": "GD_WBL_OVL_SFR", "metatype": [ { "id": "Aggregationmethod", "value": "Simple average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework Index (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The index refers to the composite measure (0–100) that summarizes how supporting frameworks shape women’s economic opportunities across ten topics: Safety, Mobility, Work, Pay, Marriage, Parenthood, Childcare, Entrepreneurship, Assets, and Pension. It is calculated by taking the unweighted average of the ten topic scores, with 100 representing the highest possible score and 0 the lowest. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "Referenceperiod", "value": "2025-2025" }, { "id": "Source", "value": "Women, Business and the Law, World Bank (WB), uri: https://wbl.worldbank.org/en/wbl-data, date accessed: 2026-03-24, date published: 2026-02-24" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Gender: Public life & decision making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "2" }, { "id": "GF.XPD.BUDG.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The indicator attempts to capture the reliability of government budgets: do governments spend what they intend to and do they collect what they set out to collect. The ability to implement the enacted budget is an important factor in government’s ability to deliver public services and achieve development objectives. The deviation between approved and actual spending is measured over a 12-month period (the budget year) and may have important implications for macroeconomic stability, public service delivery, and social welfare. A credibly implemented budget has only small deviations from the approved one. If expenditure is under-executed, beneficiaries may not receive crucial services. Over-executed budgets may result in budget deficits and increased public debt levels and can influence the macroeconomic stability. In both cases, lack of budget credibility undermines the usefulness of the budget process for policy making and implementation and erodes public trust in government." }, { "id": "IndicatorName", "value": "Primary government expenditures as a proportion of original approved budget (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although not all countries have used the PEFA methodology on an annual basis for the PEFA PI-1 indicator, the methodology relies on standard data sets for approved and final budget outturns which are commonly produced at least annually in every country. The countries that have not used the methodology to date are primarily highly developed countries which would have less difficulty in providing the necessary data than those in the lower and middle income categories that have been primary users of Public Expenditure and Financial Accountability (PEFA) to date. (As of: 2024-07-29) \n\nOne limitation of the indicator is that it is an aggregate indicator of budget reliability. While it can be disaggregated across regions, it is not disaggregated across various budget subcomponents. Different indicators are used for assessing changes in expenditure composition in the PEFA framework. Also, while this indicator is intended to measure budget reliability it should be understood that actual expenditure outturns can deviate from the originally approved budget for reasons unrelated to the accuracy of forecasts—for example, as a result of a major macroeconomic shock. However, the calibration of this indicator accommodates one unusual or “outlier” year and focuses on deviations from the forecast which occur in two of the three years covered by the assessment. Therefore, single year shocks are discounted allowing a more balanced assessment. \n\nThe broader context in which the indicator was developed is as follows. PEFA is a tool for assessing the status of public financial management and reporting on the strengths and weaknesses of Public Financial Management (PFM). A PEFA assessment provides a thorough, consistent and evidence-based analysis of PFM performance at a specific point in time and can be reapplied in successive assessments to track changes over time. The PEFA framework provides the foundation for evidence-based measurement of countries’ PFM systems using 31 performance indicators that are further disaggregated into 94 dimensions. A PEFA assessment measures the extent to which PFM systems, processes and institutions contribute to the achievement of desirable budget outcomes: aggregate fiscal discipline, strategic allocation of resources, and efficient service delivery." }, { "id": "Longdefinition", "value": "Primary government expenditures as a proportion of original approved budget measures the extent to which aggregate budget expenditure outturn reflects the amount originally approved, as defined in government budget documentation and fiscal reports. The coverage is budgetary central government (BCG) and the time period covered is the last three completed fiscal years." }, { "id": "Othernotes", "value": "Aggregate expenditure includes actual expenditures incorporating those incurred as a result of unplanned or exceptional events—for example, armed conflicts or natural disasters. Expenditures financed by windfall revenues, including privatization, should be included and noted in the supporting fiscal tables and narrative. Expenditures financed externally by loans or grants should be included, if covered by the budget along with contingency vote(s) and interest on debt. \n\nExpenditure assigned to suspense accounts is not included in the aggregate. However, if amounts are held in suspense accounts at the end of any year that could affect the scores if included in the calculations, they can be included. In such cases the reason(s) for inclusion must be clearly stated. \n\nActual expenditure outturns can deviate from the originally approved budget for reasons unrelated to the accuracy of forecasts—for example, as a result of a major macroeconomic shock. The calibration of this indicator accommodates one unusual or “outlier” year and focuses on deviations from the forecast which occur in two of the three years covered by the assessment. \nDetailed resources are available at www.pefa.org" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2024" }, { "id": "Source", "value": "Public Expenditure and Financial Accountability (PEFA), World Bank (WB), uri: https://www.pefa.org/node/5239, note: The raw data collected in order to calculate this indicator are the initially Approved and Executed Budgets. Budget Laws of countries is the usual source of the approved budget of countries. The end-of year fiscal reports (budget execution reports) are the sources of the actual spending. This data is typically obtained from websites of the Ministry of Finance (MoF) or the national Parliament, or data are collected through communication with the MoF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The PEFA PI-1 Indicator is used as a basis for the SDG 16.6.1 Indicator, following the measurement guidance and coverage. In order to make the computation and the analysis of data over time easy and applicable for all countries, it was decided that SDG 16.6.1 indicator will be based on the annual data collection on approved and executed budgets for all countries and will be calculated annually. \n\nThe simple calculation for every year for every country is for the: Aggregate expenditure outturn = Executed Budget/Approved Budget*100 \n\nIn the countries and regional groupings, analysis of the deviations are done according regions/years/countries, using the requirements of PEFA PI-1 indicator.\n\nAlthough the computation and scoring used for PI-1 indicator are not applied for the SDG 16.6.1 indicator, the categorization described below is applied and is the basis for the SDG 16.6.1 indicator. PEFA Methodology The methodology for calculating the PEFA PI-1 indicator is provided in a spreadsheet (titled “En PI-1 and PI-2 Exp Calculation-Feb 1 2016 (xls)”) and is based on the PEFA Public Expenditure and Financial Accountability (PEFA) Framework. \n\nScoring is at the heart of the indicator. A country is scored separately on a four-point ordinal scale: A, B, C, or D, according to precise criteria: (A) Aggregate expenditure outturn was between 95% and 105% of the approved aggregate budgeted expenditure in at least two of the last three years. (B) Aggregate expenditure outturn was between 90% and 110% of the approved aggregate budgeted expenditure in at least two of the last three years. (C Aggregate expenditure outturn was between 85% and 115% of the approved aggregate budgeted expenditure in at least two of the last three years. (D) Performance is less than required for a C score. In order to justify a particular score, every aspect specified in the scoring requirements must be fulfilled. \n\nIf the requirements are only partly met, the criteria are not satisfied and a lower score should be given that coincides with achievement of all requirements for the lower performance rating. A score of C reflects the basic level of performance for each indicator and dimension, consistent with good international practices. A score of D means that the feature being measured is present at less than the basic level of performance or is absent altogether, or that there is insufficient information to score the dimension. \n\nThe D score indicates performance that falls below the basic level. ‘D’ is applied if the performance observed is less than required for any higher score. For this reason, a D score is warranted when sufficient information is not available to establish the actual level of performance. A score of D due to insufficient information is distinguished from D scores for low-level performance by the use of an asterisk—that is, D* at the dimension level. The asterisk is not included at the indicator level. \n\nThe coverage is budgetary central government (BCG) and requires data for three consecutive years as a basis for assessment. The data would cover the most recent completed fiscal year for which data is available and the two immediately preceding years. \nStatistical concept(s): This indicator measures the extent to which aggregate budget expenditure outturn reflects the amount originally approved, as defined in government budget documentation and fiscal reports. The coverage is budgetary central government and the time period covers every fiscal year for the countries.\n\nRefer to Other notes for more details." }, { "id": "Topic", "value": "Public Sector: Government finance" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "HD_HCIP_EDUC_FE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): education pillar score, female (scale 0–188)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): education pillar score aggregates human capital accumulated during formal schooling. This includes pre-school, primary, secondary, and tertiary schooling. The measure also captures learning quality through harmonized learning outcomes." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ education pillar combines three measures of quantity and quality of learning: (1) expected years of schooling (EYS) — the number of years a child born today can expect to complete by age 18, constructed by summing age-appropriate enrollment rates (approximating ages before the official primary age with pre-primary, 6–11 with primary, 12–14 with lower secondary, and 15–17 with upper secondary) using UNESCO Institute for Statistics (UIS) data and validated/supplemented by World Bank country teams; (2) quality of schooling — captured by the Harmonized Learning Outcomes, which brings together and harmonizes scores from major international and regional assessments (TIMSS, PIRLS, PISA, SACMEQ, PASEC, LLECE, PILNA) and early-grade reading assessments (EGRAs); and (3) tertiary completion — the share of young adults who complete tertiary education (measured as the percentage of 25–29-year-olds with tertiary credentials), drawn from the WIDE database and national surveys.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0–188)" } ], "source_id": "2" }, { "id": "HD_HCIP_EDUC_MA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): education pillar score, male (scale 0–188)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): education pillar score aggregates human capital accumulated during formal schooling. This includes pre-school, primary, secondary, and tertiary schooling. The measure also captures learning quality through harmonized learning outcomes." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ education pillar combines three measures of quantity and quality of learning: (1) expected years of schooling (EYS) — the number of years a child born today can expect to complete by age 18, constructed by summing age-appropriate enrollment rates (approximating ages before the official primary age with pre-primary, 6–11 with primary, 12–14 with lower secondary, and 15–17 with upper secondary) using UNESCO Institute for Statistics (UIS) data and validated/supplemented by World Bank country teams; (2) quality of schooling — captured by the Harmonized Learning Outcomes, which brings together and harmonizes scores from major international and regional assessments (TIMSS, PIRLS, PISA, SACMEQ, PASEC, LLECE, PILNA) and early-grade reading assessments (EGRAs); and (3) tertiary completion — the share of young adults who complete tertiary education (measured as the percentage of 25–29-year-olds with tertiary credentials), drawn from the WIDE database and national surveys.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0–188)" } ], "source_id": "2" }, { "id": "HD_HCIP_EDUC_TO", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): education pillar score, total (scale 0–188)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): education pillar score aggregates human capital accumulated during formal schooling. This includes pre-school, primary, secondary, and tertiary schooling. The measure also captures learning quality through harmonized learning outcomes." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ education pillar combines three measures of quantity and quality of learning: (1) expected years of schooling (EYS) — the number of years a child born today can expect to complete by age 18, constructed by summing age-appropriate enrollment rates (approximating ages before the official primary age with pre-primary, 6–11 with primary, 12–14 with lower secondary, and 15–17 with upper secondary) using UNESCO Institute for Statistics (UIS) data and validated/supplemented by World Bank country teams; (2) quality of schooling — captured by the Harmonized Learning Outcomes, which brings together and harmonizes scores from major international and regional assessments (TIMSS, PIRLS, PISA, SACMEQ, PASEC, LLECE, PILNA) and early-grade reading assessments (EGRAs); and (3) tertiary completion — the share of young adults who complete tertiary education (measured as the percentage of 25–29-year-olds with tertiary credentials), drawn from the WIDE database and national surveys.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0–188)" } ], "source_id": "2" }, { "id": "HD_HCIP_HLTH_FE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): health pillar score, female (scale 0–50)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): health pillar score captures productivity effects of surviving to working age and achieving adequate early-life physical growth." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ health pillar combines two measures that together capture the latent health and nutrition conditions of a country: (1) adult survival rate, 15-60, calculated from UN Population Division mortality tables; (2) the fraction of children under five who are not stunted (height-for-age = -2 SD), a marker of adequate early nutrition and growth (Joint child malnutrition estimates (JME)). While there is no single, widely accepted measure of health status, rates of both adult survival & stunting have been linked to population-level measures of latent health. These population-level measures of health, in turn, have been linked to worker productivity.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0–50)" } ], "source_id": "2" }, { "id": "HD_HCIP_HLTH_MA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): health pillar score, male (scale 0–50)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): health pillar score captures productivity effects of surviving to working age and achieving adequate early-life physical growth." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ health pillar combines two measures that together capture the latent health and nutrition conditions of a country: (1) adult survival rate, 15-60, calculated from UN Population Division mortality tables; (2) the fraction of children under five who are not stunted (height-for-age = -2 SD), a marker of adequate early nutrition and growth (Joint child malnutrition estimates (JME)). While there is no single, widely accepted measure of health status, rates of both adult survival & stunting have been linked to population-level measures of latent health. These population-level measures of health, in turn, have been linked to worker productivity.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0–50)" } ], "source_id": "2" }, { "id": "HD_HCIP_HLTH_TO", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): health pillar score, total (scale 0–50)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): health pillar score captures productivity effects of surviving to working age and achieving adequate early-life physical growth." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ health pillar combines two measures that together capture the latent health and nutrition conditions of a country: (1) adult survival rate, 15-60, calculated from UN Population Division mortality tables; (2) the fraction of children under five who are not stunted (height-for-age = -2 SD), a marker of adequate early nutrition and growth (Joint child malnutrition estimates (JME)). While there is no single, widely accepted measure of health status, rates of both adult survival & stunting have been linked to population-level measures of latent health. These population-level measures of health, in turn, have been linked to worker productivity.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0–50)" } ], "source_id": "2" }, { "id": "HD_HCIP_OTJL_FE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): on-the-job learning pillar score, female (scale -30–87)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): on-the-job learning pillar score measures human capital accumulation after age 18 through work experience. The measure captures labor force participation, unemployment, and wage employment for youth and adults." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ on-the-job learning pillar combines three measures that capture the capacity to work and the quality of jobs for both the youth population ages 18-24 and the adult population ages 25–65 (1) the share of youth and adults in the labor force — the labor-force participation metric that reflects attachment to the labor market (ILOSTAT); (2) the share of youths and adults not in unemployment — based on unemployment rates from ILOSTAT; and (3) the share of youth and adults employed in wage versus non-wage work — the proportion of workers in paid, salaried jobs versus self-employment or unpaid family work (ILOSTAT’s modeled series). Together, these indicators capture both how long people can participate in the labor market and whether they have access to quality employment opportunities that grow human capital.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (-30–87)" } ], "source_id": "2" }, { "id": "HD_HCIP_OTJL_MA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): on-the-job learning pillar score, male (scale -30–87)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): on-the-job learning pillar score measures human capital accumulation after age 18 through work experience. The measure captures labor force participation, unemployment, and wage employment for youth and adults." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ on-the-job learning pillar combines three measures that capture the capacity to work and the quality of jobs for both the youth population ages 18-24 and the adult population ages 25–65 (1) the share of youth and adults in the labor force — the labor-force participation metric that reflects attachment to the labor market (ILOSTAT); (2) the share of youths and adults not in unemployment — based on unemployment rates from ILOSTAT; and (3) the share of youth and adults employed in wage versus non-wage work — the proportion of workers in paid, salaried jobs versus self-employment or unpaid family work (ILOSTAT’s modeled series). Together, these indicators capture both how long people can participate in the labor market and whether they have access to quality employment opportunities that grow human capital.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (-30–87)" } ], "source_id": "2" }, { "id": "HD_HCIP_OTJL_TO", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): on-the-job learning pillar score, total (scale -30–87)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): on-the-job learning pillar score measures human capital accumulation after age 18 through work experience. The measure captures labor force participation, unemployment, and wage employment for youth and adults." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ on-the-job learning pillar combines three measures that capture the capacity to work and the quality of jobs for both the youth population ages 18-24 and the adult population ages 25–65 (1) the share of youth and adults in the labor force — the labor-force participation metric that reflects attachment to the labor market (ILOSTAT); (2) the share of youths and adults not in unemployment — based on unemployment rates from ILOSTAT; and (3) the share of youth and adults employed in wage versus non-wage work — the proportion of workers in paid, salaried jobs versus self-employment or unpaid family work (ILOSTAT’s modeled series). Together, these indicators capture both how long people can participate in the labor market and whether they have access to quality employment opportunities that grow human capital.\n\n\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (-30–87)" } ], "source_id": "2" }, { "id": "HD_HCIP_OVRL_FE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): overall score, female (scale 0–325)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): overall score calculates the contributions of health, education, and on-the-job learning to worker productivity. The index measures the productivity of a child born today as a future worker relative to the benchmark of full health, complete education, and full employment in wage employment." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ pulls together 11 outcomes focused on health, education, and employment, and employs the latest research on how each affects earnings. As a result, improvements in the HCI+ can be directly interpreted as increases in workers' lifetime earnings and in GDP over the long run. The scale for the HCI+ is based on the human capital earnings function (originally developed by Mincer (1974) and others), where human capital is measured as the log of lifetime earnings. In this framework, a one-unit change in the index corresponds to a proportional change in earnings. To make the index easier to interpret, we multiply the log measure by 100.\n\nThis means that point differences in HCI+ can be read as approximate percentage differences in lifetime earnings. For example, an increase of 10 points in HCI+ corresponds roughly to a 10 percent increase in expected adult wages (and, in the long run, GDP per worker). Multiplying by 100, therefore, transforms the log measure into policy-relevant percentage units without altering the index's underlying economics.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0–325)" } ], "source_id": "2" }, { "id": "HD_HCIP_OVRL_MA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): overall score, male (scale 0–325)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): overall score calculates the contributions of health, education, and on-the-job learning to worker productivity. The index measures the productivity of a child born today as a future worker relative to the benchmark of full health, complete education, and full employment in wage employment." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ pulls together 11 outcomes focused on health, education, and employment, and employs the latest research on how each affects earnings. As a result, improvements in the HCI+ can be directly interpreted as increases in workers' lifetime earnings and in GDP over the long run. The scale for the HCI+ is based on the human capital earnings function (originally developed by Mincer (1974) and others), where human capital is measured as the log of lifetime earnings. In this framework, a one-unit change in the index corresponds to a proportional change in earnings. To make the index easier to interpret, we multiply the log measure by 100.\n\nThis means that point differences in HCI+ can be read as approximate percentage differences in lifetime earnings. For example, an increase of 10 points in HCI+ corresponds roughly to a 10 percent increase in expected adult wages (and, in the long run, GDP per worker). Multiplying by 100, therefore, transforms the log measure into policy-relevant percentage units without altering the index's underlying economics.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0–325)" } ], "source_id": "2" }, { "id": "HD_HCIP_OVRL_TO", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): overall score, total (scale 0–325)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): overall score calculates the contributions of health, education, and on-the-job learning to worker productivity. The index measures the productivity of a child born today as a future worker relative to the benchmark of full health, complete education, and full employment in wage employment." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ pulls together 11 outcomes focused on health, education, and employment, and employs the latest research on how each affects earnings. As a result, improvements in the HCI+ can be directly interpreted as increases in workers' lifetime earnings and in GDP over the long run. The scale for the HCI+ is based on the human capital earnings function (originally developed by Mincer (1974) and others), where human capital is measured as the log of lifetime earnings. In this framework, a one-unit change in the index corresponds to a proportional change in earnings. To make the index easier to interpret, we multiply the log measure by 100.\n\nThis means that point differences in HCI+ can be read as approximate percentage differences in lifetime earnings. For example, an increase of 10 points in HCI+ corresponds roughly to a 10 percent increase in expected adult wages (and, in the long run, GDP per worker). Multiplying by 100, therefore, transforms the log measure into policy-relevant percentage units without altering the index's underlying economics.\n\n\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0–325)" } ], "source_id": "2" }, { "id": "IC.BRE.BE.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Registered companies benefit from a variety of advantages, including the legal and financial services provided by courts and banks. Their employees enjoy social security protection. Additionally, the economy takes advantage of positive spillovers: where formal entrepreneurship is high, job creation and economic growth also tend to be high. As more businesses formalize, the tax base also expands, enabling the government to spend on productivity-enhancing areas and pursue other social and economic policy goals. However, entrepreneurs often encounter barriers to entry into the formal economy.\n\nThere is evidence that higher costs for business start-ups are associated with lower business entry and lower levels of employment and productivity. Cumbersome regulations for business start-ups are associated with high levels of corruption and informality. A simple business start-up process is a positive factor for fostering formal entrepreneurship. Moreover, digital technology and transparency of information can encourage businesses to register and promote private sector growth." }, { "id": "IndicatorName", "value": "B-READY: Business Entry: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Entry topic measures the process of registration and start of operations of new limited liability companies (LLCs) across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.BE.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Registered companies benefit from a variety of advantages, including the legal and financial services provided by courts and banks. Their employees enjoy social security protection. Additionally, the economy takes advantage of positive spillovers: where formal entrepreneurship is high, job creation and economic growth also tend to be high. As more businesses formalize, the tax base also expands, enabling the government to spend on productivity-enhancing areas and pursue other social and economic policy goals. However, entrepreneurs often encounter barriers to entry into the formal economy.\n\nThere is evidence that higher costs for business start-ups are associated with lower business entry and lower levels of employment and productivity. Cumbersome regulations for business start-ups are associated with high levels of corruption and informality. A simple business start-up process is a positive factor for fostering formal entrepreneurship. Moreover, digital technology and transparency of information can encourage businesses to register and promote private sector growth." }, { "id": "IndicatorName", "value": "B-READY: Business Entry Pillar 1: Quality of Regulations for Business Entry" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Entry topic measures the process of registration and start of operations of new limited liability companies (LLCs) across three different dimensions, or pillars. The first pillar assesses the quality of regulations for business entry, covering de jure features of a regulatory framework that are necessary for the adoption of good practices for business start-ups." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.BE.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Registered companies benefit from a variety of advantages, including the legal and financial services provided by courts and banks. Their employees enjoy social security protection. Additionally, the economy takes advantage of positive spillovers: where formal entrepreneurship is high, job creation and economic growth also tend to be high. As more businesses formalize, the tax base also expands, enabling the government to spend on productivity-enhancing areas and pursue other social and economic policy goals. However, entrepreneurs often encounter barriers to entry into the formal economy.\n\nThere is evidence that higher costs for business start-ups are associated with lower business entry and lower levels of employment and productivity. Cumbersome regulations for business start-ups are associated with high levels of corruption and informality. A simple business start-up process is a positive factor for fostering formal entrepreneurship. Moreover, digital technology and transparency of information can encourage businesses to register and promote private sector growth." }, { "id": "IndicatorName", "value": "B-READY: Business Entry Pillar 2: Digital Public Services and Transparency of Information for Business Entry" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Entry topic measures the process of registration and start of operations of new limited liability companies (LLCs) across three different dimensions, or pillars. The second pillar measures the availability of digital public services and transparency of information for business entry." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.BE.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Registered companies benefit from a variety of advantages, including the legal and financial services provided by courts and banks. Their employees enjoy social security protection. Additionally, the economy takes advantage of positive spillovers: where formal entrepreneurship is high, job creation and economic growth also tend to be high. As more businesses formalize, the tax base also expands, enabling the government to spend on productivity-enhancing areas and pursue other social and economic policy goals. However, entrepreneurs often encounter barriers to entry into the formal economy.\n\nThere is evidence that higher costs for business start-ups are associated with lower business entry and lower levels of employment and productivity. Cumbersome regulations for business start-ups are associated with high levels of corruption and informality. A simple business start-up process is a positive factor for fostering formal entrepreneurship. Moreover, digital technology and transparency of information can encourage businesses to register and promote private sector growth." }, { "id": "IndicatorName", "value": "B-READY: Business Entry Pillar 3: Operational Efficiency of Business Entry" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Entry topic measures the process of registration and start of operations of new limited liability companies (LLCs) across three different dimensions, or pillars. The third pillar measures the time and cost required to register new domestic and foreign firms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.BI.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The efficient and rapid exit of nonviable firms plays an important cyclical role in renewing the economy by removing firms that are not productive and making way for more productive ones. The purpose of an efficient insolvency framework is to ensure that nonviable firms are swiftly liquidated, and viable firms are effectively restructured in a sustainable way. When insolvency regimes do not have the adequate tools to handle the restructuring and liquidation of companies in a timely and effective manner these companies’ economic distress is amplified, jeopardizing the stability of the financial system. In economies where creditor recovery rates are high and resolution times are quicker, restructuring within the formal bankruptcy process fulfills its cyclical role during economic downturns by keeping companies afloat." }, { "id": "IndicatorName", "value": "B-READY: Business Insolvency: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Insolvency topic measures key features of insolvency systems on a regulatory level. It also assesses the institutional and operational infrastructure associated with insolvency proceedings (judicial services), as well as the operational efficiency of insolvency proceedings across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.BI.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The efficient and rapid exit of nonviable firms plays an important cyclical role in renewing the economy by removing firms that are not productive and making way for more productive ones. The purpose of an efficient insolvency framework is to ensure that nonviable firms are swiftly liquidated, and viable firms are effectively restructured in a sustainable way. When insolvency regimes do not have the adequate tools to handle the restructuring and liquidation of companies in a timely and effective manner these companies’ economic distress is amplified, jeopardizing the stability of the financial system. In economies where creditor recovery rates are high and resolution times are quicker, restructuring within the formal bankruptcy process fulfills its cyclical role during economic downturns by keeping companies afloat." }, { "id": "IndicatorName", "value": "B-READY: Business Insolvency Pillar 1: Quality of Regulations for Judicial Insolvency Proceedings" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Insolvency topic measures key features of insolvency systems on a regulatory level. It also assesses the institutional and operational infrastructure associated with insolvency proceedings (judicial services), as well as the operational efficiency of insolvency proceedings across three different dimensions, or pillars. The first pillar assesses the quality of regulation pertaining to judicial insolvency proceedings—liquidation and reorganization, covering de jure features of a regulatory framework that are necessary for structured debt resolution processes and effective creditor and debtor regimes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.BI.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The efficient and rapid exit of nonviable firms plays an important cyclical role in renewing the economy by removing firms that are not productive and making way for more productive ones. The purpose of an efficient insolvency framework is to ensure that nonviable firms are swiftly liquidated, and viable firms are effectively restructured in a sustainable way. When insolvency regimes do not have the adequate tools to handle the restructuring and liquidation of companies in a timely and effective manner these companies’ economic distress is amplified, jeopardizing the stability of the financial system. In economies where creditor recovery rates are high and resolution times are quicker, restructuring within the formal bankruptcy process fulfills its cyclical role during economic downturns by keeping companies afloat." }, { "id": "IndicatorName", "value": "B-READY: Business Insolvency Pillar 2: Quality of Institutional and Operational Infrastructure for Judicial Insolvency Proceedings" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Insolvency topic measures key features of insolvency systems on a regulatory level. It also assesses the institutional and operational infrastructure associated with insolvency proceedings (judicial services), as well as the operational efficiency of insolvency proceedings across three different dimensions, or pillars. The second pillar measures the quality of institutional and operational infrastructure for judicial insolvency proceedings, thus assessing the de facto aspects of insolvency resolution mechanisms and the infrastructure required to implement the legal framework on insolvency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.BI.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The efficient and rapid exit of nonviable firms plays an important cyclical role in renewing the economy by removing firms that are not productive and making way for more productive ones. The purpose of an efficient insolvency framework is to ensure that nonviable firms are swiftly liquidated, and viable firms are effectively restructured in a sustainable way. When insolvency regimes do not have the adequate tools to handle the restructuring and liquidation of companies in a timely and effective manner these companies’ economic distress is amplified, jeopardizing the stability of the financial system. In economies where creditor recovery rates are high and resolution times are quicker, restructuring within the formal bankruptcy process fulfills its cyclical role during economic downturns by keeping companies afloat." }, { "id": "IndicatorName", "value": "B-READY: Business Insolvency Pillar 3: Operational Efficiency of Resolving Judicial Insolvency Proceedings" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Insolvency topic measures key features of insolvency systems on a regulatory level. It also assesses the institutional and operational infrastructure associated with insolvency proceedings (judicial services), as well as the operational efficiency of insolvency proceedings across three different dimensions, or pillars. The third pillar measures the time and cost required to resolve in-court liquidation and reorganization proceedings." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.BL.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acquiring the physical space where a business will operate is a crucial ingredient of success for many firms, even in the digital age. Getting the right location can influence business access to customers, transportation, labor, and materials, as well as determine taxes, regulations, and environmental commitments they must comply with.\n\nWhether an entrepreneur is leasing or purchasing a commercial property, the regulatory framework and the public services related to acquiring a location can have an impact on how conducive the business environment is for individual firms and the private sector development of an economy.\n\nFirms are more likely to invest in economies with strong property rights in which they are confident that their immovable property investments will be safe. A reliable land administration system, which provides clear information on property ownership, facilitates the development of real estate markets, and supports tenure security. These factors not only provide confidence to the private sector but also indicate the economy’s prospects for economic growth. Transparency in land administration also reduces information asymmetries, which increases market efficiency, further supporting economic development. \n\nWhen investors and entrepreneurs acquire a new location for their business, the process often involves licensing requirements for altering a property or changing tenancy. Building-related permits are essential for public safety, strengthening property rights, and contributing to capital formation. Last but not least, transparent and accessible environmental regulations related to building control reduce the regulatory burden on firms by offering clarity on rules and regulations." }, { "id": "IndicatorName", "value": "B-READY: Business Location: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Location topic measures three different options—purchase, lease, or build—that are available to entrepreneurs to choose the adequate location to set up their company, across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.BL.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acquiring the physical space where a business will operate is a crucial ingredient of success for many firms, even in the digital age. Getting the right location can influence business access to customers, transportation, labor, and materials, as well as determine taxes, regulations, and environmental commitments they must comply with.\n\nWhether an entrepreneur is leasing or purchasing a commercial property, the regulatory framework and the public services related to acquiring a location can have an impact on how conducive the business environment is for individual firms and the private sector development of an economy.\n\nFirms are more likely to invest in economies with strong property rights in which they are confident that their immovable property investments will be safe. A reliable land administration system, which provides clear information on property ownership, facilitates the development of real estate markets, and supports tenure security. These factors not only provide confidence to the private sector but also indicate the economy’s prospects for economic growth. Transparency in land administration also reduces information asymmetries, which increases market efficiency, further supporting economic development. \n\nWhen investors and entrepreneurs acquire a new location for their business, the process often involves licensing requirements for altering a property or changing tenancy. Building-related permits are essential for public safety, strengthening property rights, and contributing to capital formation. Last but not least, transparent and accessible environmental regulations related to building control reduce the regulatory burden on firms by offering clarity on rules and regulations." }, { "id": "IndicatorName", "value": "B-READY: Business Location Pillar 1: Quality of Regulations for Business Location" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Location topic measures three different options—purchase, lease, or build—that are available to entrepreneurs to choose the adequate location to set up their company, across three different dimensions, or pillars. The first pillar assesses the quality of regulations pertaining to property transfer, building, and environmental permitting, covering de jure features of a regulatory framework that are necessary for immovable property lease, property ownership, urban planning, and environmental licenses." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.BL.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acquiring the physical space where a business will operate is a crucial ingredient of success for many firms, even in the digital age. Getting the right location can influence business access to customers, transportation, labor, and materials, as well as determine taxes, regulations, and environmental commitments they must comply with.\n\nWhether an entrepreneur is leasing or purchasing a commercial property, the regulatory framework and the public services related to acquiring a location can have an impact on how conducive the business environment is for individual firms and the private sector development of an economy.\n\nFirms are more likely to invest in economies with strong property rights in which they are confident that their immovable property investments will be safe. A reliable land administration system, which provides clear information on property ownership, facilitates the development of real estate markets, and supports tenure security. These factors not only provide confidence to the private sector but also indicate the economy’s prospects for economic growth. Transparency in land administration also reduces information asymmetries, which increases market efficiency, further supporting economic development. \n\nWhen investors and entrepreneurs acquire a new location for their business, the process often involves licensing requirements for altering a property or changing tenancy. Building-related permits are essential for public safety, strengthening property rights, and contributing to capital formation. Last but not least, transparent and accessible environmental regulations related to building control reduce the regulatory burden on firms by offering clarity on rules and regulations." }, { "id": "IndicatorName", "value": "B-READY: Business Location Pillar 2: Quality of Public Services and Transparency of Information for Business Location" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Location topic measures three different options—purchase, lease, or build—that are available to entrepreneurs to choose the adequate location to set up their company, across three different dimensions, or pillars. The second pillar assesses the quality of public services and transparency of information in the provision of property transfer, building, and environmental permitting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.BL.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acquiring the physical space where a business will operate is a crucial ingredient of success for many firms, even in the digital age. Getting the right location can influence business access to customers, transportation, labor, and materials, as well as determine taxes, regulations, and environmental commitments they must comply with.\n\nWhether an entrepreneur is leasing or purchasing a commercial property, the regulatory framework and the public services related to acquiring a location can have an impact on how conducive the business environment is for individual firms and the private sector development of an economy.\n\nFirms are more likely to invest in economies with strong property rights in which they are confident that their immovable property investments will be safe. A reliable land administration system, which provides clear information on property ownership, facilitates the development of real estate markets, and supports tenure security. These factors not only provide confidence to the private sector but also indicate the economy’s prospects for economic growth. Transparency in land administration also reduces information asymmetries, which increases market efficiency, further supporting economic development. \n\nWhen investors and entrepreneurs acquire a new location for their business, the process often involves licensing requirements for altering a property or changing tenancy. Building-related permits are essential for public safety, strengthening property rights, and contributing to capital formation. Last but not least, transparent and accessible environmental regulations related to building control reduce the regulatory burden on firms by offering clarity on rules and regulations." }, { "id": "IndicatorName", "value": "B-READY: Business Location Pillar 3: Operational Efficiency of Establishing a Business Location" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Location topic measures three different options—purchase, lease, or build—that are available to entrepreneurs to choose the adequate location to set up their company, across three different dimensions, or pillars. The third pillar measures the operational efficiency of establishing a business location in practice." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.DR.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Inability to resolve commercial disputes promptly and fairly can lead to adverse economic outcomes in the private sector, ranging from reduced entrepreneurship and lower investment to macroeconomic volatility. This makes efficient and quality dispute resolution essential for a healthy business environment. Having time- and cost-effective mechanisms for resolving disputes is critical because excessively long and expensive proceedings may defeat the very purpose of bringing a case to formal institutions, making them unattractive and unaffordable. In fact, correlations have been established between judicial efficiency and facilitated entrepreneurial activity. Evidence also suggests that under a more effective court system businesses are likely to have greater access to finance and borrow more. The quality of the dispute resolution process also matters. Claims should be considered with due care by credible institutions capable of issuing sound judgments. It was found that in economies with low confidence in court systems, firms are less willing to expand their businesses and look for alternative trade partners. To attract more investors, economies therefore should ensure not only judiciaries’ effectiveness but also their strength and reliability." }, { "id": "IndicatorName", "value": "B-READY: Dispute Resolution: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Dispute Resolution topic measures efficiency and quality of the resolution of commercial disputes—those arising in the business context between firms—across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.DR.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Inability to resolve commercial disputes promptly and fairly can lead to adverse economic outcomes in the private sector, ranging from reduced entrepreneurship and lower investment to macroeconomic volatility. This makes efficient and quality dispute resolution essential for a healthy business environment. Having time- and cost-effective mechanisms for resolving disputes is critical because excessively long and expensive proceedings may defeat the very purpose of bringing a case to formal institutions, making them unattractive and unaffordable. In fact, correlations have been established between judicial efficiency and facilitated entrepreneurial activity. Evidence also suggests that under a more effective court system businesses are likely to have greater access to finance and borrow more. The quality of the dispute resolution process also matters. Claims should be considered with due care by credible institutions capable of issuing sound judgments. It was found that in economies with low confidence in court systems, firms are less willing to expand their businesses and look for alternative trade partners. To attract more investors, economies therefore should ensure not only judiciaries’ effectiveness but also their strength and reliability." }, { "id": "IndicatorName", "value": "B-READY: Dispute Resolution Pillar 1: Quality of Regulations for Dispute Resolution" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Dispute Resolution topic measures efficiency and quality of the resolution of commercial disputes—those arising in the business context between firms—across three different dimensions, or pillars. The first pillar assesses the adequacy of legislation pertaining to both court processes and alternative dispute resolution (ADR), covering de jure features that are necessary for the efficient processing of cases, facilitated resolution of cross-border claims, creating alternative venues for settling disputes, and ensuring trust in relevant institutions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.DR.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Inability to resolve commercial disputes promptly and fairly can lead to adverse economic outcomes in the private sector, ranging from reduced entrepreneurship and lower investment to macroeconomic volatility. This makes efficient and quality dispute resolution essential for a healthy business environment. Having time- and cost-effective mechanisms for resolving disputes is critical because excessively long and expensive proceedings may defeat the very purpose of bringing a case to formal institutions, making them unattractive and unaffordable. In fact, correlations have been established between judicial efficiency and facilitated entrepreneurial activity. Evidence also suggests that under a more effective court system businesses are likely to have greater access to finance and borrow more. The quality of the dispute resolution process also matters. Claims should be considered with due care by credible institutions capable of issuing sound judgments. It was found that in economies with low confidence in court systems, firms are less willing to expand their businesses and look for alternative trade partners. To attract more investors, economies therefore should ensure not only judiciaries’ effectiveness but also their strength and reliability." }, { "id": "IndicatorName", "value": "B-READY: Dispute Resolution Pillar 2: Public Services for Dispute Resolution" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Dispute Resolution topic measures efficiency and quality of the resolution of commercial disputes—those arising in the business context between firms—across three different dimensions, or pillars. The second pillar focuses on judicial organizational structure, courts’ digitization and transparency, as well as ADR-related services, thus capturing the de facto provision of public services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.DR.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Inability to resolve commercial disputes promptly and fairly can lead to adverse economic outcomes in the private sector, ranging from reduced entrepreneurship and lower investment to macroeconomic volatility. This makes efficient and quality dispute resolution essential for a healthy business environment. Having time- and cost-effective mechanisms for resolving disputes is critical because excessively long and expensive proceedings may defeat the very purpose of bringing a case to formal institutions, making them unattractive and unaffordable. In fact, correlations have been established between judicial efficiency and facilitated entrepreneurial activity. Evidence also suggests that under a more effective court system businesses are likely to have greater access to finance and borrow more. The quality of the dispute resolution process also matters. Claims should be considered with due care by credible institutions capable of issuing sound judgments. It was found that in economies with low confidence in court systems, firms are less willing to expand their businesses and look for alternative trade partners. To attract more investors, economies therefore should ensure not only judiciaries’ effectiveness but also their strength and reliability." }, { "id": "IndicatorName", "value": "B-READY: Dispute Resolution Pillar 3: Ease of Resolving a Commercial Dispute" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Dispute Resolution topic measures efficiency and quality of the resolution of commercial disputes—those arising in the business context between firms—across three different dimensions, or pillars. The third pillar measures the reliability of dispute resolution, the time and cost required to resolve a dispute, as well as the time and cost associated with the recognition and enforcement of decisions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.FS.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to finance remains a major constraint for firms worldwide, despite being essential for their operations and expansion and positively associated with innovation. Additionally, access to finance affects firms’ ability to manage a volatile cash flow and directly contributes to their resilience, which was underscored during the global pandemic. Research has also shown that private sector financing in developing economies has positive macroeconomic effects as firm-level employment often benefits from improved access to finance.\n\nAccess to finance also plays an important role in maintaining a company’s financial stability. Removing bottlenecks associated with making and receiving payments further strengthens firms’ financial security. In recent years, cashless transactions (including e-payments) have continued growing. However, economies’ ever-increasing digitalization requires modern regulations that enable electronic solutions to reap the benefits of technological progress. This would unlock the extensive use of electronic payments (e-payments), which is associated with reduced tax evasion and lower informality in the private sector." }, { "id": "IndicatorName", "value": "B-READY: Financial Services: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Financial Services topic measures four areas—Commercial Lending; Secured Transactions; e-Payments; and Credit Information—across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.FS.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to finance remains a major constraint for firms worldwide, despite being essential for their operations and expansion and positively associated with innovation. Additionally, access to finance affects firms’ ability to manage a volatile cash flow and directly contributes to their resilience, which was underscored during the global pandemic. Research has also shown that private sector financing in developing economies has positive macroeconomic effects as firm-level employment often benefits from improved access to finance.\n\nAccess to finance also plays an important role in maintaining a company’s financial stability. Removing bottlenecks associated with making and receiving payments further strengthens firms’ financial security. In recent years, cashless transactions (including e-payments) have continued growing. However, economies’ ever-increasing digitalization requires modern regulations that enable electronic solutions to reap the benefits of technological progress. This would unlock the extensive use of electronic payments (e-payments), which is associated with reduced tax evasion and lower informality in the private sector." }, { "id": "IndicatorName", "value": "B-READY: Financial Services Pillar 1: Quality of Regulations for Financial Services" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Financial Services topic measures four areas—Commercial Lending; Secured Transactions; e-Payments; and Credit Information—across three different dimensions, or pillars. The first pillar assesses the effectiveness of regulation pertaining to commercial lending, secured transactions, and e-payments, covering the de jure features of regulatory frameworks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.FS.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to finance remains a major constraint for firms worldwide, despite being essential for their operations and expansion and positively associated with innovation. Additionally, access to finance affects firms’ ability to manage a volatile cash flow and directly contributes to their resilience, which was underscored during the global pandemic. Research has also shown that private sector financing in developing economies has positive macroeconomic effects as firm-level employment often benefits from improved access to finance.\n\nAccess to finance also plays an important role in maintaining a company’s financial stability. Removing bottlenecks associated with making and receiving payments further strengthens firms’ financial security. In recent years, cashless transactions (including e-payments) have continued growing. However, economies’ ever-increasing digitalization requires modern regulations that enable electronic solutions to reap the benefits of technological progress. This would unlock the extensive use of electronic payments (e-payments), which is associated with reduced tax evasion and lower informality in the private sector." }, { "id": "IndicatorName", "value": "B-READY: Financial Services Pillar 2: Accessibility of Information in Credit Infrastructure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Financial Services topic measures four areas—Commercial Lending; Secured Transactions; e-Payments; and Credit Information—across three different dimensions, or pillars. The second pillar measures the accessibility of information in credit infrastructure by evaluating the operation of credit bureaus and registries and the operation of collateral registries. Thus, the second pillar assesses de facto and some de jure aspects of financial services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.FS.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to finance remains a major constraint for firms worldwide, despite being essential for their operations and expansion and positively associated with innovation. Additionally, access to finance affects firms’ ability to manage a volatile cash flow and directly contributes to their resilience, which was underscored during the global pandemic. Research has also shown that private sector financing in developing economies has positive macroeconomic effects as firm-level employment often benefits from improved access to finance.\n\nAccess to finance also plays an important role in maintaining a company’s financial stability. Removing bottlenecks associated with making and receiving payments further strengthens firms’ financial security. In recent years, cashless transactions (including e-payments) have continued growing. However, economies’ ever-increasing digitalization requires modern regulations that enable electronic solutions to reap the benefits of technological progress. This would unlock the extensive use of electronic payments (e-payments), which is associated with reduced tax evasion and lower informality in the private sector." }, { "id": "IndicatorName", "value": "B-READY: Financial Services Pillar 3: Operational Efficiency of Receiving Financial Services" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Financial Services topic measures four areas—Commercial Lending; Secured Transactions; e-Payments; and Credit Information—across three different dimensions, or pillars. The third pillar measures the operational efficiency of (i) obtaining a loan; (ii) registering a security interest as well as the timeliness of credit information sharing; and (iii) e-payments usage and their efficiency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.IT.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "International trade is a key driver of economic growth and private sector development. Through competition among domestic and foreign firms, it promotes specialization and resource reallocation to the most productive firms. While there are winners and losers among firms, workers, and consumers, international trade can generate overall benefits for the private sector and society. To remain competitive, firms must continuously adapt, innovate, and improve their efficiency, resulting in aggregate productivity growth and welfare. Trade openness may generate further productivity gains by creating economies of scale and providing access to cheaper intermediate inputs of higher quality and variety, as well as facilitating knowledge and technology transfers. Increased access to foreign inputs may enhance productivity and export performance, and it may provide opportunities to diversify the economy and reduce its dependence on a single product or market. This shows the complementarities between exports and imports and emphasizes the importance of trade openness to reap the benefits of international trade.\n\nTo fully realize the benefits of international trade, it is necessary to have a conducive business environment that reduces trade barriers and lowers compliance and transaction costs for the private sector. A regulatory framework that establishes a nondiscriminatory, transparent, predictable, and safe trading environment generates incentives to engage in international trade and provides a level playing field. Furthermore, it is crucial to have regulations that strike a balance between public policy objectives, including protecting public health and the environment, and the requirements they impose, which can create market distortions that impede trade. Finally, policies that improve the quality of physical and digital infrastructure, as well as border management, reduce the time and cost borne by the private sector, which represents a substantial barrier to trade, and increase participation in international trade for small, medium, and large firms." }, { "id": "IndicatorName", "value": "B-READY: International Trade: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The International Trade topic measures different aspects of international trade—trade in goods, trade in services, and digital trade—across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.IT.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "International trade is a key driver of economic growth and private sector development. Through competition among domestic and foreign firms, it promotes specialization and resource reallocation to the most productive firms. While there are winners and losers among firms, workers, and consumers, international trade can generate overall benefits for the private sector and society. To remain competitive, firms must continuously adapt, innovate, and improve their efficiency, resulting in aggregate productivity growth and welfare. Trade openness may generate further productivity gains by creating economies of scale and providing access to cheaper intermediate inputs of higher quality and variety, as well as facilitating knowledge and technology transfers. Increased access to foreign inputs may enhance productivity and export performance, and it may provide opportunities to diversify the economy and reduce its dependence on a single product or market. This shows the complementarities between exports and imports and emphasizes the importance of trade openness to reap the benefits of international trade.\n\nTo fully realize the benefits of international trade, it is necessary to have a conducive business environment that reduces trade barriers and lowers compliance and transaction costs for the private sector. A regulatory framework that establishes a nondiscriminatory, transparent, predictable, and safe trading environment generates incentives to engage in international trade and provides a level playing field. Furthermore, it is crucial to have regulations that strike a balance between public policy objectives, including protecting public health and the environment, and the requirements they impose, which can create market distortions that impede trade. Finally, policies that improve the quality of physical and digital infrastructure, as well as border management, reduce the time and cost borne by the private sector, which represents a substantial barrier to trade, and increase participation in international trade for small, medium, and large firms." }, { "id": "IndicatorName", "value": "B-READY: International Trade Pillar 1: Quality of Regulations for International Trade" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The International Trade topic measures different aspects of international trade—trade in goods, trade in services, and digital trade—across three different dimensions, or pillars. The first pillar assesses the quality of regulations pertaining to international trade, covering de jure features of a regulatory framework that are necessary to establish a nondiscriminatory, transparent, predictable, and safe environment to harness the potential of international trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.IT.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "International trade is a key driver of economic growth and private sector development. Through competition among domestic and foreign firms, it promotes specialization and resource reallocation to the most productive firms. While there are winners and losers among firms, workers, and consumers, international trade can generate overall benefits for the private sector and society. To remain competitive, firms must continuously adapt, innovate, and improve their efficiency, resulting in aggregate productivity growth and welfare. Trade openness may generate further productivity gains by creating economies of scale and providing access to cheaper intermediate inputs of higher quality and variety, as well as facilitating knowledge and technology transfers. Increased access to foreign inputs may enhance productivity and export performance, and it may provide opportunities to diversify the economy and reduce its dependence on a single product or market. This shows the complementarities between exports and imports and emphasizes the importance of trade openness to reap the benefits of international trade.\n\nTo fully realize the benefits of international trade, it is necessary to have a conducive business environment that reduces trade barriers and lowers compliance and transaction costs for the private sector. A regulatory framework that establishes a nondiscriminatory, transparent, predictable, and safe trading environment generates incentives to engage in international trade and provides a level playing field. Furthermore, it is crucial to have regulations that strike a balance between public policy objectives, including protecting public health and the environment, and the requirements they impose, which can create market distortions that impede trade. Finally, policies that improve the quality of physical and digital infrastructure, as well as border management, reduce the time and cost borne by the private sector, which represents a substantial barrier to trade, and increase participation in international trade for small, medium, and large firms." }, { "id": "IndicatorName", "value": "B-READY: International Trade Pillar 2: Quality of Public Services for the Facilitation of International Trade" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The International Trade topic measures different aspects of international trade—trade in goods, trade in services, and digital trade—across three different dimensions, or pillars. The second pillar assesses digital and physical infrastructure concerning international trade and the quality of border management, thus assessing de facto provision of public services for international trade facilitation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.IT.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "International trade is a key driver of economic growth and private sector development. Through competition among domestic and foreign firms, it promotes specialization and resource reallocation to the most productive firms. While there are winners and losers among firms, workers, and consumers, international trade can generate overall benefits for the private sector and society. To remain competitive, firms must continuously adapt, innovate, and improve their efficiency, resulting in aggregate productivity growth and welfare. Trade openness may generate further productivity gains by creating economies of scale and providing access to cheaper intermediate inputs of higher quality and variety, as well as facilitating knowledge and technology transfers. Increased access to foreign inputs may enhance productivity and export performance, and it may provide opportunities to diversify the economy and reduce its dependence on a single product or market. This shows the complementarities between exports and imports and emphasizes the importance of trade openness to reap the benefits of international trade.\n\nTo fully realize the benefits of international trade, it is necessary to have a conducive business environment that reduces trade barriers and lowers compliance and transaction costs for the private sector. A regulatory framework that establishes a nondiscriminatory, transparent, predictable, and safe trading environment generates incentives to engage in international trade and provides a level playing field. Furthermore, it is crucial to have regulations that strike a balance between public policy objectives, including protecting public health and the environment, and the requirements they impose, which can create market distortions that impede trade. Finally, policies that improve the quality of physical and digital infrastructure, as well as border management, reduce the time and cost borne by the private sector, which represents a substantial barrier to trade, and increase participation in international trade for small, medium, and large firms." }, { "id": "IndicatorName", "value": "B-READY: International Trade Pillar 3: Efficiency of Importing Goods, Exporting Goods, and Engaging in Digital Trade" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The International Trade topic measures different aspects of international trade—trade in goods, trade in services, and digital trade—across three different dimensions, or pillars. The third pillar measures the time and cost to comply with export and import requirements, participation in cross-border digital trade, as well as the perceived major obstacles for international trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.LB.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Labor is arguably the most important factor of production in most businesses and the primary source of income for most people. Regulations and public services related to labor are fundamental drivers of private sector development from the perspective of both enterprises and workers. These regulations and public services affect firms’ decisions on whether to expand by hiring labor, and whether to do so formally or informally. In addition, they impact the well-being of potential workers by providing them with good jobs and opportunities for growth.\n\nFor formally employed workers, labor regulations matter—they protect their rights, reduce the risk of job loss, and promote equity and welfare. For workers employed in the informal sector, labor regulations can affect their ability to enter the formal workforce. If labor regulations make hiring costs too high or the rules too cumbersome, firms may opt to use more capital than labor or to hire informally. Sound and balanced labor regulations are essential for ensuring that both firms and workers benefit from a dynamic and innovative labor market without compromising income security or basic workers' rights. In addition, public services may address market imperfections and have important implications for the functioning of the labor market and firm choices." }, { "id": "IndicatorName", "value": "B-READY: Labor: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Labor topic measures good practices in employment regulations and public services from the perspective of both enterprises and employees across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.LB.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Labor is arguably the most important factor of production in most businesses and the primary source of income for most people. Regulations and public services related to labor are fundamental drivers of private sector development from the perspective of both enterprises and workers. These regulations and public services affect firms’ decisions on whether to expand by hiring labor, and whether to do so formally or informally. In addition, they impact the well-being of potential workers by providing them with good jobs and opportunities for growth.\n\nFor formally employed workers, labor regulations matter—they protect their rights, reduce the risk of job loss, and promote equity and welfare. For workers employed in the informal sector, labor regulations can affect their ability to enter the formal workforce. If labor regulations make hiring costs too high or the rules too cumbersome, firms may opt to use more capital than labor or to hire informally. Sound and balanced labor regulations are essential for ensuring that both firms and workers benefit from a dynamic and innovative labor market without compromising income security or basic workers' rights. In addition, public services may address market imperfections and have important implications for the functioning of the labor market and firm choices." }, { "id": "IndicatorName", "value": "B-READY: Labor Pillar 1: Quality of Labor Regulations" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Labor topic measures good practices in employment regulations and public services from the perspective of both enterprises and employees across three different dimensions, or pillars. The first pillar assesses the quality of labor regulations pertaining to workers' conditions and employment restrictions and costs, covering de jure features of the regulatory framework that are necessary for the functioning of the labor market and to provide employers and employees with their obligations and relevant safeguards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.LB.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Labor is arguably the most important factor of production in most businesses and the primary source of income for most people. Regulations and public services related to labor are fundamental drivers of private sector development from the perspective of both enterprises and workers. These regulations and public services affect firms’ decisions on whether to expand by hiring labor, and whether to do so formally or informally. In addition, they impact the well-being of potential workers by providing them with good jobs and opportunities for growth.\n\nFor formally employed workers, labor regulations matter—they protect their rights, reduce the risk of job loss, and promote equity and welfare. For workers employed in the informal sector, labor regulations can affect their ability to enter the formal workforce. If labor regulations make hiring costs too high or the rules too cumbersome, firms may opt to use more capital than labor or to hire informally. Sound and balanced labor regulations are essential for ensuring that both firms and workers benefit from a dynamic and innovative labor market without compromising income security or basic workers' rights. In addition, public services may address market imperfections and have important implications for the functioning of the labor market and firm choices." }, { "id": "IndicatorName", "value": "B-READY: Labor Pillar 2: Adequacy of Public Services for Labor" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Labor topic measures good practices in employment regulations and public services from the perspective of both enterprises and employees across three different dimensions, or pillars. The second pillar measures the adequacy of public services for labor, assessing the de facto provision of social protection and the institutional framework on which the labor market and the enforcement of labor regulations depend." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.LB.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Labor is arguably the most important factor of production in most businesses and the primary source of income for most people. Regulations and public services related to labor are fundamental drivers of private sector development from the perspective of both enterprises and workers. These regulations and public services affect firms’ decisions on whether to expand by hiring labor, and whether to do so formally or informally. In addition, they impact the well-being of potential workers by providing them with good jobs and opportunities for growth.\n\nFor formally employed workers, labor regulations matter—they protect their rights, reduce the risk of job loss, and promote equity and welfare. For workers employed in the informal sector, labor regulations can affect their ability to enter the formal workforce. If labor regulations make hiring costs too high or the rules too cumbersome, firms may opt to use more capital than labor or to hire informally. Sound and balanced labor regulations are essential for ensuring that both firms and workers benefit from a dynamic and innovative labor market without compromising income security or basic workers' rights. In addition, public services may address market imperfections and have important implications for the functioning of the labor market and firm choices." }, { "id": "IndicatorName", "value": "B-READY: Labor Pillar 3: Operational Efficiency of Labor Regulations and Public Services in Practice" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Labor topic measures good practices in employment regulations and public services from the perspective of both enterprises and employees across three different dimensions, or pillars. The third pillar measures the operational efficiency of labor regulations and public services in practice, assessing employment restrictions and cost, as well as public services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.MC.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "There is substantial economic evidence that a fair level of market competition spurs economic growth by increasing industry and firm innovation and productivity, leading to better products, more and better jobs, and higher incomes. By affecting market entry and exit, competition stimulates product innovation and service quality, protects consumers, and forces market operators to provide their products and services at cost. But competition is rarely perfect. Markets fail either due to firms’ behaviors or government interventions. Market power—a firm’s ability to raise prices well above cost, offer a low-quality good or service, and drive out competition—must be kept in check." }, { "id": "IndicatorName", "value": "B-READY: Market Competition: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Market Competition topic measures good practices related to the enforcement of competition policy, intellectual property rights and innovation policy, and regulations that focus on improving competition and innovation in markets where the government is a purchaser of services or goods, across the three different pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.MC.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "There is substantial economic evidence that a fair level of market competition spurs economic growth by increasing industry and firm innovation and productivity, leading to better products, more and better jobs, and higher incomes. By affecting market entry and exit, competition stimulates product innovation and service quality, protects consumers, and forces market operators to provide their products and services at cost. But competition is rarely perfect. Markets fail either due to firms’ behaviors or government interventions. Market power—a firm’s ability to raise prices well above cost, offer a low-quality good or service, and drive out competition—must be kept in check." }, { "id": "IndicatorName", "value": "B-READY: Market Competition Pillar 1: Quality of Regulations that Promote Market Competition" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Market Competition topic measures good practices related to the enforcement of competition policy, intellectual property rights and innovation policy, and regulations that focus on improving competition and innovation in markets where the government is a purchaser of services or goods, across the three different pillars. The first pillar assesses the quality of regulations that promote market competition, covering de jure features of a regulatory framework that enable firms to participate in fair market conditions and innovate, and where firms can participate in open and competitive government markets." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.MC.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "There is substantial economic evidence that a fair level of market competition spurs economic growth by increasing industry and firm innovation and productivity, leading to better products, more and better jobs, and higher incomes. By affecting market entry and exit, competition stimulates product innovation and service quality, protects consumers, and forces market operators to provide their products and services at cost. But competition is rarely perfect. Markets fail either due to firms’ behaviors or government interventions. Market power—a firm’s ability to raise prices well above cost, offer a low-quality good or service, and drive out competition—must be kept in check." }, { "id": "IndicatorName", "value": "B-READY: Market Competition Pillar 2: Public Services that Promote Market Competition" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Market Competition topic measures good practices related to the enforcement of competition policy, intellectual property rights and innovation policy, and regulations that focus on improving competition and innovation in markets where the government is a purchaser of services or goods, across the three different pillars. The second pillar measures the adequacy of public services that promote market competition, thus assessing the de facto provision of services that create an equal level of playing field in markets, and that foster and promote innovation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.MC.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "There is substantial economic evidence that a fair level of market competition spurs economic growth by increasing industry and firm innovation and productivity, leading to better products, more and better jobs, and higher incomes. By affecting market entry and exit, competition stimulates product innovation and service quality, protects consumers, and forces market operators to provide their products and services at cost. But competition is rarely perfect. Markets fail either due to firms’ behaviors or government interventions. Market power—a firm’s ability to raise prices well above cost, offer a low-quality good or service, and drive out competition—must be kept in check." }, { "id": "IndicatorName", "value": "B-READY: Market Competition Pillar 3: Implementation of Key Services Promoting Market Competition" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Market Competition topic measures good practices related to the enforcement of competition policy, intellectual property rights and innovation policy, and regulations that focus on improving competition and innovation in markets where the government is a purchaser of services or goods, across the three different pillars. The third pillar measures the operational efficiency in the implementation of key services promoting market competition (reflecting both the ease of compliance with the regulatory framework and the effective provision of public services directly relevant to firms that contribute in practice to the promotion of market competition)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.P1.RF", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The business environment can be defined as the set of conditions outside a firm’s control that have a significant influence on how businesses behave and perform throughout their life cycle. This set of conditions can be very large, from macroeconomic policy to microeconomic rules. B-READY concentrates on the regulatory framework and public service provision at the microeconomic level: that is, as enacted and implemented to directly affect firms’ behavior and performance. B-READY assesses an economy’s business environment by focusing on the regulatory framework and the provision of related public services directed at firms and markets, as well as the efficiency with which regulatory framework and public services are combined in practice. B-READY seeks a balanced approach when assessing the business environment: between ease of conducting a business and broader private sector benefits, between regulatory framework and public services, between de jure laws and regulations and de facto practical implementation, and between data representativeness and data comparability. For nearly all indicators, the regulatory framework pillar captures de jure information, and the public services and efficiency pillars capture de facto information." }, { "id": "IndicatorName", "value": "B-READY: Pillar 1: Regulatory Framework" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY assesses the economy’s business environment by focusing on the regulatory framework and the provision of related public services for firms and markets, as well as the operational efficiency with which they are combined in practice. B-READY’s three pillars—the Regulatory Framework, Public Services, and Operational Efficiency. The Regulatory Framework comprises the rules and regulations that firms must follow as they open, operate, and close a business." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.P2.PS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The business environment can be defined as the set of conditions outside a firm’s control that have a significant influence on how businesses behave and perform throughout their life cycle. This set of conditions can be very large, from macroeconomic policy to microeconomic rules. B-READY concentrates on the regulatory framework and public service provision at the microeconomic level: that is, as enacted and implemented to directly affect firms’ behavior and performance. B-READY assesses an economy’s business environment by focusing on the regulatory framework and the provision of related public services directed at firms and markets, as well as the efficiency with which regulatory framework and public services are combined in practice. B-READY seeks a balanced approach when assessing the business environment: between ease of conducting a business and broader private sector benefits, between regulatory framework and public services, between de jure laws and regulations and de facto practical implementation, and between data representativeness and data comparability. For nearly all indicators, the regulatory framework pillar captures de jure information, and the public services and efficiency pillars capture de facto information." }, { "id": "IndicatorName", "value": "B-READY: Pillar 2: Public Services" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY assesses the economy’s business environment by focusing on the regulatory framework and the provision of related public services for firms and markets, as well as the operational efficiency with which they are combined in practice. B-READY’s three pillars—the Regulatory Framework, Public Services, and Operational Efficiency. Public Services refers to both the facilities that governments provide directly or through private firms to support compliance with regulations and the critical institutions and infrastructure that enable business activities. Public services considered by B-READY are limited to the scope of the business environment areas related to the life cycle of the firm." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.P3.OE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The business environment can be defined as the set of conditions outside a firm’s control that have a significant influence on how businesses behave and perform throughout their life cycle. This set of conditions can be very large, from macroeconomic policy to microeconomic rules. B-READY concentrates on the regulatory framework and public service provision at the microeconomic level: that is, as enacted and implemented to directly affect firms’ behavior and performance. B-READY assesses an economy’s business environment by focusing on the regulatory framework and the provision of related public services directed at firms and markets, as well as the efficiency with which regulatory framework and public services are combined in practice. B-READY seeks a balanced approach when assessing the business environment: between ease of conducting a business and broader private sector benefits, between regulatory framework and public services, between de jure laws and regulations and de facto practical implementation, and between data representativeness and data comparability. For nearly all indicators, the regulatory framework pillar captures de jure information, and the public services and efficiency pillars capture de facto information." }, { "id": "IndicatorName", "value": "B-READY: Pillar 3: Operational Efficiency" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY assesses the economy’s business environment by focusing on the regulatory framework and the provision of related public services for firms and markets, as well as the operational efficiency with which they are combined in practice. B-READY’s three pillars—the Regulatory Framework, Public Services, and Operational Efficiency. Operational Efficiency comprises both the ease of compliance with the regulatory framework and the effective use of public services directly relevant to firms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.TX.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Taxation is a powerful policy tool that governments use to generate revenue to finance their operations and provide public goods and services. Taxation affects the private sector development through a variety of interrelated channels. On the one hand, it creates enabling conditions for the growth and development of the private sector by financing critical infrastructure, investing in human capital, ensuring law enforcement, and supporting other vital services. On the other hand, excessive taxation can distort markets, impair investment decisions, and foster tax evasion. Likewise, cumbersome regulations, complex tax reporting requirements, and inefficient and inconsistent tax procedures increase compliance costs on firms, thereby discouraging formalization. Identifying the key challenges faced by taxpayers is essential for guiding reforms that support private sector development while pursuing domestic resource mobilization objectives." }, { "id": "IndicatorName", "value": "B-READY: Taxation: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Taxation topic measures the quality of regulation, administration, and practical implementation of tax systems across the three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.TX.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Taxation is a powerful policy tool that governments use to generate revenue to finance their operations and provide public goods and services. Taxation affects the private sector development through a variety of interrelated channels. On the one hand, it creates enabling conditions for the growth and development of the private sector by financing critical infrastructure, investing in human capital, ensuring law enforcement, and supporting other vital services. On the other hand, excessive taxation can distort markets, impair investment decisions, and foster tax evasion. Likewise, cumbersome regulations, complex tax reporting requirements, and inefficient and inconsistent tax procedures increase compliance costs on firms, thereby discouraging formalization. Identifying the key challenges faced by taxpayers is essential for guiding reforms that support private sector development while pursuing domestic resource mobilization objectives." }, { "id": "IndicatorName", "value": "B-READY: Taxation Pillar 1: Quality of Regulations on Taxation" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Taxation topic measures the quality of regulation, administration, and practical implementation of tax systems across the three different dimensions, or pillars. The first pillar assesses the quality of regulation related to taxation, encompassing both the legal framework (de jure) and the implementation (de facto) of the legal requirements." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.TX.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Taxation is a powerful policy tool that governments use to generate revenue to finance their operations and provide public goods and services. Taxation affects the private sector development through a variety of interrelated channels. On the one hand, it creates enabling conditions for the growth and development of the private sector by financing critical infrastructure, investing in human capital, ensuring law enforcement, and supporting other vital services. On the other hand, excessive taxation can distort markets, impair investment decisions, and foster tax evasion. Likewise, cumbersome regulations, complex tax reporting requirements, and inefficient and inconsistent tax procedures increase compliance costs on firms, thereby discouraging formalization. Identifying the key challenges faced by taxpayers is essential for guiding reforms that support private sector development while pursuing domestic resource mobilization objectives." }, { "id": "IndicatorName", "value": "B-READY: Taxation Pillar 2: Public Services Provided by the Tax Administration" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Taxation topic measures the quality of regulation, administration, and practical implementation of tax systems across the three different dimensions, or pillars. The second pillar measures the quality of tax administration by assessing the public services related to tax matters." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.TX.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Taxation is a powerful policy tool that governments use to generate revenue to finance their operations and provide public goods and services. Taxation affects the private sector development through a variety of interrelated channels. On the one hand, it creates enabling conditions for the growth and development of the private sector by financing critical infrastructure, investing in human capital, ensuring law enforcement, and supporting other vital services. On the other hand, excessive taxation can distort markets, impair investment decisions, and foster tax evasion. Likewise, cumbersome regulations, complex tax reporting requirements, and inefficient and inconsistent tax procedures increase compliance costs on firms, thereby discouraging formalization. Identifying the key challenges faced by taxpayers is essential for guiding reforms that support private sector development while pursuing domestic resource mobilization objectives." }, { "id": "IndicatorName", "value": "B-READY: Taxation Pillar 3: Operational Efficiency of Tax Systems in Practice" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Taxation topic measures the quality of regulation, administration, and practical implementation of tax systems across the three different dimensions, or pillars. The third pillar evaluates the practical effectiveness of the implemented tax regulations and public services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.US.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By providing essential services—electricity, water, and digital connectivity—utilities play an important role in supporting economic and social development. Without these services, businesses cannot function, and households cannot lead quality lives. Yet, more than 30 percent of businesses globally identify electricity supply as a major constraint to their operations, according to the World Bank Enterprise Surveys. Disruptions in electricity supply impair firm productivity, revenues, and economic growth. Similarly, inadequate water supply can lead to decreased firm productivity, deterioration of machinery, and reduced profits. Access to reliable internet is another critical element in today’s digitalized world, where the use of digital technologies improve productivity. However, as of 2021, just over 15 percent of people globally had fixed broadband subscriptions, and only 1.4 percent in the least developed countries. The provision of basic utility services should be effective and reliable. Facilitating timely access to such services in an environmentally sustainable manner is instrumental for economic growth.\n\nThe effectiveness of regulatory frameworks, good governance, transparency, and efficiency of utility services are pivotal elements of a good business environment. An effective regulatory framework, for example, is a fundamental steppingstone for the provision of high-quality utility services. Furthermore, the reliability and sustainability of utility services should be maintained through monitoring the quality of service supply and connection safety, fostering public accountability and safety. Interoperability through agency coordination and digitalization of utilities can also help improve the quality of public services and the customer experience." }, { "id": "IndicatorName", "value": "B-READY: Utility Services: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Utility Services topic measures the effectiveness of regulatory frameworks, and the quality of governance and transparency of service delivery mechanisms, as well as the operational efficiency of providing electricity, water, and internet services. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.US.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By providing essential services—electricity, water, and digital connectivity—utilities play an important role in supporting economic and social development. Without these services, businesses cannot function, and households cannot lead quality lives. Yet, more than 30 percent of businesses globally identify electricity supply as a major constraint to their operations, according to the World Bank Enterprise Surveys. Disruptions in electricity supply impair firm productivity, revenues, and economic growth. Similarly, inadequate water supply can lead to decreased firm productivity, deterioration of machinery, and reduced profits. Access to reliable internet is another critical element in today’s digitalized world, where the use of digital technologies improve productivity. However, as of 2021, just over 15 percent of people globally had fixed broadband subscriptions, and only 1.4 percent in the least developed countries. The provision of basic utility services should be effective and reliable. Facilitating timely access to such services in an environmentally sustainable manner is instrumental for economic growth.\n\nThe effectiveness of regulatory frameworks, good governance, transparency, and efficiency of utility services are pivotal elements of a good business environment. An effective regulatory framework, for example, is a fundamental steppingstone for the provision of high-quality utility services. Furthermore, the reliability and sustainability of utility services should be maintained through monitoring the quality of service supply and connection safety, fostering public accountability and safety. Interoperability through agency coordination and digitalization of utilities can also help improve the quality of public services and the customer experience." }, { "id": "IndicatorName", "value": "B-READY: Utility Services Pillar 1: Quality of Regulations on Utility Services" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Utility Services topic measures the effectiveness of regulatory frameworks, and the quality of governance and transparency of service delivery mechanisms, as well as the operational efficiency of providing electricity, water, and internet services. Under the first pillar the Utility Services topic assesses the effectiveness of regulation pertaining to electricity, water, and internet services, covering de jure features of a regulatory framework that are necessary for the efficient deployment of connections, reliable service, safety, and environmental sustainability of provision and use of utility services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.US.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By providing essential services—electricity, water, and digital connectivity—utilities play an important role in supporting economic and social development. Without these services, businesses cannot function, and households cannot lead quality lives. Yet, more than 30 percent of businesses globally identify electricity supply as a major constraint to their operations, according to the World Bank Enterprise Surveys. Disruptions in electricity supply impair firm productivity, revenues, and economic growth. Similarly, inadequate water supply can lead to decreased firm productivity, deterioration of machinery, and reduced profits. Access to reliable internet is another critical element in today’s digitalized world, where the use of digital technologies improve productivity. However, as of 2021, just over 15 percent of people globally had fixed broadband subscriptions, and only 1.4 percent in the least developed countries. The provision of basic utility services should be effective and reliable. Facilitating timely access to such services in an environmentally sustainable manner is instrumental for economic growth.\n\nThe effectiveness of regulatory frameworks, good governance, transparency, and efficiency of utility services are pivotal elements of a good business environment. An effective regulatory framework, for example, is a fundamental steppingstone for the provision of high-quality utility services. Furthermore, the reliability and sustainability of utility services should be maintained through monitoring the quality of service supply and connection safety, fostering public accountability and safety. Interoperability through agency coordination and digitalization of utilities can also help improve the quality of public services and the customer experience." }, { "id": "IndicatorName", "value": "B-READY: Utility Services Pillar 2: Quality of Governance and Transparency of Utility Services" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Utility Services topic measures the effectiveness of regulatory frameworks, and the quality of governance and transparency of service delivery mechanisms, as well as the operational efficiency of providing electricity, water, and internet services. The second pillar of the topic measures the quality of governance and transparency in the provision of utility services, thus assessing the de facto provision of utility services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BRE.US.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By providing essential services—electricity, water, and digital connectivity—utilities play an important role in supporting economic and social development. Without these services, businesses cannot function, and households cannot lead quality lives. Yet, more than 30 percent of businesses globally identify electricity supply as a major constraint to their operations, according to the World Bank Enterprise Surveys. Disruptions in electricity supply impair firm productivity, revenues, and economic growth. Similarly, inadequate water supply can lead to decreased firm productivity, deterioration of machinery, and reduced profits. Access to reliable internet is another critical element in today’s digitalized world, where the use of digital technologies improve productivity. However, as of 2021, just over 15 percent of people globally had fixed broadband subscriptions, and only 1.4 percent in the least developed countries. The provision of basic utility services should be effective and reliable. Facilitating timely access to such services in an environmentally sustainable manner is instrumental for economic growth.\n\nThe effectiveness of regulatory frameworks, good governance, transparency, and efficiency of utility services are pivotal elements of a good business environment. An effective regulatory framework, for example, is a fundamental steppingstone for the provision of high-quality utility services. Furthermore, the reliability and sustainability of utility services should be maintained through monitoring the quality of service supply and connection safety, fostering public accountability and safety. Interoperability through agency coordination and digitalization of utilities can also help improve the quality of public services and the customer experience." }, { "id": "IndicatorName", "value": "B-READY: Utility Services Pillar 3: Operational Efficiency of Utility Service Provision" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Utility Services topic measures the effectiveness of regulatory frameworks, and the quality of governance and transparency of service delivery mechanisms, as well as the operational efficiency of providing electricity, water, and internet services. The third pillar measures the time required to obtain electricity, water, and internet connections, as well as the reliability of utility service supply." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "2" }, { "id": "IC.BUS.NDNS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Entrepreneurial activity is a pillar of economic growth. The Entrepreneurship Database is a critical source of data that facilitates the measurement of entrepreneurial activity across countries and over time. The data also allow for a deeper understanding of the relationship between new firm registration, the regulatory environment, and economic growth. Previous research using the Entrepreneurship Database has shown a significant relationship between the cost of compliance required to start a business and new firm registration." }, { "id": "IndicatorName", "value": "New business density (new registrations per 1,000 people ages 15-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. Data collected from economies categorized as offshore financial centers by Eurostat are excluded from the analysis, because registered entities in these countries may not fit the objective of the Entrepreneurship Database aiming at measuring formally registered companies with actual economic activities. The information provided by these economies likely reflects a number of shell companies, defined as companies that are registered for tax purposes but are not economically active. The information on offshore centers is collected and published by the Entrepreneurship Database, but it is not used for trend analysis purposes." }, { "id": "Longdefinition", "value": "The number of newly registered firms with limited liability per 1,000 working-age people (ages 15-64) per calendar year." }, { "id": "Othernotes", "value": "For cross-country comparability, only limited liability corporations that operate in the formal sector are included." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2022" }, { "id": "Source", "value": "Entrepreneurship Database, World Bank (WB), uri: https://www.worldbank.org/en/programs/entrepreneurship" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Entrepreneurship Database has developed a data collection methodology to systematically measure entrepreneurial activity. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data – most often due to an absence of digitized registration systems – the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and publicly available data. \nStatistical concept(s): In order to measure entrepreneurship in a way that is universally comparable, the Entrepreneurship Database employs a methodology that can be applied across heterogeneous legal regimes and economic systems. The concept of entrepreneurship can cover a wide range of activities. For the purposes of this project, entrepreneurship is defined as the activities of an individual or a group of individuals aimed at initiating economic enterprise in the formal sector under a legal form of business.\n\nThe legal form of business refers to private companies with limited liability. Limited liability companies are those in which the financial liability of the firm’s members is limited to the value of their investment in the company. A limited liability company is a separate legal entity that has its own privileges and liabilities. Although the laws on business registration vary greatly across countries, the approach to legal entities is largely uniform: any business with a unique legal entity (or “corporate personhood”) separate from its owners must be registered." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Share (proportion) [SHARE]" } ], "source_id": "2" }, { "id": "IC.BUS.NREG", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Entrepreneurial activity is a pillar of economic growth. The Entrepreneurship Database is a critical source of data that facilitates the measurement of entrepreneurial activity across countries and over time. The data also allow for a deeper understanding of the relationship between new firm registration, the regulatory environment, and economic growth. Research using the Entrepreneurship Database has shown a significant relationship between the cost of compliance required to start a business and new firm registration." }, { "id": "IndicatorName", "value": "New businesses registered (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. Data collected from economies categorized as offshore financial centers by Eurostat are excluded from the analysis, because registered entities in these countries may not fit the objective of the Entrepreneurship Database aiming at measuring formally registered companies with actual economic activities. The information provided by these economies likely reflects a number of shell companies, defined as companies that are registered for tax purposes but are not economically active. The information on offshore centers is collected and published by the Entrepreneurship Database, but it is not used for trend analysis purposes." }, { "id": "Longdefinition", "value": "New businesses registered are the number of new limited liability corporations (or its equivalent) registered in the calendar year." }, { "id": "Othernotes", "value": "For cross-country comparability, only limited liability corporations that operate in the formal sector are included." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2022" }, { "id": "Source", "value": "Entrepreneurship Database, World Bank (WB), uri: https://www.worldbank.org/en/programs/entrepreneurship" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Entrepreneurship Database has developed a data collection methodology to systematically measure entrepreneurial activity. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data – most often due to an absence of digitized registration systems – the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and publicly available data. \nStatistical concept(s): In order to measure entrepreneurship in a way that is universally comparable, the Entrepreneurship Database employs a methodology that can be applied across heterogeneous legal regimes and economic systems. The concept of entrepreneurship can cover a wide range of activities. For the purposes of this project, entrepreneurship is defined as the activities of an individual or a group of individuals aimed at initiating economic enterprise in the formal sector under a legal form of business.\n\nThe legal form of business refers to private companies with limited liability. Limited liability companies are those in which the financial liability of the firm’s members is limited to the value of their investment in the company. A limited liability company is a separate legal entity that has its own privileges and liabilities. Although the laws on business registration vary greatly across countries, the approach to legal entities is largely uniform: any business with a unique legal entity (or “corporate personhood”) separate from its owners must be registered." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "IC.CUS.DURS.EX", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nOpen markets allow firms to expand, raise standards for efficiency on exporters, and enable firms to import low cost supplies. However, trading also forces firms to deal with customs services and trade regulations, obtain export and import licenses, and in some cases, firms also face additional costs due to losses during transport. The Enterprise Surveys collect information on the operational constraints faced by exporters and importers and quantifies the trade activity of firms. Indicators provide a measure of the intensity of foreign trade in the private sector." }, { "id": "IndicatorName", "value": "Average time to clear exports through customs (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average number of days to clear direct exports through customs." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Ad hoc [adhoc]" }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data, note: All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Number of days" } ], "source_id": "2" }, { "id": "IC.CUS.DURS.IM", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Average time to clear imports through customs (days)" }, { "id": "Longdefinition", "value": "Average number of days to clear imports from customs in the manufacturing sector." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data , note: All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "2" }, { "id": "IC.ELC.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nA strong infrastructure enhances the competitiveness of an economy and generates a business environment conducive to firm growth and development. Good infrastructure efficiently connects firms to their customers and suppliers, and enables the use of modern production technologies. Conversely, deficiencies in infrastructure create barriers to productive opportunities and increase costs for all firms, from micro enterprises to large multinational corporations. \n\nThe Enterprise Surveys capture the dual challenge of providing a strong infrastructure for electricity, water supply, internet connections, etc., and the development of institutions that effectively provide and maintain public services. These indicators show the extent to which firms are faced with failures in the provision of electricity and the effect of these failures on sales. Inadequate electricity supply can increase costs, disrupt production, and reduce profitability. Additionally, these indicators measure the efficiency of the water supply for the manufacturing sector. Many manufacturing sectors depend on reliable and efficient sources of water for their operations. The indicators can also be used to evaluate the efficiency of infrastructure services by quantifying the delays in obtaining electricity, water, and telephone connections. Service delays impose additional costs on firms and may act as barriers to entry and investment." }, { "id": "IndicatorName", "value": "Time to obtain an electrical connection (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average wait, in days, experienced to obtain electrical connection from the day this establishment applied for it to the day it received the service." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Number of days" } ], "source_id": "2" }, { "id": "IC.ELC.OUTG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nA strong infrastructure enhances the competitiveness of an economy and generates a business environment conducive to firm growth and development. Good infrastructure efficiently connects firms to their customers and suppliers, and enables the use of modern production technologies. Conversely, deficiencies in infrastructure create barriers to productive opportunities and increase costs for all firms, from micro enterprises to large multinational corporations. \n\nThe Enterprise Surveys capture the dual challenge of providing a strong infrastructure for electricity, water supply, internet connections, etc., and the development of institutions that effectively provide and maintain public services. These indicators show the extent to which firms are faced with failures in the provision of electricity and the effect of these failures on sales. Inadequate electricity supply can increase costs, disrupt production, and reduce profitability. Additionally, these indicators measure the efficiency of the water supply for the manufacturing sector. Many manufacturing sectors depend on reliable and efficient sources of water for their operations. The indicators can also be used to evaluate the efficiency of infrastructure services by quantifying the delays in obtaining electricity, water, and telephone connections. Service delays impose additional costs on firms and may act as barriers to entry and investment." }, { "id": "IndicatorName", "value": "Firms experiencing electrical outages (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms that experienced power outages over the last complete fiscal year." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.BKWC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators of how firms finance their operations and of the characteristics of their financial transactions. For example, Enterprise Surveys provide indicators that compare the relative use of various sources to finance investment. Excessive reliance on internal funds is a sign of potentially inefficient financial intermediation. Another set of indicators measures the use of financial markets by individual firms. It presents the percentage of working capital that is financed by external sources to the firm, and a measure of the burden imposed by loan requirements measured by collateral levels relative to the value of the loans. Additional indicators focus on the use of financial services by private firms both on the credit side, by measuring the percentage of firms with bank loans or lines or credit, and on the deposit mobilization side, by measuring the percentage of firms with checking or savings accounts." }, { "id": "IndicatorName", "value": "Firms using banks to finance working capital (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms using bank loans to finance working capital." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\n\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.BNKL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms with a bank loan/line of credit (% of firms)" }, { "id": "Longdefinition", "value": "Percentage of firms that have bank loans or line of credit." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.BNKS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators of how firms finance their operations and of the characteristics of their financial transactions. For example, Enterprise Surveys provide indicators that compare the relative use of various sources to finance investment. Excessive reliance on internal funds is a sign of potentially inefficient financial intermediation. Another set of indicators measures the use of financial markets by individual firms. It presents the percentage of working capital that is financed by external sources to the firm, and a measure of the burden imposed by loan requirements measured by collateral levels relative to the value of the loans. Additional indicators focus on the use of financial services by private firms both on the credit side, by measuring the percentage of firms with bank loans or lines or credit, and on the deposit mobilization side, by measuring the percentage of firms with checking or savings accounts." }, { "id": "IndicatorName", "value": "Firms using banks to finance investment (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms using banks to finance purchases of fixed assets." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\n\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.BRIB.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nCorruption by public officials may present a major administrative and financial burden on firms. Corruption creates an unfavorable business environment by undermining the operational efficiency of firms and raising the costs and risks associated with doing business.\n\nInefficient regulations constrain firm efficiency as they present opportunities for soliciting bribes where firms are required to make “unofficial” payments to public officials to get things done. In many countries bribes are common and quite high and they add to the bureaucratic costs in obtaining required permits and licenses. They can be a serious impediment for firms’ growth and development." }, { "id": "IndicatorName", "value": "Bribery incidence (% of firms experiencing at least one bribe payment request)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percent of firms experiencing at least one bribe payment request across 6 public transactions dealing with utilities access, permits, licenses, and taxes." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.CDP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms that use a third party to resolve commercial disputes (% of firms)" }, { "id": "Longdefinition", "value": "Percentage of firms that use courts, arbitration, mediation, or conciliation to resolve or attempt to resolve its commercial disputes." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2021-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.CMPU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nA large informal sector has serious consequences for the formal private sector. The informal sector may pose unfair competition for formal firms and also deprive governments of potential tax revenue and diminish a government's capacity for regulatory oversight. The Enterprise Surveys capture key dimensions the degree of informality in an economy. For example, the set of indicators (unregistered start-ups) shows the percentage of firms that started operation without being formally registered. It approximates the prevalence of informality in the private economy." }, { "id": "IndicatorName", "value": "Firms competing against unregistered firms (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms competing against unregistered or informal firms." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.CO2.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms monitoring own CO2 emissions (% of firms)" }, { "id": "Longdefinition", "value": "Percentage of firms tracking their own CO2 emissions over the past three years" }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2021-2025" }, { "id": "Source", "value": "Enterprise Surveys , World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.CORR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nCorruption by public officials may present a major administrative and financial burden on firms. Corruption creates an unfavorable business environment by undermining the operational efficiency of firms and raising the costs and risks associated with doing business.\n\nInefficient regulations constrain firm efficiency as they present opportunities for soliciting bribes where firms are required to make “unofficial” payments to public officials to get things done. In many countries bribes are common and quite high and they add to the bureaucratic costs in obtaining required permits and licenses. They can be a serious impediment for firms’ growth and development." }, { "id": "IndicatorName", "value": "Informal payments to public officials (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider that firms with characteristics similar to theirs are making informal payments or giving gifts to public officials to \"get things done” with regard to customs, taxes, licenses, regulations, services etc." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\n\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.CRDC.FL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms that are fully credit constrained (% of firms)" }, { "id": "Longdefinition", "value": "Firms are categorized as fully credit constrained if they do not have access to external finance, and any of the following two conditions are met: (1) the firm did not apply for a loan for any reason other than the lack of need for it; or (2) the firm applied for a loan but the application was rejected, even when it has access to equity financing. \n\nThis indicator is based on Islam and Rodriguez Meza (2023, Islam, Asif Mohammed and Jorge Luis Rodriguez Meza. “How Prevalent Are Credit-Constrained Firms in the Formal Private Sector? Evidence Using Global Surveys”. World Bank Policy Research Working Paper; no. WPS 10502)." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2013-2025" }, { "id": "Source", "value": "Enterprise Surveys , World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.CRDC.PT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms that are partially credit constrained (% of firms)" }, { "id": "Longdefinition", "value": "Firms are categorized as partially credit constrained if any of the following conditions are met: (1) the firm applied for a loan and the application was partially approved; (2) the firm applied for a loan and the application was rejected, but the firm has access to external sources of finance excluding any equity finance; or (3) the firm has external finance but did not apply for a loan due to any reason other than no need for it. \n\nThis indicator is based on Islam and Rodriguez Meza (2023, Islam, Asif Mohammed and Jorge Luis Rodriguez Meza. “How Prevalent Are Credit-Constrained Firms in the Formal Private Sector? Evidence Using Global Surveys”. World Bank Policy Research Working Paper; no. WPS 10502)." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2013-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nGood economic governance in areas of regulations, permits and licenses are among the fundamental pillars for the creation of a favorable business environment. \n\nThe Enterprise Surveys provide qualitative and quantitative measures of regulations. For example, the Enterprise Surveys approximates the “time tax” imposed by regulations: it measures the time spent by senior management in meetings with public officials. Another indicator, the average number of visits or required meetings with tax officials, measures the average number of tax inspections or meetings with tax inspectors in each year. \n\nEffective regulations address market failures that inhibit productive investment and reconcile private and public interests. The number of permits and approvals that businesses need to obtain, and the time it takes to obtain them, are expensive and time consuming. The existing legislation of a country also determines the mix of legal forms private firms take and determines the level of protection for investors thus affecting the incentives to invest. Those indicators focus on the efficiency of business licensing and permit services. The indicators evaluate the delays faced when demanding these services." }, { "id": "IndicatorName", "value": "Time required to obtain an operating license (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The average wait, in days, to obtain an operating license, from the day of the application to the day it was granted." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2003-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "IC.FRM.ENGM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms adopting energy management measures to reduce emissions (% of firms)" }, { "id": "Longdefinition", "value": "Share of companies adopting energy-saving practices to reduce emissions over the past three years." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2021-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.EXS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms exporting directly at least 10% of sales (% of firms)" }, { "id": "Longdefinition", "value": "Percentage of firms that export directly at least 10% of their total annual sales." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.FEMM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators that describe several dimensions of gender composition in the workforce. It also collects information on the characteristics of the workforce employed in the non-agricultural private economy. The set of indicators presents the composition of the firm's workforce by type of contract and gender. Labor regulations have a direct effect on the type of employment favored by firms and they may have a different impact by gender. Other indicators present the composition of the workforce classified into temporary and permanent workers and reflect the participation of women in regular full time employment, along with the firms’ inclusion of women in formal trainings." }, { "id": "IndicatorName", "value": "Firms with female top manager (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms with females as the top manager." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity. \n\nRelevance to gender indicator: Women are vastly underrepresented in decision making positions at the top level in the private sector and this indicator monitors progress that has been made." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2007-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.FEMO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators that describe several dimensions of gender composition in the workforce. It also collects information on the characteristics of the workforce employed in the non-agricultural private economy. The set of indicators presents the composition of the firm's workforce by type of contract and gender. Labor regulations have a direct effect on the type of employment favored by firms and they may have a different impact by gender. Other indicators present the composition of the workforce classified into temporary and permanent workers and reflect the participation of women in regular full time employment, along with the firms’ inclusion of women in formal trainings." }, { "id": "IndicatorName", "value": "Firms with female participation in ownership (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms with females among the owners." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.FO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms with at least 10% foreign ownership" }, { "id": "Longdefinition", "value": "Percentage of firms that have at least 10% owned by private foreign individuals, companies or organizations." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.FREG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nA large informal sector has serious consequences for the formal private sector. The informal sector may pose unfair competition for formal firms and also deprive governments of potential tax revenue and diminish a government's capacity for regulatory oversight. The Enterprise Surveys capture key dimensions the degree of informality in an economy. For example, the set of indicators (unregistered start-ups) shows the percentage of firms that started operation without being formally registered. It approximates the prevalence of informality in the private economy." }, { "id": "IndicatorName", "value": "Firms formally registered when operations started (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms formally registered when they started operations in the country." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.LOTM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms where largest owner is also the top manager (% of firms)" }, { "id": "Longdefinition", "value": "Percent of firms where the largest owner is also the top manager." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2009-2025" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.METG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms visited or required meetings with tax officials (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms that were visited or inspected by tax officials or were required to meet with them over the last year." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Ad hoc [adhoc]" }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.NPRD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms that introduced a new product/service and process, and spent on R&D" }, { "id": "Longdefinition", "value": "Percentage of medium and large firms that introduced a new product/service and process over last 3 years, and spent on R&D over last fiscal year (excluding small firms)." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.OUTG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nA strong infrastructure enhances the competitiveness of an economy and generates a business environment conducive to firm growth and development. Good infrastructure efficiently connects firms to their customers and suppliers, and enables the use of modern production technologies. Conversely, deficiencies in infrastructure create barriers to productive opportunities and increase costs for all firms, from micro enterprises to large multinational corporations. \n\nThe Enterprise Surveys capture the dual challenge of providing a strong infrastructure for electricity, water supply, internet connections, etc., and the development of institutions that effectively provide and maintain public services. These indicators show the extent to which firms are faced with failures in the provision of electricity and the effect of these failures on sales. Inadequate electricity supply can increase costs, disrupt production, and reduce profitability. Additionally, these indicators measure the efficiency of the water supply for the manufacturing sector. Many manufacturing sectors depend on reliable and efficient sources of water for their operations. The indicators can also be used to evaluate the efficiency of infrastructure services by quantifying the delays in obtaining electricity, water, and telephone connections. Service delays impose additional costs on firms and may act as barriers to entry and investment." }, { "id": "IndicatorName", "value": "Value lost due to electrical outages (% of sales for affected firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Losses due to electrical outages, as percentage of total annual sales. The value represents average losses for all firms which reported outages (please see indicator IC.ELC.OUTG.ZS)." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.TAXE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms filling taxes electronically (% of firms)" }, { "id": "Longdefinition", "value": "Percentage of firms filing taxes electronically either fully or partially." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2021-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.FRM.TRNG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators that describe information on the characteristics of the workforce employed in the non-agricultural private economy. The set of indicators presents the composition of the firm's workforce by type of contract and gender, the composition of the workforce classified into temporary and permanent workers, and reflects the participation of women in regular full-time employment. Labor regulations have a direct effect on the type of employment favored by firms and they may have a different impact by gender." }, { "id": "IndicatorName", "value": "Firms offering formal training (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms offering formal training programs for its permanent, full-time employees." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Ad hoc [adhoc]" }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.GOV.DURS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nGood economic governance in areas of regulations, permits and licenses are among the fundamental pillars for the creation of a favorable business environment. \n\nThe Enterprise Surveys provide qualitative and quantitative measures of regulations. For example, the Enterprise Surveys approximates the “time tax” imposed by regulations: it measures the time spent by senior management in meetings with public officials. Another indicator, the average number of visits or required meetings with tax officials, measures the average number of tax inspections or meetings with tax inspectors in each year. \n\nEffective regulations address market failures that inhibit productive investment and reconcile private and public interests. The number of permits and approvals that businesses need to obtain, and the time it takes to obtain them, are expensive and time consuming. The existing legislation of a country also determines the mix of legal forms private firms take and determines the level of protection for investors thus affecting the incentives to invest. Those indicators focus on the efficiency of business licensing and permit services. The indicators evaluate the delays faced when demanding these services." }, { "id": "IndicatorName", "value": "Time spent dealing with the requirements of government regulations (% of senior management time)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average percentage of senior management’s time that is spent in a typical week dealing with requirements imposed by government regulations (eg. Taxes, customs, labor regulations, licensing and registration), including dealings with officials, completing forms, et cetera." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.MNG.IND", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Management practices index" }, { "id": "Longdefinition", "value": "Average management practices index for medium and large firms: composite index that combines information from eight management practices indicators included in the Enterprise Surveys.\n\nThe Enterprise Surveys provide indicators that describe several dimensions of management practices. These indicators measure the extent to which firms implement better practices such as taking long-term actions to fix and avoid problems in production or service-delivery; number, time-horizon, and other features of production of service-provision targets; use of bonuses or promotion to reward better performance, and demotion to limit under-performance." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2018-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Index 0-100" } ], "source_id": "2" }, { "id": "IC.TAX.GIFT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nCorruption by public officials may present a major administrative and financial burden on firms. Corruption creates an unfavorable business environment by undermining the operational efficiency of firms and raising the costs and risks associated with doing business.\n\nInefficient regulations constrain firm efficiency as they present opportunities for soliciting bribes where firms are required to make “unofficial” payments to public officials to get things done. In many countries bribes are common and quite high and they add to the bureaucratic costs in obtaining required permits and licenses. They can be a serious impediment for firms’ growth and development." }, { "id": "IndicatorName", "value": "Firms expected to give gifts in meetings with tax officials (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms expected to give gifts or informal payments during meetings with tax officials." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "IC.TAX.METG", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Number of visits or required meetings with tax officials (average for affected firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average number of visits or required meetings with tax officials." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Ad hoc [adhoc]" }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "2" }, { "id": "IE.PPI.ENGY.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investment in infrastructure projects with private participation has made important contributions to easing fiscal constraints, improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, pioneering better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure." }, { "id": "IndicatorName", "value": "Investment in energy with private participation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nInvestment commitments are the sum of investments in physical assets and payments to the government. Investments in physical assets are resources the project company commits to invest during the contract period in new facilities or in expansion and modernization of existing facilities. Payments to the government are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "The Private Participation in Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Investment in energy projects with private participation refers to commitments to infrastructure projects in energy (electricity and natural gas: generation, transmission and distribution) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2023" }, { "id": "Source", "value": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A team of researchers gather data for each of the regions using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit’s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial’s Project Finance International, Euromoney’s Project Finance, Media Analytics’ Global Water Intelligence, Pisent Masons’ Water Yearbooks, and Platt’s Power in Asia, etc.), specialist portal (such as Privatization, IPAnet, and Privatization Barometer), Internet resources (such as web sites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\n\nStatistical concept(s): PPPs is defined as “any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.”\nThe term infrastructure refers to:\n• Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n• Information and communications technology (ICT): ICT backbone infrastructure\n• Transport: Airports, railways, ports, and roads.\n• Water: potable water treatment and distribution, and sewerage collection and treatment." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" }, { "id": "Unitofmeasure", "value": "USD current [USD_CUR]" } ], "source_id": "2" }, { "id": "IE.PPI.ICTI.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investment in infrastructure projects with private participation has made important contributions to improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, looking for better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure." }, { "id": "IndicatorName", "value": "Investment in ICT with private participation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment commitments (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported. Data are available 2015 onwards only for ICT." }, { "id": "Longdefinition", "value": "The Private Participation in Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Investment in ICT projects with private participation refers to commitments to infrastructure projects in ICT (including land based and submarine cables except purely private telecoms. Instead, it will track ICT backbone infrastructure (fiber optic cables etc) that has an active government component) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: http://ppi.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A team of researchers gather data for each of the regions using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit’s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial’s Project Finance International, Euromoney’s Project Finance, Media Analytics’ Global Water Intelligence, Pisent Masons’ Water Yearbooks, and Platt’s Power in Asia, etc.), specialist portal (such as Privatization, IPAnet, and Privatization Barometer), Internet resources (such as web sites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\n\nStatistical concept(s): PPPs is defined as “any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.”\nThe term infrastructure refers to:\n• Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n• Information and communications technology (ICT): ICT backbone infrastructure\n• Transport: Airports, railways, ports, and roads.\n• Water: potable water treatment and distribution, and sewerage collection and treatment." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" }, { "id": "Unitofmeasure", "value": "USD current [USD_CUR]" } ], "source_id": "2" }, { "id": "IE.PPI.TRAN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investment in infrastructure projects with private participation has made important contributions to easing fiscal constraints, improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, pioneering better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure." }, { "id": "IndicatorName", "value": "Investment in transport with private participation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "The Private Participation In Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Investment in transport projects with private participation refers to commitments to infrastructure projects in transport [(a) airport runways and terminals, (b) railways (including fixed assets, freight, intercity passenger, and local passenger); (c) toll roads, bridges, highways, and tunnels, (d) port infrastructure, superstructures, terminals, and channels)] that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2023" }, { "id": "Source", "value": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A team of researchers gather data for each of the regions using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit’s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial’s Project Finance International, Euromoney’s Project Finance, Media Analytics’ Global Water Intelligence, Pisent Masons’ Water Yearbooks, and Platt’s Power in Asia, etc.), specialist portal (such as Privatization, IPAnet, and Privatization Barometer), Internet resources (such as web sites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their Web sites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\n\nStatistical concept(s): PPPs is defined as “any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.”\nThe term infrastructure refers to:\n• Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n• Information and communications technology (ICT): ICT backbone infrastructure\n• Transport: Airports, railways, ports, and roads.\n• Water: potable water treatment and distribution, and sewerage collection and treatment." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" }, { "id": "Unitofmeasure", "value": "USD current [USD_CUR]" } ], "source_id": "2" }, { "id": "IE.PPI.WATR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investment in infrastructure projects with private participation has made important contributions to easing fiscal constraints, improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, pioneering better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure." }, { "id": "IndicatorName", "value": "Investment in water and sanitation with private participation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "The Private Participation in Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Investment in water projects with private participation refers to commitments to infrastructure projects in potable water (treatment and distribution, and sewerage collection and treatment that has an active government component) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1987-2023" }, { "id": "Source", "value": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A team of researchers gather data for each of the regions using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit’s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial’s Project Finance International, Euromoney’s Project Finance, Media Analytics’ Global Water Intelligence, Pisent Masons’ Water Yearbooks, and Platt’s Power in Asia, etc.), specialist portal (such as Privatization, IPAnet, and Privatization Barometer), Internet resources (such as web sites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their Websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template to make sure data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then for the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\nStatistical concept(s): PPPs is defined as “any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.”\nThe term infrastructure refers to:\n• Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n• Information and communications technology (ICT): ICT backbone infrastructure\n• Transport: Airports, railways, ports, and roads.\n• Water: potable water treatment and distribution, and sewerage collection and treatment." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" }, { "id": "Unitofmeasure", "value": "USD current [USD_CUR]" } ], "source_id": "2" }, { "id": "IE.PPN.ENGY.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure. Investment in infrastructure projects with private participation has made important contributions to improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, looking for better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth." }, { "id": "IndicatorName", "value": "Public private partnerships investment in energy (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment commitments (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "The Private Participation In Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Public private partnerships in energy refers to commitments to infrastructure projects in energy (electricity and natural gas: generation, transmission, and distribution) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, and greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility). Divestitures and merchant projects are excluded. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) Payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2023" }, { "id": "Source", "value": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A team of researchers gather data using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit’s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial’s Project Finance International, Euromoney’s Project Finance, Media Analytics’ Global Water Intelligence, Pisent Masons’ Water Yearbooks, and Platt’s Power in Asia, etc.), specialist portals (such as Privatization, IPAnet, and Privatization Barometer), online resources (such as websites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\nStatistical concept(s): PPPs is defined as “any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.”\nThe term infrastructure refers to:\n• Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n• Information and communications technology (ICT): ICT backbone infrastructure\n• Transport: Airports, railways, ports, and roads.\n• Water: potable water treatment and distribution, and sewerage collection and treatment." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" }, { "id": "Unitofmeasure", "value": "USD current [USD_CUR]" } ], "source_id": "2" }, { "id": "IE.PPN.ICTI.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure. Investment in infrastructure projects with private participation has made important contributions to improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, looking for better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth." }, { "id": "IndicatorName", "value": "Public private partnerships investment in ICT (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment commitments (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "The Private Participation in Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Public private partnerships in ICT refers to commitments to projects in ICT backbone infrastructure (including land based and submarine cables) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, and greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility). It excludes divestitures and merchant projects. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) Payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars and available 2015 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A team of researchers gather data using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit’s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial’s Project Finance International, Euromoney’s Project Finance, Media Analytics’ Global Water Intelligence, Pisent Masons’ Water Yearbooks, and Platt’s Power in Asia, etc.), specialist portals (such as Privatization, IPAnet, and Privatization Barometer), online resources (such as websites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\nStatistical concept(s): PPPs is defined as “any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.”\nThe term infrastructure refers to:\n• Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n• Information and communications technology (ICT): ICT backbone infrastructure\n• Transport: Airports, railways, ports, and roads.\n• Water: potable water treatment and distribution, and sewerage collection and treatment." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" }, { "id": "Unitofmeasure", "value": "USD current" } ], "source_id": "2" }, { "id": "IE.PPN.TRAN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure. Investment in infrastructure projects with private participation has made important contributions to improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, looking for better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth." }, { "id": "IndicatorName", "value": "Public private partnerships investment in transport (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment commitments (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "The Private Participation In Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Public private partnerships in transport refers to commitments to infrastructure projects in transport [(a) airport runways and terminals, (b) railways (including fixed assets, freight, intercity passenger, and local passenger); (c) toll roads, bridges, highways, and tunnels, (d) port infrastructure, superstructures, terminals, and channels)] that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, and greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility). Divestitures and merchant projects are excluded. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) Payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2023" }, { "id": "Source", "value": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A team of researchers gather data using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit’s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial’s Project Finance International, Euromoney’s Project Finance, Media Analytics’ Global Water Intelligence, Pisent Masons’ Water Yearbooks, and Platt’s Power in Asia, etc.), specialist portals (such as Privatization, IPAnet, and Privatization Barometer), online resources (such as websites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\nStatistical concept(s): PPPs is defined as “any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.”\nThe term infrastructure refers to:\n• Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n• Information and communications technology (ICT): ICT backbone infrastructure\n• Transport: Airports, railways, ports, and roads.\n• Water: potable water treatment and distribution, and sewerage collection and treatment." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" }, { "id": "Unitofmeasure", "value": "USD current [USD_CUR]" } ], "source_id": "2" }, { "id": "IE.PPN.WATR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure. Investment in infrastructure projects with private participation has made important contributions to improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, looking for better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth." }, { "id": "IndicatorName", "value": "Public private partnerships investment in water and sanitation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment commitments (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "The Private Participation in Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Public private partnerships in water refers to commitments to infrastructure projects in potable water (treatment and distribution, and sewerage collection and treatment that has an active government component) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, and greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility). It excludes divestitures and merchant projects. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) Payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1987-2023" }, { "id": "Source", "value": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A team of researchers gather data using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit’s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial’s Project Finance International, Euromoney’s Project Finance, Media Analytics’ Global Water Intelligence, Pisent Masons’ Water Yearbooks, and Platt’s Power in Asia, etc.), specialist portals (such as Privatization, IPAnet, and Privatization Barometer), online resources (such as websites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\nStatistical concept(s): PPPs is defined as “any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.”\nThe term infrastructure refers to:\n• Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n• Information and communications technology (ICT): ICT backbone infrastructure\n• Transport: Airports, railways, ports, and roads.\n• Water: potable water treatment and distribution, and sewerage collection and treatment." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" }, { "id": "Unitofmeasure", "value": "USD current [USD_CUR]" } ], "source_id": "2" }, { "id": "IP.IDS.NRCT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The progress and well-being of humanity depend on our capacity to come up with new ideas and creations. Technological progress requires the development and application of new inventions, while a vibrant culture will constantly seek new ways to express itself. \n\nIntellectual property rights are also vital. Inventors, artists, scientists and businesses put a lot of time, money, energy and thought into developing their innovations and creations. To encourage them to do that, they need the chance to make a fair return on their investment. That means giving them rights to protect their intellectual property. Essentially, intellectual property rights such as copyright, patents and trademarks can be viewed like any other property right. They allow the creators or owners of IP to benefit from their work or from their investment in a creation by giving them control over how their property is used. \n\nIP rights have long been recognized within various legal systems. For example, patents to protect inventions were granted in Venice as far back as the fifteenth century. Modern initiatives to protect IP through international law started with the Paris Convention for the Protection of Industrial Property (1883) and the Berne Convention for the Protection of Literary and Artistic Works (1886). These days, there are more than 25 international treaties on IP administered by WIPO. IP rights are also safeguarded by Article 27 of the Universal Declaration of Human Rights.\n\nCreativity and inventiveness are vital. They spur economic growth, create new jobs and industries, and enhance the quality and enjoyment of life. The intellectual property system needs to balance the rights and interests of different groups: of creators and consumers; of businesses and their competitors; of high- and low-income countries. An efficient and fair IP system benefits everyone – including ordinary users and consumers.\n\nSome examples: (a) The multibillion-dollar film, recording, publishing and software industries – which bring pleasure to millions of people worldwide – would not thrive without copyright protection.\n(b) The patent system rewards researchers and inventors while also ensuring that they share their knowledge by making patent applications publicly available, which helps stimulate more innovation. (c) Trademark protection discourages counterfeiting, so businesses can compete on a level playing field and users can be confident they are buying the genuine article.\n\n(source: https://doi.org/10.34667/tind.42176)" }, { "id": "IndicatorName", "value": "Industrial design applications, nonresident, by count" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are based on information supplied to World Intellectual Property Organization (WIPO) by IP offices in annual surveys, supplemented by data in national IP office reports. Data may be missing for some offices or periods." }, { "id": "Longdefinition", "value": "Industrial design applications are applications to register an industrial design with a national or regional Intellectual Property (IP) offices and designations received by relevant offices through the Hague System. Non-resident application refers to an application filed with the IP office of or acting on behalf of a state or jurisdiction in which the first-named applicant in the application is not domiciled. Design count is used to render application data for industrial applications across offices comparable, as some offices follow a single-class/single-design filing system while other have a multiple class/design filing system." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2021" }, { "id": "Source", "value": "Statistics Database, World Intellectual Property Organization (WIPO), uri: www.wipo.int/ipstats/, note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Information about the World Intellectual Property Organization (WIPO) data collection can be accessed on the WIPO website: https://www.wipo.int/en/web/ip-statistics/about\n\nStatistical concept(s): Industrial designs are applied to a wide variety of industrial products and handicrafts. They refer to the ornamental or aesthetic aspects of a useful article, including compositions of lines or colors or any three-dimensional forms that give a special appearance to a product or handicraft. The holder of a registered industrial design has exclusive rights against unauthorized copying or imitation of the design by third parties. Industrial design registrations are valid for a limited period. The term of protection is usually 15 years in most jurisdictions. However, differences in legislation exist, notably in China (which provides for a 10-year term from the application date).\n\nNon-resident application refers to an application filed with the IP office of or acting on behalf of a state or jurisdiction in which the first-named applicant in the application is not domiciled. \n\nDesign count: The number of designs contained in an industrial design application or registration. Under the Hague System for the International Registration of Industrial Designs, it is possible for an applicant to obtain protection for up to 100 industrial designs for products belonging to one and the same class by filing a single application. Some national or regional IP offices allow applications to contain more than one design for the same product or within the same class, while others allow only one design per application. In order to capture the differences in application and registration numbers across offices, it is useful to compare their respective application and registration design counts." }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "IP.IDS.RSCT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The progress and well-being of humanity depend on our capacity to come up with new ideas and creations. Technological progress requires the development and application of new inventions, while a vibrant culture will constantly seek new ways to express itself. \n\nIntellectual property rights are also vital. Inventors, artists, scientists and businesses put a lot of time, money, energy and thought into developing their innovations and creations. To encourage them to do that, they need the chance to make a fair return on their investment. That means giving them rights to protect their intellectual property. Essentially, intellectual property rights such as copyright, patents and trademarks can be viewed like any other property right. They allow the creators or owners of IP to benefit from their work or from their investment in a creation by giving them control over how their property is used. \n\nIP rights have long been recognized within various legal systems. For example, patents to protect inventions were granted in Venice as far back as the fifteenth century. Modern initiatives to protect IP through international law started with the Paris Convention for the Protection of Industrial Property (1883) and the Berne Convention for the Protection of Literary and Artistic Works (1886). These days, there are more than 25 international treaties on IP administered by WIPO. IP rights are also safeguarded by Article 27 of the Universal Declaration of Human Rights.\n\nCreativity and inventiveness are vital. They spur economic growth, create new jobs and industries, and enhance the quality and enjoyment of life. The intellectual property system needs to balance the rights and interests of different groups: of creators and consumers; of businesses and their competitors; of high- and low-income countries. An efficient and fair IP system benefits everyone – including ordinary users and consumers.\n\nSome examples: (a) The multibillion-dollar film, recording, publishing and software industries – which bring pleasure to millions of people worldwide – would not thrive without copyright protection.\n(b) The patent system rewards researchers and inventors while also ensuring that they share their knowledge by making patent applications publicly available, which helps stimulate more innovation. (c) Trademark protection discourages counterfeiting, so businesses can compete on a level playing field and users can be confident they are buying the genuine article.\n\n(source: https://doi.org/10.34667/tind.42176)" }, { "id": "IndicatorName", "value": "Industrial design applications, resident, by count" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are based on information supplied to World Intellectual Property Organization (WIPO) by IP offices in annual surveys, supplemented by data in national IP office reports. Data may be missing for some offices or periods." }, { "id": "Longdefinition", "value": "Industrial design applications are applications to register an industrial design with a national or regional Intellectual Property (IP) offices and designations received by relevant offices through the Hague System. A resident application refers to an application filed with the IP office of, or acting for, the state or jurisdiction in which the first named applicant in the application is resident. Design count is used to render application data for industrial applications across offices comparable, as some offices follow a single-class/single-design filing system while other have a multiple class/design filing system." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2021" }, { "id": "Source", "value": "Statistics Database, World Intellectual Property Organization (WIPO), uri: www.wipo.int/ipstats/, note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Information about the World Intellectual Property Organization (WIPO) data collection can be accessed on the WIPO website: https://www.wipo.int/en/web/ip-statistics/about\nStatistical concept(s): Industrial designs are applied to a wide variety of industrial products and handicrafts. They refer to the ornamental or aesthetic aspects of a useful article, including compositions of lines or colors or any three-dimensional forms that give a special appearance to a product or handicraft. The holder of a registered industrial design has exclusive rights against unauthorized copying or imitation of the design by third parties. Industrial design registrations are valid for a limited period. The term of protection is usually 15 years in most jurisdictions. However, differences in legislation exist, notably in China (which provides for a 10-year term from the application date).\n\nFor statistical purposes, a resident application refers to an application filed with the IP office of, or acting for, the state or jurisdiction in which the first named applicant in the application is resident. For example, an application filed with the Japan Patent Office (JPO) by a resident of Japan is considered a resident application from the perspective of the JPO. Resident applications are sometimes referred to as “domestic applications.” A resident grant/registration is an IP right issued on the basis of a resident application.\n\nDesign count: The number of designs contained in an industrial design application or registration. Under the Hague System for the International Registration of Industrial Designs, it is possible for an applicant to obtain protection for up to 100 industrial designs for products belonging to one and the same class by filing a single application. Some national or regional IP offices allow applications to contain more than one design for the same product or within the same class, while others allow only one design per application. In order to capture the differences in application and registration numbers across offices, it is useful to compare their respective application and registration design counts." }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "IP.JRN.ARTC.SC", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A scientific journal is a periodical publication intended to further the progress of science, usually by reporting new research. Most journals are highly specialized, although some of the oldest journals such as Nature publish articles and scientific papers across a wide range of scientific fields. Scientific journals contain articles that have been peer reviewed. When a scientific journal describes experiments or calculations, they must supply enough details that an independent researcher could repeat the experiment or calculation to verify the results. Each such journal article becomes part of the permanent scientific record.\n\nSome journals, such as Nature, Science, Proceedings of the National Academy of Sciences of the United States of America (PNAS), and Physical Review Letters, have a reputation of publishing articles that mark a fundamental breakthrough in their respective fields." }, { "id": "IndicatorName", "value": "Scientific and technical journal articles" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the bibliometric database is constantly updated, the National Center for Science and Engineering Statistics (NCSES) does not recommend comparing bibliometric data across different editions of the Science and Engineering Indicators publication. For each edition of Indicators, NCSES uses a fixed snapshot of the database. This means that although trends are comparable, the exact number of articles, citations, and other data will vary across editions. For more information about comparing fixed versus dynamic journal data sets, see Schneider et al. (2019). Data before 2003 is sourced from earlier editions of the Science and Engineering Indicators report and may not be strictly comparable with 2003-2022 data.\n\nThe Scopus database is constructed from articles and conference proceedings with an English-language title and abstract; therefore, the database contains an unmeasurable bias because not all science and engineering (S&E) articles and conference proceedings meet the English language requirement (Elsevier 2020). (Source: https://ncses.nsf.gov/pubs/nsb202333/technical-appendix)" }, { "id": "Longdefinition", "value": "Article counts refer to publications from a selection of conference proceedings and peer-reviewed journals from Scopus in science and engineering fields, according to the National Center for Science and Engineering Statistics Taxonomy of Disciplines." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2022" }, { "id": "Source", "value": "Science and Engineering Indicators, National Science Foundation (NSF), uri: https://ncses.nsf.gov/indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Science and Engineering Indicators 2024 report “Publications Output: U.S. Trends and International Comparisons” uses a large database of publication records as a source of bibliometric data. Bibliometric data include each article’s title, author(s), authors’ institution(s), references, journal title, unique article-identifying information (journal volume, issue, and page numbers or digital object identifier), and year or date of publication. The PBS report uses Scopus, a bibliometric database owned by Elsevier and containing scientific literature with English titles and abstracts, to examine national and global scientific publication–related activity.? \n\nArticle counts refer to publications from a selection of conference proceedings and peer-reviewed journals in science and engineering fields from Scopus, according to the National Center for Science and Engineering Statistics Taxonomy of Disciplines: agricultural sciences, astronomy and astrophysics, biological and biomedical sciences, chemistry, computer and information sciences; engineering; geosciences, atmospheric sciences, and ocean sciences; health sciences; material sciences; mathematics and statistics; natural resources and conservation; physics; psychology; social sciences.\n\n\n\nStatistical concept(s): The number of journal articles is presented using fractional counting: a method of counting science and engineering publications in which credit for coauthored publications is divided among the collaborating institutions or regions, countries, or economies based on the proportion of their participating authors. Fractional counting allocates the publication count based on the proportion of the coauthors named on the article with institutional addresses from each region, country, or economy. Fractional counting enables the counts to sum up to the number of total articles. (Source: https://ncses.nsf.gov/pubs/nsb202333/glossary)" }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "Fractional count" } ], "source_id": "2" }, { "id": "IP.PAT.NRES", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Patent Cooperation Treaty (www.wipo.int/pct) provides a two phase system for filing patent. International applications under the treaty provide for a national patent grant only - there is no international patent. The national filing represents the applicant's seeking of patent protection for a given territory, whereas international filings, while representing a legal right, do not accurately reflect where patent protection is sought. Resident filings are those from residents of the country concerned. Nonresident filings are from applicants abroad. For regional offices applications from residents of any member state of the regional patent convention are considered nonresident filings. Some offices (notably the U.S. Patent and Trademark Office) use the residence of the inventor rather than the applicant to classify filings.\n\nPatent data are a great resource for the study of technical change in a country or region. Patent data provide a uniquely detailed source of information on inventive activity and the multiple dimensions of the inventive process (e.g. geographical location, technical and institutional origin, individuals and networks). Furthermore, patent data form a consistent basis for comparisons across time and across countries.\n\nPatent data can be used in the analysis of a wide array of topics related to technical change and patenting activity including industry-science linkages, patenting strategies by companies, internationalization of research, and indicators on the value of patents. Patent-based statistics reflect the inventive performance of countries, regions and firms, as well as other aspects of the dynamics of the innovation process such as co-operation in innovation or technology paths." }, { "id": "IndicatorName", "value": "Patent applications, nonresidents" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A patent is an exclusive right granted for a specified period (generally 20 years) for a new way of doing something or a new technical solution to a problem - an invention. The invention must be of practical use and display a characteristic unknown in the existing body of knowledge in its field. Most countries have systems to protect patentable inventions.\n\nUnless otherwise stated, statistics on the number of resident and non-resident patent applications include those filed via the PCT system as PCT national/regional phase entries." }, { "id": "Longdefinition", "value": "Patent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention--a product or process that provides a new way of doing something or offers a new technical solution to a problem. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2021" }, { "id": "Source", "value": "WIPO Patent Report: Statistics on Worldwide Patent Activity, World Intellectual Property Organization (WIPO), note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Non-resident patent applications are from applicants outside the relevant State or region. Patent data cover applications and grants classified by field of technology. International applications series distinguish four subcategories: a) patents taken out by residents of a country in that country; b) patents taken out in a country by non-residents of that country; c) total patents registered in the country or naming it; d) patents taken out outside a country by its residents. Data on patents granted only distinguish between patents awarded to residents and to non-residents. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years.\n\nPatent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention - a product or process that provides a new way of doing something or offers a new technical solution to a problem." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "2" }, { "id": "IP.PAT.RESD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Patent Cooperation Treaty (www.wipo.int/pct) provides a two phase system for filing patent. International applications under the treaty provide for a national patent grant only - there is no international patent. The national filing represents the applicant's seeking of patent protection for a given territory, whereas international filings, while representing a legal right, do not accurately reflect where patent protection is sought. Resident filings are those from residents of the country concerned. Nonresident filings are from applicants abroad. For regional offices applications from residents of any member state of the regional patent convention are considered nonresident filings. Some offices (notably the U.S. Patent and Trademark Office) use the residence of the inventor rather than the applicant to classify filings.\n\nPatent data are a great resource for the study of technical change in a country or region. Patent data provide a uniquely detailed source of information on inventive activity and the multiple dimensions of the inventive process (e.g. geographical location, technical and institutional origin, individuals and networks). Furthermore, patent data form a consistent basis for comparisons across time and across countries.\n\nPatent data can be used in the analysis of a wide array of topics related to technical change and patenting activity including industry-science linkages, patenting strategies by companies, internationalization of research, and indicators on the value of patents. Patent-based statistics reflect the inventive performance of countries, regions and firms, as well as other aspects of the dynamics of the innovation process such as co-operation in innovation or technology paths." }, { "id": "IndicatorName", "value": "Patent applications, residents" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A patent is an exclusive right granted for a specified period (generally 20 years) for a new way of doing something or a new technical solution to a problem - an invention. The invention must be of practical use and display a characteristic unknown in the existing body of knowledge in its field. Most countries have systems to protect patentable inventions." }, { "id": "Longdefinition", "value": "Patent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention--a product or process that provides a new way of doing something or offers a new technical solution to a problem. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2021" }, { "id": "Source", "value": "WIPO Patent Report: Statistics on Worldwide Patent Activity, World Intellectual Property Organization (WIPO), note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Resident patent applications are those for which the first-named applicant or assignee is a resident of the State or region concerned. In the case of regional offices such as the European Patent Office, a resident is an applicant from any of the member States of the regional patent convention.\n\nPatent data cover applications and grants classified by field of technology. International applications series distinguish four subcategories: a) patents taken out by residents of a country in that country; b) patents taken out in a country by non-residents of that country; c) total patents registered in the country or naming it; d) patents taken out outside a country by its residents. Data on patents granted only distinguish between patents awarded to residents and to non-residents. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years.\n\nPatent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention - a product or process that provides a new way of doing something or offers a new technical solution to a problem." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "2" }, { "id": "IP.TMK.NRCT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The progress and well-being of humanity depend on our capacity to come up with new ideas and creations. Technological progress requires the development and application of new inventions, while a vibrant culture will constantly seek new ways to express itself. \n\nIntellectual property rights are also vital. Inventors, artists, scientists and businesses put a lot of time, money, energy and thought into developing their innovations and creations. To encourage them to do that, they need the chance to make a fair return on their investment. That means giving them rights to protect their intellectual property. Essentially, intellectual property rights such as copyright, patents and trademarks can be viewed like any other property right. They allow the creators or owners of IP to benefit from their work or from their investment in a creation by giving them control over how their property is used. \n\nIP rights have long been recognized within various legal systems. For example, patents to protect inventions were granted in Venice as far back as the fifteenth century. Modern initiatives to protect IP through international law started with the Paris Convention for the Protection of Industrial Property (1883) and the Berne Convention for the Protection of Literary and Artistic Works (1886). These days, there are more than 25 international treaties on IP administered by WIPO. IP rights are also safeguarded by Article 27 of the Universal Declaration of Human Rights.\n\nCreativity and inventiveness are vital. They spur economic growth, create new jobs and industries, and enhance the quality and enjoyment of life. The intellectual property system needs to balance the rights and interests of different groups: of creators and consumers; of businesses and their competitors; of high- and low-income countries. An efficient and fair IP system benefits everyone – including ordinary users and consumers.\n\nSome examples: (a) The multibillion-dollar film, recording, publishing and software industries – which bring pleasure to millions of people worldwide – would not thrive without copyright protection.\n(b) The patent system rewards researchers and inventors while also ensuring that they share their knowledge by making patent applications publicly available, which helps stimulate more innovation. (c) Trademark protection discourages counterfeiting, so businesses can compete on a level playing field and users can be confident they are buying the genuine article.\n\n(source: https://doi.org/10.34667/tind.42176)" }, { "id": "IndicatorName", "value": "Trademark applications, nonresident, by count" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are based on information supplied to World Intellectual Property Organization (WIPO) by IP offices in annual surveys, supplemented by data in national IP office reports. Data may be missing for some offices or periods." }, { "id": "Longdefinition", "value": "A trademark is a sign capable of distinguishing the goods or services of one enterprise from those of other enterprises. Trademarks are protected by intellectual property rights. Non-resident application refers to an application filed with the IP office of or acting on behalf of a state or jurisdiction in which the first-named applicant in the application is not domiciled. Class count is used to render application data for trademark applications across offices comparable, as some offices follow a single-class/single-design filing system while other have a multiple class/design filing system." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2021" }, { "id": "Source", "value": "Statistics Database, World Intellectual Property Organization (WIPO), uri: www.wipo.int/ipstats/, note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Information about the World Intellectual Property Organization (WIPO) data collection can be accessed on the WIPO website: https://www.wipo.int/en/web/ip-statistics/about\nStatistical concept(s): Trademark: A sign used to distinguish the goods or services of one undertaking from those of another. A trademark may consist of words and combinations of words (for instance, names or slogans), logos, figures and images, letters, numbers, sounds, or, in rare instances, smells or moving images, or a combination thereof. The procedures for registering trademarks are governed by the legislation and procedures of national and regional IP offices and WIPO. Trademark rights are limited to the jurisdiction of the IP office that registers the trademark. Trademarks can be registered by filing an application at the relevant national or regional office(s), or by filing an international application through the Madrid System.\n\nNon-resident application refers to an application filed with the IP office of or acting on behalf of a state or jurisdiction in which the first-named applicant in the application is not domiciled.\n\nClass count: The number of classes specified in a trademark application or registration. In the international trademark system, and at certain national and regional offices, an applicant can file a trademark application specifying one or more of the 45 goods and services classes of the Nice Classification. Offices use either a multi-class or a single filing system. For example, the offices of Japan, the Republic of Korea and the United States of America (US), as well as many European IP offices, have multi-class filing systems. On the other hand, the offices of Brazil, Mexico, and South Africa follow a single-class filing system, requiring a separate application for each class in which an applicant seeks trademark protection. To capture the differences in application and registration numbers across offices, it is useful to compare their respective application and registration class counts." }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "IP.TMK.RSCT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The progress and well-being of humanity depend on our capacity to come up with new ideas and creations. Technological progress requires the development and application of new inventions, while a vibrant culture will constantly seek new ways to express itself. \n\nIntellectual property rights are also vital. Inventors, artists, scientists and businesses put a lot of time, money, energy and thought into developing their innovations and creations. To encourage them to do that, they need the chance to make a fair return on their investment. That means giving them rights to protect their intellectual property. Essentially, intellectual property rights such as copyright, patents and trademarks can be viewed like any other property right. They allow the creators or owners of IP to benefit from their work or from their investment in a creation by giving them control over how their property is used. \n\nIP rights have long been recognized within various legal systems. For example, patents to protect inventions were granted in Venice as far back as the fifteenth century. Modern initiatives to protect IP through international law started with the Paris Convention for the Protection of Industrial Property (1883) and the Berne Convention for the Protection of Literary and Artistic Works (1886). These days, there are more than 25 international treaties on IP administered by WIPO. IP rights are also safeguarded by Article 27 of the Universal Declaration of Human Rights.\n\nCreativity and inventiveness are vital. They spur economic growth, create new jobs and industries, and enhance the quality and enjoyment of life. The intellectual property system needs to balance the rights and interests of different groups: of creators and consumers; of businesses and their competitors; of high- and low-income countries. An efficient and fair IP system benefits everyone – including ordinary users and consumers.\n\nSome examples: (a) The multibillion-dollar film, recording, publishing and software industries – which bring pleasure to millions of people worldwide – would not thrive without copyright protection.\n(b) The patent system rewards researchers and inventors while also ensuring that they share their knowledge by making patent applications publicly available, which helps stimulate more innovation. (c) Trademark protection discourages counterfeiting, so businesses can compete on a level playing field and users can be confident they are buying the genuine article.\n\n(source: https://doi.org/10.34667/tind.42176)" }, { "id": "IndicatorName", "value": "Trademark applications, resident, by count" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are based on information supplied to World Intellectual Property Organization (WIPO) by IP offices in annual surveys, supplemented by data in national IP office reports. Data may be missing for some offices or periods." }, { "id": "Longdefinition", "value": "A trademark is a sign capable of distinguishing the goods or services of one enterprise from those of other enterprises. Trademarks are protected by intellectual property rights. A resident application refers to an application filed with the IP office of, or acting for, the state or jurisdiction in which the first named applicant in the application is resident. Class count is used to render application data for trademark applications across offices comparable, as some offices follow a single-class/single-design filing system while other have a multiple class/design filing system." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2021" }, { "id": "Source", "value": "Statistics Database, World Intellectual Property Organization (WIPO), uri: www.wipo.int/ipstats/, note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Information about the World Intellectual Property Organization (WIPO) data collection can be accessed on the WIPO website: https://www.wipo.int/en/web/ip-statistics/about\nStatistical concept(s): Trademark: A sign used to distinguish the goods or services of one undertaking from those of another. A trademark may consist of words and combinations of words (for instance, names or slogans), logos, figures and images, letters, numbers, sounds, or, in rare instances, smells or moving images, or a combination thereof. The procedures for registering trademarks are governed by the legislation and procedures of national and regional IP offices and WIPO. Trademark rights are limited to the jurisdiction of the IP office that registers the trademark. Trademarks can be registered by filing an application at the relevant national or regional office(s), or by filing an international application through the Madrid System.\n\nFor statistical purposes, a resident application refers to an application filed with the IP office of, or acting for, the state or jurisdiction in which the first named applicant in the application is resident. For example, an application filed with the Japan Patent Office (JPO) by a resident of Japan is considered a resident application from the perspective of the JPO. Resident applications are sometimes referred to as “domestic applications.” A resident grant/registration is an IP right issued on the basis of a resident application.\n\nClass count: The number of classes specified in a trademark application or registration. In the international trademark system, and at certain national and regional offices, an applicant can file a trademark application specifying one or more of the 45 goods and services classes of the Nice Classification. Offices use either a multi-class or a single filing system. For example, the offices of Japan, the Republic of Korea and the United States of America (US), as well as many European IP offices, have multi-class filing systems. On the other hand, the offices of Brazil, Mexico, and South Africa follow a single-class filing system, requiring a separate application for each class in which an applicant seeks trademark protection. To capture the differences in application and registration numbers across offices, it is useful to compare their respective application and registration class counts." }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "IQ.CPA.BREG.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA business regulatory environment rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThis Business Regulatory Environment criterion assesses the extent to which the legal, regulatory, and policy environment helps or hinders private business in investing, creating jobs, and becoming more productive. The\nemphasis is on direct regulations of business activity and regulation of goods and factor markets. Three sub-components are measured: (a) regulations affecting entry, exit, and competition; (b) regulations of ongoing business operations; and (c) regulations of factor markets (labor and land)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.CPA.DEBT.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA debt policy rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Debt Policy and Management criterion assesses whether the country’s debt management strategy is conducive to ensure medium-term debt sustainability and minimize budgetary risks. The criterion covers: (a) the extent to which external and domestic debt is contracted with a view to achieving/maintaining debt sustainability; and (b) the effectiveness of debt management functions (including the degree of coordination between debt management and other macroeconomic policies, the effectiveness of the debt management unit, and the existence of a debt management strategy and of a legal framework for borrowing)." }, { "id": "Othernotes", "value": "Statistical concept(s): The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector). \n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector). \n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nEach of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\nRefer to Other notes for the Statistical Concept(s)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.CPA.ECON.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA economic management cluster average (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Economic Management cluster includes monetary and exchange rate policies, fiscal policy, and debt policy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.CPA.ENVR.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA policy and institutions for environmental sustainability rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThis criterion assesses the extent to which environmental policies and institutions foster the protection and sustainable use of natural resources and the management of pollution." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.CPA.FINQ.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA quality of budgetary and financial management rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe quality of budgetary and financial management criterion assesses the extent to which there is: (a) a comprehensive and credible budget, linked to policy priorities; (b) effective financial management systems to ensure that the budget is implemented as intended in a controlled and predictable way; and (c) timely and accurate accounting and fiscal reporting, including timely audit of public accounts and effective arrangements for follow up." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "2" }, { "id": "IQ.CPA.FINS.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA financial sector rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe financial sector criterion assesses the policies and regulations that affect financial sector development. Three dimensions are covered: (a) financial stability; (b) the sector’s efficiency, depth, and resource mobilization strength; and (c) access to financial services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: http://www.worldbank.org/ida" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.CPA.FISP.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA fiscal policy rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThis CPIA fiscal policy criterion assesses the quality of the fiscal policy in its stabilization and allocation functions. The stabilization function deals with achieving macroeconomic policy objectives in conjunction with coherent monetary and exchange rate policies—smoothing business cycle fluctuations, accommodating shocks. The allocation function is concerned with the appropriate provision of public goods. The criterion pays attention to public expenditure composition, including, for example, the provision of public infrastructure and agriculture related public goods and services that support medium-term growth." }, { "id": "Othernotes", "value": "Statistical concept(s): The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector). \n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector). \n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nEach of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\nRefer to Other notes for the Statistical Concept(s)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.CPA.GNDR.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA gender equality rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe CPIA gender equality rating criterion assesses the extent to which the country has enacted and put in place institutions and programs to enforce laws and policies that: (a) promote equal access for men and women to human capital development; (b) promote equal access for men and women to productive and economic resources; and (c) give men and women equal status and protection under the law. For the human capital development dimension, the focus is on primary completion and access to secondary education, access to health care during delivery and to family planning, and adolescent fertility rate. For access to economic and productive resources, the focus is on labor force participation, land tenure and property and inheritance rights. For Agency for change and equalization of status and protection under the law the focus is on individual and family rights and personal security (violence against women, trafficking, or sexual harassment) and political participation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.CPA.HRES.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA building human resources rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe CPIA building human resources criterion assesses the national policies and public and private sector service delivery that affect access to and quality of health and education-related services. The criterion has two components: (a) health, including population and reproductive health, and nutrition as well as the prevention and treatment of communicable diseases such as HIV/AIDS, tuberculosis, and malaria; and (b) education, training and literacy programs, and early child development (ECD) programs, including both formal and non-formal programs (which may combine education, health, and nutrition interventions) aimed at children aged 0-6." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.CPA.IRAI.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "IDA resource allocation index (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score (the IDA resource allocation index) and scores for sixteen criteria that compose the CPIA. \n\nThese criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.CPA.MACR.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA macroeconomic management rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe CPIA macroeconomic management cluster assesses the monetary, exchange rate, and fiscal policy, as well as debt policy and management." }, { "id": "Othernotes", "value": "Statistical concept(s): The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector). \n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector). \n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nEach of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\nRefer to Other notes for the Statistical Concept(s)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.CPA.PADM.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA quality of public administration rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe CPIA Quality of Public Administration criterion covers the core administration defined as the civilian central government (and subnational governments, to the extent that their size or policy responsibilities are significant) excluding health and education personnel, and police. The criterion assesses the functioning of the core administration in three areas: (a) managing its own operations; (b) ensuring quality in policy implementation and regulatory management; and (c) coordinating the larger public sector Human Resources Management regime outside the core administration (de-concentrated and arms-length bodies and subsidiary governments)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.CPA.PRES.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA equity of public resource use rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Equity of public resource use assesses the extent to which the pattern of public expenditures and revenue collection affects the poor and is consistent with national poverty reduction priorities." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: All criteria within each cluster receive equal weight, and each cluster has a 25 percent weight in the overall score, which is obtained by averaging the average scores of the four clusters. For each of the 16 criteria countries are rated on a scale of 1 (low) to 6 (high). The scores depend on the level of performance in a given year assessed against the criteria, rather than on changes in performance compared with the previous year. All 16 CPIA criteria contain a detailed description of each rating level. In assessing country performance, World Bank staff evaluate the country's performance on each of the criteria and assign a rating. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge and on relevant publicly available indicators. In interpreting the assessment scores, it should be noted that the criteria are designed in a developmentally neutral manner. Accordingly, higher scores can be attained by a country that, given its stage of development, has a policy and institutional framework that more strongly fosters growth and poverty reduction.\n\nThe country teams that prepare the ratings are very familiar with the country, and their assessments are based on country diagnostic studies prepared by the World Bank or other development organizations and on their own professional judgment. An early consultation is conducted with country authorities to make sure that the assessments are informed by up-to-date information. To ensure that scores are consistent across countries, the process involves two key phases. In the benchmarking phase a small representative sample of countries drawn from all regions is rated. Country teams prepare proposals that are reviewed first at the regional level and then in a Bankwide review process. A similar process is followed to assess the performance of the remaining countries, using the benchmark countries' scores as guideposts. The final ratings are determined following a Bankwide review. The overall numerical IRAI score and the separate criteria scores were first publicly disclosed in June 2006." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "2" }, { "id": "IQ.CPA.PROP.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA property rights and rule-based governance rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Property Rights and Rule-Based Governance criterion assesses the extent to which economic activity is facilitated by an effective legal system and rule-based governance structure in which property and contract rights are reliably respected and enforced. It encompasses three dimensions: (a) legal framework for secure property and contract rights, including predictability and impartiality of laws and regulations; (b) quality of the legal and judicial system, as measured by independence, accessibility, legitimacy, efficiency, transparency, and integrity of the courts and other relevant dispute resolution mechanisms; and (c) crime and violence as an impediment to economic\nactivity and citizen security." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.CPA.PROT.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA social protection rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Social Protection criterion assess government policies in social protection and labor market regulations that reduce the risk of becoming poor, assist those who are poor to better manage further risks, and ensure a minimal level of welfare to all people. Specifically it evaluates social protection (SP) and labor policies, namely those engaged in risk prevention by supporting savings and risk pooling through social insurance, protection against destitution through redistributive safety net programs and promotion of human capital development and income generation, including labor market programs. It also assesses the functioning of an SP system, including its effectiveness in a crisis and in providing arrangements and incentives to help beneficiaries to move from protection to promotion and prevention, including through interactions with private, informal means of SP. The criterion covers: (a) the overall SP system; (b) social safety net programs; (c) labor markets programs and policies, namely those aiming to promote employment creation and productivity growth while protecting core labor standards and ensuring adequate working conditions; (d) local service delivery and civil society participation in community development programs; and (e) pension and old age savings programs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.CPA.PUBS.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA public sector management and institutions cluster average (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Public Sector Management and Institutions cluster includes property rights and rule-based governance, quality of budgetary and financial management, efficiency of revenue mobilization, quality of public administration, and transparency, accountability, and corruption in the public sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.CPA.REVN.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA efficiency of revenue mobilization rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThis Efficiency of Revenue Mobilization criterion assesses the overall pattern of revenue mobilization, not only the tax structure as it exists on paper, but revenue from all sources as they are collected. Separate sub-ratings\nare provided for (a) tax policy and (b) tax administration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "2" }, { "id": "IQ.CPA.SOCI.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA policies for social inclusion/equity cluster average (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Policies for Social Inclusion and Equity cluster includes gender equality, equity of public resource use, building human resources, social protection and labor, and policies and institutions for environmental sustainability." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.CPA.STRC.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA structural policies cluster average (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Structural Policies cluster includes trade, financial sector, and business regulatory environment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.CPA.TRAD.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The International Development Association (IDA) is the part of the World Bank Group that helps the poorest countries reduce poverty by providing concessional loans and grants for programs aimed at boosting economic growth and improving living conditions. IDA funding helps these countries deal with the complex challenges they face in meeting the Millennium Development Goals.\n\nThe World Bank's IDA Resource Allocation Index (IRAI) is based on the results of the annual Country Policy and Institutional Assessment (CPIA) exercise, which covers the IDA-eligible countries. Country assessments have been carried out annually since the mid-1970s by World Bank staff. Over time the criteria have been revised from a largely macroeconomic focus to include governance aspects and a broader coverage of social and structural dimensions. Country performance is assessed against a set of 16 criteria grouped into four clusters: economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions. IDA resources are allocated to a country on per capita terms based on its IDA country performance rating and, to a limited extent, based on its per capita gross national income. This ensures that good performers receive a higher IDA allocation in per capita terms. The IRAI is a key element in the country performance rating." }, { "id": "IndicatorName", "value": "CPIA trade rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Trade assesses how the policy framework fosters trade in goods." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: http://www.worldbank.org/ida" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: All criteria within each cluster receive equal weight, and each cluster has a 25 percent weight in the overall score, which is obtained by averaging the average scores of the four clusters. For each of the 16 criteria countries are rated on a scale of 1 (low) to 6 (high). The scores depend on the level of performance in a given year assessed against the criteria, rather than on changes in performance compared with the previous year. All 16 CPIA criteria contain a detailed description of each rating level. In assessing country performance, World Bank staff evaluate the country's performance on each of the criteria and assign a rating. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge and on relevant publicly available indicators. In interpreting the assessment scores, it should be noted that the criteria are designed in a developmentally neutral manner. Accordingly, higher scores can be attained by a country that, given its stage of development, has a policy and institutional framework that more strongly fosters growth and poverty reduction.\n\nThe country teams that prepare the ratings are very familiar with the country, and their assessments are based on country diagnostic studies prepared by the World Bank or other development organizations and on their own professional judgment. An early consultation is conducted with country authorities to make sure that the assessments are informed by up-to-date information. To ensure that scores are consistent across countries, the process involves two key phases. In the benchmarking phase a small representative sample of countries drawn from all regions is rated. Country teams prepare proposals that are reviewed first at the regional level and then in a Bankwide review process. A similar process is followed to assess the performance of the remaining countries, using the benchmark countries' scores as guideposts. The final ratings are determined following a Bankwide review. The overall numerical IRAI score and the separate criteria scores were first publicly disclosed in June 2006." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "2" }, { "id": "IQ.CPA.TRAN.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Transparency, Accountability, and Corruption in the Public Sector criterion assesses the extent to which the executive, legislators, and other high-level officials can be held accountable for their use of funds, administrative decisions, and results obtained. Accountability is generally enhanced by transparency in decision-making, access to relevant and timely information, public and media scrutiny, and by institutional checks (e.g., inspector general, ombudsman, or independent audit) on the authority of the chief executive. The criterion covers four dimensions: (a) the accountability of the executive and other top officials to effective oversight institutions; (b) access of civil society to timely and reliable information on public affairs and public policies, including fiscal information (on public expenditures, revenues, and large contract awards); (c) state capture by narrow vested interests; and (d) integrity in the management of public resources, including aid and natural resource revenues." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "2" }, { "id": "IQ.SPI.OVRL", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The new Statistical Performance Indicators (SPI) will replace the Statistical Capacity Index (SCI), which the World Bank has regularly published since 2004. Although the goals are the same, to offer a better tool to measure the statistical systems of countries, the new SPI framework has expanded into new areas including in the areas of data use, administrative data, geospatial data, data services, and data infrastructure. The SPI provides a framework that can help countries measure where they stand in several dimensions and offers an ambitious measurement agenda for the international community." }, { "id": "IndicatorName", "value": "Statistical performance indicators (SPI): Overall score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The SPI overall score is a composite score measuring country performance across five pillars: data use, data services, data products, data sources, and data infrastructure. The new Statistical Performance Indicators (SPI) will replace the Statistical Capacity Index (SCI), which the World Bank has regularly published since 2004. Although the goals are the same, to offer a better tool to measure the statistical systems of countries, the new SPI framework has expanded into new areas including in the areas of data use, administrative data, geospatial data, data services, and data infrastructure. The SPI provides a framework that can help countries measure where they stand in several dimensions and offers an ambitious measurement agenda for the international community." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2016-2024" }, { "id": "Source", "value": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Weighted average of all statistical performance indicators. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-100)" } ], "source_id": "2" }, { "id": "IQ.SPI.PIL1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The data use (outcome) pillar is segmented by five types of users: (i) the legislature, (ii) the executive branch, (iii) civil society (including sub-national actors), (iv) academia and (v) international bodies. Each dimension would have associated indicators to measure performance. A mature system would score well across all dimensions whereas a less mature one would have weaker scores along certain dimensions. The gaps would give insights into prioritization among user groups and help answer questions as to why the existing services are not resulting in higher use of national statistics in a particular segment." }, { "id": "IndicatorName", "value": "Statistical performance indicators (SPI): Pillar 1 data use score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Currently, the dashboard only features indicators for one of the five dimensions of data use, which is data use by international organizations. Indicators on whether statistical systems are providing useful data to their national governments (legislature and executive branches), to civil society, and to academia are absent. Thus the dashboard does not yet assess if national statistical systems are meeting the data needs of a large swathe of users." }, { "id": "Longdefinition", "value": "The data use overall score is a composite score measuring the demand side of the statistical system. The data use pillar is segmented by five types of users: (i) the legislature, (ii) the executive branch, (iii) civil society (including sub-national actors), (iv) academia and (v) international bodies. Each dimension would have associated indicators to measure performance. A mature system would score well across all dimensions whereas a less mature one would have weaker scores along certain dimensions. The gaps would give insights into prioritization among user groups and help answer questions as to why the existing services are not resulting in higher use of national statistics in a particular segment. Currently, the SPI only features indicators for one of the five dimensions of data use, which is data use by international organizations. Indicators on whether statistical systems are providing useful data to their national governments (legislature and executive branches), to civil society, and to academia are absent. Thus the dashboard does not yet assess if national statistical systems are meeting the data needs of a large swathe of users." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2024" }, { "id": "Source", "value": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Weighted average of statistical performance indicators related to data use. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-100)" } ], "source_id": "2" }, { "id": "IQ.SPI.PIL2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The data services (output) pillar is segmented by four service types: (i) the quality of data releases, (ii) the richness and openness of online access, (iii) the effectiveness of advisory and analytical services related to statistics, and (iv) the availability and use of data access services such as secure microdata access. Advisory and analytical services might incorporate elements related to data stewardship services including input to national data strategies, advice on data ethics and calling out misuse of data in accordance with the Fundamental Principles of Official Statistics." }, { "id": "IndicatorName", "value": "Statistical performance indicators (SPI): Pillar 2 data services score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Under the pillar of data services an area that needs improvement is the measurement of advisory and analytical services provided by NSOs, such as data stewardship services. By measuring this type of work done by NSOs that goes beyond producing data, the international community and the NSOs themselves can better assess whether this type of support is in place." }, { "id": "Longdefinition", "value": "The data services pillar overall score is a composite indicator based on four dimensions of data services: (i) the quality of data releases, (ii) the richness and openness of online access, (iii) the effectiveness of advisory and analytical services related to statistics, and (iv) the availability and use of data access services such as secure microdata access. Advisory and analytical services might incorporate elements related to data stewardship services including input to national data strategies, advice on data ethics and calling out misuse of data in accordance with the Fundamental Principles of Official Statistics." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2016-2024" }, { "id": "Source", "value": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Weighted average of statistical performance indicators related to data services. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-100)" } ], "source_id": "2" }, { "id": "IQ.SPI.PIL3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system’s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country." }, { "id": "IndicatorName", "value": "Statistical performance indicators (SPI): Pillar 3 data products score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The data products overall score is a composite score measureing whether the country is able to produce relevant indicators, primarily related to SDGs. The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system’s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Weighted average of statistical performance indicators related to data products. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-100)" } ], "source_id": "2" }, { "id": "IQ.SPI.PIL4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country’s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met." }, { "id": "IndicatorName", "value": "Statistical performance indicators (SPI): Pillar 4 data sources score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In the data sources pillar, more information is needed in the areas of administrative data, geospatial data, and private and citizen generated data. On administrative data, the picture is incomplete with no measures of whether countries have administrative data systems in place to measure health, education, labor, and social protection program statistics. For the geospatial indicator, there is a proxy measure of whether the country is able to produce indicators at the sub-national level, but as yet, no understanding of how countries are using geospatial information in other ways, for instance using satellite data. And while the world is increasingly awash with private and citizen generated data (e.g., on mobility, job search, or social networking), on a global scale there is no reliable source to measure how national statistical systems are incorporating this information." }, { "id": "Longdefinition", "value": "The data sources overall score is a composity measure of whether countries have data available from the following sources: Censuses and surveys, administrative data, geospatial data, and private sector/citizen generated data. The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country’s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2015-2024" }, { "id": "Source", "value": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Weighted average of statistical performance indicators related to data sources. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-100)" } ], "source_id": "2" }, { "id": "IQ.SPI.PIL5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The data infrastructure (capability) pillar includes hard and soft infrastructure segments, itemizing essential cross cutting requirements for an effective statistical system. The segments are: (i) legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system; (ii) standards and methods addressing compliance with recognized frameworks and concepts; (iii) skills including level of skills within the statistical system and among users (statistical literacy); (iv) partnerships reflecting the need for the statistical system to be inclusive and coherent; and (v) finance mobilized both domestically and from donors." }, { "id": "IndicatorName", "value": "Statistical performance indicators (SPI): Pillar 5 data infrastructure score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Several of the ‘soft’ components of the data infrastructure pillar lack adequate data. This includes the areas of skills and of partnerships between entities in the national statistical system. The dashboard makes use of the PARIS21 led SDG indicator on whether the statistical legislations in countries met the standards of the UN Fundamental Principles of Statistics, but this was not incorporated into the overall SPI score, because of inadequate country coverage. This is also true of the PARIS21 led SDG indicator on whether the national statistical system is fully funded. Countries would need to be encouraged to report on this information." }, { "id": "Longdefinition", "value": "The data infrastructure pillar overall score measures the hard and soft infrastructure segments, itemizing essential cross cutting requirements for an effective statistical system. The segments are: (i) legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system; (ii) standards and methods addressing compliance with recognized frameworks and concepts; (iii) skills including level of skills within the statistical system and among users (statistical literacy); (iv) partnerships reflecting the need for the statistical system to be inclusive and coherent; and (v) finance mobilized both domestically and from donors." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2016-2024" }, { "id": "Source", "value": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Weighted average of statistical performance indicators related to data infrastructure. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-100)" } ], "source_id": "2" }, { "id": "IS.AIR.DPRT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Aviation traffic data are essential for understanding the role of air transport in economic development, global connectivity, and social progress. These statistics, covering passenger volumes, freight volumes, and aircraft departures, provide a standardized measure of air transport activity across countries and regions and are closely linked to economic growth, trade integration, tourism development, and labor mobility. \n\nAviation traffic indicators also help assess the resilience and efficiency of transport systems, track the recovery from economic shocks or crises, and identify capacity constraints or infrastructure investment needs.\n\nFrom a development perspective, aviation statistics support evidence-based policy making in areas such as transport planning, regional integration, climate and emissions management, and inclusive access to services, making them a key input for monitoring progress toward sustainable and connected economies." }, { "id": "IndicatorName", "value": "Air transport, registered carrier departures worldwide" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While ICAO aviation data provide a globally standardized and comprehensive view of air transport activity, several limitations affect their accuracy and comparability. Data quality depends on the completeness and consistency of country reporting, which can vary due to differences in national statistical capacity, regulatory environments, and adherence to ICAO definitions. Some countries may not report all relevant data, leading ICAO to estimate missing values using historical submissions and published schedules, which can introduce uncertainty. Changes in airline registration, mergers, or operational practices may also affect the attribution of traffic statistics. Additionally, the aggregation of scheduled and non-scheduled services, as well as the treatment of transit and transfer passengers, may differ across countries, impacting cross-country comparability." }, { "id": "Longdefinition", "value": "Registered carrier departures worldwide are domestic takeoffs and takeoffs abroad of air carriers registered in the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Civil Aviation Statistics of the World, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers;\nICAO Staff estimates, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data is obtained from airports, airport operators, airport websites and/or civil aviation authorities. Data on passengers are reported annually by each State (for all its commercial air carriers, including scheduled and non-scheduled flights) via standardized forms sent to ICAO. \n\nEach passenger is counted once per flight number and not repeatedly on each individual stage of that flight, with a single exception that a passenger flying on both the international and domestic stages of the same flight should be counted as both a domestic and an international passenger.\n\nWhere some carriers do not report, ICAO may use historical reports or published flight-schedules to estimate their traffic.\nStatistical concept(s): Aircraft departures represent the number of take-offs of aircraft. For statistical purposes, departures are equal to the number of landings made or flight stages flown. \n\nA flight stage is the operation of an aircraft from take-off to its next landing. A flight stage is classified as either international or domestic. International flight stage is one or both terminals in the territory of a State, other than the State in which the air carrier has its principal place of business." }, { "id": "Topic", "value": "Infrastructure: Transportation" }, { "id": "Unitofmeasure", "value": "Count" } ], "source_id": "2" }, { "id": "IS.AIR.GOOD.MT.K1", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Aviation traffic data are essential for understanding the role of air transport in economic development, global connectivity, and social progress. These statistics, covering passenger volumes, freight volumes, and aircraft departures, provide a standardized measure of air transport activity across countries and regions and are closely linked to economic growth, trade integration, tourism development, and labor mobility. \n\nAviation traffic indicators also help assess the resilience and efficiency of transport systems, track the recovery from economic shocks or crises, and identify capacity constraints or infrastructure investment needs.\n\nFrom a development perspective, aviation statistics support evidence-based policy making in areas such as transport planning, regional integration, climate and emissions management, and inclusive access to services, making them a key input for monitoring progress toward sustainable and connected economies." }, { "id": "IndicatorName", "value": "Air transport, freight (million ton-km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While ICAO aviation data provide a globally standardized and comprehensive view of air transport activity, several limitations affect their accuracy and comparability. Data quality depends on the completeness and consistency of country reporting, which can vary due to differences in national statistical capacity, regulatory environments, and adherence to ICAO definitions. Some countries may not report all relevant data, leading ICAO to estimate missing values using historical submissions and published schedules, which can introduce uncertainty. Changes in airline registration, mergers, or operational practices may also affect the attribution of traffic statistics. Additionally, the aggregation of scheduled and non-scheduled services, as well as the treatment of transit and transfer passengers, may differ across countries, impacting cross-country comparability." }, { "id": "Longdefinition", "value": "Air freight is the volume of freight, express, and diplomatic bags carried on each flight stage (operation of an aircraft from takeoff to its next landing), measured in metric tons times kilometers traveled." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Civil Aviation Statistics of the World, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers;\nICAO Staff estimates, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data is obtained from airports, airport operators, airport websites and/or civil aviation authorities. Data on passengers are reported annually by each State (for all its commercial air carriers, including scheduled and non-scheduled flights) via standardized forms sent to ICAO. \n\nEach passenger is counted once per flight number and not repeatedly on each individual stage of that flight, with a single exception that a passenger flying on both the international and domestic stages of the same flight should be counted as both a domestic and an international passenger.\n\nWhere some carriers do not report, ICAO may use historical reports or published flight-schedules to estimate their traffic.\nStatistical concept(s): A metric tonne of freight or mail carried one kilometre. Freight tonne-kilometres equal the sum of the products obtained by multiplying the number of tonnes of freight, express, diplomatic bags carried on each flight stage by the stage distance. For ICAO statistical purposes freight includes express and diplomatic bags but not passenger baggage. Mail tonne-kilometres are computed in the same way as freight tonne-kilometres." }, { "id": "Topic", "value": "Infrastructure: Transportation" }, { "id": "Unitofmeasure", "value": "tonnes-km" } ], "source_id": "2" }, { "id": "IS.AIR.PSGR", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Aviation traffic data are essential for understanding the role of air transport in economic development, global connectivity, and social progress. These statistics, covering passenger volumes, freight volumes, and aircraft departures, provide a standardized measure of air transport activity across countries and regions and are closely linked to economic growth, trade integration, tourism development, and labor mobility. \n\nAviation traffic indicators also help assess the resilience and efficiency of transport systems, track the recovery from economic shocks or crises, and identify capacity constraints or infrastructure investment needs.\n\nFrom a development perspective, aviation statistics support evidence-based policy making in areas such as transport planning, regional integration, climate and emissions management, and inclusive access to services, making them a key input for monitoring progress toward sustainable and connected economies." }, { "id": "IndicatorName", "value": "Air transport, passengers carried" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While ICAO aviation data provide a globally standardized and comprehensive view of air transport activity, several limitations affect their accuracy and comparability. Data quality depends on the completeness and consistency of country reporting, which can vary due to differences in national statistical capacity, regulatory environments, and adherence to ICAO definitions. Some countries may not report all relevant data, leading ICAO to estimate missing values using historical submissions and published schedules, which can introduce uncertainty. Changes in airline registration, mergers, or operational practices may also affect the attribution of traffic statistics. Additionally, the aggregation of scheduled and non-scheduled services, as well as the treatment of transit and transfer passengers, may differ across countries, impacting cross-country comparability." }, { "id": "Longdefinition", "value": "Air carrier data per country refers to passengers carried by airlines registered in that country regardless of the origin or destination of the passengers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Civil Aviation Statistics of the World, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers;\nICAO Staff estimates, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data is obtained from airports, airport operators, airport websites and/or civil aviation authorities. Data on passengers are reported annually by each State (for all its commercial air carriers, including scheduled and non-scheduled flights) via standardized forms sent to ICAO. \n\nEach passenger is counted once per flight number and not repeatedly on each individual stage of that flight, with a single exception that a passenger flying on both the international and domestic stages of the same flight should be counted as both a domestic and an international passenger.\n\nWhere some carriers do not report, ICAO may use historical reports or published flight-schedules to estimate their traffic.\nStatistical concept(s): The number of passengers carried is obtained by counting each passenger on a particular flight (with one flight number) once only and not repeatedly on each individual stage of that flight, with a single exception that a passenger flying on both the international and domestic stages of the same flight should be counted as both a domestic and an international passenger." }, { "id": "Topic", "value": "Infrastructure: Transportation" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "IS.RRS.GOOD.MT.K6", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Transport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe railway transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. Economic growth, technological change, and market liberalization affect road transport throughout the world.\n\nRailways have helped in the industrialization process of a country by easy transportation of coal and raw-materials at a cheaper rate. As railways require huge capital outlay, they may give rise to monopolies and work against public interest at large. Even if controlled and managed by the government, lack of competition sometimes results in inefficiency and high costs. Also, many times it is not economical to operate railways in sparsely settled rural areas. Thus, in many developing countries large rural areas have no railway even today.\n\nRail transport is a major form of passenger and freight transport in many countries. It is ubiquitous in Europe, with an integrated network covering virtually the whole continent. In India, China, South Korea and Japan, many millions use trains as regular transport. In the North America, freight rail transport is widespread and heavily used in for transporting gods. The western Europe region has the highest railway density in the world and has many individual trains which operate through several countries despite technical and organizational differences in each national network. Australia has a generally sparse network, mostly along its densely populated urban centers.\n\nBulk freight handling is a key advantage for rail transport. Low or even zero transshipment costs combined with energy efficiency and low inventory costs allow trains to handle bulk much cheaper than by road. Typical bulk cargo includes coal, ore, grains and liquids. Bulk goods can be transported in open-topped cars, hopper cars and tank cars. Container trains have become the dominant type in the US for non-bulk haulage." }, { "id": "IndicatorName", "value": "Railways, goods transported (million ton-km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\" The data from UIC is based on voluntary reporting by railway companies, and can show drastic increases or decreases for some of the years due to lack of reporting by some of the companies in that country." }, { "id": "Longdefinition", "value": "Goods transported by railway are the volume of goods transported by railway, measured in metric tons times kilometers traveled." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2021" }, { "id": "Source", "value": "Railisa Database (UIC), International Union of Railways (UIC), uri: https://uic-stats.uic.org/select/;\nOECD Statistics, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Freight traffic on any mode is typically measured in tons and ton-kilometers. A ton-kilometer equals cargo weight transported times distance transported. For railways, an important measure of work performed is gross ton-kilometers, this measure includes rail wagons' empty weight for both empty and loaded movements. This measure of gross ton-kilometers is also called ‘trailing tons' or the total tons being hauled. Sometimes gross ton-kilometer measures include the weight of locomotives used to haul freight trains.\n\nThe indicator measures the tonne.kilometers of freight on the national territory of the railway.\n\nThe weight taken into account is the actual weight or chargeable weight of the goods carried. Weight means the quantity of goods in thousands of tonnes. The weight to be taken into consideration includes, in addition to the weight of the goods transported, the weight of packaging and the tare weight of containers, swap bodies, pallets as well as road vehicles transported by rail in the course of combined transport operations. If the goods are transported using the services of more than one railway undertaking (e.g. within the group etc.), when possible the weight of goods should not be counted more than once.\n\nThe number of tonne-kilometres in millions represents the weight of freight traffic (in millions of tonnes) over the charging distance (in kilometres). \n\nStatistical concept(s): Tonne-kilometre (tkm) is a unit of measurement of goods transport which represents the transport of one tonne of goods over a distance of one kilometre.\n\nThe distance to be covered is the distance actually travelled on the considered network. To avoid double counting each country should count only the tkm performed on its territory. If it is not available, then the distance charged or estimated should be taken into account." }, { "id": "Topic", "value": "Infrastructure: Transportation" }, { "id": "Unitofmeasure", "value": "Ton-km" } ], "source_id": "2" }, { "id": "IS.RRS.PASG.KM", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Transport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe railway transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. Economic growth, technological change, and market liberalization affect road transport throughout the world.\n\nRailways have helped in the industrialization process of a country by easy transportation of coal and raw-materials at a cheaper rate. As railways require huge capital outlay, they may give rise to monopolies and work against public interest at large. Even if controlled and managed by the government, lack of competition sometimes results in inefficiency and high costs. Also, many times it is not economical to operate railways in sparsely settled rural areas. Thus, in many developing countries large rural areas have no railway even today.\n\nRail transport is a major form of passenger and freight transport in many countries. Passenger trains can involve a variety of functions including long distance travel, daily commuter trips, or local urban transit services. Railways are very popular mode of transportation in Europe, with an integrated network covering virtually the whole continent. In India, China, South Korea and Japan, many millions use trains as regular transport. In North America, freight rail transport is widespread and heavily used in for transporting goods. The western Europe region has the highest railway density in the world and has many individual trains which operate through several countries despite technical and organizational differences in each national network. Australia has a generally sparse network, mostly along its densely populated urban centers." }, { "id": "IndicatorName", "value": "Railways, passengers carried (million passenger-km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\" The data from UIC is based on voluntary reporting by railway companies, and can show drastic increases or decreases for some of the years due to lack of reporting by some of the companies in that country." }, { "id": "Longdefinition", "value": "Passengers carried by railway are the number of passengers transported by rail multiplied by kilometers traveled." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2021" }, { "id": "Source", "value": "Railisa Database (UIC), International Union of Railways (UIC), uri: https://uic-stats.uic.org/select/;\nOECD Statistics, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Passenger-kilometers are usually measured on the basis of the rail travel distance between origin and destination multiplied by the number of passengers traveling between each origin and destination. This variable relates to passengers, irrespective of the fare paid and also including free travelling passengers, but excluding members of the train crew. \n\nThe number of passengers should be calculated as number of passenger journeys. A journey is the act of moving from one place (origin) to another (destination) using a given transport mode (e.g. railway). A journey may consist of one or several stages depending on whether one has to change transport means (e.g. using more than one train) in order to get from the origin to the destination. In other words, a journey consists either of a single stage or a sequence of stages using the same transport mode (e.g. railway), following each other in such a way that the destination of one stage coincides with the origin of the next.\n\nJourneys should be considered finished when an overnight stay occurs. For practical purposes, journeys can be considered finished when a change in transport mode or transport company occurs. \n\nPassenger-kilometers is the total distance travelled by all the passengers. For instance, one person travelling for 20km contributes for 20 passenger-kilometres; four people, travelling for 20km each, contribute for 80 passenger- kilometers\n\nStatistical concept(s): A passenger kilometer is performed when a passenger is carried for one kilometer. Rail passenger-kilometer (pkm) is a unit of measurement representing the transport of one rail passenger by rail over a distance of one kilometer.\n\nThe distance to be taken into consideration should be the distance actually travelled by the passenger on the network. To avoid double counting each country should count only the pkm performed on its territory. If this is not available, then the distance charged or estimated should be used." }, { "id": "Topic", "value": "Infrastructure: Transportation" }, { "id": "Unitofmeasure", "value": "Passenger-kilometers" } ], "source_id": "2" }, { "id": "IS.RRS.TOTL.KM", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Transport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, governments, and the private sector. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nRailway transport is a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. \n\nRailways have helped in the industrialization process by easy transportation of raw-materials at a cheaper rate. As railways require huge capital outlay, they may give rise to monopolies and work against public interest at large. Lack of competition sometimes results in inefficiency and high costs. Also, many times it is not economical to operate railways in sparsely settled rural areas. Thus, in many developing countries large rural areas have no railway even today.\n\nRail transport is a major form of passenger and freight transport in many countries. It is ubiquitous in Europe, with an integrated network covering virtually the whole continent. In India, China, South Korea and Japan, many millions use trains as regular transport. In the North America, freight rail transport is widespread and heavily used in for transporting gods. The western Europe region has the highest railway density in the world and has many individual trains which operate through several countries despite technical and organizational differences in each national network. Australia has a generally sparse network, mostly along its densely populated urban centers." }, { "id": "IndicatorName", "value": "Rail lines (total route-km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized\". The data from UIC is based on voluntary reporting by railway companies, and can show drastic increases or decreases for some of the years due to lack of reporting by some of the companies in that country." }, { "id": "Longdefinition", "value": "The total length of rail line in the country operated for passenger transport, goods transport, or both." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2021" }, { "id": "Source", "value": "Railisa Database, International Union of Railways (UIC), uri: https://uic-stats.uic.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Rail lines are the length of railway route available for train service, irrespective of the number of parallel tracks. It includes railway routes that are open for public passenger and freight services and excludes dedicated private resource railways.\n\nGauge:\tN (standard gauge: 1,435 m); L (broad gauge: exact rail gauge inserted); E (narrow gauge: exact rail gauge inserted). The length of railway lines worked is obtained by taking these sections including main-line track listed in the Capital Expenditure Account.\n\nSections not worked are deducted only in cases where they are permanently out of use, that is, if they are no longer maintained in working order. Lines temporarily out of use continue to form part of the length of lines worked.\n\nThe length of a section is measured in the middle of the section, from center to the center of the passenger buildings, or of the corresponding service buildings, of stations which are shown as independent points of departure or arrival for the conveyance of passengers or freight. If the boundary of the rail network falls in open track, the length of the section is measured up to that point.\n\nThe section situated between a station approach and the join to the main line of two lines or more which is used by all trains in either direction over these lines, is only counted once. However, if for one or more of these lines, tracks are normally allocated, the length of these lines is counted separately.\n\nIf between two stations there are one or more parallel tracks (siding-lines) to the main line, only the length of the latter is counted.\n\nIn the case of regular lines worked exclusively during part of the year (seasonal lines), their length is included in the end-of-year statement (Var 1112: Total length of lines worked at the end of the year in the Railisa database).\nStatistical concept(s): Railway line: one or more adjacent running tracks forming a route between two points. Where a section of network comprises two or more lines running alongside one another, there are as many lines as routes to which tracks are allotted exclusively." }, { "id": "Topic", "value": "Infrastructure: Transportation" }, { "id": "Unitofmeasure", "value": "Kilometers" } ], "source_id": "2" }, { "id": "IS.SHP.GCNW.XQ", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The liner shipping connectivity index (LSCI) aims at capturing a country's integration level into global liner shipping networks. A country's access to world markets depends largely on their transport connectivity, especially in regard to regular shipping services for the import and export of manufactured goods.\n\nTrade facilitation encompasses customs efficiency and other physical and regulatory environments where trade takes place, harmonization of standards and conformance to international regulations, and the logistics of moving goods and associated documentation through countries and ports. Though collection of trade facilitation data has improved over the last decade, data that allow meaningful evaluation, especially for developing economies, are lacking. The quality and accessibility of ports and roads affect logistics performance.\n\nAccess to global shipping and air freight networks and the quality and accessibility of ports and roads affect logistics performance. Maritime transport is the backbone of international trade and a key engine driving globalization. Around 80 per cent of global trade by volume and over 70 per cent by value is carried by sea and is handled by ports worldwide; these shares are even higher in the case of most developing countries.\n\nA total of 60 per cent of world seaborne trade by volume is loaded, and 57 per cent unloaded, in developing-country ports. That is a remarkable shift away from previous patterns, in which developing economies served mainly as loading areas for raw materials and natural resources." }, { "id": "IndicatorName", "value": "Liner shipping connectivity index (maximum value in 2004 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on trade facilitation are drawn from research by private and international agencies. Most data are perception-based evaluations by business executives and professionals. Because of different backgrounds, values, and personalities, those surveyed may evaluate the same situation differently. Caution should thus be used when interpreting perception- based indicators. Nevertheless, they convey much needed information on trade facilitation." }, { "id": "Longdefinition", "value": "The Liner Shipping Connectivity Index captures how well countries are connected to global shipping networks. It is computed by the United Nations Conference on Trade and Development (UNCTAD) based on five components of the maritime transport sector: number of ships, their container-carrying capacity, maximum vessel size, number of services, and number of companies that deploy container ships in a country's ports. For each component a country's value is divided by the maximum value of each component in 2004, the five components are averaged for each country, and the average is divided by the maximum average for 2004 and multiplied by 100. The index generates a value of 100 for the country with the highest average index in 2004. . The underlying data come from Containerisation International Online." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2021" }, { "id": "Source", "value": "Review of Maritime Transport 2010., UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Liner Shipping Connectivity Index captures how well countries are connected to global shipping networks. Starting in 2020 the index was improved and is published as a quarterly series with the index set at 100 for the country with the highest average in the first quarter of 2006. It is computed by the United Nations Conference on Trade and Development (UNCTAD) based on six components of the maritime transport sector: number of ships, their container-carrying capacity, maximum vessel size, number of services, the number of country-pairs with a direct connection, and number of companies that deploy container ships in a country's ports. The data are derived from Containerisation International Online (www.ci-online.co.uk). For each of the six components, a country's value is divided by the maximum value of that component in Q1 2006, and for each country, the average of the six components is calculated. This average is then divided by the maximum average for Q1 2006 and multiplied by 100. In this way, the index generates the value 100 for the country with the highest average index of the six components in Q1 2006. Annual values of the index equal the values of the first quarter of the same corresponding year." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "2" }, { "id": "IS.SHP.GOOD.TU", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Transport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe sea transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses, and stimulating foreign investment and international trade. Economic growth, technological change, market liberalization, and oil prices affect sea transport throughout the world." }, { "id": "IndicatorName", "value": "Container port traffic (TEU: 20 foot equivalent units)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Measures of port container traffic, much of it commodities of medium to high value added, give some indication of economic growth in a country. But when traffic is merely transshipment, much of the economic benefit goes to the terminal operator and ancillary services for ships and containers rather than to the country more broadly. In transshipment centers empty containers may account for as much as 40 percent of traffic.\n\nData cover coastal shipping as well as international journeys. Transshipment traffic is counted as two lifts at the intermediate port (once to off-load and again as an outbound lift) and includes empty units. Data for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\"" }, { "id": "Longdefinition", "value": "Port container traffic measures the flow of containers from land to sea transport modes, and vice versa, in twenty-foot equivalent units (TEUs), a standard-size container. Data refer to coastal shipping as well as international journeys. Transshipment traffic is counted as two lifts at the intermediate port (once to off-load and again as an outbound lift) and includes empty units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "UN Conference on Trade and Development (UNCTAD), uri: http://unctad.org/en/Pages/statistics.aspx" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: TEU is the standard unit, referring to 20-foot equivalent units or 20-foot-long cargo container. The size of cargo containers range from 20 feet long to more than 50 feet long. The international measure is the smallest box, the 20-footer or 20-foot-equivalent unit (TEU). Two twenty-foot containers (TEUs) equal one FEU. Container vessel capacity and port throughput capacity are frequently referred to in TEUs.\n\nFor any questions related to the country data and methodology, please contact the Review of Maritime Transport team at: unctad-rmt@un.org" }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "2" }, { "id": "IT.CEL.SETS", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. The International Telecommunication Union (ITU) estimates that there were about 6 billion mobile subscriptions globally in the early 2010s. No technology has ever spread faster around the world. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nMobile cellular telephone subscriptions are subscriptions to a public mobile telephone service using cellular technology, which provide access to the public switched telephone network (PSTN) using cellular technology. It includes postpaid and prepaid subscriptions and includes analogue and digital cellular systems.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "IndicatorName", "value": "Mobile cellular subscriptions" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU.\n\nMobile subscriptions include both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services are also excluded, but all mobile cellular subscriptions that offer voice communications are included. Both postpaid and prepaid subscriptions are included." }, { "id": "Longdefinition", "value": "Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Refers to the subscriptions to a public mobile telephone service and provides access to Public Switched Telephone Network (PSTN) using cellular technology, including number of pre-paid SIM cards active during the past three months. This includes both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services should also be excluded. This should include all mobile cellular subscriptions that offer voice communications.\n\nData on mobile cellular subscribers are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators.\n\nData for this indicator are readily available for approximately 90 percent of countries, either through ITU's World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator's website. For the rest, information can be aggregated through operators' data (mainly through annual reports) and complemented by market research reports. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): Data can be collected from all licensed mobile-cellular operators in the country, and then aggregated at the country level. If retail mobile-cellular services are also provided by nonfacilities-based operators (i.e., mobile virtual network operators), care should be taken to avoid double counting. One difficulty that may arise is that operators may have different definitions of ‘active’ and therefore may not be able to provide the data according to the recommended definition (i.e., used in the last three months)." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "number of subscriptions" } ], "source_id": "2" }, { "id": "IT.CEL.SETS.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. The International Telecommunication Union (ITU) estimates that there were about 6 billion mobile subscriptions globally in the early 2010s. No technology has ever spread faster around the world. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nMobile cellular telephone subscriptions are subscriptions to a public mobile telephone service using cellular technology, which provide access to the public switched telephone network (PSTN) using cellular technology. It includes postpaid and prepaid subscriptions and includes analogue and digital cellular systems.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "IndicatorName", "value": "Mobile cellular subscriptions (per 100 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU.\n\nMobile subscriptions include both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services are also excluded, but all mobile cellular subscriptions that offer voice communications are included. Both postpaid and prepaid subscriptions are included." }, { "id": "Longdefinition", "value": "Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Refers to the subscriptions to a public mobile telephone service and provides access to Public Switched Telephone Network (PSTN) using cellular technology, including number of pre-paid SIM cards active during the past three months. This includes both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services should also be excluded. This should include all mobile cellular subscriptions that offer voice communications.\n\nData on mobile cellular subscribers are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators.\n\nData for this indicator are readily available for approximately 90 percent of countries, either through ITU's World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator's website. For the rest, information can be aggregated through operators' data (mainly through annual reports) and complemented by market research reports.\n\nMobile cellular subscriptions (per 100 people) indicator is derived by all mobile subscriptions divided by the country's population and multiplied by 100. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): Data can be collected from all licensed mobile-cellular operators in the country, and then aggregated at the country level. If retail mobile-cellular services are also provided by nonfacilities-based operators (i.e., mobile virtual network operators), care should be taken to avoid double counting. One difficulty that may arise is that operators may have different definitions of ‘active’ and therefore may not be able to provide the data according to the recommended definition (i.e., used in the last three months). This indicator can be divided by the population and multiplied by 100 to obtain mobile cellular subscriptions per 100 people." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "number of subscriptions*100/population" } ], "source_id": "2" }, { "id": "IT.MLT.MAIN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout.\n\nFixed telephone lines are those that connect a subscriber's terminal equipment to the public switched telephone network and that have a port on a telephone exchange. This term is synonymous with the term main station or Direct Exchange Line (DEL) that is commonly used in telecommunication documents. Integrated services digital network channels and fixed wireless subscribers are included. A fixed line also refers to a phone which uses a solid medium telephone line such as a metal wire or fiber optic cable for transmission as distinguished from a mobile cellular line which uses radio waves for transmission.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "IndicatorName", "value": "Fixed telephone subscriptions" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU." }, { "id": "Longdefinition", "value": "Fixed telephone subscriptions refers to the sum of active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A fixed telephone line (previously called main telephone line in operation) is an active line connecting the subscriber's terminal equipment to the public switched telephone network (PSTN) and which has a dedicated port in the telephone exchange equipment. This term is synonymous with the terms main station or Direct Exchange Line (DEL) that are commonly used in telecommunication documents. It may not be the same as an access line or a subscriber. This should include the active number of analog fixed telephone lines, ISDN channels, fixed wireless, public payphones and VoIP subscriptions. Active lines are those that have registered an activity in the past three months.\n\nData on fixed telephone lines are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators. Data are considered to be very reliable, timely, and complete.\n\nData for this indicator are readily available for approximately 90 percent of countries, either through ITU's World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator's website. For the rest, information can be aggregated through operators' data (mainly through annual reports) and complemented by market research reports. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): Data can be collected and aggregated at the country level by asking all licensed fixed-telephone line operators how many fixed-telephone subscriptions they have." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "number of subscriptions" } ], "source_id": "2" }, { "id": "IT.MLT.MAIN.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout.\n\nFixed telephone lines are those that connect a subscriber's terminal equipment to the public switched telephone network and that have a port on a telephone exchange. This term is synonymous with the term main station or Direct Exchange Line (DEL) that is commonly used in telecommunication documents. Integrated services digital network channels and fixed wireless subscribers are included. A fixed line also refers to a phone which uses a solid medium telephone line such as a metal wire or fiber optic cable for transmission as distinguished from a mobile cellular line which uses radio waves for transmission.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "IndicatorName", "value": "Fixed telephone subscriptions (per 100 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU." }, { "id": "Longdefinition", "value": "Fixed telephone subscriptions refers to the sum of active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A fixed telephone line (previously called main telephone line in operation) is an active line connecting the subscriber's terminal equipment to the public switched telephone network (PSTN) and which has a dedicated port in the telephone exchange equipment. This term is synonymous with the terms main station or Direct Exchange Line (DEL) that are commonly used in telecommunication documents. It may not be the same as an access line or a subscriber. This should include the active number of analog fixed telephone lines, ISDN channels, fixed wireless, public payphones and VoIP subscriptions. Active lines are those that have registered an activity in the past three months.\n\nData on fixed telephone lines are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators. Data are considered to be very reliable, timely, and complete.\n\nData for this indicator are readily available for approximately 90 percent of countries, either through ITU's World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator's website. For the rest, information can be aggregated through operators' data (mainly through annual reports) and complemented by market research reports.\n\nTelephone lines (per 100 people) indicator is derived by all telephone lines divided by the country's population and multiplied by 100. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): Data can be collected and aggregated at the country level by asking all licensed fixed-telephone line operators how many fixed-telephone subscriptions they have. This indicator can be divided by the population and multiplied by 100 to obtain fixed telephone subscriptions per 100 people." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "number of subscriptions*100/population" } ], "source_id": "2" }, { "id": "IT.NET.BBND", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Fixed broadband subscriptions" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Fixed broadband subscriptions refers to fixed subscriptions to high-speed access to the public Internet (a TCP/IP connection), at downstream speeds equal to, or greater than, 256 kbit/s. This includes cable modem, DSL, fiber-to-the-home/building, other fixed (wired)-broadband subscriptions, satellite broadband and terrestrial fixed wireless broadband. This total is measured irrespective of the method of payment. It excludes subscriptions that have access to data communications (including the Internet) via mobile-cellular networks. It should include fixed WiMAX and any other fixed wireless technologies. It includes both residential subscriptions and subscriptions for organizations." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data can be collected by asking all ISPs in the country to provide the number of their fixed -broadband subscriptions (by type – cable, DSL, optical fiber, other, satellite, and terrestrial fixed wireless broadband).\nStatistical concept(s): The data can be collected by asking all ISPs in the country to provide the number of their fixed -broadband subscriptions (by type – cable, DSL, optical fiber, other, satellite, and terrestrial fixed wireless broadband)." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "number of subscriptions" } ], "source_id": "2" }, { "id": "IT.NET.BBND.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains.\n\nThere are several economic gains associated with broadband. For example, with DSL, users can use a single standard phone line for both voice and data services. This enables them to surf the Internet and call a friend at the same time - all using the same phone line. Broadband also enhances many Internet applications such as new e-government services like electronic tax filing, online health care services, e-learning and increased levels of electronic commerce.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "IndicatorName", "value": "Fixed broadband subscriptions (per 100 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are collected by national statistics offices through household surveys. Because survey questions and definitions differ, the estimates may not be strictly comparable across countries.\n\nFixed broadband Internet includes cable modem, DSL, fibre and other fixed broadband technology (such as satellite broadband Internet, Ethernet LANs, fixed-wireless access, Wireless Local Area Network, WiMAX etc.). Subscribers with access to data communications (including the Internet) via mobile cellular networks are excluded.\n\nAdvertised and real speeds can differ substantially. In some countries, regulatory authorities monitor the speed and quality of broadband services and oblige operators to provide accurate quality-of-service information to end users. Regional and global totals are calculated as unweighted sums of the country values. Regional and global penetration rates (per 100 inhabitants) are weighted averages of the country values weighted by the population of the countries/regions.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "Fixed broadband subscriptions refers to fixed subscriptions to high-speed access to the public Internet (a TCP/IP connection), at downstream speeds equal to, or greater than, 256 kbit/s. This includes cable modem, DSL, fiber-to-the-home/building, other fixed (wired)-broadband subscriptions, satellite broadband and terrestrial fixed wireless broadband. This total is measured irrespective of the method of payment. It excludes subscriptions that have access to data communications (including the Internet) via mobile-cellular networks. It should include fixed WiMAX and any other fixed wireless technologies. It includes both residential subscriptions and subscriptions for organizations." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data refer to subscriptions to high-speed access to the public Internet (a TCP/IP connection), at downstream speeds equal to, or greater than, 256 kbit/s. This includes cable modem, DSL, fibre-to-the-home/building and other fixed (wired)-broadband subscriptions. This total is measured irrespective of the method of payment. It excludes subscriptions that have access to data communications (including the Internet) via mobile-cellular networks. It excludes technologies listed under the wireless-broadband category.\n\nFixed broadband Internet subscribers per 100 people is obtained by dividing the number of fixed broadband Internet subscribers by the population and then multiplying by 100. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): The data can be collected by asking all ISPs in the country to provide the number of their fixed -broadband subscriptions (by type – cable, DSL, optical fiber, other, satellite, and terrestrial fixed wireless broadband). This indicator can be divided by the population and multiplied by 100 to obtain fixed-broadband subscriptions per 100 people." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "number of subscriptions*100/population" } ], "source_id": "2" }, { "id": "IT.NET.SECR", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Comparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. \n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met. Over the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "IndicatorName", "value": "Secure Internet servers" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Netcraft only visits sites on the standard HTTPS port, 443. Whilst it is possible to run an HTTPS server on a different port (using a URL like https://example.com:7000) this behaviour is quite rare on public websites. For example, usual ports are often used for administrative interfaces and other services not intended for the general public.\n\nNetcraft tries to visit every public secure website. Note that the survey does not include secure mail servers (SMTP) or intranet sites. So the final number of certificates for each certificate authority will be lower than the total number of server certificates sold by that authority." }, { "id": "Longdefinition", "value": "The number of distinct, publicly-trusted TLS/SSL certificates found in the Netcraft Secure Server Survey (by hosting country)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2010-2024" }, { "id": "Source", "value": "Secure Server Survey, Netcraft, uri: http://www.netcraft.com/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Netcraft's survey counts (unique) valid certificates issued by widely-trusted third-party certification authorities. A certificate must be valid, that is it must be within its validity period (certificates are usually valid for up to 39 months), and the digital signatures on the certificate must check successfully. It must be issued by third party certificate issuer that is recognised by Netcraft. \nNetcraft gathers a list of possible SSL web sites to investigate from a range of different sources. The data reflects the December survey in that year.\n\nSSL (Secure Socket Layer) is a protocol developed by Netscape for encrypted transmission over TCP/IP networks. It sets up a secure end-to-end link over which HTTP or any other application protocol can operate. The most common application of SSL is HTTPS for SSL-encrypted HTTP. It has now been replaced with a IETF-standardised version, TLS.\n\nSurveying: Netcraft only visits sites on the standard HTTPS port, 443. Whilst it is possible to run an HTTPS server on a different port (using a URL like https://example.com:7000) this behavior is quite rare on public websites. For example, usual ports are often used for administrative interfaces and other services not intended for the general public.\n\nNetcraft tries to visit every public secure website. Note that the survey does not include secure mail servers (SMTP) or intranet sites. So the final number of certificates for each certificate authority will be lower than the total number of server certificates sold by that authority. The survey makes multiple HTTPS request types to identify both web server capabilities, and the certificates in use. Netcraft makes retry visits to any non-responding IP addresses once, several hours after the failed visits. The information made available by an HTTPS server is more substantial than with http servers. The most interesting piece of information available from HTTP servers is the server signature; this can be analyzed to give straightforward empirical evidence about the relative popularity of server software on web sites across the Internet. The same information is also available from HTTPS servers. Additionally, the content of the site's X.509 certificate is available, providing details about both the company or organization owning the site, and the certificate issuer. Furthermore, in most cases the TCP/IP characteristics of the network connection allows to determine the operating system used.\n\nStatistical concept(s): The survey examines the use of encrypted transactions through extensive automated exploration, tallying the number of web sites using HTTPS. This analysis relates to those sites found in the survey where the certificate is valid for the hostname, and the certificate has been issued from a publicly-trusted root. The indicator refers to valid, third-party certificates. Included are sites found in the survey where the common name in the certificate matched the hostname, and the certificate's digital signature was not detected as being self-signed. The location is derived from the hosting location of the sites using the certificates (rather than the countries indicated on the certificates themselves.) This analysis relates to those sites found in the survey where the common name in the certificate matched the hostname, and the certificate's digital signature was not detected as being self-signed." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "Count" } ], "source_id": "2" }, { "id": "IT.NET.SECR.P6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Comparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. \n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met. Over the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "IndicatorName", "value": "Secure Internet servers (per 1 million people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Netcraft only visits sites on the standard HTTPS port, 443. Whilst it is possible to run an HTTPS server on a different port (using a URL like https://example.com:7000) this behaviour is quite rare on public websites. For example, usual ports are often used for administrative interfaces and other services not intended for the general public.\n\nNetcraft tries to visit every public secure website. Note that the survey does not include secure mail servers (SMTP) or intranet sites. So the final number of certificates for each certificate authority will be lower than the total number of server certificates sold by that authority." }, { "id": "Longdefinition", "value": "The number of distinct, publicly-trusted TLS/SSL certificates found in the Netcraft Secure Server Survey (by hosting country), per 1 million people." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2010-2024" }, { "id": "Source", "value": "Secure Server Survey, Netcraft, uri: http://www.netcraft.com/;\nWorld Bank population estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Netcraft's survey counts (unique) valid certificates issued by widely-trusted third-party certification authorities. A certificate must be valid, that is it must be within its validity period (certificates are usually valid for up to 39 months), and the digital signatures on the certificate must check successfully. It must be issued by third party certificate issuer that is recognised by Netcraft. \nNetcraft gathers a list of possible SSL web sites to investigate from a range of different sources. The data reflects the December survey in that year.\n\nSSL (Secure Socket Layer) is a protocol developed by Netscape for encrypted transmission over TCP/IP networks. It sets up a secure end-to-end link over which HTTP or any other application protocol can operate. The most common application of SSL is HTTPS for SSL-encrypted HTTP. It has now been replaced with a IETF-standardised version, TLS.\n\nSurveying: Netcraft only visits sites on the standard HTTPS port, 443. Whilst it is possible to run an HTTPS server on a different port (using a URL like https://example.com:7000) this behavior is quite rare on public websites. For example, usual ports are often used for administrative interfaces and other services not intended for the general public.\n\nNetcraft tries to visit every public secure website. Note that the survey does not include secure mail servers (SMTP) or intranet sites. So the final number of certificates for each certificate authority will be lower than the total number of server certificates sold by that authority. The survey makes multiple HTTPS request types to identify both web server capabilities, and the certificates in use. Netcraft makes retry visits to any non-responding IP addresses once, several hours after the failed visits. The information made available by an HTTPS server is more substantial than with http servers. The most interesting piece of information available from HTTP servers is the server signature; this can be analyzed to give straightforward empirical evidence about the relative popularity of server software on web sites across the Internet. The same information is also available from HTTPS servers. Additionally, the content of the site's X.509 certificate is available, providing details about both the company or organization owning the site, and the certificate issuer. Furthermore, in most cases the TCP/IP characteristics of the network connection allows to determine the operating system used.\nStatistical concept(s): The survey examines the use of encrypted transactions through extensive automated exploration, tallying the number of web sites using HTTPS. This analysis relates to those sites found in the survey where the certificate is valid for the hostname, and the certificate has been issued from a publicly-trusted root. The indicator refers to valid, third-party certificates. Included are sites found in the survey where the common name in the certificate matched the hostname, and the certificate's digital signature was not detected as being self-signed. The location is derived from the hosting location of the sites using the certificates (rather than the countries indicated on the certificates themselves.) This analysis relates to those sites found in the survey where the common name in the certificate matched the hostname, and the certificate's digital signature was not detected as being self-signed." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "Count" } ], "source_id": "2" }, { "id": "IT.NET.USER.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nToday's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains." }, { "id": "IndicatorName", "value": "Individuals using the Internet, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "This indicator refers to female individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx" }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "IT.NET.USER.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nToday's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains." }, { "id": "IndicatorName", "value": "Individuals using the Internet, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "This indicator refers to male individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx" }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "IT.NET.USER.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nToday's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains." }, { "id": "IndicatorName", "value": "Individuals using the Internet (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU), uri: https://datahub.itu.int/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): The number of in-scope individuals using the Internet is calculated by aggregating the weighted responses. The proportion of individuals using the Internet is expressed as a percentage and is calculated by dividing the total number of in-scope individuals using the Internet by the total number of in-scope individuals, and then multiplying the result by 100." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "2" }, { "id": "LP.EXP.DURS.MD", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce.\n\nA useful outcome measure of logistics performance is the time taken to complete trade transactions. The median import lead time for port and airport supply chains, as measured for the LPI, is more than 3.5 times longer in low performing countries than in high-performing countries. The difference is around three times for land supply chains. The association suggests that geographical hurdles, and perhaps internal transport markets, still pose substantial difficulties in many countries. Besides geography and speed en route, another factor in import lead times is the border process. Time can be reduced at all stages of this process, but especially in the clearance of goods on arrival. Countries with low logistics performance need to reform their border management so that they can reduce red tape, excessive and opaque procedural requirements, and physical inspections. Although the time to clear goods through customs is a fairly small fraction of total import time for all LPI quintiles, it rises sharply if goods are physically inspected. Core customs procedures are similar across quintiles. But low-performing countries have a far higher prevalence of physical inspection, even subjecting the same shipment to repeated inspections by multiple agencies.\n\nMany low-income countries have long export lead times, reducing their export competitiveness and ability to participate in international trade." }, { "id": "IndicatorName", "value": "Lead time to export, median case (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Feedback from operators is supplemented with quantitative data on the performance of key components of the logistics chain in the country of work. Thus, the LPI consists of both qualitative and quantitative measures.\n\nIn addition, despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Lead time to export is the median time (the value for 50 percent of shipments) from shipment point to port of loading. Data are from the Logistics Performance Index survey. Respondents provided separate values for the best case (10 percent of shipments) and the median case (50 percent of shipments). The data are exponentiated averages of the logarithm of single value responses and of midpoint values of range responses for the median case." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2007-2018" }, { "id": "Source", "value": "Logistic Performance Index Surveys, World Bank (WB), uri: https://lpi.worldbank.org/, note: Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.;\nTurku School of Economics, uri: https://lpi.worldbank.org/, note: Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on lead time to export are from the Logistics Performance Index (LPI) survey. Respondents provided separate values for the best case (10 percent of shipments) and the median case (50 percent of shipments) of shipments from the point of origin (the seller's factory, typically located either in the capital city or in the largest commercial center) to the port of loading or equivalent (port/airport), and excluding international shipping.\n\nThe Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and at the main express carriers. The 2012 LPI data are based on the 2011 survey, which was administered to nearly 1,000 respondents at international logistics companies in 143 countries (domestic performance indicators). The international LPI covers 155 countries. The LPI assesses both large companies and small and medium enterprises. Most of the responses are from small and medium enterprises, with large companies (those with 250 employees or more) accounting for roughly 18 percent of responses. The respondents include groups of professionals who are directly involved in day-today operations, from company headquarters and from country offices such as senior executives, area or country managers, and department managers. Many of the respondents are at corporate or regional headquarters or at country branch offices. The rest are at local branch offices or independent firms. The majority of respondents are involved in providing most logistics services as their main line of work such as warehousing and distribution, customer-tailored logistics solutions, courier services, bulk or break bulk cargo transport, and less-than-full container, full-container, or full-trailer load transport. \n\nFor the lead time to export, respondents were asked for quantitative information on their countries' international supply chains by picking choices from a dropdown menu. When a response indicates a single value, the answer is coded as the logarithm of that value. When a response indicates a range, the answer is coded as the logarithm of the midpoint of that range. Country scores are produced by exponentiating the average of responses in logarithms across all respondents for a given country. This method is equivalent to taking a geometric average in levels. Scores for regions, income groups, and LPI quintiles are simple averages of the relevant country scores." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "2" }, { "id": "LP.IMP.DURS.MD", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce.\n\nA useful outcome measure of logistics performance is the time taken to complete trade transactions. The median import lead time for port and airport supply chains, as measured for the LPI, is more than 3.5 times longer in low performing countries than in high-performing countries. The difference is around three times for land supply chains. The association suggests that geographical hurdles, and perhaps internal transport markets, still pose substantial difficulties in many countries. Besides geography and speed en route, another factor in import lead times is the border process. Time can be reduced at all stages of this process, but especially in the clearance of goods on arrival. Countries with low logistics performance need to reform their border management so that they can reduce red tape, excessive and opaque procedural requirements, and physical inspections. Although the time to clear goods through customs is a fairly small fraction of total import time for all LPI quintiles, it rises sharply if goods are physically inspected. Core customs procedures are similar across quintiles. But low-performing countries have a far higher prevalence of physical inspection, even subjecting the same shipment to repeated inspections by multiple agencies.\n\nMany low-income countries have long export lead times, reducing their export competitiveness and ability to participate in international trade." }, { "id": "IndicatorName", "value": "Lead time to import, median case (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Feedback from operators is supplemented with quantitative data on the performance of key components of the logistics chain in the country of work. Thus, the LPI consists of both qualitative and quantitative measures.\n\nIn addition, despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Lead time to import is the median time (the value for 50 percent of shipments) from port of discharge to arrival at the consignee. Data are from the Logistics Performance Index survey. Respondents provided separate values for the best case (10 percent of shipments) and the median case (50 percent of shipments). The data are exponentiated averages of the logarithm of single value responses and of midpoint values of range responses for the median case." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2007-2018" }, { "id": "Source", "value": "Logistic Performance Index Surveys, World Bank (WB), uri: https://lpi.worldbank.org/, note: Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.;\nTurku School of Economics, uri: https://lpi.worldbank.org/, note: Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on lead time to import are from the Logistics Performance Index (LPI) survey. Respondents provided separate values for the best case (10 percent of shipments) and the median case (50 percent of shipments) of shipments from the port of discharge or equivalent to the buyer's warehouse.\n\nThe Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and at the main express carriers. The 2012 LPI data are based on the 2011 survey, which was administered to nearly 1,000 respondents at international logistics companies in 143 countries (domestic performance indicators). The international LPI covers 155 countries. The LPI assesses both large companies and small and medium enterprises. Most of the responses are from small and medium enterprises, with large companies (those with 250 employees or more) accounting for roughly 18 percent of responses. The respondents include groups of professionals who are directly involved in day-today operations, from company headquarters and from country offices such as senior executives, area or country managers, and department managers. Many of the respondents are at corporate or regional headquarters or at country branch offices. The rest are at local branch offices or independent firms. The majority of respondents are involved in providing most logistics services as their main line of work such as warehousing and distribution, customer-tailored logistics solutions, courier services, bulk or break bulk cargo transport, and less-than-full container, full-container, or full-trailer load transport.\n\nFor the lead time to import, respondents were asked for quantitative information on their countries' international supply chains by picking choices from a dropdown menu. When a response indicates a single value, the answer is coded as the logarithm of that value. When a response indicates a range, the answer is coded as the logarithm of the midpoint of that range. Country scores are produced by exponentiating the average of responses in logarithms across all respondents for a given country. This method is equivalent to taking a geometric average in levels. Scores for regions, income groups, and LPI quintiles are simple averages of the relevant country scores." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "2" }, { "id": "LP.LPI.CUST.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI." }, { "id": "IndicatorName", "value": "Logistics performance index: Efficiency of customs clearance process (1=low to 5=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents evaluated efficiency of customs clearance processes (i.e. speed, simplicity and predictability of formalities), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents." }, { "id": "Othernotes", "value": "This indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/." }, { "id": "Referenceperiod", "value": "2007-2022" }, { "id": "Source", "value": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "LP.LPI.INFR.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI." }, { "id": "IndicatorName", "value": "Logistics performance index: Quality of trade and transport-related infrastructure (1=low to 5=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents evaluated the quality of trade and transport related infrastructure (e.g. ports, railroads, roads, information technology), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents." }, { "id": "Othernotes", "value": "This indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/." }, { "id": "Referenceperiod", "value": "2007-2022" }, { "id": "Source", "value": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "2" }, { "id": "LP.LPI.ITRN.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI." }, { "id": "IndicatorName", "value": "Logistics performance index: Ease of arranging competitively priced shipments (1=low to 5=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents assessed the ease of arranging competitively priced shipments to markets, on a rating ranging from 1 (very difficult) to 5 (very easy). Scores are averaged across all respondents." }, { "id": "Othernotes", "value": "This indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/." }, { "id": "Referenceperiod", "value": "2007-2022" }, { "id": "Source", "value": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "2" }, { "id": "LP.LPI.LOGS.XQ", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI." }, { "id": "IndicatorName", "value": "Logistics performance index: Competence and quality of logistics services (1=low to 5=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents evaluated the overall level of competence and quality of logistics services (e.g. transport operators, customs brokers), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents." }, { "id": "Othernotes", "value": "This indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/." }, { "id": "Referenceperiod", "value": "2007-2022" }, { "id": "Source", "value": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "2" }, { "id": "LP.LPI.OVRL.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI." }, { "id": "IndicatorName", "value": "Logistics performance index: Overall (1=low to 5=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "The Logistics Performance Index overall score reflects perceptions of a country's logistics based on the efficiency of customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The index ranges from 1 to 5, with a higher score representing better performance. \n\nData are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. \n\nScores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. \n\nDetails of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report." }, { "id": "Othernotes", "value": "This indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/." }, { "id": "Referenceperiod", "value": "2007-2022" }, { "id": "Source", "value": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "2" }, { "id": "LP.LPI.TIME.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI." }, { "id": "IndicatorName", "value": "Logistics performance index: Frequency with which shipments reach consignee within scheduled or expected time (1=low to 5=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents assessed how often the shipments to assessed markets reach the consignee within the scheduled or expected delivery time, on a rating ranging from 1 (hardly ever) to 5 (nearly always). Scores are averaged across all respondents." }, { "id": "Othernotes", "value": "This indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/." }, { "id": "Referenceperiod", "value": "2007-2022" }, { "id": "Source", "value": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "2" }, { "id": "LP.LPI.TRAC.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI." }, { "id": "IndicatorName", "value": "Logistics performance index: Ability to track and trace consignments (1=low to 5=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents evaluated the ability to track and trace consignments when shipping to the market, on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents." }, { "id": "Othernotes", "value": "This indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/." }, { "id": "Referenceperiod", "value": "2007-2022" }, { "id": "Source", "value": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "2" }, { "id": "MS.MIL.MPRT.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic." }, { "id": "IndicatorName", "value": "Arms imports (SIPRI trend indicator values)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "SIPRI calculates the volume of transfers to, from and between all parties using the TIV and the number of weapon systems or subsystems delivered in a given year. This data is intended to provide a common unit to allow the measurement if trends in the flow of arms to particular countries and regions over time. Therefore, the main priority is to ensure that the TIV system remains consistent over time, and that any changes introduced are backdated.\n\nSIPRI TIV figures do not represent sales prices for arms transfers. They should therefore not be directly compared with gross domestic product (GDP), military expenditure, sales values or the financial value of export licences in an attempt to measure the economic burden of arms imports or the economic benefits of exports. They are best used as the raw data for calculating trends in international arms transfers over periods of time, global percentages for suppliers and recipients, and percentages for the volume of transfers to or from particular states.\n\nExcluded are transfers of other military equipment such as small arms and light weapons, trucks, small artillery, ammunition, support equipment, technology transfers, and other services." }, { "id": "Longdefinition", "value": "Arms transfers (imports) cover the volume of transfers of major arms through sales and gifts, and those made through manufacturing licenses. Data cover major conventional weapons such as aircraft, armored vehicles, artillery, radar systems, missiles, and ships. Figures are SIPRI Trend Indicator Values (TIVs). A '0' indicates that the volume of deliveries is between 0 and 0.5 million SIPRI TIV." }, { "id": "Othernotes", "value": "Data for some countries are based on partial or uncertain data or rough estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Arms Transfers Programme, Stockholm International Peace Research Institute (SIPRI), uri: https://armstransfers.sipri.org/ArmsTransfer/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Stockholm International Peace Research Institute (SIPRI)'s Arms Transfers Program collects data on arms transfers from open sources. Since publicly available information is inadequate for tracking all weapons and other military equipment, SIPRI covers only what it terms major conventional weapons. Data cover the supply of weapons through sales, aid, gifts, and manufacturing licenses; therefore the term arms transfers rather than arms trade is used. SIPRI data also cover weapons supplied to or from rebel forces in an armed conflict as well as arms deliveries for which neither the supplier nor the recipient can be identified with acceptable certainty; these data are available in SIPRI's database.\n\nData cover major conventional weapons such as aircraft, armored vehicles, artillery, radar systems and other sensors, missiles, and ships designed for military use as well as some major components such as turrets for armored vehicles and engines. Excluded are other military equipment such as most small arms and light weapons, trucks, small artillery, ammunition, support equipment, technology transfers, and other services.\n\nWorld total includes arms transfers values for paramilitary groups.\n\nFor the method used for the SIPRI TIV see ." }, { "id": "Topic", "value": "Public Sector: Defense & arms trade" }, { "id": "Unitofmeasure", "value": "SIPRI trend-indicator values (TIVs)" } ], "source_id": "2" }, { "id": "MS.MIL.TOTL.P1", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic." }, { "id": "IndicatorName", "value": "Armed forces personnel, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data excludes personnel not on active duty, therefore it underestimates the share of the labor force working for the defense establishment. The cooperation of governments of all countries listed in “The Military Balance” has been sought by IISS and, in many cases, received. However, some data in “The Military Balance” is estimated." }, { "id": "Longdefinition", "value": "Armed forces personnel are active duty military personnel, including paramilitary forces if the training, organization, equipment, and control suggest they may be used to support or replace regular military forces." }, { "id": "Othernotes", "value": "Data for some countries are based on partial or uncertain data or rough estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2020" }, { "id": "Source", "value": "The Military Balance, International Institute for Strategic Studies" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Military data on manpower represent quantitative assessment of the personnel strengths of the world's armed forces. The IISS collects the data from a wide variety of sources. The numbers are based on the most accurate data available to, or on the best estimate that can be made by the International Institute for Strategic Studies (IISS) at the time of its annual publication. The current WDI indicator includes active armed forces and active paramilitary (but not reservists). Armed forces personnel comprise all servicemen and women on full-time duty, including conscripts and long-term assignments from the Reserves (“Reserve” describes formations and units not fully manned or operational in peacetime, but which can be mobilized by recalling reservists in an emergency). The indicator includes paramilitary forces. The source of the data (IISS) reports armed forces and paramilitary forces separately, however these figures are added for the purpose of computing this series. Home Guard units are counted as paramilitary." }, { "id": "Topic", "value": "Public Sector: Defense & arms trade" } ], "source_id": "2" }, { "id": "MS.MIL.TOTL.TF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic." }, { "id": "IndicatorName", "value": "Armed forces personnel (% of total labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude personnel not on active duty, therefore they underestimate the share of the labor force working for the defense establishment. Governments rarely report the size of their armed forces, so such data typically come from intelligence sources. Unless otherwise indicated, reserves includes all reservists committed to rejoining the armed forces in an emergency, except when national reserve service obligations following conscription last almost a lifetime." }, { "id": "Longdefinition", "value": "Armed forces personnel are active duty military personnel, including paramilitary forces if the training, organization, equipment, and control suggest they may be used to support or replace regular military forces. Labor force comprises all people who meet the International Labour Organization's definition of the economically active population." }, { "id": "Othernotes", "value": "Data for some countries are based on partial or uncertain data or rough estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2020" }, { "id": "Source", "value": "The Military Balance, International Institute for Strategic Studies" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Military data on manpower represent quantitative assessment of the personnel strengths of the world's armed forces. The numbers are based on the most accurate data available to, or, on the best estimate that can be made by the International Institute for Strategic Studies (IISS) at the time of its annual publication. The IISS collects the data from national governments.\n\nArmed forces personnel comprise all servicemen and women on full-time duty (including conscripts and long-term assignments from the Reserves). Reserve describes formations and units not fully manned or operational in peacetime, but which can be mobilized by recalling reservists in an emergency. IISS estimates of effective reservist strengths on the numbers available within five years of completing full-time service, unless there is good evidence that obligations are enforced for longer. Although paramilitary forces whose training, organization, equipment and control suggest they may be used to support or replace regular military forces, they are not included in the armed forces personnel. Home Guard units are counted as paramilitary.\n\nData are shown as percentage of total labor force. According to International Labour Organization (ILO armed forces occupations include all jobs held by members of the armed forces. Members of the armed forces are those personnel who are currently serving in the armed forces, including auxiliary services, whether on a voluntary or compulsory basis, and who are not free to accept civilian employment and are subject to military discipline. Included are regular members of the army, navy, air force and other military services, as well as conscripts enrolled for military training or other service for a specified period. Excluded are persons in civilian employment of government establishments concerned with defense issues; police (other than military police); customs inspectors and members of border or other armed civilian services; persons who have been temporarily withdrawn from civilian life for a short period of military training or retraining, according to national requirements, and members of military reserves not currently on active service." }, { "id": "Topic", "value": "Public Sector: Defense & arms trade" } ], "source_id": "2" }, { "id": "MS.MIL.XPND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures as a share of gross domestic product (GDP) are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nAs an \"input\" measure military expenditures are not directly related to the \"output\" of military activities, capabilities, or security. Comparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic." }, { "id": "IndicatorName", "value": "Military expenditure (current USD)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "SIPRI strives to compile reliable, consistent military expenditure data by assessing multiple sources, but accuracy depends on the quality and transparency of those sources. Challenges arise from two key issues: whether reported figures reflect actual spending and how closely they match SIPRI’s definition. While data is generally accurate in developed and many developing countries, weak governance, corruption, and secret transfers in others can lead to major discrepancies. SIPRI sometimes makes estimates, when sources conflict or lack coverage, introducing uncertainty, especially for countries like China or the UAE. Definitions also vary: official defense budgets may omit pensions, paramilitary forces, or extra- and off-budget spending such as resource funds or military commercial activities, which can be substantial but often untraceable, particularly in Africa, the Middle East, and parts of Asia. SIPRI notes these gaps in footnotes, but where figures cannot be obtained, estimates remain incomplete, limiting comparability across countries." }, { "id": "Longdefinition", "value": "Military spending USD, in current prices (converted at the exchange rate for the given year)" }, { "id": "Othernotes", "value": "Statistical concept(s): Although the lack of sufficiently detailed data makes it difficult to apply a common definition of military expenditure on a worldwide basis, SIPRI has adopted a definition as a guideline. Where possible, SIPRI military expenditure data include all current and capital expenditure on: (a) the armed forces, including peacekeeping forces; (b) defense ministries and other government agencies engaged in defense projects; (c) paramilitary forces, when judged to be trained and equipped for military operations; and (d) military space activities.\n\nThis should include expenditure on: (i) personnel, including: salaries of military and civil personnel; b. retirement pensions of military personnel, and; social services for personnel; (ii) operations and maintenance; (iii) procurement; (iv) military research and development; (v) military infrastructure spending, including military bases; and (vi) military aid (in the military expenditure of the donor country). \n\nSIPRI’s estimate of military aid includes financial contributions, training and operational costs, replacement costs of the military equipment stocks donated to recipients and payments to procure additional military equipment for the recipient. However, it does not include the estimated value of military equipment stocks donated.\n\nCivil defense and spending related to past military activities—like veterans’ benefits or demobilization—are excluded. Because many countries do not publish data detailed enough to perfectly match SIPRI’s definition, SIPRI often relies on national figures and prioritizes internal consistency over time rather than strict cross-country uniformity. As a result, SIPRI data are most reliable for analyzing trends rather than precise comparisons between countries, and users should consult footnotes for known deviations from the definition.\n\nData for some countries are based on partial or uncertain data or rough estimates. For additional details please refer to the military expenditure database on the SIPRI website: https://sipri.org/databases/milex" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "SIPRI Military Expenditure Database, Stockholm International Peace Research Institute (SIPRI), uri: https://www.sipri.org/databases" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Military expenditure data is collected from primary and secondary sources. Primary sources include official government publications such as national budgets, defense white papers, financial statistics, and responses to questionnaires from SIPRI, the UN, or the OSCE, as well as expert analyses of government budgets. Secondary sources draw on these primary materials and include international datasets produced by organizations like NATO and the IMF, as well as reference works such as the German Statistisches Jahrbuch, the Europa Yearbook, and Economist Intelligence Unit country reports. Other secondary sources are journals and newspapers. Historically, especially before 1988, secondary sources (notably IMF and UN statistics) were used more heavily due to limited availability of official national data. In recent years, the availability of primary government data has increased significantly.\n\nSIPRI uses government-reported military expenditure data as the baseline and only produces its own estimates when official data are incomplete or inconsistent across years. Estimates are created through detailed budget analysis or by merging overlapping data sources, giving priority to those that best fit SIPRI’s definition, are up-to-date, and provide continuous time series. Older pre-1988 data often required combining secondary sources like IMF GFS and UNSY, which differ in definitions (e.g., excluding military pensions). SIPRI avoids making assumptions and does not estimate spending for countries lacking any official data. In SIPRI’s database, estimated values appear in blue, while figures considered uncertain, because of weak sources or volatile conditions, appear in red. For recent years, budget projections and deflator-based adjustments are common but flagged only when uncertainty is unusually high.\n\nSIPRI presents military expenditure data on a calendar-year basis (except for the U.S., which uses financial years) and converts figures to constant prices using national consumer price indices to reflect opportunity costs. Local-currency data are converted to US dollars using average market exchange rates. \n\nMilitary spending as a share of GDP (“military burden”) is calculated using nominal local-currency values for both military expenditure and GDP. SIPRI also provides military spending as a share of total government expenditure, where IMF data permit. \n\nFor additional information, please refer to the SIPRI website: https://www.sipri.org/databases/milex \n\nRefer to Other notes for the Statistical Concept(s)." }, { "id": "Topic", "value": "Public Sector: Defense & arms trade" }, { "id": "Unitofmeasure", "value": "USD current" } ], "source_id": "2" }, { "id": "MS.MIL.XPND.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures are a rough indicator of the portion of national resources used for military activities and of the burden on the economy. Comparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic." }, { "id": "IndicatorName", "value": "Military expenditure (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "SIPRI strives to compile reliable, consistent military expenditure data by assessing multiple sources, but accuracy depends on the quality and transparency of those sources. Challenges arise from two key issues: whether reported figures reflect actual spending and how closely they match SIPRI’s definition. While data is generally accurate in developed and many developing countries, weak governance, corruption, and secret transfers in others can lead to major discrepancies. SIPRI sometimes makes estimates, when sources conflict or lack coverage, introducing uncertainty, especially for countries like China or the UAE. Definitions also vary: official defense budgets may omit pensions, paramilitary forces, or extra- and off-budget spending such as resource funds or military commercial activities, which can be substantial but often untraceable, particularly in Africa, the Middle East, and parts of Asia. SIPRI notes these gaps in footnotes, but where figures cannot be obtained, estimates remain incomplete, limiting comparability across countries." }, { "id": "Longdefinition", "value": "Military expenditure in local currency at current prices (by calendar year)" }, { "id": "Othernotes", "value": "Statistical concept(s): Although the lack of sufficiently detailed data makes it difficult to apply a common definition of military expenditure on a worldwide basis, SIPRI has adopted a definition as a guideline. Where possible, SIPRI military expenditure data include all current and capital expenditure on: (a) the armed forces, including peacekeeping forces; (b) defense ministries and other government agencies engaged in defense projects; (c) paramilitary forces, when judged to be trained and equipped for military operations; and (d) military space activities.\n\nThis should include expenditure on: (i) personnel, including: salaries of military and civil personnel; b. retirement pensions of military personnel, and; social services for personnel; (ii) operations and maintenance; (iii) procurement; (iv) military research and development; (v) military infrastructure spending, including military bases; and (vi) military aid (in the military expenditure of the donor country). \n\nSIPRI’s estimate of military aid includes financial contributions, training and operational costs, replacement costs of the military equipment stocks donated to recipients and payments to procure additional military equipment for the recipient. However, it does not include the estimated value of military equipment stocks donated.\n\nCivil defense and spending related to past military activities—like veterans’ benefits or demobilization—are excluded. Because many countries do not publish data detailed enough to perfectly match SIPRI’s definition, SIPRI often relies on national figures and prioritizes internal consistency over time rather than strict cross-country uniformity. As a result, SIPRI data are most reliable for analyzing trends rather than precise comparisons between countries, and users should consult footnotes for known deviations from the definition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "SIPRI Military Expenditure Database, Stockholm International Peace Research Institute (SIPRI), uri: https://www.sipri.org/databases" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Military expenditure data is collected from primary and secondary sources. Primary sources include official government publications such as national budgets, defense white papers, financial statistics, and responses to questionnaires from SIPRI, the UN, or the OSCE, as well as expert analyses of government budgets. Secondary sources draw on these primary materials and include international datasets produced by organizations like NATO and the IMF, as well as reference works such as the German Statistisches Jahrbuch, the Europa Yearbook, and Economist Intelligence Unit country reports. Other secondary sources are journals and newspapers. Historically, especially before 1988, secondary sources (notably IMF and UN statistics) were used more heavily due to limited availability of official national data. In recent years, the availability of primary government data has increased significantly.\n\nSIPRI uses government-reported military expenditure data as the baseline and only produces its own estimates when official data are incomplete or inconsistent across years. Estimates are created through detailed budget analysis or by merging overlapping data sources, giving priority to those that best fit SIPRI’s definition, are up-to-date, and provide continuous time series. Older pre-1988 data often required combining secondary sources like IMF GFS and UNSY, which differ in definitions (e.g., excluding military pensions). SIPRI avoids making assumptions and does not estimate spending for countries lacking any official data. In SIPRI’s database, estimated values appear in blue, while figures considered uncertain, because of weak sources or volatile conditions, appear in red. For recent years, budget projections and deflator-based adjustments are common but flagged only when uncertainty is unusually high.\n\nSIPRI presents military expenditure data on a calendar-year basis (except for the U.S., which uses financial years) and converts figures to constant prices using national consumer price indices to reflect opportunity costs. Local-currency data are converted to US dollars using average market exchange rates. \n\nMilitary spending as a share of GDP (“military burden”) is calculated using nominal local-currency values for both military expenditure and GDP. SIPRI also provides military spending as a share of total government expenditure, where IMF data permit. \n\nFor additional information, please refer to the SIPRI website: https://www.sipri.org/databases/milex \n\nRefer to Other notes for the Statistical Concept(s)." }, { "id": "Topic", "value": "Public Sector: Defense & arms trade" }, { "id": "Unitofmeasure", "value": "Domestic currency" } ], "source_id": "2" }, { "id": "MS.MIL.XPND.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures as a share of gross domestic product (GDP) are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nAs an \"input\" measure military expenditures are not directly related to the \"output\" of military activities, capabilities, or security. Comparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic." }, { "id": "IndicatorName", "value": "Military expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "SIPRI strives to compile reliable, consistent military expenditure data by assessing multiple sources, but accuracy depends on the quality and transparency of those sources. Challenges arise from two key issues: whether reported figures reflect actual spending and how closely they match SIPRI’s definition. While data is generally accurate in developed and many developing countries, weak governance, corruption, and secret transfers in others can lead to major discrepancies. SIPRI sometimes makes estimates, when sources conflict or lack coverage, introducing uncertainty, especially for countries like China or the UAE. Definitions also vary: official defense budgets may omit pensions, paramilitary forces, or extra- and off-budget spending such as resource funds or military commercial activities, which can be substantial but often untraceable, particularly in Africa, the Middle East, and parts of Asia. SIPRI notes these gaps in footnotes, but where figures cannot be obtained, estimates remain incomplete, limiting comparability across countries." }, { "id": "Longdefinition", "value": "Military expenditure by country as percentage of gross domestic product" }, { "id": "Othernotes", "value": "Statistical concept(s): Although the lack of sufficiently detailed data makes it difficult to apply a common definition of military expenditure on a worldwide basis, SIPRI has adopted a definition as a guideline. Where possible, SIPRI military expenditure data include all current and capital expenditure on: (a) the armed forces, including peacekeeping forces; (b) defense ministries and other government agencies engaged in defense projects; (c) paramilitary forces, when judged to be trained and equipped for military operations; and (d) military space activities.\n\nThis should include expenditure on: (i) personnel, including: salaries of military and civil personnel; b. retirement pensions of military personnel, and; social services for personnel; (ii) operations and maintenance; (iii) procurement; (iv) military research and development; (v) military infrastructure spending, including military bases; and (vi) military aid (in the military expenditure of the donor country). \n\nSIPRI’s estimate of military aid includes financial contributions, training and operational costs, replacement costs of the military equipment stocks donated to recipients and payments to procure additional military equipment for the recipient. However, it does not include the estimated value of military equipment stocks donated.\n\nCivil defense and spending related to past military activities—like veterans’ benefits or demobilization—are excluded. Because many countries do not publish data detailed enough to perfectly match SIPRI’s definition, SIPRI often relies on national figures and prioritizes internal consistency over time rather than strict cross-country uniformity. As a result, SIPRI data are most reliable for analyzing trends rather than precise comparisons between countries, and users should consult footnotes for known deviations from the definition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "SIPRI Military Expenditure Database, Stockholm International Peace Research Institute (SIPRI), uri: https://www.sipri.org/databases" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Military expenditure data is collected from primary and secondary sources. Primary sources include official government publications such as national budgets, defense white papers, financial statistics, and responses to questionnaires from SIPRI, the UN, or the OSCE, as well as expert analyses of government budgets. Secondary sources draw on these primary materials and include international datasets produced by organizations like NATO and the IMF, as well as reference works such as the German Statistisches Jahrbuch, the Europa Yearbook, and Economist Intelligence Unit country reports. Other secondary sources are journals and newspapers. Historically, especially before 1988, secondary sources (notably IMF and UN statistics) were used more heavily due to limited availability of official national data. In recent years, the availability of primary government data has increased significantly.\n\nSIPRI uses government-reported military expenditure data as the baseline and only produces its own estimates when official data are incomplete or inconsistent across years. Estimates are created through detailed budget analysis or by merging overlapping data sources, giving priority to those that best fit SIPRI’s definition, are up-to-date, and provide continuous time series. Older pre-1988 data often required combining secondary sources like IMF GFS and UNSY, which differ in definitions (e.g., excluding military pensions). SIPRI avoids making assumptions and does not estimate spending for countries lacking any official data. In SIPRI’s database, estimated values appear in blue, while figures considered uncertain, because of weak sources or volatile conditions, appear in red. For recent years, budget projections and deflator-based adjustments are common but flagged only when uncertainty is unusually high.\n\nSIPRI presents military expenditure data on a calendar-year basis (except for the U.S., which uses financial years) and converts figures to constant prices using national consumer price indices to reflect opportunity costs. Local-currency data are converted to US dollars using average market exchange rates. \n\nMilitary spending as a share of GDP (“military burden”) is calculated using nominal local-currency values for both military expenditure and GDP. SIPRI also provides military spending as a share of total government expenditure, where IMF data permit. \n\nFor additional information, please refer to the SIPRI website: https://www.sipri.org/databases/milex \nRefer to Other notes for the Statistical Concept(s)." }, { "id": "Topic", "value": "Public Sector: Defense & arms trade" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "2" }, { "id": "MS.MIL.XPND.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures as a share of gross domestic product (GDP) are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nAs an \"input\" measure military expenditures are not directly related to the \"output\" of military activities, capabilities, or security. Comparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic." }, { "id": "IndicatorName", "value": "Military expenditure (% of general government expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "SIPRI strives to compile reliable, consistent military expenditure data by assessing multiple sources, but accuracy depends on the quality and transparency of those sources. Challenges arise from two key issues: whether reported figures reflect actual spending and how closely they match SIPRI’s definition. While data is generally accurate in developed and many developing countries, weak governance, corruption, and secret transfers in others can lead to major discrepancies. SIPRI sometimes makes estimates, when sources conflict or lack coverage, introducing uncertainty, especially for countries like China or the UAE. Definitions also vary: official defense budgets may omit pensions, paramilitary forces, or extra- and off-budget spending such as resource funds or military commercial activities, which can be substantial but often untraceable, particularly in Africa, the Middle East, and parts of Asia. SIPRI notes these gaps in footnotes, but where figures cannot be obtained, estimates remain incomplete, limiting comparability across countries." }, { "id": "Longdefinition", "value": "Military expenditure expressed as a percentage of general government expenditure" }, { "id": "Othernotes", "value": "Statistical concept(s): Although the lack of sufficiently detailed data makes it difficult to apply a common definition of military expenditure on a worldwide basis, SIPRI has adopted a definition as a guideline. Where possible, SIPRI military expenditure data include all current and capital expenditure on: (a) the armed forces, including peacekeeping forces; (b) defense ministries and other government agencies engaged in defense projects; (c) paramilitary forces, when judged to be trained and equipped for military operations; and (d) military space activities.\n\nThis should include expenditure on: (i) personnel, including: salaries of military and civil personnel; b. retirement pensions of military personnel, and; social services for personnel; (ii) operations and maintenance; (iii) procurement; (iv) military research and development; (v) military infrastructure spending, including military bases; and (vi) military aid (in the military expenditure of the donor country). \n\nSIPRI’s estimate of military aid includes financial contributions, training and operational costs, replacement costs of the military equipment stocks donated to recipients and payments to procure additional military equipment for the recipient. However, it does not include the estimated value of military equipment stocks donated.\n\nCivil defense and spending related to past military activities—like veterans’ benefits or demobilization—are excluded. Because many countries do not publish data detailed enough to perfectly match SIPRI’s definition, SIPRI often relies on national figures and prioritizes internal consistency over time rather than strict cross-country uniformity. As a result, SIPRI data are most reliable for analyzing trends rather than precise comparisons between countries, and users should consult footnotes for known deviations from the definition.\n\nData for some countries are based on partial or uncertain data or rough estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2024" }, { "id": "Source", "value": "SIPRI Military Expenditure Database, Stockholm International Peace Research Institute (SIPRI), uri: https://www.sipri.org/databases" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Military expenditure data is collected from primary and secondary sources. Primary sources include official government publications such as national budgets, defense white papers, financial statistics, and responses to questionnaires from SIPRI, the UN, or the OSCE, as well as expert analyses of government budgets. Secondary sources draw on these primary materials and include international datasets produced by organizations like NATO and the IMF, as well as reference works such as the German Statistisches Jahrbuch, the Europa Yearbook, and Economist Intelligence Unit country reports. Other secondary sources are journals and newspapers. Historically, especially before 1988, secondary sources (notably IMF and UN statistics) were used more heavily due to limited availability of official national data. In recent years, the availability of primary government data has increased significantly.\n\nSIPRI uses government-reported military expenditure data as the baseline and only produces its own estimates when official data are incomplete or inconsistent across years. Estimates are created through detailed budget analysis or by merging overlapping data sources, giving priority to those that best fit SIPRI’s definition, are up-to-date, and provide continuous time series. Older pre-1988 data often required combining secondary sources like IMF GFS and UNSY, which differ in definitions (e.g., excluding military pensions). SIPRI avoids making assumptions and does not estimate spending for countries lacking any official data. In SIPRI’s database, estimated values appear in blue, while figures considered uncertain, because of weak sources or volatile conditions, appear in red. For recent years, budget projections and deflator-based adjustments are common but flagged only when uncertainty is unusually high.\n\nSIPRI presents military expenditure data on a calendar-year basis (except for the U.S., which uses financial years) and converts figures to constant prices using national consumer price indices to reflect opportunity costs. Local-currency data are converted to US dollars using average market exchange rates. \n\nMilitary spending as a share of GDP (“military burden”) is calculated using nominal local-currency values for both military expenditure and GDP. SIPRI also provides military spending as a share of total government expenditure, where IMF data permit. \n\nFor additional information, please refer to the SIPRI website: https://www.sipri.org/databases/milex \n\nRefer to Other notes for the Statistical Concept(s)." }, { "id": "Topic", "value": "Public Sector: Defense & arms trade" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "MS.MIL.XPRT.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic." }, { "id": "IndicatorName", "value": "Arms exports (SIPRI trend indicator values)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "SIPRI calculates the volume of transfers to, from and between all parties using the TIV and the number of weapon systems or subsystems delivered in a given year. This data is intended to provide a common unit to allow the measurement if trends in the flow of arms to particular countries and regions over time. Therefore, the main priority is to ensure that the TIV system remains consistent over time, and that any changes introduced are backdated.\n\nSIPRI TIV figures do not represent sales prices for arms transfers. They should therefore not be directly compared with gross domestic product (GDP), military expenditure, sales values or the financial value of export licences in an attempt to measure the economic burden of arms imports or the economic benefits of exports. They are best used as the raw data for calculating trends in international arms transfers over periods of time, global percentages for suppliers and recipients, and percentages for the volume of transfers to or from particular states.\n\nExcluded are transfers of other military equipment such as small arms and light weapons, trucks, small artillery, ammunition, support equipment, technology transfers, and other services." }, { "id": "Longdefinition", "value": "Arms transfers (exports) cover the volume of transfers of major arms through sales and gifts, and those made through manufacturing licenses. Data cover major conventional weapons such as aircraft, armored vehicles, artillery, radar systems, missiles, and ships. Figures are SIPRI Trend Indicator Values (TIVs). A '0' indicates that the volume of deliveries is between 0 and 0.5 million SIPRI TIV." }, { "id": "Othernotes", "value": "Data for some countries are based on partial or uncertain data or rough estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Arms Transfers Programme, Stockholm International Peace Research Institute (SIPRI), uri: https://armstransfers.sipri.org/ArmsTransfer/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Stockholm International Peace Research Institute (SIPRI)'s Arms Transfers Program collects data on arms transfers from open sources. Since publicly available information is inadequate for tracking all weapons and other military equipment, SIPRI covers only what it terms major conventional weapons. Data cover the supply of weapons through sales, aid, gifts, and manufacturing licenses; therefore the term arms transfers rather than arms trade is used. SIPRI data also cover weapons supplied to or from rebel forces in an armed conflict as well as arms deliveries for which neither the supplier nor the recipient can be identified with acceptable certainty; these data are available in SIPRI's database.\n\nData cover major conventional weapons such as aircraft, armored vehicles, artillery, radar systems and other sensors, missiles, and ships designed for military use as well as some major components such as turrets for armored vehicles and engines. Excluded are other military equipment such as most small arms and light weapons, trucks, small artillery, ammunition, support equipment, technology transfers, and other services.\n\nWorld total includes arms transfers values for paramilitary groups.\n\nFor the method used for the SIPRI TIV see ." }, { "id": "Topic", "value": "Public Sector: Defense & arms trade" }, { "id": "Unitofmeasure", "value": "SIPRI trend-indicator values (TIVs)" } ], "source_id": "2" }, { "id": "NE.CON.GOVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "General government final consumption expenditure (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total." }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NE.CON.GOVT.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "General government final consumption expenditure (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NE.CON.GOVT.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "General government final consumption expenditure (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nMeasures of growth in consumption and capital formation are subject to two kinds of inaccuracy. The first stems from the difficulty of measuring expenditures at current price levels. The second arises in deflating current price data to measure volume growth, where results depend on the relevance and reliability of the price indexes and weights used. Measuring price changes is more difficult for investment goods than for consumption goods because of the one-time nature of many investments and because the rate of technological progress in capital goods makes capturing change in quality difficult. (An example is computers - prices have fallen as quality has improved.)\n\n\n\n\n\nTo obtain government consumption in constant prices, countries may deflate current values by applying a wage (price) index or extrapolate from the change in government employment. Neither technique captures improvements in productivity or changes in the quality of government services." }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NE.CON.GOVT.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "General government final consumption expenditure (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.CON.GOVT.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "General government final consumption expenditure (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NE.CON.GOVT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "General government final consumption expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total." }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.CON.PCAP.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Households and NPISHs Final consumption expenditure per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for final consumption expenditure per person expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons.\nHouseholds and NPISHs final consumption expenditure includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), note: This information is for PPP conversion factors., publisher: International Comparison Program (ICP), date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, note: This information is for PPP conversion factors., publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB), note: This information is for households and NPISHs Final consumption expenditure per capita.;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD), note: This information is for households and NPISHs Final consumption expenditure per capita.;\nWorld Economic Outlook database, International Monetary Fund (IMF), note: This information is for households and NPISHs Final consumption expenditure per capita." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases.\n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures.\n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Current international $" } ], "source_id": "2" }, { "id": "NE.CON.PCAP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Households and NPISHs Final consumption expenditure per capita, PPP (constant 2021 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for final consumption expenditure per person expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons.\nHouseholds and NPISHs final consumption expenditure includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), note: This information is for PPP conversion factors., publisher: International Comparison Program (ICP), date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, note: This information is for PPP conversion factors., publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, note: This information is for PPP conversion factors., publisher: OECD;\nStaff estimates, World Bank (WB), note: This information is for households and NPISHs Final consumption expenditure per capita.;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD), note: This information is for households and NPISHs Final consumption expenditure per capita.;\nWorld Economic Outlook database, International Monetary Fund (IMF), note: This information is for households and NPISHs Final consumption expenditure per capita." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases.\n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures.\n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Constant 2021 international $" } ], "source_id": "2" }, { "id": "NE.CON.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households and NPISHs final consumption expenditure (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NE.CON.PRVT.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households and NPISHs final consumption expenditure (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NE.CON.PRVT.CN.AD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households and NPISHs final consumption expenditure, linked series (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NE.CON.PRVT.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households and NPISHs final consumption expenditure (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total. Household final consumption expenditure is often estimated as a residual, by subtracting all other known expenditures from GDP. The resulting aggregate may incorporate fairly large discrepancies. When household consumption is calculated separately, many of the estimates are based on household surveys, which tend to be one-year studies with limited coverage. Thus the estimates quickly become outdated and must be supplemented by estimates using price- and quantity-based statistical procedures. Complicating the issue, in many developing countries the distinction between cash outlays for personal business and those for household use may be blurred.\n\n\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\n\n\n\n\nMeasures of growth in consumption and capital formation are subject to two kinds of inaccuracy. The first stems from the difficulty of measuring expenditures at current price levels. The second arises in deflating current price data to measure volume growth, where results depend on the relevance and reliability of the price indexes and weights used. Measuring price changes is more difficult for investment goods than for consumption goods because of the one-time nature of many investments and because the rate of technological progress in capital goods makes capturing change in quality difficult. (An example is computers - prices have fallen as quality has improved.)" }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NE.CON.PRVT.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households and NPISHs final consumption expenditure (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.CON.PRVT.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households and NPISHs final consumption expenditure (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NE.CON.PRVT.PC.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households and NPISHs final consumption expenditure per capita (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total. Household final consumption expenditure is often estimated as a residual, by subtracting all other known expenditures from GDP. The resulting aggregate may incorporate fairly large discrepancies. When household consumption is calculated separately, many of the estimates are based on household surveys, which tend to be one-year studies with limited coverage. Thus the estimates quickly become outdated and must be supplemented by estimates using price- and quantity-based statistical procedures. Complicating the issue, in many developing countries the distinction between cash outlays for personal business and those for household use may be blurred.\n\n\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\n\n\n\n\nMeasures of growth in consumption and capital formation are subject to two kinds of inaccuracy. The first stems from the difficulty of measuring expenditures at current price levels. The second arises in deflating current price data to measure volume growth, where results depend on the relevance and reliability of the price indexes and weights used. Measuring price changes is more difficult for investment goods than for consumption goods because of the one-time nature of many investments and because the rate of technological progress in capital goods makes capturing change in quality difficult. (An example is computers - prices have fallen as quality has improved.)" }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NE.CON.PRVT.PC.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households final consumption expenditure per capita (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.CON.PRVT.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Households and NPISHs Final consumption expenditure, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for final consumption expenditure expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nHouseholds and NPISHs final consumption expenditure includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Current international $" } ], "source_id": "2" }, { "id": "NE.CON.PRVT.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Households and NPISHs Final consumption expenditure, PPP (constant 2021 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for final consumption expenditure expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nHouseholds and NPISHs final consumption expenditure includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Constant 2021 international $" } ], "source_id": "2" }, { "id": "NE.CON.PRVT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households and NPISHs final consumption expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total. Household final consumption expenditure is often estimated as a residual, by subtracting all other known expenditures from GDP. The resulting aggregate may incorporate fairly large discrepancies. When household consumption is calculated separately, many of the estimates are based on household surveys, which tend to be one-year studies with limited coverage. Thus the estimates quickly become outdated and must be supplemented by estimates using price- and quantity-based statistical procedures. Complicating the issue, in many developing countries the distinction between cash outlays for personal business and those for household use may be blurred.\n\n\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians." }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.CON.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Final consumption expenditure (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NE.CON.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Final consumption expenditure (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NE.CON.TOTL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Final consumption expenditure (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NE.CON.TOTL.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Final consumption expenditure (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.CON.TOTL.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Final consumption expenditure (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NE.CON.TOTL.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Final consumption expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.DAB.DEFL.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross national expenditure deflator (base year varies by country)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. A deflator is the ratio of an indicator in current prices over the same series in constant prices. The base year varies by country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "index" } ], "source_id": "2" }, { "id": "NE.DAB.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross national expenditure (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NE.DAB.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross national expenditure (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NE.DAB.TOTL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross national expenditure (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NE.DAB.TOTL.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross national expenditure (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NE.DAB.TOTL.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross national expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.EXP.GNFS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Exports of goods and services (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics." }, { "id": "Longdefinition", "value": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NE.EXP.GNFS.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Exports of goods and services (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NE.EXP.GNFS.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Exports of goods and services (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics." }, { "id": "Longdefinition", "value": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NE.EXP.GNFS.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Exports of goods and services (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.EXP.GNFS.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Exports of goods and services (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NE.EXP.GNFS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Exports of goods and services (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics." }, { "id": "Longdefinition", "value": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.GDI.FPRV.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross fixed capital formation, private sector (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Private investment covers outlays by the private sector (including private nonprofit agencies) on additions to its fixed domestic assets. Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NE.GDI.FPRV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross fixed capital formation, private sector (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Private investment covers outlays by the private sector (including private nonprofit agencies) on additions to its fixed domestic assets. Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.GDI.FTOT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross fixed capital formation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NE.GDI.FTOT.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross fixed capital formation (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NE.GDI.FTOT.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross fixed capital formation (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NE.GDI.FTOT.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross fixed capital formation (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.GDI.FTOT.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross fixed capital formation (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NE.GDI.FTOT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross fixed capital formation (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.GDI.STKB.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Changes in inventories (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Changes in inventories is the value of entries into inventories less the value of withdrawals and less the value of any recurrent losses of goods held in inventories during the accounting period.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NE.GDI.STKB.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Changes in inventories (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Changes in inventories is the value of entries into inventories less the value of withdrawals and less the value of any recurrent losses of goods held in inventories during the accounting period.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NE.GDI.STKB.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Changes in inventories (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Changes in inventories is the value of entries into inventories less the value of withdrawals and less the value of any recurrent losses of goods held in inventories during the accounting period.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NE.GDI.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross capital formation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on capital formation may be estimated from direct surveys of enterprises and administrative records or based on the commodity flow method using data from production, trade, and construction activities. The quality of data on government fixed capital formation depends on the quality of government accounting systems (which tend to be weak in developing countries). Measures of fixed capital formation by households and corporations - particularly capital outlays by small, unincorporated enterprises - are usually unreliable.\n\n\n\n\n\nEstimates of changes in inventories are rarely complete but usually include the most important activities or commodities. In some countries these estimates are derived as a composite residual along with household final consumption expenditure. According to national accounts conventions, adjustments should be made for appreciation of the value of inventory holdings due to price changes, but this is not always done. In highly inflationary economies this element can be substantial." }, { "id": "Longdefinition", "value": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NE.GDI.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross capital formation (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NE.GDI.TOTL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross capital formation (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on capital formation may be estimated from direct surveys of enterprises and administrative records or based on the commodity flow method using data from production, trade, and construction activities. The quality of data on government fixed capital formation depends on the quality of government accounting systems (which tend to be weak in developing countries). Measures of fixed capital formation by households and corporations - particularly capital outlays by small, unincorporated enterprises - are usually unreliable.\n\n\n\n\n\nEstimates of changes in inventories are rarely complete but usually include the most important activities or commodities. In some countries these estimates are derived as a composite residual along with household final consumption expenditure. According to national accounts conventions, adjustments should be made for appreciation of the value of inventory holdings due to price changes, but this is not always done. In highly inflationary economies this element can be substantial.\n\n\n\n\n\nMeasures of growth in consumption and capital formation are subject to two kinds of inaccuracy. The first stems from the difficulty of measuring expenditures at current price levels. The second arises in deflating current price data to measure volume growth, where results depend on the relevance and reliability of the price indexes and weights used. Measuring price changes is more difficult for investment goods than for consumption goods because of the one-time nature of many investments and because the rate of technological progress in capital goods makes capturing change in quality difficult. (An example is computers - prices have fallen as quality has improved.) Several countries estimate capital formation from the supply side, identifying capital goods entering an economy directly from detailed production and international trade statistics. This means that the price indexes used in deflating production and international trade, reflecting delivered or offered prices, will determine the deflator for capital formation expenditures on the demand side." }, { "id": "Longdefinition", "value": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NE.GDI.TOTL.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross capital formation (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.GDI.TOTL.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross capital formation (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NE.GDI.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross capital formation (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on capital formation may be estimated from direct surveys of enterprises and administrative records or based on the commodity flow method using data from production, trade, and construction activities. The quality of data on government fixed capital formation depends on the quality of government accounting systems (which tend to be weak in developing countries). Measures of fixed capital formation by households and corporations - particularly capital outlays by small, unincorporated enterprises - are usually unreliable.\n\n\n\n\n\nEstimates of changes in inventories are rarely complete but usually include the most important activities or commodities. In some countries these estimates are derived as a composite residual along with household final consumption expenditure. According to national accounts conventions, adjustments should be made for appreciation of the value of inventory holdings due to price changes, but this is not always done. In highly inflationary economies this element can be substantial." }, { "id": "Longdefinition", "value": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.IMP.GNFS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Imports of goods and services (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics." }, { "id": "Longdefinition", "value": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NE.IMP.GNFS.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Imports of goods and services (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NE.IMP.GNFS.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Imports of goods and services (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics." }, { "id": "Longdefinition", "value": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NE.IMP.GNFS.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Imports of goods and services (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.IMP.GNFS.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Imports of goods and services (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NE.IMP.GNFS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Imports of goods and services (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics." }, { "id": "Longdefinition", "value": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.RSB.GNFS.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "External balance on goods and services (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The balance of international trade in goods and services is the difference between the exports and imports of goods and services. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NE.RSB.GNFS.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "External balance on goods and services (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The balance of international trade in goods and services is the difference between the exports and imports of goods and services. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NE.RSB.GNFS.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "External balance on goods and services (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The balance of international trade in goods and services is the difference between the exports and imports of goods and services. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NE.RSB.GNFS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "External balance on goods and services (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The balance of international trade in goods and services is the difference between the exports and imports of goods and services. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NE.TRD.GNFS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Trade (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Trade is the sum of exports and imports of goods and services. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NV.AGR.EMPL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Agriculture, forestry, and fishing, value added per worker (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output is not easily available, especially in low- and middle-income countries, because the definition, coverage, and methodology are not always consistent across countries. For more details, see Agriculture, forestry, and fishing, value added (constant 2015 US$) [NV.AGR.TOTL.KD], Industry (including construction), value added (constant 2015 US$) [NV.IND.TOTL.KD], and Services, value added (constant 2015 US$) [NV.SRV.TOTL.KD]." }, { "id": "Longdefinition", "value": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "World Development Indicators database, World Bank (WB);\nILOSTAT database, International Labour Organization (ILO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Value added per worker is calculated by dividing value added of a sector by the number employed in the sector.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NV.AGR.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Agriculture, forestry, and fishing, value added (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently \"netted out\" using equally crude and ad hoc approximations." }, { "id": "Longdefinition", "value": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Value added" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NV.AGR.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Agriculture, forestry, and fishing, value added (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Value added" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NV.AGR.TOTL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Agriculture, forestry, and fishing, value added (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently \"netted out\" using equally crude and ad hoc approximations." }, { "id": "Longdefinition", "value": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NV.AGR.TOTL.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Agriculture, forestry, and fishing, value added (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently \"netted out\" using equally crude and ad hoc approximations." }, { "id": "Longdefinition", "value": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NV.AGR.TOTL.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Agriculture, forestry, and fishing, value added (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Value added" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NV.AGR.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Agriculture, forestry, and fishing, value added (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently \"netted out\" using equally crude and ad hoc approximations." }, { "id": "Longdefinition", "value": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period. Note: For VAB countries, gross value added at factor cost is used as the denominator." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NV.FSM.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Financial Intermediary Services Indirectly Measured (FISIM) (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Financial intermediation services which are implicitly charged in the form of either the difference between a reference rate and the interest rate actually paid to depositors, or the difference between the interest rate charged to borrowers and a reference rate. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Value added" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NV.FSM.TOTL.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Financial Intermediary Services Indirectly Measured (FISIM) (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Financial intermediation services which are implicitly charged in the form of either the difference between a reference rate and the interest rate actually paid to depositors, or the difference between the interest rate charged to borrowers and a reference rate. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1965-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Value added" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NV.IND.EMPL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Industry, including construction, value added per worker (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output is not easily available, especially in low- and middle-income countries, because the definition, coverage, and methodology are not always consistent across countries. For more details, see Agriculture, forestry, and fishing, value added (constant 2015 US$) [NV.AGR.TOTL.KD], Industry (including construction), value added (constant 2015 US$) [NV.IND.TOTL.KD], and Services, value added (constant 2015 US$) [NV.SRV.TOTL.KD]." }, { "id": "Longdefinition", "value": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. The core indicator has been divided by the number of workers in the economy to derive a measure of labor productivity. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "World Development Indicators database, World Bank (WB);\nILOSTAT database, International Labour Organization (ILO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Value added per worker is calculated by dividing value added of a sector by the number employed in the sector.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NV.IND.MANF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Manufacturing, value added (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"" }, { "id": "Longdefinition", "value": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical tranformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Value added" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NV.IND.MANF.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Manufacturing, value added (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical tranformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Value added" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NV.IND.MANF.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Manufacturing, value added (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical tranformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NV.IND.MANF.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Manufacturing, value added (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical tranformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NV.IND.MANF.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Manufacturing, value added (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical tranformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Value added" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NV.IND.MANF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Manufacturing, value added (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical tranformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NV.IND.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Industry, including construction, value added (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Value added" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NV.IND.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Industry, including construction, value added (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Value added" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NV.IND.TOTL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Industry, including construction, value added (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NV.IND.TOTL.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Industry, including construction, value added (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NV.IND.TOTL.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Industry, including construction, value added (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Value added" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NV.IND.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Industry, including construction, value added (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NV.MNF.CHEM.ZS.UN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Chemicals (% of value added in manufacturing)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"" }, { "id": "Longdefinition", "value": "Manufacturing of chemicals and chemical prodcuts includes industries classified in ISIC (Rev. 3) division 24. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of value added in manufacturing which is the contribution to the economy by the manufacturing sector (ISIC Rev. 3 major division D)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2023" }, { "id": "Source", "value": "International Yearbook of Industrial Statistics, UN Industrial Development Organization (UNIDO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NV.MNF.FBTO.ZS.UN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Food, beverages and tobacco (% of value added in manufacturing)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"" }, { "id": "Longdefinition", "value": "Manufacturing of food, beverages, and tobacco includes industries classified in ISIC (Rev. 3) divisions 15 and 16. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of value added in manufacturing which is the contribution to the economy by the manufacturing sector (ISIC Rev. 3 major division D)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2023" }, { "id": "Source", "value": "International Yearbook of Industrial Statistics, UN Industrial Development Organization (UNIDO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NV.MNF.MTRN.ZS.UN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Machinery and transport equipment (% of value added in manufacturing)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"" }, { "id": "Longdefinition", "value": "Machinery and transport equipment manufacturing includes industries classified in ISIC (Rev. 3) divisions 29-35. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of value added in manufacturing which is the contribution to the economy by the manufacturing sector (ISIC Rev. 3 major division D)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2023" }, { "id": "Source", "value": "International Yearbook of Industrial Statistics, UN Industrial Development Organization (UNIDO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NV.MNF.OTHR.ZS.UN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Other manufacturing (% of value added in manufacturing)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"" }, { "id": "Longdefinition", "value": "Other manufacturing, a residual, covers wood and related products (ISIC Rev. 3 division 20), paper and related products (ISIC Rev. 3 divisions 21 and 22), petroleum and related products (ISIC Rev. 3 division 23), basic metals and mineral products (ISIC Rev. 3 division27), fabricated metal products and professional goods (ISIC Rev. 3 division 28), and other industries (ISIC Rev. 3 divisions 25, 26, 31, 33, 36, and 37). Includes unallocated data. When data for textiles, machinery, or chemicals are shown as not available, they are included in other manufacturing. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of value added in manufacturing which is the contribution to the economy by the manufacturing sector (ISIC Rev. 3 major division D)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2023" }, { "id": "Source", "value": "International Yearbook of Industrial Statistics, UN Industrial Development Organization (UNIDO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NV.MNF.TECH.ZS.UN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Industrial development generally entails a structural transition from resource-based and low technology activities to medium and high-tech industry (MHT) activities. A modern, highly complex production structure offers better opportunities for skills development and technological innovation. MHT activities are also the high value addition industries of manufacturing with higher technological intensity and labour productivity. Increasing the share of MHT sectors also reflects the impact of innovation" }, { "id": "IndicatorName", "value": "Medium and high-tech manufacturing value added (% manufacturing value added)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Value added by economic activity should be reported at least at 3-digit ISIC for compiling MHT values. Missing values at country level are imputed based on the methodology from Competitive Industrial Performance Report (UNIDO, 2017. Conversion to USD or difference in ISIC combinations may cause discrepancy between national and international figures. For additional information please see UNIDO (2017): http://stat.unido.org/content/publications/volume-i%252c-competitive-industrial-performance-report-2016" }, { "id": "Longdefinition", "value": "The proportion of medium and high-tech industry value added in total value added of manufacturing" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2022" }, { "id": "Source", "value": "Competitive Industrial Performance (CIP) database, UN Industrial Development Organization (UNIDO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated as the share of the sum of the value added from medium and high-tech industry economic activities to manufacturing value added. The medium and high-tech industry is defined using OECD classification as the following by International Standard Industrial Classification of All Economic Activities (ISIC) Revision 3 and Revision 4 Division respectively: ISIC Rev. 3 (24, 29, 30, 31, 32, 33, 34, 35 excluding 351). Manufacturing value added is the value added of manufacturing industry, which is Section C of ISIC Rev.4, and Section D of ISIC Rev.3. Data can be found in UNIDO INDSTAT4 Database by ISIC Revision 3 and ISIC Revision 4 respectively. Data are collected using General Industrial Statistics Questionnaire which is filled by NSOs and submitted to UNIDO annually. Data for OECD countries are obtained directly from OECD. Country data are also collected from official publications and official web-sites. For additional information please see Table B.2.2 in Appendix B of UNIDO (2017): http://stat.unido.org/content/publications/volume-i%252c-competitive-industrial-performance-report-2016" }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" } ], "source_id": "2" }, { "id": "NV.MNF.TXTL.ZS.UN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Textiles and clothing (% of value added in manufacturing)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"" }, { "id": "Longdefinition", "value": "Textiles and clothing refers to industries in ISIC (rev. 3) divisions 17-19 and includes manufacturing of textiles, apparel, dying of fur, and tanning of leather. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of value added in manufacturing which is the contribution to the economy by the manufacturing sector (ISIC Rev. 3 major division D)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2023" }, { "id": "Source", "value": "International Yearbook of Industrial Statistics, UN Industrial Development Organization (UNIDO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NV.SRV.EMPL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Services, value added per worker (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output is not easily available, especially in low- and middle-income countries, because the definition, coverage, and methodology are not always consistent across countries. For more details, see Agriculture, forestry, and fishing, value added (constant 2015 US$) [NV.AGR.TOTL.KD], Industry (including construction), value added (constant 2015 US$) [NV.IND.TOTL.KD], and Services, value added (constant 2015 US$) [NV.SRV.TOTL.KD]." }, { "id": "Longdefinition", "value": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and retaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. The core indicator has been divided by the number of workers in the economy to derive a measure of labor productivity. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "World Development Indicators database, World Bank (WB);\nILOSTAT database, International Labour Organization (ILO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Value added per worker is calculated by dividing value added of a sector by the number employed in the sector.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NV.SRV.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Services, value added (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and retaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Value added" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NV.SRV.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Services, value added (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and retaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Value added" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NV.SRV.TOTL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Services, value added (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult." }, { "id": "Longdefinition", "value": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and retaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NV.SRV.TOTL.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Services, value added (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult." }, { "id": "Longdefinition", "value": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and retaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NV.SRV.TOTL.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Services, value added (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and retaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Value added" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NV.SRV.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Services, value added (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In the services industry the many self-employed workers and one-person businesses are sometimes difficult to locate, and they have little incentive to respond to surveys, let alone to report their full earnings. Compounding these problems are the many forms of economic activity that go unrecorded, including the work that women and children do for little or no pay." }, { "id": "Longdefinition", "value": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and retaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.ADJ.AEDU.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, education expenditure (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Education expenditure refers to the current operating expenditures in education, including wages and salaries and excluding capital investments in buildings and equipment. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nStatistical Yearbook, United Nations (UN), publisher: UN Statistics Division;\nOnline database, UN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.ADJ.AEDU.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, education expenditure (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Public education expenditures are considered an addition to savings. However, because of the wide variability in the effectiveness of public education expenditures, these figures cannot be construed as the value of investments in human capital. A current expenditure of $1 on education does not necessarily yield $1 of human capital. The calculation should also consider private education expenditure, but data are not available for a large number of countries." }, { "id": "Longdefinition", "value": "Education expenditure refers to the current operating expenditures in education, including wages and salaries and excluding capital investments in buildings and equipment. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nStatistical Yearbook, United Nations (UN), publisher: UN Statistics Division;\nOnline database, UN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.ADJ.DCO2.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, carbon dioxide damage (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Cost of damage due to carbon dioxide emissions from fossil fuel use and the manufacture of cement, estimated to be US$40 per ton of CO2 (the unit damage in 2017 US dollars for CO2 emitted in 2020) times the number of tons of CO2 emitted. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.ADJ.DCO2.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, carbon dioxide damage (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Cost of damage due to carbon dioxide emissions from fossil fuel use and the manufacture of cement, estimated to be US$40 per ton of CO2 (the unit damage in 2017 US dollars for CO2 emitted in 2020) times the number of tons of CO2 emitted. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.ADJ.DFOR.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, net forest depletion (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net forest depletion is calculated as the product of unit resource rents and the excess of roundwood harvest over natural growth. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.ADJ.DFOR.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, net forest depletion (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A positive net depletion figure for forest resources implies that the harvest rate exceeds the rate of natural growth; this is not the same as deforestation, which represents a change in land use. In principle, there should be an addition to savings in countries where growth exceeds harvest, but empirical estimates suggest that most of this net growth is in forested areas that cannot currently be exploited economically. Because the depletion estimates reflect only timber values, they ignore all the external and nontimber benefits associated with standing forests." }, { "id": "Longdefinition", "value": "Net forest depletion is calculated as the product of unit resource rents and the excess of roundwood harvest over natural growth. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.ADJ.DKAP.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, consumption of fixed capital (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Consumption of fixed capital represents the replacement value of capital used up in the process of production. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.ADJ.DKAP.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, consumption of fixed capital (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Consumption of fixed capital represents the replacement value of capital used up in the process of production. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nNational Accounts Statistics, United Nations (UN), publisher: UN Statistics Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.ADJ.DMIN.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, mineral depletion (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mineral depletion is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime (capped at 25 years). It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.ADJ.DMIN.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, mineral depletion (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mineral depletion is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime (capped at 25 years). It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.ADJ.DNGY.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, energy depletion (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Energy depletion is the ratio of the value of the stock of energy resources to the remaining reserve lifetime (capped at 25 years). It covers coal, crude oil, and natural gas. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.ADJ.DNGY.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, energy depletion (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Energy depletion is the ratio of the value of the stock of energy resources to the remaining reserve lifetime (capped at 25 years). It covers coal, crude oil, and natural gas. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.ADJ.DPEM.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, particulate emission damage (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Particulate emissions damage is the damage due to exposure of a country's population to ambient concentrations of particulates measuring less than 2.5 microns in diameter (PM2.5), ambient ozone pollution, and indoor concentrations of PM2.5 in households cooking with solid fuels. Damages are calculated as foregone labor income due to premature death. Estimates of health impacts from the Global Burden of Disease Study 2013 are for 1990, 1995, 2000, 2005, 2010, and 2013. Data for other years have been extrapolated from trends in mortality rates. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Global Burden of Disease 2013 study, Institute for Health Metrics and Evaluation (IHME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.ADJ.DPEM.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, particulate emission damage (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Labor productivity losses, as calculated within the framework of adjusted net savings, represent only part of the economic costs of air pollution and should be interpreted as a lower-end estimate." }, { "id": "Longdefinition", "value": "Particulate emissions damage is the damage due to exposure of a country's population to ambient concentrations of particulates measuring less than 2.5 microns in diameter (PM2.5), ambient ozone pollution, and indoor concentrations of PM2.5 in households cooking with solid fuels. Damages are calculated as foregone labor income due to premature death. Estimates of health impacts from the Global Burden of Disease Study 2013 are for 1990, 1995, 2000, 2005, 2010, and 2013. Data for other years have been extrapolated from trends in mortality rates. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Global Burden of Disease 2013 study, Institute for Health Metrics and Evaluation (IHME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.ADJ.DRES.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, natural resources depletion (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Net forest depletion is not the monetary value of deforestation. Roundwood and fuelwood production are different from deforestation, which represents a permanent change in land use and, thus, is not comparable. Areas logged out but intended for regeneration are not included in deforestation figures; rather, they are counted as producing timber depletion. Net forest depletion includes only timber values and does not include the loss of nontimber forest benefits and nonuse benefits.\n\n\n\n\n\nFor both energy and mineral depletion, unit resource rent is calculated as (unit world price - average cost) / unit world price. Marginal cost should be used instead of average cost in order to calculate the true opportunity cost of extraction; however, marginal cost is difficult to compute and data are not readily available. Unit prices refer to international rather than local prices to reflect the social cost of natural resources depletion. This differs from methodologies of national accounts, which may use local prices to measure energy or mineral GDP. This difference explains eventual discrepancies in the values for energy or mineral depletion, verses energy or mineral GDP." }, { "id": "Longdefinition", "value": "Natural resource depletion is the sum of net forest depletion, energy depletion, and mineral depletion. Net forest depletion is unit resource rents times the excess of roundwood harvest over natural growth. Energy depletion is the ratio of the value of the stock of energy resources to the remaining reserve lifetime (capped at 25 years). It covers coal, crude oil, and natural gas. Mineral depletion is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime (capped at 25 years). It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.ADJ.ICTR.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, gross savings (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because gross savings is calculated as a residual it includes errors, which may not be offsetting, in its components." }, { "id": "Longdefinition", "value": "Gross savings are the difference between gross national income and public and private consumption, plus net current transfers. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.ADJ.NNAT.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, net national savings (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net national savings are equal to gross national savings less the value of consumption of fixed capital. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.ADJ.NNAT.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, net national savings (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net national savings are equal to gross national savings less the value of consumption of fixed capital. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.ADJ.NNTY.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Adjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators." }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.ADJ.NNTY.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Adjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators." }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NY.ADJ.NNTY.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Adjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators." }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1971-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.ADJ.NNTY.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.ADJ.NNTY.PC.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income per capita (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Adjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators." }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NY.ADJ.NNTY.PC.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income per capita (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Adjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators." }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1971-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.ADJ.SVNG.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net savings, including particulate emission damage (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide and particulate emissions damage. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.ADJ.SVNG.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net savings, including particulate emission damage (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The exercise treats public education expenditures as an addition to savings. However, because of the wide variability in the effectiveness of public education expenditures, these figures cannot be construed as the value of investments in human capital. A current expenditure of $1 on education does not necessarily yield $1 of human capital. The calculation should also consider private education expenditure, but data are not available for a large number of countries.\n\n\n\n\n\nWhile extensive, the accounting of natural resource depletion and pollution costs still has some gaps. Key estimates missing on the resource side include the value of fossil water extracted from aquifers, net depletion of fish stocks, and depletion and degradation of soils. Important pollutants affecting human health and economic assets are excluded because no internationally comparable data are widely available on damage from ground-level ozone or sulfur oxides." }, { "id": "Longdefinition", "value": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide and particulate emissions damage. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.ADJ.SVNX.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net savings, excluding particulate emission damage (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide. This series excludes particulate emissions damage. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.ADJ.SVNX.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net savings, excluding particulate emission damage (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide. This series excludes particulate emissions damage. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.EXP.CAPM.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Exports as a capacity to import (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Exports as a capacity to import equals the current price value of exports of goods and services deflated by the import price index. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Other items" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NY.GDP.COAL.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Coal rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Coal rents are the difference between the value of both hard and soft coal production at world prices and their total costs of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "2" }, { "id": "NY.GDP.DEFL.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Inflation, GDP deflator (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.GDP.DEFL.KD.ZG.AD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Inflation, GDP deflator, linked series (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.GDP.DEFL.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP deflator (base year varies by country)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. The base year varies by country. This indicator is expressed as a ratio (a÷b)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "index" } ], "source_id": "2" }, { "id": "NY.GDP.DEFL.ZS.AD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP deflator, linked series (base year varies by country)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. The base year varies by country. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed as a ratio (a÷b)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "ratio" } ], "source_id": "2" }, { "id": "NY.GDP.DISC.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Discrepancy in expenditure estimate of GDP (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Although the SNA ensures there is perfect consistency between the three measures of GDP, this is a conceptual consistency that in general does not emerge naturally from data compilations. This is because of the wide disparity of data sources that must be called on and the fact that any error in any source will lead to a difference between at least two of the GDP measures. In practice it is inevitable that many such data errors will exist and will become apparent in exercises such as the balancing of supply and use tables. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NY.GDP.DISC.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Discrepancy in expenditure estimate of GDP (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Although the SNA ensures there is perfect consistency between the three measures of GDP, this is a conceptual consistency that in general does not emerge naturally from data compilations. This is because of the wide disparity of data sources that must be called on and the fact that any error in any source will lead to a difference between at least two of the GDP measures. In practice it is inevitable that many such data errors will exist and will become apparent in exercises such as the balancing of supply and use tables. This indicator is expressed in constant prices, meaning the underlying series have been adjusted to account for price changes over time. The reference year for this adjustment varies by country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NY.GDP.FCST.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross Value Added (GVA) at basic prices (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross value added at basic prices reflects the price of products receivable by the producer exclusive of taxes payable on products and inclusive of subsidies receivable on products, less intermediate consumption valued at purchasers' prices. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.GDP.FCST.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross Value Added (GVA) at basic prices (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross value added at basic prices reflects the price of products receivable by the producer exclusive of taxes payable on products and inclusive of subsidies receivable on products, less intermediate consumption valued at purchasers' prices. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NY.GDP.FCST.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross Value Added (GVA) at basic prices (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross value added at basic prices reflects the price of products receivable by the producer exclusive of taxes payable on products and inclusive of subsidies receivable on products, less intermediate consumption valued at purchasers' prices. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NY.GDP.FCST.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross Value Added (GVA) at basic prices (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross value added at basic prices reflects the price of products receivable by the producer exclusive of taxes payable on products and inclusive of subsidies receivable on products, less intermediate consumption valued at purchasers' prices. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NY.GDP.FRST.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Forest rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Forest rents are roundwood harvest times the product of regional prices and a regional rental rate." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "2" }, { "id": "NY.GDP.MINR.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Mineral rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mineral rents are the difference between the value of production for a stock of minerals at world prices and their total costs of production. Minerals included in the calculation are tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "2" }, { "id": "NY.GDP.MKTP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Gross domestic product (GDP), though widely tracked, may not always be the most relevant summary of aggregated economic performance for all economies, especially when production occurs at the expense of consuming capital stock.\n\n\n\n\n\nWhile GDP estimates based on the production approach are generally more reliable than estimates compiled from the income or expenditure side, different countries use different definitions, methods, and reporting standards. World Bank staff review the quality of national accounts data and sometimes make adjustments to improve consistency with international guidelines. Nevertheless, significant discrepancies remain between international standards and actual practice. Many statistical offices, especially those in developing countries, face severe limitations in the resources, time, training, and budgets required to produce reliable and comprehensive series of national accounts statistics.\n\n\n\n\n\nAmong the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money." }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.GDP.MKTP.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NY.GDP.MKTP.CN.AD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP, linked series (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NY.GDP.MKTP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Each industry's contribution to growth in the economy's output is measured by growth in the industry's value added. In principle, value added in constant prices can be estimated by measuring the quantity of goods and services produced in a period, valuing them at an agreed set of base year prices, and subtracting the cost of intermediate inputs, also in constant prices. This double-deflation method requires detailed information on the structure of prices of inputs and outputs.\n\n\n\n\n\nIn many industries, however, value added is extrapolated from the base year using single volume indexes of outputs or, less commonly, inputs. Particularly in the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult.\n\n\n\n\n\nMoreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in quality lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of inflation.\n\n\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\n\n\n\n\nRebasing of national accounts can alter the measured growth rate of an economy and lead to breaks in series that affect the consistency of data over time. When countries rebase their national accounts, they update the weights assigned to various components to better reflect current patterns of production or uses of output. The new base year should represent normal operation of the economy - it should be a year without major shocks or distortions. Some developing countries have not rebased their national accounts for many years. Using an old base year can be misleading because implicit price and volume weights become progressively less relevant and useful.\n\n\n\n\n\nTo obtain comparable series of constant price data for computing aggregates, the World Bank rescales GDP and value added by industrial origin to a common reference year. Because rescaling changes the implicit weights used in forming regional and income group aggregates, aggregate growth rates are not comparable with those from earlier editions with different base years. Rescaling may result in a discrepancy between the rescaled GDP and the sum of the rescaled components. To avoid distortions in the growth rates, the discrepancy is left unallocated. As a result, the weighted average of the growth rates of the components generally does not equal the GDP growth rate." }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NY.GDP.MKTP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Each industry's contribution to growth in the economy's output is measured by growth in the industry's value added. In principle, value added in constant prices can be estimated by measuring the quantity of goods and services produced in a period, valuing them at an agreed set of base year prices, and subtracting the cost of intermediate inputs, also in constant prices. This double-deflation method requires detailed information on the structure of prices of inputs and outputs.\n\n\n\n\n\nIn many industries, however, value added is extrapolated from the base year using single volume indexes of outputs or, less commonly, inputs. Particularly in the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult.\n\n\n\n\n\nMoreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in quality lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of inflation.\n\n\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\n\n\n\n\nRebasing of national accounts can alter the measured growth rate of an economy and lead to breaks in series that affect the consistency of data over time. When countries rebase their national accounts, they update the weights assigned to various components to better reflect current patterns of production or uses of output. The new base year should represent normal operation of the economy - it should be a year without major shocks or distortions. Some developing countries have not rebased their national accounts for many years. Using an old base year can be misleading because implicit price and volume weights become progressively less relevant and useful.\n\n\n\n\n\nTo obtain comparable series of constant price data for computing aggregates, the World Bank rescales GDP and value added by industrial origin to a common reference year. Because rescaling changes the implicit weights used in forming regional and income group aggregates, aggregate growth rates are not comparable with those from earlier editions with different base years. Rescaling may result in a discrepancy between the rescaled GDP and the sum of the rescaled components. To avoid distortions in the growth rates, the discrepancy is left unallocated. As a result, the weighted average of the growth rates of the components generally does not equal the GDP growth rate." }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.GDP.MKTP.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NY.GDP.MKTP.PP.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress. \n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross domestic product (GDP) expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat, date accessed: Periodical update;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-https://data-explorer.oecd.org/, publisher: OECD, date accessed: Periodical update;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases. \n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Current international $" } ], "source_id": "2" }, { "id": "NY.GDP.MKTP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP, PPP (constant 2021 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross domestic product (GDP) expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Constant 2021 international $" } ], "source_id": "2" }, { "id": "NY.GDP.NGAS.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Natural gas rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Natural gas rents are the difference between the value of natural gas production at regional prices and total costs of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "2" }, { "id": "NY.GDP.PCAP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.GDP.PCAP.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP per capita (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NY.GDP.PCAP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP per capita (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NY.GDP.PCAP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP per capita (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.GDP.PCAP.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP per capita (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NY.GDP.PCAP.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross domestic product (GDP) per person expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Current international $" } ], "source_id": "2" }, { "id": "NY.GDP.PCAP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP per capita, PPP (constant 2021 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross domestic product (GDP) per person expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Constant 2021 international $" } ], "source_id": "2" }, { "id": "NY.GDP.PETR.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Oil rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Oil rents are the difference between the value of crude oil production at regional prices and total costs of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "2" }, { "id": "NY.GDP.TOTL.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Total natural resources rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total natural resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "2" }, { "id": "NY.GDS.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross domestic savings (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant US$" } ], "source_id": "2" }, { "id": "NY.GDS.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross domestic savings (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NY.GDS.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross domestic savings (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption). This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.GDY.TOTL.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross domestic income (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Real gross domestic income (real GDI) measures the purchasing power of the total incomes generated by domestic production. It is a concept that exists in real terms only. When the terms of trade change there may be a significant divergence between the movements of GDP in volume terms and real GDI. The difference between the change in GDP in volume terms and real GDI is generally described as the “trading gain” (or loss) or, to turn this round, the trading gain or loss from changes in the terms of trade is the difference between real GDI and GDP in volume terms. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Other items" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NY.GNP.ATLS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI, Atlas method (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This figure is converted to U.S. dollars using the World Bank Atlas method. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Atlas GNI & GNI per capita" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.GNP.MKTP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.GNP.MKTP.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NY.GNP.MKTP.CN.AD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI, linked series (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NY.GNP.MKTP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NY.GNP.MKTP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.GNP.MKTP.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NY.GNP.MKTP.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross national income (GNI) expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Current international $" } ], "source_id": "2" }, { "id": "NY.GNP.MKTP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI, PPP (constant 2021 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross national income (GNI) expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Constant 2021 international $" } ], "source_id": "2" }, { "id": "NY.GNP.PCAP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita, Atlas method (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This figure is converted to U.S. dollars using the World Bank Atlas method, and divided by the midyear population. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank's official estimates of the size of economies and country classifications by income level are based on Gross National Income (GNI) per capita. For cross-national comparisons, estimates are converted from local currency units (LCU) to current U.S. dollars using the Atlas method, referring to a former World Bank publication called the Atlas of Global Development. The Atlas method smooths exchange rate fluctuations using a three-year moving average, price-adjusted conversion factor. The USD estimate of GNI per capita is derived by applying the Atlas conversion factor to estimates measured in LCU.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Atlas GNI & GNI per capita" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.GNP.PCAP.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NY.GNP.PCAP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "2" }, { "id": "NY.GNP.PCAP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.GNP.PCAP.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NY.GNP.PCAP.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross national income (GNI) per person expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Current international $" } ], "source_id": "2" }, { "id": "NY.GNP.PCAP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita, PPP (constant 2021 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross national income (GNI) per person expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Constant 2021 international $" } ], "source_id": "2" }, { "id": "NY.GNS.ICTR.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross savings (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Savings is an amount that represent the part of disposable income (adjusted for the\n\n\nchange in pension entitlements) that is not spent on final consumption. Gross savings are calculated as gross national income less total consumption, plus net transfers. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.GNS.ICTR.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross savings (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Savings is an amount that represent the part of disposable income (adjusted for the\n\n\nchange in pension entitlements) that is not spent on final consumption. Gross savings are calculated as gross national income less total consumption, plus net transfers. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NY.GNS.ICTR.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross savings (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Savings is an amount that represent the part of disposable income (adjusted for the\n\n\nchange in pension entitlements) that is not spent on final consumption. Gross savings are calculated as gross national income less total consumption, plus net transfers. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.GNS.ICTR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross savings (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Savings is an amount that represent the part of disposable income (adjusted for the\n\n\nchange in pension entitlements) that is not spent on final consumption. Gross savings are calculated as gross national income less total consumption, plus net transfers. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "NY.GSR.NFCY.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Net primary income (net income from abroad) (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net primary income includes the net labor income and net property and entrepreneurial income components of the SNA. Labor income covers compensation of employees paid to nonresident workers. Property and entrepreneurial income covers investment income from the ownership of foreign financial claims (interest, dividends, rent, etc.) and nonfinancial property income (patents, copyrights, etc.). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.GSR.NFCY.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Net primary income (net income from abroad) (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net primary income includes the net labor income and net property and entrepreneurial income components of the SNA. Labor income covers compensation of employees paid to nonresident workers. Property and entrepreneurial income covers investment income from the ownership of foreign financial claims (interest, dividends, rent, etc.) and nonfinancial property income (patents, copyrights, etc.). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NY.GSR.NFCY.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Net primary income (net income from abroad) (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net primary income includes the net labor income and net property and entrepreneurial income components of the SNA. Labor income covers compensation of employees paid to nonresident workers. Property and entrepreneurial income covers investment income from the ownership of foreign financial claims (interest, dividends, rent, etc.) and nonfinancial property income (patents, copyrights, etc.). This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NY.TAX.NIND.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Taxes less subsidies on products (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Taxes less subsidies on production includes taxes payable less subsidies receivable on goods or services produced as outputs including other taxes or subsidies on production such as those payable on the labour, machinery, buildings or other assets used in production. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.TAX.NIND.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Taxes less subsidies on products (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Taxes less subsidies on production includes taxes payable less subsidies receivable on goods or services produced as outputs including other taxes or subsidies on production such as those payable on the labour, machinery, buildings or other assets used in production. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NY.TAX.NIND.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Taxes less subsidies on products (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Taxes less subsidies on production includes taxes payable less subsidies receivable on goods or services produced as outputs including other taxes or subsidies on production such as those payable on the labour, machinery, buildings or other assets used in production. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NY.TRF.NCTR.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies." }, { "id": "IndicatorName", "value": "Net secondary income (net current transfers from abroad) (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net secondary income (from abroad) comprises transfers of income between residents of the reporting country and the rest of the world that carry no provisions for repayment. Net secondary income is equal to the unrequited transfers of income from nonresidents to residents minus the unrequited transfers from residents to nonresidents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "NY.TRF.NCTR.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies." }, { "id": "IndicatorName", "value": "Net secondary income (net current transfers from abroad) (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net secondary income (from abroad) comprises transfers of income between residents of the reporting country and the rest of the world that carry no provisions for repayment. Net secondary income is equal to the unrequited transfers of income from nonresidents to residents minus the unrequited transfers from residents to nonresidents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "2" }, { "id": "NY.TRF.NCTR.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies." }, { "id": "IndicatorName", "value": "Net secondary income (net current transfers from abroad) (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net secondary income (from abroad) comprises transfers of income between residents of the reporting country and the rest of the world that carry no provisions for repayment. Net secondary income is equal to the unrequited transfers of income from nonresidents to residents minus the unrequited transfers from residents to nonresidents. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2013" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "NY.TTF.GNFS.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Terms of trade adjustment (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The terms of trade adjustment is equal to the capacity to import (current price value of exports of goods and services deflated by the import price index) less exports of goods and services in constant prices. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Other items" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "2" }, { "id": "PA.NUS.ATLS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In a market-based economy, household, producer, and government choices about resource allocation are influenced by relative prices, including the real exchange rate, real wages, real interest rates, and other prices in the economy. Relative prices also largely reflect these agents' choices. Thus relative prices convey vital information about the interaction of economic agents in an economy and with the rest of the world." }, { "id": "IndicatorName", "value": "DEC alternative conversion factor (LCU per US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The DEC alternative conversion factor is the underlying annual exchange rate (the price of one country’s currency in relation to another country's currency) used for the World Bank Atlas method. As a rule, it is the official exchange rate reported in the IMF's International Financial Statistics. Exceptions arise where further refinements are made by World Bank staff. It is expressed in local currency units per U.S. dollar." }, { "id": "Othernotes", "value": "In the WDI database, the DEC alternative conversion factor is used to convert data in local currency units (LCU) into U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. In certain countries, multiple or dual exchange rate activity exists and must be accounted for appropriately in underlying statistics. Doing so better reflects economic reality and leads to more accurate cross-country comparisons and country classifications by income level. Consequently, an alternative conversion factor is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate effectively applied to domestic transactions of foreign currencies and traded products. This applies to only a small number of countries, as shown in the country-level metadata. An alternative conversion factor is also used when the period covered by national accounts differs from the calendar year and the alternative conversion factor will then cover the same period. Alternative conversion factors are used in the Atlas methodology and elsewhere in World Development Indicators as single-year conversion factors.\nStatistical concept(s): The World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. In certain countries, multiple or dual exchange rate activity exists and must be accounted for appropriately in underlying statistics. Doing so better reflects economic reality and leads to more accurate cross-country comparisons and country classifications by income level. Consequently, an alternative conversion factor is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate effectively applied to domestic transactions of foreign currencies and traded products. This applies to only a small number of countries, as shown in the country-level metadata. An alternative conversion factor is also used when the period covered by national accounts differs from the calendar year and the alternative conversion factor will then cover the same period. Alternative conversion factors are used in the Atlas methodology and elsewhere in World Development Indicators as single-year conversion factors." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "LCU per US$" } ], "source_id": "2" }, { "id": "PA.NUS.FCRF", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In a market-based economy, household, producer, and government choices about resource allocation are influenced by relative prices, including the real exchange rate, real wages, real interest rates, and other prices in the economy. Relative prices also largely reflect these agents' choices. Thus relative prices convey vital information about the interaction of economic agents in an economy and with the rest of the world." }, { "id": "IndicatorName", "value": "Official exchange rate (LCU per US$, period average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Official or market exchange rates are often used to convert economic statistics in local currencies to a common currency in order to make comparisons across countries. Since market rates reflect at best the relative prices of tradable goods, the volume of goods and services that a U.S. dollar buys in the United States may not correspond to what a U.S. dollar converted to another country's currency at the official exchange rate would buy in that country, particularly when nontradable goods and services account for a significant share of a country's output. An alternative exchange rate - the purchasing power parity (PPP) conversion factor - is preferred because it reflects differences in price levels for both tradable and nontradable goods and services and therefore provides a more meaningful comparison of real output." }, { "id": "Longdefinition", "value": "Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. This indicator represents the ratio of Local Currency Units relative to United States dollars.This indicator is derived as an average over the reference period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The exchange rate is the price of one currency divided by another. Official exchange rates and exchange rate arrangements are established by governments. Other exchange rates recognized by governments include market rates, which are determined largely by legal market forces, and for countries with multiple exchange arrangements, principal rates, secondary rates, and tertiary rates. Annual average exchange rates are derived as the simple average of daily exchange rates for a specified calendar year\nStatistical concept(s): The exchange rate is the price of one currency in terms of another. Official exchange rates and exchange rate arrangements are established by governments. Other exchange rates recognized by governments include market rates, which are determined largely by legal market forces, and for countries with multiple exchange arrangements, principal rates, secondary rates, and tertiary rates." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "LCU per US$" } ], "source_id": "2" }, { "id": "PA.NUS.GDP.PLI", "metatype": [ { "id": "Aggregationmethod", "value": "No aggregation provided for this indicator in WDI." }, { "id": "DataQuality", "value": "The International Comparison Program (ICP) conducts multiple rounds of validation at global, regional, and national levels in the process of producing benchmark PPP estimates which are used in the calculation of price level ratios. Please refer to its guidelines (“Operational Guidelines and Procedures for Measuring the Real Size of the World Economy”) for details of validation. https://www.worldbank.org/en/programs/icp/brief/2011-operational-guidelines" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The price level index (PLI) provides a comparison of price levels across countries. If a country’s PLI is lower than that of another country, then its items or expenditure aggregates are less expensive than those in the other country. Conversely, if a country’s PLI is higher than that of another country, then its items or expenditure aggregates are more expensive than those in the other country.\nPurchasing power parities (PPPs), PLIs, and PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n- Recommended uses of price level ratios include: to make spatial comparisons of price levels.\n- Recommended uses of price level ratios with limitations include: to analyze changes over time in relative prices; to analyze price convergence; and to make spatial comparisons of the cost of living." }, { "id": "IndicatorName", "value": "Price level index (GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global PPP estimates underlying this indicator are produced by the ICP Global Office and regional implementing agencies, based on data supplied by the national implementing agencies in the participating economies, and in accordance with the methodology recommended by the ICP Technical Advisory Group and approved by the ICP Governing Board. As such, these results are not produced by participating economies as part of their national official statistics.\nPrice level index are not recommended to use as a precise measure to establish strict rankings of countries." }, { "id": "Longdefinition", "value": "The price level index (PLI) is the ratio of a purchasing power parity (PPP) conversion factor to the corresponding market exchange rate between two countries, expressed relative to a base country that is set equal to 100. For this series the base country is the United States. It provides a measure of the differences in price level between the country and the United States by indicating the number of units of the common currency (US dollars) needed to buy the same volume of the aggregation level in each country. At the level of GDP, the price level ratio provides a measure of the differences in the general price levels of countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "World Development Indicators, World Bank (WB), uri: https://databank.worldbank.org/source/world-development-indicators, publisher: World Development Indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: For more information on market exchange rate, please refer to the metadata for \"DEC alternative conversion factor (LCU per US$)\" [PA.NUS.ATLS].\nFor the concept and methodology of PPP, please refer to the International Comparison Program (ICP)’s website (https://www.worldbank.org/en/programs/icp).\nStatistical concept(s): A measure of price level in a given country for a basket of goods and services is the ratio of the PPP for a particular basket to the market exchange rate for the currency. Thus the price level\nindex for country j with respect to a commodity group is given by\nPLIj = (PPPj / XRj)*100\nwhere XRj is the exchange rate of the currency of country j." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Indexed to United States = 100" } ], "source_id": "2" }, { "id": "PA.NUS.PPP", "metatype": [ { "id": "Aggregationmethod", "value": "No aggregation provided for this indicator." }, { "id": "DataQuality", "value": "The International Comparison Program (ICP) conducts multiple rounds of validation at global, regional, and national levels in the process of producing benchmark PPP estimates. Please refer to its guidelines (“Operational Guidelines and Procedures for Measuring the Real Size of the World Economy”) for details of validation. https://www.worldbank.org/en/programs/icp/brief/2011-operational-guidelines" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nPPPs, PLIs, and the PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress. \n- Recommended uses of PPPs include: to make spatial comparisons of GDP and its expenditure components; to make spatial comparisons of price levels; and to group countries by their per capita volume indexes and price level indexes.\n- Recommended uses of PPPs with limitations include: to analyze changes over time in relative GDP per capita and relative prices; to analyze price convergence; to make spatial comparisons of the cost of living; and to use PPPs calculated for GDP and its expenditure components as deflators for other values." }, { "id": "IndicatorName", "value": "PPP conversion factor, GDP (LCU per international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global PPP estimates provided by ICP are produced by the ICP Global Office and regional implementing agencies, based on data supplied by the national implementing agencies in the participating economies, and in accordance with the methodology recommended by the ICP Technical Advisory Group and approved by the ICP Governing Board. As such, these results are not produced by participating economies as part of their national official statistics.\n\nPPPs are not recommended to be used as: a precise measure to establish strict rankings of countries; a means of constructing national growth rates; a measure to generate output and productivity comparisons by industry; an indicator of the undervaluation or overvaluation of currencies; and as an equilibrium exchange rate." }, { "id": "Longdefinition", "value": "The purchasing power parity (PPP) conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of gross domestic product (GDP) and its expenditure components. This conversion factor is for the level of GDP and the base currency is the US dollar." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, publisher: International Comparison Program, type: International statistical program, date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat, type: International statistical program;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-https://data-explorer.oecd.org/, publisher: OECD" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model.\n\nICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. Description of WDI extrapolation approach is available here: https://datahelpdesk.worldbank.org/knowledgebase/articles/665452-how-do-you-extrapolate-the-ppp-conversion-factors\n\nFor the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. For Eurostat-OECD PPP Programme, please refer to the following websites.\n(http://www.oecd.org/sdd/prices-ppp/)\n(https://ec.europa.eu/eurostat/web/purchasing-power-parities/overview)\n\nFor more information on the ICP and PPPs, please refer to the ICP website at https://www.worldbank.org/en/programs/icp.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. \n\nPPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. See https://www.worldbank.org/en/programs/icp/methodology." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Local currency unit per international dollar" } ], "source_id": "2" }, { "id": "PA.NUS.PRVT.PLI", "metatype": [ { "id": "Aggregationmethod", "value": "No aggregation provided for this indicator in WDI." }, { "id": "DataQuality", "value": "The International Comparison Program (ICP) conducts multiple rounds of validation at global, regional, and national levels in the process of producing benchmark PPP estimates which are used in the calculation of price level ratios. Please refer to its guidelines (“Operational Guidelines and Procedures for Measuring the Real Size of the World Economy”) for details of validation. https://www.worldbank.org/en/programs/icp/brief/2011-operational-guidelines" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The price level index (PLI) provides a comparison of price levels across countries. If a country’s PLI is lower than that of another country, then its items or expenditure aggregates are less expensive than those in the other country. Conversely, if a country’s PLI is higher than that of another country, then its items or expenditure aggregates are more expensive than those in the other country.\nPurchasing power parities (PPPs), PLIs, and PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n- Recommended uses of price level ratios include: to make spatial comparisons of price levels.\n- Recommended uses of price level ratios with limitations include: to analyze changes over time in relative prices; to analyze price convergence; and to make spatial comparisons of the cost of living." }, { "id": "IndicatorName", "value": "Price level index (Households and NPISHs Final consumption expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global PPP estimates underlying this indicator are produced by the ICP Global Office and regional implementing agencies, based on data supplied by the national implementing agencies in the participating economies, and in accordance with the methodology recommended by the ICP Technical Advisory Group and approved by the ICP Governing Board. As such, these results are not produced by participating economies as part of their national official statistics.\nPrice level index are not recommended to use as a precise measure to establish strict rankings of countries." }, { "id": "Longdefinition", "value": "The price level index (PLI) is the ratio of a purchasing power parity (PPP) conversion factor to the corresponding market exchange rate between two countries, expressed relative to a base country that is set equal to 100. For this series the base country is the United States. It provides a measure of the differences in price level between the country and the United States by indicating the number of units of the common currency (US dollars) needed to buy the same volume of the aggregation level in each country. This indicator provides a measure of the differences in the price levels of countries at the level of Households and NPISHs final consumption expenditure." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "World Development Indicators, World Bank (WB), uri: https://databank.worldbank.org/source/world-development-indicators, publisher: World Development Indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: For more information on market exchange rate, please refer to the metadata for \"DEC alternative conversion factor (LCU per US$)\" [PA.NUS.ATLS].\nFor the concept and methodology of PPP, please refer to the International Comparison Program (ICP)’s website (https://www.worldbank.org/en/programs/icp).\nStatistical concept(s): A measure of price level in a given country for a basket of goods and services is the ratio of the PPP for a particular basket to the market exchange rate for the currency. Thus the price level\nindex for country j with respect to a commodity group is given by\nPLIj = (PPPj / XRj)*100\nwhere XRj is the exchange rate of the currency of country j." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Indexed to United States = 100" } ], "source_id": "2" }, { "id": "PA.NUS.PRVT.PP", "metatype": [ { "id": "Aggregationmethod", "value": "No aggregation provided for this indicator." }, { "id": "DataQuality", "value": "The International Comparison Program (ICP) conducts multiple rounds of validation at global, regional, and national levels in the process of producing benchmark PPP estimates. Please refer to its guidelines (“Operational Guidelines and Procedures for Measuring the Real Size of the World Economy”) for details of validation. https://www.worldbank.org/en/programs/icp/brief/2011-operational-guidelines" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nPPPs, PLIs, and the PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress. \n- Recommended uses of PPPs include: to make spatial comparisons of GDP and its expenditure components; to make spatial comparisons of price levels; and to group countries by their per capita volume indexes and price level indexes.\n- Recommended uses of PPPs with limitations include: to analyze changes over time in relative GDP per capita and relative prices; to analyze price convergence; to make spatial comparisons of the cost of living; and to use PPPs calculated for GDP and its expenditure components as deflators for other values." }, { "id": "IndicatorName", "value": "PPP conversion factor, households and NPISHs Final consumption expenditure (LCU per international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global PPP estimates provided by ICP are produced by the ICP Global Office and regional implementing agencies, based on data supplied by the national implementing agencies in the participating economies, and in accordance with the methodology recommended by the ICP Technical Advisory Group and approved by the ICP Governing Board. As such, these results are not produced by participating economies as part of their national official statistics.\n\nPPPs are not recommended to be used as: a precise measure to establish strict rankings of countries; a means of constructing national growth rates; a measure to generate output and productivity comparisons by industry; an indicator of the undervaluation or overvaluation of currencies; and as an equilibrium exchange rate." }, { "id": "Longdefinition", "value": "The purchasing power parity (PPP) conversion factor is a currency conversion factor and a spatial price deflator. They convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of gross domestic product (GDP) and its expenditure components. This conversion factor is for households and NPISHs Final consumption expenditure and the base currency is the US dollar." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, publisher: International Comparison Program, type: International statistical program, date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat, type: International statistical program;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-https://data-explorer.oecd.org/, publisher: OECD" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model.\n\nICP-estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. Description of WDI extrapolation approach is available here: https://datahelpdesk.worldbank.org/knowledgebase/articles/665452-how-do-you-extrapolate-the-ppp-conversion-factors\n\nFor the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. For Eurostat-OECD PPP Programme, please refer to the following websites.\n(http://www.oecd.org/sdd/prices-ppp/)\n(https://ec.europa.eu/eurostat/web/purchasing-power-parities/overview)\n\nFor more information on the ICP and PPPs, please refer to the ICP website at https://www.worldbank.org/en/programs/icp.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. \n\nPPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. See https://www.worldbank.org/en/programs/icp/methodology." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Local currency unit per international dollar" } ], "source_id": "2" }, { "id": "per_allsp.adq_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Adequacy of social protection and labor programs (% of total welfare of beneficiary households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Adequacy of social protection and labor programs (SPL) is measured by the total transfer amount received by the population participating in social insurance, social safety net, and unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_allsp.ben_q1_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Benefit incidence of social protection and labor programs to poorest quintile (% of total SPL benefits)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Benefit incidence of social protection and labor programs (SPL) to poorest quintile shows the percentage of total social protection and labor programs benefits received by the poorest 20% of the population. Social protection and labor programs include social insurance, social safety nets, and unemployment benefits and active labor market programs. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_allsp.cov_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social protection and labor programs (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social protection and labor programs (SPL) shows the percentage of population participating in social insurance, social safety net, and unemployment benefits and active labor market programs. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_lm_alllm.adq_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Adequacy of unemployment benefits and ALMP (% of total welfare of beneficiary households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Adequacy of unemployment benefits and active labor market programs (ALMP) is measured by the total transfer amount received by the population participating in unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_lm_alllm.ben_q1_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Benefit incidence of unemployment benefits and ALMP to poorest quintile (% of total U/ALMP benefits)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Benefit incidence of unemployment benefits and active labor market programs (ALMP) to poorest quintile shows the percentage of total unemployment and active labor market programs benefits received by the poorest 20% of the population. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_lm_alllm.cov_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_lm_alllm.cov_q1_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP in poorest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_lm_alllm.cov_q2_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP in 2nd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_lm_alllm.cov_q3_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP in 3rd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_lm_alllm.cov_q4_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP in 4th quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_lm_alllm.cov_q5_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP in richest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_sa_allsa.adq_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Adequacy of social safety net programs (% of total welfare of beneficiary households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Adequacy of social safety net programs is measured by the total transfer amount received by the population participating in social safety net programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Social safety net programs include cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_sa_allsa.ben_q1_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Benefit incidence of social safety net programs to poorest quintile (% of total safety net benefits)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Benefit incidence of social safety net programs to poorest quintile shows the percentage of total social safety net benefits received by the poorest 20% of the population. Social safety net programs include cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_sa_allsa.cov_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social safety net programs (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_sa_allsa.cov_q1_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social safety net programs in poorest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_sa_allsa.cov_q2_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social safety net programs in 2nd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_sa_allsa.cov_q3_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social safety net programs in 3rd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_sa_allsa.cov_q4_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social safety net programs in 4th quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_sa_allsa.cov_q5_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social safety net programs in richest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_si_allsi.adq_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Adequacy of social insurance programs (% of total welfare of beneficiary households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Adequacy of social insurance programs is measured by the total transfer amount received by the population participating in social insurance programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Social insurance programs include old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_si_allsi.ben_q1_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Benefit incidence of social insurance programs to poorest quintile (% of total social insurance benefits)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Benefit incidence of social insurance programs to poorest quintile shows the percentage of total social insurance benefits received by the poorest 20% of the population. Social insurance programs include old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_si_allsi.cov_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social insurance programs (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_si_allsi.cov_q1_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social insurance programs in poorest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_si_allsi.cov_q2_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social insurance programs in 2nd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_si_allsi.cov_q3_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social insurance programs in 3rd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_si_allsi.cov_q4_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social insurance programs in 4th quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "per_si_allsi.cov_q5_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social insurance programs in richest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "PX.REX.REER", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In a market-based economy, household, producer, and government choices about resource allocation are influenced by relative prices, including the real exchange rate, real wages, real interest rates, and other prices in the economy. Relative prices also largely reflect these agents' choices. Thus relative prices convey vital information about the interaction of economic agents in an economy and with the rest of the world." }, { "id": "IndicatorName", "value": "Real effective exchange rate index (2010 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because of conceptual and data limitations, changes in real effective exchange rates should be interpreted with caution." }, { "id": "Longdefinition", "value": "Real effective exchange rate is the nominal effective exchange rate (a measure of the value of a currency against a weighted average of several foreign currencies) divided by a price deflator or index of costs. This indicator is an index series where 2010=100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1979-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The real effective exchange rate is a nominal effective exchange rate index adjusted for relative movements in national price or cost indicators of the home country, selected countries, and the euro area. A nominal effective exchange rate index is the ratio (expressed on the base 2010 = 100) of an index of a currency's period-average exchange rate to a weighted geometric average of exchange rates for currencies of selected countries and the euro area. For most high-income countries weights are derived from industrial country trade in manufactured goods. Data are compiled from the nominal effective exchange rate index and a cost indicator of relative normalized unit labor costs in manufacturing. For selected other countries the nominal effective exchange rate index is based on manufactured goods and primary products trade with partner or competitor countries. For these countries the real effective exchange rate index is the nominal index adjusted for relative changes in consumer prices; an increase represents an appreciation of the local currency.\nStatistical concept(s): The real effective exchange rate is a nominal effective exchange rate index adjusted for relative movements in national price or cost indicators of the home country, selected countries, and the euro area. A nominal effective exchange rate index is the ratio (expressed on the base 2010 = 100) of an index of a currency's period-average exchange rate to a weighted geometric average of exchange rates for currencies of selected countries and the euro area. For most high-income countries weights are derived from industrial country trade in manufactured goods. Data are compiled from the nominal effective exchange rate index and a cost indicator of relative normalized unit labor costs in manufacturing. For selected other countries the nominal effective exchange rate index is based on manufactured goods and primary products trade with partner or competitor countries. For these countries the real effective exchange rate index is the nominal index adjusted for relative changes in consumer prices; an increase represents an appreciation of the local currency." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "index (2010 = 100)" } ], "source_id": "2" }, { "id": "SE.ADT.1524.LT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth female (% of females ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate youths divided by the total number of youths, excluding youths with unknown literacy status. \n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of females ages 15-24" } ], "source_id": "2" }, { "id": "SE.ADT.1524.LT.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEliminating gender disparities in education would help increase the status and capabilities of women. Literate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth (ages 15-24), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Gender parity index for youth literacy rate is the ratio of females to males ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female youth literacy rate by male youth literacy rate. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiteracy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "ratio" } ], "source_id": "2" }, { "id": "SE.ADT.1524.LT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth male (% of males ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate youths divided by the total number of youths, excluding youths with unknown literacy status. \n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of males ages 15-24" } ], "source_id": "2" }, { "id": "SE.ADT.1524.LT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth total (% of people ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data is calculated by dividing the number of literate persons by the total number of persons in the same age group, excluding persons with unknown literacy status.\n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "Percent of people ages 15-24" } ], "source_id": "2" }, { "id": "SE.ADT.LITR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, adult female (% of females ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status. \n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of females ages 15 and above" } ], "source_id": "2" }, { "id": "SE.ADT.LITR.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, adult male (% of males ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status. \n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of males ages 15 and above" } ], "source_id": "2" }, { "id": "SE.ADT.LITR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, adult total (% of people ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status. \n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of people ages 15 and above" } ], "source_id": "2" }, { "id": "SE.COM.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Compulsory education is recognized as a fundamental human right. The Universal Declaration of Human Rights (https://www.un.org/en/about-us/universal-declaration-of-human-rights/) advocates for free and mandatory primary education. Additionally, the Convention on the Rights of the Child (https://www.ohchr.org/en/instruments-mechanisms/instruments/convention-rights-child ) expands on this by mandating that states should strive to make secondary education available and accessible to everyone." }, { "id": "IndicatorName", "value": "Compulsory education, duration (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The presence of national legislation does not guarantee that countries will implement it effectively, nor does it ensure that parents will take advantage of the provisions available for their children." }, { "id": "Longdefinition", "value": "Duration of compulsory education is the number of years that children are legally obliged to attend school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1975-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Compulsory education is defined as educational programs that children and young people are legally obliged to attend, usually defined in terms of a number of grades or an age range, or both (UNESCO, https://unesdoc.unesco.org/ark:/48223/pf0000141639)." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "2" }, { "id": "SE.ENR.PRIM.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "IndicatorName", "value": "School enrollment, primary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in primary education is the ratio of girls to boys enrolled at primary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in primary education by male gross enrollment ratio in primary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.ENR.PRSC.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "IndicatorName", "value": "School enrollment, primary and secondary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in primary and secondary education is the ratio of girls to boys enrolled at primary and secondary levels in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in primary and secondary education by male gross enrollment ratio in primary and secondary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.ENR.SECO.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "IndicatorName", "value": "School enrollment, secondary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in secondary education is the ratio of girls to boys enrolled at secondary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in secondary education by male gross enrollment ratio in secondary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.ENR.TERT.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Education is a basic human entitlement, and it is crucial that both girls and boys are afforded equal chances to learn. The Sustainable Development Goal (SDG) Target 4.5 focuses on eliminating gender disparities in education and ensuring equal access to all levels of education for both girls and boys. This target is part of a broader commitment to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all, as outlined in SDG 4. The pursuit of gender equality in education is not only a matter of fairness and equity but also has significant implications for economic development, empowerment, and the well-being of communities and nations." }, { "id": "IndicatorName", "value": "School enrollment, tertiary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The gross enrolment ratio is a general measure of participation in tertiary education. However, it does not account for variations in the duration of programs between countries or across different levels of education and fields of study. While it is somewhat standardized by measuring it relative to a 5-year age group for all countries, it may still underestimate participation, particularly in countries with underdeveloped tertiary education systems or where offerings are limited to initial tertiary programs, which are typically shorter than 5 years in duration." }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in tertiary education is the ratio of women to men enrolled at tertiary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in tertiary education by male gross enrollment ratio in tertiary education. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "ratio" } ], "source_id": "2" }, { "id": "SE.LPV.PRIM", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Learning poverty: Share of Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The construct of “all children reading by age 10” is an ideal that embodies normative statements about both learning and access. To achieve it, not only should all children be reading proficiently after 3 full years in primary education, but they should also have entered school at age 6 or 7. \n\nBy contrast, the actual indicators used to measure learning poverty are based on grade rather than age. Since the assessments are of 4th- through 6th-graders, the children tested will have had at least 3 to 5 years in school to reach what, according to the ideal, should 10 be an age-10 minimum proficiency, or even the entire primary-school-age segment for the out-of-school indicator.\n\nDue to different assessment availability within and between countries, data comparability, both within countries over time and across countries still poses a significant challenge. The additional out of school component further limits comparability.\n\nThe learning poverty indicator is based on data covering four-fifths of children at the end of primary school. In other words, a little more than 80 percent of children in low- and middle-income countries live in a country with at least one learning assessment at the end of primary, carried out in the past 9 years. For regional and global aggregates, weighted imputations affect regions with less data coverage. The major gaps are concentrated in countries where the learning crisis is most acute. Less than half of children in Sub-Saharan Africa live in a country with a National Large-Scale Learning Assessment (NLSA) or a international of regional large-scale learning assessment (ILSA or RLSA) of adequate quality to be used for this purpose.\n\nThis extensive coverage became possible only in recent years, with the progress in measuring learning in countries and the GAML’s efforts to establish comparability, which has made possible the construction of a global indicator based on harmonized proficiency levels. Future efforts by coalition organizations are also ensuring more flexible assessment options are available for expanding data availability for countries, such as the Assessment of Minimum Proficiency Levels (AMPL) and policy linking exercises led by UIS." }, { "id": "Longdefinition", "value": "The share of 10-year-olds who cannot read and understand a short passage of age-appropriate material—in other words, those who are below the “minimum proficiency” threshold for reading. This measure is defined as the union of two deprivations: 1) schooling deprivation and 2) learning deprivation. A child is considered schooling-deprived (SD) if he or she is of primary school age and out-of-school. The dimension of learning deprivation (LD) applies only for children in school, and identifies those pupils who are below the minimum proficiency level (MPL) for reading, as defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. This “union approach” to measurement reflects the choice that, as presented in the SDGs, all age 10 children must be both in school and learning. The final learning poverty measure combines the two dimensions in a single indicator using the following formula: LP = SD + [(1-SD) x LD]" }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The learning poverty indicator brings together schooling and learning indicators. It starts with the share of children in school who haven’t achieved minimum reading proficiency (Learning Deprived) and adjusts it by the proportion of children who are out of school (Schooling Deprived). \n\n\nFormally, Learning Poverty is calculated as: [LD* (1-SD)] + [1 * SD]\n\n\nwhere LP = Learning poverty; LD = Learning deprivation or the share of children at the end of primary who read at below the minimum proficiency level, as defined by the Global Alliance to Monitor Learning (GAML) in the context of the SDG 4.1.1 monitoring; SD = Schooling deprivation or the share of primary-school-age children who are out-of-school (OOS) and in which all OOS are regarded as being below the minimum proficiency level.\n\n\nBecause out-of-school children are treated as non-proficient in reading, learning poverty will always be higher than the share of children in school who haven't achieved minimum reading proficiency. For countries with a very low schooling deprivation, the learning deprivation value will be very close to Learning Poverty. \n\n\nEstimating the current level of global and regional learning poverty requires deciding how to define “current.” We include results of assessments within four years before or after a set anchor year. This decision is driven by data availability. International and regional large-scale learning assessments used for SDG 4.1.1b reporting are carried out only every 3 to 4 years. And even where assessments have been carried out recently, there is a lag of a couple of years before the data are available. This band is intended as a moving window. In the original 2019 release, the anchor year used was 2015 (Assessments between 2011 and 2019 are included in the learning poverty estimate). In the 2022 Global Update, the anchor year was moved to 2019 (assessments between 2015 and 2023 are included).\n\n\nAggregations for each region comprise the average learning poverty of countries with available data, weighted by their population ages 10–14 years old. To obtain a global estimate, we weight the regional aggregations by the 10–14-year-old population regardless of data availability. This is equivalent to imputing missing country data using regional values.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "SE.LPV.PRIM.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Learning poverty: Share of Female Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The construct of “all children reading by age 10” is an ideal that embodies normative statements about both learning and access. To achieve it, not only should all children be reading proficiently after 3 full years in primary education, but they should also have entered school at age 6 or 7. \n\nBy contrast, the actual indicators used to measure learning poverty are based on grade rather than age. Since the assessments are of 4th- through 6th-graders, the children tested will have had at least 3 to 5 years in school to reach what, according to the ideal, should 10 be an age-10 minimum proficiency, or even the entire primary-school-age segment for the out-of-school indicator.\n\nDue to different assessment availability within and between countries, data comparability, both within countries over time and across countries still poses a significant challenge. The additional out of school component further limits comparability.\n\nThe learning poverty indicator is based on data covering four-fifths of children at the end of primary school. In other words, a little more than 80 percent of children in low- and middle-income countries live in a country with at least one learning assessment at the end of primary, carried out in the past 9 years. For regional and global aggregates, weighted imputations affect regions with less data coverage. The major gaps are concentrated in countries where the learning crisis is most acute. Less than half of children in Sub-Saharan Africa live in a country with a National Large-Scale Learning Assessment (NLSA) or a international of regional large-scale learning assessment (ILSA or RLSA) of adequate quality to be used for this purpose.\n\nThis extensive coverage became possible only in recent years, with the progress in measuring learning in countries and the GAML’s efforts to establish comparability, which has made possible the construction of a global indicator based on harmonized proficiency levels. Future efforts by coalition organizations are also ensuring more flexible assessment options are available for expanding data availability for countries, such as the Assessment of Minimum Proficiency Levels (AMPL) and policy linking exercises led by UIS." }, { "id": "Longdefinition", "value": "The share of female 10-year-olds who cannot read and understand a short passage of age-appropriate material—in other words, those who are below the “minimum proficiency” threshold for reading. This measure is defined as the union of two deprivations: 1) schooling deprivation and 2) learning deprivation. A child is considered schooling-deprived (SD) if he or she is of primary school age and out-of-school. The dimension of learning deprivation (LD) applies only for children in school, and identifies those pupils who are below the minimum proficiency level (MPL) for reading, as defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. This “union approach” to measurement reflects the choice that, as presented in the SDGs, all age 10 children must be both in school and learning. The final learning poverty measure combines the two dimensions in a single indicator using the following formula: LP = SD + [(1-SD) x LD]" }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The learning poverty indicator brings together schooling and learning indicators. It starts with the share of children in school who haven’t achieved minimum reading proficiency (Learning Deprived) and adjusts it by the proportion of children who are out of school (Schooling Deprived). \n\n\nFormally, Learning Poverty is calculated as: [LD* (1-SD)] + [1 * SD]\n\n\nwhere LP = Learning poverty; LD = Learning deprivation or the share of children at the end of primary who read at below the minimum proficiency level, as defined by the Global Alliance to Monitor Learning (GAML) in the context of the SDG 4.1.1 monitoring; SD = Schooling deprivation or the share of primary-school-age children who are out-of-school (OOS) and in which all OOS are regarded as being below the minimum proficiency level.\n\n\nBecause out-of-school children are treated as non-proficient in reading, learning poverty will always be higher than the share of children in school who haven't achieved minimum reading proficiency. For countries with a very low schooling deprivation, the learning deprivation value will be very close to Learning Poverty. \n\n\nEstimating the current level of global and regional learning poverty requires deciding how to define “current.” We include results of assessments within four years before or after a set anchor year. This decision is driven by data availability. International and regional large-scale learning assessments used for SDG 4.1.1b reporting are carried out only every 3 to 4 years. And even where assessments have been carried out recently, there is a lag of a couple of years before the data are available. This band is intended as a moving window. In the original 2019 release, the anchor year used was 2015 (Assessments between 2011 and 2019 are included in the learning poverty estimate). In the 2022 Global Update, the anchor year was moved to 2019 (assessments between 2015 and 2023 are included).\n\n\nAggregations for each region comprise the average learning poverty of countries with available data, weighted by their population ages 10–14 years old. To obtain a global estimate, we weight the regional aggregations by the 10–14-year-old population regardless of data availability. This is equivalent to imputing missing country data using regional values.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "SE.LPV.PRIM.LD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Pupils below minimum reading proficiency at end of primary (%). Low GAML threshold" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The process of equating proficiency levels on different assessments to the GAML definition is not straightforward. Even the long-running regional assessment initiatives like PASEC (West and Central Africa) and LLECE (Latin America and the Caribbean) use different definitions and a different number of levels than other assessments like PIRLS, and those might not even be the same over time. Their test development methodologies and test administration procedures also vary. Moreover, because not all countries participate in global or regional assessments, for some major countries we rely on their interim reporting using their national assessments; equating these assessments is even more challenging. UIS and the World Bank have mapped how proficiency levels between assessment can equate to one another, but they are not strictly comparable. \n\nAmong the differences across assessments, one important point concerns the age at which children are tested. The reference age for our exercise is age 10. However, all learning assessments used in this analysis are sampled based on specific grades rather than age. PIRLS and TIMSS are administered in Grade 4, meaning that the average student assessed is indeed 10 years old, but this is not the case for the regional assessments. PASEC and LLECE are administered in Grade 6, so the average age in those assessments is 12.8 and 12.4, respectively. National assessments are administered at different grades, so to incorporate those assessments, we chose for each country the grade between 4 and 6 (inclusive) for which relevant and reliable data were available. This is consistent with the SDG monitoring by UIS and GAML, which lists “End of Primary (or Grades 4 to 6)” as the relevant age category for the end-of-primary students (SDG 4.1.1b).\n\nIn some cases, National Learning Assessments that have not been policy linked are used for learning poverty, if country teams and experts determine that an assessment is of sufficiently quality or has undertaken steps to align their assessments with the Global Proficiency Framework. They will often be reported as interim learning poverty indicators, as they are not fully aligned with SDG 4.1.1b." }, { "id": "Longdefinition", "value": "The share of pupils at the end of primary schooling who are below the minimum proficiency level (MPL) for reading or learning deprived. The MPL in reading at the end of primary is defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. It is “Students independently and fluently read simple, short narrative and expository texts. They locate explicitly-stated information. They interpret and give some explanations about the key ideas in these texts. They provide simple, personal opinions or judgements about the information, events and characters in a text.” (UIS and GAML 2019). In other words, a child “attaining” minimum proficiency has the ability to read and understand a short passage of age-appropriate material, whether a simple story or non-fiction narrative of a few paragraphs. In addition to this nutshell statement, the GAML has also proposed a common terminology to describe classifications in the context of the MPL. This is a critical first step toward linking cross-national and national learning assessments with a common benchmark." }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Learning assessments used to calculate Learning Poverty have a minimum proficiency level (MPL) benchmarked by Global Alliance to Monitor Learning (GAML) under the leadership of the UNESCO Institute of Statistics (UIS), which occurred within the reporting window. To operationalize this concept, the current SDG monitoring process is followed by defining “proficiency” as reaching at least the Low International Benchmark on the international PIRLS literacy assessment. \n\n\nPIRLS is the major global primary-age assessment focused on reading, and if all countries participated in it, the task of constructing global estimates of minimum proficiency would be trivial, as it would require aggregating results from a single cross-national assessment. However, most countries participating in PIRLS are high-income, and only a small minority of low- and middle-income countries participate in the assessment. One of the main contributions of the GAML process is that it has overcome this data gap by benchmarking several major cross-national assessments—and increasingly national learning assessments as well—against the standard. \n\n\nThe MPL for each learning assessment is used to calculate the reading proficiency rate for that country, which is the share of students scoring at or above the minimum proficiency level, and conversely to calculate the learning deprivation.\n\n\nThe Proficiency and Grade Levels used for each assessment is as follows: PIRLS (grade 4) - Level 2 (Low international benchmark, 400 points); TIMSS (grade 4) - Level 2 (Low international benchmark, 400 points); LLECE (SERCE, grade 6) - Level 3 (513.66 points); PASEC (grades 5 and 6) - Level 4 (595.1 points); SEA-PLM (grade 5) - Level 6 and above; National Learning Assessment (grade 4, 5 and 6) - Varies by country. \n\n\nWhen a given country had administered multiple types of learning assessments, a hierarchy is applied in the order listed below to ensure best comparability across countries: International or Regional Learning Assessment for Reading (PIRLS, LLECE, PASEC, SEA-PLM) > TIMSS Science > Statistical or Pairwise Linking Exercises > AMPL-bs, Policy Linked National Learning Assessments or Policy Linked Service Delivery Indicators (SDIs) > Non-Policy Linked NLAs (Interim Reporting).\n\n\nNote that as the GAML and joint coalitions continue their efforts to improve learning data coverage, the hierarchy may be revised.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "SE.LPV.PRIM.LD.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Female pupils below minimum reading proficiency at end of primary (%). Low GAML threshold" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The process of equating proficiency levels on different assessments to the GAML definition is not straightforward. Even the long-running regional assessment initiatives like PASEC (West and Central Africa) and LLECE (Latin America and the Caribbean) use different definitions and a different number of levels than other assessments like PIRLS, and those might not even be the same over time. Their test development methodologies and test administration procedures also vary. Moreover, because not all countries participate in global or regional assessments, for some major countries we rely on their interim reporting using their national assessments; equating these assessments is even more challenging. UIS and the World Bank have mapped how proficiency levels between assessment can equate to one another, but they are not strictly comparable. \n\nAmong the differences across assessments, one important point concerns the age at which children are tested. The reference age for our exercise is age 10. However, all learning assessments used in this analysis are sampled based on specific grades rather than age. PIRLS and TIMSS are administered in Grade 4, meaning that the average student assessed is indeed 10 years old, but this is not the case for the regional assessments. PASEC and LLECE are administered in Grade 6, so the average age in those assessments is 12.8 and 12.4, respectively. National assessments are administered at different grades, so to incorporate those assessments, we chose for each country the grade between 4 and 6 (inclusive) for which relevant and reliable data were available. This is consistent with the SDG monitoring by UIS and GAML, which lists “End of Primary (or Grades 4 to 6)” as the relevant age category for the end-of-primary students (SDG 4.1.1b).\n\nIn some cases, National Learning Assessments that have not been policy linked are used for learning poverty, if country teams and experts determine that an assessment is of sufficiently quality or has undertaken steps to align their assessments with the Global Proficiency Framework. They will often be reported as interim learning poverty indicators, as they are not fully aligned with SDG 4.1.1b." }, { "id": "Longdefinition", "value": "The share of female pupils at the end of primary schooling who are below the minimum proficiency level (MPL) for reading or learning deprived. The MPL in reading at the end of primary is defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. It is “Students independently and fluently read simple, short narrative and expository texts. They locate explicitly-stated information. They interpret and give some explanations about the key ideas in these texts. They provide simple, personal opinions or judgements about the information, events and characters in a text.” (UIS and GAML 2019). In other words, a child “attaining” minimum proficiency has the ability to read and understand a short passage of age-appropriate material, whether a simple story or non-fiction narrative of a few paragraphs. In addition to this nutshell statement, the GAML has also proposed a common terminology to describe classifications in the context of the MPL. This is a critical first step toward linking cross-national and national learning assessments with a common benchmark." }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Learning assessments used to calculate Learning Poverty have a minimum proficiency level (MPL) benchmarked by Global Alliance to Monitor Learning (GAML) under the leadership of the UNESCO Institute of Statistics (UIS), which occurred within the reporting window. To operationalize this concept, the current SDG monitoring process is followed by defining “proficiency” as reaching at least the Low International Benchmark on the international PIRLS literacy assessment. \n\n\nPIRLS is the major global primary-age assessment focused on reading, and if all countries participated in it, the task of constructing global estimates of minimum proficiency would be trivial, as it would require aggregating results from a single cross-national assessment. However, most countries participating in PIRLS are high-income, and only a small minority of low- and middle-income countries participate in the assessment. One of the main contributions of the GAML process is that it has overcome this data gap by benchmarking several major cross-national assessments—and increasingly national learning assessments as well—against the standard. \n\n\nThe MPL for each learning assessment is used to calculate the reading proficiency rate for that country, which is the share of students scoring at or above the minimum proficiency level, and conversely to calculate the learning deprivation.\n\n\nThe Proficiency and Grade Levels used for each assessment is as follows: PIRLS (grade 4) - Level 2 (Low international benchmark, 400 points); TIMSS (grade 4) - Level 2 (Low international benchmark, 400 points); LLECE (SERCE, grade 6) - Level 3 (513.66 points); PASEC (grades 5 and 6) - Level 4 (595.1 points); SEA-PLM (grade 5) - Level 6 and above; National Learning Assessment (grade 4, 5 and 6) - Varies by country. \n\n\nWhen a given country had administered multiple types of learning assessments, a hierarchy is applied in the order listed below to ensure best comparability across countries: International or Regional Learning Assessment for Reading (PIRLS, LLECE, PASEC, SEA-PLM) > TIMSS Science > Statistical or Pairwise Linking Exercises > AMPL-bs, Policy Linked National Learning Assessments or Policy Linked Service Delivery Indicators (SDIs) > Non-Policy Linked NLAs (Interim Reporting).\n\n\nNote that as the GAML and joint coalitions continue their efforts to improve learning data coverage, the hierarchy may be revised.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "SE.LPV.PRIM.LD.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Male pupils below minimum reading proficiency at end of primary (%). Low GAML threshold" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The process of equating proficiency levels on different assessments to the GAML definition is not straightforward. Even the long-running regional assessment initiatives like PASEC (West and Central Africa) and LLECE (Latin America and the Caribbean) use different definitions and a different number of levels than other assessments like PIRLS, and those might not even be the same over time. Their test development methodologies and test administration procedures also vary. Moreover, because not all countries participate in global or regional assessments, for some major countries we rely on their interim reporting using their national assessments; equating these assessments is even more challenging. UIS and the World Bank have mapped how proficiency levels between assessment can equate to one another, but they are not strictly comparable. \n\nAmong the differences across assessments, one important point concerns the age at which children are tested. The reference age for our exercise is age 10. However, all learning assessments used in this analysis are sampled based on specific grades rather than age. PIRLS and TIMSS are administered in Grade 4, meaning that the average student assessed is indeed 10 years old, but this is not the case for the regional assessments. PASEC and LLECE are administered in Grade 6, so the average age in those assessments is 12.8 and 12.4, respectively. National assessments are administered at different grades, so to incorporate those assessments, we chose for each country the grade between 4 and 6 (inclusive) for which relevant and reliable data were available. This is consistent with the SDG monitoring by UIS and GAML, which lists “End of Primary (or Grades 4 to 6)” as the relevant age category for the end-of-primary students (SDG 4.1.1b).\n\nIn some cases, National Learning Assessments that have not been policy linked are used for learning poverty, if country teams and experts determine that an assessment is of sufficiently quality or has undertaken steps to align their assessments with the Global Proficiency Framework. They will often be reported as interim learning poverty indicators, as they are not fully aligned with SDG 4.1.1b." }, { "id": "Longdefinition", "value": "The share of male pupils at the end of primary schooling who are below the minimum proficiency level (MPL) for reading or learning deprived. The MPL in reading at the end of primary is defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. It is “Students independently and fluently read simple, short narrative and expository texts. They locate explicitly-stated information. They interpret and give some explanations about the key ideas in these texts. They provide simple, personal opinions or judgements about the information, events and characters in a text.” (UIS and GAML 2019). In other words, a child “attaining” minimum proficiency has the ability to read and understand a short passage of age-appropriate material, whether a simple story or non-fiction narrative of a few paragraphs. In addition to this nutshell statement, the GAML has also proposed a common terminology to describe classifications in the context of the MPL. This is a critical first step toward linking cross-national and national learning assessments with a common benchmark." }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Learning assessments used to calculate Learning Poverty have a minimum proficiency level (MPL) benchmarked by Global Alliance to Monitor Learning (GAML) under the leadership of the UNESCO Institute of Statistics (UIS), which occurred within the reporting window. To operationalize this concept, the current SDG monitoring process is followed by defining “proficiency” as reaching at least the Low International Benchmark on the international PIRLS literacy assessment. \n\n\nPIRLS is the major global primary-age assessment focused on reading, and if all countries participated in it, the task of constructing global estimates of minimum proficiency would be trivial, as it would require aggregating results from a single cross-national assessment. However, most countries participating in PIRLS are high-income, and only a small minority of low- and middle-income countries participate in the assessment. One of the main contributions of the GAML process is that it has overcome this data gap by benchmarking several major cross-national assessments—and increasingly national learning assessments as well—against the standard. \n\n\nThe MPL for each learning assessment is used to calculate the reading proficiency rate for that country, which is the share of students scoring at or above the minimum proficiency level, and conversely to calculate the learning deprivation.\n\n\nThe Proficiency and Grade Levels used for each assessment is as follows: PIRLS (grade 4) - Level 2 (Low international benchmark, 400 points); TIMSS (grade 4) - Level 2 (Low international benchmark, 400 points); LLECE (SERCE, grade 6) - Level 3 (513.66 points); PASEC (grades 5 and 6) - Level 4 (595.1 points); SEA-PLM (grade 5) - Level 6 and above; National Learning Assessment (grade 4, 5 and 6) - Varies by country. \n\n\nWhen a given country had administered multiple types of learning assessments, a hierarchy is applied in the order listed below to ensure best comparability across countries: International or Regional Learning Assessment for Reading (PIRLS, LLECE, PASEC, SEA-PLM) > TIMSS Science > Statistical or Pairwise Linking Exercises > AMPL-bs, Policy Linked National Learning Assessments or Policy Linked Service Delivery Indicators (SDIs) > Non-Policy Linked NLAs (Interim Reporting).\n\n\nNote that as the GAML and joint coalitions continue their efforts to improve learning data coverage, the hierarchy may be revised.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "SE.LPV.PRIM.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Learning poverty: Share of Male Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The construct of “all children reading by age 10” is an ideal that embodies normative statements about both learning and access. To achieve it, not only should all children be reading proficiently after 3 full years in primary education, but they should also have entered school at age 6 or 7. \n\nBy contrast, the actual indicators used to measure learning poverty are based on grade rather than age. Since the assessments are of 4th- through 6th-graders, the children tested will have had at least 3 to 5 years in school to reach what, according to the ideal, should 10 be an age-10 minimum proficiency, or even the entire primary-school-age segment for the out-of-school indicator.\n\nDue to different assessment availability within and between countries, data comparability, both within countries over time and across countries still poses a significant challenge. The additional out of school component further limits comparability.\n\nThe learning poverty indicator is based on data covering four-fifths of children at the end of primary school. In other words, a little more than 80 percent of children in low- and middle-income countries live in a country with at least one learning assessment at the end of primary, carried out in the past 9 years. For regional and global aggregates, weighted imputations affect regions with less data coverage. The major gaps are concentrated in countries where the learning crisis is most acute. Less than half of children in Sub-Saharan Africa live in a country with a National Large-Scale Learning Assessment (NLSA) or a international of regional large-scale learning assessment (ILSA or RLSA) of adequate quality to be used for this purpose.\n\nThis extensive coverage became possible only in recent years, with the progress in measuring learning in countries and the GAML’s efforts to establish comparability, which has made possible the construction of a global indicator based on harmonized proficiency levels. Future efforts by coalition organizations are also ensuring more flexible assessment options are available for expanding data availability for countries, such as the Assessment of Minimum Proficiency Levels (AMPL) and policy linking exercises led by UIS." }, { "id": "Longdefinition", "value": "The share of male 10-year-olds who cannot read and understand a short passage of age-appropriate material—in other words, those who are below the “minimum proficiency” threshold for reading. This measure is defined as the union of two deprivations: 1) schooling deprivation and 2) learning deprivation. A child is considered schooling-deprived (SD) if he or she is of primary school age and out-of-school. The dimension of learning deprivation (LD) applies only for children in school, and identifies those pupils who are below the minimum proficiency level (MPL) for reading, as defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. This “union approach” to measurement reflects the choice that, as presented in the SDGs, all age 10 children must be both in school and learning. The final learning poverty measure combines the two dimensions in a single indicator using the following formula: LP = SD + [(1-SD) x LD]" }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The learning poverty indicator brings together schooling and learning indicators. It starts with the share of children in school who haven’t achieved minimum reading proficiency (Learning Deprived) and adjusts it by the proportion of children who are out of school (Schooling Deprived). \n\n\nFormally, Learning Poverty is calculated as: [LD* (1-SD)] + [1 * SD]\n\n\nwhere LP = Learning poverty; LD = Learning deprivation or the share of children at the end of primary who read at below the minimum proficiency level, as defined by the Global Alliance to Monitor Learning (GAML) in the context of the SDG 4.1.1 monitoring; SD = Schooling deprivation or the share of primary-school-age children who are out-of-school (OOS) and in which all OOS are regarded as being below the minimum proficiency level.\n\n\nBecause out-of-school children are treated as non-proficient in reading, learning poverty will always be higher than the share of children in school who haven't achieved minimum reading proficiency. For countries with a very low schooling deprivation, the learning deprivation value will be very close to Learning Poverty. \n\n\nEstimating the current level of global and regional learning poverty requires deciding how to define “current.” We include results of assessments within four years before or after a set anchor year. This decision is driven by data availability. International and regional large-scale learning assessments used for SDG 4.1.1b reporting are carried out only every 3 to 4 years. And even where assessments have been carried out recently, there is a lag of a couple of years before the data are available. This band is intended as a moving window. In the original 2019 release, the anchor year used was 2015 (Assessments between 2011 and 2019 are included in the learning poverty estimate). In the 2022 Global Update, the anchor year was moved to 2019 (assessments between 2015 and 2023 are included).\n\n\nAggregations for each region comprise the average learning poverty of countries with available data, weighted by their population ages 10–14 years old. To obtain a global estimate, we weight the regional aggregations by the 10–14-year-old population regardless of data availability. This is equivalent to imputing missing country data using regional values.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "SE.LPV.PRIM.SD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Primary school age children out-of-school (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The enrollment for a given learning poverty release are not strictly comparable between countries, due to the differences in enrollment years and definitions, which are determined by best-match with the assessment year and data availability of enrollment indicators. \n\nThe measure will also differ from out of school estimates using household survey data, which UIS reports for SDG 4.1.4. Households survey estimates are not used for Schooling Deprivation because data is typically reported for countries in various years and with time gaps. School surveys are more feasibly collected annually, while household data collection occurs every few years and can also depend on country demand. School surveys also allow more global consistency as the same survey and source data are used across countries. However, the source used to compute the total school-age population differ in some cases where a country provides their national estimates over the default UNDP population data. However, there are potential trade-offs in precision from school surveys as responses come from school representatives rather than using microdata.\n\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, enrolment rate (any definition) is affected by different age-reference points for enrollment. The length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced. The population data further affects the reference age.\n\nDue to the limitations described, in some cases, country specialists will provide a data point that better reflects enrollment in the country." }, { "id": "Longdefinition", "value": "The share of children of primary-school age who are out of school or schooling deprived. This dimension is linked to the indicator 4.1.4 from the SDG 4 thematic framework. This element reflects the belief that all primary-age children should be learning in schools of some type, a belief that every country has enshrined in law and that is enshrined in the SDGs. In addition to fulfilling a universal right and serving as a necessary condition for sustained learning, schooling offers many benefits beyond learning. It contributes to children’s health and well-being such as promoting safety, nutrition, and socialization, and facilitating parents' labor market participation and, at the macro level, schooling can help build social cohesion, democracy, and peace. All those complementary functions mean that schooling has value over and above the measured cognitive learning that it leads to, and they justify including schooling deprivation in the concept of learning poverty." }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Schooling Deprivation is derived using enrollment rates computed by UIS using administrative records and follows the SDG 4.1.4 indicator. To ensure country coverage of data, we consider other enrollment definitions to use for learning poverty, if the first-best option is not available. \n\n\nWe construct an enrollment dataset from 1990 to the year of the current release, relying on multiple enrollment definitions. Our dataset is constructed from UIS (UNESCO Institute of Statistics), and other sources suggested by World Bank regional or country education specialists. Data sources are typically from administrative records (school registers or school censuses) for data on enrolment by age, and UNPD population estimates for school-age population (UIS). Enrolment by single years of age in all levels of education and the total primary-school-age population are used to compute enrollment rates.\n\n\nWe follow this hierarchy of enrollment definitions: Country specialist validated data > Adjusted Net Enrollment Rate (ANER) > Total Net Enrollment Rate (TNER) > Net enrollment rate (NER) > Gross Enrollment Rate (GER; if the gross enrollment rate is higher than 100%, it is adjusted to be 100%).\n\n\nOur preferred measure of school participation is ANER, because it accounts for some primary school aged children who might enter primary school early and advance to secondary school before they reach the official upper age limit of primary education. Adjusted net enrollment is a measure of both “stock” and “flow” and accounts for both age- and grade-based distortions, as it is the percent of primary school age children enrolled either in primary or secondary education, as opposed to gross enrollment which is the share of children of any age that are enrolled in primary school, or net enrollment which is the share of primary school age children that are enrolled in primary school. The next-best indicator is used if ANER is unavailable. In some cases, country specialists will provide a data point that reflects enrollment in the country better than UIS statistics which is used. In future Learning Poverty releases, the enrollment hierarchy may be adjusted as availability of indicator definitions change. \n\n\nThe enrollment year used is the one that best pairs with the assessment year used to compute Learning Deprivation. The year of the preferred assessment is the base. If the same enrollment year is not available, we use a step function to fill the data in with the value of the closest year. If there is data available for two years equally close to the year to fill, the older value is used. This procedure to extrapolate enrollment for missing values is required for us to pair the proficiency measure with enrollment measures from the same year, or its best proxy when enrollment is not available for the same year of the assessment.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "SE.LPV.PRIM.SD.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Female primary school age children out-of-school (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The enrollment for a given learning poverty release are not strictly comparable between countries, due to the differences in enrollment years and definitions, which are determined by best-match with the assessment year and data availability of enrollment indicators. \n\nThe measure will also differ from out of school estimates using household survey data, which UIS reports for SDG 4.1.4. Households survey estimates are not used for Schooling Deprivation because data is typically reported for countries in various years and with time gaps. School surveys are more feasibly collected annually, while household data collection occurs every few years and can also depend on country demand. School surveys also allow more global consistency as the same survey and source data are used across countries. However, the source used to compute the total school-age population differ in some cases where a country provides their national estimates over the default UNDP population data. However, there are potential trade-offs in precision from school surveys as responses come from school representatives rather than using microdata.\n\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, enrolment rate (any definition) is affected by different age-reference points for enrollment. The length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced. The population data further affects the reference age.\n\nDue to the limitations described, in some cases, country specialists will provide a data point that better reflects enrollment in the country." }, { "id": "Longdefinition", "value": "The share of female children of primary-school age who are out of school or schooling deprived. This dimension is linked to the indicator 4.1.4 from the SDG 4 thematic framework. This element reflects the belief that all primary-age children should be learning in schools of some type, a belief that every country has enshrined in law and that is enshrined in the SDGs. In addition to fulfilling a universal right and serving as a necessary condition for sustained learning, schooling offers many benefits beyond learning. It contributes to children’s health and well-being such as promoting safety, nutrition, and socialization, and facilitating parents' labor market participation and, at the macro level, schooling can help build social cohesion, democracy, and peace. All those complementary functions mean that schooling has value over and above the measured cognitive learning that it leads to, and they justify including schooling deprivation in the concept of learning poverty." }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Schooling Deprivation is derived using enrollment rates computed by UIS using administrative records and follows the SDG 4.1.4 indicator. To ensure country coverage of data, we consider other enrollment definitions to use for learning poverty, if the first-best option is not available. \n\n\nWe construct an enrollment dataset from 1990 to the year of the current release, relying on multiple enrollment definitions. Our dataset is constructed from UIS (UNESCO Institute of Statistics), and other sources suggested by World Bank regional or country education specialists. Data sources are typically from administrative records (school registers or school censuses) for data on enrolment by age, and UNPD population estimates for school-age population (UIS). Enrolment by single years of age in all levels of education and the total primary-school-age population are used to compute enrollment rates.\n\n\nWe follow this hierarchy of enrollment definitions: Country specialist validated data > Adjusted Net Enrollment Rate (ANER) > Total Net Enrollment Rate (TNER) > Net enrollment rate (NER) > Gross Enrollment Rate (GER; if the gross enrollment rate is higher than 100%, it is adjusted to be 100%).\n\n\nOur preferred measure of school participation is ANER, because it accounts for some primary school aged children who might enter primary school early and advance to secondary school before they reach the official upper age limit of primary education. Adjusted net enrollment is a measure of both “stock” and “flow” and accounts for both age- and grade-based distortions, as it is the percent of primary school age children enrolled either in primary or secondary education, as opposed to gross enrollment which is the share of children of any age that are enrolled in primary school, or net enrollment which is the share of primary school age children that are enrolled in primary school. The next-best indicator is used if ANER is unavailable. In some cases, country specialists will provide a data point that reflects enrollment in the country better than UIS statistics which is used. In future Learning Poverty releases, the enrollment hierarchy may be adjusted as availability of indicator definitions change. \n\n\nThe enrollment year used is the one that best pairs with the assessment year used to compute Learning Deprivation. The year of the preferred assessment is the base. If the same enrollment year is not available, we use a step function to fill the data in with the value of the closest year. If there is data available for two years equally close to the year to fill, the older value is used. This procedure to extrapolate enrollment for missing values is required for us to pair the proficiency measure with enrollment measures from the same year, or its best proxy when enrollment is not available for the same year of the assessment.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "SE.LPV.PRIM.SD.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Male primary school age children out-of-school (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The enrollment for a given learning poverty release are not strictly comparable between countries, due to the differences in enrollment years and definitions, which are determined by best-match with the assessment year and data availability of enrollment indicators. \n\nThe measure will also differ from out of school estimates using household survey data, which UIS reports for SDG 4.1.4. Households survey estimates are not used for Schooling Deprivation because data is typically reported for countries in various years and with time gaps. School surveys are more feasibly collected annually, while household data collection occurs every few years and can also depend on country demand. School surveys also allow more global consistency as the same survey and source data are used across countries. However, the source used to compute the total school-age population differ in some cases where a country provides their national estimates over the default UNDP population data. However, there are potential trade-offs in precision from school surveys as responses come from school representatives rather than using microdata.\n\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, enrolment rate (any definition) is affected by different age-reference points for enrollment. The length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced. The population data further affects the reference age.\n\nDue to the limitations described, in some cases, country specialists will provide a data point that better reflects enrollment in the country." }, { "id": "Longdefinition", "value": "The share of male children of primary-school age who are out of school or schooling deprived. This dimension is linked to the indicator 4.1.4 from the SDG 4 thematic framework. This element reflects the belief that all primary-age children should be learning in schools of some type, a belief that every country has enshrined in law and that is enshrined in the SDGs. In addition to fulfilling a universal right and serving as a necessary condition for sustained learning, schooling offers many benefits beyond learning. It contributes to children’s health and well-being such as promoting safety, nutrition, and socialization, and facilitating parents' labor market participation and, at the macro level, schooling can help build social cohesion, democracy, and peace. All those complementary functions mean that schooling has value over and above the measured cognitive learning that it leads to, and they justify including schooling deprivation in the concept of learning poverty." }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Schooling Deprivation is derived using enrollment rates computed by UIS using administrative records and follows the SDG 4.1.4 indicator. To ensure country coverage of data, we consider other enrollment definitions to use for learning poverty, if the first-best option is not available. \n\n\nWe construct an enrollment dataset from 1990 to the year of the current release, relying on multiple enrollment definitions. Our dataset is constructed from UIS (UNESCO Institute of Statistics), and other sources suggested by World Bank regional or country education specialists. Data sources are typically from administrative records (school registers or school censuses) for data on enrolment by age, and UNPD population estimates for school-age population (UIS). Enrolment by single years of age in all levels of education and the total primary-school-age population are used to compute enrollment rates.\n\n\nWe follow this hierarchy of enrollment definitions: Country specialist validated data > Adjusted Net Enrollment Rate (ANER) > Total Net Enrollment Rate (TNER) > Net enrollment rate (NER) > Gross Enrollment Rate (GER; if the gross enrollment rate is higher than 100%, it is adjusted to be 100%).\n\n\nOur preferred measure of school participation is ANER, because it accounts for some primary school aged children who might enter primary school early and advance to secondary school before they reach the official upper age limit of primary education. Adjusted net enrollment is a measure of both “stock” and “flow” and accounts for both age- and grade-based distortions, as it is the percent of primary school age children enrolled either in primary or secondary education, as opposed to gross enrollment which is the share of children of any age that are enrolled in primary school, or net enrollment which is the share of primary school age children that are enrolled in primary school. The next-best indicator is used if ANER is unavailable. In some cases, country specialists will provide a data point that reflects enrollment in the country better than UIS statistics which is used. In future Learning Poverty releases, the enrollment hierarchy may be adjusted as availability of indicator definitions change. \n\n\nThe enrollment year used is the one that best pairs with the assessment year used to compute Learning Deprivation. The year of the preferred assessment is the base. If the same enrollment year is not available, we use a step function to fill the data in with the value of the closest year. If there is data available for two years equally close to the year to fill, the older value is used. This procedure to extrapolate enrollment for missing values is required for us to pair the proficiency measure with enrollment measures from the same year, or its best proxy when enrollment is not available for the same year of the assessment.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "2" }, { "id": "SE.PRE.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The significance of pre-primary education lies in its role in laying a strong groundwork for children's social, emotional, and general well-being. Sustainable Development Goal (SDG) target 4.2 is dedicated to ensuring that all children can participate in high-quality early childhood development, care, and pre-primary education programs. This particular indicator is instrumental in determining the number of children eligible for pre-primary education and is a critical component for the computation of additional indicators, as well as for assessing a nation's ability to fulfill the educational needs at this fundamental level of learning." }, { "id": "IndicatorName", "value": "Preprimary education, duration (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The presence of national legislation does not guarantee that countries will implement it effectively, nor does it ensure that parents will take advantage of the provisions available for their children." }, { "id": "Longdefinition", "value": "Preprimary duration refers to the number of grades (years) in preprimary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the number of years that a country's laws or regulations specify for the preprimary stage of education. The definition of pre-primary education as programs that introduce children, typically starting at the age of 3, to a structured school environment and serve as a transition from home to school aligns with UNESCO's description(https://learningportal.iiep.unesco.org/en/glossary/pre-primary-education)." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "2" }, { "id": "SE.PRE.ENRL.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, preprimary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Preprimary school pupil-teacher ratio is the average number of pupils per teacher in preprimary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "2" }, { "id": "SE.PRE.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, preprimary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for pre-primary school is calculated by dividing the number of students enrolled in pre-primary education regardless of age by the population of the age group which officially corresponds to pre-primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.PRE.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, preprimary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for pre-primary school is calculated by dividing the number of students enrolled in pre-primary education regardless of age by the population of the age group which officially corresponds to pre-primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.PRE.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, preprimary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for pre-primary school is calculated by dividing the number of students enrolled in pre-primary education regardless of age by the population of the age group which officially corresponds to pre-primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.PRE.TCAQ.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The percentage of trained educators serves as a barometer for a country's commitment to improving its teaching workforce, and increasing this percentage aligns with the aims of Sustainable Development Goal target 4.c. Female educators, in particular, are vital as they provide inspiration and encouragement for young girls to continue their education. These professionals are instrumental in engaging and maintaining girls' attendance in schools, confronting entrenched gender biases in communities, raising parental aspirations for their daughters, and aiding in the reduction of the educational attainment disparity between male and female students.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in preprimary education, female (% of female teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in preprimary education are the percentage of preprimary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of female teachers in preprimary education" } ], "source_id": "2" }, { "id": "SE.PRE.TCAQ.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in preprimary education, male (% of male teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in preprimary education are the percentage of preprimary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of male teachers in preprimary education" } ], "source_id": "2" }, { "id": "SE.PRE.TCAQ.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in preprimary education (% of total teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in preprimary education are the percentage of preprimary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total teachers in preprimary education" } ], "source_id": "2" }, { "id": "SE.PRM.AGES", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Education is recognized as an essential human right that must be afforded to all individuals. Primary education serves as the cornerstone for the development of fundamental literacy and numeracy skills, which are crucial for a solid foundation in the learning process. It also plays a significant role in promoting personal and social development. The importance of primary education is underscored by Sustainable Development Goal (SDG) target 4.1, which advocates for inclusive and equitable quality education at the primary level for all children." }, { "id": "IndicatorName", "value": "Primary school starting age (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The theoretical entrance age to a given programme or level is typically, but not always, the most common entrance age." }, { "id": "Longdefinition", "value": "Primary school starting age is the age at which students would enter primary education, assuming they had started at the official entrance age for the lowest level of education, had studied full-time throughout and had progressed through the system without repeating or skipping a grade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator provides insights into the demand for educational services across various educational levels. Additionally, it serves as a crucial data point necessary for the generation of numerous educational indicators." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "2" }, { "id": "SE.PRM.CMPT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education." }, { "id": "IndicatorName", "value": "Primary completion rate, female (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "2" }, { "id": "SE.PRM.CMPT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education." }, { "id": "IndicatorName", "value": "Primary completion rate, male (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "2" }, { "id": "SE.PRM.CMPT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education." }, { "id": "IndicatorName", "value": "Primary completion rate, total (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "2" }, { "id": "SE.PRM.CUAT.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital?" }, { "id": "IndicatorName", "value": "Educational attainment, at least completed primary, population 25+ years, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed primary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "2" }, { "id": "SE.PRM.CUAT.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed primary, population 25+ years, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed primary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "2" }, { "id": "SE.PRM.CUAT.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed primary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "2" }, { "id": "SE.PRM.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education serves as the cornerstone for the development of fundamental literacy and numeracy skills, which are crucial for a solid foundation in the learning process. It also plays a significant role in promoting personal and social development. The importance of primary education is underscored by Sustainable Development Goal (SDG) target 4.1, which advocates completion of inclusive and equitable quality education at the primary level for all children." }, { "id": "IndicatorName", "value": "Primary education, duration (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The presence of national legislation does not guarantee that countries will implement it effectively, nor does it ensure that parents will take advantage of the provisions available for their children." }, { "id": "Longdefinition", "value": "Primary duration refers to the number of grades (years) in primary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the number of years that a country's laws or regulations specify for primary education. It aids in identifying the population of school-aged children at different educational levels. Additionally, this metric serves as crucial input data necessary for generating various indicators and evaluating a country's capacity to meet educational demand." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "2" }, { "id": "SE.PRM.ENRL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education is a fundamental human right. It serves as the cornerstone for subsequent achievement and creates opportunities for further progress. Its extensive influence extends to individuals and the collective society, playing a critical role in the alleviation of extreme poverty, the promotion of health, and the advancement of gender equality. The SDG target 4.1 advocates for the completion of free, equitable, and high-quality primary and secondary education for all children, aiming to achieve meaningful and impactful learning outcomes by the year 2030." }, { "id": "IndicatorName", "value": "Primary education, pupils" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Primary education pupils is the total number of pupils enrolled at primary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Enrollment includes Individuals officially registered in a given educational programme, or stage or module thereof, regardless of age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Educational programs at the International Standard Classification of Education (ISCED) level 1, commonly referred to as primary education, are fundamentally structured to equip students with essential literacy and numeracy skills. These programs aim to lay a robust groundwork for learning, fostering an understanding of key knowledge domains, and promoting personal and social growth, thereby readying students for the subsequent stage of lower secondary education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEntry into this level is predominantly based on age, with the typical or legally mandated age of enrollment ranging from 5 to 7 years old. The duration of primary education is generally six years, though it can vary from four to seven years, concluding when students are between 10 to 12 years old (refer to Paragraphs 132 to 134 for further details). Upon successful completion of primary education, students are eligible to progress to ISCED level 2, which corresponds to lower secondary education." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SE.PRM.ENRL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of girls allows an assessment on gender composition in school enrollment. A value greater than 50% indicates participation of more girls at a specific level or programme of education." }, { "id": "IndicatorName", "value": "Primary education, pupils (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The percentage of female enrollment is limited in assessing gender parity, because it's affected by the gender composition of population. Ratio of female to male in enrollment rate provides a population adjusted measure of gender parity." }, { "id": "Longdefinition", "value": "Female pupils as a percentage of total pupils at primary level include enrollments in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentage of female enrollment is calculated by dividing the total number of female students at a given level of education by the total enrollment at the same level, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.PRM.ENRL.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, primary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Primary school pupil-teacher ratio is the average number of pupils per teacher in primary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "2" }, { "id": "SE.PRM.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education is fundamental to future educational success and opens pathways for continued advancement. This indicator measures the overall rate of participation in primary education, signifying the education system's ability to enroll students within a specific age cohort." }, { "id": "IndicatorName", "value": "School enrollment, primary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for primary school is calculated by dividing the number of students enrolled in primary education regardless of age by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of population in the 5-year age group immediately following preprimary education" } ], "source_id": "2" }, { "id": "SE.PRM.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education is fundamental to future educational success and opens pathways for continued advancement. This indicator measures the overall rate of participation in primary education, signifying the education system's ability to enroll students within a specific age cohort." }, { "id": "IndicatorName", "value": "School enrollment, primary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for primary school is calculated by dividing the number of students enrolled in primary education regardless of age by the population of the age group which officially corresponds to primary education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of female population in the 5-year age group immediately following preprimary education" } ], "source_id": "2" }, { "id": "SE.PRM.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education is fundamental to future educational success and opens pathways for continued advancement. This indicator measures the overall rate of participation in primary education, signifying the education system's ability to enroll students within a specific age cohort." }, { "id": "IndicatorName", "value": "School enrollment, primary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for primary school is calculated by dividing the number of students enrolled in primary education regardless of age by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of male population in the 5-year age group immediately following preprimary education" } ], "source_id": "2" }, { "id": "SE.PRM.GINT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The gross intake ratio in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A low gross intake ratio in the first grade of primary education reflects the fact that many children do not enter primary education even though school attendance, at least through the primary level, is mandatory in most countries. Because the gross intake ratio includes all new entrants regardless of age, it can exceed 100 percent in some situations, such as immediately after fees have been abolished or when the number of reenrolled children is large." }, { "id": "IndicatorName", "value": "Gross intake ratio in first grade of primary education, female (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Gross intake ratio in first grade of primary education is the number of new entrants in the first grade of primary education regardless of age, expressed as a percentage of the population of the official primary entrance age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross intake ratio in the first grade of primary education is calculated by dividing the number of new entrants (enrollments minus repeaters) in the first grade of primary education, regardless of age, by the population of the official primary entrance age and multiplying the result by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "2" }, { "id": "SE.PRM.GINT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The gross intake ratio in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A low gross intake ratio in the first grade of primary education reflects the fact that many children do not enter primary education even though school attendance, at least through the primary level, is mandatory in most countries. Because the gross intake ratio includes all new entrants regardless of age, it can exceed 100 percent in some situations, such as immediately after fees have been abolished or when the number of reenrolled children is large." }, { "id": "IndicatorName", "value": "Gross intake ratio in first grade of primary education, male (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Gross intake ratio in first grade of primary education is the number of new entrants in the first grade of primary education regardless of age, expressed as a percentage of the population of the official primary entrance age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross intake ratio in the first grade of primary education is calculated by dividing the number of new entrants (enrollments minus repeaters) in the first grade of primary education, regardless of age, by the population of the official primary entrance age and multiplying the result by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "2" }, { "id": "SE.PRM.GINT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The gross intake ratio in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A low gross intake ratio in the first grade of primary education reflects the fact that many children do not enter primary education even though school attendance, at least through the primary level, is mandatory in most countries. Because the gross intake ratio includes all new entrants regardless of age, it can exceed 100 percent in some situations, such as immediately after fees have been abolished or when the number of reenrolled children is large." }, { "id": "IndicatorName", "value": "Gross intake ratio in first grade of primary education, total (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Gross intake ratio in first grade of primary education is the number of new entrants in the first grade of primary education regardless of age, expressed as a percentage of the population of the official primary entrance age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross intake ratio in the first grade of primary education is calculated by dividing the number of new entrants (enrollments minus repeaters) in the first grade of primary education, regardless of age, by the population of the official primary entrance age and multiplying the result by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "2" }, { "id": "SE.PRM.NENR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, primary (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.PRM.NENR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, primary, female (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "UNESCO Institute for Statistics (UIS), UN Educational, Scientific and Cultural Organization (UNESCO), uri: http://uis.unesco.org/, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.PRM.NENR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, primary, male (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.PRM.NINT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The net intake rate in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A high net intake rate indicates a high degree of access to primary education for the official primary school entrance age children." }, { "id": "IndicatorName", "value": "Net intake rate in grade 1, female (% of official school-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Net intake rate in grade 1 is the number of new entrants in the first grade of primary education who are of official primary school entrance age, expressed as a percentage of the population of the corresponding age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net intake rate in the first grade of primary education is calculated by dividing the number of children of official primary school entrance age who enter grade 1 of primary education for the first time by the population of the same age, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "2" }, { "id": "SE.PRM.NINT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The net intake rate in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A high net intake rate indicates a high degree of access to primary education for the official primary school entrance age children." }, { "id": "IndicatorName", "value": "Net intake rate in grade 1, male (% of official school-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Net intake rate in grade 1 is the number of new entrants in the first grade of primary education who are of official primary school entrance age, expressed as a percentage of the population of the corresponding age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net intake rate in the first grade of primary education is calculated by dividing the number of children of official primary school entrance age who enter grade 1 of primary education for the first time by the population of the same age, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "2" }, { "id": "SE.PRM.NINT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The net intake rate in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A high net intake rate indicates a high degree of access to primary education for the official primary school entrance age children." }, { "id": "IndicatorName", "value": "Net intake rate in grade 1 (% of official school-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Net intake rate in grade 1 is the number of new entrants in the first grade of primary education who are of official primary school entrance age, expressed as a percentage of the population of the corresponding age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net intake rate in the first grade of primary education is calculated by dividing the number of children of official primary school entrance age who enter grade 1 of primary education for the first time by the population of the same age, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "2" }, { "id": "SE.PRM.OENR.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Over-age students, primary, female (% of female enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Over-age students are the percentage of those enrolled who are older than the official school-age range for primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The percentage of over-age students is calculated by dividing the number of students who are older than the official school-age range for primary education by primary school enrollment, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "2" }, { "id": "SE.PRM.OENR.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Over-age students, primary, male (% of male enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Over-age students are the percentage of those enrolled who are older than the official school-age range for primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The percentage of over-age students is calculated by dividing the number of students who are older than the official school-age range for primary education by primary school enrollment, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "2" }, { "id": "SE.PRM.OENR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Over-age students, primary (% of enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Over-age students are the percentage of those enrolled who are older than the official school-age range for primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The percentage of over-age students is calculated by dividing the number of students who are older than the official school-age range for primary education by primary school enrollment, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SE.PRM.PRIV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The indicator reflects the proportion of students attending private educational institutions. Globally, private schools educate approximately 350 million children, and there has been a notable rise in the prevalence of private institutions (https://world-education-blog.org/2021/12/10/new-2021-2-gem-report-out-today-who-chooses-who-loses/). UNESCO emphasizes the importance of comprehensively understanding the contexts and frameworks within which both public and private schools function in each nation. This understanding is vital to guarantee that children's right to education is upheld and that their specific educational requirements are addressed." }, { "id": "IndicatorName", "value": "School enrollment, primary, private (% of total primary)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Religious or private schools, which are not registered with the government or don't follow the common national curriculum, may not be captured." }, { "id": "Longdefinition", "value": "Private enrollment refers to pupils or students enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body such as a nongovernmental organization, religious body, special interest group, foundation or business enterprise." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of students in private primary school is calculated by dividing the number of students enrolled in private educational institutions at primary level by total enrollment (public and private) at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of enrollment in private institutions indicates the scale and capacity of private education within a country. A high percentage suggests strong involvement of the non-governmental sector (including religious bodies, other organizations, associations, communities, private enterprises or persons) in providing organized educational programmes. However, in countries where private institutions are substantially subsidized or aided by the government, the distinction between private and public educational institutions may be less clear-cut especially when certain students are directly financed through government scholarships." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of total enrollment in primary school (both public and private)" } ], "source_id": "2" }, { "id": "SE.PRM.PRS5.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to grade 5, female (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to grade 5 (percentage of cohort reaching grade 5) is the share of children enrolled in the first grade of primary school who eventually reach grade 5. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "2" }, { "id": "SE.PRM.PRS5.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to grade 5, male (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to grade 5 (percentage of cohort reaching grade 5) is the share of children enrolled in the first grade of primary school who eventually reach grade 5. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "2" }, { "id": "SE.PRM.PRS5.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to grade 5, total (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to grade 5 (percentage of cohort reaching grade 5) is the share of children enrolled in the first grade of primary school who eventually reach grade 5. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "2" }, { "id": "SE.PRM.PRSL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to last grade of primary, female (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to last grade of primary is the percentage of children enrolled in the first grade of primary school who eventually reach the last grade of primary education. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels. Survival rate to the last grade of primary education is of particular interest for monitoring universal primary education." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "2" }, { "id": "SE.PRM.PRSL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to last grade of primary, male (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to last grade of primary is the percentage of children enrolled in the first grade of primary school who eventually reach the last grade of primary education. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels. Survival rate to the last grade of primary education is of particular interest for monitoring universal primary education." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "2" }, { "id": "SE.PRM.PRSL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to last grade of primary, total (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to last grade of primary is the percentage of children enrolled in the first grade of primary school who eventually reach the last grade of primary education. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. \n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels. Survival rate to the last grade of primary education is of particular interest for monitoring universal primary education." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "2" }, { "id": "SE.PRM.REPT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on repeaters are often used to indicate an education system's internal efficiency. Repeaters not only increase the cost of education for the family and the school system, but also use limited school resources." }, { "id": "IndicatorName", "value": "Repeaters, primary, female (% of female enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Country policies on repetition and promotion differ. In some cases the number of repeaters is controlled because of limited capacity. In other cases the number of repeaters is almost 0 because of automatic promotion – suggesting a system that is highly efficient but that may not be endowing students with enough cognitive skills." }, { "id": "Longdefinition", "value": "Repeaters in primary school are the number of students enrolled in the same grade as in the previous year, as a percentage of all students enrolled in primary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of repeaters in primary school is calculated by dividing the sum of repeaters in all grades of primary school by the total number of students enrolled in primary school, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "2" }, { "id": "SE.PRM.REPT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on repeaters are often used to indicate an education system's internal efficiency. Repeaters not only increase the cost of education for the family and the school system, but also use limited school resources." }, { "id": "IndicatorName", "value": "Repeaters, primary, male (% of male enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Country policies on repetition and promotion differ. In some cases the number of repeaters is controlled because of limited capacity. In other cases the number of repeaters is almost 0 because of automatic promotion – suggesting a system that is highly efficient but that may not be endowing students with enough cognitive skills." }, { "id": "Longdefinition", "value": "Repeaters in primary school are the number of students enrolled in the same grade as in the previous year, as a percentage of all students enrolled in primary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of repeaters in primary school is calculated by dividing the sum of repeaters in all grades of primary school by the total number of students enrolled in primary school, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "2" }, { "id": "SE.PRM.REPT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on repeaters are often used to indicate an education system's internal efficiency. Repeaters not only increase the cost of education for the family and the school system, but also use limited school resources." }, { "id": "IndicatorName", "value": "Repeaters, primary, total (% of total enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Country policies on repetition and promotion differ. In some cases the number of repeaters is controlled because of limited capacity. In other cases the number of repeaters is almost 0 because of automatic promotion – suggesting a system that is highly efficient but that may not be endowing students with enough cognitive skills." }, { "id": "Longdefinition", "value": "Repeaters in primary school are the number of students enrolled in the same grade as in the previous year, as a percentage of all students enrolled in primary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of repeaters in primary school is calculated by dividing the sum of repeaters in all grades of primary school by the total number of students enrolled in primary school, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "2" }, { "id": "SE.PRM.TCAQ.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The percentage of trained educators serves as a barometer for a country's commitment to improving its teaching workforce, and increasing this percentage aligns with the aims of Sustainable Development Goal target 4.c. Female educators, in particular, are vital as they provide inspiration and encouragement for young girls to continue their education. These professionals are instrumental in engaging and maintaining girls' attendance in schools, confronting entrenched gender biases in communities, raising parental aspirations for their daughters, and aiding in the reduction of the educational attainment disparity between male and female students.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in primary education, female (% of female teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in primary education are the percentage of primary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of female teachers in primary education" } ], "source_id": "2" }, { "id": "SE.PRM.TCAQ.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in primary education, male (% of male teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in primary education are the percentage of primary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of male teachers in primary education" } ], "source_id": "2" }, { "id": "SE.PRM.TCAQ.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in primary education (% of total teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in primary education are the percentage of primary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total teachers in primary education" } ], "source_id": "2" }, { "id": "SE.PRM.TCHR", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Teachers are pivotal in molding the future and unleashing the potential of each student. The United Nations Educational, Scientific and Cultural Organization (UNESCO) highlights a global teacher shortage as a significant obstacle in realizing Sustainable Development Goal 4, which aims for inclusive and equitable quality education." }, { "id": "IndicatorName", "value": "Primary education, teachers" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The precision of this indicator can be influenced by the enumeration method used, such as headcount or 'full-time equivalent' count of teachers." }, { "id": "Longdefinition", "value": "Primary education, teachers refers to the total number of teachers at primary level, including full-time and part-time teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Teachers refer to persons employed full-time or part-time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) or who work occasionally or in a voluntary capacity in educational institutions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The metric of personnel count primarily engaged in teaching and/or research reflects the scale, diversity, and distribution of educational staff within a nation's academic institutions. An increased count is anticipated to enrich the educational setting by means of dedicated teaching, practical research, academic pursuits, and contributions to the service of national educational policies." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SE.PRM.TCHR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although there have been advancements, girls in low-income countries continue to face significant barriers to accessing secondary education. The presence of female teachers is crucial in this context, as they act as role models, inspiring and motivating girls to pursue their education. These educators play a pivotal role in attracting and retaining girls in schools, challenging deep-seated gender stereotypes within communities, elevating parental expectations for their daughters, and contributing to the narrowing of the educational achievement gap between boys and girls." }, { "id": "IndicatorName", "value": "Primary education, teachers (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator measures the level of gender representation in the teaching profession, rather than the effectiveness and quality of teaching." }, { "id": "Longdefinition", "value": "Female teachers as a percentage of total primary education teachers includes full-time and part-time teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of female teachers in primary education is calculated by dividing the total number of female teachers at primary level of education by the total number of teachers at the same level, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of female teachers shows the level of gender representation in the teaching force. A value of greater than 50% indicates more opportunities or preference for women to participate in teaching activities." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total primary education teachers" } ], "source_id": "2" }, { "id": "SE.PRM.TENR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Relevance to gender indicator: Women teachers are important as they serve as role models to girls and help to attract and retain girls in school." }, { "id": "IndicatorName", "value": "Adjusted net enrollment rate, primary (% of primary school age children)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Adjusted net enrollment rate in primary education is calculated by dividing the number of children in the official primary school age who are enrolled in primary or secondary education by the population of the same age group and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.PRM.TENR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Relevance to gender indicator: Women teachers are important as they serve as role models to girls and help to attract and retain girls in school." }, { "id": "IndicatorName", "value": "Adjusted net enrollment rate, primary, female (% of primary school age children)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Adjusted net enrollment rate in primary education is calculated by dividing the number of children in the official primary school age who are enrolled in primary or secondary education by the population of the same age group and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.PRM.TENR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments. The adjusted net enrollment rate in primary education captures primary school-age children who have progressed to secondary education faster than their peers have and who are not counted in the traditional net enrollment rate." }, { "id": "IndicatorName", "value": "Adjusted net enrollment rate, primary, male (% of primary school age children)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Adjusted net enrollment rate in primary education is calculated by dividing the number of children in the official primary school age who are enrolled in primary or secondary education by the population of the same age group and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.PRM.UNER", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Children out of school, primary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to different data sources for enrollment and population data, the number may not capture the actual number of children not attending in primary school." }, { "id": "Longdefinition", "value": "Children out of school are the number of primary-school-age children not enrolled in primary or secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of out-of-school children is calculated by subtracting the number of primary school-age children enrolled in primary or secondary school from the total population of the official primary school-age children. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SE.PRM.UNER.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Children out of school, primary, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to different data sources for enrollment and population data, the number may not capture the actual number of children not attending in primary school." }, { "id": "Longdefinition", "value": "Children out of school are the number of primary-school-age children not enrolled in primary or secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of out-of-school children is calculated by subtracting the number of primary school-age children enrolled in primary or secondary school from the total population of the official primary school-age children. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SE.PRM.UNER.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Children out of school, female (% of female primary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The rate of out-of-school children allows to compare across countries with different population sizes. It shows the share of official primary-school-age children who never attended school or dropped out to the population of official primary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of female children in primary school age" } ], "source_id": "2" }, { "id": "SE.PRM.UNER.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Children out of school, primary, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to different data sources for enrollment and population data, the number may not capture the actual number of children not attending in primary school." }, { "id": "Longdefinition", "value": "Children out of school are the number of primary-school-age children not enrolled in primary or secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of out-of-school children is calculated by subtracting the number of primary school-age children enrolled in primary or secondary school from the total population of the official primary school-age children. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SE.PRM.UNER.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Children out of school, male (% of male primary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The rate of out-of-school children allows to compare across countries with different population sizes. It shows the share of official primary-school-age children who never attended school or dropped out to the population of official primary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of male children in primary school age" } ], "source_id": "2" }, { "id": "SE.PRM.UNER.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Children out of school (% of primary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The rate of out-of-school children allows to compare across countries with different population sizes. It shows the share of official primary-school-age children who never attended school or dropped out to the population of official primary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of children in primary school age" } ], "source_id": "2" }, { "id": "SE.SEC.AGES", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Education is recognized as an essential human right that must be afforded to all individuals. Secondary education imparts critical skills that enable individuals to engage fully in societal activities. Sustainable Development Goal (SDG) target 4.1 highlights the significance of secondary education by promoting inclusive and equitable quality education at this level for all youth." }, { "id": "IndicatorName", "value": "Lower secondary school starting age (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The theoretical entrance age to a given programme or level is typically, but not always, the most common entrance age." }, { "id": "Longdefinition", "value": "Lower secondary school starting age is the age at which students would enter lower secondary education, assuming they had started at the official entrance age for the lowest level of education, had studied full-time throughout and had progressed through the system without repeating or skipping a grade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator provides insights into the demand for educational services across various educational levels. Additionally, it serves as a crucial data point necessary for the generation of numerous educational indicators." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "2" }, { "id": "SE.SEC.CMPT.LO.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Lower secondary education serves as a critical platform for lifelong learning and human development, providing a foundation for further educational pursuits. In certain systems, it includes vocational education programs that equip individuals with skills pertinent to the workforce. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator is particularly important for policymakers who are committed to improving children's access to and participation in education. It measures the ability of the education system to enroll and retain students from the designated starting age through to the completion of all levels of lower secondary education." }, { "id": "IndicatorName", "value": "Lower secondary completion rate, female (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "2" }, { "id": "SE.SEC.CMPT.LO.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Lower secondary education serves as a critical platform for lifelong learning and human development, providing a foundation for further educational pursuits. In certain systems, it includes vocational education programs that equip individuals with skills pertinent to the workforce. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator is particularly important for policymakers who are committed to improving children's access to and participation in education. It measures the ability of the education system to enroll and retain students from the designated starting age through to the completion of all levels of lower secondary education." }, { "id": "IndicatorName", "value": "Lower secondary completion rate, male (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "2" }, { "id": "SE.SEC.CMPT.LO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Lower secondary education serves as a critical platform for lifelong learning and human development, providing a foundation for further educational pursuits. In certain systems, it includes vocational education programs that equip individuals with skills pertinent to the workforce. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator is particularly important for policymakers who are committed to improving children's access to and participation in education. It measures the ability of the education system to enroll and retain students from the designated starting age through to the completion of all levels of lower secondary education." }, { "id": "IndicatorName", "value": "Lower secondary completion rate, total (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "2" }, { "id": "SE.SEC.CUAT.LO.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed lower secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "2" }, { "id": "SE.SEC.CUAT.LO.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed lower secondary, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed lower secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "2" }, { "id": "SE.SEC.CUAT.LO.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed lower secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "2" }, { "id": "SE.SEC.CUAT.PO.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed post-secondary, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed post-secondary non-tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "2" }, { "id": "SE.SEC.CUAT.PO.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed post-secondary, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed post-secondary non-tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "2" }, { "id": "SE.SEC.CUAT.PO.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed post-secondary non-tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "2" }, { "id": "SE.SEC.CUAT.UP.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed upper secondary, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed upper secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed upper secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "2" }, { "id": "SE.SEC.CUAT.UP.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed upper secondary, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed upper secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed upper secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "2" }, { "id": "SE.SEC.CUAT.UP.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed upper secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed upper secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "2" }, { "id": "SE.SEC.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Secondary education equips individuals with vital skills that are essential for active participation in society. Sustainable Development Goal (SDG) target 4.1 promotes completion of secondary education for all young people by endorsing free, equitable, and high-quality education at this level." }, { "id": "IndicatorName", "value": "Secondary education, duration (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The presence of national legislation does not guarantee that countries will implement it effectively, nor does it ensure that parents will take advantage of the provisions available for their children." }, { "id": "Longdefinition", "value": "Secondary duration refers to the number of grades (years) in secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the number of years that a country's laws or regulations specify for secondary education. It aids in identifying the population of school-aged children at different educational levels. Additionally, this metric serves as crucial input data necessary for generating various indicators and evaluating a country's capacity to meet educational demand." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "2" }, { "id": "SE.SEC.ENRL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Secondary education provides children with essential skills that enable their full participation in society. Sustainable Development Goal target 4.1 calls for all children to complete free, equitable, and high-quality primary and secondary education, with the goal of attaining significant and effective learning outcomes by 2030." }, { "id": "IndicatorName", "value": "Secondary education, pupils" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Secondary education pupils is the total number of pupils enrolled at secondary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Enrollment includes Individuals officially registered in a given educational programme, or stage or module thereof, regardless of age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The International Standard Classification of Education (ISCED) differentiates between lower secondary education and upper secondary education within its educational programs. ISCED level 2, or lower secondary education, are designed to build upon the foundational literacy and numeracy skills acquired at ISCED level 1. The objective at this stage is to establish a base for lifelong learning and human development, which can be further enhanced by subsequent educational opportunities. Certain education systems may introduce vocational education programs at this level to equip individuals with skills that are directly applicable to the workforce.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAt ISCED level 3, or upper secondary education, programs are structured to finalize the secondary education phase, preparing students for higher education or to enter the job market with relevant skills, or in some cases, to achieve both objectives." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SE.SEC.ENRL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of girls allows an assessment on gender composition in school enrollment. A value greater than 50% indicates participation of more girls at a specific level or programme of education." }, { "id": "IndicatorName", "value": "Secondary education, pupils (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The percentage of female enrollment is limited in assessing gender parity, because it's affected by the gender composition of population. Ratio of female to male in enrollment rate provides a population adjusted measure of gender parity." }, { "id": "Longdefinition", "value": "Female pupils as a percentage of total pupils at secondary level includes enrollments in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentage of female enrollment is calculated by dividing the total number of female students at a given level of education by the total enrollment at the same level, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.SEC.ENRL.GC", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Secondary education, general pupils" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Secondary general pupils are the number of secondary students enrolled in general education programs, including teacher training." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Enrollment includes Individuals officially registered in a given educational programme, or stage or module thereof, regardless of age.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.SEC.ENRL.GC.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of girls allows an assessment on gender composition in school enrollment. A value greater than 50% indicates participation of more girls at a specific level or programme of education." }, { "id": "IndicatorName", "value": "Secondary education, general pupils (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The percentage of female enrollment is limited in assessing gender parity, because it's affected by the gender composition of population. Ratio of female to male in enrollment rate provides a population adjusted measure of gender parity." }, { "id": "Longdefinition", "value": "Secondary general pupils are the number of secondary students enrolled in general education programs, including teacher training." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentage of female enrollment is calculated by dividing the total number of female students at a given level of education by the total enrollment at the same level, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.SEC.ENRL.LO.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, lower secondary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Lower secondary school pupil-teacher ratio is the average number of pupils per teacher in lower secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1981-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "2" }, { "id": "SE.SEC.ENRL.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, secondary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Secondary school pupil-teacher ratio is the average number of pupils per teacher in secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "2" }, { "id": "SE.SEC.ENRL.UP.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, upper secondary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Upper secondary school pupil-teacher ratio is the average number of pupils per teacher in upper secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1981-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "2" }, { "id": "SE.SEC.ENRL.VO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Secondary education, vocational pupils" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Secondary vocational pupils are the number of secondary students enrolled in technical and vocational education programs, including teacher training." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Enrollment includes Individuals officially registered in a given educational programme, or stage or module thereof, regardless of age.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.SEC.ENRL.VO.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of girls allows an assessment on gender composition in school enrollment. A value greater than 50% indicates participation of more girls at a specific level or programme of education." }, { "id": "IndicatorName", "value": "Secondary education, vocational pupils (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The percentage of female enrollment is limited in assessing gender parity, because it's affected by the gender composition of population. Ratio of female to male in enrollment rate provides a population adjusted measure of gender parity." }, { "id": "Longdefinition", "value": "Secondary vocational pupils are the number of secondary students enrolled in technical and vocational education programs, including teacher training." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentage of female enrollment is calculated by dividing the total number of female students at a given level of education by the total enrollment at the same level, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.SEC.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Secondary education acts as a critical intermediary that not only builds upon the foundational knowledge acquired in primary education but also equips students for various pathways, including immediate entry into the workforce, further education in postsecondary non-tertiary institutions, or advancement to higher education. This indicator assesses the aggregate participation rate in secondary education, reflecting the education system's capacity to enroll students within a designated age group." }, { "id": "IndicatorName", "value": "School enrollment, secondary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of population in the 5-year age group immediately following primary education" } ], "source_id": "2" }, { "id": "SE.SEC.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Secondary education acts as a critical intermediary that not only builds upon the foundational knowledge acquired in primary education but also equips students for various pathways, including immediate entry into the workforce, further education in postsecondary non-tertiary institutions, or advancement to higher education. This indicator assesses the aggregate participation rate in secondary education, reflecting the education system's capacity to enroll students within a designated age group." }, { "id": "IndicatorName", "value": "School enrollment, secondary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of female population in the 5-year age group immediately following primary education" } ], "source_id": "2" }, { "id": "SE.SEC.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Secondary education acts as a critical intermediary that not only builds upon the foundational knowledge acquired in primary education but also equips students for various pathways, including immediate entry into the workforce, further education in postsecondary non-tertiary institutions, or advancement to higher education. This indicator assesses the aggregate participation rate in secondary education, reflecting the education system's capacity to enroll students within a designated age group." }, { "id": "IndicatorName", "value": "School enrollment, secondary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of male population in the 5-year age group immediately following primary education" } ], "source_id": "2" }, { "id": "SE.SEC.NENR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, secondary (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for secondary school is calculated by dividing the number of students of official school age enrolled in secondary education by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.SEC.NENR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, secondary, female (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for secondary school is calculated by dividing the number of students of official school age enrolled in secondary education by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.SEC.NENR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, secondary, male (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for secondary school is calculated by dividing the number of students of official school age enrolled in secondary education by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "2" }, { "id": "SE.SEC.PRIV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The indicator reflects the proportion of students attending private educational institutions. Globally, private schools educate approximately 350 million children, and there has been a notable rise in the prevalence of private institutions (https://world-education-blog.org/2021/12/10/new-2021-2-gem-report-out-today-who-chooses-who-loses/). UNESCO emphasizes the importance of comprehensively understanding the contexts and frameworks within which both public and private schools function in each nation. This understanding is vital to guarantee that children's right to education is upheld and that their specific educational requirements are addressed." }, { "id": "IndicatorName", "value": "School enrollment, secondary, private (% of total secondary)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Religious or private schools, which are not registered with the government or don't follow the common national curriculum, may not be captured." }, { "id": "Longdefinition", "value": "Private enrollment refers to pupils or students enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body such as a nongovernmental organization, religious body, special interest group, foundation or business enterprise." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of students in private secondary school is calculated by dividing the number of students enrolled in private educational institutions at secondary level by total enrollment (public and private) at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of enrollment in private institutions indicates the scale and capacity of private education within a country. A high percentage suggests strong involvement of the non-governmental sector (including religious bodies, other organizations, associations, communities, private enterprises or persons) in providing organized educational programmes. However, in countries where private institutions are substantially subsidized or aided by the government, the distinction between private and public educational institutions may be less clear-cut especially when certain students are directly financed through government scholarships." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of total enrollment in secondary school (both public and private)" } ], "source_id": "2" }, { "id": "SE.SEC.PROG.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The effective transition rate from primary to secondary education conveys the degree of access or transition between the two levels. As completing primary education is a prerequisite for participating in lower secondary education, growing numbers of primary completers will inevitably create pressure for more available places at the secondary level. A low effective transition rate can signal such problems as an inadequate examination and promotion system or insufficient secondary education capacity." }, { "id": "IndicatorName", "value": "Progression to secondary school, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data on the transition rate is affected when new entrants and repeaters are not correctly distinguished. Students who interrupt their studies after completing primary education could also affect data quality." }, { "id": "Longdefinition", "value": "Progression to secondary school refers to the number of new entrants to the first grade of secondary school in a given year as a percentage of the number of students enrolled in the final grade of primary school in the previous year (minus the number of repeaters from the last grade of primary education in the given year)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Effective transition rate is calculated by dividing the number of new entrants in the first grade of secondary education in a given year (t) by the number of students who enrolled in the final grade of primary education in the previous school year (t-1) minus the number of repeaters from the last grade of primary education in the given year (t), and multiplying by 100. \nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "2" }, { "id": "SE.SEC.PROG.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The effective transition rate from primary to secondary education conveys the degree of access or transition between the two levels. As completing primary education is a prerequisite for participating in lower secondary education, growing numbers of primary completers will inevitably create pressure for more available places at the secondary level. A low effective transition rate can signal such problems as an inadequate examination and promotion system or insufficient secondary education capacity." }, { "id": "IndicatorName", "value": "Progression to secondary school, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data on the transition rate is affected when new entrants and repeaters are not correctly distinguished. Students who interrupt their studies after completing primary education could also affect data quality." }, { "id": "Longdefinition", "value": "Progression to secondary school refers to the number of new entrants to the first grade of secondary school in a given year as a percentage of the number of students enrolled in the final grade of primary school in the previous year (minus the number of repeaters from the last grade of primary education in the given year)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Effective transition rate is calculated by dividing the number of new entrants in the first grade of secondary education in a given year (t) by the number of students who enrolled in the final grade of primary education in the previous school year (t-1) minus the number of repeaters from the last grade of primary education in the given year (t), and multiplying by 100. \nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "2" }, { "id": "SE.SEC.PROG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The effective transition rate from primary to secondary education conveys the degree of access or transition between the two levels. As completing primary education is a prerequisite for participating in lower secondary education, growing numbers of primary completers will inevitably create pressure for more available places at the secondary level. A low effective transition rate can signal such problems as an inadequate examination and promotion system or insufficient secondary education capacity." }, { "id": "IndicatorName", "value": "Progression to secondary school (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data on the transition rate is affected when new entrants and repeaters are not correctly distinguished. Students who interrupt their studies after completing primary education could also affect data quality." }, { "id": "Longdefinition", "value": "Progression to secondary school refers to the number of new entrants to the first grade of secondary school in a given year as a percentage of the number of students enrolled in the final grade of primary school in the previous year (minus the number of repeaters from the last grade of primary education in the given year)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Effective transition rate is calculated by dividing the number of new entrants in the first grade of secondary education in a given year (t) by the number of students who enrolled in the final grade of primary education in the previous school year (t-1) minus the number of repeaters from the last grade of primary education in the given year (t), and multiplying by 100. \nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "2" }, { "id": "SE.SEC.TCAQ.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The percentage of trained educators serves as a barometer for a country's commitment to improving its teaching workforce, and increasing this percentage aligns with the aims of Sustainable Development Goal target 4.c. Female educators, in particular, are vital as they provide inspiration and encouragement for young girls to continue their education. These professionals are instrumental in engaging and maintaining girls' attendance in schools, confronting entrenched gender biases in communities, raising parental aspirations for their daughters, and aiding in the reduction of the educational attainment disparity between male and female students.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in secondary education, female (% of female teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in secondary education are the percentage of secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of female teachers in secondary education" } ], "source_id": "2" }, { "id": "SE.SEC.TCAQ.LO.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The percentage of trained educators serves as a barometer for a country's commitment to improving its teaching workforce, and increasing this percentage aligns with the aims of Sustainable Development Goal target 4.c. Female educators, in particular, are vital as they provide inspiration and encouragement for young girls to continue their education. These professionals are instrumental in engaging and maintaining girls' attendance in schools, confronting entrenched gender biases in communities, raising parental aspirations for their daughters, and aiding in the reduction of the educational attainment disparity between male and female students.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in lower secondary education, female (% of female teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in lower secondary education are the percentage of lower secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of female teachers in lower secondary education" } ], "source_id": "2" }, { "id": "SE.SEC.TCAQ.LO.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in lower secondary education, male (% of male teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in lower secondary education are the percentage of lower secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of male teachers in lower secondary education" } ], "source_id": "2" }, { "id": "SE.SEC.TCAQ.LO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in lower secondary education (% of total teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in lower secondary education are the percentage of lower secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total teachers in lower secondary education" } ], "source_id": "2" }, { "id": "SE.SEC.TCAQ.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in secondary education, male (% of male teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in secondary education are the percentage of secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of male teachers in secondary education" } ], "source_id": "2" }, { "id": "SE.SEC.TCAQ.UP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The percentage of trained educators serves as a barometer for a country's commitment to improving its teaching workforce, and increasing this percentage aligns with the aims of Sustainable Development Goal target 4.c. Female educators, in particular, are vital as they provide inspiration and encouragement for young girls to continue their education. These professionals are instrumental in engaging and maintaining girls' attendance in schools, confronting entrenched gender biases in communities, raising parental aspirations for their daughters, and aiding in the reduction of the educational attainment disparity between male and female students.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in upper secondary education, female (% of female teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in upper secondary education are the percentage of upper secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of female teachers in upper secondary education" } ], "source_id": "2" }, { "id": "SE.SEC.TCAQ.UP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in upper secondary education, male (% of male teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in upper secondary education are the percentage of upper secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of male teachers in upper secondary education" } ], "source_id": "2" }, { "id": "SE.SEC.TCAQ.UP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in upper secondary education (% of total teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in upper secondary education are the percentage of upper secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total teachers in upper secondary education" } ], "source_id": "2" }, { "id": "SE.SEC.TCAQ.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in secondary education (% of total teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in secondary education are the percentage of secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total teachers in secondary education" } ], "source_id": "2" }, { "id": "SE.SEC.TCHR", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Teachers are pivotal in molding the future and unleashing the potential of each student. The United Nations Educational, Scientific and Cultural Organization (UNESCO) highlights a global teacher shortage as a significant obstacle in realizing Sustainable Development Goal 4, which aims for inclusive and equitable quality education." }, { "id": "IndicatorName", "value": "Secondary education, teachers" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The precision of this indicator can be influenced by the enumeration method used, such as headcount or 'full-time equivalent' count of teachers." }, { "id": "Longdefinition", "value": "Secondary education, teachers refers to the total number of teachers at secondary level, including full-time and part-time teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Teachers refer to persons employed full-time or part-time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) or who work occasionally or in a voluntary capacity in educational institutions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The metric of personnel count primarily engaged in teaching and/or research reflects the scale, diversity, and distribution of educational staff within a nation's academic institutions. An increased count is anticipated to enrich the educational setting by means of dedicated teaching, practical research, academic pursuits, and contributions to the service of national educational policies." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SE.SEC.TCHR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although there have been advancements, girls in low-income countries continue to face significant barriers to accessing secondary education. The presence of female teachers is crucial in this context, as they act as role models, inspiring and motivating girls to pursue their education. These educators play a pivotal role in attracting and retaining girls in schools, challenging deep-seated gender stereotypes within communities, elevating parental expectations for their daughters, and contributing to the narrowing of the educational achievement gap between boys and girls." }, { "id": "IndicatorName", "value": "Secondary education, teachers, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator measures the level of gender representation in the teaching profession, rather than the effectiveness and quality of teaching." }, { "id": "Longdefinition", "value": "Secondary education, teachers, female, refers to the total number of female teachers at secondary level, including full-time and part-time teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Teachers refer to persons employed full-time or part-time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) or who work occasionally or in a voluntary capacity in educational institutions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The metric of personnel count primarily engaged in teaching and/or research reflects the scale, diversity, and distribution of educational staff within a nation's academic institutions. An increased count is anticipated to enrich the educational setting by means of dedicated teaching, practical research, academic pursuits, and contributions to the service of national educational policies." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SE.SEC.TCHR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although there have been advancements, girls in low-income countries continue to face significant barriers to accessing secondary education. The presence of female teachers is crucial in this context, as they act as role models, inspiring and motivating girls to pursue their education. These educators play a pivotal role in attracting and retaining girls in schools, challenging deep-seated gender stereotypes within communities, elevating parental expectations for their daughters, and contributing to the narrowing of the educational achievement gap between boys and girls." }, { "id": "IndicatorName", "value": "Secondary education, teachers (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator measures the level of gender representation in the teaching profession, rather than the effectiveness and quality of teaching." }, { "id": "Longdefinition", "value": "Female teachers as a percentage of total secondary education teachers includes full-time and part-time teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of female teachers in secondary education is calculated by dividing the total number of female teachers at secondary level of education by the total number of teachers at the same level, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of female teachers shows the level of gender representation in the teaching force. A value of greater than 50% indicates more opportunities or preference for women to participate in teaching activities." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total secondary education teachers" } ], "source_id": "2" }, { "id": "SE.SEC.UNER.LO.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Adolescents out of school, female (% of female lower secondary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Adolescents out of school are the percentage of lower secondary school age adolescents who are not enrolled in school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The rate of out-of-school adolescents allows to compare across countries with different population sizes. It shows the share of official lower secondary age adolescents who never attended school or dropped out to the population of official lower secondary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of female adolescents in lower secondary school age" } ], "source_id": "2" }, { "id": "SE.SEC.UNER.LO.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Adolescents out of school, male (% of male lower secondary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Adolescents out of school are the percentage of lower secondary school age adolescents who are not enrolled in school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The rate of out-of-school adolescents allows to compare across countries with different population sizes. It shows the share of official lower secondary age adolescents who never attended school or dropped out to the population of official lower secondary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of male adolescents in lower secondary school age" } ], "source_id": "2" }, { "id": "SE.SEC.UNER.LO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Adolescents out of school (% of lower secondary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Adolescents out of school are the percentage of lower secondary school age adolescents who are not enrolled in school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The rate of out-of-school adolescents allows to compare across countries with different population sizes. It shows the share of official lower secondary age adolescents who never attended school or dropped out to the population of official lower secondary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of adolescents in lower secondary school age" } ], "source_id": "2" }, { "id": "SE.TER.CUAT.BA.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Bachelor's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "2" }, { "id": "SE.TER.CUAT.BA.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Bachelor's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "2" }, { "id": "SE.TER.CUAT.BA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Bachelor's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "2" }, { "id": "SE.TER.CUAT.DO.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, Doctoral or equivalent, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Doctoral or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Doctoral or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "2" }, { "id": "SE.TER.CUAT.DO.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, Doctoral or equivalent, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Doctoral or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Doctoral or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero..\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "2" }, { "id": "SE.TER.CUAT.DO.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Doctoral or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Doctoral or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "2" }, { "id": "SE.TER.CUAT.MS.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Master's or equivalent, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Master's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Master's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "2" }, { "id": "SE.TER.CUAT.MS.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Master's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Master's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "2" }, { "id": "SE.TER.CUAT.MS.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Master's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Master's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "2" }, { "id": "SE.TER.CUAT.ST.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed short-cycle tertiary, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed short-cycle tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed short-cycle tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "2" }, { "id": "SE.TER.CUAT.ST.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed short-cycle tertiary, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed short-cycle tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed short-cycle tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "2" }, { "id": "SE.TER.CUAT.ST.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed short-cycle tertiary, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed short-cycle tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed short-cycle tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "2" }, { "id": "SE.TER.ENRL.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, tertiary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Tertiary school pupil-teacher ratio is the average number of pupils per teacher in tertiary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "2" }, { "id": "SE.TER.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.3 is committed to providing equitable access to affordable and high-quality technical, vocational, and tertiary education, including university, for both women and men. This particular indicator reflects the overall capacity of the educational infrastructure to support enrolment within a specified age demographic at the tertiary level." }, { "id": "IndicatorName", "value": "School enrollment, tertiary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of population in the 5-year age group immediately following upper secondary education" } ], "source_id": "2" }, { "id": "SE.TER.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.3 is committed to providing equitable access to affordable and high-quality technical, vocational, and tertiary education, including university, for both women and men. This particular indicator reflects the overall capacity of the educational infrastructure to support enrolment within a specified age demographic at the tertiary level." }, { "id": "IndicatorName", "value": "School enrollment, tertiary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of female population in the 5-year age group immediately following upper secondary education" } ], "source_id": "2" }, { "id": "SE.TER.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.3 is committed to providing equitable access to affordable and high-quality technical, vocational, and tertiary education, including university, for both women and men. This particular indicator reflects the overall capacity of the educational infrastructure to support enrolment within a specified age demographic at the tertiary level." }, { "id": "IndicatorName", "value": "School enrollment, tertiary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of male population in the 5-year age group immediately following upper secondary education" } ], "source_id": "2" }, { "id": "SE.TER.TCHR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator reflects the gender distribution within the teaching profession. It serves as a tool for evaluating the necessity of creating opportunities and incentives to promote female participation in educational instruction at various levels. According to UNESCO, there is a global trend of women being disproportionately represented in the teaching workforce. Nonetheless, this representation declines at the tertiary education level, where men are more prevalent, and women are less likely to attain senior and leadership roles within higher education institutions." }, { "id": "IndicatorName", "value": "Tertiary education, academic staff (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator measures the level of gender representation in the teaching profession, rather than the effectiveness and quality of teaching." }, { "id": "Longdefinition", "value": "Tertiary education, academic staff (% female) is the share of female academic staff in tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of female academic staffs in tertiary education is calculated by dividing the total number of female academic staffs at tertiary level of education by the total number of academic staffs at the same level, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of female teachers shows the level of gender representation in the teaching force. A value of greater than 50% indicates more opportunities or preference for women to participate in teaching activities." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of academic staff in tertiary education" } ], "source_id": "2" }, { "id": "SE.XPD.CPRM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investing in education is a catalyst for economic expansion, productivity improvement, and the advancement of both individual and societal well-being. It also serves as a mechanism to mitigate social disparities. The value of this indicator highlights the focus of governmental policies, as evidenced by the distribution of spending by expenditure type and the nature of expenditures throughout various educational stages." }, { "id": "IndicatorName", "value": "Current education expenditure, primary (% of total expenditure in primary public institutions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator depends on comprehensive and accurate data regarding government expenditure by type and nature of spending. In some cases, data on total government expenditure on education only includes the Ministry of Education, excluding other ministries that may also allocate part of their budget to educational services." }, { "id": "Longdefinition", "value": "Current expenditure is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Current expenditure, primary is calculated by dividing all current expenditure in public institutions of primary education by total expenditure (current and capital) in public institutions of primary education, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on education spending is obtained from national governments through their responses to the annual UIS survey on formal education or the UNESCO-OECD-Eurostat (UOE) data collection initiative. The figures reported in the education expenditure questionnaire are usually derived from the annual financial reports of the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office.\nStatistical concept(s): Educational expenditure in public institutions includes all staff compensation, teaching staff/non teaching staff compensation, current expenditure other than staff compensation, expenditure on school books and teaching material." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total expenditure in primary public institutions" } ], "source_id": "2" }, { "id": "SE.XPD.CSEC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investing in education is a catalyst for economic expansion, productivity improvement, and the advancement of both individual and societal well-being. It also serves as a mechanism to mitigate social disparities. The value of this indicator highlights the focus of governmental policies, as evidenced by the distribution of spending by expenditure type and the nature of expenditures throughout various educational stages." }, { "id": "IndicatorName", "value": "Current education expenditure, secondary (% of total expenditure in secondary public institutions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator depends on comprehensive and accurate data regarding government expenditure by type and nature of spending. In some cases, data on total government expenditure on education only includes the Ministry of Education, excluding other ministries that may also allocate part of their budget to educational services." }, { "id": "Longdefinition", "value": "Current expenditure is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Current expenditure, secondary is calculated by dividing all current expenditure in public institutions of secondary education by total expenditure (current and capital) in public institutions of secondary education, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on education spending is obtained from national governments through their responses to the annual UIS survey on formal education or the UNESCO-OECD-Eurostat (UOE) data collection initiative. The figures reported in the education expenditure questionnaire are usually derived from the annual financial reports of the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office.\nStatistical concept(s): Educational expenditure in public institutions includes all staff compensation, teaching staff/non teaching staff compensation, current expenditure other than staff compensation, expenditure on school books and teaching material." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total expenditure in secondary public institutions" } ], "source_id": "2" }, { "id": "SE.XPD.CTER.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investing in education is a catalyst for economic expansion, productivity improvement, and the advancement of both individual and societal well-being. It also serves as a mechanism to mitigate social disparities. The value of this indicator highlights the focus of governmental policies, as evidenced by the distribution of spending by expenditure type and the nature of expenditures throughout various educational stages." }, { "id": "IndicatorName", "value": "Current education expenditure, tertiary (% of total expenditure in tertiary public institutions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator depends on comprehensive and accurate data regarding government expenditure by type and nature of spending. In some cases, data on total government expenditure on education only includes the Ministry of Education, excluding other ministries that may also allocate part of their budget to educational services." }, { "id": "Longdefinition", "value": "Current expenditure is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Current expenditure, tertiary is calculated by dividing all current expenditure in public institutions of tertiary education by total expenditure (current and capital) in public institutions of tertiary education, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on education spending is obtained from national governments through their responses to the annual UIS survey on formal education or the UNESCO-OECD-Eurostat (UOE) data collection initiative. The figures reported in the education expenditure questionnaire are usually derived from the annual financial reports of the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office.\nStatistical concept(s): Educational expenditure in public institutions includes all staff compensation, teaching staff/non teaching staff compensation, current expenditure other than staff compensation, expenditure on school books and teaching material." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total expenditure in tertiary public institutions" } ], "source_id": "2" }, { "id": "SE.XPD.CTOT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investing in education is a catalyst for economic expansion, productivity improvement, and the advancement of both individual and societal well-being. It also serves as a mechanism to mitigate social disparities. The value of this indicator highlights the focus of governmental policies, as evidenced by the distribution of spending by expenditure type and the nature of expenditures throughout various educational stages." }, { "id": "IndicatorName", "value": "Current education expenditure, total (% of total expenditure in public institutions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator depends on comprehensive and accurate data regarding government expenditure by type and nature of spending. In some cases, data on total government expenditure on education only includes the Ministry of Education, excluding other ministries that may also allocate part of their budget to educational services." }, { "id": "Longdefinition", "value": "Current expenditure is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Current expenditure, total is calculated by dividing all current expenditure in public institutions of all levels of education by total expenditure (current and capital) in public institutions of all levels of education, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on education spending is obtained from national governments through their responses to the annual UIS survey on formal education or the UNESCO-OECD-Eurostat (UOE) data collection initiative. The figures reported in the education expenditure questionnaire are usually derived from the annual financial reports of the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office.\nStatistical concept(s): Educational expenditure in public institutions includes all staff compensation, teaching staff/non teaching staff compensation, current expenditure other than staff compensation, expenditure on school books and teaching material." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total expenditure in public institutions" } ], "source_id": "2" }, { "id": "SE.XPD.PRIM.PC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Government expenditure per student, primary (% of GDP per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: General government expenditure per student in primary education is calculated by dividing total government expenditure on primary education by the number of students at primary level, expressed as a percentage of GDP per capita. Aggregate data are World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Data on GDP per capita come from the World Bank. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "2" }, { "id": "SE.XPD.PRIM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of government expenditure for a specific education level allows an assessment of the priority a government assigns to a level of education relative to other levels. Enrolment and the relative costs per student between different levels of education should be also taken into account." }, { "id": "IndicatorName", "value": "Expenditure on primary education (% of government expenditure on education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data disaggregated by level of education are estimates in some instances. It is often difficult to separate lower from upper secondary education expenditure, or pre-primary from primary." }, { "id": "Longdefinition", "value": "Expenditure on primary education is expressed as a percentage of total general government expenditure on education. General government usually refers to local, regional and central governments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of expenditure on primary education to total government expenditure on education is calculated by dividing government expenditure on primary education by total government expenditure on education (all levels combined), and multiplying by 100. Aggregate data are based on World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "2" }, { "id": "SE.XPD.SECO.PC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Government expenditure per student, secondary (% of GDP per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: General government expenditure per student in secondary education is calculated by dividing total government expenditure on secondary education by the number of students at secondary level, expressed as a percentage of GDP per capita. Aggregate data are World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Data on GDP per capita come from the World Bank. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "2" }, { "id": "SE.XPD.SECO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of government expenditure for a specific education level allows an assessment of the priority a government assigns to a level of education relative to other levels. Enrolment and the relative costs per student between different levels of education should be also taken into account." }, { "id": "IndicatorName", "value": "Expenditure on secondary education (% of government expenditure on education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data disaggregated by level of education are estimates in some instances. It is often difficult to separate lower from upper secondary education expenditure, or pre-primary from primary." }, { "id": "Longdefinition", "value": "Expenditure on secondary education is expressed as a percentage of total general government expenditure on education. General government usually refers to local, regional and central governments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of expenditure on secondary education to total government expenditure on education is calculated by dividing government expenditure on secondary education by total government expenditure on education (all levels combined), and multiplying by 100. Aggregate data are based on World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "2" }, { "id": "SE.XPD.TERT.PC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Government expenditure per student, tertiary (% of GDP per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: General government expenditure per student in tertiary education is calculated by dividing total government expenditure on tertiary education by the number of students at tertiary level, expressed as a percentage of GDP per capita. Aggregate data are World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Data on GDP per capita come from the World Bank. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "2" }, { "id": "SE.XPD.TERT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of government expenditure for a specific education level allows an assessment of the priority a government assigns to a level of education relative to other levels. Enrolment and the relative costs per student between different levels of education should be also taken into account." }, { "id": "IndicatorName", "value": "Expenditure on tertiary education (% of government expenditure on education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data disaggregated by level of education are estimates in some instances. It is often difficult to separate lower from upper secondary education expenditure, or pre-primary from primary." }, { "id": "Longdefinition", "value": "Expenditure on tertiary education is expressed as a percentage of total general government expenditure on education. General government usually refers to local, regional and central governments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of expenditure on tertiary education to total government expenditure on education is calculated by dividing government expenditure on tertiary education by total government expenditure on education (all levels combined), and multiplying by 100. Aggregate data are based on World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "2" }, { "id": "SE.XPD.TOTL.GB.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investment in education serves as a driving force for economic growth, enhancement of productivity, and the promotion of individual and collective prosperity. This indicator is instrumental in evaluating the extent to which a government prioritizes education, whether over time or in relation to other nations. Furthermore, it reflects the government's dedication to the investment in human capital development." }, { "id": "IndicatorName", "value": "Government expenditure on education, total (% of government expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on government expenditure on education may refer to spending by the ministry of education only (excluding spending on educational activities by other ministries). In addition, definitions and methods of data on total general government expenditure may differ across countries." }, { "id": "Longdefinition", "value": "General government expenditure on education (current, capital, and transfers) is expressed as a percentage of total general government expenditure on all sectors (including health, education, social services, etc.). It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Expenditure on education, total (% of government expenditure) is calculated by dividing total government expenditure on education by the total government expenditure on all sectors and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nInformation regarding educational expenditures is obtained from national governments through their responses to the annual survey on formal education conducted by the UNESCO Institute for Statistics (UIS). The data provided for this questionnaire often originates from the annual financial reports of the Ministry of Finance or the Ministry of Education, or from the national accounts compiled by the National Statistical Office. Additionally, comprehensive data on general government expenditure across all sectors are sourced from the International Monetary Fund's (IMF) World Economic Outlook database, which undergoes an annual update.\nStatistical concept(s): A greater allocation of government funds towards education reflects a significant emphasis on educational priorities in comparison to other public sector investments. It is important to consider, however, that the capacity of governments to spend varies, resulting in differing budget sizes. Additionally, demographic factors such as the age distribution within a country can influence the proportion of spending on education versus other areas like health or social security." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of government expenditure" } ], "source_id": "2" }, { "id": "SE.XPD.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investment in education acts as a driving force for economic growth, enhances productivity, and promotes the betterment of individual and collective welfare. This indicator evaluates the extent to which a government prioritizes education in relation to its overall economic prosperity." }, { "id": "IndicatorName", "value": "Government expenditure on education, total (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data may refer to spending by the ministry of education only (excluding spending on educational activities by other ministries)." }, { "id": "Longdefinition", "value": "General government expenditure on education (current, capital, and transfers) is expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government expenditure on education, total (% of GDP) is calculated by dividing total government expenditure for all levels of education by the GDP, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nInformation pertaining to educational expenditures is sourced from national governments, which provide the data in response to the annual survey conducted by the UNESCO Institute for Statistics (UIS) or through the joint UNESCO-OECD-Eurostat (UOE) data collection initiative. The responses to the questionnaire regarding educational spending are typically derived from the annual financial statements issued by either the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office. Additionally, data concerning GDP and overall government expenditure are accessible via the IMF’s World Economic Outlook database, which is updated annually.\nStatistical concept(s): Generally, elevated levels of the indicator suggest that a government places a high priority on educational policy. Values ranging from 4% to 6% are indicative of a country achieving the benchmark set forth by the Education 2030 Framework for Action (https://unesdoc.unesco.org/ark:/48223/pf0000245656).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEducational expenditure encompasses spending on fundamental educational goods and services, including teaching personnel, school infrastructure, textbooks, and instructional materials, as well as on ancillary educational goods and services such as support services, general administration, and other related activities.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFunding for education may originate from public sources, encompassing all government ministries and agencies that finance or support educational programs within the country, as well as from international and private sources, such as household contributions." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "2" }, { "id": "SG.DMK.ALLD.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Women participating in the three decisions (own health care, major household purchases, and visiting family) (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women participating in the three decisions (own health care, major household purchases, and visiting family) is the percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in all of the three decisions (own health care, large purchases and visits to family, relatives, and friends)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_DMAK_W_3DC; \tIndicator name from the original source: Final say in all of the decisions [Women], publisher: The DHS program (ICF), type: API, date accessed: 2023-08-18" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Women participating in the three decisions (own health care, major household purchases, and visiting family) is the number of currently married women aged 15-49 who say they alone or jointly have the final say in the three decisions, expressed as percentage of currently married women age 15-49 who have been interviewed and It’s derived by dividing the number of currently married women aged 15-49 who responded they alone or jointly have the final say in the three decisions by total number of currently married women age 15-49 who have been interviewed.\nStatistical concept(s): This indicator assesses the level of women's participation in household decision-making. It emphasizes the importance of decisions regarding their own health care, which are deemed essential to women's self-interest. The indicator also evaluates women's involvement in making substantial economic decisions, such as those related to significant purchases, to gauge their economic decision-making power within the household. Additionally, it measures women's autonomy in deciding on visits to family or friends, which can indicate their freedom of movement and the ability to engage with their birth family." }, { "id": "Topic", "value": "Gender: Public life & decision making" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "2" }, { "id": "SG.DMK.SRCR.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Women making their own informed decisions regarding sexual relations, contraceptive use and reproductive health care (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Current estimates of the indicator are based on currently married or in union women of reproductive age (15-49 years old) who are using any type of contraception. In the current Demographic and Health Surveys (DHS), the question on decision-making on use of contraception is only asked to women who are currently using contraception. Because the questions on decision- making on sexual relations and health care are restricted to women (15-49) currently married or in union, the denominator for Indicator 5.6.1 is women 15-49, who are currently married or in union and currently using contraception. However, agreement has been reached with Macro/ICF for upcoming DHS surveys to ask the question on decision on use of contraception to all married/ in union women aged 15-49 years, whether they are currently using any contraception or not. The DHS model questionnaire for Phase 7 already includes the question on decision-making for women who are not currently using any contraception." }, { "id": "Longdefinition", "value": "Proportion of women ages 15-49 years (married or in union) who make their own decision on all three selected areas i.e. can say no to sexual intercourse with their husband or partner if they do not want; decide on use of contraception; and decide on their own health care. Only women who provide a “yes” answer to all three components are considered as women who “make her own decisions regarding sexual and reproductive”." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.6.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2022" }, { "id": "Source", "value": "Demographic and Health Surveys compiled by United Nations Population Fund, United Nations (UN), uri: https://unstats.un.org/sdgs/UNSDGAPIV5/swagger/index.html, note: Indicator code from the original source: SH_FPL_INFM; \tIndicator name from the original source: Proportion of women aged 15–49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care, publisher: UN Statistics Division, type: API" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Numerator of the indicator is number of married or in union women ages 15-49 who have been interviewed and satisfy all three empowerment criteria: 1)who can say 'no' to sex; and 2)for whom the decision on contraception is not mainly made by the husband/partner; and 3) for whom decision on health care for themselves ins not usually made by the husband/partner or someone else. Denominator of the indicator is the total number of women ages 15-49 who are married or in union and who have been interviewed. \n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data for this indicator are primarily sourced from nationally representative Demographic and Health Surveys (DHS).\nStatistical concept(s): A woman is deemed to possess autonomy in reproductive health decision-making and to be empowered to assert her reproductive rights when she has the ability to: (1) make decisions regarding her own health care, independently or in conjunction with her husband or partner, (2) determine the use or non-use of contraception, on her own or together with her husband or partner, and (3) refuse sexual relations with her husband or partner if she chooses." }, { "id": "Topic", "value": "Gender: Public life & decision making" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "2" }, { "id": "SG.GEN.PARL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite much progress in recent decades, gender inequalities remain pervasive in many dimensions of life - worldwide. But while disparities exist throughout the world, they are most prevalent in developing countries. Gender inequalities in the allocation of such resources as education, health care, nutrition, and political voice matter because of the strong association with well-being, productivity, and economic growth. These patterns of inequality begin at an early age, with boys routinely receiving a larger share of education and health spending than do girls, for example.\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen are vastly underrepresented in decision-making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.\n\n\n\n\n\n\n\n\n\n\n\n\n\nA strong and vibrant democracy is possible only when parliament is fully inclusive of the population it represents. Parliaments cannot consider themselves inclusive, however, until they can boast the full participation of women. This is not just about women's right to equality and their contribution to the conduct of public affairs, but also about using women's resources and potential to determine political and development priorities that benefit societies and the global community." }, { "id": "IndicatorName", "value": "Proportion of seats held by women in national parliaments (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The number of countries covered varies with suspensions or dissolutions of parliaments. There can be difficulties in obtaining information on by-election results and replacements due to death or resignation. These changes are ad hoc events which are more difficult to keep track of. By-elections, for instance, are often not announced internationally as general elections are. Parliaments vary considerably in their internal workings and procedures, however, generally legislate, oversee government and represent the electorate. In terms of measuring women's contribution to political decision making, this indicator may not be sufficient because some women may face obstacles in fully and efficiently carrying out their parliamentary mandate.\n\n\n\n\n\n\n\n\n\n\n\nThe data is compiled by the Inter-Parliamentary Union on the basis of information provided by National Parliaments. The percentages do not take into account the case of parliaments for which no data was available at that date. Information is available in all countries where a national legislature exists and therefore does not include parliaments that have been dissolved or suspended for an indefinite period." }, { "id": "Longdefinition", "value": "Women in parliaments are the percentage of parliamentary seats in a single or lower chamber held by women." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Women are vastly underrepresented in decision making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.\n\nThis is the Sustainable Development Goal indicator 5.5.1 (a). [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2025" }, { "id": "Source", "value": "Monthly ranking of women in national parliaments, Inter-Parliamentary Union (IPU), uri: https://data.ipu.org/women-ranking/, note: For the year of 1998, the data is as of August 10, 1998., type: Excel, date accessed: 2026-03-29" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of seats held by women in national parliaments is the number of seats held by women members in single or lower chambers of national parliaments, expressed as a percentage of all occupied seats; it is derived by dividing the total number of seats occupied by women by the total number of seats in parliament.\nStatistical concept(s): This indicator assesses the extent to which women are provided with equal opportunities to participate in parliamentary decision-making processes. It applies to the sole chamber of unicameral national parliaments and the lower chamber in the case of bicameral systems. The upper chamber in bicameral parliaments is not included in this measure. Parliamentary seats are typically occupied by individuals who are victorious in general elections, though they can also be acquired through nomination, appointment, indirect election, member rotation, or by-elections. The term 'seats' refers to the total count of parliamentary mandates or the total number of parliament members." }, { "id": "Topic", "value": "Gender: Public life & decision making" }, { "id": "Unitofmeasure", "value": "% of single or lower chamber seats" } ], "source_id": "2" }, { "id": "SG.TIM.UWRK.FE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Women often spend disproportionately more time on unpaid domestic and care work than men. This unequal division of responsibilities is correlated with gender differences in economic opportunities, includign low female labor force participation, occupational sex segregation, and earnings diffrentials. The need for a gender balance in the distribution of unpaid domestic and care work has been increasingly recognized and the Sustainable Development Goals address the issue in the target 5.4." }, { "id": "IndicatorName", "value": "Proportion of time spent on unpaid domestic and care work, female (% of 24 hour day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data may not be strictly comparable across countries as the methods and sampling involved for data collection may differ." }, { "id": "Longdefinition", "value": "The average time women spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.4.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "National statistical offices or national database and publications, United Nations (UN), uri: https://unstats.un.org/sdgs/dataportal/database, note: Indicator code from the original source: SH_FPL_INFM; \tIndicator name from the original source: Proportion of time spent on unpaid domestic chores and care work, by sex, age and location (%), publisher: UN Statistics Division, type: Excel" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of time allocated to unpaid domestic and caregiving tasks is determined by dividing the daily average time spent on such activities by the total number of hours in a day (24 hours). The data for this indicator are presented as a daily time proportion. For instance, if women ages 15+ dedicate 10% of their day to unpaid domestic and caregiving work, while men of the same age bracket allocate 1%, this translates to women spending an average of 2.4 hours (or 2 hours and 24 minutes) and men spending 14.4 minutes per day on these tasks.\n\n\n\n\n\n\nTo ascertain the daily average, weekly data are averaged across all seven days.\nStatistical concept(s): This indicator measures the average amount of on unpaid domestic and care work as a proportion in a day. The objective of this indicator is to quantify the time allocation of both women and men to unpaid tasks, thereby recognizing the value of all forms of work, irrespective of monetary compensation. Furthermore, it serves as a gauge for gender equality by revealing the disparity in time spent by women and men on unpaid activities, such as household chores, caregiving, and childcare.\n\n\n\n\n\n\n\n\n\n\n\nThe daily average is derived from a mean calculated over the data collection reference period, which does not imply that individuals allocate the specified amounts of time to these activities every day." }, { "id": "Topic", "value": "Gender: Participation & access" }, { "id": "Unitofmeasure", "value": "% of a 24 hour day" } ], "source_id": "2" }, { "id": "SG.TIM.UWRK.MA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Women often spend disproportionately more time on unpaid domestic and care work than men. This unequal division of responsibilities is correlated with gender differences in economic opportunities, includign low female labor force participation, occupational sex segregation, and earnings diffrentials. The need for a gender balance in the distribution of unpaid domestic and care work has been increasingly recognized and the Sustainable Development Goals address the issue in the target 5.4." }, { "id": "IndicatorName", "value": "Proportion of time spent on unpaid domestic and care work, male (% of 24 hour day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data may not be strictly comparable across countries as the methods and sampling involved for data collection may differ." }, { "id": "Longdefinition", "value": "The average time men spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.4.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "National statistical offices or national database and publications compiled by United Nations Statistics Division., United Nations (UN), uri: https://unstats.un.org/sdgs/dataportal/database, note: Indicator code from the original source: SH_FPL_INFM; \tIndicator name from the original source: Proportion of time spent on unpaid domestic chores and care work, by sex, age and location (%), publisher: UN Statistics Division, type: Excel" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of time allocated to unpaid domestic and caregiving tasks is determined by dividing the daily average time spent on such activities by the total number of hours in a day (24 hours). The data for this indicator are presented as a daily time proportion. For instance, if women ages 15+ dedicate 10% of their day to unpaid domestic and caregiving work, while men of the same age bracket allocate 1%, this translates to women spending an average of 2.4 hours (or 2 hours and 24 minutes) and men spending 14.4 minutes per day on these tasks.\n\n\n\n\n\n\nTo ascertain the daily average, weekly data are averaged across all seven days.\nStatistical concept(s): This indicator measures the average amount of on unpaid domestic and care work as a proportion in a day. The objective of this indicator is to quantify the time allocation of both women and men to unpaid tasks, thereby recognizing the value of all forms of work, irrespective of monetary compensation. Furthermore, it serves as a gauge for gender equality by revealing the disparity in time spent by women and men on unpaid activities, such as household chores, caregiving, and childcare.\n\n\n\n\n\n\n\n\n\n\n\nThe daily average is derived from a mean calculated over the data collection reference period, which does not imply that individuals allocate the specified amounts of time to these activities every day." }, { "id": "Topic", "value": "Gender: Participation & access" }, { "id": "Unitofmeasure", "value": "% of a 24 hour day" } ], "source_id": "2" }, { "id": "SG.VAW.1549.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (% of ever-partnered women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months is the percentage of ever partnered women age 15-49 who are subjected to physical violence, sexual violence or both by a current or former intimate partner in the last 12 months." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.2.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2017" }, { "id": "Source", "value": "United Nations (UN), uri: https://unstats.un.org/sdgs/dataportal/database, note: Indicator code from the original source: VC_VAW_MARR; \tIndicator name from the original source: Proportion of ever-partnered women and girls subjected to physical and/or sexual violence by a current or former intimate partner in the previous 12 months, by age (%)\n\n\n\n\n\n\n\n\n, publisher: UN Statistics Division, type: API;\nGlobal Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/proportion-of-ever-partnered-women-and-girls-aged-15-49-years-subjected-to-physical-and-or-sexual-violence-by-a-current-or-former-intimate-partner-in-the-previous-12-months, publisher: WHO" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of ever-partnered women (ages 15 and above) subjected to any act of physical violece, sexual violence or both divided by a current or former intimate partner in the previous 12 months divided by the number of ever-partnered women (aged 15 years and above) in the population multiplied by 100. The main source of data are from the Demographic and Health Surveys (DHS), specialized surveys on violence against women and crime victimasation surveys.\nStatistical concept(s): Physical violence is defined as acts that can physically hurt the victim, including, but not limited to: being slapped or having something thrown at you that could hurt you; being pushed or shoved; being hit with a fist or something else that could hurt; being kicked, dragged or beaten up; being choked or burnt on purpose; and/or being threatened with or actually having a gun, knife or other weapon used on you. Sexual violence is operationalized as: being physically forced to have sexual intercourse when you do not want to; having sexual intercourse out of fear for what your partner might do or through coercion; and/or being forced to do something sexual that you consider humiliating or degrading (Reference: WHO, Violence Against Women Prevalence Estimates, 2018. https://www.who.int/publications/i/item/9789240022256). \n\n\n\n\n\n\n\n\n\n\n\nCurrent intimate partner includes current or most recent husbands of ever-married women (and men they live with as if married) and the current intimate partner of never-married women. Former husband/intimate partner is a husband (or partner she is living with as if married) other than the current husband (or man she is living with as if married) for currently married women, any intimate partner for never-married women who do not currently have an intimate partner, and a husband/partner other than the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Gender: Health" }, { "id": "Unitofmeasure", "value": "% of ever-partnered women and girls ages 15 years and older" } ], "source_id": "2" }, { "id": "SG.VAW.ARGU.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she argues with him (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she argues with him." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_ARG; \tIndicator name from the original source: Wife beating justified if she argues with him [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Gender: Health" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "2" }, { "id": "SG.VAW.BURN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she burns the food (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she burns the food." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_BFD; \tIndicator name from the original source: Wife beating justified if she burns the food [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Gender: Health" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "2" }, { "id": "SG.VAW.GOES.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she goes out without telling him (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she goes out without telling him." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_OUT; \tIndicator name from the original source: Wife beating justified if she goes out without telling him [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Gender: Health" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "2" }, { "id": "SG.VAW.NEGL.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she neglects the children (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she neglects the children." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_NEG; \tIndicator name from the original source: Wife beating justified if she neglects the children [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Gender: Health" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "2" }, { "id": "SG.VAW.REAS.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife (any of five reasons) (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner for any of the following five reasons: argues with him; refuses to have sex; burns the food; goes out without telling him; or when she neglects the children." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_AGR; \tIndicator name from the original source: Wife beating justified for at least one specific reason [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Gender: Health" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "2" }, { "id": "SG.VAW.REFU.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she refuses sex with him (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she refuses sex with him." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_REF; \tIndicator name from the original source: Wife beating justified if she refuses to have sex with him [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Gender: Health" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "2" }, { "id": "SH.ALC.PCAP.FE.LI", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions." }, { "id": "IndicatorName", "value": "Total alcohol consumption per capita, female (liters of pure alcohol, projected estimates, female 15+ years of age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.5.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2020" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.ALC.PCAP.LI", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions." }, { "id": "IndicatorName", "value": "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.5.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2020" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.ALC.PCAP.MA.LI", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions." }, { "id": "IndicatorName", "value": "Total alcohol consumption per capita, male (liters of pure alcohol, projected estimates, male 15+ years of age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.5.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2020" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.ANM.ALLW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of anemia among women of reproductive age (% of women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of anemia among women of reproductive age refers to the combined prevalence of both non-pregnant with haemoglobin levels below 12 g/dL and pregnant women with haemoglobin levels below 11 g/dL." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on the prevalence of anaemia and/or mean haemoglobin levels in women of reproductive age, collected between 1995 and 2019, were obtained from 408 population-representative data sources across 124 countries worldwide. A Bayesian hierarchical mixture model was employed to estimate haemoglobin distributions, systematically addressing missing data, non-linear time trends, and the representativeness of data sources. Full details on data sources are available on the GHO Anaemia page. Detailed information on the statistical methods can be found in the following reference: Finucane MM, Paciorek CJ, Stevens GA EM. Semiparametric Bayesian density estimation with disparate data sources: a meta-analysis of global childhood undernutrition. J Am Stat Assoc. 2015;110(511):889–901." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percent of women ages 15-49" } ], "source_id": "2" }, { "id": "SH.ANM.CHLD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of anemia among children (% of children ages 6-59 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data for blood haemoglobin concentrations are still limited, compared to other nutritional indicators such as hild anthropometry. As a result, the estimates may not capture the full variation across countries and regions." }, { "id": "Longdefinition", "value": "Prevalence of anemia, children ages 6-59 months, is the percentage of children ages 6-59 months whose hemoglobin level is less than 110 grams per liter, adjusted for altitude." }, { "id": "Othernotes", "value": "Anemia is defined as a low blood haemoglobin concentration. Anaemia may result from a number of causes, with the most significant contributor being iron deficiency. Anaemia resulting from iron deficiency adversely affects cognitive and motor development and causes fatigue and low productivity. Children under age 5 and pregnant women have the highest risk for anemia." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2019" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on anemia are compiled by the WHO, and a statistical model was used to estimate trends. WHO’s hemoglobin threshold concentration in blood was used." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "2" }, { "id": "SH.ANM.NPRG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of anemia among non-pregnant women (% of women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of anemia, non-pregnant women, is the percentage of non-pregnant women whose hemoglobin level is less than 120 grams per liter at sea level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on the prevalence of anaemia and/or mean haemoglobin levels in women of reproductive age, collected between 1995 and 2019, were obtained from 408 population-representative data sources across 124 countries worldwide. A Bayesian hierarchical mixture model was employed to estimate haemoglobin distributions, systematically addressing missing data, non-linear time trends, and the representativeness of data sources. Full details on data sources are available on the GHO Anaemia page. Detailed information on the statistical methods can be found in the following reference: Finucane MM, Paciorek CJ, Stevens GA EM. Semiparametric Bayesian density estimation with disparate data sources: a meta-analysis of global childhood undernutrition. J Am Stat Assoc. 2015;110(511):889–901." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percent of women ages 15-49" } ], "source_id": "2" }, { "id": "SH.CON.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "According to UNAIDS estimates, HIV incidence has fallen in many of the most severely affected countries because adolescents and young people are adopting safer sexual practices and more young people living with HIV are accessing treatment to lower their viral load. When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs)." }, { "id": "IndicatorName", "value": "Condom use, population ages 15-24, female (% of females ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Condom use, female is the percentage of the female population ages 15-24 who used a condom at last intercourse in the last 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2015" }, { "id": "Source", "value": "Demographic and Health Surveys;\nUNAIDS, Joint United Nations Programme on HIV/AIDS (UNAIDS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Household Surveys\nStatistical concept(s): When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.CON.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "According to UNAIDS estimates, HIV incidence has fallen in many of the most severely affected countries because adolescents and young people are adopting safer sexual practices and more young people living with HIV are accessing treatment to lower their viral load. When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs)." }, { "id": "IndicatorName", "value": "Condom use, population ages 15-24, male (% of males ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Condom use, male is the percentage of the male population ages 15-24 who used a condom at last intercourse in the last 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2014" }, { "id": "Source", "value": "Demographic and Health Surveys;\nUNAIDS, Joint United Nations Programme on HIV/AIDS (UNAIDS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Household Surveys\nStatistical concept(s): When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.DTH.0509", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of deaths ages 5-9 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of children ages 5-9 years" }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SH.DTH.1014", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of deaths ages 10-14 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of adolescents ages 10-14 years" }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SH.DTH.1519", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of deaths ages 15-19 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of adolescents ages 15-19 years" }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SH.DTH.2024", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of deaths ages 20-24 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of youths ages 20-24 years" }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SH.DTH.COMM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Understanding the distribution of causes of death is critical for health planning, policy formulation, and the allocation of resources. Information on the cause of death— communicable diseases and maternal, prenatal and nutrition conditions, non-communicable diseases (NCDs), injury, and, in conjunction other COVID-19 pandemic-related outcomes—provide a foundation for assessing epidemiological transitions, identifying emerging health threats, and monitoring progress toward national and global health goals, including the Sustainable Development Goals (SDGs); in particular, SDG Target 3.4 aims to reduce premature mortality from NCDs, while communicable diseases remain a key focus under Targets 3.3 and 3.8, which address disease-specific burdens and universal health coverage.\n\nInformation on the cause of death are especially important for low- and middle-income countries, where health systems must simultaneously address both infectious disease burdens and the rising prevalence of NCDs and injury-related mortality. Disaggregated cause-of-death data enable governments and development partners to prioritize interventions, assess health system performance, and guide investments in prevention and care. Moreover, monitoring mortality by cause helps identify disparities across population groups and geographies, ensuring that public health responses are equitable and data-driven." }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Limited data availability on health status remains a major constraint in assessing health conditions in low-and middle-income countries. Surveillance systems are often weak or absent for major public health issues, and available estimates of disease prevalence and incidence may be incomplete or unreliable. National capacities and commitments to data collection and reporting vary significantly. To address these gaps and enhance reliability and comparability, the World Health Organization (WHO) produces estimates using epidemiological models.\n\nThe COVID-19 pandemic introduced exceptional challenges. Even in countries with relatively complete vital registration systems, excess mortality may have been misclassified between COVID-19 and other causes of death. In countries lacking comprehensive registration systems, greater reliance on modelled estimates—including categories such as “other pandemic-related mortality”—may have resulted in underestimation or redistribution of deaths from specific causes, particularly cardiovascular diseases and other non-communicable diseases." }, { "id": "Longdefinition", "value": "Cause of death refers to the share of all deaths for all ages by underlying causes. Communicable diseases and maternal, prenatal and nutrition conditions include infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates Deaths by Cause, Age, Sex, by Country and by Region, 2000-2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on cause of death are compiled by the World Health Organization (WHO), using information from civil registration and vital statistics systems (CRVS), local health and demographic studies and other sources, supplemented by vital registration and verbal autopsy in communities as well as regular household health surveys. To address incomplete or inconsistent reporting—particularly in countries with limited vital registration coverage—WHO applies statistical models that account for underreporting, demographic differentials, and expected cause-of-death distribution. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, refer to the WHO Global Health Estimates methodological documentation.\nStatistical concept(s): Causes of death in the Global Health Estimates (GHE) are classified according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) or ICD-9. Reported data from countries are mapped to a standardized cause list structured by WHO in a three-level hierarchy. Each death is assigned to a single underlying cause based on ICD rules. To ensure internal consistency, all cause-specific mortality estimates are constrained to fit within an all-cause mortality envelope derived from demographic estimates prepared by the UN Population Division and WHO. The cause list is designed to be mutually exclusive and collectively exhaustive, allowing full decomposition of total mortality by age, sex, country, and year.\n\nFor countries with incomplete, poor-quality, or no usable cause-of-death data, WHO uses a compositional cause modeling strategy. This approach estimates the distribution of causes within broad cause groups based on epidemiological and demographic covariates. Redistribution algorithms are applied to correct for misclassification or ill-defined causes. All modeling approaches are aligned with WHO’s goal of maximizing comparability, transparency, and usability of mortality data across settings with diverse data availability and health system capacities." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.DTH.IMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of infant deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of infants dying before reaching one year of age." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SH.DTH.INJR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Understanding the distribution of causes of death is critical for health planning, policy formulation, and the allocation of resources. Information on the cause of death— communicable diseases and maternal, prenatal and nutrition conditions, non-communicable diseases (NCDs), injury, and, in conjunction other COVID-19 pandemic-related outcomes—provide a foundation for assessing epidemiological transitions, identifying emerging health threats, and monitoring progress toward national and global health goals, including the Sustainable Development Goals (SDGs); in particular, SDG Target 3.4 aims to reduce premature mortality from NCDs, while communicable diseases remain a key focus under Targets 3.3 and 3.8, which address disease-specific burdens and universal health coverage.\n\nInformation on the cause of death are especially important for low- and middle-income countries, where health systems must simultaneously address both infectious disease burdens and the rising prevalence of NCDs and injury-related mortality. Disaggregated cause-of-death data enable governments and development partners to prioritize interventions, assess health system performance, and guide investments in prevention and care. Moreover, monitoring mortality by cause helps identify disparities across population groups and geographies, ensuring that public health responses are equitable and data-driven." }, { "id": "IndicatorName", "value": "Cause of death, by injury (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Limited data availability on health status remains a major constraint in assessing health conditions in low-and middle-income countries. Surveillance systems are often weak or absent for major public health issues, and available estimates of disease prevalence and incidence may be incomplete or unreliable. National capacities and commitments to data collection and reporting vary significantly. To address these gaps and enhance reliability and comparability, the World Health Organization (WHO) produces estimates using epidemiological models.\n\nThe COVID-19 pandemic introduced exceptional challenges. Even in countries with relatively complete vital registration systems, excess mortality may have been misclassified between COVID-19 and other causes of death. In countries lacking comprehensive registration systems, greater reliance on modelled estimates—including categories such as “other pandemic-related mortality”—may have resulted in underestimation or redistribution of deaths from specific causes, particularly cardiovascular diseases and other non-communicable diseases." }, { "id": "Longdefinition", "value": "Cause of death refers to the share of all deaths for all ages by underlying causes. Injuries include unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates Deaths by Cause, Age, Sex, by Country and by Region, 2000-2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on cause of death are compiled by the World Health Organization (WHO), using information from civil registration and vital statistics systems (CRVS), local health and demographic studies and other sources, supplemented by vital registration and verbal autopsy in communities as well as regular household health surveys. To address incomplete or inconsistent reporting—particularly in countries with limited vital registration coverage—WHO applies statistical models that account for underreporting, demographic differentials, and expected cause-of-death distribution. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, refer to the WHO Global Health Estimates methodological documentation.\nStatistical concept(s): Causes of death in the Global Health Estimates (GHE) are classified according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) or ICD-9. Reported data from countries are mapped to a standardized cause list structured by WHO in a three-level hierarchy. Each death is assigned to a single underlying cause based on ICD rules. To ensure internal consistency, all cause-specific mortality estimates are constrained to fit within an all-cause mortality envelope derived from demographic estimates prepared by the UN Population Division and WHO. The cause list is designed to be mutually exclusive and collectively exhaustive, allowing full decomposition of total mortality by age, sex, country, and year.\n\nFor countries with incomplete, poor-quality, or no usable cause-of-death data, WHO uses a compositional cause modeling strategy. This approach estimates the distribution of causes within broad cause groups based on epidemiological and demographic covariates. Redistribution algorithms are applied to correct for misclassification or ill-defined causes. All modeling approaches are aligned with WHO’s goal of maximizing comparability, transparency, and usability of mortality data across settings with diverse data availability and health system capacities." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.DTH.MORT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of under-five deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of children dying before reaching age five." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SH.DTH.NCOM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Understanding the distribution of causes of death is critical for health planning, policy formulation, and the allocation of resources. Information on the cause of death— communicable diseases and maternal, prenatal and nutrition conditions, non-communicable diseases (NCDs), injury, and, in conjunction other COVID-19 pandemic-related outcomes—provide a foundation for assessing epidemiological transitions, identifying emerging health threats, and monitoring progress toward national and global health goals, including the Sustainable Development Goals (SDGs); in particular, SDG Target 3.4 aims to reduce premature mortality from NCDs, while communicable diseases remain a key focus under Targets 3.3 and 3.8, which address disease-specific burdens and universal health coverage.\n\nInformation on the cause of death are especially important for low- and middle-income countries, where health systems must simultaneously address both infectious disease burdens and the rising prevalence of NCDs and injury-related mortality. Disaggregated cause-of-death data enable governments and development partners to prioritize interventions, assess health system performance, and guide investments in prevention and care. Moreover, monitoring mortality by cause helps identify disparities across population groups and geographies, ensuring that public health responses are equitable and data-driven." }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Limited data availability on health status remains a major constraint in assessing health conditions in low-and middle-income countries. Surveillance systems are often weak or absent for major public health issues, and available estimates of disease prevalence and incidence may be incomplete or unreliable. National capacities and commitments to data collection and reporting vary significantly. To address these gaps and enhance reliability and comparability, the World Health Organization (WHO) produces estimates using epidemiological models.\n\nThe COVID-19 pandemic introduced exceptional challenges. Even in countries with relatively complete vital registration systems, excess mortality may have been misclassified between COVID-19 and other causes of death. In countries lacking comprehensive registration systems, greater reliance on modelled estimates—including categories such as “other pandemic-related mortality”—may have resulted in underestimation or redistribution of deaths from specific causes, particularly cardiovascular diseases and other non-communicable diseases." }, { "id": "Longdefinition", "value": "Cause of death refers to the share of all deaths for all ages by underlying causes. Non-communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates Deaths by Cause, Age, Sex, by Country and by Region, 2000-2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on cause of death are compiled by the World Health Organization (WHO), using information from civil registration and vital statistics systems (CRVS), local health and demographic studies and other sources, supplemented by vital registration and verbal autopsy in communities as well as regular household health surveys. To address incomplete or inconsistent reporting—particularly in countries with limited vital registration coverage—WHO applies statistical models that account for underreporting, demographic differentials, and expected cause-of-death distribution. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, refer to the WHO Global Health Estimates methodological documentation.\nStatistical concept(s): Causes of death in the Global Health Estimates (GHE) are classified according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) or ICD-9. Reported data from countries are mapped to a standardized cause list structured by WHO in a three-level hierarchy. Each death is assigned to a single underlying cause based on ICD rules. To ensure internal consistency, all cause-specific mortality estimates are constrained to fit within an all-cause mortality envelope derived from demographic estimates prepared by the UN Population Division and WHO. The cause list is designed to be mutually exclusive and collectively exhaustive, allowing full decomposition of total mortality by age, sex, country, and year.\n\nFor countries with incomplete, poor-quality, or no usable cause-of-death data, WHO uses a compositional cause modeling strategy. This approach estimates the distribution of causes within broad cause groups based on epidemiological and demographic covariates. Redistribution algorithms are applied to correct for misclassification or ill-defined causes. All modeling approaches are aligned with WHO’s goal of maximizing comparability, transparency, and usability of mortality data across settings with diverse data availability and health system capacities." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.DTH.NMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of neonatal deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of neonates dying before reaching 28 days of age." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis indicator is related to Sustainable Development Goal 3.2.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SH.DTH.OCVD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Understanding the distribution of causes of death is critical for health planning, policy formulation, and the allocation of resources. Information on the cause of death— communicable diseases and maternal, prenatal and nutrition conditions, non-communicable diseases (NCDs), injury, and, in conjunction other COVID-19 pandemic-related outcomes—provide a foundation for assessing epidemiological transitions, identifying emerging health threats, and monitoring progress toward national and global health goals, including the Sustainable Development Goals (SDGs); in particular, SDG Target 3.4 aims to reduce premature mortality from NCDs, while communicable diseases remain a key focus under Targets 3.3 and 3.8, which address disease-specific burdens and universal health coverage.\n\nInformation on the cause of death are especially important for low- and middle-income countries, where health systems must simultaneously address both infectious disease burdens and the rising prevalence of NCDs and injury-related mortality. Disaggregated cause-of-death data enable governments and development partners to prioritize interventions, assess health system performance, and guide investments in prevention and care. Moreover, monitoring mortality by cause helps identify disparities across population groups and geographies, ensuring that public health responses are equitable and data-driven." }, { "id": "IndicatorName", "value": "Cause of death, by other COVID-19 pandemic-related outcomes (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Limited data availability on health status remains a major constraint in assessing health conditions in low-and middle-income countries. Surveillance systems are often weak or absent for major public health issues, and available estimates of disease prevalence and incidence may be incomplete or unreliable. National capacities and commitments to data collection and reporting vary significantly. To address these gaps and enhance reliability and comparability, the World Health Organization (WHO) produces estimates using epidemiological models.\n\nThe COVID-19 pandemic introduced exceptional challenges. Even in countries with relatively complete vital registration systems, excess mortality may have been misclassified between COVID-19 and other causes of death. In countries lacking comprehensive registration systems, greater reliance on modelled estimates—including categories such as “other pandemic-related mortality”—may have resulted in underestimation or redistribution of deaths from specific causes, particularly cardiovascular diseases and other non-communicable diseases." }, { "id": "Longdefinition", "value": "Cause of death refers to the share of all deaths for all ages by underlying causes. Other COVID-19 pandemic-related outcomes capture all deaths due to the pandemic which were not specifically caused by COVID-19 or the indirect COVID causes. These are estimated by subtracting COVID-19 specific deaths and deaths attributed to indirect COVID-19 causes from total excess mortality." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates Deaths by Cause, Age, Sex, by Country and by Region, 2000-2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on cause of death are compiled by the World Health Organization (WHO), using information from civil registration and vital statistics systems (CRVS), local health and demographic studies and other sources, supplemented by vital registration and verbal autopsy in communities as well as regular household health surveys. To address incomplete or inconsistent reporting—particularly in countries with limited vital registration coverage—WHO applies statistical models that account for underreporting, demographic differentials, and expected cause-of-death distribution. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, refer to the WHO Global Health Estimates methodological documentation.\nStatistical concept(s): Causes of death in the Global Health Estimates (GHE) are classified according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) or ICD-9. Reported data from countries are mapped to a standardized cause list structured by WHO in a three-level hierarchy. Each death is assigned to a single underlying cause based on ICD rules. To ensure internal consistency, all cause-specific mortality estimates are constrained to fit within an all-cause mortality envelope derived from demographic estimates prepared by the UN Population Division and WHO. The cause list is designed to be mutually exclusive and collectively exhaustive, allowing full decomposition of total mortality by age, sex, country, and year.\n\nFor countries with incomplete, poor-quality, or no usable cause-of-death data, WHO uses a compositional cause modeling strategy. This approach estimates the distribution of causes within broad cause groups based on epidemiological and demographic covariates. Redistribution algorithms are applied to correct for misclassification or ill-defined causes. All modeling approaches are aligned with WHO’s goal of maximizing comparability, transparency, and usability of mortality data across settings with diverse data availability and health system capacities." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.DYN.0509", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Probability of dying among children ages 5-9 years (per 1,000)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 5-9 years of age expressed per 1,000 children aged 5, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000" } ], "source_id": "2" }, { "id": "SH.DYN.1014", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Probability of dying among adolescents ages 10-14 years (per 1,000)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 10-14 years of age expressed per 1,000 adolescents age 10, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000" } ], "source_id": "2" }, { "id": "SH.DYN.1519", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Probability of dying among adolescents ages 15-19 years (per 1,000)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 15-19 years of age expressed per 1,000 adolescents age 15, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000" } ], "source_id": "2" }, { "id": "SH.DYN.2024", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Probability of dying among youth ages 20-24 years (per 1,000)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 20-24 years of age expressed per 1,000 youths age 20, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000" } ], "source_id": "2" }, { "id": "SH.DYN.AIDS.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Women's share of population ages 15+ living with HIV (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV is the percentage of people who are infected with HIV. Female rate is as a percentage of the total population ages 15+ who are living with HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated as the number of women aged 15 and older living with HIV divided by the total number of people aged 15 and older living with HIV. Estimates of people living with HIV are produced by UNAIDS using a common modelling framework (Spectrum), which integrates country-reported HIV surveillance data, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators.\n\n\n\nReference: Annex 1. Methods for deriving UNAIDS HIV estimates, 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform:\n\nhttps://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.DYN.AIDS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS." }, { "id": "IndicatorName", "value": "Prevalence of HIV, total (% of population ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\nStatistical concept(s): HIV prevalence rates reflect the rate of HIV infection in each country's population. Low national prevalence rates can be misleading, however. They often disguise epidemics that are initially concentrated in certain localities or population groups and threaten to spill over into the wider population. In many developing countries most new infections occur in young adults, with young women especially vulnerable." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.DYN.MORT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, under-5 (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is the Sustainable Development Goal indicator 3.2.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org, publisher: UNICEF, WHO, World Bank, United Nations Population Division;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "2" }, { "id": "SH.DYN.MORT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, under-5, female (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "2" }, { "id": "SH.DYN.MORT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, under-5, male (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "2" }, { "id": "SH.DYN.NCOM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates.\n\n\nThe current estimates for 2020 and 2021 are likely underestimated in countries where high-quality vital registration data was lacking at the time of GHE2021 production. This is because the modeled estimates cannot fully account for deaths from the four major NCDs indirectly attributed to the COVID-19 pandemic. Therefore, the data for 2020 and 2021 should be interpreted with caution." }, { "id": "Longdefinition", "value": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of death between the exact ages of 30 and 70 is calculated using cause-specific mortality rates for each 5-year age group, applying standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE). These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Percent of females ages 30 years old" } ], "source_id": "2" }, { "id": "SH.DYN.NCOM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of death between the exact ages of 30 and 70 is calculated using cause-specific mortality rates for each 5-year age group, applying standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE). These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Percent of males ages 30 years old" } ], "source_id": "2" }, { "id": "SH.DYN.NCOM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS)." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.4.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of death between the exact ages of 30 and 70 is calculated using cause-specific mortality rates for each 5-year age group, applying standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE). These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Percent of people ages 30 years old" } ], "source_id": "2" }, { "id": "SH.DYN.NMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, neonatal (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Neonatal mortality rate is the number of neonates dying before reaching 28 days of age, per 1,000 live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\n\nThis is the Sustainable Development Goal indicator 3.2.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "2" }, { "id": "SH.FPL.SATM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Demand for family planning satisfied by modern methods (% of married women with demand for family planning)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Demand for family planning satisfied by modern methods refers to the percentage of married women ages 15-49 years whose need for family planning is satisfied with modern methods." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.7.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated from nationally-representative household survey data. Relevant data for this indicator are collected through various multi-country survey programs, including Contraceptive Prevalence Surveys (CPS), Demographic and Health Surveys (DHS), Fertility and Family Surveys (FFS), Reproductive Health Surveys (RHS), Multiple Indicator Cluster Surveys (MICS), Performance Monitoring and Accountability 2020 surveys (PMA), World Fertility Surveys (WFS), other international survey programs, and national surveys.\n\n\n\n\n\nData compilation involves systematic searches of websites of international survey programs, survey databases (e.g., the Integrated Household Survey Network (IHSN) database), websites of national statistical offices, SDG national reporting platforms, and ad hoc queries. Additionally, country-specific information from UNFPA country offices is utilized." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SH.H2O.BASW.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using at least basic drinking water services, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water). This indicator encompasses both people using basic water services as well as those using safely managed water services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "2" }, { "id": "SH.H2O.BASW.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using at least basic drinking water services, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water). This indicator encompasses both people using basic water services as well as those using safely managed water services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "2" }, { "id": "SH.H2O.BASW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using at least basic drinking water services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water). This indicator encompasses both people using basic water services as well as those using safely managed water services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "2" }, { "id": "SH.H2O.SMDW.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using safely managed drinking water services, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality). Many countries lack data on one or more elements of safely managed drinking water. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability). Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.1.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed drinking water services are defined as the water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "2" }, { "id": "SH.H2O.SMDW.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using safely managed drinking water services, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality). Many countries lack data on one or more elements of safely managed drinking water. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability). Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.1.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed drinking water services are defined as the water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "2" }, { "id": "SH.H2O.SMDW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using safely managed drinking water services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality). Many countries lack data on one or more elements of safely managed drinking water. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability). Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis indicator (Total) is calculated as a population-weighted average of the RURAL and URBAN aggregates when sufficient data are available for both domains. When coverage at either the RURAL or URBAN level is insufficient, but country-level TOTAL data meet the minimum population coverage threshold (30%), TOTAL aggregates are calculated directly from country-level TOTAL estimates. Because these two aggregation approaches may be applied in different years as data availability improves, methodological switches can occur and may result in discontinuities in the time series.\n\n\n\nThis indicator corresponds to Sustainable Development Goal indicator 6.1.1 (see UN SDG metadata: https://unstats.un.org/sdgs/metadata/)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed drinking water services are defined as the water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "2" }, { "id": "SH.HIV.0014", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Children (0-14) living with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Children living with HIV refers to the number of children ages 0-14 who are infected with HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SH.HIV.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Prevalence of HIV, female (% ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV, female is the percentage of females who are infected with HIV. Youth rates are as a percentage of the relevant age group." }, { "id": "Othernotes", "value": "In many developing countries most new infections occur in young adults, with young women especially vulnerable." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.HIV.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Prevalence of HIV, male (% ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV, male is the percentage of males who are infected with HIV. Youth rates are as a percentage of the relevant age group." }, { "id": "Othernotes", "value": "In many developing countries most new infections occur in young adults, with young women being especially vulnerable." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.HIV.ARTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Antiretroviral therapy (ART) is central to the global HIV response and has significantly improved both survival and quality of life for people living with HIV. Effective ART suppresses viral load to undetectable levels, preventing progression to AIDS. When viral load remains undetectable, HIV is not sexually transmitted to HIV-negative partners. \n\nDespite this progress, gaps in treatment persist. Nearly 10 million people living with HIV are not receiving ART, and according to UNAIDS, about half of them reside in Africa. Expanding access to ART remains essential for reducing HIV-related morbidity and mortality and for achieving global targets for ending AIDS as a public health threat. (Reference: https://www.unaids.org/sites/default/files/2025-07/2025-global-aids-update-JC3153_en.pdf)" }, { "id": "IndicatorName", "value": "Antiretroviral therapy coverage (% of people living with HIV)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Antiretroviral therapy coverage indicates the percentage of all people living with HIV who are receiving antiretroviral therapy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.HIV.INCD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Adults (ages 15-49) newly infected with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of adults (ages 15-49) newly infected with HIV." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SH.HIV.INCD.14", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Children (ages 0-14) newly infected with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of children (ages 0-14) newly infected with HIV." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SH.HIV.INCD.TL", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Adults (ages 15+) and children (ages 0-14) newly infected with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of adults (ages 15+) and children (ages 0-14) newly infected with HIV." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SH.HIV.INCD.TL.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Incidence of HIV, all (per 1,000 uninfected population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected populations expressed per 1,000 uninfected population in the year before the period." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.HIV.INCD.YG", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Young people (ages 15-24) newly infected with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of young people (ages 15-24) newly infected with HIV." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SH.HIV.INCD.YG.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected populations ages 15-24 expressed per 1,000 uninfected population ages 15-24 in the year before the period." }, { "id": "Othernotes", "value": "This is an age-disaggregated indicator for Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.HIV.INCD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Incidence of HIV, ages 15-49 (per 1,000 uninfected population ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected populations ages 15-49 expressed per 1,000 uninfected population in the year before the period." }, { "id": "Othernotes", "value": "This is an age-disaggregated indicator for Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.HIV.PMTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "HIV can be transmitted through sexual contact, blood transfusions, and the sharing of contaminated needles, and it can also be transmitted from mother to child during pregnancy, childbirth, or breastfeeding. Although the number of children acquiring HIV has decreased over time, mother-to-child transmission remains a significant public health concern.\n\nPrevention of mother-to-child transmission (PMTCT) is critical for reducing new pediatric HIV infections. However, many pregnant and breastfeeding women still do not begin ART or discontinue treatment during this period, contributing to continued transmission risks. Strengthening PMTCT services and ensuring continuity of care are essential for achieving global HIV prevention goals. (Reference: https://www.unaids.org/sites/default/files/2025-07/2025-global-aids-update-JC3153_en.pdf)" }, { "id": "IndicatorName", "value": "Antiretroviral therapy coverage for PMTCT (% of pregnant women living with HIV)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of pregnant women with HIV who receive antiretroviral medicine for prevention of mother-to-child transmission (PMTCT)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The coverage of antiretrovirals for PMTCT is calculated by dividing the number of pregnant women living with HIV who received antiretrovirals for PMTCT by the estimated number of pregnant women living with HIV who need antiretrovirals for PMTCT in the country. \n\n\n\n\nEstimating the Numerator: The number of pregnant women living with HIV receiving antiretrovirals for PMTCT is derived from national program data aggregated from facilities or other service delivery sites and reported by the country.\n\n\n\n\nEstimating the Denominator: The number of pregnant women living with HIV who need antiretroviral medicine for PMTCT is estimated using standardized statistical modeling based on UNAIDS/WHO methods. These methods consider various epidemic and demographic parameters, such as HIV prevalence among women of reproductive age, the effect of HIV on fertility, and national program coverage of antiretroviral therapy. These statistical modeling procedures provide a comprehensive population-based estimate of the number of pregnant women living with HIV who need antiretrovirals for PMTCT in the country." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Percent of pregnant women living with HIV" } ], "source_id": "2" }, { "id": "SH.IMM.HEPB", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, HepB3 (% of one-year-old children)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization rate, hepatitis B is the percentage of children ages 12-23 months who received hepatitis B vaccinations before 12 months or at any time before the survey. A child is considered adequately immunized after three doses." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2024" }, { "id": "Source", "value": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year. Notes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.IMM.IDPT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, DPT (% of children ages 12-23 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization, DPT, measures the percentage of children ages 12-23 months who received DPT vaccinations before 12 months or at any time before the survey. A child is considered adequately immunized against diphtheria, pertussis (or whooping cough), and tetanus (DPT) after receiving three doses of vaccine." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.b.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2024" }, { "id": "Source", "value": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year. Notes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.IMM.MEAS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, measles (% of children ages 12-23 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization, measles, measures the percentage of children ages 12-23 months who received the measles vaccination before 12 months or at any time before the survey. A child is considered adequately immunized against measles after receiving one dose of vaccine." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2024" }, { "id": "Source", "value": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF), uri: https://data.unicef.org/topic/child-health/immunization/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year. Notes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.MED.BEDS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Hospital beds are used to indicate the availability of inpatient services." }, { "id": "IndicatorName", "value": "Hospital beds (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Depending on the source and means of monitoring, data may not be exactly comparable across countries. For more information, see the original source." }, { "id": "Longdefinition", "value": "Hospital beds include inpatient beds available in public, private, general, and specialized hospitals and rehabilitation centers. In most cases beds for both acute and chronic care are included." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "WHO data, supplemented by country data, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data were compiled from the WHO Regional offices and country sources other (e.g. ministry of health, national statistical office) and modified to standardize the unit of measure of per 10 000 population by WHO.\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\n\n\nAvailability and use of health services, such as hospital beds per 1,000 people, reflect both demand- and supply-side factors. In the absence of a consistent definition this is a crude indicator of the extent of physical, financial, and other barriers to health care." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "2" }, { "id": "SH.MED.CMHW.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The WHO estimates that at least 2.5 medical staff (physicians, nurses and midwives) per 1,000 people are needed to provide adequate coverage with primary care interventions (WHO, World Health Report 2006)." }, { "id": "IndicatorName", "value": "Community health workers (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The WHO compiles data from household and labor force surveys, censuses, and administrative records. Data comparability is limited by differences in definitions and training of medical personnel varies. In addition, human resources tend to be concentrated in urban areas, so that average densities do not provide a full picture of health personnel available to the entire population." }, { "id": "Longdefinition", "value": "Community health workers include various types of community health aides, many with country-specific occupational titles such as community health officers, community health-education workers, family health workers, lady health visitors and health extension package workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2016" }, { "id": "Source", "value": "Global Health Workforce Statistics, World Health Organization (WHO);\nOrganisation for Economic Co-operation and Development (OECD);\nCountry data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The method of estimation for number of community health workers (including community health officers, community health-education workers, community health aides, family health workers and associated occupations) depends on the nature of the original data source. Enumeration based on population census data is a count of the number of people reporting 'community health worker' as their current occupation (as classified according to the tasks and duties of their job). A similar method is used for estimates based on labour force survey data, with the additional application of a sampling weight to calibrate for national representation. Data from health facility assessments and administrative reporting systems may be based on head counts of employees, staffing records, payroll records, training records, or tallies from other types of routine administrative records on human resources.\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\n\n\nData on health worker (physicians, nurses and midwives, and community health workers) density show the availability of medical personnel." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "2" }, { "id": "SH.MED.NUMW.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The WHO estimates that at least 2.5 medical staff (physicians, nurses and midwives) per 1,000 people are needed to provide adequate coverage with primary care interventions (WHO, World Health Report 2006)." }, { "id": "IndicatorName", "value": "Nurses and midwives (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The WHO compiles data from household and labor force surveys, censuses, and administrative records. Data comparability is limited by differences in definitions and training of medical personnel varies. In addition, human resources tend to be concentrated in urban areas, so that average densities do not provide a full picture of health personnel available to the entire population." }, { "id": "Longdefinition", "value": "Nurses and midwives include professional nurses, professional midwives, auxiliary nurses, auxiliary midwives, enrolled nurses, enrolled midwives and other associated personnel, such as dental nurses and primary care nurses." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.c.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "Global Health Workforce Statistics, World Health Organization (WHO);\nOrganisation for Economic Co-operation and Development (OECD);\nCountry data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National focal points share the data with WHO through the online NHWA data platform. The platform hosted in WHO, is built to facilitate data reporting on the indicators listed in the NHWA Handbook and data sharing across all the 3 levels of WHO.\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\n\n\nData on health worker (physicians, nurses and midwives, and community health workers) density show the availability of medical personnel." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "2" }, { "id": "SH.MED.PHYS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The WHO estimates that at least 2.5 medical staff (physicians, nurses and midwives) per 1,000 people are needed to provide adequate coverage with primary care interventions (WHO, World Health Report 2006)." }, { "id": "IndicatorName", "value": "Physicians (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The WHO compiles data from household and labor force surveys, censuses, and administrative records. Data comparability is limited by differences in definitions and training of medical personnel varies. In addition, human resources tend to be concentrated in urban areas, so that average densities do not provide a full picture of health personnel available to the entire population." }, { "id": "Longdefinition", "value": "Physicians include generalist and specialist medical practitioners." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.c.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Global Health Workforce Statistics, World Health Organization (WHO);\nOrganisation for Economic Co-operation and Development (OECD);\nCountry data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National focal points share the data with WHO through the online NHWA data platform. The platform hosted in WHO, is built to facilitate data reporting on the indicators listed in the NHWA Handbook and data sharing across all the 3 levels of WHO.\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\n\n\nData on health worker (physicians, nurses and midwives, and community health workers) density show the availability of medical personnel." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "2" }, { "id": "SH.MED.SAOP.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Billions people lack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked." }, { "id": "IndicatorName", "value": "Specialist surgical workforce (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Prior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that “We can’t manage what we don’t measure”, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups." }, { "id": "Longdefinition", "value": "Specialist surgical workforce is the number of specialist surgical, anaesthetic, and obstetric (SAO) providers who are working in each country per 100,000 population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2008-2018" }, { "id": "Source", "value": "Lancet Commission on Global Surgery, uri: www.lancetglobalsurgery.org, note: Data collected by the Lancet Commission on Global Surgery;\nWHO Collaborating Centre for Surgery and Public Health, note: data collected by WHO Collaborating Centre for Surgery and Public Health at Lund University from various sources including Ministries of Health or equivalent national regulatory bodies, national official entities such as medical councils, Eurostat, OECD, WHO Euro Health For All Database, WHO EURO Technical resources for health Database;\nBMJ Glob Health" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of specialist surgical, anaesthetic, and obstetric (SAO) providers who are working in each country per 100 000 population.\nStatistical concept(s): The Lancet Commission on Global Surgery, assembled in 2013 to assess surgical care around the world. Commissioners engaged in an iterative global consultative process with partners in over 110 countries to develop six core indicators of the strength of a surgical system. Two indicators assess a country’s preparedness to deliver safe surgery and anesthesia, two assess the current delivery of safe care, and two assess the state of financial risk protection for those seeking surgery." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "2" }, { "id": "SH.MLR.INCD.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes. It is preventable and curable. There are 5 parasite species that cause malaria in humans, and 2 of these species – Plasmodium falciparum and Plasmodium vivax – pose the greatest threat." }, { "id": "IndicatorName", "value": "Incidence of malaria (per 1,000 population at risk)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Incidence of malaria is the number of new cases of malaria in a year per 1,000 population at risk." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.3.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO), uri: http://apps.who.int/ghodata/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Confirmed malaria cases for countries and areas outside Africa, and for low-transmission countries and areas in Africa are adjusted for extent of health service use (treatment seeking), underreporting and lack of case confirmation (the likelihood that cases are parasite positive). In high transmission areas in which the quality of surveillance data does not permit a robust estimate from the number of reported cases, but good data on parasite prevalence is available, the number of cases can be estimated from parasite prevalence. The denominator is estimated, using official UN population and population at risk estimates for countries with sub-national endemicity.\nStatistical concept(s): Complete data on malaria cases reported through surveillance systems are the best source of data but are rarely available for large populations at high quality and accuracy. Reported data on malaria cases generally need to be adjusted for extent of health service use (treatment seeking), underreporting and lack of case confirmation (the likelihood that cases are parasite positive). WHO compiles data on reported confirmed cases of malaria and suspected cases tested with microscopy or RDT, submitted by national malaria control programmes. Underreporting is reported or estimated by countries. The extent of health service use (treatment seeking) data were obtained from nationally representative household surveys on health service use." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Per 1000 population at risk" } ], "source_id": "2" }, { "id": "SH.MLR.NETS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Use of insecticide-treated bed nets (% of under-5 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Use of insecticide-treated bed nets refers to the percentage of children under age five who slept under an insecticide-treated bednet to prevent malaria." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Malaria is endemic to the poorest countries in the world, mainly in tropical and subtropical regions of Africa, Asia, and the Americas. Insecticide-treated nets, properly used and maintained, are one of the most important malaria-preventive strategies to limit human-mosquito contact." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "2" }, { "id": "SH.MLR.TRET.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Children with fever receiving antimalarial drugs (% of children under age 5 with fever)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Malaria treatment refers to the percentage of children under age five who were ill with fever in the last two weeks and received any appropriate (locally defined) anti-malarial drugs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Malaria is endemic to the poorest countries in the world, mainly in tropical and subtropical regions of Africa, Asia, and the Americas. Prompt and effective treatment of malaria is a critical element of malaria control. It is vital that sufferers, especially children under age 5, start treatment within 24 hours of the onset of symptoms, to prevent progression - often rapid - to severe malaria and death. Data on malaria are from national-level surveys, including Multiple Indicator Cluster Surveys, Demographic and Health Surveys, and Malaria Indicator Surveys." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "2" }, { "id": "SH.MMR.DTHS", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Number of maternal deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The figures cannot be assumed to provide exact estimates." }, { "id": "Longdefinition", "value": "A maternal death refers to the death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management but not from accidental or incidental causes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO);\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SH.MMR.RISK", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Lifetime risk of maternal death (1 in: rate varies by country)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The probability cannot be assumed to provide an exact estimate of risk of maternal death." }, { "id": "Longdefinition", "value": "Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO);\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Number of 15-year old women for which 1 maternal death occurs assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future" } ], "source_id": "2" }, { "id": "SH.MMR.RISK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Lifetime risk of maternal death (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The probability cannot be assumed to provide an exact estimate of risk of maternal death." }, { "id": "Longdefinition", "value": "Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO);\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.PRG.ANEM", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of anemia among pregnant women (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data should be used with caution because surveys differ in quality, coverage, age group interviewed, and treatment of missing values across countries and over time.\n\n\n\nData on anemia are compiled by the WHO based mainly on nationally representative surveys, which measure hemoglobin in the blood. WHO's hemoglobin thresholds are then used to determine anemia status based on age, sex, and physiological status." }, { "id": "Longdefinition", "value": "Prevalence of anemia, pregnant women, is the percentage of pregnant women whose hemoglobin level is less than 110 grams per liter at sea level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Anemia is a condition in which the number of red blood cells or their oxygen-carrying capacity is insufficient to meet physiologic needs, which vary by age, sex, altitude, smoking status, and pregnancy status. In its severe form it is associated with fatigue, weakness, dizziness, and drowsiness. Children under age 5 and pregnant women have the highest risk for anemia." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "2" }, { "id": "SH.PRV.SMOK", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of current tobacco use (% of adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution." }, { "id": "Longdefinition", "value": "The percentage of the population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, total (ages 15+)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes. The indicator name and definition were updated in December, 2020." }, { "id": "Periodicity", "value": "Biennial" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nA statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. \n\n\n\nThe model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. \n\nDepending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015)." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.PRV.SMOK.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of current tobacco use, females (% of female adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution." }, { "id": "Longdefinition", "value": "The percentage of the female population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, females (% of adults)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes it. The indicator name and definition were updated in December, 2020." }, { "id": "Periodicity", "value": "Biennial" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nA statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. \n\n\n\nThe model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. \n\nDepending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015)." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.PRV.SMOK.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of current tobacco use, males (% of male adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution." }, { "id": "Longdefinition", "value": "The percentage of the male population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, males (% of adults)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes it. The indicator name and definition were updated in December, 2020." }, { "id": "Periodicity", "value": "Biennial" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nSmoking is the most common form of tobacco use and the prevalence of smoking is therefore a good measure of the tobacco epidemic. (Corrao MA, Guindon GE, Sharma N, Shokoohi DF (eds). Tobacco Control Country Profiles, 2000, American Cancer Society, Atlanta.) Tobacco use causes heart and other vascular diseases and cancers of the lung and other organs. Given the long delay between starting to smoke and the onset of disease, the health impact of smoking will increase rapidly only in the next few decades. The data presented are age-standardized rates for adults ages 15 and older from the WHO." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "2" }, { "id": "SH.SGR.CRSK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Billions people lack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked." }, { "id": "IndicatorName", "value": "Risk of catastrophic expenditure for surgical care (% of people at risk)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Prior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that “We can’t manage what we don’t measure”, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups." }, { "id": "Longdefinition", "value": "The proportion of population at risk of catastrophic expenditure when surgical care is required. Catastrophic expenditure is defined as direct out of pocket payments for surgical and anaesthesia care exceeding 10% of total income." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2003-2022" }, { "id": "Source", "value": "Program in Global Surgery and Social Change (PGSSC), Harvard Medical School, uri: https://www.pgssc.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of experiencing impoverishment when surgical care is required and the probability of experiencing catastrophic expenditure (10 percent of total income) when surgical care is required.\nStatistical concept(s): The Lancet Commission on Global Surgery, assembled in 2013 to assess surgical care around the world. Commissioners engaged in an iterative global consultative process with partners in over 110 countries to develop six core indicators of the strength of a surgical system. Two indicators assess a country’s preparedness to deliver safe surgery and anesthesia, two assess the current delivery of safe care, and two assess the state of financial risk protection for those seeking surgery." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.SGR.IRSK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Billions people lack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked." }, { "id": "IndicatorName", "value": "Risk of impoverishing expenditure for surgical care (% of people at risk)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Prior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that “We can’t manage what we don’t measure”, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups." }, { "id": "Longdefinition", "value": "The proportion of population at risk of impoverishing expenditure when surgical care is required. Impoverishing expenditure is defined as direct out of pocket payments for surgical and anaesthesia care which drive people below a poverty threshold (using a threshold of $2.15 PPP/day)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2003-2022" }, { "id": "Source", "value": "Program in Global Surgery and Social Change (PGSSC), Harvard Medical School, uri: https://www.pgssc.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of experiencing impoverishment when surgical care is required and the probability of experiencing catastrophic expenditure (10 percent of total income) when surgical care is required.\nStatistical concept(s): The Lancet Commission on Global Surgery, assembled in 2013 to assess surgical care around the world. Commissioners engaged in an iterative global consultative process with partners in over 110 countries to develop six core indicators of the strength of a surgical system. Two indicators assess a country’s preparedness to deliver safe surgery and anesthesia, two assess the current delivery of safe care, and two assess the state of financial risk protection for those seeking surgery." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.SGR.PROC.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Billions people lack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked." }, { "id": "IndicatorName", "value": "Number of surgical procedures (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Prior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that “We can’t manage what we don’t measure”, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups." }, { "id": "Longdefinition", "value": "The number of procedures undertaken in an operating theatre per 100,000 population per year in each country. A procedure is defined as the incision, excision, or manipulation of tissue that needs regional or general anaesthesia, or profound sedation to control pain." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2023" }, { "id": "Source", "value": "Lancet Commission on Global Surgery, uri: www.lancetglobalsurgery.org, note: Data from various sources compiled by the Lancet Commission on Global Surgery and the Center for Health Equity in Surgery and Anesthesia at UCSF Medical Center;\nCenter for Health Equity in Surgery and Anesthesia, note: Data from various sources compiled by the Lancet Commission on Global Surgery and the Center for Health Equity in Surgery and Anesthesia at UCSF Medical Center" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of procedures undertaken in an operating theatre per 100 000 population per year in each country. A procedure is defined as the incision, excision, or manipulation of tissue that needs regional or general anaesthesia, or profound sedation to control pain.\nStatistical concept(s): The Lancet Commission on Global Surgery, assembled in 2013 to assess surgical care around the world. Commissioners engaged in an iterative global consultative process with partners in over 110 countries to develop six core indicators of the strength of a surgical system. Two indicators assess a country’s preparedness to deliver safe surgery and anesthesia, two assess the current delivery of safe care, and two assess the state of financial risk protection for those seeking surgery." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "2" }, { "id": "SH.STA.AIRP.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries." }, { "id": "IndicatorName", "value": "Mortality rate attributed to household and ambient air pollution, age-standardized, female (per 100,000 female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions" }, { "id": "Longdefinition", "value": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2019" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "2" }, { "id": "SH.STA.AIRP.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries." }, { "id": "IndicatorName", "value": "Mortality rate attributed to household and ambient air pollution, age-standardized, male (per 100,000 male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions" }, { "id": "Longdefinition", "value": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2019" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "2" }, { "id": "SH.STA.AIRP.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries." }, { "id": "IndicatorName", "value": "Mortality rate attributed to household and ambient air pollution, age-standardized (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions" }, { "id": "Longdefinition", "value": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years)." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2019" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "2" }, { "id": "SH.STA.ANVC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Pregnant women receiving prenatal care (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources." }, { "id": "Longdefinition", "value": "Pregnant women receiving prenatal care are the percentage of women attended at least once during pregnancy by skilled health personnel for reasons related to pregnancy." }, { "id": "Othernotes", "value": "Good prenatal and postnatal care improve maternal health and reduce maternal and infant mortality." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nGood prenatal and postnatal care improves maternal health and reduces maternal and infant mortality. However, indicators on use of antenatal care services provide no information on the content or quality of the services. Data on antenatal care are obtained mostly from household surveys, which ask women who have had a live birth whether and from whom they received antenatal care." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "2" }, { "id": "SH.STA.ARIC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "ARI treatment (% of children under 5 taken to a health provider)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Children with acute respiratory infection (ARI) who are taken to a health provider refers to the percentage of children under age five with ARI in the last two weeks who were taken to an appropriate health provider, including hospital, health center, dispensary, village health worker, clinic, and private physician." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Acute respiratory infection continues to be a leading cause of death among young children. Data are drawn mostly from household health surveys in which mothers report on number of episodes and treatment for acute respiratory infection." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "2" }, { "id": "SH.STA.BASS.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using at least basic sanitation services, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation). This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "2" }, { "id": "SH.STA.BASS.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using at least basic sanitation services, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation). This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "2" }, { "id": "SH.STA.BASS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using at least basic sanitation services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation). This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "2" }, { "id": "SH.STA.BFED.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "For optimal infant and young child feeding, mothers initiate breastfeeding within one hour of birth, breastfeed exclusively for the first six months, and continue to breastfeed for two years or more while providing nutritionally adequate, safe, and age-appropriate solid, semisolid, and soft foods. Breast milk alone contains all the nutrients, antibodies, hormones, and antioxidants an infant needs to thrive. It protects babies from diarrhea and acute respiratory infections, stimulates their immune systems and response to vaccination, and may confer cognitive benefits." }, { "id": "IndicatorName", "value": "Exclusive breastfeeding (% of children under 6 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Most of the data on breastfeeding are derived from household surveys. For the data that are from household surveys, the year refers to the survey year." }, { "id": "Longdefinition", "value": "Exclusive breastfeeding refers to the percentage of children less than six months old who are fed breast milk alone (no other liquids) in the past 24 hours." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2020" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of infants aged 0–5 months who received only breast milk during the previous day by the total number of infants aged 0–5 months, then multiplying the result by 100.\n\n\nData collection involves Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), which include questions about liquids and foods given the previous day, as well as the number of milk feeds the previous day, to determine if the child is being exclusively breastfed. WHO and UNICEF jointly collect data on infant and young child feeding, pooling information from national surveys. Additionally, the WHO Programme of Nutrition, Physical Activity, and Obesity at the Regional Office for Europe independently compiles country-specific information on exclusive breastfeeding." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percent of children under 6 months" } ], "source_id": "2" }, { "id": "SH.STA.BRTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\n\n\nThe share of births attended by skilled health staff is an indicator of a health system's ability to provide adequate care for pregnant women." }, { "id": "IndicatorName", "value": "Births attended by skilled health staff (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources." }, { "id": "Longdefinition", "value": "Births attended by skilled health staff are the percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliveries on their own; and to care for newborns." }, { "id": "Othernotes", "value": "Assistance by trained professionals during birth reduces the incidence of maternal deaths during childbirth. The share of births attended by skilled health staff is an indicator of a health system’s ability to provide adequate care for pregnant women.\n\nThis is the Sustainable Development Goal indicator 3.1.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2022" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National-level household surveys are the primary sources for collecting data on skilled health personnel providing childbirth care. These surveys include Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), Reproductive Health Surveys (RHS), and other national surveys based on similar methodologies. Respondents in these surveys are asked about their last live birth and who assisted during delivery, covering a period of up to five years before the interview.\n\n\nAs part of the data harmonization process and interaction with countries, UNICEF conducts an annual country consultation. During this consultation, SDG country focal points are contacted to update and verify values included in the database and to obtain new data sources. These new data sources are reviewed and assessed jointly with WHO. Additionally, the national categories or occupational titles of skilled health personnel are verified. The reported data for some countries may include additional categories of trained personnel beyond doctors, nurses, and midwives." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Percent of live births" } ], "source_id": "2" }, { "id": "SH.STA.BRTW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Low birth-weight, which is associated with maternal malnutrition, raises the risk of infant mortality and stunts growth in infancy and childhood. There is also emerging evidence that low-birth-weight babies are more prone to non-communicable diseases such as diabetes and cardiovascular diseases. Low birth-weight can arise as a result of a baby being born too soon or too small for gestational age. Babies born prematurely, who are also small for their gestational age, have the worst prognosis.\n\n\n\nIn low- and middle-income countries low birth-weight stems primarily from poor maternal health and nutrition. Three factors have the most impact: poor maternal nutritional status before conception, mother's short stature (due mostly to under-nutrition and infections during childhood), and poor nutrition during pregnancy (UNICEF Data, https://data.unicef.org/)." }, { "id": "IndicatorName", "value": "Low-birthweight babies (% of births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Low-birthweight babies are newborns weighing less than 2,500 grams, with the measurement taken within the first hour of life, before significant postnatal weight loss has occurred." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2020" }, { "id": "Source", "value": "UNICEF-WHO Low birthweight estimates, UN Children's Fund (UNICEF), uri: data.unicef.org;\nWorld Health Organization (WHO), uri: data.unicef.org, note: UNICEF-WHO Low birthweight estimates" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Household Surveys including DHS, MICS and other national surveys\nStatistical concept(s): Low birthweight babies are more likely to die during their first month of life and those who survived face lifelong consequences including a higher risk of stunted growth, lower IQ ,and adult-onset chronic conditions such as obesity and diabetes." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.DIAB.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Diabetes, an important cause of ill health and a risk factor for other diseases in developed countries, is spreading rapidly in developing countries. Highest among the elderly, prevalence rates are rising among younger and productive populations in developing countries. Economic development has led to the spread of Western lifestyles and diet to developing countries, resulting in a substantial increase in diabetes. Without effective prevention and control programs, diabetes will likely continue to increase." }, { "id": "IndicatorName", "value": "Diabetes prevalence (% of population ages 20 to 79)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes. It is calculated by adjusting to a standard population age-structure." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Diabetes Atlas, International Diabetes Federation" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data used to estimate diabetes prevalence were gathered from various sources. Most of the data were extracted from peer-reviewed publications and national health surveys, including selected WHO STEPwise approach to surveillance (WHO STEPS) studies. Additionally, data from other official sources, such as registries and reports from health regulatory bodies, were utilized, provided there was sufficient information to assess their quality. Data sources with adequate methodological information on key areas of interest, such as the method of diagnosis and sample representativeness, were included. Given the significance of age as a major determinant for diabetes prevalence, only studies with at least three age-specific estimates were considered. After selecting the data sources, the reported age- and sex-specific data in each source were smoothed using a logistic regression model." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Percent of population ages 20 to 79" } ], "source_id": "2" }, { "id": "SH.STA.FGMS.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "FGM is a harmful practice involving the cutting or removal of the external female genitalia. It does not have any health benefits but rather causes serious risks to women’s physical and psychological health, including chronic infections, pain, menstrual problems, and complications during childbirth. FGM has been practiced mainly in the western, eastern, and north-eastern regions of Africa and some countries in the Middle East and Asia. It is reported that FGM is also found in western countries such as United Kingdom, United States, and Canada.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFGM is a violation of girls’ and women’s human rights, as well as a violation of women’s rights to health, security, and physical integrity. However, its eradication is now becoming a global concern and has even been set as one the SDGs, specifically as SDG target 5.3." }, { "id": "IndicatorName", "value": "Female genital mutilation prevalence (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on FGM should be interpreted with caution for several reasons. Women may be reluctant to disclose undergoing FGM due to its sensitivity or illegal status, and some may be unaware of the procedure, especially if performed at an early age. The data is retrospective, not reflecting recent changes, with reports from girls aged 15 to 19 years referring to events 14 to 18 years earlier. Surveys like MICS and DHS only include FGM questions in countries where the practice is prevalent, meaning FGM may still exist in countries without data, including high-income countries with migrant populations and certain low- and middle-income countries. National-level estimates may be misleading as FGM is often practiced by specific ethnic groups in certain locations, thus not accurately representing the prevalence. Reference: A Generation to Protect: Monitoring violence exploitation and abuse of children within the SDG framework (UNICEF 2020). https://data.unicef.org/wp-content/uploads/2020/06/A-Generation-to-Protect-publication-English_2020.pdf" }, { "id": "Longdefinition", "value": "Percentage of women aged 15–49 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.3.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UNICEF DATA, UN Children's Fund (UNICEF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_15-49_FGM; \tIndicator name from the original source: Percentage of girls and women (aged 15-49 years) who have undergone female genital mutilation (FGM), type: API, date accessed: 2023-12-07;\nDemographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and other surverys, DHS Program (ICF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_15-49_FGM; \tIndicator name from the original source: Percentage of girls and women (aged 15-49 years) who have undergone female genital mutilation (FGM), publisher: The DHS Program (ICF), type: API, date accessed: 2023-12-07;\nDHS API, DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: PT_F_15-49_FGM; \tIndicator name from the original source: Percentage of girls and women (aged 15-49 years) who have undergone female genital mutilation (FGM), date accessed: 2023-12-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of women ages 15-49 who have undergone FGM divided by the total number of women ages 15-49 in the population multiplied by 100. The primary sources for this indicator are the Multiple Indicator Cluster Surveys (MICS) and the Demographic and Health Surveys (DHS). The majority of the data are compiled by UNICEF, which coordinates with countries to gather the information.\nStatistical concept(s): Female genital mutilation (FGM) encompasses all practices that involve the partial or total removal of the external female genitalia, or other injury to the female genital organs, for non-medical reasons. Typically, this procedure is carried out on minors." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "2" }, { "id": "SH.STA.HYGN.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Generally, data on handwashing facilities are limited in high-income countries due to the infrequent collection of such information. In the early 2000s, even low- and middle-income countries often lacked this data. However, the recent standardization of hygiene-related questions in international surveys has led to an improvement in the availability of data." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Othernotes", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Utilizing national-level data derived from household surveys, mainly the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), the JMP calculates the proportion of the population with access to basic handwashing facilities at home with soap and water for each country by using a simple linear regression.\nStatistical concept(s): This indicator is measured using a straightforward indicator that examines the presence of handwashing facilities with soap within homes mainly through national household surveys. \n\n\n\n\n\n\n\n\n\n\nCollecting accurate information on handwashing practices presents challenges. Self-reported handwashing is an unreliable measure due to the potential for inaccurate reporting. Direct observation of handwashing can lead to observer bias, as individuals may alter their behavior when they know they are being watched, and implementing such observations on a large scale is resource-intensive. A more effective method involves survey enumerators observing the designated handwashing areas in homes and verifying the availability of water and soap, or a local substitute. This approach provides a more dependable and practical measure of handwashing behavior than relying on self-reported data." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "2" }, { "id": "SH.STA.HYGN.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Generally, data on handwashing facilities are limited in high-income countries due to the infrequent collection of such information. In the early 2000s, even low- and middle-income countries often lacked this data. However, the recent standardization of hygiene-related questions in international surveys has led to an improvement in the availability of data." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Othernotes", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Utilizing national-level data derived from household surveys, mainly the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), the JMP calculates the proportion of the population with access to basic handwashing facilities at home with soap and water for each country by using a simple linear regression.\nStatistical concept(s): This indicator is measured using a straightforward indicator that examines the presence of handwashing facilities with soap within homes mainly through national household surveys. \n\n\n\n\n\n\n\n\n\n\nCollecting accurate information on handwashing practices presents challenges. Self-reported handwashing is an unreliable measure due to the potential for inaccurate reporting. Direct observation of handwashing can lead to observer bias, as individuals may alter their behavior when they know they are being watched, and implementing such observations on a large scale is resource-intensive. A more effective method involves survey enumerators observing the designated handwashing areas in homes and verifying the availability of water and soap, or a local substitute. This approach provides a more dependable and practical measure of handwashing behavior than relying on self-reported data." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "2" }, { "id": "SH.STA.HYGN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Generally, data on handwashing facilities are limited in high-income countries due to the infrequent collection of such information. In the early 2000s, even low- and middle-income countries often lacked this data. However, the recent standardization of hygiene-related questions in international surveys has led to an improvement in the availability of data." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Generally, data on handwashing facilities are limited in high-income countries due to the infrequent collection of such information. In the early 2000s, even low- and middle-income countries often lacked this data. However, the recent standardization of hygiene-related questions in international surveys has led to an improvement in the availability of data." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Utilizing national-level data derived from household surveys, mainly the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), the JMP calculates the proportion of the population with access to basic handwashing facilities at home with soap and water for each country by using a simple linear regression.\nStatistical concept(s): This indicator is measured using a straightforward indicator that examines the presence of handwashing facilities with soap within homes mainly through national household surveys. \n\n\n\n\n\n\n\n\n\n\nCollecting accurate information on handwashing practices presents challenges. Self-reported handwashing is an unreliable measure due to the potential for inaccurate reporting. Direct observation of handwashing can lead to observer bias, as individuals may alter their behavior when they know they are being watched, and implementing such observations on a large scale is resource-intensive. A more effective method involves survey enumerators observing the designated handwashing areas in homes and verifying the availability of water and soap, or a local substitute. This approach provides a more dependable and practical measure of handwashing behavior than relying on self-reported data." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "2" }, { "id": "SH.STA.MALN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of underweight, weight for age, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of underweight, female, is the percentage of girls under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child underweight belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.MALN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of underweight, weight for age, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of underweight, male, is the percentage of boys under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child underweight belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.MALN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of underweight, weight for age (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of underweight children is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child underweight belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.MMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Maternal mortality ratio (modeled estimate, per 100,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality." }, { "id": "Longdefinition", "value": "Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs)." }, { "id": "Othernotes", "value": "This indicator represents the risk associated with each pregnancy and is also a Sustainable Development Goal Indicator (3.1.1) for monitoring maternal health." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO), uri: https://www.who.int/news/item/23-02-2023-a-woman-dies-every-two-minutes-due-to-pregnancy-or-childbirth--un-agencies;\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Per 100,000 live births" } ], "source_id": "2" }, { "id": "SH.STA.MMRT.NE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Maternal mortality ratio (national estimate, per 100,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality.\n\nMaternal mortality ratios collected directly from Demographic and Health Surveys are presented in the survey year, but reference time of these maternal mortality ratios is for the seven years preceding the survey." }, { "id": "Longdefinition", "value": "Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Maternal Mortality Estimation Inter-Agency Group (MMEIG), World Health Organization (WHO), note: The country data compiled, adjusted and used in the estimation model by the Maternal Mortality Estimation Inter-Agency Group (MMEIG). The country data were compiled from the following sources: civil registration and vital statistics; specialized studies on maternal mortality; population based surveys and censuses; other available data sources including data from surveillance sites.;\nUN Children's Fund (UNICEF), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nUN Population Fund (UNFPA), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nWorld Bank Group (WBG), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nUnited Nations (UN), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nPAHO, note: Core Indicators Portal;\nICF, note: The DHS Program, Demographic and Health Surveys" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The national estimates of maternal mortality ratios are based on national surveys, vital registration records, and surveillance data or are derived from community and hospital records.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "2" }, { "id": "SH.STA.ODFC.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to sanitation is a fundamental human right. Open defecation, which is the practice of relieving oneself outside without proper facilities, is a violation of human dignity and poses a significant threat to public health and nutrition. Poor sanitation is a leading cause of infectious diseases globally, and enhancing sanitation services has been proven to have a substantial positive effect on health outcomes. The provision of basic and safely managed sanitation can decrease the incidence of diarrheal diseases and mitigate the health consequences of other serious illnesses that cause widespread morbidity and mortality among children. Diarrheal conditions and parasitic infections debilitate children, increasing their vulnerability to malnutrition and secondary infections such as pneumonia, measles, and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe absence of adequate sanitation is especially harmful to women who are forced to defecate in the open, as it compromises their privacy and exposes them to a greater risk of assault and violence. The elimination of open defecation is a specific target within the Sustainable Development Goals (SDG target 6.2), underscoring the international commitment to addressing this critical issue." }, { "id": "IndicatorName", "value": "People practicing open defecation, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Othernotes", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "2" }, { "id": "SH.STA.ODFC.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to sanitation is a fundamental human right. Open defecation, which is the practice of relieving oneself outside without proper facilities, is a violation of human dignity and poses a significant threat to public health and nutrition. Poor sanitation is a leading cause of infectious diseases globally, and enhancing sanitation services has been proven to have a substantial positive effect on health outcomes. The provision of basic and safely managed sanitation can decrease the incidence of diarrheal diseases and mitigate the health consequences of other serious illnesses that cause widespread morbidity and mortality among children. Diarrheal conditions and parasitic infections debilitate children, increasing their vulnerability to malnutrition and secondary infections such as pneumonia, measles, and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe absence of adequate sanitation is especially harmful to women who are forced to defecate in the open, as it compromises their privacy and exposes them to a greater risk of assault and violence. The elimination of open defecation is a specific target within the Sustainable Development Goals (SDG target 6.2), underscoring the international commitment to addressing this critical issue." }, { "id": "IndicatorName", "value": "People practicing open defecation, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Othernotes", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "2" }, { "id": "SH.STA.ODFC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to sanitation is a fundamental human right. Open defecation, which is the practice of relieving oneself outside without proper facilities, is a violation of human dignity and poses a significant threat to public health and nutrition. Poor sanitation is a leading cause of infectious diseases globally, and enhancing sanitation services has been proven to have a substantial positive effect on health outcomes. The provision of basic and safely managed sanitation can decrease the incidence of diarrheal diseases and mitigate the health consequences of other serious illnesses that cause widespread morbidity and mortality among children. Diarrheal conditions and parasitic infections debilitate children, increasing their vulnerability to malnutrition and secondary infections such as pneumonia, measles, and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe absence of adequate sanitation is especially harmful to women who are forced to defecate in the open, as it compromises their privacy and exposes them to a greater risk of assault and violence. The elimination of open defecation is a specific target within the Sustainable Development Goals (SDG target 6.2), underscoring the international commitment to addressing this critical issue." }, { "id": "IndicatorName", "value": "People practicing open defecation (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "2" }, { "id": "SH.STA.ORCF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Most diarrhea-related deaths are due to dehydration, and many of these deaths can be prevented with the use of oral rehydration salts at home." }, { "id": "IndicatorName", "value": "Diarrhea treatment (% of children under 5 receiving oral rehydration and continued feeding)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Recommendations for the use of oral rehydration therapy have changed over time based on scientific progress, so it is difficult to accurately compare use rates across countries. Until the current recommended method for home management of diarrhea is adopted and applied in all countries, the data should be used with caution. Also, the prevalence of diarrhea may vary by season. Since country surveys are administered at different times, data comparability is further affected." }, { "id": "Longdefinition", "value": "Children with diarrhea who received oral rehydration and continued feeding refer to the percentage of children under age five with diarrhea in the two weeks prior to the survey who received either oral rehydration therapy or increased fluids, with continued feeding." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Mothers or caregivers of children under five years old are asked whether the child experienced diarrhea at any point in the past two weeks. If the child did have diarrhea, they are further asked whether either oral rehydration therapy or increased fluids, with continued feeding was administered. The term \"diarrhea,\" as defined by the DHS, should include all forms of diarrhea, such as bloody stools (indicative of dysentery), watery stools, and other variations. This definition encompasses both the mother's understanding and locally-used terms." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SH.STA.ORTH", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Diarrhea treatment (% of children under 5 who received ORS packet)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator does not assess the severity of the diarrhea." }, { "id": "Longdefinition", "value": "Percentage of children under age 5 with diarrhea in the two weeks preceding the survey who received oral rehydration salts (ORS packets or pre-packaged ORS fluids)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Mothers or caregivers of children under five years old are asked whether the child experienced diarrhea at any point in the past two weeks. If the child did have diarrhea, they are further asked whether Oral Rehydration Solution (ORS) was administered. The term \"diarrhea,\" as defined by the DHS, should include all forms of diarrhea, such as bloody stools (indicative of dysentery), watery stools, and other variations. This definition encompasses both the mother's understanding and locally-used terms." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SH.STA.OWGH.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight, weight for height, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.OWGH.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight, weight for height, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of overweight, male, is the percentage of boys under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.OWGH.ME.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight, female (modeled estimate, % of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time." }, { "id": "Longdefinition", "value": "Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\n\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates." }, { "id": "Periodicity", "value": "Every two years" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME);\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.OWGH.ME.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight, male (modeled estimate, % of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time." }, { "id": "Longdefinition", "value": "Prevalence of overweight, male, is the percentage of boys under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\n\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates." }, { "id": "Periodicity", "value": "Every two years" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME);\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.OWGH.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight (modeled estimate, % of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time." }, { "id": "Longdefinition", "value": "Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates." }, { "id": "Periodicity", "value": "Every two years" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME);\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.OWGH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "See SH.STA.OWGH.ME.ZS for aggregation" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight, weight for height (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.POIS.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unintentional poisoning (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unintentional poisonings is the number of deaths from unintentional poisonings in a year per 100,000 population. Unintentional poisoning can\n\n\nbe caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.9.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for unintentional poisoning mortality are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database are used, with necessary adjustments for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. For countries lacking high-quality death registration data, cause of death estimates are calculated using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates. The complete methodology can be found at the following: https://www.who.int/docs/defaultsource/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf" }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "2" }, { "id": "SH.STA.POIS.P5.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unintentional poisoning, female (per 100,000 female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unintentional poisonings is the number of female deaths from unintentional poisonings in a year per 100,000 female population. Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for unintentional poisoning mortality are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database are used, with necessary adjustments for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. For countries lacking high-quality death registration data, cause of death estimates are calculated using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates. The complete methodology can be found at the following: https://www.who.int/docs/defaultsource/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf" }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 female population" } ], "source_id": "2" }, { "id": "SH.STA.POIS.P5.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unintentional poisoning, male (per 100,000 male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unintentional poisonings is the number of male deaths from unintentional poisonings in a year per 100,000 male population. Unintentional poisoning can\n\n\nbe caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for unintentional poisoning mortality are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database are used, with necessary adjustments for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. For countries lacking high-quality death registration data, cause of death estimates are calculated using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates. The complete methodology can be found at the following: https://www.who.int/docs/defaultsource/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf" }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 male population" } ], "source_id": "2" }, { "id": "SH.STA.SMSS.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using safely managed sanitation services, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporality and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site). Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system. If no information is available, it is assumed that 50 percent is safely managed. Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed sanitation facilities are defined as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "2" }, { "id": "SH.STA.SMSS.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using safely managed sanitation services, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporality and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site). Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system. If no information is available, it is assumed that 50 percent is safely managed. Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed sanitation facilities are defined as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "2" }, { "id": "SH.STA.SMSS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using safely managed sanitation services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporality and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site). Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system. If no information is available, it is assumed that 50 percent is safely managed. Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis indicator (Total) is calculated as a population-weighted average of the RURAL and URBAN aggregates when sufficient data are available for both domains. When coverage at either the RURAL or URBAN level is insufficient, but country-level TOTAL data meet the minimum population coverage threshold (30%), TOTAL aggregates are calculated directly from country-level TOTAL estimates. Because these two aggregation approaches may be applied in different years as data availability improves, methodological switches can occur and may result in discontinuities in the time series.\n\n\n\nThis indicator corresponds to Sustainable Development Goal indicator 6.2.1 (see UN SDG metadata: https://unstats.un.org/sdgs/metadata/)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed sanitation facilities are defined as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "2" }, { "id": "SH.STA.STNT.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of stunting, female, is the percentage of girls under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.STNT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.STNT.ME.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, female (modeled estimate, % of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time." }, { "id": "Longdefinition", "value": "Prevalence of stunting, female, is the percentage of girls under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\n\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates." }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME);\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.STNT.ME.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, male (modeled estimate, % of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time." }, { "id": "Longdefinition", "value": "Prevalence of stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\n\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates." }, { "id": "Periodicity", "value": "Every two years" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME);\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.STNT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age (modeled estimate, % of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time." }, { "id": "Longdefinition", "value": "Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates." }, { "id": "Periodicity", "value": "Every two years" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, publisher: JME;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME), publisher: JME;\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME), publisher: JME" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.STNT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "See SH.STA.STNT.ME.ZS for aggregation" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.SUIC.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Suicide mortality rate, female (per 100,000 female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of suicide deaths in a year by the mid-year population for the same calendar year, then multiplying by 100,000. The estimates are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the vital registration data submitted by member states to the WHO Mortality Database are used, with adjustments made where necessary (e.g., for under-reporting of deaths, unknown age and sex, and ill-defined causes of death). For countries without high-quality death registration data, cause of death estimates are calculated using other sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not be identical to official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 female population" } ], "source_id": "2" }, { "id": "SH.STA.SUIC.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Suicide mortality rate, male (per 100,000 male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of suicide deaths in a year by the mid-year population for the same calendar year, then multiplying by 100,000. The estimates are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the vital registration data submitted by member states to the WHO Mortality Database are used, with adjustments made where necessary (e.g., for under-reporting of deaths, unknown age and sex, and ill-defined causes of death). For countries without high-quality death registration data, cause of death estimates are calculated using other sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not be identical to official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 male population" } ], "source_id": "2" }, { "id": "SH.STA.SUIC.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Suicide mortality rate (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted)." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.4.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of suicide deaths in a year by the mid-year population for the same calendar year, then multiplying by 100,000. The estimates are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the vital registration data submitted by member states to the WHO Mortality Database are used, with adjustments made where necessary (e.g., for under-reporting of deaths, unknown age and sex, and ill-defined causes of death). For countries without high-quality death registration data, cause of death estimates are calculated using other sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not be identical to official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "2" }, { "id": "SH.STA.TRAF.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Road traffic injuries and deaths is a major global public health problem. Road traffic crashes are currently the leading cause of death for children and young adults in the world. There is a strong association between the risk of road traffic death and the income level of countries. The burden of road traffic deaths is disproportionately high among low- and middle-income countries." }, { "id": "IndicatorName", "value": "Mortality caused by road traffic injury (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mortality caused by road traffic injury is estimated road traffic fatal injury deaths per 100,000 population." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.6.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2019" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The methods used for analyzing causes of death vary based on the type of data available from different countries. For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database is utilized, with necessary adjustments made for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. In contrast, for countries lacking high-quality death registration data, cause of death estimates are derived using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "2" }, { "id": "SH.STA.WASH.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unsafe drinking water, unsafe sanitation and lack of hygiene are important causes of death. Most diarrheal deaths in the world are caused by unsafe water, sanitation or hygiene. According to the World Health Organization, in addition to diarrea, the following diseases could be prevented if adequate WASH services are provided: malnutrition, intestinal nematode infections, lymphatic filariasis, trachoma, schistosomiasis and malaria." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.9.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2019" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: To estimate the portion of deaths from diarrhea and acute respiratory infections attributable to unsafe water, sanitation, and hygiene (WASH), a comparative risk assessment approach is used. This involves calculating the attributable disease deaths by combining information on the increased (or relative) risk of a disease resulting from exposure with the prevalence of that exposure in the population. This calculation yields the 'population attributable fraction' (PAF), which represents the fraction of disease in a population that can be attributed to the exposure, in this case, unsafe WASH.\n\n\nBy applying the PAF to the total deaths from diarrhea or acute respiratory infections, the number of deaths resulting from inadequate WASH can be determined. Additionally, deaths from protein-energy malnutrition attributable to inadequate WASH are estimated by evaluating the impacts of repeated infectious diarrhea episodes on nutritional status, particularly stunting. All deaths from intestinal nematode infections are attributed to inadequate WASH due to their transmission pathway." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "2" }, { "id": "SH.STA.WAST.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of wasting, weight for height, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of wasting, female, is the proportion of girls under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.WAST.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of wasting, weight for height, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of wasting, male, is the proportion of boys under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.STA.WAST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of wasting, weight for height (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of wasting is the proportion of children under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.SVR.WAST.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of severe wasting, weight for height, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of severe wasting, female, is the proportion of girls under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.SVR.WAST.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of severe wasting, weight for height, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of severe wasting, male, is the proportion of boys under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.SVR.WAST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of severe wasting, weight for height (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of severe wasting is the proportion of children under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.TBS.CURE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tuberculosis (TB) is a preventable and usually curable disease. Yet TB is one of the world’s leading causes of death from a single infectious agent. Millions of people continue to fall ill with TB every year." }, { "id": "IndicatorName", "value": "Tuberculosis treatment success rate (% of new cases)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Tuberculosis treatment success rate is the percentage of all new tuberculosis cases (or new and relapse cases for some countries) registered under a national tuberculosis control programme in a given year that successfully completed treatment, with or without bacteriological evidence of success (\"cured\" and \"treatment completed\" respectively)." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Global Tuberculosis Report, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of cases registered in a given year (excluding cases placed on a second-line drug regimen) that successfully completed treatment without bacteriological evidence of failure. All registered cases fall into one of the following five mutually exclusive categories: treatment success, failure, death, lost to follow-up, not evaluated (missing data on the outcome of treatment).\nStatistical concept(s): Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. Data on the success rate of tuberculosis treatment are provided for countries that have submitted data to the WHO. The treatment success rate for tuberculosis provides a useful indicator of the quality of health services. A low rate suggests that infectious patients may not be receiving adequate treatment. An important complement to the tuberculosis treatment success rate is the case detection rate, which indicates whether there is adequate coverage by the recommended case detection and treatment strategy." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.TBS.DTEC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tuberculosis (TB) is a preventable and usually curable disease. Yet TB is one of the world’s leading causes of death from a single infectious agent. Millions of people continue to fall ill with TB every year." }, { "id": "IndicatorName", "value": "Tuberculosis case detection rate (%, all forms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Tuberculosis case detection rate (all forms) is the number of new and relapse tuberculosis cases notified to WHO in a given year, divided by WHO's estimate of the number of incident tuberculosis cases for the same year, expressed as a percentage. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Tuberculosis Report, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of new and relapse TB cases diagnosed and treated in national TB control programmes and notified to WHO, divided by WHO's estimate of the number of incident TB cases for the same year, expressed as a percentage.\nStatistical concept(s): Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. This indicator shows the tuberculosis detection rate for all detection methods. Editions before 2010 included the tuberculosis detection rates by DOTS, the internationally recommended strategy for tuberculosis control. Thus data on the case detection rate from 2010 onward cannot be compared with data in previous editions." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.TBS.INCD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tuberculosis (TB) is a preventable and usually curable disease. Yet TB is one of the world’s leading causes of death from a single infectious agent. Millions of people continue to fall ill with TB every year." }, { "id": "IndicatorName", "value": "Incidence of tuberculosis (per 100,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nUncertainty bounds for the incidence are available at http://data.worldbank.org" }, { "id": "Longdefinition", "value": "Incidence of tuberculosis is the estimated number of new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population. All forms of TB are included, including cases in people living with HIV. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization.\n\nThis is the Sustainable Development Goal indicator 3.3.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Tuberculosis Report, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of TB incidence are produced through a consultative and analytical process led by WHO and are published annually. These estimates are based on annual case notifications, assessments of the quality and coverage of TB notification data, national surveys of the prevalence of TB disease and on information from death (vital) registration systems.\nStatistical concept(s): Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. In developed countries tuberculosis has reemerged largely as a result of cases among immigrants. Since tuberculosis incidence cannot be directly measured, estimates are obtained by eliciting expert opinion or are derived from measurements of prevalence or mortality." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Per 100,000 people" } ], "source_id": "2" }, { "id": "SH.VAC.TTNS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and ??is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Newborns protected against tetanus (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Newborns protected against tetanus are the percentage of births by women of child-bearing age who are immunized against tetanus." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2024" }, { "id": "Source", "value": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.XPD.CHEX.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Current health expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Level of current health expenditure expressed as a percentage of GDP. Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, IT and stocks of vaccines for emergency or outbreaks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.XPD.CHEX.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Current health expenditure per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current expenditures on health per capita in current US dollars. Estimates of current health expenditures include healthcare goods and services consumed during each year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "SH.XPD.CHEX.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Current health expenditure per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current expenditures on health per capita expressed in international dollars at purchasing power parity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current iternational $" } ], "source_id": "2" }, { "id": "SH.XPD.EHEX.CH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "External health expenditure (% of current health expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Share of current health expenditures funded from external sources. External sources compose of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country. External sources either flow through the government scheme or are channeled through non-governmental organizations or other schemes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.XPD.EHEX.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "External health expenditure per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current external expenditures on health per capita expressed in current US dollars. External sources are composed of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "SH.XPD.EHEX.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "External health expenditure per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current external expenditures on health per capita expressed in international dollars at purchasing power parity. External sources are composed of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current iternational $" } ], "source_id": "2" }, { "id": "SH.XPD.GHED.CH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic general government health expenditure (% of current health expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Share of current health expenditures funded from domestic public sources for health. Domestic public sources include domestic revenue as internal transfers and grants, transfers, subsidies to voluntary health insurance beneficiaries, non-profit institutions serving households (NPISH) or enterprise financing schemes as well as compulsory prepayment and social health insurance contributions. They do not include external resources spent by governments on health." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.XPD.GHED.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic general government health expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public expenditure on health from domestic sources as a share of the economy as measured by GDP." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.XPD.GHED.GE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic general government health expenditure (% of general government expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public expenditure on health from domestic sources as a share of total public expenditure. It indicates the priority of the government to spend on health from own domestic public resources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.XPD.GHED.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic general government health expenditure per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public expenditure on health from domestic sources per capita expressed in current US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "SH.XPD.GHED.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic general government health expenditure per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public expenditure on health from domestic sources per capita expressed in international dollars at purchasing power parity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current iternational $" } ], "source_id": "2" }, { "id": "SH.XPD.OOPC.CH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Out-of-pocket expenditure (% of current health expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Share of out-of-pocket payments of total current health expenditures. Out-of-pocket payments are spending on health directly out-of-pocket by households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. Aggregations are weighted by total current health expenditure (not by population).\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.XPD.OOPC.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Out-of-pocket expenditure per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Health expenditure through out-of-pocket payments per capita in USD. Out of pocket payments are spending on health directly out of pocket by households in each country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "SH.XPD.OOPC.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Out-of-pocket expenditure per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Health expenditure through out-of-pocket payments per capita in international dollars at purchasing power parity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current iternational $" } ], "source_id": "2" }, { "id": "SH.XPD.PVTD.CH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic private health expenditure (% of current health expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Share of current health expenditures funded from domestic private sources. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH.XPD.PVTD.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic private health expenditure per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current private expenditures on health per capita expressed in current US dollars. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "SH.XPD.PVTD.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic private health expenditure per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current private expenditures on health per capita expressed in international dollars at purchasing power parity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current iternational $" } ], "source_id": "2" }, { "id": "SH_UHC_FH40", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial hardship due to out-of-pocket (OOP) health spending reflects the extent to which people’s direct health payments reduce their living standards. Because OOP health payments are made at the point of care without risk pooling, they include formal and informal payments for any health service or health product, OOP health payments can occur in any health system and affect people at any income level.\n\nFinancial hardship due to OOP health spending is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services, they need without compromising their ability to meet basic needs or significantly reducing their ability to consume other goods and services. As Sustainable Development Goal (SDG) indicator 3.8.2, financial hardship forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population facing financial hardship due to out-of-pocket health expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of population spending more than 40% of household discretionary budget on out-of-pocket health expenditure (%)\n\n\n\nThe discretionary budget is defined as the total household budget, measured by consumption expenditure or income, minus the societal poverty line (SPL ). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health. \n\n\n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "The proportion of population facing financial hardship due to out-of-pocket health expenditure can be decomposed into two mutually exclusive categories: the proportion of population with impoverishing out-of-pocket health expenditure and the proportion of the population with large but non-impoverishing out-of-pocket health expenditure\n(i) Impoverishing out-of-pocket health expenditure\nOut-of-pocket health expenditure is defined as impoverishing if it exceeds 100% of household discretionary budget. This is spending that leaves the household with no discretionary budget, or with inability to meet basic needs, once out-of-pocket health expenditure is subtracted from their discretionary budget. For households with negative discretionary budgets (those living in societal poverty), any OOP health spending exceeds 100% of the household discretionary budget.\nThe proportion of population incurring impoverishing out-of-pocket health expenditure can be further decomposed into the proportion of population further impoverished and the proportion pushed into poverty due to out-of-pocket health expenditure.\nOut-of-pocket health expenditure is defined as further impoverishing for households that live in societal poverty (household total consumption or income lies below the societal poverty line) and incur any out-of-pocket health expenditure. \nOut-of-pocket health expenditure is considered as pushing into poverty if households with positive discretionary budgets spend all their discretionary budget on out-of-pocket health expenditure. In other words, household total consumption (or income) is above the societal poverty line, but its total consumption (or income) net of out-of-pocket health expenditure falls below the societal poverty line, pushing the household into societal poverty.\n(ii) Large (but not impoverishing out-of-pocket) health spending\nOut-of-pocket health spending is defined as large (but non-impoverishing) if it exceeds 40% but remains below 100% of household discretionary budget. This implies ability to meet basic needs but a substantial reduction in ability to consume other goods and services due to the need to pay for health care." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2024" }, { "id": "Source", "value": "Universal Health Coverage Dataset, World Health Organization (WHO) [WHO];\nWorld Bank (WB) [WB], note: Universal Health Coverage Dataset" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Proportion of population facing financial hardship due to out-of-pocket health expenditure (%) is measured as the population-weighted average of the number of people living in households with out-of-pocket household expenditure on health exceeding 40% of household discretionary budget.\nStatistical concept(s): The household discretionary budget is defined as total household consumption expenditure or income minus the societal poverty line (SPL). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health. Households living in societal poverty, have a negative discretionary budget.\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home). This excludes any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Refer to Other notes for details on out-of-pocket health expenditure.\nThe recommended data sources for monitoring this indicator are household budget surveys and household income and expenditure surveys, which provide information on both household consumption expenditure on health and total household consumption expenditures, and are routinely conducted by National Statistical Offices (NSOs). Preference is given for the use of consumption expenditure as a measure or household budget and discretionary budget, with income being used when consumption expenditure data is unavailable. \nTo compute regional and global aggregates for a common reference year, survey-based country estimates are first “lined-up” using a combination of interpolation/extrapolation (when there are at least two survey-based estimates available within 5 years around the reference year); econometric modelling (one survey-based estimate available around the reference year); and imputation based on income group medians (no survey-based estimates available around the reference year) (see also SDG 3.8.2 metadata description (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-02.pdf), sections 4.f and 4.g)." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH_UHC_FH40_FURTHER", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial hardship due to out-of-pocket (OOP) health spending reflects the extent to which people’s direct health payments reduce their living standards. Because OOP health payments are made at the point of care without risk pooling, they include formal and informal payments for any health service or health product, OOP health payments can occur in any health system and affect people at any income level.\n\nFinancial hardship due to OOP health spending is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services, they need without compromising their ability to meet basic needs or significantly reducing their ability to consume other goods and services. As Sustainable Development Goal (SDG) indicator 3.8.2, financial hardship forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population further impoverished due to out-of-pocket health expenditure, based on the societal poverty line (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of population living in households already in societal poverty (household total budget lies below the societal poverty line) that incur any out-of-pocket health expenditure.\n\nThe household discretionary budget is defined as the total household budget, measured by consumption expenditure or income, minus the societal poverty line (SPL). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health.\n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "The proportion of population facing financial hardship due to out-of-pocket health expenditure can be decomposed into two mutually exclusive categories: the proportion of population with impoverishing out-of-pocket health expenditure and the proportion of the population with large but non-impoverishing out-of-pocket health expenditure\n(i) Impoverishing out-of-pocket health expenditure\nOut-of-pocket health expenditure is defined as impoverishing if it exceeds 100% of household discretionary budget. This is spending that leaves the household with no discretionary budget, or with inability to meet basic needs, once out-of-pocket health expenditure is subtracted from their discretionary budget. For households with negative discretionary budgets (those living in societal poverty), any OOP health spending exceeds 100% of the household discretionary budget. \nThe proportion of population incurring impoverishing out-of-pocket health expenditure can be further decomposed into the proportion of population further impoverished and the proportion pushed into poverty due to out-of-pocket health expenditure. \nOut-of-pocket health expenditure is defined as further impoverishing for households that live in societal poverty (household total consumption or income lies below the societal poverty line) and incur any out-of-pocket health expenditure.\n Out-of-pocket health expenditure is considered as pushing into poverty if households with positive discretionary budgets spend all their discretionary budget on out-of-pocket health expenditure. In other words, household total consumption (or income) is above the societal poverty line, but its total consumption (or income) net of out-of-pocket health expenditure falls below the societal poverty line, pushing the household into societal poverty.\n(ii) Large (but not impoverishing out-of-pocket) health spending\nOut-of-pocket health spending is defined as large (but non-impoverishing) if it exceeds 40% but remains below 100% of household discretionary budget. This implies ability to meet basic needs but a substantial reduction in ability to consume other goods and services due to the need to pay for health care." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Universal Health Coverage Dataset, World Health Organization (WHO) [WHO];\nWorld Bank (WB) [WB], note: Universal Health Coverage Dataset" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Proportion of population further impoverished due to out-of-pocket health expenditure (%) is measured as the population-weighted average of the number of people living in households in societal poverty (household total consumption or income lies below the societal poverty line) that incur any out-of-pocket expenditure\nStatistical concept(s): The household discretionary budget is defined as total household consumption expenditure or income minus the societal poverty line (SPL). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health. Households living in societal poverty, have a negative discretionary budget.\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home). This excludes any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Refer to Other notes for details on out-of-pocket health expenditure.\nThe recommended data sources for monitoring this indicator are household budget surveys and household income and expenditure surveys, which provide information on both household consumption expenditure on health and total household consumption expenditures, and are routinely conducted by National Statistical Offices (NSOs). Preference is given for the use of consumption expenditure as a measure or household budget and discretionary budget, with income being used when consumption expenditure data is unavailable.\nTo compute regional and global aggregates for a common reference year, survey-based country estimates are first “lined-up” using a combination of interpolation/extrapolation (when there are at least two survey-based estimates available within 5 years around the reference year); econometric modelling (one survey-based estimate available around the reference year); and imputation based on income group medians (no survey-based estimates available around the reference year) (see also SDG 3.8.2 metadata description (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-02.pdf), sections 4.f and 4.g)." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH_UHC_FH40_IMPOV", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial hardship due to out-of-pocket health spending reflects the extent to which people’s direct health payments reduce their living standards. Because OOP health payments are made at the point of care without risk pooling, they include formal and informal payments for any health service or health product, OOP health payments can occur in any health system and affect people at any income level.\n\nFinancial hardship due to OOP health spending is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services, they need without compromising their ability to meet basic needs or significantly reducing their ability to consume other goods and services. As Sustainable Development Goal (SDG) indicator 3.8.2, financial hardship forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population facing impoverishing out-of-pocket health expenditure, based on the societal poverty line (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of population spending more than 100% of household discretionary budget on out-of-pocket health expenditure (%). This includes people living in poverty that are further impoverished due to out-of-pocket health expenditure as well as people who are pushed into poverty by out-of-pocket health expenditure. \n\nThe discretionary budget is defined as the total household budget, measured by consumption expenditure or income, minus the societal poverty line (SPL ). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health. \n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "The proportion of population facing financial hardship due to out-of-pocket health expenditure can be decomposed into two mutually exclusive categories: the proportion of population with impoverishing out-of-pocket health expenditure and the proportion of the population with large but non-impoverishing out-of-pocket health expenditure\n(i) Impoverishing out-of-pocket health expenditure\nOut-of-pocket health expenditure is defined as impoverishing if it exceeds 100% of household discretionary budget. This is spending that leaves the household with no discretionary budget, or with inability to meet basic needs, once out-of-pocket health expenditure is subtracted from their discretionary budget. For households with negative discretionary budgets (those living in societal poverty), any OOP health spending exceeds 100% of the household discretionary budget. \nThe proportion of population incurring impoverishing out-of-pocket health expenditure can be further decomposed into the proportion of population further impoverished and the proportion pushed into poverty due to out-of-pocket health expenditure. \nOut-of-pocket health expenditure is defined as further impoverishing for households that live in societal poverty (household total consumption or income lies below the societal poverty line) and incur any out-of-pocket health expenditure.\nOut-of-pocket health expenditure is considered as pushing into poverty if households with positive discretionary budgets spend all their discretionary budget on out-of-pocket health expenditure. In other words, household total consumption (or income) is above the societal poverty line, but its total consumption (or income) net of out-of-pocket health expenditure falls below the societal poverty line, pushing the household into societal poverty.\n(ii) Large (but not impoverishing out-of-pocket) health spending\nOut-of-pocket health spending is defined as large (but non-impoverishing) if it exceeds 40% but remains below 100% of household discretionary budget. This implies ability to meet basic needs but a substantial reduction in ability to consume other goods and services due to the need to pay for health care." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2024" }, { "id": "Source", "value": "Universal Health Coverage Dataset, World Health Organization (WHO) [WHO];\nWorld Bank (WB) [WB], note: Universal Health Coverage Dataset" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Proportion of population facing impoverishing out-of-pocket health expenditure (%) is measured as the population-weighted average of the number of people living in households with positive out-of-pocket household expenditure on health exceeding 100% of household discretionary budget.\nStatistical concept(s): The household discretionary budget is defined as total household consumption expenditure or income minus the societal poverty line (SPL). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health. Households living in societal poverty, have a negative discretionary budget.\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home). This excludes any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. \nThe recommended data sources for monitoring this indicator are household budget surveys and household income and expenditure surveys, which provide information on both household consumption expenditure on health and total household consumption expenditures, and are routinely conducted by National Statistical Offices (NSOs). Preference is given for the use of consumption expenditure as a measure or household budget and discretionary budget, with income being used when consumption expenditure data is unavailable.\nTo compute regional and global aggregates for a common reference year, survey-based country estimates are first “lined-up” using a combination of interpolation/extrapolation (when there are at least two survey-based estimates available within 5 years around the reference year); econometric modelling (one survey-based estimate available around the reference year); and imputation based on income group medians (no survey-based estimates available around the reference year) (see also SDG 3.8.2 metadata description (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-02.pdf), sections 4.f and 4.g).\nRefer to Other notes for details on out-of-pocket health expenditure." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH_UHC_FH40_LARGE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial hardship due to out-of-pocket (OOP) health spending reflects the extent to which people’s direct health payments reduce their living standards. Because OOP health payments are made at the point of care without risk pooling, they include formal and informal payments for any health service or health product, OOP health payments can occur in any health system and affect people at any income level.\n\nFinancial hardship due to OOP health spending is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services, they need without compromising their ability to meet basic needs or significantly reducing their ability to consume other goods and services. As Sustainable Development Goal (SDG) indicator 3.8.2, financial hardship forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population facing large (but non-impoverishing) out-of-pocket health expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of population spending more than 40% but less than 100% of household discretionary budget on out-of-pocket health expenditure (%)\n\n\n\nThe discretionary budget is defined as the total household budget, measured by consumption expenditure or income, minus the societal poverty line (SPL). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health.\n\n\n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "The proportion of population facing financial hardship due to out-of-pocket health expenditure can be decomposed into two mutually exclusive categories: the proportion of population with impoverishing out-of-pocket health expenditure and the proportion of the population with large but non-impoverishing out-of-pocket health expenditure\n(i) Impoverishing out-of-pocket health expenditure\nOut-of-pocket health expenditure is defined as impoverishing if it exceeds 100% of household discretionary budget. This is spending that leaves the household with no discretionary budget, or with inability to meet basic needs, once out-of-pocket health expenditure is subtracted from their discretionary budget. For households with negative discretionary budgets (those living in societal poverty), any OOP health spending exceeds 100% of the household discretionary budget. \nThe proportion of population incurring impoverishing out-of-pocket health expenditure can be further decomposed into the proportion of population further impoverished and the proportion pushed into poverty due to out-of-pocket health expenditure. \nOut-of-pocket health expenditure is defined as further impoverishing for households that live in societal poverty (household total consumption or income lies below the societal poverty line) and incur any out-of-pocket health expenditure.\nOut-of-pocket health expenditure is considered as pushing into poverty if households with positive discretionary budgets spend all their discretionary budget on out-of-pocket health expenditure. In other words, household total consumption (or income) is above the societal poverty line, but its total consumption (or income) net of out-of-pocket health expenditure falls below the societal poverty line, pushing the household into societal poverty.\n(ii) Large (but not impoverishing out-of-pocket) health spending\nOut-of-pocket health spending is defined as large (but non-impoverishing) if it exceeds 40% but remains below 100% of household discretionary budget. This implies ability to meet basic needs but a substantial reduction in ability to consume other goods and services due to the need to pay for health care." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2024" }, { "id": "Source", "value": "Universal Health Coverage Dataset, World Health Organization (WHO) [WHO];\nWorld Bank (WB) [WB], note: World Health Organization ( WHO )" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Proportion of population facing large (but non-impoverishing) out-of-pocket health expenditure (%) is measured as the population-weighted average of the number of people living in households with positive out-of-pocket household expenditure on health exceeding 40% but less than 100% of household discretionary budget.\nStatistical concept(s): The household discretionary budget is defined as total household consumption expenditure or income minus the societal poverty line (SPL). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health. Households living in societal poverty, have a negative discretionary budget.\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home). This excludes any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Refer to Other notes for details on out-of-pocket health expenditure. \nThe recommended data sources for monitoring this indicator are household budget surveys and household income and expenditure surveys, which provide information on both household consumption expenditure on health and total household consumption expenditures, and are routinely conducted by National Statistical Offices (NSOs). Preference is given for the use of consumption expenditure as a measure or household budget and discretionary budget, with income being used when consumption expenditure data is unavailable.\nTo compute regional and global aggregates for a common reference year, survey-based country estimates are first “lined-up” using a combination of interpolation/extrapolation (when there are at least two survey-based estimates available within 5 years around the reference year); econometric modelling (one survey-based estimate available around the reference year); and imputation based on income group medians (no survey-based estimates available around the reference year) (see also SDG 3.8.2 metadata description (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-02.pdf), sections 4.f and 4.g)." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH_UHC_FH40_PUSHED", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial hardship due to out-of-pocket (OOP) health spending reflects the extent to which people’s direct health payments reduce their living standards. Because OOP health payments are made at the point of care without risk pooling, they include formal and informal payments for any health service or health product, OOP health payments can occur in any health system and affect people at any income level.\n\nFinancial hardship due to OOP health spending is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services, they need without compromising their ability to meet basic needs or significantly reducing their ability to consume other goods and services. As Sustainable Development Goal (SDG) indicator 3.8.2, financial hardship forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed into poverty (based on the societal poverty line) due to out-of-pocket health expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of population living in households with positive discretionary budgets that spend all their discretionary budget on out-of-pocket health expenditure.\n\nThe household discretionary budget is defined as the total household budget, measured by consumption expenditure or income, minus the societal poverty line (SPL). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health.\n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "The proportion of population facing financial hardship due to out-of-pocket health expenditure can be decomposed into two mutually exclusive categories: the proportion of population with impoverishing out-of-pocket health expenditure and the proportion of the population with large but non-impoverishing out-of-pocket health expenditure\n(i) Impoverishing out-of-pocket health expenditure\nOut-of-pocket health expenditure is defined as impoverishing if it exceeds 100% of household discretionary budget. This is spending that leaves the household with no discretionary budget, or with inability to meet basic needs, once out-of-pocket health expenditure is subtracted from their discretionary budget. For households with negative discretionary budgets (those living in societal poverty), any OOP health spending exceeds 100% of the household discretionary budget. \nThe proportion of population incurring impoverishing out-of-pocket health expenditure can be further decomposed into the proportion of population further impoverished and the proportion pushed into poverty due to out-of-pocket health expenditure. \nOut-of-pocket health expenditure is defined as further impoverishing for households that live in societal poverty (household total consumption or income lies below the societal poverty line) and incur any out-of-pocket health expenditure.\nOut-of-pocket health expenditure is considered as pushing into poverty if households with positive discretionary budgets spend all their discretionary budget on out-of-pocket health expenditure. In other words, household total consumption (or income) is above the societal poverty line, but its total consumption (or income) net of out-of-pocket health expenditure falls below the societal poverty line, pushing the household into societal poverty.\n(ii) Large (but not impoverishing out-of-pocket) health spending\nOut-of-pocket health spending is defined as large (but non-impoverishing) if it exceeds 40% but remains below 100% of household discretionary budget. This implies ability to meet basic needs but a substantial reduction in ability to consume other goods and services due to the need to pay for health care." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Universal Health Coverage Dataset, World Health Organization (WHO) [WHO];\nWorld Bank (WB) [WB], note: Universal Health Coverage Dataset" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Proportion of population pushed into poverty due to out-of-pocket health expenditure (%) is measured as the population-weighted average of the number of people living in households with positive discretionary budgets that spend all their discretionary budget on out-of-pocket health expenditure.\nStatistical concept(s): The household discretionary budget is defined as total household consumption expenditure or income minus the societal poverty line (SPL). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health. Households living in societal poverty, have a negative discretionary budget.\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home). This excludes any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Refer to Other notes for details on out-of-pocket health expenditure. \nThe recommended data sources for monitoring this indicator are household budget surveys and household income and expenditure surveys, which provide information on both household consumption expenditure on health and total household consumption expenditures, and are routinely conducted by National Statistical Offices (NSOs). Preference is given for the use of consumption expenditure as a measure or household budget and discretionary budget, with income being used when consumption expenditure data is unavailable.\nTo compute regional and global aggregates for a common reference year, survey-based country estimates are first “lined-up” using a combination of interpolation/extrapolation (when there are at least two survey-based estimates available within 5 years around the reference year); econometric modelling (one survey-based estimate available around the reference year); and imputation based on income group medians (no survey-based estimates available around the reference year) (see also SDG 3.8.2 metadata description (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-02.pdf), sections 4.f and 4.g)." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SH_UHC_SCI", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "SDG Target 3.8 is defined as “Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all”. The objective is for all people and communities to receive the quality health services they need (including medicines and other health products), without financial hardship. Two indicators have been chosen to monitor target 3.8 within the SDG framework. Indicator 3.8.1, the universal health service coverage index, is for service coverage of essential health services while indicator 3.8.2 focuses on financial hardship caused by out-of-pocket health expenditure.\n\nIndicators of service coverage – defined as people receiving the service they need – are the best way to track progress in providing services under universal health coverage (UHC). Since a single health service indicator does not suffice for monitoring UHC, the UHC service coverage index is constructed from?14?tracer indicators (further organized into 4 health service areas) selected based on epidemiological and statistical criteria. This includes several indicators that are already included in other SDG targets, thereby minimizing the data collection and reporting burden.?The index is reported on a?unitless?scale of 0 to 100, with 100 being the?optimal?value." }, { "id": "IndicatorName", "value": "UHC service coverage index" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "A composite index representing coverage of essential health services based on 14 tracer indicators in the areas of reproductive, maternal, newborn and child health, infectious diseases, noncommunicable diseases and service capacity and access. It is presented on a scale of 0 to 100 index points." }, { "id": "Othernotes", "value": "Statistical concept(s): The calculation the UHC service coverage index requires first standardizing the 14 tracer indicators so that they can be combined into the index, and then computing the index from those values. \nThe 14 tracer indicators are first all placed on the same scale, with 0 being the lowest value and 100 being the optimal value. For most indicators, this scale is the natural scale of measurement, e.g., the percentage of infants who have been immunized ranges from 0 to 100 percent. However, for a few indicators, conversion and/or rescaling is required to obtain appropriate values from 0 to 100. Once all tracer indicator values are on a scale of 0 to 100, weighted geometric means are computed within each of the four health service areas, and then a geometric mean is taken of those four values. Each tracer is weighted on an indicator specific population based on the denominator of the indicator using United Nations population estimates where applicable. \nMany of the tracer indicators of health service coverage are measured by household surveys. However, administrative data, facility data, facility surveys, and sentinel surveillance systems are utilized for certain indicators. Underlying data sources for each of the 14 tracer indicators are explained in Annex 1 (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf)" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Universal Health Coverage Dataset, World Health Organization (WHO) [WHO], uri: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/uhc-index-of-service-coverage" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The UHC service coverage index is computed as the weighted geometric means of 14 tracer indicators, as follows, organized by four broad categories of service coverage:\nI. Reproductive, maternal, newborn and child health \n1. Family planning: Percentage of women of reproductive age (15-49 years) who have their need for family planning satisfied with modern methods (SDG indicator 3.7.1, metadata available at https://unstats.un.org/sdgs/metadata/files/Metadata-03-07-01.pdf)\n2. Pregnancy care: Percentage of women aged 15-49 years with a live birth in a given time period who received antenatal care four or more times\n3. Child immunization: Percentage of infants receiving three doses of diphtheria-tetanus-pertussis containing vaccine\n4. Child treatment: Percentage of children younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing due to a problem in the chest and not due to a blocked nose only) in the 2 weeks preceding the survey for whom advice or treatment was sought from a health facility or provider \nII. Infectious diseases\n5. Tuberculosis: Percentage of incident TB cases that are detected and treated\n6. HIV/AIDS: Percentage of adults and children living with HIV currently receiving antiretroviral therapy \n7. Malaria: Percentage of population in malaria-endemic areas who slept under an insecticide-treated net the previous night [only for countries with high malaria burden]\n8. Water, sanitation and hygiene: Percentage of population using at least basic sanitation services.\nIII. Noncommunicable diseases\n9. Hypertension: Coverage of treatment (taking medicine) for hypertension among adults aged 30-79 years with hypertension (age-standardized estimate) (%) \n10. Diabetes: Coverage of treatment (taking medication) for diabetes among adults aged 30 years and over with diabetes (age-standardized estimate) (%) \n11. Tobacco: Age-standardized prevalence of adults >=15 years currently using any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis (SDG indicator 3.a.1, metadata available at https://unstats.un.org/sdgs/metadata/files/Metadata-03-0a-01.pdf)\nIV. Service capacity and access\n12. Hospital access: Hospital beds density, relative to a maximum threshold of 18 per 10,000 population? \n13. Health workforce: Health workers (medical doctors, nursing and midwifery personnel) per capita, relative to a combined maximum thresholds (overlap with SDG indicator 3.c.1, see metadata at https://unstats.un.org/sdgs/metadata/files/Metadata-03-0C-01.pdf) \n14. Health security: International Health Regulations (IHR) core capacity index, which is the average percentage of attributes of 13 core capacities that have been attained?(SDG indicator 3.d.1, see metadata at https://unstats.un.org/sdgs/metadata/files/Metadata-03-0D-01.pdf)\nRefer to Other notes for the Statistical Concept(s)." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "index" } ], "source_id": "2" }, { "id": "SH_UHC_SCI_CAPACITY", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "SDG Target 3.8 is defined as “Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all”. The objective is for all people and communities to receive the quality health services they need (including medicines and other health products), without financial hardship. Two indicators have been chosen to monitor target 3.8 within the SDG framework. Indicator 3.8.1, the universal health service coverage index, is for service coverage of essential health services while indicator 3.8.2 focuses on financial hardship caused by out-of-pocket health expenditure.\n\nIndicators of service coverage – defined as people receiving the service they need – are the best way to track progress in providing services under universal health coverage (UHC). Since a single health service indicator does not suffice for monitoring UHC, the UHC service coverage index is constructed from?14?tracer indicators (further organized into 4 health service areas) selected based on epidemiological and statistical criteria. This includes several indicators that are already included in other SDG targets, thereby minimizing the data collection and reporting burden.?The index is reported on a?unitless?scale of 0 to 100, with 100 being the?optimal?value." }, { "id": "IndicatorName", "value": "UHC service coverage sub-index on service capacity and access" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "UHC service coverage sub-index on service capacity and access is based on three tracer indicators (i) hospital access, (ii) health workforce, (iii) health security. It is presented on a scale of 0 to 100 index points and is one of the four sub-indexes underlaying the overall UHC service coverage index." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Universal Health Coverage Dataset, World Health Organization (WHO) [WHO], uri: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/uhc-index-of-service-coverage" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The UHC service coverage index for service capacity and access is computed as the weighted geometric mean of the following three tracer indicators: (i) hospital access, (ii) health workforce, (iii) health security\nStatistical concept(s): The UHC service coverage sub-index on service capacity and access is one of the four sub-indexes underlaying the overall UHC service coverage index. It is computed as the weighted geometric mean of the following:\n1.?Hospital access: Hospital beds density, relative to a maximum threshold of 18 per 10,000 population? \n2.?Health workforce:?Health workers (medical doctors, nursing and midwifery personnel) per capita, relative to a combined maximum thresholds?(overlap with?SDG indicator 3.c.1, see metadata at https://unstats.un.org/sdgs/metadata/files/Metadata-03-0C-01.pdf)? \n3.?Health security:?International Health Regulations (IHR) core capacity index, which is the average percentage of attributes of 13 core capacities that have been attained?(SDG indicator 3.d.1, see metadata?at https://unstats.un.org/sdgs/metadata/files/Metadata-03-0D-01.pdf)? \nAll tracer indicators are first placed on the same scale, with 0 being the lowest value and 100 being the optimal value. For most indicators, this scale is the natural scale of measurement. However, for a few indicators, conversion and/or rescaling is required?to obtain?appropriate?values from 0 to 100. Once all?tracer indicator values?are on?a scale of 0 to 100, weighted geometric means are computed.? Each tracer indicator is weighted on an indicator specific population based on the denominator of the indicator using United Nations population estimates where applicable. \n\nMany of the tracer indicators of health service coverage are measured by household surveys. However, administrative data, facility data, facility surveys, and sentinel surveillance systems are utilized for certain indicators. Underlying data sources for each of the 14 tracer indicators are explained in Annex 1 (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf)" }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "index" } ], "source_id": "2" }, { "id": "SH_UHC_SCI_ID", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "SDG Target 3.8 is defined as “Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all”. The objective is for all people and communities to receive the quality health services they need (including medicines and other health products), without financial hardship. Two indicators have been chosen to monitor target 3.8 within the SDG framework. Indicator 3.8.1, the universal health service coverage index, is for service coverage of essential health services while indicator 3.8.2 focuses on financial hardship caused by out-of-pocket health expenditure.\n\nIndicators of service coverage – defined as people receiving the service they need – are the best way to track progress in providing services under universal health coverage (UHC). Since a single health service indicator does not suffice for monitoring UHC, the UHC service coverage index is constructed from?14?tracer indicators (further organized into 4 health service areas) selected based on epidemiological and statistical criteria. This includes several indicators that are already included in other SDG targets, thereby minimizing the data collection and reporting burden.?The index is reported on a?unitless?scale of 0 to 100, with 100 being the?optimal?value." }, { "id": "IndicatorName", "value": "UHC service coverage sub-index on infectious diseases" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "UHC service coverage sub-index on infectious diseases is based on four tracer indicators: (i) Tuberculosis, (ii) HIV/AIDS (iii) Malaria (iv) Water, sanitation and hygiene. It is presented on a scale of 0 to 100 index points and is one of the four sub-indexes underlaying the overall UHC service coverage index." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Universal Health Coverage Dataset, World Health Organization (WHO) [WHO], uri: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/uhc-index-of-service-coverage" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The UHC service coverage sub-index on infectious diseases is computed as the weighted geometric mean of the following four tracer indicators: (i) Tuberculosis, (ii) HIV/AIDS (iii) Malaria (iv) Water, sanitation and hygiene\nStatistical concept(s): The UHC service coverage sub-index on infectious diseases is one of the four sub-indexes underlaying the overall UHC service coverage index. It is computed as the weighted geometric mean of the following:\n1.?Tuberculosis:?Percentage of incident?TB cases that are detected and treated? \n2.?HIV/AIDS:?Percentage of adults and children living with HIV currently receiving antiretroviral therapy? \n3.?Malaria:?Percentage of population in malaria-endemic areas who slept under an?insecticide-treated net?the previous night [only for countries with high malaria burden]? \n4.?Water, sanitation and hygiene:?Percentage of population using?at least basic?sanitation services.? \n\nAll tracer indicators are first placed on the same scale, with 0 being the lowest value and 100 being the optimal value. For most indicators, this scale is the natural scale of measurement. However, for a few indicators, conversion and/or rescaling is required?to obtain?appropriate?values from 0 to 100. Once all?tracer indicator values?are on?a scale of 0 to 100, weighted geometric means are computed. Each tracer is weighted on an indicator specific population based on the denominator of the indicator using United Nations population estimates where applicable. \n\nMany of the tracer indicators of health service coverage are measured by household surveys. However, administrative data, facility data, facility surveys, and sentinel surveillance systems are utilized for certain indicators. Underlying data sources for each of the 14 tracer indicators are explained in Annex 1 (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf)" }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "index" } ], "source_id": "2" }, { "id": "SH_UHC_SCI_NCD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "SDG Target 3.8 is defined as “Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all”. The objective is for all people and communities to receive the quality health services they need (including medicines and other health products), without financial hardship. Two indicators have been chosen to monitor target 3.8 within the SDG framework. Indicator 3.8.1, the universal health service coverage index, is for service coverage of essential health services while indicator 3.8.2 focuses on financial hardship caused by out-of-pocket health expenditure.\n\nIndicators of service coverage – defined as people receiving the service they need – are the best way to track progress in providing services under universal health coverage (UHC). Since a single health service indicator does not suffice for monitoring UHC, the UHC service coverage index is constructed from?14?tracer indicators (further organized into 4 health service areas) selected based on epidemiological and statistical criteria. This includes several indicators that are already included in other SDG targets, thereby minimizing the data collection and reporting burden.?The index is reported on a?unitless?scale of 0 to 100, with 100 being the?optimal?value." }, { "id": "IndicatorName", "value": "UHC service coverage sub-index on non-communicable diseases" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "UHC service coverage sub-index on non-communicable diseases is based on three tracer indicators: (i) hypertension, (ii) diabetes, (iii) tobacco. It is presented on a scale of 0 to 100 index points and is one of the four sub-indexes underlaying the overall UHC service coverage index." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Universal Health Coverage Dataset, World Health Organization (WHO) [WHO], uri: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/uhc-index-of-service-coverage" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The UHC service coverage sub-index on non-communicable diseases is computed as the weighted geometric mean of the following three tracer indicators: (i) hypertension, (ii) diabetes, (iii) tobacco\nStatistical concept(s): The UHC service coverage sub-index on non-communicable diseases is one of the four sub-indexes underlaying the overall UHC service coverage index. It is computed as the weighted geometric mean of the following:\n1.?Hypertension:?Coverage of treatment (taking medicine) for hypertension among adults aged 30-79 years with hypertension (age-standardized estimate) (%) \n2.?Diabetes:? Coverage of treatment (taking medication) for diabetes among adults aged 30 years and over with diabetes (age-standardized estimate) (%) \n3.?Tobacco:?Age-standardized prevalence of adults >=15 years currently using any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis?(SDG indicator?3.a.1, metadata available at https://unstats.un.org/sdgs/metadata/files/Metadata-03-0a-01.pdf)? \n\nAll tracer indicators are first placed on the same scale, with 0 being the lowest value and 100 being the optimal value. For most indicators, this scale is the natural scale of measurement. However, for a few indicators, conversion and/or rescaling is required?to obtain?appropriate?values from 0 to 100. Once all?tracer indicator values?are on?a scale of 0 to 100, weighted geometric means are computed. Each tracer indicator is weighted on an indicator specific population based on the denominator of the indicator using United Nations population estimates where applicable. \n\nMany of the tracer indicators of health service coverage are measured by household surveys. However, administrative data, facility data, facility surveys, and sentinel surveillance systems are utilized for certain indicators. Underlying data sources for each of the 14 tracer indicators are explained in Annex 1 (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf)" }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "index" } ], "source_id": "2" }, { "id": "SH_UHC_SCI_RMNCH", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "SDG Target 3.8 is defined as “Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all”. The objective is for all people and communities to receive the quality health services they need (including medicines and other health products), without financial hardship. Two indicators have been chosen to monitor target 3.8 within the SDG framework. Indicator 3.8.1, the universal health service coverage index, is for service coverage of essential health services while indicator 3.8.2 focuses on financial hardship caused by out-of-pocket health expenditure.\n\nIndicators of service coverage – defined as people receiving the service they need – are the best way to track progress in providing services under universal health coverage (UHC). Since a single health service indicator does not suffice for monitoring UHC, the UHC service coverage index is constructed from?14?tracer indicators (further organized into 4 health service areas) selected based on epidemiological and statistical criteria. This includes several indicators that are already included in other SDG targets, thereby minimizing the data collection and reporting burden.?The index is reported on a?unitless?scale of 0 to 100, with 100 being the?optimal?value." }, { "id": "IndicatorName", "value": "UHC service coverage sub-index on reproductive, maternal, newborn and child health" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "UHC service coverage sub-index on reproductive, maternal,?newborn?and child health services is based on four tracer indicators: (i) family planning, (ii) pregnancy care, (iii) child immunization, (iv) child treatment. It is presented on a scale of 0 to 100 index points and is one of the four sub-indexes underlaying the overall UHC service coverage index." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Universal Health Coverage Dataset, World Health Organization (WHO) [WHO], uri: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/uhc-index-of-service-coverage" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The UHC service coverage sub-index on reproductive, maternal,?newborn?and child health services?is computed as the weighted geometric mean of the following four tracer indicators: (i) family planning, (ii) pregnancy care, (iii) child immunization, (iv) child treatment\nStatistical concept(s): The UHC service coverage sub-index on reproductive, maternal, newborn, and child health services is one of the four sub-indexes underlaying the overall UHC service coverage index. It is computed as the weighted geometric mean of the following: \n1.?Family planning:?Percentage of women of reproductive age (15-49 years) who have their need for family planning satisfied with modern methods? (SDG indicator?3.7.1, metadata available?at https://unstats.un.org/sdgs/metadata/files/Metadata-03-07-01.pdf)? \n2.?Pregnancy care:?Percentage of women aged 15-49 years with a live birth?in a given?time period who received antenatal care four or more times? \n3.?Child immunization:?Percentage of infants receiving three doses of diphtheria-tetanus-pertussis containing vaccine? \n4.?Child treatment:?Percentage of children younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing due to a problem in the chest and not due to a blocked nose only) in the 2 weeks preceding the survey for whom advice or treatment was sought from a health facility or provider \n\nAll tracer indicators are first placed on the same scale, with 0 being the lowest value and 100 being the optimal value. For most indicators, this scale is the natural scale of measurement. However, for a few indicators, conversion and/or rescaling is required?to obtain?appropriate?values from 0 to 100. Once all?tracer indicator values?are on?a scale of 0 to 100, weighted geometric means are computed.?Each tracer is weighted on an indicator specific population based on the denominator of the indicator using United Nations population estimates where applicable. \n\nMany of the tracer indicators of health service coverage are measured by household surveys. However, administrative data, facility data, facility surveys, and sentinel surveillance systems are utilized for certain indicators. Underlying data sources for each of the 14 tracer indicators are explained in Annex 1 (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf)." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "index" } ], "source_id": "2" }, { "id": "SI.DST.02ND.20", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Income share held by second 20%" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the second quintile, meaning the second 20% of people when ranked from lowest to highest income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.DST.03RD.20", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Income share held by third 20%" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the third quintile, meaning the third 20% of people when ranked from lowest to highest income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.DST.04TH.20", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Income share held by fourth 20%" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the fourth quintile, meaning the fourth 20% of people when ranked from lowest to highest income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.DST.05TH.20", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Income share held by highest 20%" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the fifth or top quintile, meaning the top 20% of people when ranked from lowest to highest income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.DST.10TH.10", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Income share held by highest 10%" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\nStatistical concept(s): The percentage of total income in a population that is held by the tenth or top decile, meaning the top 10% of people when ranked from lowest to highest income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.DST.50MD", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Proportion of people living below 50 percent of median income (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2021 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the tenth or top decile, meaning the top 10% of people when ranked from lowest to highest income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.DST.FRST.10", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Income share held by lowest 10%" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. No adjustment has been made for spatial differences in cost of living within countries, because the data needed for such calculations are generally unavailable. For further details on the estimation method for low- and middle-income economies, see Ravallion and Chen (1996).\n\n\n\n\n\n\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected.\nStatistical concept(s): The percentage of a population whose income or consumption falls below half of the median in their country, essentially indicating the level of \"relative poverty\" within a society. It reflects the share of the population whose income or consumption is less than half of the typical standard in their society, highlighting income inequality at the lower end of the distribution." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.DST.FRST.20", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Income share held by lowest 20%" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the bottom quintile, meaning the bottom 20% of people when ranked from lowest to highest income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.POV.DDAY", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. The World Bank defines extreme poverty as living on less than $3.00 a day (adjusted for purchasing power differences across countries). The value of $3.00 is the typical poverty line of low-income countries, which is the minimum amount of money people in low-income countries need to cover their daily basic needs, including food, clothing, and shelter. The share of population living on less than $3.00 a day is the first indicator the World Bank tracks in its Bank’s expanded vision indicators to create a world free of poverty in a livable planet. It is also the indicator the UN tracks for SDG 1.1. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at $3.00 a day (2021 PPP) (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty headcount ratio at $3.00 a day is the percentage of the population living on less than $3.00 a day at 2021 purchasing power adjusted prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\n\n\n\n\n\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.\n\n\n\n\n\n\n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\n\n\n\n\n\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): Poverty headcount ratio at $3.00 a day refers to the percentage of a population whose consumption or income per day falls short of the international poverty line of $3.00 a day (adjusted for purchasing power parity differences across countries), the poverty line typical of low-income countries." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.POV.GAPS", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The poverty gap measures the depth of poverty—that is, how far below the poverty line extreme poor are living. The poverty gap measure is used to estimate the total value of monetary transfers that could lift the poor out of poverty, assuming poverty is transitory and there are no administrative costs of transfers." }, { "id": "IndicatorName", "value": "Poverty gap at $3.00 a day (2021 PPP) (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty gap at $3.00 a day (2021 PPP) is the mean shortfall in income or consumption from the poverty line $3.00 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.Since World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in June 2025, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.Early editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, 2011, 2021, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the median of the poverty lines found in 23 low-income countries. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.The statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): The poverty gap measures the average shortfall in income or consumption of individuals living below the international poverty line of $3.00 per day, adjusted to 2021 purchasing power parity (PPP), expressed as a percentage of that poverty line. It essentially measures the depth of poverty—that is, how far the extreme poor are living below the poverty line." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.POV.GINI", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group's vision of promoting shared prosperity includes a measure that tracks the number of economies with high inequality, defined as those with a Gini index greater than 0.4" }, { "id": "IndicatorName", "value": "Gini index" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Gini coefficients are not unique. It is possible for two different Lorenz curves to give rise to the same Gini coefficient. Furthermore it is possible for the Gini coefficient of a developing country to rise (due to increasing inequality of income) while the number of people in absolute poverty decreases. This is because the Gini coefficient measures relative, not absolute, wealth.\n\n\n\n\n\n\n\nAnother limitation of the Gini coefficient is that it is not additive across groups, i.e. the total Gini of a society is not equal to the sum of the Gini's for its sub-groups. Thus, country-level Gini coefficients cannot be aggregated into regional or global Gini's, although a Gini coefficient can be computed for the aggregate.\n\n\n\n\n\n\n\nBecause the underlying household surveys differ in methods and types of welfare measures collected, data are not strictly comparable across countries or even across years within a country. Two sources of non-comparability should be noted for distributions of income in particular. First, the surveys can differ in many respects, including whether they use income or consumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much better welfare indicator, particularly in developing countries. Second, households differ in size (number of members) and in the extent of income sharing among members. And individuals differ in age and consumption needs. Differences among countries in these respects may bias comparisons of distribution. \n\n\n\n\n\n\n\nWorld Bank staff have made an effort to ensure that the data are as comparable as possible. Wherever possible, consumption has been used rather than income. Income distribution and Gini indexes for high-income economies are calculated directly from the Luxembourg Income Study database, using an estimation method consistent with that applied for developing countries." }, { "id": "Longdefinition", "value": "Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.\n\n\n\n\n\n\n\nThe Gini index provides a convenient summary measure of the degree of inequality. Data on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\nStatistical concept(s): The Gini index is the average of all pairwise absolute differences between individual consumption or income, normalized by twice the mean. More intuitively, the Gini index is the average share of mean consumption or income that needs to be transferred between two randomly selected individuals to achieve equality. A Gini index of 1 represents perfect inequality, in which total consumption or income goes to one individual. A Gini index of 0 indicates represents perfect equality, in which all individuals have the same level of consumption or income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.POV.LMIC", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank defines the poverty line of $4.20 (adjusted for purchasing power differences across countries) as a more relevant poverty line to monitor poverty in lower-middle-income countries. The value of $4.20 is the typical poverty line of lower-middle-income countries, which is the estimated minimum amount of money people in lower-middle-income countries need to cover their daily basic needs, including food, clothing, shelter, heath care, education, and so on. Policy dialogue can be facilitated with this poverty line, especially in middle-income countries." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at $4.20 a day (2021 PPP) (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty headcount ratio at $4.20 a day is the percentage of the population living on less than $4.20 a day at 2021 international prices." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\n\n\n\n\n\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.\n\n\n\n\n\n\n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\n\n\n\n\n\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): Poverty headcount ratio at $4.20 a day refers to the percentage of a population whose consumption or income per day falls short of $4.20 a day (adjusted for purchasing power parity differences across countries). $4.20 is the typical poverty line of lower-middle-income countries." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.POV.LMIC.GP", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The poverty gap measures the depth of poverty—that is, how far below the lower-middle-income poverty line the poor are living. The poverty gap measure is used to estimate the total value of monetary transfers that could lift the poor out of poverty, assuming poverty is transitory and there are no administrative costs of transfers." }, { "id": "IndicatorName", "value": "Poverty gap at $4.20 a day (2021 PPP) (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty gap at $4.20 a day (2021 PPP) is the mean shortfall in income or consumption from the poverty line $4.20 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\n\n\n\n\n\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.\n\n\n\n\n\n\n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\n\n\n\n\n\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): The poverty gap at $4.20 measures the average shortfall in income or consumption of individuals living below the poverty line of $4.20 per day, adjusted to 2021 purchasing power parity (PPP), expressed as a percentage of that poverty line. It essentially measures the depth of poverty—that is, how far the poor are living below the lower-middle-income poverty line." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.POV.MPUN", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The global MPI was developed by Sabina Alkire and Maria Emma Santos (2014), in collaboration with the Human Development Report Office (HDRO) at UNDP as an internationally comparable measure of acute poverty. Technically, the global MPI relies on the Alkire-Foster method (2011). In 2018, five out of the ten indicators have been revised to better align with the SDGs (see Alkire, Kanagaratnam, Nogales and Suppa 2022; Alkire and Kanagaratnam 2020; Alkire and Jahan 2018)." }, { "id": "IndicatorName", "value": "Multidimensional poverty headcount ratio (UNDP) (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Besides the frequency and timeliness of survey data, other limitations include if the household survey data being used is missing any of the 10 indicators, that indicator is dropped from the calculation. The weights are then adjusted so that each dimension continues to be given a weight of one-third. MPI poverty estimates are only calculated if at least one indicator in health and education dimensions is available, and if at least four indicators in the living standards dimension are available." }, { "id": "Longdefinition", "value": "The multidimensional poverty headcount ratio (UNDP) is the percentage of a population living in poverty according to UNDPs multidimensional poverty index. The index includes three dimensions -- health, education, and living standards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2013-2023" }, { "id": "Source", "value": "Alkire, S., Mishra, R., Selden, L. and Suppa, N. (2025). ‘The global Multidimensional Poverty Index (MPI) 2025: Country results and methodological note’, OPHI MPI Methodological Note 61, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford.url: https://ophi.org.uk/publications/MN-61, uri: https://ophi.org.uk/publications/MN-61, publisher: Oxford Poverty and Human Development Initiative (OPHI)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The global MPI, published annually since 2010, captures acute multidimensional poverty in developing regions of the world (Alkire and Santos, 2014). This measure is based on the dual-cutoff counting methodology developed by Alkire and Foster (2011). The global MPI is composed of three dimensions (health, education, and living standards) and 10 corresponding indicators (nutrition, child mortality, school attendance, years of schooling, electricity, drinking water, sanitation, cooking fuel, housing, and assets). The global MPI begins by constructing a deprivation profile for each household and person in it that tracks deprivations in each of the 10 indicators. For example, a household and all people living in it are deprived if any child is stunted or any child or adult for whom data are available is underweight; if any child died in the past five years; if any school-aged child is not attending school up to the age at which he or she would complete class 8 or no household member has completed six years of schooling; or if the household lacks access to electricity, an improved source of drinking water within a 30 minute walk round trip, an improved sanitation facility that is not shared, nonsolid cooking fuel, durable housing materials, and basic assets such as a radio, animal cart, phone, television, computer, refrigerator, bicycle or motorcycle. All indicators are equally weighted within each dimension, so the health and education indicators are weighted 1/6 each, and the standard of living indicators are weighted 1/18 each. A person’s deprivation score is the sum of the weighted deprivations she or he experiences. The global MPI identifies people as multidimensionally poor if their deprivation score is 1/3 or higher. MPI values are the product of the incidence (H, or the proportion of population who live in multidimensional poverty) and intensity of poverty (A, or the average deprivation score among multidimensionally poor people). MPI = H × A. The MPI ranges from 0 to 1, and higher values imply higher poverty. MPI values decline when fewer people are poor or when poor people have fewer deprivations.\nStatistical concept(s): The UNDP's multidimensional poverty index (MPI) summarizes the incidence of multiple dimensions of poverty in a country. The MPI assesses deprivation in three dimensions of well-being (health, education, and living standards) and 10 corresponding indicators (nutrition, child mortality, school attendance, years of schooling, and access to electricity, safe drinking water, improved sanitation, good cooking fuel, good housing infrastructure)." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.POV.MPWB", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The first Sustainable Development Goal calls for ending poverty in all forms by 2030. Poverty is multidimensional, containing both monetary and non-monetary dimensions. The World Bank's multidimensional poverty measure captures both elements, allowing for a more complete tracking of poverty in all forms" }, { "id": "IndicatorName", "value": "Multidimensional poverty headcount ratio (World Bank) (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "The multidimensional poverty headcount ratio (World Bank) is the percentage of a population living in poverty according to the World Bank's Multidimensional Poverty Measure. The Multidimensional Poverty Measure includes three dimensions – monetary poverty, education, and basic infrastructure services – to capture a more complete picture of poverty." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2008-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank's Multidimensional Poverty Measure (MPM) seeks to understand poverty beyond monetary deprivations by including access to education and basic infrastructure along with the monetary headcount ratio at the $3.00 international poverty line.\n\n\n\nThe measure takes inspiration and guidance from other prominent global multidimensional measures, particularly the Multidimensional Poverty Index (MPI) developed by the United Nations Development Programme (UNDP) and Oxford University but differs from them in one important aspect: it includes monetary poverty less than $3.00 per day, the new International Poverty Line at 2021 PPP (Purchasing Power Parity), as one of the dimensions. \n\n\n\nThe MPM is composed of six indicators: consumption or income, educational attainment, educational enrollment, drinking water, sanitation, and electricity. These are mapped into three dimensions of well-being: monetary, education, and basic infrastructure services.\n\n\n\nThe three MPM dimensions are weighted equally, and within each dimension each indicator is also weighted equally. Individuals are considered multidimensionally deprived if they fall short of the threshold in at least one dimension or in a combination of indicators equivalent in weight to a full dimension. In other words, households will be considered poor if they are deprived in indicators whose weight adds up to 1/3 or more. Because the monetary dimension is measured using only one indicator, anyone who is income poor is automatically also poor under the multidimensional poverty measure. \n\n\n\nSummarizing the information on the different deprivations into a single index proves useful in making comparisons across populations and across time. However, any aggregation of indicators into a single index always involves a decision on how each indicator is to be weighted.\nStatistical concept(s): The World Bank’s multi-dimensional poverty measure (MPM) summarizes the incidence of multiple dimensions of poverty in a country. It captures the idea that poverty is multi-faceted, including both monetary and non-monetary aspects. It assesses deprivation in three dimensions of well-being (monetary poverty, education, and basic infrastructure) and 6 corresponding indicators (monetary poverty, educational attainment, educational enrollment, access to electricity, access to improved sanitation, access to safe drinking water)." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.POV.NAHC", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The poverty rate as defined by national poverty lines reflects the share of the population that fails to meet the standard a country thinks is necessary to cover basic needs (typically in low- and middle-income countries) or afford a decent lifestyle (typically in high-income countries). SDG 1.2 aims to reduce by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions, by 2030." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at national poverty lines (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "National poverty headcount ratio is the percentage of the population living below the national poverty line(s). National estimates are based on population-weighted subgroup estimates from household surveys. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year." }, { "id": "Othernotes", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines., World Bank (WB), note: Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Poverty headcount ratio among the population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\n\n\n\n\n\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income. \n\n\n\n\n\n\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies. \n\n\n\n\n\n\n\nAlmost all national poverty lines in developing economies are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. \n\n\n\n\n\n\n\nThis series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year. For all other economies, the year reported is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which data collection started.\nStatistical concept(s): National poverty headcount ratio refers to the percentage of a population whose consumption or income per day falls short of the national poverty line. National poverty lines vary by country and over time. In low- and middle-income countries, national poverty lines tend to be absolute poverty lines, thus reflecting the estimated minimum amount of money needed to cover basic needs. In high-income countries, national poverty lines tend to be relative poverty lines, thus reflecting the typical amount of money needed for an individual to afford the typical standard of living and without any restraints to participating fully in the societies in which they live. National poverty lines tend to grow with economic growth, especially in high-income or upper-middle-income countries." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.POV.SOPO", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The first Sustainable Development Goal calls for ending poverty in all forms by 2030. Typically, countries' poverty lines increase in real value as their economies get richer. Essentially, this is because in a richer country it is costlier to participate in society (i.e., be considered non-poor). Yet as relative poverty lines can take on very low values for poor countries, one may want to ensure a lower bound which provides a fixed, absolute element to the SPL, which the study interprets as the cost of consuming some minimum bundle of goods. The Societal Poverty Line tracks poverty rates consistent with these considerations." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at societal poverty line (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2021 U.S. dollars and defined as max($3.00, $1.30 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $3.00. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Societal Poverty Line (SPL) adopted by the World Bank is calculated in 2021 PPP U.S. dollars as follows: SPL = max (US$3.00, US$1.30 + 0.5 median consumption). By this definition, societal poverty represents a combination of extreme poverty, which is fixed in value for everyone, and a relative dimension of well-being that differs in every country depending on the median level of consumption in that country. In countries with low median consumption (less than US$2.00 per person per day), a rise in median consumption does not change the SPL. Indeed, the SPL has the same value as the IPL in all countries with median consumption up to $3.40. However, as countries with median consumption at more than US$3.40 become richer, and the median consumption increases, the value of the SPL also rises. The slope of one-half, the rate at which the SPL is rising as countries become richer, comes from the empirical association observed between national poverty lines and different measures of overall consumption in society. It indicates that, on average, the national poverty lines are increasing at a rate equal to half the median consumption in the countries. The slope of one-half and the intercept of US$1.30 are the values that most closely fit the data provided by the national poverty lines and overall consumption in each country. The SPL and the International Poverty Line (IPL) share the same empirical underpinning. Both are anchored in the distribution of national poverty lines, which represent countries’ own judgements of what poverty means for them. Whereas the IPL focuses narrowly—and deliberately—on the choices of some of the poorest countries, the SPL is built on information from across the whole range of levels of development. In addition to fitting the data well, the slope coefficient of half the median is widely used by many countries and organizations as a measure of relative poverty and inclusion.\nStatistical concept(s): Poverty headcount ratio at societal poverty line measures the share of the population living below the societal poverty line. The societal poverty line combines elements of both absolute and relative poverty. A minimum amount of money is required for subsistence (absolute poverty), while an additional amount of money is required to afford the typical lifestyle expected in the society in which people live (relative poverty). The societal poverty line is expressed as: max (US$3.00, US$1.30 + 0.5 median consumption) in 2021 PPP U.S. dollars. $1.30 represents the standard for absolute poverty, while 0.5 median consumption represents the standard for relative poverty. In very poor countries, typically with median consumption up to $3.40, it makes more sense to consider that poverty is entirely absolute, so that the effective societal poverty line becomes the international poverty line of $3.00." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.POV.UMIC", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank defines a higher poverty line of $8.30, in addition to the international poverty line of $3.00, to account for higher living standards in a changing world (these values adjust for purchasing power differences across countries). The value of $8.30 is the typical poverty line of upper-middle-income countries, which is the estimated minimum amount of money people in upper-middle-income countries need to cover their daily basic needs, including food, clothing, shelter, heath care, education, and so on. The share of population living on less than $8.30 a day is the second indicator the World Bank tracks in its expanded vision indicators to create a world free of poverty in a livable planet. A growing majority of the world’s population live in middle-income countries (for example, about three-quarters in 2024 compared to one-quarter in 1990), so a higher poverty line would be more representative of the word’s current demographic structure. Further, a higher standard of poverty would be necessary to build resilience in a world more susceptible to climate-related risks." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at $8.30 a day (2021 PPP) (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty headcount ratio at $8.30 a day is the percentage of the population living on less than $8.30 a day at 2021 international prices." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.Since World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.Early editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.The statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): Poverty headcount ratio at $8.30 a day refers to the percentage of a population whose consumption or income per day falls short of $8.30 a day (adjusted for purchasing power parity differences across countries). $8.30 is the typical poverty line of upper-middle-income countries." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.POV.UMIC.GP", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The poverty gap measures the depth of poverty—that is, how far below the upper-middle-income poverty line the poor are living. The poverty gap measure is used to estimate the total value of monetary transfers that could lift the poor out of poverty, assuming poverty is transitory and there are no administrative costs of transfers." }, { "id": "IndicatorName", "value": "Poverty gap at $8.30 a day (2021 PPP) (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty gap at $8.30 a day (2021 PPP) is the mean shortfall in income or consumption from the poverty line $8.30 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.Since World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.Early editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.The statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): The poverty gap at $8.30 measures the average shortfall in income or consumption of individuals living below the poverty line of $8.30 per day, adjusted to 2021 purchasing power parity (PPP), expressed as a percentage of that poverty line. It essentially measures the depth of poverty—that is, how far the poor are living below the upper-middle-income poverty line." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.RMT.COST.IB.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Reducing the cost of remittance transactions has a direct impact on development by freeing additional resources that, instead of being paid as transaction cost, will remain with the senders and receivers of the flows. Remittance cost is highlighted in Sustainable Development Goal 10. Target 10.c calls for reducing to less than 3 percent the transaction costs of migrant remittances and ensure that in no corridor remittance senders are required to pay more than 5 percent by 2030." }, { "id": "IndicatorName", "value": "Average transaction cost of sending remittances to a specific country (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Remittance service providers (RSPs) are excluded when they do not disclose the exchange rate applied to the transaction" }, { "id": "Longdefinition", "value": "Average transaction cost of sending remittance to a specific country is the average of the total transaction cost in percentage of the amount sent for sending USD 200 charged by each single remittance service provider (RSP) included in the Remittance Prices Worldwide (RPW) database to a specific country." }, { "id": "Periodicity", "value": "Quarterly (represented as Annual)" }, { "id": "Referenceperiod", "value": "2016-2023" }, { "id": "Source", "value": "Remittance Prices Worldwide, World Bank (WB), uri: http://remittanceprices.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank calculates and tracks the global average cost for sending remittances following each iteration of Remittance Prices Worldwide (RPW). This is intended to provide a tool to track the trend of remittance prices by various policy makers, including measuring progress towards the commitment by the G8 member countries to reduce the cost of remittances by five percentage points over five years (the “5x5 Objective”), as well as the commitment by the G20 member countries to also reduce the global average to 5 percent. The Global Average Total Cost is calculated as the average total cost for sending USD 200 with all remittance service providers (RSPs) worldwide. In other terms, the global average total cost is the simple average of the total cost for sending USD 200 charged by each single RSP included in the RPW database, expressed as the percentage of the amount sent. The regional and national average total costs are calculated using the same methodology used to calculate the Global Average Total Cost. These represent the simple average total cost for sending USD 200 with every single RSP to a specific region of the world (regional), or to a specific country (national). The reference years reflect third quarter data here; for example, data for 2016 refers to data in the third quarter of the year. For all quarterly data, visit http://remittanceprices.worldbank.org." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "2" }, { "id": "SI.RMT.COST.OB.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Reducing the cost of remittance transactions has a direct impact on development by freeing additional resources that, instead of being paid as transaction cost, will remain with the senders and receivers of the flows. Remittance cost is highlighted in Sustainable Development Goal 10. Target 10.c calls for reducing to less than 3 percent the transaction costs of migrant remittances and ensure that in no corridor remittance senders are required to pay more than 5 percent by 2030." }, { "id": "IndicatorName", "value": "Average transaction cost of sending remittances from a specific country (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Remittance service providers (RSPs) are excluded when they do not disclose the exchange rate applied to the transaction." }, { "id": "Longdefinition", "value": "Average transaction cost of sending remittance from a specific country is the average of the total transaction cost in percentage of the amount sent for sending USD 200 charged by each single remittance service provider (RSP) included in the Remittance Prices Worldwide (RPW) database from a specific country." }, { "id": "Periodicity", "value": "Quarterly (represented as Annual)" }, { "id": "Referenceperiod", "value": "2016-2023" }, { "id": "Source", "value": "Remittance Prices Worldwide, World Bank (WB), uri: http://remittanceprices.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank calculates and tracks the global average cost for sending remittances following each iteration of Remittance Prices Worldwide (RPW). This is intended to provide a tool to track the trend of remittance prices by various policy makers, including measuring progress towards the commitment by the G8 member countries to reduce the cost of remittances by five percentage points over five years (the “5x5 Objective”), as well as the commitment by the G20 member countries to also reduce the global average to 5 percent. The Global Average Total Cost is calculated as the average total cost for sending USD 200 with all remittance service providers (RSPs) worldwide. In other terms, the global average total cost is the simple average of the total cost for sending USD 200 charged by each single RSP included in the RPW database, expressed as the percentage of the amount sent. The regional and national average total costs are calculated using the same methodology used to calculate the Global Average Total Cost. These represent the simple average total cost for sending USD 200 with every single RSP from a specific region of the world (regional), or from a specific country (national). The same applies to other averages such as the G8 average, which calculates the average cost of sending USD 200 from the G8 member countries, or the bank average, which represent the average cost of sending USD 200 with a bank worldwide. The reference years reflect third quarter data here; for example, data for 2016 refers to data in the third quarter of the year. For all quarterly data, visit http://remittanceprices.worldbank.org." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "2" }, { "id": "SI.SPR.PC40", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures improvements in the well-being of the poor, thus monitoring inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Survey mean consumption or income per capita, bottom 40% of population (2021 PPP $ per day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Longdefinition", "value": "Mean consumption or income per capita (2021 PPP $ per day) of the bottom 40%, used in calculating the growth rate in the welfare aggregate of the bottom 40% of the population in the income distribution in a country." }, { "id": "Othernotes", "value": "The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey mean consumption or income per capita, bottom 40% of population measures the amount of consumption or income per person per day in the bottom 40% of the population. It is estimated from survey data and expressed in 2021 PPP dollars.\nStatistical concept(s): Survey mean consumption or income per capita, bottom 40% of population measures the amount of consumption or income per person per day in the bottom 40% of the population. It is estimated from survey data and expressed in 2021 PPP dollars." }, { "id": "Topic", "value": "Poverty: Shared prosperity" }, { "id": "Unitofmeasure", "value": "2021 PPP $" } ], "source_id": "2" }, { "id": "SI.SPR.PC40.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures improvements in the well-being of the poor, thus monitoring inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Longdefinition", "value": "The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2021 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered." }, { "id": "Othernotes", "value": "The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The annualized growth rate in per capita real survey mean consumption of the bottom 40% is computed in the following steps. First, obtain the mean consumption or income levels of the bottom 40% of the survey distribution in two different periods. The two survey data sets should be comparable - that is, they use a similar method of sampling, collecting data, and constructing the welfare aggregate. Second, the rate of change in the survey mean values of the bottom 40% is annualized.\nStatistical concept(s): The annualized growth rate in per capita real survey mean consumption or income of the bottom 40% measures the rate of change in per capita consumption or income of the bottom 40% in a year." }, { "id": "Topic", "value": "Poverty: Shared prosperity" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.SPR.PCAP", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group has a goal of promoting shared prosperity within and across countries. Growth is good for the poor and growth in poor countries reflects in improvements in the World Bank’s new shared prosperity measure, the Global Prosperity Gap." }, { "id": "IndicatorName", "value": "Survey mean consumption or income per capita, total population (2021 PPP $ per day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Longdefinition", "value": "Mean consumption or income per capita (2021 PPP $ per day) used in calculating the growth rate in the welfare aggregate of total population." }, { "id": "Othernotes", "value": "The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. \nStatistical concept(s): Survey mean consumption or income per capita, total population measures the amount of consumption or income per person per day in the population. It is estimated from survey data and expressed in 2021 PPP dollars." }, { "id": "Topic", "value": "Poverty: Shared prosperity" }, { "id": "Unitofmeasure", "value": "2021 PPP $" } ], "source_id": "2" }, { "id": "SI.SPR.PCAP.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group has a goal of promoting shared prosperity within and across countries. Growth is good for the poor and growth in poor countries reflects in improvements in the World Bank’s new shared prosperity measure, the Global Prosperity Gap." }, { "id": "IndicatorName", "value": "Annualized average growth rate in per capita real survey mean consumption or income, total population (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Longdefinition", "value": "The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2021 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered." }, { "id": "Othernotes", "value": "The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The annualized growth rate in per capita real survey mean consumption of the total population is computed in the following steps. First, obtain the mean consumption or income levels of the total population of the survey distribution in two different periods. The two survey data sets should be comparable - that is, they use a similar method of sampling, collecting data, and constructing the welfare aggregate. Second, the rate of change in the survey mean values of the total population is annualized.\nStatistical concept(s): The annualized growth rate in per capita real survey mean consumption or income measures the rate of change in per capita consumption or income changes in a year." }, { "id": "Topic", "value": "Poverty: Shared prosperity" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SI.SPR.PGAP", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group's mission is to end extreme poverty and boost shared prosperity on a livable planet. Boosting shared prosperity is key to ensure that development gains are shared across vulnerable groups. The goal of boosting shared prosperity is defined using the prosperity gap." }, { "id": "IndicatorName", "value": "Prosperity gap (average shortfall from a prosperity standard of $28/day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The average shortfall from a prosperity standard of $28 per day (adjusted for differences in purchasing power parity across countries). It is measured as the average factor by which incomes fall short of $28." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Prosperity Gap is a measure of shared prosperity. As a distribution-sensitive measure, the gap narrows when incomes increase anywhere and falls fastest when incomes of the very poorest increase: growth in income of a person earning $3.00 per day gets ten times more weight than growth in income of a person earning $28/day. Improvements (i.e., reductions) in the Prosperity Gap reflect increases in average income, reductions in inequality within countries, and (for global/regional aggregates) reductions in inequality between countries. It is estimated from nationally representative household surveys. When survey data are missing, in order to create global and regional aggregates, data are interpolated and extrapolated following the same methods that are used for global poverty estimates: https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html\n\n\nStatistical concept(s): The Prosperity Gap measures the average factor by which everyone’s incomes in a society needs to vary to reach a prosperity standard of $28 per day (expressed in 2021 PPP dollars). Consider the following example of five individuals earning $2, $7, $14, $28, and $56. Their incomes will have to vary by a factor of 14, 4, 2, 1, and 0.5, respectively, to achieve a prosperity standard of $28. If the world consisted of only these five individuals, the Global Prosperity Gap would be the average of these factors (4.3). As shown in this example, poorer individuals contribute more to the Global Prosperity Gap. $28 is roughly the per capita household income at which countries transition from upper-middle-income to high-income status." }, { "id": "Topic", "value": "Poverty: Shared prosperity" }, { "id": "Unitofmeasure", "value": "NA" } ], "source_id": "2" }, { "id": "SL.AGR.0714.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in agriculture, female (% of female economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Agriculture corresponds to division 1 (ISIC revision 2), categories A and B (ISIC revision 3), or category A (ISIC revision 4) and includes hunting, forestry, and fishing. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.AGR.0714.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in agriculture, male (% of male economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Agriculture corresponds to division 1 (ISIC revision 2), categories A and B (ISIC revision 3), or category A (ISIC revision 4) and includes hunting, forestry, and fishing. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.AGR.0714.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in agriculture (% of economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Agriculture corresponds to division 1 (ISIC revision 2), categories A and B (ISIC revision 3), or category A (ISIC revision 4) and includes hunting, forestry, and fishing. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.AGR.EMPL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in agriculture, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "2" }, { "id": "SL.AGR.EMPL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in agriculture, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "2" }, { "id": "SL.AGR.EMPL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in agriculture (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "2" }, { "id": "SL.EMP.1524.SP.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, female (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female population ages 15-24" } ], "source_id": "2" }, { "id": "SL.EMP.1524.SP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, female (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female population ages 15-24" } ], "source_id": "2" }, { "id": "SL.EMP.1524.SP.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, male (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male population ages 15-24" } ], "source_id": "2" }, { "id": "SL.EMP.1524.SP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, male (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male population ages 15-24" } ], "source_id": "2" }, { "id": "SL.EMP.1524.SP.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, total (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-24" } ], "source_id": "2" }, { "id": "SL.EMP.1524.SP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, total (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-24" } ], "source_id": "2" }, { "id": "SL.EMP.MPYR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Employers, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a \"self-employment jobs\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of employers to the total employed is calculated as follows: Employers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "2" }, { "id": "SL.EMP.MPYR.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Employers, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a \"self-employment jobs\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of employers to the total employed is calculated as follows: Employers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "2" }, { "id": "SL.EMP.MPYR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Employers, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a \"self-employment jobs\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of employers to the total employed is calculated as follows: Employers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "2" }, { "id": "SL.EMP.SELF.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Self-employed, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include sub-categories of employers, own-account workers and members of producers' cooperatives and contributing family workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of self-employed workers to the total employed is calculated as follows: Self-employed workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "2" }, { "id": "SL.EMP.SELF.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Self-employed, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include sub-categories of employers, own-account workers and members of producers' cooperatives and contributing family workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of self-employed workers to the total employed is calculated as follows: Self-employed workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "2" }, { "id": "SL.EMP.SELF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Self-employed, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include sub-categories of employers, own-account workers and members of producers' cooperatives and contributing family workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of self-employed workers to the total employed is calculated as follows: Self-employed workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "2" }, { "id": "SL.EMP.SMGT.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator provides a meaningful measure of the percentage of females who are employed in decision-making and management roles in government, large enterprises and institutions, thus providing some insight into women’s power in decision-making and in the economy, relative to men’s power." }, { "id": "IndicatorName", "value": "Female share of employment in senior and middle management (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The main limitation of this indicator is that it fails to capture the differences in the levels of responsibility of women in their respective managerial position, or the importance of the enterprises and organizations in which they are employed. Its quality is also significantly impacted by the reliability of the employment statistics by occupation at the two-digit level of the ISCO. Whenever data at the two-digit level of the ISCO are not available, data at the one-digit level could be used as a proxy, referring only to major group 1 of ISCO-08 or ISCO-88, rather than to also refer to major group 1 minus category 14 of ISCO-08 or major group 1 minus category 13 of ISCO-88. This implies referring to the female share in total management, rather than also to the female share in senior and middle management exclusively. This proxy should be used only in case of lack of availability of data at the two-digit level of the ISCO, as total management includes junior management, and women tend to be more represented in junior management positions than in senior or middle management positions, and thus, by referring only to total management one may over-estimate women’s impact in high-level decision-making roles." }, { "id": "Longdefinition", "value": "The female share of employment in senior and middle management conveys the number of women in management as a percentage of employment in management. Employment in management is defined based on the International Standard Classification of Occupations. This series refers to senior and middle management only, thus excluding junior management (category 1 in both ISCO-08 and ISCO-88 minus category 14 in ISCO-08 and minus category 13 in ISCO-88)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2025" }, { "id": "Source", "value": "Labour Market-related SDG Indicators database (ILOSDG), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data for this indicator is collected through labor force surveys or any other household survey which collects such data through a module on employment. Establishment/firm surveys or administrative records can also provide useful data on female-occupied management positions by ISCO groups. Surveys are conducted by national statistical offices or ministries of labor in countries.\nStatistical concept(s): Employment comprises all persons of working age who, during a short reference period (one week), were engaged in any activity to produce goods or provide services for pay or profit. For further clarification, see: Resolution concerning statistics of work, employment and labor underutilization (2013).\n\n\n\n\n\n\n\nEmployment in management is determined according to the categories of the latest version of the International Standard Classification of Occupations (ISCO-08), which organizes jobs into a clearly defined set of groups based on the tasks and duties undertaken in the job. For the purposes of this indicator, it is preferable to refer separately to senior and middle management only on one hand, and to total management (including junior management) on the other. Senior and middle management correspond to sub-major groups 11, 12 and 13 in ISCO-08 and sub-major groups 11 and 12 in ISCO-88. If statistics are not available disaggregated at the sub-major group level (two-digit level of ISCO), then major group 1 of ISCO-88 and ISCO-08 can be used as a proxy and the indicator would then refer only to total management (including junior management)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment in senior and middle management" } ], "source_id": "2" }, { "id": "SL.EMP.TOTL.SP.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, female (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female population ages 15+" } ], "source_id": "2" }, { "id": "SL.EMP.TOTL.SP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, female (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female population ages 15+" } ], "source_id": "2" }, { "id": "SL.EMP.TOTL.SP.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, male (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male population ages 15+" } ], "source_id": "2" }, { "id": "SL.EMP.TOTL.SP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, male (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male population ages 15+" } ], "source_id": "2" }, { "id": "SL.EMP.TOTL.SP.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "With the aim of promoting international comparability, statistics presented on ILOSTAT are based on standard international definitions wherever feasible and may differ from official national figures. This series is based on the 13th ICLS definitions. For time series comparability, it includes countries that have implemented the 19th ICLS standards." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\n\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, total (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Comparability of employment-to-population ratios across countries is affected most significantly by variations in the definitions used for the employment and population figures. Differences result from age coverage, such as the lower and upper bounds for labor force activity. Estimates of both employment and population are also likely to vary according to whether members of the armed forces are included. Another area with scope for measurement differences has to do with the national treatment of particular groups of workers. The international definition of employment calls for inclusion of all persons who worked for at least one hour during the reference period. Workers could be in paid employment or in self-employment, including in less obvious forms of work, some of which are dealt with in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeship or non-market production. The majority of exceptions to coverage of all persons employed in a labor force survey have to do with national variations to the international recommendation applicable to the alternate employment statuses. For example, some countries measure persons employed in paid employment only and some countries measure “all persons engaged”, meaning paid employees plus working proprietors who receive some remuneration based on corporate shares. Other possible variations to the norms pertaining to measurement of total employment include hours limits (beyond one hour) placed on contributing family members for inclusion in employment. Comparisons can also be problematic when the frequency of data collection varies. The range of information collection can run from one month to 12 months in a year. Given the fact that seasonality of various kinds is undoubtedly present in all countries, employment-to-population ratios can vary for this reason alone. Countries with employment-to-population ratios based on less than full-year survey periods can be expected to have ratios that are not directly comparable with those from full-year, month-by-month collections. For example, an annual average based on 12 months of observations, all other things being equal, is likely to be different from an annual average based on four (quarterly) observations." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\n\n\nEPR (%) = 100 x Persons employed / Working-age population\n\n\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\n\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total population ages 15+" } ], "source_id": "2" }, { "id": "SL.EMP.TOTL.SP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, total (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total population ages 15+" } ], "source_id": "2" }, { "id": "SL.EMP.VULN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks. The vulnerable employment rate, which is the share of vulnerable employment in total employment, was an indicator of the (now finished) Millennium Development Goals, under the employment, target on decent work." }, { "id": "IndicatorName", "value": "Vulnerable employment, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of vulnerable employment to the total employed is calculated as follows: (Contributing family workers + own-account workers)/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "2" }, { "id": "SL.EMP.VULN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks. The vulnerable employment rate, which is the share of vulnerable employment in total employment, was an indicator of the (now finished) Millennium Development Goals, under the employment, target on decent work." }, { "id": "IndicatorName", "value": "Vulnerable employment, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of vulnerable employment to the total employed is calculated as follows: (Contributing family workers + own-account workers)/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "2" }, { "id": "SL.EMP.VULN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks. The vulnerable employment rate, which is the share of vulnerable employment in total employment, was an indicator of the (now finished) Millennium Development Goals, under the employment, target on decent work." }, { "id": "IndicatorName", "value": "Vulnerable employment, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of vulnerable employment to the total employed is calculated as follows: (Contributing family workers + own-account workers)/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "2" }, { "id": "SL.EMP.WORK.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Data disaggregated by status in employment are provided according to the latest version of the International Standard Classification of Status in Employment (ICSE-93). Data may have been regrouped from the national classifications, which may not be strictly compatible with ICSE." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\n\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Wage and salaried workers, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of wage and salaried workers to the total employed is calculated as follows: Wage and salaried workers /Total employment x 100. \n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "2" }, { "id": "SL.EMP.WORK.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Wage and salaried workers, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of wage and salaried workers to the total employed is calculated as follows: Wage and salaried workers /Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "2" }, { "id": "SL.EMP.WORK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Wage and salaried workers, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of wage and salaried workers to the total employed is calculated as follows: Wage and salaried workers /Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "2" }, { "id": "SL.FAM.0714.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, unpaid family workers, female (% of female children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Unpaid family workers are people who work without pay in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.FAM.0714.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, unpaid family workers, male (% of male children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Unpaid family workers are people who work without pay in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.FAM.0714.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, unpaid family workers (% of children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Unpaid family workers are people who work without pay in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.FAM.WORK.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Contributing family workers, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of contributing family workers to the total employed is calculated as follows: Contributing family workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "2" }, { "id": "SL.FAM.WORK.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Contributing family workers, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of contributing family workers to the total employed is calculated as follows: Contributing family workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "2" }, { "id": "SL.FAM.WORK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Contributing family workers, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of contributing family workers to the total employed is calculated as follows: Contributing family workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "2" }, { "id": "SL.GDP.PCAP.EM.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Labor productivity is used to assess a country's economic ability to create and sustain decent employment opportunities with fair and equitable remuneration. Productivity increases obtained through investment, trade, technological progress, or changes in work organization can increase social protection and reduce poverty, which in turn reduce vulnerable employment and working poverty. Productivity increases do not guarantee these improvements, but without them - and the economic growth they bring - improvements are highly unlikely.\n\n\n\nGDP per person employed is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.2.1]" }, { "id": "IndicatorName", "value": "GDP per person employed (constant 2021 PPP $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output in both national currencies and purchasing power parity dollars is not easily available, especially in developing countries, because the definition, coverage, and methodology are not always consistent across countries. For example, countries employ different methodologies for estimating the missing values for the nonmarket service sectors and use different definitions of the informal sector." }, { "id": "Longdefinition", "value": "GDP per person employed is gross domestic product (GDP) divided by total employment in the economy. Purchasing power parity (PPP) GDP is GDP converted to 2021 constant international dollars using PPP rates. An international dollar has the same purchasing power over GDP that a U.S. dollar has in the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB), note: Estimates are based on employment, population, GDP, and PPP data obtained from International Labour Organization, United Nations Population Division, Eurostat, OECD, and World Bank., type: estimates based on external database;\nInternational Labour Organization (ILO);\nUnited Nations (UN), publisher: UN Population Division;\nEurostat (ESTAT);\nOrganisation for Economic Co-operation and Development (OECD);\nWorld Development Indicators database, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are based on employment, population, GDP, and PPP data obtained from International Labour Organization, United Nations Population Division, Eurostat, OECD, and World Bank. The employment rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): GDP per person employed represents labor productivity—output per unit of labor input. To compare labor productivity levels across countries, GDP is converted to international dollars using purchasing power parity rates which take account of differences in relative prices between countries." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "constant 2017 PPP $" } ], "source_id": "2" }, { "id": "SL.IND.EMPL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in industry, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "2" }, { "id": "SL.IND.EMPL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in industry, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "2" }, { "id": "SL.IND.EMPL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in industry (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "2" }, { "id": "SL.MNF.0714.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in manufacturing, female (% of female economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Manufacturing corresponds to division 3 (ISIC revision 2), category D (ISIC revision 3), or category C (ISIC revision 4). Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.MNF.0714.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in manufacturing, male (% of male economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Manufacturing corresponds to division 3 (ISIC revision 2), category D (ISIC revision 3), or category C (ISIC revision 4). Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.MNF.0714.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in manufacturing (% of economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Manufacturing corresponds to division 3 (ISIC revision 2), category D (ISIC revision 3), or category C (ISIC revision 4). Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.SLF.0714.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, self-employed, female (% of female children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Self-employed workers are people whose remuneration depends directly on the profits derived from the goods and services they produce, with or without other employees, and include employers, own-account workers, and members of producers cooperatives." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.SLF.0714.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, self-employed, male (% of male children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Self-employed workers are people whose remuneration depends directly on the profits derived from the goods and services they produce, with or without other employees, and include employers, own-account workers, and members of producers cooperatives." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.SLF.0714.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, self-employed (% of children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Self-employed workers are people whose remuneration depends directly on the profits derived from the goods and services they produce, with or without other employees, and include employers, own-account workers, and members of producers cooperatives." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.SRV.0714.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in services, female (% of female economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Services correspond to divisions 6-9 (ISIC revision 2), categories G-P (ISIC revision 3), or categories G-U (ISIC revision 4). Services include wholesale and retail trade, hotels and restaurants, transport, financial intermediation, real estate, public administration, education, health and social work, other community services, and private household activity. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.SRV.0714.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in services, male (% of male economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Services correspond to divisions 6-9 (ISIC revision 2), categories G-P (ISIC revision 3), or categories G-U (ISIC revision 4). Services include wholesale and retail trade, hotels and restaurants, transport, financial intermediation, real estate, public administration, education, health and social work, other community services, and private household activity. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.SRV.0714.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in services (% of economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Services correspond to divisions 6-9 (ISIC revision 2), categories G-P (ISIC revision 3), or categories G-U (ISIC revision 4). Services include wholesale and retail trade, hotels and restaurants, transport, financial intermediation, real estate, public administration, education, health and social work, other community services, and private household activity. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.SRV.EMPL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in services, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "2" }, { "id": "SL.SRV.EMPL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in services, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "2" }, { "id": "SL.SRV.EMPL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in services (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "2" }, { "id": "SL.TLF.0714.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, female (% of female children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.TLF.0714.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, male (% of male children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.TLF.0714.SW.FE.TM", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Average working hours of children, study and work, female, ages 7-14 (hours per week)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Average working hours of children studying and working refer to the average weekly working hours of those children who are attending school in combination with economic activity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "2" }, { "id": "SL.TLF.0714.SW.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, study and work, female (% of female children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Study and work refer to children attending school in combination with economic activity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.TLF.0714.SW.MA.TM", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Average working hours of children, study and work, male, ages 7-14 (hours per week)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Average working hours of children studying and working refer to the average weekly working hours of those children who are attending school in combination with economic activity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "2" }, { "id": "SL.TLF.0714.SW.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, study and work, male (% of male children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Study and work refer to children attending school in combination with economic activity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.TLF.0714.SW.TM", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Average working hours of children, study and work, ages 7-14 (hours per week)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Average working hours of children studying and working refer to the average weekly working hours of those children who are attending school in combination with economic activity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "2" }, { "id": "SL.TLF.0714.SW.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, study and work (% of children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Study and work refer to children attending school in combination with economic activity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.TLF.0714.WK.FE.TM", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Average working hours of children, working only, female, ages 7-14 (hours per week)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Average working hours of children working only refers to the average weekly working hours of those children who are involved in economic activity and not attending school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "2" }, { "id": "SL.TLF.0714.WK.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, work only, female (% of female children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Work only refers to children involved in economic activity and not attending school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.TLF.0714.WK.MA.TM", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Average working hours of children, working only, male, ages 7-14 (hours per week)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Average working hours of children working only refers to the average weekly working hours of those children who are involved in economic activity and not attending school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "2" }, { "id": "SL.TLF.0714.WK.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, work only, male (% of male children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Work only refers to children involved in economic activity and not attending school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.TLF.0714.WK.TM", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Average working hours of children, working only, ages 7-14 (hours per week)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Average working hours of children working only refers to the average weekly working hours of those children who are involved in economic activity and not attending school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "2" }, { "id": "SL.TLF.0714.WK.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, work only (% of children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Work only refers to children involved in economic activity and not attending school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.TLF.0714.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, total (% of children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.TLF.ACTI.1524.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, female (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of female population ages 15-24" } ], "source_id": "2" }, { "id": "SL.TLF.ACTI.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of female population ages 15-24" } ], "source_id": "2" }, { "id": "SL.TLF.ACTI.1524.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, male (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of male population ages 15-24" } ], "source_id": "2" }, { "id": "SL.TLF.ACTI.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, male (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of male population ages 15-24" } ], "source_id": "2" }, { "id": "SL.TLF.ACTI.1524.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, total (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-24" } ], "source_id": "2" }, { "id": "SL.TLF.ACTI.1524.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, total (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-24" } ], "source_id": "2" }, { "id": "SL.TLF.ACTI.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, female (% of female population ages 15-64) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of female population ages 15-64" } ], "source_id": "2" }, { "id": "SL.TLF.ACTI.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, male (% of male population ages 15-64) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of male population ages 15-64" } ], "source_id": "2" }, { "id": "SL.TLF.ACTI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, total (% of total population ages 15-64) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-64" } ], "source_id": "2" }, { "id": "SL.TLF.ADVN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with advanced education, female (% of female working-age population with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with advanced education to the working-age population with advanced education. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of female working-age population with advanced education" } ], "source_id": "2" }, { "id": "SL.TLF.ADVN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with advanced education, male (% of male working-age population with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with advanced education to the working-age population with advanced education. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of male working-age population with advanced education" } ], "source_id": "2" }, { "id": "SL.TLF.ADVN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with advanced education (% of total working-age population with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with advanced education to the working-age population with advanced education. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total working-age population with advanced education" } ], "source_id": "2" }, { "id": "SL.TLF.BASC.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with basic education, female (% of female working-age population with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with basic education to the working-age population with basic education. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of female working-age population with basic education" } ], "source_id": "2" }, { "id": "SL.TLF.BASC.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with basic education, male (% of male working-age population with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with basic education to the working-age population with basic education. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of male working-age population with basic education" } ], "source_id": "2" }, { "id": "SL.TLF.BASC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with basic education (% of total working-age population with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with basic education to the working-age population with basic education. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total working-age population with basic education" } ], "source_id": "2" }, { "id": "SL.TLF.CACT.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, female (% of female population ages 15+) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of female population ages 15+" } ], "source_id": "2" }, { "id": "SL.TLF.CACT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, female (% of female population ages 15+) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\n\n\n\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\n\n\n\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "The labor force participation rate is the labor force as a percent of the population ages 15 and older. The labor force is the sum of all persons of working age who are employed and those who are unemployed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate (LFPR) is calculated as follows: \n\n\nLFPR (%) = 100 x Labor force / population of a given age group, where the labor force is equal to employment plus unemployment.\n\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Population censuses are another major source of data on the labor force and its components.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of female population ages 15+" } ], "source_id": "2" }, { "id": "SL.TLF.CACT.FM.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Estimates of women in the labor force and employment are generally lower than those of men and are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic. In many low-income countries women often work on farms or in other family enterprises without pay, and others work in or near their homes, mixing work and family activities during the day. In many high-income economies, women have been increasingly acquiring higher education that has led to better-compensated, longer-term careers rather than lower-skilled, shorter-term jobs. However, access to good- paying occupations for women remains unequal in many occupations and countries around the world. Labor force statistics by gender is important to monitor gender disparities in employment and unemployment patterns." }, { "id": "IndicatorName", "value": "Ratio of female to male labor force participation rate (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\n\n\n\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\n\n\n\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Ratio of female to male labor force participation rate is the proportion of female labor force participation relative to male labor force participation. The labor force participation rate is the labor force as a percent of the population ages 15 and older. The labor force is the sum of all persons of working age who are employed and those who are unemployed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100. The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\n\n\n\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\n\n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SL.TLF.CACT.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Estimates of women in the labor force and employment are generally lower than those of men and are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic. In many low-income countries women often work on farms or in other family enterprises without pay, and others work in or near their homes, mixing work and family activities during the day. In many high-income economies, women have been increasingly acquiring higher education that has led to better-compensated, longer-term careers rather than lower-skilled, shorter-term jobs. However, access to good- paying occupations for women remains unequal in many occupations and countries around the world. Labor force statistics by gender is important to monitor gender disparities in employment and unemployment patterns." }, { "id": "IndicatorName", "value": "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100. The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "SL.TLF.CACT.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, male (% of male population ages 15+) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of male population ages 15+" } ], "source_id": "2" }, { "id": "SL.TLF.CACT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, male (% of male population ages 15+) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of male population ages 15+" } ], "source_id": "2" }, { "id": "SL.TLF.CACT.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, total (% of total population ages 15+) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total population ages 15+" } ], "source_id": "2" }, { "id": "SL.TLF.CACT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, total (% of total population ages 15+) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total population ages 15+" } ], "source_id": "2" }, { "id": "SL.TLF.INTM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with intermediate education, female (% of female working-age population with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with intermediate education to the working-age population with intermediate education. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of female working-age population with intermediate education" } ], "source_id": "2" }, { "id": "SL.TLF.INTM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with intermediate education, male (% of male working-age population with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with intermediate education to the working-age population with intermediate education. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of male working-age population with intermediate education" } ], "source_id": "2" }, { "id": "SL.TLF.INTM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with intermediate education (% of total working-age population with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with intermediate education to the working-age population with intermediate education. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total working-age population with intermediate education" } ], "source_id": "2" }, { "id": "SL.TLF.PART.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Part-time employment has been seen as an instrument to increase labor supply. Indeed, as part-time work may offer the chance of a better balance between working life and family responsibilities, and suits workers who prefer shorter working hours and more time for their private life, it may allow more working-age persons to actually join the labor force. Also, policy-makers have promoted part-time work in an attempt to redistribute working time in countries of high unemployment, thus lowering politically sensitive unemployment rates without requiring an increase in the total number of hours worked.\n\n\n\nPart-time employment, however, is not always a choice. While flexibility may be one advantage of part-time work, disadvantages may exist in comparison with colleagues who work full time. Since the early 1990s, most OECD countries have introduced measures to improve the quality of part-time work, for example with respect to social benefits for part-time workers in line with those of full-time workers. Nevertheless, occupational segregation between part-time and full-time work remains an issue in most countries as it limits the occupational choices of part-time workers.\n\n\n\nLooking at part-time employment by sex is useful to see the extent to which the female labor force is more likely to work part time than the male labor force." }, { "id": "IndicatorName", "value": "Part time employment, female (% of total female employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Part-time employment rate represents the percentage of employment that is part time. Part time employment in this series is based on a common definition of less than 35 actual weekly hours worked." }, { "id": "Othernotes", "value": "Relevance to gender indicator: More and more women are working part-time and one of the concern is that part time work does not provide the stability that full time work does." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1976-2025" }, { "id": "Source", "value": "Wages and Working Time Statistics database (COND), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are typically the preferred source of information on hours of work. Such surveys can be designed to cover virtually the entire non-institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders.\n\nOther types of household surveys could also be used as sources of data on hours of work, if they have an appropriate module on the topic.\n\nIn the absence of a labor force survey or other types of household surveys with a module on working time, an establishment survey can be used as a source of statistics on hours of work. However, the statistics derived from establishments surveys would typically not refer to the whole employed population but only to employees (and often only to formal sector employees or non-agricultural formal sector employees).\nStatistical concept(s): Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total female employment" } ], "source_id": "2" }, { "id": "SL.TLF.PART.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Part-time employment has been seen as an instrument to increase labor supply. Indeed, as part-time work may offer the chance of a better balance between working life and family responsibilities, and suits workers who prefer shorter working hours and more time for their private life, it may allow more working-age persons to actually join the labor force. Also, policy-makers have promoted part-time work in an attempt to redistribute working time in countries of high unemployment, thus lowering politically sensitive unemployment rates without requiring an increase in the total number of hours worked.\n\n\n\nPart-time employment, however, is not always a choice. While flexibility may be one advantage of part-time work, disadvantages may exist in comparison with colleagues who work full time. Since the early 1990s, most OECD countries have introduced measures to improve the quality of part-time work, for example with respect to social benefits for part-time workers in line with those of full-time workers. Nevertheless, occupational segregation between part-time and full-time work remains an issue in most countries as it limits the occupational choices of part-time workers.\n\n\n\nLooking at part-time employment by sex is useful to see the extent to which the female labor force is more likely to work part time than the male labor force." }, { "id": "IndicatorName", "value": "Part time employment, male (% of total male employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Part-time employment rate represents the percentage of employment that is part time. Part time employment in this series is based on a common definition of less than 35 actual weekly hours worked." }, { "id": "Othernotes", "value": "Relevance to gender indicator: More and more women are working part-time and one of the concern is that part time work does not provide the stability that full time work does." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1976-2025" }, { "id": "Source", "value": "Wages and Working Time Statistics database (COND), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are typically the preferred source of information on hours of work. Such surveys can be designed to cover virtually the entire non-institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders.\n\nOther types of household surveys could also be used as sources of data on hours of work, if they have an appropriate module on the topic.\n\nIn the absence of a labor force survey or other types of household surveys with a module on working time, an establishment survey can be used as a source of statistics on hours of work. However, the statistics derived from establishments surveys would typically not refer to the whole employed population but only to employees (and often only to formal sector employees or non-agricultural formal sector employees).\nStatistical concept(s): Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total male employment" } ], "source_id": "2" }, { "id": "SL.TLF.PART.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Part-time employment has been seen as an instrument to increase labor supply. Indeed, as part-time work may offer the chance of a better balance between working life and family responsibilities, and suits workers who prefer shorter working hours and more time for their private life, it may allow more working-age persons to actually join the labor force. Also, policy-makers have promoted part-time work in an attempt to redistribute working time in countries of high unemployment, thus lowering politically sensitive unemployment rates without requiring an increase in the total number of hours worked.\n\n\n\nPart-time employment, however, is not always a choice. While flexibility may be one advantage of part-time work, disadvantages may exist in comparison with colleagues who work full time. Since the early 1990s, most OECD countries have introduced measures to improve the quality of part-time work, for example with respect to social benefits for part-time workers in line with those of full-time workers. Nevertheless, occupational segregation between part-time and full-time work remains an issue in most countries as it limits the occupational choices of part-time workers.\n\n\n\nLooking at part-time employment by sex is useful to see the extent to which the female labor force is more likely to work part time than the male labor force." }, { "id": "IndicatorName", "value": "Part time employment, total (% of total employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Part-time employment rate represents the percentage of employment that is part time. Part time employment in this series is based on a common definition of less than 35 actual weekly hours worked." }, { "id": "Othernotes", "value": "Relevance to gender indicator: More and more women are working part-time and one of the concern is that part time work does not provide the stability that full time work does." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1976-2025" }, { "id": "Source", "value": "Wages and Working Time Statistics database (COND), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are typically the preferred source of information on hours of work. Such surveys can be designed to cover virtually the entire non-institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders.\n\nOther types of household surveys could also be used as sources of data on hours of work, if they have an appropriate module on the topic.\n\nIn the absence of a labor force survey or other types of household surveys with a module on working time, an establishment survey can be used as a source of statistics on hours of work. However, the statistics derived from establishments surveys would typically not refer to the whole employed population but only to employees (and often only to formal sector employees or non-agricultural formal sector employees).\nStatistical concept(s): Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "2" }, { "id": "SL.TLF.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force, female (% of total labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "International Labour Organization (ILO), type: estimates based on external database;\nUnited Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are based on labor force participation rates and population data from International Labour Organization and United Nations Population Division. The labor force participation rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or voluntarily left work. In addition, persons who did not look for work but have an arrangement for a future job are also counted as unemployed. Still, some unemployment is unavoidable—at any time, some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. The labor force or the economically active portion of the population serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total labor force" } ], "source_id": "2" }, { "id": "SL.TLF.TOTL.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "International Labour Organization (ILO), type: estimates based on external database;\nUnited Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are based on labor force participation rates and population data from International Labour Organization and United Nations Population Division. The labor force participation rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or voluntarily left work. In addition, persons who did not look for work but have an arrangement for a future job are also counted as unemployed. Still, some unemployment is unavoidable—at any time, some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. The labor force or the economically active portion of the population serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "Persons" } ], "source_id": "2" }, { "id": "SL.UEM.1524.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth female (% of female labor force ages 15-24) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\n\n\n\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\n\n\n\n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female labor force ages 15-24" } ], "source_id": "2" }, { "id": "SL.UEM.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\n\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\n\n\n\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth female (% of female labor force ages 15-24) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\n\n\n\n\n\n\n\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\n\n\n\n\n\n\n\n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female labor force ages 15-24" } ], "source_id": "2" }, { "id": "SL.UEM.1524.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth male (% of male labor force ages 15-24) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male labor force ages 15-24" } ], "source_id": "2" }, { "id": "SL.UEM.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth male (% of male labor force ages 15-24) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male labor force ages 15-24" } ], "source_id": "2" }, { "id": "SL.UEM.1524.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth total (% of total labor force ages 15-24) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of total labor force ages 15-24" } ], "source_id": "2" }, { "id": "SL.UEM.1524.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth total (% of total labor force ages 15-24) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of total labor force ages 15-24" } ], "source_id": "2" }, { "id": "SL.UEM.ADVN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with advanced education, female (% of female labor force with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female labor force with advanced education" } ], "source_id": "2" }, { "id": "SL.UEM.ADVN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with advanced education, male (% of male labor force with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male labor force with advanced education" } ], "source_id": "2" }, { "id": "SL.UEM.ADVN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with advanced education (% of total labor force with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of total labor force with advanced education" } ], "source_id": "2" }, { "id": "SL.UEM.BASC.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with basic education, female (% of female labor force with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with a basic level of education who are unemployed. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female labor force with basic education" } ], "source_id": "2" }, { "id": "SL.UEM.BASC.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with basic education, male (% of male labor force with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with a basic level of education who are unemployed. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male labor force with basic education" } ], "source_id": "2" }, { "id": "SL.UEM.BASC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with basic education (% of total labor force with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with a basic level of education who are unemployed. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of total labor force with basic education" } ], "source_id": "2" }, { "id": "SL.UEM.INTM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with intermediate education, female (% of female labor force with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female labor force with intermediate education" } ], "source_id": "2" }, { "id": "SL.UEM.INTM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with intermediate education, male (% of male labor force with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male labor force with intermediate education" } ], "source_id": "2" }, { "id": "SL.UEM.INTM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with intermediate education (% of total labor force with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of total labor force with intermediate education" } ], "source_id": "2" }, { "id": "SL.UEM.NEET.FE.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, female (% of female youth population) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of youth population" } ], "source_id": "2" }, { "id": "SL.UEM.NEET.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, female (% of female youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female youth population" } ], "source_id": "2" }, { "id": "SL.UEM.NEET.MA.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, male (% of male youth population) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male youth population" } ], "source_id": "2" }, { "id": "SL.UEM.NEET.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, male (% of male youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male youth population" } ], "source_id": "2" }, { "id": "SL.UEM.NEET.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, total (% of youth population) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female youth population" } ], "source_id": "2" }, { "id": "SL.UEM.NEET.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, total (% of youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of youth population" } ], "source_id": "2" }, { "id": "SL.UEM.TOTL.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, female (% of female labor force) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female labor force" } ], "source_id": "2" }, { "id": "SL.UEM.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, female (% of female labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female labor force" } ], "source_id": "2" }, { "id": "SL.UEM.TOTL.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "With the aim of promoting international comparability, statistics presented on ILOSTAT are based on standard international definitions wherever feasible and may differ from official national figures. This series is based on the 13th ICLS definitions. For time series comparability, it includes countries that have implemented the 19th ICLS standards, for which data are also available in the Work Statistics -- 19th ICLS (WORK) database." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, male (% of male labor force) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While the unemployment rate may be considered the most informative labour market indicator, reflecting the general performance of the labour market and the economy as a whole, it should not be interpreted as a measure of economic hardship or of well-being. When based on the internationally-recommended standards, the unemployment rate simply reflects the proportion of the labour force that does not have a job but is available and actively looking for work. It says nothing about the economic resources of unemployed workers or their family members. Its use should, therefore, be limited to serving as a measurement of the utilization of labour and an indication of the failure to find work. Other measures, including income-related indicators, would be needed to evaluate economic hardship. An additional criticism of the aggregate unemployment measure is that it masks information on the composition of the jobless population and therefore misses out on the particularities of the education level, ethnic origin, socio-economic background, work experience, etc. of the unemployed. Moreover, the unemployment rate says nothing about the type of unemployment – whether it is cyclical and short-term or structural and long-term – which is a critical issue for policy makers in the development of their policy responses, especially given that structural unemployment cannot be addressed by boosting market demand only." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\n\n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male labor force" } ], "source_id": "2" }, { "id": "SL.UEM.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, male (% of male labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male labor force" } ], "source_id": "2" }, { "id": "SL.UEM.TOTL.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, total (% of total labor force) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of total labor force" } ], "source_id": "2" }, { "id": "SL.UEM.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, total (% of total labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While the unemployment rate may be considered the most informative labour market indicator, reflecting the general performance of the labour market and the economy as a whole, it should not be interpreted as a measure of economic hardship or of well-being. When based on the internationally-recommended standards, the unemployment rate simply reflects the proportion of the labour force that does not have a job but is available and actively looking for work. It says nothing about the economic resources of unemployed workers or their family members. Its use should, therefore, be limited to serving as a measurement of the utilization of labour and an indication of the failure to find work. Other measures, including income-related indicators, would be needed to evaluate economic hardship. An additional criticism of the aggregate unemployment measure is that it masks information on the composition of the jobless population and therefore misses out on the particularities of the education level, ethnic origin, socio-economic background, work experience, etc. of the unemployed. Moreover, the unemployment rate says nothing about the type of unemployment – whether it is cyclical and short-term or structural and long-term – which is a critical issue for policy makers in the development of their policy responses, especially given that structural unemployment cannot be addressed by boosting market demand only." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\n\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of total labor force" } ], "source_id": "2" }, { "id": "SL.WAG.0714.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, wage workers, female (% of female children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Wage workers (also known as employees) are people who hold explicit (written or oral) or implicit employment contracts that provide basic remuneration that does not depend directly on the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.WAG.0714.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, wage workers, male (% of male children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Wage workers (also known as employees) are people who hold explicit (written or oral) or implicit employment contracts that provide basic remuneration that does not depend directly on the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SL.WAG.0714.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, wage workers (% of children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Wage workers (also known as employees) are people who hold explicit (written or oral) or implicit employment contracts that provide basic remuneration that does not depend directly on the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "2" }, { "id": "SM.POP.ASYS.EA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on asylum-seekers is vital for understanding the demand for international protection and the capacity of states to process asylum claims, aligning with the goals of the Global Compact on Refugees. This data underpins ongoing efforts by the World Bank and the Office of the United Nations High Commissioner for Refugees (UNHCR) to strengthen asylum systems through projects like the Asylum Capacity Support Program, which assists countries in enhancing their legal frameworks and operational capacities. Moreover, the data is used in international dialogues, such as the UN High-Level Meeting on Refugees and Migrants, to advocate for fair and efficient asylum procedures and ensure that human rights obligations are upheld." }, { "id": "IndicatorName", "value": "Asylum-seekers by country or territory of asylum" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Variability in national asylum procedures and processing times can lead to inconsistencies in data reporting, affecting the accuracy of cross-country comparisons. Unregistered asylum claims or informal border crossings may result in incomplete data, underestimating the true scope of the need for protection. Additionally, the changing status of individuals, such as those asylum-seekers that are recognized as refugees, complicates the tracking and categorization of data." }, { "id": "Longdefinition", "value": "Asylum-seekers are individuals who have sought international protection and whose claims for refugee status have not yet been determined. This includes those who are in various stages of the asylum process, such as initial application, appeal, or awaiting final decision. In specific contexts, asylum-seekers may also include those who are seeking protection under complementary forms of protection, and those enjoying temporary protection, but whose claims are still under consideration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on asylum-seekers is primarily handled through administrative records maintained by national authorities responsible for processing asylum claims. These records capture various stages of the asylum process, including the number of new applications, repeat applications, and cases that are pending resolution.\n\n\n\nThe methodology for collecting asylum-seeker data is guided by the International Recommendations on Refugee Statistics (IRRS), which provide standardized guidelines for data collection and reporting. These guidelines aim to ensure that data on asylum-seekers is comparable across different countries and regions, facilitating a coherent global understanding of asylum trends. The IRRS recommends that data be disaggregated by key variables such as the stage of the asylum process, the demographic characteristics of applicants, and the country of origin." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SM.POP.ASYS.EO", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on asylum-seekers is vital for understanding the demand for international protection and the capacity of states to process asylum claims, aligning with the goals of the Global Compact on Refugees. This data underpins ongoing efforts by the World Bank and the Office of the United Nations High Commissioner for Refugees (UNHCR) to strengthen asylum systems through projects like the Asylum Capacity Support Program, which assists countries in enhancing their legal frameworks and operational capacities. Moreover, the data is used in international dialogues, such as the UN High-Level Meeting on Refugees and Migrants, to advocate for fair and efficient asylum procedures and ensure that human rights obligations are upheld." }, { "id": "IndicatorName", "value": "Asylum-seekers by country or territory of origin" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Variability in national asylum procedures and processing times can lead to inconsistencies in data reporting, affecting the accuracy of cross-country comparisons. Unregistered asylum claims or informal border crossings may result in incomplete data, underestimating the true scope of the need for protection. Additionally, the changing status of individuals, such as those asylum-seekers that are recognized as refugees, complicates the tracking and categorization of data." }, { "id": "Longdefinition", "value": "Asylum-seekers are individuals who have sought international protection and whose claims for refugee status have not yet been determined. This includes those who are in various stages of the asylum process, such as initial application, appeal, or awaiting final decision. In specific contexts, asylum-seekers may also include those who are seeking protection under complementary forms of protection, and those enjoying temporary protection, but whose claims are still under consideration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on asylum-seekers is primarily handled through administrative records maintained by national authorities responsible for processing asylum claims. These records capture various stages of the asylum process, including the number of new applications, repeat applications, and cases that are pending resolution.\n\n\n\nThe methodology for collecting asylum-seeker data is guided by the International Recommendations on Refugee Statistics (IRRS), which provide standardized guidelines for data collection and reporting. These guidelines aim to ensure that data on asylum-seekers is comparable across different countries and regions, facilitating a coherent global understanding of asylum trends. The IRRS recommends that data be disaggregated by key variables such as the stage of the asylum process, the demographic characteristics of applicants, and the country of origin." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SM.POP.FDIP", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on forcibly displaced people provide a comprehensive measure of global displacement, encompassing refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) and the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), asylum-seekers, other people in need of international protection, and internally displaced people (IDPs). This aggregate data is essential for designing holistic responses to forced displacement, supporting initiatives like the Global Compact on Refugees. It enables governments, humanitarian organizations, and development partners to better understand the scale of displacement and mobilize resources to address the multifaceted needs of displaced populations." }, { "id": "IndicatorName", "value": "Forcibly displaced people" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregating data across diverse categories introduces challenges in maintaining comparability and consistency. Variations in data collection methods and definitions—such as differing criteria for refugee or IDP status—can complicate efforts to standardize figures across countries and regions. Additionally, dynamic changes in displacement, including secondary movements or status transitions (e.g., from asylum-seeker to refugee), may lead to double counting or data gaps. The diverse sources of data, ranging from administrative records to field surveys, further underscore the importance of contextualizing and cautiously interpreting the aggregate figures." }, { "id": "Longdefinition", "value": "Forcibly displaced people are represented by the sum of (1) refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR), (2) refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), (3) asylum-seekers, (4) other people in need of international protection, and (5) internally displaced people (IDPs). Situations in which people are reported in more than one of these categories are accounted for, to the extent possible." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2010-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on forcibly displaced people combines information on five key population groups: (1) refugees (and people in a refugee-like situation) under the mandate of the United Nations High Commissioner for Refugees (UNHCR), (2) refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), (3) asylum-seekers, (4) other people in need of international protection, and (5) internally displaced people (IDPs). The primary data sources include administrative records, population censuses, and household surveys, as well as operational data from humanitarian organizations such as UNHCR, UNRWA, and the Internal Displacement Monitoring Centre (IDMC). Data is reported as stock data at specific points in time, typically the end of the calendar year, providing a snapshot of forcibly displaced populations globally. Each category follows established international methodologies to ensure consistency and comparability.\n\nAt the end of 2023 (2024), the UNRWA estimates that 70 per cent of the 1.7 (2) million IDPs in the Gaza Strip at end-2023 (-2024) were Palestine refugees under its mandate. These internally displaced refugees under the mandate of UNRWA are only counted once in the forcibly displaced total (i.e. deducted once from the total amount to avoid double counting). The forcibly displaced population category provides a comprehensive and standardized picture of displacement dynamics across different contexts and regions. Statistics for the indicator are included from 2010 onwards, as at this time data on internal displacement situations became more available and more consistent." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SM.POP.IDPC", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on internally displaced people (IDPs) is essential for guiding humanitarian assistance, informing national development plans, and shaping international responses to internal displacement, particularly in fragile and conflict-affected states. This data is crucial for initiatives like the World Bank’s Fragility, Conflict, and Violence (FCV) Strategy, which aims to address" }, { "id": "IndicatorName", "value": "Internally displaced persons (IDPs) by country or territory of asylum / origin" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Collecting accurate data on internally displaced people (IDPs) is challenging in conflict zones and areas with limited access, leading to potential underreporting or incomplete coverage. Repeated displacements and the complex dynamics of internal conflicts can result in double counting or misclassification of IDPs. Additionally, differing definitions of IDPs across countries and regions can hinder the comparability and reliability of the data, complicating international efforts to address internal displacement." }, { "id": "Longdefinition", "value": "Internally displaced people (IDPs) are persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of, or in order to avoid the effects of armed conflict, situations of generalized violence, and violations of human rights, and who have not crossed an internationally recognized State border." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2009-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download;\nGlobal Internal Displacement Database, Internal Displacement Monitoring Centre (IDMC), uri: https://www.internal-displacement.org/database/displacement-data/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on internally displaced people (IDPs) is based on a combination of population censuses, household surveys, administrative records, and operational data from humanitarian organizations. Population censuses provide a baseline count of IDPs, while household surveys are often used to collect more detailed and current data, particularly in situations where displacement is ongoing or rapidly changing.\n\n\n\nAdministrative records from local governments and humanitarian agencies provide additional data on the movements, living conditions, and access to services for IDP populations. \n\n\n\nThe Internal Displacement Monitoring Centre (IDMC) compiles and publishes IDP statistics from multiple sources. These include operational data produced by the Joint IDP Profiling Service (JIPS) and the International Organization for Migration (IOM), particularly in conflict-affected regions where official statistics may be less reliable or incomplete.\n\n\n\nThe methodology for collecting and reporting IDP data adheres to the International Recommendations on IDP Statistics (IRIS), developed by the Expert Group on Refugee, IDP and Statelessness Statistics (EGRISS). The IRIS framework provides a detailed structure for defining and measuring IDP populations, ensuring that data on IDPs is consistent, reliable, and comparable across different contexts and countries. The framework includes recommendations for both stock data (the number of IDPs at a given time) and flow data (movements into and out of the IDP population), providing a comprehensive understanding of internal displacement dynamics. This methodology has been applied in various contexts, such as in the Democratic Republic of the Congo and Colombia, where detailed IDP profiling has been essential for understanding the needs and vulnerabilities of displaced populations." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SM.POP.NETM", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\n\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. In most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers.\n\n\n\nBut refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom." }, { "id": "IndicatorName", "value": "Net migration" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "International migration is the component of population change most difficult to measure and estimate reliably. Thus, the quality and quantity of the data used in the estimation and projection of net migration varies considerably by country. Furthermore, the movement of people across international boundaries, which is very often a response to changing socio-economic, political and environmental forces, is subject to a great deal of volatility. Refugee movements, for instance, may involve large numbers of people moving across boundaries in a short time. For these reasons, projections of future international migration levels are the least robust part of current population projections and reflect mainly a continuation of recent levels and trends in net migration." }, { "id": "Longdefinition", "value": "Net migration is the net total of migrants during the period, that is, the number of immigrants minus the number of emigrants, including both citizens and noncitizens." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: When there is insufficient data, net migration is derived through the difference between the overall population growth rate and the rate of natural increase (the difference between the birth rate and the death rate) during the same period. Such calculations are usually made for intercensal periods. The estimates are also derived from the data on foreign-born population - people who have residence in one country but were born in another country. When data on the foreign-born population are not available, data on foreign population - that is, people who are citizens of a country other than the country in which they reside - are used as estimates.\nStatistical concept(s): The United Nations Population Division provides data on net migration and migrant stock. Because data on migrant stock is difficult for countries to collect, the United Nations Population Division takes into account the past migration history of a country or area, the migration policy of a country, and the influx of refugees in recent periods when deriving estimates of net migration. The data to calculate these estimates come from a variety of sources, including border statistics, administrative records, surveys, and censuses." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SM.POP.OPIP.EA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on other people in need of international protection is essential for identifying and responding to the needs of individuals who fall outside traditional refugee definitions but still require protection and assistance. This data is crucial for initiatives like the Office of the United Nations High Commissioner for Refugees’ (UNHCR) broader protection strategies and supports the World Bank’s work on mixed migration flows, where displaced populations and migrants are addressed through integrated approaches. The data also plays a key role in informing policy discussions at international forums, such as the UN General Assembly, where member states explore comprehensive protection frameworks for vulnerable groups." }, { "id": "IndicatorName", "value": "Other people in need of international protection by country or territory of asylum" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The definitions of people included in this category can be inconsistent across countries in the data that is collected and reported, affecting its comparability across contexts." }, { "id": "Longdefinition", "value": "Other people in need of international protection refer to people who are outside their country or territory of origin, typically because they have been forcibly displaced across international borders, who have not been reported under other categories (including asylum-seekers and refugees) but who likely need international protection, including protection against forced return, as well as access to basic services on a temporary or longer-term basis." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2018-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on other people in need of international protection involves methodologies similar to those used for refugees and asylum-seekers. \n\n\n\nData is primarily collected through administrative records, which track the number of individuals identified as needing international protection but who do not fall under the traditional definitions of refugees or asylum-seekers. These records are often supplemented by data from humanitarian operations and targeted surveys conducted in areas experiencing large-scale displacement.\n\n\n\nSince 2018, the Office of the United Nations High Commissioner for Refugees (UNHCR) has expanded its statistical frameworks to include this category, reflecting the growing recognition of the diverse forms of displacement and protection needs that exist beyond the traditional categories. The methodology for collecting and reporting data on these populations ensures that they are accurately represented in global statistics, and are comprehensive and comparable across different regions and timeframes, supporting more targeted and effective protection responses." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SM.POP.OPIP.EO", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on other people in need of international protection is essential for identifying and responding to the needs of individuals who fall outside traditional refugee definitions but still require protection and assistance. This data is crucial for initiatives like the Office of the United Nations High Commissioner for Refugees’ (UNHCR) broader protection strategies and supports the World Bank’s work on mixed migration flows, where displaced populations and migrants are addressed through integrated approaches. The data also plays a key role in informing policy discussions at international forums, such as the UN General Assembly, where member states explore comprehensive protection frameworks for vulnerable groups." }, { "id": "IndicatorName", "value": "Other people in need of international protection by country or territory of origin" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The definitions of people included in this category can be inconsistent across countries in the data that is collected and reported, affecting its comparability across contexts." }, { "id": "Longdefinition", "value": "Other people in need of international protection refer to people who are outside their country or territory of origin, typically because they have been forcibly displaced across international borders, who have not been reported under other categories (including asylum-seekers and refugees) but who likely need international protection, including protection against forced return, as well as access to basic services on a temporary or longer-term basis." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2018-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on other people in need of international protection involves methodologies similar to those used for refugees and asylum-seekers. \n\n\n\nData is primarily collected through administrative records, which track the number of individuals identified as needing international protection but who do not fall under the traditional definitions of refugees or asylum-seekers. These records are often supplemented by data from humanitarian operations and targeted surveys conducted in areas experiencing large-scale displacement.\n\n\n\nSince 2018, the Office of the United Nations High Commissioner for Refugees (UNHCR) has expanded its statistical frameworks to include this category, reflecting the growing recognition of the diverse forms of displacement and protection needs that exist beyond the traditional categories. The methodology for collecting and reporting data on these populations ensures that they are accurately represented in global statistics, and are comprehensive and comparable across different regions and timeframes, supporting more targeted and effective protection responses." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SM.POP.RHCR.EA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) is crucial for informing global and national policies that address the immediate and long-term needs of displaced populations. This data supports ongoing initiatives such as the World Bank’s Global Concessional Financing Facility, which provides financial resources to middle-income countries hosting large numbers of refugees, thereby fostering resilience and stability. Additionally, the data is instrumental for discussions in international forums like the Global Refugee Forum and the UNHCR Executive Committee, where stakeholders assess the effectiveness of refugee response strategies and mobilize support for burden-sharing among nations." }, { "id": "IndicatorName", "value": "Refugees under the mandate of the UNHCR by country or territory of asylum" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Inconsistencies in data collection standards across countries can complicate the comparability of refugee data globally, making it challenging to create uniform policies. The fluidity of refugee movements and states’ processing of status changes, such as shifts from asylum-seeker to refugee, can impact the timeliness of the data. Furthermore, political pressures may influence how governments report refugee figures, potentially leading to underreporting or misrepresentation of the situation." }, { "id": "Longdefinition", "value": "Refugees under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) include individuals recognized under the 1951 Convention relating to the Status of Refugees, its 1967 Protocol, the 1969 Organization of African Unity (OAU) Convention Governing the Specific Aspects of Refugee Problems in Africa, the refugee definition contained in the 1984 Cartagena Declaration on Refugees as incorporated into national laws, those recognized in accordance with the UNHCR Statute, individuals granted complementary forms of protection, and those enjoying temporary protection. The refugee population also includes people in a refugee-like situation, which is a category that is descriptive in nature and includes groups of people who are outside their country or territory of origin and who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained. Refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), Palestine Refugees, are not typically included in the statistics on refugees (and people in a refugee-like situation) under the mandate of the UNHCR." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) is based on a coordinated effort between national governments and UNHCR.\n\n\n\nThe primary sources of data include administrative records and registration systems maintained by immigration or asylum agencies, including UNHCR itself, which provide detailed demographic information, such as age, sex, and nationality. These records are generally reported as stock data, reflecting the number of refugees at a specific point in time, typically at the end of the calendar year. \n\n\n\nThe collection process follows the guidelines outlined in the International Recommendations on Refugee Statistics (IRRS), developed by the Expert Group on Refugee, IDP and Statelessness Statistics (EGRISS). The IRRS helps to standardize data collection methods across different countries. The IRRS emphasizes the importance of disaggregating data by key demographic variables and geographical locations, facilitating a more granular analysis of refugee populations." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SM.POP.RHCR.EO", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) is crucial for informing global and national policies that address the immediate and long-term needs of displaced populations. This data supports ongoing initiatives such as the World Bank’s Global Concessional Financing Facility, which provides financial resources to middle-income countries hosting large numbers of refugees, thereby fostering resilience and stability. Additionally, the data is instrumental for discussions in international forums like the Global Refugee Forum and the UNHCR Executive Committee, where stakeholders assess the effectiveness of refugee response strategies and mobilize support for burden-sharing among nations." }, { "id": "IndicatorName", "value": "Refugees under the mandate of the UNHCR by country or territory of origin" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Inconsistencies in data collection standards across countries can complicate the comparability of refugee data globally, making it challenging to create uniform policies. The fluidity of refugee movements and states’ processing of status changes, such as shifts from asylum-seeker to refugee, can impact the timeliness of the data. Furthermore, political pressures may influence how governments report refugee figures, potentially leading to underreporting or misrepresentation of the situation." }, { "id": "Longdefinition", "value": "Refugees under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) include individuals recognized under the 1951 Convention relating to the Status of Refugees, its 1967 Protocol, the 1969 Organization of African Unity (OAU) Convention Governing the Specific Aspects of Refugee Problems in Africa, the refugee definition contained in the 1984 Cartagena Declaration on Refugees as incorporated into national laws, those recognized in accordance with the UNHCR Statute, individuals granted complementary forms of protection, and those enjoying temporary protection. The refugee population also includes people in a refugee-like situation, which is a category that is descriptive in nature and includes groups of people who are outside their country or territory of origin and who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained. Refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), Palestine Refugees, are not typically included in the statistics on refugees (and people in a refugee-like situation) under the mandate of the UNHCR." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) is based on a coordinated effort between national governments and UNHCR.\n\n\n\nThe primary sources of data include administrative records and registration systems maintained by immigration or asylum agencies, including UNHCR itself, which provide detailed demographic information, such as age, sex, and nationality. These records are generally reported as stock data, reflecting the number of refugees at a specific point in time, typically at the end of the calendar year. \n\n\n\nThe collection process follows the guidelines outlined in the International Recommendations on Refugee Statistics (IRRS), developed by the Expert Group on Refugee, IDP and Statelessness Statistics (EGRISS). The IRRS helps to standardize data collection methods across different countries. The IRRS emphasizes the importance of disaggregating data by key demographic variables and geographical locations, facilitating a more granular analysis of refugee populations." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SM.POP.RRWA.EA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) is critical for addressing the unique and ongoing displacement situation of Palestinian refugees. This data informs humanitarian and development strategies in associated key regions, including the Gaza Strip, and West Bank. It supports international initiatives, particularly in areas related to health, education, and poverty reduction. Additionally, the data plays a significant role in global policy discussions to ensure adequate resources for sustaining vital services for Palestinian refugees." }, { "id": "IndicatorName", "value": "Refugees under the mandate of the UNRWA by country or territory of asylum" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The long-standing nature of Palestinian refugee status creates challenges in distinguishing generational shifts and new displacements. Data collection is further hindered by the varying legal and socio-political conditions in host countries, impacting the accuracy and comparability of reported figures. Additionally, the absence of a durable solution for Palestinian refugees exacerbates data fluidity, with changes in status, location, and service eligibility complicating longitudinal tracking. These factors underline the need for cautious interpretation and contextual understanding of data on Palestinian refugees." }, { "id": "Longdefinition", "value": "Refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), Palestine Refugees, are persons whose normal place of residence was Palestine during the period 1 June 1946 to 15 May 1948 and who lost both home and means of livelihood as a result of the 1948 conflict. Palestine Refugees, and descendants of Palestine refugee males, including legally adopted children, are eligible to register for UNRWA services. UNRWA accepts new applications from persons who wish to be registered as Palestine Refugees. Once they are registered with UNRWA, persons in this category are referred to as Registered Refugees. Refugees under the mandate of the UNRWA are not typically included in the statistics on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download;\nStatistics Bulletin, UN Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), uri: https://www.unrwa.org/what-we-do/unrwa-statistics-bulletin" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) is primarily based on administrative records maintained by the UNRWA in line with UNRWA’s internal guidelines and operational frameworks.\n\n\n\nUNRWA's registration system is a key source, capturing detailed information on individuals registered as Palestine refugees, including their demographic characteristics as well as the country or territory of asylum, to facilitate a nuanced understanding of the refugee population's distribution and characteristics.\n\n\n\nIn addition to administrative records, UNRWA also collaborates with host countries and other international organizations to enrich its data, particularly in contexts where operational challenges might limit direct data collection." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SM.POP.RRWA.EO", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) is critical for addressing the unique and ongoing displacement situation of Palestinian refugees. This data informs humanitarian and development strategies in associated key regions, including the Gaza Strip, and West Bank. It supports international initiatives, particularly in areas related to health, education, and poverty reduction. Additionally, the data plays a significant role in global policy discussions to ensure adequate resources for sustaining vital services for Palestinian refugees." }, { "id": "IndicatorName", "value": "Refugees under the mandate of the UNRWA by country or territory of origin" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The long-standing nature of Palestinian refugee status creates challenges in distinguishing generational shifts and new displacements. Data collection is further hindered by the varying legal and socio-political conditions in host countries, impacting the accuracy and comparability of reported figures. Additionally, the absence of a durable solution for Palestinian refugees exacerbates data fluidity, with changes in status, location, and service eligibility complicating longitudinal tracking. These factors underline the need for cautious interpretation and contextual understanding of data on Palestinian refugees." }, { "id": "Longdefinition", "value": "Refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), Palestine Refugees, are persons whose normal place of residence was Palestine during the period 1 June 1946 to 15 May 1948 and who lost both home and means of livelihood as a result of the 1948 conflict. Palestine Refugees, and descendants of Palestine refugee males, including legally adopted children, are eligible to register for UNRWA services. UNRWA accepts new applications from persons who wish to be registered as Palestine Refugees. Once they are registered with UNRWA, persons in this category are referred to as Registered Refugees. Refugees under the mandate of the UNRWA are not typically included in the statistics on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download;\nStatistics Bulletin, UN Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), uri: https://www.unrwa.org/what-we-do/unrwa-statistics-bulletin" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) is primarily based on administrative records maintained by the UNRWA in line with UNRWA’s internal guidelines and operational frameworks.\n\n\n\nUNRWA's registration system is a key source, capturing detailed information on individuals registered as Palestine refugees, including their demographic characteristics as well as the country or territory of asylum, to facilitate a nuanced understanding of the refugee population's distribution and characteristics.\n\n\n\nIn addition to administrative records, UNRWA also collaborates with host countries and other international organizations to enrich its data, particularly in contexts where operational challenges might limit direct data collection." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "2" }, { "id": "SM.POP.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. In most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom." }, { "id": "IndicatorName", "value": "International migrant stock, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In deriving the estimates, an international migrant was equated to a person living in a country other than that in which he or she was born. That is, the number of international migrants, also called the international migrant stock, would represent the number of foreign-born persons enumerated in the countries or areas constituting the world. However, because several countries lack data on the foreign-born, data on the number of foreigners, if available, were used instead as the basis of estimation. Consequently, the overall number of migrants in world regions or at the global level do not quite represent the overall number of foreign-born persons.\n\nThe disintegration and reunification of countries causes discontinuities in the change of the international migrant stock. Because an international migrant is equated with a person who was born outside the country in which he or she resides, when a country disintegrates, persons who had been internal migrants because they had moved from one part of the country to another may become, overnight, international migrants without having moved at that time. Such changes introduce artificial but unavoidable discontinuities in the trend of the international migrant stock. The reunification of States also introduces discontinuities, but in the opposite direction.\n\nWorld aggregates are computed by the World Bank and include economies covered by the World Development Indicators. Therefore, the world total figures or world averages may differ from those published by the United Nations Population Division (UNPD).\n\nThe proportion of international migrant stock (SM.POP.TOTL.ZS) is calculated by the UNPD using UNPD’s total population data, which may differ from the total population data in the World Development Indicators (WDI). Consequently, the number of international migrant stock (SM.POP.TOTL) may not match the result obtained by multiplying the total population in the WDI by the proportion of international migrant stock (SM.POP.TOTL.ZS)." }, { "id": "Longdefinition", "value": "International migrant stock, total is the number of people at mid-year born in a country other than that in which they live. It also includes refugees." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Migrant Stock, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The basic data to estimate the international migrant stock were obtained mostly from population censuses held during the decennial rounds of censuses. Some of the data used were obtained from population registers and nationally representative surveys.\n\nIn the majority of cases, the sources available had gathered information on the place of birth of the enumerated population, thus allowing for the identification of the foreign-born population. In estimating the international migrant stock, international migrants have been equated with the foreign-born whenever possible. In most countries lacking data on place of birth, information on the country of citizenship of those enumerated was available and was used as the basis for the identification of international migrants, thus effectively equating international migrants with foreign citizens.\n\nFor countries or areas for which no information was available on the international migrant stock, the estimates were imputed." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" } ], "source_id": "2" }, { "id": "SM.POP.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. In most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom." }, { "id": "IndicatorName", "value": "International migrant stock (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In deriving the estimates, an international migrant was equated to a person living in a country other than that in which he or she was born. That is, the number of international migrants, also called the international migrant stock, would represent the number of foreign-born persons enumerated in the countries or areas constituting the world. However, because several countries lack data on the foreign-born, data on the number of foreigners, if available, were used instead as the basis of estimation. Consequently, the overall number of migrants in world regions or at the global level do not quite represent the overall number of foreign-born persons.\n\nThe disintegration and reunification of countries causes discontinuities in the change of the international migrant stock. Because an international migrant is equated with a person who was born outside the country in which he or she resides, when a country disintegrates, persons who had been internal migrants because they had moved from one part of the country to another may become, overnight, international migrants without having moved at that time. Such changes introduce artificial but unavoidable discontinuities in the trend of the international migrant stock. The reunification of States also introduces discontinuities, but in the opposite direction.\n\nWorld aggregates are computed by the World Bank and include economies covered by the World Development Indicators. Therefore, the world total figures or world averages may differ from those published by the United Nations Population Division (UNPD).\n\nThe proportion of international migrant stock (SM.POP.TOTL.ZS) is calculated by the UNPD using UNPD’s total population data, which may differ from the total population data in the World Development Indicators (WDI). Consequently, the number of international migrant stock (SM.POP.TOTL) may not match the result obtained by multiplying the total population in the WDI by the proportion of international migrant stock (SM.POP.TOTL.ZS)." }, { "id": "Longdefinition", "value": "International migrant stock (% of population) is the proportion of people at mid-year born in a country other than that in which they live. It also includes refugees." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Migrant Stock, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The basic data to estimate the international migrant stock were obtained mostly from population censuses held during the decennial rounds of censuses. Some of the data used were obtained from population registers and nationally representative surveys.\n\nIn the majority of cases, the sources available had gathered information on the place of birth of the enumerated population, thus allowing for the identification of the foreign-born population. In estimating the international migrant stock, international migrants have been equated with the foreign-born whenever possible. In most countries lacking data on place of birth, information on the country of citizenship of those enumerated was available and was used as the basis for the identification of international migrants, thus effectively equating international migrants with foreign citizens.\n\nFor countries or areas for which no information was available on the international migrant stock, the estimates were imputed." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" } ], "source_id": "2" }, { "id": "SN.ITK.DEFC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good nutrition is the cornerstone for survival, health and development. Well-nourished children perform better in school, grow into healthy adults and in turn give their children a better start in life. Well-nourished women face fewer risks during pregnancy and childbirth, and their children set off on firmer developmental paths, both physically and mentally (UNICEF www.childinfo.org)." }, { "id": "IndicatorName", "value": "Prevalence of undernourishment (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "From a policy and program standpoint, this measure has its limits. First, food insecurity exists even where food availability is not a problem because of inadequate access of poor households to food. Second, food insecurity is an individual or household phenomenon, and the average food available to each person, even corrected for possible effects of low income, is not a good predictor of food insecurity among the population. And third, nutrition security is determined not only by food security but also by the quality of care of mothers and children and the quality of the household's health environment (Smith and Haddad 2000)." }, { "id": "Longdefinition", "value": "Prevalence of undernourishments is the percentage of the population whose habitual food consumption is insufficient to provide the dietary energy levels that are required to maintain a normal active and healthy life. Data showing as 2.5 may signify a prevalence of undernourishment below 2.5%." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 2.1.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "Food and Agriculture Organization of the United Nations (FAO), uri: http://www.fao.org/faostat/en/#home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on undernourishment are from the Food and Agriculture Organization (FAO) of the United Nations and measure food deprivation based on average food available for human consumption per person, the level of inequality in access to food, and the minimum calories required for an average person.\nStatistical concept(s): Data on undernourishment are from the Food and Agriculture Organization (FAO) of the United Nations and measure food deprivation based on average food available for human consumption per person, the level of inequality in access to food, and the minimum calories required for an average person." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SN.ITK.MSFI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Food insecurity at moderate levels of severity is typically associated with the inability to regularly eat healthy, balanced diets. As such, high prevalence of food insecurity at moderate levels can be considered a predictor of various forms of diet-related health conditions in the population, associated with micronutrient deficiency and unbalanced diets. Severe levels of food insecurity, on the other hand, imply a high probability of reduced food intake and therefore can lead to more severe forms of undernutrition, including hunger. FAO has identified the FIES as the tool with the greatest potential for becoming a global standard capable of providing comparable information on food insecurity experience across countries and population groups to track progress on reducing food insecurity and\n\nhunger" }, { "id": "IndicatorName", "value": "Prevalence of moderate or severe food insecurity in the population (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of people in the population who live in households classified as moderately or severely food insecure. A household is classified as moderately or severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to low quality diets and might have been forced to also reduce the quantity of food they would normally eat because of a lack of money or other resources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2015-2023" }, { "id": "Source", "value": "Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The assessment is conducted using data collected with the Food Insecurity Experience Scale or a compatible experience-based food security measurement questionnaire (such as the HFSSM). The probability to be food insecure is estimated using the one-parameter logistic Item Response Theory model (the Rasch model) and thresholds for classification are made cross country comparable by calibrating the metrics obtained in each country against the FIES global reference scale, maintained by FAO. The threshold to classify \"moderate or severe\" food insecurity corresponds to the severity associated with the item \"having to eat less\" on the global FIES scale. It is an indicator of lack of food access.The indicator is calculated as an average over 3 years (eg. data for 2015 is the average of 2014-2016 data)." }, { "id": "Topic", "value": "Health" } ], "source_id": "2" }, { "id": "SN.ITK.SALT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Iodine deficiency can lead to a variety of health and developmental consequences known as iodine deficiency disorders (IDDs). Iodine deficiency is a major cause of preventable mental retardation. It is especially damaging during pregnancy and in early childhood. In their most severe forms, IDDs can lead to cretinism, stillbirth and miscarriage; even mild deficiency can cause a significant loss of learning ability. Thus, it is crucially important that pregnant women and young children in particular get adequate levels of iodine.\n\nIDD can easily be prevented at low cost, however, with small quantities of iodine. One of the best and least expensive methods of preventing iodine deficiency disorder is by simply iodizing table salt, which is currently done in many countries. It represents one of the easiest and most cost-effective interventions for social and economic development." }, { "id": "IndicatorName", "value": "Consumption of iodized salt (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of households which have salt they used for cooking that tested positive (>0ppm) for presence of iodine." }, { "id": "Othernotes", "value": "Iodine deficiency is the single most important cause of preventable mental retardation, contributes significantly to the risk of stillbirth and miscarriage, and increases the incidence of infant mortality. A diet low in iodine is the main cause of iodine deficiency. It usually occurs among populations living in areas where the soil has been depleted of iodine. If soil is deficient in iodine, then so are the plants grown in it, including the grains and vegetables that people and animals consume. There are almost no countries in the world where iodine deficiency has not been a public health problem. Many newborns in low- and middle-income countries remain unprotected from the lifelong consequences of brain damage associated with iodine deficiency disorders, which affect a child's ability to learn and to earn a living as an adult, and in turn prevents children, communities, and countries from fulfilling their potential (UNICEF, www.childinfo.org). Widely used and inexpensive, iodized salt is the best source of iodine, and a global campaign to iodize edible salt is significantly reducing the risks associated with iodine deficiency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2020" }, { "id": "Source", "value": "UNICEF Global Databases on Iodized salt, UN Children's Fund (UNICEF), publisher: Division of Data, Analysis, Planning and Monitoring" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Most of the data on consumption of iodized salt are derived from household surveys. For the data that are from household surveys, the year refers to the survey year." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "2" }, { "id": "SN.ITK.SVFI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Food insecurity at moderate levels of severity is typically associated with the inability to regularly eat healthy, balanced diets. As such, high prevalence of food insecurity at moderate levels can be considered a predictor of various forms of diet-related health conditions in the population, associated with micronutrient deficiency and unbalanced diets. Severe levels of food insecurity, on the other hand, imply a high probability of reduced food intake and therefore can lead to more severe forms of undernutrition, including hunger. FAO has identified the FIES as the tool with the greatest potential for becoming a global standard capable of providing comparable information on food insecurity experience across countries and population groups to track progress on reducing food insecurity and\n\nhunger" }, { "id": "IndicatorName", "value": "Prevalence of severe food insecurity in the population (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of people in the population who live in households classified as severely food insecure. A household is classified as severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to several of the most severe experiences described in the FIES questions, such as to have been forced to reduce the quantity of the food, to have skipped meals, having gone hungry, or having to go for a whole day without eating because of a lack of money or other resources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2015-2023" }, { "id": "Source", "value": "Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The assessment is conducted using data collected with the Food Insecurity Experience Scale or a compatible experience-based food security measurement questionnaire (such as the HFSSM). The probability to be food insecure is estimated using the one-parameter logistic Item Response Theory model (the Rasch model) and thresholds for classification are made cross country comparable by calibrating the metrics obtained in each country against the FIES global reference scale, maintained by FAO. The threshold to classify \"severe\" food insecurity corresponds to the severity associated with the item \"having not eaten for an entire day\" on the global FIES scale. It is an indicator of lack of food access.The indicator is calculated as an average over 3 years (eg. data for 2015 is the average of 2014-2016 data)." }, { "id": "Topic", "value": "Health" } ], "source_id": "2" }, { "id": "SN.ITK.VITA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Vitamin A deficiency is the leading cause of preventable childhood blindness and increases the risk of death from common childhood illnesses such as diarrhoea. Periodic, high-dose vitamin A supplementation is a proven, low-cost intervention which has been shown to reduce all-cause mortality." }, { "id": "IndicatorName", "value": "Vitamin A supplementation coverage rate (% of children ages 6-59 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Vitamin A supplementation coverage rate refers to the percentage of children ages 6-59 months old receiving two high-dose vitamin A supplements in a calendar year." }, { "id": "Othernotes", "value": "Vitamin A is essential for optimal functioning of the immune system. Vitamin A deficiency, a leading cause of blindness, also causes a greater risk of dying from a range of childhood ailments such as measles, malaria, and diarrhea. In low- and middle-income countries, where vitamin A is consumed largely in fruits and vegetables, daily per capita intake is often insufficient to meet dietary requirements. Providing young children with two high-dose vitamin A capsules a year is a safe, cost-effective, efficient strategy for eliminating vitamin A deficiency and improving child survival. Giving vitamin A to new breastfeeding mothers helps protect their children during the first few months of life. Food fortification with vitamin A is being introduced in many developing countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "UNICEF Global Databases, UN Children's Fund (UNICEF), uri: https://data.unicef.org/topic/nutrition/vitamin-a-deficiency/, note: based on administrative reports from countries" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Household Surveys including DHS, MICS and other national surveys\nStatistical concept(s): The World Health Organization has classified vitamin A deficiency as a public health problem affecting many children ages 6-59 months, with the highest rates in sub-Saharan Africa and South Asia." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.ADO.TFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Adolescent childbearing is associated with a wide range of risks for young mothers. Women who become pregnant and give birth very early in their lives as well as their newborns are subject to elevated health risks." }, { "id": "IndicatorName", "value": "Adolescent fertility rate (births per 1,000 women ages 15-19)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adolescent fertility rate is the number of births per 1,000 women ages 15-19." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.7.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Adolescent fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without vital registration systems fertility rates are generally based on censuses or surveys.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Per 1000 women" } ], "source_id": "2" }, { "id": "SP.DYN.AMRT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "If available, derived from life tables of Human Mortality Database (HMD) by Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France)." }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, adult, female (per 1,000 female adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data from United Nations Population Division's World Populaton Prospects are originally 5-year period data and the presented are linearly interpolated by the World Bank for annual series. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Adult mortality rate, female, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old female dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nHuman Mortality Database, Max Planck Institute for Demographic Research, uri: www.mortality.org;\nUniversity of California, Berkeley, uri: www.mortality.org, note: Human Mortality Database;\nFrench Institute for Demographic Studies, uri: www.mortality.org, note: Human Mortality Database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors, l(x), at exact age x in a female period life table. The formula is: (l(60)-l(15))/(l(15))*1000.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data. Where reliable age-specific mortality data are available, life tables can be constructed from age-specific mortality data, and adult mortality rates can be calculated from life tables." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 female adults" } ], "source_id": "2" }, { "id": "SP.DYN.AMRT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "If available, derived from life tables of Human Mortality Database (HMD) by Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France)." }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, adult, male (per 1,000 male adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data from United Nations Population Division's World Populaton Prospects are originally 5-year period data and the presented are linearly interpolated by the World Bank for annual series. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Adult mortality rate, male, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old male dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nHuman Mortality Database, Max Planck Institute for Demographic Research, uri: www.mortality.org;\nUniversity of California, Berkeley, uri: www.mortality.org, note: Human Mortality Database;\nFrench Institute for Demographic Studies, uri: www.mortality.org, note: Human Mortality Database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors, l(x), at exact age x in a male period life table. The formula is: (l(60)-l(15))/(l(15))*1000.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data. Where reliable age-specific mortality data are available, life tables can be constructed from age-specific mortality data, and adult mortality rates can be calculated from life tables." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 male adults" } ], "source_id": "2" }, { "id": "SP.DYN.CBRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The crude birth rate is not appropriate for comparison of different populations or areas with large differences in age-distributions. When the crude death rate is subtracted from the crude birth rate, the result is the rate of natural increase, which is the rate of population change in the absence of migration." }, { "id": "IndicatorName", "value": "Birth rate, crude (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Vital registers are the preferred source for these data, but in many developing countries systems for registering births and deaths are absent or incomplete because of deficiencies in the coverage of events or geographic areas. Many developing countries carry out special household surveys that ask respondents about recent births and deaths. Estimates derived in this way are subject to sampling errors and recall errors." }, { "id": "Longdefinition", "value": "Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT);\nPopulation and Vital Statistics Report (various years), United Nations (UN), publisher: UN Statistical Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The crude birth rate is calculated as the number of births in a given period divided by the average population in that period. For human populations the period is usually one year and, if the population changes in size over the year, the divisor is taken as the population at the mid-year. The rate is usually expressed in terms of 1,000 people: for example, a crude birth rate of 9.5 (per 1000 people) in a population of 1 million would imply 9500 births per year in the entire population.\nStatistical concept(s): Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration. Vital rates are based on data from birth and death registration systems, censuses, and sample surveys by national statistical offices and other organizations, or on demographic analysis. Data for the most recent year for some high-income countries are provisional estimates based on vital registers. The estimates for many other countries are from the United Nations Population Division." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "2" }, { "id": "SP.DYN.CDRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The crude death rate is a good indicator of the general health status of a geographic area or population. The crude death rate is not appropriate for comparison of different populations or areas with large differences in age-distributions. Higher crude death rates can be found in some developed countries, despite high life expectancy, because typically these countries have a much higher proportion of older people, due to lower recent birth rates and lower age-specific mortality rates." }, { "id": "IndicatorName", "value": "Death rate, crude (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Vital registers are the preferred source for these data, but in many developing countries systems for registering births and deaths are absent or incomplete because of deficiencies in the coverage of events or geographic areas. Many developing countries carry out special household surveys that ask respondents about recent births and deaths. Estimates derived in this way are subject to sampling errors and recall errors." }, { "id": "Longdefinition", "value": "Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT);\nPopulation and Vital Statistics Report (various years), United Nations (UN), publisher: UN Statistical Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The crude death rate is calculated as the number of deaths in a given period divided by the population exposed to risk of death in that period. For human populations the period is usually one year and, if the population changes in size over the year, the divisor is taken as the population at the mid-year. The rate is usually expressed in terms of 1,000 people: for example, a crude death rate of 9.5 (per 1000 people) in a population of 1 million would imply 9500 deaths per year in the entire population.\nStatistical concept(s): Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration. Vital rates are based on data from birth and death registration systems, censuses, and sample surveys by national statistical offices and other organizations, or on demographic analysis. Data for the most recent year for some high-income countries are provisional estimates based on vital registers. The estimates for many other countries are from the United Nations Population Division." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "2" }, { "id": "SP.DYN.CONM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Contraceptive prevalence among women of reproductive age is related to maternal and child health, as well as gender equality and HIV/AIDS. Contraceptives enable women and men to make informed decisions on family planning – whether, when, and how many children they would have. \n\n\n\nPreventing unwanted pregnancies is essential to reducing maternal deaths, especially in low- and middle- income countries where maternal mortality rate is high. With effective contraception, life-threatening pregnancy complications can be reduced, and thus maternal deaths can be averted. \n\n\n\nUsing condoms (one of the modern contraceptive methods) can prevent pregnancy as well as sexually transmitted diseases, including HIV." }, { "id": "IndicatorName", "value": "Contraceptive prevalence, any modern method (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While the data availability on contraceptive use has increased, in many countries the contraceptive use data are available only for married women. \n\n\n\nThe time frame used to assess contraceptive prevalence may vary. In many surveys, it is left to the respondent to determine what is meant by “currently using” a method of contraception." }, { "id": "Longdefinition", "value": "Contraceptive prevalence, any modern method is the percentage of married women ages 15-49 who are practicing, or whose sexual partners are practicing, at least one modern method of contraception. Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Contraceptive prevalence rates are obtained mainly from nationally representative household surveys, including: Demographic and Health Surveys; Multiple Indicator Cluster Surveys; Contraceptive Prevalence Surveys; Gender and Generations Survey; Reproductive Health Surveys; and World Fertility Surveys. Additional information was provided by other international survey programs and national surveys. \n\n\n\nMarried women refer to women who are married (defined in relation to the marriage laws or customs of a country) and to women in a union, which refers to women living with their partner in the same household (also referred to as cohabiting unions, consensual unions, unmarried unions, or “living together”)." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "2" }, { "id": "SP.DYN.CONU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Contraceptive prevalence among women of reproductive age is related to maternal and child health, as well as gender equality and HIV/AIDS. Contraceptives enable women and men to make informed decisions on family planning – whether, when, and how many children they would have. \n\n\n\nPreventing unwanted pregnancies is essential to reducing maternal deaths, especially in low- and middle- income countries where maternal mortality rate is high. With effective contraception, life-threatening pregnancy complications can be reduced, and thus maternal deaths can be averted. \n\n\n\nUsing condoms (one of the modern contraceptive methods) can prevent pregnancy as well as sexually transmitted diseases, including HIV." }, { "id": "IndicatorName", "value": "Contraceptive prevalence, any method (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While the data availability on contraceptive use has increased, in many countries the contraceptive use data are available only for married women. \n\n\n\nThe time frame used to assess contraceptive prevalence may vary. In many surveys, it is left to the respondent to determine what is meant by “currently using” a method of contraception." }, { "id": "Longdefinition", "value": "Contraceptive prevalence, any method is the percentage of married women ages 15-49 who are practicing, or whose sexual partners are practicing, any method of contraception (modern or traditional). Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception. Traditional methods of contraception include rhythm (e.g., fertility awareness based methods, periodic abstinence), withdrawal and other traditional methods." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Contraceptive prevalence rates are obtained mainly from nationally representative household surveys, including: Demographic and Health Surveys; Multiple Indicator Cluster Surveys; Contraceptive Prevalence Surveys; Gender and Generations Survey; Reproductive Health Surveys; and World Fertility Surveys. Additional information was provided by other international survey programs and national surveys. \n\n\n\nMarried women refer to women who are married (defined in relation to the marriage laws or customs of a country) and to women in a union, which refers to women living with their partner in the same household (also referred to as cohabiting unions, consensual unions, unmarried unions, or “living together”)." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "2" }, { "id": "SP.DYN.IMRT.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, infant, female (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Infant mortality rate, female is the number of female infants dying before reaching one year of age, per 1,000 female live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "2" }, { "id": "SP.DYN.IMRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, infant (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "2" }, { "id": "SP.DYN.IMRT.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, infant, male (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Infant mortality rate, male is the number of male infants dying before reaching one year of age, per 1,000 male live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "2" }, { "id": "SP.DYN.LE00.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, female (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "2" }, { "id": "SP.DYN.LE00.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, total (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), uri: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices, note: Derived from male and female life expectancy at birth from sources such as statistical databases and publications from national statistical offices.;\nDemographic Statistics, Eurostat (ESTAT), note: Derived from male and female life expectancy at birth from sources such as Eurostat: Demographic Statistics." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates, or derived from male and female life expectancy at birth.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "2" }, { "id": "SP.DYN.LE00.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, male (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "2" }, { "id": "SP.DYN.TFRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries." }, { "id": "IndicatorName", "value": "Fertility rate, total (births per woman)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year." }, { "id": "Othernotes", "value": "Relevance to gender indicator: it can indicate the status of women within households and a woman’s decision about the number and spacing of children." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Total fertility rate is the sum of the age-specific fertility rates (multiplied by five, if the age-specific fertility rates are for 5-year age groups).\nStatistical concept(s): Total fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without reliable vital registration systems fertility rates are generally based on extrapolations from trends observed in censuses or surveys from earlier years." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Births per woman" } ], "source_id": "2" }, { "id": "SP.DYN.TO65.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. The lower the age specific mortality rates before age 65, the higher the proportion of people survive to age 65." }, { "id": "IndicatorName", "value": "Survival to age 65, female (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Survival to age 65 refers to the percentage of a cohort of newborn infants that would survive to age 65, if subject to age specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors, l(x), at exact age x in a female period life table. The formula is: (l(65))/(l(0))*100.\nStatistical concept(s): Survival to age 65 is calculated in a period life table. It provides a population's mortality level up to age 65 at a given time. It therefore does not reflect the mortality level that a person actually experiences during his/her life, which can be calculated in a cohort life table." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.DYN.TO65.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. The lower the age specific mortality rates before age 65, the higher the proportion of people survive to age 65." }, { "id": "IndicatorName", "value": "Survival to age 65, male (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Survival to age 65 refers to the percentage of a cohort of newborn infants that would survive to age 65, if subject to age specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors l(x) at exact age x, in a male period life table. The formula is: (l(65))/(l(0))*100.\nStatistical concept(s): Survival to age 65 is calculated in a period life table. It provides a population's mortality level up to age 65 at a given time. It therefore does not reflect the mortality level that a person actually experiences during his/her life, which can be calculated in a cohort life table." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.DYN.WFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Wanted fertility rate (births per woman)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Wanted fertility rate is an estimate of what the total fertility rate would be if all unwanted births were avoided." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Demographic and Health Surveys" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data is calculated by summing the seven age-specific wanted fertility rates, multiplying the result by five, and dividing by 1000. A birth is considered wanted if the number of living children at the time of conception is less than the ideal number of children as reported by the respondent. Special responses such as \"don't know,\" \"up to God,\" or other non-numeric responses for the ideal number of children are assumed to indicate a high ideal number of children. For more details, please refer to the DHS website: https://dhsprogram.com/data/Guide-to-DHS-Statistics/Wanted_Fertility.htm" }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Births per woman" } ], "source_id": "2" }, { "id": "SP.HOU.FEMA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The composition of households plays a pivotal role in determining the well-being of families and individuals. Research from multiple developed countries indicates that female-headed households, especially those with single mothers, face a higher risk of poverty than those with two parents (United Nations, \"Patterns and trends in household size and composition: Evidence from a United Nations dataset,\" 2019). Understanding the diversity in household structures across various populations is essential for achieving Sustainable Development Goal 1, which is dedicated to eradicating poverty in all its forms." }, { "id": "IndicatorName", "value": "Female headed households (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The definition of female-headed household differs greatly across countries, making cross-country comparison difficult. In some cases it is assumed that a woman cannot be the head of any household with an adult male, because of sex-biased stereotype. Caution should be used in interpreting the data." }, { "id": "Longdefinition", "value": "Female headed households refers to the percentage of households that are headed by females." }, { "id": "Othernotes", "value": "The composition of a household plays a role in the determining other characteristics of a household, such as how many children are sent to school and the distribution of family income." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "DHS API, DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: HC_HHHD_H_FEM; \tIndicator name from the original source: Female-headed households, publisher: DHS Program (ICF), type: API, date accessed: 2024-06-14" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of households headed by women divided by the total number of households. \n\n\n\n\n\n\n\n\n\n\n\nThe definition of a household is a person or group of related or unrelated persons who live together in the same dwelling unit(s), who acknowledge one adult male or female as the head of the household, who share the same housekeeping arrangements and who are considered a single unit.\nStatistical concept(s): The information on the characteristics of household head (e.g., sex, age) is collected the household questionnaire in the Demographic and Health Surveys (DHS). Typically, this data is obtained by detailing the connection of each member of the household to a designated central figure, who is considered the primary reference for the household." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "% of households" } ], "source_id": "2" }, { "id": "SP.M15.2024.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although the legal age of marriage is defined as 18 years in most countries, the practice of child marriage remains widespread. A women’s access to education and later her employment opportunities as well as the nature and terms of her work are often compromised by this practice. Young married girls whose schooling is cut short often lack the knowledge and skills for formal work and are limited to occupations with lower incomes and inferior working conditions. Sustainable Development Goal 5 commits to eliminate the practice of child marriage." }, { "id": "IndicatorName", "value": "Women who were first married by age 15 (% of women ages 20-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The measure of child marriage is designed to be retrospective, focusing on the age at first marriage among adult women who have already passed the risk period. Although it is feasible to assess the current marital status of girls under 15, this approach could underestimate the true extent of child marriage. This is because girls who are not married at the time of survey may still marry before reaching 15." }, { "id": "Longdefinition", "value": "Women who were first married by age 15 refers to the percentage of women ages 20-24 who were first married by age 15." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.3.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "UNICEF Data, UN Children's Fund (UNICEF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_20-24_MRD_U15; \tIndicator name from the original source: Percentage of women (aged 20-24 years) married or in union before age 15, type: API;\nDHS API, DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: MA_MBAG_W_B15; \tIndicator name from the original source: Women first married by exact age 15, type: API" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Number of women aged 20-24 who were first married or in union before age 15 divided by the total number of women aged 20-24 in the population multiplied by 100. The primary sources for this indicator are the Multiple Indicator Cluster Surveys (MICS) and the Demographic and Health Surveys (DHS). Additionally, other national household surveys and censuses contribute to the data. These figures are compiled by UNICEF, which coordinates with countries to gather the information.\nStatistical concept(s): This indicator includes both formal marriages and informal cohabiting relationships. Informal relationships are usually defined as those where a couple lives together with the intention of a long-term relationship but without a formal civil or religious ceremony. The incidence of child marriage is assessed retrospectively among women who are past the risk of marrying as children. The age range of 20 to 24 years is conventionally used to reflect the current prevalence of child marriage." }, { "id": "Topic", "value": "Gender: Agency" }, { "id": "Unitofmeasure", "value": "% of women ages 20-24" } ], "source_id": "2" }, { "id": "SP.M18.2024.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although the legal age of marriage is defined as 18 years in most countries, the practice of child marriage remains widespread. A women’s access to education and later her employment opportunities as well as the nature and terms of her work are often compromised by this practice. Young married girls whose schooling is cut short often lack the knowledge and skills for formal work and are limited to occupations with lower incomes and inferior working conditions. Sustainable Development Goal 5 commits to eliminate the practice of child marriage." }, { "id": "IndicatorName", "value": "Women who were first married by age 18 (% of women ages 20-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The measure of child marriage is designed to be retrospective, focusing on the age at first marriage among adult women who have already passed the risk period. Although it is feasible to assess the current marital status of girls under 18, this approach could underestimate the true extent of child marriage. This is because girls who are not married at the time of survey may still marry before reaching 18." }, { "id": "Longdefinition", "value": "Women who were first married by age 18 refers to the percentage of women ages 20-24 who were first married by age 18." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.3.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "UNICEF Data, UN Children's Fund (UNICEF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_20-24_MRD_U18; \tIndicator name from the original source: Percentage of women (aged 20-24 years) married or in union before age 18, type: API;\nDHS API, DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: MA_MBAG_W_B18; \tIndicator name from the original source: Women first married by exact age 18, type: API" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Number of women aged 20-24 who were first married or in union before age 18 divided by the total number of women aged 20-24 in the population multiplied by 100. The primary sources for this indicator are the Multiple Indicator Cluster Surveys (MICS) and the Demographic and Health Surveys (DHS). Additionally, other national household surveys and censuses contribute to the data. These figures are compiled by UNICEF, which coordinates with countries to gather the information.\nStatistical concept(s): This indicator includes both formal marriages and informal cohabiting relationships. Informal relationships are usually defined as those where a couple lives together with the intention of a long-term relationship but without a formal civil or religious ceremony. The incidence of child marriage is assessed retrospectively among women who are past the risk of marrying as children. The age range of 20 to 24 years is conventionally used to reflect the current prevalence of child marriage." }, { "id": "Topic", "value": "Gender: Agency" }, { "id": "Unitofmeasure", "value": "% of women ages 20-24" } ], "source_id": "2" }, { "id": "SP.MTR.1519.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Having a child during the teenage years limits girls' opportunities for better education, jobs, and income. Pregnancy is more likely to be unintended during the teenage years, and births are more likely to be premature and are associated with greater risks of complications during delivery and of death. In many countries maternal mortality is a leading cause of death among women of reproductive age, although most of those deaths are preventable. Infants of adolescent mothers are also more likely to have low birth weight, which can have a long-term impact on their health and development. Complications from pregnancy and childbirth are the leading cause of death among girls aged 15-19 years in many low- and middle-income countries." }, { "id": "IndicatorName", "value": "Teenage mothers (% of women ages 15-19 who have had children or are currently pregnant)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Teenage mothers are the percentage of women ages 15-19 who already have children or are currently pregnant." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Demographic and Health Surveys" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data represents the combined percentage of women aged 15-19 who are mothers and those who are pregnant with their first child. This information is gathered through individual interviews with women of reproductive age during household surveys, including Demographic and Health Surveys. For more details, please refer to the DHS website: https://dhsprogram.com/data/Guide-to-DHS-Statistics/Teenage_Pregnancy_and_Motherhood.htm" }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Percent of women ages 15-19 who have had children or are currently pregnant" } ], "source_id": "2" }, { "id": "SP.POP.0004.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 00-04, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 4 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.0004.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 00-04, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 4 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.0014.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SP.POP.0014.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 14 as a percentage of the total female population. Population is based on the de facto definition of population." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.0014.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SP.POP.0014.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 14 as a percentage of the total male population. Population is based on the de facto definition of population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.0014.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Staff estimates, World Bank (WB), note: Staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects., publisher: World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data.;\nWorld Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SP.POP.0014.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14 (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Population between the ages 0 to 14 as a percentage of the total population. Population is based on the de facto definition of population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects., United Nations Population Division, uri: https://population.un.org/wpp/, publisher: United Nations Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.0509.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 05-09, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 5 to 9 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.0509.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 05-09, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 5 to 9 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.1014.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 10-14, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 10 to 14 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.1014.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 10-14, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 10 to 14 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.1519.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 15-19, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 15 to 19 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.1519.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 15-19, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 15 to 19 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.1564.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SP.POP.1564.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 15 to 64 as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.1564.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SP.POP.1564.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 15 to 64 as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.1564.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SP.POP.1564.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64 (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Total population between the ages 15 to 64 as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.2024.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 20-24, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 20 to 24 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.2024.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 20-24, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 20 to 24 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.2529.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 25-29, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 25 to 29 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.2529.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 25-29, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 25 to 29 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.3034.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 30-34, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 30 to 34 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.3034.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 30-34, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 30 to 34 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.3539.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 35-39, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 35 to 39 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.3539.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 35-39, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 35 to 39 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.4044.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 40-44, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 40 to 44 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.4044.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 40-44, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 40 to 44 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.4549.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 45-49, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 45 to 49 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.4549.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 45-49, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 45 to 49 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.5054.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 50-54, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 50 to 54 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.5054.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 50-54, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 50 to 54 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.5559.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 55-59, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 55 to 59 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.5559.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 55-59, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 55 to 59 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.6064.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 60-64, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 60 to 64 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.6064.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 60-64, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 60 to 64 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.6569.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 65-69, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 65 to 69 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.6569.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 65-69, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 65 to 69 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.65UP.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SP.POP.65UP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population 65 years of age or older as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.65UP.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SP.POP.65UP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population 65 years of age or older as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.65UP.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SP.POP.65UP.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Population ages 65 and above as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.7074.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 70-74, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 70 to 74 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.7074.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 70-74, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 70 to 74 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.7579.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 75-79, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 75 to 79 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.7579.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 75-79, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 75 to 79 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.80UP.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 80 and above, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 80 and above as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.80UP.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 80 and above, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 80 and above as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.BRTH.MF", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In the absence of interference, it is expected that the sex ratio at birth is fairly stable within the range of 1.03 to 1.07 boys born per 1.00 girls. However, in some populations, the observed sex ratio at birth is well above this range because of sex-selection driven by the preference for sons over daughters." }, { "id": "IndicatorName", "value": "Sex ratio at birth (male births per female births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Sex ratio at birth refers to male births per female births." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Sex ratio at birth is calculated as number of male births divided by number of female births.\nStatistical concept(s): If the sex ratio at birth is greater than 1, it indicates more boys born that year than girls." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Male births per female births" } ], "source_id": "2" }, { "id": "SP.POP.DPND", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Age dependency ratio (% of working-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Othernotes", "value": "Relevance to gender indicator: this indicator implies the dependency burden that the working-age population bears in relation to children and the elderly. Many times single or widowed women who are the sole caregiver of a household have a high dependency ratio." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Age dependency ratio is calculated as 100 x (Population (0-14) + Population (65+)) / Population (15-64). Data are shown as the proportion of dependents per 100 working-age population.\nStatistical concept(s): Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But dependency ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.\n\n\n\nAge structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.DPND.OL", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Age dependency ratio, old (% of working-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Age dependency ratio, old is calculated as 100 x (Population (65+)) / Population (15-64). Data are shown as the proportion of old dependents per 100 working-age population.\nStatistical concept(s): Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But dependency ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.\n\n\n\nAge structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.DPND.YG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Age dependency ratio, young (% of working-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Age dependency ratio, young, is the ratio of younger dependents--people younger than 15--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Age dependency ratio, young is calculated as 100 x (Population (0-14)) / Population (15-64). Data are shown as the proportion of young dependents per 100 working-age population.\nStatistical concept(s): Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But dependency ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.\n\n\n\nAge structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.GROW", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "Derived from total population. Population source: United Nations Population Division, National Statistical Offices, Eurostat, United Nations Statistics Division." }, { "id": "Developmentrelevance", "value": "Increases in human population, whether as a result of immigration or more births than deaths, can impact natural resources and social infrastructure. This can place pressure on a country's sustainability. A significant growth in population will negatively impact the availability of land for agricultural production, and will aggravate demand for food, energy, water, social services, and infrastructure. On the other hand, decreasing population size - a result of fewer births than deaths, and people moving out of a country - can impact a government's commitment to maintain services and infrastructure." }, { "id": "IndicatorName", "value": "Population growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), note: Derived from total population, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices, note: Derived from total population;\nDemographic Statistics, Eurostat (ESTAT), note: Derived from total population;\nPopulation and Vital Statistics Report (various years), United Nations (UN), note: Derived from total population, publisher: UN Statistical Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The growth rate is computed using the exponential growth formula:\n\n\n\nr = ln(pn/p0)/n, \n\n\n\nwhere r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods.\n\n\n\nFor information on total population from which the growth rates are calculated, see total population (SP.POP.TOTL).\nStatistical concept(s): Total population growth rates are calculated on the assumption that rate of growth is constant between two points in time." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.SCIE.RD.P6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Science, technology, and innovation constitute pivotal elements for achieving sustainable growth. Sustainable Development Goal (SDG) target 9.5 is dedicated to the enhancement of scientific research and the advancement of technological capabilities within industrial sectors, with a particular focus on low- and middle-income countries. Furthermore, this target encompasses the objective of augmenting the cadre of research and development personnel, as well as escalating expenditures in research." }, { "id": "IndicatorName", "value": "Researchers in R&D (per million people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers." }, { "id": "Longdefinition", "value": "The number of researchers engaged in Research &Development (R&D), expressed as per million. Researchers are professionals who conduct research and improve or develop concepts, theories, models techniques instrumentation, software of operational methods. R&D covers basic research, applied research, and experimental development." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2024" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources/bulk, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-03-26, date published: 2025-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by taking the number of researchers in a specified year, dividing it by the total population—referencing the mid-year population figure—and then multiplying the result by one million.\n\n\n\n\n\n\n\n\n\nData are collected through national research and experimental development (R&D) surveys, either by the national statistical office or a line ministry (such as the Ministry for Science and Technology). The data compilers are the UNESCO Institute for Statistics (UIS), Organisation for Economic Co-operation and Development (OECD), Eurostat (Statistical Office of the European Union) and the Network on Science and Technology Indicators – Ibero-American and Inter-American (RICYT), African Science, Technology and Innovation (STI) Indicators Initiative (ASTII) of the African Union Development Agency-NEPAD (AUDA-NEPAD).\nStatistical concept(s): Researchers are professionals engaged in the conception or creation of new knowledge, products, processes, methods and systems, as well as in the management of these projects. Students studying at the master’s or doctoral level (ISCED2011 level 7 or 8) engaged in R&D are included. \n\n\n\n\n\n\n\n\n\nThe OECD's Frascati Manual defines research and experimental development as \"creative work undertaken on a systemic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications.\" R&D covers basic research, applied research, and experimental development (Reference: https://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en). \n\n\n\n\n\n\n\n\n\n(1) Basic research - Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view.\n\n\n\n\n\n\n\n\n\n(2) Applied research - Applied research is also original investigation undertaken in order to acquire new knowledge; it is, however, directed primarily towards a specific practical aim or objective.\n\n\n\n\n\n\n\n\n\n(3) Experimental development - Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed.\n\n\n\n\n\n\n\n\n\nThe fields of science and technology used to classify R&D according to the Revised Fields of Science and Technology Classification are:\n\n\n\n\n1. Natural sciences;\n\n\n\n\n2. Engineering and technology;\n\n\n\n\n3. Medical and health sciences;\n\n\n\n\n4. Agricultural sciences;\n\n\n\n\n5. Social sciences;\n\n\n\n\n6. Humanities and the arts.\n\n\n\n\n\n\n\n\n\nData are for full-time equivalent (FTE); the FTE of R&D personnel is defined as the ratio of working hours actually spent on R&D during a specific reference period (usually a calendar year) divided by the total number of hours conventionally worked in the same period by an individual or by a group. \n\n\n\n\n\n\n\n\n\nThe data are obtained through statistical surveys which are regularly conducted at national level covering R&D performing entities in the private and public sectors." }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "Per million people" } ], "source_id": "2" }, { "id": "SP.POP.TECH.RD.P6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Technicians in R&D (per million people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers." }, { "id": "Longdefinition", "value": "The number of technicians participated in Research & Development (R&D), expressed as per million. Technicians and equivalent staff are people who perform scientific and technical tasks involving the application of concepts and operational methods, normally under the supervision of researchers. R&D covers basic research, applied research, and experimental development." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2018" }, { "id": "Source", "value": "UNESCO Institute of Statistics (UIS), UN Educational, Scientific and Cultural Organization (UNESCO), uri: http://uis.unesco.org, note: Data as of March 2021, publisher: UNESCO Institute of Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Technicians in research and development (R&D) are persons whose main tasks require technical knowledge and experience in one or more fields of engineering, physical and life sciences, or social sciences and humanities. \n\nThe OECD's Frascati Manual defines research and experimental development as \"creative work undertaken on a systemic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications.\" R&D covers basic research, applied research, and experimental development.\n\n(1) Basic research - Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view.\n\n(2) Applied research - Applied research is also original investigation undertaken in order to acquire new knowledge; it is, however, directed primarily towards a specific practical aim or objective.\n\n(3) Experimental development - Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed.\n\nThe fields of science and technology used to classify R&D according to the Revised Fields of Science and Technology Classification are:\n1. Natural sciences;\n2. Engineering and technology;\n3. Medical and health sciences;\n4. Agricultural sciences;\n5. Social sciences;\n6. Humanities and the arts.\n\nData are for full-time equivalent (FTE); the FTE of R&D personnel is defined as the ratio of working hours actually spent on R&D during a specific reference period (usually a calendar year) divided by the total number of hours conventionally worked in the same period by an individual or by a group. \n\nThe data are obtained through statistical surveys which are regularly conducted at national level covering R&D performing entities in the private and public sectors." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "2" }, { "id": "SP.POP.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Increases in human population, whether as a result of immigration or more births than deaths, can impact natural resources and social infrastructure. This can place pressure on a country's sustainability. A significant growth in population will negatively impact the availability of land for agricultural production, and will aggravate demand for food, energy, water, social services, and infrastructure. On the other hand, decreasing population size - a result of fewer births than deaths, and people moving out of a country - can impact a government's commitment to maintain services and infrastructure." }, { "id": "IndicatorName", "value": "Population, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Current population estimates for developing countries that lack (i) reliable recent census data, and (ii) pre- and post-census estimates for countries with census data, are provided by the United Nations Population Division and other agencies. \n\n\n\nThe cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in both the model and the data.\n\n\n\nBecause future trends cannot be known with certainty, population projections have a wide range of uncertainty." }, { "id": "Longdefinition", "value": "Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates." }, { "id": "Othernotes", "value": "Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices, uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population estimates are usually based on national population censuses, and estimates of fertility, mortality and migration.\n\n\n\nErrors and undercounting in census occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census.\n\n\n\nThe quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data.\n\n\n\nThe currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census.\n\n\n\nThe United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems.\n\n\n\nInternational migration is the only other factor besides birth and death rates that directly determines a country's population change. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect.\n\n\n\nOne of the major data sources of this indicator is UN Population Division's World Population Prospects, which use the cohort component method to produce population estimates and projections.\n\n\n\nPopulation projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant.\nStatistical concept(s): Estimates of total population describe the size of total population. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SP.POP.TOTL.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Females comprise almost one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population is based on the de facto definition of population, which counts all female residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SP.POP.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age/sex distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Females comprise almost one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, female (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population is the percentage of the population that is female. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2022 Revision" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.POP.TOTL.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Males comprise about one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population is based on the de facto definition of population, which counts all male residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SP.POP.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age/sex distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Males comprise about one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, male (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population is the percentage of the population that is male. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2022 Revision" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.REG.BRTH.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 16.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2003-2021" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "2" }, { "id": "SP.REG.BRTH.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 16.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2003-2021" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "2" }, { "id": "SP.REG.BRTH.RU.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration, rural (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "2" }, { "id": "SP.REG.BRTH.UR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration, urban (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Othernotes", "value": "This is a disaggregated indicator (residence) for Sustainable Development Goal 16.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "2" }, { "id": "SP.REG.BRTH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 16.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "2" }, { "id": "SP.REG.DTHS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Completeness of death registration with cause-of-death information (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Completeness of death registration is the estimated percentage of deaths that are registered with their cause of death information in the vital registration system of a country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2017" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO), uri: http://apps.who.int/gho/data/node.main.1?lang=en" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The completeness of death registration is calculated by dividing the total number of deaths registered with cause-of-death information in the vital registration system for a given country-year by the total estimated deaths for that year for the national population. The national level of completeness is provided by the National Statistical Offices of all countries and areas to the United Nations Statistics Division as part of the annual data collection for the United Nations Demographic Yearbook. Currently, the threshold used for compiling the data for this indicator is 75 percent for death registration." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percent of completeness of death registration" } ], "source_id": "2" }, { "id": "SP.RUR.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and urban/rural distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "The rural population is calculated using the urban share reported by the United Nations Population Division.\n\nThe two distinct images - isolated farm, thriving metropolis - represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income.\n\nA 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population density and remoteness together define a set of rural areas that face special development challenges.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\"\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nRural population methodology is defined by various national statistical offices. In the United States, for example, the US Census Bureau's urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. \"Rural\" encompasses all population, housing, and territory not included within an urban area." }, { "id": "IndicatorName", "value": "Rural population" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population. Aggregation of urban and rural population may not add up to total population because of different country coverages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using World Bank's total population estimates and rural ratios derived from the United Nations World Urbanization Prospects.\nStatistical concept(s): Rural population is calculated as the difference between the total population and the urban population. Rural population is approximated as the midyear nonurban population. While a practical means of identifying the rural population, it is not a precise measure." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SP.RUR.TOTL.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and urban/rural distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "The rural population is calculated using the urban share reported by the United Nations Population Division.\n\nThe two distinct images - isolated farm, thriving metropolis - represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income.\n\nA 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population density and remoteness together define a set of rural areas that face special development challenges.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\"\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nRural population methodology is defined by various national statistical offices. In the United States, for example, the US Census Bureau's urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. \"Rural\" encompasses all population, housing, and territory not included within an urban area." }, { "id": "IndicatorName", "value": "Rural population growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. \n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Annual rural population growth rate for year t is the exponential rate of growth of midyear rural population from year t-1 to t, expressed as a percentage. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2025" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated from rural population estimates. The rural population estimates are calulcated using World Bank's total population estimates and rural ratios derived from the United Nations World Urbanization Prospects.\n\n\n\n\n\n\n\n\n\n\n\nThe growth rate is computed using the exponential growth formula:\n\n\n\n\n\n\n\n\n\n\n\nr = ln(pn/p0)/n, \n\n\n\n\n\n\n\n\n\n\n\nwhere r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods.\nStatistical concept(s): Rural population is calculated as the difference between the total population and the urban population. Rural population is approximated as the midyear nonurban population. While a practical means of identifying the rural population, it is not a precise measure." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.RUR.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The rural population is calculated using the urban share reported by the United Nations Population Division.\n\nThe two distinct images - isolated farm, thriving metropolis - represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income.\n\nA 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population density and remoteness together define a set of rural areas that face special development challenges.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\"\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nRural population methodology is defined by various national statistical offices. In the United States, for example, the US Census Bureau's urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. \"Rural\" encompasses all population, housing, and territory not included within an urban area." }, { "id": "IndicatorName", "value": "Rural population (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentages rural are calculated as the difference between 100 and the proportion of urban population in percentage.\nStatistical concept(s): Rural population is calculated as the difference between the total population and the urban population. Rural population is approximated as the midyear nonurban population. While a practical means of identifying the rural population, it is not a precise measure." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.URB.GROW", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and urban/rural distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Most countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Annual urban population growth rate for year t is the exponential rate of growth of midyear urban population from year t-1 to t, expressed as a percentage. Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank total population estimates and urban ratios from the United Nations World Urbanization Prospects." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2025" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated from urban population estimates. The urban population estimates are calulcated using World Bank's total population estimates and urban ratios from the United Nations World Urbanization Prospects.\n\n\n\n\n\n\n\n\n\n\n\nThe growth rate is computed using the exponential growth formula:\n\n\n\n\n\n\n\n\n\n\n\nr = ln(pn/p0)/n, \n\n\n\n\n\n\n\n\n\n\n\nwhere r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods.\nStatistical concept(s): Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.URB.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and urban/rural distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. \n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. Aggregation of urban and rural population may not add up to total population because of different country coverages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using World Bank's total population estimates and urban ratios from the United Nations World Urbanization Prospects.\nStatistical concept(s): Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "2" }, { "id": "SP.URB.TOTL.IN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage.\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentages urban are the numbers of persons residing in an area defined as ''urban'' per 100 total population.\nStatistical concept(s): Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "2" }, { "id": "SP.UWT.TFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Unmet need for contraception (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Unmet need for contraception is the percentage of fertile, married women of reproductive age who do not want to become pregnant and are not using contraception." }, { "id": "Othernotes", "value": "Unmet need for contraception measures the capacity women have in achieving their desired family size and birth spacing. Many couples in developing countries want to limit or postpone childbearing but are not using effective contraception. These couples have an unmet need for contraception. Common reasons are lack of knowledge about contraceptive methods and concerns about possible side effects." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2024" }, { "id": "Source", "value": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMany couples in developing countries want to limit or postpone childbearing but are not using effective contraception. These couples have an unmet need for contraception. Common reasons are lack of knowledge about contraceptive methods and concerns about possible side effects. This indicator excludes women not exposed to the risk of unintended pregnancy because of menopause, infertility, or postpartum anovulation." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "2" }, { "id": "ST.INT.ARVL", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities." }, { "id": "IndicatorName", "value": "International tourism, number of arrivals" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Tourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival. The data on inbound tourism show the arrivals of nonresident tourists (overnight visitors) at national borders. When data on international tourists are unavailable or incomplete, the data show the arrivals of international visitors, which include tourists, same-day visitors, cruise passengers, and crew members.\n\nSources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries." }, { "id": "Longdefinition", "value": "International inbound tourists (overnight visitors) are the number of tourists who travel to a country other than that in which they have their usual residence, but outside their usual environment, for a period not exceeding 12 months and whose main purpose in visiting is other than an activity remunerated from within the country visited. When data on number of tourists are not available, the number of visitors, which includes tourists, same-day visitors, cruise passengers, and crew members, is shown instead. Sources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries. The data on inbound tourists refer to the number of arrivals, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Statistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nArrivals data measure the flows of international visitors to the country of reference: each arrival corresponds to one in inbound tourism trip. If a person visits several countries during the course of a single trip, his/her arrival in each country is recorded separately. In an accounting period, arrivals are not necessarily equal to the number of persons travelling (when a person visits the same country several times a year, each trip by the same person is counted as a separate arrival).\n\nArrivals data should correspond to inbound visitors by including both tourists and same-day non-resident visitors. All other types of travelers (such as border, seasonal and other short-term workers, long-term students and others) should be excluded as they do not qualify as visitors.\n\nData are obtained from different sources: administrative records (immigration, traffic counts, and other possible types of controls), border surveys or a mix of them. If data are obtained from accommodation surveys, the number of guests is used as estimate of arrival figures; consequently, in this case, breakdowns by regions, main purpose of the trip, modes of transport used or forms of organization of the trip are based on complementary visitor surveys." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "2" }, { "id": "ST.INT.DPRT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities." }, { "id": "IndicatorName", "value": "International tourism, number of departures" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Tourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling." }, { "id": "Longdefinition", "value": "International outbound tourists are the number of departures that people make from their country of usual residence to any other country for any purpose other than a remunerated activity in the country visited. The data on outbound tourists refer to the number of departures, not to the number of people traveling. Thus a person who makes several trips from a country during a given period is counted each time as a new departure." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Statistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nDepartures data measure the flows of resident visitors leaving the country of reference. Departures are not necessarily equal to the number of arrivals reported by international destinations for the country of reference.\n\nIn many countries, the characteristics of trips and visitors are established through questions on the entry/departure cards, in surveys at the borders, at destination (accommodation surveys) or as part of household surveys (for domestic and outbound tourism). The entry/departure cards, or records of entry and departure, captured and reconciled by the immigration authorities are often the basic source for establishing the flows of inbound and outbound visitors. These cards usually collect information on a census basis on name, sex, age, nationality, current address, date of arrival (or departure in the departure card), purpose of trip, main destination visited and length of stay (expected on arrival and actual on departure for inbound visitors; expected on departure and actual on arrival for outbound visitors).\n\nData is collected using one of three methods, or a combination of these to determine the flows of outbound visitors: using an entry/departure card; a specific survey at the border, or observing them from household surveys because they belong to resident households. In the latter case, the information on outbound trips is usually collected at the same time as that on domestic trips." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "2" }, { "id": "ST.INT.RCPT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities." }, { "id": "IndicatorName", "value": "International tourism, receipts (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Tourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival. The data on inbound tourism show the arrivals of nonresident tourists (overnight visitors) at national borders. When data on international tourists are unavailable or incomplete, the data show the arrivals of international visitors, which include tourists, same-day visitors, cruise passengers, and crew members.\n\nSources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries.\n\nExpenditure associated with the activity of international visitors has been traditionally identified with the travel item of the Balance of Payments (BOP): in the case of inbound tourism, those expenditures associated with inbound visitors are registered as \"credits\" in the BOP and refers to \"travel receipts\".\n\nThe 2008 International Recommendations for Tourism Statistics consider that \"tourism industries and products\" includes transport of passengers. Consequently, a better estimate of tourism-related expenditure by inbound and outbound visitors in an international scenario would be, in terms of the BOP, the value of the travel item plus that of the passenger transport item.\n\nNevertheless, users should be aware that BOP estimates include, in addition to expenditures associated to visitors, those related to other types of travelers (these might be substantial in some countries; for instance, long-term students or patients, border and seasonal workers, etc.). Also data on expenditure by main purpose of the trip are BOP data." }, { "id": "Longdefinition", "value": "International tourism receipts are expenditures by international inbound visitors, including payments to national carriers for international transport. These receipts include any other prepayment made for goods or services received in the destination country. They also may include receipts from same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include receipts for passenger transport items. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inbound tourism expenditures may include receipts from same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include receipts for passenger transport items. Their share in exports is calculated as a ratio to exports of goods and services (all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services).\n\nStatistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization (WTO) reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nThe World Tourism Organization is improving its coverage of tourism expenditure data, using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. These data include travel and passenger transport items as defined in the IMF's Balance of Payments. When the IMF does not report data on passenger transport items, expenditure data for travel items are shown.\n\nThe aggregates are calculated using the World Bank's weighted aggregation methodology and differ from the World Tourism Organization's aggregates." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "2" }, { "id": "ST.INT.RCPT.XP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities.\n\nThis measure reflects the importance of tourism as an internationally traded service relative to other categories of exports. Such a measure reveals the degree of tourism specialization in a country's export structure and the relative capability of tourism in generating foreign revenues." }, { "id": "IndicatorName", "value": "International tourism, receipts (% of total exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Tourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival. The data on inbound tourism show the arrivals of nonresident tourists (overnight visitors) at national borders. When data on international tourists are unavailable or incomplete, the data show the arrivals of international visitors, which include tourists, same-day visitors, cruise passengers, and crew members.\n\nSources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries.\n\nExpenditure associated with the activity of international visitors has been traditionally identified with the travel item of the Balance of Payments (BOP): in the case of inbound tourism, those expenditures associated with inbound visitors are registered as \"credits\" in the BOP and refers to \"travel receipts\".\n\nThe 2008 International Recommendations for Tourism Statistics consider that \"tourism industries and products\" includes transport of passengers. Consequently, a better estimate of tourism-related expenditure by inbound and outbound visitors in an international scenario would be, in terms of the BOP, the value of the travel item plus that of the passenger transport item.\n\nNevertheless, users should be aware that BOP estimates include, in addition to expenditures associated to visitors, those related to other types of travelers (these might be substantial in some countries; for instance, long-term students or patients, border and seasonal workers, etc.). Also data on expenditure by main purpose of the trip are BOP data." }, { "id": "Longdefinition", "value": "International tourism receipts are expenditures by international inbound visitors, including payments to national carriers for international transport. These receipts include any other prepayment made for goods or services received in the destination country. They also may include receipts from same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include receipts for passenger transport items. Their share in exports is calculated as a ratio to exports of goods and services, which comprise all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism;\nIMF exports estimates, International Monetary Fund (IMF);\nWorld Bank exports estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inbound tourism expenditures may include receipts from same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include receipts for passenger transport items. Their share in exports is calculated as a ratio to exports of goods and services (all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services).\n\nInternational tourism expenditures' share in exports is calculated as a ratio to exports of goods and services, which comprise all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services.\n\nStatistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization (WTO) reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nThe World Tourism Organization is improving its coverage of tourism expenditure data, using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. These data include travel and passenger transport items as defined in the IMF's Balance of Payments. When the IMF does not report data on passenger transport items, expenditure data for travel items are shown.\n\nThe aggregates are calculated using the World Bank's weighted aggregation methodology and differ from the World Tourism Organization's aggregates." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "2" }, { "id": "ST.INT.TRNR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "International tourism, receipts for passenger transport items (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "International tourism receipts for passenger transport items are expenditures by international inbound visitors for all services provided in the international transportation by resident carriers. Also included are passenger services performed within an economy by nonresident carriers. Excluded are passenger services provided to nonresidents by resident carriers within the resident economies; these are included in travel items. In addition to the services covered by passenger fares--including fares that are a part of package tours but excluding cruise fares, which are included in travel--passenger services include such items as charges for excess baggage, vehicles, or other personal accompanying effects and expenditures for food, drink, or other items for which passengers make expenditures while on board carriers. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Tourism data is compiled based on the International Recommendations for Tourism Statistics (IRTS 2008). The World Tourism Organization enhances its coverage of tourism expenditure data by using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. When data on passenger transport items is unavailable, expenditure data for travel items are shown." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "2" }, { "id": "ST.INT.TRNX.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "International tourism, expenditures for passenger transport items (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "International tourism expenditures for passenger transport items are expenditures of international outbound visitors in other countries for all services provided during international transportation by nonresident carriers. Also included are passenger services performed within an economy by nonresident carriers. Excluded are passenger services provided to nonresidents by resident carriers within the resident economies; these are included in travel items. In addition to the services covered by passenger fares--including fares that are a part of package tours but excluding cruise fares, which are included in travel--passenger services include such items as charges for excess baggage, vehicles, or other personal accompanying effects and expenditures for food, drink, or other items for which passengers make expenditures while on board carriers. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Tourism data is compiled based on the International Recommendations for Tourism Statistics (IRTS 2008). The World Tourism Organization enhances its coverage of tourism expenditure data by using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. When data on passenger transport items is unavailable, expenditure data for travel items are shown." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "2" }, { "id": "ST.INT.TVLR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "International tourism, receipts for travel items (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "International tourism receipts for travel items are expenditures by international inbound visitors in the reporting economy. The goods and services are purchased by, or on behalf of, the traveler or provided, without a quid pro quo, for the traveler to use or give away. These receipts should include any other prepayment made for goods or services received in the destination country. They also may include receipts from same-day visitors, except in cases where these are so important as to justify a separate classification. Excluded is the international carriage of travelers, which is covered in passenger travel items. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Tourism data is compiled based on the International Recommendations for Tourism Statistics (IRTS 2008). The World Tourism Organization enhances its coverage of tourism expenditure data by using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. When data on passenger transport items is unavailable, expenditure data for travel items are shown." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "2" }, { "id": "ST.INT.TVLX.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "International tourism, expenditures for travel items (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "International tourism expenditures are expenditures of international outbound visitors in other countries. The goods and services are purchased by, or on behalf of, the traveler or provided, without a quid pro quo, for the traveler to use or give away. These may include expenditures by residents traveling abroad as same-day visitors, except in cases where these are so important as to justify a separate classification. Excluded is the international carriage of travelers, which is covered in passenger travel items. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Tourism data is compiled based on the International Recommendations for Tourism Statistics (IRTS 2008). The World Tourism Organization enhances its coverage of tourism expenditure data by using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. When data on passenger transport items is unavailable, expenditure data for travel items are shown." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "2" }, { "id": "ST.INT.XPND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities." }, { "id": "IndicatorName", "value": "International tourism, expenditures (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Tourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling.\n\nExpenditure associated with the activity of international visitors has been traditionally identified with the travel item of the Balance of Payments (BOP).\n\nThe 2008 International Recommendations for Tourism Statistics consider that \"tourism industries and products\" includes transport of passengers. Consequently, a better estimate of tourism-related expenditure by inbound and outbound visitors in an international scenario would be, in terms of the BOP, the value of the travel item plus that of the passenger transport item.\n\nNevertheless, users should be aware that BOP estimates include, in addition to expenditures associated to visitors, those related to other types of travelers (these might be substantial in some countries; for instance, long-term students or patients, border and seasonal workers, etc.). Also data on expenditure by main purpose of the trip are BOP data." }, { "id": "Longdefinition", "value": "International tourism expenditures are expenditures of international outbound visitors in other countries, including payments to foreign carriers for international transport. These expenditures may include those by residents traveling abroad as same-day visitors, except in cases where these are important enough to justify separate classification. For some countries they do not include expenditures for passenger transport items. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Outbound tourism expenditures may include those by residents traveling abroad as same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include expenditures for passenger transport items. Their share in imports is calculated as a ratio to imports of goods and services (all transactions between residents of a country and the rest of the world involving a change of ownership from nonresidents to residents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services).\n\nStatistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nThe World Tourism Organization is improving its coverage of tourism expenditure data, using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. These data include travel and passenger transport items as defined in the IMF's Balance of Payments. When the IMF does not report data on passenger transport items, expenditure data for travel items are shown.\n\nThe aggregates are calculated using the World Bank's weighted aggregation methodology and differ from the World Tourism Organization's aggregates." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "2" }, { "id": "ST.INT.XPND.MP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities.\n\nThis measure reflects the importance of tourism as an internationally traded service relative to other categories of imports. Such a measure reveals the predilection for tourism in a country's import structure and the relative degree of an economy's domestic revenue outflows due to international tourism." }, { "id": "IndicatorName", "value": "International tourism, expenditures (% of total imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Tourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling.\n\nExpenditure associated with the activity of international visitors has been traditionally identified with the travel item of the Balance of Payments (BOP).\n\nThe 2008 International Recommendations for Tourism Statistics consider that \"tourism industries and products\" includes transport of passengers. Consequently, a better estimate of tourism-related expenditure by inbound and outbound visitors in an international scenario would be, in terms of the BOP, the value of the travel item plus that of the passenger transport item.\n\nNevertheless, users should be aware that BOP estimates include, in addition to expenditures associated to visitors, those related to other types of travelers (these might be substantial in some countries; for instance, long-term students or patients, border and seasonal workers, etc.). Also data on expenditure by main purpose of the trip are BOP data." }, { "id": "Longdefinition", "value": "International tourism expenditures are expenditures of international outbound visitors in other countries, including payments to foreign carriers for international transport. These expenditures may include those by residents traveling abroad as same-day visitors, except in cases where these are important enough to justify separate classification. For some countries they do not include expenditures for passenger transport items. Their share in imports is calculated as a ratio to imports of goods and services, which comprise all transactions between residents of a country and the rest of the world involving a change of ownership from nonresidents to residents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism;\nIMF imports estimates, International Monetary Fund (IMF);\nWorld Bank imports estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Outbound tourism expenditures may include those by residents traveling abroad as same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include expenditures for passenger transport items. Their share in imports is calculated as a ratio to imports of goods and services (all transactions between residents of a country and the rest of the world involving a change of ownership from nonresidents to residents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services).\n\nInternational tourism expenditures' share in imports is calculated as a ratio to imports of goods and services, which comprise all transactions between residents of a country and the rest of the world involving a change of ownership from nonresidents to residents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services.\n\nStatistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nThe World Tourism Organization is improving its coverage of tourism expenditure data, using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. These data include travel and passenger transport items as defined in the IMF's Balance of Payments. When the IMF does not report data on passenger transport items, expenditure data for travel items are shown.\n\nThe aggregates are calculated using the World Bank's weighted aggregation methodology and differ from the World Tourism Organization's aggregates." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "2" }, { "id": "TG.VAL.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Merchandise trade (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "General merchandise trade includes goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e. It is the total of merchandise exports plus merchandise imports. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Trade Organization (WTO);\nWorld Bank GDP estimates, World Bank (WB);\nWorld Development Indicators, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Total merchandise trade" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TM.QTY.MRCH.XD.WD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Import volume index (2015 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Import volume indexes are derived from UNCTAD's volume index series and are the ratio of the import value indexes to the corresponding unit value indexes. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year's trade values at the Standard International Trade Classification three-digit level as weights." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2023" }, { "id": "Source", "value": "UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is constructed as the ratio of the import value index to the corresponding unit value index, both compiled by the United Nations Conference on Trade and Development (UNCTAD). The import value index reflects the nominal value of imports over time, while the unit value index serves as a proxy for import prices by capturing changes in average prices per unit. Unit value indexes are based primarily on data reported by national statistical authorities. To ensure data reliability, only values from countries that demonstrate internal consistency and meet quality thresholds defined by UNCTAD are used. Where data are incomplete or fail to meet these standards, UNCTAD supplements them with its own estimates.\n\nThe estimation procedure for unit value indexes involves applying trade values from the previous year as weights, using the Standard International Trade Classification (SITC) at the three-digit level of aggregation. This method helps to reduce distortions from shifts in trade structure or classification inconsistencies. The resulting indexes are chain-linked to ensure temporal comparability and are rebased to the year 2015 (2015 = 100). The final import volume index allows for the analysis of changes in the physical volume of imports over time, net of price effects, and is particularly useful for assessing trends in trade flows in real terms." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.MANF.BC.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Binding coverage, manufactured products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2022" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated as the proportion of tariff lines for manufactured products that are bound under World Trade Organization (WTO) commitments. A tariff line is considered bound if a maximum rate is legally committed in the WTO schedule of concessions. The binding coverage is computed by dividing the number of bound tariff lines by the total number of tariff lines in the relevant product category and multiplying the result by 100 to express it as a percentage. Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Manufactured products are commodities classified in Standard International Trade Classification (SITC) revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator is compiled using only officially reported bound statuses without imputation. It is methodologically consistent across countries, with tariff line concordance procedures applied to standardize classification and maintain comparability across HS revisions." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.MANF.BR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Bound rate, simple mean, manufactured products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2022" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the unweighted average of final bound tariff rates across all manufactured products tariff lines. The calculation involves summing the bound rates applied to each individual tariff line and dividing by the total number of lines within the product category. Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Manufactured products are commodities classified in Standard International Trade Classification (SITC) revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator reflects commitments under WTO agreements and is not adjusted for trade volume. It includes only those lines for which binding rates have been reported. The methodology ensures consistency across countries by converting national tariff submissions to a harmonized HS version." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.MANF.SM.AR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, applied, simple mean, manufactured products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the simple (unweighted) average of applied most-favored-nation (MFN) tariff rates across all tariff lines within the manufactured products category. Each line is given equal weight regardless of the trade volume associated with it. The mean is calculated by summing the applied rates and dividing by the number of lines. Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator excludes missing or unreported lines. Harmonization across HS revisions is conducted to ensure international comparability, and no adjustments are made beyond validation of submitted data." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.MANF.SM.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, most favored nation, simple mean, manufactured products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator calculates the simple (unweighted) mean of statutory most-favored-nation (MFN) tariff rates across all tariff lines for manufactured products. It reflects tariff rates that apply to all World Trade Organization (WTO) members on a non-discriminatory basis unless preferential agreements are in force. Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The calculation is performed by summing the MFN rates and dividing by the total number of tariff lines. No trade-based weighting is applied. Only reported rates are included, and harmonized classifications ensure comparability across countries." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.MANF.WM.AR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, applied, weighted mean, manufactured products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator computes the average of applied most-favored-nation (MFN) tariff rates for manufactured products, using import values as weights. Tariff lines with higher trade volumes have a proportionally greater influence on the final average. The weighted mean is derived by multiplying each tariff rate by its corresponding import value, summing these products, and dividing by the total import value. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Trade data used for weighting are obtained from official national or international sources. Only lines with both valid tariff rates and trade values are included. Harmonized classifications are used to align trade and tariff data consistently." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.MANF.WM.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, most favored nation, weighted mean, manufactured products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the trade-weighted average of most-favored-nation (MFN) tariff rates across all manufactured products tariff lines. Import values are used to weight each rate, reflecting the relative economic significance of different products in international trade. The weighted average is calculated by taking the sum of the products of MFN rates and import values, divided by the total import value. Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Only tariff lines with both reported MFN rates and corresponding trade values are included. The WTO applies standardized concordance procedures to match trade and tariff data using harmonized product classifications." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.MRCH.BC.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Binding coverage, all products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2022" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated as the proportion of tariff lines for all products that are bound under World Trade Organization (WTO) commitments. A tariff line is considered bound if a maximum rate is legally committed in the WTO schedule of concessions. The binding coverage is computed by dividing the number of bound tariff lines by the total number of tariff lines in the relevant product category and multiplying the result by 100 to express it as a percentage. Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator is compiled using only officially reported bound statuses without imputation. It is methodologically consistent across countries, with tariff line concordance procedures applied to standardize classification and maintain comparability across HS revisions." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.MRCH.BR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Bound rate, simple mean, all products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2022" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the unweighted average of final bound tariff rates across all all products tariff lines. The calculation involves summing the bound rates applied to each individual tariff line and dividing by the total number of lines within the product category. Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator reflects commitments under WTO agreements and is not adjusted for trade volume. It includes only those lines for which binding rates have been reported. The methodology ensures consistency across countries by converting national tariff submissions to a harmonized HS version." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.MRCH.SM.AR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Poor people in developing countries work primarily in agriculture and labor-intensive manufactures, sectors that confront the greatest trade barriers. Removing barriers to merchandise trade could increase growth in these countries - even more if trade in services were also liberalized.\n\nIn general, tariffs in high-income countries on imports from developing countries, though low, are twice those collected from other high-income countries. But protection is also an issue for developing countries, which maintain high tariffs on agricultural commodities, labor-intensive manufactures, and other products and services.\n\nCountries use a combination of tariff and nontariff measures to regulate imports. The most common form of tariff is an ad valorem duty, based on the value of the import, but tariffs may also be levied on a specific, or per unit, basis or may combine ad valorem and specific rates. Tariffs may be used to raise fiscal revenues or to protect domestic industries from foreign competition - or both. Nontariff barriers, which limit the quantity of imports of a particular good, include quotas, prohibitions, licensing schemes, export restraint arrangements, and health and quarantine measures. Because of the difficulty of combining nontariff barriers into an aggregate indicator, they are not included in the data.\n\nSome countries set fairly uniform tariff rates across all imports. Others are selective, setting high tariffs to protect favored domestic industries. The effective rate of protection - the degree to which the value added in an industry is protected - may exceed the nominal rate if the tariff system systematically differentiates among imports of raw materials, intermediate products, and finished goods." }, { "id": "IndicatorName", "value": "Tariff rate, applied, simple mean, all products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the simple (unweighted) average of applied most-favored-nation (MFN) tariff rates across all tariff lines within the all products category. Each line is given equal weight regardless of the trade volume associated with it. The mean is calculated by summing the applied rates and dividing by the number of lines. Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator excludes missing or unreported lines. Harmonization across HS revisions is conducted to ensure international comparability, and no adjustments are made beyond validation of submitted data." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.MRCH.SM.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, most favored nation, simple mean, all products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator calculates the simple (unweighted) mean of statutory most-favored-nation (MFN) tariff rates across all tariff lines for all products. It reflects tariff rates that apply to all World Trade Organization (WTO) members on a non-discriminatory basis unless preferential agreements are in force. Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The calculation is performed by summing the MFN rates and dividing by the total number of tariff lines. No trade-based weighting is applied. Only reported rates are included, and harmonized classifications ensure comparability across countries." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.MRCH.WM.AR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, applied, weighted mean, all products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator computes the average of applied most-favored-nation (MFN) tariff rates for all products, using import values as weights. Tariff lines with higher trade volumes have a proportionally greater influence on the final average. The weighted mean is derived by multiplying each tariff rate by its corresponding import value, summing these products, and dividing by the total import value. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Trade data used for weighting are obtained from official national or international sources. Only lines with both valid tariff rates and trade values are included. Harmonized classifications are used to align trade and tariff data consistently." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.MRCH.WM.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, most favored nation, weighted mean, all products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the trade-weighted average of most-favored-nation (MFN) tariff rates across all all products tariff lines. Import values are used to weight each rate, reflecting the relative economic significance of different products in international trade. The weighted average is calculated by taking the sum of the products of MFN rates and import values, divided by the total import value. Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Only tariff lines with both reported MFN rates and corresponding trade values are included. The WTO applies standardized concordance procedures to match trade and tariff data using harmonized product classifications." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.TCOM.BC.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Binding coverage, primary products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2022" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated as the proportion of tariff lines for primary products that are bound under World Trade Organization (WTO) commitments. A tariff line is considered bound if a maximum rate is legally committed in the WTO schedule of concessions. The binding coverage is computed by dividing the number of bound tariff lines by the total number of tariff lines in the relevant product category and multiplying the result by 100 to express it as a percentage. Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Primary products are commodities classified in Standard International Trade Classification (SITC) revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator is compiled using only officially reported bound statuses without imputation. It is methodologically consistent across countries, with tariff line concordance procedures applied to standardize classification and maintain comparability across HS revisions." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.TCOM.BR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Bound rate, simple mean, primary products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2022" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the unweighted average of final bound tariff rates across all primary products tariff lines. The calculation involves summing the bound rates applied to each individual tariff line and dividing by the total number of lines within the product category. Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Primary products are commodities classified in Standard International Trade Classification (SITC) revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator reflects commitments under WTO agreements and is not adjusted for trade volume. It includes only those lines for which binding rates have been reported. The methodology ensures consistency across countries by converting national tariff submissions to a harmonized HS version." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.TCOM.SM.AR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, applied, simple mean, primary products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the simple (unweighted) average of applied most-favored-nation (MFN) tariff rates across all tariff lines within the primary products category. Each line is given equal weight regardless of the trade volume associated with it. The mean is calculated by summing the applied rates and dividing by the number of lines. Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator excludes missing or unreported lines. Harmonization across HS revisions is conducted to ensure international comparability, and no adjustments are made beyond validation of submitted data." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.TCOM.SM.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, most favored nation, simple mean, primary products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator calculates the simple (unweighted) mean of statutory most-favored-nation (MFN) tariff rates across all tariff lines for primary products. It reflects tariff rates that apply to all World Trade Organization (WTO) members on a non-discriminatory basis unless preferential agreements are in force. Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The calculation is performed by summing the MFN rates and dividing by the total number of tariff lines. No trade-based weighting is applied. Only reported rates are included, and harmonized classifications ensure comparability across countries." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.TCOM.WM.AR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, applied, weighted mean, primary products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator computes the average of applied most-favored-nation (MFN) tariff rates for primary products, using import values as weights. Tariff lines with higher trade volumes have a proportionally greater influence on the final average. The weighted mean is derived by multiplying each tariff rate by its corresponding import value, summing these products, and dividing by the total import value. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Trade data used for weighting are obtained from official national or international sources. Only lines with both valid tariff rates and trade values are included. Harmonized classifications are used to align trade and tariff data consistently." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.TAX.TCOM.WM.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, most favored nation, weighted mean, primary products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the trade-weighted average of most-favored-nation (MFN) tariff rates across all primary products tariff lines. Import values are used to weight each rate, reflecting the relative economic significance of different products in international trade. The weighted average is calculated by taking the sum of the products of MFN rates and import values, divided by the total import value. Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Only tariff lines with both reported MFN rates and corresponding trade values are included. The WTO applies standardized concordance procedures to match trade and tariff data using harmonized product classifications." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.UVI.MRCH.XD.WD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Import unit value index (2015 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Import unit value indices come from UNCTAD's trade database. Unit value indices are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD’s estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates. This indicator is an index series where 2015=100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2024" }, { "id": "Source", "value": "Handbook of Statistics and data files., UN Conference on Trade and Development (UNCTAD), uri: http://unctadstat.unctad.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Trade price indices" }, { "id": "Unitofmeasure", "value": "index (2015 = 100)" } ], "source_id": "2" }, { "id": "TM.VAL.AGRI.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Agricultural raw materials imports (% of merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Agricultural raw materials comprise section 2 of SITC Rev. 3 (crude materials, inedible, except fuels) excluding divisions 22 (oil-seeds and oleaginous fruits), 27 (crude fertilizers and minerals excluding coal, petroleum, and precious stones), and 28 (metalliferous ores and scrap). This indicator is expressed as a percentage of merchandise imports which is comprised of goods whose economic ownership is changed between a non-resident and a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise import shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TM.VAL.FOOD.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Food imports (% of merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Food comprises the commodities in SITC (Rev. 3) sections 0 (food and live animals), 1 (beverages and tobacco), and 4 (animal and vegetable oils and fats) and division 22 (oil seeds, oil nuts, and oil kernels). This indicator is expressed as a percentage of merchandise imports which is comprised of goods whose economic ownership is changed between a non-resident and a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise import shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TM.VAL.FUEL.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Fuel imports (% of merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Fuels comprise the commodities in SITC (Rev. 3) section 3 (mineral fuels, lubricants and related materials). This indicator is expressed as a percentage of merchandise imports which is comprised of goods whose economic ownership is changed between a non-resident and a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise import shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TM.VAL.ICTG.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. For more information see www.itu.int/ITU-D/ict/partnership/.\n\nThe work of the Partnership is directed towards achieving internationally comparable and reliable ICT statistics. In order to achieve this, its members are involved in developing and maintaining a core list of ICT indicators. Other activities include the compilation and dissemination of ICT data, and the provision of technical assistance enabling statistical agencies to collect data that underlie the core list of ICT indicators." }, { "id": "IndicatorName", "value": "ICT goods imports (% total goods imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Detailed trade data are widely available from country trade statistics. These are collected by the UNSD and published in their UN COMTRADE database. The ICT goods trade indicators are usually compiled by interested international and national agencies using COMTRADE data. Concepts are therefore consistent with those applying to the COMTRADE database.\n\nThe main statistical issue associated with this indicator appears to be the different treatment of re-exports and re-imports by countries, depending on whether the Special or General Trade System is used.2 Re-imports are separately reported for some countries and the value of ICT re-imports (which is included in the value of ICT imports for those countries) is generally small." }, { "id": "Longdefinition", "value": "Information and communication technology goods imports include computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components, and other information and technology goods (miscellaneous)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "UNCTADstat database, UN Conference on Trade and Development (UNCTAD), uri: http://unctadstat.unctad.org/ReportFolders/reportFolders.aspx" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Information and communication technology goods imports include computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components, and other information and technology goods (miscellaneous). Software is generally excluded, as there is a preference to record it under services (not an ICT good but an ICT product) to the extent possible. However it is hard to completely exclude embedded software from certain types of ICT goods, such as video game consoles (see for example the discussion on page 30 of the OECD guide cited below). ICT goods imports as a percentage of total imports is calculated for each country by dividing the value of its ICT goods imports by the total value of its goods imports. The result is then multiplied by 100 to be expressed as a percentage.\n\nICT goods are defined according to the OECD’s Guide on Measuring the Information Society 2011 for Harmonized System (HS) 2007 and adapted to HS12 by UNCTAD in collaboration with UNSD (United Nations Statistics Division). This new list consists of 93 goods defined at the 6 digit level of the 2012 version of the HS. The technical note is available online at: http://unctad.org/en/PublicationsLibrary/tn_unctad_ict4d02_en.pdf\nData were downloaded from COMTRADE according to the reported classification (HS92, 96, 02, 07, 12) and aggregated into ICT groups by UNCTAD." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "2" }, { "id": "TM.VAL.INSF.ZS.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Insurance and financial services (% of commercial service imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Financial services covers services related to financial intermediation, financial risk management, liquidity transformation or auxiliary financial activities. It also includes insurance and pension scheme services which are services related to providing life insurance and annuities, non-life insurance, reinsurance, pensions, standardised guarantees and auxiliary services to insurance, pension schemes, and standardised guarantee schemes. This indicator is expressed as a percentage of service imports which are commercial services provided by non-residents to residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TM.VAL.MANF.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Manufactures imports (% of merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Manufactures comprise commodities in SITC (Rev. 3) sections 5 (chemicals), 6 (basic manufactures), 7 (machinery and transport equipment), and 8 (miscellaneous manufactured goods), excluding division 68 (non-ferrous metals). This indicator is expressed as a percentage of merchandise imports which is comprised of goods whose economic ownership is changed between a non-resident and a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise import shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TM.VAL.MMTL.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Ores and metals imports (% of merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Ores and metals comprise the commodities in SITC (Rev. 3) sections 27 (crude fertilizer, minerals nes); 28 (metalliferous ores, scrap); and 68 (non-ferrous metals). Imports of services are services provided by non-residents to residents. This indicator is expressed as a percentage of merchandise imports which is comprised of goods whose economic ownership is changed between a non-resident and a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise import shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TM.VAL.MRCH.AL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports from economies in the Arab World (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports from economies in the Arab World are the sum of merchandise imports by the reporting economy from economies in the Arab World. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from economies in the Arab World. The purpose is to assess the relative importance of trade with economies in the Arab World economies within the broader context of global imports. The numerator includes the total value of goods imported from economies in the Arab World, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of economies in the Arab World follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "2" }, { "id": "TM.VAL.MRCH.CD.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Merchandise imports (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The value of imports is generally recorded as the cost of the goods when purchased by the importer plus the cost of transport and insurance to the frontier of the importing country - the cost, insurance, and freight (c.i.f.) value, corresponding to the landed cost at the point of entry of foreign goods into the country. A few countries collect import data on a free on board (f.o.b.) basis and adjust them for freight and insurance costs.\n\n\n\n\n\nCountries may report trade according to the general or special system of trade. Under the general system imports include goods imported for domestic consumption and imports into bonded warehouses and free trade zones. Under the special system imports comprise goods imported for domestic consumption (including transformation and repair) and withdrawals for domestic consumption from bonded warehouses and free trade zones. Goods transported through a country en route to another are excluded.\n\n\n\n\n\nData on imports of goods are derived from the same sources as data on exports. In principle, world exports and imports should be identical. Similarly, exports from an economy should equal the sum of imports by the rest of the world from that economy. But differences in timing and definitions result in discrepancies in reported values at all levels." }, { "id": "Longdefinition", "value": "Merchandise imports includes goods whose economic ownership is changed from a non-resident to a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "TM.VAL.MRCH.HI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Low- and middle-income economies are an increasingly important part of the global trading system. Trade between high-income economies and low- and middle-income economies has grown faster than trade between high-income economies. This increased trade benefits both producers and consumers in developing and high-income economies." }, { "id": "IndicatorName", "value": "Merchandise imports from high-income economies (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated." }, { "id": "Longdefinition", "value": "Merchandise imports from high-income economies are the sum of merchandise imports by the reporting economy from high-income economies according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from high-income economies. The purpose is to assess the relative importance of trade with high-income economies economies within the broader context of global imports. The numerator includes the total value of goods imported from high-income economies, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of high-income economies follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "2" }, { "id": "TM.VAL.MRCH.OR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although global integration has increased, low- and middle-income economies still face trade barriers when accessing other markets." }, { "id": "IndicatorName", "value": "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated." }, { "id": "Longdefinition", "value": "Merchandise imports from low- and middle-income economies outside region are the sum of merchandise imports by the reporting economy from other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies outside region. The purpose is to assess the relative importance of trade with low- and middle-income economies outside region economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies outside region, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies outside region follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "2" }, { "id": "TM.VAL.MRCH.R1.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports from low- and middle-income economies in East Asia and Pacific are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the East Asia and Pacific region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in East Asia & Pacific. The purpose is to assess the relative importance of trade with low- and middle-income economies in East Asia & Pacific economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in East Asia & Pacific, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in East Asia & Pacific follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "2" }, { "id": "TM.VAL.MRCH.R2.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports from low- and middle-income economies in Europe & Central Asia (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports from low- and middle-income economies in Europe and Central Asia are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Europe and Central Asia region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in Europe & Central Asia. The purpose is to assess the relative importance of trade with low- and middle-income economies in Europe & Central Asia economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in Europe & Central Asia, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in Europe & Central Asia follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "2" }, { "id": "TM.VAL.MRCH.R3.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports from low- and middle-income economies in Latin America & the Caribbean (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports from low- and middle-income economies in Latin America and the Caribbean are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Latin America and the Caribbean region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in Latin America & the Caribbean. The purpose is to assess the relative importance of trade with low- and middle-income economies in Latin America & the Caribbean economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in Latin America & the Caribbean, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in Latin America & the Caribbean follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "2" }, { "id": "TM.VAL.MRCH.R4.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports from low- and middle-income economies in Middle East & North Africa (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports from low- and middle-income economies in Middle East and North Africa are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Middle East and North Africa region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in Middle East & North Africa. The purpose is to assess the relative importance of trade with low- and middle-income economies in Middle East & North Africa economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in Middle East & North Africa, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in Middle East & North Africa follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "2" }, { "id": "TM.VAL.MRCH.R5.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports from low- and middle-income economies in South Asia are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the South Asia region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in South Asia. The purpose is to assess the relative importance of trade with low- and middle-income economies in South Asia economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in South Asia, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in South Asia follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "2" }, { "id": "TM.VAL.MRCH.R6.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports from low- and middle-income economies in Sub-Saharan Africa (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports from low- and middle-income economies in Sub-Saharan Africa are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Sub-Saharan Africa region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in Sub-Saharan Africa. The purpose is to assess the relative importance of trade with low- and middle-income economies in Sub-Saharan Africa economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in Sub-Saharan Africa, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in Sub-Saharan Africa follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "2" }, { "id": "TM.VAL.MRCH.RS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports by the reporting economy, residual (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports by the reporting economy residuals are the total merchandise imports by the reporting economy from the rest of the world as reported in the IMF's Direction of trade database, less the sum of imports by the reporting economy from high-, low-, and middle-income economies according to the World Bank classification of economies. Includes trade with unspecified partners or with economies not covered by World Bank classification. Data are as a percentage of total merchandise imports by the economy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the total value of merchandise imports reported by a country over a specified period, typically a calendar year. The value includes all goods that physically enter the country’s economic territory and are intended for consumption, processing, or re-export. Data are expressed in nominal terms using current U.S. dollars. The data source is primarily customs administrative records submitted by importers, compiled by national statistical or trade authorities.\n\nThe reported value of imports generally includes the cost of goods plus insurance and freight (CIF), which reflects the full landed value at the port of entry. The general trade system is used in most cases, meaning that imports into bonded warehouses and free zones are also included. The data exclude services and focus solely on physical goods crossing borders. Corrections may be applied for coverage limitations, underreporting, valuation discrepancies, or misclassifications to improve comparability.\n\nSome versions of the indicator may exclude re-imports, which are goods previously exported and returned to the country without significant transformation. This distinction ensures that the import figures do not overstate economic activity by counting the same goods more than once. Users of the indicator include government agencies monitoring trade flows, businesses analyzing sourcing strategies, and researchers studying trade balances. It also provides input for calculating the balance of trade and understanding integration into global markets." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "2" }, { "id": "TM.VAL.MRCH.WL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports by the reporting economy (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports by the reporting economy are the total merchandise imports by the reporting economy from the rest of the world, as reported in the IMF's Direction of trade database. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the total value of merchandise imports reported by a country over a specified period, typically a calendar year. The value includes all goods that physically enter the country’s economic territory and are intended for consumption, processing, or re-export. Data are expressed in nominal terms using current U.S. dollars. The data source is primarily customs administrative records submitted by importers, compiled by national statistical or trade authorities.\n\nThe reported value of imports generally includes the cost of goods plus insurance and freight (CIF), which reflects the full landed value at the port of entry. The general trade system is used in most cases, meaning that imports into bonded warehouses and free zones are also included. The data exclude services and focus solely on physical goods crossing borders. Corrections may be applied for coverage limitations, underreporting, valuation discrepancies, or misclassifications to improve comparability.\n\nSome versions of the indicator may exclude re-imports, which are goods previously exported and returned to the country without significant transformation. This distinction ensures that the import figures do not overstate economic activity by counting the same goods more than once. Users of the indicator include government agencies monitoring trade flows, businesses analyzing sourcing strategies, and researchers studying trade balances. It also provides input for calculating the balance of trade and understanding integration into global markets." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "2" }, { "id": "TM.VAL.MRCH.WR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The relative importance of intraregional trade is higher for both landlocked countries and small countries with close trade links to the largest regional economy. For most low- and middle-income economies - especially smaller ones - there is a \"geographic bias\" favoring intraregional trade. Despite the broad trend toward globalization and the reduction of trade barriers, the relative share of intraregional trade increased for most economies between 1999 and 2010. This is due partly to trade-related advantages, such as proximity, lower transport costs, increased knowledge from repeated interaction, and cultural and historical affinity. The direction of trade is also influenced by preferential trade agreements that a country has made with other economies. Though formal agreements on trade liberalization do not automatically increase trade, they nevertheless affect the direction of trade between the participating economies." }, { "id": "IndicatorName", "value": "Merchandise imports from low- and middle-income economies within region (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated." }, { "id": "Longdefinition", "value": "Merchandise imports from low- and middle-income economies within region are the sum of merchandise imports by the reporting economy from other low- and middle-income economies in the same World Bank region according to the World Bank classification of economies. Data are as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data. No figures are shown for high-income economies, because they are a separate category in the World Bank classification of economies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies within region. The purpose is to assess the relative importance of trade with low- and middle-income economies within region economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies within region, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies within region follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "2" }, { "id": "TM.VAL.MRCH.XD.WD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Import value index (2015 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Import value indexes are the current value of imports (c.i.f.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2015). UNCTAD's import value indexes are reported for most economies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2023" }, { "id": "Source", "value": "UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Import Value Index (base year 2015 = 100) captures the movement in the total value of merchandise imports over time, using 2015 as the reference period. This index reflects changes in both the price and quantity of imported goods. An index value above 100 indicates that the total value of imports has increased compared to the base year, while a value below 100 suggests a decline. The indicator is constructed using current-dollar import values, typically sourced from customs data or trade statistics reports. \n\nImport values are aggregated across all products and trading partners. Because the index combines both price and volume effects, it does not isolate whether changes are due to rising prices, increased quantities, or both. For this reason, it is often interpreted alongside other indicators such as unit value or volume indexes.\n\nIndex values are calculated by comparing the nominal value of imports in each year to that of the base year, then scaling the result so that the base year equals 100. This normalization allows for straightforward interpretation and cross-country comparisons. The index is useful for identifying structural shifts in import demand, the impact of currency fluctuations, and the effects of trade policy. It can also be used in macroeconomic modeling to estimate import deflators or as an input for terms-of-trade calculations." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TM.VAL.OTHR.ZS.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Computer, communications and other services (% of commercial service imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Computer, communications and other services include such activities as international telecommunications, and postal and courier services; computer data; news-related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; and personal, cultural, and recreational services. This indicator is expressed as a percentage of service imports which are commercial services provided by non-residents to residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TM.VAL.SERV.CD.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Commercial service imports (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Commercial service imports are total service imports minus imports of government services not included elsewhere. Imports of services are services provided by non-residents to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "TM.VAL.TRAN.ZS.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Transport services (% of commercial service imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Transport is the process of carriage of people and objects from one location to another as well as related supporting and auxiliary services. Also included are postal and courier services. This indicator is expressed as a percentage of service imports which are commercial services provided by non-residents to residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TM.VAL.TRVL.ZS.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Travel services (% of commercial service imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Travel services cover goods and services for own use or to give away acquired from an economy by nonresidents during visits to that economy, or acquired from other economies by residents during visits to these other economies. This indicator is expressed as a percentage of service imports which are commercial services provided by non-residents to residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TT.PRI.MRCH.XD.WD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net barter terms of trade index (2015 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net barter terms of trade index is calculated as the percentage ratio of the export unit value indexes to the import unit value indexes, measured relative to the base year 2015." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2023" }, { "id": "Source", "value": "UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Net Barter Terms of Trade Index (base year 2015 = 100) is calculated as the ratio of an export price index to an import price index, multiplied by 100. This index measures the rate at which a country can exchange its exports for imports. A value greater than 100 means the terms of trade have improved relative to the base year, allowing the country to obtain more imports for a given level of exports. Conversely, a value below 100 indicates that the terms have worsened.\n\nExport and import price indexes are derived from unit value data or sampled transaction-level data, depending on country-specific reporting practices. The price indexes may be computed using fixed or chain-weighted methods. To ensure temporal comparability, the export and import series are indexed to a common base year (2015 = 100).\n\nThe terms of trade index is sensitive to fluctuations in global commodity prices, exchange rate movements, and changes in trade composition. It is used to assess trade gains or losses, especially for countries reliant on exports of primary commodities. Although it does not capture changes in volumes traded, it provides insight into the relative value received for exports. Policymakers and researchers may use this indicator to monitor competitiveness and evaluate exposure to external shocks." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TX.MNF.TECH.ZS.UN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Industrial development generally entails a structural transition from resource-based and low technology activities to medium and high-tech industry (MHT) activities. A modern, highly complex production structure offers better opportunities for skills development and technological innovation. MHT activities are also the high value addition industries of manufacturing with higher technological intensity and labor productivity. Increasing the share of MHT sectors also reflects the impact of innovation." }, { "id": "IndicatorName", "value": "Medium and high-tech exports (% manufactured exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Missing values at country level are imputed based on the methodology from Competitive Industrial Performance Report (UNIDO, 2017)." }, { "id": "Longdefinition", "value": "Share of medium and high-tech manufactured exports in total manufactured exports." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2022" }, { "id": "Source", "value": "Competitive Industrial Performance (CIP) database, UN Industrial Development Organization (UNIDO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data from UN COMTRADE is downloaded in SITC Revision 3, 3-digit, by reporting country, year, partner code, commodity and flow (export and re-export). SITC medium technology: 266, 267, 512, 513, 533, 553, 554, 562, 571, 572, 573, 574, 575, 579, 581, 582, 583, 591, 593, 597, 598, 653, 671, 672, 678, 711, 712,713, 714, 721, 722, 723, 724, 725, 726, 727, 728, 731, 733, 735, 737, 741, 742, 743, 744, 745, 746, 747, 748, 749, 761, 762, 763, 772, 773, 775, 778, 781, 782, 783, 784, 785, 786, 791, 793, 811, 812, 813, 872, 873, 882, 884, 885; SITC high technology: 525, 541, 542, 716, 718, 751, 752, 759, 764, 771, 774, 776, 792, 871, 874, 881, 891. Net-exports are calculated as exports minus re-exports. Manufactured exports, is the sum of the four categories resource-based exports, low-tech exports, medium tech exports and high-tech exports; and medium-high technology exports, is the sum of medium tech exports and high-tech exports. The world value of manufacturing exports is the sum of all manufacturing net exports. For additional information please see Table B.2.1 in Appendix B of UNIDO (2017): http://stat.unido.org/content/publications/volume-i%252c-competitive-industrial-performance-report-2016" }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "2" }, { "id": "TX.QTY.MRCH.XD.WD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Export volume index (2015 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Export volume indexes are derived from UNCTAD's volume index series and are the ratio of the export value indexes to the corresponding unit value indexes. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year's trade values at the Standard International Trade Classification three-digit level as weights." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2023" }, { "id": "Source", "value": "UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Export Volume Index (base year 2015 = 100) measures changes in the physical volume of merchandise exports over time, adjusting for price effects. By The Export Volume Index (base year 2015 = 100) measures changes in the physical volume of merchandise exports over time, adjusting for price effects. By removing the influence of price changes, this index isolates real growth or contraction in exported quantities. A value above 100 indicates that the volume of exports has increased relative to the base year, while a value below 100 signals a reduction.\n\nTo calculate the index, nominal export values are deflated using export price indices or unit value series, depending on the availability of data. These deflated values are then compared to the base year to produce an index that is normalized to 100 in 2015. The index covers all merchandise exports, and values are aggregated at the national level across products and partner economies.\n\nThe Export Volume Index helps distinguish between growth due to increased trade activity and growth driven solely by price inflation. It is particularly relevant for monitoring export performance in real terms, analyzing supply chain trends, and identifying structural shifts in trade composition. This index can inform decisions on industrial policy, export diversification, and investment in production capacity. It may also support economic modeling exercises requiring real trade variables." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TX.UVI.MRCH.XD.WD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Export unit value index (2015 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Export unit value indices come from UNCTAD's trade database. Unit value indices are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD’s estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, interna¬tional and national sources, and UNCTAD secretariat estimates. This indicator is an index series where 2015=100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2024" }, { "id": "Source", "value": "Handbook of Statistics and data files., UN Conference on Trade and Development (UNCTAD), uri: http://unctadstat.unctad.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Trade price indices" }, { "id": "Unitofmeasure", "value": "index (2015 = 100)" } ], "source_id": "2" }, { "id": "TX.VAL.AGRI.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Agricultural raw materials exports (% of merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Agricultural raw materials comprise section 2 of SITC Rev. 3 (crude materials, inedible, except fuels) excluding divisions 22 (oil-seeds and oleaginous fruits), 27 (crude fertilizers and minerals excluding coal, petroleum, and precious stones), and 28 (metalliferous ores and scrap). This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise export shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TX.VAL.FOOD.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Food exports (% of merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Food comprises the commodities in SITC (Rev. 3) sections 0 (food and live animals), 1 (beverages and tobacco), and 4 (animal and vegetable oils and fats) and division 22 (oil seeds, oil nuts, and oil kernels). This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise export shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TX.VAL.FUEL.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Fuel exports (% of merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Fuels comprise the commodities in SITC (Rev. 3) section 3 (mineral fuels, lubricants and related materials). This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise export shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nComtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS), World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TX.VAL.ICTG.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. For more information see www.itu.int/ITU-D/ict/partnership/.\n\nThe work of the Partnership is directed towards achieving internationally comparable and reliable ICT statistics. In order to achieve this, its members are involved in developing and maintaining a core list of ICT indicators. Other activities include the compilation and dissemination of ICT data, and the provision of technical assistance enabling statistical agencies to collect data that underlie the core list of ICT indicators." }, { "id": "IndicatorName", "value": "ICT goods exports (% of total goods exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Detailed trade data are widely available from country trade statistics. These are collected by the UNSD and published in their UN COMTRADE database. The ICT goods trade indicators are usually compiled by interested international and national agencies using COMTRADE data. Concepts are therefore consistent with those applying to the COMTRADE database.\n\nThe main statistical issue associated with this indicator appears to be the different treatment of re-exports and re-imports by countries, depending on whether the Special or General Trade System is used.2 Re-imports are separately reported for some countries and the value of ICT re-imports (which is included in the value of ICT imports for those countries) is generally small." }, { "id": "Longdefinition", "value": "Information and communication technology goods exports include computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components, and other information and technology goods (miscellaneous)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "UNCTADstat database, UN Conference on Trade and Development (UNCTAD), uri: http://unctadstat.unctad.org/ReportFolders/reportFolders.aspx" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Information and communication technology goods exports include computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components, and other information and technology goods (miscellaneous). Software is generally excluded, as there is a preference to record it under services (not an ICT good but an ICT product) to the extent possible. However it is hard to completely exclude embedded software from certain types of ICT goods, such as video game consoles (see for example the discussion on page 30 of the OECD guide cited below). ICT goods exports as a percentage of total goods exports is calculated for each country by dividing the value of its ICT goods exports by the total value of its goods exports. The result is then multiplied by 100 to be expressed as a percentage.\n\nICT goods are defined according to the OECD’s Guide on Measuring the Information Society 2011 for Harmonized System (HS) 2007 and adapted to HS12 by UNCTAD in collaboration with UNSD (United Nations Statistics Division). This new list consists of 93 goods defined at the 6 digit level of the 2012 version of the HS. The technical note is available online at: http://unctad.org/en/PublicationsLibrary/tn_unctad_ict4d02_en.pdf\nData were downloaded from COMTRADE according to the reported classification (HS92, 96, 02, 07, 12) and aggregated into ICT groups by UNCTAD." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "2" }, { "id": "TX.VAL.INSF.ZS.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Insurance and financial services (% of commercial service exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Financial services covers services related to financial intermediation, financial risk management, liquidity transformation or auxiliary financial activities. It also includes insurance and pension scheme services which are services related to providing life insurance and annuities, non-life insurance, reinsurance, pensions, standardised guarantees and auxiliary services to insurance, pension schemes, and standardised guarantee schemes. This indicator is expressed as a percentage of service exports which are commercial services provided by residents to non-residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TX.VAL.MANF.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Manufactures exports (% of merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Manufactures comprise commodities in SITC (Rev. 3) sections 5 (chemicals), 6 (basic manufactures), 7 (machinery and transport equipment), and 8 (miscellaneous manufactured goods), excluding division 68 (non-ferrous metals). This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise export shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TX.VAL.MMTL.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Ores and metals exports (% of merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Ores and metals comprise the commodities in SITC (Rev. 3) sections 27 (crude fertilizer, minerals nes); 28 (metalliferous ores, scrap); and 68 (non-ferrous metals). Exports of services are services provided by residents to non-residents. This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise export shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TX.VAL.MRCH.AL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports to economies in the Arab World (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to economies in the Arab World are the sum of merchandise exports by the reporting economy to economies in the Arab World. Data are expressed as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "2" }, { "id": "TX.VAL.MRCH.CD.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Merchandise exports (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Exports are recorded as the cost of the goods delivered to the frontier of the exporting country for shipment - the free on board (f.o.b.) value.\n\n\n\n\n\nCountries may report trade according to the general or special system of trade. Under the general system exports comprise outward-moving goods that are (a) goods wholly or partly produced in the country; (b) foreign goods, neither transformed nor declared for domestic consumption in the country, that move outward from customs storage; and (c) goods previously included as imports for domestic consumption but subsequently exported without transformation. Under the special system exports comprise categories a and c. In some compilations categories b and c are classified as re-exports. Because of differences in reporting practices, data on exports may not be fully comparable across economies.\n\n\n\n\n\nData on exports of goods are derived from the same sources as data on imports. In principle, world exports and imports should be identical. Similarly, exports from an economy should equal the sum of imports by the rest of the world from that economy. But differences in timing and definitions result in discrepancies in reported values at all levels." }, { "id": "Longdefinition", "value": "Merchandise exports includes goods whose economic ownership is changed from a resident to a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "TX.VAL.MRCH.HI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Low- and middle-income economies are an increasingly important part of the global trading system. Trade between high-income economies and low- and middle-income economies has grown faster than trade between high-income economies. This increased trade benefits both producers and consumers in developing and high-income economies.\n\nAt the regional level most exports from low- and middle-income economies are to high-income economies, but the share of intraregional trade is increasing. Geographic patterns of trade vary widely by country and commodity. Larger shares of exports from oil- and resource-rich economies are to high-income economies." }, { "id": "IndicatorName", "value": "Merchandise exports to high-income economies (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated." }, { "id": "Longdefinition", "value": "Merchandise exports to high-income economies are the sum of merchandise exports from the reporting economy to high-income economies according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "2" }, { "id": "TX.VAL.MRCH.OR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although global integration has increased, low- and middle-income economies still face trade barriers when accessing other markets." }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated." }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies outside region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "2" }, { "id": "TX.VAL.MRCH.R1.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in East Asia and Pacific are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the East Asia and Pacific region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "2" }, { "id": "TX.VAL.MRCH.R2.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in Europe & Central Asia (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in Europe and Central Asia are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Europe and Central Asia region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "2" }, { "id": "TX.VAL.MRCH.R3.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in Latin America & the Caribbean (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in Latin America and the Caribbean are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Latin America and the Caribbean region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "2" }, { "id": "TX.VAL.MRCH.R4.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in Middle East & North Africa (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in Middle East and North Africa are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Middle East and North Africa region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "2" }, { "id": "TX.VAL.MRCH.R5.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in South Asia (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in South Asia are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the South Asia region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "2" }, { "id": "TX.VAL.MRCH.R6.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in Sub-Saharan Africa (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in Sub-Saharan Africa are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Sub-Saharan Africa region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "2" }, { "id": "TX.VAL.MRCH.RS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports by the reporting economy, residual (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports by the reporting economy residuals are the total merchandise exports by the reporting economy to the rest of the world as reported in the IMF's Direction of trade database, less the sum of exports by the reporting economy to high-, low-, and middle-income economies according to the World Bank classification of economies. Includes trade with unspecified partners or with economies not covered by World Bank classification. Data are as a percentage of total merchandise exports by the economy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: \"This indicator presents the total value of all merchandise exports made by the reporting country over a given period, typically a calendar year. Exports are This This indicator presents the total value of all merchandise exports made by the reporting country over a given period, typically a calendar year. Exports are measured in nominal terms using current U.S. dollars and represent the total value of goods crossing the economic border of the country for sale, exchange, or further processing abroad. Data are reported by national customs authorities and compiled by statistical offices.\n\nExport values are recorded on a free-on-board (FOB) basis, which includes the cost of goods up to the point of shipment, excluding transportation and insurance costs beyond the exporting country. All merchandise goods are included, regardless of their final use in the destination country. Re-exports may be included or excluded depending on national reporting practices; when excluded, the values represent domestic exports only. Goods exported for processing, assembly, or resale are included if ownership is transferred to a foreign entity.\n\nThis indicator provides a comprehensive view of the country’s trade performance, export capacity, and integration into the global market. Changes in total exports can reflect economic cycles, shifts in production and competitiveness, changes in external demand, or exchange rate dynamics. Policymakers, economists, and trade analysts often rely on this measure to assess external sector performance, inform macroeconomic modeling, or design export promotion strategies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "2" }, { "id": "TX.VAL.MRCH.WL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports by the reporting economy (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports by the reporting economy are the total merchandise exports by the reporting economy to the rest of the world, as reported in the IMF's Direction of trade database. Data are in current US$." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: \"This indicator presents the total value of all merchandise exports made by the reporting country over a given period, typically a calendar year. Exports are This This indicator presents the total value of all merchandise exports made by the reporting country over a given period, typically a calendar year. Exports are measured in nominal terms using current U.S. dollars and represent the total value of goods crossing the economic border of the country for sale, exchange, or further processing abroad. Data are reported by national customs authorities and compiled by statistical offices.\n\nExport values are recorded on a free-on-board (FOB) basis, which includes the cost of goods up to the point of shipment, excluding transportation and insurance costs beyond the exporting country. All merchandise goods are included, regardless of their final use in the destination country. Re-exports may be included or excluded depending on national reporting practices; when excluded, the values represent domestic exports only. Goods exported for processing, assembly, or resale are included if ownership is transferred to a foreign entity.\n\nThis indicator provides a comprehensive view of the country’s trade performance, export capacity, and integration into the global market. Changes in total exports can reflect economic cycles, shifts in production and competitiveness, changes in external demand, or exchange rate dynamics. Policymakers, economists, and trade analysts often rely on this measure to assess external sector performance, inform macroeconomic modeling, or design export promotion strategies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "2" }, { "id": "TX.VAL.MRCH.WR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The relative importance of intraregional trade is higher for both landlocked countries and small countries with close trade links to the largest regional economy. For most low- and middle-income economies - especially smaller ones - there is a \"geographic bias\" favoring intraregional trade. Despite the broad trend toward globalization and the reduction of trade barriers, the relative share of intraregional trade increased for most economies between 1999 and 2010. This is due partly to trade-related advantages, such as proximity, lower transport costs, increased knowledge from repeated interaction, and cultural and historical affinity. The direction of trade is also influenced by preferential trade agreements that a country has made with other economies. Though formal agreements on trade liberalization do not automatically increase trade, they nevertheless affect the direction of trade between the participating economies." }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies within region (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated." }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies within region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in the same World Bank region as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data. No figures are shown for high-income economies, because they are a separate category in the World Bank classification of economies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "2" }, { "id": "TX.VAL.MRCH.XD.WD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Export value index (2015 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Export values are the current value of exports (f.o.b.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2015). UNCTAD's export value indexes are reported for most economies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2023" }, { "id": "Source", "value": "UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The value indices show the current value of exports (f.o.b.) for an economy, after conversion to United States dollars and with the reference year =100. Thus it is simply derived from monetary values from the total trade series or from the trade matrix and exchange rates." }, { "id": "Topic", "value": "Trade" } ], "source_id": "2" }, { "id": "TX.VAL.OTHR.ZS.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Computer, communications and other services (% of commercial service exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Computer, communications and other services include such activities as international telecommunications, and postal and courier services; computer data; news-related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; and personal, cultural, and recreational services. This indicator is expressed as a percentage of service exports which are commercial services provided by residents to non-residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TX.VAL.SERV.CD.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Commercial service exports (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Commercial service exports are total service exports minus exports of government services not included elsewhere. Exports of services are services provided by residents to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "2" }, { "id": "TX.VAL.TECH.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "High-technology exports represent the value of products with high research and development (R&D) intensity, such as aerospace, computers, pharmaceuticals, scientific instruments, and advanced machinery, exported by a country in a given year. High-tech exports are defined according to a standard classification (based on SITC Rev.4, using a product-level approach originally developed by OECD in collaboration with Eurostat), which aggregates product codes associated with high R&D intensity.\n\nFrom a development perspective, tracking high-technology exports reveals structural shifts in an economy: a rising volume of such exports may signal successful technology transfer, increasing domestic capacity for innovation, and greater economic sophistication. It provides empirical evidence of movement toward higher value-added production, which is often associated with stronger productivity growth, better jobs, improved trade balances, and long-term competitiveness. \n\nMoreover, by benchmarking across countries, the indicator helps policymakers and researchers assess how well an economy is diversifying away from resource- or low-technology-based exports and embedding itself in segments of global trade characterized by rapid innovation and technological change. Tracking high-technology exports also helps policymakers identify strengths and gaps in national innovation systems, benchmark progress against peers, and design targeted interventions to foster knowledge-intensive industries and diversify export portfolios." }, { "id": "IndicatorName", "value": "High-technology exports (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator is based on data reported by countries to COMTRADE. The export values presented in the World Development Indicators represent Gross Exports less Re-Exports. The values may be impacted in cases of reporting errors or missing data, for example if countries do not report Re-Exports for one or more periods." }, { "id": "Longdefinition", "value": "High-technology exports are products with high R&D intensity, such as aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2007-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), uri: comtrade.un.org, publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS), World Bank (WB), uri: https://wits.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data for high-technology exports are sourced from the United Nations Comtrade database and accessed through the World Integrated Trade Solution (WITS) platform. Export values are calculated as gross exports minus re-exports, using country-reported trade statistics. The methodology aggregates the value of exports for all products classified as high-technology according to SITC Rev.4 codes, as defined by Eurostat and the OECD. Periodic updates to product codes and conversion tables (e.g., from HS to SITC) are incorporated to maintain consistency and address data gaps. The indicator is published annually, and methodological adjustments—such as the inclusion of additional product codes to correct for conversion issues—are documented in the metadata to ensure transparency and comparability over time.\nStatistical concept(s): High technology products are defined according to SITC Rev.4 as the sum of the following products: Aerospace, Computers-office machines, Electronics-telecommunications, Pharmacy, Scientific instruments, Electrical machinery, Chemistry, Non-electrical machinery, Armament. The following product codes are used: Aerospace: (714 – 71489 -71499)+7921+7922+7924+7925+79291+79293+87411; Computers-office machines: 75194+75195+752+75997; Electronics-communication: 76331+7638+(764-76493-76499)+7722+77261+77318+77625+77627+7763+7764+7768+89844+89846; Pharmacy: 5413+5415+5416+5421+5422; Scientific instruments: 774+871+87211+(874-87411-8742)+88111+88121+88411+88419+(8996-89965-89969); Electrical machinery: (7786-77861-777866-77869)+7787+77884; Chemistry: 52222+52223+52229+52269+525+531+57433+591; Non-electrical machinery: 71489+71499+7187+72847+7311+73131+73135+73142+73144+73151+73153+73161+73163+73165+73312+73314+73316+7359+73733+73735; Armament: 891\nThe list can also be accessed on the Eurostat website. This list, based on the OECD definition, contains technical products of which the manufacturing involved a high intensity of R&D. The original high-tech products classification is based on SITC Rev. 3 and is taken from Table 4 of Annex 2 of the 1997 working paper of Thomas Hatzichronouglou, OECD. In September 2019 the definition in the World Development Indicators database was updated to SITC Rev.4 from SITC Rev. 3. The data are in current U.S. dollars and are sourced from the UN's Comtrade database.\n\nNote: The definition of high technology exports in WDI was modified as of October 2024. Specifically the list of SITC Rev.4 high technology product codes now includes product code 776 in its entirety and that all data in its sub-categories is incorporated. This means that products codes 776.11, 776.12, 776.21, 776.23, 776.29 have been added to the high-tech list of products, which does not comply with OECD’s 2008 definition of high-tech.\n\nThis change was implemented to address the problem of missing data which occurs during the data conversion process from HS2022 to SITC4. This happens if a country submits export data in the HS2022 coding system and is no longer in HS2017. Specifically, the HS2022-to-SITC4 conversion table (downloaded from COMTRADE) does not break down product code 776.4+ into sub-categories, and the export data of these sub-categories has been subsumed into 776. Since there is no break down for 776.4+, extracting data from the SITC4 dataset in the COMTRADE database will yield zero value for SITC4 codes 776.4, 776.42, 776.44, 776.46, and 776.49 as defined in the OECD high-tech definition. Hence, when calculating high-tech export values using the forward method of summing up data from these five SITC4 codes will result in missing data caused by the conversion process. This can result in a significant year-on-year decline in high-tech export values for affected countries." }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "USD current" } ], "source_id": "2" }, { "id": "TX.VAL.TECH.MF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "High-technology exports represent the value of products with high research and development (R&D) intensity, such as aerospace, computers, pharmaceuticals, scientific instruments, and advanced machinery, exported by a country in a given year. High-tech exports are defined according to a standard classification (based on SITC Rev.4, using a product-level approach originally developed by OECD in collaboration with Eurostat), which aggregates product codes associated with high R&D intensity.\n\nFrom a development perspective, tracking high-technology exports reveals structural shifts in an economy: a rising volume of such exports may signal successful technology transfer, increasing domestic capacity for innovation, and greater economic sophistication. It provides empirical evidence of movement toward higher value-added production, which is often associated with stronger productivity growth, better jobs, improved trade balances, and long-term competitiveness. \n\nMoreover, by benchmarking across countries, the indicator helps policymakers and researchers assess how well an economy is diversifying away from resource- or low-technology-based exports and embedding itself in segments of global trade characterized by rapid innovation and technological change. Tracking high-technology exports also helps policymakers identify strengths and gaps in national innovation systems, benchmark progress against peers, and design targeted interventions to foster knowledge-intensive industries and diversify export portfolios." }, { "id": "IndicatorName", "value": "High-technology exports (% of manufactured exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator is based on data reported by countries to COMTRADE. The export values presented in the World Development Indicators represent Gross Exports less Re-Exports. The values may be impacted in cases of reporting errors or missing data, for example if countries do not report Re-Exports for one or more periods." }, { "id": "Longdefinition", "value": "High-technology exports are products with high R&D intensity, such as in aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2007-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), uri: comtrade.un.org, publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS), World Bank (WB), uri: https://wits.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data for high-technology exports are sourced from the United Nations Comtrade database and accessed through the World Integrated Trade Solution (WITS) platform. Export values are calculated as gross exports minus re-exports, using country-reported trade statistics. The methodology aggregates the value of exports for all products classified as high-technology according to SITC Rev.4 codes, as defined by Eurostat and the OECD. Periodic updates to product codes and conversion tables (e.g., from HS to SITC) are incorporated to maintain consistency and address data gaps. The indicator is published annually, and methodological adjustments—such as the inclusion of additional product codes to correct for conversion issues—are documented in the metadata to ensure transparency and comparability over time.\nStatistical concept(s): High technology products are defined according to SITC Rev.4 as the sum of the following products: Aerospace, Computers-office machines, Electronics-telecommunications, Pharmacy, Scientific instruments, Electrical machinery, Chemistry, Non-electrical machinery, Armament. The following product codes are used: Aerospace: (714 – 71489 -71499)+7921+7922+7924+7925+79291+79293+87411; Computers-office machines: 75194+75195+752+75997; Electronics-communication: 76331+7638+(764-76493-76499)+7722+77261+77318+77625+77627+7763+7764+7768+89844+89846; Pharmacy: 5413+5415+5416+5421+5422; Scientific instruments: 774+871+87211+(874-87411-8742)+88111+88121+88411+88419+(8996-89965-89969); Electrical machinery: (7786-77861-777866-77869)+7787+77884; Chemistry: 52222+52223+52229+52269+525+531+57433+591; Non-electrical machinery: 71489+71499+7187+72847+7311+73131+73135+73142+73144+73151+73153+73161+73163+73165+73312+73314+73316+7359+73733+73735; Armament: 891\nThe list can also be accessed on the Eurostat website. This list, based on the OECD definition, contains technical products of which the manufacturing involved a high intensity of R&D. The original high-tech products classification is based on SITC Rev. 3 and is taken from Table 4 of Annex 2 of the 1997 working paper of Thomas Hatzichronouglou, OECD. In September 2019 the definition in the World Development Indicators database was updated to SITC Rev.4 from SITC Rev. 3. The data are in current U.S. dollars and are sourced from the UN's Comtrade database.\n\nNote: The definition of high technology exports in WDI was modified as of October 2024. Specifically the list of SITC Rev.4 high technology product codes now includes product code 776 in its entirety and that all data in its sub-categories is incorporated. This means that products codes 776.11, 776.12, 776.21, 776.23, 776.29 have been added to the high-tech list of products, which does not comply with OECD’s 2008 definition of high-tech.\n\nThis change was implemented to address the problem of missing data which occurs during the data conversion process from HS2022 to SITC4. This happens if a country submits export data in the HS2022 coding system and is no longer in HS2017. Specifically, the HS2022-to-SITC4 conversion table (downloaded from COMTRADE) does not break down product code 776.4+ into sub-categories, and the export data of these sub-categories has been subsumed into 776. Since there is no break down for 776.4+, extracting data from the SITC4 dataset in the COMTRADE database will yield zero value for SITC4 codes 776.4, 776.42, 776.44, 776.46, and 776.49 as defined in the OECD high-tech definition. Hence, when calculating high-tech export values using the forward method of summing up data from these five SITC4 codes will result in missing data caused by the conversion process. This can result in a significant year-on-year decline in high-tech export values for affected countries." }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "2" }, { "id": "TX.VAL.TRAN.ZS.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Transport services (% of commercial service exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Transport is the process of carriage of people and objects from one location to another as well as related supporting and auxiliary services. Also included are postal and courier services. This indicator is expressed as a percentage of service exports which are commercial services provided by residents to non-residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "TX.VAL.TRVL.ZS.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Travel services (% of commercial service exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Travel services cover goods and services for own use or to give away acquired from an economy by nonresidents during visits to that economy, or acquired from other economies by residents during visits to these other economies. This indicator is expressed as a percentage of service exports which are commercial services provided by residents to non-residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "2" }, { "id": "VC.BTL.DETH", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Monitoring battle-related deaths is critical for understanding the human cost of conflict and its implications for development. High levels of conflict-related mortality often coincide with displacement, destruction of infrastructure, and disruption of economic activity, which can reverse progress on poverty reduction and human development. Reliable data on conflict intensity inform policy responses aimed at peacebuilding, humanitarian assistance, and post-conflict recovery. They also support global efforts to track progress toward peace and security goals, by providing evidence for interventions that reduce violence and promote stability." }, { "id": "IndicatorName", "value": "Battle-related deaths (number of people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While the best estimate for battle-related deaths is considered a conservative minimum, the high estimate may still undercount actual fatalities due to unreported events, and users are cautioned that the apparent precision of the numbers does not eliminate underlying uncertainty." }, { "id": "Longdefinition", "value": "Battle-related deaths are deaths in battle-related conflicts between warring parties in the conflict dyad (two conflict units that are parties to a conflict). Battle-related deaths refer to those deaths caused by the warring parties that can be directly related to combat. This includes battlefield fighting, guerrilla activities (e.g. hit and-run attacks/ambushes) and all kinds of bombardments of military bases, cities and villages etc. The target for the attacks is either the military forces or representatives for the parties, though there is often substantial collateral damage in the form of civilians being killed in the crossfire, indiscriminate bombings, etc. All fatalities, military as well as civilian, incurred in such situations are counted as battle-related deaths." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1989-2024" }, { "id": "Source", "value": "UCDP Battle-related Deaths Dataset , Uppsala Conflict Data Program (UCDP), uri: https://ucdp.uu.se/downloads/, publisher: Uppsala University" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The dataset is constructed by automatically filtering and aggregating the UCDP Georeferenced Event Dataset from the incident level to the conflict/dyad-year level, and then joining this with additional conflict-year data from related UCDP datasets. Source material is gathered in a two-pass system: first, global newswire reporting is reviewed, followed by targeted searches in local and specialized sources such as local media, NGO and IGO reports, field reports, and social media. This approach ensures comprehensive coverage and cross-verification of events. Detailed procedures, including search strategies, are further described in the UCDP GED Codebook.\n\n\nStatistical concept(s): The UCDP provides three estimates for battle-related deaths (best, low, and high) to account for the uncertainty inherent in conflict reporting. The \"best estimate\" (which is the value available in the World Development Indicators database) aggregates the most reliable numbers for all incidents each year, favoring the lower figure when sources are equally credible. The \"low estimate\" sums the lowest plausible figures, while the \"high estimate\" aggregates the highest plausible numbers, including incidents with uncertain party involvement. All estimates are based on publicly accessible sources and are subject to revision as new information emerges." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" }, { "id": "Unitofmeasure", "value": "Count" } ], "source_id": "2" }, { "id": "VC.IDP.NWCV", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although all persons affected by conflict and/or human rights violations suffer, displacement from one's place of residence may make the internally displaced particularly vulnerable. Following are some of the factors that are likely to increase the need for protection:\n \n1) Internally displaced persons may be in transit from one place to another, may be in hiding, may be forced toward unhealthy or inhospitable environments, or face other circumstances that make them especially vulnerable.\n \n2) The social organization of displaced communities may have been destroyed or damaged by the act of physical displacement; family groups may be separated or disrupted; women may be forced to assume non-traditional roles or face particular vulnerabilities. Internally displaced populations, and especially groups like children, the elderly, or pregnant women, may experience profound psychosocial distress related to displacement.\n \n3) Removal from sources of income and livelihood may add to physical and psychosocial vulnerability for displaced people.\n \n4) Schooling for children and adolescents may be disrupted.\n \n5) Internal displacement to areas where local inhabitants are of different groups or inhospitable may increase risk to internally displaced communities; internally displaced persons may face language barriers during displacement.\n \n6) The condition of internal displacement may raise the suspicions of or lead to abuse by armed combatants, or other parties to conflict.\n \n7) Internally displaced persons may lack identity documents essential to receiving benefits or legal recognition; in some cases, fearing persecution, displaced persons have sometimes got rid of such documents.\n \n8) According to the Internal Displacement Monitoring Centre (IDMC) tens of millions people around the world are displaced every year within their countries by conflict, human rights violations, natural disasters and climate change. Unlike refugees who cross national borders and benefit from an established system of international protection and assistance, those forcibly uprooted within their own countries, by armed conflict, large-scale development projects, systematic violations of human rights, or natural disasters, lack predictable structures of support. Internal displacement has become one of the more pressing humanitarian, human rights and security problems confronting affected countries and the international community at large.\n \nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights." }, { "id": "IndicatorName", "value": "Internally displaced persons, new displacement associated with conflict and violence (number of cases)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Please note that most of the figures are estimates. The definition highlights two issues:\n\n1) The coercive or otherwise involuntary character of movement. The definition mentions some of the most common causes of involuntary movements, such as armed conflict, violence, human rights violations and disasters. These causes have in common that they give no choice to people but to leave their homes and deprive them of the most essential protection mechanisms, such as community networks, access to services, livelihoods. Displacement severely affects the physical, socio-economic and legal safety of people and should be systematically regarded as an indicator of potential vulnerability.\n \n2) The fact that such movement takes place within national borders. Unlike refugees, who have been deprived of the protection of their state of origin, IDPs remain legally under the protection of national authorities of their country of habitual residence. IDPs should therefore enjoy the same rights as the rest of the population. The Guiding Principles on Internal Displacement remind national authorities and other relevant actors of their responsibility to ensure that IDPs' rights are respected and fulfilled, despite the vulnerability generated by their displacement." }, { "id": "Longdefinition", "value": "Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. \"New Displacement\" refers to the number of new cases or incidents of displacement recorded over the specified year, rather than the number of people displaced. This is done because people may have been displaced more than once." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2009-2023" }, { "id": "Source", "value": "Internal Displacement Monitoring Centre (IDMC), uri: http://www.internal-displacement.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Internally displaced persons are \"persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized state border.\" Internally displaced people are often confused with refugees. Unlike refugees, internally displaced people remain under the protection of their own government, even if their reason for fleeing was similar to that of refugees. Refugees are people who have crossed an international border to find sanctuary and have been granted refugee or refugee-like status or temporary protection. For more information on methodology, please refer to the information published by IDMC: http://www.internal-displacement.org/database/" }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "2" }, { "id": "VC.IDP.NWDS", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although all persons affected by conflict and/or human rights violations suffer, displacement from one's place of residence may make the internally displaced particularly vulnerable. Following are some of the factors that are likely to increase the need for protection:\n \n1) Internally displaced persons may be in transit from one place to another, may be in hiding, may be forced toward unhealthy or inhospitable environments, or face other circumstances that make them especially vulnerable.\n \n2) The social organization of displaced communities may have been destroyed or damaged by the act of physical displacement; family groups may be separated or disrupted; women may be forced to assume non-traditional roles or face particular vulnerabilities. Internally displaced populations, and especially groups like children, the elderly, or pregnant women, may experience profound psychosocial distress related to displacement.\n \n3) Removal from sources of income and livelihood may add to physical and psychosocial vulnerability for displaced people.\n \n4) Schooling for children and adolescents may be disrupted.\n \n5) Internal displacement to areas where local inhabitants are of different groups or inhospitable may increase risk to internally displaced communities; internally displaced persons may face language barriers during displacement.\n \n6) The condition of internal displacement may raise the suspicions of or lead to abuse by armed combatants, or other parties to conflict.\n \n7) Internally displaced persons may lack identity documents essential to receiving benefits or legal recognition; in some cases, fearing persecution, displaced persons have sometimes got rid of such documents.\n \n8) According to the Internal Displacement Monitoring Centre (IDMC) tens of millions people around the world are displaced every year within their countries by conflict, human rights violations, natural disasters and climate change. Unlike refugees who cross national borders and benefit from an established system of international protection and assistance, those forcibly uprooted within their own countries, by armed conflict, large-scale development projects, systematic violations of human rights, or natural disasters, lack predictable structures of support. Internal displacement has become one of the more pressing humanitarian, human rights and security problems confronting affected countries and the international community at large.\n \nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights." }, { "id": "IndicatorName", "value": "Internally displaced persons, new displacement associated with disasters (number of cases)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Please note that most of the figures are estimates. The definition highlights two issues:\n\n1) The coercive or otherwise involuntary character of movement. The definition mentions some of the most common causes of involuntary movements, such as armed conflict, violence, human rights violations and disasters. These causes have in common that they give no choice to people but to leave their homes and deprive them of the most essential protection mechanisms, such as community networks, access to services, livelihoods. Displacement severely affects the physical, socio-economic and legal safety of people and should be systematically regarded as an indicator of potential vulnerability.\n \n2) The fact that such movement takes place within national borders. Unlike refugees, who have been deprived of the protection of their state of origin, IDPs remain legally under the protection of national authorities of their country of habitual residence. IDPs should therefore enjoy the same rights as the rest of the population. The Guiding Principles on Internal Displacement remind national authorities and other relevant actors of their responsibility to ensure that IDPs' rights are respected and fulfilled, despite the vulnerability generated by their displacement." }, { "id": "Longdefinition", "value": "Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. \"New Displacement\" refers to the number of new cases or incidents of displacement recorded over the specified year, rather than the number of people displaced. This is done because people may have been displaced more than once." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2008-2023" }, { "id": "Source", "value": "Internal Displacement Monitoring Centre (IDMC), uri: http://www.internal-displacement.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Internally displaced persons are \"persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized state border.\" Internally displaced people are often confused with refugees. Unlike refugees, internally displaced people remain under the protection of their own government, even if their reason for fleeing was similar to that of refugees. Refugees are people who have crossed an international border to find sanctuary and have been granted refugee or refugee-like status or temporary protection." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "2" }, { "id": "VC.IHR.PSRC.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Aggregate values are computed by UNODC. For additional information, please see the UNODC website: https://dataunodc.un.org/sites/dataunodc.un.org/files/metadata_intentional_homicide.pdf" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In some regions, organized crime, drug trafficking and the violent cultures of youth gangs are predominantly responsible for the high levels of homicide. There has been a sharp increase in homicides in some countries, particularly in Central America, are making the activities of organized crime and drug trafficking more visible. Greater use of firearms is often associated with the illicit activities of organized criminal groups, which are often linked to drug trafficking.\n\nKnowledge of the patterns and causes of violent crime are crucial to forming preventive strategies. Young males are the group most affected by violent crime in all regions, particularly in the Americas. Yet women of all ages are the victims of intimate partner and family-related violence in all regions and countries. Indeed, in many of them, it is within the home where a woman is most likely to be killed.\n\nData on intentional homicides are from the United Nations Office on Drugs and Crime (UNODC), which uses a variety of national and international sources on homicides - primarily criminal justice sources as well as public health data from the World Health Organization (WHO) and the Pan American Health Organization - and the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems to present accurate and comparable statistics. The UNODC defines homicide as \"unlawful death purposefully inflicted on a person by another person.\" This definition excludes deaths arising from armed conflict." }, { "id": "IndicatorName", "value": "Intentional homicides, female (per 100,000 female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Statistics reported to the United Nations in the context of its various surveys on crime levels and criminal justice trends are incidents of victimization that have been reported to the authorities in any given country. That means that this data is subject to the problems of accuracy of all official crime data. The survey results provide an overview of trends and interrelationships between various parts of the criminal justice system to promote informed decision-making in administration, nationally and internationally.\n\nThe degree to which different societies apportion the level of culpability to acts resulting in death is also subject to variation. Consequently, the comparison between countries and regions of \"intentional homicide\", or unlawful death purposefully inflicted on a person by another person, is also a comparison of the extent to which different countries deem that a killing be classified as such, as well as the capacity of their legal systems to record it. Caution should therefore be applied when evaluating and comparing homicide data." }, { "id": "Longdefinition", "value": "An intentional homicide is defined as an unlawful death inflicted upon a person with the intent to cause death or serious injury." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UNODC Research - Data Portal – Intentional Homicide, UN Office on Drugs and Crime (UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data are sourced by UNODC from either criminal justice or public health systems. In the former, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event, whereas in the latter, data are produced by health authorities certifying the cause of death of an individual. \n\nThese data are collected from national authorities with the annual United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS). National focal points working in national agencies responsible for statistics on crime and the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the UN-CTS to UNODC. Following the submission, UNODC checks for consistency and coherence with other data sources. Member States which are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union are sending their response to the UN-CTS to Eurostat for validation. \n\nData submitted by Member States through other means or taken from other sources are added to the dataset after review by Member States. \n\nThe population data is sourced from the World Population Prospect, Population Division, United Nations Department of Economic and Social Affairs. \nStatistical concept(s): The International Classification of Crime for Statistical Purposes (ICCS) is the source of the definition of intentional homicide. The definitions of the disaggregation of victims of intentional homicide included in these tables (by situational context, by relationship to perpetrator and by mechanisms) are also from the ICCS. \n\nThe ICCS includes more information on what is included and excluded in these offences. Intentional homicide (ICCS 0101): Unlawful death inflicted upon a person with the intent to cause death or serious injury. \n\nThe statistical definition contains three elements that characterize the killing of a person as “intentional homicide”: \n1. The killing of a person by another person (objective element) \n2. The intent of the perpetrator to kill or seriously injure the victim (subjective element) \n3. The unlawfulness of the killing (legal element) \n\nFor recording purposes, all killings that meet the criteria listed above are to be considered intentional homicides, irrespective of definitions provided by national legislations or practices. Killings as a result of terrorist activities are also to be classified as a form of intentional homicide." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" }, { "id": "Unitofmeasure", "value": "Rate per 100,000 population" } ], "source_id": "2" }, { "id": "VC.IHR.PSRC.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Aggregate values are computed by UNODC. For additional information, please see the UNODC website: https://dataunodc.un.org/sites/dataunodc.un.org/files/metadata_intentional_homicide.pdf" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In some regions, organized crime, drug trafficking and the violent cultures of youth gangs are predominantly responsible for the high levels of homicide. There has been a sharp increase in homicides in some countries, particularly in Central America, are making the activities of organized crime and drug trafficking more visible. Greater use of firearms is often associated with the illicit activities of organized criminal groups, which are often linked to drug trafficking.\n\nKnowledge of the patterns and causes of violent crime are crucial to forming preventive strategies. Young males are the group most affected by violent crime in all regions, particularly in the Americas. Yet women of all ages are the victims of intimate partner and family-related violence in all regions and countries. Indeed, in many of them, it is within the home where a woman is most likely to be killed.\n\nData on intentional homicides are from the United Nations Office on Drugs and Crime (UNODC), which uses a variety of national and international sources on homicides - primarily criminal justice sources as well as public health data from the World Health Organization (WHO) and the Pan American Health Organization - and the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems to present accurate and comparable statistics. The UNODC defines homicide as \"unlawful death purposefully inflicted on a person by another person.\" This definition excludes deaths arising from armed conflict." }, { "id": "IndicatorName", "value": "Intentional homicides, male (per 100,000 male)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Statistics reported to the United Nations in the context of its various surveys on crime levels and criminal justice trends are incidents of victimization that have been reported to the authorities in any given country. That means that this data is subject to the problems of accuracy of all official crime data. The survey results provide an overview of trends and interrelationships between various parts of the criminal justice system to promote informed decision-making in administration, nationally and internationally.\n\nThe degree to which different societies apportion the level of culpability to acts resulting in death is also subject to variation. Consequently, the comparison between countries and regions of \"intentional homicide\", or unlawful death purposefully inflicted on a person by another person, is also a comparison of the extent to which different countries deem that a killing be classified as such, as well as the capacity of their legal systems to record it. Caution should therefore be applied when evaluating and comparing homicide data." }, { "id": "Longdefinition", "value": "An intentional homicide is defined as an unlawful death inflicted upon a person with the intent to cause death or serious injury." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UNODC Research - Data Portal – Intentional Homicide, UN Office on Drugs and Crime (UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data are sourced by UNODC from either criminal justice or public health systems. In the former, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event, whereas in the latter, data are produced by health authorities certifying the cause of death of an individual. \n\nThese data are collected from national authorities with the annual United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS). National focal points working in national agencies responsible for statistics on crime and the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the UN-CTS to UNODC. Following the submission, UNODC checks for consistency and coherence with other data sources. Member States which are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union are sending their response to the UN-CTS to Eurostat for validation. \n\nData submitted by Member States through other means or taken from other sources are added to the dataset after review by Member States. \n\nThe population data is sourced from the World Population Prospect, Population Division, United Nations Department of Economic and Social Affairs. \nStatistical concept(s): The International Classification of Crime for Statistical Purposes (ICCS) is the source of the definition of intentional homicide. The definitions of the disaggregation of victims of intentional homicide included in these tables (by situational context, by relationship to perpetrator and by mechanisms) are also from the ICCS. \n\nThe ICCS includes more information on what is included and excluded in these offences. Intentional homicide (ICCS 0101): Unlawful death inflicted upon a person with the intent to cause death or serious injury. \n\nThe statistical definition contains three elements that characterize the killing of a person as “intentional homicide”: \n1. The killing of a person by another person (objective element) \n2. The intent of the perpetrator to kill or seriously injure the victim (subjective element) \n3. The unlawfulness of the killing (legal element) \n\nFor recording purposes, all killings that meet the criteria listed above are to be considered intentional homicides, irrespective of definitions provided by national legislations or practices. Killings as a result of terrorist activities are also to be classified as a form of intentional homicide." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" }, { "id": "Unitofmeasure", "value": "Rate per 100,000 population" } ], "source_id": "2" }, { "id": "VC.IHR.PSRC.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Aggregate values are computed by UNODC. For additional information, please see the UNODC website: https://dataunodc.un.org/sites/dataunodc.un.org/files/metadata_intentional_homicide.pdf" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In some regions, organized crime, drug trafficking and the violent cultures of youth gangs are predominantly responsible for the high levels of homicide. There has been a sharp increase in homicides in some countries, particularly in Central America, are making the activities of organized crime and drug trafficking more visible. Greater use of firearms is often associated with the illicit activities of organized criminal groups, which are often linked to drug trafficking.\n\nKnowledge of the patterns and causes of violent crime are crucial to forming preventive strategies. Young males are the group most affected by violent crime in all regions, particularly in the Americas. Yet women of all ages are the victims of intimate partner and family-related violence in all regions and countries. Indeed, in many of them, it is within the home where a woman is most likely to be killed.\n\nData on intentional homicides are from the United Nations Office on Drugs and Crime (UNODC), which uses a variety of national and international sources on homicides - primarily criminal justice sources as well as public health data from the World Health Organization (WHO) and the Pan American Health Organization - and the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems to present accurate and comparable statistics. The UNODC defines homicide as \"unlawful death purposefully inflicted on a person by another person.\" This definition excludes deaths arising from armed conflict." }, { "id": "IndicatorName", "value": "Intentional homicides (per 100,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Statistics reported to the United Nations in the context of its various surveys on crime levels and criminal justice trends are incidents of victimization that have been reported to the authorities in any given country. That means that this data is subject to the problems of accuracy of all official crime data. The survey results provide an overview of trends and interrelationships between various parts of the criminal justice system to promote informed decision-making in administration, nationally and internationally.\n\nThe degree to which different societies apportion the level of culpability to acts resulting in death is also subject to variation. Consequently, the comparison between countries and regions of \"intentional homicide\", or unlawful death purposefully inflicted on a person by another person, is also a comparison of the extent to which different countries deem that a killing be classified as such, as well as the capacity of their legal systems to record it. Caution should therefore be applied when evaluating and comparing homicide data." }, { "id": "Longdefinition", "value": "An intentional homicide is defined as an unlawful death inflicted upon a person with the intent to cause death or serious injury." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UNODC Research - Data Portal – Intentional Homicide, UN Office on Drugs and Crime (UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data are sourced by UNODC from either criminal justice or public health systems. In the former, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event, whereas in the latter, data are produced by health authorities certifying the cause of death of an individual. \n\nThese data are collected from national authorities with the annual United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS). National focal points working in national agencies responsible for statistics on crime and the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the UN-CTS to UNODC. Following the submission, UNODC checks for consistency and coherence with other data sources. Member States which are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union are sending their response to the UN-CTS to Eurostat for validation. \n\nData submitted by Member States through other means or taken from other sources are added to the dataset after review by Member States. \n\nThe population data is sourced from the World Population Prospect, Population Division, United Nations Department of Economic and Social Affairs. \nStatistical concept(s): The International Classification of Crime for Statistical Purposes (ICCS) is the source of the definition of intentional homicide. The definitions of the disaggregation of victims of intentional homicide included in these tables (by situational context, by relationship to perpetrator and by mechanisms) are also from the ICCS. \n\nThe ICCS includes more information on what is included and excluded in these offences. Intentional homicide (ICCS 0101): Unlawful death inflicted upon a person with the intent to cause death or serious injury. \n\nThe statistical definition contains three elements that characterize the killing of a person as “intentional homicide”: \n1. The killing of a person by another person (objective element) \n2. The intent of the perpetrator to kill or seriously injure the victim (subjective element) \n3. The unlawfulness of the killing (legal element) \n\nFor recording purposes, all killings that meet the criteria listed above are to be considered intentional homicides, irrespective of definitions provided by national legislations or practices. Killings as a result of terrorist activities are also to be classified as a form of intentional homicide." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" }, { "id": "Unitofmeasure", "value": "Rate per 100,000 population" } ], "source_id": "2" }, { "id": "GOV_WGI_CC.EST", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Control of Corruption - Governance estimate (approx. -2.5 to +2.5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Control of Corruption (CC) captures perceptions of the extent to which public power is used for private gain, including both petty and grand corruption, as well as capture of the state by elites and private interests. Governance estimate from the aggregation model, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Larger values correspond to better governance." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Control of Corruption" } ], "source_id": "3" }, { "id": "GOV_WGI_CC.SC", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Control of Corruption - Governance score (0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Control of Corruption (CC) captures perceptions of the extent to which public power is used for private gain, including both petty and grand corruption, as well as capture of the state by elites and private interests. Governance score is a linear transformation of the governance estimate using hypothetical worst-case and base-case countries. It is an absolute score ranging from 0-100. Larger values correspond to better governance." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Control of Corruption" } ], "source_id": "3" }, { "id": "GOV_WGI_CC.SC_LB", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Control of Corruption - Lower bound of the 90% confidence interval for the governance score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Control of Corruption (CC) captures perceptions of the extent to which public power is used for private gain, including both petty and grand corruption, as well as capture of the state by elites and private interests. Lower bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated)." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Control of Corruption" } ], "source_id": "3" }, { "id": "GOV_WGI_CC.SC_UB", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Control of Corruption - Upper bound of the 90% confidence interval for the governance score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Control of Corruption (CC) captures perceptions of the extent to which public power is used for private gain, including both petty and grand corruption, as well as capture of the state by elites and private interests. Upper bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated)." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Control of Corruption" } ], "source_id": "3" }, { "id": "GOV_WGI_CC.SE", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Control of Corruption - Standard error of the governance estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Control of Corruption (CC) captures perceptions of the extent to which public power is used for private gain, including both petty and grand corruption, as well as capture of the state by elites and private interests. Standard error indicates the precision of the governance estimate. Larger values indicate less precise estimates. A 90% confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Control of Corruption" } ], "source_id": "3" }, { "id": "GOV_WGI_CC.SR", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Control of Corruption - Number of sources" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Control of Corruption (CC) captures perceptions of the extent to which public power is used for private gain, including both petty and grand corruption, as well as capture of the state by elites and private interests. Number of sources indicates the number of underlying data sources on which the governance estimate is based." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Control of Corruption" } ], "source_id": "3" }, { "id": "GOV_WGI_GE.EST", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Government Effectiveness - Governance estimate (approx. -2.5 to +2.5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Government Effectiveness (GE) captures perceptions of the quality of public services, the civil service, policy formulation and implementation, and the credibility of a government’s decisions. Governance estimate from the aggregation model, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Larger values correspond to better governance." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Government Effectiveness" } ], "source_id": "3" }, { "id": "GOV_WGI_GE.SC", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Government Effectiveness - Governance score (0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Government Effectiveness (GE) captures perceptions of the quality of public services, the civil service, policy formulation and implementation, and the credibility of a government’s decisions. Governance score is a linear transformation of the governance estimate using hypothetical worst-case and base-case countries. It is an absolute score ranging from 0-100. Larger values correspond to better governance." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Government Effectiveness" } ], "source_id": "3" }, { "id": "GOV_WGI_GE.SC_LB", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Government Effectiveness - Lower bound of the 90% confidence interval for the governance score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Government Effectiveness (GE) captures perceptions of the quality of public services, the civil service, policy formulation and implementation, and the credibility of a government’s decisions. Lower bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated)." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Government Effectiveness" } ], "source_id": "3" }, { "id": "GOV_WGI_GE.SC_UB", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Government Effectiveness - Upper bound of the 90% confidence interval for the governance score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Government Effectiveness (GE) captures perceptions of the quality of public services, the civil service, policy formulation and implementation, and the credibility of a government’s decisions. Upper bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated)." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Government Effectiveness" } ], "source_id": "3" }, { "id": "GOV_WGI_GE.SE", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Government Effectiveness - Standard error of the governance estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Government Effectiveness (GE) captures perceptions of the quality of public services, the civil service, policy formulation and implementation, and the credibility of a government’s decisions. Standard error indicates the precision of the governance estimate. Larger values indicate less precise estimates. A 90% confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Government Effectiveness" } ], "source_id": "3" }, { "id": "GOV_WGI_GE.SR", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Government Effectiveness - Number of sources" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Government Effectiveness (GE) captures perceptions of the quality of public services, the civil service, policy formulation and implementation, and the credibility of a government’s decisions. Number of sources indicates the number of underlying data sources on which the governance estimate is based." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Government Effectiveness" } ], "source_id": "3" }, { "id": "GOV_WGI_PV.EST", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Political Stability - Governance estimate (approx. -2.5 to +2.5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Political Stability (PV) captures perceptions of the extent to which political power and governance are secure from destabilization, and of the likelihood that authority will be challenged or altered through violent, coercive, or unconstitutional means. Governance estimate from the aggregation model, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Larger values correspond to better governance." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Political Stability" } ], "source_id": "3" }, { "id": "GOV_WGI_PV.SC", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Political Stability - Governance score (0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Political Stability (PV) captures perceptions of the extent to which political power and governance are secure from destabilization, and of the likelihood that authority will be challenged or altered through violent, coercive, or unconstitutional means. Governance score is a linear transformation of the governance estimate using hypothetical worst-case and base-case countries. It is an absolute score ranging from 0-100. Larger values correspond to better governance." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Political Stability" } ], "source_id": "3" }, { "id": "GOV_WGI_PV.SC_LB", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Political Stability - Lower bound of the 90% confidence interval for the governance score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Political Stability (PV) captures perceptions of the extent to which political power and governance are secure from destabilization, and of the likelihood that authority will be challenged or altered through violent, coercive, or unconstitutional means. Lower bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated)." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Political Stability" } ], "source_id": "3" }, { "id": "GOV_WGI_PV.SC_UB", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Political Stability - Upper bound of the 90% confidence interval for the governance score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Political Stability (PV) captures perceptions of the extent to which political power and governance are secure from destabilization, and of the likelihood that authority will be challenged or altered through violent, coercive, or unconstitutional means. Upper bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated)." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Political Stability" } ], "source_id": "3" }, { "id": "GOV_WGI_PV.SE", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Political Stability - Standard error of the governance estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Political Stability (PV) captures perceptions of the extent to which political power and governance are secure from destabilization, and of the likelihood that authority will be challenged or altered through violent, coercive, or unconstitutional means. Standard error indicates the precision of the governance estimate. Larger values indicate less precise estimates. A 90% confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Political Stability" } ], "source_id": "3" }, { "id": "GOV_WGI_PV.SR", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Political Stability - Number of sources" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Political Stability (PV) captures perceptions of the extent to which political power and governance are secure from destabilization, and of the likelihood that authority will be challenged or altered through violent, coercive, or unconstitutional means. Number of sources indicates the number of underlying data sources on which the governance estimate is based." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Political Stability" } ], "source_id": "3" }, { "id": "GOV_WGI_RL.EST", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Rule of Law - Governance estimate (approx. -2.5 to +2.5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Rule of Law (RL) captures perceptions of the extent to which agents respect and follow the rules of society, including contract enforcement, property rights, the police, courts, and the likelihood of crime and violence. Governance estimate from the aggregation model, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Larger values correspond to better governance." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Rule of Law" } ], "source_id": "3" }, { "id": "GOV_WGI_RL.SC", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Rule of Law - Governance score (0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Rule of Law (RL) captures perceptions of the extent to which agents respect and follow the rules of society, including contract enforcement, property rights, the police, courts, and the likelihood of crime and violence. Governance score is a linear transformation of the governance estimate using hypothetical worst-case and base-case countries. It is an absolute score ranging from 0-100. Larger values correspond to better governance." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Rule of Law" } ], "source_id": "3" }, { "id": "GOV_WGI_RL.SC_LB", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Rule of Law - Lower bound of the 90% confidence interval for the governance score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Rule of Law (RL) captures perceptions of the extent to which agents respect and follow the rules of society, including contract enforcement, property rights, the police, courts, and the likelihood of crime and violence. Lower bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated)." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Rule of Law" } ], "source_id": "3" }, { "id": "GOV_WGI_RL.SC_UB", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Rule of Law - Upper bound of the 90% confidence interval for the governance score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Rule of Law (RL) captures perceptions of the extent to which agents respect and follow the rules of society, including contract enforcement, property rights, the police, courts, and the likelihood of crime and violence. Upper bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated)." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Rule of Law" } ], "source_id": "3" }, { "id": "GOV_WGI_RL.SE", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Rule of Law - Standard error of the governance estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Rule of Law (RL) captures perceptions of the extent to which agents respect and follow the rules of society, including contract enforcement, property rights, the police, courts, and the likelihood of crime and violence. Standard error indicates the precision of the governance estimate. Larger values indicate less precise estimates. A 90% confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Rule of Law" } ], "source_id": "3" }, { "id": "GOV_WGI_RL.SR", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Rule of Law - Number of sources" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Rule of Law (RL) captures perceptions of the extent to which agents respect and follow the rules of society, including contract enforcement, property rights, the police, courts, and the likelihood of crime and violence. Number of sources indicates the number of underlying data sources on which the governance estimate is based." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Rule of Law" } ], "source_id": "3" }, { "id": "GOV_WGI_RQ.EST", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Regulatory Quality - Governance estimate (approx. -2.5 to +2.5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Regulatory Quality (RQ) captures perceptions of the government’s ability to design and implement policies and regulations that promote private sector development. Governance estimate from the aggregation model, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Larger values correspond to better governance." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Regulatory Quality" } ], "source_id": "3" }, { "id": "GOV_WGI_RQ.SC", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Regulatory Quality - Governance score (0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Regulatory Quality (RQ) captures perceptions of the government’s ability to design and implement policies and regulations that promote private sector development. Governance score is a linear transformation of the governance estimate using hypothetical worst-case and base-case countries. It is an absolute score ranging from 0-100. Larger values correspond to better governance." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Regulatory Quality" } ], "source_id": "3" }, { "id": "GOV_WGI_RQ.SC_LB", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Regulatory Quality - Lower bound of the 90% confidence interval for the governance score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Regulatory Quality (RQ) captures perceptions of the government’s ability to design and implement policies and regulations that promote private sector development. Lower bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated)." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Regulatory Quality" } ], "source_id": "3" }, { "id": "GOV_WGI_RQ.SC_UB", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Regulatory Quality - Upper bound of the 90% confidence interval for the governance score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Regulatory Quality (RQ) captures perceptions of the government’s ability to design and implement policies and regulations that promote private sector development. Upper bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated)." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Regulatory Quality" } ], "source_id": "3" }, { "id": "GOV_WGI_RQ.SE", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Regulatory Quality - Standard error of the governance estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Regulatory Quality (RQ) captures perceptions of the government’s ability to design and implement policies and regulations that promote private sector development. Standard error indicates the precision of the governance estimate. Larger values indicate less precise estimates. A 90% confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Regulatory Quality" } ], "source_id": "3" }, { "id": "GOV_WGI_RQ.SR", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Regulatory Quality - Number of sources" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Regulatory Quality (RQ) captures perceptions of the government’s ability to design and implement policies and regulations that promote private sector development. Number of sources indicates the number of underlying data sources on which the governance estimate is based." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Regulatory Quality" } ], "source_id": "3" }, { "id": "GOV_WGI_VA.EST", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Voice and Accountability - Governance estimate (approx. -2.5 to +2.5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Voice and Accountability (VA) captures perceptions of the extent to which citizens can participate in selecting their government including electoral integrity, and of accountability mechanisms for citizens—reflected in the ability to access information, governmental oversight bodies, and a robust traditional/digital media landscape. Governance estimate from the aggregation model, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Larger values correspond to better governance." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Voice and Accountability" } ], "source_id": "3" }, { "id": "GOV_WGI_VA.SC", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Voice and Accountability - Governance score (0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Voice and Accountability (VA) captures perceptions of the extent to which citizens can participate in selecting their government including electoral integrity, and of accountability mechanisms for citizens—reflected in the ability to access information, governmental oversight bodies, and a robust traditional/digital media landscape. Governance score is a linear transformation of the governance estimate using hypothetical worst-case and base-case countries. It is an absolute score ranging from 0-100. Larger values correspond to better governance." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Voice and Accountability" } ], "source_id": "3" }, { "id": "GOV_WGI_VA.SC_LB", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Voice and Accountability - Lower bound of the 90% confidence interval for the governance score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Voice and Accountability (VA) captures perceptions of the extent to which citizens can participate in selecting their government including electoral integrity, and of accountability mechanisms for citizens—reflected in the ability to access information, governmental oversight bodies, and a robust traditional/digital media landscape. Lower bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated)." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Voice and Accountability" } ], "source_id": "3" }, { "id": "GOV_WGI_VA.SC_UB", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Voice and Accountability - Upper bound of the 90% confidence interval for the governance score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Voice and Accountability (VA) captures perceptions of the extent to which citizens can participate in selecting their government including electoral integrity, and of accountability mechanisms for citizens—reflected in the ability to access information, governmental oversight bodies, and a robust traditional/digital media landscape. Upper bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated)." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Voice and Accountability" } ], "source_id": "3" }, { "id": "GOV_WGI_VA.SE", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Voice and Accountability - Standard error of the governance estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Voice and Accountability (VA) captures perceptions of the extent to which citizens can participate in selecting their government including electoral integrity, and of accountability mechanisms for citizens—reflected in the ability to access information, governmental oversight bodies, and a robust traditional/digital media landscape. Standard error indicates the precision of the governance estimate. Larger values indicate less precise estimates. A 90% confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Voice and Accountability" } ], "source_id": "3" }, { "id": "GOV_WGI_VA.SR", "metatype": [ { "id": "Aggregationmethod", "value": "Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level." }, { "id": "IndicatorName", "value": "Voice and Accountability - Number of sources" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Voice and Accountability (VA) captures perceptions of the extent to which citizens can participate in selecting their government including electoral integrity, and of accountability mechanisms for citizens—reflected in the ability to access information, governmental oversight bodies, and a robust traditional/digital media landscape. Number of sources indicates the number of underlying data sources on which the governance estimate is based." }, { "id": "Othernotes", "value": "The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately –2.5 to 2.5 —into absolute scores on a 0–100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). “The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.” Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). “The Worldwide Governance Indicators: Methodology and 2024 Update.” Policy Research Working Paper. Washington, DC: World Bank Group." }, { "id": "Topic", "value": "Voice and Accountability" } ], "source_id": "3" }, { "id": "SN.SH.STA.MALN.ZS", "metatype": [ { "id": "IndicatorName", "value": "Sub-National Malnutrition prevalence, weight for age (% of children under 5)" }, { "id": "Longdefinition", "value": "Prevalence of child malnutrition is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's new child growth standards released in 2006." }, { "id": "Periodicity", "value": "Irregular; depending on survey data availability" }, { "id": "Source", "value": "World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries. Adjusted, comparable data are available at http://www.who.int/nutgrowthdb/en. Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "5" }, { "id": "SN.SH.STA.OWGH.ZS", "metatype": [ { "id": "IndicatorName", "value": "Sub-National Prevalence of overweight (% of children under 5)" }, { "id": "Longdefinition", "value": "Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries. Adjusted, comparable data are available at http://www.who.int/nutgrowthdb/en. Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "5" }, { "id": "SN.SH.STA.STNT.ZS", "metatype": [ { "id": "IndicatorName", "value": "Sub-National Malnutrition prevalence, height for age (% of children under 5)" }, { "id": "Longdefinition", "value": "Prevalence of child malnutrition is the percentage of children under age 5 whose height for age (stunting) is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006." }, { "id": "Periodicity", "value": "Irregular; depending on survey data availability" }, { "id": "Source", "value": "World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries. Adjusted, comparable data are available at http://www.who.int/nutgrowthdb/en. Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "5" }, { "id": "SN.SH.STA.WAST.ZS", "metatype": [ { "id": "IndicatorName", "value": "Sub-National Prevalence of wasting (% of children under 5)" }, { "id": "Limitationsandexceptions", "value": "UNICEF, WHO and the World Bank, recommend not using the data for wasting and severe wasting indicators to make inter-temporal comparisons. Wasting and severe wasting indicators are very responsive to infection and changes in food availability. A child’s weight relative to its height can drop quickly but also bounce back up with appropriate interventions or a stabilization of a crisis. Malnutrition prevalence estimates are generated from household surveys that only allow for a snapshot view at one short point in time (usually a few months long). In addition, surveys do not capture the duration of wasting and averages during the year are unavailable. Wasting and severe wasting thus, show fluctuations across surveys that do not necessarily reflect the whole spectrum of possible variability. A more appropriate way to have accurate estimates for these conditions would be to use annual incidence (i.e., number of cases that occur in a population during a given year). However, estimates of incidence at national or even regional level do not exist. Therefore, the estimates of prevalence are a proxy and should be interpreted with caution as even the presented confidence intervals may or may not span over the fluctuations that have occurred. Contrary to wasting and severe wasting, the prevalence estimates of stunting, underweight, and overweight are more stable and less reactive to rapid changes in the conditions children live in." }, { "id": "Longdefinition", "value": "Wasting prevalence is the proportion of children under five whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59." }, { "id": "Periodicity", "value": "Irregular; depending on survey data availability" }, { "id": "Source", "value": "World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries. Adjusted, comparable data are available at http://www.who.int/nutgrowthdb/en. Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "5" }, { "id": "SN.SH.SVR.WAST.ZS", "metatype": [ { "id": "IndicatorName", "value": "Sub-National Prevalence of severe wasting, weight for height (% of children under 5)" }, { "id": "Limitationsandexceptions", "value": "UNICEF, WHO and the World Bank, recommend not using the data for wasting and severe wasting indicators to make inter-temporal comparisons. Wasting and severe wasting indicators are very responsive to infection and changes in food availability. A child’s weight relative to its height can drop quickly but also bounce back up with appropriate interventions or a stabilization of a crisis. Malnutrition prevalence estimates are generated from household surveys that only allow for a snapshot view at one short point in time (usually a few months long). In addition, surveys do not capture the duration of wasting and averages during the year are unavailable. Wasting and severe wasting thus, show fluctuations across surveys that do not necessarily reflect the whole spectrum of possible variability. A more appropriate way to have accurate estimates for these conditions would be to use annual incidence (i.e., number of cases that occur in a population during a given year). However, estimates of incidence at national or even regional level do not exist. Therefore, the estimates of prevalence are a proxy and should be interpreted with caution as even the presented confidence intervals may or may not span over the fluctuations that have occurred. Contrary to wasting and severe wasting, the prevalence estimates of stunting, underweight, and overweight are more stable and less reactive to rapid changes in the conditions children live in." }, { "id": "Longdefinition", "value": "Severe wasting prevalence is the proportion of children under five whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59." }, { "id": "Periodicity", "value": "Irregular; depending on survey data availability" }, { "id": "Source", "value": "World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries. Adjusted, comparable data are available at http://www.who.int/nutgrowthdb/en. Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "5" }, { "id": "BM.GSR.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Imports of goods, services and primary income (current US$)" }, { "id": "Longdefinition", "value": "Imports of goods, services and income is the sum of goods (merchandise) imports, imports of (nonfactor) services and primary income (factor) payments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Imports of goods, services and primary income is the sum of goods (merchandise) imports, imports of (nonfactor) services and income (factor) payments. Data are in current U.S. dollars." }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" } ], "source_id": "6" }, { "id": "BN.CAB.XOKA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Current account balance (current US$)" }, { "id": "Longdefinition", "value": "Current account balance shows the difference between the sum of exports and income receivable and the sum of imports and income payable (exports and imports refer to both goods and services, while income refers to both primary and secondary income)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Current account balance shows the difference between the sum of exports and income receivable and the sum of imports and income payable (exports and imports refer to both goods and services, while income refers to both primary and secondary income)." }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Balances" } ], "source_id": "6" }, { "id": "BX.GRT.EXTA.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Grants, excluding technical cooperation (current US$)" }, { "id": "Longdefinition", "value": "Grants are defined as legally binding commitments that obligate a specific value of funds available for disbursement for which there is no repayment requirement. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Grants are defined as legally binding commitments that obligate a specific value of funds available for disbursement for which there is no repayment requirement. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "6" }, { "id": "BX.GRT.TECH.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Technical cooperation grants (current US$)" }, { "id": "Longdefinition", "value": "Technical cooperation grants include free-standing technical cooperation grants, which are intended to finance the transfer of technical and managerial skills or of technology for the purpose of building up general national capacity without reference to any specific investment projects; and investment-related technical cooperation grants, which are provided to strengthen the capacity to execute specific investment projects. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Technical cooperation grants include free-standing technical cooperation grants, which are intended to finance the transfer of technical and managerial skills or of technology for the purpose of building up general national capacity without reference to any specific investment projects; and investment-related technical cooperation grants, which are provided to strengthen the capacity to execute specific investment projects. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "6" }, { "id": "BX.GSR.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Exports of goods, services and primary income (current US$)" }, { "id": "Longdefinition", "value": "Exports of goods, services and income is the sum of goods (merchandise) exports, exports of (nonfactor) services and primary income (factor) receipts. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Exports of goods, services and primary income is the sum of goods (merchandise) exports, exports of (nonfactor) services and income (factor) receipts. Data are in current U.S. dollars." }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" } ], "source_id": "6" }, { "id": "BX.KLT.DINV.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "Note: Data starting from 2005 are based the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "IndicatorName", "value": "Foreign direct investment, net inflows in reporting economy (DRS, current US$)" }, { "id": "Longdefinition", "value": "Foreign direct investment (net) shows the net change in foreign investment in the reporting country. Foreign direct investment is defined as investment that is made to acquire a lasting management interest (usually of 10 percent of voting stock) in an enterprise operating in a country other than that of the investor (defined according to residency), the investor's purpose being an effective voice in the management of the enterprise. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows net inflows in the reporting economy. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Foreign direct investment is net inflows of investment to acquire a lasting interest in or management control over an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvested earnings, other long-term capital, and short-term capital, as shown in the balance of payments. Data are in current U.S. dollars." }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments, supplemented by data from United Nations Conference on Trade and Development and official national sources. Data starting from 2005 are based the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "6" }, { "id": "BX.KLT.DREM.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Primary income on FDI (current US$)" }, { "id": "Longdefinition", "value": "Primary income on foreign direct investment covers payments of direct investment income (debit side), which consist of income on equity (dividends, branch profits, and reinvested earnings) and income on the intercompany debt (interest). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Primary income on foreign direct investment covers payments of direct investment income (debit side), which consist of income on equity (dividends, branch profits, and reinvested earnings) and income on the intercompany debt (interest). Data are in current U.S. dollars." }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "6" }, { "id": "BX.PEF.TOTL.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "Note: Data starting from 2005 are based the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "IndicatorName", "value": "Portfolio investment, equity (DRS, current US$)" }, { "id": "Longdefinition", "value": "Portfolio equity includes net inflows from equity securities other than those recorded as direct investment and including shares, stocks, depository receipts (American or global), and direct purchases of shares in local stock markets by foreign investors. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Portfolio equity includes net inflows from equity securities other than those recorded as direct investment and including shares, stocks, depository receipts (American or global), and direct purchases of shares in local stock markets by foreign investors. Data are in current U.S. dollars." }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook. Data starting from 2005 are based the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "6" }, { "id": "BX.TRF.PWKR.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "Note: Data starting from 2005 are based the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "IndicatorName", "value": "Personal transfers and compensation of employees, received (current US$)" }, { "id": "Longdefinition", "value": "Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual(BPM6): personal transfers and compensation of employees. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual (BPM6): personal transfers and compensation of employees. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank staff estimates based on IMF balance of payments data. Data starting from 2005 are based the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "6" }, { "id": "DT.AMT.BLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves.Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves.Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.BLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.BLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.BLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.BLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.BLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves.Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves.Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.BLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "General goverment bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General goverment bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.BLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.BLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.BLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.DECB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, central bank (PPG) (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank long-term debt are aggregated. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank long-term debt are aggregated. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.DEGG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, general government sector (PPG) (AMT, current US$)" }, { "id": "Longdefinition", "value": "General government long-term debt are aggregated. General government debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government long-term debt are aggregated. General government debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.DEPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, public sector (PPG) (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector long-term debt are aggregated. Public sector debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector long-term debt are aggregated. Public sector debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "IMF repurchases (AMT, current US$)" }, { "id": "Longdefinition", "value": "IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.DLTF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, long-term + IMF (AMT, current US$)" }, { "id": "Longdefinition", "value": "Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. This item includes principal repayments on long-term debt and IMF repurchases. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. To maintain comparability between data on transactions with the IMF and data on long-term debt, use of IMF credit outstanding at the end of year (stock) is converted to dollars at the SDR exchange rate in effect at the end of year. Repurchases (flows) are converted at the average SDR exchange rate for the year in which transactions take place. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. This item includes principal repayments on long-term debt and IMF repurchases. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. To maintain comparability between data on transactions with the IMF and data on long-term debt, use of IMF credit outstanding at the end of year (stock) is converted to dollars at the SDR exchange rate in effect at the end of year. Repurchases (flows) are converted at the average SDR exchange rate for the year in which transactions take place. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, long-term (AMT, current US$)" }, { "id": "Longdefinition", "value": "Principal repayments on long-term debt are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal repayments on long-term debt are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.DOPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, other public sector (PPG) (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector long-term debt are aggregated. Other public sector debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector long-term debt are aggregated. Other public sector debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, private nonguaranteed (PNG) (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, public and publicly guaranteed (PPG) (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.MLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.MLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.MLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.MLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.MLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.MLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.MLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.MLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.MLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.MLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.OFFT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, official creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.OFFT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, official creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.OFFT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, official creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.OFFT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, official creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.OFFT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, official creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.PBND.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bonds (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. 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Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.PCBK.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, commercial banks (AMT, current US$)" }, { "id": "Longdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. 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Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.PCBK.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, commercial banks (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. 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Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, other private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. 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Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.PROP.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, other private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. 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Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector long-term debt are aggregated.Private sector guaranteed by Public Sector debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.PRVT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from private creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from private creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.PRVT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "General government debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. 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Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.PRVT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.PRVT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AMT.PRVT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "6" }, { "id": "DT.AXA.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal arrears, long-term DOD (current US$)" }, { "id": "Longdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.AXA.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal arrears, official creditors (current US$)" }, { "id": "Longdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.AXA.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal arrears, private creditors (current US$)" }, { "id": "Longdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.AXF.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal forgiven (current US$)" }, { "id": "Longdefinition", "value": "Principal forgiven is the amount of principal due or in arrears that was written off or forgiven in any given year. It includes debt forgiven within and outside Paris Club agreements, principal forgiven and principal arrears forgiven. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal forgiven is the amount of principal due or in arrears that was written off or forgiven in any given year. It includes debt forgiven within and outside Paris Club agreements, principal forgiven and principal arrears forgiven. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.AXR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal rescheduled (current US$)" }, { "id": "Longdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.AXR.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal rescheduled, official (current US$)" }, { "id": "Longdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.AXR.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal rescheduled, private (current US$)" }, { "id": "Longdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.COM.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, bilateral creditors (COM, current US$)" }, { "id": "Longdefinition", "value": "Bilateral commitments are the total amount of long-term loans for which contracts were signed in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral commitments are the total amount of long-term loans for which contracts were signed in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "6" }, { "id": "DT.COM.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, public and publicly guaranteed (COM, current US$)" }, { "id": "Longdefinition", "value": "Commitments are the total amount of long-term loans for which contracts were signed in the year specified; data for private nonguaranteed debt are not available. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Commitments are the total amount of long-term loans for which contracts were signed in the year specified; data for private nonguaranteed debt are not available. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "6" }, { "id": "DT.COM.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, IBRD (COM, current US$)" }, { "id": "Longdefinition", "value": "Commitments (IBRD) are the sum of new commitments on public and publicly guaranteed loans from the International Bank for Reconstruction and Development (IBRD). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Commitments (IBRD) are the sum of new commitments on public and publicly guaranteed loans from the International Bank for Reconstruction and Development (IBRD). Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "6" }, { "id": "DT.COM.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, IDA (COM, current US$)" }, { "id": "Longdefinition", "value": "Commitments (IDA) are the sum of new commitments on public and publicly guaranteed loans from the International Development Association (IDA). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Commitments (IDA) are the sum of new commitments on public and publicly guaranteed loans from the International Development Association (IDA). Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "6" }, { "id": "DT.COM.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, multilateral creditors (COM, current US$)" }, { "id": "Longdefinition", "value": "Multirateral commitments are the total amount of long-term loans for which contracts were signed in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Multirateral commitments are the total amount of long-term loans for which contracts were signed in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "6" }, { "id": "DT.COM.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, official creditors (COM, current US$)" }, { "id": "Longdefinition", "value": "Commitments are the amount of long-term loans for which contracts were signed in the year specified. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Commitments are the amount of long-term loans for which contracts were signed in the year specified. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "6" }, { "id": "DT.COM.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, private creditors (COM, current US$)" }, { "id": "Longdefinition", "value": "Commitments are the amount of long-term loans for which contracts were signed in the year specified; data for private nonguaranteed debt are not available. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Commitments are the amount of long-term loans for which contracts were signed in the year specified; data for private nonguaranteed debt are not available. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "6" }, { "id": "DT.CUR.DMAK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Deutsche mark (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Deutsche marks for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Deutsche marks for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "6" }, { "id": "DT.CUR.EURO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Euro (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Euros for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Euros for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "6" }, { "id": "DT.CUR.FFRC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, French franc (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in French francs for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in French francs for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "6" }, { "id": "DT.CUR.JYEN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Japanese yen (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Japanese yen for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Japanese yen for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "6" }, { "id": "DT.CUR.MULC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Multiple currencies (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in multiple currencies for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in multiple currencies for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "6" }, { "id": "DT.CUR.OTHC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, all other currencies (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in all other currencies not specified for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in all other currencies not specified for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "6" }, { "id": "DT.CUR.SDRW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, SDR (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in special drawing rights for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in special drawing rights for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "6" }, { "id": "DT.CUR.SWFR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Swiss franc (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Swiss francs for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Swiss francs for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "6" }, { "id": "DT.CUR.UKPS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Pound sterling (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in U.K. pound sterling for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in U.K. pound sterling for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "6" }, { "id": "DT.CUR.USDL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, U.S. dollars (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in U.S. dollars for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in U.S. dollars for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "6" }, { "id": "DT.DFR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt forgiveness or reduction (current US$)" }, { "id": "Longdefinition", "value": "Debt forgiveness or reduction shows the change in debt stock due to debt forgiveness or reduction. It is derived by subtracting debt forgiven and debt stock reduction from debt buyback. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt forgiveness or reduction shows the change in debt stock due to debt forgiveness or reduction. It is derived by subtracting debt forgiven and debt stock reduction from debt buyback. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.DIS.BLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.BLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.BLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.BLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.BLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.BLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.BLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.BLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.BLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.BLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.DECB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, central bank (PPG) (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank long-term debt are aggregated. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank long-term debt are aggregated. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.DEGG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, general government sector (PPG) (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.DEPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, public sector (PPG) (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "IMF purchases (DIS, current US$)" }, { "id": "Longdefinition", "value": "IMF purchases are total drawings on the General Resources Account of the IMF during the year specified, excluding drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "IMF purchases are total drawings on the General Resources Account of the IMF during the year specified, excluding drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.DLTF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, long-term + IMF (DIS, current US$)" }, { "id": "Longdefinition", "value": "Disbursements are drawings by the borrower on loan commitments during the year specified. This item includes disbursements on long-term debt and IMF purchases. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. IMF purchases are total drawings on the General Resources Account of the IMF during the year specified, excluding drawings in the reserve tranche. To maintain comparability between data on transactions with the IMF and data on long-term debt, use of IMF credit outstanding at the end of year (stock) is converted to dollars at the SDR exchange rate in effect at the end of year. Purchases are converted at the average SDR exchange rate for the year in which transactions take place. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Disbursements are drawings by the borrower on loan commitments during the year specified. This item includes disbursements on long-term debt and IMF purchases. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. IMF purchases are total drawings on the General Resources Account of the IMF during the year specified, excluding drawings in the reserve tranche. To maintain comparability between data on transactions with the IMF and data on long-term debt, use of IMF credit outstanding at the end of year (stock) is converted to dollars at the SDR exchange rate in effect at the end of year. Purchases are converted at the average SDR exchange rate for the year in which transactions take place. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, long-term (DIS, current US$)" }, { "id": "Longdefinition", "value": "Disbursements on long-term debt are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Disbursements on long-term debt are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.DOPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, other public sector (PPG) (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, private nonguaranteed (PNG) (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, public and publicly guaranteed (PPG) (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.IDAG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "IDA grants (current US$)" }, { "id": "Longdefinition", "value": "IDA grants are net disbursements of grants from the International Development Association (IDA). Data are in current U.S. dollars. Regional allocations are included in aggregate data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "IDA grants are net disbursements of grants from the International Development Association (IDA). Data are in current U.S. dollars. Regional allocations are included in aggregate data." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "6" }, { "id": "DT.DIS.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.MLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.MLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.MLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.MLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.MLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.MLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.MLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "General goverment multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.MLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.MLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.MLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.OFFT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, official creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.OFFT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, official creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.OFFT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, official creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.OFFT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, official creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.OFFT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, official creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PBND.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bonds (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PBND.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bonds (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PBND.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bonds (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PBND.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bonds (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PBND.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bonds (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PCBK.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, commercial banks (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PCBK.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, commercial banks (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PCBK.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, commercial banks (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PCBK.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, commercial banks (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PCBK.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, commercial banks (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, bonds (DIS, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PROP.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, other private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, other private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PROP.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, other private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PROP.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, other private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PROP.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, other private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PROP.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, other private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PRPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, private guaranteed by public sector (PPG) (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PRVT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from private creditors.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from private creditors.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PRVT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PRVT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PRVT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DIS.PRVT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "6" }, { "id": "DT.DOD.ALLC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Concessional external debt conveys information about the borrower's receipt of aid from official lenders at concessional terms as defined by the Development Assistance Committee (DAC) of the OECD. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Loans from major regional development banks--African Development Bank, Asian Development Bank, and the Inter-American Development Bank--and from the World Bank are classified as concessional according to each institution's classification and not according to the DAC definition, as was the practice in earlier reports. Long-term debt outstanding and disbursed is the total outstanding long-term debt at year end. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Concessional external debt conveys information about the borrower's receipt of aid from official lenders at concessional terms as defined by the Development Assistance Committee (DAC) of the OECD. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Loans from major regional development banks--African Development Bank, Asian Development Bank, and the Inter-American Development Bank--and from the World Bank are classified as concessional according to each institution's classification and not according to the DAC definition, as was the practice in earlier reports. Long-term debt outstanding and disbursed is the total outstanding long-term debt at year end. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.ALLC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Concessional debt (% of total external debt)" }, { "id": "Longdefinition", "value": "Concessional debt to total external debt stocks. Concessional debt is defined as loans with an original grant element of 35 percent or more." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Concessional debt to total external debt stocks. Concessional debt is defined as loans with an original grant element of 35 percent or more." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "6" }, { "id": "DT.DOD.BLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.BLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "General Government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General Government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.BLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Other Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.BLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.BLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.BLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.BLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.BLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.BLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.BLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.DECB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, central bank (PPG) (DOD, current US$)" }, { "id": "Longdefinition", "value": "Central Bank debt position at end of the reference period. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves.Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central Bank debt position at end of the reference period. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, total (DOD, current US$)" }, { "id": "Longdefinition", "value": "Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total external debt is debt owed to nonresidents repayable in currency, goods, or services. It is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, short-term debt, and use of IMF credit. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.DECT.CD.CG", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Total change in external debt stocks (current US$)" }, { "id": "Longdefinition", "value": "Total change in debt stocks shows the variation in debt stock between two consecutive years. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total change in debt stocks shows the variation in debt stock between two consecutive years. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.DOD.DECT.EX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "The denominator for this indicator in previous versions of Global Development Finance included workers' remittances. Workers' remittances are no longer included." }, { "id": "IndicatorName", "value": "External debt stocks (% of exports of goods, services and primary income)" }, { "id": "Longdefinition", "value": "Total external debt stocks to exports of goods, services and primary income." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total external debt stocks to exports of goods, services and primary income." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "6" }, { "id": "DT.DOD.DECT.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks (% of GNI)" }, { "id": "Longdefinition", "value": "Total external debt stocks to gross national income. Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total external debt stocks to gross national income." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "6" }, { "id": "DT.DOD.DECT.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Total external debt per capita (current US$)" }, { "id": "Longdefinition", "value": "Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total external debt is debt owed to nonresidents repayable in currency, goods, or services. It is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, short-term debt, and use of IMF credit. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.DEGG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, general government sector (PPG) (DOD, current US$)" }, { "id": "Longdefinition", "value": "General Government Sector comprises long-term external obligations of public debtors, including the national government of all levels, and political subdivisions (or an agency of either). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General Government Sector comprises long-term external obligations of public debtors, including the national government of all levels, and political subdivisions (or an agency of either). Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.DEPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, public sector (PPG) (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt comprises long-term external obligations of public debtors, including the national government of all levels, political subdivisions (or an agency of either), autonomous public bodies such as Public Corporations, State Owned Enterprises, Development Banks and Other Mixed Enterprises. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt comprises long-term external obligations of public debtors, including the national government of all levels, political subdivisions (or an agency of either), autonomous public bodies such as Public Corporations, State Owned Enterprises, Development Banks and Other Mixed Enterprises. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Use of IMF credit (DOD, current US$)" }, { "id": "Longdefinition", "value": "Use of IMF Credit: Data related to the operations of the IMF are provided by the IMF Treasurer’s Department. They are converted from special drawing rights into dollars using end-of-period exchange rates for stocks and average-over-the-period exchange rates for flows. IMF trust fund operations under the Enhanced Structural Adjustment Facility, Extended Fund Facility, Poverty Reduction and Growth Facility, and Structural Adjustment Facility (Enhanced Structural Adjustment Facility in 1999) are presented together with all of the IMF’s special facilities (buffer stock, supplemental reserve, compensatory and contingency facilities, oil facilities, and other facilities). SDR allocations are also included in this category. According to the BPM6, SDR allocations are recorded as the incurrence of a debt liability of the member receiving them (because of a requirement to repay the allocation in certain circumstances, and also because interest accrues). This debt item is introduced for the first time this year with historical data starting in 1999." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Use of IMF credit denotes members’ drawings on the IMF other than amounts drawn against the country’s reserve tranche position. Use of IMF credit includes purchases and drawings under Stand-By, Extended, Structural Adjustment, Enhanced Structural Adjustment, and Systemic Transformation Facility Arrangements as well as Trust Fund loans. SDR allocations are also included in this category." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, long-term (DOD, current US$)" }, { "id": "Longdefinition", "value": "Long-term debt is debt that has an original or extended maturity of more than one year. It has three components: public, publicly guaranteed, and private nonguaranteed debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Long-term debt is debt that has an original or extended maturity of more than one year. It has three components: public, publicly guaranteed, and private nonguaranteed debt. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.DOPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, other public sector (PPG) (DOD, current US$)" }, { "id": "Longdefinition", "value": "Other Public Sector debt comprises long-term external obligations of public debtors, excluding general government. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other Public Sector debt comprises long-term external obligations of public debtors, excluding general government. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, private nonguaranteed (PNG) (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt comprises long-term external obligations of private debtors that are not guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed external debt comprises long-term external obligations of private debtors that are not guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, public and publicly guaranteed (PPG) (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt comprises long-term external obligations of public debtors, including the national government, Public Corporations, State Owned Enterprises, Development Banks and Other Mixed Enterprises, political subdivisions (or an agency of either), autonomous public bodies, and external obligations of private debtors that are guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt comprises long-term external obligations of public debtors, including the national government, Public Corporations, State Owned Enterprises, Development Banks and Other Mixed Enterprises, political subdivisions (or an agency of either), autonomous public bodies, and external obligations of private debtors that are guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.DSDR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Use of IMF credit, SDR allocations (DOD, current US$)" }, { "id": "Longdefinition", "value": "SDR allocations are also included in this category. According to the BPM6, SDR allocations are recorded as the incurrence of a debt liability of the member receiving them (because of a requirement to repay the allocation in certain circumstances, and also because interest accrues). This debt item is introduced for the first time this year with historical data starting in 1999." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDR allocations are also included in this category. According to the BPM6, SDR allocations are recorded as the incurrence of a debt liability of the member receiving them (because of a requirement to repay the allocation in certain circumstances, and also because interest accrues). This debt item is introduced for the first time this year with historical data starting in 1999." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.DSTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, short-term (DOD, current US$)" }, { "id": "Longdefinition", "value": "Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.DSTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Short-term debt (% of total external debt)" }, { "id": "Longdefinition", "value": "Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "6" }, { "id": "DT.DOD.MDRI.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "The aggregate figure for all developing countries is sourced from OECD and includes all OECD countries and regions." }, { "id": "IndicatorName", "value": "Debt forgiveness grants (current US$)" }, { "id": "Longdefinition", "value": "Debt forgiveness grants data cover both debt cancelled by agreement between debtor and creditor and a reduction in the net present value of non-ODA debt achieved by concessional rescheduling or refinancing. The data are on a disbursement basis and cover flows from all bilateral and multilateral donors. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt forgiveness grants data cover both debt cancelled by agreement between debtor and creditor and a reduction in the net present value of non-ODA debt achieved by concessional rescheduling or refinancing. The data are on a disbursement basis and cover flows from all bilateral and multilateral donors. Data are in current U.S. dollars." }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "6" }, { "id": "DT.DOD.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.MLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.MLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.MLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.MLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.MLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral creditors are international financial institutions such as the World Bank, regional development banks, and other multilateral and intergovernmental agencies whose lending is administered on a multilateral basis. Funds administered by an international financial organization on behalf of a single donor government constitute bilateral loans (or grants). For lending by a number of multilateral creditors, the data presented in this publication are taken from the creditors’ records. Such creditors include the African Development Bank, the Asian Development Bank, the IDB, IBRD, and IDA. (IBRD and IDA are institutions of the World Bank.) Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral creditors are international financial institutions such as the World Bank, regional development banks, and other multilateral and intergovernmental agencies whose lending is administered on a multilateral basis. Funds administered by an international financial organization on behalf of a single donor government constitute bilateral loans (or grants). For lending by a number of multilateral creditors, the data presented in this publication are taken from the creditors’ records. Such creditors include the African Development Bank, the Asian Development Bank, the IDB, IBRD, and IDA. (IBRD and IDA are institutions of the World Bank.) Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.MLAT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Multilateral debt (% of total external debt)" }, { "id": "Longdefinition", "value": "Multilateral debt to total external debt stocks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Multilateral debt to total external debt stocks." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "6" }, { "id": "DT.DOD.MLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.MLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.MLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.MLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.MLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public sector guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.OFFT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, official creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.OFFT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, official creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.OFFT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, official creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.OFFT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, official creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.OFFT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, official creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PBND.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bonds (DOD, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PBND.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bonds (DOD, current US$)" }, { "id": "Longdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PBND.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bonds (DOD, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PBND.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bonds (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by public sector debt from bonds that are either publicly issued or privately placed. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by public sector debt from bonds that are either publicly issued or privately placed. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PBND.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bonds (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PCBK.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, commercial banks (DOD, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves.Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PCBK.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, commercial banks (DOD, current US$)" }, { "id": "Longdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PCBK.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, commercial banks (DOD, current US$)" }, { "id": "Longdefinition", "value": "Other public sector commercial bank loans from private banks and other private financial institutions. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector commercial bank loans from private banks and other private financial institutions. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PCBK.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, commercial banks (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by public sector commercial bank loans from private banks and other private financial institutions. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by public sector commercial bank loans from private banks and other private financial institutions. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PCBK.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, commercial banks (DOD, current US$)" }, { "id": "Longdefinition", "value": "Commercial bank loans are loans from private banks and other private financial institutions. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector guaranteed commercial bank loans from private banks and other private financial institutions. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, bonds (DOD, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Private nonguaranteed long-term debt outstanding and disbursed is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Private nonguaranteed long-term debt outstanding and disbursed is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Private nonguaranteed long-term debt outstanding and disbursed is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Private nonguaranteed long-term debt outstanding and disbursed is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PROP.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, other private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, other private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PROP.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, other private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PROP.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, other private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PROP.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, other private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PROP.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, other private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PRPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, private guaranteed by public sector (PPG) (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt comprises external obligations of private debtors that are guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt comprises external obligations of private debtors that are guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PRVS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, long-term private sector (DOD, current US$)" }, { "id": "Longdefinition", "value": "Long-term private sector external debt conveys information about the distribution of long-term debt for DRS countries by type of debtor (private banks and private entities). Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Long-term private sector external debt conveys information about the distribution of long-term debt for DRS countries by type of debtor (private banks and private entities). Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PRVT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from private creditors.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from private creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PRVT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "General government debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PRVT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PRVT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PRVT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private creditors include commercial banks, bondholders, and other private creditors. This line includes only publicly guaranteed creditors. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PUBS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, long-term public sector (DOD, current US$)" }, { "id": "Longdefinition", "value": "Long-term public sector external debt conveys information about the distribution of long-term debt for DRS countries by type of debtor (central government, state and local government, central bank, public and mixed enterprises, and official development banks). Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Long-term public sector external debt conveys information about the distribution of long-term debt for DRS countries by type of debtor (central government, state and local government, central bank, public and mixed enterprises, and official development banks). Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PVLX.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Present value of external debt (current US$)" }, { "id": "Longdefinition", "value": "Present value of debt is the discounted sum of total debt service payments due on public and publicly guaranteed long-term external debt over the life of existing loans. IMF Special Drawing Rights are excluded. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan.\nData are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Present value of debt is the discounted sum of total debt service payments due on public and publicly guaranteed long-term external debt over the life of existing loans. IMF Special Drawing Rights are excluded. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan.\nData are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.PVLX.EX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Present value of external debt (% of exports of goods, services and income)" }, { "id": "Longdefinition", "value": "Present value of debt is the discounted sum of total debt service payments due on public and publicly guaranteed long-term external debt over the life of existing loans. IMF Special Drawing Rights are excluded. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan.\nThe exports denominator is a three-year average." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Present value of debt is the discounted sum of total debt service payments due on public and publicly guaranteed long-term external debt over the life of existing loans. IMF Special Drawing Rights are excluded. The exports denominator is a three-year average." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "6" }, { "id": "DT.DOD.PVLX.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Present value of external debt (% of GNI)" }, { "id": "Longdefinition", "value": "Present value of debt is the discounted sum of total debt service payments due on public and publicly guaranteed long-term external debt over the life of existing loans. IMF Special Drawing Rights are excluded. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan.\nThe GNI denominator is a three-year average." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Present value of debt is the discounted sum of total debt service payments due on public and publicly guaranteed long-term external debt over the life of existing loans. IMF Special Drawing Rights are excluded. The GNI denominator is a three-year average." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "6" }, { "id": "DT.DOD.RSDL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Residual, debt stock-flow reconciliation (current US$)" }, { "id": "Longdefinition", "value": "The residual difference, i.e. the change in stock not explained by any of the factors identified under debt stock-flow reconciliation, is calculated as the sum of identified accounts minus the change in stock. Where the latter is large it can, in some cases, serve as an illustration of the inconsistencies in the reported data. More often however, it can be explained by specific borrowing phenomenon in individual countries. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The residual difference, i.e. the change in stock not explained by any of the factors identified under debt stock-flow reconciliation, is calculated as the sum of identified accounts minus the change in stock. Where the latter is large it can, in some cases, serve as an illustration of the inconsistencies in the reported data. More often however, it can be explained by specific borrowing phenomenon in individual countries. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.DOD.VPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, variable rate public and publicly guaranteed debt (PPG) (DOD, current US$)" }, { "id": "Longdefinition", "value": "Debt stock contracted at variable interest rate for public and publicly guaranteed long-term external debt with interest rates that float with movements in a key market rate; for example, the Secured Overnight Financing Rate (SOFR) or the Euribor. This item conveys information about the borrower's exposure to changes in international interest rates. Public and publicly guaranteed long-term long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt stock contracted at variable interest rate for public and publicly guaranteed long-term external debt with interest rates that float with movements in a key market rate; for example, the Secured Overnight Financing Rate (SOFR) or the Euribor. This item conveys information about the borrower's exposure to changes in international interest rates. Public and publicly guaranteed long-term long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DOD.VTOT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, variable rate (DOD, current US$)" }, { "id": "Longdefinition", "value": "Debt stock contracted at variable interest rate for long-term external debt with interest rates that float with movements in a key market rate; for example, the Secured Overnight Financing Rate (SOFR) or the Euribor. This item conveys information about the borrower's exposure to changes in international interest rates. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars.\nDebt stock contracted at variable interest rate for long-term external debt with interest rates that float with movements in a key market rate; for example, the Secured Overnight Financing Rate (SOFR) or the Euribor. This item conveys information about the borrower's exposure to changes in international interest rates. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt stock contracted at variable interest rate for long-term external debt with interest rates that float with movements in a key market rate; for example, the Secured Overnight Financing Rate (SOFR) or the Euribor. This item conveys information about the borrower's exposure to changes in international interest rates. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "6" }, { "id": "DT.DSB.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt buyback (current US$)" }, { "id": "Longdefinition", "value": "Debt buyback is the repurchase by a debtor of its own debt, discounted or at par. In the event of a buyback of long-term debt, the face value of the debt bought back will be recorded as a decline in the long-term debt stock, and the cash amount received by creditors will be recorded as a principal repayment. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt buyback is the repurchase by a debtor of its own debt, discounted or at par. In the event of a buyback of long-term debt, the face value of the debt bought back will be recorded as a decline in the long-term debt stock, and the cash amount received by creditors will be recorded as a principal repayment. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.DSF.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt stock reduction (current US$)" }, { "id": "Longdefinition", "value": "Debt stock reductions show the amount that has been netted out of the stock of debt using debt conversion schemes such as buybacks and equity swaps or the discounted value of long-term bonds that were issued in exchange for outstanding debt. It includes the effect of any financial operation that will reduce the debt stock other than debt stock restructuring, repayment of principal and debt forgiven. In particular, debt stock reduction will include the face value of debt bought back, the face value of debt swapped for equity (or \"nature\" or \"development\"), any face value reduction that might result as the consequence of a bond exchange, and any face value reduction resulting from an exchange of debt for discount bonds. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt stock reductions show the amount that has been netted out of the stock of debt using debt conversion schemes such as buybacks and equity swaps or the discounted value of long-term bonds that were issued in exchange for outstanding debt. It includes the effect of any financial operation that will reduce the debt stock other than debt stock restructuring, repayment of principal and debt forgiven. In particular, debt stock reduction will include the face value of debt bought back, the face value of debt swapped for equity (or \"nature\" or \"development\"), any face value reduction that might result as the consequence of a bond exchange, and any face value reduction resulting from an exchange of debt for discount bonds. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.DXR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt stock rescheduled (current US$)" }, { "id": "Longdefinition", "value": "Debt stocks rescheduled is the amount of debt outstanding rescheduled in any given year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt stocks rescheduled is the amount of debt outstanding rescheduled in any given year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.GPA.DPPG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grace period on new external debt commitments (years)" }, { "id": "Longdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "6" }, { "id": "DT.GPA.OFFT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grace period on new external debt commitments, official (years)" }, { "id": "Longdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "6" }, { "id": "DT.GPA.PRVT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grace period on new external debt commitments, private (years)" }, { "id": "Longdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "6" }, { "id": "DT.GRE.DPPG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grant element on new external debt commitments (%)" }, { "id": "Longdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Data for private nonguaranteed debt are not available." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Data for private nonguaranteed debt are not available." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "6" }, { "id": "DT.GRE.OFFT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grant element on new external debt commitments, official (%)" }, { "id": "Longdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "6" }, { "id": "DT.GRE.PRVT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grant element on new external debt commitments, private (%)" }, { "id": "Longdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "6" }, { "id": "DT.INR.DPPG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average interest on new external debt commitments (%)" }, { "id": "Longdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "6" }, { "id": "DT.INR.OFFT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average interest on new external debt commitments, official (%)" }, { "id": "Longdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "6" }, { "id": "DT.INR.PRVT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average interest on new external debt commitments, private (%)" }, { "id": "Longdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "6" }, { "id": "DT.INT.BLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.BLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.BLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.BLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sectorbilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sectorbilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.BLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.BLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.BLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.BLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.BLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.BLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.DECB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, central bank (PPG) (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank long-term debt are aggregated. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank long-term debt are aggregated. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, total (INT, current US$)" }, { "id": "Longdefinition", "value": "Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. This item includes interest paid on long-term debt, IMF charges, and interest paid on short-term debt. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. This item includes interest paid on long-term debt, IMF charges, and interest paid on short-term debt. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.DECT.EX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "The denominator for this indicator in previous versions of Global Development Finance included workers' remittances. Workers' remittances are no longer included." }, { "id": "IndicatorName", "value": "Interest payments on external debt (% of exports of goods, services and primary income)" }, { "id": "Longdefinition", "value": "Total interest payments to exports of goods, services and primary income. Total interest payment is the sum of interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and charges to the IMF." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total interest payments to exports of goods, services and primary income. Total interest payment is the sum of interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and charges to the IMF." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "6" }, { "id": "DT.INT.DECT.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt (% of GNI)" }, { "id": "Longdefinition", "value": "Total interest payments to gross national income." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total interest payments to gross national income." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "6" }, { "id": "DT.INT.DEGG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, general government sector (PPG) (INT, current US$)" }, { "id": "Longdefinition", "value": "General government long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.DEPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, public sector (PPG) (INT, current US$)" }, { "id": "Longdefinition", "value": "Public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "IMF charges (INT, current US$)" }, { "id": "Longdefinition", "value": "IMF charges cover interest payments with respect to all uses of IMF resources, excluding those resulting from drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "IMF charges cover interest payments with respect to all uses of IMF resources, excluding those resulting from drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, long-term (INT, current US$)" }, { "id": "Longdefinition", "value": "Interest payments on long-term debt are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest payments on long-term debt are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.DOPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, other public sector (PPG) (INT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, private nonguaranteed (PNG) (INT, current US$)" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, public and publicly guaranteed (PPG) (INT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.DSTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, short-term (INT, current US$)" }, { "id": "Longdefinition", "value": "Interest payments on short-term debt are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. This item includes interest paid on long-term debt, IMF charges, and interest paid on short-term debt. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest payments on short-term debt are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. This item includes interest paid on long-term debt, IMF charges, and interest paid on short-term debt. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (INT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (INT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.MLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves.Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.MLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.MLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.MLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.MLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.MLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.MLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.MLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.MLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.MLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.OFFT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, official creditors (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (INT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.OFFT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, official creditors (INT, current US$)" }, { "id": "Longdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.OFFT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, official creditors (INT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.OFFT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, official creditors (INT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.OFFT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, official creditors (INT, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.PBND.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bonds (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (INT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.PBND.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bonds (INT, current US$)" }, { "id": "Longdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.PBND.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bonds (INT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.PBND.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bonds (INT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.PBND.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bonds (INT, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.PCBK.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, commercial banks (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (INT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.PCBK.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, commercial banks (INT, current US$)" }, { "id": "Longdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.PCBK.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, commercial banks (INT, current US$)" }, { "id": "Longdefinition", "value": "Other oublic sector commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other oublic sector commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.PCBK.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, commercial banks (INT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.PCBK.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, commercial banks (INT, current US$)" }, { "id": "Longdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, bonds (INT, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (INT, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.PROP.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, other private creditors (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. 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Interest payments are actual amounts of interest paid, contracted with a variable interest rate, by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. 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Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.INT.VTOT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on variable rate external debt, long-term (INT, current US$)" }, { "id": "Longdefinition", "value": "Interest payments on long-term debt are actual amounts of interest paid, contracted with a variable interest rate, by the borrower in currency, goods, or services in the year specified. Variable interest rate is long-term external debt with interest rates that float with movements in a key market rate; for example, the Secured Overnight Financing Rate (SOFR) or the Euribor. This item conveys information about the borrower's exposure to changes in international interest rates. 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Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "6" }, { "id": "DT.IXA.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest arrears, long-term DOD (current US$)" }, { "id": "Longdefinition", "value": "Interest in arrears on long-term debt is defined as interest payment due but not paid, on a cumulative basis. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest in arrears on long-term debt is defined as interest payment due but not paid, on a cumulative basis. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.IXA.DPPG.CD.CG", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net change in interest arrears (current US$)" }, { "id": "Longdefinition", "value": "Net change in interest arrears is the variation in the total amount of interest in arrears between two consecutive years. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net change in interest arrears is the variation in the total amount of interest in arrears between two consecutive years. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.IXA.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest arrears, official creditors (current US$)" }, { "id": "Longdefinition", "value": "Interest in arrears on long-term debt is defined as interest payment due but not paid, on a cumulative basis. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest in arrears on long-term debt is defined as interest payment due but not paid, on a cumulative basis. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.IXA.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest arrears, private creditors (current US$)" }, { "id": "Longdefinition", "value": "Interest in arrears on long-term debt is defined as interest payment due but not paid, on a cumulative basis. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. 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Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.IXF.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest forgiven (current US$)" }, { "id": "Longdefinition", "value": "Interest forgiven is the amount of interest due or in arrears that was written off or forgiven in any given year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest forgiven is the amount of interest due or in arrears that was written off or forgiven in any given year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.IXR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest rescheduled (capitalized) (current US$)" }, { "id": "Longdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. (Interest capitalized is the interest that became part of the stock of debt due to a rescheduling operation.) Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. (Interest capitalized is the interest that became part of the stock of debt due to a rescheduling operation.) Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.IXR.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest rescheduled, official (current US$)" }, { "id": "Longdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organizations include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organizations include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.IXR.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest rescheduled, private (current US$)" }, { "id": "Longdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.MAT.DPPG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average maturity on new external debt commitments (years)" }, { "id": "Longdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "6" }, { "id": "DT.MAT.OFFT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average maturity on new external debt commitments, official (years)" }, { "id": "Longdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "6" }, { "id": "DT.MAT.PRVT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average maturity on new external debt commitments, private (years)" }, { "id": "Longdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "6" }, { "id": "DT.NFL.BLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, bilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.BLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.BLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.BLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.BLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.BLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.BLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.BLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.BLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.BLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.DECB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, central bank (PPG) (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank long-term debt are aggregated. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank long-term debt are aggregated. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, total (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net flows on external debt are disbursements on long-term external debt and IMF purchases minus principal repayments on long-term external debt and IMF repurchases up to 1984. Beginning in 1985 this line includes the change in stock of short-term debt (including interest arrears for long-term debt). Thus, if the change in stock is positive, a disbursement is assumed to have taken place; if negative, a repayment is assumed to have taken place. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net flows on external debt are disbursements on long-term external debt and IMF purchases minus principal repayments on long-term external debt and IMF repurchases up to 1984. Beginning in 1985 this line includes the change in stock of short-term debt (including interest arrears for long-term debt). Thus, if the change in stock is positive, a disbursement is assumed to have taken place; if negative, a repayment is assumed to have taken place. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.DEGG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, general government sector (PPG) (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.DEPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, public sector (PPG) (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, long-term (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.DOPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, other public sector (PPG) (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, private nonguaranteed (PNG) (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, public and publicly guaranteed (PPG) (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.DSTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, short-term (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.IMFC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, IMF concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IMF is the International Monetary Fund, which provides concessional lending through the Poverty Reduction and Growth Facility and the IMF Trust Fund. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IMF is the International Monetary Fund, which provides concessional lending through the Poverty Reduction and Growth Facility and the IMF Trust Fund. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.IMFN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, IMF nonconcessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IMF is the International Monetary Fund, which provides nonconcessional lending through the credit it provides to its members, mainly to meet balance of payments needs. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IMF is the International Monetary Fund, which provides nonconcessional lending through the credit it provides to its members, mainly to meet balance of payments needs. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, IBRD (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IBRD is the International Bank for Reconstruction and Development, the founding and largest member of the World Bank Group. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IBRD is the International Bank for Reconstruction and Development, the founding and largest member of the World Bank Group. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, IDA (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IDA is the International Development Association, the concessional loan window of the World Bank Group. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IDA is the International Development Association, the concessional loan window of the World Bank Group. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.MLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, multilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.MLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.MLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.MLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.MLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.MLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.MLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.MLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.MLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.MLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.MOTH.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, others (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Others is a residual category in the World Bank's Debtor Reporting System. It includes such institutions as the Caribbean Development Fund, Council of Europe, European Development Fund, Islamic Development Bank, Nordic Development Fund, and the like. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Others is a residual category in the World Bank's Debtor Reporting System. It includes such institutions as the Caribbean Development Fund, Council of Europe, European Development Fund, Islamic Development Bank, Nordic Development Fund, and the like. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.NEBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "EBRD, private nonguaranteed (NFL, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt privately placed from the European Bank for Reconstruction and Development (EBRD). Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt privately placed from the European Bank for Reconstruction and Development (EBRD). Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.NIFC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "IFC, private nonguaranteed (NFL, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt privately placed from the International Finance Corporation (IFC). Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt privately placed from the International Finance Corporation (IFC). Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.OFFT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, official creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.OFFT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, official creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.OFFT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, official creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.OFFT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, official creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.OFFT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, official creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PBND.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bonds (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt in form of bonds.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PBND.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bonds (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PBND.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bonds (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt debt from bonds that are either publicly issued or privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt debt from bonds that are either publicly issued or privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PBND.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bonds (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sectordebt from bonds that are either publicly issued or privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sectordebt from bonds that are either publicly issued or privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PBND.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bonds (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PCBK.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, commercial banks (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PCBK.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, commercial banks (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PCBK.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, commercial banks (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PCBK.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, commercial banks (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PCBK.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, commercial banks (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, bonds (NFL, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PROP.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, other private creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, other private creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PROP.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, other private creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PROP.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, other private creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PROP.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, other private creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PROP.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, other private creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PRPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, private guaranteed by public sector (PPG) (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PRVT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, private creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from private creditors.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from private creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, private creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PRVT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, private creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PRVT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, private creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PRVT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, private creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.PRVT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, private creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.RDBC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, RDB concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Concessional financial flows cover disbursements made through concessional lending facilities. Regional development banks are the African Development Bank, in Tunis, Tunisia, which serves all of Africa, including North Africa; the Asian Development Bank, in Manila, Philippines, which serves South and Central Asia and East Asia and Pacific; the European Bank for Reconstruction and Development, in London, United Kingdom, which serves Europe and Central Asia; and the Inter-American Development Bank, in Washington, D.C., which serves the Americas. Aggregates include amounts for economies not specified elsewhere. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Concessional financial flows cover disbursements made through concessional lending facilities. Regional development banks are the African Development Bank, in Tunis, Tunisia, which serves all of Africa, including North Africa; the Asian Development Bank, in Manila, Philippines, which serves South and Central Asia and East Asia and Pacific; the European Bank for Reconstruction and Development, in London, United Kingdom, which serves Europe and Central Asia; and the Inter-American Development Bank, in Washington, D.C., which serves the Americas. Aggregates include amounts for economies not specified elsewhere. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NFL.RDBN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, RDB nonconcessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Nonconcessional financial flows cover all disbursements except those made through concessional lending facilities. Regional development banks are the African Development Bank, in Tunis, Tunisia, which serves all of Africa, including North Africa; the Asian Development Bank, in Manila, Philippines, which serves South and Central Asia and East Asia and Pacific; the European Bank for Reconstruction and Development, in London, United Kingdom, which serves Europe and Central Asia; and the Inter-American Development Bank, in Washington, D.C., which serves the Americas. Aggregates include amounts for economies not specified elsewhere. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Nonconcessional financial flows cover all disbursements except those made through concessional lending facilities. Regional development banks are the African Development Bank, in Tunis, Tunisia, which serves all of Africa, including North Africa; the Asian Development Bank, in Manila, Philippines, which serves South and Central Asia and East Asia and Pacific; the European Bank for Reconstruction and Development, in London, United Kingdom, which serves Europe and Central Asia; and the Inter-American Development Bank, in Washington, D.C., which serves the Americas. Aggregates include amounts for economies not specified elsewhere. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "6" }, { "id": "DT.NTR.BLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.BLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.BLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.BLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.BLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.BLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.BLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.BLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.BLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.BLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.DECB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, central bank (PPG) (NTR, current US$)" }, { "id": "Longdefinition", "value": "The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, total (NTR, current US$)" }, { "id": "Longdefinition", "value": "Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.DEGG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, general government sector (PPG) (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.DEPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, public sector (PPG) (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, long-term (NTR, current US$)" }, { "id": "Longdefinition", "value": "Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.DOPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, other public sector (PPG) (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, private nonguaranteed (PNG) (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, public and publicly guaranteed (PPG) (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.MLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.MLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.MLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.MLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.MLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.MLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.MLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.MLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.MLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.MLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.OFFT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, official creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.OFFT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, official creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.OFFT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, official creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.OFFT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, official creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.OFFT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, official creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PBND.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bonds (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt in form of bonds.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt in form of bonds.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PBND.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bonds (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PBND.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bonds (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PBND.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bonds (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PBND.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bonds (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PCBK.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, commercial banks (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PCBK.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, commercial banks (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PCBK.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, commercial banks (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PCBK.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, commercial banks (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PCBK.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, commercial banks (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, bonds (NTR, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PROP.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, other private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, other private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PROP.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, other private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PROP.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, other private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PROP.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, other private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sectorother private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sectorother private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PROP.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, other private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PRPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, private guaranteed by public sector (PPG) (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PRVT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from private creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from private creditors.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PRVT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PRVT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PRVT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.NTR.PRVT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "6" }, { "id": "DT.TDS.BLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.BLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "General government debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.BLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.BLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.BLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.BLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.BLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.BLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.BLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.BLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.DECB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, central bank (PPG) (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, total (TDS, current US$)" }, { "id": "Longdefinition", "value": "Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.DECT.EX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "The denominator for this indicator in previous versions of Global Development Finance included workers' remittances. Workers' remittances are no longer included." }, { "id": "IndicatorName", "value": "Total debt service (% of exports of goods, services and primary income)" }, { "id": "Longdefinition", "value": "Total debt service to exports of goods, services and primary income. Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total debt service to exports of goods, services and primary income. Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "6" }, { "id": "DT.TDS.DEGG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, general government sector (PPG) (TDS, current US$)" }, { "id": "Longdefinition", "value": "General government debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.DEPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, public sector (PPG) (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "IMF repurchases and charges (TDS, current US$)" }, { "id": "Longdefinition", "value": "IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. IMF charges cover interest payments with respect to all uses of IMF resources, excluding those resulting from drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. IMF charges cover interest payments with respect to all uses of IMF resources, excluding those resulting from drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, long-term (TDS, current US$)" }, { "id": "Longdefinition", "value": "Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.DOPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, other public sector (PPG) (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, private nonguaranteed (PNG) (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private nonguaranteed debt service is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed debt service is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, public and publicly guaranteed (PPG) (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.MLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves.Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Multilateral debt service (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.MLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.MLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.MLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.MLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.MLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.MLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.MLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.MLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.MLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.OFFT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, official creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.OFFT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, official creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "General goverment debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General goverment debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.OFFT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, official creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.OFFT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, official creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.OFFT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, official creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PBND.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bonds (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt in form of bonds.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PBND.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bonds (TDS, current US$)" }, { "id": "Longdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PBND.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bonds (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PBND.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bonds (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from bonds that are either publicly issued or privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from bonds that are either publicly issued or privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PBND.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bonds (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PCBK.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, commercial banks (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. 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Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PCBK.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, commercial banks (TDS, current US$)" }, { "id": "Longdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PCBK.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, commercial banks (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PCBK.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, commercial banks (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PCBK.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, commercial banks (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, bonds (TDS, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PROP.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, other private creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, other private creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PROP.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, other private creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PROP.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, other private creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PROP.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, other private creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PROP.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, other private creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PRPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, private guaranteed by public sector (PPG) (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PRVT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, private creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from private creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from private creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, private creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PRVT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, private creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "General government guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PRVT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, private creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PRVT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, private creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TDS.PRVT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, private creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "6" }, { "id": "DT.TXR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Total amount of debt rescheduled (current US$)" }, { "id": "Longdefinition", "value": "Total amount of debt rescheduled includes the debt stock, principal, interest, charges and penalties rescheduled. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total amount of debt rescheduled includes the debt stock, principal, interest, charges and penalties rescheduled. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "6" }, { "id": "DT.UND.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Undisbursed external debt, total (UND, current US$)" }, { "id": "Longdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Undisbursed debt" } ], "source_id": "6" }, { "id": "DT.UND.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Undisbursed external debt, official creditors (UND, current US$)" }, { "id": "Longdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Undisbursed debt" } ], "source_id": "6" }, { "id": "DT.UND.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Undisbursed external debt, private creditors (UND, current US$)" }, { "id": "Longdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Undisbursed debt" } ], "source_id": "6" }, { "id": "FI.RES.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Total reserves (includes gold, current US$)" }, { "id": "Longdefinition", "value": "Total reserves comprise holdings of monetary gold, special drawing rights, reserves of IMF members held by the IMF, and holdings of foreign exchange under the control of monetary authorities. The gold component of these reserves is valued at year-end (December 31) London prices. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total reserves comprise holdings of monetary gold, special drawing rights, reserves of IMF members held by the IMF, and holdings of foreign exchange under the control of monetary authorities. The gold component of these reserves is valued at year-end (December 31) London prices. Data are in current U.S. dollars." }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "6" }, { "id": "FI.RES.TOTL.DT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Total reserves (% of total external debt)" }, { "id": "Longdefinition", "value": "International reserves to total external debt stocks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "International reserves to total external debt stocks." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "6" }, { "id": "FI.RES.TOTL.MO", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Total reserves in months of imports" }, { "id": "Longdefinition", "value": "Total reserves comprise holdings of monetary gold, special drawing rights, reserves of IMF members held by the IMF, and holdings of foreign exchange under the control of monetary authorities. The gold component of these reserves is valued at year-end (December 31) London prices. This item shows reserves expressed in terms of the number of months of imports of goods and services they could pay for [Reserves/(Imports/12)]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total reserves comprise holdings of monetary gold, special drawing rights, reserves of IMF members held by the IMF, and holdings of foreign exchange under the control of monetary authorities. The gold component of these reserves is valued at year-end (December 31) London prices. This item shows reserves expressed in terms of the number of months of imports of goods and services they could pay for [Reserves/(Imports/12)]." }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "6" }, { "id": "NY.GNP.MKTP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GNI (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" } ], "source_id": "6" }, { "id": "SP.POP.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis." }, { "id": "IndicatorName", "value": "Population, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Current population estimates for developing countries that lack (i) reliable recent census data, and (ii) pre- and post-census estimates for countries with census data, are provided by the United Nations Population Division and other agencies. \n\nThe cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in both the model and the data. In the UN estimates the five-year age group is the cohort unit and five-year period data are used; therefore interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\nBecause future trends cannot be known with certainty, population projections have a wide range of uncertainty." }, { "id": "Longdefinition", "value": "Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "(1) United Nations Population Division. World Population Prospects: 2019 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "6" }, { "id": "AED.PRIM.MATH", "metatype": [ { "id": "IndicatorName", "value": "Adjusted Primary Math Score" }, { "id": "Longdefinition", "value": "A test score that has been standardized over time and across various international and regional mathematics assessments taken at the primary school level. It is calculated by creating a ratio between U.S. primary math scores on international tests (such as PISA and TIMSS) to primary math scores on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw primary math international scores are approximated to the nearest 5-year interval, averaged, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Primary math test scores from countries which only participate in regional assessments are included in the above transformation after being multiplied by a ratio comparing average primary math scores in a given regional test and primary math scores on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted primary math test scores comparable over across time, over countries and over all assessments." }, { "id": "Shortdefinition", "value": "A test score that has been standardized over time and across various international and regional mathematics assessments taken at the primary school level. It is calculated by creating a ratio between U.S. primary math scores on international tests (such as PISA and TIMSS) to primary math scores on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw primary math international scores are approximated to the nearest 5-year interval, averaged, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Primary math test scores from countries which only participate in regional assessments are included in the above transformation after being multiplied by a ratio comparing average primary math scores in a given regional test and primary math scores on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted primary math test scores comparable over across time, over countries and over all assessments." }, { "id": "Source", "value": "Angrist, N., Patrinos, H. and Schlotter, M.. The World Bank (2013)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "AED.PRIM.MEAN", "metatype": [ { "id": "IndicatorName", "value": "Average Adjusted Primary Test Score." }, { "id": "Longdefinition", "value": "A test score that has been standardized over time, across subjects, and across various international and regional assessments taken at the primary school level. It is calculated by creating a ratio between U.S. primary scores averaged across subjects on all international tests (such as PISA and TIMSS) to average primary scores averaged across subjects on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw international primary scores are averaged across subjects and approximated to the nearest 5-year interval, averaged across tests, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Primary test scores from countries which only participate in regional assessments are included in the above transformation after being averaged across subjects and then multiplied by a ratio comparing average primary scores across subjects in a given regional test and average primary scores across subjects on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted primary test scores comparable over across time, across subjects, over countries and over all assessments." }, { "id": "Shortdefinition", "value": "A test score that has been standardized over time, across subjects, and across various international and regional assessments taken at the primary school level. It is calculated by creating a ratio between U.S. primary scores averaged across subjects on all international tests (such as PISA and TIMSS) to average primary scores averaged across subjects on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw international primary scores are averaged across subjects and approximated to the nearest 5-year interval, averaged across tests, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Primary test scores from countries which only participate in regional assessments are included in the above transformation after being averaged across subjects and then multiplied by a ratio comparing average primary scores across subjects in a given regional test and average primary scores across subjects on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted primary test scores comparable over across time, across subjects, over countries and over all assessments." }, { "id": "Source", "value": "Angrist, N., Patrinos, H. and Schlotter, M.. The World Bank (2013)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "AED.PRIM.READ", "metatype": [ { "id": "IndicatorName", "value": "Adjusted Primary Reading Score" }, { "id": "Longdefinition", "value": "A test score that has been standardized over time and across various international and regional reading assessments taken at the primary school level. It is calculated by creating a ratio between U.S. primary reading scores on international tests (such as PISA and TIMSS) to primary reading scores on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw international primary reading scores are approximated to the nearest 5-year interval, averaged, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Primary reading test scores from countries which only participate in regional assessments are included in the above transformation after being multiplied by a ratio comparing average primary reading scores in a given regional test and primary reading scores on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted primary reading test scores comparable over across time, over countries and over all assessments." }, { "id": "Shortdefinition", "value": "A test score that has been standardized over time and across various international and regional reading assessments taken at the primary school level. It is calculated by creating a ratio between U.S. primary reading scores on international tests (such as PISA and TIMSS) to primary reading scores on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw international primary reading scores are approximated to the nearest 5-year interval, averaged, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Primary reading test scores from countries which only participate in regional assessments are included in the above transformation after being multiplied by a ratio comparing average primary reading scores in a given regional test and primary reading scores on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted primary reading test scores comparable over across time, over countries and over all assessments." }, { "id": "Source", "value": "Angrist, N., Patrinos, H. and Schlotter, M.. The World Bank (2013)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "AED.PRIM.SCNC", "metatype": [ { "id": "IndicatorName", "value": "Adjusted Primary Science Score" }, { "id": "Longdefinition", "value": "A test score that has been standardized over time and across various international and regional science assessments taken at the primary school level. It is calculated by creating a ratio between U.S. primary science scores on international tests (such as PISA and TIMSS) to primary science scores on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw primary science international scores are approximated to the nearest 5-year interval, averaged, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Primary science test scores from countries which only participate in regional assessments are included in the above transformation after being multiplied by a ratio comparing average primary science scores in a given regional test and primary science scores on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted primary science test scores comparable over across time, over countries and over all assessments." }, { "id": "Shortdefinition", "value": "A test score that has been standardized over time and across various international and regional science assessments taken at the primary school level. It is calculated by creating a ratio between U.S. primary science scores on international tests (such as PISA and TIMSS) to primary science scores on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw primary science international scores are approximated to the nearest 5-year interval, averaged, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Primary science test scores from countries which only participate in regional assessments are included in the above transformation after being multiplied by a ratio comparing average primary science scores in a given regional test and primary science scores on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted primary science test scores comparable over across time, over countries and over all assessments." }, { "id": "Source", "value": "Angrist, N., Patrinos, H. and Schlotter, M.. The World Bank (2013)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "AED.PRSC.MEAN", "metatype": [ { "id": "IndicatorName", "value": "Average Adjusted Test Score" }, { "id": "Longdefinition", "value": "A test score that has been standardized over time, across subjects, across schooling levels, and across various international and regional assessments. It is calculated by creating a ratio between U.S. scores averaged across subjects and over schooling level on all international tests (such as PISA and TIMSS) to average scores averaged across subjects and over schooling levels on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw international scores are averaged across subjects and over school levels and approximated to the nearest 5-year interval, averaged across tests, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Test scores from countries which only participate in regional assessments are included in the above transformation after being averaged across subjects and over schooling levels and then multiplied by a ratio comparing average scores across subjects and schooling levels in a given regional test and average scores across subjects and schooling levels on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted test scores comparable over time, across subjects, across schooling levels, over countries and over all assessments." }, { "id": "Shortdefinition", "value": "A test score that has been standardized over time, across subjects, across schooling levels, and across various international and regional assessments. It is calculated by creating a ratio between U.S. scores averaged across subjects and over schooling level on all international tests (such as PISA and TIMSS) to average scores averaged across subjects and over schooling levels on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw international scores are averaged across subjects and over school levels and approximated to the nearest 5-year interval, averaged across tests, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Test scores from countries which only participate in regional assessments are included in the above transformation after being averaged across subjects and over schooling levels and then multiplied by a ratio comparing average scores across subjects and schooling levels in a given regional test and average scores across subjects and schooling levels on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted test scores comparable over time, across subjects, across schooling levels, over countries and over all assessments." }, { "id": "Source", "value": "Angrist, N., Patrinos, H. and Schlotter, M.. The World Bank (2013)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "AED.SECO.MATH", "metatype": [ { "id": "IndicatorName", "value": "Adjusted Secondary Math Score" }, { "id": "Longdefinition", "value": "A test score that has been standardized over time and across various international and regional mathematics assessments taken at the secondary school level. It is calculated by creating a ratio between U.S. secondary math scores on international tests (such as PISA and TIMSS) to secondary math scores on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw secondary math international scores are approximated to the nearest 5-year interval, averaged, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Secondary math test scores from countries which only participate in regional assessments are included in the above transformation after being multiplied by a ratio comparing average secondary math scores in a given regional test and secondary math scores on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted secondary math test scores comparable over across time, over countries and over all assessments." }, { "id": "Shortdefinition", "value": "A test score that has been standardized over time and across various international and regional mathematics assessments taken at the secondary school level. It is calculated by creating a ratio between U.S. secondary math scores on international tests (such as PISA and TIMSS) to secondary math scores on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw secondary math international scores are approximated to the nearest 5-year interval, averaged, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Secondary math test scores from countries which only participate in regional assessments are included in the above transformation after being multiplied by a ratio comparing average secondary math scores in a given regional test and secondary math scores on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted secondary math test scores comparable over across time, over countries and over all assessments." }, { "id": "Source", "value": "Angrist, N., Patrinos, H. and Schlotter, M.. The World Bank (2013)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "AED.SECO.MEAN", "metatype": [ { "id": "IndicatorName", "value": "Average Adjusted Secondary Test Score" }, { "id": "Longdefinition", "value": "A test score that has been standardized over time, across subjects, and across various international and regional assessments taken at the secondary school level. It is calculated by creating a ratio between U.S. secondary scores averaged across subjects on all international tests (such as PISA and TIMSS) to average secondary scores averaged across subjects on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw international secondary scores are averaged across subjects and approximated to the nearest 5-year interval, averaged across tests, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Secondary test scores from countries which only participate in regional assessments are included in the above transformation after being averaged across subjects and then multiplied by a ratio comparing average secondary scores across subjects in a given regional test and average secondary scores across subjects on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted secondary test scores comparable over time, across subjects, over countries and over all assessments." }, { "id": "Shortdefinition", "value": "A test score that has been standardized over time, across subjects, and across various international and regional assessments taken at the secondary school level. It is calculated by creating a ratio between U.S. secondary scores averaged across subjects on all international tests (such as PISA and TIMSS) to average secondary scores averaged across subjects on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw international secondary scores are averaged across subjects and approximated to the nearest 5-year interval, averaged across tests, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Secondary test scores from countries which only participate in regional assessments are included in the above transformation after being averaged across subjects and then multiplied by a ratio comparing average secondary scores across subjects in a given regional test and average secondary scores across subjects on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted secondary test scores comparable over time, across subjects, over countries and over all assessments." }, { "id": "Source", "value": "Angrist, N., Patrinos, H. and Schlotter, M.. The World Bank (2013)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "AED.SECO.READ", "metatype": [ { "id": "IndicatorName", "value": "Adjusted Secondary Reading Score" }, { "id": "Longdefinition", "value": "A test score that has been standardized over time and across various international and regional reading assessments taken at the secondary school level. It is calculated by creating a ratio between U.S. secondary reading scores on international tests (such as PISA and TIMSS) to secondary reading scores on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw secondary reading international scores are approximated to the nearest 5-year interval, averaged, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Secondary reading test scores from countries which only participate in regional assessments are included in the above transformation after being multiplied by a ratio comparing average secondary readings scores in a given regional test and secondary reading scores on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted secondary reading test scores comparable over across time, over countries and over all assessments." }, { "id": "Shortdefinition", "value": "A test score that has been standardized over time and across various international and regional reading assessments taken at the secondary school level. It is calculated by creating a ratio between U.S. secondary reading scores on international tests (such as PISA and TIMSS) to secondary reading scores on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw secondary reading international scores are approximated to the nearest 5-year interval, averaged, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Secondary reading test scores from countries which only participate in regional assessments are included in the above transformation after being multiplied by a ratio comparing average secondary readings scores in a given regional test and secondary reading scores on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted secondary reading test scores comparable over across time, over countries and over all assessments." }, { "id": "Source", "value": "Angrist, N., Patrinos, H. and Schlotter, M.. The World Bank (2013)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "AED.SECO.SCNC", "metatype": [ { "id": "IndicatorName", "value": "Adjusted Secondary Science Score" }, { "id": "Longdefinition", "value": "A test score that has been standardized over time and across various international and regional science assessments taken at the secondary school level. It is calculated by creating a ratio between U.S. secondary science scores on international tests (such as PISA and TIMSS) to secondary science scores on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw secondary science international scores are approximated to the nearest 5-year interval, averaged, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Secondary science test scores from countries which only participate in regional assessments are included in the above transformation after being multiplied by a ratio comparing average secondary science scores in a given regional test and secondary science scores on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted secondary science test scores comparable over across time, over countries and over all assessments." }, { "id": "Shortdefinition", "value": "A test score that has been standardized over time and across various international and regional science assessments taken at the secondary school level. It is calculated by creating a ratio between U.S. secondary science scores on international tests (such as PISA and TIMSS) to secondary science scores on the U.S. National Assessment of Educational Progress (NAEP) approximated to the nearest 5-year time step from 1965-2010. All raw secondary science international scores are approximated to the nearest 5-year interval, averaged, and then multiplied by this ratio. This makes these adjusted scores comparable over time, across countries and across international tests. Secondary science test scores from countries which only participate in regional assessments are included in the above transformation after being multiplied by a ratio comparing average secondary science scores in a given regional test and secondary science scores on an international assessment for all doubloon countries - countries which participate in the same regional assessment and an international test. This makes adjusted secondary science test scores comparable over across time, over countries and over all assessments." }, { "id": "Source", "value": "Angrist, N., Patrinos, H. and Schlotter, M.. The World Bank (2013)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "BAR.NOED.1519.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 15-19 with no education" }, { "id": "Longdefinition", "value": "Percentage of female population age 15-19 with no education" }, { "id": "Shortdefinition", "value": "Percentage of female population age 15-19 with no education" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.NOED.1519.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 15-19 with no education" }, { "id": "Longdefinition", "value": "Percentage of population age 15-19 with no education" }, { "id": "Shortdefinition", "value": "Percentage of population age 15-19 with no education" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.NOED.15UP.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 15+ with no education" }, { "id": "Longdefinition", "value": "Percentage of female population age 15+ with no education" }, { "id": "Shortdefinition", "value": "Percentage of female population age 15+ with no education" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.NOED.15UP.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 15+ with no education" }, { "id": "Longdefinition", "value": "Percentage of population age 15+ with no education" }, { "id": "Shortdefinition", "value": "Percentage of population age 15+ with no education" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.NOED.2024.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 20-24 with no education" }, { "id": "Longdefinition", "value": "Percentage of female population age 20-24 with no education" }, { "id": "Shortdefinition", "value": "Percentage of female population age 20-24 with no education" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.NOED.2024.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 20-24 with no education" }, { "id": "Longdefinition", "value": "Percentage of population age 20-24 with no education" }, { "id": "Shortdefinition", "value": "Percentage of population age 20-24 with no education" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.NOED.2529.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 25-29 with no education" }, { "id": "Longdefinition", "value": "Percentage of female population age 25-29 with no education" }, { "id": "Shortdefinition", "value": "Percentage of female population age 25-29 with no education" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.NOED.2529.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 25-29 with no education" }, { "id": "Longdefinition", "value": "Percentage of population age 25-29 with no education" }, { "id": "Shortdefinition", "value": "Percentage of population age 25-29 with no education" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.NOED.25UP.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 25+ with no education" }, { "id": "Longdefinition", "value": "Percentage of female population age 25+ with no education" }, { "id": "Shortdefinition", "value": "Percentage of female population age 25+ with no education" }, { "id": "Source", "value": "Robert J. 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Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 15-19 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.1519.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 15-19 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of population age 15-19 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 15-19 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.15UP.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 15+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 15+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 15+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.15UP.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 15+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of population age 15+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 15+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.2024.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 20-24 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 20-24 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 20-24 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.2024.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 20-24 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of population age 20-24 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 20-24 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.2529.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 25-29 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 25-29 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 25-29 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.2529.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 25-29 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of population age 25-29 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 25-29 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.25UP.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 25+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 25+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 25+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.25UP.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 25+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of population age 25+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 25+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.3034.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 30-34 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 30-34 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 30-34 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.3034.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 30-34 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of population age 30-34 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 30-34 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.3539.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 35-39 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 35-39 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 35-39 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.3539.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 35-39 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of population age 35-39 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 35-39 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.4044.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 40-44 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 40-44 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 40-44 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.4044.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 40-44 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of population age 40-44 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 40-44 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.4549.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 45-49 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 45-49 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 45-49 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.4549.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 45-49 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of population age 45-49 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 45-49 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.5054.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 50-54 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 50-54 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 50-54 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.5054.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 50-54 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of population age 50-54 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 50-54 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.5559.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 55-59 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 55-59 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 55-59 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.5559.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 55-59 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of population age 55-59 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 55-59 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.6064.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 60-64 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 60-64 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 60-64 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.6064.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 60-64 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of population age 60-64 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 60-64 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.6569.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 65-69 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 65-69 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 65-69 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.6569.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 65-69 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of population age 65-69 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 65-69 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.7074.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 70-74 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 70-74 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 70-74 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.7074.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 70-74 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of population age 70-74 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 70-74 with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.75UP.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 75+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 75+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 75+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.ICMP.75UP.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 75+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Longdefinition", "value": "Percentage of population age 75+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 75+ with primary schooling. Total (Incomplete and Completed Primary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.PRM.SCHL.1519", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of primary schooling, age 15-19, total" }, { "id": "Longdefinition", "value": "Average years of primary schooling, 15-19, total is the average years of primary education completed among people age 15-19." }, { "id": "Shortdefinition", "value": "Average years of primary schooling, 15-19, total is the average years of primary education completed among people age 15-19." }, { "id": "Source", "value": "Robert J. 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Completed Tertiary" }, { "id": "Shortdefinition", "value": "Percentage of population age 15+ with tertiary schooling. Completed Tertiary" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.CMPT.2024.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 20-24 with tertiary schooling. Completed Tertiary" }, { "id": "Longdefinition", "value": "Percentage of female population age 20-24 with tertiary schooling. Completed Tertiary" }, { "id": "Shortdefinition", "value": "Percentage of female population age 20-24 with tertiary schooling. Completed Tertiary" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.CMPT.2024.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 20-24 with tertiary schooling. Completed Tertiary" }, { "id": "Longdefinition", "value": "Percentage of population age 20-24 with tertiary schooling. Completed Tertiary" }, { "id": "Shortdefinition", "value": "Percentage of population age 20-24 with tertiary schooling. Completed Tertiary" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.CMPT.2529.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 25-29 with tertiary schooling. Completed Tertiary" }, { "id": "Longdefinition", "value": "Percentage of female population age 25-29 with tertiary schooling. 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Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 15-19 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.1519.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 15-19 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of population age 15-19 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 15-19 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. 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Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of population age 15+ with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 15+ with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.2024.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 20-24 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 20-24 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 20-24 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.2024.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 20-24 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of population age 20-24 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 20-24 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.2529.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 25-29 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 25-29 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 25-29 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.2529.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 25-29 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of population age 25-29 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 25-29 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.25UP.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 25+ with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 25+ with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 25+ with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.25UP.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 25+ with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of population age 25+ with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 25+ with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.3034.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 30-34 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 30-34 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 30-34 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.3034.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 30-34 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of population age 30-34 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 30-34 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.3539.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 35-39 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 35-39 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 35-39 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.3539.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 35-39 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of population age 35-39 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 35-39 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.4044.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 40-44 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 40-44 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 40-44 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.4044.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 40-44 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of population age 40-44 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 40-44 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.4549.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 45-49 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 45-49 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 45-49 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.4549.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 45-49 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of population age 45-49 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 45-49 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.5054.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 50-54 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 50-54 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 50-54 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.5054.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 50-54 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of population age 50-54 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 50-54 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.5559.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 55-59 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 55-59 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 55-59 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.5559.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 55-59 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of population age 55-59 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 55-59 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.6064.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 60-64 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 60-64 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 60-64 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.6064.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 60-64 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of population age 60-64 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 60-64 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.6569.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 65-69 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 65-69 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 65-69 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. 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Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 70-74 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 70-74 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.7074.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of population age 70-74 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of population age 70-74 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of population age 70-74 with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.ICMP.75UP.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Percentage of female population age 75+ with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Longdefinition", "value": "Percentage of female population age 75+ with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Shortdefinition", "value": "Percentage of female population age 75+ with tertiary schooling. Total (Incomplete and Completed Tertiary)" }, { "id": "Source", "value": "Robert J. 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Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.1519.FE", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 15-19, female" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 15-19, female is the average years of tertiary education completed among females age 15-19." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 15-19, female is the average years of tertiary education completed among females age 15-19." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.15UP", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 15+, total" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 15+, total is the average years of tertiary education completed among people over age 15." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 15+, total is the average years of tertiary education completed among people over age 15." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.15UP.FE", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 15+, female" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 15+, female is the average years of tertiary education completed among females over age 15." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 15+, female is the average years of tertiary education completed among females over age 15." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.2024", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 20-24, total" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 20-24, total is the average years of tertiary education completed among people age 20-24." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 20-24, total is the average years of tertiary education completed among people age 20-24." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.2024.FE", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 20-24, female" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 20-24, female is the average years of tertiary education completed among females age 20-24." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 20-24, female is the average years of tertiary education completed among females age 20-24." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.2529", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 25-29, total" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 25-29, total is the average years of tertiary education completed among people age 25-29." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 25-29, total is the average years of tertiary education completed among people age 25-29." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.2529.FE", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 25-29, female" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 25-29, female is the average years of tertiary education completed among females age 25-29." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 25-29, female is the average years of tertiary education completed among females age 25-29." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.25UP", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 25+, total" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 25+, total is the average years of tertiary education completed among people over age 25." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 25+, total is the average years of tertiary education completed among people over age 25." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.25UP.FE", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 25+, female" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 25+, female is the average years of tertiary education completed among females over age 25." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 25+, female is the average years of tertiary education completed among females over age 25." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.3034", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 30-34, total" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 30-34, total is the average years of tertiary education completed among people age 30-34." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 30-34, total is the average years of tertiary education completed among people age 30-34." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.3034.FE", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 30-34, female" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 30-34, female is the average years of tertiary education completed among females age 30-34." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 30-34, female is the average years of tertiary education completed among females age 30-34." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.3539", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 35-39, total" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 35-39, total is the average years of tertiary education completed among people age 35-39." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 35-39, total is the average years of tertiary education completed among people age 35-39." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.3539.FE", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 35-39, female" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 35-39, female is the average years of tertiary education completed among females age 35-39." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 35-39, female is the average years of tertiary education completed among females age 35-39." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.4044", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 40-44, total" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 40-44, total is the average years of tertiary education completed among people age 40-44." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 40-44, total is the average years of tertiary education completed among people age 40-44." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.4044.FE", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 40-44, female" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 40-44, female is the average years of tertiary education completed among females age 40-44." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 40-44, female is the average years of tertiary education completed among females age 40-44." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.4549", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 45-49, total" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 45-49, total is the average years of tertiary education completed among people age 45-49." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 45-49, total is the average years of tertiary education completed among people age 45-49." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.4549.FE", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 45-49, female" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 45-49, female is the average years of tertiary education completed among females age 45-49." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 45-49, female is the average years of tertiary education completed among females age 45-49." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.5054", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 50-54, total" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 50-54, total is the average years of tertiary education completed among people age 50-54." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 50-54, total is the average years of tertiary education completed among people age 50-54." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.5054.FE", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 50-54, female" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 50-54, female is the average years of tertiary education completed among females age 50-54." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 50-54, female is the average years of tertiary education completed among females age 50-54." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.5559", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 55-59, total" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 55-59, total is the average years of tertiary education completed among people age 55-59." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 55-59, total is the average years of tertiary education completed among people age 55-59." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.5559.FE", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 55-59, female" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 55-59, female is the average years of tertiary education completed among females age 55-59." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 55-59, female is the average years of tertiary education completed among females age 55-59." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.6064", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 60-64, total" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 60-64, total is the average years of tertiary education completed among people age 60-64." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 60-64, total is the average years of tertiary education completed among people age 60-64." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.6064.FE", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 60-64, female" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 60-64, female is the average years of tertiary education completed among females age 60-64." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 60-64, female is the average years of tertiary education completed among females age 60-64." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.6569", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 65-69, total" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 65-69, total is the average years of tertiary education completed among people age 65-69." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 65-69, total is the average years of tertiary education completed among people age 65-69." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.6569.FE", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 65-69, female" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 65-69, female is the average years of tertiary education completed among females age 65-69." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 65-69, female is the average years of tertiary education completed among females age 65-69." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.7074", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 70-74, total" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 70-74, total is the average years of tertiary education completed among people age 70-74." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 70-74, total is the average years of tertiary education completed among people age 70-74." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.7074.FE", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 70-74, female" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 70-74, female is the average years of tertiary education completed among females age 70-74." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 70-74, female is the average years of tertiary education completed among females age 70-74." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.75UP", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 75+, total" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 75+, total is the average years of tertiary education completed among people over age 75." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 75+, total is the average years of tertiary education completed among people over age 75." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "BAR.TER.SCHL.75UP.FE", "metatype": [ { "id": "IndicatorName", "value": "Barro-Lee: Average years of tertiary schooling, age 75+, female" }, { "id": "Longdefinition", "value": "Average years of tertiary schooling, 75+, female is the average years of tertiary education completed among females over age 75." }, { "id": "Shortdefinition", "value": "Average years of tertiary schooling, 75+, female is the average years of tertiary education completed among females over age 75." }, { "id": "Source", "value": "Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "HD.HCI.AMRT", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI): Survival Rate from Age 15-60, Total" }, { "id": "Longdefinition", "value": "Adult Survival Rate is calculated by subtracting the mortality rate for 15-60 year-olds from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "Adult Survival Rate is calculated by subtracting the mortality rate for 15-60 year-olds from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "United Nations Population Division, World Population Prospects: 2017 Revision, supplemented with data provided by World Bank Staff." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.AMRT.FE", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI): Survival Rate from Age 15-60, Female" }, { "id": "Longdefinition", "value": "Adult Survival Rate is calculated by subtracting the mortality rate for 15-60 year-olds from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "Adult Survival Rate is calculated by subtracting the mortality rate for 15-60 year-olds from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "United Nations Population Division, World Population Prospects: 2017 Revision, supplemented with data provided by World Bank Staff." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.AMRT.MA", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI): Survival Rate from Age 15-60, Male" }, { "id": "Longdefinition", "value": "Adult Survival Rate is calculated by subtracting the mortality rate for 15-60 year-olds from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "Adult Survival Rate is calculated by subtracting the mortality rate for 15-60 year-olds from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "United Nations Population Division, World Population Prospects: 2017 Revision, supplemented with data provided by World Bank Staff." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.EYRS", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI): Expected Years of School, Total" }, { "id": "Longdefinition", "value": "Expected Years of School is calculated as the sum of age-specific enrollment rates between ages 4 and 17. Age-specific enrollment rates are approximated using school enrollment rates at different levels: pre-primary enrollment rates approximate the age-specific enrolment rates for 4 and 5 year-olds; the primary rate approximates for 6-11 year-olds; the lower-secondary rate approximates for 12-14 year-olds; and the upper-secondary approximates for 15-17 year-olds. Most recent estimates are used. Year of most recent primary enrollment rate used is shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "Expected Years of School is calculated as the sum of age-specific enrollment rates between ages 4 and 17. Age-specific enrollment rates are approximated using school enrollment rates at different levels: pre-primary enrollment rates approximate the age-specific enrolment rates for 4 and 5 year-olds; the primary rate approximates for 6-11 year-olds; the lower-secondary rate approximates for 12-14 year-olds; and the upper-secondary approximates for 15-17 year-olds. Most recent estimates are used. Year of most recent primary enrollment rate used is shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "World Bank staff estimates based on data from UNESCO Institute for Statistics, supplemented with data provided by World Bank staff." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.EYRS.FE", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI): Expected Years of School, Female" }, { "id": "Longdefinition", "value": "Expected Years of School is calculated as the sum of age-specific enrollment rates between ages 4 and 17. Age-specific enrollment rates are approximated using school enrollment rates at different levels: pre-primary enrollment rates approximate the age-specific enrolment rates for 4 and 5 year-olds; the primary rate approximates for 6-11 year-olds; the lower-secondary rate approximates for 12-14 year-olds; and the upper-secondary approximates for 15-17 year-olds. Most recent estimates are used. Year of most recent primary enrollment rate used is shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "Expected Years of School is calculated as the sum of age-specific enrollment rates between ages 4 and 17. Age-specific enrollment rates are approximated using school enrollment rates at different levels: pre-primary enrollment rates approximate the age-specific enrolment rates for 4 and 5 year-olds; the primary rate approximates for 6-11 year-olds; the lower-secondary rate approximates for 12-14 year-olds; and the upper-secondary approximates for 15-17 year-olds. Most recent estimates are used. Year of most recent primary enrollment rate used is shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "World Bank staff estimates based on data from UNESCO Institute for Statistics, supplemented with data provided by World Bank staff." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.EYRS.MA", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI): Expected Years of School, Male" }, { "id": "Longdefinition", "value": "Expected Years of School is calculated as the sum of age-specific enrollment rates between ages 4 and 17. Age-specific enrollment rates are approximated using school enrollment rates at different levels: pre-primary enrollment rates approximate the age-specific enrolment rates for 4 and 5 year-olds; the primary rate approximates for 6-11 year-olds; the lower-secondary rate approximates for 12-14 year-olds; and the upper-secondary approximates for 15-17 year-olds. Most recent estimates are used. Year of most recent primary enrollment rate used is shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "Expected Years of School is calculated as the sum of age-specific enrollment rates between ages 4 and 17. Age-specific enrollment rates are approximated using school enrollment rates at different levels: pre-primary enrollment rates approximate the age-specific enrolment rates for 4 and 5 year-olds; the primary rate approximates for 6-11 year-olds; the lower-secondary rate approximates for 12-14 year-olds; and the upper-secondary approximates for 15-17 year-olds. Most recent estimates are used. Year of most recent primary enrollment rate used is shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "World Bank staff estimates based on data from UNESCO Institute for Statistics, supplemented with data provided by World Bank staff." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.HLOS", "metatype": [ { "id": "IndicatorName", "value": "Harmonized Test Scores, Total" }, { "id": "Longdefinition", "value": "Harmonized Test Scores from major international student achievement testing programs. They are measured in TIMSS-equivalent units, where 300 is minimal attainment and 625 is advanced attainment. Most recent estimates are used. The year of the most recent estimate is shown in the data notes. Test scores from the following testing programs are included: • TIMSS/PIRLS: Refers to the average of test scores from TIMSS (Trends in International Mathematics and Science Study) and PIRLS (Progress in International Reading Literacy Study), both carried out by the International Association for the Evaluation of Educational Achievement. Data from each PIRLS round is moved to the year of the nearest TIMSS round and averaged with the TIMSS data. • PISA: Refers to test scores from the Programme for International Student Assessment • PISA+TIMSS/PIRLS: Refers to the average of these programs for countries and years where both are available • SACMEQ: Refers to test scores from the Southern and Eastern Africa Consortium for Monitoring Educational Quality • PASEC: Refers to test scores from the Program of Analysis of Education Systems • LLECE: Refers to test scores from the Latin American Laboratory for Assessment of the Quality of Education • EGRA: Refers to test scores from Early Grade Reading Assessments. This indicator is part of the Human Capital Index (HCI). For more information, consult the Global Database on Education Quality (Patrinos and Angrist, 2018): http://documents.worldbank.org/curated/en/390321538076747773/Global-Dataset-on-Education-Quality-A-Review-and-Update-2000-2017" }, { "id": "Shortdefinition", "value": "Harmonized Test Scores from major international student achievement testing programs. They are measured in TIMSS-equivalent units, where 300 is minimal attainment and 625 is advanced attainment. Most recent estimates are used. The year of the most recent estimate is shown in the data notes. Test scores from the following testing programs are included: • TIMSS/PIRLS: Refers to the average of test scores from TIMSS (Trends in International Mathematics and Science Study) and PIRLS (Progress in International Reading Literacy Study), both carried out by the International Association for the Evaluation of Educational Achievement. Data from each PIRLS round is moved to the year of the nearest TIMSS round and averaged with the TIMSS data. • PISA: Refers to test scores from the Programme for International Student Assessment • PISA+TIMSS/PIRLS: Refers to the average of these programs for countries and years where both are available • SACMEQ: Refers to test scores from the Southern and Eastern Africa Consortium for Monitoring Educational Quality • PASEC: Refers to test scores from the Program of Analysis of Education Systems • LLECE: Refers to test scores from the Latin American Laboratory for Assessment of the Quality of Education • EGRA: Refers to test scores from Early Grade Reading Assessments. This indicator is part of the Human Capital Index (HCI). For more information, consult the Global Database on Education Quality (Patrinos and Angrist, 2018): http://documents.worldbank.org/curated/en/390321538076747773/Global-Dataset-on-Education-Quality-A-Review-and-Update-2000-2017" }, { "id": "Source", "value": "Patrinos and Angrist (2018). http://documents.worldbank.org/curated/en/390321538076747773/Global-Dataset-on-Education-Quality-A-Review-and-Update-2000-2019" }, { "id": "Topic", "value": "Learning Outcomes-Harmonized Test Scores" } ], "source_id": "12" }, { "id": "HD.HCI.HLOS.FE", "metatype": [ { "id": "IndicatorName", "value": "Harmonized Test Scores, Female" }, { "id": "Longdefinition", "value": "Harmonized Test Scores from major international student achievement testing programs. They are measured in TIMSS-equivalent units, where 300 is minimal attainment and 625 is advanced attainment. Most recent estimates are used. The year of the most recent estimate is shown in the data notes. Test scores from the following testing programs are included: • TIMSS/PIRLS: Refers to the average of test scores from TIMSS (Trends in International Mathematics and Science Study) and PIRLS (Progress in International Reading Literacy Study), both carried out by the International Association for the Evaluation of Educational Achievement. Data from each PIRLS round is moved to the year of the nearest TIMSS round and averaged with the TIMSS data. • PISA: Refers to test scores from the Programme for International Student Assessment • PISA+TIMSS/PIRLS: Refers to the average of these programs for countries and years where both are available • SACMEQ: Refers to test scores from the Southern and Eastern Africa Consortium for Monitoring Educational Quality • PASEC: Refers to test scores from the Program of Analysis of Education Systems • LLECE: Refers to test scores from the Latin American Laboratory for Assessment of the Quality of Education • EGRA: Refers to test scores from Early Grade Reading Assessments. This indicator is part of the Human Capital Index (HCI). For more information, consult the Global Database on Education Quality (Patrinos and Angrist, 2018): http://documents.worldbank.org/curated/en/390321538076747773/Global-Dataset-on-Education-Quality-A-Review-and-Update-2000-2017" }, { "id": "Shortdefinition", "value": "Harmonized Test Scores from major international student achievement testing programs. They are measured in TIMSS-equivalent units, where 300 is minimal attainment and 625 is advanced attainment. Most recent estimates are used. The year of the most recent estimate is shown in the data notes. Test scores from the following testing programs are included: • TIMSS/PIRLS: Refers to the average of test scores from TIMSS (Trends in International Mathematics and Science Study) and PIRLS (Progress in International Reading Literacy Study), both carried out by the International Association for the Evaluation of Educational Achievement. Data from each PIRLS round is moved to the year of the nearest TIMSS round and averaged with the TIMSS data. • PISA: Refers to test scores from the Programme for International Student Assessment • PISA+TIMSS/PIRLS: Refers to the average of these programs for countries and years where both are available • SACMEQ: Refers to test scores from the Southern and Eastern Africa Consortium for Monitoring Educational Quality • PASEC: Refers to test scores from the Program of Analysis of Education Systems • LLECE: Refers to test scores from the Latin American Laboratory for Assessment of the Quality of Education • EGRA: Refers to test scores from Early Grade Reading Assessments. This indicator is part of the Human Capital Index (HCI). For more information, consult the Global Database on Education Quality (Patrinos and Angrist, 2018): http://documents.worldbank.org/curated/en/390321538076747773/Global-Dataset-on-Education-Quality-A-Review-and-Update-2000-2017" }, { "id": "Source", "value": "Patrinos and Angrist (2018). http://documents.worldbank.org/curated/en/390321538076747773/Global-Dataset-on-Education-Quality-A-Review-and-Update-2000-2017" }, { "id": "Topic", "value": "Learning Outcomes-Harmonized Test Scores" } ], "source_id": "12" }, { "id": "HD.HCI.HLOS.MA", "metatype": [ { "id": "IndicatorName", "value": "Harmonized Test Scores, Male" }, { "id": "Longdefinition", "value": "Harmonized Test Scores from major international student achievement testing programs. They are measured in TIMSS-equivalent units, where 300 is minimal attainment and 625 is advanced attainment. Most recent estimates are used. The year of the most recent estimate is shown in the data notes. Test scores from the following testing programs are included: • TIMSS/PIRLS: Refers to the average of test scores from TIMSS (Trends in International Mathematics and Science Study) and PIRLS (Progress in International Reading Literacy Study), both carried out by the International Association for the Evaluation of Educational Achievement. Data from each PIRLS round is moved to the year of the nearest TIMSS round and averaged with the TIMSS data. • PISA: Refers to test scores from the Programme for International Student Assessment • PISA+TIMSS/PIRLS: Refers to the average of these programs for countries and years where both are available • SACMEQ: Refers to test scores from the Southern and Eastern Africa Consortium for Monitoring Educational Quality • PASEC: Refers to test scores from the Program of Analysis of Education Systems • LLECE: Refers to test scores from the Latin American Laboratory for Assessment of the Quality of Education • EGRA: Refers to test scores from Early Grade Reading Assessments. This indicator is part of the Human Capital Index (HCI). For more information, consult the Global Database on Education Quality (Patrinos and Angrist, 2018): http://documents.worldbank.org/curated/en/390321538076747773/Global-Dataset-on-Education-Quality-A-Review-and-Update-2000-2017" }, { "id": "Shortdefinition", "value": "Harmonized Test Scores from major international student achievement testing programs. They are measured in TIMSS-equivalent units, where 300 is minimal attainment and 625 is advanced attainment. Most recent estimates are used. The year of the most recent estimate is shown in the data notes. Test scores from the following testing programs are included: • TIMSS/PIRLS: Refers to the average of test scores from TIMSS (Trends in International Mathematics and Science Study) and PIRLS (Progress in International Reading Literacy Study), both carried out by the International Association for the Evaluation of Educational Achievement. Data from each PIRLS round is moved to the year of the nearest TIMSS round and averaged with the TIMSS data. • PISA: Refers to test scores from the Programme for International Student Assessment • PISA+TIMSS/PIRLS: Refers to the average of these programs for countries and years where both are available • SACMEQ: Refers to test scores from the Southern and Eastern Africa Consortium for Monitoring Educational Quality • PASEC: Refers to test scores from the Program of Analysis of Education Systems • LLECE: Refers to test scores from the Latin American Laboratory for Assessment of the Quality of Education • EGRA: Refers to test scores from Early Grade Reading Assessments. This indicator is part of the Human Capital Index (HCI). For more information, consult the Global Database on Education Quality (Patrinos and Angrist, 2018): http://documents.worldbank.org/curated/en/390321538076747773/Global-Dataset-on-Education-Quality-A-Review-and-Update-2000-2017" }, { "id": "Source", "value": "Patrinos and Angrist (2018). http://documents.worldbank.org/curated/en/390321538076747773/Global-Dataset-on-Education-Quality-A-Review-and-Update-2000-2018" }, { "id": "Topic", "value": "Learning Outcomes-Harmonized Test Scores" } ], "source_id": "12" }, { "id": "HD.HCI.LAYS", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI): Learning-Adjusted Years of School, Total" }, { "id": "Longdefinition", "value": "Learning-Adjusted Years of School are calculated by multiplying the estimates of Expected Years of School by the ratio of most recent Harmonized Test Score to 625, where 625 corresponds to advancement attainment on the TIMSS (Trends in International Mathematics and Science Study) test. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "Learning-Adjusted Years of School are calculated by multiplying the estimates of Expected Years of School by the ratio of most recent Harmonized Test Score to 625, where 625 corresponds to advancement attainment on the TIMSS (Trends in International Mathematics and Science Study) test. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "World Bank staff calculation based on methodology in Filmer et al. (2018). http://documents.worldbank.org/curated/en/243261538075151093/Learning-Adjusted-Years-of-Schooling-LAYS-Defining-A-New-Macro-Measure-of-Education" }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.LAYS.FE", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI): Learning-Adjusted Years of School, Female" }, { "id": "Longdefinition", "value": "Learning-Adjusted Years of School are calculated by multiplying the estimates of Expected Years of School by the ratio of most recent Harmonized Test Score to 625, where 625 corresponds to advancement attainment on the TIMSS (Trends in International Mathematics and Science Study) test. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "Learning-Adjusted Years of School are calculated by multiplying the estimates of Expected Years of School by the ratio of most recent Harmonized Test Score to 625, where 625 corresponds to advancement attainment on the TIMSS (Trends in International Mathematics and Science Study) test. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "World Bank staff calculation based on methodology in Filmer et al. (2018). http://documents.worldbank.org/curated/en/243261538075151093/Learning-Adjusted-Years-of-Schooling-LAYS-Defining-A-New-Macro-Measure-of-Education" }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.LAYS.MA", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI): Learning-Adjusted Years of School, Male" }, { "id": "Longdefinition", "value": "Learning-Adjusted Years of School are calculated by multiplying the estimates of Expected Years of School by the ratio of most recent Harmonized Test Score to 625, where 625 corresponds to advancement attainment on the TIMSS (Trends in International Mathematics and Science Study) test. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "Learning-Adjusted Years of School are calculated by multiplying the estimates of Expected Years of School by the ratio of most recent Harmonized Test Score to 625, where 625 corresponds to advancement attainment on the TIMSS (Trends in International Mathematics and Science Study) test. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "World Bank staff calculation based on methodology in Filmer et al. (2018). http://documents.worldbank.org/curated/en/243261538075151093/Learning-Adjusted-Years-of-Schooling-LAYS-Defining-A-New-Macro-Measure-of-Education" }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.MORT", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI): Probability of Survival to Age 5, Total" }, { "id": "Longdefinition", "value": "Probability of Survival to Age 5 is calculated by subtracting the under-5 mortality rate from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "Probability of Survival to Age 5 is calculated by subtracting the under-5 mortality rate from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), supplemented with data provided by World Bank staff." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.MORT.FE", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI): Probability of Survival to Age 5, Female" }, { "id": "Longdefinition", "value": "Probability of Survival to Age 5 is calculated by subtracting the under-5 mortality rate from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "Probability of Survival to Age 5 is calculated by subtracting the under-5 mortality rate from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), supplemented with data provided by World Bank staff." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.MORT.MA", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI): Probability of Survival to Age 5, Male" }, { "id": "Longdefinition", "value": "Probability of Survival to Age 5 is calculated by subtracting the under-5 mortality rate from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "Probability of Survival to Age 5 is calculated by subtracting the under-5 mortality rate from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), supplemented with data provided by World Bank staff." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.OVRL", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI) Score: Total (Scale 0-1)" }, { "id": "Longdefinition", "value": "The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.OVRL.FE", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI) Score: Female (Scale 0-1)" }, { "id": "Longdefinition", "value": "The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.OVRL.LB", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI) Score: Total, Lower Bound (Scale 0-1)" }, { "id": "Longdefinition", "value": "The HCI Lower Bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "The HCI Lower Bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.OVRL.LB.FE", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI) Score: Female, Lower Bound (Scale 0-1)" }, { "id": "Longdefinition", "value": "The HCI Lower Bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "The HCI Lower Bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.OVRL.LB.MA", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI) Score: Male, Lower Bound (Scale 0-1)" }, { "id": "Longdefinition", "value": "The HCI Lower Bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "The HCI Lower Bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.OVRL.MA", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI) Score: Male (Scale 0-1)" }, { "id": "Longdefinition", "value": "The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.OVRL.UB", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI) Score: Total, Upper Bound (Scale 0-1)" }, { "id": "Longdefinition", "value": "The HCI Upper Bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "The HCI Upper Bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.OVRL.UB.FE", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI) Score: Female, Upper Bound (Scale 0-1)" }, { "id": "Longdefinition", "value": "The HCI Upper Bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "The HCI Upper Bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.OVRL.UB.MA", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI) Score: Male, Upper Bound (Scale 0-1)" }, { "id": "Longdefinition", "value": "The HCI Upper Bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "The HCI Upper Bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.STNT", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI): Fraction of Children Under 5 Not Stunted, Total" }, { "id": "Longdefinition", "value": "Fraction of Children Under 5 Not Stunted is calculated by subtracting stunting rates from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "Fraction of Children Under 5 Not Stunted is calculated by subtracting stunting rates from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "UNICEF-WHO-World Bank Joint Malnutrition Estimates, supplemented with data provided by World Bank staff." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.STNT.FE", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI): Fraction of Children Under 5 Not Stunted, Female" }, { "id": "Longdefinition", "value": "Fraction of Children Under 5 Not Stunted is calculated by subtracting stunting rates from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "Fraction of Children Under 5 Not Stunted is calculated by subtracting stunting rates from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "UNICEF-WHO-World Bank Joint Malnutrition Estimates, supplemented with data provided by World Bank staff." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HD.HCI.STNT.MA", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI): Fraction of Children Under 5 Not Stunted, Male" }, { "id": "Longdefinition", "value": "Fraction of Children Under 5 Not Stunted is calculated by subtracting stunting rates from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Shortdefinition", "value": "Fraction of Children Under 5 Not Stunted is calculated by subtracting stunting rates from 1. Most recent estimates are used. Year of most recent estimate shown in data notes. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital" }, { "id": "Source", "value": "UNICEF-WHO-World Bank Joint Malnutrition Estimates, supplemented with data provided by World Bank staff." }, { "id": "Topic", "value": "Background-Human Capital Index (HCI)" } ], "source_id": "12" }, { "id": "HH.DHS.GAR.456", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gross attendance rate. Post Secondary" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary is the number of post-secondary school pupils of any age, expressed as a percentage of youth of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary is the number of post-secondary school pupils of any age, expressed as a percentage of youth of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.GAR.456.F", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gross attendance rate. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Female is the number of female post-secondary school pupils of any age, expressed as a percentage of the population of females of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Female is the number of female post-secondary school pupils of any age, expressed as a percentage of the population of females of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.GAR.456.M", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gross attendance rate. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Male is the number of male post-secondary school pupils of any age, expressed as a percentage of the population of males of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Male is the number of male post-secondary school pupils of any age, expressed as a percentage of the population of males of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.GAR.456.Q1", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gross attendance rate. Post Secondary. Quintile 1" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 1 is the number of quintile 1 post-secondary school pupils of any age, expressed as a percentage of the quintile 1 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 1 is the number of quintile 1 post-secondary school pupils of any age, expressed as a percentage of the quintile 1 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.GAR.456.Q2", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gross attendance rate. Post Secondary. Quintile 2" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 2 is the number of quintile 2 post-secondary school pupils of any age, expressed as a percentage of the quintile 2 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 2 is the number of quintile 2 post-secondary school pupils of any age, expressed as a percentage of the quintile 2 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.GAR.456.Q3", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gross attendance rate. Post Secondary. Quintile 3" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 3 is the number of quintile 3 post-secondary school pupils of any age, expressed as a percentage of the quintile 3 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 3 is the number of quintile 3 post-secondary school pupils of any age, expressed as a percentage of the quintile 3 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.GAR.456.Q4", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gross attendance rate. Post Secondary. Quintile 4" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 4 is the number of quintile 4 post-secondary school pupils of any age, expressed as a percentage of the quintile 4 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 4 is the number of quintile 4 post-secondary school pupils of any age, expressed as a percentage of the quintile 4 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.GAR.456.Q5", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gross attendance rate. Post Secondary. Quintile 5" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 5 is the number of quintile 5 post-secondary school pupils of any age, expressed as a percentage of the quintile 5 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 5 is the number of quintile 5 post-secondary school pupils of any age, expressed as a percentage of the quintile 5 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.GAR.456.R", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gross attendance rate. Post Secondary. Rural" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Rural is the number of rural post-secondary school pupils of any age, expressed as a percentage of the rural population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Rural is the number of rural post-secondary school pupils of any age, expressed as a percentage of the rural population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.GAR.456.U", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gross attendance rate. Post Secondary. Urban" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Urban is the number of urban post-secondary school pupils of any age, expressed as a percentage of the urban population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Urban is the number of urban post-secondary school pupils of any age, expressed as a percentage of the urban population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.1", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Primary" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Total is the proportion of children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Total is the proportion of children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.1.F", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Primary. Female" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Female is the proportion of female children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Female is the proportion of female children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.1.M", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Primary. Male" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Male is the proportion of male children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Male is the proportion of male children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.1.Q1", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Primary. Quintile 1" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Quintile 1 is the proportion of quintile 1 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Quintile 1 is the proportion of quintile 1 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.1.Q2", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Primary. Quintile 2" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Quintile 2 is the proportion of quintile 2 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Quintile 2 is the proportion of quintile 2 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.1.Q3", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Primary. Quintile 3" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Quintile 3 is the proportion of quintile 3 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Quintile 3 is the proportion of quintile 3 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.1.Q4", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Primary. Quintile 4" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Quintile 4 is the proportion of quintile 4 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Quintile 4 is the proportion of quintile 4 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.1.Q5", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Primary. Quintile 5" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Quintile 5 is the proportion of quintile 5 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Quintile 5 is the proportion of quintile 5 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.1.R", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Primary. Rural" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Rural is the proportion of rural children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Rural is the proportion of rural children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.1.U", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Primary. Urban" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Urban is the proportion of urban children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Urban is the proportion of urban children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.23", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Secondary" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary is the proportion of children of official secondary school age who are attending secondary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary is the proportion of children of official secondary school age who are attending secondary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.23.F", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Secondary. Female" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Female is the proportion of female children of official secondary school age who are attending secondary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Female is the proportion of female children of official secondary school age who are attending secondary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.23.M", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Secondary. Male" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Male is the proportion of male children of official secondary school age who are attending secondary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Male is the proportion of male children of official secondary school age who are attending secondary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.23.Q1", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Secondary. Quintile 1" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Quintile 1 is the proportion of quintile 1 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Quintile 1 is the proportion of quintile 1 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.23.Q2", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Secondary. Quintile 2" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Quintile 2 is the proportion of quintile 2 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Quintile 2 is the proportion of quintile 2 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.23.Q3", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Secondary. Quintile 3" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Quintile 3 is the proportion of quintile 3 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Quintile 3 is the proportion of quintile 3 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.23.Q4", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Secondary. Quintile 4" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Quintile 4 is the proportion of quintile 4 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Quintile 4 is the proportion of quintile 4 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.23.Q5", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Secondary. Quintile 5" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Quintile 5 is the proportion of quintile 5 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Quintile 5 is the proportion of quintile 5 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.23.R", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Secondary. Rural" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Rural is the proportion of rural children of official secondary school age who are attending secondary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Rural is the proportion of rural children of official secondary school age who are attending secondary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NAR.23.U", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net attendance rate. Secondary. Urban" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Urban is the proportion of urban children of official secondary school age who are attending secondary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Urban is the proportion of urban children of official secondary school age who are attending secondary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NIR.1", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net intake rate for the first grade of primary education" }, { "id": "Longdefinition", "value": "Net intake rate for the first grade of primary education is the number of new pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the children of official entrance age to primary education." }, { "id": "Shortdefinition", "value": "Net intake rate for the first grade of primary education is the number of new pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the children of official entrance age to primary education." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NIR.1.F", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net intake rate for the first grade of primary education. Female" }, { "id": "Longdefinition", "value": "Net intake rate for the first grade of primary education. Female is the number of new female pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the females of official entrance age to primary education." }, { "id": "Shortdefinition", "value": "Net intake rate for the first grade of primary education. Female is the number of new female pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the females of official entrance age to primary education." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NIR.1.M", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net intake rate for the first grade of primary education. Male" }, { "id": "Longdefinition", "value": "Net intake rate for the first grade of primary education. Male is the number of new male pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the males of official entrance age to primary education." }, { "id": "Shortdefinition", "value": "Net intake rate for the first grade of primary education. Male is the number of new male pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the males of official entrance age to primary education." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NIR.1.Q1", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net intake rate for the first grade of primary education. Quintile 1" }, { "id": "Longdefinition", "value": "Net intake rate for the first grade of primary education. Quintile 1 is the number of new quintile 1 pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the quintile 1 population of official entrance age to primary education. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net intake rate for the first grade of primary education. Quintile 1 is the number of new quintile 1 pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the quintile 1 population of official entrance age to primary education. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NIR.1.Q2", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net intake rate for the first grade of primary education. Quintile 2" }, { "id": "Longdefinition", "value": "Net intake rate for the first grade of primary education. Quintile 2 is the number of new quintile 2 pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the quintile 2 population of official entrance age to primary education. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net intake rate for the first grade of primary education. Quintile 2 is the number of new quintile 2 pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the quintile 2 population of official entrance age to primary education. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NIR.1.Q3", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net intake rate for the first grade of primary education. Quintile 3" }, { "id": "Longdefinition", "value": "Net intake rate for the first grade of primary education. Quintile 3 is the number of new quintile 3 pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the quintile 3 population of official entrance age to primary education. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net intake rate for the first grade of primary education. Quintile 3 is the number of new quintile 3 pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the quintile 3 population of official entrance age to primary education. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NIR.1.Q4", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net intake rate for the first grade of primary education. Quintile 4" }, { "id": "Longdefinition", "value": "Net intake rate for the first grade of primary education. Quintile 4 is the number of new quintile 4 pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the quintile 4 population of official entrance age to primary education. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net intake rate for the first grade of primary education. Quintile 4 is the number of new quintile 4 pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the quintile 4 population of official entrance age to primary education. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NIR.1.Q5", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net intake rate for the first grade of primary education. Quintile 5" }, { "id": "Longdefinition", "value": "Net intake rate for the first grade of primary education. Quintile 5 is the number of new quintile 5 pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the quintile 5 population of official entrance age to primary education. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net intake rate for the first grade of primary education. Quintile 5 is the number of new quintile 5 pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the quintile 5 population of official entrance age to primary education. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NIR.1.R", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net intake rate for the first grade of primary education. Rural" }, { "id": "Longdefinition", "value": "Net intake rate for the first grade of primary education. Rural is the number of new rural pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the rural population of official entrance age to primary education." }, { "id": "Shortdefinition", "value": "Net intake rate for the first grade of primary education. Rural is the number of new rural pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the rural population of official entrance age to primary education." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.NIR.1.U", "metatype": [ { "id": "IndicatorName", "value": "DHS: Net intake rate for the first grade of primary education. Urban" }, { "id": "Longdefinition", "value": "Net intake rate for the first grade of primary education. Urban is the number of new urban pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the urban population of official entrance age to primary education." }, { "id": "Shortdefinition", "value": "Net intake rate for the first grade of primary education. Urban is the number of new urban pupils attending the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the urban population of official entrance age to primary education." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOS.1", "metatype": [ { "id": "IndicatorName", "value": "DHS: Proportion of out-of-school. Primary" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary is the number of children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary is the number of children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOS.1.F", "metatype": [ { "id": "IndicatorName", "value": "DHS: Proportion of out-of-school. Primary. Female" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Female is the number of female children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of female children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Female is the number of female children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of female children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOS.1.M", "metatype": [ { "id": "IndicatorName", "value": "DHS: Proportion of out-of-school. Primary. Male" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Male is the number of male children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of male children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Male is the number of male children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of male children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOS.1.Q1", "metatype": [ { "id": "IndicatorName", "value": "DHS: Proportion of out-of-school. Primary. Quintile 1" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Quintile 1 is the number of quintile 1 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 1 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Quintile 1 is the number of quintile 1 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 1 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOS.1.Q2", "metatype": [ { "id": "IndicatorName", "value": "DHS: Proportion of out-of-school. Primary. Quintile 2" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Quintile 2 is the number of quintile 2 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 2 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Quintile 2 is the number of quintile 2 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 2 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOS.1.Q3", "metatype": [ { "id": "IndicatorName", "value": "DHS: Proportion of out-of-school. Primary. Quintile 3" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Quintile 3 is the number of quintile 3 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 3 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Quintile 3 is the number of quintile 3 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 3 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOS.1.Q4", "metatype": [ { "id": "IndicatorName", "value": "DHS: Proportion of out-of-school. Primary. Quintile 4" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Quintile 4 is the number of quintile 4 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 4 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Quintile 4 is the number of quintile 4 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 4 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOS.1.Q5", "metatype": [ { "id": "IndicatorName", "value": "DHS: Proportion of out-of-school. Primary. Quintile 5" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Quintile 5 is the number of quintile 5 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 5 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Quintile 5 is the number of quintile 5 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 5 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOS.1.R", "metatype": [ { "id": "IndicatorName", "value": "DHS: Proportion of out-of-school. Primary. Rural" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Rural is the number of rural children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of rural children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Rural is the number of rural children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of rural children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOS.1.U", "metatype": [ { "id": "IndicatorName", "value": "DHS: Proportion of out-of-school. Primary. Urban" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Urban is the number of urban children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of urban children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Urban is the number of urban children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of urban children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.DO", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Dropped out" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out is the proportion of out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out is the proportion of out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.DO.F", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Dropped out. Female" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Female is the proportion of female out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Female is the proportion of female out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.DO.M", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Dropped out. Male" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Male is the proportion of male out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Male is the proportion of male out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.DO.Q1", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Dropped out. Quintile 1" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 1 is the proportion of quintile 1 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 1 is the proportion of quintile 1 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.DO.Q2", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Dropped out. Quintile 2" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 2 is the proportion of quintile 2 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 2 is the proportion of quintile 2 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.DO.Q3", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Dropped out. Quintile 3" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 3 is the proportion of quintile 3 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 3 is the proportion of quintile 3 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.DO.Q4", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Dropped out. Quintile 4" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 4 is the proportion of quintile 4 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 4 is the proportion of quintile 4 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.DO.Q5", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Dropped out. Quintile 5" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 5 is the proportion of quintile 5 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 5 is the proportion of quintile 5 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.DO.R", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Dropped out. Rural" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Rural is the proportion of rural out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Rural is the proportion of rural out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.DO.U", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Dropped out. Urban" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Urban is the proportion of urban out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Urban is the proportion of urban out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.L", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Late entry" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry is defined as the proportion of out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry is defined as the proportion of out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.L.F", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Late entry. Female" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Female is defined as the proportion of female out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Female is defined as the proportion of female out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.L.M", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Late entry. Male" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Male is defined as the proportion of male out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Male is defined as the proportion of male out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.L.Q1", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Late entry. Quintile 1" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 1 is defined as the proportion of quintile 1 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 1 is defined as the proportion of quintile 1 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.L.Q2", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Late entry. Quintile 2" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 2 is defined as the proportion of quintile 2 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 2 is defined as the proportion of quintile 2 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.L.Q3", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Late entry. Quintile 3" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 3 is defined as the proportion of quintile 3 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 3 is defined as the proportion of quintile 3 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.L.Q4", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Late entry. Quintile 4" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 4 is defined as the proportion of quintile 4 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 4 is defined as the proportion of quintile 4 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.L.Q5", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Late entry. Quintile 5" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 5 is defined as the proportion of quintile 5 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 5 is defined as the proportion of quintile 5 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.L.R", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Late entry. Rural" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Rural is defined as the proportion of rural out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Rural is defined as the proportion of rural out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.L.U", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Late entry. Urban" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Urban is defined as the proportion of urban out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Urban is defined as the proportion of urban out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.X", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Never in school" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school is the percentage of out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school is the percentage of out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.X.F", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Never in school. Female" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Female is the percentage of female out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Female is the percentage of female out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.X.M", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Never in school. Male" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Male is the percentage of male out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Male is the percentage of male out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.X.Q1", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Never in school. Quintile 1" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 1 is the percentage of quintile 1 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 1 is the percentage of quintile 1 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.X.Q2", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Never in school. Quintile 2" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 2 is the percentage of quintile 2 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 2 is the percentage of quintile 2 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.X.Q3", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Never in school. Quintile 3" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 3 is the percentage of quintile 3 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 3 is the percentage of quintile 3 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.X.Q4", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Never in school. Quintile 4" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 4 is the percentage of quintile 4 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 4 is the percentage of quintile 4 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.X.Q5", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Never in school. Quintile 5" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 5 is the percentage of quintile 5 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 5 is the percentage of quintile 5 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.X.R", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Never in school. Rural" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Rural is the percentage of rural out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Rural is the percentage of rural out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.OOST.X.U", "metatype": [ { "id": "IndicatorName", "value": "DHS: Typology of out-of-school children. Primary. Never in school. Urban" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Urban is the percentage of urban out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Urban is the percentage of urban out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.PCR", "metatype": [ { "id": "IndicatorName", "value": "DHS: Primary completion rate" }, { "id": "Longdefinition", "value": "Primary completion rate is the total number of students of any age in the last grade of primary school, minus the number of repeaters in that grade, divided by the number of children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Shortdefinition", "value": "Primary completion rate is the total number of students of any age in the last grade of primary school, minus the number of repeaters in that grade, divided by the number of children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.PCR.F", "metatype": [ { "id": "IndicatorName", "value": "DHS: Primary completion rate. Female" }, { "id": "Longdefinition", "value": "Primary completion rate. Female is the total number of female students of any age in the last grade of primary school, minus the number of female repeaters in that grade, divided by the number of female children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Shortdefinition", "value": "Primary completion rate. Female is the total number of female students of any age in the last grade of primary school, minus the number of female repeaters in that grade, divided by the number of female children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.PCR.M", "metatype": [ { "id": "IndicatorName", "value": "DHS: Primary completion rate. Male" }, { "id": "Longdefinition", "value": "Primary completion rate. Male is the total number of male students of any age in the last grade of primary school, minus the number of male repeaters in that grade, divided by the number of male children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Shortdefinition", "value": "Primary completion rate. Male is the total number of male students of any age in the last grade of primary school, minus the number of male repeaters in that grade, divided by the number of male children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.PCR.Q1", "metatype": [ { "id": "IndicatorName", "value": "DHS: Primary completion rate. Quintile 1" }, { "id": "Longdefinition", "value": "Primary completion rate. Quintile 1 is the total number of quintile 1 students of any age in the last grade of primary school, minus the number of quintile 1 repeaters in that grade, divided by the number of quintile 1 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Primary completion rate. Quintile 1 is the total number of quintile 1 students of any age in the last grade of primary school, minus the number of quintile 1 repeaters in that grade, divided by the number of quintile 1 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.PCR.Q2", "metatype": [ { "id": "IndicatorName", "value": "DHS: Primary completion rate. Quintile 2" }, { "id": "Longdefinition", "value": "Primary completion rate. Quintile 2 is the total number of quintile 2 students of any age in the last grade of primary school, minus the number of quintile 2 repeaters in that grade, divided by the number of quintile 2 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Primary completion rate. Quintile 2 is the total number of quintile 2 students of any age in the last grade of primary school, minus the number of quintile 2 repeaters in that grade, divided by the number of quintile 2 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.PCR.Q3", "metatype": [ { "id": "IndicatorName", "value": "DHS: Primary completion rate. Quintile 3" }, { "id": "Longdefinition", "value": "Primary completion rate. Quintile 3 is the total number of quintile 3 students of any age in the last grade of primary school, minus the number of quintile 3 repeaters in that grade, divided by the number of quintile 3 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Primary completion rate. Quintile 3 is the total number of quintile 3 students of any age in the last grade of primary school, minus the number of quintile 3 repeaters in that grade, divided by the number of quintile 3 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.PCR.Q4", "metatype": [ { "id": "IndicatorName", "value": "DHS: Primary completion rate. Quintile 4" }, { "id": "Longdefinition", "value": "Primary completion rate. Quintile 4 is the total number of quintile 4 students of any age in the last grade of primary school, minus the number of quintile 4 repeaters in that grade, divided by the number of quintile 4 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Primary completion rate. Quintile 4 is the total number of quintile 4 students of any age in the last grade of primary school, minus the number of quintile 4 repeaters in that grade, divided by the number of quintile 4 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.PCR.Q5", "metatype": [ { "id": "IndicatorName", "value": "DHS: Primary completion rate. Quintile 5" }, { "id": "Longdefinition", "value": "Primary completion rate. Quintile 5 is the total number of quintile 5 students of any age in the last grade of primary school, minus the number of quintile 5 repeaters in that grade, divided by the number of quintile 5 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Primary completion rate. Quintile 5 is the total number of quintile 5 students of any age in the last grade of primary school, minus the number of quintile 5 repeaters in that grade, divided by the number of quintile 5 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.PCR.R", "metatype": [ { "id": "IndicatorName", "value": "DHS: Primary completion rate. Rural" }, { "id": "Longdefinition", "value": "Primary completion rate. Rural is the total number of rural students of any age in the last grade of primary school, minus the number of rural repeaters in that grade, divided by the number of rural children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Shortdefinition", "value": "Primary completion rate. Rural is the total number of rural students of any age in the last grade of primary school, minus the number of rural repeaters in that grade, divided by the number of rural children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.PCR.U", "metatype": [ { "id": "IndicatorName", "value": "DHS: Primary completion rate. Urban" }, { "id": "Longdefinition", "value": "Primary completion rate. Urban is the total number of urban students of any age in the last grade of primary school, minus the number of urban repeaters in that grade, divided by the number of urban children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Shortdefinition", "value": "Primary completion rate. Urban is the total number of urban students of any age in the last grade of primary school, minus the number of urban repeaters in that grade, divided by the number of urban children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.SCR", "metatype": [ { "id": "IndicatorName", "value": "DHS: Secondary completion rate" }, { "id": "Longdefinition", "value": "Secondary completion rate is the total number of students of any age in last grade of secondary school, minus the number of repeaters in that grade, divided by the number of children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Shortdefinition", "value": "Secondary completion rate is the total number of students of any age in last grade of secondary school, minus the number of repeaters in that grade, divided by the number of children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.SCR.F", "metatype": [ { "id": "IndicatorName", "value": "DHS: Secondary completion rate. Female" }, { "id": "Longdefinition", "value": "Secondary completion rate. Female is the total number of female students of any age in last grade of secondary school, minus the number of female repeaters in that grade, divided by the number of female children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Female is the total number of female students of any age in last grade of secondary school, minus the number of female repeaters in that grade, divided by the number of female children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.SCR.M", "metatype": [ { "id": "IndicatorName", "value": "DHS: Secondary completion rate. Male" }, { "id": "Longdefinition", "value": "Secondary completion rate. Male is the total number of male students of any age in last grade of secondary school, minus the number of male repeaters in that grade, divided by the number of male children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Male is the total number of male students of any age in last grade of secondary school, minus the number of male repeaters in that grade, divided by the number of male children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.SCR.Q1", "metatype": [ { "id": "IndicatorName", "value": "DHS: Secondary completion rate. Quintile 1" }, { "id": "Longdefinition", "value": "Secondary completion rate. Quintile 1 is the total number of quintile 1 students of any age in last grade of secondary school, minus the number of quintile 1 repeaters in that grade, divided by the number of quintile 1 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Quintile 1 is the total number of quintile 1 students of any age in last grade of secondary school, minus the number of quintile 1 repeaters in that grade, divided by the number of quintile 1 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.SCR.Q2", "metatype": [ { "id": "IndicatorName", "value": "DHS: Secondary completion rate. Quintile 2" }, { "id": "Longdefinition", "value": "Secondary completion rate. Quintile 2 is the total number of quintile 2 students of any age in last grade of secondary school, minus the number of quintile 2 repeaters in that grade, divided by the number of quintile 2 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Quintile 2 is the total number of quintile 2 students of any age in last grade of secondary school, minus the number of quintile 2 repeaters in that grade, divided by the number of quintile 2 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.SCR.Q3", "metatype": [ { "id": "IndicatorName", "value": "DHS: Secondary completion rate. Quintile 3" }, { "id": "Longdefinition", "value": "Secondary completion rate. Quintile 3 is the total number of quintile 3 students of any age in last grade of secondary school, minus the number of quintile 3 repeaters in that grade, divided by the number of quintile 3 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Quintile 3 is the total number of quintile 3 students of any age in last grade of secondary school, minus the number of quintile 3 repeaters in that grade, divided by the number of quintile 3 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.SCR.Q4", "metatype": [ { "id": "IndicatorName", "value": "DHS: Secondary completion rate. Quintile 4" }, { "id": "Longdefinition", "value": "Secondary completion rate. Quintile 4 is the total number of quintile 4 students of any age in last grade of secondary school, minus the number of quintile 4 repeaters in that grade, divided by the number of quintile 4 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Quintile 4 is the total number of quintile 4 students of any age in last grade of secondary school, minus the number of quintile 4 repeaters in that grade, divided by the number of quintile 4 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.SCR.Q5", "metatype": [ { "id": "IndicatorName", "value": "DHS: Secondary completion rate. Quintile 5" }, { "id": "Longdefinition", "value": "Secondary completion rate. Quintile 5 is the total number of quintile 5 students of any age in last grade of secondary school, minus the number of quintile 5 repeaters in that grade, divided by the number of quintile 5 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Quintile 5 is the total number of quintile 5 students of any age in last grade of secondary school, minus the number of quintile 5 repeaters in that grade, divided by the number of quintile 5 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.SCR.R", "metatype": [ { "id": "IndicatorName", "value": "DHS: Secondary completion rate. Rural" }, { "id": "Longdefinition", "value": "Secondary completion rate. Rural is the total number of rural students of any age in last grade of secondary school, minus the number of rural repeaters in that grade, divided by the number of rural children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Rural is the total number of rural students of any age in last grade of secondary school, minus the number of rural repeaters in that grade, divided by the number of rural children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.SCR.U", "metatype": [ { "id": "IndicatorName", "value": "DHS: Secondary completion rate. Urban" }, { "id": "Longdefinition", "value": "Secondary completion rate. Urban is the total number of urban students of any age in last grade of secondary school, minus the number of urban repeaters in that grade, divided by the number of urban children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Urban is the total number of urban students of any age in last grade of secondary school, minus the number of urban repeaters in that grade, divided by the number of urban children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.TR.12", "metatype": [ { "id": "IndicatorName", "value": "DHS: Transition rate. Primary to Secondary" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary is the proportion of pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary is the proportion of pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.TR.12.F", "metatype": [ { "id": "IndicatorName", "value": "DHS: Transition rate. Primary to Secondary. Female" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Female is the proportion of female pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Female is the proportion of female pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.TR.12.M", "metatype": [ { "id": "IndicatorName", "value": "DHS: Transition rate. Primary to Secondary. Male" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Male is the proportion of male pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Male is the proportion of male pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.TR.12.Q1", "metatype": [ { "id": "IndicatorName", "value": "DHS: Transition rate. Primary to Secondary. Quintile 1" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Quintile 1 is the proportion of quintile 1 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Quintile 1 is the proportion of quintile 1 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.TR.12.Q2", "metatype": [ { "id": "IndicatorName", "value": "DHS: Transition rate. Primary to Secondary. Quintile 2" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Quintile 2 is the proportion of quintile 2 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Quintile 2 is the proportion of quintile 2 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.TR.12.Q3", "metatype": [ { "id": "IndicatorName", "value": "DHS: Transition rate. Primary to Secondary. Quintile 3" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Quintile 3 is the proportion of quintile 3 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Quintile 3 is the proportion of quintile 3 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.TR.12.Q4", "metatype": [ { "id": "IndicatorName", "value": "DHS: Transition rate. Primary to Secondary. Quintile 4" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Quintile 4 is the proportion of quintile 4 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Quintile 4 is the proportion of quintile 4 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.TR.12.Q5", "metatype": [ { "id": "IndicatorName", "value": "DHS: Transition rate. Primary to Secondary. Quintile 5" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Quintile 5 is the proportion of quintile 5 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Quintile 5 is the proportion of quintile 5 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.TR.12.R", "metatype": [ { "id": "IndicatorName", "value": "DHS: Transition rate. Primary to Secondary. Rural" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Rural is the proportion of rural pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Rural is the proportion of rural pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.TR.12.U", "metatype": [ { "id": "IndicatorName", "value": "DHS: Transition rate. Primary to Secondary. Urban" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Urban is the proportion of urban pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Urban is the proportion of urban pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.1519", "metatype": [ { "id": "IndicatorName", "value": "DHS: Average years of schooling by age group. Age 15-19" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19 is the number of years of formal schooling received, on average, by the population of the given age group." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19 is the number of years of formal schooling received, on average, by the population of the given age group." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.1519.F", "metatype": [ { "id": "IndicatorName", "value": "DHS: Average years of schooling by age group. Age 15-19. Female" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Female is the number of years of formal schooling received, on average, by the female population of the given age group." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Female is the number of years of formal schooling received, on average, by the female population of the given age group." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.1519.M", "metatype": [ { "id": "IndicatorName", "value": "DHS: Average years of schooling by age group. Age 15-19. Male" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Male is the number of years of formal schooling received, on average, by the male population of the given age group." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Male is the number of years of formal schooling received, on average, by the male population of the given age group." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.1519.Q1", "metatype": [ { "id": "IndicatorName", "value": "DHS: Average years of schooling by age group. Age 15-19. Quintile 1" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 1 is the number of years of formal schooling received, on average, by the quintile 1 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 1 is the number of years of formal schooling received, on average, by the quintile 1 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.1519.Q2", "metatype": [ { "id": "IndicatorName", "value": "DHS: Average years of schooling by age group. Age 15-19. Quintile 2" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 2 is the number of years of formal schooling received, on average, by the quintile 2 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 2 is the number of years of formal schooling received, on average, by the quintile 2 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.1519.Q3", "metatype": [ { "id": "IndicatorName", "value": "DHS: Average years of schooling by age group. Age 15-19. Quintile 3" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 3 is the number of years of formal schooling received, on average, by the quintile 3 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 3 is the number of years of formal schooling received, on average, by the quintile 3 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.1519.Q4", "metatype": [ { "id": "IndicatorName", "value": "DHS: Average years of schooling by age group. Age 15-19. Quintile 4" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 4 is the number of years of formal schooling received, on average, by the quintile 4 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 4 is the number of years of formal schooling received, on average, by the quintile 4 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.1519.Q5", "metatype": [ { "id": "IndicatorName", "value": "DHS: Average years of schooling by age group. Age 15-19. Quintile 5" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 5 is the number of years of formal schooling received, on average, by the quintile 5 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 5 is the number of years of formal schooling received, on average, by the quintile 5 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.1519.R", "metatype": [ { "id": "IndicatorName", "value": "DHS: Average years of schooling by age group. Age 15-19. Rural" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Rural is the number of years of formal schooling received, on average, by the rural population of the given age group." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Rural is the number of years of formal schooling received, on average, by the rural population of the given age group." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.1519.U", "metatype": [ { "id": "IndicatorName", "value": "DHS: Average years of schooling by age group. Age 15-19. Urban" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Urban is the number of years of formal schooling received, on average, by the urban population of the given age group." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Urban is the number of years of formal schooling received, on average, by the urban population of the given age group." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.15UP.GIN", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gini coefficient of average years of schooling. Age 15+" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+ measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+ measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.15UP.GIN.F", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gini coefficient of average years of schooling. Age 15+. Female" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Female measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Female measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.15UP.GIN.M", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gini coefficient of average years of schooling. Age 15+. Male" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Male measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Male measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.15UP.GIN.Q1", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gini coefficient of average years of schooling. Age 15+. Quintile 1" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 1 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 1 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.15UP.GIN.Q2", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gini coefficient of average years of schooling. Age 15+. Quintile 2" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 2 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 2 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.15UP.GIN.Q3", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gini coefficient of average years of schooling. Age 15+. Quintile 3" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 3 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 3 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.15UP.GIN.Q4", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gini coefficient of average years of schooling. Age 15+. Quintile 4" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 4 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 4 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.15UP.GIN.Q5", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gini coefficient of average years of schooling. Age 15+. Quintile 5" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 5 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 5 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.15UP.GIN.R", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gini coefficient of average years of schooling. Age 15+. Rural" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Rural measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Rural measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.DHS.YRS.15UP.GIN.U", "metatype": [ { "id": "IndicatorName", "value": "DHS: Gini coefficient of average years of schooling. Age 15+. Urban" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Urban measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Urban measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.GAR.456", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gross attendance rate. Post Secondary" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary is the number of post-secondary school pupils of any age, expressed as a percentage of youth of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary is the number of post-secondary school pupils of any age, expressed as a percentage of youth of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.GAR.456.F", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gross attendance rate. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Female is the number of female post-secondary school pupils of any age, expressed as a percentage of the population of females of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Female is the number of female post-secondary school pupils of any age, expressed as a percentage of the population of females of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.GAR.456.M", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gross attendance rate. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Male is the number of male post-secondary school pupils of any age, expressed as a percentage of the population of males of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Male is the number of male post-secondary school pupils of any age, expressed as a percentage of the population of males of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.GAR.456.Q1", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gross attendance rate. Post Secondary. Quintile 1" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 1 is the number of quintile 1 post-secondary school pupils of any age, expressed as a percentage of the quintile 1 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 1 is the number of quintile 1 post-secondary school pupils of any age, expressed as a percentage of the quintile 1 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.GAR.456.Q2", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gross attendance rate. Post Secondary. Quintile 2" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 2 is the number of quintile 2 post-secondary school pupils of any age, expressed as a percentage of the quintile 2 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 2 is the number of quintile 2 post-secondary school pupils of any age, expressed as a percentage of the quintile 2 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.GAR.456.Q3", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gross attendance rate. Post Secondary. Quintile 3" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 3 is the number of quintile 3 post-secondary school pupils of any age, expressed as a percentage of the quintile 3 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 3 is the number of quintile 3 post-secondary school pupils of any age, expressed as a percentage of the quintile 3 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.GAR.456.Q4", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gross attendance rate. Post Secondary. Quintile 4" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 4 is the number of quintile 4 post-secondary school pupils of any age, expressed as a percentage of the quintile 4 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 4 is the number of quintile 4 post-secondary school pupils of any age, expressed as a percentage of the quintile 4 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.GAR.456.Q5", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gross attendance rate. Post Secondary. Quintile 5" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 5 is the number of quintile 5 post-secondary school pupils of any age, expressed as a percentage of the quintile 5 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Quintile 5 is the number of quintile 5 post-secondary school pupils of any age, expressed as a percentage of the quintile 5 population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.GAR.456.R", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gross attendance rate. Post Secondary. Rural" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Rural is the number of rural post-secondary school pupils of any age, expressed as a percentage of the rural population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Rural is the number of rural post-secondary school pupils of any age, expressed as a percentage of the rural population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.GAR.456.U", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gross attendance rate. Post Secondary. Urban" }, { "id": "Longdefinition", "value": "Gross attendance rate. Post Secondary. Urban is the number of urban post-secondary school pupils of any age, expressed as a percentage of the urban population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Shortdefinition", "value": "Gross attendance rate. Post Secondary. Urban is the number of urban post-secondary school pupils of any age, expressed as a percentage of the urban population of post-secondary school age. Post-secondary school age is defined as the age range from graduation from secondary school till the maximum age set as a parameter of the educational system." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.1", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Primary" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Total is the proportion of children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Total is the proportion of children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.1.F", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Primary. Female" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Female is the proportion of female children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Female is the proportion of female children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.1.M", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Primary. Male" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Male is the proportion of male children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Male is the proportion of male children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.1.Q1", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Primary. Quintile 1" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Quintile 1 is the proportion of quintile 1 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Quintile 1 is the proportion of quintile 1 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.1.Q2", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Primary. Quintile 2" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Quintile 2 is the proportion of quintile 2 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Quintile 2 is the proportion of quintile 2 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.1.Q3", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Primary. Quintile 3" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Quintile 3 is the proportion of quintile 3 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Quintile 3 is the proportion of quintile 3 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.1.Q4", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Primary. Quintile 4" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Quintile 4 is the proportion of quintile 4 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Quintile 4 is the proportion of quintile 4 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.1.Q5", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Primary. Quintile 5" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Quintile 5 is the proportion of quintile 5 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Quintile 5 is the proportion of quintile 5 children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.1.R", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Primary. Rural" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Rural is the proportion of rural children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Rural is the proportion of rural children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.1.U", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Primary. Urban" }, { "id": "Longdefinition", "value": "Net attendance rate. Primary. Urban is the proportion of urban children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Primary. Urban is the proportion of urban children of the official primary school age who are attending primary school. The primary school age is based on the parameters of the educational system: starting age and duration of primary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.23", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Secondary" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary is the proportion of children of official secondary school age who are attending secondary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary is the proportion of children of official secondary school age who are attending secondary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.23.F", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Secondary. Female" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Female is the proportion of female children of official secondary school age who are attending secondary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Female is the proportion of female children of official secondary school age who are attending secondary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.23.M", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Secondary. Male" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Male is the proportion of male children of official secondary school age who are attending secondary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Male is the proportion of male children of official secondary school age who are attending secondary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.23.Q1", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Secondary. Quintile 1" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Quintile 1 is the proportion of quintile 1 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Quintile 1 is the proportion of quintile 1 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.23.Q2", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Secondary. Quintile 2" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Quintile 2 is the proportion of quintile 2 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Quintile 2 is the proportion of quintile 2 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.23.Q3", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Secondary. Quintile 3" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Quintile 3 is the proportion of quintile 3 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Quintile 3 is the proportion of quintile 3 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.23.Q4", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Secondary. Quintile 4" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Quintile 4 is the proportion of quintile 4 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Quintile 4 is the proportion of quintile 4 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.23.Q5", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Secondary. Quintile 5" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Quintile 5 is the proportion of quintile 5 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Quintile 5 is the proportion of quintile 5 children of official secondary school age who are attending secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.23.R", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Secondary. Rural" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Rural is the proportion of rural children of official secondary school age who are attending secondary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Rural is the proportion of rural children of official secondary school age who are attending secondary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.NAR.23.U", "metatype": [ { "id": "IndicatorName", "value": "MICS: Net attendance rate. Secondary. Urban" }, { "id": "Longdefinition", "value": "Net attendance rate. Secondary. Urban is the proportion of urban children of official secondary school age who are attending secondary school." }, { "id": "Shortdefinition", "value": "Net attendance rate. Secondary. Urban is the proportion of urban children of official secondary school age who are attending secondary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOS.1", "metatype": [ { "id": "IndicatorName", "value": "MICS: Proportion of out-of-school. Primary" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary is the number of children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary is the number of children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOS.1.F", "metatype": [ { "id": "IndicatorName", "value": "MICS: Proportion of out-of-school. Primary. Female" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Female is the number of female children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of female children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Female is the number of female children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of female children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOS.1.M", "metatype": [ { "id": "IndicatorName", "value": "MICS: Proportion of out-of-school. Primary. Male" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Male is the number of male children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of male children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Male is the number of male children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of male children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOS.1.Q1", "metatype": [ { "id": "IndicatorName", "value": "MICS: Proportion of out-of-school. Primary. Quintile 1" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Quintile 1 is the number of quintile 1 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 1 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Quintile 1 is the number of quintile 1 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 1 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOS.1.Q2", "metatype": [ { "id": "IndicatorName", "value": "MICS: Proportion of out-of-school. Primary. Quintile 2" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Quintile 2 is the number of quintile 2 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 2 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Quintile 2 is the number of quintile 2 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 2 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOS.1.Q3", "metatype": [ { "id": "IndicatorName", "value": "MICS: Proportion of out-of-school. Primary. Quintile 3" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Quintile 3 is the number of quintile 3 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 3 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Quintile 3 is the number of quintile 3 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 3 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOS.1.Q4", "metatype": [ { "id": "IndicatorName", "value": "MICS: Proportion of out-of-school. Primary. Quintile 4" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Quintile 4 is the number of quintile 4 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 4 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Quintile 4 is the number of quintile 4 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 4 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOS.1.Q5", "metatype": [ { "id": "IndicatorName", "value": "MICS: Proportion of out-of-school. Primary. Quintile 5" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Quintile 5 is the number of quintile 5 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 5 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Quintile 5 is the number of quintile 5 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 5 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOS.1.R", "metatype": [ { "id": "IndicatorName", "value": "MICS: Proportion of out-of-school. Primary. Rural" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Rural is the number of rural children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of rural children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Rural is the number of rural children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of rural children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOS.1.U", "metatype": [ { "id": "IndicatorName", "value": "MICS: Proportion of out-of-school. Primary. Urban" }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Urban is the number of urban children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of urban children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Shortdefinition", "value": "Proportion of out-of-school. Primary. Urban is the number of urban children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of urban children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school. The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.DO", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Dropped out" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out is the proportion of out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out is the proportion of out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.DO.F", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Dropped out. Female" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Female is the proportion of female out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Female is the proportion of female out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.DO.M", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Dropped out. Male" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Male is the proportion of male out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Male is the proportion of male out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.DO.Q1", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Dropped out. Quintile 1" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 1 is the proportion of quintile 1 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 1 is the proportion of quintile 1 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.DO.Q2", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Dropped out. Quintile 2" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 2 is the proportion of quintile 2 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 2 is the proportion of quintile 2 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.DO.Q3", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Dropped out. Quintile 3" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 3 is the proportion of quintile 3 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 3 is the proportion of quintile 3 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.DO.Q4", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Dropped out. Quintile 4" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 4 is the proportion of quintile 4 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 4 is the proportion of quintile 4 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.DO.Q5", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Dropped out. Quintile 5" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 5 is the proportion of quintile 5 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Quintile 5 is the proportion of quintile 5 out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.DO.R", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Dropped out. Rural" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Rural is the proportion of rural out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Rural is the proportion of rural out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.DO.U", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Dropped out. Urban" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Urban is the proportion of urban out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Dropped out. Urban is the proportion of urban out-of-school children who were enrolled in school and dropped out. It is calculated as the proportion of children not classified as never-in-school expressed as a percent of the proportion of out-of-school children older than the age with the highest attendance rate." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.L", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Late entry" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry is defined as the proportion of out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry is defined as the proportion of out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.L.F", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Late entry. Female" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Female is defined as the proportion of female out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Female is defined as the proportion of female out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.L.M", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Late entry. Male" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Male is defined as the proportion of male out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Male is defined as the proportion of male out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.L.Q1", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Late entry. Quintile 1" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 1 is defined as the proportion of quintile 1 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 1 is defined as the proportion of quintile 1 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.L.Q2", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Late entry. Quintile 2" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 2 is defined as the proportion of quintile 2 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 2 is defined as the proportion of quintile 2 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.L.Q3", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Late entry. Quintile 3" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 3 is defined as the proportion of quintile 3 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 3 is defined as the proportion of quintile 3 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.L.Q4", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Late entry. Quintile 4" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 4 is defined as the proportion of quintile 4 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 4 is defined as the proportion of quintile 4 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.L.Q5", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Late entry. Quintile 5" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 5 is defined as the proportion of quintile 5 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Quintile 5 is defined as the proportion of quintile 5 out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.L.R", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Late entry. Rural" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Rural is defined as the proportion of rural out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Rural is defined as the proportion of rural out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.L.U", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Late entry. Urban" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Urban is defined as the proportion of urban out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Late entry. Urban is defined as the proportion of urban out-of-school children who are currently out of school but are expected to enter the education system later than they should. It is calculated based on UNESCO's methodology as the proportion leftover from those estimated as never in school, expressed as a percent of the proportion out-of-school for children younger than the age with the highest attendance rate." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.X", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Never in school" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school is the percentage of out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school is the percentage of out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.X.F", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Never in school. Female" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Female is the percentage of female out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Female is the percentage of female out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.X.M", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Never in school. Male" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Male is the percentage of male out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Male is the percentage of male out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.X.Q1", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Never in school. Quintile 1" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 1 is the percentage of quintile 1 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 1 is the percentage of quintile 1 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.X.Q2", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Never in school. Quintile 2" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 2 is the percentage of quintile 2 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 2 is the percentage of quintile 2 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.X.Q3", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Never in school. Quintile 3" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 3 is the percentage of quintile 3 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 3 is the percentage of quintile 3 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.X.Q4", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Never in school. Quintile 4" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 4 is the percentage of quintile 4 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 4 is the percentage of quintile 4 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.X.Q5", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Never in school. Quintile 5" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 5 is the percentage of quintile 5 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Quintile 5 is the percentage of quintile 5 out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.X.R", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Never in school. Rural" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Rural is the percentage of rural out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Rural is the percentage of rural out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.OOST.X.U", "metatype": [ { "id": "IndicatorName", "value": "MICS: Typology of out-of-school children. Primary. Never in school. Urban" }, { "id": "Longdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Urban is the percentage of urban out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Shortdefinition", "value": "Typology of out-of-school children. Primary. Never in school. Urban is the percentage of urban out-of-school primary-school-age children who have never attended school and are not likely to enter school in the future. Since no survey can say definitively that a school-age person will never attend school, this is a probabilistic indicator. The indicator is based on UNESCO’s methodology, which estimates the proportion of children who will never attend school as the proportion of out of school for the age group with the lowest proportion of out-of-school children. This methodology assumes no dropout before the age at which enrolment rates peak, and no late entry after the age with peak enrolment." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.PCR", "metatype": [ { "id": "IndicatorName", "value": "MICS: Primary completion rate" }, { "id": "Longdefinition", "value": "Primary completion rate is the total number of students of any age in the last grade of primary school, minus the number of repeaters in that grade, divided by the number of children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Shortdefinition", "value": "Primary completion rate is the total number of students of any age in the last grade of primary school, minus the number of repeaters in that grade, divided by the number of children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.PCR.F", "metatype": [ { "id": "IndicatorName", "value": "MICS: Primary completion rate. Female" }, { "id": "Longdefinition", "value": "Primary completion rate. Female is the total number of female students of any age in the last grade of primary school, minus the number of female repeaters in that grade, divided by the number of female children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Shortdefinition", "value": "Primary completion rate. Female is the total number of female students of any age in the last grade of primary school, minus the number of female repeaters in that grade, divided by the number of female children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.PCR.M", "metatype": [ { "id": "IndicatorName", "value": "MICS: Primary completion rate. Male" }, { "id": "Longdefinition", "value": "Primary completion rate. Male is the total number of male students of any age in the last grade of primary school, minus the number of male repeaters in that grade, divided by the number of male children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Shortdefinition", "value": "Primary completion rate. Male is the total number of male students of any age in the last grade of primary school, minus the number of male repeaters in that grade, divided by the number of male children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.PCR.Q1", "metatype": [ { "id": "IndicatorName", "value": "MICS: Primary completion rate. Quintile 1" }, { "id": "Longdefinition", "value": "Primary completion rate. Quintile 1 is the total number of quintile 1 students of any age in the last grade of primary school, minus the number of quintile 1 repeaters in that grade, divided by the number of quintile 1 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Primary completion rate. Quintile 1 is the total number of quintile 1 students of any age in the last grade of primary school, minus the number of quintile 1 repeaters in that grade, divided by the number of quintile 1 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.PCR.Q2", "metatype": [ { "id": "IndicatorName", "value": "MICS: Primary completion rate. Quintile 2" }, { "id": "Longdefinition", "value": "Primary completion rate. Quintile 2 is the total number of quintile 2 students of any age in the last grade of primary school, minus the number of quintile 2 repeaters in that grade, divided by the number of quintile 2 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Primary completion rate. Quintile 2 is the total number of quintile 2 students of any age in the last grade of primary school, minus the number of quintile 2 repeaters in that grade, divided by the number of quintile 2 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.PCR.Q3", "metatype": [ { "id": "IndicatorName", "value": "MICS: Primary completion rate. Quintile 3" }, { "id": "Longdefinition", "value": "Primary completion rate. Quintile 3 is the total number of quintile 3 students of any age in the last grade of primary school, minus the number of quintile 3 repeaters in that grade, divided by the number of quintile 3 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Primary completion rate. Quintile 3 is the total number of quintile 3 students of any age in the last grade of primary school, minus the number of quintile 3 repeaters in that grade, divided by the number of quintile 3 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.PCR.Q4", "metatype": [ { "id": "IndicatorName", "value": "MICS: Primary completion rate. Quintile 4" }, { "id": "Longdefinition", "value": "Primary completion rate. Quintile 4 is the total number of quintile 4 students of any age in the last grade of primary school, minus the number of quintile 4 repeaters in that grade, divided by the number of quintile 4 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Primary completion rate. Quintile 4 is the total number of quintile 4 students of any age in the last grade of primary school, minus the number of quintile 4 repeaters in that grade, divided by the number of quintile 4 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.PCR.Q5", "metatype": [ { "id": "IndicatorName", "value": "MICS: Primary completion rate. Quintile 5" }, { "id": "Longdefinition", "value": "Primary completion rate. Quintile 5 is the total number of quintile 5 students of any age in the last grade of primary school, minus the number of quintile 5 repeaters in that grade, divided by the number of quintile 5 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Primary completion rate. Quintile 5 is the total number of quintile 5 students of any age in the last grade of primary school, minus the number of quintile 5 repeaters in that grade, divided by the number of quintile 5 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.PCR.R", "metatype": [ { "id": "IndicatorName", "value": "MICS: Primary completion rate. Rural" }, { "id": "Longdefinition", "value": "Primary completion rate. Rural is the total number of rural students of any age in the last grade of primary school, minus the number of rural repeaters in that grade, divided by the number of rural children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Shortdefinition", "value": "Primary completion rate. Rural is the total number of rural students of any age in the last grade of primary school, minus the number of rural repeaters in that grade, divided by the number of rural children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.PCR.U", "metatype": [ { "id": "IndicatorName", "value": "MICS: Primary completion rate. Urban" }, { "id": "Longdefinition", "value": "Primary completion rate. Urban is the total number of urban students of any age in the last grade of primary school, minus the number of urban repeaters in that grade, divided by the number of urban children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Shortdefinition", "value": "Primary completion rate. Urban is the total number of urban students of any age in the last grade of primary school, minus the number of urban repeaters in that grade, divided by the number of urban children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.SCR", "metatype": [ { "id": "IndicatorName", "value": "MICS: Secondary completion rate" }, { "id": "Longdefinition", "value": "Secondary completion rate is the total number of students of any age in last grade of secondary school, minus the number of repeaters in that grade, divided by the number of children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Shortdefinition", "value": "Secondary completion rate is the total number of students of any age in last grade of secondary school, minus the number of repeaters in that grade, divided by the number of children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.SCR.F", "metatype": [ { "id": "IndicatorName", "value": "MICS: Secondary completion rate. Female" }, { "id": "Longdefinition", "value": "Secondary completion rate. Female is the total number of female students of any age in last grade of secondary school, minus the number of female repeaters in that grade, divided by the number of female children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Female is the total number of female students of any age in last grade of secondary school, minus the number of female repeaters in that grade, divided by the number of female children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.SCR.M", "metatype": [ { "id": "IndicatorName", "value": "MICS: Secondary completion rate. Male" }, { "id": "Longdefinition", "value": "Secondary completion rate. Male is the total number of male students of any age in last grade of secondary school, minus the number of male repeaters in that grade, divided by the number of male children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Male is the total number of male students of any age in last grade of secondary school, minus the number of male repeaters in that grade, divided by the number of male children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.SCR.Q1", "metatype": [ { "id": "IndicatorName", "value": "MICS: Secondary completion rate. Quintile 1" }, { "id": "Longdefinition", "value": "Secondary completion rate. Quintile 1 is the total number of quintile 1 students of any age in last grade of secondary school, minus the number of quintile 1 repeaters in that grade, divided by the number of quintile 1 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Quintile 1 is the total number of quintile 1 students of any age in last grade of secondary school, minus the number of quintile 1 repeaters in that grade, divided by the number of quintile 1 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.SCR.Q2", "metatype": [ { "id": "IndicatorName", "value": "MICS: Secondary completion rate. Quintile 2" }, { "id": "Longdefinition", "value": "Secondary completion rate. Quintile 2 is the total number of quintile 2 students of any age in last grade of secondary school, minus the number of quintile 2 repeaters in that grade, divided by the number of quintile 2 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Quintile 2 is the total number of quintile 2 students of any age in last grade of secondary school, minus the number of quintile 2 repeaters in that grade, divided by the number of quintile 2 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.SCR.Q3", "metatype": [ { "id": "IndicatorName", "value": "MICS: Secondary completion rate. Quintile 3" }, { "id": "Longdefinition", "value": "Secondary completion rate. Quintile 3 is the total number of quintile 3 students of any age in last grade of secondary school, minus the number of quintile 3 repeaters in that grade, divided by the number of quintile 3 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Quintile 3 is the total number of quintile 3 students of any age in last grade of secondary school, minus the number of quintile 3 repeaters in that grade, divided by the number of quintile 3 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.SCR.Q4", "metatype": [ { "id": "IndicatorName", "value": "MICS: Secondary completion rate. Quintile 4" }, { "id": "Longdefinition", "value": "Secondary completion rate. Quintile 4 is the total number of quintile 4 students of any age in last grade of secondary school, minus the number of quintile 4 repeaters in that grade, divided by the number of quintile 4 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Quintile 4 is the total number of quintile 4 students of any age in last grade of secondary school, minus the number of quintile 4 repeaters in that grade, divided by the number of quintile 4 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.SCR.Q5", "metatype": [ { "id": "IndicatorName", "value": "MICS: Secondary completion rate. Quintile 5" }, { "id": "Longdefinition", "value": "Secondary completion rate. Quintile 5 is the total number of quintile 5 students of any age in last grade of secondary school, minus the number of quintile 5 repeaters in that grade, divided by the number of quintile 5 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Quintile 5 is the total number of quintile 5 students of any age in last grade of secondary school, minus the number of quintile 5 repeaters in that grade, divided by the number of quintile 5 children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.SCR.R", "metatype": [ { "id": "IndicatorName", "value": "MICS: Secondary completion rate. Rural" }, { "id": "Longdefinition", "value": "Secondary completion rate. Rural is the total number of rural students of any age in last grade of secondary school, minus the number of rural repeaters in that grade, divided by the number of rural children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Rural is the total number of rural students of any age in last grade of secondary school, minus the number of rural repeaters in that grade, divided by the number of rural children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.SCR.U", "metatype": [ { "id": "IndicatorName", "value": "MICS: Secondary completion rate. Urban" }, { "id": "Longdefinition", "value": "Secondary completion rate. Urban is the total number of urban students of any age in last grade of secondary school, minus the number of urban repeaters in that grade, divided by the number of urban children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Shortdefinition", "value": "Secondary completion rate. Urban is the total number of urban students of any age in last grade of secondary school, minus the number of urban repeaters in that grade, divided by the number of urban children of official graduation age. The completion rate can exceed 100 percent if there are many overage students in the last grade of secondary school." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.TR.12", "metatype": [ { "id": "IndicatorName", "value": "MICS: Transition rate. Primary to Secondary" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary is the proportion of pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary is the proportion of pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.TR.12.F", "metatype": [ { "id": "IndicatorName", "value": "MICS: Transition rate. Primary to Secondary. Female" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Female is the proportion of female pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Female is the proportion of female pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.TR.12.M", "metatype": [ { "id": "IndicatorName", "value": "MICS: Transition rate. Primary to Secondary. Male" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Male is the proportion of male pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Male is the proportion of male pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.TR.12.Q1", "metatype": [ { "id": "IndicatorName", "value": "MICS: Transition rate. Primary to Secondary. Quintile 1" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Quintile 1 is the proportion of quintile 1 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Quintile 1 is the proportion of quintile 1 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.TR.12.Q2", "metatype": [ { "id": "IndicatorName", "value": "MICS: Transition rate. Primary to Secondary. Quintile 2" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Quintile 2 is the proportion of quintile 2 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Quintile 2 is the proportion of quintile 2 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.TR.12.Q3", "metatype": [ { "id": "IndicatorName", "value": "MICS: Transition rate. Primary to Secondary. Quintile 3" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Quintile 3 is the proportion of quintile 3 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Quintile 3 is the proportion of quintile 3 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.TR.12.Q4", "metatype": [ { "id": "IndicatorName", "value": "MICS: Transition rate. Primary to Secondary. Quintile 4" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Quintile 4 is the proportion of quintile 4 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Quintile 4 is the proportion of quintile 4 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.TR.12.Q5", "metatype": [ { "id": "IndicatorName", "value": "MICS: Transition rate. Primary to Secondary. Quintile 5" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Quintile 5 is the proportion of quintile 5 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Quintile 5 is the proportion of quintile 5 pupils in the last grade of primary who transition to the first grade of secondary the following school year. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.TR.12.R", "metatype": [ { "id": "IndicatorName", "value": "MICS: Transition rate. Primary to Secondary. Rural" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Rural is the proportion of rural pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Rural is the proportion of rural pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.TR.12.U", "metatype": [ { "id": "IndicatorName", "value": "MICS: Transition rate. Primary to Secondary. Urban" }, { "id": "Longdefinition", "value": "Transition rate. Primary to Secondary. Urban is the proportion of urban pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Shortdefinition", "value": "Transition rate. Primary to Secondary. Urban is the proportion of urban pupils in the last grade of primary who transition to the first grade of secondary the following school year." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.1519", "metatype": [ { "id": "IndicatorName", "value": "MICS: Average years of schooling by age group. Age 15-19" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19 is the number of years of formal schooling received, on average, by the population of the given age group." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19 is the number of years of formal schooling received, on average, by the population of the given age group." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.1519.F", "metatype": [ { "id": "IndicatorName", "value": "MICS: Average years of schooling by age group. Age 15-19. Female" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Female is the number of years of formal schooling received, on average, by the female population of the given age group." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Female is the number of years of formal schooling received, on average, by the female population of the given age group." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.1519.M", "metatype": [ { "id": "IndicatorName", "value": "MICS: Average years of schooling by age group. Age 15-19. Male" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Male is the number of years of formal schooling received, on average, by the male population of the given age group." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Male is the number of years of formal schooling received, on average, by the male population of the given age group." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.1519.Q1", "metatype": [ { "id": "IndicatorName", "value": "MICS: Average years of schooling by age group. Age 15-19. Quintile 1" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 1 is the number of years of formal schooling received, on average, by the quintile 1 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 1 is the number of years of formal schooling received, on average, by the quintile 1 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.1519.Q2", "metatype": [ { "id": "IndicatorName", "value": "MICS: Average years of schooling by age group. Age 15-19. Quintile 2" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 2 is the number of years of formal schooling received, on average, by the quintile 2 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 2 is the number of years of formal schooling received, on average, by the quintile 2 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.1519.Q3", "metatype": [ { "id": "IndicatorName", "value": "MICS: Average years of schooling by age group. Age 15-19. Quintile 3" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 3 is the number of years of formal schooling received, on average, by the quintile 3 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 3 is the number of years of formal schooling received, on average, by the quintile 3 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.1519.Q4", "metatype": [ { "id": "IndicatorName", "value": "MICS: Average years of schooling by age group. Age 15-19. Quintile 4" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 4 is the number of years of formal schooling received, on average, by the quintile 4 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 4 is the number of years of formal schooling received, on average, by the quintile 4 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.1519.Q5", "metatype": [ { "id": "IndicatorName", "value": "MICS: Average years of schooling by age group. Age 15-19. Quintile 5" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 5 is the number of years of formal schooling received, on average, by the quintile 5 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 5 is the number of years of formal schooling received, on average, by the quintile 5 population of the given age group. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.1519.R", "metatype": [ { "id": "IndicatorName", "value": "MICS: Average years of schooling by age group. Age 15-19. Rural" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Rural is the number of years of formal schooling received, on average, by the rural population of the given age group." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Rural is the number of years of formal schooling received, on average, by the rural population of the given age group." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.1519.U", "metatype": [ { "id": "IndicatorName", "value": "MICS: Average years of schooling by age group. Age 15-19. Urban" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Urban is the number of years of formal schooling received, on average, by the urban population of the given age group." }, { "id": "Shortdefinition", "value": "Average years of schooling by age group. Age 15-19. Urban is the number of years of formal schooling received, on average, by the urban population of the given age group." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.15UP.GIN", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gini coefficient of average years of schooling. Age 15+" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+ measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+ measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.15UP.GIN.F", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gini coefficient of average years of schooling. Age 15+. Female" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Female measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Female measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.15UP.GIN.M", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gini coefficient of average years of schooling. Age 15+. Male" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Male measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Male measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.15UP.GIN.Q1", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gini coefficient of average years of schooling. Age 15+. Quintile 1" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 1 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 1 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.15UP.GIN.Q2", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gini coefficient of average years of schooling. Age 15+. Quintile 2" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 2 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 2 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.15UP.GIN.Q3", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gini coefficient of average years of schooling. Age 15+. Quintile 3" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 3 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 3 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.15UP.GIN.Q4", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gini coefficient of average years of schooling. Age 15+. Quintile 4" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 4 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 4 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.15UP.GIN.Q5", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gini coefficient of average years of schooling. Age 15+. Quintile 5" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 5 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Quintile 5 measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality. Each poverty quintile represents one fifth of students with quintile 1 being the poorest 20 percent of students and quintile 5 being the richest 20 percent of students." }, { "id": "Source", "value": "Multiple Indicator Cluster Surveys (MICS)" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.15UP.GIN.R", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gini coefficient of average years of schooling. Age 15+. Rural" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Rural measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Rural measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "HH.MICS.YRS.15UP.GIN.U", "metatype": [ { "id": "IndicatorName", "value": "MICS: Gini coefficient of average years of schooling. Age 15+. Urban" }, { "id": "Longdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Urban measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Shortdefinition", "value": "Gini coefficient of average years of schooling. Age 15+. Urban measures the degree of inequality in years of schooling in a society. It is calculated similarly to the Gini coefficient of income or wealth. Results range from 0 to 100 with 0 indicating perfect equality and 100 indicating perfect inequality." }, { "id": "Source", "value": "World Bank staff calculations based on Multiple Indicator Cluster Survey (MICS) data" }, { "id": "Topic", "value": "Education Equality" } ], "source_id": "12" }, { "id": "IT.CMP.PCMP.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Personal computers (per 100 people)" }, { "id": "Longdefinition", "value": "Personal computers are self-contained computers designed to be used by a single individual." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "12" }, { "id": "IT.NET.USER.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "IndicatorName", "value": "Internet users (per 100 people)" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.AFA.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Afan Oromo. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.AFA.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Afan Oromo. 3rd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.AMH.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Amharic. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.AMH.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Amharic. 3rd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.BMN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Bamanankan. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.BOM.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Bomu. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.CHC.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Chichewa. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.CHC.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Chichewa. 4th Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.ENG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). English. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.ENG.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). English. 3rd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.ENG.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). English. 4th Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.FLF.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Fulfulde. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.FRN.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). French. 3rd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.HAR.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Hararigna. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.HAR.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Hararigna. 3rd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.SID.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Sidaamu Afoo. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.SID.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Sidaamu Afoo. 3rd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.SNG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Songhoi. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.SOM.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Somaligna. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.SOM.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Somaligna. 3rd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.SPN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Spanish. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.SPN.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Spanish. 3rd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.SPN.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Spanish. 4th Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.TIG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Tigrinya. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLPM.TIG.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Names Read Per Minute (Mean). Tigrinya. 3rd Grade" }, { "id": "Longdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter names that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the names of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.AKU.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Akuapem. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.ARB.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Arabic. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.ARB.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Arabic. 3rd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.AST.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Asante Twi. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.CHI.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Chitonga. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.CIN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Cinyanja. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.DAG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Dagaare. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.DAGB.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Dagbani. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.DAN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Dangme. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.ENG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). English. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.ENG.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). English. 3rd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.ENG.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). English. 4th Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.ENG.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). English. 6th Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.EWE.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Ewe. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.FAN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Fante. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.FLP.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Filipino. 3rd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.GA.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Ga. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.GON.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Gonja. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.ICI.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Icibemba. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.IND.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Indonesian. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.KAS.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Kasem. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.KII.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Kiikaonde. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.KNY.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Kinyarwanda. 4th Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.KNY.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Kinyarwanda. 6th Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.LUN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Lunda. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.LUV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Luvale. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.NZE.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Nzema. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CLSPM.SIL.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Letter Sounds Read Per Minute (Mean). Silozi. 2nd Grade" }, { "id": "Longdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average number of letter sounds that students could read per minute. In this EGRA subtask, assessors present students with a sheet listing between 50 and 100 upper- and lowercase letters of the alphabet (in some languages, graphemes, or sets of letters and/or symbols representing a single sound, are presented). Students are asked to provide the sounds of all the letters that they can in one minute. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.AFA.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Afan Oromo. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.AFA.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Afan Oromo. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.AMH.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Amharic. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.AMH.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Amharic. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ARB.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Arabic. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.BMN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Bamanankan. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.BOM.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Bomu. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.CHC.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Chichewa. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.CHC.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Chichewa. 4th Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ENG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). English. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ENG.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). English. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ENG.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). English. 4th Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ENG.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). English. 6th Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.FLF.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Fulfulde. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.FLP.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Filipino. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.HAR.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Hararigna. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.HAR.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Hararigna. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.KIS.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Kiswahili. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.KNY.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Kinyarwanda. 4th Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.KNY.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Kinyarwanda. 6th Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.SID.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Sidaamu Afoo. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.SID.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Sidaamu Afoo. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.SNG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Songhoi. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.SOM.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Somaligna. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.SOM.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Somaligna. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.SPN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Spanish. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.SPN.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Spanish. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.SPN.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Spanish. 4th Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.TIG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Tigrinya. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.TIG.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Isolated Words Read Per Minute (Mean). Tigrinya. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of familiar words correctly read per minute. The familiar word reading subtask tests students' ability to read a list of one or two syllable words drawn from a corpus of frequent words presented in random order. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.AFA.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Afan Oromo. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.AFA.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Afan Oromo. 3rd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.AKU.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Akuapem. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.AMH.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Amharic. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.AMH.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Amharic. 3rd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.ARB.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Arabic. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.ARB.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Arabic. 3rd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.AST.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Asante Twi. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.BMN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Bamanankan. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.BOM.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Bomu. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.CHC.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Chichewa. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.CHC.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Chichewa. 4th Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.CHI.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Chitonga. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.CIN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Cinyanja. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.DAG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Dagaare. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.DAGB.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Dagbani. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.DAN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Dangme. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.ENG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). English. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.ENG.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). English. 3rd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.ENG.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). English. 4th Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.ENG.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). English. 6th Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.EWE.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Ewe. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.FAN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Fante. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.FLF.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Fulfulde. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.FLP.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Filipino. 3rd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.FRN.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). French. 3rd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.GA.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Ga. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.GON.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Gonja. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.HAR.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Hararigna. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.HAR.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Hararigna. 3rd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.ICI.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Icibemba. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.IND.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Indonesian. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.KAS.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Kasem. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.KII.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Kiikaonde. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.KIS.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Kiswahili. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.KNY.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Kinyarwanda. 4th Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.KNY.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Kinyarwanda. 6th Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.LUN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Lunda. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.LUV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Luvale. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.NZE.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Nzema. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.SID.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Sidaamu Afoo. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.SID.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Sidaamu Afoo. 3rd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.SIL.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Silozi. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.SNG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Songhoi. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.SOM.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Somaligna. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.SOM.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Somaligna. 3rd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.SPN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Spanish. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.SPN.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Spanish. 3rd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.SPN.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Spanish. 4th Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.TIG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Tigrinya. 2nd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.CWPM.ZERO.TIG.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Share of students with a zero score (%). Tigrinya. 3rd Grade" }, { "id": "Longdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Percentage of students who were unable to read a single word of text on the oral reading fluency subtask. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.INIT.0.BMN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Identification of the Initial Sound of a Spoken Word - Share of students with a zero score (%). Bamanankan. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.INIT.0.BOM.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Identification of the Initial Sound of a Spoken Word - Share of students with a zero score (%). Bomu. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.INIT.0.CHC.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Identification of the Initial Sound of a Spoken Word - Share of students with a zero score (%). Chichewa. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.INIT.0.CHC.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Identification of the Initial Sound of a Spoken Word - Share of students with a zero score (%). Chichewa. 4th Grade" }, { "id": "Longdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.INIT.0.ENG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Identification of the Initial Sound of a Spoken Word - Share of students with a zero score (%). English. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.INIT.0.ENG.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Identification of the Initial Sound of a Spoken Word - Share of students with a zero score (%). English. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.INIT.0.ENG.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Identification of the Initial Sound of a Spoken Word - Share of students with a zero score (%). English. 4th Grade" }, { "id": "Longdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.INIT.0.ENG.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Identification of the Initial Sound of a Spoken Word - Share of students with a zero score (%). English. 6th Grade" }, { "id": "Longdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.INIT.0.FLF.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Identification of the Initial Sound of a Spoken Word - Share of students with a zero score (%). Fulfulde. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.INIT.0.KNY.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Identification of the Initial Sound of a Spoken Word - Share of students with a zero score (%). Kinyarwanda. 4th Grade" }, { "id": "Longdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.INIT.0.KNY.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Identification of the Initial Sound of a Spoken Word - Share of students with a zero score (%). Kinyarwanda. 6th Grade" }, { "id": "Longdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.INIT.0.SNG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Identification of the Initial Sound of a Spoken Word - Share of students with a zero score (%). Songhoi. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.INIT.0.SPN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Identification of the Initial Sound of a Spoken Word - Share of students with a zero score (%). Spanish. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.INIT.0.SPN.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Identification of the Initial Sound of a Spoken Word - Share of students with a zero score (%). Spanish. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.INIT.0.SPN.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Identification of the Initial Sound of a Spoken Word - Share of students with a zero score (%). Spanish. 4th Grade" }, { "id": "Longdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who could not identify the initial sound of any word read aloud to the student by the assessor (%). For example, the assessors ask students questions like “What is the first sound in the word ‘map’?\" and record the number of initial letters the students could identify within one minute. This EGRA subtask is a test of phonemic awareness -- the ability to identify, separate, and manipulate sounds in words. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.AFA.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Afan Oromo. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.AFA.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Afan Oromo. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.AKU.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Akuapem. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.AMH.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Amharic. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.AMH.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Amharic. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.ARB.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Arabic. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.ARB.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Arabic. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.AST.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Asante Twi. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.BMN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Bamanankan. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.BOM.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Bomu. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.CHC.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Chichewa. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.CHC.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Chichewa. 4th Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.CHI.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Chitonga. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.CIN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Cinyanja. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.DAG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Dagaare. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.DAGB.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Dagbani. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.DAN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Dangme. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.ENG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). English. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.ENG.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). English. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.ENG.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). English. 4th Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.ENG.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). English. 6th Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.EWE.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Ewe. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.FAN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Fante. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.FLF.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Fulfulde. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.FLP.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Filipino. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.GA.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Ga. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.GON.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Gonja. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.HAR.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Hararigna. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.HAR.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Hararigna. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.ICI.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Icibemba. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.IND.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Indonesian. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.KAS.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Kasem. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.KII.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Kiikaonde. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.KIS.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Kiswahili. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.KNY.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Kinyarwanda. 4th Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.KNY.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Kinyarwanda. 6th Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.LUN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Lunda. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.LUV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Luvale. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.NZE.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Nzema. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.SID.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Sidaamu Afoo. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.SID.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Sidaamu Afoo. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.SIL.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Silozi. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.SNG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Songhoi. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.SOM.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Somaligna. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.SOM.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Somaligna. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.SPN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Spanish. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.SPN.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Spanish. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.SPN.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Spanish. 4th Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.TIG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Tigrinya. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.LSTN.0.TIG.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Listening Comprehension - Share of students with a zero score (%). Tigrinya. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students scoring zero percent on the listening comprehension assessment. The listening comprehension subtask examines students' ability to respond correctly to questions about a brief passage read aloud to the student by the assessor in order to understand students’ ability to comprehend orally without having to overcome issues of decoding. Part of comprehension is understanding the meanings of the words, thus if students do not have enough vocabulary in the language, they will not be able to comprehend. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.AFA.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Afan Oromo. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.AFA.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Afan Oromo. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.AKU.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Akuapem. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.AMH.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Amharic. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.AMH.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Amharic. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.ARB.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Arabic. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.ARB.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Arabic. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.AST.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Asante Twi. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.BMN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Bamanankan. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.BOM.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Bomu. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.CHC.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Chichewa. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.CHC.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Chichewa. 4th Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.CHI.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Chitonga. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.CIN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Cinyanja. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.DAG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Dagaare. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.DAGB.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Dagbani. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.DAN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Dangme. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.ENG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). English. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.ENG.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). English. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.ENG.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). English. 4th Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.ENG.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). English. 6th Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.EWE.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Ewe. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.FAN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Fante. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.FLF.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Fulfulde. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.FLP.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Filipino. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.FRN.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). French. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.GA.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Ga. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.GON.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Gonja. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.HAR.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Hararigna. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.HAR.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Hararigna. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.ICI.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Icibemba. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.IND.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Indonesian. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.KAS.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Kasem. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.KII.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Kiikaonde. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.KIS.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Kiswahili. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.KNY.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Kinyarwanda. 4th Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.KNY.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Kinyarwanda. 6th Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.LUN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Lunda. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.LUV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Luvale. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.NZE.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Nzema. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.SID.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Sidaamu Afoo. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.SID.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Sidaamu Afoo. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.SIL.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Silozi. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.SNG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Songhoi. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.SOM.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Somaligna. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.SOM.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Somaligna. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.SPN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Spanish. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.SPN.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Spanish. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.SPN.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Spanish. 4th Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.TIG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Tigrinya. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.NCWPM.TIG.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Correct Non-Words Read Per Minute (Mean). Tigrinya. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of invented/nonsense words correctly read per minute. The indicator measures students' ability to fluently decipher/decode randomly-presented “words” that follow linguistic rules but do not actually exist in the stated language. Skill in reading nonwords can be a purer measure of decoding than using real words because children cannot recognize the words by sight. Decoding is considered a self-teaching skill that enables children to read new and unfamiliar words independently. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.AFA.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Afan Oromo. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.AFA.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Afan Oromo. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.AKU.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Akuapem. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.AMH.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Amharic. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.AMH.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Amharic. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.ARB.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Arabic. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.ARB.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Arabic. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.AST.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Asante Twi. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.BMN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Bamanankan. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.BOM.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Bomu. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.CHC.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Chichewa. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.CHC.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Chichewa. 4th Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.CHI.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Chitonga. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.CIN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Cinyanja. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.DAG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Dagaare. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.DAGB.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Dagbani. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.DAN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Dangme. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.ENG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). English. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.ENG.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). English. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.ENG.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). English. 4th Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.ENG.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). English. 6th Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.EWE.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Ewe. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.FAN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Fante. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.FLF.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Fulfulde. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.FLP.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Filipino. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.FRN.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). French. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.GA.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Ga. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.GON.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Gonja. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.HAR.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Hararigna. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.HAR.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Hararigna. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.ICI.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Icibemba. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.IND.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Indonesian. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.KAS.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Kasem. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.KII.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Kiikaonde. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.KIS.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Kiswahili. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.KNY.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Kinyarwanda. 4th Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.KNY.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Kinyarwanda. 6th Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.LUN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Lunda. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.LUV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Luvale. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.NZE.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Nzema. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.SID.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Sidaamu Afoo. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.SID.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Sidaamu Afoo. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.SIL.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Silozi. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students'ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.SNG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Songhoi. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.SOM.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Somaligna. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.SOM.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Somaligna. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.SPN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Spanish. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.SPN.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Spanish. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.SPN.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Spanish. 4th Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.TIG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Tigrinya. 2nd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.ORF.TIG.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Oral Reading Fluency - Correct Words Read Per Minute (Mean). Tigrinya. 3rd Grade" }, { "id": "Longdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Average total number of words correctly read per minute from a narrative or informational reading passage. The oral reading fluency/paragraph reading subtask examines students' ability to read a narrative or informational text with accuracy, with little effort, and at a sufficient rate. Assessors ask students to read the paragraph and stop them after one minute to record the number of words correctly read. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org ." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.AFA.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Afan Oromo. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.AFA.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Afan Oromo. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.AKU.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Akuapem. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.AMH.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Amharic. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.AMH.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Amharic. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.ARB.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Arabic. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.ARB.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Arabic. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.AST.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Asante Twi. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.BMN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Bamanankan. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.BOM.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Bomu. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.CHC.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Chichewa. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.CHC.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Chichewa. 4th Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.CHI.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Chitonga. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.CIN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Cinyanja. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.DAG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Dagaare. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.DAGB.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Dagbani. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.DAN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Dangme. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.ENG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). English. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.ENG.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). English. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.ENG.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). English. 4th Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.ENG.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). English. 6th Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.EWE.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Ewe. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.FAN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Fante. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.FLF.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Fulfulde. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.FLP.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Filipino. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.FRN.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). French. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.GA.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Ga. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.GON.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Gonja. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.HAR.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Hararigna. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.HAR.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Hararigna. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.ICI.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Icibemba. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.IND.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Indonesian. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.KAS.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Kasem. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.KII.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Kiikaonde. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.KIS.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Kiswahili. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.KNY.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Kinyarwanda. 4th Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.KNY.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Kinyarwanda. 6th Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.LUN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Lunda. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.LUV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Luvale. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.NZE.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Nzema. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.SID.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Sidaamu Afoo. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.SID.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Sidaamu Afoo. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.SIL.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Silozi. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.SNG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Songhoi. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.SOM.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Somaligna. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.SOM.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Somaligna. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.SPN.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Spanish. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.SPN.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Spanish. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.SPN.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Spanish. 4th Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.TIG.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Tigrinya. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.0.TIG.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students with a zero score (%). Tigrinya. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored zero percent on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.AFA.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Afan Oromo. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.AFA.ADV.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Afan Oromo. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.AKU.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Akuapem. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.AMH.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Amharic. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.AMH.ADV.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Amharic. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.ARB.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Arabic. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.ARB.ADV.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Arabic. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.AST.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Asante Twi. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.BMN.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Bamanankan. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.BOM.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Bomu. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.CHC.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Chichewa. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.CHC.ADV.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Chichewa. 4th Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.CHI.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Chitonga. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.CIN.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Cinyanja. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.DAG.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Dagaare. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.DAGB.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Dagbani. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.DAN.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Dangme. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.ENG.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). English. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.ENG.ADV.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). English. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.ENG.ADV.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). English. 4th Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.ENG.ADV.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). English. 6th Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.EWE.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Ewe. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.FAN.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Fante. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.FLF.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Fulfulde. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.FLP.ADV.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Filipino. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.FRN.ADV.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). French. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.GA.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Ga. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.GON.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Gonja. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.HAR.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Hararigna. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.HAR.ADV.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Hararigna. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.ICI.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Icibemba. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.IND.ADV.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Indonesian. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.KAS.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Kasem. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.KII.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Kiikaonde. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.KIS.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Kiswahili. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.KNY.ADV.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Kinyarwanda. 4th Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.KNY.ADV.6GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Kinyarwanda. 6th Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.LUN.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Lunda. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.LUV.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Luvale. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.NZE.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Nzema. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.SID.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Sidaamu Afoo. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.SID.ADV.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Sidaamu Afoo. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.SIL.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Silozi. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.SNG.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Songhoi. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.SOM.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Somaligna. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.SOM.ADV.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Somaligna. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.SPN.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Spanish. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.SPN.ADV.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Spanish. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.SPN.ADV.4GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Spanish. 4th Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.TIG.ADV.2GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Tigrinya. 2nd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.EGRA.READ.TIG.ADV.3GRD", "metatype": [ { "id": "IndicatorName", "value": "EGRA: Reading Comprehension - Share of students scoring at least 80 percent (%). Tigrinya. 3rd Grade" }, { "id": "Longdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Othernotes", "value": "EGRA" }, { "id": "Shortdefinition", "value": "Share of students who scored 80 percent or higher on the reading comprehension assessment. The reading comprehension subtask follows the oral reading fluency subtask; students are required to respond to literal and inferential questions about the oral reading fluency subtask text they have just read. The questions are read aloud to the student by the assessor. Users are discouraged from using these data to make direct comparisons across countries or languages. Consult the EdData website and the specific country report for more information: www.eddataglobal.org." }, { "id": "Source", "value": "Early Grade Reading Assessment (EGRA): https://www.eddataglobal.org/reading/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the mathematics scale for 3rd grade students. Total" }, { "id": "Longdefinition", "value": "Average score on the 3rd grade mathematics assessment. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 3rd grade mathematics assessment. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.0", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 3rd grade students by mathematics proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade students below the lowest proficiency level (scoring below 391.5) on the LLECE mathematics scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade students below the lowest proficiency level (scoring below 391.5) on the LLECE mathematics scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.0.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 3rd grade students by mathematics proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade female students below the lowest proficiency level (scoring below 391.5) on the LLECE mathematics scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade female students below the lowest proficiency level (scoring below 391.5) on the LLECE mathematics scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.0.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 3rd grade students by mathematics proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade male students below the lowest proficiency level (scoring below 391.5) on the LLECE mathematics scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade male students below the lowest proficiency level (scoring below 391.5) on the LLECE mathematics scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.1", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 3rd grade students by mathematics proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade students scoring at least 391.5 but lower than 489.01 points on the LLECE mathematics scale. At Level 1, students can recognize the relationship between natural numbers and common bi-dimensional geometric shapes in simple drawings. They can locate relative positions of an object in a spatial representation. Students can interpret tables and graphs in order to obtain direct information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade students scoring at least 391.5 but lower than 489.01 points on the LLECE mathematics scale. At Level 1, students can recognize the relationship between natural numbers and common bi-dimensional geometric shapes in simple drawings. They can locate relative positions of an object in a spatial representation. Students can interpret tables and graphs in order to obtain direct information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.1.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 3rd grade students by mathematics proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade female students scoring at least 391.5 but lower than 489.01 points on the LLECE mathematics scale. At Level 1, students can recognize the relationship between natural numbers and common bi-dimensional geometric shapes in simple drawings. They can locate relative positions of an object in a spatial representation. Students can interpret tables and graphs in order to obtain direct information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade female students scoring at least 391.5 but lower than 489.01 points on the LLECE mathematics scale. At Level 1, students can recognize the relationship between natural numbers and common bi-dimensional geometric shapes in simple drawings. They can locate relative positions of an object in a spatial representation. Students can interpret tables and graphs in order to obtain direct information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.1.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 3rd grade students by mathematics proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade male students scoring at least 391.5 but lower than 489.01 points on the LLECE mathematics scale. At Level 1, students can recognize the relationship between natural numbers and common bi-dimensional geometric shapes in simple drawings. They can locate relative positions of an object in a spatial representation. Students can interpret tables and graphs in order to obtain direct information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade male students scoring at least 391.5 but lower than 489.01 points on the LLECE mathematics scale. At Level 1, students can recognize the relationship between natural numbers and common bi-dimensional geometric shapes in simple drawings. They can locate relative positions of an object in a spatial representation. Students can interpret tables and graphs in order to obtain direct information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.2", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 3rd grade students by mathematics proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade students scoring at least 489.01 but less than 558.54 points on the LLECE mathematics scale. At Level 2, students recognize the organization of the decimal-positional numeral system and identify the constituent elements of geometrical shapes. Students can identify a trajectory on a plane and the most suitable measurement unit or instrument in order to measure a known object’s attribute. Students interpret tables and charts in order to obtain information and compare data. Students solve addition or multiplication problems involving proportional relationships using natural numbers. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade students scoring at least 489.01 but less than 558.54 points on the LLECE mathematics scale. At Level 2, students recognize the organization of the decimal-positional numeral system and identify the constituent elements of geometrical shapes. Students can identify a trajectory on a plane and the most suitable measurement unit or instrument in order to measure a known object’s attribute. Students interpret tables and charts in order to obtain information and compare data. Students solve addition or multiplication problems involving proportional relationships using natural numbers. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.2.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 3rd grade students by mathematics proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade female students scoring at least 489.01 but less than 558.54 points on the LLECE mathematics scale. At Level 2, students recognize the organization of the decimal-positional numeral system and identify the constituent elements of geometrical shapes. Students can identify a trajectory on a plane and the most suitable measurement unit or instrument in order to measure a known object’s attribute. Students interpret tables and charts in order to obtain information and compare data. Students solve addition or multiplication problems involving proportional relationships using natural numbers. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade female students scoring at least 489.01 but less than 558.54 points on the LLECE mathematics scale. At Level 2, students recognize the organization of the decimal-positional numeral system and identify the constituent elements of geometrical shapes. Students can identify a trajectory on a plane and the most suitable measurement unit or instrument in order to measure a known object’s attribute. Students interpret tables and charts in order to obtain information and compare data. Students solve addition or multiplication problems involving proportional relationships using natural numbers. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.2.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 3rd grade students by mathematics proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade male students scoring at least 489.01 but less than 558.54 points on the LLECE mathematics scale. At Level 2, students recognize the organization of the decimal-positional numeral system and identify the constituent elements of geometrical shapes. Students can identify a trajectory on a plane and the most suitable measurement unit or instrument in order to measure a known object’s attribute. Students interpret tables and charts in order to obtain information and compare data. Students solve addition or multiplication problems involving proportional relationships using natural numbers. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade male students scoring at least 489.01 but less than 558.54 points on the LLECE mathematics scale. At Level 2, students recognize the organization of the decimal-positional numeral system and identify the constituent elements of geometrical shapes. Students can identify a trajectory on a plane and the most suitable measurement unit or instrument in order to measure a known object’s attribute. Students interpret tables and charts in order to obtain information and compare data. Students solve addition or multiplication problems involving proportional relationships using natural numbers. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.3", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 3rd grade students by mathematics proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade students scoring at least 558.54 but less than 621.68 points on the LLECE mathematics scale. At Level 3, students can solve multiplication problems or problems which require the use of an addition equation or two separate operations. Students can solve addition problems involving measurement units and their equivalences or problems which require using common fractions. Students must identify the graphic or addition numerical sequence rule being used in order to continue it. Students can identify the elements of unusual geometrical shapes and interpret different types of graphs in order to retrieve information and solve problems that involve operating with the data. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade students scoring at least 558.54 but less than 621.68 points on the LLECE mathematics scale. At Level 3, students can solve multiplication problems or problems which require the use of an addition equation or two separate operations. Students can solve addition problems involving measurement units and their equivalences or problems which require using common fractions. Students must identify the graphic or addition numerical sequence rule being used in order to continue it. Students can identify the elements of unusual geometrical shapes and interpret different types of graphs in order to retrieve information and solve problems that involve operating with the data. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.3.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 3rd grade students by mathematics proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade female students scoring at least 558.54 but less than 621.68 points on the LLECE mathematics scale. At Level 3, students can solve multiplication problems or problems which require the use of an addition equation or two separate operations. Students can solve addition problems involving measurement units and their equivalences or problems which require using common fractions. Students must identify the graphic or addition numerical sequence rule being used in order to continue it. Students can identify the elements of unusual geometrical shapes and interpret different types of graphs in order to retrieve information and solve problems that involve operating with the data. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade female students scoring at least 558.54 but less than 621.68 points on the LLECE mathematics scale. At Level 3, students can solve multiplication problems or problems which require the use of an addition equation or two separate operations. Students can solve addition problems involving measurement units and their equivalences or problems which require using common fractions. Students must identify the graphic or addition numerical sequence rule being used in order to continue it. Students can identify the elements of unusual geometrical shapes and interpret different types of graphs in order to retrieve information and solve problems that involve operating with the data. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.3.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 3rd grade students by mathematics proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade male students scoring at least 558.54 but less than 621.68 points on the LLECE mathematics scale. At Level 3, students can solve multiplication problems or problems which require the use of an addition equation or two separate operations. Students can solve addition problems involving measurement units and their equivalences or problems which require using common fractions. Students must identify the graphic or addition numerical sequence rule being used in order to continue it. Students can identify the elements of unusual geometrical shapes and interpret different types of graphs in order to retrieve information and solve problems that involve operating with the data. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade male students scoring at least 558.54 but less than 621.68 points on the LLECE mathematics scale. At Level 3, students can solve multiplication problems or problems which require the use of an addition equation or two separate operations. Students can solve addition problems involving measurement units and their equivalences or problems which require using common fractions. Students must identify the graphic or addition numerical sequence rule being used in order to continue it. Students can identify the elements of unusual geometrical shapes and interpret different types of graphs in order to retrieve information and solve problems that involve operating with the data. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.4", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 3rd grade students by mathematics proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade students scoring 621.68 or higher on the LLECE mathematics scale. At Level 4, students can recognize a numerical sequence rule and identify it. Students can solve multiplication problems with one unknown or problems which require the use of equivalences between commonly used measures of length. Students can identify an element on a bi-dimensional plane and the properties of the sides of a square or rectangle in order to solve a problem. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade students scoring 621.68 or higher on the LLECE mathematics scale. At Level 4, students can recognize a numerical sequence rule and identify it. Students can solve multiplication problems with one unknown or problems which require the use of equivalences between commonly used measures of length. Students can identify an element on a bi-dimensional plane and the properties of the sides of a square or rectangle in order to solve a problem. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.4.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 3rd grade students by mathematics proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade female students scoring 621.68 or higher on the LLECE mathematics scale. At Level 4, students can recognize a numerical sequence rule and identify it. Students can solve multiplication problems with one unknown or problems which require the use of equivalences between commonly used measures of length. Students can identify an element on a bi-dimensional plane and the properties of the sides of a square or rectangle in order to solve a problem. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade female students scoring 621.68 or higher on the LLECE mathematics scale. At Level 4, students can recognize a numerical sequence rule and identify it. Students can solve multiplication problems with one unknown or problems which require the use of equivalences between commonly used measures of length. Students can identify an element on a bi-dimensional plane and the properties of the sides of a square or rectangle in order to solve a problem. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.4.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 3rd grade students by mathematics proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade male students scoring 621.68 or higher on the LLECE mathematics scale. At Level 4, students can recognize a numerical sequence rule and identify it. Students can solve multiplication problems with one unknown or problems which require the use of equivalences between commonly used measures of length. Students can identify an element on a bi-dimensional plane and the properties of the sides of a square or rectangle in order to solve a problem. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade male students scoring 621.68 or higher on the LLECE mathematics scale. At Level 4, students can recognize a numerical sequence rule and identify it. Students can solve multiplication problems with one unknown or problems which require the use of equivalences between commonly used measures of length. Students can identify an element on a bi-dimensional plane and the properties of the sides of a square or rectangle in order to solve a problem. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the mathematics scale for 3rd grade students. Female" }, { "id": "Longdefinition", "value": "Average score on the 3rd grade mathematics assessment for female students. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 3rd grade mathematics assessment for female students. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the mathematics scale for 3rd grade students. Male" }, { "id": "Longdefinition", "value": "Average score on the 3rd grade mathematics assessment for male students. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 3rd grade mathematics assessment for male students. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.P10", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 3rd Grade Mathematics Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.P25", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 3rd Grade Mathematics Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.P50", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 3rd Grade Mathematics Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.P75", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 3rd Grade Mathematics Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT3.P90", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 3rd Grade Mathematics Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT4", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the mathematics scale for 4th grade students. Total" }, { "id": "Longdefinition", "value": "Average score on the 4th grade mathematics assessment. 1997 means were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 4th grade mathematics assessment. 1997 means were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT4.P10", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 4th Grade Mathematics Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT4.P25", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 4th Grade Mathematics Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT4.P50", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 4th Grade Mathematics Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT4.P75", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 4th Grade Mathematics Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT4.P90", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 4th Grade Mathematics Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the mathematics scale for 6th grade students. Total" }, { "id": "Longdefinition", "value": "Average score on the 6th grade mathematics assessment. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 6th grade mathematics assessment. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.0", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 6th grade students by mathematics proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students below the lowest proficiency level (scoring below 309.64) on the LLECE mathematics scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students below the lowest proficiency level (scoring below 309.64) on the LLECE mathematics scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.0.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 6th grade students by mathematics proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students below the lowest proficiency level (scoring below 309.64) on the LLECE mathematics scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students below the lowest proficiency level (scoring below 309.64) on the LLECE mathematics scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.0.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 6th grade students by mathematics proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students below the lowest proficiency level (scoring below 309.64) on the LLECE mathematics scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students below the lowest proficiency level (scoring below 309.64) on the LLECE mathematics scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.1", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 6th grade students by mathematics proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at least 309.64 but lower than 413.58 points on the LLECE mathematics scale. At Level 1, students can 1) arrange natural numbers (up to 5 digits) and decimals (up to thousands) in sequence; 2) recognize common geometrical shapes and the unit consistent with the attribute being measured; 3) interpret information presented in graphic images in order to compare it and change it to a different form of representation; 4) solve problems involving a single addition using natural numbers. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at least 309.64 but lower than 413.58 points on the LLECE mathematics scale. At Level 1, students can 1) arrange natural numbers (up to 5 digits) and decimals (up to thousands) in sequence; 2) recognize common geometrical shapes and the unit consistent with the attribute being measured; 3) interpret information presented in graphic images in order to compare it and change it to a different form of representation; 4) solve problems involving a single addition using natural numbers. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.1.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 6th grade students by mathematics proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at least 309.64 but lower than 413.58 points on the LLECE mathematics scale. At Level 1, students can 1) arrange natural numbers (up to 5 digits) and decimals (up to thousands) in sequence; 2) recognize common geometrical shapes and the unit consistent with the attribute being measured; 3) interpret information presented in graphic images in order to compare it and change it to a different form of representation; 4) solve problems involving a single addition using natural numbers. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at least 309.64 but lower than 413.58 points on the LLECE mathematics scale. At Level 1, students can 1) arrange natural numbers (up to 5 digits) and decimals (up to thousands) in sequence; 2) recognize common geometrical shapes and the unit consistent with the attribute being measured; 3) interpret information presented in graphic images in order to compare it and change it to a different form of representation; 4) solve problems involving a single addition using natural numbers. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.1.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 6th grade students by mathematics proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at least 309.64 but lower than 413.58 points on the LLECE mathematics scale. At Level 1, students can 1) arrange natural numbers (up to 5 digits) and decimals (up to thousands) in sequence; 2) recognize common geometrical shapes and the unit consistent with the attribute being measured; 3) interpret information presented in graphic images in order to compare it and change it to a different form of representation; 4) solve problems involving a single addition using natural numbers. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at least 309.64 but lower than 413.58 points on the LLECE mathematics scale. At Level 1, students can 1) arrange natural numbers (up to 5 digits) and decimals (up to thousands) in sequence; 2) recognize common geometrical shapes and the unit consistent with the attribute being measured; 3) interpret information presented in graphic images in order to compare it and change it to a different form of representation; 4) solve problems involving a single addition using natural numbers. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.2", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 6th grade students by mathematics proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at least 413.58 but lower than 514.41 points on the LLECE mathematics scale. At Level 2, students can 1) analyze and identify the structure of the positional decimal number system, estimate weight (mass) expressing it in units consistent with the attribute being measured; 2) recognize commonly used geometrical shapes and their properties in order to solve problems; 3) interpret, compare and work with information presented through various graphic images; 4) identify the regularity of a simple pattern sequence; 5) solve addition problems in different numerical fields (natural numbers, decimals) including commonly used fractions or equivalent measures; 6) solve multiplication or division problems, or two natural number operations, or operations that include direct proportionality relations. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at least 413.58 but lower than 514.41 points on the LLECE mathematics scale. At Level 2, students can 1) analyze and identify the structure of the positional decimal number system, estimate weight (mass) expressing it in units consistent with the attribute being measured; 2) recognize commonly used geometrical shapes and their properties in order to solve problems; 3) interpret, compare and work with information presented through various graphic images; 4) identify the regularity of a simple pattern sequence; 5) solve addition problems in different numerical fields (natural numbers, decimals) including commonly used fractions or equivalent measures; 6) solve multiplication or division problems, or two natural number operations, or operations that include direct proportionality relations. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.2.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 6th grade students by mathematics proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at least 413.58 but lower than 514.41 points on the LLECE mathematics scale. At Level 2, students can 1) analyze and identify the structure of the positional decimal number system, estimate weight (mass) expressing it in units consistent with the attribute being measured; 2) recognize commonly used geometrical shapes and their properties in order to solve problems; 3) interpret, compare and work with information presented through various graphic images; 4) identify the regularity of a simple pattern sequence; 5) solve addition problems in different numerical fields (natural numbers, decimals) including commonly used fractions or equivalent measures; 6) solve multiplication or division problems, or two natural number operations, or operations that include direct proportionality relations. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at least 413.58 but lower than 514.41 points on the LLECE mathematics scale. At Level 2, students can 1) analyze and identify the structure of the positional decimal number system, estimate weight (mass) expressing it in units consistent with the attribute being measured; 2) recognize commonly used geometrical shapes and their properties in order to solve problems; 3) interpret, compare and work with information presented through various graphic images; 4) identify the regularity of a simple pattern sequence; 5) solve addition problems in different numerical fields (natural numbers, decimals) including commonly used fractions or equivalent measures; 6) solve multiplication or division problems, or two natural number operations, or operations that include direct proportionality relations. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.2.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 6th grade students by mathematics proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at least 413.58 but lower than 514.41 points on the LLECE mathematics scale. At Level 2, students can 1) analyze and identify the structure of the positional decimal number system, estimate weight (mass) expressing it in units consistent with the attribute being measured; 2) recognize commonly used geometrical shapes and their properties in order to solve problems; 3) interpret, compare and work with information presented through various graphic images; 4) identify the regularity of a simple pattern sequence; 5) solve addition problems in different numerical fields (natural numbers, decimals) including commonly used fractions or equivalent measures; 6) solve multiplication or division problems, or two natural number operations, or operations that include direct proportionality relations. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at least 413.58 but lower than 514.41 points on the LLECE mathematics scale. At Level 2, students can 1) analyze and identify the structure of the positional decimal number system, estimate weight (mass) expressing it in units consistent with the attribute being measured; 2) recognize commonly used geometrical shapes and their properties in order to solve problems; 3) interpret, compare and work with information presented through various graphic images; 4) identify the regularity of a simple pattern sequence; 5) solve addition problems in different numerical fields (natural numbers, decimals) including commonly used fractions or equivalent measures; 6) solve multiplication or division problems, or two natural number operations, or operations that include direct proportionality relations. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.3", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 6th grade students by mathematics proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at least 514.41 but lower than 624.60 points on the LLECE mathematics scale. At Level 3, students can 1) compare fractions, use the concept of percentages when analyzing information and solving problems that require this type of calculation; 2) identify parallelism and perpendicularity on a plane, as well as bodies and their elements without the benefit of graphic support; 3) solve problems that require interpreting the constituent elements of a division or equivalent measures; 4) recognize central angles and commonly used geometrical shapes, such as circles, and resort to their properties for solving problems; 5) solve problems involving areas and perimeters of triangles and quadrilaterals; 6) make generalizations in order to continue a graphic sequence or find the numerical sequence rule that applies to a relatively complex pattern. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at least 514.41 but lower than 624.60 points on the LLECE mathematics scale. At Level 3, students can 1) compare fractions, use the concept of percentages when analyzing information and solving problems that require this type of calculation; 2) identify parallelism and perpendicularity on a plane, as well as bodies and their elements without the benefit of graphic support; 3) solve problems that require interpreting the constituent elements of a division or equivalent measures; 4) recognize central angles and commonly used geometrical shapes, such as circles, and resort to their properties for solving problems; 5) solve problems involving areas and perimeters of triangles and quadrilaterals; 6) make generalizations in order to continue a graphic sequence or find the numerical sequence rule that applies to a relatively complex pattern. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.3.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 6th grade students by mathematics proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at least 514.41 but lower than 624.60 points on the LLECE mathematics scale. At Level 3, students can 1) compare fractions, use the concept of percentages when analyzing information and solving problems that require this type of calculation; 2) identify parallelism and perpendicularity on a plane, as well as bodies and their elements without the benefit of graphic support; 3) solve problems that require interpreting the constituent elements of a division or equivalent measures; 4) recognize central angles and commonly used geometrical shapes, such as circles, and resort to their properties for solving problems; 5) solve problems involving areas and perimeters of triangles and quadrilaterals; 6) make generalizations in order to continue a graphic sequence or find the numerical sequence rule that applies to a relatively complex pattern. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at least 514.41 but lower than 624.60 points on the LLECE mathematics scale. At Level 3, students can 1) compare fractions, use the concept of percentages when analyzing information and solving problems that require this type of calculation; 2) identify parallelism and perpendicularity on a plane, as well as bodies and their elements without the benefit of graphic support; 3) solve problems that require interpreting the constituent elements of a division or equivalent measures; 4) recognize central angles and commonly used geometrical shapes, such as circles, and resort to their properties for solving problems; 5) solve problems involving areas and perimeters of triangles and quadrilaterals; 6) make generalizations in order to continue a graphic sequence or find the numerical sequence rule that applies to a relatively complex pattern. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.3.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 6th grade students by mathematics proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at least 514.41 but lower than 624.60 points on the LLECE mathematics scale. At Level 3, students can 1) compare fractions, use the concept of percentages when analyzing information and solving problems that require this type of calculation; 2) identify parallelism and perpendicularity on a plane, as well as bodies and their elements without the benefit of graphic support; 3) solve problems that require interpreting the constituent elements of a division or equivalent measures; 4) recognize central angles and commonly used geometrical shapes, such as circles, and resort to their properties for solving problems; 5) solve problems involving areas and perimeters of triangles and quadrilaterals; 6) make generalizations in order to continue a graphic sequence or find the numerical sequence rule that applies to a relatively complex pattern. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at least 514.41 but lower than 624.60 points on the LLECE mathematics scale. At Level 3, students can 1) compare fractions, use the concept of percentages when analyzing information and solving problems that require this type of calculation; 2) identify parallelism and perpendicularity on a plane, as well as bodies and their elements without the benefit of graphic support; 3) solve problems that require interpreting the constituent elements of a division or equivalent measures; 4) recognize central angles and commonly used geometrical shapes, such as circles, and resort to their properties for solving problems; 5) solve problems involving areas and perimeters of triangles and quadrilaterals; 6) make generalizations in order to continue a graphic sequence or find the numerical sequence rule that applies to a relatively complex pattern. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.4", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 6th grade students by mathematics proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at least 624.60 points on the LLECE mathematics scale. At Level 4, students can 1) find averages and do calculations using the four basic operations in the field of natural numbers; 2) identify parallelism and perpendicularity in a real situation and the graphic images of a percentage; 3) solve problems involving properties of angles, triangles and quadrilaterals as part of different shapes, or involving operations with two decimal numbers; 4) solve problems involving fractions; 5) make generalizations in order to continue a complex graphic sequence pattern. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at least 624.60 points on the LLECE mathematics scale. At Level 4, students can 1) find averages and do calculations using the four basic operations in the field of natural numbers; 2) identify parallelism and perpendicularity in a real situation and the graphic images of a percentage; 3) solve problems involving properties of angles, triangles and quadrilaterals as part of different shapes, or involving operations with two decimal numbers; 4) solve problems involving fractions; 5) make generalizations in order to continue a complex graphic sequence pattern. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.4.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 6th grade students by mathematics proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at least 624.60 points on the LLECE mathematics scale. At Level 4, students can 1) find averages and do calculations using the four basic operations in the field of natural numbers; 2) identify parallelism and perpendicularity in a real situation and the graphic images of a percentage; 3) solve problems involving properties of angles, triangles and quadrilaterals as part of different shapes, or involving operations with two decimal numbers; 4) solve problems involving fractions; 5) make generalizations in order to continue a complex graphic sequence pattern. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at least 624.60 points on the LLECE mathematics scale. At Level 4, students can 1) find averages and do calculations using the four basic operations in the field of natural numbers; 2) identify parallelism and perpendicularity in a real situation and the graphic images of a percentage; 3) solve problems involving properties of angles, triangles and quadrilaterals as part of different shapes, or involving operations with two decimal numbers; 4) solve problems involving fractions; 5) make generalizations in order to continue a complex graphic sequence pattern. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.4.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 6th grade students by mathematics proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at least 624.60 points on the LLECE mathematics scale. At Level 4, students can 1) find averages and do calculations using the four basic operations in the field of natural numbers; 2) identify parallelism and perpendicularity in a real situation and the graphic images of a percentage; 3) solve problems involving properties of angles, triangles and quadrilaterals as part of different shapes, or involving operations with two decimal numbers; 4) solve problems involving fractions; 5) make generalizations in order to continue a complex graphic sequence pattern. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at least 624.60 points on the LLECE mathematics scale. At Level 4, students can 1) find averages and do calculations using the four basic operations in the field of natural numbers; 2) identify parallelism and perpendicularity in a real situation and the graphic images of a percentage; 3) solve problems involving properties of angles, triangles and quadrilaterals as part of different shapes, or involving operations with two decimal numbers; 4) solve problems involving fractions; 5) make generalizations in order to continue a complex graphic sequence pattern. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the mathematics scale for 6th grade students. Female" }, { "id": "Longdefinition", "value": "Average score on the 6th grade mathematics assessment for female students. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 6th grade mathematics assessment for female students. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the mathematics scale for 6th grade students. Male" }, { "id": "Longdefinition", "value": "Average score on the 6th grade mathematics assessment for male students. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 6th grade mathematics assessment for male students. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.P10", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 6th Grade Mathematics Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.P25", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 6th Grade Mathematics Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.P50", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 6th Grade Mathematics Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.P75", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 6th Grade Mathematics Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.MAT6.P90", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 6th Grade Mathematics Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the reading scale for 3rd grade students. Total" }, { "id": "Longdefinition", "value": "Average score on the 3rd grade reading assessment. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 3rd grade reading assessment. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.0", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 3rd grade students by reading proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade students below the lowest proficiency level (scoring below 367.36) on the LLECE reading scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade students below the lowest proficiency level (scoring below 367.36) on the LLECE reading scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.0.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 3rd grade students by reading proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade female students below the lowest proficiency level (scoring below 367.36) on the LLECE reading scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade female students below the lowest proficiency level (scoring below 367.36) on the LLECE reading scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.0.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 3rd grade students by reading proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade male students below the lowest proficiency level (scoring below 367.36) on the LLECE reading scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade male students below the lowest proficiency level (scoring below 367.36) on the LLECE reading scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.1", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 3rd grade students by reading proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade students scoring at least 367.36 but less than 461.32 on the LLECE reading scale. Students at this level can locate information with a single meaning in a prominent part of the text, repeated literally or synonymously, and isolated from other information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade students scoring at least 367.36 but less than 461.32 on the LLECE reading scale. Students at this level can locate information with a single meaning in a prominent part of the text, repeated literally or synonymously, and isolated from other information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.1.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 3rd grade students by reading proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade female students scoring at least 367.36 but less than 461.32 on the LLECE reading scale. Students at this level can locate information with a single meaning in a prominent part of the text, repeated literally or synonymously, and isolated from other information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade female students scoring at least 367.36 but less than 461.32 on the LLECE reading scale. Students at this level can locate information with a single meaning in a prominent part of the text, repeated literally or synonymously, and isolated from other information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.1.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 3rd grade students by reading proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade male students scoring at least 367.36 but less than 461.32 on the LLECE reading scale. Students at this level can locate information with a single meaning in a prominent part of the text, repeated literally or synonymously, and isolated from other information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade male students scoring at least 367.36 but less than 461.32 on the LLECE reading scale. Students at this level can locate information with a single meaning in a prominent part of the text, repeated literally or synonymously, and isolated from other information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.2", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 3rd grade students by reading proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade students scoring at least 461.32 but less than 552.14 on the LLECE reading scale. Students at this level can: 1) locate information in a brief text that cannot be distinguished from other conceptually similar information; 2) Discriminate words with a single meaning; 3) Recognize simple sentence reformulations; and 4) Recognize redundancies between graphic and verbal codes. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade students scoring at least 461.32 but less than 552.14 on the LLECE reading scale. Students at this level can: 1) locate information in a brief text that cannot be distinguished from other conceptually similar information; 2) Discriminate words with a single meaning; 3) Recognize simple sentence reformulations; and 4) Recognize redundancies between graphic and verbal codes. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.2.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 3rd grade students by reading proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade female students scoring at least 461.32 but less than 552.14 on the LLECE reading scale. Students at this level can: 1) locate information in a brief text that cannot be distinguished from other conceptually similar information; 2) discriminate words with a single meaning; 3) recognize simple sentence reformulations; and 4) recognize redundancies between graphic and verbal codes. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade female students scoring at least 461.32 but less than 552.14 on the LLECE reading scale. Students at this level can: 1) locate information in a brief text that cannot be distinguished from other conceptually similar information; 2) discriminate words with a single meaning; 3) recognize simple sentence reformulations; and 4) recognize redundancies between graphic and verbal codes. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.2.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 3rd grade students by reading proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade male students scoring at least 461.32 but less than 552.14 on the LLECE reading scale. Students at this level can: 1) locate information in a brief text that cannot be distinguished from other conceptually similar information; 2) discriminate words with a single meaning; 3) recognize simple sentence reformulations; and 4) recognize redundancies between graphic and verbal codes. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade male students scoring at least 461.32 but less than 552.14 on the LLECE reading scale. Students at this level can: 1) locate information in a brief text that cannot be distinguished from other conceptually similar information; 2) discriminate words with a single meaning; 3) recognize simple sentence reformulations; and 4) recognize redundancies between graphic and verbal codes. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.3", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 3rd grade students by reading proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade students scoring at least 552.14 but less than 637.49 on the LLECE reading scale. Students at this level can: 1) locate information discriminating it from adjacent information; 2) interpret reformulations that synthesize several data; 3) infer information based on knowledge about the world; 4) discriminate the meaning of words that have several other meanings based on the text. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade students scoring at least 552.14 but less than 637.49 on the LLECE reading scale. Students at this level can: 1) locate information discriminating it from adjacent information; 2) interpret reformulations that synthesize several data; 3) infer information based on knowledge about the world; 4) discriminate the meaning of words that have several other meanings based on the text. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.3.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 3rd grade students by reading proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade female students scoring at least 552.14 but less than 637.49 on the LLECE reading scale. Students at this level can: 1) locate information discriminating it from adjacent information; 2) interpret reformulations that synthesize several data; 3) infer information based on knowledge about the world; 4) discriminate the meaning of words that have several other meanings based on the text. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade female students scoring at least 552.14 but less than 637.49 on the LLECE reading scale. Students at this level can: 1) locate information discriminating it from adjacent information; 2) interpret reformulations that synthesize several data; 3) infer information based on knowledge about the world; 4) discriminate the meaning of words that have several other meanings based on the text. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.3.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 3rd grade students by reading proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade male students scoring at least 552.14 but less than 637.49 on the LLECE reading scale. Students at this level can: 1) locate information discriminating it from adjacent information; 2) interpret reformulations that synthesize several data; 3) infer information based on knowledge about the world; 4) discriminate the meaning of words that have several other meanings based on the text. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade male students scoring at least 552.14 but less than 637.49 on the LLECE reading scale. Students at this level can: 1) locate information discriminating it from adjacent information; 2) interpret reformulations that synthesize several data; 3) infer information based on knowledge about the world; 4) discriminate the meaning of words that have several other meanings based on the text. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.4", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 3rd grade students by reading proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade students scoring higher than 637.49 on the LLECE reading scale. Students at this level can: 1) integrate and generalize information given in a paragraph or in the verbal codes and graph; 2) replace non-explicit information; 3) read the text identifying new information; 4) translate from one code to another (from numeric to verbal, and verbal to graphic). Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade students scoring higher than 637.49 on the LLECE reading scale. Students at this level can: 1) integrate and generalize information given in a paragraph or in the verbal codes and graph; 2) replace non-explicit information; 3) read the text identifying new information; 4) translate from one code to another (from numeric to verbal, and verbal to graphic). Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.4.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 3rd grade students by reading proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade female students scoring higher than 637.49 on the LLECE reading scale. Students at this level can: 1) integrate and generalize information given in a paragraph or in the verbal codes and graph; 2) replace non-explicit information; 3) read the text identifying new information; 4) translate from one code to another (from numeric to verbal, and verbal to graphic). Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade female students scoring higher than 637.49 on the LLECE reading scale. Students at this level can: 1) integrate and generalize information given in a paragraph or in the verbal codes and graph; 2) replace non-explicit information; 3) read the text identifying new information; 4) translate from one code to another (from numeric to verbal, and verbal to graphic). Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.4.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 3rd grade students by reading proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 3rd grade male students scoring higher than 637.49 on the LLECE reading scale. Students at this level can: 1) integrate and generalize information given in a paragraph or in the verbal codes and graph; 2) replace non-explicit information; 3) read the text identifying new information; 4) translate from one code to another (from numeric to verbal, and verbal to graphic). Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 3rd grade male students scoring higher than 637.49 on the LLECE reading scale. Students at this level can: 1) integrate and generalize information given in a paragraph or in the verbal codes and graph; 2) replace non-explicit information; 3) read the text identifying new information; 4) translate from one code to another (from numeric to verbal, and verbal to graphic). Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the reading scale for 3rd grade students. Female" }, { "id": "Longdefinition", "value": "Average score on the 3rd grade reading assessment for female students. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 3rd grade reading assessment for female students. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the reading scale for 3rd grade students. Male" }, { "id": "Longdefinition", "value": "Average score on the 3rd grade reading assessment for male students. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 3rd grade reading assessment for male students. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.P10", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 3rd Grade Reading Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.P25", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 3rd Grade Reading Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.P50", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 3rd Grade Reading Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.P75", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 3rd Grade Reading Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA3.P90", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 3rd Grade Reading Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 2006 scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. 2006 scores are not comparable with 1997 scores. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA4", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the reading scale for 4th grade students. Total" }, { "id": "Longdefinition", "value": "Average score on the 4th grade reading assessment. 1997 means were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 4th grade reading assessment. 1997 means were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA4.P10", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 4th Grade Reading Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA4.P25", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 4th Grade Reading Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA4.P50", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 4th Grade Reading Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA4.P75", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 4th Grade Reading Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA4.P90", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 4th Grade Reading Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. 1997 scores were calculated on a scale with a mean of 250 points and a standard deviation of 50 points. 4th grade students were only assessed in 1997. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the reading scale for 6th grade students. Total" }, { "id": "Longdefinition", "value": "Average score on the 6th grade reading assessment. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 6th grade reading assessment. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.0", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 6th grade students by reading proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students below the lowest proficiency level (scoring below 299.59) on the LLECE reading scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students below the lowest proficiency level (scoring below 299.59) on the LLECE reading scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.0.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 6th grade students by reading proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students below the lowest proficiency level (scoring below 299.59) on the LLECE reading scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students below the lowest proficiency level (scoring below 299.59) on the LLECE reading scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.0.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 6th grade students by reading proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students below the lowest proficiency level (scoring below 299.59) on the LLECE reading scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students below the lowest proficiency level (scoring below 299.59) on the LLECE reading scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.1", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 6th grade students by reading proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at least 299.59 but lower than 424.54 points on the LLECE reading scale. At Level 1, students can locate information with a single meaning in a prominent or central part of the text (beginning or end) that is repeated literally or synonymously and is isolated from other information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at least 299.59 but lower than 424.54 points on the LLECE reading scale. At Level 1, students can locate information with a single meaning in a prominent or central part of the text (beginning or end) that is repeated literally or synonymously and is isolated from other information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.1.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 6th grade students by reading proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at least 299.59 but lower than 424.54 points on the LLECE reading scale. At Level 1, students can locate information with a single meaning in a prominent or central part of the text (beginning or end) that is repeated literally or synonymously and is isolated from other information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at least 299.59 but lower than 424.54 points on the LLECE reading scale. At Level 1, students can locate information with a single meaning in a prominent or central part of the text (beginning or end) that is repeated literally or synonymously and is isolated from other information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.1.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 6th grade students by reading proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at least 299.59 but lower than 424.54 points on the LLECE reading scale. At Level 1, students can locate information with a single meaning in a prominent or central part of the text (beginning or end) that is repeated literally or synonymously and is isolated from other information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at least 299.59 but lower than 424.54 points on the LLECE reading scale. At Level 1, students can locate information with a single meaning in a prominent or central part of the text (beginning or end) that is repeated literally or synonymously and is isolated from other information. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.2", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 6th grade students by reading proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at least 424.54 but lower than 513.66 points on the LLECE reading scale. At Level 2, students can 1) locate information in the middle of a text that must be distinguished from a different piece of information found in a different segment; and 2) identify words with a single meaning. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at least 424.54 but lower than 513.66 points on the LLECE reading scale. At Level 2, students can 1) locate information in the middle of a text that must be distinguished from a different piece of information found in a different segment; and 2) identify words with a single meaning. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.2.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 6th grade students by reading proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at least 424.54 but lower than 513.66 points on the LLECE reading scale. At Level 2, students can 1) locate information in the middle of a text that must be distinguished from a different piece of information found in a different segment; and 2) identify words with a single meaning. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at least 424.54 but lower than 513.66 points on the LLECE reading scale. At Level 2, students can 1) locate information in the middle of a text that must be distinguished from a different piece of information found in a different segment; and 2) identify words with a single meaning. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.2.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 6th grade students by reading proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at least 424.54 but lower than 513.66 points on the LLECE reading scale. At Level 2, students can 1) locate information in the middle of a text that must be distinguished from a different piece of information found in a different segment; and 2) identify words with a single meaning. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at least 424.54 but lower than 513.66 points on the LLECE reading scale. At Level 2, students can 1) locate information in the middle of a text that must be distinguished from a different piece of information found in a different segment; and 2) identify words with a single meaning. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.3", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 6th grade students by reading proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at least 513.66 but lower than 593.59 points on the LLECE reading scale. At Level 3, students can 1) locate information and separate it from other nearby information; 2) interpret reformulations and syntheses; 3) integrate data distributed across a paragraph; 4) reinstate implicit information in the paragraph; 5) re-read in search of specific data; 6) identify a single meaning in words that have several meanings; 7) recognize the meaning of parts of words (affixes) using the text as a reference. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at least 513.66 but lower than 593.59 points on the LLECE reading scale. At Level 3, students can 1) locate information and separate it from other nearby information; 2) interpret reformulations and syntheses; 3) integrate data distributed across a paragraph; 4) reinstate implicit information in the paragraph; 5) re-read in search of specific data; 6) identify a single meaning in words that have several meanings; 7) recognize the meaning of parts of words (affixes) using the text as a reference. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.3.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 6th grade students by reading proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at least 513.66 but lower than 593.59 points on the LLECE reading scale. At Level 3, students can 1) locate information and separate it from other nearby information; 2) interpret reformulations and syntheses; 3) integrate data distributed across a paragraph; 4) reinstate implicit information in the paragraph; 5) re-read in search of specific data; 6) identify a single meaning in words that have several meanings; 7) recognize the meaning of parts of words (affixes) using the text as a reference. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at least 513.66 but lower than 593.59 points on the LLECE reading scale. At Level 3, students can 1) locate information and separate it from other nearby information; 2) interpret reformulations and syntheses; 3) integrate data distributed across a paragraph; 4) reinstate implicit information in the paragraph; 5) re-read in search of specific data; 6) identify a single meaning in words that have several meanings; 7) recognize the meaning of parts of words (affixes) using the text as a reference. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.3.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 6th grade students by reading proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at least 513.66 but lower than 593.59 points on the LLECE reading scale. At Level 3, students can 1) locate information and separate it from other nearby information; 2) interpret reformulations and syntheses; 3) integrate data distributed across a paragraph; 4) reinstate implicit information in the paragraph; 5) re-read in search of specific data; 6) identify a single meaning in words that have several meanings; 7) recognize the meaning of parts of words (affixes) using the text as a reference. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at least 513.66 but lower than 593.59 points on the LLECE reading scale. At Level 3, students can 1) locate information and separate it from other nearby information; 2) interpret reformulations and syntheses; 3) integrate data distributed across a paragraph; 4) reinstate implicit information in the paragraph; 5) re-read in search of specific data; 6) identify a single meaning in words that have several meanings; 7) recognize the meaning of parts of words (affixes) using the text as a reference. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.4", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 6th grade students by reading proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at least 593.59 points on the LLECE reading scale. At Level 4, students can 1) integrate, rank and generalize information distributed across the text; 2) establish equivalences among more than two codes (verbal, numerical and graphic); 3) reinstate implicit information associated with the entire text; 4) recognize the possible meanings of technical terms or figurative language; 5) distinguish various tenses and nuances (certainty, doubt) used in a text. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at least 593.59 points on the LLECE reading scale. At Level 4, students can 1) integrate, rank and generalize information distributed across the text; 2) establish equivalences among more than two codes (verbal, numerical and graphic); 3) reinstate implicit information associated with the entire text; 4) recognize the possible meanings of technical terms or figurative language; 5) distinguish various tenses and nuances (certainty, doubt) used in a text. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.4.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 6th grade students by reading proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at least 593.59 points on the LLECE reading scale. At Level 4, students can 1) integrate, rank and generalize information distributed across the text; 2) establish equivalences among more than two codes (verbal, numerical and graphic); 3) reinstate implicit information associated with the entire text; 4) recognize the possible meanings of technical terms or figurative language; 5) distinguish various tenses and nuances (certainty, doubt) used in a text. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at least 593.59 points on the LLECE reading scale. At Level 4, students can 1) integrate, rank and generalize information distributed across the text; 2) establish equivalences among more than two codes (verbal, numerical and graphic); 3) reinstate implicit information associated with the entire text; 4) recognize the possible meanings of technical terms or figurative language; 5) distinguish various tenses and nuances (certainty, doubt) used in a text. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.4.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 6th grade students by reading proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at least 593.59 points on the LLECE reading scale. At Level 4, students can 1) integrate, rank and generalize information distributed across the text; 2) establish equivalences among more than two codes (verbal, numerical and graphic); 3) reinstate implicit information associated with the entire text; 4) recognize the possible meanings of technical terms or figurative language; 5) distinguish various tenses and nuances (certainty, doubt) used in a text. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at least 593.59 points on the LLECE reading scale. At Level 4, students can 1) integrate, rank and generalize information distributed across the text; 2) establish equivalences among more than two codes (verbal, numerical and graphic); 3) reinstate implicit information associated with the entire text; 4) recognize the possible meanings of technical terms or figurative language; 5) distinguish various tenses and nuances (certainty, doubt) used in a text. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the reading scale for 6th grade students. Female" }, { "id": "Longdefinition", "value": "Average score on the 6th grade reading assessment for female students. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 6th grade reading assessment for female students. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the reading scale for 6th grade students. Male" }, { "id": "Longdefinition", "value": "Average score on the 6th grade reading assessment for male students. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 6th grade reading assessment for male students. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.P10", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 6th Grade Reading Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.P25", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 6th Grade Reading Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.P50", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 6th Grade Reading Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.P75", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 6th Grade Reading Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.REA6.P90", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 6th Grade Reading Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the science scale for 6th grade students. Total" }, { "id": "Longdefinition", "value": "Average score on the 6th grade science assessment. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 6th grade science assessment. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.0", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 6th grade students by science proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students below the lowest proficiency level (scoring below 351.31) on the LLECE science scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students below the lowest proficiency level (scoring below 351.31) on the LLECE science scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.0.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 6th grade students by science proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students below the lowest proficiency level (scoring below 351.31) on the LLECE science scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students below the lowest proficiency level (scoring below 351.31) on the LLECE science scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.0.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 6th grade students by science proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students below the lowest proficiency level (scoring below 351.31) on the LLECE science scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students below the lowest proficiency level (scoring below 351.31) on the LLECE science scale. Students below Level 1 have not been able to acquire the abilities required in Level I. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.1", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 6th grade students by science proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at least 351.31 but lower than 472.06 points on the LLECE science scale. At Level 1, students can 1) relate scientific knowledge to daily situations that are of common occurrence in their context; 2) explain their immediate world based on their own experiences and observations, and establish a simple and lineal relation with previously acquired scientific knowledge; and 3) describe concrete and simple events involving cognitive processes such as remembering, evoking and identifying. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at least 351.31 but lower than 472.06 points on the LLECE science scale. At Level 1, students can 1) relate scientific knowledge to daily situations that are of common occurrence in their context; 2) explain their immediate world based on their own experiences and observations, and establish a simple and lineal relation with previously acquired scientific knowledge; and 3) describe concrete and simple events involving cognitive processes such as remembering, evoking and identifying. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.1.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 6th grade students by science proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at least 351.31 but lower than 472.06 points on the LLECE science scale. At Level 1, students can 1) relate scientific knowledge to daily situations that are of common occurrence in their context; 2) explain their immediate world based on their own experiences and observations, and establish a simple and lineal relation with previously acquired scientific knowledge; and 3) describe concrete and simple events involving cognitive processes such as remembering, evoking and identifying. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at least 351.31 but lower than 472.06 points on the LLECE science scale. At Level 1, students can 1) relate scientific knowledge to daily situations that are of common occurrence in their context; 2) explain their immediate world based on their own experiences and observations, and establish a simple and lineal relation with previously acquired scientific knowledge; and 3) describe concrete and simple events involving cognitive processes such as remembering, evoking and identifying. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.1.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 6th grade students by science proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at least 351.31 but lower than 472.06 points on the LLECE science scale. At Level 1, students can 1) relate scientific knowledge to daily situations that are of common occurrence in their context; 2) explain their immediate world based on their own experiences and observations, and establish a simple and lineal relation with previously acquired scientific knowledge; and 3) describe concrete and simple events involving cognitive processes such as remembering, evoking and identifying. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at least 351.31 but lower than 472.06 points on the LLECE science scale. At Level 1, students can 1) relate scientific knowledge to daily situations that are of common occurrence in their context; 2) explain their immediate world based on their own experiences and observations, and establish a simple and lineal relation with previously acquired scientific knowledge; and 3) describe concrete and simple events involving cognitive processes such as remembering, evoking and identifying. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.2", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 6th grade students by science proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at least 472.06 but lower than 590.29 points on the LLECE science scale. At Level 2, students can 1) apply school-acquired scientific knowledge; 2) compare, organize and interpret information presented in various formats (tables, charts, graphs, pictures); 3) identify causality relations and classify living beings according to a given criterion; 4) access information presented in different formats, which requires the use of much more complex skills than required in Level 1. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at least 472.06 but lower than 590.29 points on the LLECE science scale. At Level 2, students can 1) apply school-acquired scientific knowledge; 2) compare, organize and interpret information presented in various formats (tables, charts, graphs, pictures); 3) identify causality relations and classify living beings according to a given criterion; 4) access information presented in different formats, which requires the use of much more complex skills than required in Level 1. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.2.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 6th grade students by science proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at least 472.06 but lower than 590.29 points on the LLECE science scale. At Level 2, students can 1) apply school-acquired scientific knowledge; 2) compare, organize and interpret information presented in various formats (tables, charts, graphs, pictures); 3) identify causality relations and classify living beings according to a given criterion; 4) access information presented in different formats, which requires the use of much more complex skills than required in Level 1. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at least 472.06 but lower than 590.29 points on the LLECE science scale. At Level 2, students can 1) apply school-acquired scientific knowledge; 2) compare, organize and interpret information presented in various formats (tables, charts, graphs, pictures); 3) identify causality relations and classify living beings according to a given criterion; 4) access information presented in different formats, which requires the use of much more complex skills than required in Level 1. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.2.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 6th grade students by science proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at least 472.06 but lower than 590.29 points on the LLECE science scale. At Level 2, students can 1) apply school-acquired scientific knowledge; 2) compare, organize and interpret information presented in various formats (tables, charts, graphs, pictures); 3) identify causality relations and classify living beings according to a given criterion; 4) access information presented in different formats, which requires the use of much more complex skills than required in Level 1. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at least 472.06 but lower than 590.29 points on the LLECE science scale. At Level 2, students can 1) apply school-acquired scientific knowledge; 2) compare, organize and interpret information presented in various formats (tables, charts, graphs, pictures); 3) identify causality relations and classify living beings according to a given criterion; 4) access information presented in different formats, which requires the use of much more complex skills than required in Level 1. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.3", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 6th grade students by science proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at least 590.29 but lower than 704.75 points on the LLECE science scale. At Level 3, students can 1) explain everyday situations on the basis of scientific evidence; 2) use simple descriptive models to interpret natural phenomena; and 3) draw conclusions from the description of experimental activities. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at least 590.29 but lower than 704.75 points on the LLECE science scale. At Level 3, students can 1) explain everyday situations on the basis of scientific evidence; 2) use simple descriptive models to interpret natural phenomena; and 3) draw conclusions from the description of experimental activities. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.3.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 6th grade students by science proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at least 590.29 but lower than 704.75 points on the LLECE science scale. At Level 3, students can 1) explain everyday situations on the basis of scientific evidence; 2) use simple descriptive models to interpret natural phenomena; and 3) draw conclusions from the description of experimental activities. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at least 590.29 but lower than 704.75 points on the LLECE science scale. At Level 3, students can 1) explain everyday situations on the basis of scientific evidence; 2) use simple descriptive models to interpret natural phenomena; and 3) draw conclusions from the description of experimental activities. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.3.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 6th grade students by science proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at least 590.29 but lower than 704.75 points on the LLECE science scale. At Level 3, students can 1) explain everyday situations on the basis of scientific evidence; 2) use simple descriptive models to interpret natural phenomena; and 3) draw conclusions from the description of experimental activities. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at least 590.29 but lower than 704.75 points on the LLECE science scale. At Level 3, students can 1) explain everyday situations on the basis of scientific evidence; 2) use simple descriptive models to interpret natural phenomena; and 3) draw conclusions from the description of experimental activities. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.4", "metatype": [ { "id": "IndicatorName", "value": "LLECE: 6th grade students by science proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at least 704.75 points on the LLECE science scale. At Level 4, students can 1) use and transfer scientific knowledge to diverse types of situations, which requires a high degree of formalization and abstraction and 2) identify the scientific knowledge involved in a problem that is more formally stated and may relate to aspects, dimensions or analyses detached from the immediate setting. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at least 704.75 points on the LLECE science scale. At Level 4, students can 1) use and transfer scientific knowledge to diverse types of situations, which requires a high degree of formalization and abstraction and 2) identify the scientific knowledge involved in a problem that is more formally stated and may relate to aspects, dimensions or analyses detached from the immediate setting. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.4.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Female 6th grade students by science proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at least 704.75 points on the LLECE science scale. At Level 4, students can 1) use and transfer scientific knowledge to diverse types of situations, which requires a high degree of formalization and abstraction and 2) identify the scientific knowledge involved in a problem that is more formally stated and may relate to aspects, dimensions or analyses detached from the immediate setting. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at least 704.75 points on the LLECE science scale. At Level 4, students can 1) use and transfer scientific knowledge to diverse types of situations, which requires a high degree of formalization and abstraction and 2) identify the scientific knowledge involved in a problem that is more formally stated and may relate to aspects, dimensions or analyses detached from the immediate setting. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.4.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Male 6th grade students by science proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at least 704.75 points on the LLECE science scale. At Level 4, students can 1) use and transfer scientific knowledge to diverse types of situations, which requires a high degree of formalization and abstraction and 2) identify the scientific knowledge involved in a problem that is more formally stated and may relate to aspects, dimensions or analyses detached from the immediate setting. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at least 704.75 points on the LLECE science scale. At Level 4, students can 1) use and transfer scientific knowledge to diverse types of situations, which requires a high degree of formalization and abstraction and 2) identify the scientific knowledge involved in a problem that is more formally stated and may relate to aspects, dimensions or analyses detached from the immediate setting. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.FE", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the science scale for 6th grade students. Female" }, { "id": "Longdefinition", "value": "Average score on the 6th grade science assessment for female students. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 6th grade science assessment for female students. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.MA", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Mean performance on the science scale for 6th grade students. Male" }, { "id": "Longdefinition", "value": "Average score on the 6th grade science assessment for male students. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "Average score on the 6th grade science assessment for male students. Means were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.P10", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 6th Grade Science Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.P25", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 6th Grade Science Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.P50", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 6th Grade Science Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.P75", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 6th Grade Science Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.LLECE.SCI6.P90", "metatype": [ { "id": "IndicatorName", "value": "LLECE: Distribution of 6th Grade Science Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Scores were calculated on a scale with a mean of 500 and a standard deviation of 100 points. Data reflects country performance in the stated year according to LLECE reports. Consult the LLECE website for more detailed information: http://www.llece.org/" }, { "id": "Source", "value": "Latin American Laboratory for Assessment of the Quality of Education (LLECE)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.FRE.P05", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 5th Grade French Scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.FRE.P10", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 5th Grade French Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.FRE.P25", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 5th Grade French Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.FRE.P50", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 5th Grade French Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.FRE.P75", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 5th Grade French Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.FRE.P90", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 5th Grade French Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.FRE.P95", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 5th Grade French Scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.FRE5", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the French language scale (100 points) for 5th grade students. Total" }, { "id": "Longdefinition", "value": "Mean percentage correct for 5th grade students on the French language exam. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Mean percentage correct for 5th grade students on the French language exam. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.FRE5.FE", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the French language scale (100 points) for 5th grade students. Female" }, { "id": "Longdefinition", "value": "Mean percentage correct for 5th grade female students on the French language exam. Average differences are only significant between males and females in Benin, Burkina Faso, Democratic Republic of Congo, and Burundi. Since comparison is suggested by giving average scores for males/females, please take into account that these are only raw effects of gender. PASEC uses econometric models to validate the effect of gender in achievement. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Mean percentage correct for 5th grade female students on the French language exam. Average differences are only significant between males and females in Benin, Burkina Faso, Democratic Republic of Congo, and Burundi. Since comparison is suggested by giving average scores for males/females, please take into account that these are only raw effects of gender. PASEC uses econometric models to validate the effect of gender in achievement. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.FRE5.HIG", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 5th grade students reaching the Knowledge Base Rate on the French language scale (%)" }, { "id": "Longdefinition", "value": "Share of students scoring at least 40 percent on the PASEC French language exam. The Knowledge Base Rate is the minimum learning goal based on the programs of the level selected and appropriate to the scale of the tests used. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Share of students scoring at least 40 percent on the PASEC French language exam. The Knowledge Base Rate is the minimum learning goal based on the programs of the level selected and appropriate to the scale of the tests used. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.FRE5.HIG.FE", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Female 5th grade students reaching the Knowledge Base Rate on the French language scale (%)" }, { "id": "Longdefinition", "value": "Share of female students scoring at least 40 percent on the PASEC French language exam. The Knowledge Base Rate is the minimum learning goal based on the programs of the level selected and appropriate to the scale of the tests used. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Share of female students scoring at least 40 percent on the PASEC French language exam. The Knowledge Base Rate is the minimum learning goal based on the programs of the level selected and appropriate to the scale of the tests used. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.FRE5.HIG.MA", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Male 5th grade students reaching the Knowledge Base Rate on the French language scale (%)" }, { "id": "Longdefinition", "value": "Share of male students scoring at least 40 percent on the PASEC French language exam. The Knowledge Base Rate is the minimum learning goal based on the programs of the level selected and appropriate to the scale of the tests used. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Share of male students scoring at least 40 percent on the PASEC French language exam. The Knowledge Base Rate is the minimum learning goal based on the programs of the level selected and appropriate to the scale of the tests used. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.FRE5.LO", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 5th grade students below the Failure Rate on the French language scale (%)" }, { "id": "Longdefinition", "value": "Share of students scoring less than 25 percent on the PASEC French language exam. The Failure Rate is the score a pupil could obtain by completing the PASEC test randomly. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Share of students scoring less than 25 percent on the PASEC French language exam. The Failure Rate is the score a pupil could obtain by completing the PASEC test randomly. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.FRE5.LO.FE", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Female 5th grade students below the Failure Rate on the French language scale (%)" }, { "id": "Longdefinition", "value": "Share of female students scoring less than 25 percent on the PASEC French language exam. The Failure Rate is the score a pupil could obtain by completing the PASEC test randomly. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Share of female students scoring less than 25 percent on the PASEC French language exam. The Failure Rate is the score a pupil could obtain by completing the PASEC test randomly. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.FRE5.LO.MA", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Male 5th grade students below the Failure Rate on the French language scale (%)" }, { "id": "Longdefinition", "value": "Share of male students scoring less than 25 percent on the PASEC French language exam. The Failure Rate is the score a pupil could obtain by completing the PASEC test randomly. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Share of male students scoring less than 25 percent on the PASEC French language exam. The Failure Rate is the score a pupil could obtain by completing the PASEC test randomly. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.FRE5.MA", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the French language scale (100 points) for 5th grade students. Male" }, { "id": "Longdefinition", "value": "Mean percentage correct for 5th male grade students on the French language exam. Average differences are only significant between males and females in Benin, Burkina Faso, Democratic Republic of Congo and Burundi. Since comparison is suggested by giving average scores for males/females, please take into account that these are only raw effects of gender. PASEC uses econometric models to validate the effect of gender in achievement. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Mean percentage correct for 5th male grade students on the French language exam. Average differences are only significant between males and females in Benin, Burkina Faso, Democratic Republic of Congo and Burundi. Since comparison is suggested by giving average scores for males/females, please take into account that these are only raw effects of gender. PASEC uses econometric models to validate the effect of gender in achievement. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the mathematics scale for 2nd grade students. Total" }, { "id": "Longdefinition", "value": "Average score of Grade 2 students on the PASEC mathematics scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of Grade 2 students on the PASEC mathematics scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.ADD1", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Percentage of 2nd grade students correcting answering the addition problem: 8+5" }, { "id": "Longdefinition", "value": "Share of 2nd grade students correctly answering the stated problem. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Share of 2nd grade students correctly answering the stated problem. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.ADD2", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Percentage of 2nd grade students correcting answering the addition problem: 14+23" }, { "id": "Longdefinition", "value": "Share of 2nd grade students correctly answering the stated problem. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Share of 2nd grade students correctly answering the stated problem. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.ADD3", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Percentage of 2nd grade students correcting answering the addition problem: 39+26" }, { "id": "Longdefinition", "value": "Share of 2nd grade students correctly answering the stated problem. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Share of 2nd grade students correctly answering the stated problem. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.CNT.0T60", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade students by last number reached when counting out loud. 0 to 60" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students who could not reach 61 when counting out loud. Counting requires students to know the numbers and understand the organization of the number sequence. It is an important prerequisite to acquire basic number concepts. The test administrator asked the pupils to start counting from one and up to the greatest possible number, meaning until they make their first mistake, hesitate for more than 5 seconds on a number or until 2 minutes have passed. The test administrator enters the last number read correctly or reached after 2 minutes have gone by. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students who could not reach 61 when counting out loud. Counting requires students to know the numbers and understand the organization of the number sequence. It is an important prerequisite to acquire basic number concepts. The test administrator asked the pupils to start counting from one and up to the greatest possible number, meaning until they make their first mistake, hesitate for more than 5 seconds on a number or until 2 minutes have passed. The test administrator enters the last number read correctly or reached after 2 minutes have gone by. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.CNT.61T80", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade students by last number reached when counting out loud. 61 to 80" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students who counted to at least 61 but not beyond 80. Counting requires students to know the numbers and understand the organization of the number sequence. It is an important prerequisite to acquire basic number concepts. The test administrator asked the pupils to start counting from one and up to the greatest possible number, meaning until they make their first mistake, hesitate for more than 5 seconds on a number or until 2 minutes have passed. The test administrator enters the last number read correctly or reached after 2 minutes have gone by. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students who counted to at least 61 but not beyond 80. Counting requires students to know the numbers and understand the organization of the number sequence. It is an important prerequisite to acquire basic number concepts. The test administrator asked the pupils to start counting from one and up to the greatest possible number, meaning until they make their first mistake, hesitate for more than 5 seconds on a number or until 2 minutes have passed. The test administrator enters the last number read correctly or reached after 2 minutes have gone by. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.CNT.80UP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade students by last number reached when counting out loud. 80+" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students who could count beyond 80. Counting requires students to know the numbers and understand the organization of the number sequence. It is an important prerequisite to acquire basic number concepts. The test administrator asked the pupils to start counting from one and up to the greatest possible number, meaning until they make their first mistake, hesitate for more than 5 seconds on a number or until 2 minutes have passed. The test administrator enters the last number read correctly or reached after 2 minutes have gone by. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students who could count beyond 80. Counting requires students to know the numbers and understand the organization of the number sequence. It is an important prerequisite to acquire basic number concepts. The test administrator asked the pupils to start counting from one and up to the greatest possible number, meaning until they make their first mistake, hesitate for more than 5 seconds on a number or until 2 minutes have passed. The test administrator enters the last number read correctly or reached after 2 minutes have gone by. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.FE", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the mathematics scale for 2nd grade students. Female" }, { "id": "Longdefinition", "value": "Average score of female Grade 2 students on the PASEC mathematics scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of female Grade 2 students on the PASEC mathematics scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.L0", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 2nd grade students by mathematics proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students scoring below 400.3 on the PASEC mathematics scale. Pupils below level 1 do not display the competencies measured by this test. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students scoring below 400.3 on the PASEC mathematics scale. Pupils below level 1 do not display the competencies measured by this test. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.L1", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 2nd grade students by mathematics proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students scoring at least 400.3 but lower than 489 on the PASEC mathematics scale. Students at Level 1 have not passed the Sufficient Competency Threshold, but they have developed their knowledge of the mathematical language and mastered the first concepts of quantity (quantification, comparison) with objects and numbers under twenty. They can appraise the relative size of objects, recognize simple geometric shapes. They have developed an awareness of the first concepts of spatial orientation (inside, outside). Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students scoring at least 400.3 but lower than 489 on the PASEC mathematics scale. Students at Level 1 have not passed the Sufficient Competency Threshold, but they have developed their knowledge of the mathematical language and mastered the first concepts of quantity (quantification, comparison) with objects and numbers under twenty. They can appraise the relative size of objects, recognize simple geometric shapes. They have developed an awareness of the first concepts of spatial orientation (inside, outside). Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.L2", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 2nd grade students by mathematics proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students scoring at least 489 but lower than 577.7 on the PASEC mathematics scale. Students at Level 2 have passed the Sufficient Competency Threshold. Pupils can recognize numbers up to one hundred, compare them, complete logical series and perform operations (sums and subtractions) with numbers under fifty. They have developed awareness of spatial orientation (below, above, beside). They have begun to develop an ability to solve basic problems with numbers under twenty using reasoning skills. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students scoring at least 489 but lower than 577.7 on the PASEC mathematics scale. Students at Level 2 have passed the Sufficient Competency Threshold. Pupils can recognize numbers up to one hundred, compare them, complete logical series and perform operations (sums and subtractions) with numbers under fifty. They have developed awareness of spatial orientation (below, above, beside). They have begun to develop an ability to solve basic problems with numbers under twenty using reasoning skills. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.L3", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 2nd grade students by mathematics proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students scoring at least 577.7 on the PASEC mathematics scale. Students at Level 3 have passed the Sufficient Competency Threshold. Students at this level have mastered the oral number sequence (counting up to sixty in two minutes) and are able to compare numbers, complete logical series and perform operations (sums and subtractions) with numbers over fifty. They can solve basic problems with numbers under twenty using reasoning skills. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students scoring at least 577.7 on the PASEC mathematics scale. Students at Level 3 have passed the Sufficient Competency Threshold. Students at this level have mastered the oral number sequence (counting up to sixty in two minutes) and are able to compare numbers, complete logical series and perform operations (sums and subtractions) with numbers over fifty. They can solve basic problems with numbers under twenty using reasoning skills. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.MA", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the mathematics scale for 2nd grade students. Male" }, { "id": "Longdefinition", "value": "Average score of male Grade 2 students on the PASEC mathematics scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of male Grade 2 students on the PASEC mathematics scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.MG.GAP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Average performance gap between 2nd grade students in multigrade and standard classes. Mathematics" }, { "id": "Longdefinition", "value": "Average difference in scores between students in multigrade classes and standard classes. Standard classes have one full-time teacher per class/grade, and multigrade classes have pupils from different grades in a single pedagogical group with a single teacher. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average difference in scores between students in multigrade classes and standard classes. Standard classes have one full-time teacher per class/grade, and multigrade classes have pupils from different grades in a single pedagogical group with a single teacher. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.NPP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the mathematics scale for 2nd grade students who did not attend pre-primary education" }, { "id": "Longdefinition", "value": "Average score of Grade 2 students who did not attend any form of pre-primary education. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of Grade 2 students who did not attend any form of pre-primary education. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.P05", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade mathematics scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.P1", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade mathematics scores: 1st Percentile Score" }, { "id": "Longdefinition", "value": "The 1st percentile score is the score below which 1 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 1st percentile score is the score below which 1 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.P10", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade mathematics scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.P25", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade mathematics scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.P50", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade mathematics scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.P75", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade mathematics scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.P90", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade mathematics scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.P95", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade mathematics scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 95 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 95 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.P99", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade mathematics scores: 99th Percentile Score" }, { "id": "Longdefinition", "value": "The 99th percentile score is the score below which 99 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 99th percentile score is the score below which 99 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.PP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the mathematics scale for 2nd grade students who attended pre-primary education" }, { "id": "Longdefinition", "value": "Average score of Grade 2 students who attended any form of pre-primary education. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of Grade 2 students who attended any form of pre-primary education. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.PP.GAP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Average performance gap between 2nd grade students who attended/did not attend pre-primary education. Mathematics" }, { "id": "Longdefinition", "value": "Average difference in scores between students in who attended or did not attend any form of pre-primary education. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average difference in scores between students in who attended or did not attend any form of pre-primary education. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.PRI.GAP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Average performance gap between 2nd grade students in private and public education. Mathematics" }, { "id": "Longdefinition", "value": "Average difference between pupil scores in private and public educational institutions controlling for the territorial planning index, which is calculated based on the availability of the following infrastructure and services: a paved road; electricity; a lower secondary school; an upper secondary school; a hospital; a medical or healthcare center; a police station; a bank; a savings bank; a post office; and a cultural center or library. When public and private schools are located in areas with similar infrastructure and service levels as measured by the territorial planning index, the gaps between pupils’ scores tend to be smaller. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average difference between pupil scores in private and public educational institutions controlling for the territorial planning index, which is calculated based on the availability of the following infrastructure and services: a paved road; electricity; a lower secondary school; an upper secondary school; a hospital; a medical or healthcare center; a police station; a bank; a savings bank; a post office; and a cultural center or library. When public and private schools are located in areas with similar infrastructure and service levels as measured by the territorial planning index, the gaps between pupils’ scores tend to be smaller. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.RU.GAP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Average performance gap between 2nd grade students in rural and urban areas. Mathematics" }, { "id": "Longdefinition", "value": "Average difference between pupil scores in rural and urban areas controlling for the territorial planning index, which is calculated based on the availability of the following infrastructure and services: a paved road; electricity; a lower secondary school; an upper secondary school; a hospital; a medical or healthcare center; a police station; a bank; a savings bank; a post office; and a cultural center or library. When urban and rural schools are located in areas with similar infrastructure and service levels as measured by the territorial planning index, the gaps between pupils’ scores tend to be smaller. Urban areas include towns and town suburbs, and rural areas include large villages (several hundred family lots) and small villages (up to one hundred family lots). These urban/rural definitions are standard across countries to facilitate the comparison of trends from one country to another. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average difference between pupil scores in rural and urban areas controlling for the territorial planning index, which is calculated based on the availability of the following infrastructure and services: a paved road; electricity; a lower secondary school; an upper secondary school; a hospital; a medical or healthcare center; a police station; a bank; a savings bank; a post office; and a cultural center or library. When urban and rural schools are located in areas with similar infrastructure and service levels as measured by the territorial planning index, the gaps between pupils’ scores tend to be smaller. Urban areas include towns and town suburbs, and rural areas include large villages (several hundred family lots) and small villages (up to one hundred family lots). These urban/rural definitions are standard across countries to facilitate the comparison of trends from one country to another. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.SUB1", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Percentage of 2nd grade students correcting answering the subtraction problem: 13-7" }, { "id": "Longdefinition", "value": "Share of 2nd grade students correctly answering the stated problem. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Share of 2nd grade students correctly answering the stated problem. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.SUB2", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Percentage of 2nd grade students correcting answering the subtraction problem: 34-11" }, { "id": "Longdefinition", "value": "Share of 2nd grade students correctly answering the stated problem. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Share of 2nd grade students correctly answering the stated problem. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.2.SUB3", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Percentage of 2nd grade students correcting answering the subtraction problem: 50-18" }, { "id": "Longdefinition", "value": "Share of 2nd grade students correctly answering the stated problem. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Share of 2nd grade students correctly answering the stated problem. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the mathematics scale for 6th grade students. Total" }, { "id": "Longdefinition", "value": "Average score of Grade 6 students on the PASEC mathematics scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of Grade 6 students on the PASEC mathematics scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.FE", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the mathematics scale for 6th grade students. Female" }, { "id": "Longdefinition", "value": "Average score of female Grade 6 students on the PASEC mathematics scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of female Grade 6 students on the PASEC mathematics scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.L0", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 6th grade students by mathematics proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Share of 6th grade students who scored below 433.3 on the PASEC mathematics scale. Pupils below Level 1 are not able to correctly answer a majority of the most basic test questions; these pupils do not display the competencies measured by this test. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Share of 6th grade students who scored below 433.3 on the PASEC mathematics scale. Pupils below Level 1 are not able to correctly answer a majority of the most basic test questions; these pupils do not display the competencies measured by this test. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.L1", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 6th grade students by mathematics proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Share of 6th grade students scoring higher than 433.3 but below 521.5 on the PASEC mathematics scale. Students at Level 1 have not reached the Sufficient Competency Threshold, but they can answer very brief questions by calling upon factual knowledge or a specific procedure. In arithmetic, they are able to carry out the four basic operations with whole numbers. In measurement, they recognize the length measurement unit: the meter. In geometry, they are able to orientate themselves in space by identifying directions and positions and by reading coordinates on a graph. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Share of 6th grade students scoring higher than 433.3 but below 521.5 on the PASEC mathematics scale. Students at Level 1 have not reached the Sufficient Competency Threshold, but they can answer very brief questions by calling upon factual knowledge or a specific procedure. In arithmetic, they are able to carry out the four basic operations with whole numbers. In measurement, they recognize the length measurement unit: the meter. In geometry, they are able to orientate themselves in space by identifying directions and positions and by reading coordinates on a graph. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.L2", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 6th grade students by mathematics proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Share of 6th grade students scoring higher than 521.5 but below 609.6 on the PASEC mathematics scale. Students at Level 2 have reached the Sufficient Competency Threshold, and are able to answer brief arithmetic, measurement and geometry questions by resorting to the three assessed processes: knowing, applying and reasoning. Some questions call on factual knowledge or a scientific approach; others require analysis of a situation prior to determining the appropriate approach. In arithmetic, students can perform operations with decimal numbers and can also solve familiar problems by analyzing the wording of the question or extracting data from a double-entry table. They know how to complete logical series with decimal numbers or fractions. In measurement, pupils can tell the time and convert units of measurement with or without a conversion table. They are also able to solve arithmetic problems involving operations with units of length or days, hours and minutes. In geometry, pupils know the names of certain solids, basic geometric shapes and some characteristic lines (diagonal, median). Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Share of 6th grade students scoring higher than 521.5 but below 609.6 on the PASEC mathematics scale. Students at Level 2 have reached the Sufficient Competency Threshold, and are able to answer brief arithmetic, measurement and geometry questions by resorting to the three assessed processes: knowing, applying and reasoning. Some questions call on factual knowledge or a scientific approach; others require analysis of a situation prior to determining the appropriate approach. In arithmetic, students can perform operations with decimal numbers and can also solve familiar problems by analyzing the wording of the question or extracting data from a double-entry table. They know how to complete logical series with decimal numbers or fractions. In measurement, pupils can tell the time and convert units of measurement with or without a conversion table. They are also able to solve arithmetic problems involving operations with units of length or days, hours and minutes. In geometry, pupils know the names of certain solids, basic geometric shapes and some characteristic lines (diagonal, median). Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.L3", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 6th grade students by mathematics proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Share of 6th grade students scoring at least 609.6 on the PASEC mathematics scale. Students at Level 3 have reached the Sufficient Competency Threshold. They are able to answer arithmetic and measurement questions that require them to analyze situations and decide on the appropriate approach. In arithmetic, they can solve problems involving fractions or decimal numbers, and in measurement, they can solve problems involving surface area or perimeter calculations. Pupils can find data on a diagram prior to calculating distances. They are also able to perform calculations and conversions involving hours, minutes and seconds. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Share of 6th grade students scoring at least 609.6 on the PASEC mathematics scale. Students at Level 3 have reached the Sufficient Competency Threshold. They are able to answer arithmetic and measurement questions that require them to analyze situations and decide on the appropriate approach. In arithmetic, they can solve problems involving fractions or decimal numbers, and in measurement, they can solve problems involving surface area or perimeter calculations. Pupils can find data on a diagram prior to calculating distances. They are also able to perform calculations and conversions involving hours, minutes and seconds. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.MA", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the mathematics scale for 6th grade students. Male" }, { "id": "Longdefinition", "value": "Average score of male Grade 6 students on the PASEC mathematics scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of male Grade 6 students on the PASEC mathematics scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.MG.GAP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Average performance gap between 6th grade students in multigrade and standard classes. Mathematics" }, { "id": "Longdefinition", "value": "Average difference in scores between students in multigrade classes and standard classes. Standard classes have one full-time teacher per class/grade, and multigrade classes have pupils from different grades in a single pedagogical group with a single teacher. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average difference in scores between students in multigrade classes and standard classes. Standard classes have one full-time teacher per class/grade, and multigrade classes have pupils from different grades in a single pedagogical group with a single teacher. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.NPP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the mathematics scale for 6th grade students who did not attend pre-primary education" }, { "id": "Longdefinition", "value": "Average score of Grade 6 students who did not attend any form of pre-primary education. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of Grade 6 students who did not attend any form of pre-primary education. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.P05", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade mathematics scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.P1", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade mathematics scores: 1st Percentile Score" }, { "id": "Longdefinition", "value": "The 1st percentile score is the score below which 1 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 1st percentile score is the score below which 1 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.P10", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade mathematics scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.P25", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade mathematics scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.P50", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade mathematics scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.P75", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade mathematics scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.P90", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade mathematics scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.P95", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade mathematics scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 95 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 95 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.P99", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade mathematics scores: 99th Percentile Score" }, { "id": "Longdefinition", "value": "The 99th percentile score is the score below which 99 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 99th percentile score is the score below which 99 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.PP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the mathematics scale for 6th grade students who attended pre-primary education" }, { "id": "Longdefinition", "value": "Average score of Grade 6 students who attended any form of pre-primary education. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of Grade 6 students who attended any form of pre-primary education. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.PP.GAP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Average performance gap between 6th grade students who attended/did not attend pre-primary education. Mathematics" }, { "id": "Longdefinition", "value": "Average difference in scores between students in who attended or did not attend any form of pre-primary education. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average difference in scores between students in who attended or did not attend any form of pre-primary education. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.PRI.GAP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Average performance gap between 6th grade students in private and public education. Mathematics" }, { "id": "Longdefinition", "value": "Average difference between pupil scores in private and public educational institutions controlling for the socioeconomic index and the territorial planning index, which is calculated based on the availability of the following infrastructure and services: a paved road; electricity; a lower secondary school; an upper secondary school; a hospital; a medical or healthcare center; a police station; a bank; a savings bank; a post office; and a cultural center or library. When public and private schools are located in areas with similar infrastructure and service levels as measured by the territorial planning index, the gaps between pupils’ scores tend to be smaller. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average difference between pupil scores in private and public educational institutions controlling for the socioeconomic index and the territorial planning index, which is calculated based on the availability of the following infrastructure and services: a paved road; electricity; a lower secondary school; an upper secondary school; a hospital; a medical or healthcare center; a police station; a bank; a savings bank; a post office; and a cultural center or library. When public and private schools are located in areas with similar infrastructure and service levels as measured by the territorial planning index, the gaps between pupils’ scores tend to be smaller. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.6.RU.GAP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Average performance gap between 6th grade students in rural and urban areas. Mathematics" }, { "id": "Longdefinition", "value": "Average difference between pupil scores in rural and urban areas controlling for the socioeconomic index and the territorial planning index, which is calculated based on the availability of the following infrastructure and services: a paved road; electricity; a lower secondary school; an upper secondary school; a hospital; a medical or healthcare center; a police station; a bank; a savings bank; a post office; and a cultural center or library. When urban and rural schools are located in areas with similar infrastructure and service levels as measured by the territorial planning index, the gaps between pupils’ scores tend to be smaller. Urban areas include towns and town suburbs, and rural areas include large villages (several hundred family lots) and small villages (up to one hundred family lots). These urban/rural definitions are standard across countries to facilitate the comparison of trends from one country to another. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average difference between pupil scores in rural and urban areas controlling for the socioeconomic index and the territorial planning index, which is calculated based on the availability of the following infrastructure and services: a paved road; electricity; a lower secondary school; an upper secondary school; a hospital; a medical or healthcare center; a police station; a bank; a savings bank; a post office; and a cultural center or library. When urban and rural schools are located in areas with similar infrastructure and service levels as measured by the territorial planning index, the gaps between pupils’ scores tend to be smaller. Urban areas include towns and town suburbs, and rural areas include large villages (several hundred family lots) and small villages (up to one hundred family lots). These urban/rural definitions are standard across countries to facilitate the comparison of trends from one country to another. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.P05", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 5th Grade Mathematics Scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.P10", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 5th Grade Mathematics Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.P25", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 5th Grade Mathematics Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.P50", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 5th Grade Mathematics Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.P75", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 5th Grade Mathematics Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.P90", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 5th Grade Mathematics Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT.P95", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 5th Grade Mathematics Scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT5", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the mathematics scale (100 points) for 5th grade students. Total" }, { "id": "Longdefinition", "value": "Mean percentage correct for 5th grade students on the mathematics exam. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Mean percentage correct for 5th grade students on the mathematics exam. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT5.FE", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the mathematics scale (100 points) for 5th grade students. Female" }, { "id": "Longdefinition", "value": "Mean percentage correct for female 5th grade students on the mathematics exam. Average differences are only significant between males and females in Benin, Gabon, Burkina Faso, Senegal, Burundi, Cote d'Ivoire, Togo and Comoros. Since comparison is suggested by giving average scores for males/females, please take into account that these are only raw effects of gender. PASEC uses econometric models to validate the effect of gender in achievement. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Mean percentage correct for female 5th grade students on the mathematics exam. Average differences are only significant between males and females in Benin, Gabon, Burkina Faso, Senegal, Burundi, Cote d'Ivoire, Togo and Comoros. Since comparison is suggested by giving average scores for males/females, please take into account that these are only raw effects of gender. PASEC uses econometric models to validate the effect of gender in achievement. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT5.HIG", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 5th grade students reaching the Knowledge Base Rate on the mathematics scale (%)" }, { "id": "Longdefinition", "value": "Share of students scoring at least 40 percent on the PASEC mathematics exam. The Knowledge Base Rate is the minimum learning goal based on the programs of the level selected and appropriate to the scale of the tests used. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Share of students scoring at least 40 percent on the PASEC mathematics exam. The Knowledge Base Rate is the minimum learning goal based on the programs of the level selected and appropriate to the scale of the tests used. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT5.HIG.FE", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Female 5th grade students reaching the Knowledge Base Rate on the mathematics scale (%)" }, { "id": "Longdefinition", "value": "Share of female students scoring at least 40 percent on the PASEC mathematics exam. The Knowledge Base Rate is the minimum learning goal based on the programs of the level selected and appropriate to the scale of the tests used. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Share of female students scoring at least 40 percent on the PASEC mathematics exam. The Knowledge Base Rate is the minimum learning goal based on the programs of the level selected and appropriate to the scale of the tests used. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT5.HIG.MA", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Male 5th grade students reaching the Knowledge Base Rate on the mathematics scale (%)" }, { "id": "Longdefinition", "value": "Share of male students scoring at least 40 percent on the PASEC mathematics exam. The Knowledge Base Rate is the minimum learning goal based on the programs of the level selected and appropriate to the scale of the tests used. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Share of male students scoring at least 40 percent on the PASEC mathematics exam. The Knowledge Base Rate is the minimum learning goal based on the programs of the level selected and appropriate to the scale of the tests used. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT5.LO", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 5th grade students below the Failure Rate on the mathematics scale (%)" }, { "id": "Longdefinition", "value": "Share of students scoring less than 25 percent on the PASEC mathematics exam. The Failure Rate is the score a pupil could obtain by completing the PASEC test randomly. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Share of students scoring less than 25 percent on the PASEC mathematics exam. The Failure Rate is the score a pupil could obtain by completing the PASEC test randomly. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT5.LO.FE", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Female 5th grade students below the Failure Rate on the mathematics scale (%)" }, { "id": "Longdefinition", "value": "Share of female students scoring less than 25 percent on the PASEC mathematics exam. The Failure Rate is the score a pupil could obtain by completing the PASEC test randomly. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Share of female students scoring less than 25 percent on the PASEC mathematics exam. The Failure Rate is the score a pupil could obtain by completing the PASEC test randomly. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT5.LO.MA", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Male 5th grade students below the Failure Rate on the mathematics scale (%)" }, { "id": "Longdefinition", "value": "Share of male students scoring less than 25 percent on the PASEC mathematics exam. The Failure Rate is the score a pupil could obtain by completing the PASEC test randomly. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Share of male students scoring less than 25 percent on the PASEC mathematics exam. The Failure Rate is the score a pupil could obtain by completing the PASEC test randomly. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.MAT5.MA", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the mathematics scale (100 points) for 5th grade students. Male" }, { "id": "Longdefinition", "value": "Mean percentage correct for male 5th grade students on the mathematics exam. Average differences are only significant between males and females in Benin, Gabon, Burkina Faso, Senegal, Burundi, Cote d'Ivoire, Togo and Comoros. Since comparison is suggested by giving average scores for males/females, please take into account that these are only raw effects of gender. PASEC uses econometric models to validate the effect of gender in achievement. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Shortdefinition", "value": "Mean percentage correct for male 5th grade students on the mathematics exam. Average differences are only significant between males and females in Benin, Gabon, Burkina Faso, Senegal, Burundi, Cote d'Ivoire, Togo and Comoros. Since comparison is suggested by giving average scores for males/females, please take into account that these are only raw effects of gender. PASEC uses econometric models to validate the effect of gender in achievement. Data reflects country performance in the stated year according to PASEC. 2004-2005 PASEC data is only comparable with other country data from 2004-2005. 2006-2010 data is not comparable with data from 2004-2005. Consult the PASEC website for more detailed information: http://www.confemen.org/le-pasec/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the language scale for 2nd grade students. Total" }, { "id": "Longdefinition", "value": "Average score of Grade 2 students on the PASEC language scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of Grade 2 students on the PASEC language scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.FE", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the language scale for 2nd grade students. Female" }, { "id": "Longdefinition", "value": "Average score of female Grade 2 students on the PASEC language scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of female Grade 2 students on the PASEC language scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.L0", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 2nd grade students by language proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students scoring below 399.1 on the PASEC reading scale. Pupils below Level 1 do not display the competencies measured by the PASEC reading test. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students scoring below 399.1 on the PASEC reading scale. Pupils below Level 1 do not display the competencies measured by the PASEC reading test. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.L1", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 2nd grade students by language proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students scoring at least 399.1 but not higher than 469.5 on the PASEC reading scale. Students at proficiency level 1 are classified as Early Readers who have not reached the Sufficient Competency Threshold. Students at this level are able to understand very short and familiar oral messages to recognize familiar objects. They have great difficulty decoding written language and performing graphophonological identification (letters, syllables, graphemes and phonemes). Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students scoring at least 399.1 but not higher than 469.5 on the PASEC reading scale. Students at proficiency level 1 are classified as Early Readers who have not reached the Sufficient Competency Threshold. Students at this level are able to understand very short and familiar oral messages to recognize familiar objects. They have great difficulty decoding written language and performing graphophonological identification (letters, syllables, graphemes and phonemes). Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.L2", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 2nd grade students by language proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students scoring at least 469.5 but not higher than 540 on the PASEC reading scale. Students at proficiency level 2 are classified as Emerging Readers who have not reached the Sufficient Competency Threshold. These students are in the process of developing the first basic links between the oral and written language. They can perform basic graphophonological decoding, recognition and identification tasks (letters, syllables, graphemes and phonemes). Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students scoring at least 469.5 but not higher than 540 on the PASEC reading scale. Students at proficiency level 2 are classified as Emerging Readers who have not reached the Sufficient Competency Threshold. These students are in the process of developing the first basic links between the oral and written language. They can perform basic graphophonological decoding, recognition and identification tasks (letters, syllables, graphemes and phonemes). Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.L3", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 2nd grade students by language proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students scoring at least 540 but not higher than 610.4 on the PASEC reading scale. Students at proficiency level 3 are classified as Novice Readers who have reached the Sufficient Competency Threshold. These students have demonstrated their skills at written language decoding, listening comprehension and reading comprehension. They are able to identify the meaning of isolated words in reading comprehension, and in listening comprehension, they are able to understand explicit information in a short passage containing familiar vocabulary. They have developed links between oral and written language thus improving their decoding skills and expanding their vocabulary. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students scoring at least 540 but not higher than 610.4 on the PASEC reading scale. Students at proficiency level 3 are classified as Novice Readers who have reached the Sufficient Competency Threshold. These students have demonstrated their skills at written language decoding, listening comprehension and reading comprehension. They are able to identify the meaning of isolated words in reading comprehension, and in listening comprehension, they are able to understand explicit information in a short passage containing familiar vocabulary. They have developed links between oral and written language thus improving their decoding skills and expanding their vocabulary. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.L4", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 2nd grade students by language proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students scoring at least 610.4 on the PASEC reading scale. Students at proficiency level 4 are classified as Intermediate Readers who have reached the Sufficient Competency Threshold. These students have acquired written language decoding and listening comprehension competencies that enable them to understand explicit information in words, sentences and short passages. They can combine their decoding skills and their mastery of the oral language to grasp the literal meaning of a short passage. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students scoring at least 610.4 on the PASEC reading scale. Students at proficiency level 4 are classified as Intermediate Readers who have reached the Sufficient Competency Threshold. These students have acquired written language decoding and listening comprehension competencies that enable them to understand explicit information in words, sentences and short passages. They can combine their decoding skills and their mastery of the oral language to grasp the literal meaning of a short passage. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.LTR.0T5", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade students by average number of letters read accurately in one minute. 0 to 5 Letters" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students who accurately read 5 or less letters per minute on the PASEC reading assessment. Pupils’ ability to read letters of the alphabet correctly and quickly shows how well pupils have mastered initial letter decoding skills. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students who accurately read 5 or less letters per minute on the PASEC reading assessment. Pupils’ ability to read letters of the alphabet correctly and quickly shows how well pupils have mastered initial letter decoding skills. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.LTR.11T20", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade students by average number of letters read accurately in one minute. 11 to 20 Letters" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students who accurately read 11 to 20 letters per minute on the PASEC reading assessment. Pupils’ ability to read letters of the alphabet correctly and quickly shows how well pupils have mastered initial letter decoding skills. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students who accurately read 11 to 20 letters per minute on the PASEC reading assessment. Pupils’ ability to read letters of the alphabet correctly and quickly shows how well pupils have mastered initial letter decoding skills. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.LTR.20UP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade students by average number of letters read accurately in one minute. 20+ Letters" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students who accurately read more than 20 letters per minute on the PASEC reading assessment. Pupils’ ability to read letters of the alphabet correctly and quickly shows how well pupils have mastered initial letter decoding skills. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students who accurately read more than 20 letters per minute on the PASEC reading assessment. Pupils’ ability to read letters of the alphabet correctly and quickly shows how well pupils have mastered initial letter decoding skills. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.LTR.6T10", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade students by average number of letters read accurately in one minute. 6 to 10 Letters" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students who accurately read 6 to 10 letters per minute on the PASEC reading assessment. Pupils’ ability to read letters of the alphabet correctly and quickly shows how well pupils have mastered initial letter decoding skills. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students who accurately read 6 to 10 letters per minute on the PASEC reading assessment. Pupils’ ability to read letters of the alphabet correctly and quickly shows how well pupils have mastered initial letter decoding skills. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.MA", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the language scale for 2nd grade students. Male" }, { "id": "Longdefinition", "value": "Average score of male Grade 2 students on the PASEC language scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of male Grade 2 students on the PASEC language scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.MG.GAP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Average performance gap between 2nd grade students in multigrade and standard classes. Language" }, { "id": "Longdefinition", "value": "Average difference in scores between students in multigrade classes and standard classes. Standard classes have one full-time teacher per class/grade, and multigrade classes have pupils from different grades in a single pedagogical group with a single teacher. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average difference in scores between students in multigrade classes and standard classes. Standard classes have one full-time teacher per class/grade, and multigrade classes have pupils from different grades in a single pedagogical group with a single teacher. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.NPP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the language scale for 2nd grade students who did not attend pre-primary education" }, { "id": "Longdefinition", "value": "Average score of Grade 2 students who did not attend any form of pre-primary education. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of Grade 2 students who did not attend any form of pre-primary education. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.P05", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade language scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.P1", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade language scores: 1st Percentile Score" }, { "id": "Longdefinition", "value": "The 1st percentile score is the score below which 1 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 1st percentile score is the score below which 1 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.P10", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade language scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.P25", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade language scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.P50", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade language scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.P75", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade language scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.P90", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade language scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.P95", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade language scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 95 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 95 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.P99", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade language scores: 99th Percentile Score" }, { "id": "Longdefinition", "value": "The 99th percentile score is the score below which 99 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 99th percentile score is the score below which 99 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.PP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the language scale for 2nd grade students who attended pre-primary education" }, { "id": "Longdefinition", "value": "Average score of Grade 2 students who attended any form of pre-primary education. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of Grade 2 students who attended any form of pre-primary education. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.PP.GAP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Average performance gap between 2nd grade students who attended/did not attend pre-primary education. Language" }, { "id": "Longdefinition", "value": "Average difference in scores between students in who attended or did not attend any form of pre-primary education. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average difference in scores between students in who attended or did not attend any form of pre-primary education. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.PRI.GAP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Average performance gap between 2nd grade students in private and public education. Language" }, { "id": "Longdefinition", "value": "Average difference between pupil scores in private and public educational institutions controlling for the territorial planning index, which is calculated based on the availability of the following infrastructure and services: a paved road; electricity; a lower secondary school; an upper secondary school; a hospital; a medical or healthcare center; a police station; a bank; a savings bank; a post office; and a cultural center or library. When public and private schools are located in areas with similar infrastructure and service levels as measured by the territorial planning index, the gaps between pupils’ scores tend to be smaller. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average difference between pupil scores in private and public educational institutions controlling for the territorial planning index, which is calculated based on the availability of the following infrastructure and services: a paved road; electricity; a lower secondary school; an upper secondary school; a hospital; a medical or healthcare center; a police station; a bank; a savings bank; a post office; and a cultural center or library. When public and private schools are located in areas with similar infrastructure and service levels as measured by the territorial planning index, the gaps between pupils’ scores tend to be smaller. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.RU.GAP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Average performance gap between 2nd grade students in rural and urban areas. Language" }, { "id": "Longdefinition", "value": "Average difference between pupil scores in rural and urban areas controlling for the territorial planning index, which is calculated based on the availability of the following infrastructure and services: a paved road; electricity; a lower secondary school; an upper secondary school; a hospital; a medical or healthcare center; a police station; a bank; a savings bank; a post office; and a cultural center or library. When urban and rural schools are located in areas with similar infrastructure and service levels as measured by the territorial planning index, the gaps between pupils’ scores tend to be smaller. Urban areas include towns and town suburbs, and rural areas include large villages (several hundred family lots) and small villages (up to one hundred family lots). These urban/rural definitions are standard across countries to facilitate the comparison of trends from one country to another. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average difference between pupil scores in rural and urban areas controlling for the territorial planning index, which is calculated based on the availability of the following infrastructure and services: a paved road; electricity; a lower secondary school; an upper secondary school; a hospital; a medical or healthcare center; a police station; a bank; a savings bank; a post office; and a cultural center or library. When urban and rural schools are located in areas with similar infrastructure and service levels as measured by the territorial planning index, the gaps between pupils’ scores tend to be smaller. Urban areas include towns and town suburbs, and rural areas include large villages (several hundred family lots) and small villages (up to one hundred family lots). These urban/rural definitions are standard across countries to facilitate the comparison of trends from one country to another. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.WRD.0", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade students by average number of words read accurately in one minute. 0 Words" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students who accurately read zero words within one minute. The exercise requires pupils to read isolated, familiar and mostly irregular words to determine which pupils have developed sufficient written language decoding skills to adopt a lexical approach to reading. Pupils have up to five seconds to read each word; This time limit ensures that students are not decoding words through the sub-lexical assembly process. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students who accurately read zero words within one minute. The exercise requires pupils to read isolated, familiar and mostly irregular words to determine which pupils have developed sufficient written language decoding skills to adopt a lexical approach to reading. Pupils have up to five seconds to read each word; This time limit ensures that students are not decoding words through the sub-lexical assembly process. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.WRD.11T20", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade students by average number of words read accurately in one minute. 11 to 20 Words" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students who accurately read 11 to 20 words within one minute. The exercise requires pupils to read isolated, familiar and mostly irregular words to determine which pupils have developed sufficient written language decoding skills to adopt a lexical approach to reading. Pupils have up to five seconds to read each word; This time limit ensures that students are not decoding words through the sub-lexical assembly process. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students who accurately read 11 to 20 words within one minute. The exercise requires pupils to read isolated, familiar and mostly irregular words to determine which pupils have developed sufficient written language decoding skills to adopt a lexical approach to reading. Pupils have up to five seconds to read each word; This time limit ensures that students are not decoding words through the sub-lexical assembly process. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.WRD.1T5", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade students by average number of words read accurately in one minute. 1 to 5 Words" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students who accurately read 1 to 5 words within one minute. The exercise requires pupils to read isolated, familiar and mostly irregular words to determine which pupils have developed sufficient written language decoding skills to adopt a lexical approach to reading. Pupils have up to five seconds to read each word; This time limit ensures that students are not decoding words through the sub-lexical assembly process. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students who accurately read 1 to 5 words within one minute. The exercise requires pupils to read isolated, familiar and mostly irregular words to determine which pupils have developed sufficient written language decoding skills to adopt a lexical approach to reading. Pupils have up to five seconds to read each word; This time limit ensures that students are not decoding words through the sub-lexical assembly process. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.WRD.20UP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade students by average number of words read accurately in one minute. 20+ Words" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students who accurately read more than 20 words within one minute. The exercise requires pupils to read isolated, familiar and mostly irregular words to determine which pupils have developed sufficient written language decoding skills to adopt a lexical approach to reading. Pupils have up to five seconds to read each word; This time limit ensures that students are not decoding words through the sub-lexical assembly process. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students who accurately read more than 20 words within one minute. The exercise requires pupils to read isolated, familiar and mostly irregular words to determine which pupils have developed sufficient written language decoding skills to adopt a lexical approach to reading. Pupils have up to five seconds to read each word; This time limit ensures that students are not decoding words through the sub-lexical assembly process. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.2.WRD.6T10", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 2nd grade students by average number of words read accurately in one minute. 6 to 10 Words" }, { "id": "Longdefinition", "value": "Percentage of 2nd grade students who accurately read 6 to 10 words within one minute. The exercise requires pupils to read isolated, familiar and mostly irregular words to determine which pupils have developed sufficient written language decoding skills to adopt a lexical approach to reading. Pupils have up to five seconds to read each word; This time limit ensures that students are not decoding words through the sub-lexical assembly process. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Percentage of 2nd grade students who accurately read 6 to 10 words within one minute. The exercise requires pupils to read isolated, familiar and mostly irregular words to determine which pupils have developed sufficient written language decoding skills to adopt a lexical approach to reading. Pupils have up to five seconds to read each word; This time limit ensures that students are not decoding words through the sub-lexical assembly process. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the reading scale for 6th grade students. Total" }, { "id": "Longdefinition", "value": "Average score of Grade 6 students on the PASEC reading scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of Grade 6 students on the PASEC reading scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.FE", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the reading scale for 6th grade students. Female" }, { "id": "Longdefinition", "value": "Average score of female Grade 6 students on the PASEC reading scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of female Grade 6 students on the PASEC reading scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.L0", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 6th grade students by reading proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Share of 6th grade students scoring below 365 on the PASEC reading scale. Pupils below Level 1 are not able to correctly answer a majority of the most basic test questions; Pupils at this level do not display the competencies measured by this test. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Share of 6th grade students scoring below 365 on the PASEC reading scale. Pupils below Level 1 are not able to correctly answer a majority of the most basic test questions; Pupils at this level do not display the competencies measured by this test. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.L1", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 6th grade students by reading proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Share of 6th grade students scoring higher than 365 but lower than 441.7 on the PASEC reading scale. Students at level 1 have not achieved the Sufficient Competency Threshold. They have developed decoding skills and can draw on them to understand isolated words taken from their everyday lives but are in difficulty when it comes to understanding the meaning of short and simple texts. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Share of 6th grade students scoring higher than 365 but lower than 441.7 on the PASEC reading scale. Students at level 1 have not achieved the Sufficient Competency Threshold. They have developed decoding skills and can draw on them to understand isolated words taken from their everyday lives but are in difficulty when it comes to understanding the meaning of short and simple texts. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.L2", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 6th grade students by reading proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Share of 6th grade students scoring higher than 441.7 but lower than 518.4 on the PASEC reading scale. Students at level 2 have not achieved the Sufficient Competency Threshold, but they can draw on their orthographic decoding skills to identify and understand isolated words taken from their everyday lives. They are also able to paraphrase explicit information from a text and locate information in short and medium length texts by identifying clues in the text and questions. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Share of 6th grade students scoring higher than 441.7 but lower than 518.4 on the PASEC reading scale. Students at level 2 have not achieved the Sufficient Competency Threshold, but they can draw on their orthographic decoding skills to identify and understand isolated words taken from their everyday lives. They are also able to paraphrase explicit information from a text and locate information in short and medium length texts by identifying clues in the text and questions. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.L3", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 6th grade students by reading proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Share of 6th grade students scoring higher than 518.4 but lower than 595.1 on the PASEC reading scale. Students at level 3 have achieved the Sufficient Competency Threshold. They are able to combine two pieces of explicit information from a document and carry out simple inferences in a narrative or informative text. They can extract implicit information from written material while giving meaning to implicit connectors, anaphora or referents. Students can also locate explicit information in long texts and discontinuous documents. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Share of 6th grade students scoring higher than 518.4 but lower than 595.1 on the PASEC reading scale. Students at level 3 have achieved the Sufficient Competency Threshold. They are able to combine two pieces of explicit information from a document and carry out simple inferences in a narrative or informative text. They can extract implicit information from written material while giving meaning to implicit connectors, anaphora or referents. Students can also locate explicit information in long texts and discontinuous documents. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.L4", "metatype": [ { "id": "IndicatorName", "value": "PASEC: 6th grade students by reading proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Share of 6th grade students scoring higher than 595.1 on the PASEC reading scale. Students at level 4 have achieved the Sufficient Competency Threshold and have an overall understanding of narrative passages, informative texts and documents. They are able to interpret several implicit ideas in these texts while drawing from their experience and knowledge. When reading literary texts, pupils can to identify the author’s intention, determine implicit meaning and interpret characters’ feelings. When reading informative texts and documents, they can connect information and compare data prior to using it. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Share of 6th grade students scoring higher than 595.1 on the PASEC reading scale. Students at level 4 have achieved the Sufficient Competency Threshold and have an overall understanding of narrative passages, informative texts and documents. They are able to interpret several implicit ideas in these texts while drawing from their experience and knowledge. When reading literary texts, pupils can to identify the author’s intention, determine implicit meaning and interpret characters’ feelings. When reading informative texts and documents, they can connect information and compare data prior to using it. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.MA", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the reading scale for 6th grade students. Male" }, { "id": "Longdefinition", "value": "Average score of male Grade 6 students on the PASEC reading scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of male Grade 6 students on the PASEC reading scale. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.MG.GAP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Average performance gap between 6th grade students in multigrade and standard classes. Reading" }, { "id": "Longdefinition", "value": "Average difference in scores between students in multigrade classes and standard classes. Standard classes have one full-time teacher per class/grade, and multigrade classes have pupils from different grades in a single pedagogical group with a single teacher. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average difference in scores between students in multigrade classes and standard classes. Standard classes have one full-time teacher per class/grade, and multigrade classes have pupils from different grades in a single pedagogical group with a single teacher. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.NPP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the reading scale for 6th grade students who did not attend pre-primary education" }, { "id": "Longdefinition", "value": "Average score of Grade 6 students who did not attend any form of pre-primary education. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of Grade 6 students who did not attend any form of pre-primary education. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.P05", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade reading scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.P1", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade reading scores: 1st Percentile Score" }, { "id": "Longdefinition", "value": "The 1st percentile score is the score below which 1 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 1st percentile score is the score below which 1 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.P10", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade reading scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.P25", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade reading scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.P50", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade reading scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.P75", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade reading scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.P90", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade reading scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.P95", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade reading scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 95 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 95 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.P99", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Distribution of 6th grade reading scores: 99th Percentile Score" }, { "id": "Longdefinition", "value": "The 99th percentile score is the score below which 99 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "The 99th percentile score is the score below which 99 percent of students scored. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.PP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Mean performance on the reading scale for 6th grade students who attended pre-primary education" }, { "id": "Longdefinition", "value": "Average score of Grade 6 students who attended any form of pre-primary education. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average score of Grade 6 students who attended any form of pre-primary education. PASEC performance scales have an international average of 500 points and a standard deviation of 100 points with all countries being given equal weighting. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.PP.GAP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Average performance gap between 6th grade students who attended/did not attend pre-primary education. Reading" }, { "id": "Longdefinition", "value": "Average difference in scores between students in who attended or did not attend any form of pre-primary education. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average difference in scores between students in who attended or did not attend any form of pre-primary education. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.PRI.GAP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Average performance gap between 6th grade students in private and public education. Reading" }, { "id": "Longdefinition", "value": "Average difference between pupil scores in private and public educational institutions controlling for the socioeconomic index and the territorial planning index, which is calculated based on the availability of the following infrastructure and services: a paved road; electricity; a lower secondary school; an upper secondary school; a hospital; a medical or healthcare center; a police station; a bank; a savings bank; a post office; and a cultural center or library. When public and private schools are located in areas with similar infrastructure and service levels as measured by the territorial planning index, the gaps between pupils’ scores tend to be smaller. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average difference between pupil scores in private and public educational institutions controlling for the socioeconomic index and the territorial planning index, which is calculated based on the availability of the following infrastructure and services: a paved road; electricity; a lower secondary school; an upper secondary school; a hospital; a medical or healthcare center; a police station; a bank; a savings bank; a post office; and a cultural center or library. When public and private schools are located in areas with similar infrastructure and service levels as measured by the territorial planning index, the gaps between pupils’ scores tend to be smaller. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PASEC.REA.6.RU.GAP", "metatype": [ { "id": "IndicatorName", "value": "PASEC: Average performance gap between 6th grade students in rural and urban areas. Reading" }, { "id": "Longdefinition", "value": "Average difference between pupil scores in rural and urban areas controlling for the socioeconomic index and the territorial planning index, which is calculated based on the availability of the following infrastructure and services: a paved road; electricity; a lower secondary school; an upper secondary school; a hospital; a medical or healthcare center; a police station; a bank; a savings bank; a post office; and a cultural center or library. When urban and rural schools are located in areas with similar infrastructure and service levels as measured by the territorial planning index, the gaps between pupils’ scores tend to be smaller. Urban areas include towns and town suburbs, and rural areas include large villages (several hundred family lots) and small villages (up to one hundred family lots). These urban/rural definitions are standard across countries to facilitate the comparison of trends from one country to another. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Shortdefinition", "value": "Average difference between pupil scores in rural and urban areas controlling for the socioeconomic index and the territorial planning index, which is calculated based on the availability of the following infrastructure and services: a paved road; electricity; a lower secondary school; an upper secondary school; a hospital; a medical or healthcare center; a police station; a bank; a savings bank; a post office; and a cultural center or library. When urban and rural schools are located in areas with similar infrastructure and service levels as measured by the territorial planning index, the gaps between pupils’ scores tend to be smaller. Urban areas include towns and town suburbs, and rural areas include large villages (several hundred family lots) and small villages (up to one hundred family lots). These urban/rural definitions are standard across countries to facilitate the comparison of trends from one country to another. See footnotes for the statistical significance of each data point. Data were published for this indicator beginning with PASEC 2014. Consult the PASEC website for more detailed information: http://www.pasec.confemen.org/" }, { "id": "Source", "value": "Programme d'Analyse des Systèmes Educatifs de la CONFEMEN/Program for the Analysis of CONFEMEN Education Systems (PASEC): http://www.pasec.confemen.org/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Mean Adult Literacy Proficiency. Total" }, { "id": "Longdefinition", "value": "Literacy is defined as the ability to understand, evaluate, use and engage with written texts to participate in society, to achieve one’s goals, and to develop one’s knowledge and potential. Literacy encompasses a range of skills from the decoding of written words and sentences to the comprehension, interpretation, and evaluation of complex texts. It does not, however, involve the production of text (writing). Information on the skills of adults with low levels of proficiency is provided by an assessment of reading components that covers text vocabulary, sentence comprehension and passage fluency. The target population for the survey was the non institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. Literacy-related non-respondents are not included in the calculation of the mean scores which, thus, present an upper bound of the estimated literacy proficiency of the population. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Literacy is defined as the ability to understand, evaluate, use and engage with written texts to participate in society, to achieve one’s goals, and to develop one’s knowledge and potential. Literacy encompasses a range of skills from the decoding of written words and sentences to the comprehension, interpretation, and evaluation of complex texts. It does not, however, involve the production of text (writing). Information on the skills of adults with low levels of proficiency is provided by an assessment of reading components that covers text vocabulary, sentence comprehension and passage fluency. The target population for the survey was the non institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. Literacy-related non-respondents are not included in the calculation of the mean scores which, thus, present an upper bound of the estimated literacy proficiency of the population. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.1", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by literacy proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of adults scoring 176 to less than 226 points on the 0 to 500 point scale. Most of the tasks at this level require the respondent to read relatively short digital or print continuous, non-continuous, or mixed texts to locate a single piece of information that is identical to or synonymous with the information given in the question or directive. Some tasks, such as those involving non-continuous texts, may require the respondent to enter personal information onto a document. Little, if any, competing information is present. Some tasks may require simple cycling through more than one piece of information. Knowledge and skill in recognizing basic vocabulary determining the meaning of sentences, and reading paragraphs of text is expected. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of adults scoring 176 to less than 226 points on the 0 to 500 point scale. Most of the tasks at this level require the respondent to read relatively short digital or print continuous, non-continuous, or mixed texts to locate a single piece of information that is identical to or synonymous with the information given in the question or directive. Some tasks, such as those involving non-continuous texts, may require the respondent to enter personal information onto a document. Little, if any, competing information is present. Some tasks may require simple cycling through more than one piece of information. Knowledge and skill in recognizing basic vocabulary determining the meaning of sentences, and reading paragraphs of text is expected. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.2", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by literacy proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of adults scoring 226 to less than 276 points on the 0 to 500 point scale. At this level, the medium of texts may be digital or printed, and texts may comprise continuous, non-continuous, or mixed types. Tasks at this level require respondents to make matches between the text and information, and may require paraphrasing or low-level inferences. Some competing pieces of information may be present. Some tasks require the respondent to either A) cycle through or integrate two or more pieces of information based on criteria; B) compare and contrast or reason about information requested in the question; or C) navigate within digital texts to access and identify information from various parts of a document. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of adults scoring 226 to less than 276 points on the 0 to 500 point scale. At this level, the medium of texts may be digital or printed, and texts may comprise continuous, non-continuous, or mixed types. Tasks at this level require respondents to make matches between the text and information, and may require paraphrasing or low-level inferences. Some competing pieces of information may be present. Some tasks require the respondent to either A) cycle through or integrate two or more pieces of information based on criteria; B) compare and contrast or reason about information requested in the question; or C) navigate within digital texts to access and identify information from various parts of a document. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.3", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by literacy proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of adults scoring 276 to less than 326 points on the 0 to 500 point scale. Texts at this level are often dense or lengthy, and include continuous, non-continuous, mixed, or multiple pages of text. Understanding text and rhetorical structures become more central to successfully completing tasks, especially navigating complex digital texts. Tasks require the respondent to identify, interpret, or evaluate one or more pieces of information, and often require varying levels of inference. Many tasks require the respondent to construct meaning across larger chunks of text or perform multi-step operations in order to identify and formulate responses. Often tasks also demand that the respondent disregard irrelevant or inappropriate content to answer accurately. Competing information is often present, but it is not more prominent than the correct information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of adults scoring 276 to less than 326 points on the 0 to 500 point scale. Texts at this level are often dense or lengthy, and include continuous, non-continuous, mixed, or multiple pages of text. Understanding text and rhetorical structures become more central to successfully completing tasks, especially navigating complex digital texts. Tasks require the respondent to identify, interpret, or evaluate one or more pieces of information, and often require varying levels of inference. Many tasks require the respondent to construct meaning across larger chunks of text or perform multi-step operations in order to identify and formulate responses. Often tasks also demand that the respondent disregard irrelevant or inappropriate content to answer accurately. Competing information is often present, but it is not more prominent than the correct information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.4", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by literacy proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of adults scoring 326 to less than 376 points on the 0 to 500 point scale. Tasks at this level often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of adults scoring 326 to less than 376 points on the 0 to 500 point scale. Tasks at this level often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.5", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by literacy proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of adults scoring equal to or higher than 376 points on the 0 to 500 point scale. At this level, tasks may require the respondent to search for and integrate information across multiple, dense texts; construct syntheses of similar and contrasting ideas or points of view; or evaluate evidence based arguments. Application and evaluation of logical and conceptual models of ideas may be required to accomplish tasks. Evaluating reliability of evidentiary sources and selecting key information is frequently a requirement. Tasks often require respondents to be aware of subtle, rhetorical cues and to make high-level inferences or use specialized background knowledge. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of adults scoring equal to or higher than 376 points on the 0 to 500 point scale. At this level, tasks may require the respondent to search for and integrate information across multiple, dense texts; construct syntheses of similar and contrasting ideas or points of view; or evaluate evidence based arguments. Application and evaluation of logical and conceptual models of ideas may be required to accomplish tasks. Evaluating reliability of evidentiary sources and selecting key information is frequently a requirement. Tasks often require respondents to be aware of subtle, rhetorical cues and to make high-level inferences or use specialized background knowledge. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.BE", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by literacy proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of adults scoring below 176 points on the 0 to 500 point scale. The tasks at this level require the respondent to read brief texts on familiar topics to locate a single piece of specific information. There is seldom any competing information in the text and the requested information is identical in form to information in the question or directive. The respondent may be required to locate information in short continuous texts. However, in this case, the information can be located as if the text were non-continuous in format. Only basic vocabulary knowledge is required, and the reader is not required to understand the structure of sentences or paragraphs or make use of other text features. Tasks below Level 1 do not make use of any features specific to digital texts. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of adults scoring below 176 points on the 0 to 500 point scale. The tasks at this level require the respondent to read brief texts on familiar topics to locate a single piece of specific information. There is seldom any competing information in the text and the requested information is identical in form to information in the question or directive. The respondent may be required to locate information in short continuous texts. However, in this case, the information can be located as if the text were non-continuous in format. Only basic vocabulary knowledge is required, and the reader is not required to understand the structure of sentences or paragraphs or make use of other text features. Tasks below Level 1 do not make use of any features specific to digital texts. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.FE", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Mean Adult Literacy Proficiency. Female" }, { "id": "Longdefinition", "value": "Literacy is defined as the ability to understand, evaluate, use and engage with written texts to participate in society, to achieve one’s goals, and to develop one’s knowledge and potential. Literacy encompasses a range of skills from the decoding of written words and sentences to the comprehension, interpretation, and evaluation of complex texts. It does not, however, involve the production of text (writing). Information on the skills of adults with low levels of proficiency is provided by an assessment of reading components that covers text vocabulary, sentence comprehension and passage fluency. The target population for the survey was the non institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. Literacy-related non-respondents are not included in the calculation of the mean scores which, thus, present an upper bound of the estimated literacy proficiency of the population. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Literacy is defined as the ability to understand, evaluate, use and engage with written texts to participate in society, to achieve one’s goals, and to develop one’s knowledge and potential. Literacy encompasses a range of skills from the decoding of written words and sentences to the comprehension, interpretation, and evaluation of complex texts. It does not, however, involve the production of text (writing). Information on the skills of adults with low levels of proficiency is provided by an assessment of reading components that covers text vocabulary, sentence comprehension and passage fluency. The target population for the survey was the non institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. Literacy-related non-respondents are not included in the calculation of the mean scores which, thus, present an upper bound of the estimated literacy proficiency of the population. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.FE.1", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by literacy proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring 176 to less than 226 points on the 0 to 500 point scale. Most of the tasks at this level require the respondent to read relatively short digital or print continuous, non-continuous, or mixed texts to locate a single piece of information that is identical to or synonymous with the information given in the question or directive. Some tasks, such as those involving non-continuous texts, may require the respondent to enter personal information onto a document. Little, if any, competing information is present. Some tasks may require simple cycling through more than one piece of information. Knowledge and skill in recognizing basic vocabulary determining the meaning of sentences, and reading paragraphs of text is expected. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring 176 to less than 226 points on the 0 to 500 point scale. Most of the tasks at this level require the respondent to read relatively short digital or print continuous, non-continuous, or mixed texts to locate a single piece of information that is identical to or synonymous with the information given in the question or directive. Some tasks, such as those involving non-continuous texts, may require the respondent to enter personal information onto a document. Little, if any, competing information is present. Some tasks may require simple cycling through more than one piece of information. Knowledge and skill in recognizing basic vocabulary determining the meaning of sentences, and reading paragraphs of text is expected. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.FE.2", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by literacy proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring 226 to less than 276 points on the 0 to 500 point scale. At this level, the medium of texts may be digital or printed, and texts may comprise continuous, non-continuous, or mixed types. Tasks at this level require respondents to make matches between the text and information, and may require paraphrasing or low-level inferences. Some competing pieces of information may be present. Some tasks require the respondent to either A) cycle through or integrate two or more pieces of information based on criteria; B) compare and contrast or reason about information requested in the question; or C) navigate within digital texts to access and identify information from various parts of a document. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring 226 to less than 276 points on the 0 to 500 point scale. At this level, the medium of texts may be digital or printed, and texts may comprise continuous, non-continuous, or mixed types. Tasks at this level require respondents to make matches between the text and information, and may require paraphrasing or low-level inferences. Some competing pieces of information may be present. Some tasks require the respondent to either A) cycle through or integrate two or more pieces of information based on criteria; B) compare and contrast or reason about information requested in the question; or C) navigate within digital texts to access and identify information from various parts of a document. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.FE.3", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by literacy proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring 276 to less than 326 points on the 0 to 500 point scale. Texts at this level are often dense or lengthy, and include continuous, non-continuous, mixed, or multiple pages of text. Understanding text and rhetorical structures become more central to successfully completing tasks, especially navigating complex digital texts. Tasks require the respondent to identify, interpret, or evaluate one or more pieces of information, and often require varying levels of inference. Many tasks require the respondent to construct meaning across larger chunks of text or perform multi-step operations in order to identify and formulate responses. Often tasks also demand that the respondent disregard irrelevant or inappropriate content to answer accurately. Competing information is often present, but it is not more prominent than the correct information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring 276 to less than 326 points on the 0 to 500 point scale. Texts at this level are often dense or lengthy, and include continuous, non-continuous, mixed, or multiple pages of text. Understanding text and rhetorical structures become more central to successfully completing tasks, especially navigating complex digital texts. Tasks require the respondent to identify, interpret, or evaluate one or more pieces of information, and often require varying levels of inference. Many tasks require the respondent to construct meaning across larger chunks of text or perform multi-step operations in order to identify and formulate responses. Often tasks also demand that the respondent disregard irrelevant or inappropriate content to answer accurately. Competing information is often present, but it is not more prominent than the correct information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.FE.4", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by literacy proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring 326 to less than 376 points on the 0 to 500 point scale. Tasks at this level often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring 326 to less than 376 points on the 0 to 500 point scale. Tasks at this level often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.FE.45", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by literacy proficiency level (%). Level 4 & 5" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring at least 326 on the 0 to 500 point scale. Tasks at level 4 often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. Tasks at level 5 often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring at least 326 on the 0 to 500 point scale. Tasks at level 4 often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. Tasks at level 5 often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.FE.5", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by literacy proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring equal to or higher than 376 points on the 0 to 500 point scale. At this level, tasks may require the respondent to search for and integrate information across multiple, dense texts; construct syntheses of similar and contrasting ideas or points of view; or evaluate evidence based arguments. Application and evaluation of logical and conceptual models of ideas may be required to accomplish tasks. Evaluating reliability of evidentiary sources and selecting key information is frequently a requirement. Tasks often require respondents to be aware of subtle, rhetorical cues and to make high-level inferences or use specialized background knowledge. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring equal to or higher than 376 points on the 0 to 500 point scale. At this level, tasks may require the respondent to search for and integrate information across multiple, dense texts; construct syntheses of similar and contrasting ideas or points of view; or evaluate evidence based arguments. Application and evaluation of logical and conceptual models of ideas may be required to accomplish tasks. Evaluating reliability of evidentiary sources and selecting key information is frequently a requirement. Tasks often require respondents to be aware of subtle, rhetorical cues and to make high-level inferences or use specialized background knowledge. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.FE.BE", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by literacy proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring below 176 points on the 0 to 500 point scale. The tasks at this level require the respondent to read brief texts on familiar topics to locate a single piece of specific information. There is seldom any competing information in the text and the requested information is identical in form to information in the question or directive. The respondent may be required to locate information in short continuous texts. However, in this case, the information can be located as if the text were non-continuous in format. Only basic vocabulary knowledge is required, and the reader is not required to understand the structure of sentences or paragraphs or make use of other text features. Tasks below Level 1 do not make use of any features specific to digital texts. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring below 176 points on the 0 to 500 point scale. The tasks at this level require the respondent to read brief texts on familiar topics to locate a single piece of specific information. There is seldom any competing information in the text and the requested information is identical in form to information in the question or directive. The respondent may be required to locate information in short continuous texts. However, in this case, the information can be located as if the text were non-continuous in format. Only basic vocabulary knowledge is required, and the reader is not required to understand the structure of sentences or paragraphs or make use of other text features. Tasks below Level 1 do not make use of any features specific to digital texts. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.MA", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Mean Adult Literacy Proficiency. Male" }, { "id": "Longdefinition", "value": "Literacy is defined as the ability to understand, evaluate, use and engage with written texts to participate in society, to achieve one’s goals, and to develop one’s knowledge and potential. Literacy encompasses a range of skills from the decoding of written words and sentences to the comprehension, interpretation, and evaluation of complex texts. It does not, however, involve the production of text (writing). Information on the skills of adults with low levels of proficiency is provided by an assessment of reading components that covers text vocabulary, sentence comprehension and passage fluency. The target population for the survey was the non institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. Literacy-related non-respondents are not included in the calculation of the mean scores which, thus, present an upper bound of the estimated literacy proficiency of the population. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Literacy is defined as the ability to understand, evaluate, use and engage with written texts to participate in society, to achieve one’s goals, and to develop one’s knowledge and potential. Literacy encompasses a range of skills from the decoding of written words and sentences to the comprehension, interpretation, and evaluation of complex texts. It does not, however, involve the production of text (writing). Information on the skills of adults with low levels of proficiency is provided by an assessment of reading components that covers text vocabulary, sentence comprehension and passage fluency. The target population for the survey was the non institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. Literacy-related non-respondents are not included in the calculation of the mean scores which, thus, present an upper bound of the estimated literacy proficiency of the population. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.MA.1", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by literacy proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring 176 to less than 226 points on the 0 to 500 point scale. Most of the tasks at this level require the respondent to read relatively short digital or print continuous, non-continuous, or mixed texts to locate a single piece of information that is identical to or synonymous with the information given in the question or directive. Some tasks, such as those involving non-continuous texts, may require the respondent to enter personal information onto a document. Little, if any, competing information is present. Some tasks may require simple cycling through more than one piece of information. Knowledge and skill in recognizing basic vocabulary determining the meaning of sentences, and reading paragraphs of text is expected. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring 176 to less than 226 points on the 0 to 500 point scale. Most of the tasks at this level require the respondent to read relatively short digital or print continuous, non-continuous, or mixed texts to locate a single piece of information that is identical to or synonymous with the information given in the question or directive. Some tasks, such as those involving non-continuous texts, may require the respondent to enter personal information onto a document. Little, if any, competing information is present. Some tasks may require simple cycling through more than one piece of information. Knowledge and skill in recognizing basic vocabulary determining the meaning of sentences, and reading paragraphs of text is expected. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.MA.2", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by literacy proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring 226 to less than 276 points on the 0 to 500 point scale. At this level, the medium of texts may be digital or printed, and texts may comprise continuous, non-continuous, or mixed types. Tasks at this level require respondents to make matches between the text and information, and may require paraphrasing or low-level inferences. Some competing pieces of information may be present. Some tasks require the respondent to either A) cycle through or integrate two or more pieces of information based on criteria; B) compare and contrast or reason about information requested in the question; or C) navigate within digital texts to access and identify information from various parts of a document. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring 226 to less than 276 points on the 0 to 500 point scale. At this level, the medium of texts may be digital or printed, and texts may comprise continuous, non-continuous, or mixed types. Tasks at this level require respondents to make matches between the text and information, and may require paraphrasing or low-level inferences. Some competing pieces of information may be present. Some tasks require the respondent to either A) cycle through or integrate two or more pieces of information based on criteria; B) compare and contrast or reason about information requested in the question; or C) navigate within digital texts to access and identify information from various parts of a document. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.MA.3", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by literacy proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring 276 to less than 326 points on the 0 to 500 point scale. Texts at this level are often dense or lengthy, and include continuous, non-continuous, mixed, or multiple pages of text. Understanding text and rhetorical structures become more central to successfully completing tasks, especially navigating complex digital texts. Tasks require the respondent to identify, interpret, or evaluate one or more pieces of information, and often require varying levels of inference. Many tasks require the respondent to construct meaning across larger chunks of text or perform multi-step operations in order to identify and formulate responses. Often tasks also demand that the respondent disregard irrelevant or inappropriate content to answer accurately. Competing information is often present, but it is not more prominent than the correct information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring 276 to less than 326 points on the 0 to 500 point scale. Texts at this level are often dense or lengthy, and include continuous, non-continuous, mixed, or multiple pages of text. Understanding text and rhetorical structures become more central to successfully completing tasks, especially navigating complex digital texts. Tasks require the respondent to identify, interpret, or evaluate one or more pieces of information, and often require varying levels of inference. Many tasks require the respondent to construct meaning across larger chunks of text or perform multi-step operations in order to identify and formulate responses. Often tasks also demand that the respondent disregard irrelevant or inappropriate content to answer accurately. Competing information is often present, but it is not more prominent than the correct information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.MA.4", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by literacy proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring 326 to less than 376 points on the 0 to 500 point scale. Tasks at this level often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring 326 to less than 376 points on the 0 to 500 point scale. Tasks at this level often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.MA.45", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by literacy proficiency level (%). Level 4 & 5" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring at least 326 on the 0 to 500 point scale. Tasks at level 4 often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. Tasks at level 5 often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring at least 326 on the 0 to 500 point scale. Tasks at level 4 often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. Tasks at level 5 often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.MA.5", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by literacy proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring equal to or higher than 376 points on the 0 to 500 point scale. At this level, tasks may require the respondent to search for and integrate information across multiple, dense texts; construct syntheses of similar and contrasting ideas or points of view; or evaluate evidence based arguments. Application and evaluation of logical and conceptual models of ideas may be required to accomplish tasks. Evaluating reliability of evidentiary sources and selecting key information is frequently a requirement. Tasks often require respondents to be aware of subtle, rhetorical cues and to make high-level inferences or use specialized background knowledge. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring equal to or higher than 376 points on the 0 to 500 point scale. At this level, tasks may require the respondent to search for and integrate information across multiple, dense texts; construct syntheses of similar and contrasting ideas or points of view; or evaluate evidence based arguments. Application and evaluation of logical and conceptual models of ideas may be required to accomplish tasks. Evaluating reliability of evidentiary sources and selecting key information is frequently a requirement. Tasks often require respondents to be aware of subtle, rhetorical cues and to make high-level inferences or use specialized background knowledge. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.MA.BE", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by literacy proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring below 176 points on the 0 to 500 point scale. The tasks at this level require the respondent to read brief texts on familiar topics to locate a single piece of specific information. There is seldom any competing information in the text and the requested information is identical in form to information in the question or directive. The respondent may be required to locate information in short continuous texts. However, in this case, the information can be located as if the text were non-continuous in format. Only basic vocabulary knowledge is required, and the reader is not required to understand the structure of sentences or paragraphs or make use of other text features. Tasks below Level 1 do not make use of any features specific to digital texts. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring below 176 points on the 0 to 500 point scale. The tasks at this level require the respondent to read brief texts on familiar topics to locate a single piece of specific information. There is seldom any competing information in the text and the requested information is identical in form to information in the question or directive. The respondent may be required to locate information in short continuous texts. However, in this case, the information can be located as if the text were non-continuous in format. Only basic vocabulary knowledge is required, and the reader is not required to understand the structure of sentences or paragraphs or make use of other text features. Tasks below Level 1 do not make use of any features specific to digital texts. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.P05", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Literacy Scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.P10", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Literacy Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.P25", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Literacy Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.P50", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Literacy Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.P75", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Literacy Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.P90", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Literacy Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.P95", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Literacy Scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 90 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 90 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.YOU", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Mean Young Adult Literacy Proficiency. Total" }, { "id": "Longdefinition", "value": "Literacy is defined as the ability to understand, evaluate, use and engage with written texts to participate in society, to achieve one’s goals, and to develop one’s knowledge and potential. Literacy encompasses a range of skills from the decoding of written words and sentences to the comprehension, interpretation, and evaluation of complex texts. It does not, however, involve the production of text (writing). Information on the skills of adults with low levels of proficiency is provided by an assessment of reading components that covers text vocabulary, sentence comprehension and passage fluency. The target population was the non institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. Literacy-related non-respondents are not included in the calculation of the mean scores which, thus, present an upper bound of the estimated literacy proficiency of the population. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Literacy is defined as the ability to understand, evaluate, use and engage with written texts to participate in society, to achieve one’s goals, and to develop one’s knowledge and potential. Literacy encompasses a range of skills from the decoding of written words and sentences to the comprehension, interpretation, and evaluation of complex texts. It does not, however, involve the production of text (writing). Information on the skills of adults with low levels of proficiency is provided by an assessment of reading components that covers text vocabulary, sentence comprehension and passage fluency. The target population was the non institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. Literacy-related non-respondents are not included in the calculation of the mean scores which, thus, present an upper bound of the estimated literacy proficiency of the population. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.YOU.1", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by literacy proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring 176 to less than 226 points on the 0 to 500 point scale. Most of the tasks at this level require the respondent to read relatively short digital or print continuous, non-continuous, or mixed texts to locate a single piece of information that is identical to or synonymous with the information given in the question or directive. Some tasks, such as those involving non-continuous texts, may require the respondent to enter personal information onto a document. Little, if any, competing information is present. Some tasks may require simple cycling through more than one piece of information. Knowledge and skill in recognizing basic vocabulary determining the meaning of sentences, and reading paragraphs of text is expected. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring 176 to less than 226 points on the 0 to 500 point scale. Most of the tasks at this level require the respondent to read relatively short digital or print continuous, non-continuous, or mixed texts to locate a single piece of information that is identical to or synonymous with the information given in the question or directive. Some tasks, such as those involving non-continuous texts, may require the respondent to enter personal information onto a document. Little, if any, competing information is present. Some tasks may require simple cycling through more than one piece of information. Knowledge and skill in recognizing basic vocabulary determining the meaning of sentences, and reading paragraphs of text is expected. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.YOU.2", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by literacy proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring 226 to less than 276 points on the 0 to 500 point scale. At this level, the medium of texts may be digital or printed, and texts may comprise continuous, non-continuous, or mixed types. Tasks at this level require respondents to make matches between the text and information, and may require paraphrasing or low-level inferences. Some competing pieces of information may be present. Some tasks require the respondent to either A) cycle through or integrate two or more pieces of information based on criteria; B) compare and contrast or reason about information requested in the question; or C) navigate within digital texts to access and identify information from various parts of a document. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring 226 to less than 276 points on the 0 to 500 point scale. At this level, the medium of texts may be digital or printed, and texts may comprise continuous, non-continuous, or mixed types. Tasks at this level require respondents to make matches between the text and information, and may require paraphrasing or low-level inferences. Some competing pieces of information may be present. Some tasks require the respondent to either A) cycle through or integrate two or more pieces of information based on criteria; B) compare and contrast or reason about information requested in the question; or C) navigate within digital texts to access and identify information from various parts of a document. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.YOU.3", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by literacy proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring 276 to less than 326 points on the 0 to 500 point scale. Texts at this level are often dense or lengthy, and include continuous, non-continuous, mixed, or multiple pages of text. Understanding text and rhetorical structures become more central to successfully completing tasks, especially navigating complex digital texts. Tasks require the respondent to identify, interpret, or evaluate one or more pieces of information, and often require varying levels of inference. Many tasks require the respondent to construct meaning across larger chunks of text or perform multi-step operations in order to identify and formulate responses. Often tasks also demand that the respondent disregard irrelevant or inappropriate content to answer accurately. Competing information is often present, but it is not more prominent than the correct information. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring 276 to less than 326 points on the 0 to 500 point scale. Texts at this level are often dense or lengthy, and include continuous, non-continuous, mixed, or multiple pages of text. Understanding text and rhetorical structures become more central to successfully completing tasks, especially navigating complex digital texts. Tasks require the respondent to identify, interpret, or evaluate one or more pieces of information, and often require varying levels of inference. Many tasks require the respondent to construct meaning across larger chunks of text or perform multi-step operations in order to identify and formulate responses. Often tasks also demand that the respondent disregard irrelevant or inappropriate content to answer accurately. Competing information is often present, but it is not more prominent than the correct information. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.YOU.4", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by literacy proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring 326 to less than 376 points on the 0 to 500 point scale. Tasks at this level often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring 326 to less than 376 points on the 0 to 500 point scale. Tasks at this level often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.YOU.45", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by literacy proficiency level (%). Level 4 & 5" }, { "id": "Longdefinition", "value": "Percentage of young adults scoring at least 326 on the 0 to 500 point scale. Tasks at level 4 often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. Tasks at level 5 often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. The target population for the survey was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young adults scoring at least 326 on the 0 to 500 point scale. Tasks at level 4 often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. Tasks at level 5 often require respondents to perform multiple-step operations to integrate, interpret, or synthesize information from complex or lengthy continuous, non-continuous, mixed, or multiple type texts. Complex inferences and application of background knowledge may be needed to perform the task successfully. Many tasks require identifying and understanding one or more specific, non-central idea(s) in the text in order to interpret or evaluate subtle evidence-claim or persuasive discourse relationships. Conditional information is frequently present in tasks at this level and must be taken into consideration by the respondent. Competing information is present and sometimes seemingly as prominent as correct information. The target population for the survey was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.YOU.5", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by literacy proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring equal to or higher than 376 points on the 0 to 500 point scale. At this level, tasks may require the respondent to search for and integrate information across multiple, dense texts; construct syntheses of similar and contrasting ideas or points of view; or evaluate evidence based arguments. Application and evaluation of logical and conceptual models of ideas may be required to accomplish tasks. Evaluating reliability of evidentiary sources and selecting key information is frequently a requirement. Tasks often require respondents to be aware of subtle, rhetorical cues and to make high-level inferences or use specialized background knowledge. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring equal to or higher than 376 points on the 0 to 500 point scale. At this level, tasks may require the respondent to search for and integrate information across multiple, dense texts; construct syntheses of similar and contrasting ideas or points of view; or evaluate evidence based arguments. Application and evaluation of logical and conceptual models of ideas may be required to accomplish tasks. Evaluating reliability of evidentiary sources and selecting key information is frequently a requirement. Tasks often require respondents to be aware of subtle, rhetorical cues and to make high-level inferences or use specialized background knowledge. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.YOU.BE", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by literacy proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring below 176 points on the 0 to 500 point scale. The tasks at this level require the respondent to read brief texts on familiar topics to locate a single piece of specific information. There is seldom any competing information in the text and the requested information is identical in form to information in the question or directive. The respondent may be required to locate information in short continuous texts. However, in this case, the information can be located as if the text were non-continuous in format. Only basic vocabulary knowledge is required, and the reader is not required to understand the structure of sentences or paragraphs or make use of other text features. Tasks below Level 1 do not make use of any features specific to digital texts. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring below 176 points on the 0 to 500 point scale. The tasks at this level require the respondent to read brief texts on familiar topics to locate a single piece of specific information. There is seldom any competing information in the text and the requested information is identical in form to information in the question or directive. The respondent may be required to locate information in short continuous texts. However, in this case, the information can be located as if the text were non-continuous in format. Only basic vocabulary knowledge is required, and the reader is not required to understand the structure of sentences or paragraphs or make use of other text features. Tasks below Level 1 do not make use of any features specific to digital texts. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The percentage of adults scoring at different levels of proficiency adds up to 100% when the percentage of literacy-related non-respondents are taken into account. Adults in this category were not able to complete the background questionnaire due to language difficulties or learning and mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.YOU.FE", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Mean Young Adult Literacy Proficiency. Female" }, { "id": "Longdefinition", "value": "Literacy is defined as the ability to understand, evaluate, use and engage with written texts to participate in society, to achieve one’s goals, and to develop one’s knowledge and potential. Literacy encompasses a range of skills from the decoding of written words and sentences to the comprehension, interpretation, and evaluation of complex texts. It does not, however, involve the production of text (writing). Information on the skills of adults with low levels of proficiency is provided by an assessment of reading components that covers text vocabulary, sentence comprehension and passage fluency. The target population was the non institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. Literacy-related non-respondents are not included in the calculation of the mean scores which, thus, present an upper bound of the estimated literacy proficiency of the population. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Literacy is defined as the ability to understand, evaluate, use and engage with written texts to participate in society, to achieve one’s goals, and to develop one’s knowledge and potential. Literacy encompasses a range of skills from the decoding of written words and sentences to the comprehension, interpretation, and evaluation of complex texts. It does not, however, involve the production of text (writing). Information on the skills of adults with low levels of proficiency is provided by an assessment of reading components that covers text vocabulary, sentence comprehension and passage fluency. The target population was the non institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. Literacy-related non-respondents are not included in the calculation of the mean scores which, thus, present an upper bound of the estimated literacy proficiency of the population. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.LIT.YOU.MA", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Mean Young Adult Literacy Proficiency. Male" }, { "id": "Longdefinition", "value": "Literacy is defined as the ability to understand, evaluate, use and engage with written texts to participate in society, to achieve one’s goals, and to develop one’s knowledge and potential. Literacy encompasses a range of skills from the decoding of written words and sentences to the comprehension, interpretation, and evaluation of complex texts. It does not, however, involve the production of text (writing). Information on the skills of adults with low levels of proficiency is provided by an assessment of reading components that covers text vocabulary, sentence comprehension and passage fluency. The target population was the non institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. Literacy-related non-respondents are not included in the calculation of the mean scores which, thus, present an upper bound of the estimated literacy proficiency of the population. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Literacy is defined as the ability to understand, evaluate, use and engage with written texts to participate in society, to achieve one’s goals, and to develop one’s knowledge and potential. Literacy encompasses a range of skills from the decoding of written words and sentences to the comprehension, interpretation, and evaluation of complex texts. It does not, however, involve the production of text (writing). Information on the skills of adults with low levels of proficiency is provided by an assessment of reading components that covers text vocabulary, sentence comprehension and passage fluency. The target population was the non institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. Literacy-related non-respondents are not included in the calculation of the mean scores which, thus, present an upper bound of the estimated literacy proficiency of the population. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Mean Adult Numeracy Proficiency. Total" }, { "id": "Longdefinition", "value": "Numeracy is defined as the ability to access, use, interpret and communicate mathematical information and ideas in order to engage in and manage the mathematical demands of a range of situations in adult life. To this end, numeracy involves managing a situation or solving a problem in a real context, by responding to mathematical content/information/ideas represented in multiple ways. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Numeracy is defined as the ability to access, use, interpret and communicate mathematical information and ideas in order to engage in and manage the mathematical demands of a range of situations in adult life. To this end, numeracy involves managing a situation or solving a problem in a real context, by responding to mathematical content/information/ideas represented in multiple ways. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.1", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by numeracy proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of adults scoring 176 to less than 226 points on the 0 to 500 point scale. Tasks at this level require the respondent to carry out basic mathematical processes in common, concrete contexts where the mathematical content is explicit with little text and minimal distractors. Tasks usually require one-step or simple processes involving counting, sorting, performing basic arithmetic operations, understanding simple percents such as 50%, and locating and identifying elements of simple or common graphical or spatial representations. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of adults scoring 176 to less than 226 points on the 0 to 500 point scale. Tasks at this level require the respondent to carry out basic mathematical processes in common, concrete contexts where the mathematical content is explicit with little text and minimal distractors. Tasks usually require one-step or simple processes involving counting, sorting, performing basic arithmetic operations, understanding simple percents such as 50%, and locating and identifying elements of simple or common graphical or spatial representations. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.2", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by numeracy proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of adults scoring 226 to less than 276 points on the 0 to 500 point scale. Tasks at this level require the respondent to identify and act on mathematical information and ideas embedded in a range of common contexts where the mathematical content is fairly explicit or visual with relatively few distractors. Tasks tend to require the application of two or more steps or processes involving calculation with whole numbers and common decimals, percents and fractions; simple measurement and spatial representation; estimation; and interpretation of relatively simple data and statistics in texts, tables and graphs. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of adults scoring 226 to less than 276 points on the 0 to 500 point scale. Tasks at this level require the respondent to identify and act on mathematical information and ideas embedded in a range of common contexts where the mathematical content is fairly explicit or visual with relatively few distractors. Tasks tend to require the application of two or more steps or processes involving calculation with whole numbers and common decimals, percents and fractions; simple measurement and spatial representation; estimation; and interpretation of relatively simple data and statistics in texts, tables and graphs. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.3", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by numeracy proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of adults scoring 276 to less than 326 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand mathematical information that may be less explicit, embedded in contexts that are not always familiar and represented in more complex ways. Tasks require several steps and may involve the choice of problem-solving strategies and relevant processes. Tasks tend to require the application of number sense and spatial sense; recognizing and working with mathematical relationships, patterns, and proportions expressed in verbal or numerical form; and interpretation and basic analysis of data and statistics in texts, tables and graphs. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of adults scoring 276 to less than 326 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand mathematical information that may be less explicit, embedded in contexts that are not always familiar and represented in more complex ways. Tasks require several steps and may involve the choice of problem-solving strategies and relevant processes. Tasks tend to require the application of number sense and spatial sense; recognizing and working with mathematical relationships, patterns, and proportions expressed in verbal or numerical form; and interpretation and basic analysis of data and statistics in texts, tables and graphs. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.4", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by numeracy proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of adults scoring 326 to less than 376 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand a broad range of mathematical information that may be complex, abstract or embedded in unfamiliar contexts. These tasks involve undertaking multiple steps and choosing relevant problem-solving strategies and processes. Tasks tend to require analysis and more complex reasoning about quantities and data; statistics and chance; spatial relationships; and change, proportions and formulas. Tasks at this level may also require understanding arguments or communicating well-reasoned explanations for answers or choices. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of adults scoring 326 to less than 376 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand a broad range of mathematical information that may be complex, abstract or embedded in unfamiliar contexts. These tasks involve undertaking multiple steps and choosing relevant problem-solving strategies and processes. Tasks tend to require analysis and more complex reasoning about quantities and data; statistics and chance; spatial relationships; and change, proportions and formulas. Tasks at this level may also require understanding arguments or communicating well-reasoned explanations for answers or choices. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.5", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by numeracy proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of adults scoring equal to or higher than 376 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand complex representations and abstract and formal mathematical and statistical ideas, possibly embedded in complex texts. Respondents may have to integrate multiple types of mathematical information where considerable translation or interpretation is required; draw inferences; develop or work with mathematical arguments or models; and justify, evaluate and critically reflect upon solutions or choices. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of adults scoring equal to or higher than 376 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand complex representations and abstract and formal mathematical and statistical ideas, possibly embedded in complex texts. Respondents may have to integrate multiple types of mathematical information where considerable translation or interpretation is required; draw inferences; develop or work with mathematical arguments or models; and justify, evaluate and critically reflect upon solutions or choices. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.BE", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by numeracy proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of adults scoring below 176 points on the 0 to 500 point scale on the 0 to 500 point scale. Tasks at this level require the respondents to carry out simple processes such as counting, sorting, performing basic arithmetic operations with whole numbers or money, or recognizing common spatial representations in concrete, familiar contexts where the mathematical content is explicit with little or no text or distractors. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of adults scoring below 176 points on the 0 to 500 point scale on the 0 to 500 point scale. Tasks at this level require the respondents to carry out simple processes such as counting, sorting, performing basic arithmetic operations with whole numbers or money, or recognizing common spatial representations in concrete, familiar contexts where the mathematical content is explicit with little or no text or distractors. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.FE", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Mean Adult Numeracy Proficiency. Female" }, { "id": "Longdefinition", "value": "Numeracy is defined as the ability to access, use, interpret and communicate mathematical information and ideas in order to engage in and manage the mathematical demands of a range of situations in adult life. To this end, numeracy involves managing a situation or solving a problem in a real context, by responding to mathematical content/information/ideas represented in multiple ways. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Numeracy is defined as the ability to access, use, interpret and communicate mathematical information and ideas in order to engage in and manage the mathematical demands of a range of situations in adult life. To this end, numeracy involves managing a situation or solving a problem in a real context, by responding to mathematical content/information/ideas represented in multiple ways. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.FE.1", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by numeracy proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring 176 to less than 226 points on the 0 to 500 point scale. Tasks at this level require the respondent to carry out basic mathematical processes in common, concrete contexts where the mathematical content is explicit with little text and minimal distractors. Tasks usually require one-step or simple processes involving counting, sorting, performing basic arithmetic operations, understanding simple percents such as 50%, and locating and identifying elements of simple or common graphical or spatial representations. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring 176 to less than 226 points on the 0 to 500 point scale. Tasks at this level require the respondent to carry out basic mathematical processes in common, concrete contexts where the mathematical content is explicit with little text and minimal distractors. Tasks usually require one-step or simple processes involving counting, sorting, performing basic arithmetic operations, understanding simple percents such as 50%, and locating and identifying elements of simple or common graphical or spatial representations. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.FE.2", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by numeracy proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring 226 to less than 276 points on the 0 to 500 point scale. Tasks at this level require the respondent to identify and act on mathematical information and ideas embedded in a range of common contexts where the mathematical content is fairly explicit or visual with relatively few distractors. Tasks tend to require the application of two or more steps or processes involving calculation with whole numbers and common decimals, percents and fractions; simple measurement and spatial representation; estimation; and interpretation of relatively simple data and statistics in texts, tables and graphs. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring 226 to less than 276 points on the 0 to 500 point scale. Tasks at this level require the respondent to identify and act on mathematical information and ideas embedded in a range of common contexts where the mathematical content is fairly explicit or visual with relatively few distractors. Tasks tend to require the application of two or more steps or processes involving calculation with whole numbers and common decimals, percents and fractions; simple measurement and spatial representation; estimation; and interpretation of relatively simple data and statistics in texts, tables and graphs. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.FE.3", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by numeracy proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring 276 to less than 326 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand mathematical information that may be less explicit, embedded in contexts that are not always familiar and represented in more complex ways. Tasks require several steps and may involve the choice of problem-solving strategies and relevant processes. Tasks tend to require the application of number sense and spatial sense; recognizing and working with mathematical relationships, patterns, and proportions expressed in verbal or numerical form; and interpretation and basic analysis of data and statistics in texts, tables and graphs. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring 276 to less than 326 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand mathematical information that may be less explicit, embedded in contexts that are not always familiar and represented in more complex ways. Tasks require several steps and may involve the choice of problem-solving strategies and relevant processes. Tasks tend to require the application of number sense and spatial sense; recognizing and working with mathematical relationships, patterns, and proportions expressed in verbal or numerical form; and interpretation and basic analysis of data and statistics in texts, tables and graphs. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.FE.4", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by numeracy proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring 326 to less than 376 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand a broad range of mathematical information that may be complex, abstract or embedded in unfamiliar contexts. These tasks involve undertaking multiple steps and choosing relevant problem-solving strategies and processes. Tasks tend to require analysis and more complex reasoning about quantities and data; statistics and chance; spatial relationships; and change, proportions and formulas. Tasks at this level may also require understanding arguments or communicating well-reasoned explanations for answers or choices. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring 326 to less than 376 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand a broad range of mathematical information that may be complex, abstract or embedded in unfamiliar contexts. These tasks involve undertaking multiple steps and choosing relevant problem-solving strategies and processes. Tasks tend to require analysis and more complex reasoning about quantities and data; statistics and chance; spatial relationships; and change, proportions and formulas. Tasks at this level may also require understanding arguments or communicating well-reasoned explanations for answers or choices. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.FE.45", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by numeracy proficiency level (%). Level 4 & 5" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring at least 326 on the 0 to 500 point scale. Tasks at level 4 require the respondent to understand a broad range of mathematical information that may be complex, abstract or embedded in unfamiliar contexts. These tasks involve undertaking multiple steps and choosing relevant problem-solving strategies and processes. Tasks tend to require analysis and more complex reasoning about quantities and data; statistics and chance; spatial relationships; and change, proportions and formulas. Tasks at this level may also require understanding arguments or communicating well-reasoned explanations for answers or choices. Tasks at level 5 require the respondent to understand complex representations and abstract and formal mathematical and statistical ideas, possibly embedded in complex texts. Respondents may have to integrate multiple types of mathematical information where considerable translation or interpretation is required; draw inferences; develop or work with mathematical arguments or models; and justify, evaluate and critically reflect upon solutions or choices. The target population was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring at least 326 on the 0 to 500 point scale. Tasks at level 4 require the respondent to understand a broad range of mathematical information that may be complex, abstract or embedded in unfamiliar contexts. These tasks involve undertaking multiple steps and choosing relevant problem-solving strategies and processes. Tasks tend to require analysis and more complex reasoning about quantities and data; statistics and chance; spatial relationships; and change, proportions and formulas. Tasks at this level may also require understanding arguments or communicating well-reasoned explanations for answers or choices. Tasks at level 5 require the respondent to understand complex representations and abstract and formal mathematical and statistical ideas, possibly embedded in complex texts. Respondents may have to integrate multiple types of mathematical information where considerable translation or interpretation is required; draw inferences; develop or work with mathematical arguments or models; and justify, evaluate and critically reflect upon solutions or choices. The target population was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.FE.5", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by numeracy proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring equal to or higher than 376 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand complex representations and abstract and formal mathematical and statistical ideas, possibly embedded in complex texts. Respondents may have to integrate multiple types of mathematical information where considerable translation or interpretation is required; draw inferences; develop or work with mathematical arguments or models; and justify, evaluate and critically reflect upon solutions or choices. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring equal to or higher than 376 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand complex representations and abstract and formal mathematical and statistical ideas, possibly embedded in complex texts. Respondents may have to integrate multiple types of mathematical information where considerable translation or interpretation is required; draw inferences; develop or work with mathematical arguments or models; and justify, evaluate and critically reflect upon solutions or choices. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.FE.BE", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by numeracy proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring below 176 points on the 0 to 500 point scale on the 0 to 500 point scale. Tasks at this level require the respondents to carry out simple processes such as counting, sorting, performing basic arithmetic operations with whole numbers or money, or recognizing common spatial representations in concrete, familiar contexts where the mathematical content is explicit with little or no text or distractors. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring below 176 points on the 0 to 500 point scale on the 0 to 500 point scale. Tasks at this level require the respondents to carry out simple processes such as counting, sorting, performing basic arithmetic operations with whole numbers or money, or recognizing common spatial representations in concrete, familiar contexts where the mathematical content is explicit with little or no text or distractors. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.MA", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Mean Adult Numeracy Proficiency. Male" }, { "id": "Longdefinition", "value": "Numeracy is defined as the ability to access, use, interpret and communicate mathematical information and ideas in order to engage in and manage the mathematical demands of a range of situations in adult life. To this end, numeracy involves managing a situation or solving a problem in a real context, by responding to mathematical content/information/ideas represented in multiple ways. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Numeracy is defined as the ability to access, use, interpret and communicate mathematical information and ideas in order to engage in and manage the mathematical demands of a range of situations in adult life. To this end, numeracy involves managing a situation or solving a problem in a real context, by responding to mathematical content/information/ideas represented in multiple ways. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.MA.1", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by numeracy proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring 176 to less than 226 points on the 0 to 500 point scale. Tasks at this level require the respondent to carry out basic mathematical processes in common, concrete contexts where the mathematical content is explicit with little text and minimal distractors. Tasks usually require one-step or simple processes involving counting, sorting, performing basic arithmetic operations, understanding simple percents such as 50%, and locating and identifying elements of simple or common graphical or spatial representations. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring 176 to less than 226 points on the 0 to 500 point scale. Tasks at this level require the respondent to carry out basic mathematical processes in common, concrete contexts where the mathematical content is explicit with little text and minimal distractors. Tasks usually require one-step or simple processes involving counting, sorting, performing basic arithmetic operations, understanding simple percents such as 50%, and locating and identifying elements of simple or common graphical or spatial representations. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.MA.2", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by numeracy proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring 226 to less than 276 points on the 0 to 500 point scale. Tasks at this level require the respondent to identify and act on mathematical information and ideas embedded in a range of common contexts where the mathematical content is fairly explicit or visual with relatively few distractors. Tasks tend to require the application of two or more steps or processes involving calculation with whole numbers and common decimals, percents and fractions; simple measurement and spatial representation; estimation; and interpretation of relatively simple data and statistics in texts, tables and graphs. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring 226 to less than 276 points on the 0 to 500 point scale. Tasks at this level require the respondent to identify and act on mathematical information and ideas embedded in a range of common contexts where the mathematical content is fairly explicit or visual with relatively few distractors. Tasks tend to require the application of two or more steps or processes involving calculation with whole numbers and common decimals, percents and fractions; simple measurement and spatial representation; estimation; and interpretation of relatively simple data and statistics in texts, tables and graphs. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.MA.3", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by numeracy proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring 276 to less than 326 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand mathematical information that may be less explicit, embedded in contexts that are not always familiar and represented in more complex ways. Tasks require several steps and may involve the choice of problem-solving strategies and relevant processes. Tasks tend to require the application of number sense and spatial sense; recognizing and working with mathematical relationships, patterns, and proportions expressed in verbal or numerical form; and interpretation and basic analysis of data and statistics in texts, tables and graphs. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring 276 to less than 326 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand mathematical information that may be less explicit, embedded in contexts that are not always familiar and represented in more complex ways. Tasks require several steps and may involve the choice of problem-solving strategies and relevant processes. Tasks tend to require the application of number sense and spatial sense; recognizing and working with mathematical relationships, patterns, and proportions expressed in verbal or numerical form; and interpretation and basic analysis of data and statistics in texts, tables and graphs. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.MA.4", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by numeracy proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring 326 to less than 376 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand a broad range of mathematical information that may be complex, abstract or embedded in unfamiliar contexts. These tasks involve undertaking multiple steps and choosing relevant problem-solving strategies and processes. Tasks tend to require analysis and more complex reasoning about quantities and data; statistics and chance; spatial relationships; and change, proportions and formulas. Tasks at this level may also require understanding arguments or communicating well-reasoned explanations for answers or choices. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring 326 to less than 376 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand a broad range of mathematical information that may be complex, abstract or embedded in unfamiliar contexts. These tasks involve undertaking multiple steps and choosing relevant problem-solving strategies and processes. Tasks tend to require analysis and more complex reasoning about quantities and data; statistics and chance; spatial relationships; and change, proportions and formulas. Tasks at this level may also require understanding arguments or communicating well-reasoned explanations for answers or choices. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.MA.45", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by numeracy proficiency level (%). Level 4 & 5" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring at least 326 on the 0 to 500 point scale. Tasks at level 4 require the respondent to understand a broad range of mathematical information that may be complex, abstract or embedded in unfamiliar contexts. These tasks involve undertaking multiple steps and choosing relevant problem-solving strategies and processes. Tasks tend to require analysis and more complex reasoning about quantities and data; statistics and chance; spatial relationships; and change, proportions and formulas. Tasks at this level may also require understanding arguments or communicating well-reasoned explanations for answers or choices. Tasks at level 5 require the respondent to understand complex representations and abstract and formal mathematical and statistical ideas, possibly embedded in complex texts. Respondents may have to integrate multiple types of mathematical information where considerable translation or interpretation is required; draw inferences; develop or work with mathematical arguments or models; and justify, evaluate and critically reflect upon solutions or choices. The target population was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring at least 326 on the 0 to 500 point scale. Tasks at level 4 require the respondent to understand a broad range of mathematical information that may be complex, abstract or embedded in unfamiliar contexts. These tasks involve undertaking multiple steps and choosing relevant problem-solving strategies and processes. Tasks tend to require analysis and more complex reasoning about quantities and data; statistics and chance; spatial relationships; and change, proportions and formulas. Tasks at this level may also require understanding arguments or communicating well-reasoned explanations for answers or choices. Tasks at level 5 require the respondent to understand complex representations and abstract and formal mathematical and statistical ideas, possibly embedded in complex texts. Respondents may have to integrate multiple types of mathematical information where considerable translation or interpretation is required; draw inferences; develop or work with mathematical arguments or models; and justify, evaluate and critically reflect upon solutions or choices. The target population was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.MA.5", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by numeracy proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring equal to or higher than 376 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand complex representations and abstract and formal mathematical and statistical ideas, possibly embedded in complex texts. Respondents may have to integrate multiple types of mathematical information where considerable translation or interpretation is required; draw inferences; develop or work with mathematical arguments or models; and justify, evaluate and critically reflect upon solutions or choices. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring equal to or higher than 376 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand complex representations and abstract and formal mathematical and statistical ideas, possibly embedded in complex texts. Respondents may have to integrate multiple types of mathematical information where considerable translation or interpretation is required; draw inferences; develop or work with mathematical arguments or models; and justify, evaluate and critically reflect upon solutions or choices. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.MA.BE", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by numeracy proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring below 176 points on the 0 to 500 point scale on the 0 to 500 point scale. Tasks at this level require the respondents to carry out simple processes such as counting, sorting, performing basic arithmetic operations with whole numbers or money, or recognizing common spatial representations in concrete, familiar contexts where the mathematical content is explicit with little or no text or distractors. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring below 176 points on the 0 to 500 point scale on the 0 to 500 point scale. Tasks at this level require the respondents to carry out simple processes such as counting, sorting, performing basic arithmetic operations with whole numbers or money, or recognizing common spatial representations in concrete, familiar contexts where the mathematical content is explicit with little or no text or distractors. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.P05", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Numeracy Scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.P10", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Numeracy Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.P25", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Numeracy Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.P50", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Numeracy Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.P75", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Numeracy Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.P90", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Numeracy Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.P95", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Numeracy Scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 90 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 90 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.YOU", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Mean Young Adult Numeracy Proficiency. Total" }, { "id": "Longdefinition", "value": "Numeracy is defined as the ability to access, use, interpret and communicate mathematical information and ideas in order to engage in and manage the mathematical demands of a range of situations in adult life. To this end, numeracy involves managing a situation or solving a problem in a real context, by responding to mathematical content/information/ideas represented in multiple ways. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Numeracy is defined as the ability to access, use, interpret and communicate mathematical information and ideas in order to engage in and manage the mathematical demands of a range of situations in adult life. To this end, numeracy involves managing a situation or solving a problem in a real context, by responding to mathematical content/information/ideas represented in multiple ways. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.YOU.1", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by numeracy proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring 176 to less than 226 points on the 0 to 500 point scale. Tasks at this level require the respondent to carry out basic mathematical processes in common, concrete contexts where the mathematical content is explicit with little text and minimal distractors. Tasks usually require one-step or simple processes involving counting, sorting, performing basic arithmetic operations, understanding simple percents such as 50%, and locating and identifying elements of simple or common graphical or spatial representations. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring 176 to less than 226 points on the 0 to 500 point scale. Tasks at this level require the respondent to carry out basic mathematical processes in common, concrete contexts where the mathematical content is explicit with little text and minimal distractors. Tasks usually require one-step or simple processes involving counting, sorting, performing basic arithmetic operations, understanding simple percents such as 50%, and locating and identifying elements of simple or common graphical or spatial representations. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.YOU.2", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by numeracy proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring 226 to less than 276 points on the 0 to 500 point scale. Tasks at this level require the respondent to identify and act on mathematical information and ideas embedded in a range of common contexts where the mathematical content is fairly explicit or visual with relatively few distractors. Tasks tend to require the application of two or more steps or processes involving calculation with whole numbers and common decimals, percents and fractions; simple measurement and spatial representation; estimation; and interpretation of relatively simple data and statistics in texts, tables and graphs. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring 226 to less than 276 points on the 0 to 500 point scale. Tasks at this level require the respondent to identify and act on mathematical information and ideas embedded in a range of common contexts where the mathematical content is fairly explicit or visual with relatively few distractors. Tasks tend to require the application of two or more steps or processes involving calculation with whole numbers and common decimals, percents and fractions; simple measurement and spatial representation; estimation; and interpretation of relatively simple data and statistics in texts, tables and graphs. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.YOU.3", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by numeracy proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring 276 to less than 326 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand mathematical information that may be less explicit, embedded in contexts that are not always familiar and represented in more complex ways. Tasks require several steps and may involve the choice of problem-solving strategies and relevant processes. Tasks tend to require the application of number sense and spatial sense; recognizing and working with mathematical relationships, patterns, and proportions expressed in verbal or numerical form; and interpretation and basic analysis of data and statistics in texts, tables and graphs. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring 276 to less than 326 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand mathematical information that may be less explicit, embedded in contexts that are not always familiar and represented in more complex ways. Tasks require several steps and may involve the choice of problem-solving strategies and relevant processes. Tasks tend to require the application of number sense and spatial sense; recognizing and working with mathematical relationships, patterns, and proportions expressed in verbal or numerical form; and interpretation and basic analysis of data and statistics in texts, tables and graphs. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.YOU.4", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by numeracy proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring 326 to less than 376 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand a broad range of mathematical information that may be complex, abstract or embedded in unfamiliar contexts. These tasks involve undertaking multiple steps and choosing relevant problem-solving strategies and processes. Tasks tend to require analysis and more complex reasoning about quantities and data; statistics and chance; spatial relationships; and change, proportions and formulas. Tasks at this level may also require understanding arguments or communicating well-reasoned explanations for answers or choices. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring 326 to less than 376 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand a broad range of mathematical information that may be complex, abstract or embedded in unfamiliar contexts. These tasks involve undertaking multiple steps and choosing relevant problem-solving strategies and processes. Tasks tend to require analysis and more complex reasoning about quantities and data; statistics and chance; spatial relationships; and change, proportions and formulas. Tasks at this level may also require understanding arguments or communicating well-reasoned explanations for answers or choices. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.YOU.45", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by numeracy proficiency level (%). Level 4 & 5" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring at least 326 on the 0 to 500 point scale. Tasks at level 4 require the respondent to understand a broad range of mathematical information that may be complex, abstract or embedded in unfamiliar contexts. These tasks involve undertaking multiple steps and choosing relevant problem-solving strategies and processes. Tasks tend to require analysis and more complex reasoning about quantities and data; statistics and chance; spatial relationships; and change, proportions and formulas. Tasks at this level may also require understanding arguments or communicating well-reasoned explanations for answers or choices. Tasks at level 5 require the respondent to understand complex representations and abstract and formal mathematical and statistical ideas, possibly embedded in complex texts. Respondents may have to integrate multiple types of mathematical information where considerable translation or interpretation is required; draw inferences; develop or work with mathematical arguments or models; and justify, evaluate and critically reflect upon solutions or choices. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring at least 326 on the 0 to 500 point scale. Tasks at level 4 require the respondent to understand a broad range of mathematical information that may be complex, abstract or embedded in unfamiliar contexts. These tasks involve undertaking multiple steps and choosing relevant problem-solving strategies and processes. Tasks tend to require analysis and more complex reasoning about quantities and data; statistics and chance; spatial relationships; and change, proportions and formulas. Tasks at this level may also require understanding arguments or communicating well-reasoned explanations for answers or choices. Tasks at level 5 require the respondent to understand complex representations and abstract and formal mathematical and statistical ideas, possibly embedded in complex texts. Respondents may have to integrate multiple types of mathematical information where considerable translation or interpretation is required; draw inferences; develop or work with mathematical arguments or models; and justify, evaluate and critically reflect upon solutions or choices. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.YOU.5", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by numeracy proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring equal to or higher than 376 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand complex representations and abstract and formal mathematical and statistical ideas, possibly embedded in complex texts. Respondents may have to integrate multiple types of mathematical information where considerable translation or interpretation is required; draw inferences; develop or work with mathematical arguments or models; and justify, evaluate and critically reflect upon solutions or choices. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring equal to or higher than 376 points on the 0 to 500 point scale. Tasks at this level require the respondent to understand complex representations and abstract and formal mathematical and statistical ideas, possibly embedded in complex texts. Respondents may have to integrate multiple types of mathematical information where considerable translation or interpretation is required; draw inferences; develop or work with mathematical arguments or models; and justify, evaluate and critically reflect upon solutions or choices. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.YOU.BE", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by numeracy proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring below 176 points on the 0 to 500 point scale on the 0 to 500 point scale. Tasks at this level require the respondents to carry out simple processes such as counting, sorting, performing basic arithmetic operations with whole numbers or money, or recognizing common spatial representations in concrete, familiar contexts where the mathematical content is explicit with little or no text or distractors. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring below 176 points on the 0 to 500 point scale on the 0 to 500 point scale. Tasks at this level require the respondents to carry out simple processes such as counting, sorting, performing basic arithmetic operations with whole numbers or money, or recognizing common spatial representations in concrete, familiar contexts where the mathematical content is explicit with little or no text or distractors. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. The proportion of adults scoring at different levels of proficiency adds up to 100% when the percentage of numeracy-related non-respondents are taken into account. Adults in the missing category were not able to provide enough background information to impute proficiency scores because of language difficulties, or learning or mental disabilities (literacy-related non-response). For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.YOU.FE", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Mean Young Adult Numeracy Proficiency. Female" }, { "id": "Longdefinition", "value": "Numeracy is defined as the ability to access, use, interpret and communicate mathematical information and ideas in order to engage in and manage the mathematical demands of a range of situations in adult life. To this end, numeracy involves managing a situation or solving a problem in a real context, by responding to mathematical content/information/ideas represented in multiple ways. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Numeracy is defined as the ability to access, use, interpret and communicate mathematical information and ideas in order to engage in and manage the mathematical demands of a range of situations in adult life. To this end, numeracy involves managing a situation or solving a problem in a real context, by responding to mathematical content/information/ideas represented in multiple ways. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.NUM.YOU.MA", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Mean Young Adult Numeracy Proficiency. Male" }, { "id": "Longdefinition", "value": "Numeracy is defined as the ability to access, use, interpret and communicate mathematical information and ideas in order to engage in and manage the mathematical demands of a range of situations in adult life. To this end, numeracy involves managing a situation or solving a problem in a real context, by responding to mathematical content/information/ideas represented in multiple ways. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Numeracy is defined as the ability to access, use, interpret and communicate mathematical information and ideas in order to engage in and manage the mathematical demands of a range of situations in adult life. To this end, numeracy involves managing a situation or solving a problem in a real context, by responding to mathematical content/information/ideas represented in multiple ways. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.1", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by proficiency level in problem solving in technology-rich environments (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of adults scoring 241 to less than 291 points on the 0 to 500 point scale. At this level, tasks typically require the use of widely available and familiar technology applications, such as e-mail software or a web browser. There is little or no navigation required to access the information or commands required to solve the problem. The tasks involve few steps and a minimal number of operators. Only simple forms of reasoning, such as assigning items to categories, are required; there is no need to contrast or integrate information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of adults scoring 241 to less than 291 points on the 0 to 500 point scale. At this level, tasks typically require the use of widely available and familiar technology applications, such as e-mail software or a web browser. There is little or no navigation required to access the information or commands required to solve the problem. The tasks involve few steps and a minimal number of operators. Only simple forms of reasoning, such as assigning items to categories, are required; there is no need to contrast or integrate information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.2", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by proficiency level in problem solving in technology-rich environments (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of adults scoring 291 to less than 341 points on the 0 to 500 point scale. At this level, tasks typically require the use of both generic and more specific technology applications. For instance, the respondent may have to make use of a novel online form. Some navigation across pages and applications is required to solve the problem. The task may involve multiple steps and operators. The goal of the problem may have to be defined by the respondent, though the criteria to be met are explicit. There are higher monitoring demands. Some unexpected outcomes or impasses may appear. The task may require evaluating the relevance of a set of items to discard distractors. Some integration and inferential reasoning may be needed. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of adults scoring 291 to less than 341 points on the 0 to 500 point scale. At this level, tasks typically require the use of both generic and more specific technology applications. For instance, the respondent may have to make use of a novel online form. Some navigation across pages and applications is required to solve the problem. The task may involve multiple steps and operators. The goal of the problem may have to be defined by the respondent, though the criteria to be met are explicit. There are higher monitoring demands. Some unexpected outcomes or impasses may appear. The task may require evaluating the relevance of a set of items to discard distractors. Some integration and inferential reasoning may be needed. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.3", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by proficiency level in problem solving in technology-rich environments (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of adults scoring equal to or higher than 341 points on the 0 to 500 point scale. At this level, tasks typically require the use of both generic and more specific technology applications. Some navigation across pages and applications is required to solve the problem. The use of tools (e.g. a sort function) is required to make progress towards the solution. The task may involve multiple steps and operators. The goal of the problem may have to be defined by the respondent, and the criteria to be met may or may not be explicit. There are typically high monitoring demands. Unexpected outcomes and impasses are likely to occur. The task may require evaluating the relevance and reliability of information in order to discard distractors. Integration and inferential reasoning may be needed to a large extent. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of adults scoring equal to or higher than 341 points on the 0 to 500 point scale. At this level, tasks typically require the use of both generic and more specific technology applications. Some navigation across pages and applications is required to solve the problem. The use of tools (e.g. a sort function) is required to make progress towards the solution. The task may involve multiple steps and operators. The goal of the problem may have to be defined by the respondent, and the criteria to be met may or may not be explicit. There are typically high monitoring demands. Unexpected outcomes and impasses are likely to occur. The task may require evaluating the relevance and reliability of information in order to discard distractors. Integration and inferential reasoning may be needed to a large extent. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.BE", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by proficiency level in problem solving in technology-rich environments (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of adults scoring below 241 points on the 0 to 500 point scale. Tasks are based on well-defined problems involving the use of only one function within a generic interface to meet one explicit criterion without any categorical or inferential reasoning, or transforming of information. Few steps are required and no sub-goal has to be generated. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of adults scoring below 241 points on the 0 to 500 point scale. Tasks are based on well-defined problems involving the use of only one function within a generic interface to meet one explicit criterion without any categorical or inferential reasoning, or transforming of information. Few steps are required and no sub-goal has to be generated. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.FAIL", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by proficiency level in problem solving in technology-rich environments (%). Failed the ICT Core Test" }, { "id": "Longdefinition", "value": "Adults in this category had prior computer experience but failed the ICT core test, which assesses basic ICT skills, such as the capacity to use a mouse or scroll through a web page, needed to take the computer-based assessment. Therefore, they did not take part in the computer-based assessment, but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Adults in this category had prior computer experience but failed the ICT core test, which assesses basic ICT skills, such as the capacity to use a mouse or scroll through a web page, needed to take the computer-based assessment. Therefore, they did not take part in the computer-based assessment, but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.FE.1", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by proficiency level in problem solving in technology-rich environments (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring 241 to less than 291 points on the 0 to 500 point scale. At this level, tasks typically require the use of widely available and familiar technology applications, such as e-mail software or a web browser. There is little or no navigation required to access the information or commands required to solve the problem. The tasks involve few steps and a minimal number of operators. Only simple forms of reasoning, such as assigning items to categories, are required; there is no need to contrast or integrate information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring 241 to less than 291 points on the 0 to 500 point scale. At this level, tasks typically require the use of widely available and familiar technology applications, such as e-mail software or a web browser. There is little or no navigation required to access the information or commands required to solve the problem. The tasks involve few steps and a minimal number of operators. Only simple forms of reasoning, such as assigning items to categories, are required; there is no need to contrast or integrate information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.FE.2", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by proficiency level in problem solving in technology-rich environments (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring 291 to less than 341 points on the 0 to 500 point scale. At this level, tasks typically require the use of both generic and more specific technology applications. For instance, the respondent may have to make use of a novel online form. Some navigation across pages and applications is required to solve the problem. The task may involve multiple steps and operators. The goal of the problem may have to be defined by the respondent, though the criteria to be met are explicit. There are higher monitoring demands. Some unexpected outcomes or impasses may appear. The task may require evaluating the relevance of a set of items to discard distractors. Some integration and inferential reasoning may be needed. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring 291 to less than 341 points on the 0 to 500 point scale. At this level, tasks typically require the use of both generic and more specific technology applications. For instance, the respondent may have to make use of a novel online form. Some navigation across pages and applications is required to solve the problem. The task may involve multiple steps and operators. The goal of the problem may have to be defined by the respondent, though the criteria to be met are explicit. There are higher monitoring demands. Some unexpected outcomes or impasses may appear. The task may require evaluating the relevance of a set of items to discard distractors. Some integration and inferential reasoning may be needed. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.FE.3", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by proficiency level in problem solving in technology-rich environments (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring equal to or higher than 341 points on the 0 to 500 point scale. At this level, tasks typically require the use of both generic and more specific technology applications. Some navigation across pages and applications is required to solve the problem. The use of tools (e.g. a sort function) is required to make progress towards the solution. The task may involve multiple steps and operators. The goal of the problem may have to be defined by the respondent, and the criteria to be met may or may not be explicit. There are typically high monitoring demands. Unexpected outcomes and impasses are likely to occur. The task may require evaluating the relevance and reliability of information in order to discard distractors. Integration and inferential reasoning may be needed to a large extent. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring equal to or higher than 341 points on the 0 to 500 point scale. At this level, tasks typically require the use of both generic and more specific technology applications. Some navigation across pages and applications is required to solve the problem. The use of tools (e.g. a sort function) is required to make progress towards the solution. The task may involve multiple steps and operators. The goal of the problem may have to be defined by the respondent, and the criteria to be met may or may not be explicit. There are typically high monitoring demands. Unexpected outcomes and impasses are likely to occur. The task may require evaluating the relevance and reliability of information in order to discard distractors. Integration and inferential reasoning may be needed to a large extent. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.FE.BE", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by proficiency level in problem solving in technology-rich environments (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of female adults scoring below 241 points on the 0 to 500 point scale. Tasks are based on well-defined problems involving the use of only one function within a generic interface to meet one explicit criterion without any categorical or inferential reasoning, or transforming of information. Few steps are required and no sub-goal has to be generated. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults scoring below 241 points on the 0 to 500 point scale. Tasks are based on well-defined problems involving the use of only one function within a generic interface to meet one explicit criterion without any categorical or inferential reasoning, or transforming of information. Few steps are required and no sub-goal has to be generated. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.FE.FAIL", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by proficiency level in problem solving in technology-rich environments (%). Failed ICT Core Test" }, { "id": "Longdefinition", "value": "Percentage of female adults who had prior computer experience but failed the ICT core test, which assesses the basic ICT skills needed to take the computer-based assessment, such as the capacity to use a mouse or scroll through a web page. Therefore, they did not take part in the computer-based assessment, but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults who had prior computer experience but failed the ICT core test, which assesses the basic ICT skills needed to take the computer-based assessment, such as the capacity to use a mouse or scroll through a web page. Therefore, they did not take part in the computer-based assessment, but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.FE.FAILNO", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by proficiency level in problem solving in technology-rich environments (%). No computer experience or failed the ICT core test" }, { "id": "Longdefinition", "value": "Percentage of female adults who either 1) reported having no prior computer experience or 2) had prior computer experience but failed the ICT core test, which assesses basic ICT skills needed to take the computer-based assessment, such as the capacity to use a mouse or scroll through a web page. This group of adults did not take part in the computer-based assessment and instead took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults who either 1) reported having no prior computer experience or 2) had prior computer experience but failed the ICT core test, which assesses basic ICT skills needed to take the computer-based assessment, such as the capacity to use a mouse or scroll through a web page. This group of adults did not take part in the computer-based assessment and instead took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.FE.NO", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by proficiency level in problem solving in technology-rich environments (%). No computer experience" }, { "id": "Longdefinition", "value": "Percentage of female adults who reported having no prior computer experience; therefore, they did not take part in the computer-based assessment but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults who reported having no prior computer experience; therefore, they did not take part in the computer-based assessment but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.FE.OPT", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Female adults by proficiency level in problem solving in technology-rich environments (%). Opted out of computer-based assessment" }, { "id": "Longdefinition", "value": "Percentage of female adults who opted to take the paper-based assessment without first taking the ICT core assessment, even if they reported some prior experience with computers. They also did not take part in the computer-based assessment, but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of female adults who opted to take the paper-based assessment without first taking the ICT core assessment, even if they reported some prior experience with computers. They also did not take part in the computer-based assessment, but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.MA.1", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by proficiency level in problem solving in technology-rich environments (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring 241 to less than 291 points on the 0 to 500 point scale. At this level, tasks typically require the use of widely available and familiar technology applications, such as e-mail software or a web browser. There is little or no navigation required to access the information or commands required to solve the problem. The tasks involve few steps and a minimal number of operators. Only simple forms of reasoning, such as assigning items to categories, are required; there is no need to contrast or integrate information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring 241 to less than 291 points on the 0 to 500 point scale. At this level, tasks typically require the use of widely available and familiar technology applications, such as e-mail software or a web browser. There is little or no navigation required to access the information or commands required to solve the problem. The tasks involve few steps and a minimal number of operators. Only simple forms of reasoning, such as assigning items to categories, are required; there is no need to contrast or integrate information. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.MA.2", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by proficiency level in problem solving in technology-rich environments (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring 291 to less than 341 points on the 0 to 500 point scale. At this level, tasks typically require the use of both generic and more specific technology applications. For instance, the respondent may have to make use of a novel online form. Some navigation across pages and applications is required to solve the problem. The task may involve multiple steps and operators. The goal of the problem may have to be defined by the respondent, though the criteria to be met are explicit. There are higher monitoring demands. Some unexpected outcomes or impasses may appear. The task may require evaluating the relevance of a set of items to discard distractors. Some integration and inferential reasoning may be needed. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring 291 to less than 341 points on the 0 to 500 point scale. At this level, tasks typically require the use of both generic and more specific technology applications. For instance, the respondent may have to make use of a novel online form. Some navigation across pages and applications is required to solve the problem. The task may involve multiple steps and operators. The goal of the problem may have to be defined by the respondent, though the criteria to be met are explicit. There are higher monitoring demands. Some unexpected outcomes or impasses may appear. The task may require evaluating the relevance of a set of items to discard distractors. Some integration and inferential reasoning may be needed. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.MA.3", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by proficiency level in problem solving in technology-rich environments (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring equal to or higher than 341 points on the 0 to 500 point scale. At this level, tasks typically require the use of both generic and more specific technology applications. Some navigation across pages and applications is required to solve the problem. The use of tools (e.g. a sort function) is required to make progress towards the solution. The task may involve multiple steps and operators. The goal of the problem may have to be defined by the respondent, and the criteria to be met may or may not be explicit. There are typically high monitoring demands. Unexpected outcomes and impasses are likely to occur. The task may require evaluating the relevance and reliability of information in order to discard distractors. Integration and inferential reasoning may be needed to a large extent. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring equal to or higher than 341 points on the 0 to 500 point scale. At this level, tasks typically require the use of both generic and more specific technology applications. Some navigation across pages and applications is required to solve the problem. The use of tools (e.g. a sort function) is required to make progress towards the solution. The task may involve multiple steps and operators. The goal of the problem may have to be defined by the respondent, and the criteria to be met may or may not be explicit. There are typically high monitoring demands. Unexpected outcomes and impasses are likely to occur. The task may require evaluating the relevance and reliability of information in order to discard distractors. Integration and inferential reasoning may be needed to a large extent. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.MA.BE", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by proficiency level in problem solving in technology-rich environments (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of male adults scoring below 241 points on the 0 to 500 point scale. Tasks are based on well-defined problems involving the use of only one function within a generic interface to meet one explicit criterion without any categorical or inferential reasoning, or transforming of information. Few steps are required and no sub-goal has to be generated. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults scoring below 241 points on the 0 to 500 point scale. Tasks are based on well-defined problems involving the use of only one function within a generic interface to meet one explicit criterion without any categorical or inferential reasoning, or transforming of information. Few steps are required and no sub-goal has to be generated. The target population for the survey was the non-institutionalized population, aged 16-65 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.MA.FAIL", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by proficiency level in problem solving in technology-rich environments (%). Failed ICT Core Test" }, { "id": "Longdefinition", "value": "Percentage of male adults who had prior computer experience but failed the ICT core test, which assesses the basic ICT skills needed to take the computer-based assessment, such as the capacity to use a mouse or scroll through a web page. Therefore, they did not take part in the computer-based assessment, but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults who had prior computer experience but failed the ICT core test, which assesses the basic ICT skills needed to take the computer-based assessment, such as the capacity to use a mouse or scroll through a web page. Therefore, they did not take part in the computer-based assessment, but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.MA.FAILNO", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by proficiency level in problem solving in technology-rich environments (%). No computer experience or failed the ICT core test" }, { "id": "Longdefinition", "value": "Percentage of male adults who either 1) reported having no prior computer experience or 2) had prior computer experience but failed the ICT core test, which assesses basic ICT skills needed to take the computer-based assessment, such as the capacity to use a mouse or scroll through a web page. This group of adults did not take part in the computer-based assessment and instead took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults who either 1) reported having no prior computer experience or 2) had prior computer experience but failed the ICT core test, which assesses basic ICT skills needed to take the computer-based assessment, such as the capacity to use a mouse or scroll through a web page. This group of adults did not take part in the computer-based assessment and instead took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.MA.NO", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by proficiency level in problem solving in technology-rich environments (%). No computer experience" }, { "id": "Longdefinition", "value": "Percentage of male adults who reported having no prior computer experience; therefore, they did not take part in the computer-based assessment but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults who reported having no prior computer experience; therefore, they did not take part in the computer-based assessment but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.MA.OPT", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Male adults by proficiency level in problem solving in technology-rich environments (%). Opted out of computer-based assessment" }, { "id": "Longdefinition", "value": "Percentage of male adults who opted to take the paper-based assessment without first taking the ICT core assessment, even if they reported some prior experience with computers. They also did not take part in the computer-based assessment, but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of male adults who opted to take the paper-based assessment without first taking the ICT core assessment, even if they reported some prior experience with computers. They also did not take part in the computer-based assessment, but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.NO", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by proficiency level in problem solving in technology-rich environments (%). No computer experience" }, { "id": "Longdefinition", "value": "Adults in this category reported having no prior computer experience; therefore, they did not take part in the computer-based assessment but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Adults in this category reported having no prior computer experience; therefore, they did not take part in the computer-based assessment but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.OPT", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Adults by proficiency level in problem solving in technology-rich environments (%). Opted out of computer-based assessment" }, { "id": "Longdefinition", "value": "Adults in this category opted to take the paper-based assessment without first taking the ICT core assessment, even if they reported some prior experience with computers. They also did not take part in the computer-based assessment, but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Adults in this category opted to take the paper-based assessment without first taking the ICT core assessment, even if they reported some prior experience with computers. They also did not take part in the computer-based assessment, but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.P05", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Problem Solving in Technology-Rich Environments Scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.P10", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Problem Solving in Technology-Rich Environments Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.P25", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Problem Solving in Technology-Rich Environments Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.P50", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Problem Solving in Technology-Rich Environments Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.P75", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Problem Solving in Technology-Rich Environments Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.P90", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Problem Solving in Technology-Rich Environments Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.P95", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Distribution of Adult Problem Solving in Technology-Rich Environments Scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 90 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 90 percent of adults (age 16 to 65) scored. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.YOU.1", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by proficiency level in problem solving in technology-rich environments (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring 241 to less than 291 points on the 0 to 500 point scale. At this level, tasks typically require the use of widely available and familiar technology applications, such as e-mail software or a web browser. There is little or no navigation required to access the information or commands required to solve the problem. The tasks involve few steps and a minimal number of operators. Only simple forms of reasoning, such as assigning items to categories, are required; there is no need to contrast or integrate information. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring 241 to less than 291 points on the 0 to 500 point scale. At this level, tasks typically require the use of widely available and familiar technology applications, such as e-mail software or a web browser. There is little or no navigation required to access the information or commands required to solve the problem. The tasks involve few steps and a minimal number of operators. Only simple forms of reasoning, such as assigning items to categories, are required; there is no need to contrast or integrate information. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.YOU.2", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by proficiency level in problem solving in technology-rich environments (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring 291 to less than 341 points on the 0 to 500 point scale. At this level, tasks typically require the use of both generic and more specific technology applications. For instance, the respondent may have to make use of a novel online form. Some navigation across pages and applications is required to solve the problem. The task may involve multiple steps and operators. The goal of the problem may have to be defined by the respondent, though the criteria to be met are explicit. There are higher monitoring demands. Some unexpected outcomes or impasses may appear. The task may require evaluating the relevance of a set of items to discard distractors. Some integration and inferential reasoning may be needed. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring 291 to less than 341 points on the 0 to 500 point scale. At this level, tasks typically require the use of both generic and more specific technology applications. For instance, the respondent may have to make use of a novel online form. Some navigation across pages and applications is required to solve the problem. The task may involve multiple steps and operators. The goal of the problem may have to be defined by the respondent, though the criteria to be met are explicit. There are higher monitoring demands. Some unexpected outcomes or impasses may appear. The task may require evaluating the relevance of a set of items to discard distractors. Some integration and inferential reasoning may be needed. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.YOU.3", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by proficiency level in problem solving in technology-rich environments (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring equal to or higher than 341 points on the 0 to 500 point scale. At this level, tasks typically require the use of both generic and more specific technology applications. Some navigation across pages and applications is required to solve the problem. The use of tools (e.g. a sort function) is required to make progress towards the solution. The task may involve multiple steps and operators. The goal of the problem may have to be defined by the respondent, and the criteria to be met may or may not be explicit. There are typically high monitoring demands. Unexpected outcomes and impasses are likely to occur. The task may require evaluating the relevance and reliability of information in order to discard distractors. Integration and inferential reasoning may be needed to a large extent. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring equal to or higher than 341 points on the 0 to 500 point scale. At this level, tasks typically require the use of both generic and more specific technology applications. Some navigation across pages and applications is required to solve the problem. The use of tools (e.g. a sort function) is required to make progress towards the solution. The task may involve multiple steps and operators. The goal of the problem may have to be defined by the respondent, and the criteria to be met may or may not be explicit. There are typically high monitoring demands. Unexpected outcomes and impasses are likely to occur. The task may require evaluating the relevance and reliability of information in order to discard distractors. Integration and inferential reasoning may be needed to a large extent. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.YOU.BE", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by proficiency level in problem solving in technology-rich environments (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults scoring below 241 points on the 0 to 500 point scale. Tasks are based on well-defined problems involving the use of only one function within a generic interface to meet one explicit criterion without any categorical or inferential reasoning, or transforming of information. Few steps are required and no sub-goal has to be generated. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults scoring below 241 points on the 0 to 500 point scale. Tasks are based on well-defined problems involving the use of only one function within a generic interface to meet one explicit criterion without any categorical or inferential reasoning, or transforming of information. Few steps are required and no sub-goal has to be generated. The target population was the non-institutionalized population, aged 16-24 years, residing in the country at the time of data collection, irrespective of nationality, citizenship or language status. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.YOU.FAIL", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by proficiency level in problem solving in technology-rich environments (%). Failed ICT Core Test" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults who had prior computer experience but failed the ICT core test, which assesses basic ICT skills needed to take the computer-based assessment, such as the capacity to use a mouse or scroll through a web page. Therefore, they did not take part in the computer-based assessment, but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults who had prior computer experience but failed the ICT core test, which assesses basic ICT skills needed to take the computer-based assessment, such as the capacity to use a mouse or scroll through a web page. Therefore, they did not take part in the computer-based assessment, but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.YOU.FAILNO", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by proficiency level in problem solving in technology-rich environments (%). No computer experience or failed the ICT core test" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults who either 1) reported having no prior computer experience or 2) had prior computer experience but failed the ICT core test, which assesses basic ICT skills needed to take the computer-based assessment, such as the capacity to use a mouse or scroll through a web page. This group of adults did not take part in the computer-based assessment and instead took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults who either 1) reported having no prior computer experience or 2) had prior computer experience but failed the ICT core test, which assesses basic ICT skills needed to take the computer-based assessment, such as the capacity to use a mouse or scroll through a web page. This group of adults did not take part in the computer-based assessment and instead took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.YOU.NO", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by proficiency level in problem solving in technology-rich environments (%). No computer experience" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults who reported having no prior computer experience; therefore, they did not take part in the computer-based assessment but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults who reported having no prior computer experience; therefore, they did not take part in the computer-based assessment but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIAAC.TEC.YOU.OPT", "metatype": [ { "id": "IndicatorName", "value": "PIAAC: Young adults by proficiency level in problem solving in technology-rich environments (%). Opted out of computer-based assessment" }, { "id": "Longdefinition", "value": "Percentage of young (age 16-24) adults who opted to take the paper-based assessment without first taking the ICT core assessment, even if they reported some prior experience with computers. They did not take part in the computer-based assessment, but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Shortdefinition", "value": "Percentage of young (age 16-24) adults who opted to take the paper-based assessment without first taking the ICT core assessment, even if they reported some prior experience with computers. They did not take part in the computer-based assessment, but took the paper-based version of the assessment, which does not include the problem solving in technology-rich environment domain. For more information, consult the OECD PIAAC website: http://www.oecd.org/site/piaac/" }, { "id": "Source", "value": "OECD Programme for the International Assessment of Adult Competencies (PIAAC)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Mean performance on the reading scale for fourth grade students. Total" }, { "id": "Longdefinition", "value": "Mean performance on the reading scale for fourth grade students. Total is the average scale score for fourth graders on the PIRLS reading assessment. The scale centerpoint is 500. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Mean performance on the reading scale for fourth grade students. Total is the average scale score for fourth graders on the PIRLS reading assessment. The scale centerpoint is 500. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.ADV", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Fourth grade students reaching the advanced international benchmark in reading achievement (%)" }, { "id": "Longdefinition", "value": "Fourth grade students reaching the advanced international benchmark in reading achievement (%) is the share of fourth grade students scoring at least 625 on the reading assessment. When reading Literary Texts, students at the advanced international benchmarking level can: A) Integrate ideas and evidence across a text to appreciate overall themes, and B) Interpret story events and character actions to provide reasons, motivations, feelings, and character traits with full text-based support. When reading Informational Texts, students at the advanced international benchmark can A) Distinguish and interpret complex information from different parts of text, and provide full text-based support, B) Integrate information across a text to provide explanations, interpret significance, and sequence activities, and C) Evaluate visual and textual features to explain their function. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Advanced benchmark was called the \"Top 10% Benchmark\" (90th percentile score), which corresponded to a scale score of 615. The 2001 data in this database were recalculated based on a 625 Advanced Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Fourth grade students reaching the advanced international benchmark in reading achievement (%) is the share of fourth grade students scoring at least 625 on the reading assessment. When reading Literary Texts, students at the advanced international benchmarking level can: A) Integrate ideas and evidence across a text to appreciate overall themes, and B) Interpret story events and character actions to provide reasons, motivations, feelings, and character traits with full text-based support. When reading Informational Texts, students at the advanced international benchmark can A) Distinguish and interpret complex information from different parts of text, and provide full text-based support, B) Integrate information across a text to provide explanations, interpret significance, and sequence activities, and C) Evaluate visual and textual features to explain their function. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Advanced benchmark was called the \"Top 10% Benchmark\" (90th percentile score), which corresponded to a scale score of 615. The 2001 data in this database were recalculated based on a 625 Advanced Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.ADV.FE", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Female 4th grade students reaching the advanced international benchmark in reading achievement (%)" }, { "id": "Longdefinition", "value": "Female 4th grade students reaching the advanced international benchmark in reading achievement (%) is the share of female 4th grade students scoring at least 625 on the reading assessment. When reading Literary Texts, students at the advanced international benchmarking level can: A) Integrate ideas and evidence across a text to appreciate overall themes, and B) Interpret story events and character actions to provide reasons, motivations, feelings, and character traits with full text-based support. When reading Informational Texts, students at the advanced international benchmark can A) Distinguish and interpret complex information from different parts of text, and provide full text-based support, B) Integrate information across a text to provide explanations, interpret significance, and sequence activities, and C) Evaluate visual and textual features to explain their function. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Advanced benchmark was called the \"Top 10% Benchmark\" (90th percentile score), which corresponded to a scale score of 615. The 2001 data in this database were recalculated based on a 625 Advanced Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Female 4th grade students reaching the advanced international benchmark in reading achievement (%) is the share of female 4th grade students scoring at least 625 on the reading assessment. When reading Literary Texts, students at the advanced international benchmarking level can: A) Integrate ideas and evidence across a text to appreciate overall themes, and B) Interpret story events and character actions to provide reasons, motivations, feelings, and character traits with full text-based support. When reading Informational Texts, students at the advanced international benchmark can A) Distinguish and interpret complex information from different parts of text, and provide full text-based support, B) Integrate information across a text to provide explanations, interpret significance, and sequence activities, and C) Evaluate visual and textual features to explain their function. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Advanced benchmark was called the \"Top 10% Benchmark\" (90th percentile score), which corresponded to a scale score of 615. The 2001 data in this database were recalculated based on a 625 Advanced Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.ADV.MA", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Male 4th grade students reaching the advanced international benchmark in reading achievement (%)" }, { "id": "Longdefinition", "value": "Male 4th grade students reaching the advanced international benchmark in reading achievement (%) is the share of male 4th grade students scoring at least 625 on the reading assessment. When reading Literary Texts, students at the advanced international benchmarking level can: A) Integrate ideas and evidence across a text to appreciate overall themes, and B) Interpret story events and character actions to provide reasons, motivations, feelings, and character traits with full text-based support. When reading Informational Texts, students at the advanced international benchmark can A) Distinguish and interpret complex information from different parts of text, and provide full text-based support, B) Integrate information across a text to provide explanations, interpret significance, and sequence activities, and C) Evaluate visual and textual features to explain their function. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Advanced benchmark was called the \"Top 10% Benchmark\" (90th percentile score), which corresponded to a scale score of 615. The 2001 data in this database were recalculated based on a 625 Advanced Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Male 4th grade students reaching the advanced international benchmark in reading achievement (%) is the share of male 4th grade students scoring at least 625 on the reading assessment. When reading Literary Texts, students at the advanced international benchmarking level can: A) Integrate ideas and evidence across a text to appreciate overall themes, and B) Interpret story events and character actions to provide reasons, motivations, feelings, and character traits with full text-based support. When reading Informational Texts, students at the advanced international benchmark can A) Distinguish and interpret complex information from different parts of text, and provide full text-based support, B) Integrate information across a text to provide explanations, interpret significance, and sequence activities, and C) Evaluate visual and textual features to explain their function. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Advanced benchmark was called the \"Top 10% Benchmark\" (90th percentile score), which corresponded to a scale score of 615. The 2001 data in this database were recalculated based on a 625 Advanced Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.BL", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Fourth grade students who did not reach the low international benchmark in reading achievement (%)" }, { "id": "Longdefinition", "value": "Fourth grade students who did not reach the low international benchmark in reading achievement (%) is the share of fourth grade students who did not score at least 400 on the reading assessment. These figures were calculated by the World Bank EdStats team by subtracting the share of students reaching the low international benchmark from 100. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Fourth grade students who did not reach the low international benchmark in reading achievement (%) is the share of fourth grade students who did not score at least 400 on the reading assessment. These figures were calculated by the World Bank EdStats team by subtracting the share of students reaching the low international benchmark from 100. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.BL.FE", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Female 4th grade students who did not reach the low international benchmark in reading achievement (%)" }, { "id": "Longdefinition", "value": "Female 4th grade students who did not reach the low international benchmark in reading achievement (%) is the share of female 4th grade students who did not score at least 400 on the reading assessment. These figures were calculated by the World Bank EdStats team by subtracting the share of students reaching the low international benchmark from 100. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Female 4th grade students who did not reach the low international benchmark in reading achievement (%) is the share of female 4th grade students who did not score at least 400 on the reading assessment. These figures were calculated by the World Bank EdStats team by subtracting the share of students reaching the low international benchmark from 100. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.BL.MA", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Male 4th grade students who did not reach the low international benchmark in reading achievement (%)" }, { "id": "Longdefinition", "value": "Male 4th grade students who did not reach the low international benchmark in reading achievement (%) is the share of male 4th grade students who did not score at least 400 on the reading assessment. These figures were calculated by the World Bank EdStats team by subtracting the share of students reaching the low international benchmark from 100. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Male 4th grade students who did not reach the low international benchmark in reading achievement (%) is the share of male 4th grade students who did not score at least 400 on the reading assessment. These figures were calculated by the World Bank EdStats team by subtracting the share of students reaching the low international benchmark from 100. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.FE", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Mean performance on the reading scale for fourth grade students. Female" }, { "id": "Longdefinition", "value": "Mean performance on the reading scale for fourth grade students. Female is the average scale score for female fourth graders on the PIRLS reading assessment. The scale centerpoint is 500. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Mean performance on the reading scale for fourth grade students. Female is the average scale score for female fourth graders on the PIRLS reading assessment. The scale centerpoint is 500. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.HI", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Fourth grade students reaching the high international benchmark in reading achievement (%)" }, { "id": "Longdefinition", "value": "Fourth grade students reaching the high international benchmark in reading achievement (%) is the share of fourth grade students scoring at least 550 on the reading assessment. When reading Literary Texts, students at the high benchmarking level can A) Locate and distinguish significant actions and details embedded across the text, B) Make inferences to explain relationships between intentions, actions, events, and feelings, and give text-based support, C) Interpret and integrate story events and character actions and traits from different parts of the text, D) Evaluate the significance of events and actions across the entire story, and E) Recognize the use of some language features (e.g., metaphor, tone, imagery). When reading Informational Texts, students at this benchmarking level can: A) Locate and distinguish relevant information within a dense text or a complex table, B) Make inferences about logical connections to provide explanations and reasons, C) Integrate textual and visual information to interpret the relationship between ideas, and D) Evaluate content and textual elements to make a generalization. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the High Benchmark was called the \"Upper Quarter Benchmark\" or 75th percentile score, which corresponded to a scale score of 570. The 2001 data in this database were recalculated based on a 550 High Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Fourth grade students reaching the high international benchmark in reading achievement (%) is the share of fourth grade students scoring at least 550 on the reading assessment. When reading Literary Texts, students at the high benchmarking level can A) Locate and distinguish significant actions and details embedded across the text, B) Make inferences to explain relationships between intentions, actions, events, and feelings, and give text-based support, C) Interpret and integrate story events and character actions and traits from different parts of the text, D) Evaluate the significance of events and actions across the entire story, and E) Recognize the use of some language features (e.g., metaphor, tone, imagery). When reading Informational Texts, students at this benchmarking level can: A) Locate and distinguish relevant information within a dense text or a complex table, B) Make inferences about logical connections to provide explanations and reasons, C) Integrate textual and visual information to interpret the relationship between ideas, and D) Evaluate content and textual elements to make a generalization. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the High Benchmark was called the \"Upper Quarter Benchmark\" or 75th percentile score, which corresponded to a scale score of 570. The 2001 data in this database were recalculated based on a 550 High Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.HI.FE", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Female 4th grade students reaching the high international benchmark in reading achievement (%)" }, { "id": "Longdefinition", "value": "Female 4th grade students reaching the high international benchmark in reading achievement (%) is the share of female 4th grade students scoring at least 550 on the reading assessment. When reading Literary Texts, students at the high benchmarking level can A) Locate and distinguish significant actions and details embedded across the text, B) Make inferences to explain relationships between intentions, actions, events, and feelings, and give text-based support, C) Interpret and integrate story events and character actions and traits from different parts of the text, D) Evaluate the significance of events and actions across the entire story, and E) Recognize the use of some language features (e.g., metaphor, tone, imagery). When reading Informational Texts, students at this benchmarking level can: A) Locate and distinguish relevant information within a dense text or a complex table, B) Make inferences about logical connections to provide explanations and reasons, C) Integrate textual and visual information to interpret the relationship between ideas, and D) Evaluate content and textual elements to make a generalization. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the High Benchmark was called the \"Upper Quarter Benchmark\" or 75th percentile score, which corresponded to a scale score of 570. The 2001 data in this database were recalculated based on a 550 High Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Female 4th grade students reaching the high international benchmark in reading achievement (%) is the share of female 4th grade students scoring at least 550 on the reading assessment. When reading Literary Texts, students at the high benchmarking level can A) Locate and distinguish significant actions and details embedded across the text, B) Make inferences to explain relationships between intentions, actions, events, and feelings, and give text-based support, C) Interpret and integrate story events and character actions and traits from different parts of the text, D) Evaluate the significance of events and actions across the entire story, and E) Recognize the use of some language features (e.g., metaphor, tone, imagery). When reading Informational Texts, students at this benchmarking level can: A) Locate and distinguish relevant information within a dense text or a complex table, B) Make inferences about logical connections to provide explanations and reasons, C) Integrate textual and visual information to interpret the relationship between ideas, and D) Evaluate content and textual elements to make a generalization. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the High Benchmark was called the \"Upper Quarter Benchmark\" or 75th percentile score, which corresponded to a scale score of 570. The 2001 data in this database were recalculated based on a 550 High Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.HI.MA", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Male 4th grade students reaching the high international benchmark in reading achievement (%)" }, { "id": "Longdefinition", "value": "Male 4th grade students reaching the high international benchmark in reading achievement (%) is the share of male 4th grade students scoring at least 550 on the reading assessment. When reading Literary Texts, students at the high benchmarking level can A) Locate and distinguish significant actions and details embedded across the text, B) Make inferences to explain relationships between intentions, actions, events, and feelings, and give text-based support, C) Interpret and integrate story events and character actions and traits from different parts of the text, D) Evaluate the significance of events and actions across the entire story, and E) Recognize the use of some language features (e.g., metaphor, tone, imagery). When reading Informational Texts, students at this benchmarking level can: A) Locate and distinguish relevant information within a dense text or a complex table, B) Make inferences about logical connections to provide explanations and reasons, C) Integrate textual and visual information to interpret the relationship between ideas, and D) Evaluate content and textual elements to make a generalization. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the High Benchmark was called the \"Upper Quarter Benchmark\" or 75th percentile score, which corresponded to a scale score of 570. The 2001 data in this database were recalculated based on a 550 High Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Male 4th grade students reaching the high international benchmark in reading achievement (%) is the share of male 4th grade students scoring at least 550 on the reading assessment. When reading Literary Texts, students at the high benchmarking level can A) Locate and distinguish significant actions and details embedded across the text, B) Make inferences to explain relationships between intentions, actions, events, and feelings, and give text-based support, C) Interpret and integrate story events and character actions and traits from different parts of the text, D) Evaluate the significance of events and actions across the entire story, and E) Recognize the use of some language features (e.g., metaphor, tone, imagery). When reading Informational Texts, students at this benchmarking level can: A) Locate and distinguish relevant information within a dense text or a complex table, B) Make inferences about logical connections to provide explanations and reasons, C) Integrate textual and visual information to interpret the relationship between ideas, and D) Evaluate content and textual elements to make a generalization. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the High Benchmark was called the \"Upper Quarter Benchmark\" or 75th percentile score, which corresponded to a scale score of 570. The 2001 data in this database were recalculated based on a 550 High Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.INT", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Fourth grade students reaching the intermediate international benchmark in reading achievement (%)" }, { "id": "Longdefinition", "value": "Fourth grade students reaching the intermediate international benchmark in reading achievement (%) is the share of fourth grade students scoring at least 475 on the reading assessment. When reading Literary Texts, students at the intermediate benchmarking level can A) Retrieve and reproduce explicitly stated actions, events, and feelings, B) Make straightforward inferences about the attributes, feelings, and motivations of main characters, C) Interpret obvious reasons and causes and give simple explanations, and D) Begin to recognize language features and style. When reading Informational Texts, students at the informational level can A) Locate and reproduce two or three pieces of information from within the text, and B) Use subheadings, text boxes, and illustrations to locate parts of the text. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Intermediate Benchmark was called the \"Median Benchmark\" (50th percentile score or median), which corresponded to a scale score of 510. The 2001 data in this database were recalculated based on a 475 Intermediate Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Fourth grade students reaching the intermediate international benchmark in reading achievement (%) is the share of fourth grade students scoring at least 475 on the reading assessment. When reading Literary Texts, students at the intermediate benchmarking level can A) Retrieve and reproduce explicitly stated actions, events, and feelings, B) Make straightforward inferences about the attributes, feelings, and motivations of main characters, C) Interpret obvious reasons and causes and give simple explanations, and D) Begin to recognize language features and style. When reading Informational Texts, students at the informational level can A) Locate and reproduce two or three pieces of information from within the text, and B) Use subheadings, text boxes, and illustrations to locate parts of the text. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Intermediate Benchmark was called the \"Median Benchmark\" (50th percentile score or median), which corresponded to a scale score of 510. The 2001 data in this database were recalculated based on a 475 Intermediate Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.INT.FE", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Female 4th grade students reaching the intermediate international benchmark in reading achievement (%)" }, { "id": "Longdefinition", "value": "Female 4th grade students reaching the intermediate international benchmark in reading achievement (%) is the share of female 4th grade students scoring at least 475 on the reading assessment. When reading Literary Texts, students at the intermediate benchmarking level can A) Retrieve and reproduce explicitly stated actions, events, and feelings, B) Make straightforward inferences about the attributes, feelings, and motivations of main characters, C) Interpret obvious reasons and causes and give simple explanations, and D) Begin to recognize language features and style. When reading Informational Texts, students at the informational level can A) Locate and reproduce two or three pieces of information from within the text, and B) Use subheadings, text boxes, and illustrations to locate parts of the text. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Intermediate Benchmark was called the \"Median Benchmark\" (50th percentile score or median), which corresponded to a scale score of 510. The 2001 data in this database were recalculated based on a 475 Intermediate Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Female 4th grade students reaching the intermediate international benchmark in reading achievement (%) is the share of female 4th grade students scoring at least 475 on the reading assessment. When reading Literary Texts, students at the intermediate benchmarking level can A) Retrieve and reproduce explicitly stated actions, events, and feelings, B) Make straightforward inferences about the attributes, feelings, and motivations of main characters, C) Interpret obvious reasons and causes and give simple explanations, and D) Begin to recognize language features and style. When reading Informational Texts, students at the informational level can A) Locate and reproduce two or three pieces of information from within the text, and B) Use subheadings, text boxes, and illustrations to locate parts of the text. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Intermediate Benchmark was called the \"Median Benchmark\" (50th percentile score or median), which corresponded to a scale score of 510. The 2001 data in this database were recalculated based on a 475 Intermediate Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.INT.MA", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Male 4th grade students reaching the intermediate international benchmark in reading achievement (%)" }, { "id": "Longdefinition", "value": "Male 4th grade students reaching the intermediate international benchmark in reading achievement (%) is the share of male 4th grade students scoring at least 475 on the reading assessment. When reading Literary Texts, students at the intermediate benchmarking level can A) Retrieve and reproduce explicitly stated actions, events, and feelings, B) Make straightforward inferences about the attributes, feelings, and motivations of main characters, C) Interpret obvious reasons and causes and give simple explanations, and D) Begin to recognize language features and style. When reading Informational Texts, students at the informational level can A) Locate and reproduce two or three pieces of information from within the text, and B) Use subheadings, text boxes, and illustrations to locate parts of the text. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Intermediate Benchmark was called the \"Median Benchmark\" (50th percentile score or median), which corresponded to a scale score of 510. The 2001 data in this database were recalculated based on a 475 Intermediate Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Male 4th grade students reaching the intermediate international benchmark in reading achievement (%) is the share of male 4th grade students scoring at least 475 on the reading assessment. When reading Literary Texts, students at the intermediate benchmarking level can A) Retrieve and reproduce explicitly stated actions, events, and feelings, B) Make straightforward inferences about the attributes, feelings, and motivations of main characters, C) Interpret obvious reasons and causes and give simple explanations, and D) Begin to recognize language features and style. When reading Informational Texts, students at the informational level can A) Locate and reproduce two or three pieces of information from within the text, and B) Use subheadings, text boxes, and illustrations to locate parts of the text. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Intermediate Benchmark was called the \"Median Benchmark\" (50th percentile score or median), which corresponded to a scale score of 510. The 2001 data in this database were recalculated based on a 475 Intermediate Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.LOW", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Fourth grade students reaching the low international benchmark in reading achievement (%)" }, { "id": "Longdefinition", "value": "Fourth grade students reaching the low international benchmark in reading achievement (%) is the share of fourth grade students scoring at least 400 on the reading assessment. When reading Literary Texts, students at the low benchmarking level can locate and retrieve an explicitly stated detail. When reading Informational Texts, students at the low benchmark can locate and reproduce explicitly stated information that is at the beginning of the text. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Low Benchmark was called the \"Lower Quarter Benchmark\" (25th percentile score), which corresponded to a scale score of 435. The 2001 data in this database were recalculated based on a 400 Low Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Fourth grade students reaching the low international benchmark in reading achievement (%) is the share of fourth grade students scoring at least 400 on the reading assessment. When reading Literary Texts, students at the low benchmarking level can locate and retrieve an explicitly stated detail. When reading Informational Texts, students at the low benchmark can locate and reproduce explicitly stated information that is at the beginning of the text. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Low Benchmark was called the \"Lower Quarter Benchmark\" (25th percentile score), which corresponded to a scale score of 435. The 2001 data in this database were recalculated based on a 400 Low Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.LOW.FE", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Female 4th grade students reaching the low international benchmark in reading achievement (%)" }, { "id": "Longdefinition", "value": "Female 4th grade students reaching the low international benchmark in reading achievement (%) is the share of female 4th grade students scoring at least 400 on the reading assessment. When reading Literary Texts, students at the low benchmarking level can locate and retrieve an explicitly stated detail. When reading Informational Texts, students at the low benchmark can locate and reproduce explicitly stated information that is at the beginning of the text. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Low Benchmark was called the \"Lower Quarter Benchmark\" (25th percentile score), which corresponded to a scale score of 435. The 2001 data in this database were recalculated based on a 400 Low Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Female 4th grade students reaching the low international benchmark in reading achievement (%) is the share of female 4th grade students scoring at least 400 on the reading assessment. When reading Literary Texts, students at the low benchmarking level can locate and retrieve an explicitly stated detail. When reading Informational Texts, students at the low benchmark can locate and reproduce explicitly stated information that is at the beginning of the text. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Low Benchmark was called the \"Lower Quarter Benchmark\" (25th percentile score), which corresponded to a scale score of 435. The 2001 data in this database were recalculated based on a 400 Low Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.LOW.MA", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Male 4th grade students reaching the low international benchmark in reading achievement (%)" }, { "id": "Longdefinition", "value": "Male 4th grade students reaching the low international benchmark in reading achievement (%) is the share of male 4th grade students scoring at least 400 on the reading assessment. When reading Literary Texts, students at the low benchmarking level can locate and retrieve an explicitly stated detail. When reading Informational Texts, students at the low benchmark can locate and reproduce explicitly stated information that is at the beginning of the text. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Low Benchmark was called the \"Lower Quarter Benchmark\" (25th percentile score), which corresponded to a scale score of 435. The 2001 data in this database were recalculated based on a 400 Low Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Male 4th grade students reaching the low international benchmark in reading achievement (%) is the share of male 4th grade students scoring at least 400 on the reading assessment. When reading Literary Texts, students at the low benchmarking level can locate and retrieve an explicitly stated detail. When reading Informational Texts, students at the low benchmark can locate and reproduce explicitly stated information that is at the beginning of the text. The procedure for identifying International Benchmarks changed from the PIRLS 2001 method of using percentiles to a method based on scores that do not change from PIRLS cycle to cycle. In PIRLS 2001, the Low Benchmark was called the \"Lower Quarter Benchmark\" (25th percentile score), which corresponded to a scale score of 435. The 2001 data in this database were recalculated based on a 400 Low Benchmark score and are different than the data found in the 2001 PIRLS report. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.MA", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Mean performance on the reading scale for fourth grade students. Male" }, { "id": "Longdefinition", "value": "Mean performance on the reading scale for fourth grade students. Male is the average scale score for male fourth graders on the PIRLS reading assessment. The scale centerpoint is 500. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "Mean performance on the reading scale for fourth grade students. Male is the average scale score for male fourth graders on the PIRLS reading assessment. The scale centerpoint is 500. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.P05", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Distribution of Reading Scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.P10", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Distribution of Reading Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.P25", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Distribution of Reading Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.P50", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Distribution of Reading Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.P75", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Distribution of Reading Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.P90", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Distribution of Reading Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PIRLS.REA.P95", "metatype": [ { "id": "IndicatorName", "value": "PIRLS: Distribution of Reading Scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year, but may not be comparable across years or countries. Consult the PIRLS website for more detailed information: http://timssandpirls.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA) Progress in International Reading Literacy Study (PIRLS)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT", "metatype": [ { "id": "IndicatorName", "value": "PISA: Mean performance on the mathematics scale" }, { "id": "Longdefinition", "value": "Average score of 15-year-old students on the PISA mathematics scale. The metric for the overall mathematics scale is based on a mean for OECD countries of 500 points and a standard deviation of 100 points. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Average score of 15-year-old students on the PISA mathematics scale. The metric for the overall mathematics scale is based on a mean for OECD countries of 500 points and a standard deviation of 100 points. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.0", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by mathematics proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students below the lowest proficiency level (scoring 358 or below) on the PISA mathematics scale. Students below Level 1 may be able to perform very direct and straightforward mathematical tasks, such as reading a single value from a well-labeled chart or table where the labels on the chart match the words in the stimulus and question, so that the selection criteria are clear and the relationship between the chart and the aspects of the context depicted are evident, and performing arithmetic calculations with whole numbers by following clear and well-defined instructions. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old students below the lowest proficiency level (scoring 358 or below) on the PISA mathematics scale. Students below Level 1 may be able to perform very direct and straightforward mathematical tasks, such as reading a single value from a well-labeled chart or table where the labels on the chart match the words in the stimulus and question, so that the selection criteria are clear and the relationship between the chart and the aspects of the context depicted are evident, and performing arithmetic calculations with whole numbers by following clear and well-defined instructions. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.0.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by mathematics proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students below the lowest proficiency level (scoring 358 or below) on the PISA mathematics scale. Students below Level 1 may be able to perform very direct and straightforward mathematical tasks, such as reading a single value from a well-labeled chart or table where the labels on the chart match the words in the stimulus and question, so that the selection criteria are clear and the relationship between the chart and the aspects of the context depicted are evident, and performing arithmetic calculations with whole numbers by following clear and well-defined instructions. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old female students below the lowest proficiency level (scoring 358 or below) on the PISA mathematics scale. Students below Level 1 may be able to perform very direct and straightforward mathematical tasks, such as reading a single value from a well-labeled chart or table where the labels on the chart match the words in the stimulus and question, so that the selection criteria are clear and the relationship between the chart and the aspects of the context depicted are evident, and performing arithmetic calculations with whole numbers by following clear and well-defined instructions. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.0.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by mathematics proficiency level (%). Below Level 1" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students below the lowest proficiency level (scoring 358 or below) on the PISA mathematics scale. Students below Level 1 may be able to perform very direct and straightforward mathematical tasks, such as reading a single value from a well-labeled chart or table where the labels on the chart match the words in the stimulus and question, so that the selection criteria are clear and the relationship between the chart and the aspects of the context depicted are evident, and performing arithmetic calculations with whole numbers by following clear and well-defined instructions. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old male students below the lowest proficiency level (scoring 358 or below) on the PISA mathematics scale. Students below Level 1 may be able to perform very direct and straightforward mathematical tasks, such as reading a single value from a well-labeled chart or table where the labels on the chart match the words in the stimulus and question, so that the selection criteria are clear and the relationship between the chart and the aspects of the context depicted are evident, and performing arithmetic calculations with whole numbers by following clear and well-defined instructions. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.1", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by mathematics proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 358 but lower than or equal to 420 points on the PISA mathematics scale. At Level 1, students can answer questions involving familiar contexts where all relevant information is present and the questions are clearly defined. They are able to identify information and to carry out routine procedures according to direct instructions in explicit situations. They can perform actions that are almost always obvious and follow immediately from the given stimuli. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.1.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by mathematics proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 358 but lower than or equal to 420 points on the PISA mathematics scale. At Level 1, students can answer questions involving familiar contexts where all relevant information is present and the questions are clearly defined. They are able to identify information and to carry out routine procedures according to direct instructions in explicit situations. They can perform actions that are almost always obvious and follow immediately from the given stimuli. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old female students scoring higher than 358 but lower than or equal to 420 points on the PISA mathematics scale. At Level 1, students can answer questions involving familiar contexts where all relevant information is present and the questions are clearly defined. They are able to identify information and to carry out routine procedures according to direct instructions in explicit situations. They can perform actions that are almost always obvious and follow immediately from the given stimuli. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.1.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by mathematics proficiency level (%). Level 1" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 358 but lower than or equal to 420 points on the PISA mathematics scale. At Level 1, students can answer questions involving familiar contexts where all relevant information is present and the questions are clearly defined. They are able to identify information and to carry out routine procedures according to direct instructions in explicit situations. They can perform actions that are almost always obvious and follow immediately from the given stimuli. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old male students scoring higher than 358 but lower than or equal to 420 points on the PISA mathematics scale. At Level 1, students can answer questions involving familiar contexts where all relevant information is present and the questions are clearly defined. They are able to identify information and to carry out routine procedures according to direct instructions in explicit situations. They can perform actions that are almost always obvious and follow immediately from the given stimuli. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.2", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by mathematics proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 420 but lower than or equal to 482 points on the PISA mathematics scale. At Level 2, students can interpret and recognize situations in contexts that require no more than direct inference. They can extract relevant information from a single source and make use of a single representational mode. Students at this level can employ basic algorithms, formulae, procedures, or conventions to solve problems involving whole numbers. They are capable of making literal interpretations of the results. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old students scoring higher than 420 but lower than or equal to 482 points on the PISA mathematics scale. At Level 2, students can interpret and recognize situations in contexts that require no more than direct inference. They can extract relevant information from a single source and make use of a single representational mode. Students at this level can employ basic algorithms, formulae, procedures, or conventions to solve problems involving whole numbers. They are capable of making literal interpretations of the results. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.2.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by mathematics proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 420 but lower than or equal to 482 points on the PISA mathematics scale. At Level 2, students can interpret and recognize situations in contexts that require no more than direct inference. They can extract relevant information from a single source and make use of a single representational mode. Students at this level can employ basic algorithms, formulae, procedures, or conventions to solve problems involving whole numbers. They are capable of making literal interpretations of the results. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old female students scoring higher than 420 but lower than or equal to 482 points on the PISA mathematics scale. At Level 2, students can interpret and recognize situations in contexts that require no more than direct inference. They can extract relevant information from a single source and make use of a single representational mode. Students at this level can employ basic algorithms, formulae, procedures, or conventions to solve problems involving whole numbers. They are capable of making literal interpretations of the results. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.2.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by mathematics proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 420 but lower than or equal to 482 points on the PISA mathematics scale. At Level 2, students can interpret and recognize situations in contexts that require no more than direct inference. They can extract relevant information from a single source and make use of a single representational mode. Students at this level can employ basic algorithms, formulae, procedures, or conventions to solve problems involving whole numbers. They are capable of making literal interpretations of the results. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old male students scoring higher than 420 but lower than or equal to 482 points on the PISA mathematics scale. At Level 2, students can interpret and recognize situations in contexts that require no more than direct inference. They can extract relevant information from a single source and make use of a single representational mode. Students at this level can employ basic algorithms, formulae, procedures, or conventions to solve problems involving whole numbers. They are capable of making literal interpretations of the results. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.3", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by mathematics proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 482 but lower than or equal to 545 points on the PISA mathematics scale. At Level 3, students can execute clearly described procedures, including those that require sequential decisions. Their interpretations are sufficiently sound to be a base for building a simple model or for selecting and applying simple problem- solving strategies. Students at this level can interpret and use representations based on different information sources and reason directly from them. They typically show some ability to handle percentages, fractions and decimal numbers, and to work with proportional relationships. Their solutions reflect that they have engaged in basic interpretation and reasoning. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old students scoring higher than 482 but lower than or equal to 545 points on the PISA mathematics scale. At Level 3, students can execute clearly described procedures, including those that require sequential decisions. Their interpretations are sufficiently sound to be a base for building a simple model or for selecting and applying simple problem- solving strategies. Students at this level can interpret and use representations based on different information sources and reason directly from them. They typically show some ability to handle percentages, fractions and decimal numbers, and to work with proportional relationships. Their solutions reflect that they have engaged in basic interpretation and reasoning. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.3.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by mathematics proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 482 but lower than or equal to 545 points on the PISA mathematics scale. At Level 3, students can execute clearly described procedures, including those that require sequential decisions. Their interpretations are sufficiently sound to be a base for building a simple model or for selecting and applying simple problem- solving strategies. Students at this level can interpret and use representations based on different information sources and reason directly from them. They typically show some ability to handle percentages, fractions and decimal numbers, and to work with proportional relationships. Their solutions reflect that they have engaged in basic interpretation and reasoning. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old female students scoring higher than 482 but lower than or equal to 545 points on the PISA mathematics scale. At Level 3, students can execute clearly described procedures, including those that require sequential decisions. Their interpretations are sufficiently sound to be a base for building a simple model or for selecting and applying simple problem- solving strategies. Students at this level can interpret and use representations based on different information sources and reason directly from them. They typically show some ability to handle percentages, fractions and decimal numbers, and to work with proportional relationships. Their solutions reflect that they have engaged in basic interpretation and reasoning. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.3.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by mathematics proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 482 but lower than or equal to 545 points on the PISA mathematics scale. At Level 3, students can execute clearly described procedures, including those that require sequential decisions. Their interpretations are sufficiently sound to be a base for building a simple model or for selecting and applying simple problem- solving strategies. Students at this level can interpret and use representations based on different information sources and reason directly from them. They typically show some ability to handle percentages, fractions and decimal numbers, and to work with proportional relationships. Their solutions reflect that they have engaged in basic interpretation and reasoning. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old male students scoring higher than 482 but lower than or equal to 545 points on the PISA mathematics scale. At Level 3, students can execute clearly described procedures, including those that require sequential decisions. Their interpretations are sufficiently sound to be a base for building a simple model or for selecting and applying simple problem- solving strategies. Students at this level can interpret and use representations based on different information sources and reason directly from them. They typically show some ability to handle percentages, fractions and decimal numbers, and to work with proportional relationships. Their solutions reflect that they have engaged in basic interpretation and reasoning. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.4", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by mathematics proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 545 but lower than or equal to 607 on the PISA mathematics scale. At Level 4, students can work effectively with explicit models for complex concrete situations that may involve constraints or call for making assumptions. They can select and integrate different representations, including symbolic, linking them directly to aspects of real-world situations. Students at this level can utilize their limited range of skills and can reason with some insight, in straightforward contexts. They can construct and communicate explanations and arguments based on their interpretations, arguments, and actions. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old students scoring higher than 545 but lower than or equal to 607 on the PISA mathematics scale. At Level 4, students can work effectively with explicit models for complex concrete situations that may involve constraints or call for making assumptions. They can select and integrate different representations, including symbolic, linking them directly to aspects of real-world situations. Students at this level can utilize their limited range of skills and can reason with some insight, in straightforward contexts. They can construct and communicate explanations and arguments based on their interpretations, arguments, and actions. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.4.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by mathematics proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 545 but lower than or equal to 607 on the PISA mathematics scale. At Level 4, students can work effectively with explicit models for complex concrete situations that may involve constraints or call for making assumptions. They can select and integrate different representations, including symbolic, linking them directly to aspects of real-world situations. Students at this level can utilize their limited range of skills and can reason with some insight, in straightforward contexts. They can construct and communicate explanations and arguments based on their interpretations, arguments, and actions. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old female students scoring higher than 545 but lower than or equal to 607 on the PISA mathematics scale. At Level 4, students can work effectively with explicit models for complex concrete situations that may involve constraints or call for making assumptions. They can select and integrate different representations, including symbolic, linking them directly to aspects of real-world situations. Students at this level can utilize their limited range of skills and can reason with some insight, in straightforward contexts. They can construct and communicate explanations and arguments based on their interpretations, arguments, and actions. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.4.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by mathematics proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 545 but lower than or equal to 607 on the PISA mathematics scale. At Level 4, students can work effectively with explicit models for complex concrete situations that may involve constraints or call for making assumptions. They can select and integrate different representations, including symbolic, linking them directly to aspects of real-world situations. Students at this level can utilize their limited range of skills and can reason with some insight, in straightforward contexts. They can construct and communicate explanations and arguments based on their interpretations, arguments, and actions. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old male students scoring higher than 545 but lower than or equal to 607 on the PISA mathematics scale. At Level 4, students can work effectively with explicit models for complex concrete situations that may involve constraints or call for making assumptions. They can select and integrate different representations, including symbolic, linking them directly to aspects of real-world situations. Students at this level can utilize their limited range of skills and can reason with some insight, in straightforward contexts. They can construct and communicate explanations and arguments based on their interpretations, arguments, and actions. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.5", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by mathematics proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 607 but lower than or equal to 669 on the PISA mathematics scale. At Level 5, students can develop and work with models for complex situations, identifying constraints and specifying assumptions. They can select, compare, and evaluate appropriate problem-solving strategies for dealing with complex problems related to these models. Students at this level can work strategically using broad, well-developed thinking and reasoning skills, appropriate linked representations, symbolic and formal characterizations, and insight pertaining to these situations. They begin to reflect on their work and can formulate and communicate their interpretations and reasoning. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old students scoring higher than 607 but lower than or equal to 669 on the PISA mathematics scale. At Level 5, students can develop and work with models for complex situations, identifying constraints and specifying assumptions. They can select, compare, and evaluate appropriate problem-solving strategies for dealing with complex problems related to these models. Students at this level can work strategically using broad, well-developed thinking and reasoning skills, appropriate linked representations, symbolic and formal characterizations, and insight pertaining to these situations. They begin to reflect on their work and can formulate and communicate their interpretations and reasoning. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.5.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by mathematics proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 607 but lower than or equal to 669 on the PISA mathematics scale. At Level 5, students can develop and work with models for complex situations, identifying constraints and specifying assumptions. They can select, compare, and evaluate appropriate problem-solving strategies for dealing with complex problems related to these models. Students at this level can work strategically using broad, well-developed thinking and reasoning skills, appropriate linked representations, symbolic and formal characterizations, and insight pertaining to these situations. They begin to reflect on their work and can formulate and communicate their interpretations and reasoning. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old female students scoring higher than 607 but lower than or equal to 669 on the PISA mathematics scale. At Level 5, students can develop and work with models for complex situations, identifying constraints and specifying assumptions. They can select, compare, and evaluate appropriate problem-solving strategies for dealing with complex problems related to these models. Students at this level can work strategically using broad, well-developed thinking and reasoning skills, appropriate linked representations, symbolic and formal characterizations, and insight pertaining to these situations. They begin to reflect on their work and can formulate and communicate their interpretations and reasoning. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.5.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by mathematics proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 607 but lower than or equal to 669 on the PISA mathematics scale. At Level 5, students can develop and work with models for complex situations, identifying constraints and specifying assumptions. They can select, compare, and evaluate appropriate problem-solving strategies for dealing with complex problems related to these models. Students at this level can work strategically using broad, well-developed thinking and reasoning skills, appropriate linked representations, symbolic and formal characterizations, and insight pertaining to these situations. They begin to reflect on their work and can formulate and communicate their interpretations and reasoning. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old male students scoring higher than 607 but lower than or equal to 669 on the PISA mathematics scale. At Level 5, students can develop and work with models for complex situations, identifying constraints and specifying assumptions. They can select, compare, and evaluate appropriate problem-solving strategies for dealing with complex problems related to these models. Students at this level can work strategically using broad, well-developed thinking and reasoning skills, appropriate linked representations, symbolic and formal characterizations, and insight pertaining to these situations. They begin to reflect on their work and can formulate and communicate their interpretations and reasoning. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.6", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by mathematics proficiency level (%). Level 6" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 669 on the PISA mathematics scale. At Level 6, students can conceptualize, generalize and utilize information based on their investigations and modeling of complex problem situations, and can use their knowledge in relatively non-standard contexts. They can link different information sources and representations and flexibly translate among them. Students at this level are capable of advanced mathematical thinking and reasoning. These students can apply this insight and understanding, along with a mastery of symbolic and formal mathematical operations and relationships, to develop new approaches and strategies for attacking novel situations. Students at this level can reflect on their actions, and can formulate and precisely communicate their actions and reflections regarding their findings, interpretations, arguments, and the appropriateness of these to the original situation. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old students scoring higher than 669 on the PISA mathematics scale. At Level 6, students can conceptualize, generalize and utilize information based on their investigations and modeling of complex problem situations, and can use their knowledge in relatively non-standard contexts. They can link different information sources and representations and flexibly translate among them. Students at this level are capable of advanced mathematical thinking and reasoning. These students can apply this insight and understanding, along with a mastery of symbolic and formal mathematical operations and relationships, to develop new approaches and strategies for attacking novel situations. Students at this level can reflect on their actions, and can formulate and precisely communicate their actions and reflections regarding their findings, interpretations, arguments, and the appropriateness of these to the original situation. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.6.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by mathematics proficiency level (%). Level 6" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 669 on the PISA mathematics scale. At Level 6, students can conceptualize, generalize and utilize information based on their investigations and modeling of complex problem situations, and can use their knowledge in relatively non-standard contexts. They can link different information sources and representations and flexibly translate among them. Students at this level are capable of advanced mathematical thinking and reasoning. These students can apply this insight and understanding, along with a mastery of symbolic and formal mathematical operations and relationships, to develop new approaches and strategies for attacking novel situations. Students at this level can reflect on their actions, and can formulate and precisely communicate their actions and reflections regarding their findings, interpretations, arguments, and the appropriateness of these to the original situation. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old female students scoring higher than 669 on the PISA mathematics scale. At Level 6, students can conceptualize, generalize and utilize information based on their investigations and modeling of complex problem situations, and can use their knowledge in relatively non-standard contexts. They can link different information sources and representations and flexibly translate among them. Students at this level are capable of advanced mathematical thinking and reasoning. These students can apply this insight and understanding, along with a mastery of symbolic and formal mathematical operations and relationships, to develop new approaches and strategies for attacking novel situations. Students at this level can reflect on their actions, and can formulate and precisely communicate their actions and reflections regarding their findings, interpretations, arguments, and the appropriateness of these to the original situation. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.6.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by mathematics proficiency level (%). Level 6" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 669 on the PISA mathematics scale. At Level 6, students can conceptualize, generalize and utilize information based on their investigations and modeling of complex problem situations, and can use their knowledge in relatively non-standard contexts. They can link different information sources and representations and flexibly translate among them. Students at this level are capable of advanced mathematical thinking and reasoning. These students can apply this insight and understanding, along with a mastery of symbolic and formal mathematical operations and relationships, to develop new approaches and strategies for attacking novel situations. Students at this level can reflect on their actions, and can formulate and precisely communicate their actions and reflections regarding their findings, interpretations, arguments, and the appropriateness of these to the original situation. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old male students scoring higher than 669 on the PISA mathematics scale. At Level 6, students can conceptualize, generalize and utilize information based on their investigations and modeling of complex problem situations, and can use their knowledge in relatively non-standard contexts. They can link different information sources and representations and flexibly translate among them. Students at this level are capable of advanced mathematical thinking and reasoning. These students can apply this insight and understanding, along with a mastery of symbolic and formal mathematical operations and relationships, to develop new approaches and strategies for attacking novel situations. Students at this level can reflect on their actions, and can formulate and precisely communicate their actions and reflections regarding their findings, interpretations, arguments, and the appropriateness of these to the original situation. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Mean performance on the mathematics scale. Female" }, { "id": "Longdefinition", "value": "Average score of 15-year-old female students on the PISA mathematics scale. The metric for the overall mathematics scale is based on a mean for OECD countries of 500 points and a standard deviation of 100 points. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Average score of 15-year-old female students on the PISA mathematics scale. The metric for the overall mathematics scale is based on a mean for OECD countries of 500 points and a standard deviation of 100 points. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Mean performance on the mathematics scale. Male" }, { "id": "Longdefinition", "value": "Average score of 15-year-old male students on the PISA mathematics scale. The metric for the overall mathematics scale is based on a mean for OECD countries of 500 points and a standard deviation of 100 points. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Average score of 15-year-old male students on the PISA mathematics scale. The metric for the overall mathematics scale is based on a mean for OECD countries of 500 points and a standard deviation of 100 points. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.P05", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Mathematics Scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.P10", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Mathematics Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.P25", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Mathematics Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.P50", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Mathematics Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Othernotes", "value": "PISA" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.P75", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Mathematics Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.P90", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Mathematics Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.MAT.P95", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Mathematics Scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Mean performance on the reading scale" }, { "id": "Longdefinition", "value": "Average score of 15-year-old students on the PISA reading scale. The metric for the overall reading scale is based on a mean for participating OECD countries set at 500, with a standard deviation of 100. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Average score of 15-year-old students on the PISA reading scale. The metric for the overall reading scale is based on a mean for participating OECD countries set at 500, with a standard deviation of 100. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.0.B1C", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by reading proficiency level (%). Below Level 1C" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring students below the lowest proficiency level (1C) on the PISA reading scale. Students with scores below Level 1C (less or equal to 189 points) usually do not succeed at the most basic reading tasks that PISA measures. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old students scoring students below the lowest proficiency level (1C) on the PISA reading scale. Students with scores below Level 1C (less or equal to 189 points) usually do not succeed at the most basic reading tasks that PISA measures. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.0.B1C.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by reading proficiency level (%). Below Level 1C" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring students below the lowest proficiency level (1C) on the PISA reading scale. Students with scores below Level 1C (less or equal to 189 points) usually do not succeed at the most basic reading tasks that PISA measures. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old female students scoring students below the lowest proficiency level (1C) on the PISA reading scale. Students with scores below Level 1C (less or equal to 189 points) usually do not succeed at the most basic reading tasks that PISA measures. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.0.B1C.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by reading proficiency level (%). Below Level 1C" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring students below the lowest proficiency level (1C) on the PISA reading scale. Students with scores below Level 1C (less or equal to 189 points) usually do not succeed at the most basic reading tasks that PISA measures. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old male students scoring students below the lowest proficiency level (1C) on the PISA reading scale. Students with scores below Level 1C (less or equal to 189 points) usually do not succeed at the most basic reading tasks that PISA measures. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.1A", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by reading proficiency level (%). Level 1A" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 335 but lower than or equal to 407 on the PISA reading scale. Readers at Level 1a can understand the literal meaning of sentences or short passages. Readers at this level can also recognize the main theme or the author’s purpose in a piece of text about a familiar topic, and make a simple connection between several adjacent pieces of information, or between the given information and their own prior knowledge. They can select a relevant page from a small set based on simple prompts, and locate one or more independent pieces of information within short texts. Level 1a readers can reflect on the overall purpose and on the relative importance of information (e.g. the main idea vs. non-essential detail) in simple texts containing explicit cues. Most tasks at this level contain explicit cues regarding what needs to be done, how to do it, and where in the text(s) readers should focus their attention. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 1 data have been included in this database as Level 1A because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old students scoring higher than 335 but lower than or equal to 407 on the PISA reading scale. Readers at Level 1a can understand the literal meaning of sentences or short passages. Readers at this level can also recognize the main theme or the author’s purpose in a piece of text about a familiar topic, and make a simple connection between several adjacent pieces of information, or between the given information and their own prior knowledge. They can select a relevant page from a small set based on simple prompts, and locate one or more independent pieces of information within short texts. Level 1a readers can reflect on the overall purpose and on the relative importance of information (e.g. the main idea vs. non-essential detail) in simple texts containing explicit cues. Most tasks at this level contain explicit cues regarding what needs to be done, how to do it, and where in the text(s) readers should focus their attention. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 1 data have been included in this database as Level 1A because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.1A.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by reading proficiency level (%). Level 1A" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 335 but lower than or equal to 407 on the PISA reading scale. Readers at Level 1a can understand the literal meaning of sentences or short passages. Readers at this level can also recognize the main theme or the author’s purpose in a piece of text about a familiar topic, and make a simple connection between several adjacent pieces of information, or between the given information and their own prior knowledge. They can select a relevant page from a small set based on simple prompts, and locate one or more independent pieces of information within short texts. Level 1a readers can reflect on the overall purpose and on the relative importance of information (e.g. the main idea vs. non-essential detail) in simple texts containing explicit cues. Most tasks at this level contain explicit cues regarding what needs to be done, how to do it, and where in the text(s) readers should focus their attention. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 1 data have been included in this database as Level 1A because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old female students scoring higher than 335 but lower than or equal to 407 on the PISA reading scale. Readers at Level 1a can understand the literal meaning of sentences or short passages. Readers at this level can also recognize the main theme or the author’s purpose in a piece of text about a familiar topic, and make a simple connection between several adjacent pieces of information, or between the given information and their own prior knowledge. They can select a relevant page from a small set based on simple prompts, and locate one or more independent pieces of information within short texts. Level 1a readers can reflect on the overall purpose and on the relative importance of information (e.g. the main idea vs. non-essential detail) in simple texts containing explicit cues. Most tasks at this level contain explicit cues regarding what needs to be done, how to do it, and where in the text(s) readers should focus their attention. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 1 data have been included in this database as Level 1A because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.1A.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by reading proficiency level (%). Level 1A" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 335 but lower than or equal to 407 on the PISA reading scale. Readers at Level 1a can understand the literal meaning of sentences or short passages. Readers at this level can also recognize the main theme or the author’s purpose in a piece of text about a familiar topic, and make a simple connection between several adjacent pieces of information, or between the given information and their own prior knowledge. They can select a relevant page from a small set based on simple prompts, and locate one or more independent pieces of information within short texts. Level 1a readers can reflect on the overall purpose and on the relative importance of information (e.g. the main idea vs. non-essential detail) in simple texts containing explicit cues. Most tasks at this level contain explicit cues regarding what needs to be done, how to do it, and where in the text(s) readers should focus their attention. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 1 data have been included in this database as Level 1A because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old male students scoring higher than 335 but lower than or equal to 407 on the PISA reading scale. Readers at Level 1a can understand the literal meaning of sentences or short passages. Readers at this level can also recognize the main theme or the author’s purpose in a piece of text about a familiar topic, and make a simple connection between several adjacent pieces of information, or between the given information and their own prior knowledge. They can select a relevant page from a small set based on simple prompts, and locate one or more independent pieces of information within short texts. Level 1a readers can reflect on the overall purpose and on the relative importance of information (e.g. the main idea vs. non-essential detail) in simple texts containing explicit cues. Most tasks at this level contain explicit cues regarding what needs to be done, how to do it, and where in the text(s) readers should focus their attention. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 1 data have been included in this database as Level 1A because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.1B", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by reading proficiency level (%). Level 1B" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 262 but lower than or equal to 335 on the PISA reading scale. Readers at Level 1b can evaluate the literal meaning of simple sentences. They can also interpret the literal meaning of texts by making simple connections between adjacent pieces of information in the question and/or the text. Readers at this level can scan for and locate a single piece of prominently placed, explicitly stated information in a single sentence, a short text or a simple list. They can access a relevant page from a small set based on simple prompts when explicit cues are present. Tasks at Level 1b explicitly direct readers to consider relevant factors in the task and in the text. Texts at this level are short and typically provide support to the reader, such as through repetition of information, pictures or familiar symbols. There is minimal competing information. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Below Level 1 to Level 5) than later assessments. Data for 2000, 2003, and 2006 for Level 1B were not included in PISA reports, but values in this database were calculated based on the current Level 1B score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old students scoring higher than 262 but lower than or equal to 335 on the PISA reading scale. Readers at Level 1b can evaluate the literal meaning of simple sentences. They can also interpret the literal meaning of texts by making simple connections between adjacent pieces of information in the question and/or the text. Readers at this level can scan for and locate a single piece of prominently placed, explicitly stated information in a single sentence, a short text or a simple list. They can access a relevant page from a small set based on simple prompts when explicit cues are present. Tasks at Level 1b explicitly direct readers to consider relevant factors in the task and in the text. Texts at this level are short and typically provide support to the reader, such as through repetition of information, pictures or familiar symbols. There is minimal competing information. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Below Level 1 to Level 5) than later assessments. Data for 2000, 2003, and 2006 for Level 1B were not included in PISA reports, but values in this database were calculated based on the current Level 1B score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.1B.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by reading proficiency level (%). Level 1B" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 262 but lower than or equal to 335 on the PISA reading scale. Readers at Level 1b can evaluate the literal meaning of simple sentences. They can also interpret the literal meaning of texts by making simple connections between adjacent pieces of information in the question and/or the text. Readers at this level can scan for and locate a single piece of prominently placed, explicitly stated information in a single sentence, a short text or a simple list. They can access a relevant page from a small set based on simple prompts when explicit cues are present. Tasks at Level 1b explicitly direct readers to consider relevant factors in the task and in the text. Texts at this level are short and typically provide support to the reader, such as through repetition of information, pictures or familiar symbols. There is minimal competing information. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Below Level 1 to Level 5) than later assessments. Data for 2000, 2003, and 2006 for Level 1B were not included in PISA reports, but values in this database were calculated based on the current Level 1B score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old female students scoring higher than 262 but lower than or equal to 335 on the PISA reading scale. Readers at Level 1b can evaluate the literal meaning of simple sentences. They can also interpret the literal meaning of texts by making simple connections between adjacent pieces of information in the question and/or the text. Readers at this level can scan for and locate a single piece of prominently placed, explicitly stated information in a single sentence, a short text or a simple list. They can access a relevant page from a small set based on simple prompts when explicit cues are present. Tasks at Level 1b explicitly direct readers to consider relevant factors in the task and in the text. Texts at this level are short and typically provide support to the reader, such as through repetition of information, pictures or familiar symbols. There is minimal competing information. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Below Level 1 to Level 5) than later assessments. Data for 2000, 2003, and 2006 for Level 1B were not included in PISA reports, but values in this database were calculated based on the current Level 1B score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.1B.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by reading proficiency level (%). Level 1B" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 262 but lower than or equal to 335 on the PISA reading scale. Readers at Level 1b can evaluate the literal meaning of simple sentences. They can also interpret the literal meaning of texts by making simple connections between adjacent pieces of information in the question and/or the text. Readers at this level can scan for and locate a single piece of prominently placed, explicitly stated information in a single sentence, a short text or a simple list. They can access a relevant page from a small set based on simple prompts when explicit cues are present. Tasks at Level 1b explicitly direct readers to consider relevant factors in the task and in the text. Texts at this level are short and typically provide support to the reader, such as through repetition of information, pictures or familiar symbols. There is minimal competing information. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Below Level 1 to Level 5) than later assessments. Data for 2000, 2003, and 2006 for Level 1B were not included in PISA reports, but values in this database were calculated based on the current Level 1B score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old male students scoring higher than 262 but lower than or equal to 335 on the PISA reading scale. Readers at Level 1b can evaluate the literal meaning of simple sentences. They can also interpret the literal meaning of texts by making simple connections between adjacent pieces of information in the question and/or the text. Readers at this level can scan for and locate a single piece of prominently placed, explicitly stated information in a single sentence, a short text or a simple list. They can access a relevant page from a small set based on simple prompts when explicit cues are present. Tasks at Level 1b explicitly direct readers to consider relevant factors in the task and in the text. Texts at this level are short and typically provide support to the reader, such as through repetition of information, pictures or familiar symbols. There is minimal competing information. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Below Level 1 to Level 5) than later assessments. Data for 2000, 2003, and 2006 for Level 1B were not included in PISA reports, but values in this database were calculated based on the current Level 1B score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.1C", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by reading proficiency level (%). Level 1C" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 189 but lower than or equal to 262 on the PISA reading scale. Readers at Level 1C can understand and affirm the meaning of short, syntactically simple sentences on a literal level, and read for a clear and simple purpose within a limited amount of time. Tasks at this level involve simple vocabulary and syntactic structures. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old students scoring higher than 189 but lower than or equal to 262 on the PISA reading scale. Readers at Level 1C can understand and affirm the meaning of short, syntactically simple sentences on a literal level, and read for a clear and simple purpose within a limited amount of time. Tasks at this level involve simple vocabulary and syntactic structures. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.1C.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by reading proficiency level (%). Level 1C" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 189 but lower than or equal to 262 on the PISA reading scale. Readers at Level 1C can understand and affirm the meaning of short, syntactically simple sentences on a literal level, and read for a clear and simple purpose within a limited amount of time. Tasks at this level involve simple vocabulary and syntactic structures. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old female students scoring higher than 189 but lower than or equal to 262 on the PISA reading scale. Readers at Level 1C can understand and affirm the meaning of short, syntactically simple sentences on a literal level, and read for a clear and simple purpose within a limited amount of time. Tasks at this level involve simple vocabulary and syntactic structures. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.1C.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by reading proficiency level (%). Level 1C" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 189 but lower than or equal to 262 on the PISA reading scale. Readers at Level 1C can understand and affirm the meaning of short, syntactically simple sentences on a literal level, and read for a clear and simple purpose within a limited amount of time. Tasks at this level involve simple vocabulary and syntactic structures. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old male students scoring higher than 189 but lower than or equal to 262 on the PISA reading scale. Readers at Level 1C can understand and affirm the meaning of short, syntactically simple sentences on a literal level, and read for a clear and simple purpose within a limited amount of time. Tasks at this level involve simple vocabulary and syntactic structures. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.2", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by reading proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 407 but lower than or equal to 480 on the PISA reading scale. Readers at Level 2 can identify the main idea in a piece of text of moderate length. They can understand relationships or construe meaning within a limited part of the text when the information is not prominent by producing basic inferences, and/or when the text(s) include some distracting information. They can select and access a page in a set based on explicit though sometimes complex prompts, and locate one or more pieces of information based on multiple, partly implicit criteria. Readers at Level 2 can, when explicitly cued, reflect on the overall purpose, or on the purpose of specific details, in texts of moderate length. They can reflect on simple visual or typographical features. They can compare claims and evaluate the reasons supporting them based on short, explicit statements. Tasks at Level 2 may involve comparisons or contrasts based on a single feature in the text. Typical reflective tasks at this level require readers to make a comparison or several connections between the text and outside knowledge by drawing on personal experience and attitudes. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 2 data have been included in this database as Level 2 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old students scoring higher than 407 but lower than or equal to 480 on the PISA reading scale. Readers at Level 2 can identify the main idea in a piece of text of moderate length. They can understand relationships or construe meaning within a limited part of the text when the information is not prominent by producing basic inferences, and/or when the text(s) include some distracting information. They can select and access a page in a set based on explicit though sometimes complex prompts, and locate one or more pieces of information based on multiple, partly implicit criteria. Readers at Level 2 can, when explicitly cued, reflect on the overall purpose, or on the purpose of specific details, in texts of moderate length. They can reflect on simple visual or typographical features. They can compare claims and evaluate the reasons supporting them based on short, explicit statements. Tasks at Level 2 may involve comparisons or contrasts based on a single feature in the text. Typical reflective tasks at this level require readers to make a comparison or several connections between the text and outside knowledge by drawing on personal experience and attitudes. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 2 data have been included in this database as Level 2 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.2.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by reading proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 407 but lower than or equal to 480 on the PISA reading scale. Readers at Level 2 can identify the main idea in a piece of text of moderate length. They can understand relationships or construe meaning within a limited part of the text when the information is not prominent by producing basic inferences, and/or when the text(s) include some distracting information. They can select and access a page in a set based on explicit though sometimes complex prompts, and locate one or more pieces of information based on multiple, partly implicit criteria. Readers at Level 2 can, when explicitly cued, reflect on the overall purpose, or on the purpose of specific details, in texts of moderate length. They can reflect on simple visual or typographical features. They can compare claims and evaluate the reasons supporting them based on short, explicit statements. Tasks at Level 2 may involve comparisons or contrasts based on a single feature in the text. Typical reflective tasks at this level require readers to make a comparison or several connections between the text and outside knowledge by drawing on personal experience and attitudes. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 2 data have been included in this database as Level 2 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old female students scoring higher than 407 but lower than or equal to 480 on the PISA reading scale. Readers at Level 2 can identify the main idea in a piece of text of moderate length. They can understand relationships or construe meaning within a limited part of the text when the information is not prominent by producing basic inferences, and/or when the text(s) include some distracting information. They can select and access a page in a set based on explicit though sometimes complex prompts, and locate one or more pieces of information based on multiple, partly implicit criteria. Readers at Level 2 can, when explicitly cued, reflect on the overall purpose, or on the purpose of specific details, in texts of moderate length. They can reflect on simple visual or typographical features. They can compare claims and evaluate the reasons supporting them based on short, explicit statements. Tasks at Level 2 may involve comparisons or contrasts based on a single feature in the text. Typical reflective tasks at this level require readers to make a comparison or several connections between the text and outside knowledge by drawing on personal experience and attitudes. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 2 data have been included in this database as Level 2 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.2.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by reading proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 407 but lower than or equal to 480 on the PISA reading scale. Readers at Level 2 can identify the main idea in a piece of text of moderate length. They can understand relationships or construe meaning within a limited part of the text when the information is not prominent by producing basic inferences, and/or when the text(s) include some distracting information. They can select and access a page in a set based on explicit though sometimes complex prompts, and locate one or more pieces of information based on multiple, partly implicit criteria. Readers at Level 2 can, when explicitly cued, reflect on the overall purpose, or on the purpose of specific details, in texts of moderate length. They can reflect on simple visual or typographical features. They can compare claims and evaluate the reasons supporting them based on short, explicit statements. Tasks at Level 2 may involve comparisons or contrasts based on a single feature in the text. Typical reflective tasks at this level require readers to make a comparison or several connections between the text and outside knowledge by drawing on personal experience and attitudes. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 2 data have been included in this database as Level 2 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old male students scoring higher than 407 but lower than or equal to 480 on the PISA reading scale. Readers at Level 2 can identify the main idea in a piece of text of moderate length. They can understand relationships or construe meaning within a limited part of the text when the information is not prominent by producing basic inferences, and/or when the text(s) include some distracting information. They can select and access a page in a set based on explicit though sometimes complex prompts, and locate one or more pieces of information based on multiple, partly implicit criteria. Readers at Level 2 can, when explicitly cued, reflect on the overall purpose, or on the purpose of specific details, in texts of moderate length. They can reflect on simple visual or typographical features. They can compare claims and evaluate the reasons supporting them based on short, explicit statements. Tasks at Level 2 may involve comparisons or contrasts based on a single feature in the text. Typical reflective tasks at this level require readers to make a comparison or several connections between the text and outside knowledge by drawing on personal experience and attitudes. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 2 data have been included in this database as Level 2 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.3", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by reading proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 480 but lower than or equal to 553 on the PISA reading scale. Readers at Level 3 can represent the literal meaning of single or multiple texts in the absence of explicit content or organizational clues. Readers can integrate content and generate both basic and more advanced inferences. They can also integrate several parts of a piece of text in order to identify the main idea, understand a relationship or construe the meaning of a word or phrase when the required information is featured on a single page. They can search for information based on indirect prompts, and locate target information that is not in a prominent position and/or is in the presence of distractors. In some cases, readers at this level recognize the relationship between several pieces of information based on multiple criteria. Level 3 readers can reflect on a piece of text or a small set of texts, and compare and contrast several authors’ viewpoints based on explicit information. Reflective tasks at this level may require the reader to perform comparisons, generate explanations or evaluate a feature of the text. Some reflective tasks require readers to demonstrate a detailed understanding of a piece of text dealing with a familiar topic, whereas others require a basic understanding of less-familiar content. Tasks at Level 3 require the reader to take many features into account when comparing, contrasting or categorizing information. The required information is often not prominent or there may be a considerable amount of competing information. Texts typical of this level may include other obstacles, such as ideas that are contrary to expectation or negatively worded. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 3 data have been included in this database as Level 3 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old students scoring higher than 480 but lower than or equal to 553 on the PISA reading scale. Readers at Level 3 can represent the literal meaning of single or multiple texts in the absence of explicit content or organizational clues. Readers can integrate content and generate both basic and more advanced inferences. They can also integrate several parts of a piece of text in order to identify the main idea, understand a relationship or construe the meaning of a word or phrase when the required information is featured on a single page. They can search for information based on indirect prompts, and locate target information that is not in a prominent position and/or is in the presence of distractors. In some cases, readers at this level recognize the relationship between several pieces of information based on multiple criteria. Level 3 readers can reflect on a piece of text or a small set of texts, and compare and contrast several authors’ viewpoints based on explicit information. Reflective tasks at this level may require the reader to perform comparisons, generate explanations or evaluate a feature of the text. Some reflective tasks require readers to demonstrate a detailed understanding of a piece of text dealing with a familiar topic, whereas others require a basic understanding of less-familiar content. Tasks at Level 3 require the reader to take many features into account when comparing, contrasting or categorizing information. The required information is often not prominent or there may be a considerable amount of competing information. Texts typical of this level may include other obstacles, such as ideas that are contrary to expectation or negatively worded. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 3 data have been included in this database as Level 3 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.3.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by reading proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 480 but lower than or equal to 553 on the PISA reading scale. Readers at Level 3 can represent the literal meaning of single or multiple texts in the absence of explicit content or organizational clues. Readers can integrate content and generate both basic and more advanced inferences. They can also integrate several parts of a piece of text in order to identify the main idea, understand a relationship or construe the meaning of a word or phrase when the required information is featured on a single page. They can search for information based on indirect prompts, and locate target information that is not in a prominent position and/or is in the presence of distractors. In some cases, readers at this level recognize the relationship between several pieces of information based on multiple criteria. Level 3 readers can reflect on a piece of text or a small set of texts, and compare and contrast several authors’ viewpoints based on explicit information. Reflective tasks at this level may require the reader to perform comparisons, generate explanations or evaluate a feature of the text. Some reflective tasks require readers to demonstrate a detailed understanding of a piece of text dealing with a familiar topic, whereas others require a basic understanding of less-familiar content. Tasks at Level 3 require the reader to take many features into account when comparing, contrasting or categorizing information. The required information is often not prominent or there may be a considerable amount of competing information. Texts typical of this level may include other obstacles, such as ideas that are contrary to expectation or negatively worded. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 3 data have been included in this database as Level 3 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old female students scoring higher than 480 but lower than or equal to 553 on the PISA reading scale. Readers at Level 3 can represent the literal meaning of single or multiple texts in the absence of explicit content or organizational clues. Readers can integrate content and generate both basic and more advanced inferences. They can also integrate several parts of a piece of text in order to identify the main idea, understand a relationship or construe the meaning of a word or phrase when the required information is featured on a single page. They can search for information based on indirect prompts, and locate target information that is not in a prominent position and/or is in the presence of distractors. In some cases, readers at this level recognize the relationship between several pieces of information based on multiple criteria. Level 3 readers can reflect on a piece of text or a small set of texts, and compare and contrast several authors’ viewpoints based on explicit information. Reflective tasks at this level may require the reader to perform comparisons, generate explanations or evaluate a feature of the text. Some reflective tasks require readers to demonstrate a detailed understanding of a piece of text dealing with a familiar topic, whereas others require a basic understanding of less-familiar content. Tasks at Level 3 require the reader to take many features into account when comparing, contrasting or categorizing information. The required information is often not prominent or there may be a considerable amount of competing information. Texts typical of this level may include other obstacles, such as ideas that are contrary to expectation or negatively worded. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 3 data have been included in this database as Level 3 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.3.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by reading proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 480 but lower than or equal to 553 on the PISA reading scale. Readers at Level 3 can represent the literal meaning of single or multiple texts in the absence of explicit content or organizational clues. Readers can integrate content and generate both basic and more advanced inferences. They can also integrate several parts of a piece of text in order to identify the main idea, understand a relationship or construe the meaning of a word or phrase when the required information is featured on a single page. They can search for information based on indirect prompts, and locate target information that is not in a prominent position and/or is in the presence of distractors. In some cases, readers at this level recognize the relationship between several pieces of information based on multiple criteria. Level 3 readers can reflect on a piece of text or a small set of texts, and compare and contrast several authors’ viewpoints based on explicit information. Reflective tasks at this level may require the reader to perform comparisons, generate explanations or evaluate a feature of the text. Some reflective tasks require readers to demonstrate a detailed understanding of a piece of text dealing with a familiar topic, whereas others require a basic understanding of less-familiar content. Tasks at Level 3 require the reader to take many features into account when comparing, contrasting or categorizing information. The required information is often not prominent or there may be a considerable amount of competing information. Texts typical of this level may include other obstacles, such as ideas that are contrary to expectation or negatively worded. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 3 data have been included in this database as Level 3 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old male students scoring higher than 480 but lower than or equal to 553 on the PISA reading scale. Readers at Level 3 can represent the literal meaning of single or multiple texts in the absence of explicit content or organizational clues. Readers can integrate content and generate both basic and more advanced inferences. They can also integrate several parts of a piece of text in order to identify the main idea, understand a relationship or construe the meaning of a word or phrase when the required information is featured on a single page. They can search for information based on indirect prompts, and locate target information that is not in a prominent position and/or is in the presence of distractors. In some cases, readers at this level recognize the relationship between several pieces of information based on multiple criteria. Level 3 readers can reflect on a piece of text or a small set of texts, and compare and contrast several authors’ viewpoints based on explicit information. Reflective tasks at this level may require the reader to perform comparisons, generate explanations or evaluate a feature of the text. Some reflective tasks require readers to demonstrate a detailed understanding of a piece of text dealing with a familiar topic, whereas others require a basic understanding of less-familiar content. Tasks at Level 3 require the reader to take many features into account when comparing, contrasting or categorizing information. The required information is often not prominent or there may be a considerable amount of competing information. Texts typical of this level may include other obstacles, such as ideas that are contrary to expectation or negatively worded. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. PISA 2000/2003/2006 Level 3 data have been included in this database as Level 3 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.4", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by reading proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 553 but lower than or equal to 626 on the PISA reading scale. At Level 4, readers can comprehend extended passages in single or multiple-text settings. They interpret the meaning of nuances of language in a section of text by taking into account the text as a whole. In other interpretative tasks, students demonstrate understanding and application of ad hoc categories. They can compare perspectives and draw inferences based on multiple sources. Readers can search, locate and integrate several pieces of embedded information in the presence of plausible distractors. They can generate inferences based on the task statement in order to assess the relevance of target information. They can handle tasks that require them to memories prior task context. In addition, students at this level can evaluate the relationship between specific statements and a person’s overall stance or conclusion about a topic. They can reflect on the strategies that authors use to convey their points, based on salient features of texts (e.g., titles and illustrations). They can compare and contrast claims explicitly made in several texts and assess the reliability of a source based on salient criteria. Texts at Level 4 are often long or complex, and their content or form may not be standard. Many of the tasks are situated in multiple-text settings. The texts and the tasks contain indirect or implicit cues. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5). PISA 2000/2003/2006 Level 4 data have been included in this database as Level 4 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old students scoring higher than 553 but lower than or equal to 626 on the PISA reading scale. At Level 4, readers can comprehend extended passages in single or multiple-text settings. They interpret the meaning of nuances of language in a section of text by taking into account the text as a whole. In other interpretative tasks, students demonstrate understanding and application of ad hoc categories. They can compare perspectives and draw inferences based on multiple sources. Readers can search, locate and integrate several pieces of embedded information in the presence of plausible distractors. They can generate inferences based on the task statement in order to assess the relevance of target information. They can handle tasks that require them to memories prior task context. In addition, students at this level can evaluate the relationship between specific statements and a person’s overall stance or conclusion about a topic. They can reflect on the strategies that authors use to convey their points, based on salient features of texts (e.g., titles and illustrations). They can compare and contrast claims explicitly made in several texts and assess the reliability of a source based on salient criteria. Texts at Level 4 are often long or complex, and their content or form may not be standard. Many of the tasks are situated in multiple-text settings. The texts and the tasks contain indirect or implicit cues. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5). PISA 2000/2003/2006 Level 4 data have been included in this database as Level 4 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.4.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by reading proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 553 but lower than or equal to 626 on the PISA reading scale. At Level 4, readers can comprehend extended passages in single or multiple-text settings. They interpret the meaning of nuances of language in a section of text by taking into account the text as a whole. In other interpretative tasks, students demonstrate understanding and application of ad hoc categories. They can compare perspectives and draw inferences based on multiple sources. Readers can search, locate and integrate several pieces of embedded information in the presence of plausible distractors. They can generate inferences based on the task statement in order to assess the relevance of target information. They can handle tasks that require them to memories prior task context. In addition, students at this level can evaluate the relationship between specific statements and a person’s overall stance or conclusion about a topic. They can reflect on the strategies that authors use to convey their points, based on salient features of texts (e.g., titles and illustrations). They can compare and contrast claims explicitly made in several texts and assess the reliability of a source based on salient criteria. Texts at Level 4 are often long or complex, and their content or form may not be standard. Many of the tasks are situated in multiple-text settings. The texts and the tasks contain indirect or implicit cues. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5). PISA 2000/2003/2006 Level 4 data have been included in this database as Level 4 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old female students scoring higher than 553 but lower than or equal to 626 on the PISA reading scale. At Level 4, readers can comprehend extended passages in single or multiple-text settings. They interpret the meaning of nuances of language in a section of text by taking into account the text as a whole. In other interpretative tasks, students demonstrate understanding and application of ad hoc categories. They can compare perspectives and draw inferences based on multiple sources. Readers can search, locate and integrate several pieces of embedded information in the presence of plausible distractors. They can generate inferences based on the task statement in order to assess the relevance of target information. They can handle tasks that require them to memories prior task context. In addition, students at this level can evaluate the relationship between specific statements and a person’s overall stance or conclusion about a topic. They can reflect on the strategies that authors use to convey their points, based on salient features of texts (e.g., titles and illustrations). They can compare and contrast claims explicitly made in several texts and assess the reliability of a source based on salient criteria. Texts at Level 4 are often long or complex, and their content or form may not be standard. Many of the tasks are situated in multiple-text settings. The texts and the tasks contain indirect or implicit cues. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5). PISA 2000/2003/2006 Level 4 data have been included in this database as Level 4 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.4.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by reading proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 553 but lower than or equal to 626 on the PISA reading scale. At Level 4, readers can comprehend extended passages in single or multiple-text settings. They interpret the meaning of nuances of language in a section of text by taking into account the text as a whole. In other interpretative tasks, students demonstrate understanding and application of ad hoc categories. They can compare perspectives and draw inferences based on multiple sources. Readers can search, locate and integrate several pieces of embedded information in the presence of plausible distractors. They can generate inferences based on the task statement in order to assess the relevance of target information. They can handle tasks that require them to memories prior task context. In addition, students at this level can evaluate the relationship between specific statements and a person’s overall stance or conclusion about a topic. They can reflect on the strategies that authors use to convey their points, based on salient features of texts (e.g., titles and illustrations). They can compare and contrast claims explicitly made in several texts and assess the reliability of a source based on salient criteria. Texts at Level 4 are often long or complex, and their content or form may not be standard. Many of the tasks are situated in multiple-text settings. The texts and the tasks contain indirect or implicit cues. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5). PISA 2000/2003/2006 Level 4 data have been included in this database as Level 4 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old male students scoring higher than 553 but lower than or equal to 626 on the PISA reading scale. At Level 4, readers can comprehend extended passages in single or multiple-text settings. They interpret the meaning of nuances of language in a section of text by taking into account the text as a whole. In other interpretative tasks, students demonstrate understanding and application of ad hoc categories. They can compare perspectives and draw inferences based on multiple sources. Readers can search, locate and integrate several pieces of embedded information in the presence of plausible distractors. They can generate inferences based on the task statement in order to assess the relevance of target information. They can handle tasks that require them to memories prior task context. In addition, students at this level can evaluate the relationship between specific statements and a person’s overall stance or conclusion about a topic. They can reflect on the strategies that authors use to convey their points, based on salient features of texts (e.g., titles and illustrations). They can compare and contrast claims explicitly made in several texts and assess the reliability of a source based on salient criteria. Texts at Level 4 are often long or complex, and their content or form may not be standard. Many of the tasks are situated in multiple-text settings. The texts and the tasks contain indirect or implicit cues. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5). PISA 2000/2003/2006 Level 4 data have been included in this database as Level 4 because they are based on an identical score range. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.5", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by reading proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 626 but lower than or equal to 698 on the PISA reading scale. Readers at Level 5 can comprehend lengthy texts, inferring which information in the text is relevant even though the information of interest may be easily overlooked. They can perform causal or other forms of reasoning based on a deep understanding of extended pieces of text. They can also answer indirect questions by inferring the relationship between the question and one or several pieces of information distributed within or across multiple texts and sources. Reflective tasks require the production or critical evaluation of hypotheses, drawing on specific information. Readers can establish distinctions between content and purpose, and between fact and opinion as applied to complex or abstract statements. They can assess neutrality and bias based on explicit or implicit cues pertaining to both the content and/or source of the information. They can also draw conclusions regarding the reliability of the claims or conclusions offered in a piece of text. For all aspects of reading, tasks at Level 5 typically involve dealing with concepts that are abstract or counterintuitive, and going through several steps until the goal is reached. In addition, tasks at this level may require the reader to handle several long texts, switching back and forth across texts in order to compare and contrast information. In this database, 2000/2003/2006 Level 5 data are calculated figures based on the current Level 5 (626 to 698) rather than the figures presented for Level 5 (626+) in PISA Reports for 2000, 2003, and 2006. Use caution in comparing results across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old students scoring higher than 626 but lower than or equal to 698 on the PISA reading scale. Readers at Level 5 can comprehend lengthy texts, inferring which information in the text is relevant even though the information of interest may be easily overlooked. They can perform causal or other forms of reasoning based on a deep understanding of extended pieces of text. They can also answer indirect questions by inferring the relationship between the question and one or several pieces of information distributed within or across multiple texts and sources. Reflective tasks require the production or critical evaluation of hypotheses, drawing on specific information. Readers can establish distinctions between content and purpose, and between fact and opinion as applied to complex or abstract statements. They can assess neutrality and bias based on explicit or implicit cues pertaining to both the content and/or source of the information. They can also draw conclusions regarding the reliability of the claims or conclusions offered in a piece of text. For all aspects of reading, tasks at Level 5 typically involve dealing with concepts that are abstract or counterintuitive, and going through several steps until the goal is reached. In addition, tasks at this level may require the reader to handle several long texts, switching back and forth across texts in order to compare and contrast information. In this database, 2000/2003/2006 Level 5 data are calculated figures based on the current Level 5 (626 to 698) rather than the figures presented for Level 5 (626+) in PISA Reports for 2000, 2003, and 2006. Use caution in comparing results across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.5.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by reading proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of female 15-year-old students scoring higher than 626 but lower than or equal to 698 on the PISA reading scale. Readers at Level 5 can comprehend lengthy texts, inferring which information in the text is relevant even though the information of interest may be easily overlooked. They can perform causal or other forms of reasoning based on a deep understanding of extended pieces of text. They can also answer indirect questions by inferring the relationship between the question and one or several pieces of information distributed within or across multiple texts and sources. Reflective tasks require the production or critical evaluation of hypotheses, drawing on specific information. Readers can establish distinctions between content and purpose, and between fact and opinion as applied to complex or abstract statements. They can assess neutrality and bias based on explicit or implicit cues pertaining to both the content and/or source of the information. They can also draw conclusions regarding the reliability of the claims or conclusions offered in a piece of text. For all aspects of reading, tasks at Level 5 typically involve dealing with concepts that are abstract or counterintuitive, and going through several steps until the goal is reached. In addition, tasks at this level may require the reader to handle several long texts, switching back and forth across texts in order to compare and contrast information. In this database, 2000/2003/2006 Level 5 data are calculated figures based on the current Level 5 (626 to 698) rather than the figures presented for Level 5 (626+) in PISA Reports for 2000, 2003, and 2006. Use caution in comparing results across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of female 15-year-old students scoring higher than 626 but lower than or equal to 698 on the PISA reading scale. Readers at Level 5 can comprehend lengthy texts, inferring which information in the text is relevant even though the information of interest may be easily overlooked. They can perform causal or other forms of reasoning based on a deep understanding of extended pieces of text. They can also answer indirect questions by inferring the relationship between the question and one or several pieces of information distributed within or across multiple texts and sources. Reflective tasks require the production or critical evaluation of hypotheses, drawing on specific information. Readers can establish distinctions between content and purpose, and between fact and opinion as applied to complex or abstract statements. They can assess neutrality and bias based on explicit or implicit cues pertaining to both the content and/or source of the information. They can also draw conclusions regarding the reliability of the claims or conclusions offered in a piece of text. For all aspects of reading, tasks at Level 5 typically involve dealing with concepts that are abstract or counterintuitive, and going through several steps until the goal is reached. In addition, tasks at this level may require the reader to handle several long texts, switching back and forth across texts in order to compare and contrast information. In this database, 2000/2003/2006 Level 5 data are calculated figures based on the current Level 5 (626 to 698) rather than the figures presented for Level 5 (626+) in PISA Reports for 2000, 2003, and 2006. Use caution in comparing results across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.5.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by reading proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of male 15-year-old students scoring higher than 626 but lower than or equal to 698 on the PISA reading scale. Readers at Level 5 can comprehend lengthy texts, inferring which information in the text is relevant even though the information of interest may be easily overlooked. They can perform causal or other forms of reasoning based on a deep understanding of extended pieces of text. They can also answer indirect questions by inferring the relationship between the question and one or several pieces of information distributed within or across multiple texts and sources. Reflective tasks require the production or critical evaluation of hypotheses, drawing on specific information. Readers can establish distinctions between content and purpose, and between fact and opinion as applied to complex or abstract statements. They can assess neutrality and bias based on explicit or implicit cues pertaining to both the content and/or source of the information. They can also draw conclusions regarding the reliability of the claims or conclusions offered in a piece of text. For all aspects of reading, tasks at Level 5 typically involve dealing with concepts that are abstract or counterintuitive, and going through several steps until the goal is reached. In addition, tasks at this level may require the reader to handle several long texts, switching back and forth across texts in order to compare and contrast information. In this database, 2000/2003/2006 Level 5 data are calculated figures based on the current Level 5 (626 to 698) rather than the figures presented for Level 5 (626+) in PISA Reports for 2000, 2003, and 2006. Use caution in comparing results across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of male 15-year-old students scoring higher than 626 but lower than or equal to 698 on the PISA reading scale. Readers at Level 5 can comprehend lengthy texts, inferring which information in the text is relevant even though the information of interest may be easily overlooked. They can perform causal or other forms of reasoning based on a deep understanding of extended pieces of text. They can also answer indirect questions by inferring the relationship between the question and one or several pieces of information distributed within or across multiple texts and sources. Reflective tasks require the production or critical evaluation of hypotheses, drawing on specific information. Readers can establish distinctions between content and purpose, and between fact and opinion as applied to complex or abstract statements. They can assess neutrality and bias based on explicit or implicit cues pertaining to both the content and/or source of the information. They can also draw conclusions regarding the reliability of the claims or conclusions offered in a piece of text. For all aspects of reading, tasks at Level 5 typically involve dealing with concepts that are abstract or counterintuitive, and going through several steps until the goal is reached. In addition, tasks at this level may require the reader to handle several long texts, switching back and forth across texts in order to compare and contrast information. In this database, 2000/2003/2006 Level 5 data are calculated figures based on the current Level 5 (626 to 698) rather than the figures presented for Level 5 (626+) in PISA Reports for 2000, 2003, and 2006. Use caution in comparing results across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.6", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by reading proficiency level (%). Level 6" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 698 on the PISA reading scale. Readers at Level 6 can comprehend lengthy and abstract texts in which the information of interest is deeply embedded and only indirectly related to the task. They can compare, contrast and integrate information representing multiple and potentially conflicting perspectives, using multiple criteria and generating inferences across distant pieces of information to determine how the information may be used. Readers at Level 6 can reflect deeply on the text’s source in relation to its content, using criteria external to the text. They can compare and contrast information across texts, identifying and resolving inter-textual discrepancies and conflicts through inferences about the sources of information, their explicit or vested interests, and other cues as to the validity of the information. Tasks at Level 6 typically require the reader to set up elaborate plans, combining multiple criteria and generating inferences to relate the task and the text(s). Materials at this level include one or several complex and abstract text(s), involving multiple and possibly discrepant perspectives. Target information may take the form of details that are deeply embedded within or across texts and potentially obscured by competing information. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. Level 6 data were not available in the 2000/2003/2006 PISA Reports, so these figures were calculated based on the current PISA Reading Proficiency Level 6 (698+). Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old students scoring higher than 698 on the PISA reading scale. Readers at Level 6 can comprehend lengthy and abstract texts in which the information of interest is deeply embedded and only indirectly related to the task. They can compare, contrast and integrate information representing multiple and potentially conflicting perspectives, using multiple criteria and generating inferences across distant pieces of information to determine how the information may be used. Readers at Level 6 can reflect deeply on the text’s source in relation to its content, using criteria external to the text. They can compare and contrast information across texts, identifying and resolving inter-textual discrepancies and conflicts through inferences about the sources of information, their explicit or vested interests, and other cues as to the validity of the information. Tasks at Level 6 typically require the reader to set up elaborate plans, combining multiple criteria and generating inferences to relate the task and the text(s). Materials at this level include one or several complex and abstract text(s), involving multiple and possibly discrepant perspectives. Target information may take the form of details that are deeply embedded within or across texts and potentially obscured by competing information. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. Level 6 data were not available in the 2000/2003/2006 PISA Reports, so these figures were calculated based on the current PISA Reading Proficiency Level 6 (698+). Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.6.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by reading proficiency level (%). Level 6" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 698 on the PISA reading scale. Readers at Level 6 can comprehend lengthy and abstract texts in which the information of interest is deeply embedded and only indirectly related to the task. They can compare, contrast and integrate information representing multiple and potentially conflicting perspectives, using multiple criteria and generating inferences across distant pieces of information to determine how the information may be used. Readers at Level 6 can reflect deeply on the text’s source in relation to its content, using criteria external to the text. They can compare and contrast information across texts, identifying and resolving inter-textual discrepancies and conflicts through inferences about the sources of information, their explicit or vested interests, and other cues as to the validity of the information. Tasks at Level 6 typically require the reader to set up elaborate plans, combining multiple criteria and generating inferences to relate the task and the text(s). Materials at this level include one or several complex and abstract text(s), involving multiple and possibly discrepant perspectives. Target information may take the form of details that are deeply embedded within or across texts and potentially obscured by competing information. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. Level 6 data were not available in the 2000/2003/2006 PISA Reports, so these figures were calculated based on the current PISA Reading Proficiency Level 6 (698+). Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old female students scoring higher than 698 on the PISA reading scale. Readers at Level 6 can comprehend lengthy and abstract texts in which the information of interest is deeply embedded and only indirectly related to the task. They can compare, contrast and integrate information representing multiple and potentially conflicting perspectives, using multiple criteria and generating inferences across distant pieces of information to determine how the information may be used. Readers at Level 6 can reflect deeply on the text’s source in relation to its content, using criteria external to the text. They can compare and contrast information across texts, identifying and resolving inter-textual discrepancies and conflicts through inferences about the sources of information, their explicit or vested interests, and other cues as to the validity of the information. Tasks at Level 6 typically require the reader to set up elaborate plans, combining multiple criteria and generating inferences to relate the task and the text(s). Materials at this level include one or several complex and abstract text(s), involving multiple and possibly discrepant perspectives. Target information may take the form of details that are deeply embedded within or across texts and potentially obscured by competing information. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. Level 6 data were not available in the 2000/2003/2006 PISA Reports, so these figures were calculated based on the current PISA Reading Proficiency Level 6 (698+). Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.6.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by reading proficiency level (%). Level 6" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 698 on the PISA reading scale. Readers at Level 6 can comprehend lengthy and abstract texts in which the information of interest is deeply embedded and only indirectly related to the task. They can compare, contrast and integrate information representing multiple and potentially conflicting perspectives, using multiple criteria and generating inferences across distant pieces of information to determine how the information may be used. Readers at Level 6 can reflect deeply on the text’s source in relation to its content, using criteria external to the text. They can compare and contrast information across texts, identifying and resolving inter-textual discrepancies and conflicts through inferences about the sources of information, their explicit or vested interests, and other cues as to the validity of the information. Tasks at Level 6 typically require the reader to set up elaborate plans, combining multiple criteria and generating inferences to relate the task and the text(s). Materials at this level include one or several complex and abstract text(s), involving multiple and possibly discrepant perspectives. Target information may take the form of details that are deeply embedded within or across texts and potentially obscured by competing information. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. Level 6 data were not available in the 2000/2003/2006 PISA Reports, so these figures were calculated based on the current PISA Reading Proficiency Level 6 (698+). Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Percentage of 15-year-old male students scoring higher than 698 on the PISA reading scale. Readers at Level 6 can comprehend lengthy and abstract texts in which the information of interest is deeply embedded and only indirectly related to the task. They can compare, contrast and integrate information representing multiple and potentially conflicting perspectives, using multiple criteria and generating inferences across distant pieces of information to determine how the information may be used. Readers at Level 6 can reflect deeply on the text’s source in relation to its content, using criteria external to the text. They can compare and contrast information across texts, identifying and resolving inter-textual discrepancies and conflicts through inferences about the sources of information, their explicit or vested interests, and other cues as to the validity of the information. Tasks at Level 6 typically require the reader to set up elaborate plans, combining multiple criteria and generating inferences to relate the task and the text(s). Materials at this level include one or several complex and abstract text(s), involving multiple and possibly discrepant perspectives. Target information may take the form of details that are deeply embedded within or across texts and potentially obscured by competing information. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Levels 1 to 5) than later assessments. Level 6 data were not available in the 2000/2003/2006 PISA Reports, so these figures were calculated based on the current PISA Reading Proficiency Level 6 (698+). Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Mean performance on the reading scale. Female" }, { "id": "Longdefinition", "value": "Average score of 15-year-old female students on the PISA reading scale. The metric for the overall reading scale is based on a mean for participating OECD countries set at 500, with a standard deviation of 100. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Average score of 15-year-old female students on the PISA reading scale. The metric for the overall reading scale is based on a mean for participating OECD countries set at 500, with a standard deviation of 100. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Mean performance on the reading scale. Male" }, { "id": "Longdefinition", "value": "Average score of 15-year-old male students on the PISA reading scale. The metric for the overall reading scale is based on a mean for participating OECD countries set at 500, with a standard deviation of 100. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Average score of 15-year-old male students on the PISA reading scale. The metric for the overall reading scale is based on a mean for participating OECD countries set at 500, with a standard deviation of 100. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.P05", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Reading Scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.P10", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Reading Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.P25", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Reading Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.P50", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Reading Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Othernotes", "value": "PISA" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.P75", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Reading Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.P90", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Reading Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.REA.P95", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Reading Scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI", "metatype": [ { "id": "IndicatorName", "value": "PISA: Mean performance on the science scale" }, { "id": "Longdefinition", "value": "Average score of 15-year-old students on the PISA science scale. In PISA 2006 the mean science score for OECD countries was initially set at 500 points (for 30 OECD countries), then was re-set at 498 points after taking into account new OECD countries. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Average score of 15-year-old students on the PISA science scale. In PISA 2006 the mean science score for OECD countries was initially set at 500 points (for 30 OECD countries), then was re-set at 498 points after taking into account new OECD countries. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.0", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by science proficiency level (%). Below Level 1B" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring below the lowest proficiency level (less than or equal to 261) on the PISA science scale. No item in the PISA assessment can indicate what students who perform below Level 1b can do. Students below Level 1b may have acquired some elements of science knowledge and skills, but based on the tasks included in the PISA test, their ability can only be described in terms of what they cannot do – and they are unlikely to be able to solve, other than by guessing, any of the PISA tasks. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.0.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by science proficiency level (%). Below Level 1B" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring below the lowest proficiency level (less than or equal to 261) on the PISA science scale. No item in the PISA assessment can indicate what students who perform below Level 1b can do. Students below Level 1b may have acquired some elements of science knowledge and skills, but based on the tasks included in the PISA test, their ability can only be described in terms of what they cannot do – and they are unlikely to be able to solve, other than by guessing, any of the PISA tasks. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.0.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by science proficiency level (%). Below Level 1B" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring below the lowest proficiency level (less than or equal to 261) on the PISA science scale. No item in the PISA assessment can indicate what students who perform below Level 1b can do. Students below Level 1b may have acquired some elements of science knowledge and skills, but based on the tasks included in the PISA test, their ability can only be described in terms of what they cannot do – and they are unlikely to be able to solve, other than by guessing, any of the PISA tasks. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.1A", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by science proficiency level (%). Level 1A" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 335 but less than or equal to 410 on the PISA science scale. At Level 1A, students are able to use basic or everyday content and procedural knowledge to recognise or identify explanations of simple scientific phenomenon. With support, they can undertake structured scientific enquiries with no more than two variables. They are able to identify simple causal or correlational relationships and interpret graphical and visual data that require a low level of cognitive demand. Level 1a students can select the best scientific explanation for given data in familiar personal, local and global contexts. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.1A.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by science proficiency level (%). Level 1A" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 335 but less than or equal to 410 on the PISA science scale. At Level 1A, students are able to use basic or everyday content and procedural knowledge to recognise or identify explanations of simple scientific phenomenon. With support, they can undertake structured scientific enquiries with no more than two variables. They are able to identify simple causal or correlational relationships and interpret graphical and visual data that require a low level of cognitive demand. Level 1a students can select the best scientific explanation for given data in familiar personal, local and global contexts. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.1A.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by science proficiency level (%). Level 1A" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 335 but less than or equal to 410 on the PISA science scale. At Level 1A, students are able to use basic or everyday content and procedural knowledge to recognise or identify explanations of simple scientific phenomenon. With support, they can undertake structured scientific enquiries with no more than two variables. They are able to identify simple causal or correlational relationships and interpret graphical and visual data that require a low level of cognitive demand. Level 1a students can select the best scientific explanation for given data in familiar personal, local and global contexts. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.1B", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by science proficiency level (%). Level 1B" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 261 but less than or equal to 335 on the PISA science scale. At Level 1B, students can use basic or everyday scientific knowledge to recognise aspects of familiar or simple phenomenon. They are able to identify simple patterns in data, recognise basic scientific terms and follow explicit instructions to carry out a scientific procedure. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.1B.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by science proficiency level (%). Level 1B" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 261 but less than or equal to 335 on the PISA science scale. At Level 1B, students can use basic or everyday scientific knowledge to recognise aspects of familiar or simple phenomenon. They are able to identify simple patterns in data, recognise basic scientific terms and follow explicit instructions to carry out a scientific procedure. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.1B.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by science proficiency level (%). Level 1B" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 261 but less than or equal to 335 on the PISA science scale. At Level 1B, students can use basic or everyday scientific knowledge to recognise aspects of familiar or simple phenomenon. They are able to identify simple patterns in data, recognise basic scientific terms and follow explicit instructions to carry out a scientific procedure. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.2", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by science proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 410 but less than or equal to 484 on the PISA science scale. At Level 2, students are able to draw on everyday content knowledge and basic procedural knowledge to identify an appropriate scientific explanation, interpret data, and identify the question being addressed in a simple experimental design. They can use basic or everyday scientific knowledge to identify a valid conclusion from a simple data set. Level 2 students demonstrate basic epistemic knowledge by being able to identify questions that can be investigated scientifically. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.2.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by science proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 410 but less than or equal to 484 on the PISA science scale. At Level 2, students are able to draw on everyday content knowledge and basic procedural knowledge to identify an appropriate scientific explanation, interpret data, and identify the question being addressed in a simple experimental design. They can use basic or everyday scientific knowledge to identify a valid conclusion from a simple data set. Level 2 students demonstrate basic epistemic knowledge by being able to identify questions that can be investigated scientifically. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.2.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by science proficiency level (%). Level 2" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 410 but less than or equal to 484 on the PISA science scale. At Level 2, students are able to draw on everyday content knowledge and basic procedural knowledge to identify an appropriate scientific explanation, interpret data, and identify the question being addressed in a simple experimental design. They can use basic or everyday scientific knowledge to identify a valid conclusion from a simple data set. Level 2 students demonstrate basic epistemic knowledge by being able to identify questions that can be investigated scientifically. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.3", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by science proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 484 but less than or equal to 559 on the PISA science scale. At Level 3, students can draw upon moderately complex content knowledge to identify or construct explanations of familiar phenomena. In less familiar or more complex situations, they can construct explanations with relevant cueing or support. They can draw on elements of procedural or epistemic knowledge to carry out a simple experiment in a constrained context. Level 3 students are able to distinguish between scientific and non-scientific issues and identify the evidence supporting a scientific claim. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.3.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by science proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 484 but less than or equal to 559 on the PISA science scale. At Level 3, students can draw upon moderately complex content knowledge to identify or construct explanations of familiar phenomena. In less familiar or more complex situations, they can construct explanations with relevant cueing or support. They can draw on elements of procedural or epistemic knowledge to carry out a simple experiment in a constrained context. Level 3 students are able to distinguish between scientific and non-scientific issues and identify the evidence supporting a scientific claim. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.3.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by science proficiency level (%). Level 3" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 484 but less than or equal to 559 on the PISA science scale. At Level 3, students can draw upon moderately complex content knowledge to identify or construct explanations of familiar phenomena. In less familiar or more complex situations, they can construct explanations with relevant cueing or support. They can draw on elements of procedural or epistemic knowledge to carry out a simple experiment in a constrained context. Level 3 students are able to distinguish between scientific and non-scientific issues and identify the evidence supporting a scientific claim. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.4", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by science proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 559 but less than or equal to 633 on the PISA science scale. At Level 4, students can use more complex or more abstract content knowledge, which is either provided or recalled, to construct explanations of more complex or less familiar events and processes. They can conduct experiments involving two or more independent variables in a constrained context. They are able to justify an experimental design, drawing on elements of procedural and epistemic knowledge. Level 4 students can interpret data drawn from a moderately complex data set or less familiar context, draw appropriate conclusions that go beyond the data and provide justifications for their choices. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.4.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by science proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 559 but less than or equal to 633 on the PISA science scale. At Level 4, students can use more complex or more abstract content knowledge, which is either provided or recalled, to construct explanations of more complex or less familiar events and processes. They can conduct experiments involving two or more independent variables in a constrained context. They are able to justify an experimental design, drawing on elements of procedural and epistemic knowledge. Level 4 students can interpret data drawn from a moderately complex data set or less familiar context, draw appropriate conclusions that go beyond the data and provide justifications for their choices. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.4.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by science proficiency level (%). Level 4" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 559 but less than or equal to 633 on the PISA science scale. At Level 4, students can use more complex or more abstract content knowledge, which is either provided or recalled, to construct explanations of more complex or less familiar events and processes. They can conduct experiments involving two or more independent variables in a constrained context. They are able to justify an experimental design, drawing on elements of procedural and epistemic knowledge. Level 4 students can interpret data drawn from a moderately complex data set or less familiar context, draw appropriate conclusions that go beyond the data and provide justifications for their choices. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.5", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by science proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 633 but less than or equal to 708 on the PISA science scale. At Level 5, students can use abstract scientific ideas or concepts to explain unfamiliar and more complex phenomena, events and processes involving multiple causal links. They are able to apply more sophisticated epistemic knowledge to evaluate alternative experimental designs and justify their choices and use theoretical knowledge to interpret information or make predictions. Level 5 students can evaluate ways of exploring a given question scientifically and identify limitations in interpretations of data sets including sources and the effects of uncertainty in scientific data. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.5.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by science proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 633 but less than or equal to 708 on the PISA science scale. At Level 5, students can use abstract scientific ideas or concepts to explain unfamiliar and more complex phenomena, events and processes involving multiple causal links. They are able to apply more sophisticated epistemic knowledge to evaluate alternative experimental designs and justify their choices and use theoretical knowledge to interpret information or make predictions. Level 5 students can evaluate ways of exploring a given question scientifically and identify limitations in interpretations of data sets including sources and the effects of uncertainty in scientific data. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.5.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by science proficiency level (%). Level 5" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 633 but less than or equal to 708 on the PISA science scale. At Level 5, students can use abstract scientific ideas or concepts to explain unfamiliar and more complex phenomena, events and processes involving multiple causal links. They are able to apply more sophisticated epistemic knowledge to evaluate alternative experimental designs and justify their choices and use theoretical knowledge to interpret information or make predictions. Level 5 students can evaluate ways of exploring a given question scientifically and identify limitations in interpretations of data sets including sources and the effects of uncertainty in scientific data. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.6", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by science proficiency level (%). Level 6" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring higher than 708 on the PISA science scale. At Level 6, students can draw on a range of interrelated scientific ideas and concepts from the physical, life and earth and space sciences and use content, procedural and epistemic knowledge in order to offer explanatory hypotheses of novel scientific phenomena, events and processes or to make predictions. In interpreting data and evidence, they are able to discriminate between relevant and irrelevant information and can draw on knowledge external to the normal school curriculum. They can distinguish between arguments that are based on scientific evidence and theory and those based on other considerations. Level 6 students can evaluate competing designs of complex experiments, field studies or simulations and justify their choices. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.6.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Female 15-year-olds by science proficiency level (%). Level 6" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old female students scoring higher than 708 on the PISA science scale. At Level 6, students can draw on a range of interrelated scientific ideas and concepts from the physical, life and earth and space sciences and use content, procedural and epistemic knowledge in order to offer explanatory hypotheses of novel scientific phenomena, events and processes or to make predictions. In interpreting data and evidence, they are able to discriminate between relevant and irrelevant information and can draw on knowledge external to the normal school curriculum. They can distinguish between arguments that are based on scientific evidence and theory and those based on other considerations. Level 6 students can evaluate competing designs of complex experiments, field studies or simulations and justify their choices. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.6.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Male 15-year-olds by science proficiency level (%). Level 6" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old male students scoring higher than 708 on the PISA science scale. At Level 6, students can draw on a range of interrelated scientific ideas and concepts from the physical, life and earth and space sciences and use content, procedural and epistemic knowledge in order to offer explanatory hypotheses of novel scientific phenomena, events and processes or to make predictions. In interpreting data and evidence, they are able to discriminate between relevant and irrelevant information and can draw on knowledge external to the normal school curriculum. They can distinguish between arguments that are based on scientific evidence and theory and those based on other considerations. Level 6 students can evaluate competing designs of complex experiments, field studies or simulations and justify their choices. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.FE", "metatype": [ { "id": "IndicatorName", "value": "PISA: Mean performance on the science scale. Female" }, { "id": "Longdefinition", "value": "Average score of 15-year-old female students on the PISA science scale. In PISA 2006 the mean science score for OECD countries was initially set at 500 points (for 30 OECD countries), then was re-set at 498 points after taking into account new OECD countries. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Average score of 15-year-old female students on the PISA science scale. In PISA 2006 the mean science score for OECD countries was initially set at 500 points (for 30 OECD countries), then was re-set at 498 points after taking into account new OECD countries. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.MA", "metatype": [ { "id": "IndicatorName", "value": "PISA: Mean performance on the science scale. Male" }, { "id": "Longdefinition", "value": "Average score of 15-year-old male students on the PISA science scale. In PISA 2006 the mean science score for OECD countries was initially set at 500 points (for 30 OECD countries), then was re-set at 498 points after taking into account new OECD countries. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "Average score of 15-year-old male students on the PISA science scale. In PISA 2006 the mean science score for OECD countries was initially set at 500 points (for 30 OECD countries), then was re-set at 498 points after taking into account new OECD countries. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.P05", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Science Scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.P10", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Science Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.P25", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Science Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.P50", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Science Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Othernotes", "value": "PISA" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.P75", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Science Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.P90", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Science Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.PISA.SCI.P95", "metatype": [ { "id": "IndicatorName", "value": "PISA: Distribution of Science Scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA)" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Mean performance on the 6th grade mathematics scale" }, { "id": "Longdefinition", "value": "Mean performance on the mathematics scale is the mean mathematics score for 6th grade students. Mean scores are on SACMEQ scales for mathematics, which have averages of 500 and standard deviations of 100. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Mean performance on the mathematics scale is the mean mathematics score for 6th grade students. Mean scores are on SACMEQ scales for mathematics, which have averages of 500 and standard deviations of 100. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Mean performance on the 6th grade mathematics scale. Female" }, { "id": "Longdefinition", "value": "Mean performance on the mathematics scale, female is the mean mathematics score for female 6th grade students. Mean scores are on SACMEQ scales for mathematics, which have averages of 500 and standard deviations of 100. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Mean performance on the mathematics scale, female is the mean mathematics score for female 6th grade students. Mean scores are on SACMEQ scales for mathematics, which have averages of 500 and standard deviations of 100. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L1", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: 6th grade students by mathematics proficiency level (%). Level 1 - Pre-Numeracy" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at the Pre-Numeracy level (Level 1 of 8) on the mathematics assessment. At this level, students can apply single step addition or subtraction operations, recognize simple shapes, match numbers and pictures, and count in whole numbers. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at the Pre-Numeracy level (Level 1 of 8) on the mathematics assessment. At this level, students can apply single step addition or subtraction operations, recognize simple shapes, match numbers and pictures, and count in whole numbers. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L1.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Female 6th grade students by mathematics proficiency level (%). Level 1 - Pre-Numeracy" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at the Pre-Numeracy level (Level 1 of 8) on the mathematics assessment. At this level, students can apply single step addition or subtraction operations, recognize simple shapes, match numbers and pictures, and count in whole numbers. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at the Pre-Numeracy level (Level 1 of 8) on the mathematics assessment. At this level, students can apply single step addition or subtraction operations, recognize simple shapes, match numbers and pictures, and count in whole numbers. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L1.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Male 6th grade students by mathematics proficiency level (%). Level 1 - Pre-Numeracy" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at the Pre-Numeracy level (Level 1 of 8) on the mathematics assessment. At this level, students can apply single step addition or subtraction operations, recognize simple shapes, match numbers and pictures, and count in whole numbers. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at the Pre-Numeracy level (Level 1 of 8) on the mathematics assessment. At this level, students can apply single step addition or subtraction operations, recognize simple shapes, match numbers and pictures, and count in whole numbers. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L2", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: 6th grade students by mathematics proficiency level (%). Level 2 - Emergent Numeracy" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at the Emergent Numeracy level (Level 2 of 8) on the mathematics assessment. At this level, students can apply a two-step addition or subtraction operation involving carrying, checking (through very basic estimation), or conversion of pictures to numbers. They can estimate the length of familiar objects and recognize common two-dimensional shapes. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at the Emergent Numeracy level (Level 2 of 8) on the mathematics assessment. At this level, students can apply a two-step addition or subtraction operation involving carrying, checking (through very basic estimation), or conversion of pictures to numbers. They can estimate the length of familiar objects and recognize common two-dimensional shapes. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L2.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Female 6th grade students by mathematics proficiency level (%). Level 2 - Emergent Numeracy" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at the Emergent Numeracy level (Level 2 of 8) on the mathematics assessment. At this level, students can apply a two-step addition or subtraction operation involving carrying, checking (through very basic estimation), or conversion of pictures to numbers. They can estimate the length of familiar objects and recognize common two-dimensional shapes. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at the Emergent Numeracy level (Level 2 of 8) on the mathematics assessment. At this level, students can apply a two-step addition or subtraction operation involving carrying, checking (through very basic estimation), or conversion of pictures to numbers. They can estimate the length of familiar objects and recognize common two-dimensional shapes. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L2.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Male 6th grade students by mathematics proficiency level (%). Level 2 - Emergent Numeracy" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at the Emergent Numeracy level (Level 2 of 8) on the mathematics assessment. At this level, students can apply a two-step addition or subtraction operation involving carrying, checking (through very basic estimation), or conversion of pictures to numbers. They can estimate the length of familiar objects and recognize common two-dimensional shapes. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at the Emergent Numeracy level (Level 2 of 8) on the mathematics assessment. At this level, students can apply a two-step addition or subtraction operation involving carrying, checking (through very basic estimation), or conversion of pictures to numbers. They can estimate the length of familiar objects and recognize common two-dimensional shapes. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L3", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: 6th grade students by mathematics proficiency level (%). Level 3 - Basic Numeracy" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at the Basic Numeracy level (Level 3 of 8) on the mathematics assessment. At this level, students can translate verbal information presented in a sentence, simple graph or table, using one arithmetic operation in several repeated steps. They can translate graphical information into fractions, interpret place value of whole numbers up to thousands, and interpret simple common everyday units of measurement. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at the Basic Numeracy level (Level 3 of 8) on the mathematics assessment. At this level, students can translate verbal information presented in a sentence, simple graph or table, using one arithmetic operation in several repeated steps. They can translate graphical information into fractions, interpret place value of whole numbers up to thousands, and interpret simple common everyday units of measurement. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L3.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Female 6th grade students by mathematics proficiency level (%). Level 3 - Basic Numeracy" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at the Basic Numeracy level (Level 3 of 8) on the mathematics assessment. At this level, students can translate verbal information presented in a sentence, simple graph or table, using one arithmetic operation in several repeated steps. They can translate graphical information into fractions, interpret place value of whole numbers up to thousands, and interpret simple common everyday units of measurement. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at the Basic Numeracy level (Level 3 of 8) on the mathematics assessment. At this level, students can translate verbal information presented in a sentence, simple graph or table, using one arithmetic operation in several repeated steps. They can translate graphical information into fractions, interpret place value of whole numbers up to thousands, and interpret simple common everyday units of measurement. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L3.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Male 6th grade students by mathematics proficiency level (%). Level 3 - Basic Numeracy" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at the Basic Numeracy level (Level 3 of 8) on the mathematics assessment. At this level, students can translate verbal information presented in a sentence, simple graph or table, using one arithmetic operation in several repeated steps. They can translate graphical information into fractions, interpret place value of whole numbers up to thousands, and interpret simple common everyday units of measurement. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at the Basic Numeracy level (Level 3 of 8) on the mathematics assessment. At this level, students can translate verbal information presented in a sentence, simple graph or table, using one arithmetic operation in several repeated steps. They can translate graphical information into fractions, interpret place value of whole numbers up to thousands, and interpret simple common everyday units of measurement. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L4", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: 6th grade students by mathematics proficiency level (%). Level 4 - Beginning Numeracy" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at the Beginning Numeracy level (Level 4 of 8) on the mathematics assessment. At this level, students can translate verbal or graphic information into simple arithmetic problems, and use multiple different arithmetic operations (in the correct order) on whole numbers, fractions, and/or decimals. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at the Beginning Numeracy level (Level 4 of 8) on the mathematics assessment. At this level, students can translate verbal or graphic information into simple arithmetic problems, and use multiple different arithmetic operations (in the correct order) on whole numbers, fractions, and/or decimals. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L4.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Female 6th grade students by mathematics proficiency level (%). Level 4 - Beginning Numeracy" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at the Beginning Numeracy level (Level 4 of 8) on the mathematics assessment. At this level, students can translate verbal or graphic information into simple arithmetic problems, and use multiple different arithmetic operations (in the correct order) on whole numbers, fractions, and/or decimals. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at the Beginning Numeracy level (Level 4 of 8) on the mathematics assessment. At this level, students can translate verbal or graphic information into simple arithmetic problems, and use multiple different arithmetic operations (in the correct order) on whole numbers, fractions, and/or decimals. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L4.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Male 6th grade students by mathematics proficiency level (%). Level 4 - Beginning Numeracy" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at the Beginning Numeracy level (Level 4 of 8) on the mathematics assessment. At this level, students can translate verbal or graphic information into simple arithmetic problems, and use multiple different arithmetic operations (in the correct order) on whole numbers, fractions, and/or decimals. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at the Beginning Numeracy level (Level 4 of 8) on the mathematics assessment. At this level, students can translate verbal or graphic information into simple arithmetic problems, and use multiple different arithmetic operations (in the correct order) on whole numbers, fractions, and/or decimals. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L5", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: 6th grade students by mathematics proficiency level (%). Level 5 - Competent Numeracy" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at the Competent Numeracy level (Level 5 of 8) on the mathematics assessment. At this level, students can translate verbal, graphic, or tabular information into an arithmetic form in order to solve a given problem. They can solve multiple-operation problems (using the correct order of arithmetic operations) involving everyday units of measurement and/or whole and mixed numbers, and convert basic measurement units from one level of measurement to another (for example, meters to centimeters). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at the Competent Numeracy level (Level 5 of 8) on the mathematics assessment. At this level, students can translate verbal, graphic, or tabular information into an arithmetic form in order to solve a given problem. They can solve multiple-operation problems (using the correct order of arithmetic operations) involving everyday units of measurement and/or whole and mixed numbers, and convert basic measurement units from one level of measurement to another (for example, meters to centimeters). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L5.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Female 6th grade students by mathematics proficiency level (%). Level 5 - Competent Numeracy" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at the Competent Numeracy level (Level 5 of 8) on the mathematics assessment. At this level, students can translate verbal, graphic, or tabular information into an arithmetic form in order to solve a given problem. They can solve multiple-operation problems (using the correct order of arithmetic operations) involving everyday units of measurement and/or whole and mixed numbers, and convert basic measurement units from one level of measurement to another (for example, meters to centimeters). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at the Competent Numeracy level (Level 5 of 8) on the mathematics assessment. At this level, students can translate verbal, graphic, or tabular information into an arithmetic form in order to solve a given problem. They can solve multiple-operation problems (using the correct order of arithmetic operations) involving everyday units of measurement and/or whole and mixed numbers, and convert basic measurement units from one level of measurement to another (for example, meters to centimeters). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L5.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Male 6th grade students by mathematics proficiency level (%). Level 5 - Competent Numeracy" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at the Competent Numeracy level (Level 5 of 8) on the mathematics assessment. At this level, students can translate verbal, graphic, or tabular information into an arithmetic form in order to solve a given problem. They can solve multiple-operation problems (using the correct order of arithmetic operations) involving everyday units of measurement and/or whole and mixed numbers, and convert basic measurement units from one level of measurement to another (for example, meters to centimeters). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at the Competent Numeracy level (Level 5 of 8) on the mathematics assessment. At this level, students can translate verbal, graphic, or tabular information into an arithmetic form in order to solve a given problem. They can solve multiple-operation problems (using the correct order of arithmetic operations) involving everyday units of measurement and/or whole and mixed numbers, and convert basic measurement units from one level of measurement to another (for example, meters to centimeters). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L6", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: 6th grade students by mathematics proficiency level (%). Level 6 - Mathematically Skilled" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at the Mathematically Skilled numeracy level (Level 6 of 8) on the mathematics assessment. At this level, students can solve multiple-operation problems (using the correct order of arithmetic operations) involving fractions, ratios, and decimals. They can translate verbal and graphic representation information into symbolic, algebraic, and equation form in order to solve a given mathematical problem. They can check and estimate answers using external knowledge (not provided within the problem). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at the Mathematically Skilled numeracy level (Level 6 of 8) on the mathematics assessment. At this level, students can solve multiple-operation problems (using the correct order of arithmetic operations) involving fractions, ratios, and decimals. They can translate verbal and graphic representation information into symbolic, algebraic, and equation form in order to solve a given mathematical problem. They can check and estimate answers using external knowledge (not provided within the problem). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L6.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Female 6th grade students by mathematics proficiency level (%). Level 6 - Mathematically Skilled" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at the Mathematically Skilled numeracy level (Level 6 of 8) on the mathematics assessment. At this level, students can solve multiple-operation problems (using the correct order of arithmetic operations) involving fractions, ratios, and decimals. They can translate verbal and graphic representation information into symbolic, algebraic, and equation form in order to solve a given mathematical problem. They can check and estimate answers using external knowledge (not provided within the problem). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at the Mathematically Skilled numeracy level (Level 6 of 8) on the mathematics assessment. At this level, students can solve multiple-operation problems (using the correct order of arithmetic operations) involving fractions, ratios, and decimals. They can translate verbal and graphic representation information into symbolic, algebraic, and equation form in order to solve a given mathematical problem. They can check and estimate answers using external knowledge (not provided within the problem). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L6.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Male 6th grade students by mathematics proficiency level (%). Level 6 - Mathematically Skilled" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at the Mathematically Skilled numeracy level (Level 6 of 8) on the mathematics assessment. At this level, students can solve multiple-operation problems (using the correct order of arithmetic operations) involving fractions, ratios, and decimals. They can translate verbal and graphic representation information into symbolic, algebraic, and equation form in order to solve a given mathematical problem. They can check and estimate answers using external knowledge (not provided within the problem). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at the Mathematically Skilled numeracy level (Level 6 of 8) on the mathematics assessment. At this level, students can solve multiple-operation problems (using the correct order of arithmetic operations) involving fractions, ratios, and decimals. They can translate verbal and graphic representation information into symbolic, algebraic, and equation form in order to solve a given mathematical problem. They can check and estimate answers using external knowledge (not provided within the problem). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L7", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: 6th grade students by mathematics proficiency level (%). Level 7 - Concrete Problem Solving" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at the Concrete Problem Solving numeracy level (Level 7 of 8) on the mathematics assessment. At this level, students can extract and convert (for example, with respect to measurement units) information from tables, charts, visual and symbolic presentations in order to identify, and then solve multi-step problems. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at the Concrete Problem Solving numeracy level (Level 7 of 8) on the mathematics assessment. At this level, students can extract and convert (for example, with respect to measurement units) information from tables, charts, visual and symbolic presentations in order to identify, and then solve multi-step problems. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L7.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Female 6th grade students by mathematics proficiency level (%). Level 7 - Concrete Problem Solving" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at the Concrete Problem Solving numeracy level (Level 7 of 8) on the mathematics assessment. At this level, students can extract and convert (for example, with respect to measurement units) information from tables, charts, visual and symbolic presentations in order to identify, and then solve multi-step problems. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at the Concrete Problem Solving numeracy level (Level 7 of 8) on the mathematics assessment. At this level, students can extract and convert (for example, with respect to measurement units) information from tables, charts, visual and symbolic presentations in order to identify, and then solve multi-step problems. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L7.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Male 6th grade students by mathematics proficiency level (%). Level 7 - Concrete Problem Solving" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at the Concrete Problem Solving numeracy level (Level 7 of 8) on the mathematics assessment. At this level, students can extract and convert (for example, with respect to measurement units) information from tables, charts, visual and symbolic presentations in order to identify, and then solve multi-step problems. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at the Concrete Problem Solving numeracy level (Level 7 of 8) on the mathematics assessment. At this level, students can extract and convert (for example, with respect to measurement units) information from tables, charts, visual and symbolic presentations in order to identify, and then solve multi-step problems. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L8", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: 6th grade students by mathematics proficiency level (%). Level 8 - Abstract Problem Solving" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at the Abstract Problem Solving numeracy level (Level 8 of 8) on the mathematics assessment. At this level, students can identify the nature of an unstated mathematical problem embedded within verbal or graphic information, and then translate this into symbolic, algebraic, or equation form in order to solve the problem. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at the Abstract Problem Solving numeracy level (Level 8 of 8) on the mathematics assessment. At this level, students can identify the nature of an unstated mathematical problem embedded within verbal or graphic information, and then translate this into symbolic, algebraic, or equation form in order to solve the problem. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L8.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Female 6th grade students by mathematics proficiency level (%). Level 8 - Abstract Problem Solving" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at the Abstract Problem Solving numeracy level (Level 8 of 8) on the mathematics assessment. At this level, students can identify the nature of an unstated mathematical problem embedded within verbal or graphic information, and then translate this into symbolic, algebraic, or equation form in order to solve the problem. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at the Abstract Problem Solving numeracy level (Level 8 of 8) on the mathematics assessment. At this level, students can identify the nature of an unstated mathematical problem embedded within verbal or graphic information, and then translate this into symbolic, algebraic, or equation form in order to solve the problem. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.L8.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Male 6th grade students by mathematics proficiency level (%). Level 8 - Abstract Problem Solving" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at the Abstract Problem Solving numeracy level (Level 8 of 8) on the mathematics assessment. At this level, students can identify the nature of an unstated mathematical problem embedded within verbal or graphic information, and then translate this into symbolic, algebraic, or equation form in order to solve the problem. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at the Abstract Problem Solving numeracy level (Level 8 of 8) on the mathematics assessment. At this level, students can identify the nature of an unstated mathematical problem embedded within verbal or graphic information, and then translate this into symbolic, algebraic, or equation form in order to solve the problem. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.MAT.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Mean performance on the 6th grade mathematics scale. Male" }, { "id": "Longdefinition", "value": "Mean performance on the mathematics scale, male is the mean mathematics score for male 6th grade students. Mean scores are on SACMEQ scales for mathematics, which have averages of 500 and standard deviations of 100. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Mean performance on the mathematics scale, male is the mean mathematics score for male 6th grade students. Mean scores are on SACMEQ scales for mathematics, which have averages of 500 and standard deviations of 100. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Mean performance on the 6th grade reading scale" }, { "id": "Longdefinition", "value": "Mean performance on the reading scale, total is the mean reading score for all 6th grade students. Mean scores are on SACMEQ scales for reading, which have averages of 500 and standard deviations of 100. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Mean performance on the reading scale, total is the mean reading score for all 6th grade students. Mean scores are on SACMEQ scales for reading, which have averages of 500 and standard deviations of 100. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Mean performance on the 6th grade reading scale. Female" }, { "id": "Longdefinition", "value": "Mean performance on the reading scale, female is the mean reading score for female 6th grade students. Mean scores are on SACMEQ scales for reading, which have averages of 500 and standard deviations of 100. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Mean performance on the reading scale, female is the mean reading score for female 6th grade students. Mean scores are on SACMEQ scales for reading, which have averages of 500 and standard deviations of 100. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L1", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: 6th grade students by reading proficiency level (%). Level 1 - Pre-Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at the Pre-Reading level (Level 1 of 8) on the reading assessment. At this level, the student can match words and pictures involving concrete concepts and everyday objects, and follow short simple written instructions. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at the Pre-Reading level (Level 1 of 8) on the reading assessment. At this level, the student can match words and pictures involving concrete concepts and everyday objects, and follow short simple written instructions. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L1.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Female 6th grade students by reading proficiency level (%). Level 1 - Pre-Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at the Pre-Reading level (Level 1 of 8) on the reading assessment. At this level, the student can match words and pictures involving concrete concepts and everyday objects, and follow short simple written instructions. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at the Pre-Reading level (Level 1 of 8) on the reading assessment. At this level, the student can match words and pictures involving concrete concepts and everyday objects, and follow short simple written instructions. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L1.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Male 6th grade students by reading proficiency level (%). Level 1 - Pre-Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at the Pre-Reading level (Level 1 of 8) on the reading assessment. At this level, the student can match words and pictures involving concrete concepts and everyday objects, and follow short simple written instructions. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at the Pre-Reading level (Level 1 of 8) on the reading assessment. At this level, the student can match words and pictures involving concrete concepts and everyday objects, and follow short simple written instructions. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L2", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: 6th grade students by reading proficiency level (%). Level 2 - Emergent Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at the Emergent Reading level (Level 2 of 8) on the reading assessment. Students at this level can match words and pictures involving prepositions and abstract concepts, and use cuing systems (by sounding out, using simple sentence structure, and familiar words) to interpret phrases by reading on. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at the Emergent Reading level (Level 2 of 8) on the reading assessment. Students at this level can match words and pictures involving prepositions and abstract concepts, and use cuing systems (by sounding out, using simple sentence structure, and familiar words) to interpret phrases by reading on. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L2.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Female 6th grade students by reading proficiency level (%). Level 2 - Emergent Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at the Emergent Reading level (Level 2 of 8) on the reading assessment. Students at this level can match words and pictures involving prepositions and abstract concepts, and use cuing systems (by sounding out, using simple sentence structure, and familiar words) to interpret phrases by reading on. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at the Emergent Reading level (Level 2 of 8) on the reading assessment. Students at this level can match words and pictures involving prepositions and abstract concepts, and use cuing systems (by sounding out, using simple sentence structure, and familiar words) to interpret phrases by reading on. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L2.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Male 6th grade students by reading proficiency level (%). Level 2 - Emergent Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at the Emergent Reading level (Level 2 of 8) on the reading assessment. Students at this level can match words and pictures involving prepositions and abstract concepts, and use cuing systems (by sounding out, using simple sentence structure, and familiar words) to interpret phrases by reading on. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at the Emergent Reading level (Level 2 of 8) on the reading assessment. Students at this level can match words and pictures involving prepositions and abstract concepts, and use cuing systems (by sounding out, using simple sentence structure, and familiar words) to interpret phrases by reading on. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L3", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: 6th grade students by reading proficiency level (%). Level 3 - Basic Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at the Basic Reading level (Level 3 of 8) on the reading assessment. Students at this level can interpret meaning (by matching words and phrases, completing a sentence, or matching adjacent words) in a short and simple text by reading on or reading back. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at the Basic Reading level (Level 3 of 8) on the reading assessment. Students at this level can interpret meaning (by matching words and phrases, completing a sentence, or matching adjacent words) in a short and simple text by reading on or reading back. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L3.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Female 6th grade students by reading proficiency level (%). Level 3 - Basic Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at the Basic Reading level (Level 3 of 8) on the reading assessment. Students at this level can interpret meaning (by matching words and phrases, completing a sentence, or matching adjacent words) in a short and simple text by reading on or reading back. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at the Basic Reading level (Level 3 of 8) on the reading assessment. Students at this level can interpret meaning (by matching words and phrases, completing a sentence, or matching adjacent words) in a short and simple text by reading on or reading back. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L3.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Male 6th grade students by reading proficiency level (%). Level 3 - Basic Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at the Basic Reading level (Level 3 of 8) on the reading assessment. Students at this level can interpret meaning (by matching words and phrases, completing a sentence, or matching adjacent words) in a short and simple text by reading on or reading back. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at the Basic Reading level (Level 3 of 8) on the reading assessment. Students at this level can interpret meaning (by matching words and phrases, completing a sentence, or matching adjacent words) in a short and simple text by reading on or reading back. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L4", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: 6th grade students by reading proficiency level (%). Level 4 - Reading for Meaning" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at the Reading for Meaning level (Level 4 of 8) on the reading assessment. At this level, students can read on or read back in order to link and interpret information located in various parts of the text. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at the Reading for Meaning level (Level 4 of 8) on the reading assessment. At this level, students can read on or read back in order to link and interpret information located in various parts of the text. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L4.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Female 6th grade students by reading proficiency level (%). Level 4 - Reading for Meaning" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at the Reading for Meaning level (Level 4 of 8) on the reading assessment. At this level, students can read on or read back in order to link and interpret information located in various parts of the text. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at the Reading for Meaning level (Level 4 of 8) on the reading assessment. At this level, students can read on or read back in order to link and interpret information located in various parts of the text. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L4.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Male 6th grade students by reading proficiency level (%). Level 4 - Reading for Meaning" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at the Reading for Meaning level (Level 4 of 8) on the reading assessment. At this level, students can read on or read back in order to link and interpret information located in various parts of the text. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at the Reading for Meaning level (Level 4 of 8) on the reading assessment. At this level, students can read on or read back in order to link and interpret information located in various parts of the text. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L5", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: 6th grade students by reading proficiency level (%). Level 5 - Interpretive Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at the Interpretive Reading level (Level 5 of 8) on the reading assessment. At this level, students can read on and reads back in order to combine and interpret information from various parts of the text in association with external information (based on recalled factual knowledge) that ‘completes’ and contextualizes meaning. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at the Interpretive Reading level (Level 5 of 8) on the reading assessment. At this level, students can read on and reads back in order to combine and interpret information from various parts of the text in association with external information (based on recalled factual knowledge) that ‘completes’ and contextualizes meaning. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L5.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Female 6th grade students by reading proficiency level (%). Level 5 - Interpretive Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at the Interpretive Reading level (Level 5 of 8) on the reading assessment. At this level, students can read on and reads back in order to combine and interpret information from various parts of the text in association with external information (based on recalled factual knowledge) that ‘completes’ and contextualizes meaning. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at the Interpretive Reading level (Level 5 of 8) on the reading assessment. At this level, students can read on and reads back in order to combine and interpret information from various parts of the text in association with external information (based on recalled factual knowledge) that ‘completes’ and contextualizes meaning. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L5.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Male 6th grade students by reading proficiency level (%). Level 5 - Interpretive Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at the Interpretive Reading level (Level 5 of 8) on the reading assessment. At this level, students can read on and reads back in order to combine and interpret information from various parts of the text in association with external information (based on recalled factual knowledge) that ‘completes’ and contextualizes meaning. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at the Interpretive Reading level (Level 5 of 8) on the reading assessment. At this level, students can read on and reads back in order to combine and interpret information from various parts of the text in association with external information (based on recalled factual knowledge) that ‘completes’ and contextualizes meaning. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L6", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: 6th grade students by reading proficiency level (%). Level 6 - Inferential Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at the Inferential Reading level (Level 6 of 8) on the reading assessment. At this level, students can read on and read back through longer texts (narrative, document, or expository) in order to combine information from various parts of the texts so as to infer the writer’s purpose. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at the Inferential Reading level (Level 6 of 8) on the reading assessment. At this level, students can read on and read back through longer texts (narrative, document, or expository) in order to combine information from various parts of the texts so as to infer the writer’s purpose. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L6.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Female 6th grade students by reading proficiency level (%). Level 6 - Inferential Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at the Inferential Reading level (Level 6 of 8) on the reading assessment. At this level, students can read on and read back through longer texts (narrative, document, or expository) in order to combine information from various parts of the texts so as to infer the writer’s purpose. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at the Inferential Reading level (Level 6 of 8) on the reading assessment. At this level, students can read on and read back through longer texts (narrative, document, or expository) in order to combine information from various parts of the texts so as to infer the writer’s purpose. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L6.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Male 6th grade students by reading proficiency level (%). Level 6 - Inferential Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at the Inferential Reading level (Level 6 of 8) on the reading assessment. At this level, students can read on and read back through longer texts (narrative, document, or expository) in order to combine information from various parts of the texts so as to infer the writer’s purpose. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at the Inferential Reading level (Level 6 of 8) on the reading assessment. At this level, students can read on and read back through longer texts (narrative, document, or expository) in order to combine information from various parts of the texts so as to infer the writer’s purpose. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L7", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: 6th grade students by reading proficiency level (%). Level 7 - Analytical Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at the Analytical Reading level (Level 7 of 8) on the reading assessment. At this level, students can locate information in longer texts (narrative, document, or expository) by reading on and reading backing order to combine information from various parts of the text so as to infer the writer’s personal beliefs (value systems, prejudices, and/or biases). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at the Analytical Reading level (Level 7 of 8) on the reading assessment. At this level, students can locate information in longer texts (narrative, document, or expository) by reading on and reading backing order to combine information from various parts of the text so as to infer the writer’s personal beliefs (value systems, prejudices, and/or biases). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L7.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Female 6th grade students by reading proficiency level (%). Level 7 - Analytical Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at the Analytical Reading level (Level 7 of 8) on the reading assessment. At this level, students can locate information in longer texts (narrative, document, or expository) by reading on and reading backing order to combine information from various parts of the text so as to infer the writer’s personal beliefs (value systems, prejudices, and/or biases). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at the Analytical Reading level (Level 7 of 8) on the reading assessment. At this level, students can locate information in longer texts (narrative, document, or expository) by reading on and reading backing order to combine information from various parts of the text so as to infer the writer’s personal beliefs (value systems, prejudices, and/or biases). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L7.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Male 6th grade students by reading proficiency level (%). Level 7 - Analytical Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at the Analytical Reading level (Level 7 of 8) on the reading assessment. At this level, students can locate information in longer texts (narrative, document, or expository) by reading on and reading backing order to combine information from various parts of the text so as to infer the writer’s personal beliefs (value systems, prejudices, and/or biases). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at the Analytical Reading level (Level 7 of 8) on the reading assessment. At this level, students can locate information in longer texts (narrative, document, or expository) by reading on and reading backing order to combine information from various parts of the text so as to infer the writer’s personal beliefs (value systems, prejudices, and/or biases). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L8", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: 6th grade students by reading proficiency level (%). Level 8 - Critical Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade students scoring at the Critical Reading level (Level 8 of 8) on the reading assessment. At this level, students can locate information in longer texts (narrative, document, and expository) by reading on and reading back in order to combine information from various parts of the text so as to infer and evaluate what the writer has assumed about the topic and the characteristics of the reader – such as age, knowledge, and personal beliefs (values systems, prejudices, and/or biases). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade students scoring at the Critical Reading level (Level 8 of 8) on the reading assessment. At this level, students can locate information in longer texts (narrative, document, and expository) by reading on and reading back in order to combine information from various parts of the text so as to infer and evaluate what the writer has assumed about the topic and the characteristics of the reader – such as age, knowledge, and personal beliefs (values systems, prejudices, and/or biases). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L8.FE", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Female 6th grade students by reading proficiency level (%). Level 8 - Critical Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade female students scoring at the Critical Reading level (Level 8 of 8) on the reading assessment. At this level, students can locate information in longer texts (narrative, document, and expository) by reading on and reading back in order to combine information from various parts of the text so as to infer and evaluate what the writer has assumed about the topic and the characteristics of the reader – such as age, knowledge, and personal beliefs (values systems, prejudices, and/or biases). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade female students scoring at the Critical Reading level (Level 8 of 8) on the reading assessment. At this level, students can locate information in longer texts (narrative, document, and expository) by reading on and reading back in order to combine information from various parts of the text so as to infer and evaluate what the writer has assumed about the topic and the characteristics of the reader – such as age, knowledge, and personal beliefs (values systems, prejudices, and/or biases). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.L8.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Male 6th grade students by reading proficiency level (%). Level 8 - Critical Reading" }, { "id": "Longdefinition", "value": "Percentage of 6th grade male students scoring at the Critical Reading level (Level 8 of 8) on the reading assessment. At this level, students can locate information in longer texts (narrative, document, and expository) by reading on and reading back in order to combine information from various parts of the text so as to infer and evaluate what the writer has assumed about the topic and the characteristics of the reader – such as age, knowledge, and personal beliefs (values systems, prejudices, and/or biases). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Percentage of 6th grade male students scoring at the Critical Reading level (Level 8 of 8) on the reading assessment. At this level, students can locate information in longer texts (narrative, document, and expository) by reading on and reading back in order to combine information from various parts of the text so as to infer and evaluate what the writer has assumed about the topic and the characteristics of the reader – such as age, knowledge, and personal beliefs (values systems, prejudices, and/or biases). Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.SACMEQ.REA.MA", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ: Mean performance on the 6th grade reading scale. Male" }, { "id": "Longdefinition", "value": "Mean performance on the reading scale, male is the mean reading score for male 6th grade students. Mean scores are on SACMEQ scales for reading, which have averages of 500 and standard deviations of 100. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Shortdefinition", "value": "Mean performance on the reading scale, male is the mean reading score for male 6th grade students. Mean scores are on SACMEQ scales for reading, which have averages of 500 and standard deviations of 100. Data reflects country performance in the stated year according to SACMEQ, but may not be comparable across years or countries. Consult the SACMEQ website for more detailed information: http://www.sacmeq.org/" }, { "id": "Source", "value": "Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) Data Archive, www.sacmeq.org" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Mean performance on the mathematics scale for fourth grade students, total" }, { "id": "Longdefinition", "value": "TIMSS: Mean performance on the mathematics scale for fourth grade students, total is the average scale score for 4th graders on the mathematics assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Mean performance on the mathematics scale for fourth grade students, total is the average scale score for 4th graders on the mathematics assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.ADV", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Fourth grade students reaching the advanced international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Fourth grade students reaching the advanced international benchmark of mathematics achievement (%) is the share of 4th grade students scoring at least 625 on the mathematics assessment. Students at this benchmark can apply their understanding and knowledge in a variety of relatively complex situations and explain their reasoning. They can solve a variety of multi-step word problems involving whole numbers. Students at this level show an increasing understanding of fractions and decimals. They can apply knowledge of a range of two- and three-dimensional shapes in a variety of situations. They can interpret and represent data to solve multi-step problems. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Othernotes", "value": "Proficiency" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Statisticalconceptandmethodology", "value": "TIMSS" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.ADV.FE", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Female 4th grade students reaching the advanced international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 4th grade students reaching the advanced international benchmark of mathematics achievement (%) is the share of female 4th grade students scoring at least 625 on the mathematics assessment. Students at this benchmark can apply their understanding and knowledge in a variety of relatively complex situations and explain their reasoning. They can solve a variety of multi-step word problems involving whole numbers including proportions. Students at this level show an increasing understanding of fractions and decimals. Students can apply geometric knowledge of a range of two- and three-dimensional shapes in a variety of situations. They can draw a conclusion from data in a table and justify their conclusion. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 4th grade students reaching the advanced international benchmark of mathematics achievement (%) is the share of female 4th grade students scoring at least 625 on the mathematics assessment. Students at this benchmark can apply their understanding and knowledge in a variety of relatively complex situations and explain their reasoning. They can solve a variety of multi-step word problems involving whole numbers including proportions. Students at this level show an increasing understanding of fractions and decimals. Students can apply geometric knowledge of a range of two- and three-dimensional shapes in a variety of situations. They can draw a conclusion from data in a table and justify their conclusion. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.ADV.MA", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Male 4th grade students reaching the advanced international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 4th grade students reaching the advanced international benchmark of mathematics achievement (%) is the share of male 4th grade students scoring at least 625 on the mathematics assessment. Students at this benchmark can apply their understanding and knowledge in a variety of relatively complex situations and explain their reasoning. They can solve a variety of multi-step word problems involving whole numbers including proportions. Students at this level show an increasing understanding of fractions and decimals. Students can apply geometric knowledge of a range of two- and three-dimensional shapes in a variety of situations. They can draw a conclusion from data in a table and justify their conclusion. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 4th grade students reaching the advanced international benchmark of mathematics achievement (%) is the share of male 4th grade students scoring at least 625 on the mathematics assessment. Students at this benchmark can apply their understanding and knowledge in a variety of relatively complex situations and explain their reasoning. They can solve a variety of multi-step word problems involving whole numbers including proportions. Students at this level show an increasing understanding of fractions and decimals. Students can apply geometric knowledge of a range of two- and three-dimensional shapes in a variety of situations. They can draw a conclusion from data in a table and justify their conclusion. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.BL", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Fourth grade students who did not reach the low international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Fourth grade students who did not reach the low international benchmark of mathematics achievement (%) is the share of 4th grade students scoring below 400 on the mathematics assessment. The indicator is the percentage of students with achievement too low for estimation on TIMSS and TIMSS Numeracy combined. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Othernotes", "value": "Proficiency" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Statisticalconceptandmethodology", "value": "TIMSS" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.BL.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 4th grade students who did not reach the low international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 4th grade students who did not reach the low international benchmark of mathematics achievement (%) is the share of female 4th grade students scoring below 400 on the mathematics assessment. The indicator is the percentage of students with achievement too low for estimation on TIMSS and TIMSS Numeracy combined. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 4th grade students who did not reach the low international benchmark of mathematics achievement (%) is the share of female 4th grade students scoring below 400 on the mathematics assessment. The indicator is the percentage of students with achievement too low for estimation on TIMSS and TIMSS Numeracy combined. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.BL.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 4th grade students who did not reach the low international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 4th grade students who did not reach the low international benchmark of mathematics achievement (%) is the share of male 4th grade students scoring below 400 on the mathematics assessment. The indicator is the percentage of students with achievement too low for estimation on TIMSS and TIMSS Numeracy combined. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 4th grade students who did not reach the low international benchmark of mathematics achievement (%) is the share of male 4th grade students scoring below 400 on the mathematics assessment. The indicator is the percentage of students with achievement too low for estimation on TIMSS and TIMSS Numeracy combined. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.FE", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Mean performance on the mathematics scale for fourth grade students, female" }, { "id": "Longdefinition", "value": "TIMSS: Mean performance on the mathematics scale for fourth grade students, female is the average scale score for female 4th graders on the mathematics assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Mean performance on the mathematics scale for fourth grade students, female is the average scale score for female 4th graders on the mathematics assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.HI", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Fourth grade students reaching the high international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Fourth grade students reaching the high international benchmark of mathematics achievement (%) is the share of 4th grade students scoring at least 550 on the mathematics assessment. Students at this benchmark can apply conceptual understanding to solve problems. They can apply conceptual understanding of whole numbers to solve two-step word problems. They show understanding of the number line, multiples, factors, and rounding numbers, and operations with fractions and decimals. Students can solve simple measurement problems. They demonstrate understanding of geometric properties of shapes and angles. Students can interpret and use data in tables and a variety of graphs to solve problems. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Fourth grade students reaching the high international benchmark of mathematics achievement (%) is the share of 4th grade students scoring at least 550 on the mathematics assessment. Students at this benchmark can apply conceptual understanding to solve problems. They can apply conceptual understanding of whole numbers to solve two-step word problems. They show understanding of the number line, multiples, factors, and rounding numbers, and operations with fractions and decimals. Students can solve simple measurement problems. They demonstrate understanding of geometric properties of shapes and angles. Students can interpret and use data in tables and a variety of graphs to solve problems. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.HI.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 4th grade students reaching the high international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 4th grade students reaching the high international benchmark of mathematics achievement (%) is the share of female 4th grade students scoring at least 550 on the mathematics assessment. Students at this benchmark can apply conceptual understanding to solve problems. They can apply conceptual understanding of whole numbers to solve two-step word problems. They show understanding of the number line, multiples, factors, and rounding numbers, and operations with fractions and decimals. Students can solve simple measurement problems. They demonstrate understanding of geometric properties of shapes and angles. Students can interpret and use data in tables and a variety of graphs to solve problems. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 4th grade students reaching the high international benchmark of mathematics achievement (%) is the share of female 4th grade students scoring at least 550 on the mathematics assessment. Students at this benchmark can apply conceptual understanding to solve problems. They can apply conceptual understanding of whole numbers to solve two-step word problems. They show understanding of the number line, multiples, factors, and rounding numbers, and operations with fractions and decimals. Students can solve simple measurement problems. They demonstrate understanding of geometric properties of shapes and angles. Students can interpret and use data in tables and a variety of graphs to solve problems. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.HI.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 4th grade students reaching the high international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 4th grade students reaching the high international benchmark of mathematics achievement (%) is the share of male 4th grade students scoring at least 550 on the mathematics assessment. Students at this benchmark can apply conceptual understanding to solve problems. They can apply conceptual understanding of whole numbers to solve two-step word problems. They show understanding of the number line, multiples, factors, and rounding numbers, and operations with fractions and decimals. Students can solve simple measurement problems. They demonstrate understanding of geometric properties of shapes and angles. Students can interpret and use data in tables and a variety of graphs to solve problems. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 4th grade students reaching the high international benchmark of mathematics achievement (%) is the share of male 4th grade students scoring at least 550 on the mathematics assessment. Students at this benchmark can apply conceptual understanding to solve problems. They can apply conceptual understanding of whole numbers to solve two-step word problems. They show understanding of the number line, multiples, factors, and rounding numbers, and operations with fractions and decimals. Students can solve simple measurement problems. They demonstrate understanding of geometric properties of shapes and angles. Students can interpret and use data in tables and a variety of graphs to solve problems. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.INT", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Fourth grade students reaching the intermediate international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Fourth grade students reaching the intermediate international benchmark of mathematics achievement (%) is the share of 4th grade students scoring at least 475 on the mathematics assessment. Students at this benchmark can apply basic mathematical knowledge in simple situations. They can compute with three- and four-digit whole numbers in a variety of situations. They have some understanding of decimals and fractions. Students can identify and draw shapes with simple properties. They can read, label, and interpret information in graphs and tables. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Fourth grade students reaching the intermediate international benchmark of mathematics achievement (%) is the share of 4th grade students scoring at least 475 on the mathematics assessment. Students at this benchmark can apply basic mathematical knowledge in simple situations. They can compute with three- and four-digit whole numbers in a variety of situations. They have some understanding of decimals and fractions. Students can identify and draw shapes with simple properties. They can read, label, and interpret information in graphs and tables. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.INT.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 4th grade students reaching the intermediate international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 4th grade students reaching the intermediate international benchmark of mathematics achievement (%) is the share of female 4th grade students scoring at least 475 on the mathematics assessment. Students at this benchmark can apply basic mathematical knowledge in simple situations. They can compute with three- and four-digit whole numbers in a variety of situations. They have some understanding of decimals and fractions. Students can identify and draw shapes with simple properties. They can read, label, and interpret information in graphs and tables. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 4th grade students reaching the intermediate international benchmark of mathematics achievement (%) is the share of female 4th grade students scoring at least 475 on the mathematics assessment. Students at this benchmark can apply basic mathematical knowledge in simple situations. They can compute with three- and four-digit whole numbers in a variety of situations. They have some understanding of decimals and fractions. Students can identify and draw shapes with simple properties. They can read, label, and interpret information in graphs and tables. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.INT.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 4th grade students reaching the intermediate international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 4th grade students reaching the intermediate international benchmark of mathematics achievement (%) is the share of male 4th grade students scoring at least 475 on the mathematics assessment. Students at this benchmark can apply basic mathematical knowledge in simple situations. They can compute with three- and four-digit whole numbers in a variety of situations. They have some understanding of decimals and fractions. Students can identify and draw shapes with simple properties. They can read, label, and interpret information in graphs and tables. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 4th grade students reaching the intermediate international benchmark of mathematics achievement (%) is the share of male 4th grade students scoring at least 475 on the mathematics assessment. Students at this benchmark can apply basic mathematical knowledge in simple situations. They can compute with three- and four-digit whole numbers in a variety of situations. They have some understanding of decimals and fractions. Students can identify and draw shapes with simple properties. They can read, label, and interpret information in graphs and tables. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.LOW", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Fourth grade students reaching the low international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Fourth grade students reaching the low international benchmark of mathematics achievement (%) is the share of 4th grade students scoring at least 400 on the mathematics assessment. Students at this benchmark have some basic mathematical knowledge. They can add, subtract, multiply, and divide one- and two-digit whole numbers. They can solve simple word problems. They have some knowledge of simple fractions and common geometric shapes. Students can read and complete simple bar graphs and tables. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Fourth grade students reaching the low international benchmark of mathematics achievement (%) is the share of 4th grade students scoring at least 400 on the mathematics assessment. Students at this benchmark have some basic mathematical knowledge. They can add, subtract, multiply, and divide one- and two-digit whole numbers. They can solve simple word problems. They have some knowledge of simple fractions and common geometric shapes. Students can read and complete simple bar graphs and tables. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.LOW.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 4th grade students reaching the low international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 4th grade students reaching the low international benchmark of mathematics achievement (%) is the share of female 4th grade students scoring at least 400 on the mathematics assessment. Students at this benchmark have some basic mathematical knowledge. They can add, subtract, multiply, and divide one- and two-digit whole numbers. They can solve simple word problems. They have some knowledge of simple fractions and common geometric shapes. Students can read and complete simple bar graphs and tables. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 4th grade students reaching the low international benchmark of mathematics achievement (%) is the share of female 4th grade students scoring at least 400 on the mathematics assessment. Students at this benchmark have some basic mathematical knowledge. They can add, subtract, multiply, and divide one- and two-digit whole numbers. They can solve simple word problems. They have some knowledge of simple fractions and common geometric shapes. Students can read and complete simple bar graphs and tables. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.LOW.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 4th grade students reaching the low international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 4th grade students reaching the low international benchmark of mathematics achievement (%) is the share of male 4th grade students scoring at least 400 on the mathematics assessment. Students at this benchmark have some basic mathematical knowledge. They can add, subtract, multiply, and divide one- and two-digit whole numbers. They can solve simple word problems. They have some knowledge of simple fractions and common geometric shapes. Students can read and complete simple bar graphs and tables. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 4th grade students reaching the low international benchmark of mathematics achievement (%) is the share of male 4th grade students scoring at least 400 on the mathematics assessment. Students at this benchmark have some basic mathematical knowledge. They can add, subtract, multiply, and divide one- and two-digit whole numbers. They can solve simple word problems. They have some knowledge of simple fractions and common geometric shapes. Students can read and complete simple bar graphs and tables. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.MA", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Mean performance on the mathematics scale for fourth grade students, male" }, { "id": "Longdefinition", "value": "TIMSS: Mean performance on the mathematics scale for fourth grade students, male is the average scale score for male 4th graders on the mathematics assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Mean performance on the mathematics scale for fourth grade students, male is the average scale score for male 4th graders on the mathematics assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.P05", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 4th Grade Mathematics Scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.P10", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 4th Grade Mathematics Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.P25", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 4th Grade Mathematics Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.P50", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 4th Grade Mathematics Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.P75", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 4th Grade Mathematics Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.P90", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 4th Grade Mathematics Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT4.P95", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Distribution of 4th Grade Mathematics Scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 95 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 95 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Mean performance on the mathematics scale for eighth grade students, total" }, { "id": "Longdefinition", "value": "TIMSS: Mean performance on the mathematics scale for eighth grade students, total is the average scale score for 8th graders on the mathematics assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Mean performance on the mathematics scale for eighth grade students, total is the average scale score for 8th graders on the mathematics assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.ADV", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Eighth grade students reaching the advanced international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Eighth grade students reaching the advanced international benchmark of mathematics achievement (%) is the share of 8th grade students scoring at least 625 on the mathematics assessment. Students can apply and reason in a variety of problem situations, solve linear equations, and make generalizations. They can solve a variety of fraction, proportion, and percent problems and justify their conclusions. They can understand linear functions and algebraic expressions. Students can use their knowledge of geometric figures to solve a wide range of problems involving angles, area, and surface area. They can calculate means and medians, and understand how changing data points can impact the mean. Students can interpret a wide variety of data displays to draw and justify conclusions, and solve multistep problems. They can solve problems involving expected values. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Eighth grade students reaching the advanced international benchmark of mathematics achievement (%) is the share of 8th grade students scoring at least 625 on the mathematics assessment. Students can apply and reason in a variety of problem situations, solve linear equations, and make generalizations. They can solve a variety of fraction, proportion, and percent problems and justify their conclusions. They can understand linear functions and algebraic expressions. Students can use their knowledge of geometric figures to solve a wide range of problems involving angles, area, and surface area. They can calculate means and medians, and understand how changing data points can impact the mean. Students can interpret a wide variety of data displays to draw and justify conclusions, and solve multistep problems. They can solve problems involving expected values. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.ADV.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 8th grade students reaching the advanced international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 8th grade students reaching the advanced international benchmark of mathematics achievement (%) is the share of female 8th grade students scoring at least 625 on the mathematics assessment. Students can apply and reason in a variety of problem situations, solve linear equations, and make generalizations. They can solve a variety of fraction, proportion, and percent problems and justify their conclusions. They can understand linear functions and algebraic expressions. Students can use their knowledge of geometric figures to solve a wide range of problems involving angles, area, and surface area. They can calculate means and medians, and understand how changing data points can impact the mean. Students can interpret a wide variety of data displays to draw and justify conclusions, and solve multistep problems. They can solve problems involving expected values. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 8th grade students reaching the advanced international benchmark of mathematics achievement (%) is the share of female 8th grade students scoring at least 625 on the mathematics assessment. Students can apply and reason in a variety of problem situations, solve linear equations, and make generalizations. They can solve a variety of fraction, proportion, and percent problems and justify their conclusions. They can understand linear functions and algebraic expressions. Students can use their knowledge of geometric figures to solve a wide range of problems involving angles, area, and surface area. They can calculate means and medians, and understand how changing data points can impact the mean. Students can interpret a wide variety of data displays to draw and justify conclusions, and solve multistep problems. They can solve problems involving expected values. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.ADV.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 8th grade students reaching the advanced international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 8th grade students reaching the advanced international benchmark of mathematics achievement (%) is the share of male 8th grade students scoring at least 625 on the mathematics assessment. Students can apply and reason in a variety of problem situations, solve linear equations, and make generalizations. They can solve a variety of fraction, proportion, and percent problems and justify their conclusions. They can understand linear functions and algebraic expressions. Students can use their knowledge of geometric figures to solve a wide range of problems involving angles, area, and surface area. They can calculate means and medians, and understand how changing data points can impact the mean. Students can interpret a wide variety of data displays to draw and justify conclusions, and solve multistep problems. They can solve problems involving expected values. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 8th grade students reaching the advanced international benchmark of mathematics achievement (%) is the share of male 8th grade students scoring at least 625 on the mathematics assessment. Students can apply and reason in a variety of problem situations, solve linear equations, and make generalizations. They can solve a variety of fraction, proportion, and percent problems and justify their conclusions. They can understand linear functions and algebraic expressions. Students can use their knowledge of geometric figures to solve a wide range of problems involving angles, area, and surface area. They can calculate means and medians, and understand how changing data points can impact the mean. Students can interpret a wide variety of data displays to draw and justify conclusions, and solve multistep problems. They can solve problems involving expected values. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.BL", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Eighth grade students who did not reach the low international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Eighth grade students who did not reach the low international benchmark of mathematics achievement (%) is the share of 8th grade students scoring below 400 on the mathematics assessment. It is the percentage of students with achievement too low for estimation. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. However, such students were assigned scale scores (plausible values) by the achievement scaling procedure, despite concerns about their reliability. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Othernotes", "value": "Proficiency" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Statisticalconceptandmethodology", "value": "TIMSS" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.BL.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 8th grade students who did not reach the low international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 8th grade students who did not reach the low international benchmark of mathematics achievement (%) is the share of female 8th grade students scoring below 400 on the mathematics assessment. It is the percentage of students with achievement too low for estimation. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. However, such students were assigned scale scores (plausible values) by the achievement scaling procedure, despite concerns about their reliability. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 8th grade students who did not reach the low international benchmark of mathematics achievement (%) is the share of female 8th grade students scoring below 400 on the mathematics assessment. It is the percentage of students with achievement too low for estimation. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. However, such students were assigned scale scores (plausible values) by the achievement scaling procedure, despite concerns about their reliability. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.BL.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 8th grade students who did not reach the low international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 8th grade students who did not reach the low international benchmark of mathematics achievement (%) is the share of male 8th grade students scoring below 400 on the mathematics assessment. It is the percentage of students with achievement too low for estimation. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. However, such students were assigned scale scores (plausible values) by the achievement scaling procedure, despite concerns about their reliability. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 8th grade students who did not reach the low international benchmark of mathematics achievement (%) is the share of male 8th grade students scoring below 400 on the mathematics assessment. It is the percentage of students with achievement too low for estimation. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. However, such students were assigned scale scores (plausible values) by the achievement scaling procedure, despite concerns about their reliability. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.FE", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Mean performance on the mathematics scale for eighth grade students, female" }, { "id": "Longdefinition", "value": "TIMSS: Mean performance on the mathematics scale for eighth grade students, female is the average scale score for female 8th graders on the mathematics assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Mean performance on the mathematics scale for eighth grade students, female is the average scale score for female 8th graders on the mathematics assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.HI", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Eighth grade students reaching the high international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Eighth grade students reaching the high international benchmark of mathematics achievement (%) is the share of 8th grade students scoring at least 550 on the mathematics assessment. Students at this benchmark can apply their understanding and knowledge in a variety of relatively complex situations. They can solve problems with fractions, decimals, ratios, and proportions. Students at this level show basic procedural knowledge related to algebraic expressions and equations. They can solve a variety of problems with angles, including problems involving triangles, parallel lines, rectangles, and congruent and similar figures. Students can interpret data in a variety of graphs and solve simple problems involving outcomes and probabilities. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Eighth grade students reaching the high international benchmark of mathematics achievement (%) is the share of 8th grade students scoring at least 550 on the mathematics assessment. Students at this benchmark can apply their understanding and knowledge in a variety of relatively complex situations. They can solve problems with fractions, decimals, ratios, and proportions. Students at this level show basic procedural knowledge related to algebraic expressions and equations. They can solve a variety of problems with angles, including problems involving triangles, parallel lines, rectangles, and congruent and similar figures. Students can interpret data in a variety of graphs and solve simple problems involving outcomes and probabilities. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.HI.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 8th grade students reaching the high international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 8th grade students reaching the high international benchmark of mathematics achievement (%) is the share of female 8th grade students scoring at least 550 on the mathematics assessment. Students at this benchmark can apply their understanding and knowledge in a variety of relatively complex situations. They can solve problems with fractions, decimals, ratios, and proportions. Students at this level show basic procedural knowledge related to algebraic expressions and equations. They can solve a variety of problems with angles, including problems involving triangles, parallel lines, rectangles, and congruent and similar figures. Students can interpret data in a variety of graphs and solve simple problems involving outcomes and probabilities. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 8th grade students reaching the high international benchmark of mathematics achievement (%) is the share of female 8th grade students scoring at least 550 on the mathematics assessment. Students at this benchmark can apply their understanding and knowledge in a variety of relatively complex situations. They can solve problems with fractions, decimals, ratios, and proportions. Students at this level show basic procedural knowledge related to algebraic expressions and equations. They can solve a variety of problems with angles, including problems involving triangles, parallel lines, rectangles, and congruent and similar figures. Students can interpret data in a variety of graphs and solve simple problems involving outcomes and probabilities. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.HI.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 8th grade students reaching the high international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 8th grade students reaching the high international benchmark of mathematics achievement (%) is the share of male 8th grade students scoring at least 550 on the mathematics assessment. Students at this benchmark can apply their understanding and knowledge in a variety of relatively complex situations. They can solve problems with fractions, decimals, ratios, and proportions. Students at this level show basic procedural knowledge related to algebraic expressions and equations. They can solve a variety of problems with angles, including problems involving triangles, parallel lines, rectangles, and congruent and similar figures. Students can interpret data in a variety of graphs and solve simple problems involving outcomes and probabilities. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 8th grade students reaching the high international benchmark of mathematics achievement (%) is the share of male 8th grade students scoring at least 550 on the mathematics assessment. Students at this benchmark can apply their understanding and knowledge in a variety of relatively complex situations. They can solve problems with fractions, decimals, ratios, and proportions. Students at this level show basic procedural knowledge related to algebraic expressions and equations. They can solve a variety of problems with angles, including problems involving triangles, parallel lines, rectangles, and congruent and similar figures. Students can interpret data in a variety of graphs and solve simple problems involving outcomes and probabilities. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.INT", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Eighth grade students reaching the intermediate international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Eighth grade students reaching the intermediate international benchmark of mathematics achievement (%) is the share of 8th grade students scoring at least 475 on the mathematics assessment. Students at this benchmark can apply basic mathematical knowledge in a variety of situations. They can solve problems involving whole numbers, negative numbers, fractions, decimals, and ratios. Students have some basic knowledge about properties of two-dimensional shapes. They can read and interpret data in graphs and have some rudimentary knowledge of probability. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Eighth grade students reaching the intermediate international benchmark of mathematics achievement (%) is the share of 8th grade students scoring at least 475 on the mathematics assessment. Students at this benchmark can apply basic mathematical knowledge in a variety of situations. They can solve problems involving whole numbers, negative numbers, fractions, decimals, and ratios. Students have some basic knowledge about properties of two-dimensional shapes. They can read and interpret data in graphs and have some rudimentary knowledge of probability. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.INT.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 8th grade students reaching the intermediate international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 8th grade students reaching the intermediate international benchmark of mathematics achievement (%) is the share of female 8th grade students scoring at least 475 on the mathematics assessment. Students can apply basic mathematical knowledge in a variety of situations. They can solve problems involving whole numbers, negative numbers, fractions, decimals, and ratios. Students have some basic knowledge about properties of two-dimensional shapes. They can read and interpret data in graphs and have some rudimentary knowledge of probability. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 8th grade students reaching the intermediate international benchmark of mathematics achievement (%) is the share of female 8th grade students scoring at least 475 on the mathematics assessment. Students can apply basic mathematical knowledge in a variety of situations. They can solve problems involving whole numbers, negative numbers, fractions, decimals, and ratios. Students have some basic knowledge about properties of two-dimensional shapes. They can read and interpret data in graphs and have some rudimentary knowledge of probability. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.INT.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 8th grade students reaching the intermediate international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 8th grade students reaching the intermediate international benchmark of mathematics achievement (%) is the share of male 8th grade students scoring at least 475 on the mathematics assessment. Students can apply basic mathematical knowledge in a variety of situations. They can solve problems involving whole numbers, negative numbers, fractions, decimals, and ratios. Students have some basic knowledge about properties of two-dimensional shapes. They can read and interpret data in graphs and have some rudimentary knowledge of probability. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 8th grade students reaching the intermediate international benchmark of mathematics achievement (%) is the share of male 8th grade students scoring at least 475 on the mathematics assessment. Students can apply basic mathematical knowledge in a variety of situations. They can solve problems involving whole numbers, negative numbers, fractions, decimals, and ratios. Students have some basic knowledge about properties of two-dimensional shapes. They can read and interpret data in graphs and have some rudimentary knowledge of probability. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.LOW", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Eighth grade students reaching the low international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Eighth grade students reaching the low international benchmark of mathematics achievement (%) is the share of 8th grade students scoring at least 400 on the mathematics assessment. Students at this benchmark have some knowledge of whole numbers and basic graphs. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Eighth grade students reaching the low international benchmark of mathematics achievement (%) is the share of 8th grade students scoring at least 400 on the mathematics assessment. Students at this benchmark have some knowledge of whole numbers and basic graphs. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.LOW.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 8th grade students reaching the low international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 8th grade students reaching the low international benchmark of mathematics achievement (%) is the share of female 8th grade students scoring at least 400 on the mathematics assessment. Students at this benchmark have some knowledge of whole numbers and basic graphs. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 8th grade students reaching the low international benchmark of mathematics achievement (%) is the share of female 8th grade students scoring at least 400 on the mathematics assessment. Students at this benchmark have some knowledge of whole numbers and basic graphs. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.LOW.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 8th grade students reaching the low international benchmark of mathematics achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 8th grade students reaching the low international benchmark of mathematics achievement (%) is the share of male 8th grade students scoring at least 400 on the mathematics assessment. Students at this benchmark have some knowledge of whole numbers and basic graphs. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 8th grade students reaching the low international benchmark of mathematics achievement (%) is the share of male 8th grade students scoring at least 400 on the mathematics assessment. Students at this benchmark have some knowledge of whole numbers and basic graphs. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.MA", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Mean performance on the mathematics scale for eighth grade students, male" }, { "id": "Longdefinition", "value": "TIMSS: Mean performance on the mathematics scale for eighth grade students, male is the average scale score for male 8th graders on the mathematics assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Mean performance on the mathematics scale for eighth grade students, male is the average scale score for male 8th graders on the mathematics assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.P05", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 8th Grade Mathematics Scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.P10", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 8th Grade Mathematics Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.P25", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 8th Grade Mathematics Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.P50", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 8th Grade Mathematics Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.P75", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 8th Grade Mathematics Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.P90", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 8th Grade Mathematics Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.MAT8.P95", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Distribution of 8th Grade Mathematics Scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 95 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 95 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Mean performance on the science scale for fourth grade students, total" }, { "id": "Longdefinition", "value": "TIMSS: Mean performance on the science scale for fourth grade students, total is the average scale score for 4th graders on the science assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Mean performance on the science scale for fourth grade students, total is the average scale score for 4th graders on the science assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.ADV", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Fourth grade students reaching the advanced international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Fourth grade students reaching the advanced international benchmark of science achievement (%) is the share of 4th grade students scoring at least 625 on the science assessment. Students reaching this benchmark can communicate their understanding of life, physical, and Earth sciences and demonstrate some knowledge of the process of scientific inquiry. Students demonstrate knowledge of characteristics and life processes of a variety of organisms. They can communicate understanding of relationships in ecosystems and interactions between organisms and their environment. They communicate understanding of properties and states of matter and physical and chemical changes. Students communicate understanding of Earth’s physical characteristics, processes, and history and show knowledge of Earth’s revolution and rotation. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Fourth grade students reaching the advanced international benchmark of science achievement (%) is the share of 4th grade students scoring at least 625 on the science assessment. Students reaching this benchmark can communicate their understanding of life, physical, and Earth sciences and demonstrate some knowledge of the process of scientific inquiry. Students demonstrate knowledge of characteristics and life processes of a variety of organisms. They can communicate understanding of relationships in ecosystems and interactions between organisms and their environment. They communicate understanding of properties and states of matter and physical and chemical changes. Students communicate understanding of Earth’s physical characteristics, processes, and history and show knowledge of Earth’s revolution and rotation. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.ADV.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 4th grade students reaching the advanced international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 4th grade students reaching the advanced international benchmark of science achievement (%) is the share of female 4th grade students scoring at least 625 on the science assessment. Students reaching this benchmark can communicate their understanding of life, physical, and Earth sciences and demonstrate some knowledge of the process of scientific inquiry. Students demonstrate knowledge of characteristics and life processes of a variety of organisms. They can communicate understanding of relationships in ecosystems and interactions between organisms and their environment. They communicate understanding of properties and states of matter and physical and chemical changes. Students communicate understanding of Earth’s physical characteristics, processes, and history and show knowledge of Earth’s revolution and rotation. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 4th grade students reaching the advanced international benchmark of science achievement (%) is the share of female 4th grade students scoring at least 625 on the science assessment. Students reaching this benchmark can communicate their understanding of life, physical, and Earth sciences and demonstrate some knowledge of the process of scientific inquiry. Students demonstrate knowledge of characteristics and life processes of a variety of organisms. They can communicate understanding of relationships in ecosystems and interactions between organisms and their environment. They communicate understanding of properties and states of matter and physical and chemical changes. Students communicate understanding of Earth’s physical characteristics, processes, and history and show knowledge of Earth’s revolution and rotation. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.ADV.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 4th grade students reaching the advanced international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 4th grade students reaching the advanced international benchmark of science achievement (%) is the share of male 4th grade students scoring at least 625 on the science assessment. Students reaching this benchmark can communicate their understanding of life, physical, and Earth sciences and demonstrate some knowledge of the process of scientific inquiry. Students demonstrate knowledge of characteristics and life processes of a variety of organisms. They can communicate understanding of relationships in ecosystems and interactions between organisms and their environment. They communicate understanding of properties and states of matter and physical and chemical changes. Students communicate understanding of Earth’s physical characteristics, processes, and history and show knowledge of Earth’s revolution and rotation. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 4th grade students reaching the advanced international benchmark of science achievement (%) is the share of male 4th grade students scoring at least 625 on the science assessment. Students reaching this benchmark can communicate their understanding of life, physical, and Earth sciences and demonstrate some knowledge of the process of scientific inquiry. Students demonstrate knowledge of characteristics and life processes of a variety of organisms. They can communicate understanding of relationships in ecosystems and interactions between organisms and their environment. They communicate understanding of properties and states of matter and physical and chemical changes. Students communicate understanding of Earth’s physical characteristics, processes, and history and show knowledge of Earth’s revolution and rotation. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.BL", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Fourth grade students who did not reach the low international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Fourth grade students who did not reach the low international benchmark of science achievement (%) is the share of 4th grade students scoring below 400 on the science assessment. The indicator is the percentage of students with achievement too low for estimation. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Othernotes", "value": "Proficiency" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Statisticalconceptandmethodology", "value": "TIMSS" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.BL.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 4th grade students who did not reach the low international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 4th grade students who did not reach the low international benchmark of science achievement (%) is the share of female 4th grade students scoring below 400 on the science assessment. The indicator is the percentage of students with achievement too low for estimation. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 4th grade students who did not reach the low international benchmark of science achievement (%) is the share of female 4th grade students scoring below 400 on the science assessment. The indicator is the percentage of students with achievement too low for estimation. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.BL.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 4th grade students who did not reach the low international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 4th grade students who did not reach the low international benchmark of science achievement (%) is the share of male 4th grade students scoring below 400 on the science assessment. The indicator is the percentage of students with achievement too low for estimation. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 4th grade students who did not reach the low international benchmark of science achievement (%) is the share of male 4th grade students scoring below 400 on the science assessment. The indicator is the percentage of students with achievement too low for estimation. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.FE", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Mean performance on the science scale for fourth grade students, female" }, { "id": "Longdefinition", "value": "TIMSS: Mean performance on the science scale for fourth grade students, female is the average scale score for female 4th graders on the science assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Mean performance on the science scale for fourth grade students, female is the average scale score for female 4th graders on the science assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.HI", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Fourth grade students reaching the high international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Fourth grade students reaching the high international benchmark of science achievement (%) is the share of 4th grade students scoring at least 550 on the science assessment. Students reaching this benchmark can communicate and apply knowledge of life, physical, and Earth sciences. Students communicate knowledge of characteristics of plants, animals, and their life cycles, and apply knowledge of ecosystems and of humans’ and organisms’ interactions with their environment. Students demonstrate knowledge of states and properties of matter and of energy transfer in practical contexts, and show some understanding of forces and motion. Students know various facts about the Earth’s physical characteristics and show basic understanding of the Earth-Moon-Sun system. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Fourth grade students reaching the high international benchmark of science achievement (%) is the share of 4th grade students scoring at least 550 on the science assessment. Students reaching this benchmark can communicate and apply knowledge of life, physical, and Earth sciences. Students communicate knowledge of characteristics of plants, animals, and their life cycles, and apply knowledge of ecosystems and of humans’ and organisms’ interactions with their environment. Students demonstrate knowledge of states and properties of matter and of energy transfer in practical contexts, and show some understanding of forces and motion. Students know various facts about the Earth’s physical characteristics and show basic understanding of the Earth-Moon-Sun system. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.HI.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 4th grade students reaching the high international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 4th grade students reaching the high international benchmark of science achievement (%) is the share of female 4th grade students scoring at least 550 on the science assessment. Students reaching this benchmark can communicate and apply knowledge of life, physical, and Earth sciences. Students communicate knowledge of characteristics of plants, animals, and their life cycles, and apply knowledge of ecosystems and of humans’ and organisms’ interactions with their environment. Students demonstrate knowledge of states and properties of matter and of energy transfer in practical contexts, and show some understanding of forces and motion. Students know various facts about the Earth’s physical characteristics and show basic understanding of the Earth-Moon-Sun system. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 4th grade students reaching the high international benchmark of science achievement (%) is the share of female 4th grade students scoring at least 550 on the science assessment. Students reaching this benchmark can communicate and apply knowledge of life, physical, and Earth sciences. Students communicate knowledge of characteristics of plants, animals, and their life cycles, and apply knowledge of ecosystems and of humans’ and organisms’ interactions with their environment. Students demonstrate knowledge of states and properties of matter and of energy transfer in practical contexts, and show some understanding of forces and motion. Students know various facts about the Earth’s physical characteristics and show basic understanding of the Earth-Moon-Sun system. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.HI.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 4th grade students reaching the high international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 4th grade students reaching the high international benchmark of science achievement (%) is the share of male 4th grade students scoring at least 550 on the science assessment. Students reaching this benchmark can communicate and apply knowledge of life, physical, and Earth sciences. Students communicate knowledge of characteristics of plants, animals, and their life cycles, and apply knowledge of ecosystems and of humans’ and organisms’ interactions with their environment. Students demonstrate knowledge of states and properties of matter and of energy transfer in practical contexts, and show some understanding of forces and motion. Students know various facts about the Earth’s physical characteristics and show basic understanding of the Earth-Moon-Sun system. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 4th grade students reaching the high international benchmark of science achievement (%) is the share of male 4th grade students scoring at least 550 on the science assessment. Students reaching this benchmark can communicate and apply knowledge of life, physical, and Earth sciences. Students communicate knowledge of characteristics of plants, animals, and their life cycles, and apply knowledge of ecosystems and of humans’ and organisms’ interactions with their environment. Students demonstrate knowledge of states and properties of matter and of energy transfer in practical contexts, and show some understanding of forces and motion. Students know various facts about the Earth’s physical characteristics and show basic understanding of the Earth-Moon-Sun system. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.INT", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Fourth grade students reaching the intermediate international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Fourth grade students reaching the intermediate international benchmark of science achievement (%) is the share of 4th grade students scoring at least 475 on the science assessment. Students reaching this benchmark show knowledge and understanding of some aspects of science. Students demonstrate some basic knowledge of plants and animals. They demonstrate knowledge about some properties of matter and some facts related to electricity, and can apply elementary knowledge of forces and motion. They show some understanding of Earth’s physical characteristics. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Fourth grade students reaching the intermediate international benchmark of science achievement (%) is the share of 4th grade students scoring at least 475 on the science assessment. Students reaching this benchmark show knowledge and understanding of some aspects of science. Students demonstrate some basic knowledge of plants and animals. They demonstrate knowledge about some properties of matter and some facts related to electricity, and can apply elementary knowledge of forces and motion. They show some understanding of Earth’s physical characteristics. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.INT.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 4th grade students reaching the intermediate international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 4th grade students reaching the intermediate international benchmark of science achievement (%) is the share of female 4th grade students scoring at least 475 on the science assessment. Students reaching this benchmark show knowledge and understanding of some aspects of science. Students demonstrate some basic knowledge of plants and animals. They demonstrate knowledge about some properties of matter and some facts related to electricity, and can apply elementary knowledge of forces and motion. They show some understanding of Earth’s physical characteristics. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 4th grade students reaching the intermediate international benchmark of science achievement (%) is the share of female 4th grade students scoring at least 475 on the science assessment. Students reaching this benchmark show knowledge and understanding of some aspects of science. Students demonstrate some basic knowledge of plants and animals. They demonstrate knowledge about some properties of matter and some facts related to electricity, and can apply elementary knowledge of forces and motion. They show some understanding of Earth’s physical characteristics. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.INT.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 4th grade students reaching the intermediate international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 4th grade students reaching the intermediate international benchmark of science achievement (%) is the share of male 4th grade students scoring at least 475 on the science assessment. Students reaching this benchmark show knowledge and understanding of some aspects of science. Students demonstrate some basic knowledge of plants and animals. They demonstrate knowledge about some properties of matter and some facts related to electricity, and can apply elementary knowledge of forces and motion. They show some understanding of Earth’s physical characteristics. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 4th grade students reaching the intermediate international benchmark of science achievement (%) is the share of male 4th grade students scoring at least 475 on the science assessment. Students reaching this benchmark show knowledge and understanding of some aspects of science. Students demonstrate some basic knowledge of plants and animals. They demonstrate knowledge about some properties of matter and some facts related to electricity, and can apply elementary knowledge of forces and motion. They show some understanding of Earth’s physical characteristics. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.LOW", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Fourth grade students reaching the low international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Fourth grade students reaching the low international benchmark of science achievement (%) is the share of 4th grade students scoring at least 400 on the science assessment. Students reaching this benchmark show limited understanding of scientific concepts and limited knowledge of foundational science facts. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Fourth grade students reaching the low international benchmark of science achievement (%) is the share of 4th grade students scoring at least 400 on the science assessment. Students reaching this benchmark show limited understanding of scientific concepts and limited knowledge of foundational science facts. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.LOW.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 4th grade students reaching the low international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 4th grade students reaching the low international benchmark of science achievement (%) is the share of female 4th grade students scoring at least 400 on the science assessment. Students reaching this benchmark show limited understanding of scientific concepts and limited knowledge of foundational science facts. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 4th grade students reaching the low international benchmark of science achievement (%) is the share of female 4th grade students scoring at least 400 on the science assessment. Students reaching this benchmark show limited understanding of scientific concepts and limited knowledge of foundational science facts. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.LOW.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 4th grade students reaching the low international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 4th grade students reaching the low international benchmark of science achievement (%) is the share of male 4th grade students scoring at least 400 on the science assessment. Students reaching this benchmark show limited understanding of scientific concepts and limited knowledge of foundational science facts. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 4th grade students reaching the low international benchmark of science achievement (%) is the share of male 4th grade students scoring at least 400 on the science assessment. Students reaching this benchmark show limited understanding of scientific concepts and limited knowledge of foundational science facts. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.MA", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Mean performance on the science scale for fourth grade students, male" }, { "id": "Longdefinition", "value": "TIMSS: Mean performance on the science scale for fourth grade students, male is the average scale score for male 4th graders on the science assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Mean performance on the science scale for fourth grade students, male is the average scale score for male 4th graders on the science assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.P05", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 4th Grade Science Scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.P10", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 4th Grade Science Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.P25", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 4th Grade Science Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.P50", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 4th Grade Science Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.P75", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 4th Grade Science Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.P90", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 4th Grade Science Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI4.P95", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Distribution of 4th Grade Science Scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 95 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 95 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Mean performance on the science scale for eighth grade students, total" }, { "id": "Longdefinition", "value": "TIMSS: Mean performance on the science scale for eighth grade students, total is the average scale score for 8th graders on the science assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Mean performance on the science scale for eighth grade students, total is the average scale score for 8th graders on the science assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.ADV", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Eighth grade students reaching the advanced international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Eighth grade students reaching the advanced international benchmark of science achievement (%) is the share of 8th grade students scoring at least 625 on the science assessment. Students at this benchmark can communicate understanding of concepts related to biology, chemistry, physics, and Earth science in a variety of contexts. Students can classify animals into taxonomic groups. They can apply knowledge of cell structures and their functions. Students show some understanding of diversity, adaptation, and natural selection. They also recognize the interdependence of populations of organisms in an ecosystem. Students demonstrate knowledge of the composition of matter and the periodic table of the elements. Students use physical properties of matter to sort, classify, and compare substances and materials. They also recognize evidence that a chemical reaction has occurred. Students communicate understanding of particle spacing and motion in different physical states. Students apply knowledge of energy transfer and electrical circuits, can relate the properties of light and sound to common phenomena, and demonstrate understanding of forces in everyday contexts. Students communicate understanding of Earth’s structure, physical features, and processes. They demonstrate knowledge of the Earth’s resources and their conservation. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Eighth grade students reaching the advanced international benchmark of science achievement (%) is the share of 8th grade students scoring at least 625 on the science assessment. Students at this benchmark can communicate understanding of concepts related to biology, chemistry, physics, and Earth science in a variety of contexts. Students can classify animals into taxonomic groups. They can apply knowledge of cell structures and their functions. Students show some understanding of diversity, adaptation, and natural selection. They also recognize the interdependence of populations of organisms in an ecosystem. Students demonstrate knowledge of the composition of matter and the periodic table of the elements. Students use physical properties of matter to sort, classify, and compare substances and materials. They also recognize evidence that a chemical reaction has occurred. Students communicate understanding of particle spacing and motion in different physical states. Students apply knowledge of energy transfer and electrical circuits, can relate the properties of light and sound to common phenomena, and demonstrate understanding of forces in everyday contexts. Students communicate understanding of Earth’s structure, physical features, and processes. They demonstrate knowledge of the Earth’s resources and their conservation. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.ADV.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 8th grade students reaching the advanced international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 8th grade students reaching the advanced international benchmark of science achievement (%) is the share of female 8th grade students scoring at least 625 on the science assessment. Students at this benchmark can communicate understanding of concepts related to biology, chemistry, physics, and Earth science in a variety of contexts. Students can classify animals into taxonomic groups. They can apply knowledge of cell structures and their functions. Students show some understanding of diversity, adaptation, and natural selection. They also recognize the interdependence of populations of organisms in an ecosystem. Students demonstrate knowledge of the composition of matter and the periodic table of the elements. Students use physical properties of matter to sort, classify, and compare substances and materials. They also recognize evidence that a chemical reaction has occurred. Students communicate understanding of particle spacing and motion in different physical states. Students apply knowledge of energy transfer and electrical circuits, can relate the properties of light and sound to common phenomena, and demonstrate understanding of forces in everyday contexts. Students communicate understanding of Earth’s structure, physical features, and processes. They demonstrate knowledge of the Earth’s resources and their conservation. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 8th grade students reaching the advanced international benchmark of science achievement (%) is the share of female 8th grade students scoring at least 625 on the science assessment. Students at this benchmark can communicate understanding of concepts related to biology, chemistry, physics, and Earth science in a variety of contexts. Students can classify animals into taxonomic groups. They can apply knowledge of cell structures and their functions. Students show some understanding of diversity, adaptation, and natural selection. They also recognize the interdependence of populations of organisms in an ecosystem. Students demonstrate knowledge of the composition of matter and the periodic table of the elements. Students use physical properties of matter to sort, classify, and compare substances and materials. They also recognize evidence that a chemical reaction has occurred. Students communicate understanding of particle spacing and motion in different physical states. Students apply knowledge of energy transfer and electrical circuits, can relate the properties of light and sound to common phenomena, and demonstrate understanding of forces in everyday contexts. Students communicate understanding of Earth’s structure, physical features, and processes. They demonstrate knowledge of the Earth’s resources and their conservation. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.ADV.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 8th grade students reaching the advanced international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 8th grade students reaching the advanced international benchmark of science achievement (%) is the share of male 8th grade students scoring at least 625 on the science assessment. Students at this benchmark communicate understanding of concepts related to biology, chemistry, physics, and Earth science in a variety of contexts. Students can classify animals into taxonomic groups. They can apply knowledge of cell structures and their functions. Students show some understanding of diversity, adaptation, and natural selection. They also recognize the interdependence of populations of organisms in an ecosystem. Students demonstrate knowledge of the composition of matter and the periodic table of the elements. Students use physical properties of matter to sort, classify, and compare substances and materials. They also recognize evidence that a chemical reaction has occurred. Students communicate understanding of particle spacing and motion in different physical states. Students apply knowledge of energy transfer and electrical circuits, can relate the properties of light and sound to common phenomena, and demonstrate understanding of forces in everyday contexts. Students communicate understanding of Earth’s structure, physical features, and processes. They demonstrate knowledge of the Earth’s resources and their conservation. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 8th grade students reaching the advanced international benchmark of science achievement (%) is the share of male 8th grade students scoring at least 625 on the science assessment. Students at this benchmark communicate understanding of concepts related to biology, chemistry, physics, and Earth science in a variety of contexts. Students can classify animals into taxonomic groups. They can apply knowledge of cell structures and their functions. Students show some understanding of diversity, adaptation, and natural selection. They also recognize the interdependence of populations of organisms in an ecosystem. Students demonstrate knowledge of the composition of matter and the periodic table of the elements. Students use physical properties of matter to sort, classify, and compare substances and materials. They also recognize evidence that a chemical reaction has occurred. Students communicate understanding of particle spacing and motion in different physical states. Students apply knowledge of energy transfer and electrical circuits, can relate the properties of light and sound to common phenomena, and demonstrate understanding of forces in everyday contexts. Students communicate understanding of Earth’s structure, physical features, and processes. They demonstrate knowledge of the Earth’s resources and their conservation. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.BL", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Eighth grade students who did not reach the low international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Eighth grade students who did not reach the low international benchmark of science achievement (%) is the share of 8th grade students scoring below 400 on the science assessment. It is the percentage of students with achievement too low for estimation. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. However, such students were assigned scale scores (plausible values) by the achievement scaling procedure, despite concerns about their reliability. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Othernotes", "value": "Proficiency" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Statisticalconceptandmethodology", "value": "TIMSS" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.BL.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 8th grade students who did not reach the low international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 8th grade students who did not reach the low international benchmark of science achievement (%) is the share of female 8th grade students scoring below 400 on the science assessment. It is the percentage of students with achievement too low for estimation. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. However, such students were assigned scale scores (plausible values) by the achievement scaling procedure, despite concerns about their reliability. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 8th grade students who did not reach the low international benchmark of science achievement (%) is the share of female 8th grade students scoring below 400 on the science assessment. It is the percentage of students with achievement too low for estimation. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. However, such students were assigned scale scores (plausible values) by the achievement scaling procedure, despite concerns about their reliability. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.BL.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 8th grade students who did not reach the low international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 8th grade students who did not reach the low international benchmark of science achievement (%) is the share of male 8th grade students scoring below 400 on the science assessment. It is the percentage of students with achievement too low for estimation. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. However, such students were assigned scale scores (plausible values) by the achievement scaling procedure, despite concerns about their reliability. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 8th grade students who did not reach the low international benchmark of science achievement (%) is the share of male 8th grade students scoring below 400 on the science assessment. It is the percentage of students with achievement too low for estimation. Students were considered to have achievement too low for estimation if their performance on the assessment was no better than could be achieved by simply guessing on the multiple-choice assessment items. However, such students were assigned scale scores (plausible values) by the achievement scaling procedure, despite concerns about their reliability. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.FE", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Mean performance on the science scale for eighth grade students, female" }, { "id": "Longdefinition", "value": "TIMSS: Mean performance on the science scale for eighth grade students, female is the average scale score for female 8th graders on the science assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Mean performance on the science scale for eighth grade students, female is the average scale score for female 8th graders on the science assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.HI", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Eighth grade students reaching the high international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Eighth grade students reaching the high international benchmark of science achievement (%) is the share of 8th grade students scoring at least 550 on the science assessment. Students at this benchmark can apply understanding of concepts from biology, chemistry, physics, and Earth science. Students can apply knowledge of the characteristics of groups of animals, life processes in humans, cells and their functions, genetic inheritance, ecosystems, and nutrition. Students show some knowledge and understanding of the composition and properties of matter and chemical reactions. They can apply basic knowledge of energy transformation and transfer, electrical circuits, properties of magnets, light, sound, and forces. They can apply knowledge of Earth’s physical features, processes, cycles, and history, and show some understanding of Earth’s resources and their use. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Eighth grade students reaching the high international benchmark of science achievement (%) is the share of 8th grade students scoring at least 550 on the science assessment. Students at this benchmark can apply understanding of concepts from biology, chemistry, physics, and Earth science. Students can apply knowledge of the characteristics of groups of animals, life processes in humans, cells and their functions, genetic inheritance, ecosystems, and nutrition. Students show some knowledge and understanding of the composition and properties of matter and chemical reactions. They can apply basic knowledge of energy transformation and transfer, electrical circuits, properties of magnets, light, sound, and forces. They can apply knowledge of Earth’s physical features, processes, cycles, and history, and show some understanding of Earth’s resources and their use. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.HI.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 8th grade students reaching the high international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 8th grade students reaching the high international benchmark of science achievement (%) is the share of female 8th grade students scoring at least 550 on the science assessment. Students at this benchmark can apply understanding of concepts from biology, chemistry, physics, and Earth science. Students can apply knowledge of the characteristics of groups of animals, life processes in humans, cells and their functions, genetic inheritance, ecosystems, and nutrition. Students show some knowledge and understanding of the composition and properties of matter and chemical reactions. They can apply basic knowledge of energy transformation and transfer, electrical circuits, properties of magnets, light, sound, and forces. They can apply knowledge of Earth’s physical features, processes, cycles, and history, and show some understanding of Earth’s resources and their use. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 8th grade students reaching the high international benchmark of science achievement (%) is the share of female 8th grade students scoring at least 550 on the science assessment. Students at this benchmark can apply understanding of concepts from biology, chemistry, physics, and Earth science. Students can apply knowledge of the characteristics of groups of animals, life processes in humans, cells and their functions, genetic inheritance, ecosystems, and nutrition. Students show some knowledge and understanding of the composition and properties of matter and chemical reactions. They can apply basic knowledge of energy transformation and transfer, electrical circuits, properties of magnets, light, sound, and forces. They can apply knowledge of Earth’s physical features, processes, cycles, and history, and show some understanding of Earth’s resources and their use. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.HI.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 8th grade students reaching the high international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 8th grade students reaching the high international benchmark of science achievement (%) is the share of male 8th grade students scoring at least 550 on the science assessment. Students at this benchmark can apply understanding of concepts from biology, chemistry, physics, and Earth science. Students can apply knowledge of the characteristics of groups of animals, life processes in humans, cells and their functions, genetic inheritance, ecosystems, and nutrition. Students show some knowledge and understanding of the composition and properties of matter and chemical reactions. They can apply basic knowledge of energy transformation and transfer, electrical circuits, properties of magnets, light, sound, and forces. They can apply knowledge of Earth’s physical features, processes, cycles, and history, and show some understanding of Earth’s resources and their use. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 8th grade students reaching the high international benchmark of science achievement (%) is the share of male 8th grade students scoring at least 550 on the science assessment. Students at this benchmark can apply understanding of concepts from biology, chemistry, physics, and Earth science. Students can apply knowledge of the characteristics of groups of animals, life processes in humans, cells and their functions, genetic inheritance, ecosystems, and nutrition. Students show some knowledge and understanding of the composition and properties of matter and chemical reactions. They can apply basic knowledge of energy transformation and transfer, electrical circuits, properties of magnets, light, sound, and forces. They can apply knowledge of Earth’s physical features, processes, cycles, and history, and show some understanding of Earth’s resources and their use. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.INT", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Eighth grade students reaching the intermediate international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Eighth grade students reaching the intermediate international benchmark of science achievement (%) is the share of 8th grade students scoring at least 475 on the science assessment. Students at this benchmark can show and apply some knowledge of biology and the physical sciences. Students demonstrate some knowledge of characteristics of animals and apply knowledge of ecosystems. They show some knowledge of the properties of matter, chemical changes, and a few physics concepts. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Eighth grade students reaching the intermediate international benchmark of science achievement (%) is the share of 8th grade students scoring at least 475 on the science assessment. Students at this benchmark can show and apply some knowledge of biology and the physical sciences. Students demonstrate some knowledge of characteristics of animals and apply knowledge of ecosystems. They show some knowledge of the properties of matter, chemical changes, and a few physics concepts. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.INT.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 8th grade students reaching the intermediate international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 8th grade students reaching the intermediate international benchmark of science achievement (%) is the share of female 8th grade students scoring at least 475 on the science assessment. Students at this benchmark can show and apply some knowledge of biology and the physical sciences. Students demonstrate some knowledge of characteristics of animals and apply knowledge of ecosystems. They show some knowledge of the properties of matter, chemical changes, and a few physics concepts. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 8th grade students reaching the intermediate international benchmark of science achievement (%) is the share of female 8th grade students scoring at least 475 on the science assessment. Students at this benchmark can show and apply some knowledge of biology and the physical sciences. Students demonstrate some knowledge of characteristics of animals and apply knowledge of ecosystems. They show some knowledge of the properties of matter, chemical changes, and a few physics concepts. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.INT.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 8th grade students reaching the intermediate international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 8th grade students reaching the intermediate international benchmark of science achievement (%) is the share of male 8th grade students scoring at least 475 on the science assessment. Students at this benchmark can show and apply some knowledge of biology and the physical sciences. Students demonstrate some knowledge of characteristics of animals and apply knowledge of ecosystems. They show some knowledge of the properties of matter, chemical changes, and a few physics concepts. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 8th grade students reaching the intermediate international benchmark of science achievement (%) is the share of male 8th grade students scoring at least 475 on the science assessment. Students at this benchmark can show and apply some knowledge of biology and the physical sciences. Students demonstrate some knowledge of characteristics of animals and apply knowledge of ecosystems. They show some knowledge of the properties of matter, chemical changes, and a few physics concepts. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.LOW", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Eighth grade students reaching the low international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Eighth grade students reaching the low international benchmark of science achievement (%) is the share of 8th grade students scoring at least 400 on the science assessment. Students at this benchmark show limited understanding of scientific principles and concepts and limited knowledge of science facts. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Eighth grade students reaching the low international benchmark of science achievement (%) is the share of 8th grade students scoring at least 400 on the science assessment. Students at this benchmark show limited understanding of scientific principles and concepts and limited knowledge of science facts. Data reflect country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.LOW.FE", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Female 8th grade students reaching the low international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Female 8th grade students reaching the low international benchmark of science achievement (%) is the share of female 8th grade students scoring at least 400 on the science assessment. Students at this benchmark show limited understanding of scientific principles and concepts and limited knowledge of science facts. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Female 8th grade students reaching the low international benchmark of science achievement (%) is the share of female 8th grade students scoring at least 400 on the science assessment. Students at this benchmark show limited understanding of scientific principles and concepts and limited knowledge of science facts. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.LOW.MA", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Male 8th grade students reaching the low international benchmark of science achievement (%)" }, { "id": "Longdefinition", "value": "TIMSS: Male 8th grade students reaching the low international benchmark of science achievement (%) is the share of male 8th grade students scoring at least 400 on the science assessment. Students at this benchmark show limited understanding of scientific principles and concepts and limited knowledge of science facts. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Male 8th grade students reaching the low international benchmark of science achievement (%) is the share of male 8th grade students scoring at least 400 on the science assessment. Students at this benchmark show limited understanding of scientific principles and concepts and limited knowledge of science facts. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.MA", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Mean performance on the science scale for eighth grade students, male" }, { "id": "Longdefinition", "value": "TIMSS: Mean performance on the science scale for eighth grade students, male is the average scale score for male 8th graders on the science assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "TIMSS: Mean performance on the science scale for eighth grade students, male is the average scale score for male 8th graders on the science assessment. The scale centerpoint is 500. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.P05", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 8th Grade Science Scores: 5th Percentile Score" }, { "id": "Longdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 5th percentile score is the score below which 5 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.P10", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 8th Grade Science Scores: 10th Percentile Score" }, { "id": "Longdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 10th percentile score is the score below which 10 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.P25", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 8th Grade Science Scores: 25th Percentile Score" }, { "id": "Longdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 25th percentile score is the score below which 25 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.P50", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 8th Grade Science Scores: 50th Percentile Score" }, { "id": "Longdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 50th percentile score is the score below which 50 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.P75", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 8th Grade Science Scores: 75th Percentile Score" }, { "id": "Longdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 75th percentile score is the score below which 75 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.P90", "metatype": [ { "id": "IndicatorName", "value": "TIMSS: Distribution of 8th Grade Science Scores: 90th Percentile Score" }, { "id": "Longdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 90th percentile score is the score below which 90 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "LO.TIMSS.SCI8.P95", "metatype": [ { "id": "Dataset", "value": "TIMSS" }, { "id": "IndicatorName", "value": "TIMSS: Distribution of 8th Grade Science Scores: 95th Percentile Score" }, { "id": "Longdefinition", "value": "The 95th percentile score is the score below which 95 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Shortdefinition", "value": "The 95th percentile score is the score below which 95 percent of students scored. Data reflects country performance in the stated year according to TIMSS reports, but may not be comparable across years or countries. Consult the TIMSS website for more detailed information: http://timss.bc.edu/" }, { "id": "Source", "value": "International Association for the Evaluation of Educational Achievement (IEA)'s Trends in International Mathematics and Science Study" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "12" }, { "id": "NY.GDP.MKTP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "IndicatorName", "value": "GDP (current US$)" }, { "id": "Limitationsandexceptions", "value": "Gross domestic product (GDP), though widely tracked, may not always be the most relevant summary of aggregated economic performance for all economies, especially when production occurs at the expense of consuming capital stock.\n\nWhile GDP estimates based on the production approach are generally more reliable than estimates compiled from the income or expenditure side, different countries use different definitions, methods, and reporting standards. World Bank staff review the quality of national accounts data and sometimes make adjustments to improve consistency with international guidelines. Nevertheless, significant discrepancies remain between international standards and actual practice. Many statistical offices, especially those in developing countries, face severe limitations in the resources, time, training, and budgets required to produce reliable and comprehensive series of national accounts statistics.\n\nAmong the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money." }, { "id": "Longdefinition", "value": "GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars. Dollar figures for GDP are converted from domestic currencies using single year official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" } ], "source_id": "12" }, { "id": "NY.GDP.MKTP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "BasePeriod", "value": "2010" }, { "id": "IndicatorName", "value": "GDP (constant 2010 US$)" }, { "id": "Limitationsandexceptions", "value": "Each industry's contribution to growth in the economy's output is measured by growth in the industry's value added. In principle, value added in constant prices can be estimated by measuring the quantity of goods and services produced in a period, valuing them at an agreed set of base year prices, and subtracting the cost of intermediate inputs, also in constant prices. This double-deflation method requires detailed information on the structure of prices of inputs and outputs.\n\nIn many industries, however, value added is extrapolated from the base year using single volume indexes of outputs or, less commonly, inputs. Particularly in the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult.\n\nMoreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in quality lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of inflation.\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\nRebasing of national accounts can alter the measured growth rate of an economy and lead to breaks in series that affect the consistency of data over time. When countries rebase their national accounts, they update the weights assigned to various components to better reflect current patterns of production or uses of output. The new base year should represent normal operation of the economy - it should be a year without major shocks or distortions. Some developing countries have not rebased their national accounts for many years. Using an old base year can be misleading because implicit price and volume weights become progressively less relevant and useful.\n\nTo obtain comparable series of constant price data for computing aggregates, the World Bank rescales GDP and value added by industrial origin to a common reference year. Because rescaling changes the implicit weights used in forming regional and income group aggregates, aggregate growth rates are not comparable with those from earlier editions with different base years. Rescaling may result in a discrepancy between the rescaled GDP and the sum of the rescaled components. To avoid distortions in the growth rates, the discrepancy is left unallocated. As a result, the weighted average of the growth rates of the components generally does not equal the GDP growth rate." }, { "id": "Longdefinition", "value": "GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2010 U.S. dollars. Dollar figures for GDP are converted from domestic currencies using 2010 official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2010 prices: Aggregate indicators" } ], "source_id": "12" }, { "id": "NY.GDP.MKTP.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "IndicatorName", "value": "GDP, PPP (current international $)" }, { "id": "Longdefinition", "value": "PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current international dollars. For most economies PPP figures are extrapolated from the 2011 International Comparison Program (ICP) benchmark estimates or imputed using a statistical model based on the 2011 ICP. For 47 high- and upper middle-income economies conversion factors are provided by Eurostat and the Organisation for Economic Co-operation and Development (OECD)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, International Comparison Program database." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "12" }, { "id": "NY.GDP.MKTP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "BasePeriod", "value": "2011" }, { "id": "IndicatorName", "value": "GDP, PPP (constant 2011 international $)" }, { "id": "Longdefinition", "value": "PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2011 international dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, International Comparison Program database." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "12" }, { "id": "NY.GDP.PCAP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "GDP per capita (current US$)" }, { "id": "Longdefinition", "value": "GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" } ], "source_id": "12" }, { "id": "NY.GDP.PCAP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "BasePeriod", "value": "2010" }, { "id": "IndicatorName", "value": "GDP per capita (constant 2010 US$)" }, { "id": "Longdefinition", "value": "GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2010 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2010 prices: Aggregate indicators" } ], "source_id": "12" }, { "id": "NY.GDP.PCAP.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "GDP per capita, PPP (current international $)" }, { "id": "Longdefinition", "value": "GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current international dollars based on the 2011 ICP round." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, International Comparison Program database." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "12" }, { "id": "NY.GDP.PCAP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "BasePeriod", "value": "2011" }, { "id": "IndicatorName", "value": "GDP per capita, PPP (constant 2011 international $)" }, { "id": "Longdefinition", "value": "GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2011 international dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, International Comparison Program database." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "12" }, { "id": "NY.GNP.MKTP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "IndicatorName", "value": "GNI (current US$)" }, { "id": "Longdefinition", "value": "GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" } ], "source_id": "12" }, { "id": "NY.GNP.MKTP.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "IndicatorName", "value": "GNI, PPP (current international $)" }, { "id": "Longdefinition", "value": "PPP GNI (formerly PPP GNP) is gross national income (GNI) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNI as a U.S. dollar has in the United States. Gross national income is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current international dollars. For most economies PPP figures are extrapolated from the 2011 International Comparison Program (ICP) benchmark estimates or imputed using a statistical model based on the 2011 ICP. For 47 high- and upper middle-income economies conversion factors are provided by Eurostat and the Organisation for Economic Co-operation and Development (OECD)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, International Comparison Program database." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "12" }, { "id": "NY.GNP.PCAP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "GNI per capita, Atlas method (current US$)" }, { "id": "Longdefinition", "value": "GNI per capita (formerly GNP per capita) is the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Atlas GNI & GNI per capita" } ], "source_id": "12" }, { "id": "NY.GNP.PCAP.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "GNI per capita, PPP (current international $)" }, { "id": "Longdefinition", "value": "GNI per capita based on purchasing power parity (PPP). PPP GNI is gross national income (GNI) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNI as a U.S. dollar has in the United States. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current international dollars based on the 2011 ICP round." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, International Comparison Program database." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "12" }, { "id": "OECD.TSAL.0.E0", "metatype": [ { "id": "IndicatorName", "value": "Annual statutory teacher salaries in public institutions in USD. Pre-Primary. Starting salary" }, { "id": "Longdefinition", "value": "Starting salaries refer to the average scheduled gross salary per year for a full-time teacher with the minimum training necessary to be fully qualified at the beginning of the teaching career. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Shortdefinition", "value": "Starting salaries refer to the average scheduled gross salary per year for a full-time teacher with the minimum training necessary to be fully qualified at the beginning of the teaching career. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "OECD.TSAL.0.E10", "metatype": [ { "id": "IndicatorName", "value": "Annual statutory teacher salaries in public institutions in USD. Pre-Primary. 10 years of experience" }, { "id": "Longdefinition", "value": "Salaries after 10 years of experience refer to the scheduled annual salary of a full-time classroom teacher with the minimum training necessary to be fully qualified plus 10 years of experience. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Shortdefinition", "value": "Salaries after 10 years of experience refer to the scheduled annual salary of a full-time classroom teacher with the minimum training necessary to be fully qualified plus 10 years of experience. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "OECD.TSAL.0.E15", "metatype": [ { "id": "IndicatorName", "value": "Annual statutory teacher salaries in public institutions in USD. Pre-Primary. 15 years of experience" }, { "id": "Longdefinition", "value": "Salaries after 15 years of experience refer to the scheduled annual salary of a full-time classroom teacher with the minimum training necessary to be fully qualified plus 15 years of experience. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Shortdefinition", "value": "Salaries after 15 years of experience refer to the scheduled annual salary of a full-time classroom teacher with the minimum training necessary to be fully qualified plus 15 years of experience. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "OECD.TSAL.0.ETOP", "metatype": [ { "id": "IndicatorName", "value": "Annual statutory teacher salaries in public institutions in USD. Pre-Primary. Top of scale" }, { "id": "Longdefinition", "value": "Top of scale salaries reported refer to the scheduled maximum annual salary of a full-time classroom teacher with the minimum training to be fully qualified for the job. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Shortdefinition", "value": "Top of scale salaries reported refer to the scheduled maximum annual salary of a full-time classroom teacher with the minimum training to be fully qualified for the job. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "OECD.TSAL.1.E0", "metatype": [ { "id": "IndicatorName", "value": "Annual statutory teacher salaries in public institutions in USD. Primary. Starting salary" }, { "id": "Longdefinition", "value": "Starting salaries refer to the average scheduled gross salary per year for a full-time teacher with the minimum training necessary to be fully qualified at the beginning of the teaching career. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Shortdefinition", "value": "Starting salaries refer to the average scheduled gross salary per year for a full-time teacher with the minimum training necessary to be fully qualified at the beginning of the teaching career. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "OECD.TSAL.1.E10", "metatype": [ { "id": "IndicatorName", "value": "Annual statutory teacher salaries in public institutions in USD. Primary. 10 years of experience" }, { "id": "Longdefinition", "value": "Salaries after 10 years of experience refer to the scheduled annual salary of a full-time classroom teacher with the minimum training necessary to be fully qualified plus 10 years of experience. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Shortdefinition", "value": "Salaries after 10 years of experience refer to the scheduled annual salary of a full-time classroom teacher with the minimum training necessary to be fully qualified plus 10 years of experience. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "OECD.TSAL.1.E15", "metatype": [ { "id": "IndicatorName", "value": "Annual statutory teacher salaries in public institutions in USD. Primary. 15 years of experience" }, { "id": "Longdefinition", "value": "Salaries after 15 years of experience refer to the scheduled annual salary of a full-time classroom teacher with the minimum training necessary to be fully qualified plus 15 years of experience. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Shortdefinition", "value": "Salaries after 15 years of experience refer to the scheduled annual salary of a full-time classroom teacher with the minimum training necessary to be fully qualified plus 15 years of experience. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "OECD.TSAL.1.ETOP", "metatype": [ { "id": "IndicatorName", "value": "Annual statutory teacher salaries in public institutions in USD. Primary. Top of scale" }, { "id": "Longdefinition", "value": "Top of scale salaries reported refer to the scheduled maximum annual salary of a full-time classroom teacher with the minimum training to be fully qualified for the job. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Shortdefinition", "value": "Top of scale salaries reported refer to the scheduled maximum annual salary of a full-time classroom teacher with the minimum training to be fully qualified for the job. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "OECD.TSAL.2.E0", "metatype": [ { "id": "IndicatorName", "value": "Annual statutory teacher salaries in public institutions in USD. Lower Secondary. Starting salary" }, { "id": "Longdefinition", "value": "Starting salaries refer to the average scheduled gross salary per year for a full-time teacher with the minimum training necessary to be fully qualified at the beginning of the teaching career. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Shortdefinition", "value": "Starting salaries refer to the average scheduled gross salary per year for a full-time teacher with the minimum training necessary to be fully qualified at the beginning of the teaching career. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "OECD.TSAL.2.E10", "metatype": [ { "id": "IndicatorName", "value": "Annual statutory teacher salaries in public institutions in USD. Lower Secondary. 10 years of experience" }, { "id": "Longdefinition", "value": "Salaries after 10 years of experience refer to the scheduled annual salary of a full-time classroom teacher with the minimum training necessary to be fully qualified plus 10 years of experience. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Shortdefinition", "value": "Salaries after 10 years of experience refer to the scheduled annual salary of a full-time classroom teacher with the minimum training necessary to be fully qualified plus 10 years of experience. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "OECD.TSAL.2.E15", "metatype": [ { "id": "IndicatorName", "value": "Annual statutory teacher salaries in public institutions in USD. Lower Secondary. 15 years of experience" }, { "id": "Longdefinition", "value": "Salaries after 15 years of experience refer to the scheduled annual salary of a full-time classroom teacher with the minimum training necessary to be fully qualified plus 15 years of experience. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Shortdefinition", "value": "Salaries after 15 years of experience refer to the scheduled annual salary of a full-time classroom teacher with the minimum training necessary to be fully qualified plus 15 years of experience. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "OECD.TSAL.2.ETOP", "metatype": [ { "id": "IndicatorName", "value": "Annual statutory teacher salaries in public institutions in USD. Lower Secondary. Top of scale" }, { "id": "Longdefinition", "value": "Top of scale salaries reported refer to the scheduled maximum annual salary of a full-time classroom teacher with the minimum training to be fully qualified for the job. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Shortdefinition", "value": "Top of scale salaries reported refer to the scheduled maximum annual salary of a full-time classroom teacher with the minimum training to be fully qualified for the job. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "OECD.TSAL.3.E0", "metatype": [ { "id": "IndicatorName", "value": "Annual statutory teacher salaries in public institutions in USD. Upper Secondary. Starting salary" }, { "id": "Longdefinition", "value": "Starting salaries refer to the average scheduled gross salary per year for a full-time upper secondary teacher (general programs only) with the minimum training necessary to be fully qualified at the beginning of the teaching career. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Shortdefinition", "value": "Starting salaries refer to the average scheduled gross salary per year for a full-time upper secondary teacher (general programs only) with the minimum training necessary to be fully qualified at the beginning of the teaching career. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "OECD.TSAL.3.E10", "metatype": [ { "id": "IndicatorName", "value": "Annual statutory teacher salaries in public institutions in USD. Upper Secondary. 10 years of experience" }, { "id": "Longdefinition", "value": "Salaries after 10 years of experience refer to the scheduled annual salary of a full-time upper secondary classroom teacher (general programs only) with the minimum training necessary to be fully qualified plus 10 years of experience. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Shortdefinition", "value": "Salaries after 10 years of experience refer to the scheduled annual salary of a full-time upper secondary classroom teacher (general programs only) with the minimum training necessary to be fully qualified plus 10 years of experience. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "OECD.TSAL.3.E15", "metatype": [ { "id": "IndicatorName", "value": "Annual statutory teacher salaries in public institutions in USD. Upper Secondary. 15 years of experience" }, { "id": "Longdefinition", "value": "Salaries after 15 years of experience refer to the scheduled annual salary of a full-time upper secondary classroom teacher (general programs only) with the minimum training necessary to be fully qualified plus 15 years of experience. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Shortdefinition", "value": "Salaries after 15 years of experience refer to the scheduled annual salary of a full-time upper secondary classroom teacher (general programs only) with the minimum training necessary to be fully qualified plus 15 years of experience. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "OECD.TSAL.3.ETOP", "metatype": [ { "id": "IndicatorName", "value": "Annual statutory teacher salaries in public institutions in USD. Upper Secondary. Top of scale" }, { "id": "Longdefinition", "value": "Top of scale salaries reported refer to the scheduled maximum annual salary of a full-time upper secondary classroom teacher (general programs only) with the minimum training to be fully qualified for the job. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Shortdefinition", "value": "Top of scale salaries reported refer to the scheduled maximum annual salary of a full-time upper secondary classroom teacher (general programs only) with the minimum training to be fully qualified for the job. Salaries are in equivalent USD converted using PPPs for private consumption. Statutory salaries refer to scheduled salaries according to official pay scales, while actual salaries refer to the average annual salary earned by a full-time teacher. The salaries reported are gross (total sum paid by the employer) less the employer’s contribution to social security and pension, according to existing salary scales. Salaries are “before tax”, i.e. before deductions for income tax. Teachers’ salaries are one component of teachers’ total compensation. Other benefits, such as regional allowances for teaching in remote areas, family allowances, reduced rates on public transport and tax allowances on the purchase of cultural materials, may also form part of teachers’ total remuneration. There are also large differences in taxation and social-benefits systems in OECD countries. All this should be borne in mind when comparing statutory salaries across countries. Data after 2009 is not comparable to data for 2009 and before due to changes in methodology. For more information, consult the OECD's Education at a Glance website: http://www.oecd.org/edu/eag.htm" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.2.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. Lower Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.2.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. Lower Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.2.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. Lower Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.3.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. Upper Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.3.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. Upper Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.3.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. Upper Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.4.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.4.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.4.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. Post Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.NED.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. No Education. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.NED.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. No Education. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.NED.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. No Education. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.S1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. Incomplete Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.S1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. Incomplete Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.1519.S1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15-19 by highest level of educational attainment. Incomplete Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.2.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. Lower Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.2.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. Lower Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.2.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. Lower Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.3.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. Upper Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.3.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. Upper Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.3.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. Upper Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.4.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.4.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.4.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. Post Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.NED.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. No Education. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.NED.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. No Education. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.NED.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. No Education. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.S1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. Incomplete Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.S1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. Incomplete Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.15UP.S1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 15+ by highest level of educational attainment. Incomplete Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.2.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. Lower Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.2.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. Lower Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.2.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. Lower Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.3.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. Upper Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.3.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. Upper Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.3.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. Upper Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.4.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.4.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.4.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. Post Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.NED.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. No Education. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.NED.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. No Education. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.NED.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. No Education. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.S1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. Incomplete Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.S1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. Incomplete Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2024.S1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-24 by highest level of educational attainment. Incomplete Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.2.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. Lower Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.2.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. Lower Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.2.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. Lower Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.3.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. Upper Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.3.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. Upper Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.3.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. Upper Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.4.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.4.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.4.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. Post Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.NED.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. No Education. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.NED.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. No Education. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.NED.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. No Education. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.S1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. Incomplete Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.S1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. Incomplete Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2039.S1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-39 by highest level of educational attainment. Incomplete Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.2.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. Lower Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.2.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. Lower Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.2.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. Lower Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.3.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. Upper Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.3.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. Upper Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.3.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. Upper Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.4.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.4.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.4.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. Post Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.NED.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. No Education. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.NED.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. No Education. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.NED.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. No Education. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.S1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. Incomplete Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.S1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. Incomplete Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2064.S1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 20-64 by highest level of educational attainment. Incomplete Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.2.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. Lower Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.2.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. Lower Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.2.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. Lower Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.3.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. Upper Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.3.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. Upper Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.3.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. Upper Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.4.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.4.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.4.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. Post Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.NED.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. No Education. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.NED.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. No Education. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.NED.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. No Education. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.S1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. Incomplete Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.S1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. Incomplete Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.2529.S1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25-29 by highest level of educational attainment. Incomplete Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.2.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. Lower Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.2.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. Lower Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.2.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. Lower Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.3.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. Upper Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.3.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. Upper Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.3.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. Upper Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.4.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.4.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.4.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. Post Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.NED.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. No Education. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.NED.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. No Education. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.NED.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. No Education. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.S1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. Incomplete Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.S1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. Incomplete Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.25UP.S1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 25+ by highest level of educational attainment. Incomplete Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.2.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. Lower Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.2.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. Lower Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.2.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. Lower Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.3.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. Upper Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.3.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. Upper Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.3.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. Upper Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.4.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.4.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.4.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. Post Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.NED.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. No Education. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.NED.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. No Education. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.NED.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. No Education. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.S1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. Incomplete Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.S1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. Incomplete Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.4064.S1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 40-64 by highest level of educational attainment. Incomplete Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.2.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. Lower Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.2.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. Lower Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.2.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. Lower Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.3.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. Upper Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.3.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. Upper Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.3.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. Upper Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.4.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.4.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.4.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. Post Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.NED.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. No Education. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.NED.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. No Education. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.NED.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. No Education. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.S1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. Incomplete Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.S1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. Incomplete Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.60UP.S1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 60+ by highest level of educational attainment. Incomplete Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.2.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. Lower Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.2.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. Lower Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.2.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. Lower Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.3.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. Upper Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.3.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. Upper Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.3.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. Upper Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.4.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.4.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.4.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. Post Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.NED.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. No Education. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.NED.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. No Education. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.NED.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. No Education. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.S1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. Incomplete Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.S1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. Incomplete Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.80UP.S1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the population age 80+ by highest level of educational attainment. Incomplete Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.2.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. Lower Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.2.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. Lower Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.2.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. Lower Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.3.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. Upper Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.3.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. Upper Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.3.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. Upper Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.4.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.4.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.4.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. Post Secondary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.NED.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. No Education. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.NED.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. No Education. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.NED.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. No Education. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.S1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. Incomplete Primary. Female" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.S1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. Incomplete Primary. Male" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.ATT.ALL.S1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Percentage of the total population by highest level of educational attainment. Incomplete Primary. Total" }, { "id": "Longdefinition", "value": "Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.0T19.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 0-19. Female" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.0T19.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 0-19. Male" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.0T19.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 0-19. Total" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.1519.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 15-19. Female" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.1519.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 15-19. Male" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.1519.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 15-19. Total" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.15UP.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 15+. Female" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.15UP.GPI", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean Years of Schooling. Age 15+. Gender Gap" }, { "id": "Longdefinition", "value": "The difference between male and female mean number of years spent in school by age group. It is calculated by subtracting the female value from the male value. Data can be negative if the mean years of schooling for females is higher than for males. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.15UP.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 15+. Male" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.15UP.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 15+. Total" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.2024.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 20-24. Female" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.2024.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 20-24. Male" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.2024.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 20-24. Total" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.2039.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 20-39. Female" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.2039.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 20-39. Male" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.2039.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 20-39. Total" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.2064.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 20-64. Female" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.2064.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 20-64. Male" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.2064.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 20-64. Total" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.2529.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 25-29. Female" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.2529.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 25-29. Male" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.2529.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 25-29. Total" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.25UP.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 25+. Female" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.25UP.GPI", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean Years of Schooling. Age 25+. Gender Gap" }, { "id": "Longdefinition", "value": "The difference between male and female mean number of years spent in school by age group. It is calculated by subtracting the female value from the male value. Data can be negative if the mean years of schooling for females is higher than for males. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.25UP.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 25+. Male" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.25UP.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 25+. Total" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.4064.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 40-64. Female" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.4064.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 40-64. Male" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.4064.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 40-64. Total" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.60UP.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 60+. Female" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.60UP.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 60+. Male" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.60UP.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 60+. Total" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.65UP.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 65+. Female" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.65UP.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 65+. Male" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.65UP.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 65+. Total" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.80UP.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 80+. Female" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.80UP.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 80+. Male" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.MYS.80UP.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Mean years of schooling. Age 80+. Total" }, { "id": "Longdefinition", "value": "Mean number of years spent in school by age group and gender. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. Primary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. Primary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. Primary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.2.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. Lower Secondary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.2.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. Lower Secondary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.2.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. Lower Secondary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.3.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. Upper Secondary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.3.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. Upper Secondary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.3.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. Upper Secondary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.4.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.4.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.4.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. Post Secondary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.NED.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. No Education. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.NED.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. No Education. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.NED.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. No Education. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.S1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. Incomplete Primary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.S1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. Incomplete Primary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.1519.S1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 15-19 in thousands by highest level of educational attainment. Incomplete Primary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. Primary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. Primary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. Primary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.2.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. Lower Secondary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.2.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. Lower Secondary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.2.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. Lower Secondary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.3.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. Upper Secondary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.3.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. Upper Secondary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.3.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. Upper Secondary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.4.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.4.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.4.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. Post Secondary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.NED.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. No Education. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.NED.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. No Education. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.NED.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. No Education. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.S1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. Incomplete Primary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.S1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. Incomplete Primary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2024.S1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 20-24 in thousands by highest level of educational attainment. Incomplete Primary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. Primary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. Primary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. Primary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.2.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. Lower Secondary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.2.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. Lower Secondary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.2.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. Lower Secondary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.3.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. Upper Secondary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.3.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. Upper Secondary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.3.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. Upper Secondary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.4.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.4.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.4.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. Post Secondary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.NED.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. No Education. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.NED.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. No Education. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.NED.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. No Education. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.S1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. Incomplete Primary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.S1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. Incomplete Primary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.2529.S1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population age 25-29 in thousands by highest level of educational attainment. Incomplete Primary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. Primary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. Primary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. Primary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.2.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. Lower Secondary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.2.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. Lower Secondary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.2.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. Lower Secondary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.3.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. Upper Secondary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.3.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. Upper Secondary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.3.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. Upper Secondary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.4.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. Post Secondary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.4.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. Post Secondary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.4.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. Post Secondary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.NED.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. No Education. Female" }, { "id": "Longdefinition", "value": "Total population in thousands that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.NED.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. No Education. Male" }, { "id": "Longdefinition", "value": "Total population in thousands that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.NED.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. No Education. Total" }, { "id": "Longdefinition", "value": "Total population in thousands that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.S1.FE", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. Incomplete Primary. Female" }, { "id": "Longdefinition", "value": "Total population in thousands that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.S1.MA", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. Incomplete Primary. Male" }, { "id": "Longdefinition", "value": "Total population in thousands that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "PRJ.POP.ALL.S1.MF", "metatype": [ { "id": "BasePeriod", "value": "Projections (2010 to 2100)" }, { "id": "IndicatorName", "value": "Projection: Population in thousands by highest level of educational attainment. Incomplete Primary. Total" }, { "id": "Longdefinition", "value": "Total population in thousands that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Source", "value": "Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 1: Enabling Environment" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL1.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 1 Lever 1: Legal Framework" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL1.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 1 Lever 2: Organizational Structure" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL1.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 1 Lever 3: Human Resources" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL1.LVL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 1 Lever 4: Infrastructural capacity" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL1.LVL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 1 Lever 5: Budget" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL1.LVL6", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 1 Lever 6: Data-driven Culture" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 2: System Soundness" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL2.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 2 Lever 1: Data Architecture" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL2.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 2 Lever 2: Data Coverage" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL2.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 2 Lever 3: Data Analytics" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL2.LVL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 2 Lever 4: Dynamic System" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL2.LVL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 2 Lever 5: Serviceability" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 3: Quality data" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL3.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 3 Lever 1: Methodological Soundness" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL3.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 3 Lever 2: Accuracy and Reliability" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL3.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 3 Lever 3: Integrity" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL3.LVL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 3 Lever 4: Periodicity and Timeliness" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 4: Utilization in decision making" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL4.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 4 Lever 1: Openness" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL4.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 4 Lever 2: Operational Use" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL4.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 4 Lever 3: Accessibility" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.EMIS.GOAL4.LVL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Education Management Information Systems) Policy Goal 4 Lever 4: Effectiveness in Disseminating Findings" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Education Management Information Systems (SABER)" } ], "source_id": "12" }, { "id": "SABER.ERL.CHLD.GOAL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Early Childhood Development) Policy Goal 1: Establishing an Enabling Environment" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Early Child Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.ERL.CHLD.GOAL1.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Early Childhood Development) Policy Goal 1 Lever 1: Legal Framework" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Early Child Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.ERL.CHLD.GOAL1.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Early Childhood Development) Policy Goal 1 Lever 2: Inter-sectoral Coordination" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Early Child Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.ERL.CHLD.GOAL1.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Early Childhood Development) Policy Goal 1 Lever 3: School Finance" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Early Child Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.ERL.CHLD.GOAL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Early Childhood Development) Policy Goal 2: Scope of Programs" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Early Child Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.ERL.CHLD.GOAL2.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Early Childhood Development) Policy Goal 2 Lever 1: Scope of Programs" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Early Child Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.ERL.CHLD.GOAL2.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Early Childhood Development) Policy Goal 2 Lever 2: Coverage" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Early Child Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.ERL.CHLD.GOAL2.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Early Childhood Development) Policy Goal 2 Lever 3: Equity" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Early Child Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.ERL.CHLD.GOAL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Early Childhood Development) Policy Goal 3: Monitoring and Assuring Quality" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Early Child Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.ERL.CHLD.GOAL3.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Early Childhood Development) Policy Goal 3 Lever 1: Data Availability" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Early Child Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.ERL.CHLD.GOAL3.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Early Childhood Development) Policy Goal 3 Lever 2: Quality Standards" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Early Child Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.ERL.CHLD.GOAL3.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Early Childhood Development) Policy Goal 3 Lever 3: Compliance with Standards" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Early Child Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 5: Encouraging innovation by providers" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL5.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 5 Lever 1: Teacher standards" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL5.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 5 Lever 2: Appointment and deployment of teachers" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL5.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 5 Lever 3: Teacher salaries" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL5.LVL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 5 Lever 4: Teacher dismissal" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL5.LVL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 5 Lever 5: Curriculum delivery" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL5.LVL6", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 5 Lever 6: Classroom resourcing" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL5.LVL7", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 5 Lever 7: Budget autonomy" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL6", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 6: Holding schools accountable" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL6.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 6 Lever 1: Student standards" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL6.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 6 Lever 2: Student assessment" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL6.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 6 Lever 3: Financial reporting" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL6.LVL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 6 Lever 4: Inspection" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL6.LVL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 6 Lever 5: Improvement planning" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL6.LVL6", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 6 Lever 6: Sanctions and rewards" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL7", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 7: Empowering all parents, students, and communities" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL7.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 7 Lever 1: Information" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL7.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 7 Lever 2: Voice" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL7.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 7 Lever 3: Selection" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL7.LVL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 7 Lever 4: Contributions" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL8", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 8: Promoting diversity of supply" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL8.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 8 Lever 1: Ownership" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL8.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 8 Lever 2: Certification standards" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL8.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 8 Lever 3: Market entry information" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL8.LVL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 8 Lever 4: Regulatory fees" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL8.LVL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 8 Lever 5: Funding" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL8.LVL6", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 8 Lever 6: Incentives" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.GRVT.GOAL8.LVL7", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector, Government funded) Policy Goal 8 Lever 7: Planning" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.HLTH.GOAL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Health and School Feeding) Policy Goal 1: Policy Frameworks" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Health and School Feeding (SABER)" } ], "source_id": "12" }, { "id": "SABER.HLTH.GOAL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Health and School Feeding) Policy Goal 2: Financial Capacity" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Health and School Feeding (SABER)" } ], "source_id": "12" }, { "id": "SABER.HLTH.GOAL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Health and School Feeding) Policy Goal 3: Institutional Capacity and Coordination" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Health and School Feeding (SABER)" } ], "source_id": "12" }, { "id": "SABER.HLTH.GOAL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Health and School Feeding) Policy Goal 4: Design and Implementation" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Health and School Feeding (SABER)" } ], "source_id": "12" }, { "id": "SABER.HLTH.GOAL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Health and School Feeding) Policy Goal 5: Community Roles–Reaching Beyond Schools" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Health and School Feeding (SABER)" } ], "source_id": "12" }, { "id": "SABER.HLTH.GOAL6", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Health and School Feeding) Policy Goal 6: Health-related school policies" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Health and School Feeding (SABER)" } ], "source_id": "12" }, { "id": "SABER.HLTH.GOAL7", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Health and School Feeding) Policy Goal 7: Safe, Supportive School Environments" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Health and School Feeding (SABER)" } ], "source_id": "12" }, { "id": "SABER.HLTH.GOAL8", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Health and School Feeding) Policy Goal 8: School-Based Health and Nutrition Services" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Health and School Feeding (SABER)" } ], "source_id": "12" }, { "id": "SABER.HLTH.GOAL9", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Health and School Feeding) Policy Goal 9: Skills-Based Health Education" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Health and School Feeding (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 1: Encouraging innovation by providers" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL1.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 1 Lever 1: Teacher standards" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL1.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 1 Lever 2: Teacher appointment and deployment" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL1.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 1 Lever 3: Teacher salaries" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL1.LVL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 1 Lever 4: Teacher dismissal" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL1.LVL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 1 Lever 5: Curriculum Delivery" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL1.LVL6", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 1 Lever 6: Classroom resourcing" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 2: Holding schools accountable" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL2.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 2 Lever 1: Student standards" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL2.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 2 Lever 2: Student assessment" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL2.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 2 Lever 3: Inspection" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL2.LVL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 2 Lever 4: Improvement planning" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL2.LVL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 2 Lever 5: Sanctions" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 3: Empowering all parents, students, and communities" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL3.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 3 Lever 1: Information" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL3.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 3 Lever 2: Voice" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL3.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 3 Lever 3: Financial Support" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 4: Promoting diversity of supply" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL4.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 4 Lever 1: Tuition fees" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL4.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 4 Lever 2: Ownership" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL4.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 4 Lever 3: Certification standards" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL4.LVL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 4 Lever 4: Market entry" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.PRVT.GOAL4.LVL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Engaging the Private Sector) Policy Goal 4 Lever 5: Regulatory fees" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Engaging the Private Sector (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 1: Level of autonomy in the planning and management of school budget" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL1.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 1 Lever 1: Legal authority over the management of the operational budget" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL1.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 1 Lever 2: Legal authority over the management of the non-teaching staff salaries" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL1.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 1 Lever 3: Legal authority over the management of teacher salaries" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL1.LVL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 1 Lever 4: Legal authority to raise additional funds for the school" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL1.LVL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 1 Lever 5: Collaborative budget planning" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 2: Level of autonomy in personnel management" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL2.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 2 Lever 1: Autonomy in teacher appointment and deployment decisions" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL2.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 2 Lever 2: Autonomy in non-teaching staff appointment and deployment decisions" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL2.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 2 Lever 3: Autonomy in school principal appointment and deployment decisions" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 3: Role of the school council on school governance" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL3.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 3 Lever 1: Participation of the school councils in budget preparation" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL3.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 3 Lever 2: Participation of the school councils in financial oversight" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL3.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 3 Lever 3: Participation of the school councils in personnel management" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL3.LVL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 3 Lever 4: Participation of the school councils in school activities" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL3.LVL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 3 Lever 5: Participation of the school councils in learning inputs" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL3.LVL6", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 3 Lever 6: Transparency in community participation" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 4: School and student assessment" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL4.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 4 Lever 1: Existence and Frequency of school assessments" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL4.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 4 Lever 2: Use of school assessments for making school adjustments" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL4.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 4 Lever 3: Existence and Frequency of standardized student assessments" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL4.LVL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 4 Lever 4: Use of standardized student assessments for pedagogical, operational, and personnel adjustments" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL4.LVL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 4 Lever 5: Publication of student assessments" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 5: School Accountability" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL5.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 5 Lever 1: Guidelines for the use of results of student assessments" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL5.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 5 Lever 2: Analysis of school and student performance" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL5.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 5 Lever 3: Degree of financial accountability at the central, regional, municipal, local, and school level" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL5.LVL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 5 Lever 4: Degree of accountability in school operations" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.ATNM.GOAL5.LVL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Autonomy and Accountability) Policy Goal 5 Lever 5: Degree of learning accountability" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Autonomy and Accountability (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 1: Ensuring basic conditions for learning" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL1.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 1 Lever 1: Are there policies and systems set up to provide basic educational inputs to all?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL1.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 1 Lever 2: Are there basic educational inputs for all primary school students?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 2: Monitoring learning conditions and outcomes" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL2.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 2 Lever 1: Does the government provide more resources to students from disadvantaged backgrounds?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL2.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 2 Lever 2: Do payments for schooling represent a high share of income for low income households?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 3: Overseeing service delivery" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL3.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 3 Lever 1: Are resources allocated and disbursed in a manner that is transparent and effective?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL3.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 3 Lever 2: Do monitoring and auditing processes encourage accountability in the use of funding?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 4: Budgeting with adequate and transparent information" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL4.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 4 Lever 1: Is there an informed budget process?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL4.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 4 Lever 2: Is the budget comprehensive and transparent?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 5: Providing more resources to students who need them" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL5.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 5 Lever 1: Are more public resources available to students from disadvantaged backgrounds?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL5.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 5 Lever 2: Do payments for schooling represent a small share of income for low income families?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL6", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 6: Managing resources efficiently" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL6.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 6 Lever 1: Are there systems in place to verify the use of educational resources?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.SCH.FNNC.GOAL6.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (School Finance) Policy Goal 6 Lever 2: Are education expenditures audited?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "School Finance (SABER)" } ], "source_id": "12" }, { "id": "SABER.STD.ASS.GOAL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Student Assessment) Policy Goal 1: Classroom Assessment" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Student Assessment (SABER)" } ], "source_id": "12" }, { "id": "SABER.STD.ASS.GOAL1.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Student Assessment) Policy Goal 1 Lever 1: Enabling Context and System Alignment" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Student Assessment (SABER)" } ], "source_id": "12" }, { "id": "SABER.STD.ASS.GOAL1.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Student Assessment) Policy Goal 1 Lever 2: Assessment Quality" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Student Assessment (SABER)" } ], "source_id": "12" }, { "id": "SABER.STD.ASS.GOAL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Student Assessment) Policy Goal 2: Examinations" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Student Assessment (SABER)" } ], "source_id": "12" }, { "id": "SABER.STD.ASS.GOAL2.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Student Assessment) Policy Goal 2 Lever 1: Enabling Context" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Student Assessment (SABER)" } ], "source_id": "12" }, { "id": "SABER.STD.ASS.GOAL2.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Student Assessment) Policy Goal 2 Lever 2: System Alignment" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Student Assessment (SABER)" } ], "source_id": "12" }, { "id": "SABER.STD.ASS.GOAL2.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Student Assessment) Policy Goal 2 Lever 3: Assessment Quality" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Student Assessment (SABER)" } ], "source_id": "12" }, { "id": "SABER.STD.ASS.GOAL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Student Assessment) Policy Goal 3: National Large-Scale Assessment (NLSA)" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Student Assessment (SABER)" } ], "source_id": "12" }, { "id": "SABER.STD.ASS.GOAL3.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Student Assessment) Policy Goal 3 Lever 1: Enabling Context" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Student Assessment (SABER)" } ], "source_id": "12" }, { "id": "SABER.STD.ASS.GOAL3.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Student Assessment) Policy Goal 3 Lever 2: System Alignment" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Student Assessment (SABER)" } ], "source_id": "12" }, { "id": "SABER.STD.ASS.GOAL3.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Student Assessment) Policy Goal 3 Lever 3: Assessment Quality" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Student Assessment (SABER)" } ], "source_id": "12" }, { "id": "SABER.STD.ASS.GOAL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Student Assessment) Policy Goal 4: International Large-Scale Assessment (ILSA)" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Student Assessment (SABER)" } ], "source_id": "12" }, { "id": "SABER.STD.ASS.GOAL4.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Student Assessment) Policy Goal 4 Lever 1: Enabling Context" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Student Assessment (SABER)" } ], "source_id": "12" }, { "id": "SABER.STD.ASS.GOAL4.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Student Assessment) Policy Goal 4 Lever 2: System Alignment" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Student Assessment (SABER)" } ], "source_id": "12" }, { "id": "SABER.STD.ASS.GOAL4.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Student Assessment) Policy Goal 4 Lever 3: Assessment Quality" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Student Assessment (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 1: Setting clear expectations for teachers" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL1.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 1 Lever 1: Are there clear expectations for teachers?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL1.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 1 Lever 2: Is there useful guidance on the use of teachers' working time?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 2: Attracting the best into teaching" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL2.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 2 Lever 1: Are entry requirements set up to attract talented candidates?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL2.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 2 Lever 2: Is teacher pay appealing for talented candidates?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL2.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 2 Lever 3: Are working conditions appealing for talented applicants?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL2.LVL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 2 Lever 4: Are there attractive career opportunities?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 3: Preparing teachers with useful training and experience" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL3.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 3 Lever 1: Are there minimum standards for pre-service teaching education programs?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL3.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 3 Lever 2: To what extent are teacher-entrants required to be familiar with classroom practice?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 4: Matching teachers' skills with students' needs" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL4.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 4 Lever 1: Are there incentives for teachers to work at hard-to-staff schools?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL4.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 4 Lever 2: Are there incentives for teachers to teach critical shortage subjects?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 5: Leading teachers with strong principals" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL5.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 5 Lever 1: Does the education system invest in developing qualified school leaders?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL5.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 5 Lever 2: Are principals expected to support and improve instructional practice?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL6", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 6: Monitoring teaching and learning" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL6.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 6 Lever 1: Are there systems in place to assess student learning in order to inform teaching and policy?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL6.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 6 Lever 2: Are there systems in place to monitor teacher performance?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL6.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 6 Lever 3: Are there multiple mechanisms to evaluate teacher performance?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL7", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 7: Supporting teachers to improve instruction" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL7.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 7 Lever 1: Are there opportunities for professional development?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL7.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 7 Lever 2: Is teacher professional development collaborative and focused on instructional improvement?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL7.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 7 Lever 3: Is teacher professional development assigned based on perceived needs?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL8", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 8: Motivating teachers to perform" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL8.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 8 Lever 1: Are career opportunities linked to performance?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL8.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 8 Lever 2: Are there mechanisms to hold teachers accountable?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TECH.GOAL8.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Teachers) Policy Goal 8 Lever 3: Is teacher compensation linked to performance?" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Teachers (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 1: Vision for Tertiary Education" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL1.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 1 Lever 1: Clear vision" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 2: Regulatory Framework for Tertiary Education" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL2.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 2 Lever 1: Steering the system" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 3: Governance" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL3.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 3 Lever 1: Articulation" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL3.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 3 Lever 2: Institutional autonomy" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL4", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 4: Finance" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL4.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 4 Lever 1: Coverage of resource allocation" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL4.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 4 Lever 2: Resource allocation" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL4.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 4 Lever 3: Resource utilization (Equity)" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL5", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 5: Quality Assurance" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL5.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 5 Lever 1: Accreditation and Institutional Quality Standards" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL6", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 6: The Relevance of Tertiary Education for Economic and Social Needs" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL6.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 6 Lever 1: Economic Development" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL6.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 6 Lever 2: Fostering RDI and Innovation" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.TER.GOAL6.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Tertiary Education) Policy Goal 6 Lever 3: Fostering Social and Cultural Development and Environmental Protection and Sustainability" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Tertiary Education (SABER)" } ], "source_id": "12" }, { "id": "SABER.WORK.GOAL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Workforce Development) Policy Goal 1: Strategic Framework" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Workforce Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.WORK.GOAL1.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Workforce Development) Policy Goal 1 Lever 1: Setting a Strategic Direction" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Workforce Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.WORK.GOAL1.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Workforce Development) Policy Goal 1 Lever 2: Fostering a Demand-Driven Approach" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Workforce Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.WORK.GOAL1.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Workforce Development) Policy Goal 1 Lever 3: Strengthening Critical Coordination" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Workforce Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.WORK.GOAL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Workforce Development) Policy Goal 2: System Oversight" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Workforce Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.WORK.GOAL2.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Workforce Development) Policy Goal 2 Lever 1: Ensuring Efficiency and Equity in Funding" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Workforce Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.WORK.GOAL2.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Workforce Development) Policy Goal 2 Lever 2: Assuring Relevant and Reliable Standards" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Workforce Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.WORK.GOAL2.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Workforce Development) Policy Goal 2 Lever 3: Diversifying Pathways for Skills Acquisition" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Workforce Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.WORK.GOAL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Workforce Development) Policy Goal 3: Service Delivery" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Workforce Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.WORK.GOAL3.LVL1", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Workforce Development) Policy Goal 3 Lever 1: Enabling Diversity and Excellence in Training Provision" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Workforce Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.WORK.GOAL3.LVL2", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Workforce Development) Policy Goal 3 Lever 2: Fostering Relevance in Public Training Programs" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Workforce Development (SABER)" } ], "source_id": "12" }, { "id": "SABER.WORK.GOAL3.LVL3", "metatype": [ { "id": "IndicatorName", "value": "SABER: (Workforce Development) Policy Goal 3 Lever 3: Enhancing Evidence-based Accountability for Results" }, { "id": "Longdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Relatedsourcelinks", "value": "http://saber.worldbank.org/index.cfm" }, { "id": "Shortdefinition", "value": "Data Interpretation: 1=Latent; 2=Emerging; 3=Established; 4=Advanced. For additional information, visit the SABER: (website: http://saber.worldbank.org/index.cfm" }, { "id": "Source", "value": "Systems Approach for Better Education Results (SABER), World Bank" }, { "id": "Topic", "value": "Workforce Development (SABER)" } ], "source_id": "12" }, { "id": "SE.ADT.1524.LT.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth literacy rate, population 15-24 years, female (%)" }, { "id": "Longdefinition", "value": "Number of females age 15 to 24 years who can both read and write with understanding a short simple statement on their everyday life, divided by the female population in that age group. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. Divide the number of people aged 15 to 24 years who are literate by the total population in the same age group and multiply the result by 100." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "SE.ADT.1524.LT.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth literacy rate, population 15-24 years, male (%)" }, { "id": "Longdefinition", "value": "Number of males age 15 to 24 years who can both read and write with understanding a short simple statement on their everyday life, divided by the male population in that age group. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. Divide the number of people aged 15 to 24 years who are literate by the total population in the same age group and multiply the result by 100." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "SE.ADT.1524.LT.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth literacy rate, population 15-24 years, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of people age 15 to 24 years who can both read and write with understanding a short simple statement on their everyday life, divided by the population in that age group. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. Divide the number of people aged 15 to 24 years who are literate by the total population in the same age group and multiply the result by 100." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "SE.ADT.LITR.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Adult literacy rate, population 15+ years, female (%)" }, { "id": "Longdefinition", "value": "Percentage of females age 15 and above who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of female literates aged 15 years and over by the corresponding age group population and multiplying the result by 100." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "SE.ADT.LITR.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Adult literacy rate, population 15+ years, male (%)" }, { "id": "Longdefinition", "value": "Percentage of males age 15 and above who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of male literates aged 15 years and over by the corresponding age group population and multiplying the result by 100." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "SE.ADT.LITR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Adult literacy rate, population 15+ years, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of the population age 15 and above who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of literates aged 15 years and over by the corresponding age group population and multiplying the result by 100." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "SE.CLO.15Y.LDGAP.M.PS.2000.1", "metatype": [ { "id": "IndicatorName", "value": "Learning Deprivation Gap;PISA 2000 for grade 15Y using MPL Level 2 for math, First Quintile" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Learning Deprivation Gap, that measures the average distance of a learning deprived child to the minimum proficiency level and indicates the average increase in learning required to eliminate learning deprivation." }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.CLO.15Y.LDGAP.M.PS.2000.2", "metatype": [ { "id": "IndicatorName", "value": "Learning Deprivation Gap;PISA 2000 for grade 15Y using MPL Level 2 for math, Second Quintile" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Learning Deprivation Gap, that measures the average distance of a learning deprived child to the minimum proficiency level and indicates the average increase in learning required to eliminate learning deprivation." }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.CLO.15Y.LDGAP.M.PS.2000.3", "metatype": [ { "id": "IndicatorName", "value": "Learning Deprivation Gap;PISA 2000 for grade 15Y using MPL Level 2 for math, Third Quintile" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Learning Deprivation Gap, that measures the average 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For more information please see Azevedo, Joao Pedro, and others. 2019. Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It. World Bank Policy Research Working Paper series. 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(400 points) for science, Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.CLO.9.PRF.S.TMS.2011.R", "metatype": [ { "id": "IndicatorName", "value": "Above Proficiency;TIMSS 2011 for grade 9 using MPL Low (400 points) for science, Rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.CLO.9.PRF.S.TMS.2011.U", "metatype": [ { "id": "IndicatorName", "value": "Above Proficiency;TIMSS 2011 for grade 9 using MPL Low (400 points) for science, Urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.CLO.9.PRF.S.TMS.2015.1", 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"License_Type", "value": "CC BY-4.0" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.CLO.9.PRF.S.TMS.2019.5", "metatype": [ { "id": "IndicatorName", "value": "Above Proficiency;TIMSS 2019 for grade 9 using MPL Low (400 points) for science, Fifth Quintile" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.CLO.9.PRF.S.TMS.2019.A", "metatype": [ { "id": "IndicatorName", "value": "Above Proficiency;TIMSS 2019 for grade 9 using MPL Low (400 points) for science" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.CLO.9.PRF.S.TMS.2019.F", "metatype": [ { "id": "IndicatorName", "value": "Above Proficiency;TIMSS 2019 for grade 9 using MPL Low (400 points) for science, Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.CLO.9.PRF.S.TMS.2019.M", "metatype": [ { "id": "IndicatorName", "value": "Above Proficiency;TIMSS 2019 for grade 9 using MPL Low (400 points) for science, Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.CLO.9.PRF.S.TMS.2019.R", "metatype": [ { "id": "IndicatorName", "value": "Above Proficiency;TIMSS 2019 for grade 9 using MPL Low (400 points) for science, Rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.CLO.9.PRF.S.TMS.2019.U", "metatype": [ { "id": "IndicatorName", "value": "Above Proficiency;TIMSS 2019 for grade 9 using MPL Low (400 points) for science, Urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.COM.DURS", "metatype": [ { "id": "IndicatorName", "value": "Duration of compulsory education (years)" }, { "id": "Longdefinition", "value": "Number of years that children are legally obliged to attend school." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Background" } ], "source_id": "12" }, { "id": "SE.ENR.PRIM.FM.ZS", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, primary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female gross enrolment ratio for primary to male gross enrolment ratio for primary. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.ENR.PRSC.FM.ZS", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, primary and secondary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female gross enrolment ratio for primary and secondary to male gross enrolment ratio for primary and secondary. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.ENR.SECO.FM.ZS", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, secondary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female gross enrolment ratio for secondary to male gross enrolment ratio for secondary. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.GEPD.PRIM", "metatype": [ { "id": "IndicatorName", "value": "Learning Poverty Rate" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.GEPD.PRIM.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of 10 year old children who are out-of-school or in-school and not achieving basic proficiency on reading." }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.GEPD.PRIM.BMP", "metatype": [ { "id": "IndicatorName", "value": "Proficiency by End of Primary" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.GEPD.PRIM.BMP.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy and numeracy by end of primary, as reported by UIS" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.LPV.POP.0516", "metatype": [ { "id": "IndicatorName", "value": "Population Age 0516 for Reference Year 2019" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "World Bank staff estimates using the World Bank's total population and age sex distributions of the United Nations Population Division's as of June, 2022." }, { "id": "Notesfromoriginalsource", "value": "World Bank" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.POP.0516.FE", "metatype": [ { "id": "IndicatorName", "value": "Population Age 0516 for Reference Year 2019" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "World Bank staff estimates using the World Bank's total population and age sex distributions of the United Nations Population Division's as of June, 2022." }, { "id": "Notesfromoriginalsource", "value": "World Bank" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.POP.0516.MA", "metatype": [ { "id": "IndicatorName", "value": "Population Age 0516 for Reference Year 2019" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "World Bank staff estimates using the World Bank's total population and age sex distributions of the United Nations Population Division's as of June, 2022." }, { "id": "Notesfromoriginalsource", "value": "World Bank" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.POP.10", "metatype": [ { "id": "IndicatorName", "value": "Population Age 10 for Reference Year 2019" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "World Bank staff estimates using the World Bank's total population and age sex distributions of the United Nations Population Division's as of June, 2022." }, { "id": "Notesfromoriginalsource", "value": "World Bank" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.POP.10.FE", "metatype": [ { "id": "IndicatorName", "value": "Population Age 10 for Reference Year 2019" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "World Bank staff estimates using the World Bank's total population and age sex distributions of the United Nations Population Division's as of June, 2022." }, { "id": "Notesfromoriginalsource", "value": "World Bank" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.POP.10.MA", "metatype": [ { "id": "IndicatorName", "value": "Population Age 10 for Reference Year 2019" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "World Bank staff estimates using the World Bank's total population and age sex distributions of the United Nations Population Division's as of June, 2022." }, { "id": "Notesfromoriginalsource", "value": "World Bank" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.POP.1014", "metatype": [ { "id": "IndicatorName", "value": "Population Age 1014 for Reference Year 2019" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "World Bank staff estimates using the World Bank's total population and age sex distributions of the United Nations Population Division's as of June, 2022." }, { "id": "Notesfromoriginalsource", "value": "World Bank" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.POP.1014.FE", "metatype": [ { "id": "IndicatorName", "value": "Population Age 1014 for Reference Year 2019" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "World Bank staff estimates using the World Bank's total population and age sex distributions of the United Nations Population Division's as of June, 2022." }, { "id": "Notesfromoriginalsource", "value": "World Bank" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.POP.1014.MA", "metatype": [ { "id": "IndicatorName", "value": "Population Age 1014 for Reference Year 2019" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "World Bank staff estimates using the World Bank's total population and age sex distributions of the United Nations Population Division's as of June, 2022." }, { "id": "Notesfromoriginalsource", "value": "World Bank" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.POP.9PLUS", "metatype": [ { "id": "IndicatorName", "value": "Population Age 9plus for Reference Year 2019" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "World Bank staff estimates using the World Bank's total population and age sex distributions of the United Nations Population Division's as of June, 2022." }, { "id": "Notesfromoriginalsource", "value": "World Bank" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.POP.9PLUS.FE", "metatype": [ { "id": "IndicatorName", "value": "Population Age 9plus for Reference Year 2019" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "World Bank staff estimates using the World Bank's total population and age sex distributions of the United Nations Population Division's as of June, 2022." }, { "id": "Notesfromoriginalsource", "value": "World Bank" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.POP.9PLUS.MA", "metatype": [ { "id": "IndicatorName", "value": "Population Age 9plus for Reference Year 2019" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "World Bank staff estimates using the World Bank's total population and age sex distributions of the United Nations Population Division's as of June, 2022." }, { "id": "Notesfromoriginalsource", "value": "World Bank" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.POP.PRIM", "metatype": [ { "id": "IndicatorName", "value": "Population Age primary for Reference Year 2019" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "World Bank staff estimates using the World Bank's total population and age sex distributions of the United Nations Population Division's as of June, 2022." }, { "id": "Notesfromoriginalsource", "value": "World Bank" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.POP.PRIM.FE", "metatype": [ { "id": "IndicatorName", "value": "Population Age primary for Reference Year 2019" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "World Bank staff estimates using the World Bank's total population and age sex distributions of the United Nations Population Division's as of June, 2022." }, { "id": "Notesfromoriginalsource", "value": "World Bank" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.POP.PRIM.MA", "metatype": [ { "id": "IndicatorName", "value": "Population Age primary for Reference Year 2019" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "World Bank staff estimates using the World Bank's total population and age sex distributions of the United Nations Population Division's as of June, 2022." }, { "id": "Notesfromoriginalsource", "value": "World Bank" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM", "metatype": [ { "id": "IndicatorName", "value": "Learning poverty: Share of Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator brings together schooling and learning. It starts with the share of children who have not achieved minimum reading proficiency and adjusts it by the proportion of children who are out of school. The data used to calculate Learning Poverty has been made possible thanks to the work of the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS), which established Minimum Proficiency Levels (MPLs) that enable countries to benchmark learning across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro; et al. 2021. Will Every Child Be Able to Read by 2030? Defining Learning Poverty and Mapping the Dimensions of the Challenge. Policy Research Working Paper;No. 9588. World Bank, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/35300" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.BMP", "metatype": [ { "id": "IndicatorName", "value": "Pupils below minimum reading proficiency at end of primary (%). Low GAML threshold" }, { "id": "Longdefinition", "value": "This indicator uses on Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments." }, { "id": "Notesfromoriginalsource", "value": "This indicator uses on Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro, and others. 2019. \"Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.\" World Bank Policy Research Working Paper series. Washington, DC: World Bank." }, { "id": "Othernotes", "value": "Education Statistics" }, { "id": "Otherweblinks", "value": "http://datatopics.worldbank.org/education/files/LearningPoverty/EndingLearningPoverty.pdf" }, { "id": "Shortdefinition", "value": "This indicator uses on Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro, and others. 2019. â??Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.â? World Bank Policy Research Working Paper series. Washington, DC: World Bank." }, { "id": "Source", "value": "Word Bank and UIS" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.BMP.FE", "metatype": [ { "id": "IndicatorName", "value": "Female pupils below minimum reading proficiency at end of primary (%). 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Washington, DC: World Bank." }, { "id": "Othernotes", "value": "Education Statistics" }, { "id": "Otherweblinks", "value": "http://datatopics.worldbank.org/education/files/LearningPoverty/EndingLearningPoverty.pdf" }, { "id": "Shortdefinition", "value": "This indicator uses on Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro, and others. 2019. â??Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.â? World Bank Policy Research Working Paper series. Washington, DC: World Bank." }, { "id": "Source", "value": "Word Bank and UIS" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.BMP.MA", "metatype": [ { "id": "IndicatorName", "value": "Male pupils below minimum reading proficiency at end of primary (%). 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Washington, DC: World Bank." }, { "id": "Othernotes", "value": "Education Statistics" }, { "id": "Otherweblinks", "value": "http://datatopics.worldbank.org/education/files/LearningPoverty/EndingLearningPoverty.pdf" }, { "id": "Shortdefinition", "value": "This indicator uses on Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro, and others. 2019. â??Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.â? World Bank Policy Research Working Paper series. Washington, DC: World Bank." }, { "id": "Source", "value": "Word Bank and UIS" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.FE", "metatype": [ { "id": "IndicatorName", "value": "Learning poverty: Share of Female Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator brings together schooling and learning. It starts with the share of children who have not achieved minimum reading proficiency and adjusts it by the proportion of children who are out of school. The data used to calculate Learning Poverty has been made possible thanks to the work of the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS), which established Minimum Proficiency Levels (MPLs) that enable countries to benchmark learning across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro; et al. 2021. Will Every Child Be Able to Read by 2030? Defining Learning Poverty and Mapping the Dimensions of the Challenge. Policy Research Working Paper;No. 9588. World Bank, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/35300" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.LD", "metatype": [ { "id": "IndicatorName", "value": "Pupils below minimum reading proficiency at end of primary (%). 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World Bank. https://openknowledge.worldbank.org/handle/10986/35300" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.LD.FE", "metatype": [ { "id": "IndicatorName", "value": "Female pupils below minimum reading proficiency at end of primary (%). Low GAML threshold" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator uses the Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro; et al. 2021. Will Every Child Be Able to Read by 2030? Defining Learning Poverty and Mapping the Dimensions of the Challenge. Policy Research Working Paper;No. 9588. World Bank, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/35300" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.LD.MA", "metatype": [ { "id": "IndicatorName", "value": "Male pupils below minimum reading proficiency at end of primary (%). Low GAML threshold" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator uses the Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro; et al. 2021. Will Every Child Be Able to Read by 2030? Defining Learning Poverty and Mapping the Dimensions of the Challenge. Policy Research Working Paper;No. 9588. World Bank, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/35300" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.LDGAP", "metatype": [ { "id": "IndicatorName", "value": "Learning Deprivation Gap" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator uses the Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro. 2020. Learning Poverty: Measures and Simulations. Policy Research Working Paper;No. 9446. World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/34654" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.LDGAP.FE", "metatype": [ { "id": "IndicatorName", "value": "Learning Deprivation Gap, Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator uses the Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro. 2020. Learning Poverty: Measures and Simulations. Policy Research Working Paper;No. 9446. 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World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/34654" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.LDSEV", "metatype": [ { "id": "IndicatorName", "value": "Learning Deprivation Severity" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator uses the Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro. 2020. Learning Poverty: Measures and Simulations. Policy Research Working Paper;No. 9446. World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/34654" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.LDSEV.FE", "metatype": [ { "id": "IndicatorName", "value": "Learning Deprivation Severity, Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator uses the Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro. 2020. Learning Poverty: Measures and Simulations. Policy Research Working Paper;No. 9446. World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/34654" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.LDSEV.MA", "metatype": [ { "id": "IndicatorName", "value": "Learning Deprivation Severity, Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator uses the Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro. 2020. Learning Poverty: Measures and Simulations. Policy Research Working Paper;No. 9446. World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/34654" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.LPGAP", "metatype": [ { "id": "IndicatorName", "value": "Learning Poverty Gap" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator uses the Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro. 2020. Learning Poverty: Measures and Simulations. Policy Research Working Paper;No. 9446. World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/34654" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.LPGAP.FE", "metatype": [ { "id": "IndicatorName", "value": "Learning Poverty Gap, Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator uses the Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro. 2020. Learning Poverty: Measures and Simulations. Policy Research Working Paper;No. 9446. 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World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/34654" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.LPSEV", "metatype": [ { "id": "IndicatorName", "value": "Learning Poverty Severity" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator uses the Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro. 2020. Learning Poverty: Measures and Simulations. Policy Research Working Paper;No. 9446. World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/34654" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.LPSEV.FE", "metatype": [ { "id": "IndicatorName", "value": "Learning Poverty Severity, Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator uses the Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro. 2020. Learning Poverty: Measures and Simulations. Policy Research Working Paper;No. 9446. World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/34654" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.LPSEV.MA", "metatype": [ { "id": "IndicatorName", "value": "Learning Poverty Severity, Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator uses the Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro. 2020. Learning Poverty: Measures and Simulations. Policy Research Working Paper;No. 9446. World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/34654" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.MA", "metatype": [ { "id": "IndicatorName", "value": "Learning poverty: Share of Male Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator brings together schooling and learning. It starts with the share of children who have not achieved minimum reading proficiency and adjusts it by the proportion of children who are out of school. The data used to calculate Learning Poverty has been made possible thanks to the work of the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS), which established Minimum Proficiency Levels (MPLs) that enable countries to benchmark learning across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro; et al. 2021. Will Every Child Be Able to Read by 2030? Defining Learning Poverty and Mapping the Dimensions of the Challenge. Policy Research Working Paper;No. 9588. World Bank, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/35300" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.OOS", "metatype": [ { "id": "IndicatorName", "value": "Primary school age children out-of-school (%)" }, { "id": "Longdefinition", "value": "The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by UIS." }, { "id": "Notesfromoriginalsource", "value": "The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by UIS. For more information please see Azevedo, Joao Pedro, and others. 2019. \"Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.\" World Bank Policy Research Working Paper series. Washington, DC: World Bank." }, { "id": "Othernotes", "value": "Education Statistics" }, { "id": "Otherweblinks", "value": "http://datatopics.worldbank.org/education/files/LearningPoverty/EndingLearningPoverty.pdf" }, { "id": "Shortdefinition", "value": "The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by UIS. For more information please see Azevedo, Joao Pedro, and others. 2019. â??Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.â? World Bank Policy Research Working Paper series. Washington, DC: World Bank." }, { "id": "Source", "value": "Word Bank and UIS" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.OOS.FE", "metatype": [ { "id": "IndicatorName", "value": "Female primary school age children out-of-school (%)" }, { "id": "Longdefinition", "value": "The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by UIS." }, { "id": "Notesfromoriginalsource", "value": "The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by UIS. For more information please see Azevedo, Joao Pedro, and others. 2019. \"Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.\" World Bank Policy Research Working Paper series. Washington, DC: World Bank." }, { "id": "Othernotes", "value": "Education Statistics" }, { "id": "Otherweblinks", "value": "http://datatopics.worldbank.org/education/files/LearningPoverty/EndingLearningPoverty.pdf" }, { "id": "Shortdefinition", "value": "The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by UIS. For more information please see Azevedo, Joao Pedro, and others. 2019. â??Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.â? World Bank Policy Research Working Paper series. Washington, DC: World Bank." }, { "id": "Source", "value": "Word Bank and UIS" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.OOS.MA", "metatype": [ { "id": "IndicatorName", "value": "Male primary school age children out-of-school (%)" }, { "id": "Longdefinition", "value": "The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by UIS." }, { "id": "Notesfromoriginalsource", "value": "The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by UIS. For more information please see Azevedo, Joao Pedro, and others. 2019. \"Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.\" World Bank Policy Research Working Paper series. Washington, DC: World Bank." }, { "id": "Othernotes", "value": "Education Statistics" }, { "id": "Otherweblinks", "value": "http://datatopics.worldbank.org/education/files/LearningPoverty/EndingLearningPoverty.pdf" }, { "id": "Shortdefinition", "value": "The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by UIS. For more information please see Azevedo, Joao Pedro, and others. 2019. â??Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.â? World Bank Policy Research Working Paper series. Washington, DC: World Bank." }, { "id": "Source", "value": "Word Bank and UIS" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.SD", "metatype": [ { "id": "IndicatorName", "value": "Primary school age children out-of-school (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by the UNESCO Institute for Statistics (UIS). For more information, please see Azevedo, Joao Pedro; et al. 2021. Will Every Child Be Able to Read by 2030? Defining Learning Poverty and Mapping the Dimensions of the Challenge. Policy Research Working Paper;No. 9588. World Bank, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/35300" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.SD.FE", "metatype": [ { "id": "IndicatorName", "value": "Female primary school age children out-of-school (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by the UNESCO Institute for Statistics (UIS). For more information please see Azevedo, Joao Pedro; et al. 2021. Will Every Child Be Able to Read by 2030? Defining Learning Poverty and Mapping the Dimensions of the Challenge. Policy Research Working Paper;No. 9588. World Bank, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/35300" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.LPV.PRIM.SD.MA", "metatype": [ { "id": "IndicatorName", "value": "Male primary school age children out-of-school (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by the UNESCO Institute for Statistics (UIS). For more information please see Azevedo, Joao Pedro; et al. 2021. Will Every Child Be Able to Read by 2030? Defining Learning Poverty and Mapping the Dimensions of the Challenge. Policy Research Working Paper;No. 9588. World Bank, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/35300" }, { "id": "Notesfromoriginalsource", "value": "World Bank and UIS" }, { "id": "Source", "value": "Education Statistics" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "12" }, { "id": "SE.PRE.ENRL", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in pre-primary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in public and private pre-primary education institutions (ISCED 0.2) regardless of age. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years; and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "SE.PRE.ENRL.FE", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in pre-primary education, female (number)" }, { "id": "Longdefinition", "value": "Total number of female students enrolled in public and private pre-primary education institutions (ISCED 0.2) regardless of age. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years; and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "SE.PRE.ENRR", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, pre-primary, both sexes (%)" }, { "id": "Longdefinition", "value": "Total enrollment in pre-primary education, regardless of age, expressed as a percentage of the total population of official pre-primary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Pre-Primary" } ], "source_id": "12" }, { "id": "SE.PRE.ENRR.FE", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, pre-primary, female (%)" }, { "id": "Longdefinition", "value": "Total female enrollment in pre-primary education, regardless of age, expressed as a percentage of the total female population of official pre-primary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Pre-Primary" } ], "source_id": "12" }, { "id": "SE.PRE.ENRR.MA", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, pre-primary, male (%)" }, { "id": "Longdefinition", "value": "Total male enrollment in pre-primary education, regardless of age, expressed as a percentage of the total male population of official pre-primary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Pre-Primary" } ], "source_id": "12" }, { "id": "SE.PRE.PRIV.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of enrolment in pre-primary education in private institutions (%)" }, { "id": "Longdefinition", "value": "Total number of students in pre-primary education enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body (e.g., non-governmental organisation, religious body, special interest group, foundation or business enterprise), expressed as a percentage of total number of students enrolled in pre-primary education. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years; and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "SE.PRE.TCHR", "metatype": [ { "id": "IndicatorName", "value": "Teachers in pre-primary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of teachers in public and private pre-primary education institutions. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.PRE.TCHR.FE", "metatype": [ { "id": "IndicatorName", "value": "Teachers in pre-primary education, female (number)" }, { "id": "Longdefinition", "value": "Total number of female teachers in public and private pre-primary education institutions. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.PRE.TCHR.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of teachers in pre-primary education who are female (%)" }, { "id": "Longdefinition", "value": "Number of female teachers at the pre-primary level expressed as a percentage of the total number of teachers (male and female) at the pre-primary level in a given school year. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.PRM.AGES", "metatype": [ { "id": "IndicatorName", "value": "Official entrance age to primary education (years)" }, { "id": "Longdefinition", "value": "Age at which students would enter primary education, assuming they had started at the official entrance age for the lowest level of education, had studied full-time throughout and had progressed through the system without repeating or skipping a grade." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.ATTD", "metatype": [ { "id": "IndicatorName", "value": "Student Attendance" }, { "id": "Notesfromoriginalsource", "value": "School Survey. Percent of 4th grade students who are present during an unannounced visit." }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ATTD.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of 4th grade students present during an unannounced visit" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ATTD.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of 4th grade students present during an unannounced visit - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ATTD.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of 4th grade students present during an unannounced visit - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ATTD.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of 4th grade students present during an unannounced visit - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ATTD.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of 4th grade students present during an unannounced visit - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BFIN", "metatype": [ { "id": "IndicatorName", "value": "Financing" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BFIN.1", "metatype": [ { "id": "IndicatorName", "value": "Financing score; where a score of 1 indicates low effectiveness and 5 indicates high effectiveness in terms of adequacy, efficiency, and equity." }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BFIN.2", "metatype": [ { "id": "IndicatorName", "value": "(Financing) - Adequacy expressed by the per child spending" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BFIN.3", "metatype": [ { "id": "IndicatorName", "value": "(Financing) Efficiency - Expressed by the score from the Public Expenditure and Financial Accountability (PEFA) assessment; where 0 is the lowest possible efficiency and 1 is the highest" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BFIN.4", "metatype": [ { "id": "IndicatorName", "value": "(Financing) Efficiency - Expressed by the relationship between financing and outcomes; where 0 is the lowest possible efficiency and 1 is the highest" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BFIN.5", "metatype": [ { "id": "IndicatorName", "value": "(Financing) - Equity" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BIMP", "metatype": [ { "id": "IndicatorName", "value": "Impartial Decision-Making" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BIMP.1", "metatype": [ { "id": "IndicatorName", "value": "Average score for Impartial Decision-Making; where a score of 1 indicates low effectiveness and 5 indicates high effectiveness" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BIMP.2", "metatype": [ { "id": "IndicatorName", "value": "(Impartial Decision-Making) average score for politicized personnel management" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BIMP.3", "metatype": [ { "id": "IndicatorName", "value": "(Impartial Decision-Making) average score for politicized policy-making" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BIMP.4", "metatype": [ { "id": "IndicatorName", "value": "(Impartial Decision-Making) average score for politicized policy implementation" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BIMP.5", "metatype": [ { "id": "IndicatorName", "value": "(Impartial Decision-Making) average score for employee unions as facilitators" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BMAC", "metatype": [ { "id": "IndicatorName", "value": "Mandates & Accountability" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BMAC.1", "metatype": [ { "id": "IndicatorName", "value": "Average score for Mandates & Accountability; where a score of 1 indicates low effectiveness and 5 indicates high effectiveness" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BMAC.2", "metatype": [ { "id": "IndicatorName", "value": "(Mandates & Accountability) Average score for coherence" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BMAC.3", "metatype": [ { "id": "IndicatorName", "value": "(Mandates & Accountability) Average score for transparency" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BMAC.4", "metatype": [ { "id": "IndicatorName", "value": "(Mandates & Accountability) Average score for accountability of public officials" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BNLG", "metatype": [ { "id": "IndicatorName", "value": "National Learning Goals" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BNLG.1", "metatype": [ { "id": "IndicatorName", "value": "Average score for National Learning Goals; where a score of 1 indicates low effectiveness and 5 indicates high effectiveness" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BNLG.2", "metatype": [ { "id": "IndicatorName", "value": "(National Learning Goals) Average score for targeting" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BNLG.3", "metatype": [ { "id": "IndicatorName", "value": "(National Learning Goals) Average score for monitoring" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BNLG.4", "metatype": [ { "id": "IndicatorName", "value": "(National Learning Goals) Average score for incentives" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BNLG.5", "metatype": [ { "id": "IndicatorName", "value": "(National Learning Goals) Average score for community engagement" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BQBR", "metatype": [ { "id": "IndicatorName", "value": "Characteristics of Bureaucracy" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BQBR.1", "metatype": [ { "id": "IndicatorName", "value": "Average score for Characteristics of Bureaucracy; where a score of 1 indicates low effectiveness and 5 indicates high effectiveness" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BQBR.2", "metatype": [ { "id": "IndicatorName", "value": "(Characteristics of Bureaucracy) average score for knowledge and skills" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BQBR.3", "metatype": [ { "id": "IndicatorName", "value": "(Characteristics of Bureaucracy) average score for work environment" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BQBR.4", "metatype": [ { "id": "IndicatorName", "value": "(Characteristics of Bureaucracy) average score for merit" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.BQBR.5", "metatype": [ { "id": "IndicatorName", "value": "(Characteristics of Bureaucracy) average score for motivation and attitudes" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.CMPT.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Gross intake ratio to the last grade of primary education, female (%)" }, { "id": "Longdefinition", "value": "Total number of new female entrants in the last grade of primary education, regardless of age, expressed as percentage of the total female population of the theoretical entrance age to the last grade of primary. The ratio can exceed 100% due to over-aged and under-aged children who enter primary school late/early and/or repeat grades. This indicator has been used as the Primary Completion Rate. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of new female entrants in the last grade of primary education, regardless of age, expressed as percentage of the total female population of the theoretical entrance age to the last grade of primary. The ratio can exceed 100% due to over-aged and under-aged children who enter primary school late/early and/or repeat grades. This indicator has been used as the Primary Completion Rate. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.CMPT.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Gross intake ratio to the last grade of primary education, male (%)" }, { "id": "Longdefinition", "value": "Total number of new male entrants in the last grade of primary education, regardless of age, expressed as percentage of the total male population of the theoretical entrance age to the last grade of primary. The ratio can exceed 100% due to over-aged and under-aged children who enter primary school late/early and/or repeat grades. This indicator has been used as the Primary Completion Rate. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of new male entrants in the last grade of primary education, regardless of age, expressed as percentage of the total male population of the theoretical entrance age to the last grade of primary. The ratio can exceed 100% due to over-aged and under-aged children who enter primary school late/early and/or repeat grades. This indicator has been used as the Primary Completion Rate. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.CMPT.ZS", "metatype": [ { "id": "IndicatorName", "value": "Gross intake ratio to the last grade of primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of new entrants in the last grade of primary education, regardless of age, expressed as percentage of the total population of the theoretical entrance age to the last grade of primary. The ratio can exceed 100% due to over-aged and under-aged children who enter primary school late/early and/or repeat grades. This indicator has been used as the Primary Completion Rate. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of new entrants in the last grade of primary education, regardless of age, expressed as percentage of the total population of the theoretical entrance age to the last grade of primary. The ratio can exceed 100% due to over-aged and under-aged children who enter primary school late/early and/or repeat grades. This indicator has been used as the Primary Completion Rate. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.CONT", "metatype": [ { "id": "IndicatorName", "value": "Content Knowledge" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.CONT.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subjects they teach" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.CONT.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subjects they teach - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.CONT.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subjects they teach - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.CONT.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subjects they teach - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.CONT.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subjects they teach - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.CONT.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of language" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.CONT.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of language - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.CONT.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of language - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.CONT.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of language - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.CONT.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of language - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.CONT.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of mathematics" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.CONT.3.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of mathematics - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.CONT.3.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of mathematics - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.CONT.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of mathematics - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.CONT.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of mathematics - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.DURS", "metatype": [ { "id": "IndicatorName", "value": "Theoretical duration of primary education (years)" }, { "id": "Longdefinition", "value": "Number of grades (years) in primary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.EFFT", "metatype": [ { "id": "IndicatorName", "value": "Teacher Effort" }, { "id": "Notesfromoriginalsource", "value": "School survey. Percent of teachers present. Teacher is coded absent if they are: , not in school - in school but absent from the class." }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.EFFT.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their classrooms, when they are scheduled to be teaching, during an announced visit" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.EFFT.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their classrooms, when they are scheduled to be teaching, during an announced visit - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.EFFT.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their classrooms, when they are scheduled to be teaching, during an announced visit - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.EFFT.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their classrooms, when they are scheduled to be teaching, during an announced visit - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.EFFT.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their classrooms, when they are scheduled to be teaching, during an announced visit - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.EFFT.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their schools during an announced visit" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.EFFT.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their schools during an announced visit - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.EFFT.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their schools during an announced visit - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.EFFT.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their schools during an announced visit - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.EFFT.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their schools during an announced visit - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ENRL", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in primary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in public and private primary education institutions regardless of age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.ENRL.FE", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in primary education, female (number)" }, { "id": "Longdefinition", "value": "Total number of female students enrolled in public and private primary education institutions regardless of age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.ENRR", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, primary, both sexes (%)" }, { "id": "Longdefinition", "value": "Total enrollment in primary education, regardless of age, expressed as a percentage of the population of official primary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.ENRR.FE", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, primary, female (%)" }, { "id": "Longdefinition", "value": "Total female enrollment in primary education, regardless of age, expressed as a percentage of the female population of official primary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.ENRR.MA", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, primary, male (%)" }, { "id": "Longdefinition", "value": "Total male enrollment in primary education, regardless of age, expressed as a percentage of the male population of official primary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR", "metatype": [ { "id": "IndicatorName", "value": "Instructional Leadership" }, { "id": "Notesfromoriginalsource", "value": "School survey. Total score starts at 1 and points added are the sum of whether a teacher has: , Had a classroom observation in past year - Had a discussion based on that observation that lasted longer than 30 min - Received actionable feedback from that observation - Teacher had a lesson plan and discussed it with another person" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of instructional leadership" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of instructional leadership - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of instructional leadership - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of instructional leadership - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of instructional leadership - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having had their class observed" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having had their class observed - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having had their class observed - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having had their class observed - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having had their class observed - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the classroom observation happened recently" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.3.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the classroom observation happened recently - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.3.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the classroom observation happened recently - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the classroom observation happened recently - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the classroom observation happened recently - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having discussed the results of the classroom observation" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.4.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having discussed the results of the classroom observation - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.4.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having discussed the results of the classroom observation - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.4.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having discussed the results of the classroom observation - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.4.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having discussed the results of the classroom observation - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the discussion was over 30 minutes" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.5.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the discussion was over 30 minutes - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.5.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the discussion was over 30 minutes - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.5.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the discussion was over 30 minutes - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.5.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the discussion was over 30 minutes - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they were provided with feedback in that discussion" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.6.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they were provided with feedback in that discussion - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.6.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they were provided with feedback in that discussion - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.6.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they were provided with feedback in that discussion - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.6.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they were provided with feedback in that discussion - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.7", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having lesson plans" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.7.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having lesson plans - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.7.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having lesson plans - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.7.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having lesson plans - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.7.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having lesson plans - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they had discussed their lesson plans with someone else (pricinpal, pedagogical coordinator, another teacher)" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.8.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they had discussed their lesson plans with someone else (pricinpal, pedagogical coordinator, another teacher) - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.8.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they had discussed their lesson plans with someone else (pricinpal, pedagogical coordinator, another teacher) - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.8.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they had discussed their lesson plans with someone else (pricinpal, pedagogical coordinator, another teacher) - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ILDR.8.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they had discussed their lesson plans with someone else (pricinpal, pedagogical coordinator, another teacher) - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.IMON", "metatype": [ { "id": "IndicatorName", "value": "Monitoring" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.IMON.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools that report there is someone monitoring that basic inputs are available to students" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.IMON.10", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Number of basic infrastructure features clearly articulated as needing to be monitored" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.IMON.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools that report that parents or community members are involved in the monitoring of availability of basic inputs" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.IMON.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools that report that there is an inventory to monitor availability of basic inputs" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.IMON.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools that report there is someone monitoring that basic infrastructure is available" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.IMON.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools that report that parents or community members are involved in the monitoring of availability of basic infrastructure" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.IMON.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools that report that there is an inventory to monitor availability of basic infrastructure" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.IMON.7", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is the responsibility of monitoring basic inputs clearly articulated in the policies?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.IMON.8", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Number of basic inputs clearly articulated as needing to be monitored" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.IMON.9", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is the responsibility of monitoring basic infrastructure clearly articulated in the policies?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.IMON.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Inputs & Infrastructure) - Monitoring" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.IMON.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Inputs & Infrastructure) - Monitoring" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR", "metatype": [ { "id": "IndicatorName", "value": "Basic Infrastructure" }, { "id": "Notesfromoriginalsource", "value": "School survey. Total score is the sum of whether a school has: , Access to adequate drinking water -Functional toilets that are separate for boys/girls, private, useable, and have hand washing facilities - Electricity - Internet - School is accessible for those with disabilities (road access, a school ramp for wheelchairs, an entrance wide enough for wheelchairs, ramps to classrooms where needed, accessible toilets, and disability screening for seeing, hearing, and learning disabilities with partial credit for having 1 or 2 or the 3).)" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average number of infrastructure aspects present in schools" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average number of infrastructure aspects present in schools - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average number of infrastructure aspects present in schools - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with drinking water" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with drinking water - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with drinking water - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with functioning toilets" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with functioning toilets - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with functioning toilets - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to electricity" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.4.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to electricity - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.4.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to electricity - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to internet" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.5.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to internet - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.5.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to internet - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools accessible to children with special needs" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.6.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools accessible to children with special needs - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INFR.6.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools accessible to children with special needs - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INPT", "metatype": [ { "id": "IndicatorName", "value": "Basic Inputs" }, { "id": "Notesfromoriginalsource", "value": "School survey. Total score is the sum of whether a school has: , Functional blackboard - Pens, pencils, exercise books - Textbooks - Fraction of students in class with a desk - Used ICT in class and have access to ICT in the school." }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INPT.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average number of classroom inputs in classrooms" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INPT.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average number of classroom inputs in classrooms - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INPT.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average number of classroom inputs in classrooms - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INPT.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of classrooms with a functional blackboard and chalk" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INPT.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of classrooms with a functional blackboard and chalk - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INPT.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of classrooms with a functional blackboard and chalk - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INPT.3", "metatype": [ { "id": "IndicatorName", "value": "(De facto) Percent of classrooms equipped with pens/pencils, textbooks, and exercise books" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INPT.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De facto) Percent of classrooms equipped with pens/pencils, textbooks, and exercise books - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INPT.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De facto) Percent of classrooms equipped with pens/pencils, textbooks, and exercise books - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INPT.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of classrooms with basic classroom furniture" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INPT.4.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of classrooms with basic classroom furniture - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INPT.4.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of classrooms with basic classroom furniture - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INPT.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to EdTech" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INPT.5.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to EdTech - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.INPT.5.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to EdTech - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD", "metatype": [ { "id": "IndicatorName", "value": "Standards" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy in place to require that students have access to the prescribed textbooks?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD.10", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if there is a policy in place to require that schools have access to drinking water?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD.11", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy in place to require that schools have functioning toilets?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD.12", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if there is a policy in place to require that schools have functioning toilets?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD.13", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy in place to require that schools are accessible to children with special needs?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD.14", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if there is there a policy in place to require that schools are accessible to children with special needs?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if there is a policy in place to require that students have access to the prescribed textbooks?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD.3", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a national connectivity program?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if there is a national connectivity program?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD.5", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy in place to require that students have access to PCs, laptops, tablets, and/or other computing devices?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if there is a policy in place to require that students have access to PCs, laptops, tablets, and/or other computing devices?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD.7", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy in place to require that schools have access to electricity?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if there is a policy in place to require that schools have access to electricity?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD.9", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy in place to require that schools have access to drinking water?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Inputs & Infrastructure) - Standards" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.ISTD.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Inputs & Infrastructure) - Standards" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP", "metatype": [ { "id": "IndicatorName", "value": "Student Readiness" }, { "id": "Notesfromoriginalsource", "value": "School survey. Percent of sampled 1st grade students scoring at least 80% on GEPD Direct Assessment. In such assessment, total equal points (100) are allocated equally across the 4 domains measured. These include numeracy, literacy, socioemotional skills, and executive function. Within each domain, all questions are given an equal weight." }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average developmental score for 1st Graders" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average developmental score for 1st Graders - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average developmental score for 1st Graders - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average developmental score for 1st Graders - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average developmental score for 1st Graders - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average numeracy score for 1st Graders" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average numeracy score for 1st Graders - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average numeracy score for 1st Graders - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average numeracy score for 1st Graders - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average numeracy score for 1st Graders - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average literacy score for 1st Graders" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.3.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average literacy score for 1st Graders - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.3.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average literacy score for 1st Graders - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average literacy score for 1st Graders - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average literacy score for 1st Graders - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average executive funcion score for 1st Graders" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.4.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average executive funcion score for 1st Graders - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.4.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average executive funcion score for 1st Graders - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.4.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average executive funcion score for 1st Graders - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.4.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average executive funcion score for 1st Graders - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average socioemotional score for 1st Graders" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.5.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average socioemotional score for 1st Graders - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.5.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average socioemotional score for 1st Graders - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.5.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average socioemotional score for 1st Graders - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCAP.5.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average socioemotional score for 1st Graders - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCBC", "metatype": [ { "id": "IndicatorName", "value": "Center-Based Care" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCBC.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy that guarantees free education for some or all grades and ages included in pre-primary education (for children age 0-83 months)?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCBC.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children age 36-59 months who are attending an early childhood education programme" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCBC.3", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are there developmental standards established for early childhood care and education?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCBC.4", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) According to laws and regulations, are there requirement to become an early childhood educator, pre-primary teacher?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCBC.5", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) According to policy, are ECCE professionals working at public or private centers required to complete in-service training in ECCE service delivery?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCBC.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Learners) - Center-Based Care" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LCBC.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Learners) - Center-Based Care" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LERN", "metatype": [ { "id": "IndicatorName", "value": "Proficiency on GEPD Assessment" }, { "id": "Notesfromoriginalsource", "value": "The fraction of students scoring at least 20/24 points on 4th grade language and 14/17 points on the math student assessment. Our team consulted several content experts to advise on how many of our math and language items a minimally proficient 4th grade student should be able to get correct to decide these thresholds." }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LERN.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy and numeracy according to GEPD School Survey" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LERN.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy and numeracy according to GEPD School Survey - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LERN.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy and numeracy according to GEPD School Survey - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LERN.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy and numeracy according to GEPD School Survey - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LERN.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy and numeracy according to GEPD School Survey - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LERN.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy according to GEPD School Survey" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LERN.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy according to GEPD School Survey - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LERN.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy according to GEPD School Survey - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LERN.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy according to GEPD School Survey - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LERN.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy according to GEPD School Survey - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LERN.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in numeracy according to GEPD School Survey" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LERN.3.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in numeracy according to GEPD School Survey - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LERN.3.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in numeracy according to GEPD School Survey - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LERN.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in numeracy according to GEPD School Survey - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LERN.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in numeracy according to GEPD School Survey - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LFCP", "metatype": [ { "id": "IndicatorName", "value": "Caregiver Financial Capacity" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LFCP.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are anti poverty interventions that focus on ECD publicly supported?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LFCP.2", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are cash transfers conditional on ECD services/enrollment publicly supported?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LFCP.3", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are cash transfers focused partially on ECD publicly supported?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LFCP.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Coverage of social protection programs" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LFCP.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Learners) - Caregiver Capacity – Financial Capacity" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LFCP.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Learners) - Caregiver Capacity – Financial Capacity" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LHTH", "metatype": [ { "id": "IndicatorName", "value": "Health Programs" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LHTH.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are young children required to receive a complete course of childhood immunizations?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LHTH.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children who at age 24-35 months had received all vaccinations recommended in the national immunization schedule" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LHTH.3", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy that assures access to healthcare for young children? Either by offering these services free or by subsidizing them" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LHTH.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children under 5 covered by health insurance" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LHTH.5", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are deworming pills funded and distributed by the government?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LHTH.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children age 6-59 months who received deworming medication" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LHTH.7", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy that guarantees pregnant women free antenatal visits and skilled delivery?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LHTH.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of women age 15-49 years with a live birth in the last 2 years whose most recent live birth was delivered in a health facility" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LHTH.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Learners) - Health" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LHTH.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Learners) - Health" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LNTN", "metatype": [ { "id": "IndicatorName", "value": "Nutrition Programs" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LNTN.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Does a national policy to encourage salt iodization exist?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LNTN.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of households with salt testing positive for any iodide among households" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LNTN.3", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Does a national policy exist to encourage iron fortification of staples like wheat, maize, or rice?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LNTN.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children age 6–23 months who had at least the minimum dietary diversity and the minimum meal frequency during the previous day" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LNTN.5", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Does a national policy exist to encourage breastfeeding?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LNTN.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children born in the five (three) years preceding the survey who were ever breastfed" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LNTN.7", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a publicly funded school feeding program?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LNTN.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools reporting having publicly funded school feeding program" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LNTN.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Learners) - Nutrition Programs" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LNTN.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Learners) - Nutrition Programs" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LSKC", "metatype": [ { "id": "IndicatorName", "value": "Caregiver Skills Capacity" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LSKC.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Does the government offer programs that aim to share good parenting practices with caregivers?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LSKC.2", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are any of the following publicly-supported delivery channels used to reach families in order to promote early childhood stimulation? Home visits, Group sessions, Community health programs, Health center waiting rooms, School-based groups, Mass media/Information campaigns" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LSKC.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children under age 5 who have three or more children’s books" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LSKC.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children age 24-59 months engaged in four or more activities to provide early stimulation and responsive care in the last 3 days with any adult in the household" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LSKC.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Learners) - Caregiver Capacity – Skills Capacity" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.LSKC.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Learners) - Caregiver Capacity – Skills Capacity" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.OPMN", "metatype": [ { "id": "IndicatorName", "value": "Operational Management" }, { "id": "Notesfromoriginalsource", "value": "School Survey. Principals/head teachers are given two vignettes: , One on solving the problem of a hypothetical leaky roof - One on solving a problem of inadequate numbers of textbooks. Each vignette is worth 2 points. The indicator will measure two things: presence of functions and quality of functions. In each vignette: - 0.5 points are awarded for someone specific having the responsibility to fix - 0.5 point is awarded if the school can fully fund the repair, 0.25 points is awarded if the school must get partial help from the community, and 0 points are awarded if the full cost must be born by the community - 1 point is awarded if the problem is fully resolved in a timely manner, with partial credit given if problem can only be partly resolved." }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.OPMN.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of core operational management functions" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.OPMN.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of core operational management functions - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.OPMN.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of core operational management functions - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.OPMN.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of core operational management functions - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.OPMN.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of core operational management functions - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.OPMN.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for infrastructure repair/maintenance" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.OPMN.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for infrastructure repair/maintenance - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.OPMN.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for infrastructure repair/maintenance - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.OPMN.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for infrastructure repair/maintenance - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.OPMN.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for infrastructure repair/maintenance - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.OPMN.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for ensuring availability of school inputs" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.OPMN.3.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for ensuring availability of school inputs - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.OPMN.3.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for ensuring availability of school inputs - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.OPMN.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for ensuring availability of school inputs - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.OPMN.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for ensuring availability of school inputs - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG", "metatype": [ { "id": "IndicatorName", "value": "Pedagogical Skills" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good pedagogical skills (3 or above on Teach overall score)" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good pedagogical skills (3 or above on Teach overall score) - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good pedagogical skills (3 or above on Teach overall score) - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good pedagogical skills (3 or above on Teach overall score) - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good pedagogical skills (3 or above on Teach overall score) - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good classroom culture practices (3 or above on Teach Classroom Culture score)" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good classroom culture practices (3 or above on Teach Classroom Culture score) - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good classroom culture practices (3 or above on Teach Classroom Culture score) - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good classroom culture practices (3 or above on Teach Classroom Culture score) - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good classroom culture practices (3 or above on Teach Classroom Culture score) - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good instruction practices (3 or above on Teach Instruction score)" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.3.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good instruction practices (3 or above on Teach Instruction score) - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.3.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good instruction practices (3 or above on Teach Instruction score) - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good instruction practices (3 or above on Teach Instruction score) - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good instruction practices (3 or above on Teach Instruction score) - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good practices on socioemotional skills (3 or above on Teach Socioemotional Skills score)" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.4.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good practices on socioemotional skills (3 or above on Teach Socioemotional Skills score) - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.4.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good practices on socioemotional skills (3 or above on Teach Socioemotional Skills score) - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.4.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good practices on socioemotional skills (3 or above on Teach Socioemotional Skills score) - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PEDG.4.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good practices on socioemotional skills (3 or above on Teach Socioemotional Skills score) - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW", "metatype": [ { "id": "IndicatorName", "value": "School Knowledge" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals are familiar with certain key aspects of the day-to-day workings of the school" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals are familiar with certain key aspects of the day-to-day workings of the school - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals are familiar with certain key aspects of the day-to-day workings of the school - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals are familiar with certain key aspects of the day-to-day workings of the school - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals are familiar with certain key aspects of the day-to-day workings of the school - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' content knowledge" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' content knowledge - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' content knowledge - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' content knowledge - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' content knowledge - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' experience" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.3.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' experience - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.3.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' experience - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' experience - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' experience - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with availability of classroom inputs" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.4.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with availability of classroom inputs - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.4.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with availability of classroom inputs - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.4.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with availability of classroom inputs - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PKNW.4.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with availability of classroom inputs - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PMAN", "metatype": [ { "id": "IndicatorName", "value": "Management Practices" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PMAN.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master two key managerial skills - problem-solving in the short-term, and goal-setting in the long term" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PMAN.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master two key managerial skills - problem-solving in the short-term, and goal-setting in the long term - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PMAN.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master two key managerial skills - problem-solving in the short-term, and goal-setting in the long term - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PMAN.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master two key managerial skills - problem-solving in the short-term, and goal-setting in the long term - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PMAN.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master two key managerial skills - problem-solving in the short-term, and goal-setting in the long term - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PMAN.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master problem-solving in the short-term" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PMAN.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master problem-solving in the short-term - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PMAN.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master problem-solving in the short-term - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PMAN.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master problem-solving in the short-term - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PMAN.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master problem-solving in the short-term - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PMAN.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master goal-setting in the long term" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PMAN.3.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master goal-setting in the long term - Female" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PMAN.3.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master goal-setting in the long term - Male" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PMAN.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master goal-setting in the long term - Rural" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PMAN.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master goal-setting in the long term - Urban" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PRIV.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of enrolment in primary education in private institutions (%)" }, { "id": "Longdefinition", "value": "Total number of students in primary education enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body (e.g., non-governmental organisation, religious body, special interest group, foundation or business enterprise), expressed as a percentage of total number of students enrolled in primary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.PROE", "metatype": [ { "id": "IndicatorName", "value": "Proficiency by Grade 2/3" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PROE.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy and numeracy by grade 2/3, as reported by UIS" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.PRS5.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Survival rate to Grade 5 of primary education, female (%)" }, { "id": "Longdefinition", "value": "Percentage of a cohort of female students enrolled in the first grade of primary education in a given school year who are expected to reach grade 5, regardless of repetition. Divide the total number of students belonging to a school-cohort who reached each successive grade of primary education by the number of students in the school-cohort i.e. those originally enrolled in the first grade of primary education, and multiply the result by 100. The survival rate is calculated on the basis of the reconstructed cohort method, which uses data on enrolment and repeaters for two consecutive years." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.PRS5.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Survival rate to Grade 5 of primary education, male (%)" }, { "id": "Longdefinition", "value": "Percentage of a cohort of male students enrolled in the first grade of primary education in a given school year who are expected to reach grade 5, regardless of repetition. Divide the total number of students belonging to a school-cohort who reached each successive grade of primary education by the number of students in the school-cohort i.e. those originally enrolled in the first grade of primary education, and multiply the result by 100. The survival rate is calculated on the basis of the reconstructed cohort method, which uses data on enrolment and repeaters for two consecutive years." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.PRS5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Survival rate to Grade 5 of primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of a cohort of students enrolled in the first grade of primary education in a given school year who are expected to reach grade 5, regardless of repetition. Divide the total number of students belonging to a school-cohort who reached each successive grade of primary education by the number of students in the school-cohort i.e. those originally enrolled in the first grade of primary education, and multiply the result by 100. The survival rate is calculated on the basis of the reconstructed cohort method, which uses data on enrolment and repeaters for two consecutive years." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.PRSL.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Survival rate to the last grade of primary education, female (%)" }, { "id": "Longdefinition", "value": "Percentage of a cohort of female students enrolled in the first grade of primary education in a given school year who are expected to reach the last grade of primary education, regardless of repetition. Divide the total number of students belonging to a school-cohort who reached each successive grade of primary education by the number of students in the school-cohort i.e. those originally enrolled in the first grade of primary education, and multiply the result by 100. The survival rate is calculated on the basis of the reconstructed cohort method, which uses data on enrolment and repeaters for two consecutive years." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.PRSL.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Survival rate to the last grade of primary education, male (%)" }, { "id": "Longdefinition", "value": "Percentage of a cohort of male students enrolled in the first grade of primary education in a given school year who are expected to reach the last grade of primary education, regardless of repetition. Divide the total number of students belonging to a school-cohort who reached each successive grade of primary education by the number of students in the school-cohort i.e. those originally enrolled in the first grade of primary education, and multiply the result by 100. The survival rate is calculated on the basis of the reconstructed cohort method, which uses data on enrolment and repeaters for two consecutive years." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.PRSL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Survival rate to the last grade of primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of a cohort of students enrolled in the first grade of primary education in a given school year who are expected to reach the last grade of primary education, regardless of repetition. Divide the total number of students belonging to a school-cohort who reached each successive grade of primary education by the number of students in the school-cohort i.e. those originally enrolled in the first grade of primary education, and multiply the result by 100. The survival rate is calculated on the basis of the reconstructed cohort method, which uses data on enrolment and repeaters for two consecutive years." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.SATT", "metatype": [ { "id": "IndicatorName", "value": "Attraction" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SATT.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Do the national policies governing the education system portray the position of principal or head teacher as professionalized and distinct figure within schools?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SATT.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average principal salary as percent of GDP per capita" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SATT.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals reporting being satisfied or very satisfied with their social status in the community" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SATT.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (School Management) - Attraction" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SATT.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (School Management) - Attraction" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN", "metatype": [ { "id": "IndicatorName", "value": "Clarity of Functions" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if the policies governing schools assign responsibility for the implementation of the maintenance and expansion of school infrastructure?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN.10", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Do the policies governing schools assign the responsibility of student learning assessments?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN.11", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if the policies governing schools assign responsibility for the implementation of principal hiring and assignment?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN.12", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Do the policies governing schools assign the responsibility of principal hiring and assignment?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN.13", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if the policies governing schools assign responsibility for the implementation of principal supervision and training?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN.14", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Do the policies governing schools assign the responsibility of principal supervision and training?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN.2", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Do the policies governing schools assign the responsibility of maintenance and expansion of school infrastructure?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if the policies governing schools assign responsibility for the implementation of the procurement of materials?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN.4", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Do the policies governing schools assign the responsibility of procurement of materials?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if the policies governing schools assign responsibility for the implementation of teacher hiring and assignment?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN.6", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Do the policies governing schools assign the responsibility of teacher hiring and assignment?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN.7", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if the policies governing schools assign responsibility for the implementation of teacher supervision, training, and coaching?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN.8", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Do the policies governing schools assign the responsibility of teacher supervision, training, and coaching?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN.9", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if the policies governing schools assign responsibility for the implementation of student learning assessments?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (School Management) - Clarity of Functions" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SCFN.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (School Management) - Clarity of Functions" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SEVL", "metatype": [ { "id": "IndicatorName", "value": "Evaluation" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SEVL.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy that specifies the need to monitor principal or head teacher performance?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SEVL.2", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is the criteria to evaluate principals clear and includes multiple factors?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SEVL.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report having been evaluated during the last school year" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SEVL.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report having been evaluated on multiple factors" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SEVL.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report there would be consequences after two negative evaluations" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SEVL.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report there would be consequences after two positive evaluations" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SEVL.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (School Management) - Evaluation" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SEVL.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (School Management) - Evaluation" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD", "metatype": [ { "id": "IndicatorName", "value": "Selection & Deployment" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a systematic approach/rubric for the selection of principals?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.10", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the most important factor considered when selecting a principal is years of experience" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.11", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the most important factor considered when selecting a principal is quality of teaching" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.12", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the most important factor considered when selecting a principal is demonstrated management qualities" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.13", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the most important factor considered when selecting a principal is having a good relationship with the owner of the school" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.14", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the most important factor considered when selecting a principal is political affiliations" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.15", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the most important factor considered when selecting a principal is ethnic group" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.16", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the most important factor considered when selecting a principal is knowledge of the local community" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.2", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) How are the principals selected? Based on the requirements, is the selection system meritocratic?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the factors considered when selecting a principal include years of experience" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the factors considered when selecting a principal include quality of teaching" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the factors considered when selecting a principal include demonstrated management qualities" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the factors considered when selecting a principal include good relationship with the owner of the school" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.7", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the factors considered when selecting a principal include political affiliations" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the factors considered when selecting a principal include ethnic group" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.9", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the factors considered when selecting a principal include knowledge of the local community" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (School Management) - Selection & Deployment" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSLD.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (School Management) - Selection & Deployment" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSUP", "metatype": [ { "id": "IndicatorName", "value": "Support" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSUP.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are principals required to have training on how to manage a school?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSUP.10", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report having used the skills they gained at the last training they attended" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSUP.11", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average number of trainings that principals report having been offered to them in the past year" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSUP.2", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are principals required to have management training for new principals?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSUP.3", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are principals required to have in-service training?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSUP.4", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are principals required to have mentoring/coaching by experienced principals?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSUP.5", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) How many times per year do principals have trainings?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSUP.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report ever having received formal training" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSUP.7", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report having received management training for new principals" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSUP.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report having received in-service training" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSUP.9", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report having received mentoring/coaching by experienced principals" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSUP.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (School Management) - Support" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.SSUP.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (School Management) - Support" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TATT", "metatype": [ { "id": "IndicatorName", "value": "Attraction" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TATT.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Average starting public-school teacher salary as percent of GDP per capita" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TATT.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting being satisfied or very satisfied with their social status in the community" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TATT.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting being satisfied or very satisfied with their job as teacher" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TATT.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having received financial bonuses in addition to their salaries" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TATT.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that there are incentives (financial or otherwise) for teachers to teach certain subjects/grades and/or in certain areas" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TATT.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that performance matters for promotions" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TATT.7", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a well-established career path for teachers?" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TATT.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that report salary delays in the past 12 months" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TATT.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Teaching) - Attraction" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TATT.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Teaching) - Attraction" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TCAQ.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in primary education, female (%)" }, { "id": "Longdefinition", "value": "Number of female teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the primary level in the given country, expressed as a percentage of the total number of female teachers at the primary level." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Number of female teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the primary level in the given country, expressed as a percentage of the total number of female teachers at the primary level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.PRM.TCAQ.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in primary education, male (%)" }, { "id": "Longdefinition", "value": "Number of male teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the primary level in the given country, expressed as a percentage of the total number of male teachers at the primary level." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Number of male teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the primary level in the given country, expressed as a percentage of the total number of male teachers at the primary level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.PRM.TCAQ.ZS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the primary level in the given country, expressed as a percentage of the total number of teachers at the primary level." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Number of teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the primary level in the given country, expressed as a percentage of the total number of teachers at the primary level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.PRM.TCHR", "metatype": [ { "id": "IndicatorName", "value": "Teachers in primary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of teachers in public and private primary education institutions. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.PRM.TCHR.FE", "metatype": [ { "id": "IndicatorName", "value": "Teachers in primary education, female (number)" }, { "id": "Longdefinition", "value": "Total number of female teachers in public and private primary education institutions. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.PRM.TCHR.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of teachers in primary education who are female (%)" }, { "id": "Longdefinition", "value": "Number of female teachers at the primary level expressed as a percentage of the total number of teachers (male and female) at the primary level in a given school year. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.PRM.TENR", "metatype": [ { "id": "IndicatorName", "value": "Net Adjusted Enrollment Rate" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TENR.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of primary school age children who are enrolled at primary education" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TEVL", "metatype": [ { "id": "IndicatorName", "value": "Evaluation" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TEVL.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Legislation assigns responsibility of evaluating the performance of teachers to a public authority (national, regional, local)" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TEVL.2", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Legislation assigns responsibility of evaluating the performance of teachers to the schools" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TEVL.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that report being evaluated in the past 12 months" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TEVL.4", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) The criteria to evaluate teachers is clear" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TEVL.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Number of criteria used to evaluate teachers" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TEVL.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that report there would be consequences after two negative evaluations" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TEVL.7", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that report there would be consequences after two positive evaluations" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TEVL.8", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) There are clear consequences for teachers who receive two or more negative evaluations" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TEVL.9", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) There are clear consequences for teachers who receive two or more positive evaluations" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TEVL.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Teaching) - Evaluation" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TEVL.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Teaching) - Evaluation" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TINM", "metatype": [ { "id": "IndicatorName", "value": "Intrinsic Motivation" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TINM.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"It is acceptable for a teacher to be absent if the assigned curriculum has been completed\"" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TINM.10", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"Students can change even their basic intelligence level considerably\"" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TINM.11", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers who state that intrinsic motivation was the main reason to become teachers" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TINM.12", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) New teachers are required to undergo a probationary period" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TINM.13", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) New teachers are required to undergo a probationary period" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TINM.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"It is acceptable for a teacher to be absent if students are left with work to do\"" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TINM.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"It is acceptable for a teacher to be absent if the teacher is doing something useful for the community\"" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TINM.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"Students deserve more attention if they attend school regularly\"" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TINM.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"Students deserve more attention if they come to school with materials\"" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TINM.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"Students deserve more attention if they are motivated to learn\"" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TINM.7", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"Students have a certain amount of intelligence and they really can’t do much to change it\"" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TINM.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"To be honest, students can’t really change how intelligent they are\"" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TINM.9", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"Students can always substantially change how intelligent they are\"" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TINM.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Teaching) - Intrinsic Motivation" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TINM.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Teaching) - Intrinsic Motivation" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TMNA", "metatype": [ { "id": "IndicatorName", "value": "Monitoring & Accountability" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TMNA.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Information on teacher presence/absenteeism is being collected on a regular basis" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TMNA.2", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Teachers receive monetary compensation for being present" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TMNA.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Teacher report receiving monetary compensation (aside from salary) for being present" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TMNA.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that report having been absent because of administrative processes" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TMNA.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that report that there would be consequences for being absent 40% of the time" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TMNA.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Teaching) - Monitoring & Accountability" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TMNA.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Teaching) - Monitoring & Accountability" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSDP", "metatype": [ { "id": "IndicatorName", "value": "Selection & Deployment" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSDP.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Requirements to enter into initial education programs" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSDP.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average quality of applicants accepted into initial education programs" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSDP.3", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Requirements to become a primary school teacher" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSDP.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Requirements to become a primary school teacher" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSDP.5", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Requirements to fulfill a transfer request" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSDP.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Requirements to fulfill a transfer request" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSDP.7", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Selectivity of teacher hiring process" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSDP.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Teaching) - Selection & Deployment" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSDP.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Teaching) - Selection & Deployment" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSUP", "metatype": [ { "id": "IndicatorName", "value": "Support" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSUP.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Practicum required as part of pre-service training" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSUP.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent reporting they completed a practicum as part of pre-service training" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSUP.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they participated in an induction and/or mentorship program" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSUP.4", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Participation in professional development has professional implications for teachers" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSUP.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having attended in-service trainings in the past 12 months" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSUP.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average length of the trainings attended" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSUP.7", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average span of time (in weeks) of those trainings" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSUP.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average percent of time spent inside the classrooms during the trainings" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSUP.9", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that report having opportunities to come together with other teachers to discuss ways of improving teaching" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSUP.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Teaching) - Support" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.TSUP.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Teaching) - Support" }, { "id": "Notesfromoriginalsource", "value": "NA" }, { "id": "Source", "value": "Global Education Policy Dashboard, World Bank" }, { "id": "Topic", "value": "Learning Poverty" } ], "source_id": "12" }, { "id": "SE.PRM.UNER", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school children of primary school age, both sexes (number)" }, { "id": "Longdefinition", "value": "Children in the official primary school age range who are not enrolled in either primary or secondary schools." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.UNER.FE", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school children of primary school age, female (number)" }, { "id": "Longdefinition", "value": "Female children in the official primary school age range who are not enrolled in either primary or secondary schools." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.PRM.UNER.MA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school children of primary school age, male (number)" }, { "id": "Longdefinition", "value": "Male children in the official primary school age range who are not enrolled in either primary or secondary schools." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "SE.SCH.LIFE", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, primary to tertiary, both sexes (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.SCH.LIFE.FE", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, primary to tertiary, female (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.SCH.LIFE.MA", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, primary to tertiary, male (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.SEC.AGES", "metatype": [ { "id": "IndicatorName", "value": "Official entrance age to lower secondary education (years)" }, { "id": "Longdefinition", "value": "Age at which students would enter lower secondary education, assuming they had started at the official entrance age for the lowest level of education, had studied full-time throughout and had progressed through the system without repeating or skipping a grade." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.CMPT.LO.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Gross intake ratio to the last grade of lower secondary general education, female (%)" }, { "id": "Longdefinition", "value": "Total number of new entrants into the last grade of lower secondary general education, regardless of age, expressed as a percentage of the population at the intended entrance age to the last grade of lower secondary general education. The intended entrance age to the last grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. This indicator has been used as Lower Secondary Completion Rate. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of new entrants into the last grade of lower secondary general education, regardless of age, expressed as a percentage of the population at the intended entrance age to the last grade of lower secondary general education. The intended entrance age to the last grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. This indicator has been used as Lower Secondary Completion Rate. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.CMPT.LO.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Gross intake ratio to the last grade of lower secondary general education, male (%)" }, { "id": "Longdefinition", "value": "Total number of new entrants into the last grade of lower secondary general education, regardless of age, expressed as a percentage of the population at the intended entrance age to the last grade of lower secondary general education. The intended entrance age to the last grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. This indicator has been used as Lower Secondary Completion Rate. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of new entrants into the last grade of lower secondary general education, regardless of age, expressed as a percentage of the population at the intended entrance age to the last grade of lower secondary general education. The intended entrance age to the last grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. This indicator has been used as Lower Secondary Completion Rate. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.CMPT.LO.ZS", "metatype": [ { "id": "IndicatorName", "value": "Gross intake ratio to the last grade of lower secondary general education, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of new entrants into the last grade of lower secondary general education, regardless of age, expressed as a percentage of the population at the intended entrance age to the last grade of lower secondary general education. The intended entrance age to the last grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. This indicator has been used as Lower Secondary Completion Rate. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of new entrants into the last grade of lower secondary general education, regardless of age, expressed as a percentage of the population at the intended entrance age to the last grade of lower secondary general education. The intended entrance age to the last grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. This indicator has been used as Lower Secondary Completion Rate. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.DURS", "metatype": [ { "id": "IndicatorName", "value": "Theoretical duration of secondary education (years)" }, { "id": "Longdefinition", "value": "Number of grades (years) in secondary education (ISCED 2 and 3)." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.DURS.LO ", "metatype": [ { "id": "IndicatorName", "value": "Theoretical duration of lower secondary education (years)" }, { "id": "Longdefinition", "value": "Number of grades (years) in lower secondary education." } ], "source_id": "12" }, { "id": "SE.SEC.DURS.LO", "metatype": [ { "id": "Shortdefinition", "value": "Number of grades (years) in lower secondary education." } ], "source_id": "12" }, { "id": "SE.SEC.DURS.LO ", "metatype": [ { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "SE.SEC.DURS.LO", "metatype": [ { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.DURS.UP", "metatype": [ { "id": "IndicatorName", "value": "Theoretical duration of upper secondary education (years)" }, { "id": "Longdefinition", "value": "Number of grades (years) in upper secondary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.ENRL", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in secondary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of students enrolled at public and private secondary education institutions regardless of age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.ENRL.FE", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in secondary education, female (number)" }, { "id": "Longdefinition", "value": "Total number of female students enrolled at public and private secondary education institutions regardless of age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.ENRL.VO.ZS", "metatype": [ { "id": "IndicatorName", "value": "Share of all students in secondary education enrolled in vocational programmes (%)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in vocational programmes at the secondary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the secondary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Shortdefinition", "value": "Total number of students enrolled in vocational programmes at the secondary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the secondary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.ENRR", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, secondary, both sexes (%)" }, { "id": "Longdefinition", "value": "Total enrollment in secondary education, regardless of age, expressed as a percentage of the population of official secondary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.ENRR.FE", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, secondary, female (%)" }, { "id": "Longdefinition", "value": "Total female enrollment in secondary education, regardless of age, expressed as a percentage of the female population of official secondary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.ENRR.LO", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, lower secondary, both sexes (%)" }, { "id": "Longdefinition", "value": "Total enrollment in lower secondary education, regardless of age, expressed as a percentage of the total population of official lower secondary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.ENRR.LO.FE", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, lower secondary, female (%)" }, { "id": "Longdefinition", "value": "Total female enrollment in lower secondary education, regardless of age, expressed as a percentage of the total female population of official lower secondary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.ENRR.LO.MA", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, lower secondary, male (%)" }, { "id": "Longdefinition", "value": "Total male enrollment in lower secondary education, regardless of age, expressed as a percentage of the total male population of official lower secondary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.ENRR.MA", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, secondary, male (%)" }, { "id": "Longdefinition", "value": "Total male enrollment in secondary education, regardless of age, expressed as a percentage of the male population of official secondary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.ENRR.UP", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, upper secondary, both sexes (%)" }, { "id": "Longdefinition", "value": "Total enrollment in upper secondary education, regardless of age, expressed as a percentage of the total population of official upper secondary education age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.ENRR.UP.FE ", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, upper secondary, female (%)" }, { "id": "Longdefinition", "value": "Total female enrollment in upper secondary education, regardless of age, expressed as a percentage of the female population of official upper secondary education age." } ], "source_id": "12" }, { "id": "SE.SEC.ENRR.UP.FE", "metatype": [ { "id": "Shortdefinition", "value": "Total female enrollment in upper secondary education, regardless of age, expressed as a percentage of the female population of official upper secondary education age." } ], "source_id": "12" }, { "id": "SE.SEC.ENRR.UP.FE ", "metatype": [ { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "SE.SEC.ENRR.UP.FE", "metatype": [ { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.ENRR.UP.MA ", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, upper secondary, male (%)" }, { "id": "Longdefinition", "value": "Total male enrollment in upper secondary education, regardless of age, expressed as a percentage of the male population of official upper secondary education age." } ], "source_id": "12" }, { "id": "SE.SEC.ENRR.UP.MA", "metatype": [ { "id": "Shortdefinition", "value": "Total male enrollment in upper secondary education, regardless of age, expressed as a percentage of the male population of official upper secondary education age." } ], "source_id": "12" }, { "id": "SE.SEC.ENRR.UP.MA ", "metatype": [ { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "SE.SEC.ENRR.UP.MA", "metatype": [ { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.PRIV.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of enrolment in secondary education in private institutions (%)" }, { "id": "Longdefinition", "value": "Total number of students in secondary education enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body (e.g., non-governmental organisation, religious body, special interest group, foundation or business enterprise), expressed as a percentage of total number of students enrolled in secondary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "SE.SEC.TCAQ.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in secondary education, female (%)" }, { "id": "Longdefinition", "value": "Number of female teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the secondary level in the given country, expressed as a percentage of the total number of female teachers at the secondary level." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Number of female teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the secondary level in the given country, expressed as a percentage of the total number of female teachers at the secondary level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.SEC.TCAQ.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in secondary education, male (%)" }, { "id": "Longdefinition", "value": "Number of male teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the secondary level in the given country, expressed as a percentage of the total number of male teachers at the secondary level." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Number of male teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the secondary level in the given country, expressed as a percentage of the total number of male teachers at the secondary level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.SEC.TCAQ.ZS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in secondary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the secondary level in the given country, expressed as a percentage of the total number of teachers at the secondary level." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Number of teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the secondary level in the given country, expressed as a percentage of the total number of teachers at the secondary level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.SEC.TCHR", "metatype": [ { "id": "IndicatorName", "value": "Teachers in secondary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of teachers in public and private secondary education institutions (ISCED 2 and 3). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.SEC.TCHR.FE", "metatype": [ { "id": "IndicatorName", "value": "Teachers in secondary education, female (number)" }, { "id": "Longdefinition", "value": "Total number of female teachers in public and private secondary education institutions (ISCED 2 and 3). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.SEC.TCHR.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of teachers in secondary education who are female (%)" }, { "id": "Longdefinition", "value": "Number of female teachers at the secondary level expressed as a percentage of the total number of teachers (male and female) at the secondary level in a given school year. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.TER.CMPL.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Gross graduation ratio from first degree programmes (ISCED 6 and 7) in tertiary education, female (%)" }, { "id": "Longdefinition", "value": "Number of female graduates from first degree programmes (at ISCED 6 and 7) expressed as a percentage of the female population of the theoretical graduation age of the most common first degree programme." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.CMPL.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Gross graduation ratio from first degree programmes (ISCED 6 and 7) in tertiary education, male (%)" }, { "id": "Longdefinition", "value": "Number of male graduates from first degree programmes (at ISCED 6 and 7) expressed as a percentage of the male population of the theoretical graduation age of the most common first degree programme." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.CMPL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Gross graduation ratio from first degree programmes (ISCED 6 and 7) in tertiary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of graduates from first degree programmes (at ISCED 6 and 7) expressed as a percentage of the population of the theoretical graduation age of the most common first degree programme." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.ENRL", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in tertiary education, all programmes, both sexes (number)" }, { "id": "Longdefinition", "value": "The total number of students enrolled at public and private tertiary education institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.ENRL.FE", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in tertiary education, all programmes, female (number)" }, { "id": "Longdefinition", "value": "The total number of female students enrolled at public and private tertiary education institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.ENRR", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio for tertiary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Total enrollment in tertiary education (ISCED 5 to 8), regardless of age, expressed as a percentage of the total population of the five-year age group following on from secondary school leaving." }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Total enrollment in tertiary education (ISCED 5 to 8), regardless of age, expressed as a percentage of the total population of the five-year age group following on from secondary school leaving." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.ENRR.FE", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio for tertiary education, female (%)" }, { "id": "Longdefinition", "value": "Total female enrollment in tertiary education (ISCED 5 to 8), regardless of age, expressed as a percentage of the total female population of the five-year age group following on from secondary school leaving." }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Total female enrollment in tertiary education (ISCED 5 to 8), regardless of age, expressed as a percentage of the total female population of the five-year age group following on from secondary school leaving." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.ENRR.MA", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio for tertiary education, male (%)" }, { "id": "Longdefinition", "value": "Total male enrollment in tertiary education (ISCED 5 to 8), regardless of age, expressed as a percentage of the total male population of the five-year age group following on from secondary school leaving." }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Total male enrollment in tertiary education (ISCED 5 to 8), regardless of age, expressed as a percentage of the total male population of the five-year age group following on from secondary school leaving." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.GRAD.AG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of graduates from tertiary education graduating from Agriculture, Forestry, Fisheries and Veterinary programmes, both sexes (%)" }, { "id": "Longdefinition", "value": "Share of all tertiary graduates who completed Agriculture, Forestry, Fisheries and Veterinary programmes in the reference year." }, { "id": "Shortdefinition", "value": "Share of all tertiary graduates who completed Agriculture, Forestry, Fisheries and Veterinary programmes in the reference year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.GRAD.ED.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of graduates from tertiary education graduating from Education programmes, both sexes (%)" }, { "id": "Longdefinition", "value": "Share of all tertiary graduates who completed education programmes in the reference year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.GRAD.EN.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of graduates from tertiary education graduating from Engineering, Manufacturing and Construction programmes, both sexes (%)" }, { "id": "Longdefinition", "value": "Share of all tertiary graduates who completed engineering, manufacturing and construction programmes in the reference year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.GRAD.HL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of graduates from tertiary education graduating from Health and Welfare programmes, both sexes (%)" }, { "id": "Longdefinition", "value": "Share of all tertiary graduates who completed health and welfare programmes in the reference year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.GRAD.HU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of graduates from tertiary education graduating from Arts and Humanities programmes, both sexes (%)" }, { "id": "Longdefinition", "value": "Share of all tertiary graduates who completed humanities and arts programmes in the reference year." }, { "id": "Shortdefinition", "value": "Share of all tertiary graduates who completed humanities and arts programmes in the reference year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.GRAD.OT.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of graduates from tertiary education graduating from programmes in unspecified fields, both sexes (%)" }, { "id": "Longdefinition", "value": "Share of all tertiary graduates who completed programmes in unspecified fields in the reference year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.GRAD.SC.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of graduates from tertiary education graduating from Natural Sciences, Mathematics and Statistics programmes, both sexes (%)" }, { "id": "Longdefinition", "value": "Share of all tertiary graduates who completed Natural Sciences, Mathematics and Statistics programmes in the reference year." }, { "id": "Shortdefinition", "value": "Share of all tertiary graduates who completed Natural Sciences, Mathematics and Statistics programmes in the reference year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.GRAD.SS.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of graduates from tertiary education graduating from Social Sciences, Journalism and Information programmes, both sexes (%)" }, { "id": "Longdefinition", "value": "Share of all tertiary graduates who completed Social Sciences, Journalism and Information programmes in the reference year." }, { "id": "Shortdefinition", "value": "Share of all tertiary graduates who completed Social Sciences, Journalism and Information programmes in the reference year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.GRAD.SV.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of graduates from tertiary education graduating from Services programmes, both sexes (%)" }, { "id": "Longdefinition", "value": "Share of all tertiary graduates who completed services programmes in the reference year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.PRIV.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of enrolment in tertiary education in private institutions (%)" }, { "id": "Longdefinition", "value": "Total number of students in tertiary education enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body (e.g., non-governmental organisation, religious body, special interest group, foundation or business enterprise), expressed as a percentage of total number of students enrolled in tertiary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.TER.TCHR", "metatype": [ { "id": "IndicatorName", "value": "Teachers in tertiary education programmes, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of teachers in public and private tertiary education institutions (ISCED 5-8). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.TER.TCHR.FE", "metatype": [ { "id": "IndicatorName", "value": "Teachers in tertiary education programmes, female (number)" }, { "id": "Longdefinition", "value": "Total number of female teachers in public and private tertiary education institutions (ISCED 5-8). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.TER.TCHR.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of teachers in tertiary education who are female (%)" }, { "id": "Longdefinition", "value": "Number of female teachers at the tertiary level expressed as a percentage of the total number of teachers (male and female) at the tertiary level in a given school year. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "SE.TOT.ENRR", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, primary to tertiary, both sexes (%)" }, { "id": "Longdefinition", "value": "Total enrollment in primary, secondary and tertiary education, regardless of age, expressed as a percentage of the total population of primary school age, secondary school age, and the five-year age group following on from secondary school leaving. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "SE.XPD.CUR.TOTL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Current expenditure as % of total expenditure in public institutions (%)" }, { "id": "Longdefinition", "value": "Current expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional). Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration). Divide all current expenditure in public institutions by total expenditure (current and capital) in public institutions, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Current expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional). Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration). Divide all current expenditure in public institutions by total expenditure (current and capital) in public institutions, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "SE.XPD.TOTL.GB.ZS", "metatype": [ { "id": "IndicatorName", "value": "Expenditure on education as % of total government expenditure (%)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers), expressed as a percentage of total general government expenditure on all sectors (including health, education, social services, etc.). It includes expenditure funded by transfers from international sources to government. Public education expenditure includes spending by local/municipal, regional and national governments (excluding household contributions) on educational institutions (both public and private), education administration, and subsidies for private entities (students/households and other privates entities). In some instances data on total public expenditure on education refers only to the ministry of education and can exclude other ministries that spend a part of their budget on educational activities. The indicator is calculated by dividing total public expenditure on education incurred by all government agencies/departments by the total government expenditure and multiplying by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers), expressed as a percentage of total general government expenditure on all sectors (including health, education, social services, etc.). It includes expenditure funded by transfers from international sources to government. Public education expenditure includes spending by local/municipal, regional and national governments (excluding household contributions) on educational institutions (both public and private), education administration, and subsidies for private entities (students/households and other privates entities). In some instances data on total public expenditure on education refers only to the ministry of education and can exclude other ministries that spend a part of their budget on educational activities. The indicator is calculated by dividing total public expenditure on education incurred by all government agencies/departments by the total government expenditure and multiplying by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "SE.XPD.TOTL.GD.ZS", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on education as % of GDP (%)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "SH.DYN.AIDS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "Prevalence of HIV, total (% of population ages 15-49)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "12" }, { "id": "SH.DYN.MORT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys." }, { "id": "IndicatorName", "value": "Mortality rate, under-5 (per 1,000 live births)" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "12" }, { "id": "SL.TLF.ADVN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "Labor force with advanced education, female (% of female working-age population with advanced education)" }, { "id": "Longdefinition", "value": "The percentage of the working age population with an advanced level of education who are in the labor force. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in March 1, 2020." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "12" }, { "id": "SL.TLF.ADVN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "Labor force with advanced education, male (% of male working-age population with advanced education)" }, { "id": "Longdefinition", "value": "The percentage of the working age population with an advanced level of education who are in the labor force. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in March 1, 2020." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "12" }, { "id": "SL.TLF.ADVN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "Labor force with advanced education (% of total working-age population with advanced education)" }, { "id": "Longdefinition", "value": "The percentage of the working age population with an advanced level of education who are in the labor force. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in March 1, 2020." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "12" }, { "id": "SL.TLF.BASC.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "Labor force with basic education, female (% of female working-age population with basic education)" }, { "id": "Longdefinition", "value": "The percentage of the working age population with a basic level of education who are in the labor force. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in March 1, 2020." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "12" }, { "id": "SL.TLF.BASC.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "Labor force with basic education, male (% of male working-age population with basic education)" }, { "id": "Longdefinition", "value": "The percentage of the working age population with a basic level of education who are in the labor force. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in March 1, 2020." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "12" }, { "id": "SL.TLF.BASC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "Labor force with basic education (% of total working-age population with basic education)" }, { "id": "Longdefinition", "value": "The percentage of the working age population with a basic level of education who are in the labor force. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in March 1, 2020." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "12" }, { "id": "SL.TLF.INTM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "Labor force with intermediate education, female (% of female working-age population with intermediate education)" }, { "id": "Longdefinition", "value": "The percentage of the working age population with an intermediate level of education who are in the labor force. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in March 1, 2020." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "12" }, { "id": "SL.TLF.INTM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "Labor force with intermediate education, male (% of male working-age population with intermediate education)" }, { "id": "Longdefinition", "value": "The percentage of the working age population with an intermediate level of education who are in the labor force. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in March 1, 2020." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "12" }, { "id": "SL.TLF.INTM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "Labor force with intermediate education (% of total working-age population with intermediate education)" }, { "id": "Longdefinition", "value": "The percentage of the working age population with an intermediate level of education who are in the labor force. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in March 1, 2020." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "12" }, { "id": "SL.TLF.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Labor force, female (% of total labor force)" }, { "id": "Longdefinition", "value": "Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived using data from International Labour Organization, ILOSTAT database. The data retrieved in March 1, 2020." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "12" }, { "id": "SL.TLF.TOTL.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Labor force, total" }, { "id": "Longdefinition", "value": "Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived using data from International Labour Organization, ILOSTAT database. The data retrieved in March 1, 2020." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "12" }, { "id": "SL.UEM.NEET.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, female (% of female youth population)" }, { "id": "Limitationsandexceptions", "value": "Data should be used cautiously because of differences in age coverage." }, { "id": "Longdefinition", "value": "Share of youth not in education, employment or training (NEET) is the proportion of young people who are not in education, employment, or training to the population of the corresponding age group: youth (ages 15 to 24); persons ages 15 to 29; or both age groups." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in March 1, 2020." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work in a recent past period, and currently available for and seeking for employment. But there may be persons who do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. NEET rates capture more broadly untapped potential youth, including such individuals who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\").\n\nYouth are defined as persons ages 15 to 24; young adults are those ages 25 to 29; and adults are those ages 25 and above. However, countries vary somewhat in their operational definitions. In particular, the lower age limit for young people is usually determined by the minimum age for leaving school, where this exists." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "12" }, { "id": "SL.UEM.NEET.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, male (% of male youth population)" }, { "id": "Limitationsandexceptions", "value": "Data should be used cautiously because of differences in age coverage." }, { "id": "Longdefinition", "value": "Share of youth not in education, employment or training (NEET) is the proportion of young people who are not in education, employment, or training to the population of the corresponding age group: youth (ages 15 to 24); persons ages 15 to 29; or both age groups." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in March 1, 2020." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work in a recent past period, and currently available for and seeking for employment. But there may be persons who do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. NEET rates capture more broadly untapped potential youth, including such individuals who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\").\n\nYouth are defined as persons ages 15 to 24; young adults are those ages 25 to 29; and adults are those ages 25 and above. However, countries vary somewhat in their operational definitions. In particular, the lower age limit for young people is usually determined by the minimum age for leaving school, where this exists." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "12" }, { "id": "SL.UEM.NEET.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, total (% of youth population)" }, { "id": "Limitationsandexceptions", "value": "Data should be used cautiously because of differences in age coverage." }, { "id": "Longdefinition", "value": "Share of youth not in education, employment or training (NEET) is the proportion of young people who are not in education, employment, or training to the population of the corresponding age group: youth (ages 15 to 24); persons ages 15 to 29; or both age groups." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in March 1, 2020." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work in a recent past period, and currently available for and seeking for employment. But there may be persons who do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. NEET rates capture more broadly untapped potential youth, including such individuals who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\").\n\nYouth are defined as persons ages 15 to 24; young adults are those ages 25 to 29; and adults are those ages 25 and above. However, countries vary somewhat in their operational definitions. In particular, the lower age limit for young people is usually determined by the minimum age for leaving school, where this exists." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "12" }, { "id": "SL.UEM.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, female (% of female labor force) (modeled ILO estimate)" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in March 1, 2020." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "12" }, { "id": "SL.UEM.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, male (% of male labor force) (modeled ILO estimate)" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in March 1, 2020." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "12" }, { "id": "SL.UEM.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, total (% of total labor force) (modeled ILO estimate)" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in March 1, 2020." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "12" }, { "id": "SP.POP.0014.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "IndicatorName", "value": "Population ages 0-14, female" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "12" }, { "id": "SP.POP.0014.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 0-14, male" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "12" }, { "id": "SP.POP.0014.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 0-14, total" }, { "id": "Longdefinition", "value": "Total population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "12" }, { "id": "SP.POP.0014.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Population ages 0-14 (% of total population)" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Population between the ages 0 to 14 as a percentage of the total population. Population is based on the de facto definition of population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "12" }, { "id": "SP.POP.0305.FE.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 3-5, female" }, { "id": "Longdefinition", "value": "Population, ages 3-5, female is the total number of females age 3-5." }, { "id": "Shortdefinition", "value": "Population, ages 3-5, female is the total number of females age 3-5." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0305.MA.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 3-5, male" }, { "id": "Longdefinition", "value": "Population, ages 3-5, male is the total number of males age 3-5." }, { "id": "Shortdefinition", "value": "Population, ages 3-5, male is the total number of males age 3-5." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0305.TO.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 3-5, total" }, { "id": "Longdefinition", "value": "Population, ages 3-5, total is the total population age 3-5." }, { "id": "Shortdefinition", "value": "Population, ages 3-5, total is the total population age 3-5." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0406.FE.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 4-6, female" }, { "id": "Longdefinition", "value": "Population, ages 4-6, female is the total number of females age 4-6." }, { "id": "Shortdefinition", "value": "Population, ages 4-6, female is the total number of females age 4-6." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0406.MA.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 4-6, male" }, { "id": "Longdefinition", "value": "Population, ages 4-6, male is the total number of males age 4-6." }, { "id": "Shortdefinition", "value": "Population, ages 4-6, male is the total number of males age 4-6." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0406.TO.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 4-6, total" }, { "id": "Longdefinition", "value": "Population, ages 4-6, total is the total population age 4-6." }, { "id": "Shortdefinition", "value": "Population, ages 4-6, total is the total population age 4-6." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0509.FE.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 5-9, female" }, { "id": "Longdefinition", "value": "Population, ages 5-9, female is the total number of females age 5-9." }, { "id": "Shortdefinition", "value": "Population, ages 5-9, female is the total number of females age 5-9." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0509.MA.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 5-9, male" }, { "id": "Longdefinition", "value": "Population, ages 5-9, male is the total number of males age 5-9." }, { "id": "Shortdefinition", "value": "Population, ages 5-9, male is the total number of males age 5-9." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0509.TO.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 5-9, total" }, { "id": "Longdefinition", "value": "Population, ages 5-9, total is the total population age 5-9." }, { "id": "Shortdefinition", "value": "Population, ages 5-9, total is the total population age 5-9." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0510.FE.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 5-10, female" }, { "id": "Longdefinition", "value": "Population, ages 5-10, female is the total number of females age 5-10." }, { "id": "Shortdefinition", "value": "Population, ages 5-10, female is the total number of females age 5-10." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0510.MA.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 5-10, male" }, { "id": "Longdefinition", "value": "Population, ages 5-10, male is the total number of males age 5-10." }, { "id": "Shortdefinition", "value": "Population, ages 5-10, male is the total number of males age 5-10." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0510.TO.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 5-10, total" }, { "id": "Longdefinition", "value": "Population, ages 5-10, total is the total population age 5-10." }, { "id": "Shortdefinition", "value": "Population, ages 5-10, total is the total population age 5-10." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0511.FE.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 5-11, female" }, { "id": "Longdefinition", "value": "Population, ages 5-11, female is the total number of females age 5-11." }, { "id": "Shortdefinition", "value": "Population, ages 5-11, female is the total number of females age 5-11." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0511.MA.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 5-11, male" }, { "id": "Longdefinition", "value": "Population, ages 5-11, male is the total number of males age 5-11." }, { "id": "Shortdefinition", "value": "Population, ages 5-11, male is the total number of males age 5-11." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0511.TO.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 5-11, total" }, { "id": "Longdefinition", "value": "Population, ages 5-11, total is the total population age 5-11." }, { "id": "Shortdefinition", "value": "Population, ages 5-11, total is the total population age 5-11." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0609.FE.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 6-9, female" }, { "id": "Longdefinition", "value": "Population, ages 6-9, male is the total number of males age 6-9." }, { "id": "Shortdefinition", "value": "Population, ages 6-9, male is the total number of males age 6-9." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0609.MA.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 6-9, male" }, { "id": "Longdefinition", "value": "Population, ages 6-9, total is the total population age 6-9." }, { "id": "Shortdefinition", "value": "Population, ages 6-9, total is the total population age 6-9." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0609.TO.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 6-9, total" }, { "id": "Longdefinition", "value": "Population, ages 6-9, female is the total number of females age 6-9." }, { "id": "Shortdefinition", "value": "Population, ages 6-9, female is the total number of females age 6-9." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0610.FE.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 6-10, female" }, { "id": "Longdefinition", "value": "Population, ages 6-10, female is the total number of females age 6-10." }, { "id": "Shortdefinition", "value": "Population, ages 6-10, female is the total number of females age 6-10." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0610.MA.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 6-10, male" }, { "id": "Longdefinition", "value": "Population, ages 6-10, male is the total number of males age 6-10." }, { "id": "Shortdefinition", "value": "Population, ages 6-10, male is the total number of males age 6-10." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0610.TO.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 6-10, total" }, { "id": "Longdefinition", "value": "Population, ages 6-10, total is the total population age 6-10." }, { "id": "Shortdefinition", "value": "Population, ages 6-10, total is the total population age 6-10." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0611.FE.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 6-11, female" }, { "id": "Longdefinition", "value": "Population, ages 6-11, female is the total number of females age 6-11." }, { "id": "Shortdefinition", "value": "Population, ages 6-11, female is the total number of females age 6-11." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0611.MA.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 6-11, male" }, { "id": "Longdefinition", "value": "Population, ages 6-11, male is the total number of males age 6-11." }, { "id": "Shortdefinition", "value": "Population, ages 6-11, male is the total number of males age 6-11." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0611.TO.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 6-11, total" }, { "id": "Longdefinition", "value": "Population, ages 6-11, total is the total population age 6-11." }, { "id": "Shortdefinition", "value": "Population, ages 6-11, total is the total population age 6-11." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0612.FE.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 6-12, female" }, { "id": "Longdefinition", "value": "Population, ages 6-12, female is the total number of females age 6-12." }, { "id": "Shortdefinition", "value": "Population, ages 6-12, female is the total number of females age 6-12." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0612.MA.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 6-12, male" }, { "id": "Longdefinition", "value": "Population, ages 6-12, male is the total number of males age 6-12." }, { "id": "Shortdefinition", "value": "Population, ages 6-12, male is the total number of males age 6-12." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0612.TO.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 6-12, total" }, { "id": "Longdefinition", "value": "Population, ages 6-12, total is the total population age 6-12." }, { "id": "Shortdefinition", "value": "Population, ages 6-12, total is the total population age 6-12." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0709.FE.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 7-9, female" }, { "id": "Longdefinition", "value": "Population, ages 7-9, female is the total number of females age 7-9." }, { "id": "Shortdefinition", "value": "Population, ages 7-9, female is the total number of females age 7-9." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0709.MA.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 7-9, male" }, { "id": "Longdefinition", "value": "Population, ages 7-9, male is the total number of males age 7-9." }, { "id": "Shortdefinition", "value": "Population, ages 7-9, male is the total number of males age 7-9." }, { "id": "Source", "value": "UNESCO Institute for Statistics (Derived)" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.0709.TO.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, ages 7-9, total" }, { "id": "Longdefinition", "value": 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Population: Structure" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics." }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.AG22.FE.UN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 22, female, UNESCO" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics." }, { "id": "Othernotes", "value": "Health: Population: Structure" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics." }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.AG22.MA.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, age 22, male" }, { "id": "Longdefinition", "value": "Population, age 22, male refers to the male population at the specified age." }, { "id": "Shortdefinition", "value": "Population, age 22, male refers to the male population at the specified age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.AG22.TO.UN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 22, total, UNESCO" }, { "id": "Longdefinition", "value": "Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics." }, { "id": "Othernotes", "value": "Health: Population: Structure" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics." }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.AG23.FE.UN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 23, female, UNESCO" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics." }, { "id": "Othernotes", "value": "Health: Population: Structure" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics." }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.AG23.MA.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, age 23, male" }, { "id": "Longdefinition", "value": "Population, age 23, male refers to the male population at the specified age." }, { "id": "Shortdefinition", "value": "Population, age 23, male refers to the male population at the specified age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.AG23.TO.UN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 23, total, UNESCO" }, { "id": "Longdefinition", "value": "Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics." }, { "id": "Othernotes", "value": "Health: Population: Structure" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics." }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.AG24.FE.UN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 24, female, UNESCO" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics." }, { "id": "Othernotes", "value": "Health: Population: Structure" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics." }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.AG24.MA.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, age 24, male" }, { "id": "Longdefinition", "value": "Population, age 24, male refers to the male population at the specified age." }, { "id": "Shortdefinition", "value": "Population, age 24, male refers to the male population at the specified age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.AG24.TO.UN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 24, total, UNESCO" }, { "id": "Longdefinition", "value": "Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics." }, { "id": "Othernotes", "value": "Health: Population: Structure" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics." }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.AG25.FE.UN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 25, female, UNESCO" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics." }, { "id": "Othernotes", "value": "Health: Population: Structure" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics." }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.AG25.MA.UN", "metatype": [ { "id": "IndicatorName", "value": "Population, age 25, male" }, { "id": "Longdefinition", "value": "Population, age 25, male refers to the male population at the specified age." }, { "id": "Shortdefinition", "value": "Population, age 25, male refers to the male population at the specified age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.AG25.TO.UN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 25, total, UNESCO" }, { "id": "Longdefinition", "value": "Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics." }, { "id": "Othernotes", "value": "Health: Population: Structure" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics." }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.POP.GROW", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Population growth (annual %)" }, { "id": "Longdefinition", "value": "Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Annual population growth rate. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2019 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "12" }, { "id": "SP.POP.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis." }, { "id": "IndicatorName", "value": "Population, total" }, { "id": "Limitationsandexceptions", "value": "Current population estimates for developing countries that lack (i) reliable recent census data, and (ii) pre- and post-census estimates for countries with census data, are provided by the United Nations Population Division and other agencies. \n\nThe cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in both the model and the data. In the UN estimates the five-year age group is the cohort unit and five-year period data are used; therefore interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\nBecause future trends cannot be known with certainty, population projections have a wide range of uncertainty." }, { "id": "Longdefinition", "value": "Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "(1) United Nations Population Division. World Population Prospects: 2019 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "12" }, { "id": "SP.POP.TOTL.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population, female" }, { "id": "Longdefinition", "value": "Female population is based on the de facto definition of population, which counts all female residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "12" }, { "id": "SP.POP.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Population, female (% of total population)" }, { "id": "Longdefinition", "value": "Female population is the percentage of the population that is female. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "12" }, { "id": "SP.POP.TOTL.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population, male" }, { "id": "Longdefinition", "value": "Male population is based on the de facto definition of population, which counts all male residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "12" }, { "id": "SP.POP.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Population, male (% of total population)" }, { "id": "Longdefinition", "value": "Male population is the percentage of the population that is male. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "12" }, { "id": "SP.PRE.TOTL.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, pre-primary education, female (number)" }, { "id": "Longdefinition", "value": "Female population of the age-group theoretically corresponding to pre-primary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Female population of the age-group theoretically corresponding to pre-primary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.PRE.TOTL.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, pre-primary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Population of the age-group theoretically corresponding to pre-primary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Population of the age-group theoretically corresponding to pre-primary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.PRE.TOTL.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, pre-primary education, male (number)" }, { "id": "Longdefinition", "value": "Male population of the age-group theoretically corresponding to pre-primary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Male population of the age-group theoretically corresponding to pre-primary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.PRM.GRAD.FE", "metatype": [ { "id": "IndicatorName", "value": "School age population, last grade of primary education, female (number)" }, { "id": "Longdefinition", "value": "Female population of the age-group theoretically corresponding to the last grade of primary school as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Female population of the age-group theoretically corresponding to the last grade of primary school as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.PRM.GRAD.MA", "metatype": [ { "id": "IndicatorName", "value": "School age population, last grade of primary education, male (number)" }, { "id": "Longdefinition", "value": "Male population of the age-group theoretically corresponding to the last grade of primary school as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Male population of the age-group theoretically corresponding to the last grade of primary school as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.PRM.GRAD.TO", "metatype": [ { "id": "IndicatorName", "value": "School age population, last grade of primary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Population of the age-group theoretically corresponding to the last grade of primary school as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Population of the age-group theoretically corresponding to the last grade of primary school as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.PRM.TOTL.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, primary education, female (number)" }, { "id": "Longdefinition", "value": "Female population of the age-group theoretically corresponding to primary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Female population of the age-group theoretically corresponding to primary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.PRM.TOTL.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, primary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Population of the age-group theoretically corresponding to primary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Population of the age-group theoretically corresponding to primary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.PRM.TOTL.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, primary education, male (number)" }, { "id": "Longdefinition", "value": "Male population of the age-group theoretically corresponding to primary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Male population of the age-group theoretically corresponding to primary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.SEC.LTOT.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, lower secondary education, female (number)" }, { "id": "Longdefinition", "value": "Female population of the age-group theoretically corresponding to lower secondary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Female population of the age-group theoretically corresponding to lower secondary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.SEC.LTOT.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, lower secondary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Population of the age-group theoretically corresponding to lower secondary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Population of the age-group theoretically corresponding to lower secondary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.SEC.LTOT.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, lower secondary education, male (number)" }, { "id": "Longdefinition", "value": "Male population of the age-group theoretically corresponding to lower secondary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Male population of the age-group theoretically corresponding to lower secondary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.SEC.TOTL.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, secondary education, female (number)" }, { "id": "Longdefinition", "value": "Female population of the age-group theoretically corresponding to secondary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Female population of the age-group theoretically corresponding to secondary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.SEC.TOTL.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, secondary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Population of the age-group theoretically corresponding to secondary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Population of the age-group theoretically corresponding to secondary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.SEC.TOTL.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, secondary education, male (number)" }, { "id": "Longdefinition", "value": "Male population of the age-group theoretically corresponding to secondary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Male population of the age-group theoretically corresponding to secondary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.SEC.UTOT.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, upper secondary education, female (number)" }, { "id": "Longdefinition", "value": "Female population of the age-group theoretically corresponding to upper secondary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Female population of the age-group theoretically corresponding to upper secondary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.SEC.UTOT.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, upper secondary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Population of the age-group theoretically corresponding to upper secondary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Population of the age-group theoretically corresponding to upper secondary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.SEC.UTOT.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, upper secondary education, male (number)" }, { "id": "Longdefinition", "value": "Male population of the age-group theoretically corresponding to upper secondary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Male population of the age-group theoretically corresponding to upper secondary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.TER.TOTL.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, tertiary education, female (number)" }, { "id": "Longdefinition", "value": "Female population of the age-group theoretically corresponding to tertiary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Female population of the age-group theoretically corresponding to tertiary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.TER.TOTL.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, tertiary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Population of the age-group theoretically corresponding to tertiary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Population of the age-group theoretically corresponding to tertiary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "SP.TER.TOTL.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "School age population, tertiary education, male (number)" }, { "id": "Longdefinition", "value": "Male population of the age-group theoretically corresponding to tertiary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Male population of the age-group theoretically corresponding to tertiary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.ADMI.ENDOFLOWERSEC.MAT", "metatype": [ { "id": "IndicatorName", "value": "Administration of a nationally-representative learning assessment at the end of lower secondary education in mathematics (number)" }, { "id": "Longdefinition", "value": "This indicator evaluates whether a national or cross-national assessment of learning outcomes was conducted in the last 5 years in (a) reading, writing or language and (b) mathematics at the relevant stages of education. The indicator is expressed as a simple ‘yes’ or ‘no’ for each subject area and each stage of education. The implementation of a learning assessment in the reference period serves as an educational vital tool to assess progress. 'Yes’ values indicate that the country is monitoring learning outcomes regularly at the given stage of education and in the given subject areas. This will enable the country to review and adapt as necessary its national policies on education and learning to ensure that all children and young people have the opportunity to acquire basic skills at each education level and in each subject area. Data on the administration of a large-scale assessment from a national representative sample from national learning assessment offices, ministries of education or other bodies responsible for learning assessments, including regional or international organizations running learning assessments (e.g. Conférence des ministres de l'Éducation des États et gouvernements de la Francophonie (CONFEMEN), Educational Quality and Assessment Programme (EQAP), International Association for the Evaluation of Educational Achievement (IEA), Organisation for Economic Co-operation and Development (OECD), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ) and Third Regional Comparative and Explanatory Study (TERCE). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "This indicator evaluates whether a national or cross-national assessment of learning outcomes was conducted in the last 5 years in (a) reading, writing or language and (b) mathematics at the relevant stages of education. The indicator is expressed as a simple ‘yes’ or ‘no’ for each subject area and each stage of education. The implementation of a learning assessment in the reference period serves as an educational vital tool to assess progress. 'Yes’ values indicate that the country is monitoring learning outcomes regularly at the given stage of education and in the given subject areas. This will enable the country to review and adapt as necessary its national policies on education and learning to ensure that all children and young people have the opportunity to acquire basic skills at each education level and in each subject area. Data on the administration of a large-scale assessment from a national representative sample from national learning assessment offices, ministries of education or other bodies responsible for learning assessments, including regional or international organizations running learning assessments (e.g. Conférence des ministres de l'Éducation des États et gouvernements de la Francophonie (CONFEMEN), Educational Quality and Assessment Programme (EQAP), International Association for the Evaluation of Educational Achievement (IEA), Organisation for Economic Co-operation and Development (OECD), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ) and Third Regional Comparative and Explanatory Study (TERCE). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ADMI.ENDOFLOWERSEC.READ", "metatype": [ { "id": "IndicatorName", "value": "Administration of a nationally-representative learning assessment at the end of lower secondary education in reading (number)" }, { "id": "Longdefinition", "value": "This indicator evaluates whether a national or cross-national assessment of learning outcomes was conducted in the last 5 years in (a) reading, writing or language and (b) mathematics at the relevant stages of education. The indicator is expressed as a simple ‘yes’ or ‘no’ for each subject area and each stage of education. The implementation of a learning assessment in the reference period serves as an educational vital tool to assess progress. 'Yes’ values indicate that the country is monitoring learning outcomes regularly at the given stage of education and in the given subject areas. This will enable the country to review and adapt as necessary its national policies on education and learning to ensure that all children and young people have the opportunity to acquire basic skills at each education level and in each subject area. Data on the administration of a large-scale assessment from a national representative sample from national learning assessment offices, ministries of education or other bodies responsible for learning assessments, including regional or international organizations running learning assessments (e.g. Conférence des ministres de l'Éducation des États et gouvernements de la Francophonie (CONFEMEN), Educational Quality and Assessment Programme (EQAP), International Association for the Evaluation of Educational Achievement (IEA), Organisation for Economic Co-operation and Development (OECD), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ) and Third Regional Comparative and Explanatory Study (TERCE). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "This indicator evaluates whether a national or cross-national assessment of learning outcomes was conducted in the last 5 years in (a) reading, writing or language and (b) mathematics at the relevant stages of education. The indicator is expressed as a simple ‘yes’ or ‘no’ for each subject area and each stage of education. The implementation of a learning assessment in the reference period serves as an educational vital tool to assess progress. 'Yes’ values indicate that the country is monitoring learning outcomes regularly at the given stage of education and in the given subject areas. This will enable the country to review and adapt as necessary its national policies on education and learning to ensure that all children and young people have the opportunity to acquire basic skills at each education level and in each subject area. Data on the administration of a large-scale assessment from a national representative sample from national learning assessment offices, ministries of education or other bodies responsible for learning assessments, including regional or international organizations running learning assessments (e.g. Conférence des ministres de l'Éducation des États et gouvernements de la Francophonie (CONFEMEN), Educational Quality and Assessment Programme (EQAP), International Association for the Evaluation of Educational Achievement (IEA), Organisation for Economic Co-operation and Development (OECD), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ) and Third Regional Comparative and Explanatory Study (TERCE). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ADMI.ENDOFPRIM.MAT", "metatype": [ { "id": "IndicatorName", "value": "Administration of a nationally-representative learning assessment at the end of primary in mathematics (number)" }, { "id": "Longdefinition", "value": "This indicator evaluates whether a national or cross-national assessment of learning outcomes was conducted in the last 5 years in (a) reading, writing or language and (b) mathematics at the relevant stages of education. The indicator is expressed as a simple ‘yes’ or ‘no’ for each subject area and each stage of education. The implementation of a learning assessment in the reference period serves as an educational vital tool to assess progress. 'Yes’ values indicate that the country is monitoring learning outcomes regularly at the given stage of education and in the given subject areas. This will enable the country to review and adapt as necessary its national policies on education and learning to ensure that all children and young people have the opportunity to acquire basic skills at each education level and in each subject area. Data on the administration of a large-scale assessment from a national representative sample from national learning assessment offices, ministries of education or other bodies responsible for learning assessments, including regional or international organizations running learning assessments (e.g. Conférence des ministres de l'Éducation des États et gouvernements de la Francophonie (CONFEMEN), Educational Quality and Assessment Programme (EQAP), International Association for the Evaluation of Educational Achievement (IEA), Organisation for Economic Co-operation and Development (OECD), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ) and Third Regional Comparative and Explanatory Study (TERCE). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "This indicator evaluates whether a national or cross-national assessment of learning outcomes was conducted in the last 5 years in (a) reading, writing or language and (b) mathematics at the relevant stages of education. The indicator is expressed as a simple ‘yes’ or ‘no’ for each subject area and each stage of education. The implementation of a learning assessment in the reference period serves as an educational vital tool to assess progress. 'Yes’ values indicate that the country is monitoring learning outcomes regularly at the given stage of education and in the given subject areas. This will enable the country to review and adapt as necessary its national policies on education and learning to ensure that all children and young people have the opportunity to acquire basic skills at each education level and in each subject area. Data on the administration of a large-scale assessment from a national representative sample from national learning assessment offices, ministries of education or other bodies responsible for learning assessments, including regional or international organizations running learning assessments (e.g. Conférence des ministres de l'Éducation des États et gouvernements de la Francophonie (CONFEMEN), Educational Quality and Assessment Programme (EQAP), International Association for the Evaluation of Educational Achievement (IEA), Organisation for Economic Co-operation and Development (OECD), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ) and Third Regional Comparative and Explanatory Study (TERCE). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ADMI.ENDOFPRIM.READ", "metatype": [ { "id": "IndicatorName", "value": "Administration of a nationally-representative learning assessment at the end of primary in reading (number)" }, { "id": "Longdefinition", "value": "This indicator evaluates whether a national or cross-national assessment of learning outcomes was conducted in the last 5 years in (a) reading, writing or language and (b) mathematics at the relevant stages of education. The indicator is expressed as a simple ‘yes’ or ‘no’ for each subject area and each stage of education. The implementation of a learning assessment in the reference period serves as an educational vital tool to assess progress. 'Yes’ values indicate that the country is monitoring learning outcomes regularly at the given stage of education and in the given subject areas. This will enable the country to review and adapt as necessary its national policies on education and learning to ensure that all children and young people have the opportunity to acquire basic skills at each education level and in each subject area. Data on the administration of a large-scale assessment from a national representative sample from national learning assessment offices, ministries of education or other bodies responsible for learning assessments, including regional or international organizations running learning assessments (e.g. Conférence des ministres de l'Éducation des États et gouvernements de la Francophonie (CONFEMEN), Educational Quality and Assessment Programme (EQAP), International Association for the Evaluation of Educational Achievement (IEA), Organisation for Economic Co-operation and Development (OECD), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ) and Third Regional Comparative and Explanatory Study (TERCE). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "This indicator evaluates whether a national or cross-national assessment of learning outcomes was conducted in the last 5 years in (a) reading, writing or language and (b) mathematics at the relevant stages of education. The indicator is expressed as a simple ‘yes’ or ‘no’ for each subject area and each stage of education. The implementation of a learning assessment in the reference period serves as an educational vital tool to assess progress. 'Yes’ values indicate that the country is monitoring learning outcomes regularly at the given stage of education and in the given subject areas. This will enable the country to review and adapt as necessary its national policies on education and learning to ensure that all children and young people have the opportunity to acquire basic skills at each education level and in each subject area. Data on the administration of a large-scale assessment from a national representative sample from national learning assessment offices, ministries of education or other bodies responsible for learning assessments, including regional or international organizations running learning assessments (e.g. Conférence des ministres de l'Éducation des États et gouvernements de la Francophonie (CONFEMEN), Educational Quality and Assessment Programme (EQAP), International Association for the Evaluation of Educational Achievement (IEA), Organisation for Economic Co-operation and Development (OECD), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ) and Third Regional Comparative and Explanatory Study (TERCE). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ADMI.GRADE2OR3PRIM.MAT", "metatype": [ { "id": "IndicatorName", "value": "Administration of a nationally representative learning assessment in Grade 2 or 3 in mathematics (number)" }, { "id": "Longdefinition", "value": "This indicator evaluates whether a national or cross-national assessment of learning outcomes was conducted in the last 5 years in (a) reading, writing or language and (b) mathematics at the relevant stages of education. The indicator is expressed as a simple ‘yes’ or ‘no’ for each subject area and each stage of education. The implementation of a learning assessment in the reference period serves as an educational vital tool to assess progress. 'Yes’ values indicate that the country is monitoring learning outcomes regularly at the given stage of education and in the given subject areas. This will enable the country to review and adapt as necessary its national policies on education and learning to ensure that all children and young people have the opportunity to acquire basic skills at each education level and in each subject area. Data on the administration of a large-scale assessment from a national representative sample from national learning assessment offices, ministries of education or other bodies responsible for learning assessments, including regional or international organizations running learning assessments (e.g. Conférence des ministres de l'Éducation des États et gouvernements de la Francophonie (CONFEMEN), Educational Quality and Assessment Programme (EQAP), International Association for the Evaluation of Educational Achievement (IEA), Organisation for Economic Co-operation and Development (OECD), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ) and Third Regional Comparative and Explanatory Study (TERCE). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "This indicator evaluates whether a national or cross-national assessment of learning outcomes was conducted in the last 5 years in (a) reading, writing or language and (b) mathematics at the relevant stages of education. The indicator is expressed as a simple ‘yes’ or ‘no’ for each subject area and each stage of education. The implementation of a learning assessment in the reference period serves as an educational vital tool to assess progress. 'Yes’ values indicate that the country is monitoring learning outcomes regularly at the given stage of education and in the given subject areas. This will enable the country to review and adapt as necessary its national policies on education and learning to ensure that all children and young people have the opportunity to acquire basic skills at each education level and in each subject area. Data on the administration of a large-scale assessment from a national representative sample from national learning assessment offices, ministries of education or other bodies responsible for learning assessments, including regional or international organizations running learning assessments (e.g. Conférence des ministres de l'Éducation des États et gouvernements de la Francophonie (CONFEMEN), Educational Quality and Assessment Programme (EQAP), International Association for the Evaluation of Educational Achievement (IEA), Organisation for Economic Co-operation and Development (OECD), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ) and Third Regional Comparative and Explanatory Study (TERCE). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ADMI.GRADE2OR3PRIM.READ", "metatype": [ { "id": "IndicatorName", "value": "Administration of a nationally representative learning assessment in Grade 2 or 3 in reading (number)" }, { "id": "Longdefinition", "value": "This indicator evaluates whether a national or cross-national assessment of learning outcomes was conducted in the last 5 years in (a) reading, writing or language and (b) mathematics at the relevant stages of education. The indicator is expressed as a simple ‘yes’ or ‘no’ for each subject area and each stage of education. The implementation of a learning assessment in the reference period serves as an educational vital tool to assess progress. 'Yes’ values indicate that the country is monitoring learning outcomes regularly at the given stage of education and in the given subject areas. This will enable the country to review and adapt as necessary its national policies on education and learning to ensure that all children and young people have the opportunity to acquire basic skills at each education level and in each subject area. Data on the administration of a large-scale assessment from a national representative sample from national learning assessment offices, ministries of education or other bodies responsible for learning assessments, including regional or international organizations running learning assessments (e.g. Conférence des ministres de l'Éducation des États et gouvernements de la Francophonie (CONFEMEN), Educational Quality and Assessment Programme (EQAP), International Association for the Evaluation of Educational Achievement (IEA), Organisation for Economic Co-operation and Development (OECD), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ) and Third Regional Comparative and Explanatory Study (TERCE). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "This indicator evaluates whether a national or cross-national assessment of learning outcomes was conducted in the last 5 years in (a) reading, writing or language and (b) mathematics at the relevant stages of education. The indicator is expressed as a simple ‘yes’ or ‘no’ for each subject area and each stage of education. The implementation of a learning assessment in the reference period serves as an educational vital tool to assess progress. 'Yes’ values indicate that the country is monitoring learning outcomes regularly at the given stage of education and in the given subject areas. This will enable the country to review and adapt as necessary its national policies on education and learning to ensure that all children and young people have the opportunity to acquire basic skills at each education level and in each subject area. Data on the administration of a large-scale assessment from a national representative sample from national learning assessment offices, ministries of education or other bodies responsible for learning assessments, including regional or international organizations running learning assessments (e.g. Conférence des ministres de l'Éducation des États et gouvernements de la Francophonie (CONFEMEN), Educational Quality and Assessment Programme (EQAP), International Association for the Evaluation of Educational Achievement (IEA), Organisation for Economic Co-operation and Development (OECD), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ) and Third Regional Comparative and Explanatory Study (TERCE). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.AIDEDUC.LOWINCOMECOUNT", "metatype": [ { "id": "IndicatorName", "value": "Percentage of total aid to education allocated to least developed countries (%)" }, { "id": "Longdefinition", "value": "Total gross official development assistance (ODA) for education in least developed countries (including early childhood, primary, secondary, post-secondary non-tertiary and tertiary education) as well as scholarships and student costs in donor countries expressed as a percentage of total gross official development assistance to education. A high value indicates that least developed countries are being prioritised to receive aid for education. Least developed countries are those defined by the UN Office of the High Representative for Least Developed Countries, Landlocked States and Small Island Developing States (UN-OHRLLS). Official development assistance is defined as grants or loans to countries and territories and to multilateral institutions provided by state and local governments or their executive agencies with the objective of promoting the economic development and welfare of developing countries and territories. Such grants or loans are provided on concessional financial terms and, in the case of loans, contain a grant element of at least 25 per cent. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Expenditures" }, { "id": "Shortdefinition", "value": "Total gross official development assistance (ODA) for education in least developed countries (including early childhood, primary, secondary, post-secondary non-tertiary and tertiary education) as well as scholarships and student costs in donor countries expressed as a percentage of total gross official development assistance to education. A high value indicates that least developed countries are being prioritised to receive aid for education. Least developed countries are those defined by the UN Office of the High Representative for Least Developed Countries, Landlocked States and Small Island Developing States (UN-OHRLLS). Official development assistance is defined as grants or loans to countries and territories and to multilateral institutions provided by state and local governments or their executive agencies with the objective of promoting the economic development and welfare of developing countries and territories. Such grants or loans are provided on concessional financial terms and, in the case of loans, contain a grant element of at least 25 per cent. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Expenditures" } ], "source_id": "12" }, { "id": "UIS.AIR.1.GLAST.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross intake ratio to the last grade of primary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.AIR.2.GPV.GLAST.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross intake ratio to the last grade of lower secondary general education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ASTAFF.6T8", "metatype": [ { "id": "IndicatorName", "value": "Teachers in tertiary education ISCED 6, 7 and 8 programmes, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of teachers in public and private tertiary education institutions (ISCED 6-8). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Total number of teachers in public and private tertiary education institutions (ISCED 6-8). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.ASTAFF.6T8.F", "metatype": [ { "id": "IndicatorName", "value": "Teachers in tertiary education ISCED 6, 7 and 8 programmes, female (number)" }, { "id": "Longdefinition", "value": "Total number of female teachers in public and private short-cycle tertiary education institutions (ISCED 6-8). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Total number of female teachers in public and private short-cycle tertiary education institutions (ISCED 6-8). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.ASTAFF.6T8.M", "metatype": [ { "id": "IndicatorName", "value": "Teachers in tertiary education ISCED 6, 7 and 8 programmes, male (number)" }, { "id": "Longdefinition", "value": "Total number of male teachers in public and private short-cycle tertiary education institutions (ISCED 6-8). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Total number of male teachers in public and private short-cycle tertiary education institutions (ISCED 6-8). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.CEAge.1", "metatype": [ { "id": "IndicatorName", "value": "Official entrance age to compulsory education (years)" }, { "id": "Longdefinition", "value": "Official age when students should enter compulsory education assuming they start at the official entrance age for the lowest level of education, study full-time throughout and progressed through the system without repeating or skipping a grade. The theoretical entrance age to a given programme or level is typically, but not always, the most common entrance age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.CEAGE.1", "metatype": [ { "id": "Topic", "value": "Background" } ], "source_id": "12" }, { "id": "UIS.CR.1", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q1", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q1.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, poorest quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q1.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, poorest quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q1.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, poorest quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q2", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, second quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q2.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, second quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q2.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, second quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, second quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q2.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, second quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q3", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q3.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, middle quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q3.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, middle quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q3.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, middle quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q4", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q4.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, fourth quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q4.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, fourth quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q4.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, fourth quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q5", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q5.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, richest quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q5.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, richest quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.Q5.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, richest quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q1", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q2", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, second quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, second quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q3", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q4", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q5", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.RUR.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, rural, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q1", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q2", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, second quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, second quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q3", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q4", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q5", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.URB.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, urban, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.1.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, primary education, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.CR.2", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q1", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q1.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, poorest quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q1.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, poorest quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q1.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, poorest quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q2", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, second quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q2.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, second quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q2.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, second quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, second quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q2.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, second quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q3", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q3.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, middle quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q3.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, middle quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q3.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, middle quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q4", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q4.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, fourth quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q4.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, fourth quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q4.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, fourth quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q5", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q5.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, richest quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q5.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, richest quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.Q5.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, richest quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q1", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q2", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, second quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, second quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q3", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q4", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q5", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.RUR.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, rural, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q1", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q2", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, second quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, second quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q3", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q4", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q5", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.URB.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, urban, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.2.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, lower secondary education, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q1", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q1.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, poorest quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q1.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, poorest quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q1.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, poorest quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q2", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, second quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q2.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, second quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q2.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, second quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, second quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q2.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, second quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q3", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q3.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, middle quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q3.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, middle quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q3.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, middle quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q4", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q4.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, fourth quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q4.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, fourth quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q4.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, fourth quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q5", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q5.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, richest quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q5.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, richest quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.Q5.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, richest quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q1", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q2", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, second quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, second quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q3", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q4", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q5", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.RUR.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, rural, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q1", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q2", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, second quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, second quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q3", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q4", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q5", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The number of persons in the relevant age group who have completed the last grade of the given level of education is expressed as a percentage of the total population (in the survey sample) of the same age group. The primary completion rate is the percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of primary education who have completed that grade. The intended age for the last grade of primary education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.URB.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, urban, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.CR.3.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Completion rate, upper secondary education, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.E.0.F", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in early childhood education, female (number)" }, { "id": "Longdefinition", "value": "Total number of female students enrolled in public and private early childhood education institutions (ISCED 0) regardless of age. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years; and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.E.0.M", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in early childhood education, male (number)" }, { "id": "Longdefinition", "value": "Total number of male students enrolled in public and private early childhood education institutions (ISCED 0) regardless of age. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years; and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1." }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Total number of male students enrolled in public and private early childhood education institutions (ISCED 0) regardless of age. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years; and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.E.0.T", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in early childhood education, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in public and private early childhood education institutions (ISCED 0) regardless of age. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years; and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.E.01.F", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in early childhood educational development programmes, female (number)" }, { "id": "Longdefinition", "value": "Total number of females enrolled in early childhood educational development institutions (ISCED 0.1) regardless of age. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years; and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.E.01.M", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in early childhood educational development programmes, male (number)" }, { "id": "Longdefinition", "value": "Total number of males enrolled in early childhood educational development institutions (ISCED 0.1) regardless of age. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years; and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1." }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Total number of males enrolled in early childhood educational development institutions (ISCED 0.1) regardless of age. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years; and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.E.01.T", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in early childhood educational development programmes, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of children enrolled in early childhood educational development institutions (ISCED 0.1) regardless of age. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years; and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.E.02.M", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in pre-primary education, male (number)" }, { "id": "Longdefinition", "value": "Total number of male students enrolled in public and private pre-primary education institutions (ISCED 0.2) regardless of age. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years; and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1." }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Total number of male students enrolled in public and private pre-primary education institutions (ISCED 0.2) regardless of age. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years; and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.E.1.M", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in primary education, male (number)" }, { "id": "Longdefinition", "value": "Total number of male students enrolled in public and private primary education institutions regardless of age." }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of male students enrolled in public and private primary education institutions regardless of age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.E.2", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in lower secondary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in public and private lower secondary education institutions regardless of age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.E.2.F", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in lower secondary education, female (number)" }, { "id": "Longdefinition", "value": "Total number of female students enrolled in public and private lower secondary education institutions regardless of age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.E.2.M", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in lower secondary education, male (number)" }, { "id": "Longdefinition", "value": "Total number of male students enrolled in public and private lower secondary education institutions regardless of age." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of male students enrolled in public and private lower secondary education institutions regardless of age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.E.23.M", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in secondary education, male (number)" }, { "id": "Longdefinition", "value": "Total number of male students enrolled at public and private secondary education institutions regardless of age." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of male students enrolled at public and private secondary education institutions regardless of age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.E.3", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in upper secondary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in public and private upper secondary education institutions regardless of age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.E.3.F", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in upper secondary education, female (number)" }, { "id": "Longdefinition", "value": "Total number of female students enrolled in public and private upper secondary education institutions regardless of age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.E.3.M", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in upper secondary education, male (number)" }, { "id": "Longdefinition", "value": "Total number of male students enrolled in public and private upper secondary education institutions regardless of age. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total number of male students enrolled in public and private upper secondary education institutions regardless of age. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.E.4", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in post-secondary non-tertiary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in public and private post-secondary non-tertiary education institutions regardless of age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.E.4.F", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in post-secondary non-tertiary education, female (number)" }, { "id": "Longdefinition", "value": "Total number of female students enrolled in public and private post-secondary non-tertiary education institutions regardless of age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.E.4.M", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in post-secondary non-tertiary education, male (number)" }, { "id": "Longdefinition", "value": "Total number of male students enrolled in public and private post-secondary non-tertiary education institutions regardless of age." }, { "id": "Notesfromoriginalsource", "value": "Vocational & Post-secondary Non-Tertiary" }, { "id": "Shortdefinition", "value": "Total number of male students enrolled in public and private post-secondary non-tertiary education institutions regardless of age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Vocational & Post-secondary Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.E.5", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in tertiary education, ISCED 5 programmes, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in public and private short-cycle tertiary education programmes (ISCED 5)." }, { "id": "Referenceperiod", "value": "Enrolment in tertiary education, ISCED 5 programmes, both sexes (number)" }, { "id": "Shortdefinition", "value": "Total number of students enrolled in public and private short-cycle tertiary education programmes (ISCED 5)." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.E.5.F", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in tertiary education, ISCED 5 programmes, female (number)" }, { "id": "Longdefinition", "value": "Total number of female students enrolled in public and private short-cycle tertiary education programmes (ISCED 5)." }, { "id": "Referenceperiod", "value": "Enrolment in tertiary education, ISCED 5 programmes, female (number)" }, { "id": "Shortdefinition", "value": "Total number of female students enrolled in public and private short-cycle tertiary education programmes (ISCED 5)." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.E.5.M", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in tertiary education, ISCED 5 programmes, male (number)" }, { "id": "Longdefinition", "value": "Total number of male students enrolled in public and private short-cycle tertiary education programmes (ISCED 5)." }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Total number of male students enrolled in public and private short-cycle tertiary education programmes (ISCED 5)." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.E.58.M", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in tertiary education, all programmes, male (number)" }, { "id": "Longdefinition", "value": "The total number of male students enrolled at public and private tertiary education institutions." }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The total number of male students enrolled at public and private tertiary education institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.E.6", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in tertiary education, ISCED 6 programmes, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in public and private tertiary education institutions in programmes on the bachelors or equivalent (ISCED 6) level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.E.6.F", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in tertiary education, ISCED 6 programmes, female (number)" }, { "id": "Longdefinition", "value": "Total number of female students enrolled in public and private tertiary education institutions in programmes on the bachelors or equivalent (ISCED 6) level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.E.6.M", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in tertiary education, ISCED 6 programmes, male (number)" }, { "id": "Longdefinition", "value": "Total number of male students enrolled in public and private tertiary education institutions in programmes on the bachelors or equivalent (ISCED 6) level." }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Total number of male students enrolled in public and private tertiary education institutions in programmes on the bachelors or equivalent (ISCED 6) level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.E.7", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in tertiary education, ISCED 7 programmes, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in public and private tertiary education institutions in programmes on the masters or equivalent (ISCED 7) level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.E.7.F", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in tertiary education, ISCED 7 programmes, female (number)" }, { "id": "Longdefinition", "value": "Total number of female students enrolled in public and private tertiary education institutions in programmes on the masters or equivalent (ISCED 7) level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.E.7.M", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in tertiary education, ISCED 7 programmes, male (number)" }, { "id": "Longdefinition", "value": "Total number of male students enrolled in public and private tertiary education institutions in programmes on the masters or equivalent (ISCED 7) level." }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Total number of male students enrolled in public and private tertiary education institutions in programmes on the masters or equivalent (ISCED 7) level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.E.8", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in tertiary education, ISCED 8 programmes, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in public and private tertiary education institutions in programmes on the doctoral or equivalent (ISCED 8) level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.E.8.F", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in tertiary education, ISCED 8 programmes, female (number)" }, { "id": "Longdefinition", "value": "Total number of female students enrolled in public and private tertiary education institutions in programmes on the doctoral or equivalent (ISCED 8) level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.E.8.M", "metatype": [ { "id": "IndicatorName", "value": "Enrolment in tertiary education, ISCED 8 programmes, male (number)" }, { "id": "Longdefinition", "value": "Total number of male students enrolled in public and private tertiary education institutions in programmes on the doctoral or equivalent (ISCED 8) level." }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Total number of male students enrolled in public and private tertiary education institutions in programmes on the doctoral or equivalent (ISCED 8) level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.EA.1.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is primary, both sexes" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with completed primary (ISCED 1) as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above with completed primary education as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of population (age 25 and over) with completed primary (ISCED 1) as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above with completed primary education as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.1.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is primary, female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with completed primary (ISCED 1) as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed primary education as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of female population (age 25 and over) with completed primary (ISCED 1) as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed primary education as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.1.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is primary, male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with completed primary (ISCED 1) as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed primary education as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of male population (age 25 and over) with completed primary (ISCED 1) as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed primary education as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.1T6.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least completed primary education (ISCED 1 or higher). Total" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with at least completed primary education (ISCED 1 or higher). This indicator is calculated by dividing the number of persons aged 25 years and above with completed primary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Othernotes", "value": "Cumulative Attainment" }, { "id": "Shortdefinition", "value": "The percentage of population (age 25 and over) with at least completed primary education (ISCED 1 or higher). This indicator is calculated by dividing the number of persons aged 25 years and above with completed primary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.1T6.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least completed primary education (ISCED 1 or higher). Female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with at least completed primary education (ISCED 1 or higher). This indicator is calculated by dividing the number of females aged 25 years and above who completed primary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Othernotes", "value": "Cumulative Attainment" }, { "id": "Shortdefinition", "value": "The percentage of female population (age 25 and over) with at least completed primary education (ISCED 1 or higher). This indicator is calculated by dividing the number of females aged 25 years and above who completed primary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.1T6.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least completed primary education (ISCED 1 or higher). Male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with at least completed primary education (ISCED 1 or higher). This indicator is calculated by dividing the number of males aged 25 years and above who completed primary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Othernotes", "value": "Cumulative Attainment" }, { "id": "Shortdefinition", "value": "The percentage of male population (age 25 and over) with at least completed primary education (ISCED 1 or higher). This indicator is calculated by dividing the number of males aged 25 years and above who completed primary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.1T8.AG25T99.GPIA", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least completed primary education (ISCED 1 or higher). Adjusted Gender Parity Index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Attainment" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.2.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is lower secondary, both sexes" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with completed lower secondary education (ISCED 2) as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed lower secondary education as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of population (age 25 and over) with completed lower secondary education (ISCED 2) as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed lower secondary education as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.2.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is lower secondary, female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with completed lower secondary education (ISCED 2) as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed lower secondary education as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of female population (age 25 and over) with completed lower secondary education (ISCED 2) as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed lower secondary education as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.2.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is lower secondary, male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with completed lower secondary education (ISCED 2) as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed lower secondary education as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of male population (age 25 and over) with completed lower secondary education (ISCED 2) as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed lower secondary education as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.2T6.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least completed lower secondary education (ISCED 2 or higher). Total" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with at least completed lower secondary education (ISCED 2 or higher). This indicator is calculated by dividing the number of persons aged 25 years and above with completed lower secondary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Othernotes", "value": "Cumulative Attainment" }, { "id": "Shortdefinition", "value": "The percentage of population (age 25 and over) with at least completed lower secondary education (ISCED 2 or higher). This indicator is calculated by dividing the number of persons aged 25 years and above with completed lower secondary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.2T6.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least completed lower secondary education (ISCED 2 or higher). Female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with at least completed lower secondary education (ISCED 2 or higher). This indicator is calculated by dividing the number of females aged 25 years and above who completed lower secondary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Othernotes", "value": "Cumulative Attainment" }, { "id": "Shortdefinition", "value": "The percentage of female population (age 25 and over) with at least completed lower secondary education (ISCED 2 or higher). This indicator is calculated by dividing the number of females aged 25 years and above who completed lower secondary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.2T6.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least completed lower secondary education (ISCED 2 or higher). Male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with at least completed lower secondary education (ISCED 2 or higher). This indicator is calculated by dividing the number of males aged 25 years and above who completed lower secondary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Othernotes", "value": "Cumulative Attainment" }, { "id": "Shortdefinition", "value": "The percentage of male population (age 25 and over) with at least completed lower secondary education (ISCED 2 or higher). This indicator is calculated by dividing the number of males aged 25 years and above who completed lower secondary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.2T8.AG25T99.GPIA", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least completed lower secondary education (ISCED 2 or higher). Adjusted Gender Parity Index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Attainment" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.3.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is upper secondary, both sexes" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with completed upper secondary education (ISCED 3) as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed upper secondary education as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of population (age 25 and over) with completed upper secondary education (ISCED 3) as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed upper secondary education as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.3.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is upper secondary, female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with completed upper secondary education (ISCED 3) as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed upper secondary education as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of female population (age 25 and over) with completed upper secondary education (ISCED 3) as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed upper secondary education as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.3.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is upper secondary, male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with completed upper secondary education (ISCED 3) as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed upper secondary education as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of male population (age 25 and over) with completed upper secondary education (ISCED 3) as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed upper secondary education as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.3T6.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least completed upper secondary education (ISCED 3 or higher). Total" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with at least completed upper secondary education (ISCED 3 or higher). This indicator is calculated by dividing the number of persons aged 25 years and above with completed upper secondary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Othernotes", "value": "Cumulative Attainment" }, { "id": "Shortdefinition", "value": "The percentage of population (age 25 and over) with at least completed upper secondary education (ISCED 3 or higher). This indicator is calculated by dividing the number of persons aged 25 years and above with completed upper secondary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.3T6.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least completed upper secondary education (ISCED 3 or higher). Female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with at least completed upper secondary education (ISCED 3 or higher). This indicator is calculated by dividing the number of females aged 25 years and above who completed upper secondary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Othernotes", "value": "Cumulative Attainment" }, { "id": "Shortdefinition", "value": "The percentage of female population (age 25 and over) with at least completed upper secondary education (ISCED 3 or higher). This indicator is calculated by dividing the number of females aged 25 years and above who completed upper secondary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.3T6.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least completed upper secondary education (ISCED 3 or higher). Male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with at least completed upper secondary education (ISCED 3 or higher). This indicator is calculated by dividing the number of males aged 25 years and above who completed upper secondary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Othernotes", "value": "Cumulative Attainment" }, { "id": "Shortdefinition", "value": "The percentage of male population (age 25 and over) with at least completed upper secondary education (ISCED 3 or higher). This indicator is calculated by dividing the number of males aged 25 years and above who completed upper secondary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.3T8.AG25T99.GPIA", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least completed upper secondary education (ISCED 3 or higher). Adjusted Gender Parity Index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Attainment" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.4.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is post-secondary non-tertiary, both sexes" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with completed post-secondary education (ISCED 4) as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed post-secondary education as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of population (age 25 and over) with completed post-secondary education (ISCED 4) as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed post-secondary education as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.4.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is post-secondary non-tertiary, female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with completed post-secondary education (ISCED 4) as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed post-secondary education as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of female population (age 25 and over) with completed post-secondary education (ISCED 4) as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed post-secondary education as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.4.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is post-secondary non-tertiary, male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with completed post-secondary education (ISCED 4) as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed post-secondary education as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of male population (age 25 and over) with completed post-secondary education (ISCED 4) as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed post-secondary education as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.4T6.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least completed post-secondary education (ISCED 4 or higher). Total" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with at least completed post-secondary education (ISCED 4 or higher). This indicator is calculated by dividing the number of persons aged 25 years and above with completed post-secondary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Othernotes", "value": "Cumulative Attainment" }, { "id": "Shortdefinition", "value": "The percentage of population (age 25 and over) with at least completed post-secondary education (ISCED 4 or higher). This indicator is calculated by dividing the number of persons aged 25 years and above with completed post-secondary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.4T6.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least completed post-secondary education (ISCED 4 or higher). Female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with at least completed post-secondary education (ISCED 4 or higher). This indicator is calculated by dividing the number of females aged 25 years and above who completed post-secondary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Othernotes", "value": "Cumulative Attainment" }, { "id": "Shortdefinition", "value": "The percentage of female population (age 25 and over) with at least completed post-secondary education (ISCED 4 or higher). This indicator is calculated by dividing the number of females aged 25 years and above who completed post-secondary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.4T6.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least completed post-secondary education (ISCED 4 or higher). Male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with at least completed post-secondary education (ISCED 4 or higher). This indicator is calculated by dividing the number of males aged 25 years and above who completed post-secondary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Othernotes", "value": "Cumulative Attainment" }, { "id": "Shortdefinition", "value": "The percentage of male population (age 25 and over) with at least completed post-secondary education (ISCED 4 or higher). This indicator is calculated by dividing the number of males aged 25 years and above who completed post-secondary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.4T8.AG25T99.GPIA", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least completed post-secondary education (ISCED 4 or higher). Adjusted Gender Parity Index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Attainment" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.5.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is short cycle tertiary, both sexes" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with a completed short-cycle tertiary degree (ISCED 5) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed a short-cycle tertiary degree as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of population (age 25 and over) with a completed short-cycle tertiary degree (ISCED 5) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed a short-cycle tertiary degree as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.5.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is short cycle tertiary, female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with a completed short-cycle tertiary degree (ISCED 5) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed a short-cycle tertiary degree as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of female population (age 25 and over) with a completed short-cycle tertiary degree (ISCED 5) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed a short-cycle tertiary degree as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.5.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is short cycle tertiary, male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with a completed short-cycle tertiary degree (ISCED 5) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed a short-cycle tertiary degree as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of male population (age 25 and over) with a completed short-cycle tertiary degree (ISCED 5) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed a short-cycle tertiary degree as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.5T8.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Total" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with a completed short-cycle tertiary degree (ISCED 5) or higher. This indicator is calculated by dividing the number of persons aged 25 years and above with a completed short-cycle tertiary degree by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.5T8.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with a completed short-cycle tertiary degree (ISCED 5) or higher. This indicator is calculated by dividing the number of females aged 25 years and above who completed a short-cycle tertiary degree by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.5T8.AG25T99.GPIA", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Adjusted Gender Parity Index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Attainment" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.5T8.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with a completed short-cycle tertiary degree (ISCED 5) or higher. This indicator is calculated by dividing the number of males aged 25 years and above who completed a short-cycle tertiary degree by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.6.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is Bachelor's or equivalent (ISCED 6), both sexes" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with a completed bachelor's or equivalent degree (ISCED 6) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed a bachelor's or equivalent degree as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of population (age 25 and over) with a completed bachelor's or equivalent degree (ISCED 6) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed a bachelor's or equivalent degree as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.6.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is Bachelor's or equivalent (ISCED 6), female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with a completed bachelor's or equivalent degree (ISCED 6) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed a bachelor's or equivalent degree as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of female population (age 25 and over) with a completed bachelor's or equivalent degree (ISCED 6) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed a bachelor's or equivalent degree as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.6.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is Bachelor's or equivalent (ISCED 6), male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with a completed bachelor's or equivalent degree (ISCED 6) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed a bachelor's or equivalent degree as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of male population (age 25 and over) with a completed bachelor's or equivalent degree (ISCED 6) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed a bachelor's or equivalent degree as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.6T8.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least a completed bachelor's or equivalent degree (ISCED 6 or higher). Total" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with a completed bachelor's or equivalent degree (ISCED 6) or higher. This indicator is calculated by dividing the number of persons aged 25 years and above with a completed bachelor's or equivalent degree by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.6T8.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least a completed bachelor's or equivalent degree (ISCED 6 or higher). Female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with a completed bachelor's or equivalent degree (ISCED 6) or higher. This indicator is calculated by dividing the number of females aged 25 years and above who completed a bachelor's or equivalent degree by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.6T8.AG25T99.GPIA", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least a completed bachelor's or equivalent degree (ISCED 6 or higher). Adjusted Gender Parity Index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Attainment" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.6T8.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least a completed bachelor's or equivalent degree (ISCED 6 or higher). Male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with a completed bachelor's or equivalent degree (ISCED 6) or higher. This indicator is calculated by dividing the number of males aged 25 years and above who completed a bachelor's or equivalent degree by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.7.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is Master's or equivalent (ISCED 7), both sexes" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with a completed master's or equivalent degree (ISCED 7) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed a master's or equivalent degree as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of population (age 25 and over) with a completed master's or equivalent degree (ISCED 7) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed a master's or equivalent degree as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.7.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is Master's or equivalent (ISCED 7), female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with a completed master's or equivalent degree (ISCED 7) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed a master's or equivalent degree as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of female population (age 25 and over) with a completed master's or equivalent degree (ISCED 7) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed a master's or equivalent degree as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.7.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is Master's or equivalent (ISCED 7), male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with a completed master's or equivalent degree (ISCED 7) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed a master's or equivalent degree as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of male population (age 25 and over) with a completed master's or equivalent degree (ISCED 7) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed a master's or equivalent degree as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.7T8.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least a completed master's degree or equivalent (ISCED 7 or higher). Total" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with a completed master's or equivalent degree (ISCED 7) or higher. This indicator is calculated by dividing the number of persons aged 25 years and above with a completed master's or equivalent degree by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.7T8.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least a completed master's degree or equivalent (ISCED 7 or higher). Female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with a completed master's or equivalent degree (ISCED 7) or higher. This indicator is calculated by dividing the number of females aged 25 years and above who completed a master's or equivalent degree by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.7T8.AG25T99.GPIA", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least a completed master's degree or equivalent (ISCED 7 or higher). Adjusted Gender Parity Index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Attainment" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.7T8.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least a completed master's degree or equivalent (ISCED 7 or higher). Male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with a completed master's or equivalent degree (ISCED 7) or higher. This indicator is calculated by dividing the number of males aged 25 years and above who completed a master's or equivalent degree by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.8.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with a doctoral degree or equivalent (ISCED 8). Total" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with a completed doctoral or equivalent degree (ISCED 8). This indicator is calculated by dividing the number of persons aged 25 years and above with a completed doctoral or equivalent degree by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.8.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with a doctoral degree or equivalent (ISCED 8). Female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with a completed doctoral or equivalent degree (ISCED 8). This indicator is calculated by dividing the number of females aged 25 years and above who completed a doctoral or equivalent degree by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.8.AG25T99.GPIA", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with a doctoral degree or equivalent (ISCED 8). Adjusted Gender Parity Index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Attainment" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.8.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with a doctoral degree or equivalent (ISCED 8). Male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with a completed doctoral or equivalent degree (ISCED 8). This indicator is calculated by dividing the number of males aged 25 years and above who completed a doctoral or equivalent degree by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.MEAN.1T6.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Mean years of schooling (ISCED 1 or higher), population 25+ years, both sexes" }, { "id": "Longdefinition", "value": "Mean years of schooling (MYS) provides the average number of years of education (primary/ISCED 1 or higher) completed by a country’s adult population (25 years and older), excluding years spent repeating grades. For further information and specific calculation methods, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "Mean years of schooling (MYS) provides the average number of years of education (primary/ISCED 1 or higher) completed by a country’s adult population (25 years and older), excluding years spent repeating grades. For further information and specific calculation methods, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.MEAN.1T6.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Mean years of schooling (ISCED 1 or higher), population 25+ years, female" }, { "id": "Longdefinition", "value": "Mean years of schooling (MYS) provides the average number of years of education (primary/ISCED 1 or higher) completed by a country’s female adult population (25 years and older), excluding years spent repeating grades. For further information and specific calculation methods, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "Mean years of schooling (MYS) provides the average number of years of education (primary/ISCED 1 or higher) completed by a country’s female adult population (25 years and older), excluding years spent repeating grades. For further information and specific calculation methods, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.MEAN.1T6.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Mean years of schooling (ISCED 1 or higher), population 25+ years, male" }, { "id": "Longdefinition", "value": "Mean years of schooling (MYS) provides the average number of years of education (primary/ISCED 1 or higher) completed by a country’s male adult population (25 years and older), excluding years spent repeating grades. For further information and specific calculation methods, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "Mean years of schooling (MYS) provides the average number of years of education (primary/ISCED 1 or higher) completed by a country’s male adult population (25 years and older), excluding years spent repeating grades. For further information and specific calculation methods, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.NS.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with no schooling, both sexes" }, { "id": "Longdefinition", "value": "The percentage of the population (age 25 and over) with no education. This indicator is calculated by dividing the number of persons aged 25 years and above with no education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of the population (age 25 and over) with no education. This indicator is calculated by dividing the number of persons aged 25 years and above with no education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.NS.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with no schooling, female" }, { "id": "Longdefinition", "value": "The percentage of the female population (age 25 and over) with no education. This indicator is calculated by dividing the number of females aged 25 years and above with no education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of the female population (age 25 and over) with no education. This indicator is calculated by dividing the number of females aged 25 years and above with no education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.NS.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with no schooling, male" }, { "id": "Longdefinition", "value": "The percentage of the male population (age 25 and over) with no education. This indicator is calculated by dividing the number of males aged 25 years and above with no education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of the male population (age 25 and over) with no education. This indicator is calculated by dividing the number of males aged 25 years and above with no education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.S1.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is incomplete primary, both sexes" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with incomplete primary as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above with incomplete primary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of population (age 25 and over) with incomplete primary as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above with incomplete primary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.S1.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is incomplete primary, female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with incomplete primary as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above with incomplete primary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of female population (age 25 and over) with incomplete primary as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above with incomplete primary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.S1.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ whose highest level of education is incomplete primary, male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with incomplete primary as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above with incomplete primary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Shortdefinition", "value": "The percentage of male population (age 25 and over) with incomplete primary as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above with incomplete primary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.S1T8.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least some primary (ISCED 1). Total" }, { "id": "Longdefinition", "value": "The percentage of population (age 25 and over) with at least some primary education (ISCED 1). This indicator is calculated by dividing the number of persons aged 25 years and above with some primary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Attainment" }, { "id": "Shortdefinition", "value": "The percentage of population (age 25 and over) with at least some primary education (ISCED 1). This indicator is calculated by dividing the number of persons aged 25 years and above with some primary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.S1T8.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least some primary (ISCED 1). Female" }, { "id": "Longdefinition", "value": "The percentage of female population (age 25 and over) with at least some primary education (ISCED 1). This indicator is calculated by dividing the number of females aged 25 years and above with some primary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Attainment" }, { "id": "Shortdefinition", "value": "The percentage of female population (age 25 and over) with at least some primary education (ISCED 1). This indicator is calculated by dividing the number of females aged 25 years and above with some primary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.S1T8.AG25T99.GPIA", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least some primary (ISCED 1). Adjusted Gender Parity Index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Attainment" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.S1T8.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with at least some primary (ISCED 1). Male" }, { "id": "Longdefinition", "value": "The percentage of male population (age 25 and over) with at least some primary education (ISCED 1). This indicator is calculated by dividing the number of males aged 25 years and above with some primary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Attainment" }, { "id": "Shortdefinition", "value": "The percentage of male population (age 25 and over) with at least some primary education (ISCED 1). This indicator is calculated by dividing the number of males aged 25 years and above with some primary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.UK.AG25T99", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with unknown educational attainment. Total" }, { "id": "Longdefinition", "value": "The percentage of the population (age 25 and over) with unknown educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above with unknown educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Othernotes", "value": "Single Level Attainment/ Not Cumulative" }, { "id": "Shortdefinition", "value": "The percentage of the population (age 25 and over) with unknown educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above with unknown educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.UK.AG25T99.F", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with unknown educational attainment. Female" }, { "id": "Longdefinition", "value": "The percentage of the female population (age 25 and over) with unknown educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above with unknown educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Othernotes", "value": "Single Level Attainment/ Not Cumulative" }, { "id": "Shortdefinition", "value": "The percentage of the female population (age 25 and over) with unknown educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above with unknown educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.EA.UK.AG25T99.M", "metatype": [ { "id": "IndicatorName", "value": "UIS: Percentage of population age 25+ with unknown educational attainment. Male" }, { "id": "Longdefinition", "value": "The percentage of the male population (age 25 and over) with unknown educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above with unknown educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Othernotes", "value": "Single Level Attainment/ Not Cumulative" }, { "id": "Shortdefinition", "value": "The percentage of the male population (age 25 and over) with unknown educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above with unknown educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Attainment" } ], "source_id": "12" }, { "id": "UIS.ESG.LOWERSEC", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ESG.LOWERSEC.COGN", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience, Cognitive dimension, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ESG.LOWERSEC.COGN.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience, Cognitive dimension, female (%)" }, { "id": "Longdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ESG.LOWERSEC.COGN.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience‚ Cognitive dimension‚ adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ESG.LOWERSEC.COGN.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience, Cognitive dimension, male (%)" }, { "id": "Longdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ESG.LOWERSEC.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience, female (%)" }, { "id": "Longdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ESG.LOWERSEC.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience‚ adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ESG.LOWERSEC.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience, male (%)" }, { "id": "Longdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ESG.LOWERSEC.NCOG.CONF", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience, Non-cognitive dimension, Confidence, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ESG.LOWERSEC.NCOG.CONF.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience, Non-cognitive dimension, Confidence, female (%)" }, { "id": "Longdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ESG.LOWERSEC.NCOG.CONF.GPI", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience‚ Non-cognitive dimension‚ Confidence‚ adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ESG.LOWERSEC.NCOG.CONF.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience, Non-cognitive dimension, Confidence, male (%)" }, { "id": "Longdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience. This indicator serves to identify country's students capacity to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity. It is about lifelong learning and is an integral part of quality education. The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ESG.LOWERSEC.NCOG.ENJO", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience, Non-cognitive dimension, Enjoyment, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students in lower secondary education indicating enjoyment of environmental science and geoscience. Students who express high enjoyment of learning environmental science and geoscience and agree with expressions such as “I like to conduct science experiments”, “I learn many interesting things in science”, or “I like science”, and express disagreement to expressions such as “Science is boring” or “I wish I did not have to study science.” The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students in lower secondary education indicating enjoyment of environmental science and geoscience. Students who express high enjoyment of learning environmental science and geoscience and agree with expressions such as “I like to conduct science experiments”, “I learn many interesting things in science”, or “I like science”, and express disagreement to expressions such as “Science is boring” or “I wish I did not have to study science.” The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ESG.LOWERSEC.NCOG.ENJO.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience, Non-cognitive dimension, Enjoyment, female (%)" }, { "id": "Longdefinition", "value": "Percentage of students in lower secondary education indicating enjoyment of environmental science and geoscience. Students who express high enjoyment of learning environmental science and geoscience and agree with expressions such as “I like to conduct science experiments”, “I learn many interesting things in science”, or “I like science”, and express disagreement to expressions such as “Science is boring” or “I wish I did not have to study science.” The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students in lower secondary education indicating enjoyment of environmental science and geoscience. Students who express high enjoyment of learning environmental science and geoscience and agree with expressions such as “I like to conduct science experiments”, “I learn many interesting things in science”, or “I like science”, and express disagreement to expressions such as “Science is boring” or “I wish I did not have to study science.” The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ESG.LOWERSEC.NCOG.ENJO.GPI", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience‚ Non-cognitive dimension‚ Enjoyment‚ adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ESG.LOWERSEC.NCOG.ENJO.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing proficiency in knowledge of environmental science and geoscience, Non-cognitive dimension, Enjoyment, male (%)" }, { "id": "Longdefinition", "value": "Percentage of students in lower secondary education indicating enjoyment of environmental science and geoscience. Students who express high enjoyment of learning environmental science and geoscience and agree with expressions such as “I like to conduct science experiments”, “I learn many interesting things in science”, or “I like science”, and express disagreement to expressions such as “Science is boring” or “I wish I did not have to study science.” The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students in lower secondary education indicating enjoyment of environmental science and geoscience. Students who express high enjoyment of learning environmental science and geoscience and agree with expressions such as “I like to conduct science experiments”, “I learn many interesting things in science”, or “I like science”, and express disagreement to expressions such as “Science is boring” or “I wish I did not have to study science.” The data was sourced from the IEA Trends in International Mathematics and Science Study (TIMSS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.EV1524P.2T5.V", "metatype": [ { "id": "IndicatorName", "value": "Proportion of 15-24 year-olds enrolled in vocational education, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of young people aged 15-24 years participating in technical or vocational education either in formal or non-formal (e.g. work-based or other settings) education, on a given date or during a specified period. The figure is calculated as the number of young people aged 15-24 years participating in technical and vocational education at secondary, post-secondary non-tertiary or tertiary levels of education is expressed as a percentage of the population of the same age group. The data source is administrative data from schools and other places of education and training or household survey data on enrolment in technical and vocational programmes by single year of age; population censuses and surveys for population estimates for the age group 15-24 years (if using administrative data on enrolment). Technical and vocational education and training can be offered in a variety of settings including schools and universities, workplace environments and others. Administrative data often capture only provision in formal settings such as schools and universities. Participation rates do not capture the intensity or quality of the provision nor the outcomes of the education and training on offer. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Vocational & Post-secondary Non-Tertiary" }, { "id": "Shortdefinition", "value": "Percentage of young people aged 15-24 years participating in technical or vocational education either in formal or non-formal (e.g. work-based or other settings) education, on a given date or during a specified period. The figure is calculated as the number of young people aged 15-24 years participating in technical and vocational education at secondary, post-secondary non-tertiary or tertiary levels of education is expressed as a percentage of the population of the same age group. The data source is administrative data from schools and other places of education and training or household survey data on enrolment in technical and vocational programmes by single year of age; population censuses and surveys for population estimates for the age group 15-24 years (if using administrative data on enrolment). Technical and vocational education and training can be offered in a variety of settings including schools and universities, workplace environments and others. Administrative data often capture only provision in formal settings such as schools and universities. Participation rates do not capture the intensity or quality of the provision nor the outcomes of the education and training on offer. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Vocational & Post-secondary Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.EV1524P.2T5.V.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of 15-24 year-olds enrolled in vocational education, female (%)" }, { "id": "Longdefinition", "value": "Percentage of young people aged 15-24 years participating in technical or vocational education either in formal or non-formal (e.g. work-based or other settings) education, on a given date or during a specified period. The figure is calculated as the number of young people aged 15-24 years participating in technical and vocational education at secondary, post-secondary non-tertiary or tertiary levels of education is expressed as a percentage of the population of the same age group. The data source is administrative data from schools and other places of education and training or household survey data on enrolment in technical and vocational programmes by single year of age; population censuses and surveys for population estimates for the age group 15-24 years (if using administrative data on enrolment). Technical and vocational education and training can be offered in a variety of settings including schools and universities, workplace environments and others. Administrative data often capture only provision in formal settings such as schools and universities. Participation rates do not capture the intensity or quality of the provision nor the outcomes of the education and training on offer. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Vocational & Post-secondary Non-Tertiary" }, { "id": "Shortdefinition", "value": "Percentage of young people aged 15-24 years participating in technical or vocational education either in formal or non-formal (e.g. work-based or other settings) education, on a given date or during a specified period. The figure is calculated as the number of young people aged 15-24 years participating in technical and vocational education at secondary, post-secondary non-tertiary or tertiary levels of education is expressed as a percentage of the population of the same age group. The data source is administrative data from schools and other places of education and training or household survey data on enrolment in technical and vocational programmes by single year of age; population censuses and surveys for population estimates for the age group 15-24 years (if using administrative data on enrolment). Technical and vocational education and training can be offered in a variety of settings including schools and universities, workplace environments and others. Administrative data often capture only provision in formal settings such as schools and universities. Participation rates do not capture the intensity or quality of the provision nor the outcomes of the education and training on offer. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Vocational & Post-secondary Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.EV1524P.2T5.V.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of 15-24 year-olds enrolled in vocational education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Vocational & Post-secondary Non-Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Vocational & Post-secondary Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.EV1524P.2T5.V.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of 15-24 year-olds enrolled in vocational education, male (%)" }, { "id": "Longdefinition", "value": "Percentage of young people aged 15-24 years participating in technical or vocational education either in formal or non-formal (e.g. work-based or other settings) education, on a given date or during a specified period. The figure is calculated as the number of young people aged 15-24 years participating in technical and vocational education at secondary, post-secondary non-tertiary or tertiary levels of education is expressed as a percentage of the population of the same age group. The data source is administrative data from schools and other places of education and training or household survey data on enrolment in technical and vocational programmes by single year of age; population censuses and surveys for population estimates for the age group 15-24 years (if using administrative data on enrolment). Technical and vocational education and training can be offered in a variety of settings including schools and universities, workplace environments and others. Administrative data often capture only provision in formal settings such as schools and universities. Participation rates do not capture the intensity or quality of the provision nor the outcomes of the education and training on offer. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Vocational & Post-secondary Non-Tertiary" }, { "id": "Shortdefinition", "value": "Percentage of young people aged 15-24 years participating in technical or vocational education either in formal or non-formal (e.g. work-based or other settings) education, on a given date or during a specified period. The figure is calculated as the number of young people aged 15-24 years participating in technical and vocational education at secondary, post-secondary non-tertiary or tertiary levels of education is expressed as a percentage of the population of the same age group. The data source is administrative data from schools and other places of education and training or household survey data on enrolment in technical and vocational programmes by single year of age; population censuses and surveys for population estimates for the age group 15-24 years (if using administrative data on enrolment). Technical and vocational education and training can be offered in a variety of settings including schools and universities, workplace environments and others. Administrative data often capture only provision in formal settings such as schools and universities. Participation rates do not capture the intensity or quality of the provision nor the outcomes of the education and training on offer. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Vocational & Post-secondary Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.FHLANGILP.1", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in primary education who have their first or home language as language of instruction, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students in primary education who have their first language or home language as language of instruction expressed as a percentage of the total number of students at the same level stage of education and year. The higher the percentage of children taught in their home language, the higher the probability that they acquire cognitive skills such as beginning reading and writing. Data for this indicator are compiled by the UNESCO Institute for Statistics from sources like El Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (LLECE), Programme d'analyse des systèmes éducatifs de la confemen (PASEC), Trends in International Mathematics and Science Study (TIMSS), and Multiple Indicator Cluster Surveys (MICS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of students in primary education who have their first language or home language as language of instruction expressed as a percentage of the total number of students at the same level stage of education and year. The higher the percentage of children taught in their home language, the higher the probability that they acquire cognitive skills such as beginning reading and writing. Data for this indicator are compiled by the UNESCO Institute for Statistics from sources like El Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (LLECE), Programme d'analyse des systèmes éducatifs de la confemen (PASEC), Trends in International Mathematics and Science Study (TIMSS), and Multiple Indicator Cluster Surveys (MICS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.FHLANGILP.1.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in primary education who have their first or home language as language of instruction, female (%)" }, { "id": "Longdefinition", "value": "Number of students in primary education who have their first language or home language as language of instruction expressed as a percentage of the total number of students at the same level stage of education and year. The higher the percentage of children taught in their home language, the higher the probability that they acquire cognitive skills such as beginning reading and writing. Data for this indicator are compiled by the UNESCO Institute for Statistics from sources like El Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (LLECE), Programme d'analyse des systèmes éducatifs de la confemen (PASEC), Trends in International Mathematics and Science Study (TIMSS), and Multiple Indicator Cluster Surveys (MICS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of students in primary education who have their first language or home language as language of instruction expressed as a percentage of the total number of students at the same level stage of education and year. The higher the percentage of children taught in their home language, the higher the probability that they acquire cognitive skills such as beginning reading and writing. Data for this indicator are compiled by the UNESCO Institute for Statistics from sources like El Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (LLECE), Programme d'analyse des systèmes éducatifs de la confemen (PASEC), Trends in International Mathematics and Science Study (TIMSS), and Multiple Indicator Cluster Surveys (MICS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.FHLANGILP.1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in primary education who have their first or home language as language of instruction, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.FHLANGILP.1.HIGHSES", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in primary education who have their first or home language as language of instruction, very affluent socioeconomic background, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students in primary education who have their first language or home language as language of instruction expressed as a percentage of the total number of students at the same level stage of education and year. The higher the percentage of children taught in their home language, the higher the probability that they acquire cognitive skills such as beginning reading and writing. Data for this indicator are compiled by the UNESCO Institute for Statistics from sources like El Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (LLECE), Programme d'analyse des systèmes éducatifs de la confemen (PASEC), Trends in International Mathematics and Science Study (TIMSS), and Multiple Indicator Cluster Surveys (MICS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of students in primary education who have their first language or home language as language of instruction expressed as a percentage of the total number of students at the same level stage of education and year. The higher the percentage of children taught in their home language, the higher the probability that they acquire cognitive skills such as beginning reading and writing. Data for this indicator are compiled by the UNESCO Institute for Statistics from sources like El Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (LLECE), Programme d'analyse des systèmes éducatifs de la confemen (PASEC), Trends in International Mathematics and Science Study (TIMSS), and Multiple Indicator Cluster Surveys (MICS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.FHLANGILP.1.LOWSES", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in primary education who have their first or home language as language of instruction, very poor socioeconomic background, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students in primary education who have their first language or home language as language of instruction expressed as a percentage of the total number of students at the same level stage of education and year. The higher the percentage of children taught in their home language, the higher the probability that they acquire cognitive skills such as beginning reading and writing. Data for this indicator are compiled by the UNESCO Institute for Statistics from sources like El Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (LLECE), Programme d'analyse des systèmes éducatifs de la confemen (PASEC), Trends in International Mathematics and Science Study (TIMSS), and Multiple Indicator Cluster Surveys (MICS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of students in primary education who have their first language or home language as language of instruction expressed as a percentage of the total number of students at the same level stage of education and year. The higher the percentage of children taught in their home language, the higher the probability that they acquire cognitive skills such as beginning reading and writing. Data for this indicator are compiled by the UNESCO Institute for Statistics from sources like El Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (LLECE), Programme d'analyse des systèmes éducatifs de la confemen (PASEC), Trends in International Mathematics and Science Study (TIMSS), and Multiple Indicator Cluster Surveys (MICS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.FHLANGILP.1.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in primary education who have their first or home language as language of instruction, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.FHLANGILP.1.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in primary education who have their first or home language as language of instruction, male (%)" }, { "id": "Longdefinition", "value": "Number of students in primary education who have their first language or home language as language of instruction expressed as a percentage of the total number of students at the same level stage of education and year. The higher the percentage of children taught in their home language, the higher the probability that they acquire cognitive skills such as beginning reading and writing. Data for this indicator are compiled by the UNESCO Institute for Statistics from sources like El Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (LLECE), Programme d'analyse des systèmes éducatifs de la confemen (PASEC), Trends in International Mathematics and Science Study (TIMSS), and Multiple Indicator Cluster Surveys (MICS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of students in primary education who have their first language or home language as language of instruction expressed as a percentage of the total number of students at the same level stage of education and year. The higher the percentage of children taught in their home language, the higher the probability that they acquire cognitive skills such as beginning reading and writing. Data for this indicator are compiled by the UNESCO Institute for Statistics from sources like El Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (LLECE), Programme d'analyse des systèmes éducatifs de la confemen (PASEC), Trends in International Mathematics and Science Study (TIMSS), and Multiple Indicator Cluster Surveys (MICS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.FHLANGILP.1.RUR", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in primary education who have their first or home language as language of instruction, rural, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students in primary education who have their first language or home language as language of instruction expressed as a percentage of the total number of students at the same level stage of education and year. The higher the percentage of children taught in their home language, the higher the probability that they acquire cognitive skills such as beginning reading and writing. Data for this indicator are compiled by the UNESCO Institute for Statistics from sources like El Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (LLECE), Programme d'analyse des systèmes éducatifs de la confemen (PASEC), Trends in International Mathematics and Science Study (TIMSS), and Multiple Indicator Cluster Surveys (MICS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of students in primary education who have their first language or home language as language of instruction expressed as a percentage of the total number of students at the same level stage of education and year. The higher the percentage of children taught in their home language, the higher the probability that they acquire cognitive skills such as beginning reading and writing. Data for this indicator are compiled by the UNESCO Institute for Statistics from sources like El Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (LLECE), Programme d'analyse des systèmes éducatifs de la confemen (PASEC), Trends in International Mathematics and Science Study (TIMSS), and Multiple Indicator Cluster Surveys (MICS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.FHLANGILP.1.URB", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in primary education who have their first or home language as language of instruction, urban, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students in primary education who have their first language or home language as language of instruction expressed as a percentage of the total number of students at the same level stage of education and year. The higher the percentage of children taught in their home language, the higher the probability that they acquire cognitive skills such as beginning reading and writing. Data for this indicator are compiled by the UNESCO Institute for Statistics from sources like El Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (LLECE), Programme d'analyse des systèmes éducatifs de la confemen (PASEC), Trends in International Mathematics and Science Study (TIMSS), and Multiple Indicator Cluster Surveys (MICS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of students in primary education who have their first language or home language as language of instruction expressed as a percentage of the total number of students at the same level stage of education and year. The higher the percentage of children taught in their home language, the higher the probability that they acquire cognitive skills such as beginning reading and writing. Data for this indicator are compiled by the UNESCO Institute for Statistics from sources like El Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (LLECE), Programme d'analyse des systèmes éducatifs de la confemen (PASEC), Trends in International Mathematics and Science Study (TIMSS), and Multiple Indicator Cluster Surveys (MICS). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.FHLANGILP.1.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in primary education who have their first or home language as language of instruction, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.FOSGP.5T8.F400", "metatype": [ { "id": "IndicatorName", "value": "Percentage of graduates from tertiary education graduating from Business, Administration and Law programmes, both sexes (%)" }, { "id": "Longdefinition", "value": "Share of all tertiary graduates who completed Business, Administration and Law programmes in the reference year." }, { "id": "Shortdefinition", "value": "Share of all tertiary graduates who completed Business, Administration and Law programmes in the reference year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.FOSGP.5T8.F500600700", "metatype": [ { "id": "IndicatorName", "value": "Percentage of graduates from Science, Technology, Engineering and Mathematics programmes in tertiary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Share of all tertiary graduates who completed Science, Technology, Engineering and Mathematics in the reference year." }, { "id": "Shortdefinition", "value": "Share of all tertiary graduates who completed Science, Technology, Engineering and Mathematics in the reference year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.FOSGP.5T8.F600", "metatype": [ { "id": "IndicatorName", "value": "Percentage of graduates from tertiary education graduating from Information and Communication Technologies programmes, both sexes (%)" }, { "id": "Longdefinition", "value": "Share of all tertiary graduates who completed Information and Communication Technologies programmes in the reference year." }, { "id": "Shortdefinition", "value": "Share of all tertiary graduates who completed Information and Communication Technologies programmes in the reference year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.FOSGP.5T8.FNON500600700", "metatype": [ { "id": "IndicatorName", "value": "Percentage of graduates from programmes other than Science, Technology, Engineering and Mathematics in tertiary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Share of all tertiary graduates who completed Science, Technology, Engineering and Mathematics in the reference year." }, { "id": "Shortdefinition", "value": "Share of all tertiary graduates who completed Science, Technology, Engineering and Mathematics in the reference year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.FTP.2", "metatype": [ { "id": "IndicatorName", "value": "Percentage of teachers in lower secondary education who are female (%)" }, { "id": "Longdefinition", "value": "Number of female teachers at the lower secondary level expressed as a percentage of the total number of teachers (male and female) at the lower secondary level in a given school year. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.FTP.3", "metatype": [ { "id": "IndicatorName", "value": "Percentage of teachers in upper secondary education who are female (%)" }, { "id": "Longdefinition", "value": "Number of female teachers at the upper secondary level expressed as a percentage of the total number of teachers (male and female) at the upper secondary level in a given school year. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.FTP.4", "metatype": [ { "id": "IndicatorName", "value": "Percentage of teachers in post-secondary non-tertiary education who are female (%)" }, { "id": "Longdefinition", "value": "Number of female teachers at the post-secondary non-tertiary level expressed as a percentage of the total number of teachers (male and female) at the post-secondary non-tertiary level in a given school year. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q1", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q1.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, poorest quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q1.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, poorest quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q1.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, poorest quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q2", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, second quintile, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q2.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, second quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q2.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, second quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, second quintile, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q2.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, second quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q3", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q3.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, middle quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q3.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, middle quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q3.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, middle quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q4", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q4.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, fourth quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q4.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, fourth quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q4.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, fourth quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q5", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q5.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, richest quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q5.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, richest quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.Q5.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, richest quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q1", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q2", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, second quintile, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, second quintile, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q3", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q4", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q5", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.RUR.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, rural, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q1", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q2", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, second quintile, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, second quintile, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q3", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q4", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q5", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Number of students attending tertiary education at any time during the reference academic year, regardless of age, expressed as a percentage of the official school-age population corresponding to tertiary education. For the tertiary level, the population used is the 5-year age group starting from the official secondary school graduation age. The GAR can exceed 100% due to the inclusion of over-aged and under-aged students. Reasons include early or late entry, and grade repetition. For tertiary education, the GAR can exceed 100% due to the inclusion of students outside the 5-year age group starting from the official secondary school graduation age. Data are compiled by the UNESCO Institute for Statistics and sourced from national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.URB.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, urban, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GAR.5T8.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross attendance ratio for tertiary education, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.COG", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Cognitive Dimension, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.COG.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Cognitive Dimension, female (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.COG.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Cognitive Dimension, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.COG.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Cognitive Dimension, male (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, female (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, male (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.FREE", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Freedom, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.FREE.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Freedom, female (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.FREE.GPI", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability‚ Non-cognitive Dimension‚ Freedom, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.FREE.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Freedom, male (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.GEQU", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Gender equality, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.GEQU.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Gender equality, female (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.GEQU.GPI", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability‚ Non-cognitive Dimension‚ Gender equality, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.GEQU.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Gender equality, male (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.GLOC", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Global-local thinking, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.GLOC.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Global-local thinking, female (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.GLOC.GPI", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability‚ Non-cognitive Dimension‚ Global-local thinking, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.GLOC.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Global-local thinking, male (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.MULT", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Multiculturalism, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.MULT.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Multiculturalism, female (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.MULT.GPI", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability‚ Non-cognitive Dimension‚ Multiculturalism, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.MULT.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Multiculturalism, male (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.PEAC", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Peace, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.PEAC.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Peace, female (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.PEAC.GPI", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability‚ Non-cognitive Dimension‚ Peace, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.PEAC.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Peace, male (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.SDEV", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Sustainable development, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.SDEV.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Sustainable development, female (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.SDEV.GPI", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability‚ Non-cognitive Dimension‚ Sustainable development, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.SDEV.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Sustainable development, male (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.SJUS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Social Justice, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.SJUS.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Social Justice, female (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.SJUS.GPI", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability‚ Non-cognitive Dimension‚ Social Justice, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GCS.LOWERSEC.NCOG.SJUS.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary education showing adequate understanding of issues relating to global citizenship and sustainability, Non-cognitive Dimension, Social Justice, male (%)" }, { "id": "Longdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of students with an adequate understanding of issues relating to global citizenship and sustainability, a concept which nurtures respect for all, building a sense of belonging to a common humanity and helping learners become responsible and active global citizens. Global citizenship education aims to empower learners to assume active roles to face and resolve global challenges and to become proactive contributors to a more peaceful, tolerant, and inclusive and secure world. The value is calculated as the number of students at the relevant stage of education in a given year achieving or exceeding the pre-defined level of adequate understanding of issues relating to global citizenship and sustainability within a given country or region and expressed as a percentage of the total number of students at the same stage of education and year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GER.0", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, early childhood education, both sexes (%)" }, { "id": "Longdefinition", "value": "Total enrollment in early childhood education, regardless of age, expressed as a percentage of the total population of official early childhood education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Shortdefinition", "value": "Total enrollment in early childhood education, regardless of age, expressed as a percentage of the total population of official early childhood education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.GER.0.F", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, early childhood education, female (%)" }, { "id": "Longdefinition", "value": "Total female enrollment in early childhood education, regardless of age, expressed as a percentage of the total female population of official early childhood education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Shortdefinition", "value": "Total female enrollment in early childhood education, regardless of age, expressed as a percentage of the total female population of official early childhood education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.GER.0.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, early childhood education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.GER.0.M", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, early childhood education, male (%)" }, { "id": "Longdefinition", "value": "Total male enrollment in early childhood education, regardless of age, expressed as a percentage of the total male population of official early childhood education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Shortdefinition", "value": "Total male enrollment in early childhood education, regardless of age, expressed as a percentage of the total male population of official early childhood education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.GER.01", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, early childhood educational development programmes, both sexes (%)" }, { "id": "Longdefinition", "value": "Total enrollment in early childhood educational development programmes, regardless of age, expressed as a percentage of the total population of the official age for early childhood educational development programmes. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Shortdefinition", "value": "Total enrollment in early childhood educational development programmes, regardless of age, expressed as a percentage of the total population of the official age for early childhood educational development programmes. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.GER.01.F", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, early childhood educational development programmes, female (%)" }, { "id": "Longdefinition", "value": "Total female enrollment in early childhood educational development programmes, regardless of age, expressed as a percentage of the total female population of the official age for early childhood educational development programmes. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Shortdefinition", "value": "Total female enrollment in early childhood educational development programmes, regardless of age, expressed as a percentage of the total female population of the official age for early childhood educational development programmes. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.GER.01.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, early childhood educational development programmes, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.GER.01.M", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, early childhood educational development programmes, male (%)" }, { "id": "Longdefinition", "value": "Total male enrollment in early childhood educational development programmes, regardless of age, expressed as a percentage of the total male population of the official age for early childhood educational development programmes. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Shortdefinition", "value": "Total male enrollment in early childhood educational development programmes, regardless of age, expressed as a percentage of the total male population of the official age for early childhood educational development programmes. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.GER.02.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, pre-primary, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.GER.12", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, primary and lower secondary, both sexes (%)" }, { "id": "Longdefinition", "value": "Total enrollment in primary and lower secondary education, regardless of age, expressed as a percentage of the population of official primary and lower secondary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GER.12.F", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, primary and lower secondary, female (%)" }, { "id": "Longdefinition", "value": "Total female enrollment in primary and lower secondary education, regardless of age, expressed as a percentage of the female population of official primary and lower secondary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GER.12.GPI", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, primary and lower secondary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female gross enrolment ratio for primary and lower secondary to male gross enrolment ratio for primary and lower secondary. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GER.12.M", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, primary and lower secondary, male (%)" }, { "id": "Longdefinition", "value": "Total male enrollment in primary and lower secondary education, regardless of age, expressed as a percentage of the male population of official primary and lower secondary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GER.123", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, primary and secondary, both sexes (%)" }, { "id": "Longdefinition", "value": "Total enrollment in primary and secondary education, regardless of age, expressed as a percentage of the total population of official primary and secondary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GER.123.F", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, primary and secondary, female (%)" }, { "id": "Longdefinition", "value": "Total female enrollment in primary and secondary education, regardless of age, expressed as a percentage of the total female population of official primary and secondary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GER.123.M", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, primary and secondary, male (%)" }, { "id": "Longdefinition", "value": "Total male enrollment in primary and secondary education, regardless of age, expressed as a percentage of the total male population of official primary and secondary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GER.1t6.F", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, primary to tertiary, female (%)" }, { "id": "Longdefinition", "value": "Total female enrollment in primary, secondary and tertiary education, regardless of age, expressed as a percentage of the total female population of primary school age, secondary school age, and the five-year age group following on from secondary school leaving. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.GER.1T6.F", "metatype": [ { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GER.1t6.GPI", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, primary to tertiary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female gross enrolment ratio for primary to tertiary to the male value for the same indicator. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.GER.1T6.GPI", "metatype": [ { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GER.1t6.M", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, primary to tertiary, male (%)" }, { "id": "Longdefinition", "value": "Total male enrollment in primary, secondary and tertiary education, regardless of age, expressed as a percentage of the total male population of primary school age, secondary school age, and the five-year age group following on from secondary school leaving. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.GER.1T6.M", "metatype": [ { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GER.2.GPI", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, lower secondary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female gross enrolment ratio for lower secondary to the male gross enrolment ratio for lower secondary. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GER.3.GPI", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, upper secondary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female gross enrolment ratio for upper secondary to the male gross enrolment ratio for upper secondary. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GER.4", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, post-secondary non-tertiary, both sexes (%)" }, { "id": "Longdefinition", "value": "Total enrollment in post-secondary non-tertiary education, regardless of age, expressed as a percentage of the total population of official post-secondary non-tertiary education age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Post-Secondary/Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.GER.4.F", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, post-secondary non-tertiary, female (%)" }, { "id": "Longdefinition", "value": "Total female enrollment in post-secondary non-tertiary education, regardless of age, expressed as a percentage of the female population of official post-secondary non-tertiary education age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Post-Secondary/Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.GER.4.GPI", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, post-secondary non-tertiary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female gross enrolment ratio for post-secondary non-tertiary education to the male gross enrolment ratio for post-secondary non-tertiary education. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Post-Secondary/Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.GER.4.M", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio, post-secondary non-tertiary, male (%)" }, { "id": "Longdefinition", "value": "Total male enrollment in post-secondary non-tertiary education, regardless of age, expressed as a percentage of the male population of official post-secondary non-tertiary education age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Post-Secondary/Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.GER.5T8.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Gross enrolment ratio for tertiary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GGR.5.A.GPI", "metatype": [ { "id": "IndicatorName", "value": "Gross graduation ratio from first degree programmes (ISCED 6 and 7) in tertiary education, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female gross graduation ratio to the male value for the same indicator. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.GTVP.2.GPV", "metatype": [ { "id": "IndicatorName", "value": "Share of all students in lower secondary education enrolled in general programmes (%)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in general programmes at the lower secondary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the lower secondary level. General education is designed to develop learners’ general knowledge, skills and competencies and literacy and numeracy skills, often to prepare students for more advanced educational programmes at the same or higher ISCED levels and to lay the foundation for lifelong learning. General educational programmes are typically school- or college-based. General education includes educational programmes that are designed to prepare students for entry into vocational education, but that do not prepare for employment in a particular occupation or trade or class of occupations or trades, nor lead directly to a labour market relevant qualification." }, { "id": "Shortdefinition", "value": "Total number of students enrolled in general programmes at the lower secondary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the lower secondary level. General education is designed to develop learners’ general knowledge, skills and competencies and literacy and numeracy skills, often to prepare students for more advanced educational programmes at the same or higher ISCED levels and to lay the foundation for lifelong learning. General educational programmes are typically school- or college-based. General education includes educational programmes that are designed to prepare students for entry into vocational education, but that do not prepare for employment in a particular occupation or trade or class of occupations or trades, nor lead directly to a labour market relevant qualification." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GTVP.2.V", "metatype": [ { "id": "IndicatorName", "value": "Share of all students in lower secondary education enrolled in vocational programmes (%)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in vocational programmes at the lower secondary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the lower secondary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Shortdefinition", "value": "Total number of students enrolled in vocational programmes at the lower secondary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the lower secondary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GTVP.23.GPV", "metatype": [ { "id": "IndicatorName", "value": "Share of all students in secondary education enrolled in general programmes (%)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in general programmes at the secondary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the secondary level. General education is designed to develop learners’ general knowledge, skills and competencies and literacy and numeracy skills, often to prepare students for more advanced educational programmes at the same or higher ISCED levels and to lay the foundation for lifelong learning. General educational programmes are typically school- or college-based. General education includes educational programmes that are designed to prepare students for entry into vocational education, but that do not prepare for employment in a particular occupation or trade or class of occupations or trades, nor lead directly to a labour market relevant qualification." }, { "id": "Shortdefinition", "value": "Total number of students enrolled in general programmes at the secondary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the secondary level. General education is designed to develop learners’ general knowledge, skills and competencies and literacy and numeracy skills, often to prepare students for more advanced educational programmes at the same or higher ISCED levels and to lay the foundation for lifelong learning. General educational programmes are typically school- or college-based. General education includes educational programmes that are designed to prepare students for entry into vocational education, but that do not prepare for employment in a particular occupation or trade or class of occupations or trades, nor lead directly to a labour market relevant qualification." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GTVP.3.GPV", "metatype": [ { "id": "IndicatorName", "value": "Share of all students in upper secondary education enrolled in general programmes (%)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in general programmes at the upper secondary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the upper secondary level. General education is designed to develop learners’ general knowledge, skills and competencies and literacy and numeracy skills, often to prepare students for more advanced educational programmes at the same or higher ISCED levels and to lay the foundation for lifelong learning. General educational programmes are typically school- or college-based. General education includes educational programmes that are designed to prepare students for entry into vocational education, but that do not prepare for employment in a particular occupation or trade or class of occupations or trades, nor lead directly to a labour market relevant qualification." }, { "id": "Shortdefinition", "value": "Total number of students enrolled in general programmes at the upper secondary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the upper secondary level. General education is designed to develop learners’ general knowledge, skills and competencies and literacy and numeracy skills, often to prepare students for more advanced educational programmes at the same or higher ISCED levels and to lay the foundation for lifelong learning. General educational programmes are typically school- or college-based. General education includes educational programmes that are designed to prepare students for entry into vocational education, but that do not prepare for employment in a particular occupation or trade or class of occupations or trades, nor lead directly to a labour market relevant qualification." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GTVP.3.V", "metatype": [ { "id": "IndicatorName", "value": "Share of all students in upper secondary education enrolled in vocational programmes (%)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in vocational programmes at the upper secondary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the upper secondary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Shortdefinition", "value": "Total number of students enrolled in vocational programmes at the upper secondary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the upper secondary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.GTVP.4.GPV", "metatype": [ { "id": "IndicatorName", "value": "Share of all students in post-secondary non-tertiary education enrolled in general programmes (%)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in general programmes at the post-secondary non-tertiary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the post-secondary non-tertiary level. General education is designed to develop learners’ general knowledge, skills and competencies and literacy and numeracy skills, often to prepare students for more advanced educational programmes at the same or higher ISCED levels and to lay the foundation for lifelong learning. General educational programmes are typically school- or college-based. General education includes educational programmes that are designed to prepare students for entry into vocational education, but that do not prepare for employment in a particular occupation or trade or class of occupations or trades, nor lead directly to a labour market relevant qualification." }, { "id": "Shortdefinition", "value": "Total number of students enrolled in general programmes at the post-secondary non-tertiary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the post-secondary non-tertiary level. General education is designed to develop learners’ general knowledge, skills and competencies and literacy and numeracy skills, often to prepare students for more advanced educational programmes at the same or higher ISCED levels and to lay the foundation for lifelong learning. General educational programmes are typically school- or college-based. General education includes educational programmes that are designed to prepare students for entry into vocational education, but that do not prepare for employment in a particular occupation or trade or class of occupations or trades, nor lead directly to a labour market relevant qualification." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Post-Secondary/Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.GTVP.4.V", "metatype": [ { "id": "IndicatorName", "value": "Share of all students in post-secondary non-tertiary education enrolled in vocational programmes (%)" }, { "id": "Longdefinition", "value": "Total number of students enrolled in vocational programmes at the post-secondary non-tertiary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the post-secondary non-tertiary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Shortdefinition", "value": "Total number of students enrolled in vocational programmes at the post-secondary non-tertiary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the post-secondary non-tertiary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Post-Secondary/Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLATTACH", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have sent e-mails with attached files (e.g. document, picture, video), both sexes (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLATTACH.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have sent e-mails with attached files (e.g. document, picture, video), female (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLATTACH.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have sent e-mails with attached files (e.g. document, picture, video), adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLATTACH.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have sent e-mails with attached files (e.g. document, picture, video), male (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLCONNEC", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have connected and installed new devices (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLCONNEC.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have connected and installed new devices, female (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLCONNEC.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have connected and installed new devices, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLCONNEC.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have connected and installed new devices, male (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLCOPI", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have copied or moved a file or folder (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLCOPI.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have copied or moved a file or folder, female (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLCOPI.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have copied or moved a file or folder, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLCOPI.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have copied or moved a file or folder, male (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLCREAT", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have created electronic presentations with presentation software (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLCREAT.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have created electronic presentations with presentation software, female (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLCREAT.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have created electronic presentations with presentation software, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLCREAT.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have created electronic presentations with presentation software, male (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLDUPLIC", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have used copy and paste tools to duplicate or move information within a document , both sexes (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLDUPLIC.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have used copy and paste tools to duplicate or move information within a document, female (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLDUPLIC.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have used copy and paste tools to duplicate or move information within a document, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLDUPLIC.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have used copy and paste tools to duplicate or move information within a document, male (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLFORMULA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have used basic arithmetic formulae in a spreadsheet, both sexes (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLFORMULA.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have used basic arithmetic formulae in a spreadsheet, female (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLFORMULA.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have used basic arithmetic formulae in a spreadsheet, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLFORMULA.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have used basic arithmetic formulae in a spreadsheet, male (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLPROGLANG", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have wrote a computer program using a specialised programming language, both sexes (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLPROGLANG.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have wrote a computer program using a specialised programming language, female (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLPROGLANG.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have wrote a computer program using a specialised programming language, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLPROGLANG.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have wrote a computer program using a specialised programming language, male (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLSOFTWARE", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have found, downloaded, installed and configured software, both sexes (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLSOFTWARE.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have found, downloaded, installed and configured software, female (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLSOFTWARE.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have found, downloaded, installed and configured software, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLSOFTWARE.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have found, downloaded, installed and configured software, male (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLTRANSFERFILE", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have transferred files between a computer and other devices, both sexes (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLTRANSFERFILE.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have transferred files between a computer and other devices, female (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLTRANSFERFILE.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have transferred files between a computer and other devices, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ICTSKILLTRANSFERFILE.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of youth and adults who have transferred files between a computer and other devices, male (%)" }, { "id": "Longdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Technology Skills" }, { "id": "Shortdefinition", "value": "The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries. The figure is calculated as the percentage of people in a given population who have responded ‘yes’ to a selected number of variables e.g. the use of ICT skills in various subject areas or learning domains, the use of ICT skills inside or outside of school and/or workplace, the minimum amount of time spent using ICT skills inside and outside of school and/or workplace, availability of internet access inside or outside of school and/or workplace, etc. in the past 3 months, regardless of where that activity took place. The data are self-reported information on the use of ICT skills in household surveys. One of the main challenges of measurement for this indicator is that it is based only on the information people self-report. They provide information on the types of activities they have undertaken but not on their proficiency level. While self-reporting offers a cost efficient approach to data collection, it is important to consider that the results can vary between groups from different cultural and personal backgrounds. Women, for example, tend to under-report their abilities in using computers and the Internet, while men tend to overstate them. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Technology Skills" } ], "source_id": "12" }, { "id": "UIS.ILLPOP.AG25T64", "metatype": [ { "id": "IndicatorName", "value": "Illiterate population, 25-64 years, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of adults from age 25 to age 64 who cannot both read and write with understanding a short simple statement on their everyday life." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.ILLPOP.AG25T64.F", "metatype": [ { "id": "IndicatorName", "value": "Illiterate population, 25-64 years, female (number)" }, { "id": "Longdefinition", "value": "Total number of females from age 25 to age 64 who cannot both read and write with understanding a short simple statement on their everyday life." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.ILLPOP.AG25T64.M", "metatype": [ { "id": "IndicatorName", "value": "Illiterate population, 25-64 years, male (number)" }, { "id": "Longdefinition", "value": "Total number of males from age 25 to age 64 who cannot both read and write with understanding a short simple statement on their everyday life." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.ILLPOPF.AG25T64", "metatype": [ { "id": "IndicatorName", "value": "Illiterate population, 25-64 years, % female" }, { "id": "Longdefinition", "value": "Share of the adult illiterate population (age 25-64) that is female." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LP.Ag15t24", "metatype": [ { "id": "IndicatorName", "value": "Youth illiterate population, 15-24 years, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of youth between age 15 and age 24 who cannot both read and write with understanding a short simple statement on their everyday life." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.LP.AG15T24", "metatype": [ { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LP.Ag15t24.F", "metatype": [ { "id": "IndicatorName", "value": "Youth illiterate population, 15-24 years, female (number)" }, { "id": "Longdefinition", "value": "Total number of females between age 15 and age 24 who cannot both read and write with understanding a short simple statement on their everyday life." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.LP.AG15T24.F", "metatype": [ { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LP.Ag15t24.M", "metatype": [ { "id": "IndicatorName", "value": "Youth illiterate population, 15-24 years, male (number)" }, { "id": "Longdefinition", "value": "Total number of males between age 15 and age 24 who cannot both read and write with understanding a short simple statement on their everyday life." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.LP.AG15T24.M", "metatype": [ { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LP.Ag15t99", "metatype": [ { "id": "IndicatorName", "value": "Adult illiterate population, 15+ years, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of adults over age 15 who cannot both read and write with understanding a short simple statement on their everyday life." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.LP.AG15T99", "metatype": [ { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LP.Ag15t99.F", "metatype": [ { "id": "IndicatorName", "value": "Adult illiterate population, 15+ years, female (number)" }, { "id": "Longdefinition", "value": "Total number of females over age 15 who cannot both read and write with understanding a short simple statement on their everyday life." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.LP.AG15T99.F", "metatype": [ { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LP.Ag15t99.M", "metatype": [ { "id": "IndicatorName", "value": "Adult illiterate population, 15+ years, male (number)" }, { "id": "Longdefinition", "value": "Total number of males over age 15 who cannot both read and write with understanding a short simple statement on their everyday life." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.LP.AG15T99.M", "metatype": [ { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LP.Ag65", "metatype": [ { "id": "IndicatorName", "value": "Elderly illiterate population, 65+ years, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of adults over age 65 who cannot both read and write with understanding a short simple statement on their everyday life." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.LP.AG65", "metatype": [ { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LP.Ag65.F", "metatype": [ { "id": "IndicatorName", "value": "Elderly illiterate population, 65+ years, female (number)" }, { "id": "Longdefinition", "value": "Total number of females over age 65 who cannot both read and write with understanding a short simple statement on their everyday life." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.LP.AG65.F", "metatype": [ { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LP.Ag65.M", "metatype": [ { "id": "IndicatorName", "value": "Elderly illiterate population, 65+ years, male (number)" }, { "id": "Longdefinition", "value": "Total number of males over age 65 who cannot both read and write with understanding a short simple statement on their everyday life." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.LP.AG65.M", "metatype": [ { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LPP.Ag15t24", "metatype": [ { "id": "IndicatorName", "value": "Youth illiterate population, 15-24 years, % female" }, { "id": "Longdefinition", "value": "Share of the youth illiterate population that is female." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.LPP.AG15T24", "metatype": [ { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LPP.Ag15t99", "metatype": [ { "id": "IndicatorName", "value": "Adult illiterate population, 15+ years, % female" }, { "id": "Longdefinition", "value": "Share of the adult illiterate population (age 15+) that is female." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.LPP.AG15T99", "metatype": [ { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LPP.Ag65", "metatype": [ { "id": "IndicatorName", "value": "Elderly illiterate population, 65+ years, % female" }, { "id": "Longdefinition", "value": "Share of the elderly illiterate population that is female." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.LPP.AG65", "metatype": [ { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T24.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Youth literacy rate, population 15-24 years, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T24.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Youth literacy rate, population 15-24 years, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T24.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Youth literacy rate, population 15-24 years, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T24.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Youth literacy rate, population 15-24 years, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T24.RUR", "metatype": [ { "id": "IndicatorName", "value": "Youth literacy rate, population 15-24 years, rural, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of people age 15 to 24 years in rural areas who can both read and write with understanding a short simple statement on their everyday life, divided by the population in that age group. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. Divide the number of people aged 15 to 24 years who are literate by the total population in the same age group and multiply the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Number of people age 15 to 24 years in rural areas who can both read and write with understanding a short simple statement on their everyday life, divided by the population in that age group. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. Divide the number of people aged 15 to 24 years who are literate by the total population in the same age group and multiply the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T24.RUR.F", "metatype": [ { "id": "IndicatorName", "value": "Youth literacy rate, population 15-24 years, rural, female (%)" }, { "id": "Longdefinition", "value": "Number of females age 15 to 24 years in rural areas who can both read and write with understanding a short simple statement on their everyday life, divided by the female population in that age group. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. Divide the number of people aged 15 to 24 years who are literate by the total population in the same age group and multiply the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Number of females age 15 to 24 years in rural areas who can both read and write with understanding a short simple statement on their everyday life, divided by the female population in that age group. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. Divide the number of people aged 15 to 24 years who are literate by the total population in the same age group and multiply the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T24.RUR.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Youth literacy rate, population 15-24 years, rural, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T24.RUR.M", "metatype": [ { "id": "IndicatorName", "value": "Youth literacy rate, population 15-24 years, rural, male (%)" }, { "id": "Longdefinition", "value": "Number of males age 15 to 24 years in rural areas who can both read and write with understanding a short simple statement on their everyday life, divided by the male population in that age group. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. Divide the number of people aged 15 to 24 years who are literate by the total population in the same age group and multiply the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Number of males age 15 to 24 years in rural areas who can both read and write with understanding a short simple statement on their everyday life, divided by the male population in that age group. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. Divide the number of people aged 15 to 24 years who are literate by the total population in the same age group and multiply the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T24.URB", "metatype": [ { "id": "IndicatorName", "value": "Youth literacy rate, population 15-24 years, urban, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of people age 15 to 24 years in urban areas who can both read and write with understanding a short simple statement on their everyday life, divided by the population in that age group. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. Divide the number of people aged 15 to 24 years who are literate by the total population in the same age group and multiply the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Number of people age 15 to 24 years in urban areas who can both read and write with understanding a short simple statement on their everyday life, divided by the population in that age group. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. Divide the number of people aged 15 to 24 years who are literate by the total population in the same age group and multiply the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T24.URB.F", "metatype": [ { "id": "IndicatorName", "value": "Youth literacy rate, population 15-24 years, urban, female (%)" }, { "id": "Longdefinition", "value": "Number of females age 15 to 24 years in urban areas who can both read and write with understanding a short simple statement on their everyday life, divided by the female population in that age group. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. Divide the number of people aged 15 to 24 years who are literate by the total population in the same age group and multiply the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Number of females age 15 to 24 years in urban areas who can both read and write with understanding a short simple statement on their everyday life, divided by the female population in that age group. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. Divide the number of people aged 15 to 24 years who are literate by the total population in the same age group and multiply the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T24.URB.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Youth literacy rate, population 15-24 years, urban, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T24.URB.M", "metatype": [ { "id": "IndicatorName", "value": "Youth literacy rate, population 15-24 years, urban, male (%)" }, { "id": "Longdefinition", "value": "Number of males age 15 to 24 years in urban areas who can both read and write with understanding a short simple statement on their everyday life, divided by the male population in that age group. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. Divide the number of people aged 15 to 24 years who are literate by the total population in the same age group and multiply the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Number of males age 15 to 24 years in urban areas who can both read and write with understanding a short simple statement on their everyday life, divided by the male population in that age group. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. Divide the number of people aged 15 to 24 years who are literate by the total population in the same age group and multiply the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T99.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adult literacy rate, population 15+ years, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T99.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adult literacy rate, population 15+ years, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T99.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adult literacy rate, population 15+ years, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T99.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adult literacy rate, population 15+ years, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T99.RUR", "metatype": [ { "id": "IndicatorName", "value": "Adult literacy rate, population 15+ years, rural, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of adults in rural areas over age 15 who cannot both read and write with understanding a short simple statement on their everyday life. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Total number of adults in rural areas over age 15 who cannot both read and write with understanding a short simple statement on their everyday life. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T99.RUR.F", "metatype": [ { "id": "IndicatorName", "value": "Adult literacy rate, population 15+ years, rural, female (%)" }, { "id": "Longdefinition", "value": "Total number of females in rural areas over age 15 who cannot both read and write with understanding a short simple statement on their everyday life. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Total number of females in rural areas over age 15 who cannot both read and write with understanding a short simple statement on their everyday life. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T99.RUR.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adult literacy rate, population 15+ years, rural, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T99.RUR.M", "metatype": [ { "id": "IndicatorName", "value": "Adult literacy rate, population 15+ years, rural, male (%)" }, { "id": "Longdefinition", "value": "Total number of males in rural areas over age 15 who cannot both read and write with understanding a short simple statement on their everyday life. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Total number of males in rural areas over age 15 who cannot both read and write with understanding a short simple statement on their everyday life. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T99.URB", "metatype": [ { "id": "IndicatorName", "value": "Adult literacy rate, population 15+ years, urban, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of adults in urban areas over age 15 who cannot both read and write with understanding a short simple statement on their everyday life. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Total number of adults in urban areas over age 15 who cannot both read and write with understanding a short simple statement on their everyday life. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T99.URB.F", "metatype": [ { "id": "IndicatorName", "value": "Adult literacy rate, population 15+ years, urban, female (%)" }, { "id": "Longdefinition", "value": "Total number of females in urban areas over age 15 who cannot both read and write with understanding a short simple statement on their everyday life. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Total number of females in urban areas over age 15 who cannot both read and write with understanding a short simple statement on their everyday life. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T99.URB.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adult literacy rate, population 15+ years, urban, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG15T99.URB.M", "metatype": [ { "id": "IndicatorName", "value": "Adult literacy rate, population 15+ years, urban, male (%)" }, { "id": "Longdefinition", "value": "Total number of males in urban areas over age 15 who cannot both read and write with understanding a short simple statement on their everyday life. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Total number of males in urban areas over age 15 who cannot both read and write with understanding a short simple statement on their everyday life. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG25T64", "metatype": [ { "id": "IndicatorName", "value": "Literacy rate, population 25-64 years, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of the population between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of literates aged 25-64 years by the corresponding age group population and multiplying the result by 100." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG25T64.F", "metatype": [ { "id": "IndicatorName", "value": "Literacy rate, population 25-64 years, female (%)" }, { "id": "Longdefinition", "value": "Percentage of the female population between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of female literates aged 25-64 years by the corresponding age group population and multiplying the result by 100." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG25T64.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Literacy rate, population 25-64 years, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG25T64.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Literacy rate, population 25-64 years, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG25T64.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Literacy rate, population 25-64 years, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG25T64.M", "metatype": [ { "id": "IndicatorName", "value": "Literacy rate, population 25-64 years, male (%)" }, { "id": "Longdefinition", "value": "Percentage of the male population between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of male literates aged 25-64 years by the corresponding age group population and multiplying the result by 100." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG25T64.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Literacy rate, population 25-64 years, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG25T64.RUR", "metatype": [ { "id": "IndicatorName", "value": "Literacy rate, population 25-64 years, rural, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of the population in rural areas between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of literates aged 25-64 years by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Percentage of the population in rural areas between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of literates aged 25-64 years by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG25T64.RUR.F", "metatype": [ { "id": "IndicatorName", "value": "Literacy rate, population 25-64 years, rural, female (%)" }, { "id": "Longdefinition", "value": "Percentage of the female population in rural areas between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of female literates aged 25-64 years by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Percentage of the female population in rural areas between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of female literates aged 25-64 years by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG25T64.RUR.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Literacy rate, population 25-64 years, rural, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG25T64.RUR.M", "metatype": [ { "id": "IndicatorName", "value": "Literacy rate, population 25-64 years, rural, male (%)" }, { "id": "Longdefinition", "value": "Percentage of the male population in rural areas between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of male literates aged 25-64 years by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Percentage of the male population in rural areas between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of male literates aged 25-64 years by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG25T64.URB", "metatype": [ { "id": "IndicatorName", "value": "Literacy rate, population 25-64 years, urban, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of the population in urban areas between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of literates aged 25-64 years by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Percentage of the population in urban areas between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of literates aged 25-64 years by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG25T64.URB.F", "metatype": [ { "id": "IndicatorName", "value": "Literacy rate, population 25-64 years, urban, female (%)" }, { "id": "Longdefinition", "value": "Percentage of the female population in urban areas between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of female literates aged 25-64 years by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Percentage of the female population in urban areas between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of female literates aged 25-64 years by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG25T64.URB.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Literacy rate, population 25-64 years, urban, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG25T64.URB.M", "metatype": [ { "id": "IndicatorName", "value": "Literacy rate, population 25-64 years, urban, male (%)" }, { "id": "Longdefinition", "value": "Percentage of the male population in urban areas between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of male literates aged 25-64 years by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Percentage of the male population in urban areas between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of male literates aged 25-64 years by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.Ag65", "metatype": [ { "id": "IndicatorName", "value": "Elderly literacy rate, population 65+ years, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of the population age 65 and above who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of literates aged 65 years and over by the corresponding age group population and multiplying the result by 100." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.LR.AG65", "metatype": [ { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.Ag65.F", "metatype": [ { "id": "IndicatorName", "value": "Elderly literacy rate, population 65+ years, female (%)" }, { "id": "Longdefinition", "value": "Percentage of females age 65 and above who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of female literates aged 65 years and over by the corresponding age group population and multiplying the result by 100." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.LR.AG65.F", "metatype": [ { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.Ag65.M", "metatype": [ { "id": "IndicatorName", "value": "Elderly literacy rate, population 65+ years, male (%)" }, { "id": "Longdefinition", "value": "Percentage of males age 65 and above who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of male literates aged 65 years and over by the corresponding age group population and multiplying the result by 100." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.LR.AG65.M", "metatype": [ { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG65T99.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Elderly literacy rate, population 65+ years, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG65T99.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Elderly literacy rate, population 65+ years, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG65T99.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Elderly literacy rate, population 65+ years, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG65T99.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Elderly literacy rate, population 65+ years, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG65T99.RUR", "metatype": [ { "id": "IndicatorName", "value": "Elderly literacy rate, population 65+ years, rural, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of the population age 65 and above in rural areas who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of literates aged 65 years and over by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Percentage of the population age 65 and above in rural areas who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of literates aged 65 years and over by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG65T99.RUR.F", "metatype": [ { "id": "IndicatorName", "value": "Elderly literacy rate, population 65+ years, rural, female (%)" }, { "id": "Longdefinition", "value": "Percentage of females age 65 and above in rural areas who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of female literates aged 65 years and over by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Percentage of females age 65 and above in rural areas who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of female literates aged 65 years and over by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG65T99.RUR.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Elderly literacy rate, population 65+ years, rural, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG65T99.RUR.M", "metatype": [ { "id": "IndicatorName", "value": "Elderly literacy rate, population 65+ years, rural, male (%)" }, { "id": "Longdefinition", "value": "Percentage of males age 65 and above in rural areas who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of male literates aged 65 years and over by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Percentage of males age 65 and above in rural areas who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of male literates aged 65 years and over by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG65T99.URB", "metatype": [ { "id": "IndicatorName", "value": "Elderly literacy rate, population 65+ years, urban, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of the population age 65 and above in urban areas who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of literates aged 65 years and over by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Percentage of the population age 65 and above in urban areas who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of literates aged 65 years and over by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG65T99.URB.F", "metatype": [ { "id": "IndicatorName", "value": "Elderly literacy rate, population 65+ years, urban, female (%)" }, { "id": "Longdefinition", "value": "Percentage of females age 65 and above in urban areas who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of female literates aged 65 years and over by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Percentage of females age 65 and above in urban areas who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of female literates aged 65 years and over by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG65T99.URB.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Elderly literacy rate, population 65+ years, urban, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.LR.AG65T99.URB.M", "metatype": [ { "id": "IndicatorName", "value": "Elderly literacy rate, population 65+ years, urban, male (%)" }, { "id": "Longdefinition", "value": "Percentage of males age 65 and above in urban areas who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of male literates aged 65 years and over by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Percentage of males age 65 and above in urban areas who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of male literates aged 65 years and over by the corresponding age group population and multiplying the result by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.MATH.G2T3", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in mathematics, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.G2T3.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in mathematics, female (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.G2T3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in mathematics, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.G2T3.HIGHSES", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in mathematics, very affluent socioeconomic background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.G2T3.LANGTEST", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in mathematics, spoke the language of the test at home, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.G2T3.LOWSES", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in mathematics, very poor socioeconomic background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.G2T3.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in mathematics, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.G2T3.LTPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in mathematics, adjusted speaks language of the test parity index (LTPIA)" }, { "id": "Longdefinition", "value": "The adjusted speaks language of the test parity index (LTPIA) is calculated by dividing the value for the indicator for students who do not speak the language of the test at home by the value for the indicator for students who speak the language of the test at home. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted speaks language of the test parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LTPI equal to 1 indicates parity between students who do and do not speak the language of the test at home. In general, a value less than 1 indicates disparity in favor of students who speak the language of test at home. A value greater than 1 indicates disparity in favor of students who do not speak the language of the test at home. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The adjusted speaks language of the test parity index (LTPIA) is calculated by dividing the value for the indicator for students who do not speak the language of the test at home by the value for the indicator for students who speak the language of the test at home. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted speaks language of the test parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LTPI equal to 1 indicates parity between students who do and do not speak the language of the test at home. In general, a value less than 1 indicates disparity in favor of students who speak the language of test at home. A value greater than 1 indicates disparity in favor of students who do not speak the language of the test at home. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.G2T3.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in mathematics, male (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.G2T3.NATIVE", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in mathematics, non-immigrant background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.G2T3.NONLANGTEST", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in mathematics, did not speak the language of the test at home, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.G2T3.NONNATIVE", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in mathematics, immigrant background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.G2T3.NPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in mathematics, adjusted native parity index (NPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.G2T3.RURAL", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in mathematics, rural areas, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.G2T3.URBAN", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in mathematics, urban areas, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.G2T3.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in mathematics, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.LOWERSEC", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in mathematics, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.MATH.LOWERSEC.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in mathematics, female (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.MATH.LOWERSEC.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary achieving at least a minimum proficiency level in mathematics, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.MATH.LOWERSEC.HIGHSES", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in mathematics, very affluent socioeconomic background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.MATH.LOWERSEC.LANGTEST", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in mathematics, spoke the language of the test at home, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.MATH.LOWERSEC.LOWSES", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in mathematics, very poor socioeconomic background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.MATH.LOWERSEC.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary achieving at least a minimum proficiency level in mathematics, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.MATH.LOWERSEC.LTPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in mathematics, adjusted speaks language of the test parity index (LTPIA)" }, { "id": "Longdefinition", "value": "The adjusted speaks language of the test parity index (LTPIA) is calculated by dividing the value for the indicator for students who do not speak the language of the test at home by the value for the indicator for students who speak the language of the test at home. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted speaks language of the test parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LTPI equal to 1 indicates parity between students who do and do not speak the language of the test at home. In general, a value less than 1 indicates disparity in favor of students who speak the language of test at home. A value greater than 1 indicates disparity in favor of students who do not speak the language of the test at home. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The adjusted speaks language of the test parity index (LTPIA) is calculated by dividing the value for the indicator for students who do not speak the language of the test at home by the value for the indicator for students who speak the language of the test at home. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted speaks language of the test parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LTPI equal to 1 indicates parity between students who do and do not speak the language of the test at home. In general, a value less than 1 indicates disparity in favor of students who speak the language of test at home. A value greater than 1 indicates disparity in favor of students who do not speak the language of the test at home. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.MATH.LOWERSEC.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in mathematics, male (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.MATH.LOWERSEC.NATIVE", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in mathematics, non-immigrant background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.MATH.LOWERSEC.NONLANGTEST", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in mathematics, did not speak the language of the test at home, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.MATH.LOWERSEC.NONNATIVE", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in mathematics, immigrant background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.MATH.LOWERSEC.NPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in mathematics, adjusted native parity index (NPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.MATH.LOWERSEC.RURAL", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in mathematics, rural areas, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.MATH.LOWERSEC.URBAN", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in mathematics, urban areas, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.MATH.LOWERSEC.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary achieving at least a minimum proficiency level in mathematics, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.MATH.PRIMARY", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in mathematics, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.PRIMARY.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in mathematics, female (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.PRIMARY.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary achieving at least a minimum proficiency level in mathematics, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.PRIMARY.HIGHSES", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in mathematics, very affluent socioeconomic background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.PRIMARY.LANGTEST", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in mathematics, spoke the language of the test at home, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.PRIMARY.LOWSES", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in mathematics, very poor socioeconomic background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.PRIMARY.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary achieving at least a minimum proficiency level in mathematics, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.PRIMARY.LTPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in mathematics, adjusted speaks language of the test parity index (LTPIA)" }, { "id": "Longdefinition", "value": "The adjusted speaks language of the test parity index (LTPIA) is calculated by dividing the value for the indicator for students who do not speak the language of the test at home by the value for the indicator for students who speak the language of the test at home. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted speaks language of the test parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LTPI equal to 1 indicates parity between students who do and do not speak the language of the test at home. In general, a value less than 1 indicates disparity in favor of students who speak the language of test at home. A value greater than 1 indicates disparity in favor of students who do not speak the language of the test at home. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The adjusted speaks language of the test parity index (LTPIA) is calculated by dividing the value for the indicator for students who do not speak the language of the test at home by the value for the indicator for students who speak the language of the test at home. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted speaks language of the test parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LTPI equal to 1 indicates parity between students who do and do not speak the language of the test at home. In general, a value less than 1 indicates disparity in favor of students who speak the language of test at home. A value greater than 1 indicates disparity in favor of students who do not speak the language of the test at home. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.PRIMARY.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in mathematics, male (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.PRIMARY.NATIVE", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in mathematics, non-immigrant background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.PRIMARY.NONLANGTEST", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in mathematics, did not speak the language of the test at home, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.PRIMARY.NONNATIVE", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in mathematics, immigrant background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.PRIMARY.NPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in mathematics, adjusted native parity index (NPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.PRIMARY.RURAL", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in mathematics, rural areas, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.PRIMARY.URBAN", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in mathematics, urban areas, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in mathematics. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MATH.PRIMARY.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary achieving at least a minimum proficiency level in mathematics, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.MENF.56", "metatype": [ { "id": "IndicatorName", "value": "Net flow of internationally mobile students (inbound - outbound), both sexes (number)" }, { "id": "Longdefinition", "value": "Number of tertiary students from abroad (inbound students) studying in a given country minus the number of students at the same level from a given country studying abroad (outbound students)." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.MENFR.56", "metatype": [ { "id": "IndicatorName", "value": "Net flow ratio of internationally mobile students (inbound - outbound), both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of tertiary students from abroad (inbound students) studying in a given country minus the number of students at the same level of education from that country studying abroad (outbound students), expressed as a percentage of total tertiary enrolment in that country." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.MS.56.F", "metatype": [ { "id": "IndicatorName", "value": "Total inbound internationally mobile students, female (number)" }, { "id": "Longdefinition", "value": "Total number of female students who have crossed a national or territorial border for the purpose of education and are now enrolled in tertiary institutions outside their country of origin." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.MS.56.M", "metatype": [ { "id": "IndicatorName", "value": "Total inbound internationally mobile students, male (number)" }, { "id": "Longdefinition", "value": "Total number of male students who have crossed a national or territorial border for the purpose of education and are now enrolled in tertiary institutions outside their country of origin." }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Total number of male students who have crossed a national or territorial border for the purpose of education and are now enrolled in tertiary institutions outside their country of origin." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.MS.56.T", "metatype": [ { "id": "IndicatorName", "value": "Total inbound internationally mobile students, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of students who have crossed a national or territorial border for the purpose of education and are now enrolled in tertiary institutions outside their country of origin." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.MSEP.56", "metatype": [ { "id": "IndicatorName", "value": "Inbound mobility rate, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students from abroad studying in a given country, as a percentage of the total tertiary enrollment in that country." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.MSEP.56.F", "metatype": [ { "id": "IndicatorName", "value": "Inbound mobility rate, female (%)" }, { "id": "Longdefinition", "value": "Number of female students from abroad studying in a given country, as a percentage of the total female tertiary enrollment in that country." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.MSEP.56.M", "metatype": [ { "id": "IndicatorName", "value": "Inbound mobility rate, male (%)" }, { "id": "Longdefinition", "value": "Number of male students from abroad studying in a given country, as a percentage of the total male tertiary enrollment in that country." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.N.ATTACKS", "metatype": [ { "id": "IndicatorName", "value": "Number of attacks on students, personnel and institutions" }, { "id": "Longdefinition", "value": "Number of violent attacks, threats or deliberate use of force in a given time period (e.g. the last 12 months, a school year or a calendar year) directed against students, teachers and other personnel or against education buildings, materials and facilities, including transport. The indicator focuses on attacks carried out for political, military, ideological, sectarian, ethnic or religious reasons by armed forces or nonstate armed groups. The indicator is a broad measure of the safety of learning environments, particularly in relation to armed conflict and political violence. Available data for global tracking are presently collected from reporting by a wide variety of stakeholders, including national and international NGOs working at the country-level and national and international media reports. Attacks on education include the following sub-categories: • Attacks on schools: targeted violent attacks on preschool, kindergarten, primary, and secondary school buildings or infrastructure by state military forces or non-state armed groups in the form of arson; suicide, car, or other bombs aimed at a school; or artillery fire directed at a school. In addition, this category includes indiscriminate attacks that result in the damage or destruction of school infrastructure as well as explosions that occur in close proximity to a school. • Attacks on students, teachers, and other education personnel: killings, injuries, torture, abductions, forced disappearances, or threats of violence, including coercion or extortion involving violent threats directed towards students and education staff who work at the primary and secondary levels. Since it is sometimes difficult to identify why a teacher or school staff member is killed if the assassination occurs outside of school, this category also includes such attacks in cases where there is an established pattern of that kind of violence. The category of attacks on students, teachers, and other education personnel also includes cases where police or state security forces violently repress student protests that either occur at school, or, if they occur off-campus, focus on education-related policies and laws. • Military use of schools and universities: cases in which armed forces or non-state armed groups take over schools or universities as bases, barracks and temporary shelters to house soldiers or fighters, fighting positions, weapons storage facilities, detention and interrogation centres, or for other military purposes. • Recruitment of children at schools or along school routes: cases in which armed forces or nonstate armed groups use schools or school routes as locales for recruiting children under the age of 18 into their fighting forces in violation of international standards. • Sexual violence by parties to the conflict: incidents of sexual abuse and harassment perpetrated at schools or universities or along school routes. • Attacks on higher education: include targeted violent attacks on universities in the form of bombings, airstrikes, arson, or other means, as well as targeted killings, abductions, or threats directed at university students, faculty, or staff. The category includes cases of violent repression of student protests that either occur at institutions of higher education, or, if they occur off-campus, focus on education-related policies and laws. This indicator is based on data compiled by the Global Coalition to Protect Education from Attack (GCPEA) for its report Education under Attack. Information from three types of data sources: reports released by UN agencies, development and humanitarian NGOs, human rights organizations, government bodies, and think tanks; media reports; and information shared with GCPEA by staff members of international and national organizations working in the countries profiled in this study. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "School Safety/Violence" }, { "id": "Shortdefinition", "value": "Number of violent attacks, threats or deliberate use of force in a given time period (e.g. the last 12 months, a school year or a calendar year) directed against students, teachers and other personnel or against education buildings, materials and facilities, including transport. The indicator focuses on attacks carried out for political, military, ideological, sectarian, ethnic or religious reasons by armed forces or nonstate armed groups. The indicator is a broad measure of the safety of learning environments, particularly in relation to armed conflict and political violence. Available data for global tracking are presently collected from reporting by a wide variety of stakeholders, including national and international NGOs working at the country-level and national and international media reports. Attacks on education include the following sub-categories: • Attacks on schools: targeted violent attacks on preschool, kindergarten, primary, and secondary school buildings or infrastructure by state military forces or non-state armed groups in the form of arson; suicide, car, or other bombs aimed at a school; or artillery fire directed at a school. In addition, this category includes indiscriminate attacks that result in the damage or destruction of school infrastructure as well as explosions that occur in close proximity to a school. • Attacks on students, teachers, and other education personnel: killings, injuries, torture, abductions, forced disappearances, or threats of violence, including coercion or extortion involving violent threats directed towards students and education staff who work at the primary and secondary levels. Since it is sometimes difficult to identify why a teacher or school staff member is killed if the assassination occurs outside of school, this category also includes such attacks in cases where there is an established pattern of that kind of violence. The category of attacks on students, teachers, and other education personnel also includes cases where police or state security forces violently repress student protests that either occur at school, or, if they occur off-campus, focus on education-related policies and laws. • Military use of schools and universities: cases in which armed forces or non-state armed groups take over schools or universities as bases, barracks and temporary shelters to house soldiers or fighters, fighting positions, weapons storage facilities, detention and interrogation centres, or for other military purposes. • Recruitment of children at schools or along school routes: cases in which armed forces or nonstate armed groups use schools or school routes as locales for recruiting children under the age of 18 into their fighting forces in violation of international standards. • Sexual violence by parties to the conflict: incidents of sexual abuse and harassment perpetrated at schools or universities or along school routes. • Attacks on higher education: include targeted violent attacks on universities in the form of bombings, airstrikes, arson, or other means, as well as targeted killings, abductions, or threats directed at university students, faculty, or staff. The category includes cases of violent repression of student protests that either occur at institutions of higher education, or, if they occur off-campus, focus on education-related policies and laws. This indicator is based on data compiled by the Global Coalition to Protect Education from Attack (GCPEA) for its report Education under Attack. Information from three types of data sources: reports released by UN agencies, development and humanitarian NGOs, human rights organizations, government bodies, and think tanks; media reports; and information shared with GCPEA by staff members of international and national organizations working in the countries profiled in this study. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "School Safety/Violence" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q1", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q1.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, poorest quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q1.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, poorest quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q1.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, poorest quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q2", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, second quintile, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q2.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, second quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q2.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, second quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, second quintile, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q2.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, second quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q3", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q3.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, middle quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q3.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, middle quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q3.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, middle quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q4", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q4.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, fourth quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q4.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, fourth quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q4.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, fourth quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q5", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q5.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, richest quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q5.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, richest quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.Q5.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, richest quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q1", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q2", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, second quintile, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, second quintile, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q3", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q4", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q5", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.RUR.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, rural, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q1", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q2", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, second quintile, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, second quintile, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q3", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q4", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q5", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.URB.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, urban, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NARA.AGM1.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net attendance rate, one year before the official primary entry age, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NART.1", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q1", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q1.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, poorest quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q1.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, poorest quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q1.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, poorest quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q2", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, second quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q2.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, second quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q2.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, second quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, second quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q2.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, second quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q3", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q3.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, middle quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q3.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, middle quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q3.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, middle quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q4", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q4.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, fourth quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q4.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, fourth quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q4.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, fourth quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q5", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q5.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, richest quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q5.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, richest quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.Q5.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, richest quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q1", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q2", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, second quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, second quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q3", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q4", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q5", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.RUR.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, rural, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q1", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q2", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, second quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, second quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q3", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q4", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q5", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for a given level of education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for the given level of education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for primary education is due to attendance of pre-primary education. Data Source: UNESCO Institute for Statistics calculations based on national census data and household surveys. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.URB.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, urban, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.1.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, primary, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NART.2", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q1", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q1.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, poorest quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q1.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, poorest quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q1.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, poorest quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q2", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, second quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q2.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, second quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q2.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, second quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, second quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q2.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, second quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q3", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q3.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, middle quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q3.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, middle quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q3.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, middle quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q4", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q4.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, fourth quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q4.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, fourth quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q4.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, fourth quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q5", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q5.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, richest quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q5.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, richest quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.Q5.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, richest quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q1", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q2", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, second quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, second quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q3", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q4", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q5", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.RUR.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, rural, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official lower secondary school age group who attended primary or secondary education at any time during the reference academic year, expressed as a percentage of the corresponding population. The UNESCO Institute for Statistics (UIS) calculates household survey-based education indicators using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). School participation in household surveys and censuses is commonly measured by whether pupils or students attended a given grade or level of education at least one day during the academic reference year. Therefore, indicators of school participation derived from household survey data refer to attendance, e.g. “net attendance rate” or “adjusted net attendance rate”. The comparable indicator for administrative data is \"Total net enrolment rate, lower secondary\" because the data is based on numbers of students officially enrolled in educational institutions in the stated year. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q1", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q2", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, second quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, second quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q3", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q4", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q5", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for lower secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for lower secondary education is due to attendance in of pre-primary or primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.URB.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, urban, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.2.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, lower secondary, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q1", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q1.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, poorest quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q1.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, poorest quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q1.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, poorest quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q2", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, second quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q2.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, second quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q2.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, second quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, second quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q2.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, second quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q3", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q3.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, middle quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q3.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, middle quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q3.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, middle quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q4", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q4.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, fourth quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q4.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, fourth quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q4.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, fourth quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q5", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q5.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, richest quintile, female, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q5.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, richest quintile, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.Q5.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, richest quintile, male, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q1", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q2", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, second quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, second quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q3", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q4", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q5", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.RUR.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, rural, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, female, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, male, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q1", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, poorest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, poorest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, poorest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, poorest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q2", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, second quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, second quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, second quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, second quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q3", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, middle quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, middle quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, middle quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, middle quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q4", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, fourth quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, fourth quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, fourth quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, fourth quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q5", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, richest quintile, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, richest quintile, female (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, richest quintile, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, richest quintile, male (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are attending school at any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are attending school at any level of education by the population of the same age group and multiply the result by 100. The difference between the total NAR and the adjusted NAR provides a measure of the proportion of children in the official relevant school age group who are attending levels of education below the one intended for their age. The difference between the total NAR and the adjusted NAR for upper secondary education is due to attendance in of pre-primary, primary or lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.URB.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, urban, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NART.3.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Total net attendance rate, upper secondary, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NERA.AGM1", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net enrolment rate, one year before the official primary entry age, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of children of the age one year before the official entry age to primary education who participate in an organized learning programme expressed as a percentage of the total population of the same age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "The number of children of the age one year before the official entry age to primary education who participate in an organized learning programme expressed as a percentage of the total population of the same age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NERA.AGM1.F", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net enrolment rate, one year before the official primary entry age, female (%)" }, { "id": "Longdefinition", "value": "The number of female children of the age one year before the official entry age to primary education who participate in an organized learning programme expressed as a percentage of the total population of the same age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "The number of female children of the age one year before the official entry age to primary education who participate in an organized learning programme expressed as a percentage of the total population of the same age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NERA.AGM1.GPIA.CP", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net enrolment rate, one year before the official primary entry age, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.NERA.AGM1.M", "metatype": [ { "id": "IndicatorName", "value": "Adjusted net enrolment rate, one year before the official primary entry age, male (%)" }, { "id": "Longdefinition", "value": "The number of male children of the age one year before the official entry age to primary education who participate in an organized learning programme expressed as a percentage of the total population of the same age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "The number of male children of the age one year before the official entry age to primary education who participate in an organized learning programme expressed as a percentage of the total population of the same age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NERT.1", "metatype": [ { "id": "IndicatorName", "value": "Total net enrolment rate, primary, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for primary education who are enrolled in any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for primary education who are enrolled in any level of education by the population of the same age group and multiply the result by 100. The difference between the total NER and the adjusted NER provides a measure of the proportion of children in the official relevant school age group who are enrolled in levels of education below the one intended for their age. The difference between the total NER and the adjusted NER for primary education is due to enrolment in pre-primary education. The total NER should be based on total enrolment of the official relevant school age group in any level of education for all types of schools and education institutions, including public, private and all other institutions that provide organized educational programmes." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NERT.1.F", "metatype": [ { "id": "IndicatorName", "value": "Total net enrolment rate, primary, female (%)" }, { "id": "Longdefinition", "value": "Total number of female students of the official age group for primary education who are enrolled in any level of education, expressed as a percentage of the corresponding female population. Divide the total number of female students in the official school age range for primary education who are enrolled in any level of education by the female population of the same age group and multiply the result by 100. The difference between the total NER and the adjusted NER provides a measure of the proportion of children in the official relevant school age group who are enrolled in levels of education below the one intended for their age. The difference between the total NER and the adjusted NER for primary education is due to enrolment in pre-primary education. The total NER should be based on total enrolment of the official relevant school age group in any level of education for all types of schools and education institutions, including public, private and all other institutions that provide organized educational programmes." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NERT.1.GPI", "metatype": [ { "id": "IndicatorName", "value": "Total net enrolment rate, primary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female total net enrolment rate for primary to the male total net enrolment rate for primary. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NERT.1.M", "metatype": [ { "id": "IndicatorName", "value": "Total net enrolment rate, primary, male (%)" }, { "id": "Longdefinition", "value": "Total number of male students of the official age group for primary education who are enrolled in any level of education, expressed as a percentage of the corresponding male population. Divide the total number of male students in the official school age range for primary education who are enrolled in any level of education by the male population of the same age group and multiply the result by 100. The difference between the total NER and the adjusted NER provides a measure of the proportion of children in the official relevant school age group who are enrolled in levels of education below the one intended for their age. The difference between the total NER and the adjusted NER for primary education is due to enrolment in pre-primary education. The total NER should be based on total enrolment of the official relevant school age group in any level of education for all types of schools and education institutions, including public, private and all other institutions that provide organized educational programmes." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.NERT.2", "metatype": [ { "id": "IndicatorName", "value": "Total net enrolment rate, lower secondary, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for lower secondary education who are enrolled in any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for lower secondary education who are enrolled in any level of education by the population of the same age group and multiply the result by 100. The difference between the total NER and the adjusted NER provides a measure of the proportion of children in the official relevant school age group who are enrolled in levels of education below the one intended for their age. The difference between the total NER and the adjusted NER for lower secondary education is due to enrolment in pre-primary or primary education. The total NER should be based on total enrolment of the official relevant school age group in any level of education for all types of schools and education institutions, including public, private and all other institutions that provide organized educational programmes." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NERT.2.F", "metatype": [ { "id": "IndicatorName", "value": "Total net enrolment rate, lower secondary, female (%)" }, { "id": "Longdefinition", "value": "Total number of female students of the official age group for lower secondary education who are enrolled in any level of education, expressed as a percentage of the corresponding female population. Divide the total number of female students in the official school age range for lower secondary education who are enrolled in any level of education by the female population of the same age group and multiply the result by 100. The difference between the total NER and the adjusted NER provides a measure of the proportion of children in the official relevant school age group who are enrolled in levels of education below the one intended for their age. The difference between the total NER and the adjusted NER for lower secondary education is due to enrolment in pre-primary or primary education. The total NER should be based on total enrolment of the official relevant school age group in any level of education for all types of schools and education institutions, including public, private and all other institutions that provide organized educational programmes." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NERT.2.GPI", "metatype": [ { "id": "IndicatorName", "value": "Total net enrolment rate, lower secondary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female total net enrolment rate for lower secondary to the male total net enrolment rate for lower secondary. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NERT.2.M", "metatype": [ { "id": "IndicatorName", "value": "Total net enrolment rate, lower secondary, male (%)" }, { "id": "Longdefinition", "value": "Total number of male students of the official age group for lower secondary education who are enrolled in any level of education, expressed as a percentage of the corresponding male population. Divide the total number of male students in the official school age range for lower secondary education who are enrolled in any level of education by the male population of the same age group and multiply the result by 100. The difference between the total NER and the adjusted NER provides a measure of the proportion of children in the official relevant school age group who are enrolled in levels of education below the one intended for their age. The difference between the total NER and the adjusted NER for lower secondary education is due to enrolment in pre-primary or primary education. The total NER should be based on total enrolment of the official relevant school age group in any level of education for all types of schools and education institutions, including public, private and all other institutions that provide organized educational programmes." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NERT.3", "metatype": [ { "id": "IndicatorName", "value": "Total net enrolment rate, upper secondary, both sexes (%)" }, { "id": "Longdefinition", "value": "Total number of students of the official age group for upper secondary education who are enrolled in any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are enrolled in any level of education by the population of the same age group and multiply the result by 100. The difference between the total NER and the adjusted NER provides a measure of the proportion of children in the official relevant school age group who are enrolled in levels of education below the one intended for their age. The difference between the total NER and the adjusted NER for upper secondary education is due to enrolment in pre-primary or primary education. The total NER should be based on total enrolment of the official relevant school age group in any level of education for all types of schools and education institutions, including public, private and all other institutions that provide organized educational programmes. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total number of students of the official age group for upper secondary education who are enrolled in any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for upper secondary education who are enrolled in any level of education by the population of the same age group and multiply the result by 100. The difference between the total NER and the adjusted NER provides a measure of the proportion of children in the official relevant school age group who are enrolled in levels of education below the one intended for their age. The difference between the total NER and the adjusted NER for upper secondary education is due to enrolment in pre-primary or primary education. The total NER should be based on total enrolment of the official relevant school age group in any level of education for all types of schools and education institutions, including public, private and all other institutions that provide organized educational programmes. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NERT.3.F", "metatype": [ { "id": "IndicatorName", "value": "Total net enrolment rate, upper secondary, female (%)" }, { "id": "Longdefinition", "value": "Total number of female students of the official age group for upper secondary education who are enrolled in any level of education, expressed as a percentage of the corresponding female population. Divide the total number of female students in the official school age range for upper secondary education who are enrolled in any level of education by the female population of the same age group and multiply the result by 100. The difference between the total NER and the adjusted NER provides a measure of the proportion of children in the official relevant school age group who are enrolled in levels of education below the one intended for their age. The difference between the total NER and the adjusted NER for upper secondary education is due to enrolment in pre-primary or primary education. The total NER should be based on total enrolment of the official relevant school age group in any level of education for all types of schools and education institutions, including public, private and all other institutions that provide organized educational programmes. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total number of female students of the official age group for upper secondary education who are enrolled in any level of education, expressed as a percentage of the corresponding female population. Divide the total number of female students in the official school age range for upper secondary education who are enrolled in any level of education by the female population of the same age group and multiply the result by 100. The difference between the total NER and the adjusted NER provides a measure of the proportion of children in the official relevant school age group who are enrolled in levels of education below the one intended for their age. The difference between the total NER and the adjusted NER for upper secondary education is due to enrolment in pre-primary or primary education. The total NER should be based on total enrolment of the official relevant school age group in any level of education for all types of schools and education institutions, including public, private and all other institutions that provide organized educational programmes. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NERT.3.GPI", "metatype": [ { "id": "IndicatorName", "value": "Total net enrolment rate, upper secondary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female total net enrolment rate for upper secondary to the male total net enrolment rate for upper secondary. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Ratio of female total net enrolment rate for upper secondary to the male total net enrolment rate for upper secondary. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.NERT.3.M", "metatype": [ { "id": "IndicatorName", "value": "Total net enrolment rate, upper secondary, male (%)" }, { "id": "Longdefinition", "value": "Total number of male students of the official age group for upper secondary education who are enrolled in any level of education, expressed as a percentage of the corresponding male population. Divide the total number of male students in the official school age range for upper secondary education who are enrolled in any level of education by the male population of the same age group and multiply the result by 100. The difference between the total NER and the adjusted NER provides a measure of the proportion of children in the official relevant school age group who are enrolled in levels of education below the one intended for their age. The difference between the total NER and the adjusted NER for upper secondary education is due to enrolment in pre-primary or primary education. The total NER should be based on total enrolment of the official relevant school age group in any level of education for all types of schools and education institutions, including public, private and all other institutions that provide organized educational programmes. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total number of male students of the official age group for upper secondary education who are enrolled in any level of education, expressed as a percentage of the corresponding male population. Divide the total number of male students in the official school age range for upper secondary education who are enrolled in any level of education by the male population of the same age group and multiply the result by 100. The difference between the total NER and the adjusted NER provides a measure of the proportion of children in the official relevant school age group who are enrolled in levels of education below the one intended for their age. The difference between the total NER and the adjusted NER for upper secondary education is due to enrolment in pre-primary or primary education. The total NER should be based on total enrolment of the official relevant school age group in any level of education for all types of schools and education institutions, including public, private and all other institutions that provide organized educational programmes. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OAEPG.1", "metatype": [ { "id": "IndicatorName", "value": "Percentage of pupils enrolled in primary education who are at least 2 years over-age for their current grade, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of pupils in primary education who are at least 2 years above the intended age for their grade. The intended age for a given grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. A low value of this indicator will show that the majority of students start school on time and progress with minimum levels of grade repetition. Late school entry and significant grade repetition exacerbate over-age progression and should be discouraged as both are associated with lower levels of student learning achievement. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of pupils in primary education who are at least 2 years above the intended age for their grade. The intended age for a given grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. A low value of this indicator will show that the majority of students start school on time and progress with minimum levels of grade repetition. Late school entry and significant grade repetition exacerbate over-age progression and should be discouraged as both are associated with lower levels of student learning achievement. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.OAEPG.1.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of pupils enrolled in primary education who are at least 2 years over-age for their current grade, female (%)" }, { "id": "Longdefinition", "value": "Percentage of pupils in primary education who are at least 2 years above the intended age for their grade. The intended age for a given grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. A low value of this indicator will show that the majority of students start school on time and progress with minimum levels of grade repetition. Late school entry and significant grade repetition exacerbate over-age progression and should be discouraged as both are associated with lower levels of student learning achievement. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of pupils in primary education who are at least 2 years above the intended age for their grade. The intended age for a given grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. A low value of this indicator will show that the majority of students start school on time and progress with minimum levels of grade repetition. Late school entry and significant grade repetition exacerbate over-age progression and should be discouraged as both are associated with lower levels of student learning achievement. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.OAEPG.1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of pupils enrolled in primary education who are at least 2 years over-age for their current grade, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.OAEPG.1.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of pupils enrolled in primary education who are at least 2 years over-age for their current grade, male (%)" }, { "id": "Longdefinition", "value": "Percentage of pupils in primary education who are at least 2 years above the intended age for their grade. The intended age for a given grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. A low value of this indicator will show that the majority of students start school on time and progress with minimum levels of grade repetition. Late school entry and significant grade repetition exacerbate over-age progression and should be discouraged as both are associated with lower levels of student learning achievement. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of pupils in primary education who are at least 2 years above the intended age for their grade. The intended age for a given grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. A low value of this indicator will show that the majority of students start school on time and progress with minimum levels of grade repetition. Late school entry and significant grade repetition exacerbate over-age progression and should be discouraged as both are associated with lower levels of student learning achievement. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.OAEPG.2.GPV", "metatype": [ { "id": "IndicatorName", "value": "Percentage of pupils enrolled in lower secondary general education who are at least 2 years over-age for their current grade, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of pupils in each level of education (primary and lower secondary general education) who are at least 2 years above the intended age for their grade. The intended age for a given grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. The value is calculated as the sum of enrolments across all grades in the given level of education which are 2 or more years older than the intended age for the given grade is expressed as a percentage of the total enrolment in the given level of education. A low value of this indicator will show that the majority of students start school on time and progress with minimum levels of grade repetition. Late school entry and significant grade repetition exacerbate over-age progression and should be discouraged as both are associated with lower levels of student learning achievement. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of pupils in each level of education (primary and lower secondary general education) who are at least 2 years above the intended age for their grade. The intended age for a given grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. The value is calculated as the sum of enrolments across all grades in the given level of education which are 2 or more years older than the intended age for the given grade is expressed as a percentage of the total enrolment in the given level of education. A low value of this indicator will show that the majority of students start school on time and progress with minimum levels of grade repetition. Late school entry and significant grade repetition exacerbate over-age progression and should be discouraged as both are associated with lower levels of student learning achievement. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OAEPG.2.GPV.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of pupils enrolled in lower secondary general education who are at least 2 years over-age for their current grade, female (%)" }, { "id": "Longdefinition", "value": "Percentage of pupils in each level of education (primary and lower secondary general education) who are at least 2 years above the intended age for their grade. The intended age for a given grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. The value is calculated as the sum of enrolments across all grades in the given level of education which are 2 or more years older than the intended age for the given grade is expressed as a percentage of the total enrolment in the given level of education. A low value of this indicator will show that the majority of students start school on time and progress with minimum levels of grade repetition. Late school entry and significant grade repetition exacerbate over-age progression and should be discouraged as both are associated with lower levels of student learning achievement. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of pupils in each level of education (primary and lower secondary general education) who are at least 2 years above the intended age for their grade. The intended age for a given grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. The value is calculated as the sum of enrolments across all grades in the given level of education which are 2 or more years older than the intended age for the given grade is expressed as a percentage of the total enrolment in the given level of education. A low value of this indicator will show that the majority of students start school on time and progress with minimum levels of grade repetition. Late school entry and significant grade repetition exacerbate over-age progression and should be discouraged as both are associated with lower levels of student learning achievement. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OAEPG.2.GPV.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of pupils enrolled in lower secondary general education who are at least 2 years over-age for their current grade, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OAEPG.2.GPV.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of pupils enrolled in lower secondary general education who are at least 2 years over-age for their current grade, male (%)" }, { "id": "Longdefinition", "value": "Percentage of pupils in each level of education (primary and lower secondary general education) who are at least 2 years above the intended age for their grade. The intended age for a given grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. The value is calculated as the sum of enrolments across all grades in the given level of education which are 2 or more years older than the intended age for the given grade is expressed as a percentage of the total enrolment in the given level of education. A low value of this indicator will show that the majority of students start school on time and progress with minimum levels of grade repetition. Late school entry and significant grade repetition exacerbate over-age progression and should be discouraged as both are associated with lower levels of student learning achievement. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of pupils in each level of education (primary and lower secondary general education) who are at least 2 years above the intended age for their grade. The intended age for a given grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. The value is calculated as the sum of enrolments across all grades in the given level of education which are 2 or more years older than the intended age for the given grade is expressed as a percentage of the total enrolment in the given level of education. A low value of this indicator will show that the majority of students start school on time and progress with minimum levels of grade repetition. Late school entry and significant grade repetition exacerbate over-age progression and should be discouraged as both are associated with lower levels of student learning achievement. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ODAFLOW.VOLUMESCHOLARSHIP", "metatype": [ { "id": "IndicatorName", "value": "Volume of official development assistance flows for scholarships by sector and type of study, constant US$" }, { "id": "Longdefinition", "value": "Gross disbursements of total official development assistance (ODA) for scholarships in donor countries expressed in US dollars at the average annual exchange rate. Scholarships are financial aid awards for individual students and contributions to trainees. The beneficiary students and trainees are nationals of developing countries. Financial aid awards include bilateral grants to students in institutions of higher education following full-time studies or training courses in the donor country. Administrative data from donor countries and other aid providers on gross disbursements of total official development assistance to education are compiled by the Development Assistance Committee (DAC) of the Organization for Economic Co-operation and Development from returns submitted by its member countries and other aid providers. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Expenditures" }, { "id": "Shortdefinition", "value": "Gross disbursements of total official development assistance (ODA) for scholarships in donor countries expressed in US dollars at the average annual exchange rate. Scholarships are financial aid awards for individual students and contributions to trainees. The beneficiary students and trainees are nationals of developing countries. Financial aid awards include bilateral grants to students in institutions of higher education following full-time studies or training courses in the donor country. Administrative data from donor countries and other aid providers on gross disbursements of total official development assistance to education are compiled by the Development Assistance Committee (DAC) of the Organization for Economic Co-operation and Development from returns submitted by its member countries and other aid providers. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Expenditures" } ], "source_id": "12" }, { "id": "UIS.OE.56.40510 ", "metatype": [ { "id": "IndicatorName", "value": "Total outbound internationally mobile tertiary students studying abroad, all countries, both sexes (number)" }, { "id": "Longdefinition", "value": "Students who have crossed a national or territorial border for the purpose of education and are now enrolled outside their country of origin." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.OE.56.40510", "metatype": [ { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.OFST.1T2.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school children and adolescents of primary and lower secondary school age, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of children and adolescents of primary and lower secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children and adolescents of primary and lower secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OFST.1T2.F.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school children and adolescents of primary and lower secondary school age, female (number)" }, { "id": "Longdefinition", "value": "Number of children and adolescents of primary and lower secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children and adolescents of primary and lower secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OFST.1T2.M.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school children and adolescents of primary and lower secondary school age, male (number)" }, { "id": "Longdefinition", "value": "Number of children and adolescents of primary and lower secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children and adolescents of primary and lower secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OFST.1T3.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school children, adolescents and youth of primary and secondary school age, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of children, adolescents, and youth of primary, lower secondary, and upper secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children, adolescents, and youth of primary, lower secondary, and upper secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OFST.1T3.F.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school children, adolescents and youth of primary and secondary school age, female (number)" }, { "id": "Longdefinition", "value": "Number of children, adolescents, and youth of primary, lower secondary, and upper secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children, adolescents, and youth of primary, lower secondary, and upper secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OFST.1T3.M.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school children, adolescents and youth of primary and secondary school age, male (number)" }, { "id": "Longdefinition", "value": "Number of children, adolescents, and youth of primary, lower secondary, and upper secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children, adolescents, and youth of primary, lower secondary, and upper secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OFST.2", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school adolescents of lower secondary school age, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of lower secondary school age adolescents who are not enrolled in lower secondary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OFST.2.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school adolescents of lower secondary school age, female (number)" }, { "id": "Longdefinition", "value": "Total number of female lower secondary school age adolescents who are not enrolled in lower secondary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OFST.2.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school adolescents of lower secondary school age, male (number)" }, { "id": "Longdefinition", "value": "Total number of male lower secondary school age adolescents who are not enrolled in lower secondary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OFST.2T3.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school adolescents and youth of secondary school age, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of adolescents and youth of secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of adolescents and youth of secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OFST.2T3.F.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school adolescents and youth of secondary school age, female (number)" }, { "id": "Longdefinition", "value": "Number of adolescents and youth of secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of adolescents and youth of secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OFST.2T3.M.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school adolescents and youth of secondary school age, male (number)" }, { "id": "Longdefinition", "value": "Number of adolescents and youth of secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of adolescents and youth of secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OFST.3.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school youth of upper secondary school age, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of upper secondary school age youth who are not enrolled in upper secondary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OFST.3.F.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school youth of upper secondary school age, female (number)" }, { "id": "Longdefinition", "value": "Total number of upper secondary school age female youth who are not enrolled in upper secondary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OFST.3.M.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school youth of upper secondary school age, male (number)" }, { "id": "Longdefinition", "value": "Total number of upper secondary school age male youth who are not enrolled in upper secondary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.OFST.AGM1.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school children, one year younger than official primary entry age, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of children one year before primary entry age who are not enrolled or attending pre-primary education during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Number of children one year before primary entry age who are not enrolled or attending pre-primary education during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.OFST.AGM1.F.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school children, one year younger than official primary entry age, female (number)" }, { "id": "Longdefinition", "value": "Number of children one year before primary entry age who are not enrolled or attending pre-primary education during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Number of children one year before primary entry age who are not enrolled or attending pre-primary education during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.OFST.AGM1.M.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school children, one year younger than official primary entry age, male (number)" }, { "id": "Longdefinition", "value": "Number of children one year before primary entry age who are not enrolled or attending pre-primary education during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Number of children one year before primary entry age who are not enrolled or attending pre-primary education during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.OMR.56", "metatype": [ { "id": "IndicatorName", "value": "Outbound mobility ratio, all regions, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of students from a given country studying abroad as a percentage of the total tertiary enrolment in that country." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.ONTRACK.THREE.DOMAINS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of children aged 24-59 months who are developmentally on track in health, learning and psychosocial well-being, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of children aged 24 to 59 months who are developmentally on track in health, learning and psychosocial well-being divided by the total number of children aged 24 to 59 months in the population multiplied by 100. The higher the combined score represented by the indicator is, the higher the percentage of kids ready to start primary education. Data are based on UNICEF's Early Childhood Development Index (ECDI). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Number of children aged 24 to 59 months who are developmentally on track in health, learning and psychosocial well-being divided by the total number of children aged 24 to 59 months in the population multiplied by 100. The higher the combined score represented by the indicator is, the higher the percentage of kids ready to start primary education. Data are based on UNICEF's Early Childhood Development Index (ECDI). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.ONTRACK.THREE.DOMAINS.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of children aged 24-59 months who are developmentally on track in health, learning and psychosocial well-being, female (%)" }, { "id": "Longdefinition", "value": "Number of children aged 24 to 59 months who are developmentally on track in health, learning and psychosocial well-being divided by the total number of children aged 24 to 59 months in the population multiplied by 100. The higher the combined score represented by the indicator is, the higher the percentage of kids ready to start primary education. Data are based on UNICEF's Early Childhood Development Index (ECDI). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Number of children aged 24 to 59 months who are developmentally on track in health, learning and psychosocial well-being divided by the total number of children aged 24 to 59 months in the population multiplied by 100. The higher the combined score represented by the indicator is, the higher the percentage of kids ready to start primary education. Data are based on UNICEF's Early Childhood Development Index (ECDI). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.ONTRACK.THREE.DOMAINS.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of children aged 24-59 months who are developmentally on track in health, learning and psychosocial well-being, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.ONTRACK.THREE.DOMAINS.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of children aged 24-59 months who are developmentally on track in health, learning and psychosocial well-being, male (%)" }, { "id": "Longdefinition", "value": "Number of children aged 24 to 59 months who are developmentally on track in health, learning and psychosocial well-being divided by the total number of children aged 24 to 59 months in the population multiplied by 100. The higher the combined score represented by the indicator is, the higher the percentage of kids ready to start primary education. Data are based on UNICEF's Early Childhood Development Index (ECDI). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Number of children aged 24 to 59 months who are developmentally on track in health, learning and psychosocial well-being divided by the total number of children aged 24 to 59 months in the population multiplied by 100. The higher the combined score represented by the indicator is, the higher the percentage of kids ready to start primary education. Data are based on UNICEF's Early Childhood Development Index (ECDI). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.PER.11T15.BULLIED", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students experiencing bullying in the last 12 months, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students who, during a school year, were physically attacked, participated in a physical fight, experiencing bullying, corporal punishment, harassment, sexual discrimination or abuse. Bullying, includes verbal and relational abuse. This indicator intends to measure experiences related to bullying such as being called by an offensive nickname, being threatened to be hurt, or other students posting offensive pictures or texts about them. Bullying has been linked to reduce academic and health outcomes for victims and for perpetrators. The figure is calculated as the number of students in a given level of education reporting that they have experienced any of the different types of violence or abuse in the past year expressed as a percentage of all students at the same level of education. Data for this indicator may come from two different school based surveys coordinated by the World Health Organization (WHO): a) The Global School-based Student Health Survey (GSHS) developed by the World Health Organization (WHO) and the US Center for Disease Control and Prevention (CDC) in collaboration with UNICEF, UNESCO, and UNAIDS. The GSHS is conducted primarily among students aged 13-17 years and has a global coverage; b) The Health Behaviour in School-aged Children (HBSC) study is a WHO collaborative cross-national study of adolescents' health and well-being administered in schools every four years and using a questionnaire for 11-, 13- and 15-year-olds. In addition, data points could be reported using student background data from international student assessments. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "School Safety/Violence" }, { "id": "Shortdefinition", "value": "Percentage of students who, during a school year, were physically attacked, participated in a physical fight, experiencing bullying, corporal punishment, harassment, sexual discrimination or abuse. Bullying, includes verbal and relational abuse. This indicator intends to measure experiences related to bullying such as being called by an offensive nickname, being threatened to be hurt, or other students posting offensive pictures or texts about them. Bullying has been linked to reduce academic and health outcomes for victims and for perpetrators. The figure is calculated as the number of students in a given level of education reporting that they have experienced any of the different types of violence or abuse in the past year expressed as a percentage of all students at the same level of education. Data for this indicator may come from two different school based surveys coordinated by the World Health Organization (WHO): a) The Global School-based Student Health Survey (GSHS) developed by the World Health Organization (WHO) and the US Center for Disease Control and Prevention (CDC) in collaboration with UNICEF, UNESCO, and UNAIDS. The GSHS is conducted primarily among students aged 13-17 years and has a global coverage; b) The Health Behaviour in School-aged Children (HBSC) study is a WHO collaborative cross-national study of adolescents' health and well-being administered in schools every four years and using a questionnaire for 11-, 13- and 15-year-olds. In addition, data points could be reported using student background data from international student assessments. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "School Safety/Violence" } ], "source_id": "12" }, { "id": "UIS.PER.11T15.BULLIED.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students experiencing bullying in the last 12 months, female (%)" }, { "id": "Longdefinition", "value": "Percentage of students who, during a school year, were physically attacked, participated in a physical fight, experiencing bullying, corporal punishment, harassment, sexual discrimination or abuse. Bullying, includes verbal and relational abuse. This indicator intends to measure experiences related to bullying such as being called by an offensive nickname, being threatened to be hurt, or other students posting offensive pictures or texts about them. Bullying has been linked to reduce academic and health outcomes for victims and for perpetrators. The figure is calculated as the number of students in a given level of education reporting that they have experienced any of the different types of violence or abuse in the past year expressed as a percentage of all students at the same level of education. Data for this indicator may come from two different school based surveys coordinated by the World Health Organization (WHO): a) The Global School-based Student Health Survey (GSHS) developed by the World Health Organization (WHO) and the US Center for Disease Control and Prevention (CDC) in collaboration with UNICEF, UNESCO, and UNAIDS. The GSHS is conducted primarily among students aged 13-17 years and has a global coverage; b) The Health Behaviour in School-aged Children (HBSC) study is a WHO collaborative cross-national study of adolescents' health and well-being administered in schools every four years and using a questionnaire for 11-, 13- and 15-year-olds. In addition, data points could be reported using student background data from international student assessments. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "School Safety/Violence" }, { "id": "Shortdefinition", "value": "Percentage of students who, during a school year, were physically attacked, participated in a physical fight, experiencing bullying, corporal punishment, harassment, sexual discrimination or abuse. Bullying, includes verbal and relational abuse. This indicator intends to measure experiences related to bullying such as being called by an offensive nickname, being threatened to be hurt, or other students posting offensive pictures or texts about them. Bullying has been linked to reduce academic and health outcomes for victims and for perpetrators. The figure is calculated as the number of students in a given level of education reporting that they have experienced any of the different types of violence or abuse in the past year expressed as a percentage of all students at the same level of education. Data for this indicator may come from two different school based surveys coordinated by the World Health Organization (WHO): a) The Global School-based Student Health Survey (GSHS) developed by the World Health Organization (WHO) and the US Center for Disease Control and Prevention (CDC) in collaboration with UNICEF, UNESCO, and UNAIDS. The GSHS is conducted primarily among students aged 13-17 years and has a global coverage; b) The Health Behaviour in School-aged Children (HBSC) study is a WHO collaborative cross-national study of adolescents' health and well-being administered in schools every four years and using a questionnaire for 11-, 13- and 15-year-olds. In addition, data points could be reported using student background data from international student assessments. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "School Safety/Violence" } ], "source_id": "12" }, { "id": "UIS.PER.11T15.BULLIED.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students experiencing bullying in the last 12 months, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "School Safety/Violence" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "School Safety/Violence" } ], "source_id": "12" }, { "id": "UIS.PER.11T15.BULLIED.HIGHSES", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students experiencing bullying in the last 12 months, high socio-economic status, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students who, during a school year, were physically attacked, participated in a physical fight, experiencing bullying, corporal punishment, harassment, sexual discrimination or abuse. Bullying, includes verbal and relational abuse. This indicator intends to measure experiences related to bullying such as being called by an offensive nickname, being threatened to be hurt, or other students posting offensive pictures or texts about them. Bullying has been linked to reduce academic and health outcomes for victims and for perpetrators. The figure is calculated as the number of students in a given level of education reporting that they have experienced any of the different types of violence or abuse in the past year expressed as a percentage of all students at the same level of education. Data for this indicator may come from two different school based surveys coordinated by the World Health Organization (WHO): a) The Global School-based Student Health Survey (GSHS) developed by the World Health Organization (WHO) and the US Center for Disease Control and Prevention (CDC) in collaboration with UNICEF, UNESCO, and UNAIDS. The GSHS is conducted primarily among students aged 13-17 years and has a global coverage; b) The Health Behaviour in School-aged Children (HBSC) study is a WHO collaborative cross-national study of adolescents' health and well-being administered in schools every four years and using a questionnaire for 11-, 13- and 15-year-olds. In addition, data points could be reported using student background data from international student assessments. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "School Safety/Violence" }, { "id": "Shortdefinition", "value": "Percentage of students who, during a school year, were physically attacked, participated in a physical fight, experiencing bullying, corporal punishment, harassment, sexual discrimination or abuse. Bullying, includes verbal and relational abuse. This indicator intends to measure experiences related to bullying such as being called by an offensive nickname, being threatened to be hurt, or other students posting offensive pictures or texts about them. Bullying has been linked to reduce academic and health outcomes for victims and for perpetrators. The figure is calculated as the number of students in a given level of education reporting that they have experienced any of the different types of violence or abuse in the past year expressed as a percentage of all students at the same level of education. Data for this indicator may come from two different school based surveys coordinated by the World Health Organization (WHO): a) The Global School-based Student Health Survey (GSHS) developed by the World Health Organization (WHO) and the US Center for Disease Control and Prevention (CDC) in collaboration with UNICEF, UNESCO, and UNAIDS. The GSHS is conducted primarily among students aged 13-17 years and has a global coverage; b) The Health Behaviour in School-aged Children (HBSC) study is a WHO collaborative cross-national study of adolescents' health and well-being administered in schools every four years and using a questionnaire for 11-, 13- and 15-year-olds. In addition, data points could be reported using student background data from international student assessments. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "School Safety/Violence" } ], "source_id": "12" }, { "id": "UIS.PER.11T15.BULLIED.LOWSES", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students experiencing bullying in the last 12 months, low socio-economic status, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students who, during a school year, were physically attacked, participated in a physical fight, experiencing bullying, corporal punishment, harassment, sexual discrimination or abuse. Bullying, includes verbal and relational abuse. This indicator intends to measure experiences related to bullying such as being called by an offensive nickname, being threatened to be hurt, or other students posting offensive pictures or texts about them. Bullying has been linked to reduce academic and health outcomes for victims and for perpetrators. The figure is calculated as the number of students in a given level of education reporting that they have experienced any of the different types of violence or abuse in the past year expressed as a percentage of all students at the same level of education. Data for this indicator may come from two different school based surveys coordinated by the World Health Organization (WHO): a) The Global School-based Student Health Survey (GSHS) developed by the World Health Organization (WHO) and the US Center for Disease Control and Prevention (CDC) in collaboration with UNICEF, UNESCO, and UNAIDS. The GSHS is conducted primarily among students aged 13-17 years and has a global coverage; b) The Health Behaviour in School-aged Children (HBSC) study is a WHO collaborative cross-national study of adolescents' health and well-being administered in schools every four years and using a questionnaire for 11-, 13- and 15-year-olds. In addition, data points could be reported using student background data from international student assessments. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "School Safety/Violence" }, { "id": "Shortdefinition", "value": "Percentage of students who, during a school year, were physically attacked, participated in a physical fight, experiencing bullying, corporal punishment, harassment, sexual discrimination or abuse. Bullying, includes verbal and relational abuse. This indicator intends to measure experiences related to bullying such as being called by an offensive nickname, being threatened to be hurt, or other students posting offensive pictures or texts about them. Bullying has been linked to reduce academic and health outcomes for victims and for perpetrators. The figure is calculated as the number of students in a given level of education reporting that they have experienced any of the different types of violence or abuse in the past year expressed as a percentage of all students at the same level of education. Data for this indicator may come from two different school based surveys coordinated by the World Health Organization (WHO): a) The Global School-based Student Health Survey (GSHS) developed by the World Health Organization (WHO) and the US Center for Disease Control and Prevention (CDC) in collaboration with UNICEF, UNESCO, and UNAIDS. The GSHS is conducted primarily among students aged 13-17 years and has a global coverage; b) The Health Behaviour in School-aged Children (HBSC) study is a WHO collaborative cross-national study of adolescents' health and well-being administered in schools every four years and using a questionnaire for 11-, 13- and 15-year-olds. In addition, data points could be reported using student background data from international student assessments. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "School Safety/Violence" } ], "source_id": "12" }, { "id": "UIS.PER.11T15.BULLIED.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students experiencing bullying in the last 12 months, male (%)" }, { "id": "Longdefinition", "value": "Percentage of students who, during a school year, were physically attacked, participated in a physical fight, experiencing bullying, corporal punishment, harassment, sexual discrimination or abuse. Bullying, includes verbal and relational abuse. This indicator intends to measure experiences related to bullying such as being called by an offensive nickname, being threatened to be hurt, or other students posting offensive pictures or texts about them. Bullying has been linked to reduce academic and health outcomes for victims and for perpetrators. The figure is calculated as the number of students in a given level of education reporting that they have experienced any of the different types of violence or abuse in the past year expressed as a percentage of all students at the same level of education. Data for this indicator may come from two different school based surveys coordinated by the World Health Organization (WHO): a) The Global School-based Student Health Survey (GSHS) developed by the World Health Organization (WHO) and the US Center for Disease Control and Prevention (CDC) in collaboration with UNICEF, UNESCO, and UNAIDS. The GSHS is conducted primarily among students aged 13-17 years and has a global coverage; b) The Health Behaviour in School-aged Children (HBSC) study is a WHO collaborative cross-national study of adolescents' health and well-being administered in schools every four years and using a questionnaire for 11-, 13- and 15-year-olds. In addition, data points could be reported using student background data from international student assessments. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "School Safety/Violence" }, { "id": "Shortdefinition", "value": "Percentage of students who, during a school year, were physically attacked, participated in a physical fight, experiencing bullying, corporal punishment, harassment, sexual discrimination or abuse. Bullying, includes verbal and relational abuse. This indicator intends to measure experiences related to bullying such as being called by an offensive nickname, being threatened to be hurt, or other students posting offensive pictures or texts about them. Bullying has been linked to reduce academic and health outcomes for victims and for perpetrators. The figure is calculated as the number of students in a given level of education reporting that they have experienced any of the different types of violence or abuse in the past year expressed as a percentage of all students at the same level of education. Data for this indicator may come from two different school based surveys coordinated by the World Health Organization (WHO): a) The Global School-based Student Health Survey (GSHS) developed by the World Health Organization (WHO) and the US Center for Disease Control and Prevention (CDC) in collaboration with UNICEF, UNESCO, and UNAIDS. The GSHS is conducted primarily among students aged 13-17 years and has a global coverage; b) The Health Behaviour in School-aged Children (HBSC) study is a WHO collaborative cross-national study of adolescents' health and well-being administered in schools every four years and using a questionnaire for 11-, 13- and 15-year-olds. In addition, data points could be reported using student background data from international student assessments. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "School Safety/Violence" } ], "source_id": "12" }, { "id": "UIS.PER.11T15.BULLIED.NATIVE", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students experiencing bullying in the last 12 months, non-immigrant background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students who, during a school year, were physically attacked, participated in a physical fight, experiencing bullying, corporal punishment, harassment, sexual discrimination or abuse. Bullying, includes verbal and relational abuse. This indicator intends to measure experiences related to bullying such as being called by an offensive nickname, being threatened to be hurt, or other students posting offensive pictures or texts about them. Bullying has been linked to reduce academic and health outcomes for victims and for perpetrators. The figure is calculated as the number of students in a given level of education reporting that they have experienced any of the different types of violence or abuse in the past year expressed as a percentage of all students at the same level of education. Data for this indicator may come from two different school based surveys coordinated by the World Health Organization (WHO): a) The Global School-based Student Health Survey (GSHS) developed by the World Health Organization (WHO) and the US Center for Disease Control and Prevention (CDC) in collaboration with UNICEF, UNESCO, and UNAIDS. The GSHS is conducted primarily among students aged 13-17 years and has a global coverage; b) The Health Behaviour in School-aged Children (HBSC) study is a WHO collaborative cross-national study of adolescents' health and well-being administered in schools every four years and using a questionnaire for 11-, 13- and 15-year-olds. In addition, data points could be reported using student background data from international student assessments. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "School Safety/Violence" }, { "id": "Shortdefinition", "value": "Percentage of students who, during a school year, were physically attacked, participated in a physical fight, experiencing bullying, corporal punishment, harassment, sexual discrimination or abuse. Bullying, includes verbal and relational abuse. This indicator intends to measure experiences related to bullying such as being called by an offensive nickname, being threatened to be hurt, or other students posting offensive pictures or texts about them. Bullying has been linked to reduce academic and health outcomes for victims and for perpetrators. The figure is calculated as the number of students in a given level of education reporting that they have experienced any of the different types of violence or abuse in the past year expressed as a percentage of all students at the same level of education. Data for this indicator may come from two different school based surveys coordinated by the World Health Organization (WHO): a) The Global School-based Student Health Survey (GSHS) developed by the World Health Organization (WHO) and the US Center for Disease Control and Prevention (CDC) in collaboration with UNICEF, UNESCO, and UNAIDS. The GSHS is conducted primarily among students aged 13-17 years and has a global coverage; b) The Health Behaviour in School-aged Children (HBSC) study is a WHO collaborative cross-national study of adolescents' health and well-being administered in schools every four years and using a questionnaire for 11-, 13- and 15-year-olds. In addition, data points could be reported using student background data from international student assessments. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "School Safety/Violence" } ], "source_id": "12" }, { "id": "UIS.PER.11T15.BULLIED.NON", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students experiencing bullying in the last 12 months, immigrant background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of students who, during a school year, were physically attacked, participated in a physical fight, experiencing bullying, corporal punishment, harassment, sexual discrimination or abuse. Bullying, includes verbal and relational abuse. This indicator intends to measure experiences related to bullying such as being called by an offensive nickname, being threatened to be hurt, or other students posting offensive pictures or texts about them. Bullying has been linked to reduce academic and health outcomes for victims and for perpetrators. The figure is calculated as the number of students in a given level of education reporting that they have experienced any of the different types of violence or abuse in the past year expressed as a percentage of all students at the same level of education. Data for this indicator may come from two different school based surveys coordinated by the World Health Organization (WHO): a) The Global School-based Student Health Survey (GSHS) developed by the World Health Organization (WHO) and the US Center for Disease Control and Prevention (CDC) in collaboration with UNICEF, UNESCO, and UNAIDS. The GSHS is conducted primarily among students aged 13-17 years and has a global coverage; b) The Health Behaviour in School-aged Children (HBSC) study is a WHO collaborative cross-national study of adolescents' health and well-being administered in schools every four years and using a questionnaire for 11-, 13- and 15-year-olds. In addition, data points could be reported using student background data from international student assessments. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "School Safety/Violence" }, { "id": "Shortdefinition", "value": "Percentage of students who, during a school year, were physically attacked, participated in a physical fight, experiencing bullying, corporal punishment, harassment, sexual discrimination or abuse. Bullying, includes verbal and relational abuse. This indicator intends to measure experiences related to bullying such as being called by an offensive nickname, being threatened to be hurt, or other students posting offensive pictures or texts about them. Bullying has been linked to reduce academic and health outcomes for victims and for perpetrators. The figure is calculated as the number of students in a given level of education reporting that they have experienced any of the different types of violence or abuse in the past year expressed as a percentage of all students at the same level of education. Data for this indicator may come from two different school based surveys coordinated by the World Health Organization (WHO): a) The Global School-based Student Health Survey (GSHS) developed by the World Health Organization (WHO) and the US Center for Disease Control and Prevention (CDC) in collaboration with UNICEF, UNESCO, and UNAIDS. The GSHS is conducted primarily among students aged 13-17 years and has a global coverage; b) The Health Behaviour in School-aged Children (HBSC) study is a WHO collaborative cross-national study of adolescents' health and well-being administered in schools every four years and using a questionnaire for 11-, 13- and 15-year-olds. In addition, data points could be reported using student background data from international student assessments. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "School Safety/Violence" } ], "source_id": "12" }, { "id": "UIS.PER.11T15.BULLIED.NPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students experiencing bullying in the last 12 months, adjusted native parity index (NPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "School Safety/Violence" }, { "id": "Shortdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "School Safety/Violence" } ], "source_id": "12" }, { "id": "UIS.PER.11T15.BULLIED.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students experiencing bullying in the last 12 months, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "School Safety/Violence" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "School Safety/Violence" } ], "source_id": "12" }, { "id": "UIS.PLILLITP", "metatype": [ { "id": "IndicatorName", "value": "Participants in literacy programmes as a % of the illiterate population, both sexes" }, { "id": "Longdefinition", "value": "Number of youth (aged 15-24 years) and adults (aged 15 years and older) participating in literacy programmes expressed as a percentage of the illiterate population of the same age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Number of youth (aged 15-24 years) and adults (aged 15 years and older) participating in literacy programmes expressed as a percentage of the illiterate population of the same age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.PLILLITP.F", "metatype": [ { "id": "IndicatorName", "value": "Participants in literacy programmes as a % of the illiterate population, female" }, { "id": "Longdefinition", "value": "Number of youth (aged 15-24 years) and adults (aged 15 years and older) participating in literacy programmes expressed as a percentage of the illiterate population of the same age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Number of youth (aged 15-24 years) and adults (aged 15 years and older) participating in literacy programmes expressed as a percentage of the illiterate population of the same age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.PLILLITP.M", "metatype": [ { "id": "IndicatorName", "value": "Participants in literacy programmes as a % of the illiterate population, male" }, { "id": "Longdefinition", "value": "Number of youth (aged 15-24 years) and adults (aged 15 years and older) participating in literacy programmes expressed as a percentage of the illiterate population of the same age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "Number of youth (aged 15-24 years) and adults (aged 15 years and older) participating in literacy programmes expressed as a percentage of the illiterate population of the same age. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.POSTIMUENV", "metatype": [ { "id": "IndicatorName", "value": "Percentage of children under 5 years experiencing positive and stimulating home learning environments, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children aged 36-59 months who live in households where their mother, father or other adult household members engage with them in the following types of activities: reading or looking at picture books; telling stories; singing songs; taking children outside the home; playing; and naming, counting and/or drawing. The indicators aims to evaluate learning environment to ensure that it promotes and does not harm children's development. Source data are from household surveys with measures of positive and stimulating home learning environments for young children, which have been used in multiple countries and are available from surveys and assessments, including the Multiple Indicator Cluster Surveys (MICS), Programa Regional de Indicadores de Desarrollo Infantil (PRIDI) in Latin America, Young Lives and others. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Percentage of children aged 36-59 months who live in households where their mother, father or other adult household members engage with them in the following types of activities: reading or looking at picture books; telling stories; singing songs; taking children outside the home; playing; and naming, counting and/or drawing. The indicators aims to evaluate learning environment to ensure that it promotes and does not harm children's development. Source data are from household surveys with measures of positive and stimulating home learning environments for young children, which have been used in multiple countries and are available from surveys and assessments, including the Multiple Indicator Cluster Surveys (MICS), Programa Regional de Indicadores de Desarrollo Infantil (PRIDI) in Latin America, Young Lives and others. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.POSTIMUENV.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of children under 5 years experiencing positive and stimulating home learning environments, female (%)" }, { "id": "Longdefinition", "value": "Percentage of children aged 36-59 months who live in households where their mother, father or other adult household members engage with them in the following types of activities: reading or looking at picture books; telling stories; singing songs; taking children outside the home; playing; and naming, counting and/or drawing. The indicators aims to evaluate learning environment to ensure that it promotes and does not harm children's development. Source data are from household surveys with measures of positive and stimulating home learning environments for young children, which have been used in multiple countries and are available from surveys and assessments, including the Multiple Indicator Cluster Surveys (MICS), Programa Regional de Indicadores de Desarrollo Infantil (PRIDI) in Latin America, Young Lives and others. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Percentage of children aged 36-59 months who live in households where their mother, father or other adult household members engage with them in the following types of activities: reading or looking at picture books; telling stories; singing songs; taking children outside the home; playing; and naming, counting and/or drawing. The indicators aims to evaluate learning environment to ensure that it promotes and does not harm children's development. Source data are from household surveys with measures of positive and stimulating home learning environments for young children, which have been used in multiple countries and are available from surveys and assessments, including the Multiple Indicator Cluster Surveys (MICS), Programa Regional de Indicadores de Desarrollo Infantil (PRIDI) in Latin America, Young Lives and others. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.POSTIMUENV.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of children under 5 years experiencing positive and stimulating home learning environments, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.POSTIMUENV.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of children under 5 years experiencing positive and stimulating home learning environments, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.POSTIMUENV.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of children under 5 years experiencing positive and stimulating home learning environments, male (%)" }, { "id": "Longdefinition", "value": "Percentage of children aged 36-59 months who live in households where their mother, father or other adult household members engage with them in the following types of activities: reading or looking at picture books; telling stories; singing songs; taking children outside the home; playing; and naming, counting and/or drawing. The indicators aims to evaluate learning environment to ensure that it promotes and does not harm children's development. Source data are from household surveys with measures of positive and stimulating home learning environments for young children, which have been used in multiple countries and are available from surveys and assessments, including the Multiple Indicator Cluster Surveys (MICS), Programa Regional de Indicadores de Desarrollo Infantil (PRIDI) in Latin America, Young Lives and others. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Percentage of children aged 36-59 months who live in households where their mother, father or other adult household members engage with them in the following types of activities: reading or looking at picture books; telling stories; singing songs; taking children outside the home; playing; and naming, counting and/or drawing. The indicators aims to evaluate learning environment to ensure that it promotes and does not harm children's development. Source data are from household surveys with measures of positive and stimulating home learning environments for young children, which have been used in multiple countries and are available from surveys and assessments, including the Multiple Indicator Cluster Surveys (MICS), Programa Regional de Indicadores de Desarrollo Infantil (PRIDI) in Latin America, Young Lives and others. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.POSTIMUENV.RUR", "metatype": [ { "id": "IndicatorName", "value": "Percentage of children under 5 years experiencing positive and stimulating home learning environments, rural (%)" }, { "id": "Longdefinition", "value": "Percentage of children aged 36-59 months who live in households where their mother, father or other adult household members engage with them in the following types of activities: reading or looking at picture books; telling stories; singing songs; taking children outside the home; playing; and naming, counting and/or drawing. The indicators aims to evaluate learning environment to ensure that it promotes and does not harm children's development. Source data are from household surveys with measures of positive and stimulating home learning environments for young children, which have been used in multiple countries and are available from surveys and assessments, including the Multiple Indicator Cluster Surveys (MICS), Programa Regional de Indicadores de Desarrollo Infantil (PRIDI) in Latin America, Young Lives and others. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Percentage of children aged 36-59 months who live in households where their mother, father or other adult household members engage with them in the following types of activities: reading or looking at picture books; telling stories; singing songs; taking children outside the home; playing; and naming, counting and/or drawing. The indicators aims to evaluate learning environment to ensure that it promotes and does not harm children's development. Source data are from household surveys with measures of positive and stimulating home learning environments for young children, which have been used in multiple countries and are available from surveys and assessments, including the Multiple Indicator Cluster Surveys (MICS), Programa Regional de Indicadores de Desarrollo Infantil (PRIDI) in Latin America, Young Lives and others. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.POSTIMUENV.URB", "metatype": [ { "id": "IndicatorName", "value": "Percentage of children under 5 years experiencing positive and stimulating home learning environments, urban (%)" }, { "id": "Longdefinition", "value": "Percentage of children aged 36-59 months who live in households where their mother, father or other adult household members engage with them in the following types of activities: reading or looking at picture books; telling stories; singing songs; taking children outside the home; playing; and naming, counting and/or drawing. The indicators aims to evaluate learning environment to ensure that it promotes and does not harm children's development. Source data are from household surveys with measures of positive and stimulating home learning environments for young children, which have been used in multiple countries and are available from surveys and assessments, including the Multiple Indicator Cluster Surveys (MICS), Programa Regional de Indicadores de Desarrollo Infantil (PRIDI) in Latin America, Young Lives and others. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Percentage of children aged 36-59 months who live in households where their mother, father or other adult household members engage with them in the following types of activities: reading or looking at picture books; telling stories; singing songs; taking children outside the home; playing; and naming, counting and/or drawing. The indicators aims to evaluate learning environment to ensure that it promotes and does not harm children's development. Source data are from household surveys with measures of positive and stimulating home learning environments for young children, which have been used in multiple countries and are available from surveys and assessments, including the Multiple Indicator Cluster Surveys (MICS), Programa Regional de Indicadores de Desarrollo Infantil (PRIDI) in Latin America, Young Lives and others. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.POSTIMUENV.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of children under 5 years experiencing positive and stimulating home learning environments, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.POSTIMUENV.WQ1", "metatype": [ { "id": "IndicatorName", "value": "Percentage of children under 5 years experiencing positive and stimulating home learning environments, poorest quintile (%)" }, { "id": "Longdefinition", "value": "Percentage of children aged 36-59 months who live in households where their mother, father or other adult household members engage with them in the following types of activities: reading or looking at picture books; telling stories; singing songs; taking children outside the home; playing; and naming, counting and/or drawing. The indicators aims to evaluate learning environment to ensure that it promotes and does not harm children's development. Source data are from household surveys with measures of positive and stimulating home learning environments for young children, which have been used in multiple countries and are available from surveys and assessments, including the Multiple Indicator Cluster Surveys (MICS), Programa Regional de Indicadores de Desarrollo Infantil (PRIDI) in Latin America, Young Lives and others. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Percentage of children aged 36-59 months who live in households where their mother, father or other adult household members engage with them in the following types of activities: reading or looking at picture books; telling stories; singing songs; taking children outside the home; playing; and naming, counting and/or drawing. The indicators aims to evaluate learning environment to ensure that it promotes and does not harm children's development. Source data are from household surveys with measures of positive and stimulating home learning environments for young children, which have been used in multiple countries and are available from surveys and assessments, including the Multiple Indicator Cluster Surveys (MICS), Programa Regional de Indicadores de Desarrollo Infantil (PRIDI) in Latin America, Young Lives and others. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.POSTIMUENV.WQ5", "metatype": [ { "id": "IndicatorName", "value": "Percentage of children under 5 years experiencing positive and stimulating home learning environments, richest quintile (%)" }, { "id": "Longdefinition", "value": "Percentage of children aged 36-59 months who live in households where their mother, father or other adult household members engage with them in the following types of activities: reading or looking at picture books; telling stories; singing songs; taking children outside the home; playing; and naming, counting and/or drawing. The indicators aims to evaluate learning environment to ensure that it promotes and does not harm children's development. Source data are from household surveys with measures of positive and stimulating home learning environments for young children, which have been used in multiple countries and are available from surveys and assessments, including the Multiple Indicator Cluster Surveys (MICS), Programa Regional de Indicadores de Desarrollo Infantil (PRIDI) in Latin America, Young Lives and others. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Percentage of children aged 36-59 months who live in households where their mother, father or other adult household members engage with them in the following types of activities: reading or looking at picture books; telling stories; singing songs; taking children outside the home; playing; and naming, counting and/or drawing. The indicators aims to evaluate learning environment to ensure that it promotes and does not harm children's development. Source data are from household surveys with measures of positive and stimulating home learning environments for young children, which have been used in multiple countries and are available from surveys and assessments, including the Multiple Indicator Cluster Surveys (MICS), Programa Regional de Indicadores de Desarrollo Infantil (PRIDI) in Latin America, Young Lives and others. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.PRP.0", "metatype": [ { "id": "IndicatorName", "value": "Percentage of enrolment in early childhood education programmes in private institutions (%)" }, { "id": "Longdefinition", "value": "Total number of students in early childhood education programmes enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body (e.g., non-governmental organisation, religious body, special interest group, foundation or business enterprise), expressed as a percentage of total number of students enrolled in early childhood education programmes. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years; and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.PRP.01", "metatype": [ { "id": "IndicatorName", "value": "Percentage of enrolment in early childhood educational development programmes in private institutions (%)" }, { "id": "Longdefinition", "value": "Total number of students in early childhood educational development programmes enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body (e.g., non-governmental organisation, religious body, special interest group, foundation or business enterprise), expressed as a percentage of total number of students enrolled in early childhood educational development programmes. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years; and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.PRP.2", "metatype": [ { "id": "IndicatorName", "value": "Percentage of enrolment in lower secondary education in private institutions (%)" }, { "id": "Longdefinition", "value": "Total number of students in lower secondary education enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body (e.g., non-governmental organisation, religious body, special interest group, foundation or business enterprise), expressed as a percentage of total number of students enrolled in lower secondary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.PRP.3", "metatype": [ { "id": "IndicatorName", "value": "Percentage of enrolment in upper secondary education in private institutions (%)" }, { "id": "Longdefinition", "value": "Total number of students in upper secondary education enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body (e.g., non-governmental organisation, religious body, special interest group, foundation or business enterprise), expressed as a percentage of total number of students enrolled in upper secondary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.PRP.4", "metatype": [ { "id": "IndicatorName", "value": "Percentage of enrolment in post-secondary non-tertiary education in private institutions (%)" }, { "id": "Longdefinition", "value": "Total number of students in post-secondary non-tertiary education enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body (e.g., non-governmental organisation, religious body, special interest group, foundation or business enterprise), expressed as a percentage of total number of students enrolled in post-secondary non-tertiary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Post-Secondary/Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.PRYA.12MO", "metatype": [ { "id": "IndicatorName", "value": "Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of youth and adults in a given age range (e.g. 15-24 years, 25-64 years, etc.) participating in formal or non-formal education or training in a given time period (e.g. last 12 months). Formal education and training is defined as education provided by the system of schools, colleges, universities and other formal educational institutions that normally constitutes a continuous ‘ladder’ of full-time education for children and young people, generally beginning at the age of 5 to 7 and continuing to up to 20 or 25 years old. In some countries, the upper parts of this ‘ladder’ are organized programmes of joint part-time employment and part-time participation in the regular school and university system. Non-formal education and training is defined as any organized and sustained learning activities that do not correspond exactly to the above definition of formal education. Non-formal education may therefore take place both within and outside educational institutions and cater to people of all ages. Depending on national contexts, it may cover educational programmes to impart adult literacy, life-skills, work-skills, and general culture. The source data are administrative data from schools and other places of education and training or household survey data on participants in formal and non-formal education and training by single year of age; population censuses and surveys for population estimates by single year of age (if using administrative data on enrolment). Formal and non-formal education and training can be offered in a variety of settings including schools and universities, workplace environments and others and can have a variety of durations. Administrative data often capture only provision in formal settings such as schools and universities. Participation rates do not capture the intensity or quality of the provision nor the outcomes of the education and training on offer. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Vocational & Post-secondary Non-Tertiary" }, { "id": "Shortdefinition", "value": "Percentage of youth and adults in a given age range (e.g. 15-24 years, 25-64 years, etc.) participating in formal or non-formal education or training in a given time period (e.g. last 12 months). Formal education and training is defined as education provided by the system of schools, colleges, universities and other formal educational institutions that normally constitutes a continuous ‘ladder’ of full-time education for children and young people, generally beginning at the age of 5 to 7 and continuing to up to 20 or 25 years old. In some countries, the upper parts of this ‘ladder’ are organized programmes of joint part-time employment and part-time participation in the regular school and university system. Non-formal education and training is defined as any organized and sustained learning activities that do not correspond exactly to the above definition of formal education. Non-formal education may therefore take place both within and outside educational institutions and cater to people of all ages. Depending on national contexts, it may cover educational programmes to impart adult literacy, life-skills, work-skills, and general culture. The source data are administrative data from schools and other places of education and training or household survey data on participants in formal and non-formal education and training by single year of age; population censuses and surveys for population estimates by single year of age (if using administrative data on enrolment). Formal and non-formal education and training can be offered in a variety of settings including schools and universities, workplace environments and others and can have a variety of durations. Administrative data often capture only provision in formal settings such as schools and universities. Participation rates do not capture the intensity or quality of the provision nor the outcomes of the education and training on offer. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Vocational & Post-secondary Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.PRYA.12MO.F", "metatype": [ { "id": "IndicatorName", "value": "Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, female (%)" }, { "id": "Longdefinition", "value": "Percentage of youth and adults in a given age range (e.g. 15-24 years, 25-64 years, etc.) participating in formal or non-formal education or training in a given time period (e.g. last 12 months). Formal education and training is defined as education provided by the system of schools, colleges, universities and other formal educational institutions that normally constitutes a continuous ‘ladder’ of full-time education for children and young people, generally beginning at the age of 5 to 7 and continuing to up to 20 or 25 years old. In some countries, the upper parts of this ‘ladder’ are organized programmes of joint part-time employment and part-time participation in the regular school and university system. Non-formal education and training is defined as any organized and sustained learning activities that do not correspond exactly to the above definition of formal education. Non-formal education may therefore take place both within and outside educational institutions and cater to people of all ages. Depending on national contexts, it may cover educational programmes to impart adult literacy, life-skills, work-skills, and general culture. The source data are administrative data from schools and other places of education and training or household survey data on participants in formal and non-formal education and training by single year of age; population censuses and surveys for population estimates by single year of age (if using administrative data on enrolment). Formal and non-formal education and training can be offered in a variety of settings including schools and universities, workplace environments and others and can have a variety of durations. Administrative data often capture only provision in formal settings such as schools and universities. Participation rates do not capture the intensity or quality of the provision nor the outcomes of the education and training on offer. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Vocational & Post-secondary Non-Tertiary" }, { "id": "Shortdefinition", "value": "Percentage of youth and adults in a given age range (e.g. 15-24 years, 25-64 years, etc.) participating in formal or non-formal education or training in a given time period (e.g. last 12 months). Formal education and training is defined as education provided by the system of schools, colleges, universities and other formal educational institutions that normally constitutes a continuous ‘ladder’ of full-time education for children and young people, generally beginning at the age of 5 to 7 and continuing to up to 20 or 25 years old. In some countries, the upper parts of this ‘ladder’ are organized programmes of joint part-time employment and part-time participation in the regular school and university system. Non-formal education and training is defined as any organized and sustained learning activities that do not correspond exactly to the above definition of formal education. Non-formal education may therefore take place both within and outside educational institutions and cater to people of all ages. Depending on national contexts, it may cover educational programmes to impart adult literacy, life-skills, work-skills, and general culture. The source data are administrative data from schools and other places of education and training or household survey data on participants in formal and non-formal education and training by single year of age; population censuses and surveys for population estimates by single year of age (if using administrative data on enrolment). Formal and non-formal education and training can be offered in a variety of settings including schools and universities, workplace environments and others and can have a variety of durations. Administrative data often capture only provision in formal settings such as schools and universities. Participation rates do not capture the intensity or quality of the provision nor the outcomes of the education and training on offer. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Vocational & Post-secondary Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.PRYA.12MO.GPI", "metatype": [ { "id": "IndicatorName", "value": "Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Vocational & Post-secondary Non-Tertiary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Vocational & Post-secondary Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.PRYA.12MO.M", "metatype": [ { "id": "IndicatorName", "value": "Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, male (%)" }, { "id": "Longdefinition", "value": "Percentage of youth and adults in a given age range (e.g. 15-24 years, 25-64 years, etc.) participating in formal or non-formal education or training in a given time period (e.g. last 12 months). Formal education and training is defined as education provided by the system of schools, colleges, universities and other formal educational institutions that normally constitutes a continuous ‘ladder’ of full-time education for children and young people, generally beginning at the age of 5 to 7 and continuing to up to 20 or 25 years old. In some countries, the upper parts of this ‘ladder’ are organized programmes of joint part-time employment and part-time participation in the regular school and university system. Non-formal education and training is defined as any organized and sustained learning activities that do not correspond exactly to the above definition of formal education. Non-formal education may therefore take place both within and outside educational institutions and cater to people of all ages. Depending on national contexts, it may cover educational programmes to impart adult literacy, life-skills, work-skills, and general culture. The source data are administrative data from schools and other places of education and training or household survey data on participants in formal and non-formal education and training by single year of age; population censuses and surveys for population estimates by single year of age (if using administrative data on enrolment). Formal and non-formal education and training can be offered in a variety of settings including schools and universities, workplace environments and others and can have a variety of durations. Administrative data often capture only provision in formal settings such as schools and universities. Participation rates do not capture the intensity or quality of the provision nor the outcomes of the education and training on offer. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Vocational & Post-secondary Non-Tertiary" }, { "id": "Shortdefinition", "value": "Percentage of youth and adults in a given age range (e.g. 15-24 years, 25-64 years, etc.) participating in formal or non-formal education or training in a given time period (e.g. last 12 months). Formal education and training is defined as education provided by the system of schools, colleges, universities and other formal educational institutions that normally constitutes a continuous ‘ladder’ of full-time education for children and young people, generally beginning at the age of 5 to 7 and continuing to up to 20 or 25 years old. In some countries, the upper parts of this ‘ladder’ are organized programmes of joint part-time employment and part-time participation in the regular school and university system. Non-formal education and training is defined as any organized and sustained learning activities that do not correspond exactly to the above definition of formal education. Non-formal education may therefore take place both within and outside educational institutions and cater to people of all ages. Depending on national contexts, it may cover educational programmes to impart adult literacy, life-skills, work-skills, and general culture. The source data are administrative data from schools and other places of education and training or household survey data on participants in formal and non-formal education and training by single year of age; population censuses and surveys for population estimates by single year of age (if using administrative data on enrolment). Formal and non-formal education and training can be offered in a variety of settings including schools and universities, workplace environments and others and can have a variety of durations. Administrative data often capture only provision in formal settings such as schools and universities. Participation rates do not capture the intensity or quality of the provision nor the outcomes of the education and training on offer. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Vocational & Post-secondary Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.PTRHC.02.QUALIFIED", "metatype": [ { "id": "IndicatorName", "value": "Pupil-qualified teacher ratio in pre-primary education (headcount basis)" }, { "id": "Longdefinition", "value": "Average number of pupils per qualified teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of qualified teachers at the same level. A qualified teacher is one who has the minimum academic qualifications necessary to teach at a specific level of education in a given country. This is usually related to the subject(s) they teach. In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Average number of pupils per qualified teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of qualified teachers at the same level. A qualified teacher is one who has the minimum academic qualifications necessary to teach at a specific level of education in a given country. This is usually related to the subject(s) they teach. In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.PTRHC.02.TRAINED", "metatype": [ { "id": "IndicatorName", "value": "Pupil-trained teacher ratio in pre-primary education (headcount basis)" }, { "id": "Longdefinition", "value": "Average number of pupils per trained teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of trained teachers at the same level. A trained teacher is defined as a teacher who has fulfilled at least the minimum organized teacher-training requirements (pre-service or in-service) to teach a specific level of education according to the relevant national policy or law. These requirements usually include pedagogical knowledge (broad principles and strategies of classroom management and organization that transcend the subject matter being taught - typically approaches, methods and techniques of teaching), and professional knowledge (knowledge of statutory instruments and other legal frameworks that govern the teaching profession). Some programmes may also cover content knowledge (knowledge of the curriculum and the subject matter to be taught and the use of relevant materials). In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Average number of pupils per trained teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of trained teachers at the same level. A trained teacher is defined as a teacher who has fulfilled at least the minimum organized teacher-training requirements (pre-service or in-service) to teach a specific level of education according to the relevant national policy or law. These requirements usually include pedagogical knowledge (broad principles and strategies of classroom management and organization that transcend the subject matter being taught - typically approaches, methods and techniques of teaching), and professional knowledge (knowledge of statutory instruments and other legal frameworks that govern the teaching profession). Some programmes may also cover content knowledge (knowledge of the curriculum and the subject matter to be taught and the use of relevant materials). In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.PTRHC.1.QUALIFIED", "metatype": [ { "id": "IndicatorName", "value": "Pupil-qualified teacher ratio in primary education (headcount basis)" }, { "id": "Longdefinition", "value": "Average number of pupils per qualified teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of qualified teachers at the same level. A qualified teacher is one who has the minimum academic qualifications necessary to teach at a specific level of education in a given country. This is usually related to the subject(s) they teach. In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Average number of pupils per qualified teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of qualified teachers at the same level. A qualified teacher is one who has the minimum academic qualifications necessary to teach at a specific level of education in a given country. This is usually related to the subject(s) they teach. In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.PTRHC.1.TRAINED", "metatype": [ { "id": "IndicatorName", "value": "Pupil-trained teacher ratio in primary education (headcount basis)" }, { "id": "Longdefinition", "value": "Average number of pupils per trained teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of trained teachers at the same level. A trained teacher is defined as a teacher who has fulfilled at least the minimum organized teacher-training requirements (pre-service or in-service) to teach a specific level of education according to the relevant national policy or law. These requirements usually include pedagogical knowledge (broad principles and strategies of classroom management and organization that transcend the subject matter being taught - typically approaches, methods and techniques of teaching), and professional knowledge (knowledge of statutory instruments and other legal frameworks that govern the teaching profession). Some programmes may also cover content knowledge (knowledge of the curriculum and the subject matter to be taught and the use of relevant materials). In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Average number of pupils per trained teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of trained teachers at the same level. A trained teacher is defined as a teacher who has fulfilled at least the minimum organized teacher-training requirements (pre-service or in-service) to teach a specific level of education according to the relevant national policy or law. These requirements usually include pedagogical knowledge (broad principles and strategies of classroom management and organization that transcend the subject matter being taught - typically approaches, methods and techniques of teaching), and professional knowledge (knowledge of statutory instruments and other legal frameworks that govern the teaching profession). Some programmes may also cover content knowledge (knowledge of the curriculum and the subject matter to be taught and the use of relevant materials). In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.PTRHC.2.QUALIFIED", "metatype": [ { "id": "IndicatorName", "value": "Pupil-qualified teacher ratio in lower secondary (headcount basis)" }, { "id": "Longdefinition", "value": "Average number of pupils per qualified teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of qualified teachers at the same level. A qualified teacher is one who has the minimum academic qualifications necessary to teach at a specific level of education in a given country. This is usually related to the subject(s) they teach. In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Average number of pupils per qualified teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of qualified teachers at the same level. A qualified teacher is one who has the minimum academic qualifications necessary to teach at a specific level of education in a given country. This is usually related to the subject(s) they teach. In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.PTRHC.2.TRAINED", "metatype": [ { "id": "IndicatorName", "value": "Pupil-trained teacher ratio in lower secondary education (headcount basis)" }, { "id": "Longdefinition", "value": "Average number of pupils per trained teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of trained teachers at the same level. A trained teacher is defined as a teacher who has fulfilled at least the minimum organized teacher-training requirements (pre-service or in-service) to teach a specific level of education according to the relevant national policy or law. These requirements usually include pedagogical knowledge (broad principles and strategies of classroom management and organization that transcend the subject matter being taught - typically approaches, methods and techniques of teaching), and professional knowledge (knowledge of statutory instruments and other legal frameworks that govern the teaching profession). Some programmes may also cover content knowledge (knowledge of the curriculum and the subject matter to be taught and the use of relevant materials). In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Average number of pupils per trained teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of trained teachers at the same level. A trained teacher is defined as a teacher who has fulfilled at least the minimum organized teacher-training requirements (pre-service or in-service) to teach a specific level of education according to the relevant national policy or law. These requirements usually include pedagogical knowledge (broad principles and strategies of classroom management and organization that transcend the subject matter being taught - typically approaches, methods and techniques of teaching), and professional knowledge (knowledge of statutory instruments and other legal frameworks that govern the teaching profession). Some programmes may also cover content knowledge (knowledge of the curriculum and the subject matter to be taught and the use of relevant materials). In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.PTRHC.2T3.QUALIFIED", "metatype": [ { "id": "IndicatorName", "value": "Pupil-qualified teacher ratio in secondary (headcount basis)" }, { "id": "Longdefinition", "value": "Average number of pupils per qualified teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of qualified teachers at the same level. A qualified teacher is one who has the minimum academic qualifications necessary to teach at a specific level of education in a given country. This is usually related to the subject(s) they teach. In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Average number of pupils per qualified teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of qualified teachers at the same level. A qualified teacher is one who has the minimum academic qualifications necessary to teach at a specific level of education in a given country. This is usually related to the subject(s) they teach. In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.PTRHC.2T3.TRAINED", "metatype": [ { "id": "IndicatorName", "value": "Pupil-trained teacher ratio in secondary education (headcount basis)" }, { "id": "Longdefinition", "value": "Average number of pupils per trained teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of trained teachers at the same level. A trained teacher is defined as a teacher who has fulfilled at least the minimum organized teacher-training requirements (pre-service or in-service) to teach a specific level of education according to the relevant national policy or law. These requirements usually include pedagogical knowledge (broad principles and strategies of classroom management and organization that transcend the subject matter being taught - typically approaches, methods and techniques of teaching), and professional knowledge (knowledge of statutory instruments and other legal frameworks that govern the teaching profession). Some programmes may also cover content knowledge (knowledge of the curriculum and the subject matter to be taught and the use of relevant materials). In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Average number of pupils per trained teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of trained teachers at the same level. A trained teacher is defined as a teacher who has fulfilled at least the minimum organized teacher-training requirements (pre-service or in-service) to teach a specific level of education according to the relevant national policy or law. These requirements usually include pedagogical knowledge (broad principles and strategies of classroom management and organization that transcend the subject matter being taught - typically approaches, methods and techniques of teaching), and professional knowledge (knowledge of statutory instruments and other legal frameworks that govern the teaching profession). Some programmes may also cover content knowledge (knowledge of the curriculum and the subject matter to be taught and the use of relevant materials). In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.PTRHC.3.QUALIFIED", "metatype": [ { "id": "IndicatorName", "value": "Pupil-qualified teacher ratio in upper secondary (headcount basis)" }, { "id": "Longdefinition", "value": "Average number of pupils per qualified teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of qualified teachers at the same level. A qualified teacher is one who has the minimum academic qualifications necessary to teach at a specific level of education in a given country. This is usually related to the subject(s) they teach. In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Average number of pupils per qualified teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of qualified teachers at the same level. A qualified teacher is one who has the minimum academic qualifications necessary to teach at a specific level of education in a given country. This is usually related to the subject(s) they teach. In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.PTRHC.3.TRAINED", "metatype": [ { "id": "IndicatorName", "value": "Pupil-trained teacher ratio in upper secondary education (headcount basis)" }, { "id": "Longdefinition", "value": "Average number of pupils per trained teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of trained teachers at the same level. A trained teacher is defined as a teacher who has fulfilled at least the minimum organized teacher-training requirements (pre-service or in-service) to teach a specific level of education according to the relevant national policy or law. These requirements usually include pedagogical knowledge (broad principles and strategies of classroom management and organization that transcend the subject matter being taught - typically approaches, methods and techniques of teaching), and professional knowledge (knowledge of statutory instruments and other legal frameworks that govern the teaching profession). Some programmes may also cover content knowledge (knowledge of the curriculum and the subject matter to be taught and the use of relevant materials). In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Average number of pupils per trained teacher at a given level of education, based on headcounts of both pupils and teachers. Divide the total number of pupils enrolled at the specified level of education by the number of trained teachers at the same level. A trained teacher is defined as a teacher who has fulfilled at least the minimum organized teacher-training requirements (pre-service or in-service) to teach a specific level of education according to the relevant national policy or law. These requirements usually include pedagogical knowledge (broad principles and strategies of classroom management and organization that transcend the subject matter being taught - typically approaches, methods and techniques of teaching), and professional knowledge (knowledge of statutory instruments and other legal frameworks that govern the teaching profession). Some programmes may also cover content knowledge (knowledge of the curriculum and the subject matter to be taught and the use of relevant materials). In computing and interpreting this indicator, one should take into account the existence of part-time teaching, school-shifts, multi-grade classes and other practices that may affect the precision and meaningfulness of pupil-teacher ratios. When feasible, the number of part-time teachers is converted to ‘full-time equivalent’ teachers; a double-shift teacher is counted twice, etc. Teachers are defined as persons whose professional activity involves the transmitting of knowledge, attitudes and skills that are stipulated in a formal curriculum programme to students enrolled in a formal educational institution." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.02", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in pre-primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.02.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in pre-primary education, female (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.02.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in pre-primary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.02.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in pre-primary education, male (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.1", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.1.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in primary education, female (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in primary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.1.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in primary education, male (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.2", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in lower secondary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.2.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in lower secondary education, female (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in lower secondary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.2.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in lower secondary education, male (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.2T3", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in secondary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.2T3.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in secondary education, female (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.2T3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in secondary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.2T3.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in secondary education, male (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.3", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in upper secondary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. 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For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.3.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in upper secondary education, female (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in upper secondary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.QUTP.3.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of qualified teachers in upper secondary education, male (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have at least the minimum academic qualifications required for teaching their subjects at the relevant level in a given country, in a given academic year. A high value indicates that students are being taught by teachers who are academically well qualified in the subjects they teach. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.R.1", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in primary education, all grades, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in primary education, all grades, female (number)" }, { "id": "Longdefinition", "value": "Number of female pupils enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G1", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 1 of primary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G1.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 1 of primary education, female (number)" }, { "id": "Longdefinition", "value": "Number of female pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G1.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 1 of primary education, male (number)" }, { "id": "Longdefinition", "value": "Number of male pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of male pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G2", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 2 of primary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G2.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 2 of primary education, female (number)" }, { "id": "Longdefinition", "value": "Number of female pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G2.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 2 of primary education, male (number)" }, { "id": "Longdefinition", "value": "Number of male pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of male pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G3", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 3 of primary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G3.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 3 of primary education, female (number)" }, { "id": "Longdefinition", "value": "Number of female pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G3.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 3 of primary education, male (number)" }, { "id": "Longdefinition", "value": "Number of male pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of male pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G4", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 4 of primary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G4.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 4 of primary education, female (number)" }, { "id": "Longdefinition", "value": "Number of female pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G4.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 4 of primary education, male (number)" }, { "id": "Longdefinition", "value": "Number of male pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of male pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G5", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 5 of primary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G5.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 5 of primary education, female (number)" }, { "id": "Longdefinition", "value": "Number of female pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G5.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 5 of primary education, male (number)" }, { "id": "Longdefinition", "value": "Number of male pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of male pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G6", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 6 of primary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G6.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 6 of primary education, female (number)" }, { "id": "Longdefinition", "value": "Number of female pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G6.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 6 of primary education, male (number)" }, { "id": "Longdefinition", "value": "Number of male pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of male pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G7", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 7 of primary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G7.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 7 of primary education, female (number)" }, { "id": "Longdefinition", "value": "Number of female pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.G7.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 7 of primary education, male (number)" }, { "id": "Longdefinition", "value": "Number of male pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of male pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.Guk", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in grade unknown of primary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils enrolled in the same grade for a second (or further) year." } ], "source_id": "12" }, { "id": "UIS.R.1.GUK", "metatype": [ { "id": "Notesfromoriginalsource", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.Guk", "metatype": [ { "id": "Shortdefinition", "value": "Number of pupils enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.R.1.GUK", "metatype": [ { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.Guk.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in grade unknown of primary education, female (number)" }, { "id": "Longdefinition", "value": "Number of female pupils enrolled in the same grade for a second (or further) year." } ], "source_id": "12" }, { "id": "UIS.R.1.GUK.F", "metatype": [ { "id": "Notesfromoriginalsource", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.Guk.F", "metatype": [ { "id": "Shortdefinition", "value": "Number of female pupils enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.R.1.GUK.F", "metatype": [ { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.GUK.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in grade unknown of primary education, male (number)" }, { "id": "Longdefinition", "value": "Number of male pupils enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of male pupils enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.1.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in primary education, all grades, male (number)" }, { "id": "Longdefinition", "value": "Number of male pupils enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of male pupils enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in lower secondary general education, all grades, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in lower secondary general education, all grades, female (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G1", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 1 of lower secondary general education, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G1.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 1 of lower secondary general education, female (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G1.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 1 of lower secondary general education, male (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G2", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 2 of lower secondary general education, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G2.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 2 of lower secondary general education, female (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G2.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 2 of lower secondary general education, male (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G3", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 3 of lower secondary general education, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G3.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 3 of lower secondary general education, female (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G3.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 3 of lower secondary general education, male (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G4", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 4 of lower secondary general education, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G4.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 4 of lower secondary general education, female (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G4.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 4 of lower secondary general education, male (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G5", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 5 of lower secondary general education, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G5.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 5 of lower secondary general education, female (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G5.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 5 of lower secondary general education, male (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G6", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 6 of lower secondary general education, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G6.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 6 of lower secondary general education, female (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.G6.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in Grade 6 of lower secondary general education, male (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.GUK", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in grade unknown of lower secondary general education, both sexes (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.GUK.F", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in grade unknown of lower secondary general education, female (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.GUK.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in grade unknown of lower secondary general education, male (number)" }, { "id": "Longdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of pupils in the specified grade who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.R.2.GPV.M", "metatype": [ { "id": "IndicatorName", "value": "Repeaters in lower secondary general education, all grades, male (number)" }, { "id": "Longdefinition", "value": "Number of male pupils in lower secondary education who are enrolled in the same grade for a second (or further) year." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of male pupils in lower secondary education who are enrolled in the same grade for a second (or further) year." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.G2T3", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in reading, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.G2T3.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in reading, female (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.G2T3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in reading, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.G2T3.HIGHSES", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in reading, very affluent socioeconomic background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.G2T3.LANGTEST", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in reading, spoke the language of the test at home, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.G2T3.LOWSES", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in reading, very poor socioeconomic background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.G2T3.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in reading, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.G2T3.LTPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in reading, adjusted speaks language of the test parity index (LTPIA)" }, { "id": "Longdefinition", "value": "The adjusted speaks language of the test parity index (LTPIA) is calculated by dividing the value for the indicator for students who do not speak the language of the test at home by the value for the indicator for students who speak the language of the test at home. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted speaks language of the test parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LTPI equal to 1 indicates parity between students who do and do not speak the language of the test at home. In general, a value less than 1 indicates disparity in favor of students who speak the language of test at home. A value greater than 1 indicates disparity in favor of students who do not speak the language of the test at home. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The adjusted speaks language of the test parity index (LTPIA) is calculated by dividing the value for the indicator for students who do not speak the language of the test at home by the value for the indicator for students who speak the language of the test at home. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted speaks language of the test parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LTPI equal to 1 indicates parity between students who do and do not speak the language of the test at home. In general, a value less than 1 indicates disparity in favor of students who speak the language of test at home. A value greater than 1 indicates disparity in favor of students who do not speak the language of the test at home. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.G2T3.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in reading, male (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.G2T3.NATIVE", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in reading, non-immigrant background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.G2T3.NONLANGTEST", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in reading, did not speak the language of the test at home, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.G2T3.NONNATIVE", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in reading, immigrant background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.G2T3.NPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in reading, adjusted native parity index (NPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.G2T3.RURAL", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in reading, rural areas, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.G2T3.URBAN", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in reading, urban areas, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children in Grade 2 or 3 reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.G2T3.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students in Grade 2 or 3 achieving at least a minimum proficiency level in reading, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.LOWERSEC", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in reading, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.LOWERSEC.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in reading, female (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.LOWERSEC.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary achieving at least a minimum proficiency level in reading, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.LOWERSEC.HIGHSES", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in reading, very affluent socioeconomic background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.LOWERSEC.LANGTEST", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in reading, spoke the language of the test at home, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.LOWERSEC.LOWSES", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in reading, very poor socioeconomic background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.LOWERSEC.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in reading, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.LOWERSEC.LTPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in reading, adjusted speaks language of the test parity index (LTPIA)" }, { "id": "Longdefinition", "value": "The adjusted speaks language of the test parity index (LTPIA) is calculated by dividing the value for the indicator for students who do not speak the language of the test at home by the value for the indicator for students who speak the language of the test at home. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted speaks language of the test parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LTPI equal to 1 indicates parity between students who do and do not speak the language of the test at home. In general, a value less than 1 indicates disparity in favor of students who speak the language of test at home. A value greater than 1 indicates disparity in favor of students who do not speak the language of the test at home. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The adjusted speaks language of the test parity index (LTPIA) is calculated by dividing the value for the indicator for students who do not speak the language of the test at home by the value for the indicator for students who speak the language of the test at home. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted speaks language of the test parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LTPI equal to 1 indicates parity between students who do and do not speak the language of the test at home. In general, a value less than 1 indicates disparity in favor of students who speak the language of test at home. A value greater than 1 indicates disparity in favor of students who do not speak the language of the test at home. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.LOWERSEC.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in reading, male (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.LOWERSEC.NATIVE", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in reading, non-immigrant background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.LOWERSEC.NONLANGTEST", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in reading, did not speak the language of the test at home, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.LOWERSEC.NONNATIVE", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in reading, immigrant background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.LOWERSEC.NPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in reading, adjusted native parity index (NPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.LOWERSEC.RURAL", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in reading, rural areas, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.LOWERSEC.URBAN", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in reading, urban areas, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of lower secondary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.LOWERSEC.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of lower secondary education achieving at least a minimum proficiency level in reading, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.READ.PRIMARY", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in reading, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.PRIMARY.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in reading, female (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.PRIMARY.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary achieving at least a minimum proficiency level in reading, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.PRIMARY.HIGHSES", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in reading, very affluent socioeconomic background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.PRIMARY.LANGTEST", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in reading, spoke the language of the test at home, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.PRIMARY.LOWSES", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in reading, very poor socioeconomic background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.PRIMARY.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in reading, adjusted location parity index (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.PRIMARY.LTPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in reading, adjusted speaks language of the test parity index (LTPIA)" }, { "id": "Longdefinition", "value": "The adjusted speaks language of the test parity index (LTPIA) is calculated by dividing the value for the indicator for students who do not speak the language of the test at home by the value for the indicator for students who speak the language of the test at home. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted speaks language of the test parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LTPI equal to 1 indicates parity between students who do and do not speak the language of the test at home. In general, a value less than 1 indicates disparity in favor of students who speak the language of test at home. A value greater than 1 indicates disparity in favor of students who do not speak the language of the test at home. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The adjusted speaks language of the test parity index (LTPIA) is calculated by dividing the value for the indicator for students who do not speak the language of the test at home by the value for the indicator for students who speak the language of the test at home. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted speaks language of the test parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LTPI equal to 1 indicates parity between students who do and do not speak the language of the test at home. In general, a value less than 1 indicates disparity in favor of students who speak the language of test at home. A value greater than 1 indicates disparity in favor of students who do not speak the language of the test at home. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.PRIMARY.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in reading, male (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.PRIMARY.NATIVE", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in reading, non-immigrant background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.PRIMARY.NONLANGTEST", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in reading, did not speak the language of the test at home, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.PRIMARY.NONNATIVE", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in reading, immigrant background, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.PRIMARY.NPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in reading, adjusted native parity index (NPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.PRIMARY.RURAL", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in reading, rural areas, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.PRIMARY.URBAN", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in reading, urban areas, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of children at the end of primary education reaching at least a minimum proficiency level in reading. A minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. The indicator is calculated as the number of children and/or young people at the relevant stage of education n in a given year t achieving or exceeding the pre-defined proficiency level in a given subject s, expressed as a percentage of the total number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s. The higher the value of the indicator, the higher the proportion of children or young adults who have acquired the minimum level of meaningful competencies. Data are calculated by the UNESCO Institute for Statistics from sources that include the Programme for International Student Assessment (PISA), Programme for International Student Assessment for Development (PISA-D), Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), El Laboratorio Latino americano de Evaluación de la Calidad de la Educación (LLECE), Programme d’analyse des systèmes éducatifs de la confemen (PASEC), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ), Pacific Islands Literacy and Numeracy Assessment (PILNA), national assessments data collected through the Catalogue of Learning Assessments (CLA) and/or available in national reports, and population-based assessments (Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA), UNICEF Multiple Indicator Cluster Surveys (MICS), People’s Action for Learning (PAL) NETWORK: e.g. Annual Status of Education Report (ASER), UWEZO, etc.)). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.READ.PRIMARY.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of students at the end of primary education achieving at least a minimum proficiency level in reading, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in primary education (all grades), both sexes (%)" }, { "id": "Longdefinition", "value": "Number of repeaters in primary education in a given school year, expressed as a percentage of enrolment in primary education in the previous school year. Divide the number of repeaters in primary education in school year t+1 by the number of pupils from the same cohort enrolled in primary education in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.F", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in primary education (all grades), female (%)" }, { "id": "Longdefinition", "value": "Number of female repeaters in primary education in a given school year, expressed as a percentage of female enrolment in primary education in the previous school year. Divide the number of female repeaters in primary education in school year t+1 by the number of female pupils from the same cohort enrolled in primary education in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G1", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 1 of primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of repeaters in a given grade in a given school year, expressed as a percentage of enrolment in that grade the previous school year. Divide the number of repeaters in a given grade in school year t+1 by the number of pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G1.F", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 1 of primary education, female (%)" }, { "id": "Longdefinition", "value": "Number of female repeaters in a given grade in a given school year, expressed as a percentage of female enrolment in that grade the previous school year. Divide the number of female repeaters in a given grade in school year t+1 by the number of female pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G1.M", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 1 of primary education, male (%)" }, { "id": "Longdefinition", "value": "Number of male repeaters in a given grade in a given school year, expressed as a percentage of male enrolment in that grade the previous school year. Divide the number of male repeaters in a given grade in school year t+1 by the number of male pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G2", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 2 of primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of repeaters in a given grade in a given school year, expressed as a percentage of enrolment in that grade the previous school year. Divide the number of repeaters in a given grade in school year t+1 by the number of pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G2.F", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 2 of primary education, female (%)" }, { "id": "Longdefinition", "value": "Number of female repeaters in a given grade in a given school year, expressed as a percentage of female enrolment in that grade the previous school year. Divide the number of female repeaters in a given grade in school year t+1 by the number of female pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G2.M", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 2 of primary education, male (%)" }, { "id": "Longdefinition", "value": "Number of male repeaters in a given grade in a given school year, expressed as a percentage of male enrolment in that grade the previous school year. Divide the number of male repeaters in a given grade in school year t+1 by the number of male pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G3", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 3 of primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of repeaters in a given grade in a given school year, expressed as a percentage of enrolment in that grade the previous school year. Divide the number of repeaters in a given grade in school year t+1 by the number of pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G3.F", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 3 of primary education, female (%)" }, { "id": "Longdefinition", "value": "Number of female repeaters in a given grade in a given school year, expressed as a percentage of female enrolment in that grade the previous school year. Divide the number of female repeaters in a given grade in school year t+1 by the number of female pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G3.M", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 3 of primary education, male (%)" }, { "id": "Longdefinition", "value": "Number of male repeaters in a given grade in a given school year, expressed as a percentage of male enrolment in that grade the previous school year. Divide the number of male repeaters in a given grade in school year t+1 by the number of male pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G4", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 4 of primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of repeaters in a given grade in a given school year, expressed as a percentage of enrolment in that grade the previous school year. Divide the number of repeaters in a given grade in school year t+1 by the number of pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G4.F", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 4 of primary education, female (%)" }, { "id": "Longdefinition", "value": "Number of female repeaters in a given grade in a given school year, expressed as a percentage of female enrolment in that grade the previous school year. Divide the number of female repeaters in a given grade in school year t+1 by the number of female pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G4.M", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 4 of primary education, male (%)" }, { "id": "Longdefinition", "value": "Number of male repeaters in a given grade in a given school year, expressed as a percentage of male enrolment in that grade the previous school year. Divide the number of male repeaters in a given grade in school year t+1 by the number of male pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G5", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 5 of primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of repeaters in a given grade in a given school year, expressed as a percentage of enrolment in that grade the previous school year. Divide the number of repeaters in a given grade in school year t+1 by the number of pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G5.F", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 5 of primary education, female (%)" }, { "id": "Longdefinition", "value": "Number of female repeaters in a given grade in a given school year, expressed as a percentage of female enrolment in that grade the previous school year. Divide the number of female repeaters in a given grade in school year t+1 by the number of female pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G5.M", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 5 of primary education, male (%)" }, { "id": "Longdefinition", "value": "Number of male repeaters in a given grade in a given school year, expressed as a percentage of male enrolment in that grade the previous school year. Divide the number of male repeaters in a given grade in school year t+1 by the number of male pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G6", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 6 of primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of repeaters in a given grade in a given school year, expressed as a percentage of enrolment in that grade the previous school year. Divide the number of repeaters in a given grade in school year t+1 by the number of pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G6.F", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 6 of primary education, female (%)" }, { "id": "Longdefinition", "value": "Number of female repeaters in a given grade in a given school year, expressed as a percentage of female enrolment in that grade the previous school year. Divide the number of female repeaters in a given grade in school year t+1 by the number of female pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G6.M", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 6 of primary education, male (%)" }, { "id": "Longdefinition", "value": "Number of male repeaters in a given grade in a given school year, expressed as a percentage of male enrolment in that grade the previous school year. Divide the number of male repeaters in a given grade in school year t+1 by the number of male pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G7", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 7 of primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of repeaters in a given grade in a given school year, expressed as a percentage of enrolment in that grade the previous school year. Divide the number of repeaters in a given grade in school year t+1 by the number of pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G7.F", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 7 of primary education, female (%)" }, { "id": "Longdefinition", "value": "Number of female repeaters in a given grade in a given school year, expressed as a percentage of female enrolment in that grade the previous school year. Divide the number of female repeaters in a given grade in school year t+1 by the number of female pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.G7.M", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 7 of primary education, male (%)" }, { "id": "Longdefinition", "value": "Number of male repeaters in a given grade in a given school year, expressed as a percentage of male enrolment in that grade the previous school year. Divide the number of male repeaters in a given grade in school year t+1 by the number of male pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.1.M", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in primary education (all grades), male (%)" }, { "id": "Longdefinition", "value": "Number of male repeaters in primary education in a given school year, expressed as a percentage of male enrolment in primary education in the previous school year. Divide the number of male repeaters in primary education in school year t+1 by the number of male pupils from the same cohort enrolled in primary education in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in lower secondary general education (all grades), both sexes (%)" }, { "id": "Longdefinition", "value": "Number of repeaters in lower secondary education in a given school year, expressed as a percentage of enrolment in lower secondary education in the previous school year. Divide the number of repeaters in lower secondary education in school year t+1 by the number of pupils from the same cohort enrolled in lower secondary education in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.F", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in lower secondary general education (all grades), female (%)" }, { "id": "Longdefinition", "value": "Number of female repeaters in lower secondary education in a given school year, expressed as a percentage of female enrolment in lower secondary education in the previous school year. Divide the number of female repeaters in lower secondary education in school year t+1 by the number of female pupils from the same cohort enrolled in lower secondary education in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.G1", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 1 of lower secondary general education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of repeaters in a given grade in a given school year, expressed as a percentage of enrolment in that grade the previous school year. Divide the number of repeaters in a given grade in school year t+1 by the number of pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.G1.F", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 1 of lower secondary general education, female (%)" }, { "id": "Longdefinition", "value": "Number of female repeaters in a given grade in a given school year, expressed as a percentage of female enrolment in that grade the previous school year. Divide the number of female repeaters in a given grade in school year t+1 by the number of female pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.G1.M", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 1 of lower secondary general education, male (%)" }, { "id": "Longdefinition", "value": "Number of male repeaters in a given grade in a given school year, expressed as a percentage of male enrolment in that grade the previous school year. Divide the number of male repeaters in a given grade in school year t+1 by the number of male pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.G2", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 2 of lower secondary general education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of repeaters in a given grade in a given school year, expressed as a percentage of enrolment in that grade the previous school year. Divide the number of repeaters in a given grade in school year t+1 by the number of pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.G2.F", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 2 of lower secondary general education, female (%)" }, { "id": "Longdefinition", "value": "Number of female repeaters in a given grade in a given school year, expressed as a percentage of female enrolment in that grade the previous school year. Divide the number of female repeaters in a given grade in school year t+1 by the number of female pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.G2.M", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 2 of lower secondary general education, male (%)" }, { "id": "Longdefinition", "value": "Number of male repeaters in a given grade in a given school year, expressed as a percentage of male enrolment in that grade the previous school year. Divide the number of male repeaters in a given grade in school year t+1 by the number of male pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.G3", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 3 of lower secondary general education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of repeaters in a given grade in a given school year, expressed as a percentage of enrolment in that grade the previous school year. Divide the number of repeaters in a given grade in school year t+1 by the number of pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.G3.F", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 3 of lower secondary general education, female (%)" }, { "id": "Longdefinition", "value": "Number of female repeaters in a given grade in a given school year, expressed as a percentage of female enrolment in that grade the previous school year. Divide the number of female repeaters in a given grade in school year t+1 by the number of female pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.G3.M", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 3 of lower secondary general education, male (%)" }, { "id": "Longdefinition", "value": "Number of male repeaters in a given grade in a given school year, expressed as a percentage of male enrolment in that grade the previous school year. Divide the number of male repeaters in a given grade in school year t+1 by the number of male pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.G4", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 4 of lower secondary general education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of repeaters in a given grade in a given school year, expressed as a percentage of enrolment in that grade the previous school year. Divide the number of repeaters in a given grade in school year t+1 by the number of pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.G4.F", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 4 of lower secondary general education, female (%)" }, { "id": "Longdefinition", "value": "Number of female repeaters in a given grade in a given school year, expressed as a percentage of female enrolment in that grade the previous school year. Divide the number of female repeaters in a given grade in school year t+1 by the number of female pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.G4.M", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 4 of lower secondary general education, male (%)" }, { "id": "Longdefinition", "value": "Number of male repeaters in a given grade in a given school year, expressed as a percentage of male enrolment in that grade the previous school year. Divide the number of male repeaters in a given grade in school year t+1 by the number of male pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.G5", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 5 of lower secondary general education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of repeaters in a given grade in a given school year, expressed as a percentage of enrolment in that grade the previous school year. Divide the number of repeaters in a given grade in school year t+1 by the number of pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.G5.F", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 5 of lower secondary general education, female (%)" }, { "id": "Longdefinition", "value": "Number of female repeaters in a given grade in a given school year, expressed as a percentage of female enrolment in that grade the previous school year. Divide the number of female repeaters in a given grade in school year t+1 by the number of female pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.G5.M", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in Grade 5 of lower secondary general education, male (%)" }, { "id": "Longdefinition", "value": "Number of male repeaters in a given grade in a given school year, expressed as a percentage of male enrolment in that grade the previous school year. Divide the number of male repeaters in a given grade in school year t+1 by the number of male pupils from the same cohort enrolled in the same grade in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.REPR.2.GPV.M", "metatype": [ { "id": "IndicatorName", "value": "Repetition rate in lower secondary general education (all grades), male (%)" }, { "id": "Longdefinition", "value": "Number of male repeaters in lower secondary education in a given school year, expressed as a percentage of male enrolment in lower secondary education in the previous school year. Divide the number of male repeaters in lower secondary education in school year t+1 by the number of male pupils from the same cohort enrolled in lower secondary education in the previous school year t." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.1", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, both sexes (%)" }, { "id": "Longdefinition", "value": "The number of students of the official age for primary education is subtracted from the total population of the same age. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of students of the official age for primary education is subtracted from the total population of the same age. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.1.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, female (%)" }, { "id": "Longdefinition", "value": "The number of female students of the official age for primary education is subtracted from the total female population of the same age. The result is expressed as a percentage of the female population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of female students of the official age for primary education is subtracted from the total female population of the same age. The result is expressed as a percentage of the female population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.1.GPIA.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.1.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, male (%)" }, { "id": "Longdefinition", "value": "The number of male students of the official age for primary education is subtracted from the total male population of the same age. The result is expressed as a percentage of the male population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The number of male students of the official age for primary education is subtracted from the total male population of the same age. The result is expressed as a percentage of the male population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.1T2.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children and adolescents of primary and lower secondary school age, both sexes (%)" }, { "id": "Longdefinition", "value": "Proportion of children and adolescents in the official age range for primary and lower secondary education who are not enrolled in primary or lower secondary education. To calculate the indicator, the number of students of the official age for primary and lower secondary education enrolled in primary or lower secondary education is subtracted from the total population of primary and lower secondary education. The result is expressed as a percentage of the population of the official age for primary and lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of children and adolescents in the official age range for primary and lower secondary education who are not enrolled in primary or lower secondary education. To calculate the indicator, the number of students of the official age for primary and lower secondary education enrolled in primary or lower secondary education is subtracted from the total population of primary and lower secondary education. The result is expressed as a percentage of the population of the official age for primary and lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.1T2.F.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children and adolescents of primary and lower secondary school age, female (%)" }, { "id": "Longdefinition", "value": "Proportion of children and adolescents in the official age range for primary and lower secondary education who are not enrolled in primary or lower secondary education. To calculate the indicator, the number of students of the official age for primary and lower secondary education enrolled in primary or lower secondary education is subtracted from the total population of primary and lower secondary education. The result is expressed as a percentage of the population of the official age for primary and lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of children and adolescents in the official age range for primary and lower secondary education who are not enrolled in primary or lower secondary education. To calculate the indicator, the number of students of the official age for primary and lower secondary education enrolled in primary or lower secondary education is subtracted from the total population of primary and lower secondary education. The result is expressed as a percentage of the population of the official age for primary and lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.1T2.GPIA.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children and adolescents of primary and lower secondary school age, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.1T2.M.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children and adolescents of primary and lower secondary school age, male (%)" }, { "id": "Longdefinition", "value": "Proportion of children and adolescents in the official age range for primary and lower secondary education who are not enrolled in primary or lower secondary education. To calculate the indicator, the number of students of the official age for primary and lower secondary education enrolled in primary or lower secondary education is subtracted from the total population of primary and lower secondary education. The result is expressed as a percentage of the population of the official age for primary and lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of children and adolescents in the official age range for primary and lower secondary education who are not enrolled in primary or lower secondary education. To calculate the indicator, the number of students of the official age for primary and lower secondary education enrolled in primary or lower secondary education is subtracted from the total population of primary and lower secondary education. The result is expressed as a percentage of the population of the official age for primary and lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.1T3.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children, adolescents and youth of primary, lower secondary and upper secondary school age, both sexes (%)" }, { "id": "Longdefinition", "value": "Proportion of children, adolescents, and youth in the official age range for primary, lower secondary, and upper secondary education who are not enrolled in primary, lower secondary, or upper secondary education. To calculate the indicator, the number of students of the official age for primary, lower secondary, and upper secondary education enrolled in primary, lower secondary, and upper secondary education is subtracted from the total population of primary, lower secondary, and upper secondary education. The result is expressed as a percentage of the population of the official age for primary, lower secondary, and upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of children, adolescents, and youth in the official age range for primary, lower secondary, and upper secondary education who are not enrolled in primary, lower secondary, or upper secondary education. To calculate the indicator, the number of students of the official age for primary, lower secondary, and upper secondary education enrolled in primary, lower secondary, and upper secondary education is subtracted from the total population of primary, lower secondary, and upper secondary education. The result is expressed as a percentage of the population of the official age for primary, lower secondary, and upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.1T3.F.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children, adolescents and youth of primary, lower secondary and upper secondary school age, female (%)" }, { "id": "Longdefinition", "value": "Proportion of children, adolescents, and youth in the official age range for primary, lower secondary, and upper secondary education who are not enrolled in primary, lower secondary, or upper secondary education. To calculate the indicator, the number of students of the official age for primary, lower secondary, and upper secondary education enrolled in primary, lower secondary, and upper secondary education is subtracted from the total population of primary, lower secondary, and upper secondary education. The result is expressed as a percentage of the population of the official age for primary, lower secondary, and upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of children, adolescents, and youth in the official age range for primary, lower secondary, and upper secondary education who are not enrolled in primary, lower secondary, or upper secondary education. To calculate the indicator, the number of students of the official age for primary, lower secondary, and upper secondary education enrolled in primary, lower secondary, and upper secondary education is subtracted from the total population of primary, lower secondary, and upper secondary education. The result is expressed as a percentage of the population of the official age for primary, lower secondary, and upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.1T3.GPIA.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children, adolescents and youth of primary, lower secondary and upper secondary school age, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.1T3.M.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children, adolescents and youth of primary, lower secondary and upper secondary school age, male (%)" }, { "id": "Longdefinition", "value": "Proportion of children, adolescents, and youth in the official age range for primary, lower secondary, and upper secondary education who are not enrolled in primary, lower secondary, or upper secondary education. To calculate the indicator, the number of students of the official age for primary, lower secondary, and upper secondary education enrolled in primary, lower secondary, and upper secondary education is subtracted from the total population of primary, lower secondary, and upper secondary education. The result is expressed as a percentage of the population of the official age for primary, lower secondary, and upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of children, adolescents, and youth in the official age range for primary, lower secondary, and upper secondary education who are not enrolled in primary, lower secondary, or upper secondary education. To calculate the indicator, the number of students of the official age for primary, lower secondary, and upper secondary education enrolled in primary, lower secondary, and upper secondary education is subtracted from the total population of primary, lower secondary, and upper secondary education. The result is expressed as a percentage of the population of the official age for primary, lower secondary, and upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.2", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of adolescents of official lower secondary school age who are not enrolled in lower secondary school expressed as a percentage of the population of official lower secondary school age." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of adolescents of official lower secondary school age who are not enrolled in lower secondary school expressed as a percentage of the population of official lower secondary school age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.2.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, female (%)" }, { "id": "Longdefinition", "value": "Number of females of official lower secondary school age who are not enrolled in lower secondary school expressed as a percentage of the female population of official lower secondary school age." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of females of official lower secondary school age who are not enrolled in lower secondary school expressed as a percentage of the female population of official lower secondary school age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.2.GPIA.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.2.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, male (%)" }, { "id": "Longdefinition", "value": "Number of males of official lower secondary school age who are not enrolled in lower secondary school expressed as a percentage of the male population of official lower secondary school age." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of males of official lower secondary school age who are not enrolled in lower secondary school expressed as a percentage of the male population of official lower secondary school age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.2T3.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents and youth of lower and upper secondary school age, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of adolescents and youth of lower secondary and upper secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of adolescents and youth of lower secondary and upper secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.2T3.F.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents and youth of lower and upper secondary school age, female (%)" }, { "id": "Longdefinition", "value": "Number of adolescents and youth of lower secondary and upper secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of adolescents and youth of lower secondary and upper secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.2T3.GPIA.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents and youth of lower and upper secondary school age, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.2T3.M.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents and youth of lower and upper secondary school age, male (%)" }, { "id": "Longdefinition", "value": "Number of adolescents and youth of lower secondary and upper secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of adolescents and youth of lower secondary and upper secondary school age who are not enrolled or attending school during in a given academic year. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.3.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of youth of official upper secondary school age who are not enrolled in upper secondary school expressed as a percentage of the population of official upper secondary school age." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of youth of official upper secondary school age who are not enrolled in upper secondary school expressed as a percentage of the population of official upper secondary school age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.3.F.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, female (%)" }, { "id": "Longdefinition", "value": "Number of females of official upper secondary school age who are not enrolled in upper secondary school expressed as a percentage of the female population of official upper secondary school age." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of females of official upper secondary school age who are not enrolled in upper secondary school expressed as a percentage of the female population of official upper secondary school age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.3.GPIA.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.3.M.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, male (%)" }, { "id": "Longdefinition", "value": "Number of males of official upper secondary school age who are not enrolled in upper secondary school expressed as a percentage of the male population of official upper secondary school age." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of males of official upper secondary school age who are not enrolled in upper secondary school expressed as a percentage of the male population of official upper secondary school age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.AGM1.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children one year younger than official primary entrance age, both sexes (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for one year before primary education who are not enrolled in pre-primary education. To calculate the indicator, the number of students of the official age for one year before primary education enrolled in pre-primary education is subtracted from the total population for one year before primary education. The result is expressed as a percentage of the population of the official age for the year before primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for one year before primary education who are not enrolled in pre-primary education. To calculate the indicator, the number of students of the official age for one year before primary education enrolled in pre-primary education is subtracted from the total population for one year before primary education. The result is expressed as a percentage of the population of the official age for the year before primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.ROFST.AGM1.F.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children one year younger than official primary entrance age, female (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for one year before primary education who are not enrolled in pre-primary education. To calculate the indicator, the number of students of the official age for one year before primary education enrolled in pre-primary education is subtracted from the total population for one year before primary education. The result is expressed as a percentage of the population of the official age for the year before primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for one year before primary education who are not enrolled in pre-primary education. To calculate the indicator, the number of students of the official age for one year before primary education enrolled in pre-primary education is subtracted from the total population for one year before primary education. The result is expressed as a percentage of the population of the official age for the year before primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.ROFST.AGM1.GPIA.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children one year younger than official age, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.ROFST.AGM1.M.CP", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children one year younger than official primary entrance age, male (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for one year before primary education who are not enrolled in pre-primary education. To calculate the indicator, the number of students of the official age for one year before primary education enrolled in pre-primary education is subtracted from the total population for one year before primary education. The result is expressed as a percentage of the population of the official age for the year before primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for one year before primary education who are not enrolled in pre-primary education. To calculate the indicator, the number of students of the official age for one year before primary education enrolled in pre-primary education is subtracted from the total population for one year before primary education. The result is expressed as a percentage of the population of the official age for the year before primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, female, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, male, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q1", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, poorest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, poorest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q1.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, poorest quintile, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, poorest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q1.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, poorest quintile, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, poorest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q1.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, poorest quintile, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q2", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, second quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, second quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q2.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, second quintile, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, second quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q2.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, second quintile, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, second quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q2.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, second quintile, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q3", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, middle quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, middle quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q3.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, middle quintile, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, middle quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q3.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, middle quintile, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, middle quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q3.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, middle quintile, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q4", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, fourth quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, fourth quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q4.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, fourth quintile, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, fourth quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q4.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, fourth quintile, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, fourth quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q4.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, fourth quintile, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q5", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, richest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, richest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q5.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, richest quintile, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, richest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q5.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, richest quintile, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, richest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.Q5.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, richest quintile, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, female, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, male, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q1", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, poorest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, poorest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, primary education, rural, poorest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, poorest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q2", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, second quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, second quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, primary education, rural, second quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, second quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q3", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, middle quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, middle quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, primary education, rural, middle quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, middle quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q4", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, fourth quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, fourth quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, primary education, rural, fourth quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, fourth quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q5", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, richest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, richest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, primary education, rural, richest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, richest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.RUR.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, rural, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, female, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Number of children of official primary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official primary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, male, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q1", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, poorest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, poorest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, primary education, urban, poorest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, poorest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q2", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, second quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, second quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, primary education, urban, second quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, second quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q3", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, middle quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, middle quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, primary education, urban, middle quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, middle quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q4", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, fourth quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, fourth quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, primary education, urban, fourth quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, fourth quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q5", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, richest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, richest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, primary education, urban, richest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, richest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Proportion of children in the official age range for primary education who are not enrolled in primary education. To calculate the indicator, the number of students of the official age for primary education enrolled in primary education is subtracted from the total population of primary education. The result is expressed as a percentage of the population of the official age for primary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.URB.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, urban, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.1.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for children of primary school age, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, female, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, male, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q1", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, poorest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, poorest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q1.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, poorest quintile, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, poorest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q1.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, poorest quintile, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, poorest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q1.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, poorest quintile, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q2", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, second quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, second quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q2.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, second quintile, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, second quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q2.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, second quintile, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, second quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q2.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, second quintile, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q3", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, middle quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, middle quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q3.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, middle quintile, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, middle quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q3.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, middle quintile, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, middle quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q3.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, middle quintile, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q4", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, fourth quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, fourth quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q4.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, fourth quintile, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, fourth quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q4.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, fourth quintile, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, fourth quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q4.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, fourth quintile, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q5", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, richest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, richest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q5.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, richest quintile, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, richest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q5.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, richest quintile, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, richest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.Q5.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, richest quintile, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, female, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, male, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q1", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, poorest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, poorest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, poorest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, poorest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q2", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, second quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, second quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, second quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, second quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q3", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, middle quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, middle quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, middle quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, middle quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q4", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, fourth quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, fourth quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, fourth quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, fourth quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q5", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, richest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, richest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, richest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, richest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.RUR.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, rural, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, female, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of children of official lower secondary school age who did not attend primary or secondary school at any time during the reference academic year, expressed as a percentage of the number of official lower secondary school age children in the household survey sample. Children attending pre-primary or non-formal education are considered out of school. The UNESCO Institute for Statistics (UIS) releases estimates of out-of-school children calculated from both administrative and household survey sources (Demographic and Health Surveys and Multiple Indicator Cluster Surveys). Administrative records and household surveys are two data sources which differ in fundamental ways: who collects the data, as well as how, when and for what purpose. As a result, the out-of-school children estimates calculated from one data source may not match those based on other data sources. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, male, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q1", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, poorest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, poorest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, poorest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, poorest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q2", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, second quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, second quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, second quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, second quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q3", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, middle quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, middle quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, middle quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, middle quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q4", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, fourth quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, fourth quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, fourth quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, fourth quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q5", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, richest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, richest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, richest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, richest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of adolescents in the official age range for lower secondary education who are not enrolled in lower secondary education. To calculate the indicator, the number of students of the official age for lower secondary education enrolled in lower secondary education is subtracted from the total population of lower secondary education. The result is expressed as a percentage of the population of the official age for lower secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.URB.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, urban, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.2.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for adolescents of lower secondary school age, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, female, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, male, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q1", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, poorest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, poorest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q1.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, poorest quintile, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, poorest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q1.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, poorest quintile, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, poorest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q1.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, poorest quintile, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q2", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, second quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, second quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q2.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, second quintile, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, second quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q2.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, second quintile, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, second quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q2.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, second quintile, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q3", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, middle quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, middle quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q3.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, middle quintile, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, middle quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q3.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, middle quintile, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, middle quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q3.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, middle quintile, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q4", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, fourth quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, fourth quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q4.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, fourth quintile, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, fourth quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q4.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, fourth quintile, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, fourth quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q4.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, fourth quintile, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q5", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, richest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, richest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q5.F.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, richest quintile, female, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, richest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q5.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, richest quintile, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, richest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.Q5.M.LPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, richest quintile, male, adjusted location parity index (household survey data) (LPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Location Parity Index (LPIA) is calculated by dividing the rural value for the indicator by the urban value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted location parity index is symmetrical around 1 and lies in the range 0-2. An adjusted LPI equal to 1 indicates parity between rural and urban locations. In general, a value less than 1 indicates disparity in favor of urban locations and a value greater than 1 indicates disparity in favor of rural locations. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, female, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, male, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q1", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, poorest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, poorest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, poorest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, poorest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q2", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, second quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, second quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, second quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, second quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q3", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, middle quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, middle quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, middle quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, middle quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q4", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, fourth quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, fourth quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, fourth quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, fourth quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q5", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, richest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, richest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, richest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, richest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.RUR.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, rural, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.F.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, female, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.M.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, male, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q1", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, poorest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q1.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, poorest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, poorest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q1.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, poorest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q2", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, second quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q2.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, second quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, second quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q2.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, second quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q3", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, middle quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q3.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, middle quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, middle quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q3.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, middle quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q4", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, fourth quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q4.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, fourth quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q4.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, fourth quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q4.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, fourth quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q5", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, richest quintile, both sexes (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q5.F", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, richest quintile, female (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q5.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, richest quintile, adjusted gender parity index (household survey data) (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.Q5.M", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, richest quintile, male (household survey data) (%)" }, { "id": "Longdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Proportion of youth in the official age range for upper secondary education who are not enrolled in upper secondary education. To calculate the indicator, the number of students of the official age for upper secondary education enrolled in upper secondary education is subtracted from the total population of upper secondary education. The result is expressed as a percentage of the population of the official age for upper secondary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.URB.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, urban, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.ROFST.H.3.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Out-of-school rate for youth of upper secondary school age, adjusted wealth parity index (household survey data) (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SAP.0", "metatype": [ { "id": "IndicatorName", "value": "School age population, early childhood education, both sexes (number)" }, { "id": "Longdefinition", "value": "Population of the age-group theoretically corresponding to early childhood education (ISCED 0) as indicated by theoretical entrance age and duration. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years, and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Population" }, { "id": "Shortdefinition", "value": "Population of the age-group theoretically corresponding to early childhood education (ISCED 0) as indicated by theoretical entrance age and duration. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years, and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.0.F", "metatype": [ { "id": "IndicatorName", "value": "School age population, early childhood education, female (number)" }, { "id": "Longdefinition", "value": "Female population of the age-group theoretically corresponding to early childhood education (ISCED 0) as indicated by theoretical entrance age and duration. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years, and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Population" }, { "id": "Shortdefinition", "value": "Female population of the age-group theoretically corresponding to early childhood education (ISCED 0) as indicated by theoretical entrance age and duration. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years, and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.0.M", "metatype": [ { "id": "IndicatorName", "value": "School age population, early childhood education, male (number)" }, { "id": "Longdefinition", "value": "Male population of the age-group theoretically corresponding to early childhood education (ISCED 0) as indicated by theoretical entrance age and duration. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years, and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Population" }, { "id": "Shortdefinition", "value": "Male population of the age-group theoretically corresponding to early childhood education (ISCED 0) as indicated by theoretical entrance age and duration. Within ISCED 0, early childhood educational development programmes are targeted at children aged 0 to 2 years, and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.01", "metatype": [ { "id": "IndicatorName", "value": "School age population, early childhood educational development programmes, both sexes (number)" }, { "id": "Longdefinition", "value": "Population of the age-group theoretically corresponding to early childhood educational development as indicated by theoretical entrance age and duration. Early childhood educational development programmes are targeted at children aged 0 to 2 years, and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Population" }, { "id": "Shortdefinition", "value": "Population of the age-group theoretically corresponding to early childhood educational development as indicated by theoretical entrance age and duration. Early childhood educational development programmes are targeted at children aged 0 to 2 years, and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.01.F", "metatype": [ { "id": "IndicatorName", "value": "School age population, early childhood educational development programmes, female (number)" }, { "id": "Longdefinition", "value": "Female population of the age-group theoretically corresponding to early childhood educational development education as indicated by theoretical entrance age and duration. Early childhood educational development programmes are targeted at children aged 0 to 2 years, and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Population" }, { "id": "Shortdefinition", "value": "Female population of the age-group theoretically corresponding to early childhood educational development education as indicated by theoretical entrance age and duration. Early childhood educational development programmes are targeted at children aged 0 to 2 years, and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.01.M", "metatype": [ { "id": "IndicatorName", "value": "School age population, early childhood educational development programmes, male (number)" }, { "id": "Longdefinition", "value": "Male population of the age-group theoretically corresponding to early childhood educational development education as indicated by theoretical entrance age and duration. Early childhood educational development programmes are targeted at children aged 0 to 2 years, and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Population" }, { "id": "Shortdefinition", "value": "Male population of the age-group theoretically corresponding to early childhood educational development education as indicated by theoretical entrance age and duration. Early childhood educational development programmes are targeted at children aged 0 to 2 years, and pre-primary education programmes are targeted at children aged 3 years until the age to start ISCED 1. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.1.AGM1", "metatype": [ { "id": "IndicatorName", "value": "School age population, one year before than official primary entry age, both sexes (number)" }, { "id": "Longdefinition", "value": "Population of the age-group theoretically corresponding to one year before the official primary entry age as indicated by theoretical entrance age and duration. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Population" }, { "id": "Shortdefinition", "value": "Population of the age-group theoretically corresponding to one year before the official primary entry age as indicated by theoretical entrance age and duration. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.1.AGM1.F", "metatype": [ { "id": "IndicatorName", "value": "School age population, one year before than official primary entry age, female (number)" }, { "id": "Longdefinition", "value": "Female population of the age-group theoretically corresponding to one year before the official primary entry age as indicated by theoretical entrance age and duration. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Population" }, { "id": "Shortdefinition", "value": "Female population of the age-group theoretically corresponding to one year before the official primary entry age as indicated by theoretical entrance age and duration. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.1.AGM1.M", "metatype": [ { "id": "IndicatorName", "value": "School age population, one year before than official primary entry age, male (number)" }, { "id": "Longdefinition", "value": "Male population of the age-group theoretically corresponding to one year before the official primary entry age as indicated by theoretical entrance age and duration. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Population" }, { "id": "Shortdefinition", "value": "Male population of the age-group theoretically corresponding to one year before the official primary entry age as indicated by theoretical entrance age and duration. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.1.G1", "metatype": [ { "id": "IndicatorName", "value": "Population of the official entrance age to primary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Population of the age-group theoretically corresponding to the official entrance age to primary education. The official entrance age is the age at which students would enter a given programme or level of education assuming they start at the official entrance age for the lowest level of education, study full-time throughout and progressed through the system without repeating or skipping a grade. The theoretical entrance age to a given programme or level is typically, but not always, the most common entrance age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.1.G1.F", "metatype": [ { "id": "IndicatorName", "value": "Population of the official entrance age to primary education, female (number)" }, { "id": "Longdefinition", "value": "Female population of the age-group theoretically corresponding to the official entrance age to primary education. The official entrance age is the age at which students would enter a given programme or level of education assuming they start at the official entrance age for the lowest level of education, study full-time throughout and progressed through the system without repeating or skipping a grade. The theoretical entrance age to a given programme or level is typically, but not always, the most common entrance age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.1.G1.M", "metatype": [ { "id": "IndicatorName", "value": "Population of the official entrance age to primary education, male (number)" }, { "id": "Longdefinition", "value": "Male population of the age-group theoretically corresponding to the official entrance age to primary education. The official entrance age is the age at which students would enter a given programme or level of education assuming they start at the official entrance age for the lowest level of education, study full-time throughout and progressed through the system without repeating or skipping a grade. The theoretical entrance age to a given programme or level is typically, but not always, the most common entrance age." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.23.GPV.G1", "metatype": [ { "id": "IndicatorName", "value": "Population of the official entrance age to secondary general education, both sexes (number)" }, { "id": "Longdefinition", "value": "Population of the age-group theoretically corresponding to secondary general education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.23.GPV.G1.F", "metatype": [ { "id": "IndicatorName", "value": "Population of the official entrance age to secondary general education, female (number)" }, { "id": "Longdefinition", "value": "Female population of the age-group theoretically corresponding to secondary general education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.23.GPV.G1.M", "metatype": [ { "id": "IndicatorName", "value": "Population of the official entrance age to secondary general education, male (number)" }, { "id": "Longdefinition", "value": "Male population of the age-group theoretically corresponding to secondary general education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.4", "metatype": [ { "id": "IndicatorName", "value": "School age population, post-secondary non-tertiary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Population of the age-group theoretically corresponding to post-secondary non-tertiary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Population of the age-group theoretically corresponding to post-secondary non-tertiary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.4.F", "metatype": [ { "id": "IndicatorName", "value": "School age population, post-secondary non-tertiary education, female (number)" }, { "id": "Longdefinition", "value": "Female population of the age-group theoretically corresponding to post-secondary non-tertiary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Female population of the age-group theoretically corresponding to post-secondary non-tertiary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.4.M", "metatype": [ { "id": "IndicatorName", "value": "School age population, post-secondary non-tertiary education, male (number)" }, { "id": "Longdefinition", "value": "Male population of the age-group theoretically corresponding to post-secondary non-tertiary education as indicated by theoretical entrance age and duration." }, { "id": "Shortdefinition", "value": "Male population of the age-group theoretically corresponding to post-secondary non-tertiary education as indicated by theoretical entrance age and duration." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.CE", "metatype": [ { "id": "IndicatorName", "value": "Population of compulsory school age, both sexes (number)" }, { "id": "Longdefinition", "value": "Population of children within the age span that children are legally obliged to attend school." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.CE.F", "metatype": [ { "id": "IndicatorName", "value": "Population of compulsory school age, female (number)" }, { "id": "Longdefinition", "value": "Population of female children within the age span that children are legally obliged to attend school." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SAP.CE.M", "metatype": [ { "id": "IndicatorName", "value": "Population of compulsory school age, male (number)" }, { "id": "Longdefinition", "value": "Population of male children within the age span that children are legally obliged to attend school." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Population" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.1.WCOMPUT", "metatype": [ { "id": "IndicatorName", "value": "Proportion of primary schools with access to computers for pedagogical purposes (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Computers for pedagogical use: Use of computers to support course delivery or independent teaching and learning needs. This may include activities using computers or the Internet to meet information needs for research purposes; develop presentations; perform hands-on exercises and experiments; share information; and participate in online discussion forums for educational purposes. A computer is a programmable electronic device that can store, retrieve and process data, as well as share information in a highly-structured manner. It performs high-speed mathematical or logical operations according to a set of instructions or algorithms. Computers include the following types: a desktop computer usually remains fixed in one place; normally the user is placed in front of it, behind the keyboard; a laptop computer is small enough to carry and usually enables the same tasks as a desktop computer; it includes notebooks and netbooks but does not include tablets and similar handheld devices; and a tablet (or similar handheld computer) is a computer that is integrated into a flat touch screen, operated by touching the screen rather than using a physical keyboard. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Computers for pedagogical use: Use of computers to support course delivery or independent teaching and learning needs. This may include activities using computers or the Internet to meet information needs for research purposes; develop presentations; perform hands-on exercises and experiments; share information; and participate in online discussion forums for educational purposes. A computer is a programmable electronic device that can store, retrieve and process data, as well as share information in a highly-structured manner. It performs high-speed mathematical or logical operations according to a set of instructions or algorithms. Computers include the following types: a desktop computer usually remains fixed in one place; normally the user is placed in front of it, behind the keyboard; a laptop computer is small enough to carry and usually enables the same tasks as a desktop computer; it includes notebooks and netbooks but does not include tablets and similar handheld devices; and a tablet (or similar handheld computer) is a computer that is integrated into a flat touch screen, operated by touching the screen rather than using a physical keyboard. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.1.WELEC", "metatype": [ { "id": "IndicatorName", "value": "Proportion of primary schools with access to electricity (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Electricity is defined as regularly and readily available sources of power (e.g. grid/mains connection, wind, water, solar and fuel-powered generator, etc.) that enable the adequate and sustainable use of ICT infrastructure for educational purposes. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Electricity is defined as regularly and readily available sources of power (e.g. grid/mains connection, wind, water, solar and fuel-powered generator, etc.) that enable the adequate and sustainable use of ICT infrastructure for educational purposes. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.1.WHIVSEXED", "metatype": [ { "id": "IndicatorName", "value": "Percentage of primary schools providing life skills-based HIV and sexuality education" }, { "id": "Longdefinition", "value": "Percentage of schools providing life skills-based HIV and sexuality education within the formal curriculum or as part of extra-curricular activities. The value is the number of schools at each level of education providing life skills-based HIV and sexuality education expressed as a percentage of all schools at the given level of education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of schools providing life skills-based HIV and sexuality education within the formal curriculum or as part of extra-curricular activities. The value is the number of schools at each level of education providing life skills-based HIV and sexuality education expressed as a percentage of all schools at the given level of education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.1.WINFSTUDIS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of primary schools with access to adapted infrastructure and materials for students with disabilities (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Adapted infrastructure is defined as any built environment related to education facilities that are accessible to all users, including those with different types of disability, to be able to gain access to use and exit from them. Accessibility includes ease of independent approach, entry, evacuation and/or use of a building and its services and facilities (such as water and sanitation), by all of the building's potential users with an assurance of individual health, safety and welfare during the course of those activities. Adapted materials include learning materials and assistive products that enable students and teachers with disabilities/functioning limitations to access learning and to participate fully in the school environment. Accessible learning materials include textbooks, instructional materials, assessments and other materials that are available and provided in appropriate formats such as audio, braille, sign language and simplified formats that can be used by students and teachers with disabilities/functioning limitations. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Adapted infrastructure is defined as any built environment related to education facilities that are accessible to all users, including those with different types of disability, to be able to gain access to use and exit from them. Accessibility includes ease of independent approach, entry, evacuation and/or use of a building and its services and facilities (such as water and sanitation), by all of the building's potential users with an assurance of individual health, safety and welfare during the course of those activities. Adapted materials include learning materials and assistive products that enable students and teachers with disabilities/functioning limitations to access learning and to participate fully in the school environment. Accessible learning materials include textbooks, instructional materials, assessments and other materials that are available and provided in appropriate formats such as audio, braille, sign language and simplified formats that can be used by students and teachers with disabilities/functioning limitations. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.1.WINTERN", "metatype": [ { "id": "IndicatorName", "value": "Proportion of primary schools with access to Internet for pedagogical purposes (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Internet for pedagogical purposes is defined as internet that is available for enhancing teaching and learning and is accessible by pupils. Internet is defined as a worldwide interconnected computer network, which provides pupils access to a number of communication services including the World Wide Web and carries e-mail, news, entertainment and data files, irrespective of the device used (i.e. not assumed to be only via a computer and thus can also be accessed by mobile telephone, tablet, PDA, games machine, digital TV etc.). Access can be via a fixed narrowband, fixed broadband, or via mobile network. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Internet for pedagogical purposes is defined as internet that is available for enhancing teaching and learning and is accessible by pupils. Internet is defined as a worldwide interconnected computer network, which provides pupils access to a number of communication services including the World Wide Web and carries e-mail, news, entertainment and data files, irrespective of the device used (i.e. not assumed to be only via a computer and thus can also be accessed by mobile telephone, tablet, PDA, games machine, digital TV etc.). Access can be via a fixed narrowband, fixed broadband, or via mobile network. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.1.WTOILA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of primary schools with single-sex basic sanitation facilities (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic sanitation facilities are defined as functional improved sanitation facilities separated for males and females on or near the premises. Improved sanitation facilities include a pit latrine with slab, a ventilated improved pit latrine, a flush toilet, a pour-flush toilet or a composting toilet. Unimproved facilities include a pit latrine without a slab, hanging toilets and bucket toilets. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic sanitation facilities are defined as functional improved sanitation facilities separated for males and females on or near the premises. Improved sanitation facilities include a pit latrine with slab, a ventilated improved pit latrine, a flush toilet, a pour-flush toilet or a composting toilet. Unimproved facilities include a pit latrine without a slab, hanging toilets and bucket toilets. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.1.WWASH", "metatype": [ { "id": "IndicatorName", "value": "Proportion of primary schools with basic handwashing facilities (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic handwashing facilities are defined as functional handwashing facilities, with soap and water available to all girls and boys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic handwashing facilities are defined as functional handwashing facilities, with soap and water available to all girls and boys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.1.WWATA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of primary schools with access to basic drinking water (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic drinking water is defined as a functional improved drinking water source on or near the premises and water points accessible to all users during school hours. An improved drinking water source is a water delivery point that by the nature of its design protects the water from external contamination, particularly of fecal origin. Examples of improved drinking water facilities include piped water, protected wells, tubewells and boreholes, protected springs and rainwater, purchased bottled water and tanker-trucks. Unimproved water sources include unprotected wells and springs and surface water (e.g. rivers, lakes). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Primary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic drinking water is defined as a functional improved drinking water source on or near the premises and water points accessible to all users during school hours. An improved drinking water source is a water delivery point that by the nature of its design protects the water from external contamination, particularly of fecal origin. Examples of improved drinking water facilities include piped water, protected wells, tubewells and boreholes, protected springs and rainwater, purchased bottled water and tanker-trucks. Unimproved water sources include unprotected wells and springs and surface water (e.g. rivers, lakes). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.2.WCOMPUT", "metatype": [ { "id": "IndicatorName", "value": "Proportion of lower secondary schools with access to computers for pedagogical purposes (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Computers for pedagogical use: Use of computers to support course delivery or independent teaching and learning needs. This may include activities using computers or the Internet to meet information needs for research purposes; develop presentations; perform hands-on exercises and experiments; share information; and participate in online discussion forums for educational purposes. A computer is a programmable electronic device that can store, retrieve and process data, as well as share information in a highly-structured manner. It performs high-speed mathematical or logical operations according to a set of instructions or algorithms. Computers include the following types: a desktop computer usually remains fixed in one place; normally the user is placed in front of it, behind the keyboard; a laptop computer is small enough to carry and usually enables the same tasks as a desktop computer; it includes notebooks and netbooks but does not include tablets and similar handheld devices; and a tablet (or similar handheld computer) is a computer that is integrated into a flat touch screen, operated by touching the screen rather than using a physical keyboard. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Computers for pedagogical use: Use of computers to support course delivery or independent teaching and learning needs. This may include activities using computers or the Internet to meet information needs for research purposes; develop presentations; perform hands-on exercises and experiments; share information; and participate in online discussion forums for educational purposes. A computer is a programmable electronic device that can store, retrieve and process data, as well as share information in a highly-structured manner. It performs high-speed mathematical or logical operations according to a set of instructions or algorithms. Computers include the following types: a desktop computer usually remains fixed in one place; normally the user is placed in front of it, behind the keyboard; a laptop computer is small enough to carry and usually enables the same tasks as a desktop computer; it includes notebooks and netbooks but does not include tablets and similar handheld devices; and a tablet (or similar handheld computer) is a computer that is integrated into a flat touch screen, operated by touching the screen rather than using a physical keyboard. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.2.WELEC", "metatype": [ { "id": "IndicatorName", "value": "Proportion of lower secondary schools with access to electricity (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Electricity is defined as regularly and readily available sources of power (e.g. grid/mains connection, wind, water, solar and fuel-powered generator, etc.) that enable the adequate and sustainable use of ICT infrastructure for educational purposes. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Electricity is defined as regularly and readily available sources of power (e.g. grid/mains connection, wind, water, solar and fuel-powered generator, etc.) that enable the adequate and sustainable use of ICT infrastructure for educational purposes. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.2.WHIVSEXED", "metatype": [ { "id": "IndicatorName", "value": "Percentage of lower secondary schools providing life skills-based HIV and sexuality education" }, { "id": "Longdefinition", "value": "Percentage of schools providing life skills-based HIV and sexuality education within the formal curriculum or as part of extra-curricular activities. The value is the number of schools at each level of education providing life skills-based HIV and sexuality education expressed as a percentage of all schools at the given level of education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools providing life skills-based HIV and sexuality education within the formal curriculum or as part of extra-curricular activities. The value is the number of schools at each level of education providing life skills-based HIV and sexuality education expressed as a percentage of all schools at the given level of education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.2.WINFSTUDIS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of lower secondary schools with access to adapted infrastructure and materials for students with disabilities (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Adapted infrastructure is defined as any built environment related to education facilities that are accessible to all users, including those with different types of disability, to be able to gain access to use and exit from them. Accessibility includes ease of independent approach, entry, evacuation and/or use of a building and its services and facilities (such as water and sanitation), by all of the building's potential users with an assurance of individual health, safety and welfare during the course of those activities. Adapted materials include learning materials and assistive products that enable students and teachers with disabilities/functioning limitations to access learning and to participate fully in the school environment. Accessible learning materials include textbooks, instructional materials, assessments and other materials that are available and provided in appropriate formats such as audio, braille, sign language and simplified formats that can be used by students and teachers with disabilities/functioning limitations. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Adapted infrastructure is defined as any built environment related to education facilities that are accessible to all users, including those with different types of disability, to be able to gain access to use and exit from them. Accessibility includes ease of independent approach, entry, evacuation and/or use of a building and its services and facilities (such as water and sanitation), by all of the building's potential users with an assurance of individual health, safety and welfare during the course of those activities. Adapted materials include learning materials and assistive products that enable students and teachers with disabilities/functioning limitations to access learning and to participate fully in the school environment. Accessible learning materials include textbooks, instructional materials, assessments and other materials that are available and provided in appropriate formats such as audio, braille, sign language and simplified formats that can be used by students and teachers with disabilities/functioning limitations. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.2.WINTERN", "metatype": [ { "id": "IndicatorName", "value": "Proportion of lower secondary schools with access to Internet for pedagogical purposes (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Internet for pedagogical purposes is defined as internet that is available for enhancing teaching and learning and is accessible by pupils. Internet is defined as a worldwide interconnected computer network, which provides pupils access to a number of communication services including the World Wide Web and carries e-mail, news, entertainment and data files, irrespective of the device used (i.e. not assumed to be only via a computer and thus can also be accessed by mobile telephone, tablet, PDA, games machine, digital TV etc.). Access can be via a fixed narrowband, fixed broadband, or via mobile network. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Internet for pedagogical purposes is defined as internet that is available for enhancing teaching and learning and is accessible by pupils. Internet is defined as a worldwide interconnected computer network, which provides pupils access to a number of communication services including the World Wide Web and carries e-mail, news, entertainment and data files, irrespective of the device used (i.e. not assumed to be only via a computer and thus can also be accessed by mobile telephone, tablet, PDA, games machine, digital TV etc.). Access can be via a fixed narrowband, fixed broadband, or via mobile network. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.2.WTOILA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of lower secondary schools with single-sex basic sanitation facilities (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic sanitation facilities are defined as functional improved sanitation facilities separated for males and females on or near the premises. Improved sanitation facilities include a pit latrine with slab, a ventilated improved pit latrine, a flush toilet, a pour-flush toilet or a composting toilet. Unimproved facilities include a pit latrine without a slab, hanging toilets and bucket toilets. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic sanitation facilities are defined as functional improved sanitation facilities separated for males and females on or near the premises. Improved sanitation facilities include a pit latrine with slab, a ventilated improved pit latrine, a flush toilet, a pour-flush toilet or a composting toilet. Unimproved facilities include a pit latrine without a slab, hanging toilets and bucket toilets. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.2.WWASH", "metatype": [ { "id": "IndicatorName", "value": "Proportion of lower secondary schools with basic handwashing facilities (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic handwashing facilities are defined as functional handwashing facilities, with soap and water available to all girls and boys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic handwashing facilities are defined as functional handwashing facilities, with soap and water available to all girls and boys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.2.WWATA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of lower secondary schools with access to basic drinking water (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic drinking water is defined as a functional improved drinking water source on or near the premises and water points accessible to all users during school hours. An improved drinking water source is a water delivery point that by the nature of its design protects the water from external contamination, particularly of fecal origin. Examples of improved drinking water facilities include piped water, protected wells, tubewells and boreholes, protected springs and rainwater, purchased bottled water and tanker-trucks. Unimproved water sources include unprotected wells and springs and surface water (e.g. rivers, lakes). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic drinking water is defined as a functional improved drinking water source on or near the premises and water points accessible to all users during school hours. An improved drinking water source is a water delivery point that by the nature of its design protects the water from external contamination, particularly of fecal origin. Examples of improved drinking water facilities include piped water, protected wells, tubewells and boreholes, protected springs and rainwater, purchased bottled water and tanker-trucks. Unimproved water sources include unprotected wells and springs and surface water (e.g. rivers, lakes). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.2T3.WCOMPUT", "metatype": [ { "id": "IndicatorName", "value": "Proportion of secondary schools with access to computers for pedagogical purposes (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Computers for pedagogical use: Use of computers to support course delivery or independent teaching and learning needs. This may include activities using computers or the Internet to meet information needs for research purposes; develop presentations; perform hands-on exercises and experiments; share information; and participate in online discussion forums for educational purposes. A computer is a programmable electronic device that can store, retrieve and process data, as well as share information in a highly-structured manner. It performs high-speed mathematical or logical operations according to a set of instructions or algorithms. Computers include the following types: a desktop computer usually remains fixed in one place; normally the user is placed in front of it, behind the keyboard; a laptop computer is small enough to carry and usually enables the same tasks as a desktop computer; it includes notebooks and netbooks but does not include tablets and similar handheld devices; and a tablet (or similar handheld computer) is a computer that is integrated into a flat touch screen, operated by touching the screen rather than using a physical keyboard. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Computers for pedagogical use: Use of computers to support course delivery or independent teaching and learning needs. This may include activities using computers or the Internet to meet information needs for research purposes; develop presentations; perform hands-on exercises and experiments; share information; and participate in online discussion forums for educational purposes. A computer is a programmable electronic device that can store, retrieve and process data, as well as share information in a highly-structured manner. It performs high-speed mathematical or logical operations according to a set of instructions or algorithms. Computers include the following types: a desktop computer usually remains fixed in one place; normally the user is placed in front of it, behind the keyboard; a laptop computer is small enough to carry and usually enables the same tasks as a desktop computer; it includes notebooks and netbooks but does not include tablets and similar handheld devices; and a tablet (or similar handheld computer) is a computer that is integrated into a flat touch screen, operated by touching the screen rather than using a physical keyboard. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.2T3.WINTERN", "metatype": [ { "id": "IndicatorName", "value": "Proportion of secondary schools with access to Internet for pedagogical purposes (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Internet for pedagogical purposes is defined as internet that is available for enhancing teaching and learning and is accessible by pupils. Internet is defined as a worldwide interconnected computer network, which provides pupils access to a number of communication services including the World Wide Web and carries e-mail, news, entertainment and data files, irrespective of the device used (i.e. not assumed to be only via a computer and thus can also be accessed by mobile telephone, tablet, PDA, games machine, digital TV etc.). Access can be via a fixed narrowband, fixed broadband, or via mobile network. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Internet for pedagogical purposes is defined as internet that is available for enhancing teaching and learning and is accessible by pupils. Internet is defined as a worldwide interconnected computer network, which provides pupils access to a number of communication services including the World Wide Web and carries e-mail, news, entertainment and data files, irrespective of the device used (i.e. not assumed to be only via a computer and thus can also be accessed by mobile telephone, tablet, PDA, games machine, digital TV etc.). Access can be via a fixed narrowband, fixed broadband, or via mobile network. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.3.WCOMPUT", "metatype": [ { "id": "IndicatorName", "value": "Proportion of upper secondary schools with access to computers for pedagogical purposes (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Computers for pedagogical use: Use of computers to support course delivery or independent teaching and learning needs. This may include activities using computers or the Internet to meet information needs for research purposes; develop presentations; perform hands-on exercises and experiments; share information; and participate in online discussion forums for educational purposes. A computer is a programmable electronic device that can store, retrieve and process data, as well as share information in a highly-structured manner. It performs high-speed mathematical or logical operations according to a set of instructions or algorithms. Computers include the following types: a desktop computer usually remains fixed in one place; normally the user is placed in front of it, behind the keyboard; a laptop computer is small enough to carry and usually enables the same tasks as a desktop computer; it includes notebooks and netbooks but does not include tablets and similar handheld devices; and a tablet (or similar handheld computer) is a computer that is integrated into a flat touch screen, operated by touching the screen rather than using a physical keyboard. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Computers for pedagogical use: Use of computers to support course delivery or independent teaching and learning needs. This may include activities using computers or the Internet to meet information needs for research purposes; develop presentations; perform hands-on exercises and experiments; share information; and participate in online discussion forums for educational purposes. A computer is a programmable electronic device that can store, retrieve and process data, as well as share information in a highly-structured manner. It performs high-speed mathematical or logical operations according to a set of instructions or algorithms. Computers include the following types: a desktop computer usually remains fixed in one place; normally the user is placed in front of it, behind the keyboard; a laptop computer is small enough to carry and usually enables the same tasks as a desktop computer; it includes notebooks and netbooks but does not include tablets and similar handheld devices; and a tablet (or similar handheld computer) is a computer that is integrated into a flat touch screen, operated by touching the screen rather than using a physical keyboard. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.3.WELEC", "metatype": [ { "id": "IndicatorName", "value": "Proportion of upper secondary schools with access to electricity (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Electricity is defined as regularly and readily available sources of power (e.g. grid/mains connection, wind, water, solar and fuel-powered generator, etc.) that enable the adequate and sustainable use of ICT infrastructure for educational purposes. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Electricity is defined as regularly and readily available sources of power (e.g. grid/mains connection, wind, water, solar and fuel-powered generator, etc.) that enable the adequate and sustainable use of ICT infrastructure for educational purposes. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.3.WHIVSEXED", "metatype": [ { "id": "IndicatorName", "value": "Percentage of upper secondary schools providing life skills-based HIV and sexuality education" }, { "id": "Longdefinition", "value": "Percentage of schools providing life skills-based HIV and sexuality education within the formal curriculum or as part of extra-curricular activities. The value is the number of schools at each level of education providing life skills-based HIV and sexuality education expressed as a percentage of all schools at the given level of education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools providing life skills-based HIV and sexuality education within the formal curriculum or as part of extra-curricular activities. The value is the number of schools at each level of education providing life skills-based HIV and sexuality education expressed as a percentage of all schools at the given level of education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.3.WINFSTUDIS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of upper secondary schools with access to adapted infrastructure and materials for students with disabilities (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Adapted infrastructure is defined as any built environment related to education facilities that are accessible to all users, including those with different types of disability, to be able to gain access to use and exit from them. Accessibility includes ease of independent approach, entry, evacuation and/or use of a building and its services and facilities (such as water and sanitation), by all of the building's potential users with an assurance of individual health, safety and welfare during the course of those activities. Adapted materials include learning materials and assistive products that enable students and teachers with disabilities/functioning limitations to access learning and to participate fully in the school environment. Accessible learning materials include textbooks, instructional materials, assessments and other materials that are available and provided in appropriate formats such as audio, braille, sign language and simplified formats that can be used by students and teachers with disabilities/functioning limitations. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Adapted infrastructure is defined as any built environment related to education facilities that are accessible to all users, including those with different types of disability, to be able to gain access to use and exit from them. Accessibility includes ease of independent approach, entry, evacuation and/or use of a building and its services and facilities (such as water and sanitation), by all of the building's potential users with an assurance of individual health, safety and welfare during the course of those activities. Adapted materials include learning materials and assistive products that enable students and teachers with disabilities/functioning limitations to access learning and to participate fully in the school environment. Accessible learning materials include textbooks, instructional materials, assessments and other materials that are available and provided in appropriate formats such as audio, braille, sign language and simplified formats that can be used by students and teachers with disabilities/functioning limitations. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.3.WINTERN", "metatype": [ { "id": "IndicatorName", "value": "Proportion of upper secondary schools with access to Internet for pedagogical purposes (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Internet for pedagogical purposes is defined as internet that is available for enhancing teaching and learning and is accessible by pupils. Internet is defined as a worldwide interconnected computer network, which provides pupils access to a number of communication services including the World Wide Web and carries e-mail, news, entertainment and data files, irrespective of the device used (i.e. not assumed to be only via a computer and thus can also be accessed by mobile telephone, tablet, PDA, games machine, digital TV etc.). Access can be via a fixed narrowband, fixed broadband, or via mobile network. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Internet for pedagogical purposes is defined as internet that is available for enhancing teaching and learning and is accessible by pupils. Internet is defined as a worldwide interconnected computer network, which provides pupils access to a number of communication services including the World Wide Web and carries e-mail, news, entertainment and data files, irrespective of the device used (i.e. not assumed to be only via a computer and thus can also be accessed by mobile telephone, tablet, PDA, games machine, digital TV etc.). Access can be via a fixed narrowband, fixed broadband, or via mobile network. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.3.WTOILA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of upper secondary schools with single-sex basic sanitation facilities (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic sanitation facilities are defined as functional improved sanitation facilities separated for males and females on or near the premises. Improved sanitation facilities include a pit latrine with slab, a ventilated improved pit latrine, a flush toilet, a pour-flush toilet or a composting toilet. Unimproved facilities include a pit latrine without a slab, hanging toilets and bucket toilets. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic sanitation facilities are defined as functional improved sanitation facilities separated for males and females on or near the premises. Improved sanitation facilities include a pit latrine with slab, a ventilated improved pit latrine, a flush toilet, a pour-flush toilet or a composting toilet. Unimproved facilities include a pit latrine without a slab, hanging toilets and bucket toilets. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.3.WWASH", "metatype": [ { "id": "IndicatorName", "value": "Proportion of upper secondary schools with basic handwashing facilities (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic handwashing facilities are defined as functional handwashing facilities, with soap and water available to all girls and boys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic handwashing facilities are defined as functional handwashing facilities, with soap and water available to all girls and boys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SCHBSP.3.WWATA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of upper secondary schools with access to basic drinking water (%)" }, { "id": "Longdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic drinking water is defined as a functional improved drinking water source on or near the premises and water points accessible to all users during school hours. An improved drinking water source is a water delivery point that by the nature of its design protects the water from external contamination, particularly of fecal origin. Examples of improved drinking water facilities include piped water, protected wells, tubewells and boreholes, protected springs and rainwater, purchased bottled water and tanker-trucks. Unimproved water sources include unprotected wells and springs and surface water (e.g. rivers, lakes). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service. The value is calculated as the number of schools in a given level of education with access to the relevant facilities expressed as a percentage of all schools at that level of education. Basic drinking water is defined as a functional improved drinking water source on or near the premises and water points accessible to all users during school hours. An improved drinking water source is a water delivery point that by the nature of its design protects the water from external contamination, particularly of fecal origin. Examples of improved drinking water facilities include piped water, protected wells, tubewells and boreholes, protected springs and rainwater, purchased bottled water and tanker-trucks. Unimproved water sources include unprotected wells and springs and surface water (e.g. rivers, lakes). For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SLE.02", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, pre-primary, both sexes (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Shortdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.SLE.02.F", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, pre-primary, female (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Shortdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.SLE.02.GPI", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, pre-primary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female school life expectancy to the male school life expectancy. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Shortdefinition", "value": "Ratio of female school life expectancy to the male school life expectancy. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.SLE.02.M", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, pre-primary, male (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Shortdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.SLE.1", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, primary, both sexes (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SLE.1.F", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, primary, female (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SLE.1.GPI", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, primary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female school life expectancy to the male school life expectancy. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SLE.1.M", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, primary, male (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SLE.12", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, primary and lower secondary, both sexes (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified levels of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SLE.12.F", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, primary and lower secondary, female (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified levels of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SLE.12.M", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, primary and lower secondary, male (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified levels of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SLE.123", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, primary and secondary, both sexes (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SLE.123.F", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, primary and secondary, female (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SLE.123.GPI", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, primary and secondary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female school life expectancy to the male school life expectancy. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SLE.123.M", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, primary and secondary, male (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SLE.1T2.GPI", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, primary and lower secondary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female school life expectancy to the male school life expectancy. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Ratio of female school life expectancy to the male school life expectancy. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SLE.1t6.GPI", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, primary to tertiary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female school life expectancy to the male school life expectancy. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.SLE.1T6.GPI", "metatype": [ { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.SLE.23", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, secondary, both sexes (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SLE.23.F", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, secondary, female (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SLE.23.GPI", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, secondary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female school life expectancy to the male school life expectancy. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SLE.23.M", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, secondary, male (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.SLE.4", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, post-secondary non-tertiary, both sexes (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Post-Secondary/Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.SLE.4.F", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, post-secondary non-tertiary, female (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Post-Secondary/Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.SLE.4.GPI", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, post-secondary non-tertiary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female school life expectancy to the male school life expectancy. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Post-Secondary/Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.SLE.4.M", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, post-secondary non-tertiary, male (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Post-Secondary/Non-Tertiary" } ], "source_id": "12" }, { "id": "UIS.SLE.56", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, tertiary, both sexes (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.SLE.56.F", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, tertiary, female (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.SLE.56.GPI", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, tertiary, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female school life expectancy to the male school life expectancy. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.SLE.56.M", "metatype": [ { "id": "IndicatorName", "value": "School life expectancy, tertiary, male (years)" }, { "id": "Longdefinition", "value": "Number of years a person of school entrance age can expect to spend within the specified level of education. For a child of a certain age a, the school life expectancy is calculated as the sum of the age specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high SLE indicates greater probability for children to spend more years in education and higher overall retention within the education system. It must be noted that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition. Since school life expectancy is an average based on participation in different levels of education, the expected number of years of schooling may be pulled down by the magnitude of children who never go to school. Those children who are in school may benefit from many more years of education than the average." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.SR.1.G4", "metatype": [ { "id": "IndicatorName", "value": "Survival rate to Grade 4 of primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of a cohort of students enrolled in the first grade of primary education in a given school year who are expected to reach grade 4, regardless of repetition. Divide the total number of students belonging to a school-cohort who reached each successive grade of primary education by the number of students in the school-cohort i.e. those originally enrolled in the first grade of primary education, and multiply the result by 100. The survival rate is calculated on the basis of the reconstructed cohort method, which uses data on enrolment and repeaters for two consecutive years." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SR.1.G4.F", "metatype": [ { "id": "IndicatorName", "value": "Survival rate to Grade 4 of primary education, female (%)" }, { "id": "Longdefinition", "value": "Percentage of a cohort of female students enrolled in the first grade of primary education in a given school year who are expected to reach grade 4, regardless of repetition. Divide the total number of students belonging to a school-cohort who reached each successive grade of primary education by the number of students in the school-cohort i.e. those originally enrolled in the first grade of primary education, and multiply the result by 100. The survival rate is calculated on the basis of the reconstructed cohort method, which uses data on enrolment and repeaters for two consecutive years." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SR.1.G4.GPI", "metatype": [ { "id": "IndicatorName", "value": "Survival rate to Grade 4 of primary education, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female survival rate to grade 4 to the male survival rate to grade 4. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SR.1.G4.M", "metatype": [ { "id": "IndicatorName", "value": "Survival rate to Grade 4 of primary education, male (%)" }, { "id": "Longdefinition", "value": "Percentage of a cohort of male students enrolled in the first grade of primary education in a given school year who are expected to reach grade 4, regardless of repetition. Divide the total number of students belonging to a school-cohort who reached each successive grade of primary education by the number of students in the school-cohort i.e. those originally enrolled in the first grade of primary education, and multiply the result by 100. The survival rate is calculated on the basis of the reconstructed cohort method, which uses data on enrolment and repeaters for two consecutive years." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SR.1.G5.GPI", "metatype": [ { "id": "IndicatorName", "value": "Survival rate to Grade 5 of primary education, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female survival rate to grade 5 to the male survival rate to grade 5. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.SR.1.Glast.GPI", "metatype": [ { "id": "IndicatorName", "value": "Survival rate to the last grade of primary education, gender parity index (GPI)" }, { "id": "Longdefinition", "value": "Ratio of female survival rate to the last grade of primary education to the male survival rate to the last grade of primary education. It is calculated by dividing the female value for the indicator by the male value for the indicator. A GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.SR.1.GLAST.GPI", "metatype": [ { "id": "Topic", "value": "Primary" } ], "source_id": "12" }, { "id": "UIS.T.01", "metatype": [ { "id": "IndicatorName", "value": "Teachers in early childhood educational development programmes, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of teachers in public and private early childhood educational development programmes. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Total number of teachers in public and private early childhood educational development programmes. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.01.F", "metatype": [ { "id": "IndicatorName", "value": "Teachers in early childhood educational development programmes, female (number)" }, { "id": "Longdefinition", "value": "Total number of female teachers in public and private early childhood educational development programmes. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Total number of female teachers in public and private early childhood educational development programmes. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.01.M", "metatype": [ { "id": "IndicatorName", "value": "Teachers in early childhood educational development programmes, male (number)" }, { "id": "Longdefinition", "value": "Total number of male teachers in public and private early childhood educational development programmes. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Total number of male teachers in public and private early childhood educational development programmes. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.02.M", "metatype": [ { "id": "IndicatorName", "value": "Teachers in pre-primary education, male (number)" }, { "id": "Longdefinition", "value": "Total number of male teachers in public and private pre-primary education institutions. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Total number of male teachers in public and private pre-primary education institutions. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.1.M", "metatype": [ { "id": "IndicatorName", "value": "Teachers in primary education, male (number)" }, { "id": "Longdefinition", "value": "Total number of male teachers in public and private primary education institutions. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Total number of male teachers in public and private primary education institutions. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.2", "metatype": [ { "id": "IndicatorName", "value": "Teachers in lower secondary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of teachers in public and private lower secondary education institutions (ISCED 2). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.2.F", "metatype": [ { "id": "IndicatorName", "value": "Teachers in lower secondary education, female (number)" }, { "id": "Longdefinition", "value": "Total number of female teachers in public and private lower secondary education institutions (ISCED 2). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.2.M", "metatype": [ { "id": "IndicatorName", "value": "Teachers in lower secondary education, male (number)" }, { "id": "Longdefinition", "value": "Total number of male teachers in public and private lower secondary education institutions (ISCED 2). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Total number of male teachers in public and private lower secondary education institutions (ISCED 2). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.23.M", "metatype": [ { "id": "IndicatorName", "value": "Teachers in secondary education, male (number)" }, { "id": "Longdefinition", "value": "Total number of male teachers in public and private secondary education institutions (ISCED 2 and 3). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Total number of male teachers in public and private secondary education institutions (ISCED 2 and 3). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.3", "metatype": [ { "id": "IndicatorName", "value": "Teachers in upper secondary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of teachers in public and private upper secondary education institutions (ISCED 3). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.3.F", "metatype": [ { "id": "IndicatorName", "value": "Teachers in upper secondary education, female (number)" }, { "id": "Longdefinition", "value": "Total number of female teachers in public and private upper secondary education institutions (ISCED 3). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.3.M", "metatype": [ { "id": "IndicatorName", "value": "Teachers in upper secondary education, male (number)" }, { "id": "Longdefinition", "value": "Total number of male teachers in public and private upper secondary education institutions (ISCED 3). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Total number of male teachers in public and private upper secondary education institutions (ISCED 3). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.4", "metatype": [ { "id": "IndicatorName", "value": "Teachers in post-secondary non-tertiary education, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of teachers in public and private post-secondary non-tertiary education institutions (ISCED 4). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.4.F", "metatype": [ { "id": "IndicatorName", "value": "Teachers in post-secondary non-tertiary education, female (number)" }, { "id": "Longdefinition", "value": "Total number of female teachers in public and private post-secondary non-tertiary education institutions (ISCED 4). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.4.M", "metatype": [ { "id": "IndicatorName", "value": "Teachers in post-secondary non-tertiary education, male (number)" }, { "id": "Longdefinition", "value": "Total number of male teachers in public and private post-secondary non-tertiary education institutions (ISCED 4). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Total number of male teachers in public and private post-secondary non-tertiary education institutions (ISCED 4). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.5", "metatype": [ { "id": "IndicatorName", "value": "Teachers in tertiary education ISCED 5 programmes, both sexes (number)" }, { "id": "Longdefinition", "value": "Total number of teachers in public and private short-cycle tertiary education institutions (ISCED 5). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Referenceperiod", "value": "Teachers in tertiary education ISCED 5 programmes, both sexes (number)" }, { "id": "Shortdefinition", "value": "Total number of teachers in public and private short-cycle tertiary education institutions (ISCED 5). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.5.F", "metatype": [ { "id": "IndicatorName", "value": "Teachers in tertiary education ISCED 5 programmes, female (number)" }, { "id": "Longdefinition", "value": "Total number of female teachers in public and private short-cycle tertiary education institutions (ISCED 5). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Referenceperiod", "value": "Teachers in tertiary education ISCED 5 programmes, female (number)" }, { "id": "Shortdefinition", "value": "Total number of female teachers in public and private short-cycle tertiary education institutions (ISCED 5). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.5.M", "metatype": [ { "id": "IndicatorName", "value": "Teachers in tertiary education ISCED 5 programmes, male (number)" }, { "id": "Longdefinition", "value": "Total number of male teachers in public and private short-cycle tertiary education institutions (ISCED 5). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Total number of male teachers in public and private short-cycle tertiary education institutions (ISCED 5). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.T.58.M", "metatype": [ { "id": "IndicatorName", "value": "Teachers in tertiary education programmes, male (number)" }, { "id": "Longdefinition", "value": "Total number of male teachers in public and private tertiary education institutions (ISCED 5-8). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Total number of male teachers in public and private tertiary education institutions (ISCED 5-8). Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity in educational institutions. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.02", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from pre-primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.02.F", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from pre-primary education, female (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.02.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from pre-primary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.02.M", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from pre-primary education, male (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.1.F", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from primary education, female (%)" }, { "id": "Longdefinition", "value": "Number of female teachers who left the primary education system in year t+1, expressed as a percentage of female teachers in service in primary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Number of female teachers who left the primary education system in year t+1, expressed as a percentage of female teachers in service in primary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from primary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.1.M", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from primary education, male (%)" }, { "id": "Longdefinition", "value": "Number of male teachers who left the primary education system in year t+1, expressed as a percentage of male teachers in service in primary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Number of male teachers who left the primary education system in year t+1, expressed as a percentage of male teachers in service in primary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.1.T", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of teachers who left the primary education system in year t+1, expressed as a percentage of teachers in service in primary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Number of teachers who left the primary education system in year t+1, expressed as a percentage of teachers in service in primary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.2.F", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from lower secondary education, female (%)" }, { "id": "Longdefinition", "value": "Number of female teachers who left the lower secondary education system in year t+1, expressed as a percentage of female teachers in service in lower secondary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Number of female teachers who left the lower secondary education system in year t+1, expressed as a percentage of female teachers in service in lower secondary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from lower secondary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.2.M", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from lower secondary education, male (%)" }, { "id": "Longdefinition", "value": "Number of male teachers who left the lower secondary education system in year t+1, expressed as a percentage of male teachers in service in lower secondary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Number of male teachers who left the lower secondary education system in year t+1, expressed as a percentage of male teachers in service in lower secondary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.2.T", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from lower secondary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of teachers who left the lower secondary education system in year t+1, expressed as a percentage of teachers in service in lower secondary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Number of teachers who left the lower secondary education system in year t+1, expressed as a percentage of teachers in service in lower secondary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.2T3", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from secondary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.2T3.F", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from secondary education, female (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.2T3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from secondary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.2T3.GPV", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from general secondary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.2T3.GPV.F", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from general secondary education, female (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.2T3.GPV.M", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from general secondary education, male (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.2T3.M", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from secondary education, male (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.2T3.V", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from vocational secondary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.2T3.V.F", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from vocational secondary education, female (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.2T3.V.M", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from vocational secondary education, male (%)" }, { "id": "Longdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Percentage of teachers at a given level of education leaving the profession in a given school year. The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1. A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Assessing and monitoring teacher attrition is essential to ensuring a sufficient supply of qualified and well-trained teachers as well as to their effective deployment, support and management. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.3.F", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from upper secondary education, female (%)" }, { "id": "Longdefinition", "value": "Number of female teachers who left the upper secondary education system in year t+1, expressed as a percentage of female teachers in service in upper secondary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Number of female teachers who left the upper secondary education system in year t+1, expressed as a percentage of female teachers in service in upper secondary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from upper secondary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.3.M", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from upper secondary education, male (%)" }, { "id": "Longdefinition", "value": "Number of male teachers who left the upper secondary education system in year t+1, expressed as a percentage of male teachers in service in upper secondary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Number of male teachers who left the upper secondary education system in year t+1, expressed as a percentage of male teachers in service in upper secondary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TATTRR.3.T", "metatype": [ { "id": "IndicatorName", "value": "Teacher attrition rate from upper secondary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of teachers who left the upper secondary education system in year t+1, expressed as a percentage of teachers in service in upper secondary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Number of teachers who left the upper secondary education system in year t+1, expressed as a percentage of teachers in service in upper secondary education system in year t. For more information, consult the UIS website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.thAge.0", "metatype": [ { "id": "IndicatorName", "value": "Official entrance age to early childhood education (years)" }, { "id": "Longdefinition", "value": "Age at which students would enter early childhood education." } ], "source_id": "12" }, { "id": "UIS.THAGE.0", "metatype": [ { "id": "Shortdefinition", "value": "Age at which students would enter early childhood education." } ], "source_id": "12" }, { "id": "UIS.thAge.0", "metatype": [ { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.THAGE.0", "metatype": [ { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.THAGE.01", "metatype": [ { "id": "IndicatorName", "value": "Official entrance age to early childhood educational development (years)" }, { "id": "Longdefinition", "value": "Age at which students would enter early childhood educational development. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Age at which students would enter early childhood educational development. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.THAGE.02", "metatype": [ { "id": "IndicatorName", "value": "Official entrance age to pre-primary education (years)" }, { "id": "Longdefinition", "value": "Age at which students would enter pre-primary education." }, { "id": "Shortdefinition", "value": "Age at which students would enter pre-primary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.thAge.3.A.GPV", "metatype": [ { "id": "IndicatorName", "value": "Official entrance age to upper secondary education (years)" }, { "id": "Longdefinition", "value": "Age at which students would enter upper secondary education, assuming they had started at the official entrance age for the lowest level of education, had studied full-time throughout and had progressed through the system without repeating or skipping a grade." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.THAGE.3.A.GPV", "metatype": [ { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.thAge.4.A.GPV", "metatype": [ { "id": "IndicatorName", "value": "Official entrance age to post-secondary non-tertiary education (years)" }, { "id": "Longdefinition", "value": "Age at which students would enter post-secondary education, assuming they had started at the official entrance age for the lowest level of education, had studied full-time throughout and had progressed through the system without repeating or skipping a grade." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.THAGE.4.A.GPV", "metatype": [ { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.thDur.0", "metatype": [ { "id": "IndicatorName", "value": "Theoretical duration of early childhood education (years)" }, { "id": "Longdefinition", "value": "Number of grades (years) in early childhood education." } ], "source_id": "12" }, { "id": "UIS.THDUR.0", "metatype": [ { "id": "Shortdefinition", "value": "Number of grades (years) in early childhood education." } ], "source_id": "12" }, { "id": "UIS.thDur.0", "metatype": [ { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.THDUR.0", "metatype": [ { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.THDUR.01", "metatype": [ { "id": "IndicatorName", "value": "Theoretical duration of early childhood educational development (years)" }, { "id": "Longdefinition", "value": "Number of grades (years) in early childhood educational development." }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Relatedindicators", "value": "Core" }, { "id": "Shortdefinition", "value": "Number of grades (years) in early childhood educational development." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.THDUR.02", "metatype": [ { "id": "IndicatorName", "value": "Theoretical duration of pre-primary education (years)" }, { "id": "Longdefinition", "value": "Number of grades (years) in pre-primary education." }, { "id": "Shortdefinition", "value": "Number of grades (years) in pre-primary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.thDur.4.A.GPV", "metatype": [ { "id": "IndicatorName", "value": "Theoretical duration of post-secondary non-tertiary education (years)" }, { "id": "Longdefinition", "value": "Number of grades (years) in post-secondary education." }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.THDUR.4.A.GPV", "metatype": [ { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.TRTP.02", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in pre-primary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the pre-primary level in the given country, expressed as a percentage of the total number of teachers at the pre-primary level." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Number of teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the pre-primary level in the given country, expressed as a percentage of the total number of teachers at the pre-primary level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TRTP.02.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in pre-primary education, female (%)" }, { "id": "Longdefinition", "value": "Number of female teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the pre-primary level in the given country, expressed as a percentage of the total number of female teachers at the pre-primary level." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Number of female teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the pre-primary level in the given country, expressed as a percentage of the total number of female teachers at the pre-primary level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TRTP.02.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in pre-primary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TRTP.02.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in pre-primary education, male (%)" }, { "id": "Longdefinition", "value": "Number of male teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the pre-primary level in the given country, expressed as a percentage of the total number of male teachers at the pre-primary level." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Number of male teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the pre-primary level in the given country, expressed as a percentage of the total number of male teachers at the pre-primary level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TRTP.1.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in primary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TRTP.2", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in lower secondary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the lower secondary level in the given country, expressed as a percentage of the total number of teachers at the lower secondary level." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Number of teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the lower secondary level in the given country, expressed as a percentage of the total number of teachers at the lower secondary level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TRTP.2.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in lower secondary education, female (%)" }, { "id": "Longdefinition", "value": "Number of female teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the lower secondary level in the given country, expressed as a percentage of the total number of female teachers at the lower secondary level." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Number of female teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the lower secondary level in the given country, expressed as a percentage of the total number of female teachers at the lower secondary level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TRTP.2.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in lower secondary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TRTP.2.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in lower secondary education, male (%)" }, { "id": "Longdefinition", "value": "Number of male teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the lower secondary level in the given country, expressed as a percentage of the total number of male teachers at the lower secondary level." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Number of male teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the lower secondary level in the given country, expressed as a percentage of the total number of male teachers at the lower secondary level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TRTP.2T3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in secondary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TRTP.3", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in upper secondary education, both sexes (%)" }, { "id": "Longdefinition", "value": "Number of teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the upper secondary level in the given country, expressed as a percentage of the total number of teachers at the upper secondary level." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Number of teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the upper secondary level in the given country, expressed as a percentage of the total number of teachers at the upper secondary level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TRTP.3.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in upper secondary education, female (%)" }, { "id": "Longdefinition", "value": "Number of female teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the upper secondary level in the given country, expressed as a percentage of the total number of female teachers at the upper secondary level." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Number of female teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the upper secondary level in the given country, expressed as a percentage of the total number of female teachers at the upper secondary level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TRTP.3.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in upper secondary education, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.TRTP.3.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of teachers with the minimum required qualifications in upper secondary education, male (%)" }, { "id": "Longdefinition", "value": "Number of male teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the upper secondary level in the given country, expressed as a percentage of the total number of male teachers at the upper secondary level." }, { "id": "Notesfromoriginalsource", "value": "Teachers" }, { "id": "Shortdefinition", "value": "Number of male teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching at the upper secondary level in the given country, expressed as a percentage of the total number of male teachers at the upper secondary level." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Teachers" } ], "source_id": "12" }, { "id": "UIS.X.PPP.02.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on pre-primary education, PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on pre-primary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on pre-primary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPP.1.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on primary education, PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on primary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on primary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPP.2.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on lower secondary education, PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on lower secondary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on lower secondary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPP.2T3.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on secondary education, PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on secondary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on secondary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPP.2T4.V.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on secondary and post-secondary non-tertiary vocational education only, PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on secondary and post-secondary non-tertiary vocational education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on secondary and post-secondary non-tertiary vocational education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPP.3.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on upper secondary education, PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on upper secondary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on upper secondary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPP.4.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on post-secondary non-tertiary education, PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on post-secondary non-tertiary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on post-secondary non-tertiary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPP.5T8.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on tertiary education, PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPP.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on education, PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers), in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPP.UK.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on education not specified by level, PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers) not specified by level, in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers) not specified by level, in millions PPP$ (at purchasing power parity), in nominal value. It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPPCONST.02.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on pre-primary education, constant PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on pre-primary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on pre-primary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPPCONST.1.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on primary education, constant PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on primary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on primary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPPCONST.2.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on lower secondary education, constant PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on lower secondary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on lower secondary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPPCONST.2T3.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on secondary education, constant PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on secondary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on secondary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPPCONST.2T4.V.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on secondary and post-secondary non-tertiary vocational education only, constant PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on secondary and post-secondary non-tertiary vocational education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on secondary and post-secondary non-tertiary vocational education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPPCONST.3.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on upper secondary education, constant PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on upper secondary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on upper secondary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPPCONST.4.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on post-secondary non-tertiary education, constant PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on post-secondary non-tertiary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on post-secondary non-tertiary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPPCONST.5T8.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on tertiary education, constant PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPPCONST.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on education, constant PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers), in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.PPPCONST.UK.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on education not specified by level, constant PPP$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers) not specified by level, in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers) not specified by level, in millions PPP$ (at purchasing power parity) in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to PPP$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant PPP$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.US.02.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on pre-primary education, US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on pre-primary education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on pre-primary education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.US.1.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on primary education, US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on primary education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on primary education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.US.2.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on lower secondary education, US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on lower secondary education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on lower secondary education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.US.2T3.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on secondary education, US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on secondary education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on secondary education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.US.2T4.V.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on secondary and post-secondary non-tertiary vocational education only, US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on secondary and post-secondary non-tertiary vocational education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on secondary and post-secondary non-tertiary vocational education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.US.3.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on upper secondary education, US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on upper secondary education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on upper secondary education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.US.4.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on post-secondary non-tertiary education, US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on post-secondary non-tertiary education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on post-secondary non-tertiary education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.US.5T8.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on tertiary education, US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.US.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on education, US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers) in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.US.UK.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on education not specified by level, US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers) not specified by level in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers) not specified by level in millions US$ (nominal value). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.USCONST.02.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on pre-primary education, constant US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on pre-primary education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on pre-primary education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.USCONST.1.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on primary education, constant US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on primary education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on primary education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.USCONST.2.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on lower secondary education, constant US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on lower secondary education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on lower secondary education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.USCONST.2T3.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on secondary education, constant US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on secondary education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on secondary education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.USCONST.2T4.V.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on secondary and post-secondary non-tertiary vocational education only, constant US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on secondary and post-secondary non-tertiary vocational education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on secondary and post-secondary non-tertiary vocational education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.USCONST.3.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on upper secondary education, constant US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on upper secondary education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on upper secondary education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.USCONST.4.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on post-secondary non-tertiary education, constant US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on post-secondary non-tertiary education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on post-secondary non-tertiary education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.USCONST.5T8.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on tertiary education, constant US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.USCONST.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on education, constant US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers) in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.X.USCONST.UK.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on education not specified by level, constant US$ (millions)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers) not specified by level in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on education (current, capital, and transfers) not specified by level in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XGDP.0.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on pre-primary education as % of GDP (%)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on pre-primary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on pre-primary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XGDP.1.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on primary education as % of GDP (%)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on primary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on primary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XGDP.2.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on lower secondary education as a percentage of GDP (%)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on lower secondary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on lower secondary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XGDP.23.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on secondary education as % of GDP (%)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on secondary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on secondary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XGDP.2T4.V.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on secondary and post-secondary non-tertiary vocational education as % of GDP (%)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on secondary and post-secondary non-tertiary vocational education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on secondary and post-secondary non-tertiary vocational education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XGDP.3.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on upper secondary education as a percentage of GDP (%)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on upper secondary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on upper secondary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XGDP.4.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on post-secondary non-tertiary education as % of GDP (%)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on post-secondary non-tertiary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on post-secondary non-tertiary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XGDP.56.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Government expenditure on tertiary education as % of GDP (%)" }, { "id": "Longdefinition", "value": "Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.0.FDPUB.FNCAP", "metatype": [ { "id": "IndicatorName", "value": "Capital expenditure as % of total expenditure in pre-primary public institutions (%)" }, { "id": "Longdefinition", "value": "Capital expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Capital expenditure is for education goods or assets that yield benefits for a period of more than one year. It includes expenditure for construction, renovation and major repairs of buildings and the purchase of heavy equipment or vehicles. Divide capital expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Capital expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Capital expenditure is for education goods or assets that yield benefits for a period of more than one year. It includes expenditure for construction, renovation and major repairs of buildings and the purchase of heavy equipment or vehicles. Divide capital expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.0.FDPUB.FNCUR", "metatype": [ { "id": "IndicatorName", "value": "Current expenditure as % of total expenditure in pre-primary public institutions (%)" }, { "id": "Longdefinition", "value": "Current expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration). Divide all current expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Current expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration). Divide all current expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.0.FDPUB.FNNONS", "metatype": [ { "id": "IndicatorName", "value": "Current expenditure other than staff compensation as % of total expenditure in pre-primary public institutions (%)" }, { "id": "Longdefinition", "value": "Current expenditure other than for staff compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure other than for staff compensation includes expenditure on school books and teaching materials, ancillary services (ex. food, transport), and administration and other support activities. Divide current expenditure other than staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Current expenditure other than for staff compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure other than for staff compensation includes expenditure on school books and teaching materials, ancillary services (ex. food, transport), and administration and other support activities. Divide current expenditure other than staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.0.FDPUB.FNS", "metatype": [ { "id": "IndicatorName", "value": "All staff compensation as % of total expenditure in pre-primary public institutions (%)" }, { "id": "Longdefinition", "value": "All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.02.FDPUB.FNNTS", "metatype": [ { "id": "IndicatorName", "value": "Non-teaching staff compensation as a percentage of total expenditure in pre-primary public institutions (%)" }, { "id": "Longdefinition", "value": "Non-teacher (e.g. school directors, support staff, administrative staff in local or central Ministries) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate the indicator, divide non-teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Non-teacher (e.g. school directors, support staff, administrative staff in local or central Ministries) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate the indicator, divide non-teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.02.FDPUB.FNTS", "metatype": [ { "id": "IndicatorName", "value": "Teaching staff compensation as a percentage of total expenditure in pre-primary public institutions (%)" }, { "id": "Longdefinition", "value": "Teacher compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate this indicator, divide teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Teacher compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate this indicator, divide teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.1.FDPUB.FNBOOKS", "metatype": [ { "id": "IndicatorName", "value": "Expenditure on school books and teaching material as % of total expenditure in primary public institutions (%)" }, { "id": "Longdefinition", "value": "Expenditure on school books and teaching material expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Divide expenditure on school books and teaching material in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions, in some cases government budget classifications may differ. It is also often difficult to identify expenditure on school books and teaching material, as not all accounting systems include such a classification. In some poorer countries, foreign donors and/or households may fund a large portion of expenditure on school books and teaching material, and where country respondents only have data on government expenditure, the amounts may be underestimated or missing entirely. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Expenditure on school books and teaching material expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Divide expenditure on school books and teaching material in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions, in some cases government budget classifications may differ. It is also often difficult to identify expenditure on school books and teaching material, as not all accounting systems include such a classification. In some poorer countries, foreign donors and/or households may fund a large portion of expenditure on school books and teaching material, and where country respondents only have data on government expenditure, the amounts may be underestimated or missing entirely. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.1.FDPUB.FNCAP", "metatype": [ { "id": "IndicatorName", "value": "Capital expenditure as % of total expenditure in primary public institutions (%)" }, { "id": "Longdefinition", "value": "Capital expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Capital expenditure is for education goods or assets that yield benefits for a period of more than one year. It includes expenditure for construction, renovation and major repairs of buildings and the purchase of heavy equipment or vehicles. Divide capital expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Capital expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Capital expenditure is for education goods or assets that yield benefits for a period of more than one year. It includes expenditure for construction, renovation and major repairs of buildings and the purchase of heavy equipment or vehicles. Divide capital expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.1.FDPUB.FNCUR", "metatype": [ { "id": "IndicatorName", "value": "Current expenditure as % of total expenditure in primary public institutions (%)" }, { "id": "Longdefinition", "value": "Current expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration). Divide all current expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Current expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration). Divide all current expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.1.FDPUB.FNNONS", "metatype": [ { "id": "IndicatorName", "value": "Current expenditure other than staff compensation as % of total expenditure in primary public institutions (%)" }, { "id": "Longdefinition", "value": "Current expenditure other than for staff compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure other than for staff compensation includes expenditure on school books and teaching materials, ancillary services (ex. food, transport), and administration and other support activities. Divide current expenditure other than staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Current expenditure other than for staff compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure other than for staff compensation includes expenditure on school books and teaching materials, ancillary services (ex. food, transport), and administration and other support activities. Divide current expenditure other than staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.1.FDPUB.FNNTS", "metatype": [ { "id": "IndicatorName", "value": "Non-teaching staff compensation as a percentage of total expenditure in primary public institutions (%)" }, { "id": "Longdefinition", "value": "Non-teacher (e.g. school directors, support staff, administrative staff in local or central Ministries) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate the indicator, divide non-teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Non-teacher (e.g. school directors, support staff, administrative staff in local or central Ministries) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate the indicator, divide non-teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.1.FDPUB.FNS", "metatype": [ { "id": "IndicatorName", "value": "All staff compensation as % of total expenditure in primary public institutions (%)" }, { "id": "Longdefinition", "value": "All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.1.FDPUB.FNTS", "metatype": [ { "id": "IndicatorName", "value": "Teaching staff compensation as a percentage of total expenditure in primary public institutions (%)" }, { "id": "Longdefinition", "value": "Teacher compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate this indicator, divide teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Teacher compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate this indicator, divide teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.2.FDPUB.FNCAP", "metatype": [ { "id": "IndicatorName", "value": "Capital expenditure as % of total expenditure in lower secondary public institutions (%)" }, { "id": "Longdefinition", "value": "Capital expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Capital expenditure is for education goods or assets that yield benefits for a period of more than one year. It includes expenditure for construction, renovation and major repairs of buildings and the purchase of heavy equipment or vehicles. Divide capital expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Capital expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Capital expenditure is for education goods or assets that yield benefits for a period of more than one year. It includes expenditure for construction, renovation and major repairs of buildings and the purchase of heavy equipment or vehicles. Divide capital expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.2.FDPUB.FNCUR", "metatype": [ { "id": "IndicatorName", "value": "Current expenditure as % of total expenditure in lower secondary public institutions (%)" }, { "id": "Longdefinition", "value": "Current expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration). Divide all current expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Current expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration). Divide all current expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.2.FDPUB.FNNONS", "metatype": [ { "id": "IndicatorName", "value": "Current expenditure other than staff compensation as % of total expenditure in lower secondary public institutions (%)" }, { "id": "Longdefinition", "value": "Current expenditure other than for staff compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure other than for staff compensation includes expenditure on school books and teaching materials, ancillary services (ex. food, transport), and administration and other support activities. Divide current expenditure other than staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Current expenditure other than for staff compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure other than for staff compensation includes expenditure on school books and teaching materials, ancillary services (ex. food, transport), and administration and other support activities. Divide current expenditure other than staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.2.FDPUB.FNNTS", "metatype": [ { "id": "IndicatorName", "value": "Non-teaching staff compensation as a percentage of total expenditure in lower secondary public institutions (%)" }, { "id": "Longdefinition", "value": "Non-teacher (e.g. school directors, support staff, administrative staff in local or central Ministries) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate the indicator, divide non-teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Non-teacher (e.g. school directors, support staff, administrative staff in local or central Ministries) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate the indicator, divide non-teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.2.FDPUB.FNS", "metatype": [ { "id": "IndicatorName", "value": "All staff compensation as % of total expenditure in lower secondary public institutions (%)" }, { "id": "Longdefinition", "value": "All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.2.FDPUB.FNTS", "metatype": [ { "id": "IndicatorName", "value": "Teaching staff compensation as a percentage of total expenditure in lower secondary public institutions (%)" }, { "id": "Longdefinition", "value": "Teacher compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate this indicator, divide teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Teacher compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate this indicator, divide teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.23.FDPUB.FNBOOKS", "metatype": [ { "id": "IndicatorName", "value": "Expenditure on school books and teaching material as % of total expenditure in secondary public institutions (%)" }, { "id": "Longdefinition", "value": "Expenditure on school books and teaching material expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Divide expenditure on school books and teaching material in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions, in some cases government budget classifications may differ. It is also often difficult to identify expenditure on school books and teaching material, as not all accounting systems include such a classification. In some poorer countries, foreign donors and/or households may fund a large portion of expenditure on school books and teaching material, and where country respondents only have data on government expenditure, the amounts may be underestimated or missing entirely. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Expenditure on school books and teaching material expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Divide expenditure on school books and teaching material in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions, in some cases government budget classifications may differ. It is also often difficult to identify expenditure on school books and teaching material, as not all accounting systems include such a classification. In some poorer countries, foreign donors and/or households may fund a large portion of expenditure on school books and teaching material, and where country respondents only have data on government expenditure, the amounts may be underestimated or missing entirely. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.23.FDPUB.FNCAP", "metatype": [ { "id": "IndicatorName", "value": "Capital expenditure as % of total expenditure in secondary public institutions (%)" }, { "id": "Longdefinition", "value": "Capital expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Capital expenditure is for education goods or assets that yield benefits for a period of more than one year. It includes expenditure for construction, renovation and major repairs of buildings and the purchase of heavy equipment or vehicles. Divide capital expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Capital expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Capital expenditure is for education goods or assets that yield benefits for a period of more than one year. It includes expenditure for construction, renovation and major repairs of buildings and the purchase of heavy equipment or vehicles. Divide capital expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.23.FDPUB.FNCUR", "metatype": [ { "id": "IndicatorName", "value": "Current expenditure as % of total expenditure in secondary public institutions (%)" }, { "id": "Longdefinition", "value": "Current expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration). Divide all current expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Current expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration). Divide all current expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.23.FDPUB.FNNONS", "metatype": [ { "id": "IndicatorName", "value": "Current expenditure other than staff compensation as % of total expenditure in secondary public institutions (%)" }, { "id": "Longdefinition", "value": "Current expenditure other than for staff compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure other than for staff compensation includes expenditure on school books and teaching materials, ancillary services (ex. food, transport), and administration and other support activities. Divide current expenditure other than staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Current expenditure other than for staff compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure other than for staff compensation includes expenditure on school books and teaching materials, ancillary services (ex. food, transport), and administration and other support activities. Divide current expenditure other than staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.23.FDPUB.FNNTS", "metatype": [ { "id": "IndicatorName", "value": "Non-teaching staff compensation as a percentage of total expenditure in secondary public institutions (%)" }, { "id": "Longdefinition", "value": "Non-teacher (e.g. school directors, support staff, administrative staff in local or central Ministries) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate the indicator, divide non-teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Non-teacher (e.g. school directors, support staff, administrative staff in local or central Ministries) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate the indicator, divide non-teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.23.FDPUB.FNS", "metatype": [ { "id": "IndicatorName", "value": "All staff compensation as % of total expenditure in secondary public institutions (%)" }, { "id": "Longdefinition", "value": "All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.23.FDPUB.FNTS", "metatype": [ { "id": "IndicatorName", "value": "Teaching staff compensation as a percentage of total expenditure in secondary public institutions (%)" }, { "id": "Longdefinition", "value": "Teacher compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate this indicator, divide teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Teacher compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate this indicator, divide teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.3.FDPUB.FNCAP", "metatype": [ { "id": "IndicatorName", "value": "Capital expenditure as % of total expenditure in upper-secondary public institutions (%)" }, { "id": "Longdefinition", "value": "Capital expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Capital expenditure is for education goods or assets that yield benefits for a period of more than one year. It includes expenditure for construction, renovation and major repairs of buildings and the purchase of heavy equipment or vehicles. Divide capital expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Capital expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Capital expenditure is for education goods or assets that yield benefits for a period of more than one year. It includes expenditure for construction, renovation and major repairs of buildings and the purchase of heavy equipment or vehicles. Divide capital expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.3.FDPUB.FNCUR", "metatype": [ { "id": "IndicatorName", "value": "Current expenditure as % of total expenditure in upper-secondary public institutions (%)" }, { "id": "Longdefinition", "value": "Current expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration). Divide all current expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Current expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration). Divide all current expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.3.FDPUB.FNNONS", "metatype": [ { "id": "IndicatorName", "value": "Current expenditure other than staff compensation as % of total expenditure in upper secondary public institutions (%)" }, { "id": "Longdefinition", "value": "Current expenditure other than for staff compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure other than for staff compensation includes expenditure on school books and teaching materials, ancillary services (ex. food, transport), and administration and other support activities. Divide current expenditure other than staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Current expenditure other than for staff compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure other than for staff compensation includes expenditure on school books and teaching materials, ancillary services (ex. food, transport), and administration and other support activities. Divide current expenditure other than staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.3.FDPUB.FNNTS", "metatype": [ { "id": "IndicatorName", "value": "Non-teaching staff compensation as a percentage of total expenditure in upper secondary public institutions (%)" }, { "id": "Longdefinition", "value": "Non-teacher (e.g. school directors, support staff, administrative staff in local or central Ministries) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate the indicator, divide non-teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Non-teacher (e.g. school directors, support staff, administrative staff in local or central Ministries) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate the indicator, divide non-teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.3.FDPUB.FNS", "metatype": [ { "id": "IndicatorName", "value": "All staff compensation as % of total expenditure in upper secondary public institutions (%)" }, { "id": "Longdefinition", "value": "All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.3.FDPUB.FNTS", "metatype": [ { "id": "IndicatorName", "value": "Teaching staff compensation as a percentage of total expenditure in upper secondary public institutions (%)" }, { "id": "Longdefinition", "value": "Teacher compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate this indicator, divide teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Teacher compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate this indicator, divide teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.4.FDPUB.FNCAP", "metatype": [ { "id": "IndicatorName", "value": "Capital expenditure as % of total expenditure in post-secondary non-tertiary public institutions (%)" }, { "id": "Longdefinition", "value": "Capital expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Capital expenditure is for education goods or assets that yield benefits for a period of more than one year. It includes expenditure for construction, renovation and major repairs of buildings and the purchase of heavy equipment or vehicles. Divide capital expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Capital expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Capital expenditure is for education goods or assets that yield benefits for a period of more than one year. It includes expenditure for construction, renovation and major repairs of buildings and the purchase of heavy equipment or vehicles. Divide capital expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.4.FDPUB.FNCUR", "metatype": [ { "id": "IndicatorName", "value": "Current expenditure as % of total expenditure in post-secondary non-tertiary public institutions (%)" }, { "id": "Longdefinition", "value": "Current expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration). Divide all current expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Current expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration). Divide all current expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.4.FDPUB.FNNONS", "metatype": [ { "id": "IndicatorName", "value": "Current expenditure other than staff compensation as % of total expenditure in post-secondary non-tertiary public institutions (%)" }, { "id": "Longdefinition", "value": "Current expenditure other than for staff compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure other than for staff compensation includes expenditure on school books and teaching materials, ancillary services (ex. food, transport), and administration and other support activities. Divide current expenditure other than staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Current expenditure other than for staff compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure other than for staff compensation includes expenditure on school books and teaching materials, ancillary services (ex. food, transport), and administration and other support activities. Divide current expenditure other than staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.4.FDPUB.FNNTS", "metatype": [ { "id": "IndicatorName", "value": "Non-teaching staff compensation as a percentage of total expenditure in post-secondary non-tertiary public institutions (%)" }, { "id": "Longdefinition", "value": "Non-teacher (e.g. school directors, support staff, administrative staff in local or central Ministries) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate the indicator, divide non-teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Non-teacher (e.g. school directors, support staff, administrative staff in local or central Ministries) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate the indicator, divide non-teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.4.FDPUB.FNS", "metatype": [ { "id": "IndicatorName", "value": "All staff compensation as % of total expenditure in post-secondary non-tertiary public institutions (%)" }, { "id": "Longdefinition", "value": "All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.4.FDPUB.FNTS", "metatype": [ { "id": "IndicatorName", "value": "Teaching staff compensation as a percentage of total expenditure in post-secondary non-tertiary public institutions (%)" }, { "id": "Longdefinition", "value": "Teacher compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate this indicator, divide teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Teacher compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate this indicator, divide teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSpendP.56.Fdpub.Fncap", "metatype": [ { "id": "IndicatorName", "value": "Capital expenditure as % of total expenditure in tertiary public institutions (%)" }, { "id": "Longdefinition", "value": "Capital expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Capital expenditure is for education goods or assets that yield benefits for a period of more than one year. It includes expenditure for construction, renovation and major repairs of buildings and the purchase of heavy equipment or vehicles. Divide capital expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Capital expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Capital expenditure is for education goods or assets that yield benefits for a period of more than one year. It includes expenditure for construction, renovation and major repairs of buildings and the purchase of heavy equipment or vehicles. Divide capital expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.56.FDPUB.FNCAP", "metatype": [ { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSpendP.56.Fdpub.Fncur", "metatype": [ { "id": "IndicatorName", "value": "Current expenditure as % of total expenditure in tertiary public institutions (%)" }, { "id": "Longdefinition", "value": "Current expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration). Divide all current expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Current expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration). Divide all current expenditure in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.56.FDPUB.FNCUR", "metatype": [ { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSpendP.56.Fdpub.Fnnons", "metatype": [ { "id": "IndicatorName", "value": "Current expenditure other than staff compensation as % of total expenditure in tertiary public institutions (%)" }, { "id": "Longdefinition", "value": "Current expenditure other than for staff compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure other than for staff compensation includes expenditure on school books and teaching materials, ancillary services (ex. food, transport), and administration and other support activities. Divide current expenditure other than staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Current expenditure other than for staff compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure other than for staff compensation includes expenditure on school books and teaching materials, ancillary services (ex. food, transport), and administration and other support activities. Divide current expenditure other than staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.56.FDPUB.FNNONS", "metatype": [ { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.56.FDPUB.FNNTS", "metatype": [ { "id": "IndicatorName", "value": "Non-teaching staff compensation as a percentage of total expenditure in tertiary public institutions (%)" }, { "id": "Longdefinition", "value": "Non-teacher (e.g. school directors, support staff, administrative staff in local or central Ministries) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate the indicator, divide non-teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Non-teacher (e.g. school directors, support staff, administrative staff in local or central Ministries) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate the indicator, divide non-teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSpendP.56.Fdpub.Fns", "metatype": [ { "id": "IndicatorName", "value": "All staff compensation as % of total expenditure in tertiary public institutions (%)" }, { "id": "Longdefinition", "value": "All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.56.FDPUB.FNS", "metatype": [ { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.56.FDPUB.FNTS", "metatype": [ { "id": "IndicatorName", "value": "Teaching staff compensation as a percentage of total expenditure in tertiary public institutions (%)" }, { "id": "Longdefinition", "value": "Teacher compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate this indicator, divide teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Teacher compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate this indicator, divide teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.FDPUB.FNCAP", "metatype": [ { "id": "IndicatorName", "value": "Capital expenditure as % of total expenditure in public institutions (%)" }, { "id": "Longdefinition", "value": "Capital expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional). Financial aid to students and other transfers are excluded from direct expenditure. Capital expenditure is for education goods or assets that yield benefits for a period of more than one year. It includes expenditure for construction, renovation and major repairs of buildings and the purchase of heavy equipment or vehicles. Divide capital expenditure in public institutions by total expenditure (current and capital) in public institutions, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Capital expenditure expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional). Financial aid to students and other transfers are excluded from direct expenditure. Capital expenditure is for education goods or assets that yield benefits for a period of more than one year. It includes expenditure for construction, renovation and major repairs of buildings and the purchase of heavy equipment or vehicles. Divide capital expenditure in public institutions by total expenditure (current and capital) in public institutions, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.FDPUB.FNNONS", "metatype": [ { "id": "IndicatorName", "value": "Current expenditure other than staff compensation as % of total expenditure in public institutions (%)" }, { "id": "Longdefinition", "value": "Current expenditure other than for staff compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional). Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure other than for staff compensation includes expenditure on school books and teaching materials, ancillary services (ex. food, transport), and administration and other support activities. Divide current expenditure other than staff compensation in public institutions by total expenditure (current and capital) in public institutions, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Current expenditure other than for staff compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional). Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure other than for staff compensation includes expenditure on school books and teaching materials, ancillary services (ex. food, transport), and administration and other support activities. Divide current expenditure other than staff compensation in public institutions by total expenditure (current and capital) in public institutions, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.FDPUB.FNNTS", "metatype": [ { "id": "IndicatorName", "value": "Non-teaching staff compensation as a percentage of total expenditure in public institutions (%)" }, { "id": "Longdefinition", "value": "Non-teacher (e.g. school directors, support staff, administrative staff in local or central Ministries) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate the indicator, divide non-teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Non-teacher (e.g. school directors, support staff, administrative staff in local or central Ministries) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate the indicator, divide non-teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.FDPUB.FNS", "metatype": [ { "id": "IndicatorName", "value": "All staff compensation as % of total expenditure in public institutions (%)" }, { "id": "Longdefinition", "value": "All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional). Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions by total expenditure (current and capital) in public institutions, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional). Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions by total expenditure (current and capital) in public institutions, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XSPENDP.FDPUB.FNTS", "metatype": [ { "id": "IndicatorName", "value": "Teaching staff compensation as a percentage of total expenditure in public institutions (%)" }, { "id": "Longdefinition", "value": "Teacher compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate this indicator, divide teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Teacher compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. To calculate this indicator, divide teacher compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. Limitations: Although countries responding to the UIS questionnaire on educational expenditure are required to follow common definitions for staff compensation, in some cases government budget classifications may differ. It is also often difficult to separate staff compensation between teachers and non-teachers, as these are usually grouped together in country's accounting systems. In general, respondent countries must use estimation methods to separate teacher and non-teacher staff compensation. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.GDPCAP.02.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Initial government funding per pre-primary student as a percentage of GDP per capita" }, { "id": "Longdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.GDPCAP.1.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Initial government funding per primary student as a percentage of GDP per capita" }, { "id": "Longdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.GDPCAP.1.FSHH", "metatype": [ { "id": "IndicatorName", "value": "Initial household funding per primary student as a percentage of GDP per capita" }, { "id": "Longdefinition", "value": "Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport) and purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes). 'Initial funding' means that government transfers to households, such as scholarships and other financial aid for education, are subtracted from what is spent by households. Note that in some countries for some education levels, the value of this indicator may be 0, since on average households may be receiving as much, or more, in financial aid from the government than what they are spending on education. Calculation: Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport), plus purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes), minus government education transfers to households (such as scholarships or other education-specific financial aid). When expressed as a share of GDP, this is then divided by the country's Gross Domestic Product (GDP). Limitations: Indicators for household expenditure on education should be interpreted with caution since data comes from household surveys which may not all follow the same definitions and concepts. These types of surveys are also not carried out in all countries with regularity, and for some categories (such as pupils in pre-primary education), the sample sizes may be low. In some cases where data on government transfers to households (scholarships and other financial aid) was not available, they could not be subtracted from amounts paid by households. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport) and purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes). 'Initial funding' means that government transfers to households, such as scholarships and other financial aid for education, are subtracted from what is spent by households. Note that in some countries for some education levels, the value of this indicator may be 0, since on average households may be receiving as much, or more, in financial aid from the government than what they are spending on education. Calculation: Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport), plus purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes), minus government education transfers to households (such as scholarships or other education-specific financial aid). When expressed as a share of GDP, this is then divided by the country's Gross Domestic Product (GDP). Limitations: Indicators for household expenditure on education should be interpreted with caution since data comes from household surveys which may not all follow the same definitions and concepts. These types of surveys are also not carried out in all countries with regularity, and for some categories (such as pupils in pre-primary education), the sample sizes may be low. In some cases where data on government transfers to households (scholarships and other financial aid) was not available, they could not be subtracted from amounts paid by households. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.GDPCAP.2.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Initial government funding per lower secondary student as a percentage of GDP per capita" }, { "id": "Longdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.GDPCAP.23.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Initial government funding per secondary student as a percentage of GDP per capita" }, { "id": "Longdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.GDPCAP.23.FSHH", "metatype": [ { "id": "IndicatorName", "value": "Initial household funding per secondary student as a percentage of GDP per capita" }, { "id": "Longdefinition", "value": "Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport) and purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes). 'Initial funding' means that government transfers to households, such as scholarships and other financial aid for education, are subtracted from what is spent by households. Note that in some countries for some education levels, the value of this indicator may be 0, since on average households may be receiving as much, or more, in financial aid from the government than what they are spending on education. Calculation: Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport), plus purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes), minus government education transfers to households (such as scholarships or other education-specific financial aid). When expressed as a share of GDP, this is then divided by the country's Gross Domestic Product (GDP). Limitations: Indicators for household expenditure on education should be interpreted with caution since data comes from household surveys which may not all follow the same definitions and concepts. These types of surveys are also not carried out in all countries with regularity, and for some categories (such as pupils in pre-primary education), the sample sizes may be low. In some cases where data on government transfers to households (scholarships and other financial aid) was not available, they could not be subtracted from amounts paid by households. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport) and purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes). 'Initial funding' means that government transfers to households, such as scholarships and other financial aid for education, are subtracted from what is spent by households. Note that in some countries for some education levels, the value of this indicator may be 0, since on average households may be receiving as much, or more, in financial aid from the government than what they are spending on education. Calculation: Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport), plus purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes), minus government education transfers to households (such as scholarships or other education-specific financial aid). When expressed as a share of GDP, this is then divided by the country's Gross Domestic Product (GDP). Limitations: Indicators for household expenditure on education should be interpreted with caution since data comes from household surveys which may not all follow the same definitions and concepts. These types of surveys are also not carried out in all countries with regularity, and for some categories (such as pupils in pre-primary education), the sample sizes may be low. In some cases where data on government transfers to households (scholarships and other financial aid) was not available, they could not be subtracted from amounts paid by households. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.GDPCAP.3.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Initial government funding per upper secondary student as a percentage of GDP per capita" }, { "id": "Longdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.GDPCAP.5T8.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Initial government funding per tertiary student as a percentage of GDP per capita" }, { "id": "Longdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.GDPCAP.5T8.FSHH", "metatype": [ { "id": "IndicatorName", "value": "Initial household funding per tertiary student as a percentage of GDP per capita" }, { "id": "Longdefinition", "value": "Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport) and purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes). 'Initial funding' means that government transfers to households, such as scholarships and other financial aid for education, are subtracted from what is spent by households. Note that in some countries for some education levels, the value of this indicator may be 0, since on average households may be receiving as much, or more, in financial aid from the government than what they are spending on education. Calculation: Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport), plus purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes), minus government education transfers to households (such as scholarships or other education-specific financial aid). When expressed as a share of GDP, this is then divided by the country's Gross Domestic Product (GDP). Limitations: Indicators for household expenditure on education should be interpreted with caution since data comes from household surveys which may not all follow the same definitions and concepts. These types of surveys are also not carried out in all countries with regularity, and for some categories (such as pupils in pre-primary education), the sample sizes may be low. In some cases where data on government transfers to households (scholarships and other financial aid) was not available, they could not be subtracted from amounts paid by households. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport) and purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes). 'Initial funding' means that government transfers to households, such as scholarships and other financial aid for education, are subtracted from what is spent by households. Note that in some countries for some education levels, the value of this indicator may be 0, since on average households may be receiving as much, or more, in financial aid from the government than what they are spending on education. Calculation: Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport), plus purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes), minus government education transfers to households (such as scholarships or other education-specific financial aid). When expressed as a share of GDP, this is then divided by the country's Gross Domestic Product (GDP). Limitations: Indicators for household expenditure on education should be interpreted with caution since data comes from household surveys which may not all follow the same definitions and concepts. These types of surveys are also not carried out in all countries with regularity, and for some categories (such as pupils in pre-primary education), the sample sizes may be low. In some cases where data on government transfers to households (scholarships and other financial aid) was not available, they could not be subtracted from amounts paid by households. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.PPPCONST.02.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Initial government funding per pre-primary student, constant PPP$" }, { "id": "Longdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed at constant purchasing power parity (constant PPP$). Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed at constant purchasing power parity (constant PPP$). Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.PPPCONST.1.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Initial government funding per primary student, constant PPP$" }, { "id": "Longdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed at constant purchasing power parity (constant PPP$). Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed at constant purchasing power parity (constant PPP$). Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.PPPCONST.1.FSHH", "metatype": [ { "id": "IndicatorName", "value": "Initial household funding per primary student, constant PPP$" }, { "id": "Longdefinition", "value": "Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport) and purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes). 'Initial funding' means that government transfers to households, such as scholarships and other financial aid for education, are subtracted from what is spent by households. Note that in some countries for some education levels, the value of this indicator may be 0, since on average households may be receiving as much, or more, in financial aid from the government than what they are spending on education. Calculation: Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport), plus purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes), minus government education transfers to households (such as scholarships or other education-specific financial aid). Limitations: Indicators for household expenditure on education should be interpreted with caution since data comes from household surveys which may not all follow the same definitions and concepts. These types of surveys are also not carried out in all countries with regularity, and for some categories (such as pupils in pre-primary education), the sample sizes may be low. In some cases where data on government transfers to households (scholarships and other financial aid) was not available, they could not be subtracted from amounts paid by households. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport) and purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes). 'Initial funding' means that government transfers to households, such as scholarships and other financial aid for education, are subtracted from what is spent by households. Note that in some countries for some education levels, the value of this indicator may be 0, since on average households may be receiving as much, or more, in financial aid from the government than what they are spending on education. Calculation: Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport), plus purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes), minus government education transfers to households (such as scholarships or other education-specific financial aid). Limitations: Indicators for household expenditure on education should be interpreted with caution since data comes from household surveys which may not all follow the same definitions and concepts. These types of surveys are also not carried out in all countries with regularity, and for some categories (such as pupils in pre-primary education), the sample sizes may be low. In some cases where data on government transfers to households (scholarships and other financial aid) was not available, they could not be subtracted from amounts paid by households. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.PPPCONST.2.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Initial government funding per lower secondary student, constant PPP$" }, { "id": "Longdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed at constant purchasing power parity (constant PPP$). Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed at constant purchasing power parity (constant PPP$). Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.PPPCONST.23.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Initial government funding per secondary student, constant PPP$" }, { "id": "Longdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed at constant purchasing power parity (constant PPP$). Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed at constant purchasing power parity (constant PPP$). Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.PPPCONST.23.FSHH", "metatype": [ { "id": "IndicatorName", "value": "Initial household funding per secondary student, constant PPP$" }, { "id": "Longdefinition", "value": "Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport) and purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes). 'Initial funding' means that government transfers to households, such as scholarships and other financial aid for education, are subtracted from what is spent by households. Note that in some countries for some education levels, the value of this indicator may be 0, since on average households may be receiving as much, or more, in financial aid from the government than what they are spending on education. Calculation: Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport), plus purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes), minus government education transfers to households (such as scholarships or other education-specific financial aid). Limitations: Indicators for household expenditure on education should be interpreted with caution since data comes from household surveys which may not all follow the same definitions and concepts. These types of surveys are also not carried out in all countries with regularity, and for some categories (such as pupils in pre-primary education), the sample sizes may be low. In some cases where data on government transfers to households (scholarships and other financial aid) was not available, they could not be subtracted from amounts paid by households. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport) and purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes). 'Initial funding' means that government transfers to households, such as scholarships and other financial aid for education, are subtracted from what is spent by households. Note that in some countries for some education levels, the value of this indicator may be 0, since on average households may be receiving as much, or more, in financial aid from the government than what they are spending on education. Calculation: Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport), plus purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes), minus government education transfers to households (such as scholarships or other education-specific financial aid). Limitations: Indicators for household expenditure on education should be interpreted with caution since data comes from household surveys which may not all follow the same definitions and concepts. These types of surveys are also not carried out in all countries with regularity, and for some categories (such as pupils in pre-primary education), the sample sizes may be low. In some cases where data on government transfers to households (scholarships and other financial aid) was not available, they could not be subtracted from amounts paid by households. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.PPPCONST.3.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Initial government funding per upper secondary student, constant PPP$" }, { "id": "Longdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed at constant purchasing power parity (constant PPP$). Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed at constant purchasing power parity (constant PPP$). Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.PPPCONST.5T8.FSGOV", "metatype": [ { "id": "IndicatorName", "value": "Initial government funding per tertiary student, constant PPP$" }, { "id": "Longdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed at constant purchasing power parity (constant PPP$). Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed at constant purchasing power parity (constant PPP$). Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.XUNIT.PPPCONST.5T8.FSHH", "metatype": [ { "id": "IndicatorName", "value": "Initial household funding per tertiary student, constant PPP$" }, { "id": "Longdefinition", "value": "Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport) and purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes). 'Initial funding' means that government transfers to households, such as scholarships and other financial aid for education, are subtracted from what is spent by households. Note that in some countries for some education levels, the value of this indicator may be 0, since on average households may be receiving as much, or more, in financial aid from the government than what they are spending on education. Calculation: Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport), plus purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes), minus government education transfers to households (such as scholarships or other education-specific financial aid). Limitations: Indicators for household expenditure on education should be interpreted with caution since data comes from household surveys which may not all follow the same definitions and concepts. These types of surveys are also not carried out in all countries with regularity, and for some categories (such as pupils in pre-primary education), the sample sizes may be low. In some cases where data on government transfers to households (scholarships and other financial aid) was not available, they could not be subtracted from amounts paid by households. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Shortdefinition", "value": "Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport) and purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes). 'Initial funding' means that government transfers to households, such as scholarships and other financial aid for education, are subtracted from what is spent by households. Note that in some countries for some education levels, the value of this indicator may be 0, since on average households may be receiving as much, or more, in financial aid from the government than what they are spending on education. Calculation: Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport), plus purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes), minus government education transfers to households (such as scholarships or other education-specific financial aid). Limitations: Indicators for household expenditure on education should be interpreted with caution since data comes from household surveys which may not all follow the same definitions and concepts. These types of surveys are also not carried out in all countries with regularity, and for some categories (such as pupils in pre-primary education), the sample sizes may be low. In some cases where data on government transfers to households (scholarships and other financial aid) was not available, they could not be subtracted from amounts paid by households. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education Expenditures" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFILITERACY", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, both sexes (%)" }, { "id": "Longdefinition", "value": "The percentage of youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in literacy. Functional literacy is defined by UIS as the capacity of a person to engage in all those activities in which literacy is required for effective function of his or her group and community and also for enabling him or her to continue to use reading, writing and calculation for his or her own and the community’s development. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The percentage of youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in literacy. Functional literacy is defined by UIS as the capacity of a person to engage in all those activities in which literacy is required for effective function of his or her group and community and also for enabling him or her to continue to use reading, writing and calculation for his or her own and the community’s development. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFILITERACY.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, female (%)" }, { "id": "Longdefinition", "value": "The percentage of female youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in literacy. Functional literacy is defined by UIS as the capacity of a person to engage in all those activities in which literacy is required for effective function of his or her group and community and also for enabling him or her to continue to use reading, writing and calculation for his or her own and the community’s development. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The percentage of female youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in literacy. Functional literacy is defined by UIS as the capacity of a person to engage in all those activities in which literacy is required for effective function of his or her group and community and also for enabling him or her to continue to use reading, writing and calculation for his or her own and the community’s development. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFILITERACY.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFILITERACY.HSES", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, high socio-economic status (%)" }, { "id": "Longdefinition", "value": "The percentage of high socio-economic status youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in literacy. Functional literacy is defined by UIS as the capacity of a person to engage in all those activities in which literacy is required for effective function of his or her group and community and also for enabling him or her to continue to use reading, writing and calculation for his or her own and the community’s development. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The percentage of high socio-economic status youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in literacy. Functional literacy is defined by UIS as the capacity of a person to engage in all those activities in which literacy is required for effective function of his or her group and community and also for enabling him or her to continue to use reading, writing and calculation for his or her own and the community’s development. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFILITERACY.LSES", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, low socio-economic status (%)" }, { "id": "Longdefinition", "value": "The percentage of low socio-economic status youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in literacy. Functional literacy is defined by UIS as the capacity of a person to engage in all those activities in which literacy is required for effective function of his or her group and community and also for enabling him or her to continue to use reading, writing and calculation for his or her own and the community’s development. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The percentage of low socio-economic status youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in literacy. Functional literacy is defined by UIS as the capacity of a person to engage in all those activities in which literacy is required for effective function of his or her group and community and also for enabling him or her to continue to use reading, writing and calculation for his or her own and the community’s development. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFILITERACY.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, male (%)" }, { "id": "Longdefinition", "value": "The percentage of male youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in literacy. Functional literacy is defined by UIS as the capacity of a person to engage in all those activities in which literacy is required for effective function of his or her group and community and also for enabling him or her to continue to use reading, writing and calculation for his or her own and the community’s development. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The percentage of male youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in literacy. Functional literacy is defined by UIS as the capacity of a person to engage in all those activities in which literacy is required for effective function of his or her group and community and also for enabling him or her to continue to use reading, writing and calculation for his or her own and the community’s development. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFILITERACY.NAT", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, non-immigrant background (%)" }, { "id": "Longdefinition", "value": "The percentage of non-immigrant youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in literacy. Functional literacy is defined by UIS as the capacity of a person to engage in all those activities in which literacy is required for effective function of his or her group and community and also for enabling him or her to continue to use reading, writing and calculation for his or her own and the community’s development. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The percentage of non-immigrant youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in literacy. Functional literacy is defined by UIS as the capacity of a person to engage in all those activities in which literacy is required for effective function of his or her group and community and also for enabling him or her to continue to use reading, writing and calculation for his or her own and the community’s development. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFILITERACY.NON", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, immigrant background (%)" }, { "id": "Longdefinition", "value": "The percentage of immigrant youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in literacy. Functional literacy is defined by UIS as the capacity of a person to engage in all those activities in which literacy is required for effective function of his or her group and community and also for enabling him or her to continue to use reading, writing and calculation for his or her own and the community’s development. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The percentage of immigrant youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in literacy. Functional literacy is defined by UIS as the capacity of a person to engage in all those activities in which literacy is required for effective function of his or her group and community and also for enabling him or her to continue to use reading, writing and calculation for his or her own and the community’s development. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFILITERACY.NPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, adjusted native parity index (NPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFILITERACY.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFINUMERACY", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, both sexes (%)" }, { "id": "Longdefinition", "value": "The percentage of youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in numeracy. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The percentage of youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in numeracy. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFINUMERACY.F", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, female (%)" }, { "id": "Longdefinition", "value": "The percentage of female youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in numeracy. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The percentage of female youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in numeracy. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFINUMERACY.GPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, adjusted gender parity index (GPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Gender Parity Index (GPIA) is calculated by dividing the female value for the indicator by the male value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted gender parity index is symmetrical around 1 and lies in the range 0-2. An adjusted GPI equal to 1 indicates parity between females and males. In general, a value less than 1 indicates disparity in favor of males and a value greater than 1 indicates disparity in favor of females. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFINUMERACY.HSES", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, high socio-economic status (%)" }, { "id": "Longdefinition", "value": "The percentage of high socio-economic status youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in numeracy. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The percentage of high socio-economic status youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in numeracy. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFINUMERACY.LSES", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, low socio-economic status (%)" }, { "id": "Longdefinition", "value": "The percentage of low socio-economic status youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in numeracy. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The percentage of low socio-economic status youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in numeracy. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFINUMERACY.M", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, male (%)" }, { "id": "Longdefinition", "value": "The percentage of male youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in numeracy. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The percentage of male youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in numeracy. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFINUMERACY.NAT", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, non-immigrant background (%)" }, { "id": "Longdefinition", "value": "The percentage of non-immigrant youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in numeracy. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The percentage of non-immigrant youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in numeracy. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFINUMERACY.NON", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, immigrant background (%)" }, { "id": "Longdefinition", "value": "The percentage of immigrant youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in numeracy. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The percentage of immigrant youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in numeracy. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFINUMERACY.NPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, adjusted native parity index (NPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Native Parity Index (NPIA) is calculated by dividing the immigrant value for the indicator by the non-immigrant value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted native parity index is symmetrical around 1 and lies in the range 0-2. An adjusted NPI equal to 1 indicates parity between immigrants and non-immigrants. In general, a value less than 1 indicates disparity in favor of non-immigrants and a value greater than 1 indicates disparity in favor of immigrants. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YADULT.PROFINUMERACY.WPIA", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, adjusted wealth parity index (WPIA)" }, { "id": "Longdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Literacy" }, { "id": "Shortdefinition", "value": "The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Literacy" } ], "source_id": "12" }, { "id": "UIS.YEARS.FC.COMP.02", "metatype": [ { "id": "IndicatorName", "value": "Number of years of compulsory pre-primary education guaranteed in legal frameworks" }, { "id": "Longdefinition", "value": "Number of years of compulsory pre-primary education guaranteed in legal frameworks. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Number of years of compulsory pre-primary education guaranteed in legal frameworks. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.YEARS.FC.COMP.1T3", "metatype": [ { "id": "IndicatorName", "value": "Number of years of compulsory primary and secondary education guaranteed in legal frameworks" }, { "id": "Longdefinition", "value": "Number of years of compulsory primary and secondary education. Most countries have legislation specifying the ages and the level of education (typically pre-primary or primary education) at which children should start school. Such legislation usually also specifies either the number of years of education that are guaranteed or the age at which young people may leave education or, in some cases, both. The number of years of primary and secondary education to which children are legally entitled should ideally be the number of grades of primary and secondary education which young people are expected to have completed before being legally eligible to leave school. Years of pre-primary education covered by the legal entitlement should be excluded from this indicator. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of years of compulsory primary and secondary education. Most countries have legislation specifying the ages and the level of education (typically pre-primary or primary education) at which children should start school. Such legislation usually also specifies either the number of years of education that are guaranteed or the age at which young people may leave education or, in some cases, both. The number of years of primary and secondary education to which children are legally entitled should ideally be the number of grades of primary and secondary education which young people are expected to have completed before being legally eligible to leave school. Years of pre-primary education covered by the legal entitlement should be excluded from this indicator. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.YEARS.FC.FREE.02", "metatype": [ { "id": "IndicatorName", "value": "Number of years of free pre-primary education guaranteed in legal frameworks" }, { "id": "Longdefinition", "value": "Number of years of free pre-primary education guaranteed in legal frameworks. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Early Childhood Education" }, { "id": "Shortdefinition", "value": "Number of years of free pre-primary education guaranteed in legal frameworks. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Early Childhood Education" } ], "source_id": "12" }, { "id": "UIS.YEARS.FC.FREE.1T3", "metatype": [ { "id": "IndicatorName", "value": "Number of years of free primary and secondary education guaranteed in legal frameworks" }, { "id": "Longdefinition", "value": "Number of years of primary and secondary education to which children and young people are legally entitled that are free from tuition fees. Most countries have legislation specifying the ages and the level of education (typically pre-primary or primary education) at which children should start school. Such legislation usually also specifies either the number of years of education that are guaranteed or the age at which young people may leave education or, in some cases, both. The number of years of primary and secondary education to which children are legally entitled should ideally be the number of grades of primary and secondary education which young people are expected to have completed before being legally eligible to leave school. Years of pre-primary education covered by the legal entitlement should be excluded from this indicator. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Secondary" }, { "id": "Shortdefinition", "value": "Number of years of primary and secondary education to which children and young people are legally entitled that are free from tuition fees. Most countries have legislation specifying the ages and the level of education (typically pre-primary or primary education) at which children should start school. Such legislation usually also specifies either the number of years of education that are guaranteed or the age at which young people may leave education or, in some cases, both. The number of years of primary and secondary education to which children are legally entitled should ideally be the number of grades of primary and secondary education which young people are expected to have completed before being legally eligible to leave school. Years of pre-primary education covered by the legal entitlement should be excluded from this indicator. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Secondary" } ], "source_id": "12" }, { "id": "UIS.YR.END.01T5", "metatype": [ { "id": "IndicatorName", "value": "End of the academic school year (pre-primary to post-secondary non tertiary education)" }, { "id": "Longdefinition", "value": "End of the academic school year (pre-primary to post-secondary non tertiary education). For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Background" }, { "id": "Shortdefinition", "value": "End of the academic school year (pre-primary to post-secondary non tertiary education). For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Background" } ], "source_id": "12" }, { "id": "UIS.YR.END.6T8", "metatype": [ { "id": "IndicatorName", "value": "End of the academic school year (tertiary education)" }, { "id": "Longdefinition", "value": "End of the academic school year for tertiary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "End of the academic school year for tertiary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.YR.END.MON.01T5", "metatype": [ { "id": "IndicatorName", "value": "End month of the academic school year (pre-primary to post-secondary non-tertiary education)" }, { "id": "Longdefinition", "value": "End month of the academic school year (pre-primary to post-secondary non-tertiary education). For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Background" }, { "id": "Shortdefinition", "value": "End month of the academic school year (pre-primary to post-secondary non-tertiary education). For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Background" } ], "source_id": "12" }, { "id": "UIS.YR.END.MON.6T8", "metatype": [ { "id": "IndicatorName", "value": "End month of the academic school year (tertiary education)" }, { "id": "Longdefinition", "value": "Ending month of the academic school year for tertiary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Ending month of the academic school year for tertiary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.YR.ST.01T5", "metatype": [ { "id": "IndicatorName", "value": "Start of the academic school year (pre-primary to post-secondary non tertiary education)" }, { "id": "Longdefinition", "value": "Start of the academic school year (pre-primary to post-secondary non tertiary education). For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Background" }, { "id": "Shortdefinition", "value": "Start of the academic school year (pre-primary to post-secondary non tertiary education). For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Background" } ], "source_id": "12" }, { "id": "UIS.YR.ST.6T8", "metatype": [ { "id": "IndicatorName", "value": "Start of the academic school year (tertiary education)" }, { "id": "Longdefinition", "value": "Start of the academic school year for tertiary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Start of the academic school year for tertiary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "UIS.YR.ST.MON.01T5", "metatype": [ { "id": "IndicatorName", "value": "Start month of the academic school year (pre-primary to post-secondary non-tertiary education)" }, { "id": "Longdefinition", "value": "Start month of the academic school year (pre-primary to post-secondary non-tertiary education). For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Notesfromoriginalsource", "value": "Background" }, { "id": "Shortdefinition", "value": "Start month of the academic school year (pre-primary to post-secondary non-tertiary education). For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Background" } ], "source_id": "12" }, { "id": "UIS.YR.ST.MON.6T8", "metatype": [ { "id": "IndicatorName", "value": "Start month of the academic school year (tertiary education)" }, { "id": "Longdefinition", "value": "Starting month of the academic school year for tertiary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Notesfromoriginalsource", "value": "Tertiary" }, { "id": "Shortdefinition", "value": "Starting month of the academic school year for tertiary education. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Tertiary" } ], "source_id": "12" }, { "id": "IC.FRM.BRIB.GRAFT2", "metatype": [ { "id": "IndicatorName", "value": "Bribery depth (% of public transactions where a gift or informal payment was requested)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The depth of Bribery is the percentage of instances in which a firm was either expected or requested to provide a gift or informal payment during solicitations for public services, licenses or permits. This measure uses data from 6 survey questions for each firm. For purposes of computation, a refusal to answer a particular survey question is\nconsidered an affirmative answer." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Corruption" } ], "source_id": "13" }, { "id": "IC.FRM.BRIB.GRAFT3", "metatype": [ { "id": "IndicatorName", "value": "Bribery incidence (percent of firms experiencing at least one bribe payment request)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percent of firms experiencing at least one bribe payment request across 6 public transactions dealing with utilities access, permits, licenses, and taxes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Corruption" } ], "source_id": "13" }, { "id": "IC.FRM.CORR.CORR1", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms expected to give gifts in meetings with tax officials" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms expected to give gifts or informal payments during meetings with tax officials." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Corruption" } ], "source_id": "13" }, { "id": "IC.FRM.CORR.CORR10", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms expected to give gifts to get an operating license" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms expected to give gifts or informal payments to get an operating license. Spontaneous refusals to the question are treated as a Yes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Corruption" } ], "source_id": "13" }, { "id": "IC.FRM.CORR.CORR11", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms identifying corruption as a major constraint" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms identifying corruption as a \"major\" or \"very severe\" obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Corruption" } ], "source_id": "13" }, { "id": "IC.FRM.CORR.CORR2", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms expected to give gifts to secure government contract" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider that firms with characteristics similar to theirs are making informal payments or giving gifts to public officials to secure government contract." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Corruption" } ], "source_id": "13" }, { "id": "IC.FRM.CORR.CORR3", "metatype": [ { "id": "IndicatorName", "value": "Value of gift expected to secure a government contract (% of contract value)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of contract value expected as a gift to secure government contract. Only firms that have confirmed that they have secured or attempted to secure a government contract in the last 12 months were required to answer this question." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Corruption" } ], "source_id": "13" }, { "id": "IC.FRM.CORR.CORR4", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms expected to give gifts to public officials \"to get things done\"" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider that firms with characteristics similar to theirs are making informal payments or giving gifts to public officials to \"get things done\" with regard to customs, taxes, licenses, regulations, services etc." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Corruption" } ], "source_id": "13" }, { "id": "IC.FRM.CORR.CORR6", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms expected to give gifts to get an electrical connection" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms expected to give gifts or informal payments to get an electrical connection." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Corruption" } ], "source_id": "13" }, { "id": "IC.FRM.CORR.CORR7", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms expected to give gifts to get a water connection" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms expected to give gifts or informal payments to get a water connection." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Corruption" } ], "source_id": "13" }, { "id": "IC.FRM.CORR.CORR8", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms expected to give gifts to get a construction permit" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms expected to give gifts or informal payments to get a construction permit." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Corruption" } ], "source_id": "13" }, { "id": "IC.FRM.CORR.CORR9", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms expected to give gifts to get an import license" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms expected to give gifts or informal payments to get an import license." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Corruption" } ], "source_id": "13" }, { "id": "IC.FRM.CORR.CRIME9", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms identifying the courts system as a major constraint" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms identifying functioning of the courts as major constraint. The computation of the indicator is based on the rating of the obstacle as a potential constraint to the current operations of the establishment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Corruption" } ], "source_id": "13" }, { "id": "IC.FRM.CRM.CRIME1", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms paying for security" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms paying for security, for example equipment, personnel, or professional security services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Crime" } ], "source_id": "13" }, { "id": "IC.FRM.CRM.CRIME10", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms experiencing losses due to theft and vandalism" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "If there were losses, estimated losses as a result of theft, robbery, vandalism or arson that occurred on establishment's premises calculated as a percentage of annual sales." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Crime" } ], "source_id": "13" }, { "id": "IC.FRM.CRM.CRIME2_C", "metatype": [ { "id": "IndicatorName", "value": "If the establishment pays for security, average security costs (% of annual sales)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average security costs as a percentage of total annual sales for firms that pay for security." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Crime" } ], "source_id": "13" }, { "id": "IC.FRM.CRM.CRIME3_C", "metatype": [ { "id": "IndicatorName", "value": "If there were losses, average losses due to theft and vandalism (% of annual sales)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "If there were losses, estimated losses as a result of theft, robbery, vandalism or arson that occurred on\n establishment's premises calculated as a percentage of annual sales." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Crime" } ], "source_id": "13" }, { "id": "IC.FRM.CRM.CRIME5", "metatype": [ { "id": "IndicatorName", "value": "Products shipped to supply domestic markets that were lost due to theft (% of product value)*" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Value of losses of products, due to theft, while in transit to domestic markets." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Crime" } ], "source_id": "13" }, { "id": "IC.FRM.CRM.CRIME8", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms identifying crime, theft and disorder as a major constraint" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms identifying crime, theft and disorder as a \"major\" or \"very severe\" obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Crime" } ], "source_id": "13" }, { "id": "IC.FRM.EMP.GROW.PEFT2", "metatype": [ { "id": "IndicatorName", "value": "Annual employment growth (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Annualized growth of permanent full-time workers expressed as a percentage. Annual employment growth is the change in full-time employment reported in the current fiscal year from a previous period. For most countries the difference between the two fiscal year periods is two years. However, for some countries the interval is three years. Hence, an annualized measure is used." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Performance" } ], "source_id": "13" }, { "id": "IC.FRM.FCHAR.CAR1", "metatype": [ { "id": "IndicatorName", "value": "Age of the establishment (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Age of the firm based on the year in which the firm began operations." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Firm Characteristics" } ], "source_id": "13" }, { "id": "IC.FRM.FCHAR.CAR2", "metatype": [ { "id": "IndicatorName", "value": "Proportion of private domestic ownership in a firm (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of the firm owned by domestic individuals, companies or organizations." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Firm Characteristics" } ], "source_id": "13" }, { "id": "IC.FRM.FCHAR.CAR7", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms with at least 10% of foreign ownership" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms that have at least 10% owned by private foreign individuals, companies or organizations." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Firm Characteristics" } ], "source_id": "13" }, { "id": "IC.FRM.FCHAR.CAR8", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms with at least 10% of government/state ownership" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms where the government or state has at least a 10% share in ownership of the firm." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Firm Characteristics" } ], "source_id": "13" }, { "id": "IC.FRM.FCHAR.LFORM3", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms with legal status of Sole Proprietorship" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percent of firms with legal status of Sole Proprietorship" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Firm Characteristics" } ], "source_id": "13" }, { "id": "IC.FRM.FIAS.PEFT4", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms buying fixed assets" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percent of firms buying fixed assets such as machinery, equipment, land or buildings." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Performance" } ], "source_id": "13" }, { "id": "IC.FRM.FIN.FIN1", "metatype": [ { "id": "IndicatorName", "value": "Proportion of investment financed internally (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Estimated proportion of purchases of fixed assets that was financed from internal funds/retained earnings." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Finance" } ], "source_id": "13" }, { "id": "IC.FRM.FIN.FIN10", "metatype": [ { "id": "IndicatorName", "value": "Value of collateral needed for a loan (% of the loan amount)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Value of collateral needed for a loan or line of credit as a percentage of the loan value or the value of the line of\ncredit." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Finance" } ], "source_id": "13" }, { "id": "IC.FRM.FIN.FIN11", "metatype": [ { "id": "IndicatorName", "value": "Proportion of loans requiring collateral (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Loans requiring collateral in order to get the financing." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Finance" } ], "source_id": "13" }, { "id": "IC.FRM.FIN.FIN12", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms using banks to finance investments" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms using banks to finance purchases of fixed assets." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Finance" } ], "source_id": "13" }, { "id": "IC.FRM.FIN.FIN13", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms using banks to finance working capital" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms using bank loans to finance working capital." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Finance" } ], "source_id": "13" }, { "id": "IC.FRM.FIN.FIN14", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms with a bank loan/line of credit" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms with bank loans or line of credit." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Finance" } ], "source_id": "13" }, { "id": "IC.FRM.FIN.FIN15", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms with a checking or savings account" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms with a checking or savings account." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Finance" } ], "source_id": "13" }, { "id": "IC.FRM.FIN.FIN16", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms identifying access to finance as a major constraint" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms identifying access/cost of finance as a \"major\" or \"very severe\" obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Finance" } ], "source_id": "13" }, { "id": "IC.FRM.FIN.FIN2", "metatype": [ { "id": "IndicatorName", "value": "Proportion of investment financed by banks (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Estimated proportion of purchases of fixed assets that was financed from bank loans." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Finance" } ], "source_id": "13" }, { "id": "IC.FRM.FIN.FIN20", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms not needing a loan" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percent of firms that did not apply for a loan in the last fiscal year because they did not need a loan. The denominator is the number of firms who did and did not apply for a loan. The numerator is the number of firms\nwho did not apply for a loan and also stated that they did not need a loan." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Finance" } ], "source_id": "13" }, { "id": "IC.FRM.FIN.FIN21", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms whose recent loan application was rejected" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percent of firms whose recent loan application was rejected." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Finance" } ], "source_id": "13" }, { "id": "IC.FRM.FIN.FIN22", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms using supplier/customer credit to finance working capital" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms using credit from suppliers and advances from customers to finance working capital." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Finance" } ], "source_id": "13" }, { "id": "IC.FRM.FIN.FIN7", "metatype": [ { "id": "IndicatorName", "value": "Proportion of working capital financed by banks (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of the working capital that was financed by bank loans." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Finance" } ], "source_id": "13" }, { "id": "IC.FRM.GEN.GEND1", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms with female participation in ownership" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms with females among the owners." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Gender" } ], "source_id": "13" }, { "id": "IC.FRM.GEN.GEND2", "metatype": [ { "id": "IndicatorName", "value": "Proportion of permanent full-time workers that are female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of full-time workers that are female." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Gender" } ], "source_id": "13" }, { "id": "IC.FRM.GEN.GEND3", "metatype": [ { "id": "IndicatorName", "value": "Proportion of permanent full-time non-production workers that are female (%)*" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of permanent full-time non-production workers that are female." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Gender" } ], "source_id": "13" }, { "id": "IC.FRM.GEN.GEND4", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms with a female top manager" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms with females as the top manager." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Gender" } ], "source_id": "13" }, { "id": "IC.FRM.GEN.GEND5", "metatype": [ { "id": "IndicatorName", "value": "Proportion of permanent full-time production workers that are female (%)*" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of permanent full-time production workers that are female." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Gender" } ], "source_id": "13" }, { "id": "IC.FRM.GEN.GEND6", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms with majority female ownership" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms with majority female ownership" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Gender" } ], "source_id": "13" }, { "id": "IC.FRM.INFOR.INFOR1", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms competing against unregistered or informal firms" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms competing against unregistered or informal firms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Informality" } ], "source_id": "13" }, { "id": "IC.FRM.INFOR.INFOR2", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms identifying practices of competitors in the informal sector as a major constraint" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms identifying practices of competitors in the informal sector as major constraint. The computation of the indicator is based on the rating of the obstacle as a potential constraint to the current operations of the establishment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Informality" } ], "source_id": "13" }, { "id": "IC.FRM.INFOR.INFOR4", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms formally registered when they started operations in the country" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms formally registered when they started operations in the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Informality" } ], "source_id": "13" }, { "id": "IC.FRM.INFOR.INFOR5", "metatype": [ { "id": "IndicatorName", "value": "Number of years firm operated without formal registration" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average number of years firms operated without formal registration. This indicator is computed only for the firms that did not have a formal registration when they started their operations in the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Informality" } ], "source_id": "13" }, { "id": "IC.FRM.INFRA.IN1", "metatype": [ { "id": "IndicatorName", "value": "Days to obtain an electrical connection (upon application)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average wait, in days, experienced to obtain electrical connection from the day this establishment applied for it to\nthe day it received the service." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Infrastructure" } ], "source_id": "13" }, { "id": "IC.FRM.INFRA.IN10_C", "metatype": [ { "id": "IndicatorName", "value": "If a generator is used, average proportion of electricity from a generator (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "If a generator is used, what percentage of electricity comes from a generator" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Infrastructure" } ], "source_id": "13" }, { "id": "IC.FRM.INFRA.IN11", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms identifying transportation as a major constraint" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms identifying transportation as a major constraint. The computation of the indicator is based on\nthe rating of the obstacle as a potential constraint to the current operations of the establishment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Infrastructure" } ], "source_id": "13" }, { "id": "IC.FRM.INFRA.IN12", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms identifying electricity as a major constraint" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms identifying electricity as a major constraint. The computation of the indicator is based on the\nrating of the obstacle as a potential constraint to the current operations of the establishment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Infrastructure" } ], "source_id": "13" }, { "id": "IC.FRM.INFRA.IN14", "metatype": [ { "id": "IndicatorName", "value": "Proportion of products lost to breakage or spoilage during shipping to domestic markets (%)*" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of products shipped to supply domestic markets lost due to breakage or spoilage." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Infrastructure" } ], "source_id": "13" }, { "id": "IC.FRM.INFRA.IN16", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms experiencing electrical outages" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms that experienced power outages over the last complete fiscal year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Infrastructure" } ], "source_id": "13" }, { "id": "IC.FRM.INFRA.IN17", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms experiencing water insufficiencies" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms that experienced insufficient water supply for production over the last complete fiscal year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Infrastructure" } ], "source_id": "13" }, { "id": "IC.FRM.INFRA.IN2", "metatype": [ { "id": "IndicatorName", "value": "Number of electrical outages in a typical month" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average number of power outages in a typical month." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Infrastructure" } ], "source_id": "13" }, { "id": "IC.FRM.INFRA.IN3_C", "metatype": [ { "id": "IndicatorName", "value": "If there were outages, average duration of a typical electrical outage (hours)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average duration of power outages (hours) conditional on having a power outage." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Infrastructure" } ], "source_id": "13" }, { "id": "IC.FRM.INFRA.IN4_C", "metatype": [ { "id": "IndicatorName", "value": "If there were outages, average losses due to electrical outages (% of annual sales)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "If there were outages, average losses due to electrical outages, as percentage of total annual sales." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Infrastructure" } ], "source_id": "13" }, { "id": "IC.FRM.INFRA.IN6", "metatype": [ { "id": "IndicatorName", "value": "Number of water insufficiencies in a typical month*" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average number of water shortages in a typical month in the last fiscal year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Infrastructure" } ], "source_id": "13" }, { "id": "IC.FRM.INFRA.IN9", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms owning or sharing a generator" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms owning or sharing a generator." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Infrastructure" } ], "source_id": "13" }, { "id": "IC.FRM.INNOV.T1", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms with an internationally-recognized quality certification" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms that have an internationally-recognized quality certification, i.e. ISO 9000, 9002 or 14000." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Firm Characteristics" } ], "source_id": "13" }, { "id": "IC.FRM.INNOV.T10", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms that spend on R&D" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms that spent on formal research and development activities during the last fiscal year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Innovation and Technology" } ], "source_id": "13" }, { "id": "IC.FRM.INNOV.T2", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms with an annual financial statement reviewed by external auditors" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms with their annual financial statement reviewed by an external auditor." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Firm Characteristics" } ], "source_id": "13" }, { "id": "IC.FRM.INNOV.T3", "metatype": [ { "id": "IndicatorName", "value": "Capacity utilization (%)*" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Capacity utilization based on comparison of the current output with the maximum output possible using the\ncurrent inputs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Performance" } ], "source_id": "13" }, { "id": "IC.FRM.INNOV.T4", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms using technology licensed from foreign companies*" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms using technology licensed from foreign companies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Innovation and Technology" } ], "source_id": "13" }, { "id": "IC.FRM.INNOV.T5", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms having their own Web site" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms using website for business related activities, i.e. sales, product promotion etc." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Innovation and Technology" } ], "source_id": "13" }, { "id": "IC.FRM.INNOV.T6", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms using e-mail to interact with clients/suppliers" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms using email to interact with clients or suppliers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Innovation and Technology" } ], "source_id": "13" }, { "id": "IC.FRM.INNOV.T7", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms that introduced a new product/service" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms that introduced new or significantly improved products or services over the last three years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Innovation and Technology" } ], "source_id": "13" }, { "id": "IC.FRM.INNOV.T8", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms whose new product/service is also new to the main market" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms that introduced new or significantly improved products or services over the last three years\n that were also new for the firms' main market." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Innovation and Technology" } ], "source_id": "13" }, { "id": "IC.FRM.INNOV.T9", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms that introduced a process innovation" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms that introduced any new or significantly improved process." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Innovation and Technology" } ], "source_id": "13" }, { "id": "IC.FRM.OBS.OBST1", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms choosing access to finance as their biggest obstacle" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider access to finance to be the Biggest Obstacle" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Biggest Obstacle" } ], "source_id": "13" }, { "id": "IC.FRM.OBS.OBST10", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms choosing labor regulations as their biggest obstacle" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider labor regulations to be the Biggest Obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Biggest Obstacle" } ], "source_id": "13" }, { "id": "IC.FRM.OBS.OBST11", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms choosing political instability as their biggest obstacle" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider political instability to be the Biggest Obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Biggest Obstacle" } ], "source_id": "13" }, { "id": "IC.FRM.OBS.OBST12", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms choosing practices of the informal sector as their biggest obstacle" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider practices of competitors in the informal sector to be the Biggest Obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Biggest Obstacle" } ], "source_id": "13" }, { "id": "IC.FRM.OBS.OBST13", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms choosing tax administration as their biggest obstacle" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider the tax administration to be the Biggest Obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Biggest Obstacle" } ], "source_id": "13" }, { "id": "IC.FRM.OBS.OBST14", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms choosing tax rates as their biggest obstacle" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider tax rates to be the Biggest Obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Biggest Obstacle" } ], "source_id": "13" }, { "id": "IC.FRM.OBS.OBST15", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms choosing transportation as their biggest obstacle" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider transport to be the Biggest Obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Biggest Obstacle" } ], "source_id": "13" }, { "id": "IC.FRM.OBS.OBST2", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms choosing access to land as their biggest obstacle" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider access to land to be the Biggest Obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Biggest Obstacle" } ], "source_id": "13" }, { "id": "IC.FRM.OBS.OBST3", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms choosing business licensing and permits as their biggest obstacle" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider business licensing and permits to be the Biggest Obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Biggest Obstacle" } ], "source_id": "13" }, { "id": "IC.FRM.OBS.OBST4", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms choosing corruption as their biggest obstacle" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider corruption to be the Biggest Obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Biggest Obstacle" } ], "source_id": "13" }, { "id": "IC.FRM.OBS.OBST5", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms choosing courts as their biggest obstacle" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider courts to be the Biggest Obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Biggest Obstacle" } ], "source_id": "13" }, { "id": "IC.FRM.OBS.OBST6", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms choosing crime, theft and disorder as their biggest obstacle" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider crime, theft and disorder to be the Biggest Obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Biggest Obstacle" } ], "source_id": "13" }, { "id": "IC.FRM.OBS.OBST7", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms choosing customs and trade regulations as their biggest obstacle" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider customs and trade regulations to be the Biggest Obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Biggest Obstacle" } ], "source_id": "13" }, { "id": "IC.FRM.OBS.OBST8", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms choosing electricity as their biggest obstacle" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider electricity to be the Biggest Obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Biggest Obstacle" } ], "source_id": "13" }, { "id": "IC.FRM.OBS.OBST9", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms choosing inadequately educated workforce as their biggest obstacle" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider an inadequately educated workforce to be the Biggest Obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Biggest Obstacle" } ], "source_id": "13" }, { "id": "IC.FRM.PROD.GROW.PEFT3", "metatype": [ { "id": "IndicatorName", "value": "Real annual labor productivity growth (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Annual labor productivity growth is measured by a percentage change in labor productivity between the last completed fiscal year and a previous period, where labor productivity is sales divided by the number of full- time permanent workers. All sales values are deflated to 2009 using each country's GDP deflators." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Performance" } ], "source_id": "13" }, { "id": "IC.FRM.REG.BUS1", "metatype": [ { "id": "IndicatorName", "value": "Days to obtain an import license" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average wait, in days, to obtain import license." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Regulations and Taxes" } ], "source_id": "13" }, { "id": "IC.FRM.REG.BUS2", "metatype": [ { "id": "IndicatorName", "value": "Days to obtain an operating license" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The average wait, in days, to obtain an operating license." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Regulations and Taxes" } ], "source_id": "13" }, { "id": "IC.FRM.REG.BUS3", "metatype": [ { "id": "IndicatorName", "value": "Days to obtain a construction-related permit" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average wait, in days, to obtain construction-related permit." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Regulations and Taxes" } ], "source_id": "13" }, { "id": "IC.FRM.REG.BUS5", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms identifying business licensing and permits as a major constraint" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms identifying business licensing and permits as \"major\" or \"very severe\" obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Regulations and Taxes" } ], "source_id": "13" }, { "id": "IC.FRM.REG.REG1", "metatype": [ { "id": "IndicatorName", "value": "Senior management time spent dealing with the requirements of government regulation (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average percentage of senior management's time that is spent in a typical week dealing with requirements imposed by government regulations (eg. taxes, customs, labor regulations, licensing and registration), including dealings with officials, completing forms, et cetera." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Regulations and Taxes" } ], "source_id": "13" }, { "id": "IC.FRM.REG.REG2_C", "metatype": [ { "id": "IndicatorName", "value": "If there were visits, average number of visits or required meetings with tax officials" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "If there were a positive number of visits or required meeting with tax officials, what was the average number?" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Regulations and Taxes" } ], "source_id": "13" }, { "id": "IC.FRM.REG.REG4", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms identifying tax rates as a major constraint" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms identifying tax rates as a \"major\" or \"very severe\" obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Regulations and Taxes" } ], "source_id": "13" }, { "id": "IC.FRM.REG.REG5", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms identifying tax administration as a major constraint" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms identifying tax administration as a \"major\" or \"very severe\" obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Regulations and Taxes" } ], "source_id": "13" }, { "id": "IC.FRM.REG.REG6", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms visited or required to meet with tax officials" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms that were visited or inspected by tax officials or were required to meet with them over the last year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Regulations and Taxes" } ], "source_id": "13" }, { "id": "IC.FRM.SLS.GROW.PEFT1", "metatype": [ { "id": "IndicatorName", "value": "Real annual sales growth (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Real annual sales growth is measured as a percentage change in sales between the last completed fiscal year and a previous period. All sales values are deflated to 2009 using each country's GDP deflators." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Performance" } ], "source_id": "13" }, { "id": "IC.FRM.TRD.TR1", "metatype": [ { "id": "IndicatorName", "value": "Days to clear direct exports through customs" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average number of days to clear direct exports through customs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Trade" } ], "source_id": "13" }, { "id": "IC.FRM.TRD.TR11", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms using material inputs and/or supplies of foreign origin*" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms that use material inputs and/or supplies of foreign origin." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Trade" } ], "source_id": "13" }, { "id": "IC.FRM.TRD.TR16", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms exporting directly (at least 10% of sales)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms that export directly at least 10% of their total annual sales." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Trade" } ], "source_id": "13" }, { "id": "IC.FRM.TRD.TR17", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms exporting directly or indirectly (at least 10% of sales)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms that export directly or indirectly at least 10% of their total annual sales." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Trade" } ], "source_id": "13" }, { "id": "IC.FRM.TRD.TR2", "metatype": [ { "id": "IndicatorName", "value": "Days to clear imports from customs*" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average number of days to clear imports from customs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Trade" } ], "source_id": "13" }, { "id": "IC.FRM.TRD.TR5", "metatype": [ { "id": "IndicatorName", "value": "Proportion of total sales that are exported directly (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Sales exported directly as percentage of total sales." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Trade" } ], "source_id": "13" }, { "id": "IC.FRM.TRD.TR8", "metatype": [ { "id": "IndicatorName", "value": "Proportion of total inputs that are of foreign origin (%)*" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of material inputs and/or supplies of foreign origin." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Trade" } ], "source_id": "13" }, { "id": "IC.FRM.TRD.TR9", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms identifying customs and trade regulations as a major constraint" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms identifying customs and trade regulations as a \"major\" or \"very severe\" obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Trade" } ], "source_id": "13" }, { "id": "IC.FRM.WRKF.WK1", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms offering formal training" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms offering formal training programs for its permanent, full-time employees." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Workforce" } ], "source_id": "13" }, { "id": "IC.FRM.WRKF.WK10", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms identifying an inadequately educated workforce as a major constraint" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms identifying labor skill level as a major constraint. The computation of the indicator is based on\nthe rating of the obstacle as a potential constraint to the current operations of the establishment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Workforce" } ], "source_id": "13" }, { "id": "IC.FRM.WRKF.WK14", "metatype": [ { "id": "IndicatorName", "value": "Number of workers" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of workers, including permanent and temporary workers. The number of temporary workers is adjusted for the number of months of their employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Workforce" } ], "source_id": "13" }, { "id": "IC.FRM.WRKF.WK15", "metatype": [ { "id": "IndicatorName", "value": "Proportion of production workers (out of all permanent workers)*" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of production workers out of all permanent workers*." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Workforce" } ], "source_id": "13" }, { "id": "IC.FRM.WRKF.WK17", "metatype": [ { "id": "IndicatorName", "value": "Proportion of temporary workers (out of all workers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The proportion of temporary workers out of all workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Workforce" } ], "source_id": "13" }, { "id": "IC.FRM.WRKF.WK18", "metatype": [ { "id": "IndicatorName", "value": "Proportion of permanent workers (out of all workers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The proportion of permanent workers out of all workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Workforce" } ], "source_id": "13" }, { "id": "IC.FRM.WRKF.WK19", "metatype": [ { "id": "IndicatorName", "value": "Proportion of skilled workers (out of all production workers) (%)*" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of skilled workers out of all production workers*." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Workforce" } ], "source_id": "13" }, { "id": "IC.FRM.WRKF.WK2", "metatype": [ { "id": "IndicatorName", "value": "Proportion of workers offered formal training (%)*" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of permanent, full-time employees that have received formal training." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Workforce" } ], "source_id": "13" }, { "id": "IC.FRM.WRKF.WK8", "metatype": [ { "id": "IndicatorName", "value": "Years of the top manager's experience working in the firm's sector" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Years of experience of the top manager working in the sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Workforce" } ], "source_id": "13" }, { "id": "IC.FRM.WRKF.WK9", "metatype": [ { "id": "IndicatorName", "value": "Percent of firms identifying labor regulations as a major constraint" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms identifying labor regulations as a \"major\" or \"very severe\" obstacle." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Workforce" } ], "source_id": "13" }, { "id": "account.t.d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "Generalcomments", "value": "The aggregated data for MEA and SAS is based on the FY26 classification." }, { "id": "IndicatorName", "value": "Account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having an account (by themselves or together with someone else) at a bank or similar financial institution (see the definition for \"bank or similar financial institution account\") or report personally using a mobile money service in the past year (see the definition for \"mobile money account\"), (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "account.t.d.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "Generalcomments", "value": "The aggregated data for MEA and SAS is based on the FY26 classification." }, { "id": "IndicatorName", "value": "Account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having an account (by themselves or together with someone else) at a bank or similar financial institution (see the definition for \"bank or similar financial institution account\") or report personally using a mobile money service in the past year (see the definition for \"mobile money account\"), women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "account.t.d.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "Generalcomments", "value": "The aggregated data for MEA and SAS is based on the FY26 classification." }, { "id": "IndicatorName", "value": "Account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having an account (by themselves or together with someone else) at a bank or similar financial institution (see the definition for \"bank or similar financial institution account\") or report personally using a mobile money service in the past year (see the definition for \"mobile money account\"), men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "borrow.any.t.d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed any money (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money (by themselves or together with someone else) for any reason and from any source in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "borrow.any.t.d.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed any money, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money (by themselves or together with someone else) for any reason and from any source in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "borrow.any.t.d.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed any money, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money (by themselves or together with someone else) for any reason and from any source in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Own a mobile phone (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report owning a mobile phone that they use to make and receive personal calls, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con1.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Own a mobile phone, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report owning a mobile phone that they use to make and receive personal calls, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con1.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Own a mobile phone, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report owning a mobile phone that they use to make and receive personal calls, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con11", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "SIM number registered in own name (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that the SIM number in their mobile phone is registered in their name, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con11.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "SIM number registered in own name, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that the SIM number in their mobile phone is registered in their name, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con11.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "SIM number registered in own name, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that the SIM number in their mobile phone is registered in their name, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con12d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Daily mobile phone use (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they use a mobile phone daily, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con12d.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Daily mobile phone use, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they use a mobile phone daily, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con12d.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Daily mobile phone use, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they use a mobile phone daily, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con14", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Read a text message on a mobile phone (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they read a text message on a mobile phone. This can include an SMS or text message or a message on a messaging app, such as WhatsApp, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con14.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Read a text message on a mobile phone, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they read a text message on a mobile phone. This can include an SMS or text message or a message on a messaging app, such as WhatsApp, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con14.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Read a text message on a mobile phone, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they read a text message on a mobile phone. This can include an SMS or text message or a message on a messaging app, such as WhatsApp, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con16", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Can send a text message on a mobile phone (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they sent a text message through a mobile phone. This can include an SMS or text message or a message on a messaging app, such as WhatsApp, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con16.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Can send a text message on a mobile phone, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they sent a text message through a mobile phone. This can include an SMS or text message or a message on a messaging app, such as WhatsApp, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con16.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Can send a text message on a mobile phone, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they sent a text message through a mobile phone. This can include an SMS or text message or a message on a messaging app, such as WhatsApp, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con17a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Prefers receiving government news by SMS or text (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they would prefer receiving important government news by SMS or text, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con17a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Prefers receiving government news by SMS or text, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they would prefer receiving important government news by SMS or text, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con17a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Prefers receiving government news by SMS or text, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they would prefer receiving important government news by SMS or text, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con17b", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Prefers receiving government news by phone call (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they would prefer receiving important government news by phone call, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con17b.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Prefers receiving government news by phone call, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they would prefer receiving important government news by phone call, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con17b.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Prefers receiving government news by phone call, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they would prefer receiving important government news by phone call, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con18", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Phone has a lock (PIN, password, fingerprint) (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report a lock on the mobile phone that they use, such as a PIN, password, or fingerprint, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con18.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Phone has a lock (PIN, password, fingerprint), women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report a lock on the mobile phone that they use, such as a PIN, password, or fingerprint, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con18.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Phone has a lock (PIN, password, fingerprint), men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report a lock on the mobile phone that they use, such as a PIN, password, or fingerprint, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con19", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Can change PIN or password on mobile phone without help (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they can change the PIN or password, without help, on the mobile phone they use, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con19.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Can change PIN or password on mobile phone without help, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they can change the PIN or password, without help, on the mobile phone they use, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con19.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Can change PIN or password on mobile phone without help, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they can change the PIN or password, without help, on the mobile phone they use, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con20", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Family member sets rules for mobile phone use (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that someone, such as a family member, sets rules about how they can use a mobile phone, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con20.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Family member sets rules for mobile phone use, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that someone, such as a family member, sets rules about how they can use a mobile phone, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con20.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Family member sets rules for mobile phone use, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that someone, such as a family member, sets rules about how they can use a mobile phone, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con21", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received phone call or SMS asking for money in the past 12 months (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they have received a phone call, SMS, or text message on their mobile phone from someone they didn't know, asking them to send money, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con21.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received phone call or SMS asking for money in the past 12 months, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they have received a phone call, SMS, or text message on their mobile phone from someone they didn't know, asking them to send money, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con21.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received phone call or SMS asking for money in the past 12 months, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they have received a phone call, SMS, or text message on their mobile phone from someone they didn't know, asking them to send money, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con26d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Daily internet use (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they use the internet daily, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con26d.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Daily internet use, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they use the internet daily, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con26d.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Daily internet use, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they use the internet daily, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con27", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Buys data package for internet use (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they buy a data package to use the internet, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con27.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Buys data package for internet use, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they buy a data package to use the internet, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con27.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Buys data package for internet use, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they buy a data package to use the internet, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con28m", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Buys data package monthly (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they buy a monthly data package to use the internet, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con28m.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Buys data package monthly, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they buy a monthly data package to use the internet, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con28m.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Buys data package monthly, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they buy a monthly data package to use the internet, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent a voice message in past three months (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they sent a voice message from a mobile phone in the past three months, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent a voice message in past three months, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they sent a voice message from a mobile phone in the past three months, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent a voice message in past three months, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they sent a voice message from a mobile phone in the past three months, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30b", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent a photo in past three months (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they sent a photo from a mobile phone in the past three months, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30b.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent a photo in past three months, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they sent a photo from a mobile phone in the past three months, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30b.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent a photo in past three months, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they sent a photo from a mobile phone in the past three months, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30c", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used social media in past three months (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they used social media, such as Facebook or TikTok, on a mobile phone in the past three months, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30c.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used social media in past three months, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they used social media, such as Facebook or TikTok, on a mobile phone in the past three months, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30c.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used social media in past three months, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they used social media, such as Facebook or TikTok, on a mobile phone in the past three months, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Read news online in past three months (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they read about news online in the past three months, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30d.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Read news online in past three months, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they read about news online in the past three months, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30d.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Read news online in past three months, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they read about news online in the past three months, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30e", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Accessed online info to learn/train/educate in past three months (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they accessed information online to learn, train, or educate themselves or someone else in the past three months, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30e.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Accessed online info to learn/train/educate in past three months, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they accessed information online to learn, train, or educate themselves or someone else in the past three months, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30e.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Accessed online info to learn/train/educate in past three months, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they accessed information online to learn, train, or educate themselves or someone else in the past three months, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30g", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Accessed government services/info online in past three months (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they accessed government services or searched for government information online in the past three months, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30g.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Accessed government services/info online in past three months, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they accessed government services or searched for government information online in the past three months, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con30g.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Accessed government services/info online in past three months, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they accessed government services or searched for government information online in the past three months, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con31a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Does not own a smartphone due to cost (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who don't own a smartphone, because they don't have enough money to buy one, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con31a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Does not own a smartphone due to cost, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who don't own a smartphone, because they don't have enough money to buy one, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con31a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Does not own a smartphone due to cost, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who don't own a smartphone, because they don't have enough money to buy one, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con9a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main mobile phone is a smartphone (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that the main mobile phone they use is a smartphone, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con9a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main mobile phone is a smartphone, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that the main mobile phone they use is a smartphone, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con9a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main mobile phone is a smartphone, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that the main mobile phone they use is a smartphone, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con9b", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main mobile phone is a basic text phone (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that the main mobile phone they use is a basic text phone, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con9b.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main mobile phone is a basic text phone, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that the main mobile phone they use is a basic text phone, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "con9b.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main mobile phone is a basic text phone, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that the main mobile phone they use is a basic text phone, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "dig.acc", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Digitally enabled account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of adults who report having a mobile money account or an account at a bank or similar financial institution that allows the account owner to make or receive payments using a card or phone, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "dig.acc.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Digitally enabled account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of adults who report having a mobile money account or an account at a bank or similar financial institution that allows the account owner to make or receive payments using a card or phone, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "dig.acc.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Digitally enabled account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of adults who report having a mobile money account or an account at a bank or similar financial institution that allows the account owner to make or receive payments using a card or phone, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fh1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent domestic remittances (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally sending any of their money in the past year to a relative or friend living in a different area of their country. This can be money they hand-delivered personally or sent in some other way, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fh1.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent domestic remittances, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally sending any of their money in the past year to a relative or friend living in a different area of their country. This can be money they hand-delivered personally or sent in some other way, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fh1.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent domestic remittances, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally sending any of their money in the past year to a relative or friend living in a different area of their country. This can be money they hand-delivered personally or sent in some other way, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fh1.fh2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent or received domestic remittances (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally sending or receiving any of their money in the past year to or from a relative or friend living in a different area of their country, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fh1.fh2.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent or received domestic remittances, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally sending or receiving any of their money in the past year to or from a relative or friend living in a different area of their country, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fh1.fh2.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent or received domestic remittances, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally sending or receiving any of their money in the past year to or from a relative or friend living in a different area of their country, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fh2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received domestic remittances (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any money in the past year from a relative or friend living in a different area of their country. This includes any money received in-person, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fh2.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received domestic remittances, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any money in the past year from a relative or friend living in a different area of their country. This includes any money received in-person, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fh2.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received domestic remittances, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any money in the past year from a relative or friend living in a different area of their country. This includes any money received in-person, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fh2a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received international remittances (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they recieved international remittances in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fh2a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received international remittances , women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they recieved international remittances in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fh2a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received international remittances , men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they recieved international remittances in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fiaccount.t.d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Bank or similar financial institution account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another similar financial institution, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fiaccount.t.d.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Bank or similar financial institution account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another similar financial institution, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fiaccount.t.d.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Bank or similar financial institution account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another similar financial institution, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin10", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Owns a credit card (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having a credit card, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin10.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Owns a credit card, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having a credit card, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin10.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Owns a credit card, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having a credit card, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin11e", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "No account because someone in the family has one (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report not having a bank or similar financial institution account because someone else in their family already has one, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin11e.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "No account because someone in the family has one, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report not having a bank or similar financial institution account because someone else in their family already has one, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin11e.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "No account because someone in the family has one, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report not having a bank or similar financial institution account because someone else in their family already has one, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin17a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved at a bank or similar financial institution (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving or setting aside any money at a bank or similar financial institution in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin17a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved at a bank or similar financial institution, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving or setting aside any money at a bank or similar financial institution in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin17a.17a1.d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved at a bank or similar financial institution or using a mobile money account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving or setting aside any money at a bank or similar financial institution or using a mobile money account to save in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin17a.17a1.d.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved at a bank or similar financial institution or using a mobile money account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving or setting aside any money at a bank or similar financial institution or using a mobile money account to save in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin17a.17a1.d.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved at a bank or similar financial institution or using a mobile money account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving or setting aside any money at a bank or similar financial institution or using a mobile money account to save in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin17a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved at a bank or similar financial institution, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving or setting aside any money at a bank or similar financial institution in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin17b", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved money using a mobile money account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving money using a mobile money account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin17b.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved money using a mobile money account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving money using a mobile money account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin17b.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved money using a mobile money account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving money using a mobile money account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin17c", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved using a savings club or a person outside the family (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving or setting aside any money in the past year by using an informal savings club or a person outside the family, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin17c.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved using a savings club or a person outside the family, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving or setting aside any money in the past year by using an informal savings club or a person outside the family, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin17c.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved using a savings club or a person outside the family, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving or setting aside any money in the past year by using an informal savings club or a person outside the family, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin17dm", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved or set aside money into an account, monthly (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving or setting aside any money in the past year on monthly basis using an account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin17dm.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved or set aside money into an account, monthly, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving or setting aside any money in the past year on monthly basis using an account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin17dm.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved or set aside money into an account, monthly, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving or setting aside any money in the past year on monthly basis using an account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin19", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made regular payments to insurance agent or company (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they made made any regular payments to an insurance agent or company in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin19.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made regular payments to insurance agent or company , women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they made made any regular payments to an insurance agent or company in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin19.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made regular payments to insurance agent or company , men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they made made any regular payments to an insurance agent or company in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin2.t.d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Owns a debit card (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having an ATM or debit card, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin2.t.d.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Owns a debit card, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having an ATM or debit card, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin2.t.d.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Owns a debit card, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having an ATM or debit card, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed from a formal bank or similar financial institution (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money from a bank or similar financial institution or using a credit card in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed from a formal bank or similar financial institution, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money from a bank or similar financial institution or using a credit card in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed from a formal bank or similar financial institution, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money from a bank or similar financial institution or using a credit card in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22a.22a1.22g.d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed any money from a formal bank or similar financial institution or using a mobile money account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money from a bank or similar financial institution, or using a credit card, or using a mobile money account in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22a.22a1.22g.d.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed any money from a formal bank or similar financial institution or using a mobile money account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money from a bank or similar financial institution, or using a credit card, or using a mobile money account in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22a.22a1.22g.d.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed any money from a formal bank or similar financial institution or using a mobile money account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money from a bank or similar financial institution, or using a credit card, or using a mobile money account in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22b", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed from family or friends (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money from family, relatives, or friends in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22b.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed from family or friends, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money from family, relatives, or friends in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22b.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed from family or friends, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money from family, relatives, or friends in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22c", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed from a savings club (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money from an informal savings club in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22c.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed from a savings club, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money from an informal savings club in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22c.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed from a savings club, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money from an informal savings club in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed for health or medical purposes (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money for health or medical purposes in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22d.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed for health or medical purposes, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money for health or medical purposes in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22d.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed for health or medical purposes, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money for health or medical purposes in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22e", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed to start or operate a business (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money to start or operate a business in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22e.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed to start or operate a business, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money to start or operate a business in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22e.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Borrowed to start or operate a business, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money to start or operate a business in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22f", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Purchased food on credit (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report buying household food and paying for it at a later date, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22f.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Purchased food on credit, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report buying household food and paying for it at a later date, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22f.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Purchased food on credit, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report buying household food and paying for it at a later date, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22g", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used a credit card (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using their own credit card in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22g.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used a credit card, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using their own credit card in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22g.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used a credit card, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using their own credit card in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22h", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Paid off all credit card balances in full by their due date (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they paid off all credit card balances in full by their due date, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22h.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Paid off all credit card balances in full by their due date, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they paid off all credit card balances in full by their due date, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin22h.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Paid off all credit card balances in full by their due date, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they paid off all credit card balances in full by their due date, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: not possible (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is not possible for them to come up with the emergency funds in 30 days. This includes people who say do not know or refused, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aN.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: not possible, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is not possible for them to come up with the emergency funds in 30 days. This includes people who say do not know or refused, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aN.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: not possible, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is not possible for them to come up with the emergency funds in 30 days. This includes people who say do not know or refused, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aND", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: possible and not difficult at all (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible and not difficult at all for them to come up with the funds in 30 days, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aND.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: possible and not difficult at all, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible and not difficult at all for them to come up with the funds in 30 days, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aND.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: possible and not difficult at all, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible and not difficult at all for them to come up with the funds in 30 days, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aP", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: possible (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible--whether\"difficult,\"\"somewhat difficult,\"or\"not very difficult\"--for them to come up with the emergency funds in 30 days, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aP.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: possible, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible--whether\"difficult,\"\"somewhat difficult,\"or\"not very difficult\"--for them to come up with the emergency funds in 30 days, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aP.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: possible, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible--whether\"difficult,\"\"somewhat difficult,\"or\"not very difficult\"--for them to come up with the emergency funds in 30 days, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aSD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: possible and somewhat difficult (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible and somewhat difficult for them to come up with the emergency funds in 30 days, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aSD.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: possible and somewhat difficult, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible and somewhat difficult for them to come up with the emergency funds in 30 days, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aSD.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: possible and somewhat difficult, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible and somewhat difficult for them to come up with the emergency funds in 30 days, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aSD_ND", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: possible and not difficult or somewhat difficult (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible and not difficult at all or somewhat difficult to come up with the funds in 30 days, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aSD_ND.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: possible and not difficult or somewhat difficult, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible and not difficult at all or somewhat difficult to come up with the funds in 30 days, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aSD_ND.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: possible and not difficult or somewhat difficult, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible and not difficult at all or somewhat difficult to come up with the funds in 30 days, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aVD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: possible and very difficult (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible and very difficult for them to come up with the emergency funds in 30 days, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aVD.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: possible and very difficult, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible and very difficult for them to come up with the emergency funds in 30 days, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24aVD.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Coming up with emergency funds in 30 days: possible and very difficult, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible and very difficult for them to come up with the emergency funds in 30 days, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24ba", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Less than two weeks can be covered using savings, borrowing, selling something, seeking help from friends and family, or other ways, in case household loses its main source of income (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they could cover expenses by using savings, borrowing, selling something they own, seeking help from family and friends or through some other way for less than two weeks, in case they their household lost its main source of income, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24ba.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Less than two weeks can be covered using savings, borrowing, selling something, seeking help from friends and family, or other ways, in case household loses its main source of income, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they could cover expenses by using savings, borrowing, selling something they own, seeking help from family and friends or through some other way for less than two weeks, in case they their household lost its main source of income, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24ba.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Less than two weeks can be covered using savings, borrowing, selling something, seeking help from friends and family, or other ways, in case household loses its main source of income, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they could cover expenses by using savings, borrowing, selling something they own, seeking help from family and friends or through some other way for less than two weeks, in case they their household lost its main source of income, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24bb", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "About one month can be covered using savings, borrowing, selling something, seeking help from friends and family, or other ways, in case household loses its main source of income (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they could cover expenses by using savings, borrowing, selling something they own, seeking help from family and friends or through some other way for about one month, in case they their household lost its main source of income, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24bb.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "About one month can be covered using savings, borrowing, selling something, seeking help from friends and family, or other ways, in case household loses its main source of income, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they could cover expenses by using savings, borrowing, selling something they own, seeking help from family and friends or through some other way for about one month, in case they their household lost its main source of income, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24bb.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "About one month can be covered using savings, borrowing, selling something, seeking help from friends and family, or other ways, in case household loses its main source of income, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they could cover expenses by using savings, borrowing, selling something they own, seeking help from family and friends or through some other way for about one month, in case they their household lost its main source of income, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24bc", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "About two months can be covered using savings, borrowing, selling something, seeking help from friends and family, or other ways, in case household loses its main source of income (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they could cover expenses by using savings, borrowing, selling something they own, seeking help from family and friends or through some other way for about two months, in case they their household lost its main source of income, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24bc.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "About two months can be covered using savings, borrowing, selling something, seeking help from friends and family, or other ways, in case household loses its main source of income, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they could cover expenses by using savings, borrowing, selling something they own, seeking help from family and friends or through some other way for about two months, in case they their household lost its main source of income, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24bc.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "About two months can be covered using savings, borrowing, selling something, seeking help from friends and family, or other ways, in case household loses its main source of income, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they could cover expenses by using savings, borrowing, selling something they own, seeking help from family and friends or through some other way for about two months, in case they their household lost its main source of income, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24bd", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "More than two months can be covered using savings, borrowing, selling something, seeking help from friends and family, or other ways, in case household loses its main source of income (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they could cover expenses by using savings, borrowing, selling something they own, seeking help from family and friends or through some other way for more than two months, in case they their household lost its main source of income, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24bd.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "More than two months can be covered using savings, borrowing, selling something, seeking help from friends and family, or other ways, in case household loses its main source of income, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they could cover expenses by using savings, borrowing, selling something they own, seeking help from family and friends or through some other way for more than two months, in case they their household lost its main source of income, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24bd.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "More than two months can be covered using savings, borrowing, selling something, seeking help from friends and family, or other ways, in case household loses its main source of income, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they could cover expenses by using savings, borrowing, selling something they own, seeking help from family and friends or through some other way for more than two months, in case they their household lost its main source of income, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24bor", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: loan from a bank, employer, or private lender (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible for them to come up with emergency funds in 30 days and list borrowing from a bank, employer, or private lender as their main source of emergency funds, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24bor.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: loan from a bank, employer, or private lender, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible for them to come up with emergency funds in 30 days and list borrowing from a bank, employer, or private lender as their main source of emergency funds, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24bor.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: loan from a bank, employer, or private lender, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible for them to come up with emergency funds in 30 days and list borrowing from a bank, employer, or private lender as their main source of emergency funds, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24c", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Experienced a natural disaster or severe weather event (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they personally experienced a natural disaster or severe weather event in the past three years, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24c.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Experienced a natural disaster or severe weather event, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they personally experienced a natural disaster or severe weather event in the past three years, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24c.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Experienced a natural disaster or severe weather event, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they personally experienced a natural disaster or severe weather event in the past three years, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24d1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Natural disaster in the past three years: Income lost or unable to work, by self or household member (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they or someone in their household lost income or were unable to work as a result of natural disasters or severe weather events in the past three years, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24d1.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Natural disaster in the past three years: Income lost or unable to work, by self or household member, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they or someone in their household lost income or were unable to work as a result of natural disasters or severe weather events in the past three years, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24d1.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Natural disaster in the past three years: Income lost or unable to work, by self or household member, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they or someone in their household lost income or were unable to work as a result of natural disasters or severe weather events in the past three years, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24d2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Natural disaster in the past three years: Damage to home or livestock (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they experienced damage to their home or livestock as a result of natural disasters or severe weather events in the past three years, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24d2.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Natural disaster in the past three years: Damage to home or livestock, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they experienced damage to their home or livestock as a result of natural disasters or severe weather events in the past three years, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24d2.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Natural disaster in the past three years: Damage to home or livestock, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they experienced damage to their home or livestock as a result of natural disasters or severe weather events in the past three years, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24fam", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: family or friends (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible for them to come up with emergency funds in 30 days and list family, relatives, or friends as their main source of emergency funds, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24fam.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: family or friends, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible for them to come up with emergency funds in 30 days and list family, relatives, or friends as their main source of emergency funds, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24fam.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: family or friends, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible for them to come up with emergency funds in 30 days and list family, relatives, or friends as their main source of emergency funds, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24fam_SD_ND", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: family or friends, possible and not difficult or somewhat difficult (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say that their main source of emergency money is family and friends and that it is possible and not difficult at all or somewhat difficult to come up with the funds in 30 days, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24fam_SD_ND.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: family or friends, possible and not difficult or somewhat difficult, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say that their main source of emergency money is family and friends and that it is possible and not difficult at all or somewhat difficult to come up with the funds in 30 days, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24fam_SD_ND.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: family or friends, possible and not difficult or somewhat difficult, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say that their main source of emergency money is family and friends and that it is possible and not difficult at all or somewhat difficult to come up with the funds in 30 days, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24fam_VD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: family or friends, possible and very difficult (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say that their main source of emergency money is family and friends and that it is possible and very difficult to come up with the funds in 30 days, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24fam_VD.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: family or friends, possible and very difficult, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say that their main source of emergency money is family and friends and that it is possible and very difficult to come up with the funds in 30 days, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24fam_VD.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: family or friends, possible and very difficult, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say that their main source of emergency money is family and friends and that it is possible and very difficult to come up with the funds in 30 days, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24sav", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: savings (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible for them to come up with emergency funds in 30 days and list savings as their main source of emergency funds, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24sav.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: savings, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible for them to come up with emergency funds in 30 days and list savings as their main source of emergency funds, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24sav.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: savings, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible for them to come up with emergency funds in 30 days and list savings as their main source of emergency funds, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24sav_SD_ND", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: savings, possible and not difficult or somewhat difficult (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say that their main source of emergency money is savings and that it is possible and not difficult at all or somewhat difficult to come up with the funds in 30 days, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24sav_SD_ND.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: savings, possible and not difficult or somewhat difficult, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say that their main source of emergency money is savings and that it is possible and not difficult at all or somewhat difficult to come up with the funds in 30 days, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24sav_SD_ND.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: savings, possible and not difficult or somewhat difficult, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say that their main source of emergency money is savings and that it is possible and not difficult at all or somewhat difficult to come up with the funds in 30 days, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24sell", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: sale of assets (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible for them to come up with emergency funds in 30 days and list selling assets as their main source of emergency funds, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24sell.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: sale of assets, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible for them to come up with emergency funds in 30 days and list selling assets as their main source of emergency funds, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24sell.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: sale of assets, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible for them to come up with emergency funds in 30 days and list selling assets as their main source of emergency funds, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24work", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: work (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible for them to come up with emergency funds in 30 days and list money from working as their main source of emergency funds, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24work.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: work, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible for them to come up with emergency funds in 30 days and list money from working as their main source of emergency funds, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24work.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: work, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say it is possible for them to come up with emergency funds in 30 days and list money from working as their main source of emergency funds, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24work_SD_ND", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: work, possible and not difficult or somewhat difficult (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say that their main source of emergency money is work and that it is possible and not difficult at all or somewhat difficult to come up with the funds in 30 days, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24work_SD_ND.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: work, possible and not difficult or somewhat difficult, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say that their main source of emergency money is work and that it is possible and not difficult at all or somewhat difficult to come up with the funds in 30 days, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin24work_SD_ND.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Main source of emergency funds in 30 days: work, possible and not difficult or somewhat difficult, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who say that their main source of emergency money is work and that it is possible and not difficult at all or somewhat difficult to come up with the funds in 30 days, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin25e1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used mobile phone or card to pay for household food or cleaning supplies (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using mobile phone or card to pay for household food or cleaning supplies, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin25e1.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used mobile phone or card to pay for household food or cleaning supplies, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using mobile phone or card to pay for household food or cleaning supplies, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin25e1.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used mobile phone or card to pay for household food or cleaning supplies, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using mobile phone or card to pay for household food or cleaning supplies, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin25e2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used mobile phone or card to pay for in-store purchase (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile phone or card to pay for instore purchase in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin25e2.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used mobile phone or card to pay for in-store purchase, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile phone or card to pay for instore purchase in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin25e2.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used mobile phone or card to pay for in-store purchase, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile phone or card to pay for instore purchase in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin25e2b", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Did NOT use mobile phone or card to pay for in-store purchase (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report not using a mobile phone or card to pay for instore purchase in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin25e2b.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Did NOT use mobile phone or card to pay for in-store purchase , women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report not using a mobile phone or card to pay for instore purchase in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin25e2b.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Did NOT use mobile phone or card to pay for in-store purchase , men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report not using a mobile phone or card to pay for instore purchase in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin25e4d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Reason for cash payment for in-store purchases: used to paying it with cash (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report the main reason they used cash to pay for instore purchase in the past year, is because they are used to paying by cash, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin25e4d.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Reason for cash payment for in-store purchases: used to paying it with cash, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report the main reason they used cash to pay for instore purchase in the past year, is because they are used to paying by cash, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin25e4d.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Reason for cash payment for in-store purchases: used to paying it with cash, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report the main reason they used cash to pay for instore purchase in the past year, is because they are used to paying by cash, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin26a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to pay bills (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile phone or the internet to pay bills in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin26a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to pay bills, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile phone or the internet to pay bills in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin26a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to pay bills, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile phone or the internet to pay bills in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin26b", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to buy something online (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile phone or the internet to buy something online in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin26b.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to buy something online, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile phone or the internet to buy something online in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin26b.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to buy something online, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile phone or the internet to buy something online in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin27a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a digital online merchant payment for an online purchase (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using the internet to buy something online in the past year and paid online, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin27a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a digital online merchant payment for an online purchase, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using the internet to buy something online in the past year and paid online, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin27a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a digital online merchant payment for an online purchase, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using the internet to buy something online in the past year and paid online, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin28", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent domestic remittances: using an account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally sending any of their money in the past year to a relative or friend living in a different area of their country, and sent it using a bank or similar financial institution account or a mobile money account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin28.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent domestic remittances: using an account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally sending any of their money in the past year to a relative or friend living in a different area of their country, and sent it using a bank or similar financial institution account or a mobile money account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin28.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent domestic remittances: using an account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally sending any of their money in the past year to a relative or friend living in a different area of their country, and sent it using a bank or similar financial institution account or a mobile money account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin28.29", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent or received domestic remittances: using an account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally sending or receiving any of their money in the past year to or from a relative or friend living in a different area of their country, and sent or received the money using a bank or similar financial institution account or a mobile money account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin28.29.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent or received domestic remittances: using an account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally sending or receiving any of their money in the past year to or from a relative or friend living in a different area of their country, and sent or received the money using a bank or similar financial institution account or a mobile money account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin28.29.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Sent or received domestic remittances: using an account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally sending or receiving any of their money in the past year to or from a relative or friend living in a different area of their country, and sent or received the money using a bank or similar financial institution account or a mobile money account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin29", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received domestic remittances: into an account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any money in the past year from a relative or friend living in a different area of their country, and received it into a bank or similar financial institution account or a mobile money account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin29.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received domestic remittances: into an account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any money in the past year from a relative or friend living in a different area of their country, and received it into a bank or similar financial institution account or a mobile money account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin29.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received domestic remittances: into an account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any money in the past year from a relative or friend living in a different area of their country, and received it into a bank or similar financial institution account or a mobile money account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used a card or mobile phone to make payments (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they used a card or a mobile phone to make payments, buy things, or to send or receive money using your account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin3.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used a card or mobile phone to make payments, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they used a card or a mobile phone to make payments, buy things, or to send or receive money using your account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin3.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used a card or mobile phone to make payments, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report they used a card or a mobile phone to make payments, buy things, or to send or receive money using your account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin30", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a utility payment (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin30.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a utility payment, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin30.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a utility payment, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin31a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a utility payment: using a bank or similar financial institution account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year directly from a bank or similar financial institution account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin31a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a utility payment: using a bank or similar financial institution account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year directly from a bank or similar financial institution account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin31a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a utility payment: using a bank or similar financial institution account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year directly from a bank or similar financial institution account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin31a.31b", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a utility payment: using an account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year directly from a bank or similar financial institution account or by using a mobile phone, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin31a.31b.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a utility payment: using an account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year directly from a bank or similar financial institution account or by using a mobile phone, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin31a.31b.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a utility payment: using an account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year directly from a bank or similar financial institution account or by using a mobile phone, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin31b", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a utility payment: using a mobile phone (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year using a mobile phone, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin31b.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a utility payment: using a mobile phone, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year using a mobile phone, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin31b.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a utility payment: using a mobile phone, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year using a mobile phone, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin31d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a utility payment: using cash only (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year using cash only, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin31d.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a utility payment: using cash only, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year using cash only, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin31d.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a utility payment: using cash only, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year using cash only, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received wages (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report receiving any money from an employer in the past year in the form of a salary or wages for doing work. This does not include any money received directly from clients or customers, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received wages, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report receiving any money from an employer in the past year in the form of a salary or wages for doing work. This does not include any money received directly from clients or customers, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received wages, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report receiving any money from an employer in the past year in the form of a salary or wages for doing work. This does not include any money received directly from clients or customers, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.33", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received public sector wages (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed by the government, military, or public sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.33.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received public sector wages, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed by the government, military, or public sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.33.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received public sector wages, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed by the government, military, or public sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.33.34a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received public sector wages: into a bank or similar financial institution account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed by the government, military, or public sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, and who report receiving that money directly into a bank or similar financial institution account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.33.34a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received public sector wages: into a bank or similar financial institution account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed by the government, military, or public sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, and who report receiving that money directly into a bank or similar financial institution account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.33.34a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received public sector wages: into a bank or similar financial institution account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed by the government, military, or public sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, and who report receiving that money directly into a bank or similar financial institution account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.33.acc", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received public sector wages: into an account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed by the government, military, or public sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, and who received that money directly into a bank or similar financial institution account, into a card, or into a mobile money account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.33.acc.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received public sector wages: into an account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed by the government, military, or public sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, and who received that money directly into a bank or similar financial institution account, into a card, or into a mobile money account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.33.acc.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received public sector wages: into an account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed by the government, military, or public sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, and who received that money directly into a bank or similar financial institution account, into a card, or into a mobile money account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.acc", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received wages: into an account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report receiving any money from an employer in the past year in the form of a salary or wages for doing work, and who received it directly into a bank or similar financial institution account, into a card, or through a mobile phone, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.acc.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received wages: into an account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report receiving any money from an employer in the past year in the form of a salary or wages for doing work, and who received it directly into a bank or similar financial institution account, into a card, or through a mobile phone, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.acc.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received wages: into an account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report receiving any money from an employer in the past year in the form of a salary or wages for doing work, and who received it directly into a bank or similar financial institution account, into a card, or through a mobile phone, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.n33", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received private sector wages (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed in the private sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.n33.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received private sector wages, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed in the private sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.n33.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received private sector wages, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed in the private sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.n33.34a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received private sector wages: into a bank or similar financial institution account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed in the private sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, and who received that money directly into a bank or similar financial institution account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.n33.34a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received private sector wages: into a bank or similar financial institution account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed in the private sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, and who received that money directly into a bank or similar financial institution account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.n33.34a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received private sector wages: into a bank or similar financial institution account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed in the private sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, and who received that money directly into a bank or similar financial institution account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.n33.34c", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received private sector wages: in cash only (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed in the private sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work in cash only, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.n33.34c.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received private sector wages: in cash only, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed in the private sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work in cash only, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.n33.34c.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received private sector wages: in cash only, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed in the private sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work in cash only, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.n33.acc", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received private sector wages: into an account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed in the private sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, and who received that money directly into a bank or similar financial institution account, into a card, or through a mobile phone, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.n33.acc.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received private sector wages: into an account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed in the private sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, and who received that money directly into a bank or similar financial institution account, into a card, or through a mobile phone, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin32.n33.acc.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received private sector wages: into an account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report being employed in the private sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, and who received that money directly into a bank or similar financial institution account, into a card, or through a mobile phone, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin34a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received wages: into a bank or similar financial institution account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report receiving any money from an employer in the past year in the form of a salary or wages for doing work, and who received it directly into a bank or similar financial institution account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin34a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received wages: into a bank or similar financial institution account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report receiving any money from an employer in the past year in the form of a salary or wages for doing work, and who received it directly into a bank or similar financial institution account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin34a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received wages: into a bank or similar financial institution account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report receiving any money from an employer in the past year in the form of a salary or wages for doing work, and who received it directly into a bank or similar financial institution account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin34c", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received wages: in cash only (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report receiving any money from an employer in the past year in the form of a salary or wages for doing work, and who received it in cash only, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin34c.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received wages: in cash only, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report receiving any money from an employer in the past year in the form of a salary or wages for doing work, and who received it in cash only, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin34c.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received wages: in cash only, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report receiving any money from an employer in the past year in the form of a salary or wages for doing work, and who received it in cash only, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin36b", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Wage payments: leave some money in the account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report leaving some money in the account when their employer paid them salary or wages into their account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin36b.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Wage payments: leave some money in the account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report leaving some money in the account when their employer paid them salary or wages into their account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin36b.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Wage payments: leave some money in the account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report leaving some money in the account when their employer paid them salary or wages into their account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any transfer from the government in the past year. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It does not include a pension from the government, military, or public sector; wages; or any other payments related to work, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any transfer from the government in the past year. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It does not include a pension from the government, military, or public sector; wages; or any other payments related to work, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any transfer from the government in the past year. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It does not include a pension from the government, military, or public sector; wages; or any other payments related to work, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.38", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer or pension (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any transfer or pension from the government in the past year. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes payments for a pension from the government, military, or public sector, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.38.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer or pension, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any transfer or pension from the government in the past year. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes payments for a pension from the government, military, or public sector, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.38.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer or pension, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any transfer or pension from the government in the past year. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes payments for a pension from the government, military, or public sector, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.38.39.acc", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer or pension: into an account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any transfer or pension from the government in the past year into a bank or similar financial institution account, into a card, or through a mobile phone, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.38.39.acc.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer or pension: into an account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any transfer or pension from the government in the past year into a bank or similar financial institution account, into a card, or through a mobile phone, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.38.39.acc.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer or pension: into an account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any transfer or pension from the government in the past year into a bank or similar financial institution account, into a card, or through a mobile phone, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.38.39a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer or pension: into bank or similar financial institution account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any government transfer or pension from the government in the past year into a bank or similar financial institution account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.38.39a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer or pension: into bank or similar financial institution account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any government transfer or pension from the government in the past year into a bank or similar financial institution account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.38.39a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer or pension: into bank or similar financial institution account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any government transfer or pension from the government in the past year into a bank or similar financial institution account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.39.acc", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer: into an account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any transfer from the government in the past year directly into a bank or similar financial institution account, into a card, or through a mobile phone, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.39.acc.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer: into an account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any transfer from the government in the past year directly into a bank or similar financial institution account, into a card, or through a mobile phone, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.39.acc.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer: into an account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any transfer from the government in the past year directly into a bank or similar financial institution account, into a card, or through a mobile phone, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.39a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer: into a bank or similar financial institution account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any transfer from the government in the past year directly into a bank or similar financial institution account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.39a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer: into a bank or similar financial institution account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any transfer from the government in the past year directly into a bank or similar financial institution account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin37.39a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government transfer: into a bank or similar financial institution account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any transfer from the government in the past year directly into a bank or similar financial institution account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin38", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received a public sector pension (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving a pension from the government, military, or public sector in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin38.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received a public sector pension, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving a pension from the government, military, or public sector in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin38.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received a public sector pension, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving a pension from the government, military, or public sector in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin38.39.acc", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received a public sector pension: into an account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving a pension from the government, military, or public sector in the past year directly into a bank or similar financial institution account, into a card, or through a mobile phone, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin38.39.acc.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received a public sector pension: into an account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving a pension from the government, military, or public sector in the past year directly into a bank or similar financial institution account, into a card, or through a mobile phone, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin38.39.acc.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received a public sector pension: into an account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving a pension from the government, military, or public sector in the past year directly into a bank or similar financial institution account, into a card, or through a mobile phone, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin38.39a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received a public sector pension: into a bank or similar financial institution account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving a pension from the government, military, or public sector in the past year directly into a bank or similar financial institution account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin38.39a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received a public sector pension: into a bank or similar financial institution account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving a pension from the government, military, or public sector in the past year directly into a bank or similar financial institution account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin38.39a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received a public sector pension: into a bank or similar financial institution account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving a pension from the government, military, or public sector in the past year directly into a bank or similar financial institution account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin4.d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "First bank or similar financial institution account ever was opened to receive a wage payment or money from the government (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report opening a bank or similar financial institution account for the first time to receive money from the government or to receive a wage payment, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin4.d.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "First bank or similar financial institution account ever was opened to receive a wage payment or money from the government, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report opening a bank or similar financial institution account for the first time to receive money from the government or to receive a wage payment, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin4.d.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "First bank or similar financial institution account ever was opened to receive a wage payment or money from the government, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report opening a bank or similar financial institution account for the first time to receive money from the government or to receive a wage payment, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin42", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received payments for the sale of agricultural products, livestock, or crops (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving payments from any source for the sale of agricultural products, crops, produce, or livestock in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin42.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received payments for the sale of agricultural products, livestock, or crops, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving payments from any source for the sale of agricultural products, crops, produce, or livestock in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin42.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received payments for the sale of agricultural products, livestock, or crops, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving payments from any source for the sale of agricultural products, crops, produce, or livestock in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin43c", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received payments for agricultural products: in cash only (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving money from any source for the sale of agricultural products, crops, produce, or livestock in the past year in cash only, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin43c.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received payments for agricultural products: in cash only, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving money from any source for the sale of agricultural products, crops, produce, or livestock in the past year in cash only, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin43c.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received payments for agricultural products: in cash only, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving money from any source for the sale of agricultural products, crops, produce, or livestock in the past year in cash only, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin45a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Most worrying financial issue: money for old age (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents whose primary financial worry is that they won't have enough money for old age, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin45a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Most worrying financial issue: money for old age, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents whose primary financial worry is that they won't have enough money for old age, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin45a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Most worrying financial issue: money for old age, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents whose primary financial worry is that they won't have enough money for old age, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin45c", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Most worrying financial issue: paying for medical costs in case of a serious illness or accident (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents whose primary financial issue is that they won't have enough money for medical costs in the case of a serious illness or accident, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin45c.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Most worrying financial issue: paying for medical costs in case of a serious illness or accident, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents whose primary financial issue is that they won't have enough money for medical costs in the case of a serious illness or accident, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin45c.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Most worrying financial issue: paying for medical costs in case of a serious illness or accident, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents whose primary financial issue is that they won't have enough money for medical costs in the case of a serious illness or accident, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin45d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Most worrying financial issue: money to pay for monthly expenses or bills (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents whose primary financial issue is that they won't have enough money for monthly expenses or bills, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin45d.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Most worrying financial issue: money to pay for monthly expenses or bills, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents whose primary financial issue is that they won't have enough money for monthly expenses or bills, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin45d.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Most worrying financial issue: money to pay for monthly expenses or bills, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents whose primary financial issue is that they won't have enough money for monthly expenses or bills, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin45e", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Most worrying financial issue: paying school or education fees (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents whose primary financial issue is that they won't have enough money to pay school fees, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin45e.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Most worrying financial issue: paying school or education fees, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents whose primary financial issue is that they won't have enough money to pay school fees, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin45e.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Most worrying financial issue: paying school or education fees, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents whose primary financial issue is that they won't have enough money to pay school fees, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin8", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Store money in account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report keeping money in their bank or similar financial institution account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin8.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Store money in account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report keeping money in their bank or similar financial institution account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin8.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Store money in account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report keeping money in their bank or similar financial institution account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin9a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received information about account balance from bank through email, SMS, or text message on mobile in the past 12 months (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having a bank or similar financial institution account,and report receiving any information about their account balance from their bank, such as through email, SMS, or text message, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin9a.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received information about account balance from bank through email, SMS, or text message on mobile in the past 12 months, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having a bank or similar financial institution account,and report receiving any information about their account balance from their bank, such as through email, SMS, or text message, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin9a.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received information about account balance from bank through email, SMS, or text message on mobile in the past 12 months, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having a bank or similar financial institution account,and report receiving any information about their account balance from their bank, such as through email, SMS, or text message, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin9b", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to check account balance (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile phone or the internet to check their balance for a bank or similar financial institution account in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin9b.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to check account balance, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile phone or the internet to check their balance for a bank or similar financial institution account in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fin9b.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to check account balance, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile phone or the internet to check their balance for a bank or similar financial institution account in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fing2p", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government payments (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any payment from the government (government transfers, public sector pension, or public sector wages) in the past year. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fing2p.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government payments, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any payment from the government (government transfers, public sector pension, or public sector wages) in the past year. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fing2p.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government payments, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving any payment from the government (government transfers, public sector pension, or public sector wages) in the past year. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fing2p.acc", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government payments: into an account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving payments from the government (government transfers, public sector pension, or public sector wages) in the past year directly into a bank or similar financial institution account, into a card, or through a mobile phone, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fing2p.acc.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government payments: into an account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving payments from the government (government transfers, public sector pension, or public sector wages) in the past year directly into a bank or similar financial institution account, into a card, or through a mobile phone, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fing2p.acc.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government payments: into an account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving payments from the government (government transfers, public sector pension, or public sector wages) in the past year directly into a bank or similar financial institution account, into a card, or through a mobile phone, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fing2p.fin", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government payments: into a bank or similar financial institution account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving payments from the government (government transfers, public sector pension, or public sector wages) in the past year directly into a bank or similar financial institution account, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fing2p.fin.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government payments: into a bank or similar financial institution account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving payments from the government (government transfers, public sector pension, or public sector wages) in the past year directly into a bank or similar financial institution account, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "fing2p.fin.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received government payments: into a bank or similar financial institution account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally receiving payments from the government (government transfers, public sector pension, or public sector wages) in the past year directly into a bank or similar financial institution account, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "FP.CPI.TOTL.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A general and continuing increase in an economy’s price level is called inflation. The increase in the average prices of goods and services in the economy should be distinguished from a change in the relative prices of individual goods and services. Generally accompanying an overall increase in the price level is a change in the structure of relative prices, but it is only the average increase, not the relative price changes, that constitutes inflation. A commonly used measure of inflation is the consumer price index, which measures the prices of a representative basket of goods and services purchased by a typical household. The consumer price index is usually calculated on the basis of periodic surveys of consumer prices. Other price indices are derived implicitly from indexes of current and constant price series." }, { "id": "IndicatorName", "value": "Inflation, consumer prices (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Consumer Prices Indices are compiled in accordance with international standards: Consumer Price Index Manual, 2020 or 2004 version. Specific information on how countries compile their CPI statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of a consumer price index series is to measure the rate at which prices of consumption goods and services are changing from one period to another." }, { "id": "Topic", "value": "Economic and Social Context" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "14" }, { "id": "g20.any", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made or received a digital payment (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using mobile money, a debit or credit card, or a mobile phone to make a payment from an account--or report using the internet to pay bills or to buy something online or in a store--in the past year. This includes respondents who report paying bills, sending or receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly from or into a bank or similar financial institution account or through a mobile money account in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "g20.any.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made or received a digital payment, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using mobile money, a debit or credit card, or a mobile phone to make a payment from an account--or report using the internet to pay bills or to buy something online or in a store--in the past year. This includes respondents who report paying bills, sending or receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly from or into a bank or similar financial institution account or through a mobile money account in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "g20.any.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made or received a digital payment, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using mobile money, a debit or credit card, or a mobile phone to make a payment from an account--or report using the internet to pay bills or to buy something online or in a store--in the past year. This includes respondents who report paying bills, sending or receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly from or into a bank or similar financial institution account or through a mobile money account in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "g20.made", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a digital payment (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using mobile money, a debit or credit card, or a mobile phone to make a payment from an account; or who report using the internet to pay bills or to buy something online or in a store in the past year. This includes respondents who report paying bills or sending remittances directly from a bank or similar financial institution account or through a mobile money account in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "g20.made.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a digital payment, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using mobile money, a debit or credit card, or a mobile phone to make a payment from an account; or who report using the internet to pay bills or to buy something online or in a store in the past year. This includes respondents who report paying bills or sending remittances directly from a bank or similar financial institution account or through a mobile money account in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "g20.made.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a digital payment, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using mobile money, a debit or credit card, or a mobile phone to make a payment from an account; or who report using the internet to pay bills or to buy something online or in a store in the past year. This includes respondents who report paying bills or sending remittances directly from a bank or similar financial institution account or through a mobile money account in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "g20.received", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received digital payments (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile money account, a debit or credit card, or a mobile phone to receive a payment into an account in the past year. This includes respondents who report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a bank or similar financial institution account or into a mobile money account in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "g20.received.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received digital payments, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile money account, a debit or credit card, or a mobile phone to receive a payment into an account in the past year. This includes respondents who report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a bank or similar financial institution account or into a mobile money account in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "g20.received.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Received digital payments, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile money account, a debit or credit card, or a mobile phone to receive a payment into an account in the past year. This includes respondents who report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a bank or similar financial institution account or into a mobile money account in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "GD_WBL_AST_ENF_INHERCHLD_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Assets, Public authorities enforce existing legislation granting equal inheritance rights to sons and daughters" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation granting equal inheritance rights to sons and daughters in practice.Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level scores are calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) for each indicator at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied, as described in detail in the enforcement perceptions scoring sections in the relevant topic chapters.\n\nFor details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ast_Enf_InherChld_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_AST_ENF_INHERSPSE_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Assets, Public authorities enforce existing legislation granting equal inheritance rights to male and female surviving spouses" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation granting equal inheritance rights to male and female surviving spouses in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level scores are calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) for each indicator at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied, as described in detail in the enforcement perceptions scoring sections in the relevant topic chapters.\n\nFor details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ast_Enf_InherSpse_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_AST_ENF_NONMONCON_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Assets, Public authorities enforce existing legislation that provides for the valuation of nonmonetary contributions in the case of the dissolution of marriage" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation that provides for the valuation of nonmonetary contributions in the case of the dissolution of marriage in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level scores are calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) for each indicator at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied, as described in detail in the enforcement perceptions scoring sections in the relevant topic chapters.\n\nFor details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ast_Enf_NonMonCon_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_AST_ENF_OWNERSHIP_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Assets, Public authorities enforce equal rights over immovable property" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce equal rights over immovable property in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level scores are calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) for each indicator at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied, as described in detail in the enforcement perceptions scoring sections in the relevant topic chapters.\n\nFor details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ast_Enf_Ownership_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_AST_ENF_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Assets, Score (scale 0–100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Assets topic enforcement perceptions score measures the extent to which gender differences in property and inheritance laws are enforced in practice or the consequent rights are upheld in practice. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: pillar3_assets" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_AST_LAW_INHERCHLD", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Assets, The law grants equal inheritance rights to sons and daughters" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law: (1) explicitly grants sons and daughters equal rights to inherit assets from their parents; OR (2) mandates equal shares of inheritance for sons and daughters; OR (3) does not specify any legal distinctions between boys and girls in matters of inheritance.\n\nScore = 1 if one of the conditions is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ast_Law_InherChld" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_AST_LAW_INHERSPSE", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Assets, The law grants equal inheritance rights to male and female surviving spouses" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law: (1) recognizes equal inheritance rights and, if established, the same order of succession for male and female surviving spouses; AND widows do not lose their right to inherited property upon remarriage; OR (2) provides for equal shares of inheritance and, if established, the same order of succession for male and female surviving spouses; AND widows do not lose their right to inherited property upon remarriage.\n\nScore = 1 if any of the two conditions is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ast_Law_InherSpse" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_AST_LAW_NONMONCON", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Assets, The law provides for the valuation of nonmonetary contributions in case of the dissolution of marriage" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) there is an explicit legal recognition of nonmonetary contributions, and the law provides for equal or equitable division of property, or the transfer of a lump sum based on nonmonetary contributions; OR (2) the default marital property regime is full community, partial community, or deferred community of property. Score = 1 if any of the two conditions is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ast_Law_NonMonCon" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_AST_LAW_OWNERSHIP", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Assets, The law grants women equal rights over immovable property" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law grants men and women equal ownership rights and administrative authority over immovable property, including land. Score = 1 if condition is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ast_Law_Ownership" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_AST_LAW_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Assets, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Assets topic legal frameworks score measures gender differences in property and inheritance law, assessing women’s equal access to immovable assets including land, administrative authority over property, and Inheritance rights. It is divided into four indicators, some of which consist of several questions. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Ast_Law_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_AST_SFR_ACCSTOINFO", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Assets, Awareness measures are in place to improve women's access to information about property and inheritance rights" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) information on property or inheritance rights is provided by a public entity; (AND) this information covers at least one of the following thematic areas: land, housing, property, or inheritance; (AND) this information addresses at least one of the following aspects: how to claim property rights or how to divide or settle property in cases of divorce or death; (AND) the information is disseminated through websites, reports, programs, or educational activities; (AND) The information has been released or updated within the past five years preceding the data collection cut-off date and information provided by a private or nongovernmental agency is insufficient; OR; (2) there is a helpline provided by a public entity to advise property owners in the language spoken by the majority of the people, and this service has been operational during the reporting cycle; (AND) This information covers at least one of the following thematic areas: land, housing, property, or inheritance; (AND) This information addresses aspects related to how to claim or register property rights; OR (3) detailed guidelines on how to register property are available in the language spoken by the majority of people. \n\nScore = 1 if any of the three conditions is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Ast_SFR_AccstoInfo" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_AST_SFR_JOINTTITLING", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Assets, The law enables the joint titling of matrimonial property (land, residential or commercial building) for both spouses" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law: (1) mandates or presumes joint titling of matrimonial property; (2) provides for joint titling of matrimonial property. If first condition is met, score=1; if second condition is met, score= 0.5; if none of the two conditions are met, score=0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ast_SFR_JointTitling" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_AST_SFR_PROPSDD", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Assets, The government publishes anonymized sex-disaggregated data on property ownership" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the government publishes anonymized sex-disaggregated data on the following: (1) land ownership; (2) housing. Partial score of 0.5 is assigned to each of the two underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Ast_SFR_PropSDD" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_AST_SFR_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Assets, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Assets topic supportive frameworks score examines policies that support women in property ownership and registration, focusing on the availability of statistical data on women’s property ownership, awareness campaigns, joint titling, and mechanisms for property ownership and registration. It is divided into four indicators, some of which consist of several questions. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ast_SFR_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_AST_SFR_WOMREGPROP", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Assets, There are mechanisms or incentives to encourage women to register immovable property" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) there is at least one effective policy or program administered or at least partially funded by a public entity aiming to enable women’s access to property ownership or registration, which includes concrete action items designed to address the gender gap in property ownership; OR (2) the government offers incentives, such as reduced fees, subsidized services, or affordable registration programs, either specifically designed for women or stipulating joint titling. \n\nScore = 1 if any of the conditions is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Ast_SFR_WomRegProp" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_CHC_ENF_CTRCHC_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Childcare, Public authorities enforce existing legislation establishing the provision of center-based childcare services" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation establishing the provision of center-based childcare services in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of laws establishing center-based childcare services and 4 indicates full enforcement of such laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Chc_Enf_CtrChc_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_CHC_ENF_QUALSTD_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Childcare, Public authorities enforce existing legislation establishing quality standards for the provision of center-based childcare services" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation establishing quality standards for the provision of center-based childcare services in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of laws establishing quality standards for the provision of center-based childcare services, and 4 indicates full enforcement of such laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Chc_Enf_QualStd_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_CHC_ENF_SUPPFAM_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Childcare, Public authorities enforce existing legislation establishing support for families for childcare services" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation establishing support for families for childcare services in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of laws establishing support for families for childcare services and 4 indicates full enforcement of such laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Chc_Enf_SuppFam_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_CHC_ENF_SUPPNS_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Childcare, Public authorities enforce existing legislation establishing support for nonstate childcare providers" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation establishing support for nonstate childcare providers in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of laws establishing support for families for childcare services and 4 indicates full enforcement of such laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Chc_Enf_SuppNS_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_CHC_ENF_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Childcare, Score (scale 0–100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Childcare topic enforcement perceptions score measures the extent to which laws related to childcare services are enforced in practice. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: pillar3_childcare" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_CHC_LAW_CTRCHC", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Childcare, The law establishes the provision of center-based childcare services" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law establishes the provision of childcare services for children aged 0–2 years (including 2 years and 11 months) in center-based settings (nurseries, day care centers, creches, or formal preschools) by any of the following: (1) the government, (2) private centers, or (3) employers without conditions on the presence of female employees. Score = 1 if the condition is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Chc_Law_CtrChc" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_CHC_LAW_QUALSTD", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Childcare, The law establishes quality standards for the provision of center-based childcare services" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law establishes the following quality standards for the provision of center-based childcare services (public or private): (1) caregiver-to-child ratio or maximum group size; (2) a minimum level of specialized education or training for educators; (3) mandatory periodic inspection of childcare centers by authorized bodies or mandatory periodic reporting by childcare centers to authorized bodies. Partial score of 0.33 is assigned to each of the three underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Chc_Law_QualStd" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_CHC_LAW_SUPPFAM", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Childcare, The law establishes any form of support for families for childcare services" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law establishes any form of financial or tax support for families for childcare services. Score = 1 if the condition is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Chc_Law_SuppFam" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_CHC_LAW_SUPPNS", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Childcare, The law establishes any form of support for nonstate childcare providers" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law establishes any form of financial or tax support for private centers or employers for childcare services. Score = 1 if the condition is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Chc_Law_SuppNS" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_CHC_LAW_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Childcare, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Childcare topic legal frameworks score measures laws that regulate the availability, public financing, and quality of childcare services. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Chc_Law_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_CHC_SFR_GOVQREP", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Childcare, The government publishes reports on the quality of childcare services" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the government publishes reports on the quality of childcare services. A score of 1 is assigned when the government provides an open-data information system or publish reports that assess individual childcare providers on the quality of provided services. A score of 0.5 is assigned when the government publishes reports of a broad nature benchmarking the quality of childcare services without assessing the individual providers. A score of 0 is assigned the government has not published any such reports. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Chc_SFR_GovQRep" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_CHC_SFR_NS", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Childcare, There is a clearly outlined application procedure to request financial support from the government for childcare services by nonstate childcare providers" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether there is a clearly outlined application procedure for private centers or employers to request financial support from the government for the provision of childcare services. Score = 1 if the condition is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Chc_SFR_NS" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_CHC_SFR_PARNONTAX", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Childcare, There is a clearly outlined application procedure to request financial support from the government for childcare services by parents" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether there is a clearly outlined application procedure for parents to request financial support from the government for childcare services. Score = 1 if the condition is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Chc_SFR_ParNonTax" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_CHC_SFR_REG", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Childcare, There is a publicly available registry or database of childcare providers" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether there is a publicly available registry or database of childcare providers. Score = 1 if the condition is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Chc_SFR_Reg" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_CHC_SFR_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Childcare, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Childcare topic supportive frameworks score measures policies and practices that support parents in making informed decisions about childcare, including access to publicly available registries of childcare providers, financial support for both parents and nonstate childcare providers, and monitoring of highquality services through publicly available regular quality reports. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Chc_SFR_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_ENT_ENF_CREDISC_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Entrepreneurship, Public authorities enforce existing legislation that prohibits gender-based discrimination in access to credit" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation that prohibits gender-based discrimination in access to credit in practice.Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level scores are calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) for each indicator at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied, as described in detail in the enforcement perceptions scoring sections in the relevant topic chapters.\n\nFor details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ent_Enf_CreDisc_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_ENT_ENF_ENTACTV_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Entrepreneurship, Public authorities enforce a woman's right to undertake entrepreneurial activities in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce a woman's right to undertake entrepreneurial activities in the same way as a man in practice.Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level scores are calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) for each indicator at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied, as described in detail in the enforcement perceptions scoring sections in the relevant topic chapters.\n\nFor details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ent_Enf_EntActv_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_ENT_ENF_GNDRPROC_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Entrepreneurship, Public authorities enforce existing legislation including gender-responsive procurement provisions for public procurement processes" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation including gender-responsive procurement provisions for public procurement processes in practice.Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level scores are calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) for each indicator at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied, as described in detail in the enforcement perceptions scoring sections in the relevant topic chapters.\n\nFor details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ent_Enf_GndrProc_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_ENT_ENF_QUOTA_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Entrepreneurship, Public authorities enforce existing legislation prescribing gender quotas for corporate boards" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation prescribing gender quotas for corporate boards in practice.Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level scores are calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) for each indicator at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied, as described in detail in the enforcement perceptions scoring sections in the relevant topic chapters.\n\nFor details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ent_Enf_Quota_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_ENT_ENF_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Entrepreneurship, Score (scale 0–100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Entrepreneurship topic enforcement perception score measures the extent to which laws on women’s ability to establish and run a business are enforced in practice or the consequent rights are upheld in practice. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: pillar3_entrepreneurship" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_ENT_LAW_CREDISC", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Entrepreneurship, The law prohibits discrimination in access to credit based on gender" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law: (1) explicitly prohibits gender-based discrimination in access to financial services, credit, or loans; (OR) the law prescribes equal access for both women and men to financial products or services; AND (2) if the specific provision on nondiscrimination in access to credit is regulated in a central bank regulation or circular, it must be legally binding; AND (3) the legally binding instruments are accompanied by effective remedies after violation of the principle.\n\nScore = 1 if all three conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Ent_Law_CreDisc" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_ENT_LAW_ENTACTV", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Entrepreneurship, The law allows a woman to undertake entrepreneurial activities in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law allows a woman to undertake entrepreneurial activities in the same way as a man, including the ability to sign contracts, register a business, and operate a business without legal restrictions based on gender. Score = 1 if women have the same legal capacity as men to undertake all entrepreneurial activities; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ent_Law_EntActv" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_ENT_LAW_GNDRPROC", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Entrepreneurship, The law includes gender-responsive procurement provisions for public procurement processes" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law includes gender-responsive provisions in public procurement processes. Score = 1 if gender-responsive procurement provisions are established in law; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Ent_Law_GndrProc" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_ENT_LAW_QUOTA", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Entrepreneurship, The law prescribes a gender quota for corporate boards" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law prescribes a mandatory gender quota for corporate boards. Score = 1 if the quota is 40% or higher; score = 0.5 if the quota is below 40% but greater than 0%, or if the law requires at least one woman on the board; score = 0 if there is no mandatory quota. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Ent_Law_Quota" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_ENT_LAW_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Entrepreneurship, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Entrepreneurship topic legal framework score measures legal constraints to a woman’s ability to start and run a business and assesses the existence of enabling provisions, including non-discrimination in access to credit provisions, gender-responsive criteria in public procurement laws, and binding quotas for women on public corporate boards. It is divided into four indicators, some of which consist of several questions. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ent_Law_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_ENT_SFR_ACCESSTOFIN", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Entrepreneurship, There are government-led services on increasing access to financial services or resources available for women and female entrepreneurs" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) there is a national financial inclusion strategy or a national strategy with a dedicated section focusing on women’s financial inclusion; (2) the government provides programs on access to financial services or resources to women or female entrepreneurs. \n\nPartial score of 0.5 is assigned to each of the three underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Ent_SFR_AccessToFin" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_ENT_SFR_ENTSDD", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Entrepreneurship, The government publishes anonymized sex-disaggregated data on women-owned or women-led businesses and on women in leadership positions" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the government publishes anonymized sex-disaggregated data on: (1) women-owned or women-led businesses; (2) women in corporate leadership positions. Partial score of 0.5 is assigned to each of the three underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Ent_SFR_ENTSDD" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_ENT_SFR_GOVLEDSUPP", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Entrepreneurship, There are government-led programs supporting female entrepreneurs with training or business development" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) there is at least one program administered or at least partially funded by a public entity that provides support in any of the following areas: technical or soft skill training, mentoring, or coaching opportunities, business development such as business training, advisory services, technology transfer, business incubation, or business formalization services; AND (2) the program was active during the reporting cycle; AND (3) the program explicitly mentions women and/or female entrepreneurs as part of the target audience. Score = 1 if all three conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Ent_SFR_GovLedSupp" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_ENT_SFR_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Entrepreneurship, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Entrepreneurship topic supportive framework score examines policies and practices that support female entrepreneurship, including the availability of regularly published sex-disaggregated data on women’s business activities, government-led programs or national strategies to facilitate women’s access to financial services, and government-led programs to support women entrepreneurs. It is divided into four indicators, some of which consist of several questions. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Ent_SFR_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_ENT_SFR_WOMNBZSUPP", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Entrepreneurship, There is a comprehensive framework to support women entrepreneurs, women-owned or women-led businesses" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) there is a plan or strategy supporting female entrepreneurs; (2) the plan or strategy that supports female entrepreneurs provide for specific targets and indicators; (3) the plan or strategy that supports female entrepreneurs provide for monitoring and evaluation mechanisms; (3) there is an agency supporting female entrepreneurs; (4) there is a nationally applicable definition on what constitutes a women-owned business or women-led business.\n\nPartial score of 0.2 is assigned to each of the three underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Ent_SFR_WomnBzSupp" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MAR_ENF_DIVORCE_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Marriage, Public authorities enforce a woman's right to obtain a judgment of divorce in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce a woman's right to obtain a judgment of divorce in the same way as a man in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link:https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mar_Enf_Divorce_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MAR_ENF_HOH_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Marriage, Public authorities enforce a woman's right to be the head of household or head of family in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce a woman's right to be the \"head of household\" or \"head of family\" in the same way as a man in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link:https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mar_Enf_HoH_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MAR_ENF_OBEDIENCE_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Marriage, Public authorities enforce a married woman's right to not be required to obey her husband" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce a married woman's right to not be required to obey her husband in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link:https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mar_Enf_Obedience_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MAR_ENF_REMARRY_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Marriage, Public authorities enforce equal rights between women and men to remarry" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce equal rights between women and men to remarry in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link:https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mar_Enf_Remarry_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MAR_ENF_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Marriage, Score (scale 0–100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Marriage topic enforcement perceptions score measures the extent to which legal constraints related to marriage and divorce are enforced in practice or the consequent rights are upheld in practice. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: pillar3_marriage" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MAR_LAW_DIVORCE", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Marriage, The law allows a woman to obtain a judgment of divorce in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) the process of divorce is equal for a man and a woman; OR (2) the evidentiary rules in divorce proceedings are the same for men and women: for example, the burden of proof is the same for a man as for a woman; OR (3) there are additional protections for women, such as the prohibition for a husband to initiate divorce proceedings if the wife is pregnant. \n\nScore = 1 if one of the three conditions is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Mar_Law_Divorce" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MAR_LAW_HOH", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Marriage, The law allows a woman to be head of household or head of family in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) the law does not designate the husband as the “head of household” or the “head of family” or stipulates that the husband leads the family; AND (2) the husband is not by default the family member who receives the family book or an equivalent document that is required to access services and benefits; AND (3) the family law, civil law, or personal status law does not place any restrictions on women to be considered as “head of household” or “head of family. Differences under tax law are not considered under this indicator.\n\nScore = 1 if all three conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Mar_Law_HoH" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MAR_LAW_OBEDIENCE", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Marriage, The law is free of legal provisions that require a married woman to obey her husband" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) there is no provision requiring a married woman to obey her husband; OR (2) the law stipulates that spouses have equal rights and duties. Score = 1 if any of the conditions is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Mar_Law_Obedience" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MAR_LAW_REMARRY", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Marriage, The law grants a woman the same rights to remarry as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law does not contain any of the following three constraints: (1) the law limits the ability of a woman to remarry in a way that does not apply to a man—for example, by requiring a waiting period before being able to remarry, while the same period does not apply to a man; OR (2) a woman is required to provide a certificate showing that she is not pregnant before being able to remarry; OR (3) divorce is not legally allowed. \n\nScore = 1 if none of the three conditions is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Mar_Law_Remarry" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MAR_LAW_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Marriage, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Marriage topic legal frameworks score measures laws related to marriage and divorce because equal rights in marriage and divorce are critical to a woman’s agency, financial security, and health. It is divided into four indicators. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mar_Law_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MAR_SFR_FAMCOURT", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Marriage, There are specialized family courts" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) there are operational, specialized family courts that are (a) available at various levels of the\njudicial hierarchy; (b) not personal law or religious courts; and (c) dedicated to settling family law disputes on at least two of the following types of claims: divorce, alimony, or child custody; OR (2) there are operational, specialized chambers within courts that are (a) available at various levels of the judicial hierarchy; (b) not personal law or religious courts; and (c) dedicated to settling family law disputes or at least two of the following types of claims: divorce, alimony, or child custody; OR (3) there are judges at the various levels of the judicial hierarchy who receive specialized training in family law issues and disputes.\n\nScore = 1 if one of the three conditions is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Mar_SFR_FamCourt" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MAR_SFR_FASTTRACK", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Marriage, There is a fast-track process or procedure for family law disputes" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the following exists: (1) a fast-track or expedited process for family law disputes; OR (2) nonmandatory alternative dispute resolution mechanisms for family law disputes. Score = 1 if any of the conditions is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Mar_SFR_FastTrack" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MAR_SFR_LEGALAID", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Marriage, Legal aid, provided through a government institution or government-funded institution, is available for family disputes" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) there is legal aid either provided by the government itself or through a nongovernmental organization at least partially financed by the government, even if access is subject to certain income criteria. Universities (whether public or private) providing legal aid services are insufficient to obtain a score on this indicator; AND (2) the services provided may include aspects such as legal advice, assistance, and representation for family law matters including at least two of the following types of disputes: marriage, divorce, custody, or alimony; OR covering civil law case in jurisdictions where family law falls under the broader scope of civil law; AND (3) the service has sufficient prospects of success and is not frivolous. \n\nScore = 1 if all three conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Mar_SFR_LegalAid" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MAR_SFR_NOFAULTDIV", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Marriage, A woman can obtain a judgment of divorce without having to prove fault" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether a woman can obtain the following: (1) a divorce judgment without proving fault or waiting; (2) a no-fault divorce after a mandatory separation period. If first condition is met, score=1; if second condition is met, score=0.5; if none of the conditions is met, score=0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mar_SFR_NoFaultDiv" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MAR_SFR_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Marriage, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Marriage topic supportive frameworks score examines policies and practices that support the implementation of equal rights in marriage and divorce, including the fast-track processes in family disputes, specialized family courts, and legal aid in family law cases. It is divided into four indicators, some of which consist of several question. It is calculated as the unweighted average of the indicator scores in that topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mar_SFR_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MOB_ENF_CHOOSELIVE_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Mobility, Public authorities enforce a woman's right to choose where to live in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce a woman's right to choose where to live in the same way as a man in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level scores are calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) for each indicator at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mob_Enf_ChooseLive_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MOB_ENF_CITIZEN_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Mobility, Public authorities enforce equal rights between women and men to confer citizenship on their spouse and children" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce equal rights between women and men to confer citizenship on their spouse and children in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level scores are calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) for each indicator at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mob_Enf_Citizen_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MOB_ENF_LEAVEHOME_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Mobility, Public authorities enforce a woman's right to leave the marital home and travel domestically in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce a woman's right to leave the marital home and travel domestically in the same way as a man in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level scores are calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) for each indicator at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mob_Enf_LeaveHome_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MOB_ENF_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Mobility, Score (scale 0–100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Mobility enforcement perceptions topic score measures the extent to which laws constraining a woman’s agency and freedom of movement are enforced in practice or the consequent rights are upheld in practice. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: pillar3_mobility" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MOB_ENF_TRAVELINTL_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Mobility, Public authorities enforce a woman's right to travel internationally in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce a woman's right to travel internationally in the same way as a man in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level scores are calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) for each indicator at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mob_Enf_TravelIntl_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MOB_LAW_CHOOSELIVE", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Mobility, The law allows a woman to choose where to live in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses the following: (1) whether there are no restrictions in the law on a woman choosing where to live; OR (2) there is an explicit recognition of women’s rights to freely choose where to live. Score = 1 if any of the conditions is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mob_Law_ChooseLive" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MOB_LAW_CITIZEN", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Mobility, A woman and a man have equal rights to confer citizenship on their spouse and children" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether a woman has the same legal rights to confer nationality to the following: (1) her children, (2) her husband. Partial score of 0.5 is assigned to each of the two underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Mob_Law_Citizen" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MOB_LAW_LEAVEHOME", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Mobility, The law allows a woman to leave the marital home and travel domestically in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) there are no restrictions in the law on a woman traveling alone domestically; OR (2) there are no barriers preventing a woman to travel domestically; (3) OR there is an explicit recognition of women’s rights to travel domestically in the same way as a man. Score = 1 if any of of the conditions is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mob_Law_LeaveHome" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MOB_LAW_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Mobility, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Mobility legal frameworks topic score measures laws constraining a woman’s agency, freedom of movement, and ability to confer citizenship to her children and spouse. It is divided into four indicators, some of which consist of several questions. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mob_Law_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MOB_LAW_TRAVELINTL", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Mobility, The law allows a woman to travel internationally in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law allows a woman the following: (1) to travel abroad; (2) to apply for a passport in the same way as a man. Partial score of 0.5 is assigned to each of the two underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Mob_Law_TravelIntl" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MOB_SFR_IDPROCESS", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Mobility, The application processes for official identity documents are the same for a woman and a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) application processes for official identity documents are uniform for both women and men; AND (2) there are no procedural barriers preventing a woman from applying for an official identity document in the same way as a man; AND (3) there are no additional document requirements for women that men are not subject to. Score = 1 if all three conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mob_SFR_IDProcess" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MOB_SFR_MOBCONSTR", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Mobility, Women are free from government-imposed mobility constraints, including when traveling with their children" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) there are no procedural or administrative differences between women and men in their ability to travel with their children; OR (2) spousal consent is required to travel internationally with the child, but it applies equally to men and women; OR (3) there are no government-imposed mobility constraints placed on women. Score = 1 if any of the conditions is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mob_SFR_MobConstr" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MOB_SFR_PASSPORT", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Mobility, Passport application processes are the same for a woman and a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) the application procedures and forms for passports are uniform for both women and men; AND (2) there are no procedural barriers preventing a woman from applying for a passport in the same way as a man; AND (3) there are no additional document requirements for women that men are not subject to. Score = 1 if all three conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mob_SFR_Passport" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MOB_SFR_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Mobility, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Mobility topic supportive frameworks score examines policies and practices that support the implementation of women’s agency and freedom of movement, including gender-based barriers in the processes of applying for official identity documents and passports as well as gender-sensitive public transportation policies and plans, and whether women face government-imposed mobility constraints. It is divided into four indicators, one of which consists of two questions. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Mob_SFR_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_MOB_SFR_TRANSPNEED", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Mobility, A policy or plan considers women’s mobility needs in public transportation systems" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether a policy or plan address the following: (1) recognizes women’s needs in accessing and using public transportation, and (2) sets specific objectives and targets associated with women’s transportation needs. Partial score of 0.5 is assigned to each of the three underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Mob_SFR_TranspNeed" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_OVL_ENF", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions Index (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The index is a composite measure (0–100) that summarizes how enforcement perceptions shape women’s economic opportunities across ten topics: Safety, Mobility, Work, Pay, Marriage, Parenthood, Childcare, Entrepreneurship, Assets, and Pension. It is calculated as the unweighted average of the ten topic scores, with 100 representing the highest possible score and 0 the lowest. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Pillar_3_Score" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_OVL_LAW", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework Index (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The index refers to the composite measure (0–100) that summarizes how legal frameworks shape women’s economic opportunities across ten topics: Safety, Mobility, Work, Pay, Marriage, Parenthood, Childcare, Entrepreneurship, Assets, and Pension. It is calculated by taking the unweighted average of the ten topic scores, with 100 representing the highest possible score and 0 the lowest. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: LF_econ_index" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_OVL_SFR", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework Index (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The index refers to the composite measure (0–100) that summarizes how supporting frameworks shape women’s economic opportunities across ten topics: Safety, Mobility, Work, Pay, Marriage, Parenthood, Childcare, Entrepreneurship, Assets, and Pension. It is calculated by taking the unweighted average of the ten topic scores, with 100 representing the highest possible score and 0 the lowest. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: SF_econ_index" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAR_ENF_MATBENE_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Parenthood, Public authorities enforce existing legislation on leave benefits for mothers paid by the government" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation on leave benefits for mothers paid by the government in practice.Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Par_Enf_MatBene_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAR_ENF_MATLEAVE_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Parenthood, Public authorities enforce existing legislation on paid leave for mothers" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation on paid leave for mothers in practice.Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Par_Enf_MatLeave_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAR_ENF_NONDISMPREG_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Parenthood, Public authorities enforce existing legislation prohibiting dismissal of pregnant workers" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation prohibiting dismissal of pregnant workers in practice.Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Par_Enf_NonDismPreg_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAR_ENF_PATLEAVE_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Parenthood, Public authorities enforce existing legislation on paid leave for fathers" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation on paid leave for fathers in practice.Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Par_Enf_PatLeave_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAR_ENF_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Parenthood, Score (scale 0–100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Parenthood topic enforcement perceptions score measures the extent to which laws on women’s work during and after pregnancy are enforced in practice. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: pillar3_parenthood" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAR_LAW_MATBENE", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Parenthood, Leave benefits for mothers are paid by the government" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether according to the law leave benefits are paid by the following: (1) solely by the government, (2) shared between government and employer; (3) paid solely by an employer. Score=1 if leave benefits are fully paid or administered by the government (including compulsory social insurance schemes, public funds, or government-mandated private insurance), or if employer-paid benefits are fully reimbursed by the government. Score=0.5 if the cost of leave benefits is shared between the employer and the government. Score=0 if leave benefits are fully paid or administered by the employer without government reimbursement, or if there is no paid leave for mothers. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Par_Law_MatBene" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAR_LAW_MATLEAVE", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Parenthood, There is paid leave available to mothers" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether law provides paid maternity leave. Score=1 if the length of paid leave for mothers is at least 98 calendar days. Score=0 if there is no paid leave. If the length is between 1 and 97 days, the score is calculated using a linear function, such that the score increases as the length of leave increases. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Par_Law_MatLeave" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAR_LAW_NONDISMPREG", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Parenthood, Dismissal of pregnant workers is prohibited" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) the law explicitly prohibits the dismissal of women during pregnancy and after childbirth (AND) the prohibition of dismissal is not restricted to a specific period (for example, only the period of maternity leave, or a limited period during the pregnancy) or to cases where pregnancy result in illness or disability; OR (2) pregnancy and childbirth cannot serve as grounds for terminating a contract; OR (3) the dismissal of women during pregnancy and after childbirth is considered a form of unlawful termination, unfair dismissal, or wrongful discharge.\n\nScore = 1 if one or more of the three conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Par_Law_NonDismPreg" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAR_LAW_PATLEAVE", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Parenthood, There is paid leave available to fathers" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether law provides paternity leave. Score=1 if the length of paid leave for fathers is at least 14 calendar days. Score=0 if there is no paid leave for fathers. If the length of paid leave for fathers is less than 14 calendar days and greater than 0, the score for indicator is calculated using a linear function, indicating that as the length of leave for fathers increases, the score increases. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Par_Law_PatLeave" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAR_LAW_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Parenthood, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Parenthood legal frameworks topic score measures the availability of maternity and paternity leave, whether the cost is covered by the government, and whether dismissal of pregnant workers is prohibited. It is divided into four indicators, some of which consist of several questions. It is calculated as the unweighted average of the indicator scores in that topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Par_Law_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAR_SFR_APPLYMATBEN", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Parenthood, It is possible to apply for maternity benefits using a single government application process" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses the following: (1) whether maternity benefits are paid or administered solely by the government (or) payment is shared between the government and the employer as assessed under indicator; AND (2) a single government application process exists for applying for maternity benefits (or) employers can apply for maternity benefits on behalf of beneficiaries. Score = 1 both conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Par_SFR_ApplyMatBen" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAR_SFR_INCENTFATHERS", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Parenthood, Incentives are in place to encourage fathers to take paternity leave upon the birth of a child" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the following is in place: (1) fathers are entitled to paid paternity or parental leave as assessed under legal frameworks indicator; AND (2) there are incentives to promote fathers’ uptake of paternity or parental leave. Score = 1 if both conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Par_SFR_IncentFathers" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAR_SFR_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Parenthood, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Parenthood supportive frameworks topic score examines policies and practices that support the implementation of laws pertaining to parents’ ability to continue working after having children, including the ease of application and incentives for father’s leave and availability of data on women’s unpaid care work. It is divided into four indicators. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Par_SFR_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAR_SFR_UNPAIDCARE", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Parenthood, The government publishes anonymized sex-disaggregated data on unpaid care work" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) a public entity, national statistical office, or social security administration has collected and published anonymized sex-disaggregated data on unpaid care work; AND (2) the data were published within the past three years preceding the data collection cutoff date; AND (3) the data are presented in a structured and comprehensive table format, either on a government-associated website or in an associated report. Score = 1 if all three conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Par_SFR_UnpaidCare" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAR_SFR_UNPAIDDOMCARE", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Parenthood, There are government-led initiatives aimed at promoting equal sharing of unpaid domestic and care work responsibilities between men and women" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether government has currently undertaken or supported initiatives to promote the equal sharing of unpaid domestic and care work responsibilities between men and women. Score = 1 if the condition is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Par_SFR_UnpaidDomCare" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAY_ENF_DANGEROUS_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Pay, Public authorities enforce a woman's right to work in a job deemed dangerous in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce a woman's right to work in a job deemed dangerous in the same way as a man in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Pay_Enf_Dangerous_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAY_ENF_ECONSECT_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Pay, Public authorities enforce a woman's right to work in different economic sectors in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce a woman's right to work in different economic sectors in the same way as a man in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Pay_Enf_EconSect_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAY_ENF_EQREMEQVAL_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Pay, Public authorities enforce existing legislation mandating equal remuneration for work of equal value" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation mandating equal remuneration for work of equal value in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Pay_Enf_EqRemEqVal_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAY_ENF_NIGHTWRK_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Pay, Public authorities enforce a woman's right to work at night in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce a woman's right to work at night in the same way as a man in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Pay_Enf_NightWrk_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAY_ENF_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Pay, Score (scale 0–100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Pay topic enforcement perception score measures the extent to which laws on occupational segregation and the gender wage gap are enforced in practice or the consequent rights are upheld in practice. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: pillar3_pay" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAY_LAW_DANGEROUS", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Pay, A woman can work in a job deemed dangerous in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) a woman work in a job deemed hazardous in the same way as a man; (2) a woman can work in a job deemed arduous in the same way as a man; (3) a woman can work in a job deemed morally inappropriate in the same way as a man; (4) the law is free of legal provisions that explicitly give the relevant government authority the power to restrict or prohibit women from working in jobs deemed dangerous. Partial score of 0.25 is assigned to each of the four underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Pay_Law_Dangerous" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAY_LAW_ECONSECT", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Pay, A woman can work in different economic sectors in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether no laws prohibit or restrict a woman who is not pregnant and not nursing from working in the following sectors: (1) mining; (2) construction; (3) manufacturing; (4) agriculture; (5) transportation; (6) energy; (7) water sector; and (8) whether the law does not give the relevant authority the power to prohibit or restrict a woman’s ability to work in different economic sectors, regardless of any decisions issued by that authority. Each of the eight component questions are individually assessed. The final indicator score is calculated based on the number of component questions that receive a positive answer as follows: a score of 1 is assigned if the response to all eight component questions are 'Yes'; a score of 0.75 is assigned if the response to seven or six components questions are 'Yes'; a score of 0.50 is assigned if the response to five or four component questions are 'Yes'; a score of 0.25 is assigned if the response to three or two questions are 'Yes'; a score of 0 is assigned if the response to one or none of the component questions are 'Yes'. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Pay_Law_EconSect" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAY_LAW_EQREMEQVAL", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Pay, The law mandates equal remuneration for work of equal value" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether law mandates the following: (1) employers must pay equal remuneration to male and female employees who perform work of equal value in accordance with the definitions of “remuneration” and “work of equal value” provided by the ILO; AND (2) the law does not limit the principle of equal remuneration to equal work, the same work, similar work, or work of a similar nature; AND (3) the law does not limit the broad concept of “remuneration” to only basic wages or salary. Score = 1 if all three conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Pay_Law_EqRemEqVal" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAY_LAW_NIGHTWRK", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Pay, A woman can work at night in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) the law does not prohibit a woman who is not pregnant and not nursing from working at night in the same way as a man; AND (2) the law does not broadly prohibit a woman, including one with children over the age of one, from working at night and does not limit the hours that she can work at night; AND (3) the law does not give the relevant authority the power to restrict or prohibit a woman’s ability to work at night, regardless of any decisions issued by that authority. Score = 1 if all three conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Pay_Law_NightWrk" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAY_LAW_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Pay, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Pay legal frameworks topic score measures laws related to equal remuneration for women and men for work of equal value and women’s work at night, in different economic sectors, and in jobs deemed dangerous. It is divided into four indicators, some of which consist of several questions. It is calculated as the unweighted average of the indicator scores in that topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Pay_Law_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAY_SFR_ECSEC", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Pay, The government provides anonymized sex-disaggregated data on employment and salaries in different economic sectors" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the government provides the anonymized sex-disaggregated data on the following: (1) employment in different economic sectors; (2) salaries in different economic sectors. Partial score of 0.5 is assigned to each of the two underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Pay_SFR_EcSec" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAY_SFR_OSH", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Pay, There are gender-sensitive occupational safety and health (OSH) public policies applicable to the private sector" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) the law does not prohibit women from working in certain jobs or economic sectors, or during certain hours, as assessed under questions I; AND (2) there is an active occupational safety and health policy, at either the national, state, or municipal level (that is, a country’s main business city); AND (3) the policy explicitly considers either women’s occupational safety and health, specific risks that disproportionately affect working women (such as reproductive hazards, violence at work, stressful workplace factors, inadequate personal protective equipment, tools and machinery, among others), or the establishment of a national institute that examines gender issues in occupational safety and health. Score = 1 if all three conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Pay_SFR_OSH" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAY_SFR_PAYGAP", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Pay, There are pay transparency measures to address the pay gap or mechanisms to enforce equal pay legislation" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the following are in place: (1) pay transparency measures to address the pay gap; (2) enforcement mechanisms to ensure compliance with equal pay legislation. Score = 1 if any of the two conditions is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Pay_SFR_PayGap" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAY_SFR_STEM", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Pay, There are government-led initiatives aimed at incentivizing women to work in science, technology, engineering and mathematics (STEM) fields" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) there is at least one initiative administered or at least partially funded by a public entity to incentivize women to work in science, technology, engineering, and mathematics (STEM) fields; AND (2) the initiative is in effect during the reporting cycle or has been published within the past five years preceding the data collection cutoff date if there is no expiration date specified in the document. Score = 1 if both conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Pay_SFR_STEM" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PAY_SFR_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Pay, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Pay Supportive Framework topic score examines policies and practices that support the implementation of equal pay legislation, including pay transparency measures and enforcement mechanisms, and the availability of statistical sex-disaggregated data on women’s employment and salaries in different economic sectors. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Pay_SFR_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PEN_ENF_CARECRED_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Pension, Public authorities enforce existing legislation accounting for periods of absence due to childcare in pension benefits" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation accounting for periods of absence due to childcare in pension benefits in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link:https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Pen_Enf_CareCred_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PEN_ENF_MANDAGEEQ_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Pension, Public authorities enforce existing legislation on mandatory retirement ages" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation on mandatory retirement ages in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link:https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Pen_Enf_MandAgeEq_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PEN_ENF_PENSAGEEQ_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Pension, Public authorities enforce existing legislation on retirement ages and benefits" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation on retirement ages and benefits in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link:https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Pen_Enf_PensAgeEq_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PEN_ENF_SURVBENE_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Pension, Public authorities enforce existing legislation mandating equal survivor benefits for spouses" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation mandating equal survivor benefits for spouses in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link:https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Pen_Enf_SurvBene_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PEN_ENF_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Pension, Score (scale 0–100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Pension topic enforcement perception score measures the extent to which laws on the size of a woman’s pension are enforced in practice. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: pillar3_pension" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PEN_LAW_CARECRED", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Pension, Periods of absence due to childcare are accounted for in the calculation of pension benefits" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) pension contributions are paid or credited during maternity or parental leave or the leave period is considered a qualifying period of employment used for the purpose of calculating pension benefits; OR (2) there are mechanisms to compensate for any gaps in contributions due to maternity or parental leave and to ensure that the leave period does not reduce the assessment base for pension amounts; OR (3) there are no mandatory contributory pension schemes, but there is a noncontributory universal social pension (that is, independent of contributions and income level).\n\nScore = 1 if one of the conditions is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Pen_Law_CareCred" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PEN_LAW_MANDAGEEQ", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Pension, The mandatory retirement age for a woman and a man is the same" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law mandates the same compulsory retirement age for women and men. The indicator is assigned a score of 1 if the age for a woman and a man is the same, and 0 if the difference in ages is equal to or greater than 5 years. If the difference in ages is greater than zero and less than 5 years, the score is calculated using a linear function, indicating that as the age gap between men and women decreases, the score increases. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Pen_Law_MandAgeEq" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PEN_LAW_PENSAGEEQ", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Pension, The ages at which a woman and a man can retire are the same" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether statutory retirement ages for women and men are equal for both full and partial pension benefits. The indicator is assigned a score of 1 if the age for a woman and a man is the same, and 0 if the difference in ages is equal to or greater than 5 years. If the difference in ages is greater than zero and less than 5 years, the score is calculated using a linear function, indicating that as the age gap between men and women decreases, the score increases. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Pen_Law_PensAgeEq" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PEN_LAW_SURVBENE", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Pension, The law mandates equal survivor benefits for spouses" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law specifies the following: (1) equal eligibility criteria for widows and widowers to access survivor pension benefits; (2) minimum age at which spouses can receive survivor benefits; (3) benefit payments are paid in installments for widows and widowers. Partial score of 0.33 is assigned to each of the three underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Pen_Law_SurvBene" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PEN_LAW_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Pension, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Pension topic legal framework score measures differences in retirement ages, whether the law allows for pension care credits to compensate for a woman’s career interruptions, and whether the law mandates survivor pension benefits for spouses. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Pen_Law_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PEN_SFR_BENEFITS", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Pension, The government publishes anonymized sex-disaggregated data on actual retirement ages and actual amounts of pension benefits" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the government publishes anonymized sex-disaggregated data on the following: (1) actual retirement ages; (2) actual amount of received pension benefits. Partial score of 0.5 is assigned to each of the three underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Pen_SFR_Benefits" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PEN_SFR_INCRBENE", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Pension, Incentives are in place to increase women’s pension benefits" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether there are laws or policies in place that provide incentives to increase a woman’s pension benefits. Score = 1 if the condition is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Pen_SFR_IncrBene" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PEN_SFR_INFOPENBENE", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Pension, Awareness measures are in place to improve access to information about pensions" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) there is a mandatory pension system; AND (2) the system is operational; AND (3) the information has been released or updated within the past five years preceding the data collection cut-off date. Score = 1 if all three conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Pen_SFR_InfoPenBene" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PEN_SFR_PROCEDURE", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Pension, A procedure is in place for pension beneficiaries to challenge the decisions of the competent authority regarding their benefits" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) there is a mandatory pension system; AND (2) the system is operational; AND (3) there is a judicial or administrative procedure for pension beneficiaries to challenge the decisions of the competent authority about their benefits. Score = 1 if all three conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Pen_SFR_Procedure" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_PEN_SFR_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Pension, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Pension topic supportive frameworks score examines policies and practices that support the implementation of laws pertaining to women’s old age security, including incentives to increase women’s retirement benefits, dedicated procedures to challenge benefit decisions, measures to raise awareness about pension benefits, and the existence of sex-disaggregated data on retirement ages and amounts of pension benefits. It is divided into four indicators, some of which consist of several questions. It is calculated as the unweighted average of the indicator scores in that topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Pen_SFR_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_SAF_ENF_CHILDMARR_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Safety, Public authorities enforce existing legislation addressing child marriage" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation addressing child marriage in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Saf_Enf_ChildMarr_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_SAF_ENF_DOMVIOL_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Safety, Public authorities enforce existing legislation addressing domestic violence" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation addressing domestic violence in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Saf_Enf_DomViol_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_SAF_ENF_FEMICIDE_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Safety, Public authorities enforce existing legislation addressing femicide" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation addressing femicide in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Saf_Enf_Femicide_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_SAF_ENF_SEXHARASS_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Safety, Public authorities enforce existing legislation addressing sexual harassment" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation addressing sexual harassment in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Saf_Enf_SexHarass_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_SAF_ENF_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Safety, Score (scale 0–100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Safety topic enforcement perception score measures the extent to which laws on protecting women from gender-based violence are enforced in practice. It is calculated as the unweighted average of the indicator scores in the topic and then rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: pillar3_safety" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_SAF_LAW_CHILDMARR", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Safety, The law addresses child marriage" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law establishes the following: (1) the legal age of marriage is 18 or higher for boys and girls; (2) the law is free of parental consent exceptions to the legal age of marriage; (3) marriage under the legal age is void or voidable; (4) penalties for adults who authorize, celebrate, register, or enter into child marriage. Each component is individually assessed. Score = 1 if all four conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Saf_Law_ChildMarr" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_SAF_LAW_DOMVIOL", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Safety, The law addresses domestic violence" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the following exists: (1) law or legal provisions on domestic violence; (2) law establishes criminal penalties for domestic violence; (3) law establishes protection orders for victims of domestic violence; (4) law on domestic violence addresses physical, psychological, financial/economic, and sexual violence (including marital rape), where partial score of 0.25 is assigned to each form of violence addressed. The total indicator score = 1 if all four conditions are met and all four forms of domestic violence are addressed in the law. Partial score is assigned if all four conditions are met, but only some and not all four forms of domestic violence are addressed in the law; otherwise score=0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Saf_Law_DomViol" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_SAF_LAW_FEMICIDE", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Safety, The law addresses femicide" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law addresses any of the following: (1) specifically criminalize femicide (the intentional killing of a woman with a gender-related motivation; (2) provides for aggravated penalties for the intentional killing of women. Score = 1 if the condition is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Saf_Law_Femicide" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_SAF_LAW_SEXHARASS", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Safety, The law addresses sexual harassment" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether there law addresses the following: (1) sexual harassment in employment with criminal penalties or civil remedies; (2) sexual harassment in education/schools with criminal penalties or civil remedies; (3) sexual harassment in public places (or on transportation) with criminal penalties or civil remedies; (4) cyber-harassment or cyber-stalking with criminal penalties or civil remedies. Partial score of 0.25 is assigned to each of the four underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Saf_Law_SexHarass" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_SAF_LAW_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Safety, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Safety topic legal frameworks score measures laws addressing child marriage, sexual harassment, domestic violence, and femicide. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Saf_Law_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_SAF_SFR_ACTPLN", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Safety, There is an action plan or strategy on violence against women" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) the action plan or strategy on violence against women provides for prevention measures; (2) it indicates the institutions responsible for its implementation; (3) provides for targets and indicators. Partial score of 0.33 is assigned to each of the three underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Saf_SFR_ActPln" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_SAF_SFR_AJUSTICE", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Safety, There are mechanisms to facilitate access to justice for cases of violence against women" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) there are special police or prosecutorial units; (2) there are courts or procedures on violence against women; (2) there is legal aid provided for cases of violence against women. Partial score of 0.33 is assigned to each of the three underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Saf_SFR_AJustice" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_SAF_SFR_MONITOR", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Safety, There is an institutional mechanism to monitor the implementation of legislation, national plans, and/or programs on violence against women" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the following is established: (1) a specific government mechanism or agency that oversees the implementation of legislation and policies on violence against women; and (2) the entity in place, such as a ministry or committee, is in charge of monitoring implementation of legislation, national plans, and/or programs on violence against women. Score = 1 if both conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Saf_SFR_Monitor" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_SAF_SFR_SERVICES", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Safety, The government provides or funds services for women affected by violence" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the following services exist for women affected by violence: (1) shelters; (2) health services; (3) psychological services; (4) livelihood support services. Partial score of 0.25 is assigned to each of the four underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Saf_SFR_Services" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_SAF_SFR_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Safety, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Safety topic supportive frameworks score examines policies and practices that support the implementation of gender-based violence legislation, including the existence of action plans or strategies, mechanisms to facilitate access to justice, services for women affected by violence, and monitoring agencies. It is divided into four indicators, some of which consist of several questions. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Saf_SFR_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_WRK_ENF_GETJOB_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Work, Public authorities enforce existing legislation allowing employees to request flexible work" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation allowing employees to request flexible work in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Wrk_Enf_ReqFlex_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_WRK_ENF_NONDISCEMP_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Work, Public authorities enforce existing legislation prohibiting discrimination in recruitment based on marital status, parental status, or age" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation prohibiting discrimination in recruitment based on marital status, parental status, or age in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Wrk_Enf_NonDiscRec_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_WRK_ENF_NONDISCREC_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Work, Public authorities enforce a woman's right to get a job in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce a woman's right to get a job in the same way as a man in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Wrk_Enf_GetJob_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_WRK_ENF_REQFLEX_E", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Work, Public authorities enforce existing legislation prohibiting discrimination in employment based on gender" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses expert perceptions of the extent to which public authorities enforce existing legislation prohibiting discrimination in employment based on gender in practice. Experts rate enforcement on a five-point Likert scale (0–4), where 0 indicates no enforcement of protective or beneficial laws, no upholding of women’s rights, or full enforcement of restrictive laws, and 4 indicates full enforcement of protective or beneficial laws, the full upholding of women’s rights, or no enforcement of restrictive laws.\n\nEnforcement perceptions indicator-level score is contingent upon the score of the corresponding legal frameworks indicator and calculated by taking the median value of individual expert responses on the five-point Likert scale (0–4) at the economy level. For indicators that correspond to a partially scored legal frameworks indicator, a weighted approach is applied. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Wrk_Enf_NonDiscEmp_is" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_WRK_ENF_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Enforcement Perceptions, Work, Score (scale 0–100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Work topic enforcement perception score measures the extent to which laws related to a woman's decision to enter and remain in the labor force are enforced in practice or the consequent rights are upheld in practice. It is calculated as the unweighted average of the indicator scores in the topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: pillar3_work" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_WRK_LAW_GETJOB", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Work, The law allows women to get a job in the same way as a man" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) there are no restrictions on a woman’s legal capacity and ability to get a job or pursue a trade or profession; AND (2) the law does not mandate that a woman must seek formal approval or provide additional documentation (such as written consent or authorization) from her husband or guardian; AND (3) the law does not impose legal consequences, such as loss of maintenance or financial support, on women who work against their husband’s or family’s wishes, treating it as a form of disobedience. Score = 1 if all three conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Wrk_Law_GetJob" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_WRK_LAW_NONDISCEMP", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Work, The law prohibits discrimination in employment based on gender" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) the law prohibits employers from discriminating based on gender, or the law mandates equal treatment of women and men in employment; AND (2) the law does not prohibit discrimination in only one aspect of employment, such as pay or dismissal. Score = 1 if both conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Wrk_Law_NonDiscEmp" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_WRK_LAW_NONDISCREC", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Work, The law prohibits discrimination in recruitment based on marital status, parental status, or age" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law prohibits discrimination in recruitment based on the following: (1) marital status; (2) parental status; and (3) age. Partial score of 0.33 is assigned to each of the three underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Wrk_Law_NonDiscRec" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_WRK_LAW_REQFLEX", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Work, The law allows employees to request flexible work" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether the law allows employees to request flexibility regarding the following: (1) time of work; (2) place of work. Partial score of 0.5 is assigned to each of the two underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Wrk_Law_ReqFlex" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_WRK_LAW_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Legal Framework, Work, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Work legal frameworks topic score measures laws protecting against discrimination based on gender in recruitment and employment and laws providing flexible work arrangements. It is divided into four indicators, some of which consist of several questions. It is calculated as the unweighted average of the indicator scores in that topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)." }, { "id": "Othernotes", "value": "Original WBL code: Wrk_Law_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_WRK_SFR_COMPLAINTS", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Work, A specialized body receives complaints about gender discrimination in employment" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) an specialized and independent body has a mandate to receive, adjudicate, or refer to the competent courts, complaints made by public and private actors related to discrimination in employment based on gender; AND (2) the mandate extends to cases about employment relations in the private sector; AND (3) the body is operational. Score = 1 if all three conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Wrk_SFR_Complaints" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_WRK_SFR_FLEXGUIDE", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Work, The government provides instructional resources for the private sector to adopt flexible work arrangements" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether a public entity has provided instructional resources to private sector employers with information on how to implement flexible work arrangements. Score = 1 if condition is met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Wrk_SFR_Flexguide" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_WRK_SFR_LBRMKT_SCORED", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Work, A national government plan or strategy focuses on women's access to the labor market" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether there exist a national government plan or strategy which includes the following: (1) measures to increase women’s access to the labor market; (2) institutional arrangements to monitor the implementation of the plan or strategy; (3) result targets and indicators. Partial score of 0.33 is assigned to each of the three underlying components realized. The total indicator score is the sum of component scores, capped at 1. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Wrk_SFR_LbrMkt_Scored" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_WRK_SFR_RECRUIT", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Work, The government provides awareness-raising measures on fair recruitment policies free from discrimination based on gender" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The indicator assesses whether: (1) a public entity has introduced awareness-raising measures or published other instructional resources; information provided by a private or nongovernmental agency is insufficient; AND (2) the awareness measures provides private sector employers with information on how to implement nondiscrimination based on gender in recruitment practices; AND (3) the information has been released or updated within the past five years preceding the data collection cut-off date. Score = 1 if all the conditions are met; otherwise, score = 0. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Wrk_SFR_Recruit" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "GD_WBL_WRK_SFR_T", "metatype": [ { "id": "Developmentrelevance", "value": "Women, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women’s economic participation. Laws are fundamental to women’s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women’s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth." }, { "id": "Generalcomments", "value": "1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group." }, { "id": "IndicatorName", "value": "WBL: Supportive Framework, Work, Score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY 3.0 IGO" }, { "id": "License_URL", "value": "http://creativecommons.org / licenses/by/3.0/igo" }, { "id": "Limitationsandexceptions", "value": "WBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women’s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets." }, { "id": "Longdefinition", "value": "The Work supportive frameworks topic score examines policies and practices that support the implementation of laws related to the workplace, including the existence of institutions to receive complaints related to discrimination in employment, instructional resources published by the government on nondiscrimination and flexible work arrangements, and national plans to foster women’s labor market inclusion. It is divided into four indicators, some of which consist of several questions. It is calculated as the unweighted average of the indicator scores in that topic and rescaled to a 0–100 scale, where 0 represents the lowest possible score and 100 the highest possible score. For details on underlying questions and scoring assumptions, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)" }, { "id": "Othernotes", "value": "Original WBL code: Wrk_SFR_ts" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "14" }, { "id": "HD_HCIP_EDUC_FE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): education pillar score, female (scale 0–188)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): education pillar score aggregates human capital accumulated during formal schooling. This includes pre-school, primary, secondary, and tertiary schooling. The measure also captures learning quality through harmonized learning outcomes." }, { "id": "Othernotes", "value": "The original Human Capital Index (HCI) was created to estimate a child's potential productivity by age 18, using five core indicators across survival, education, and health. The index, ranging from 0 to 1, measures how closely a child’s expected productivity approaches an ideal benchmark of full health and quality education. \nThe Human Capital Index Plus (HCI+) will retain HCI’s foundation in health and education but expand its scope to include job-related indicators, linking human capital to actual economic participation. After 2025, HCI+ replaced the original HCI as the primary index, with the initial version archived at https://datacatalog.worldbank.org/search/dataset/0038030/Human-Capital-Index." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ education pillar combines three measures of quantity and quality of learning: (1) expected years of schooling (EYS) — the number of years a child born today can expect to complete by age 18, constructed by summing age-appropriate enrollment rates (approximating ages before the official primary age with pre-primary, 6–11 with primary, 12–14 with lower secondary, and 15–17 with upper secondary) using UNESCO Institute for Statistics (UIS) data and validated/supplemented by World Bank country teams; (2) quality of schooling — captured by the Harmonized Learning Outcomes, which brings together and harmonizes scores from major international and regional assessments (TIMSS, PIRLS, PISA, SACMEQ, PASEC, LLECE, PILNA) and early-grade reading assessments (EGRAs); and (3) tertiary completion — the share of young adults who complete tertiary education (measured as the percentage of 25–29-year-olds with tertiary credentials), drawn from the WIDE database and national surveys.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Unitofmeasure", "value": "Index (0–188)" } ], "source_id": "14" }, { "id": "HD_HCIP_EDUC_MA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): education pillar score, male (scale 0–188)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): education pillar score aggregates human capital accumulated during formal schooling. This includes pre-school, primary, secondary, and tertiary schooling. The measure also captures learning quality through harmonized learning outcomes." }, { "id": "Othernotes", "value": "The original Human Capital Index (HCI) was created to estimate a child's potential productivity by age 18, using five core indicators across survival, education, and health. The index, ranging from 0 to 1, measures how closely a child’s expected productivity approaches an ideal benchmark of full health and quality education. \nThe Human Capital Index Plus (HCI+) will retain HCI’s foundation in health and education but expand its scope to include job-related indicators, linking human capital to actual economic participation. After 2025, HCI+ replaced the original HCI as the primary index, with the initial version archived at https://datacatalog.worldbank.org/search/dataset/0038030/Human-Capital-Index." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ education pillar combines three measures of quantity and quality of learning: (1) expected years of schooling (EYS) — the number of years a child born today can expect to complete by age 18, constructed by summing age-appropriate enrollment rates (approximating ages before the official primary age with pre-primary, 6–11 with primary, 12–14 with lower secondary, and 15–17 with upper secondary) using UNESCO Institute for Statistics (UIS) data and validated/supplemented by World Bank country teams; (2) quality of schooling — captured by the Harmonized Learning Outcomes, which brings together and harmonizes scores from major international and regional assessments (TIMSS, PIRLS, PISA, SACMEQ, PASEC, LLECE, PILNA) and early-grade reading assessments (EGRAs); and (3) tertiary completion — the share of young adults who complete tertiary education (measured as the percentage of 25–29-year-olds with tertiary credentials), drawn from the WIDE database and national surveys.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Unitofmeasure", "value": "Index (0–188)" } ], "source_id": "14" }, { "id": "HD_HCIP_EDUC_TO", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): education pillar score, total (scale 0–188)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): education pillar score aggregates human capital accumulated during formal schooling. This includes pre-school, primary, secondary, and tertiary schooling. The measure also captures learning quality through harmonized learning outcomes." }, { "id": "Othernotes", "value": "The original Human Capital Index (HCI) was created to estimate a child's potential productivity by age 18, using five core indicators across survival, education, and health. The index, ranging from 0 to 1, measures how closely a child’s expected productivity approaches an ideal benchmark of full health and quality education. \nThe Human Capital Index Plus (HCI+) will retain HCI’s foundation in health and education but expand its scope to include job-related indicators, linking human capital to actual economic participation. After 2025, HCI+ replaced the original HCI as the primary index, with the initial version archived at https://datacatalog.worldbank.org/search/dataset/0038030/Human-Capital-Index." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ education pillar combines three measures of quantity and quality of learning: (1) expected years of schooling (EYS) — the number of years a child born today can expect to complete by age 18, constructed by summing age-appropriate enrollment rates (approximating ages before the official primary age with pre-primary, 6–11 with primary, 12–14 with lower secondary, and 15–17 with upper secondary) using UNESCO Institute for Statistics (UIS) data and validated/supplemented by World Bank country teams; (2) quality of schooling — captured by the Harmonized Learning Outcomes, which brings together and harmonizes scores from major international and regional assessments (TIMSS, PIRLS, PISA, SACMEQ, PASEC, LLECE, PILNA) and early-grade reading assessments (EGRAs); and (3) tertiary completion — the share of young adults who complete tertiary education (measured as the percentage of 25–29-year-olds with tertiary credentials), drawn from the WIDE database and national surveys.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Unitofmeasure", "value": "Index (0–188)" } ], "source_id": "14" }, { "id": "HD_HCIP_HLTH_FE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): health pillar score, female (scale 0–50)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): health pillar score captures productivity effects of surviving to working age and achieving adequate early-life physical growth." }, { "id": "Othernotes", "value": "The original Human Capital Index (HCI) was created to estimate a child's potential productivity by age 18, using five core indicators across survival, education, and health. The index, ranging from 0 to 1, measures how closely a child’s expected productivity approaches an ideal benchmark of full health and quality education. \nThe Human Capital Index Plus (HCI+) will retain HCI’s foundation in health and education but expand its scope to include job-related indicators, linking human capital to actual economic participation. After 2025, HCI+ replaced the original HCI as the primary index, with the initial version archived at https://datacatalog.worldbank.org/search/dataset/0038030/Human-Capital-Index." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ health pillar combines two measures that together capture the latent health and nutrition conditions of a country: (1) adult survival rate, 15-60, calculated from UN Population Division mortality tables; (2) the fraction of children under five who are not stunted (height-for-age = -2 SD), a marker of adequate early nutrition and growth (Joint child malnutrition estimates (JME)). While there is no single, widely accepted measure of health status, rates of both adult survival & stunting have been linked to population-level measures of latent health. These population-level measures of health, in turn, have been linked to worker productivity.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Unitofmeasure", "value": "Index (0–50)" } ], "source_id": "14" }, { "id": "HD_HCIP_HLTH_MA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): health pillar score, male (scale 0–50)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): health pillar score captures productivity effects of surviving to working age and achieving adequate early-life physical growth." }, { "id": "Othernotes", "value": "The original Human Capital Index (HCI) was created to estimate a child's potential productivity by age 18, using five core indicators across survival, education, and health. The index, ranging from 0 to 1, measures how closely a child’s expected productivity approaches an ideal benchmark of full health and quality education. \nThe Human Capital Index Plus (HCI+) will retain HCI’s foundation in health and education but expand its scope to include job-related indicators, linking human capital to actual economic participation. After 2025, HCI+ replaced the original HCI as the primary index, with the initial version archived at https://datacatalog.worldbank.org/search/dataset/0038030/Human-Capital-Index." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ health pillar combines two measures that together capture the latent health and nutrition conditions of a country: (1) adult survival rate, 15-60, calculated from UN Population Division mortality tables; (2) the fraction of children under five who are not stunted (height-for-age = -2 SD), a marker of adequate early nutrition and growth (Joint child malnutrition estimates (JME)). While there is no single, widely accepted measure of health status, rates of both adult survival & stunting have been linked to population-level measures of latent health. These population-level measures of health, in turn, have been linked to worker productivity.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Unitofmeasure", "value": "Index (0–50)" } ], "source_id": "14" }, { "id": "HD_HCIP_HLTH_TO", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): health pillar score, total (scale 0–50)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): health pillar score captures productivity effects of surviving to working age and achieving adequate early-life physical growth." }, { "id": "Othernotes", "value": "The original Human Capital Index (HCI) was created to estimate a child's potential productivity by age 18, using five core indicators across survival, education, and health. The index, ranging from 0 to 1, measures how closely a child’s expected productivity approaches an ideal benchmark of full health and quality education. \nThe Human Capital Index Plus (HCI+) will retain HCI’s foundation in health and education but expand its scope to include job-related indicators, linking human capital to actual economic participation. After 2025, HCI+ replaced the original HCI as the primary index, with the initial version archived at https://datacatalog.worldbank.org/search/dataset/0038030/Human-Capital-Index." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ health pillar combines two measures that together capture the latent health and nutrition conditions of a country: (1) adult survival rate, 15-60, calculated from UN Population Division mortality tables; (2) the fraction of children under five who are not stunted (height-for-age = -2 SD), a marker of adequate early nutrition and growth (Joint child malnutrition estimates (JME)). While there is no single, widely accepted measure of health status, rates of both adult survival & stunting have been linked to population-level measures of latent health. These population-level measures of health, in turn, have been linked to worker productivity.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Unitofmeasure", "value": "Index (0–50)" } ], "source_id": "14" }, { "id": "HD_HCIP_OTJL_FE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): on-the-job learning pillar score, female (scale -30–87)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): on-the-job learning pillar score measures human capital accumulation after age 18 through work experience. The measure captures labor force participation, unemployment, and wage employment for youth and adults." }, { "id": "Othernotes", "value": "The original Human Capital Index (HCI) was created to estimate a child's potential productivity by age 18, using five core indicators across survival, education, and health. The index, ranging from 0 to 1, measures how closely a child’s expected productivity approaches an ideal benchmark of full health and quality education. \nThe Human Capital Index Plus (HCI+) will retain HCI’s foundation in health and education but expand its scope to include job-related indicators, linking human capital to actual economic participation. After 2025, HCI+ replaced the original HCI as the primary index, with the initial version archived at https://datacatalog.worldbank.org/search/dataset/0038030/Human-Capital-Index." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ on-the-job learning pillar combines three measures that capture the capacity to work and the quality of jobs for both the youth population ages 18-24 and the adult population ages 25–65 (1) the share of youth and adults in the labor force — the labor-force participation metric that reflects attachment to the labor market (ILOSTAT); (2) the share of youths and adults not in unemployment — based on unemployment rates from ILOSTAT; and (3) the share of youth and adults employed in wage versus non-wage work — the proportion of workers in paid, salaried jobs versus self-employment or unpaid family work (ILOSTAT’s modeled series). Together, these indicators capture both how long people can participate in the labor market and whether they have access to quality employment opportunities that grow human capital.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Unitofmeasure", "value": "Index (-30–87)" } ], "source_id": "14" }, { "id": "HD_HCIP_OTJL_MA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): on-the-job learning pillar score, male (scale -30–87)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): on-the-job learning pillar score measures human capital accumulation after age 18 through work experience. The measure captures labor force participation, unemployment, and wage employment for youth and adults." }, { "id": "Othernotes", "value": "The original Human Capital Index (HCI) was created to estimate a child's potential productivity by age 18, using five core indicators across survival, education, and health. The index, ranging from 0 to 1, measures how closely a child’s expected productivity approaches an ideal benchmark of full health and quality education. \nThe Human Capital Index Plus (HCI+) will retain HCI’s foundation in health and education but expand its scope to include job-related indicators, linking human capital to actual economic participation. After 2025, HCI+ replaced the original HCI as the primary index, with the initial version archived at https://datacatalog.worldbank.org/search/dataset/0038030/Human-Capital-Index." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ on-the-job learning pillar combines three measures that capture the capacity to work and the quality of jobs for both the youth population ages 18-24 and the adult population ages 25–65 (1) the share of youth and adults in the labor force — the labor-force participation metric that reflects attachment to the labor market (ILOSTAT); (2) the share of youths and adults not in unemployment — based on unemployment rates from ILOSTAT; and (3) the share of youth and adults employed in wage versus non-wage work — the proportion of workers in paid, salaried jobs versus self-employment or unpaid family work (ILOSTAT’s modeled series). Together, these indicators capture both how long people can participate in the labor market and whether they have access to quality employment opportunities that grow human capital.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Unitofmeasure", "value": "Index (-30–87)" } ], "source_id": "14" }, { "id": "HD_HCIP_OTJL_TO", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): on-the-job learning pillar score, total (scale -30–87)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): on-the-job learning pillar score measures human capital accumulation after age 18 through work experience. The measure captures labor force participation, unemployment, and wage employment for youth and adults." }, { "id": "Othernotes", "value": "The original Human Capital Index (HCI) was created to estimate a child's potential productivity by age 18, using five core indicators across survival, education, and health. The index, ranging from 0 to 1, measures how closely a child’s expected productivity approaches an ideal benchmark of full health and quality education. \nThe Human Capital Index Plus (HCI+) will retain HCI’s foundation in health and education but expand its scope to include job-related indicators, linking human capital to actual economic participation. After 2025, HCI+ replaced the original HCI as the primary index, with the initial version archived at https://datacatalog.worldbank.org/search/dataset/0038030/Human-Capital-Index." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ on-the-job learning pillar combines three measures that capture the capacity to work and the quality of jobs for both the youth population ages 18-24 and the adult population ages 25–65 (1) the share of youth and adults in the labor force — the labor-force participation metric that reflects attachment to the labor market (ILOSTAT); (2) the share of youths and adults not in unemployment — based on unemployment rates from ILOSTAT; and (3) the share of youth and adults employed in wage versus non-wage work — the proportion of workers in paid, salaried jobs versus self-employment or unpaid family work (ILOSTAT’s modeled series). Together, these indicators capture both how long people can participate in the labor market and whether they have access to quality employment opportunities that grow human capital.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Unitofmeasure", "value": "Index (-30–87)" } ], "source_id": "14" }, { "id": "HD_HCIP_OVRL_FE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): overall score, female (scale 0–325)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): overall score calculates the contributions of health, education, and on-the-job learning to worker productivity. The index measures the productivity of a child born today as a future worker relative to the benchmark of full health, complete education, and full employment in wage employment." }, { "id": "Othernotes", "value": "The original Human Capital Index (HCI) was created to estimate a child's potential productivity by age 18, using five core indicators across survival, education, and health. The index, ranging from 0 to 1, measures how closely a child’s expected productivity approaches an ideal benchmark of full health and quality education. \nThe Human Capital Index Plus (HCI+) will retain HCI’s foundation in health and education but expand its scope to include job-related indicators, linking human capital to actual economic participation. After 2025, HCI+ replaced the original HCI as the primary index, with the initial version archived at https://datacatalog.worldbank.org/search/dataset/0038030/Human-Capital-Index." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ pulls together 11 outcomes focused on health, education, and employment, and employs the latest research on how each affects earnings. As a result, improvements in the HCI+ can be directly interpreted as increases in workers' lifetime earnings and in GDP over the long run. The scale for the HCI+ is based on the human capital earnings function (originally developed by Mincer (1974) and others), where human capital is measured as the log of lifetime earnings. In this framework, a one-unit change in the index corresponds to a proportional change in earnings. To make the index easier to interpret, we multiply the log measure by 100.\nThis means that point differences in HCI+ can be read as approximate percentage differences in lifetime earnings. For example, an increase of 10 points in HCI+ corresponds roughly to a 10 percent increase in expected adult wages (and, in the long run, GDP per worker). Multiplying by 100, therefore, transforms the log measure into policy-relevant percentage units without altering the index's underlying economics.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Unitofmeasure", "value": "Index (0–325)" } ], "source_id": "14" }, { "id": "HD_HCIP_OVRL_MA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): overall score, male (scale 0–325)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): overall score calculates the contributions of health, education, and on-the-job learning to worker productivity. The index measures the productivity of a child born today as a future worker relative to the benchmark of full health, complete education, and full employment in wage employment." }, { "id": "Othernotes", "value": "The original Human Capital Index (HCI) was created to estimate a child's potential productivity by age 18, using five core indicators across survival, education, and health. The index, ranging from 0 to 1, measures how closely a child’s expected productivity approaches an ideal benchmark of full health and quality education. \nThe Human Capital Index Plus (HCI+) will retain HCI’s foundation in health and education but expand its scope to include job-related indicators, linking human capital to actual economic participation. After 2025, HCI+ replaced the original HCI as the primary index, with the initial version archived at https://datacatalog.worldbank.org/search/dataset/0038030/Human-Capital-Index." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ pulls together 11 outcomes focused on health, education, and employment, and employs the latest research on how each affects earnings. As a result, improvements in the HCI+ can be directly interpreted as increases in workers' lifetime earnings and in GDP over the long run. The scale for the HCI+ is based on the human capital earnings function (originally developed by Mincer (1974) and others), where human capital is measured as the log of lifetime earnings. In this framework, a one-unit change in the index corresponds to a proportional change in earnings. To make the index easier to interpret, we multiply the log measure by 100.\nThis means that point differences in HCI+ can be read as approximate percentage differences in lifetime earnings. For example, an increase of 10 points in HCI+ corresponds roughly to a 10 percent increase in expected adult wages (and, in the long run, GDP per worker). Multiplying by 100, therefore, transforms the log measure into policy-relevant percentage units without altering the index's underlying economics.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Unitofmeasure", "value": "Index (0–325)" } ], "source_id": "14" }, { "id": "HD_HCIP_OVRL_TO", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index plus (HCI+): overall score, total (scale 0–325)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The human capital index plus (HCI+): overall score calculates the contributions of health, education, and on-the-job learning to worker productivity. The index measures the productivity of a child born today as a future worker relative to the benchmark of full health, complete education, and full employment in wage employment." }, { "id": "Othernotes", "value": "The original Human Capital Index (HCI) was created to estimate a child's potential productivity by age 18, using five core indicators across survival, education, and health. The index, ranging from 0 to 1, measures how closely a child’s expected productivity approaches an ideal benchmark of full health and quality education. \nThe Human Capital Index Plus (HCI+) will retain HCI’s foundation in health and education but expand its scope to include job-related indicators, linking human capital to actual economic participation. After 2025, HCI+ replaced the original HCI as the primary index, with the initial version archived at https://datacatalog.worldbank.org/search/dataset/0038030/Human-Capital-Index." }, { "id": "Periodicity", "value": "Five-Year Interval" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The HCI+ pulls together 11 outcomes focused on health, education, and employment, and employs the latest research on how each affects earnings. As a result, improvements in the HCI+ can be directly interpreted as increases in workers' lifetime earnings and in GDP over the long run. The scale for the HCI+ is based on the human capital earnings function (originally developed by Mincer (1974) and others), where human capital is measured as the log of lifetime earnings. In this framework, a one-unit change in the index corresponds to a proportional change in earnings. To make the index easier to interpret, we multiply the log measure by 100.\nThis means that point differences in HCI+ can be read as approximate percentage differences in lifetime earnings. For example, an increase of 10 points in HCI+ corresponds roughly to a 10 percent increase in expected adult wages (and, in the long run, GDP per worker). Multiplying by 100, therefore, transforms the log measure into policy-relevant percentage units without altering the index's underlying economics.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars—health, education, and on-the-job learning—into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\nReferences: Decerf, Benoît; D’Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. © World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. © World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO." }, { "id": "Unitofmeasure", "value": "Index (0–325)" } ], "source_id": "14" }, { "id": "IC.FRM.FEMM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators that describe several dimensions of gender composition in the workforce. It also collects information on the characteristics of the workforce employed in the non-agricultural private economy. The set of indicators presents the composition of the firm's workforce by type of contract and gender. Labor regulations have a direct effect on the type of employment favored by firms and they may have a different impact by gender. Other indicators present the composition of the workforce classified into temporary and permanent workers and reflect the participation of women in regular full time employment, along with the firms’ inclusion of women in formal trainings." }, { "id": "IndicatorName", "value": "Firms with female top manager (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms with females as the top manager." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity. \n\nRelevance to gender indicator: Women are vastly underrepresented in decision making positions at the top level in the private sector and this indicator monitors progress that has been made." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2007-2024" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Leadership" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "IC.FRM.FEMO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators that describe several dimensions of gender composition in the workforce. It also collects information on the characteristics of the workforce employed in the non-agricultural private economy. The set of indicators presents the composition of the firm's workforce by type of contract and gender. Labor regulations have a direct effect on the type of employment favored by firms and they may have a different impact by gender. Other indicators present the composition of the workforce classified into temporary and permanent workers and reflect the participation of women in regular full time employment, along with the firms’ inclusion of women in formal trainings." }, { "id": "IndicatorName", "value": "Firms with female participation in ownership (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms with females among the owners." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2024" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Entrepreneurship" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "IC.REG.COST.PC.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Cost of business start-up procedures, female (% of GNI per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Cost to register a business is normalized by presenting it as a percentage of gross national income (GNI) per capita." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nEntrepreneurs around the world face a range of challenges. One of them is inefficient regulation. This indicator measures the number of procedures, time, cost and paid-in minimum capital requirement for a small- to medium-size limited liability company to start up and formally operate in each economy’s largest business city. To make the data comparable across 190 economies, Doing Business uses a standardized business that is 100% domestically owned, has a start-up capital equivalent to 10 times the income per capita, engages in general industrial or commercial activities and employs between 10 and 50 people one month after the commencement of operations, all of whom are domestic nationals. The starting a business indicators consider two cases of local limited liability companies that are identical in all aspects, except that one company is owned by five married women and the other by five married men. \n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.REG.COST.PC.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Cost of business start-up procedures, male (% of GNI per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Cost to register a business is normalized by presenting it as a percentage of gross national income (GNI) per capita." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nEntrepreneurs around the world face a range of challenges. One of them is inefficient regulation. This indicator measures the paid-in minimum capital requirement for a small- to medium-size limited liability company to start up and formally operate in each economy’s largest business city. To make the data comparable across 190 economies, Doing Business uses a standardized business that is 100% domestically owned, has a start-up capital equivalent to 10 times the income per capita, engages in general industrial or commercial activities and employs between 10 and 50 people one month after the commencement of operations, all of whom are domestic nationals. The starting a business indicators consider two cases of local limited liability companies that are identical in all aspects, except that one company is owned by five married women and the other by five married men. \n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.REG.COST.PC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Cost of business start-up procedures (% of GNI per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Cost to register a business is normalized by presenting it as a percentage of gross national income (GNI) per capita." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nEntrepreneurs around the world face a range of challenges. One of them is inefficient regulation. The indicator measures the procedures, time, cost and paid-in minimum capital required for a small or medium-size limited liability company to start up and formally operate.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.REG.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time required to start a business (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Time required to start a business is the number of calendar days needed to complete the procedures to legally operate a business. If a procedure can be speeded up at additional cost, the fastest procedure, independent of cost, is chosen." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nEntrepreneurs around the world face a range of challenges. One of them is inefficient regulation. The indicator measures the procedures, time, cost and paid-in minimum capital required for a small or medium-size limited liability company to start up and formally operate.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.REG.DURS.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time required to start a business, female (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Time required to start a business is the number of calendar days needed to complete the procedures to legally operate a business. If a procedure can be speeded up at additional cost, the fastest procedure, independent of cost, is chosen." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nEntrepreneurs around the world face a range of challenges. One of them is inefficient regulation. This indicator measures the time for a small- to medium-size limited liability company to start up and formally operate in each economy’s largest business city. To make the data comparable across 190 economies, Doing Business uses a standardized business that is 100% domestically owned, has a start-up capital equivalent to 10 times the income per capita, engages in general industrial or commercial activities and employs between 10 and 50 people one month after the commencement of operations, all of whom are domestic nationals. The starting a business indicators consider two cases of local limited liability companies that are identical in all aspects, except that one company is owned by five married women and the other by five married men. \n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.REG.DURS.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The Doing Business data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time required to start a business, male (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Time required to start a business is the number of calendar days needed to complete the procedures to legally operate a business. If a procedure can be speeded up at additional cost, the fastest procedure, independent of cost, is chosen." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nEntrepreneurs around the world face a range of challenges. One of them is inefficient regulation. This indicator measures the time for a small- to medium-size limited liability company to start up and formally operate in each economy’s largest business city. To make the data comparable across 190 economies, Doing Business uses a standardized business that is 100% domestically owned, has a start-up capital equivalent to 10 times the income per capita, engages in general industrial or commercial activities and employs between 10 and 50 people one month after the commencement of operations, all of whom are domestic nationals. The starting a business indicators consider two cases of local limited liability companies that are identical in all aspects, except that one company is owned by five married women and the other by five married men. \n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.REG.PROC", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Start-up procedures to register a business (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Start-up procedures are those required to start a business, including interactions to obtain necessary permits and licenses and to complete all inscriptions, verifications, and notifications to start operations. Data are for businesses with specific characteristics of ownership, size, and type of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nEntrepreneurs around the world face a range of challenges. One of them is inefficient regulation. The indicator measures the procedures, time, cost and paid-in minimum capital required for a small or medium-size limited liability company to start up and formally operate.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.REG.PROC.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Start-up procedures to register a business, female (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Start-up procedures are those required to start a business, including interactions to obtain necessary permits and licenses and to complete all inscriptions, verifications, and notifications to start operations. Data are for businesses with specific characteristics of ownership, size, and type of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nEntrepreneurs around the world face a range of challenges. One of them is inefficient regulation. This indicator measures the number of procedure for a small- to medium-size limited liability company to start up and formally operate in each economy’s largest business city. To make the data comparable across 190 economies, Doing Business uses a standardized business that is 100% domestically owned, has a start-up capital equivalent to 10 times the income per capita, engages in general industrial or commercial activities and employs between 10 and 50 people one month after the commencement of operations, all of whom are domestic nationals. The starting a business indicators consider two cases of local limited liability companies that are identical in all aspects, except that one company is owned by five married women and the other by five married men. \n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.REG.PROC.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Start-up procedures to register a business, male (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Start-up procedures are those required to start a business, including interactions to obtain necessary permits and licenses and to complete all inscriptions, verifications, and notifications to start operations. Data are for businesses with specific characteristics of ownership, size, and type of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "This indicator measures the number of procedures for a small- to medium-size limited liability company to start up and formally operate in each economy’s largest business city. To make the data comparable across 190 economies, Doing Business uses a standardized business that is 100% domestically owned, has a start-up capital equivalent to 10 times the income per capita, engages in general industrial or commercial activities and employs between 10 and 50 people one month after the commencement of operations, all of whom are domestic nationals. The starting a business indicators consider two cases of local limited liability companies that are identical in all aspects, except that one company is owned by five married women and the other by five married men." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.WEF.LLCD.FE", "metatype": [ { "id": "Developmentrelevance", "value": "Measuring women’s entrepreneurial activity is critically important for a better understanding of how female entrepreneurs contribute to the economy and society. The lack of comprehensive sex-disaggregated data on business entry and ownership presents a significant obstacle to the global and diversified analysis of female entrepreneurship. Due to insufficient standardized and country-comparable data, the diagnostics of gender gaps in entrepreneurship are limited.\nIn here, to measure female entrepreneurial activity, annual data is collected directly from 73 company registrars on the number of female/male business owners of LLCs, female/ male sole proprietors and female/ male directors of LLCs, over the past four years.\n\nThe importance of female entrepreneurship for economic development is widely recognized. Numerous studies demonstrate the positive impact of female entrepreneurs on economic growth and development, as well as sustainable and durable peace. Moreover, economies characterized by high levels of female entrepreneurial activity are more resilient to financial crises and experience economic slowdowns less frequently. Despite different methodologies, these studies find significant socioeconomic benefits of female entrepreneurship." }, { "id": "Generalcomments", "value": "For cross-country comparability, only limited liability corporations that operate in the formal sector are included." }, { "id": "IndicatorName", "value": "Number of female directors" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. The Entrepreneurship Database facilitates the analysis of the growth of the formal private sector and the identification of factors that encourage firms to begin operations in or transition to the formal sector. Data is collected all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured.\n\nThe data itself only provides a snapshot of a given economy's business demographics, and cannot by itself explain the factors that affect the business creation cycle. However, when the Entrepreneurship Database is combined with other data such as the Doing Business Report, Investment Climate Assessments, and/or OECD Entrepreneurship Indicators, researchers and policymakers can better understand the dynamics of the business creation process." }, { "id": "Longdefinition", "value": "Number of female directors is the number of female individuals who conduct the affairs of newly registered limited liability companies in the calendar year." }, { "id": "Source", "value": "World Bank's Entrepreneurship Survey and database (https://www.worldbank.org/en/programs/entrepreneurship). Downloaded on November 29, 2023." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group’s Entrepreneurship Database. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries in 73 economies. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data - most often due to an absence of digitized registration systems - the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and private vendors or publicly available data.\n\nThe data includes all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.WEF.LLCD.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Measuring women’s entrepreneurial activity is critically important for a better understanding of how female entrepreneurs contribute to the economy and society. The lack of comprehensive sex-disaggregated data on business entry and ownership presents a significant obstacle to the global and diversified analysis of female entrepreneurship. Due to insufficient standardized and country-comparable data, the diagnostics of gender gaps in entrepreneurship are limited.\nIn here, to measure female entrepreneurial activity, annual data is collected directly from 73 company registrars on the number of female/male business owners of LLCs, female/ male sole proprietors and female/ male directors of LLCs, over the past four years.\n\nThe importance of female entrepreneurship for economic development is widely recognized. Numerous studies demonstrate the positive impact of female entrepreneurs on economic growth and development, as well as sustainable and durable peace. Moreover, economies characterized by high levels of female entrepreneurial activity are more resilient to financial crises and experience economic slowdowns less frequently. Despite different methodologies, these studies find significant socioeconomic benefits of female entrepreneurship." }, { "id": "Generalcomments", "value": "For cross-country comparability, only limited liability corporations that operate in the formal sector are included." }, { "id": "IndicatorName", "value": "Share of female directors (% of total directors)" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. The Entrepreneurship Database facilitates the analysis of the growth of the formal private sector and the identification of factors that encourage firms to begin operations in or transition to the formal sector. Data is collected all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured.\n\nThe data itself only provides a snapshot of a given economy's business demographics, and cannot by itself explain the factors that affect the business creation cycle. However, when the Entrepreneurship Database is combined with other data such as the Doing Business Report, Investment Climate Assessments, and/or OECD Entrepreneurship Indicators, researchers and policymakers can better understand the dynamics of the business creation process." }, { "id": "Longdefinition", "value": "Share of female directors is the proportion of female directors of newly registered limited liability companies out of the total number of directors of newly registered limited liability companies in the economy in the calendar year. A director is defined as an individual who conducts the affairs of newly registered limited liability companies in the calendar year." }, { "id": "Source", "value": "World Bank's Entrepreneurship Survey and database (https://www.worldbank.org/en/programs/entrepreneurship). Downloaded on November 29, 2023." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group’s Entrepreneurship Database. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries in 73 economies. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data - most often due to an absence of digitized registration systems - the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and private vendors or publicly available data.\n\nThe data includes all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.WEF.LLCD.MA", "metatype": [ { "id": "Developmentrelevance", "value": "Measuring women’s entrepreneurial activity is critically important for a better understanding of how female entrepreneurs contribute to the economy and society. The lack of comprehensive sex-disaggregated data on business entry and ownership presents a significant obstacle to the global and diversified analysis of female entrepreneurship. Due to insufficient standardized and country-comparable data, the diagnostics of gender gaps in entrepreneurship are limited.\nIn here, to measure female entrepreneurial activity, annual data is collected directly from 73 company registrars on the number of female/male business owners of LLCs, female/ male sole proprietors and female/ male directors of LLCs, over the past four years.\n\nThe importance of female entrepreneurship for economic development is widely recognized. Numerous studies demonstrate the positive impact of female entrepreneurs on economic growth and development, as well as sustainable and durable peace. Moreover, economies characterized by high levels of female entrepreneurial activity are more resilient to financial crises and experience economic slowdowns less frequently. Despite different methodologies, these studies find significant socioeconomic benefits of female entrepreneurship." }, { "id": "Generalcomments", "value": "For cross-country comparability, only limited liability corporations that operate in the formal sector are included." }, { "id": "IndicatorName", "value": "Number of male directors" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. The Entrepreneurship Database facilitates the analysis of the growth of the formal private sector and the identification of factors that encourage firms to begin operations in or transition to the formal sector. Data is collected all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured.\n\nThe data itself only provides a snapshot of a given economy's business demographics, and cannot by itself explain the factors that affect the business creation cycle. However, when the Entrepreneurship Database is combined with other data such as the Doing Business Report, Investment Climate Assessments, and/or OECD Entrepreneurship Indicators, researchers and policymakers can better understand the dynamics of the business creation process." }, { "id": "Longdefinition", "value": "Number of male directors is the number of female individuals who conduct the affairs of newly registered limited liability companies in the calendar year." }, { "id": "Source", "value": "World Bank's Entrepreneurship Survey and database (https://www.worldbank.org/en/programs/entrepreneurship). Downloaded on November 29, 2023." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group’s Entrepreneurship Database. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries in 73 economies. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data - most often due to an absence of digitized registration systems - the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and private vendors or publicly available data.\n\nThe data includes all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.WEF.LLCD.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Measuring women’s entrepreneurial activity is critically important for a better understanding of how female entrepreneurs contribute to the economy and society. The lack of comprehensive sex-disaggregated data on business entry and ownership presents a significant obstacle to the global and diversified analysis of female entrepreneurship. Due to insufficient standardized and country-comparable data, the diagnostics of gender gaps in entrepreneurship are limited.\nIn here, to measure female entrepreneurial activity, annual data is collected directly from 73 company registrars on the number of female/male business owners of LLCs, female/ male sole proprietors and female/ male directors of LLCs, over the past four years.\n\nThe importance of female entrepreneurship for economic development is widely recognized. Numerous studies demonstrate the positive impact of female entrepreneurs on economic growth and development, as well as sustainable and durable peace. Moreover, economies characterized by high levels of female entrepreneurial activity are more resilient to financial crises and experience economic slowdowns less frequently. Despite different methodologies, these studies find significant socioeconomic benefits of female entrepreneurship." }, { "id": "Generalcomments", "value": "For cross-country comparability, only limited liability corporations that operate in the formal sector are included." }, { "id": "IndicatorName", "value": "Share of male directors (% of total directors)" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. The Entrepreneurship Database facilitates the analysis of the growth of the formal private sector and the identification of factors that encourage firms to begin operations in or transition to the formal sector. Data is collected all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured.\n\nThe data itself only provides a snapshot of a given economy's business demographics, and cannot by itself explain the factors that affect the business creation cycle. However, when the Entrepreneurship Database is combined with other data such as the Doing Business Report, Investment Climate Assessments, and/or OECD Entrepreneurship Indicators, researchers and policymakers can better understand the dynamics of the business creation process." }, { "id": "Longdefinition", "value": "Share of male directors is the proportion of male directors of newly registered limited liability companies out of the total number of directors of newly registered limited liability companies in the economy in the calendar year. A director is defined as an individual who conducts the affairs of newly registered limited liability companies in the calendar year." }, { "id": "Source", "value": "World Bank's Entrepreneurship Survey and database (https://www.worldbank.org/en/programs/entrepreneurship). Downloaded on November 29, 2023." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group’s Entrepreneurship Database. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries in 73 economies. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data - most often due to an absence of digitized registration systems - the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and private vendors or publicly available data.\n\nThe data includes all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.WEF.LLCO.FE", "metatype": [ { "id": "Developmentrelevance", "value": "Measuring women’s entrepreneurial activity is critically important for a better understanding of how female entrepreneurs contribute to the economy and society. The lack of comprehensive sex-disaggregated data on business entry and ownership presents a significant obstacle to the global and diversified analysis of female entrepreneurship. Due to insufficient standardized and country-comparable data, the diagnostics of gender gaps in entrepreneurship are limited.\nIn here, to measure female entrepreneurial activity, annual data is collected directly from 73 company registrars on the number of female/male business owners of LLCs, female/ male sole proprietors and female/ male directors of LLCs, over the past four years.\n\nThe importance of female entrepreneurship for economic development is widely recognized. Numerous studies demonstrate the positive impact of female entrepreneurs on economic growth and development, as well as sustainable and durable peace. Moreover, economies characterized by high levels of female entrepreneurial activity are more resilient to financial crises and experience economic slowdowns less frequently. Despite different methodologies, these studies find significant socioeconomic benefits of female entrepreneurship." }, { "id": "Generalcomments", "value": "For cross-country comparability, only limited liability corporations that operate in the formal sector are included." }, { "id": "IndicatorName", "value": "Number of female business owners" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. The Entrepreneurship Database facilitates the analysis of the growth of the formal private sector and the identification of factors that encourage firms to begin operations in or transition to the formal sector. Data is collected all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured.\n\nThe data itself only provides a snapshot of a given economy's business demographics, and cannot by itself explain the factors that affect the business creation cycle. However, when the Entrepreneurship Database is combined with other data such as the Doing Business Report, Investment Climate Assessments, and/or OECD Entrepreneurship Indicators, researchers and policymakers can better understand the dynamics of the business creation process." }, { "id": "Longdefinition", "value": "Number of female business owners is the number of female individuals that own at least one share of a limited liability company that was newly registered in the calendar year." }, { "id": "Source", "value": "World Bank's Entrepreneurship Survey and database (https://www.worldbank.org/en/programs/entrepreneurship). Downloaded on November 29, 2023." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group’s Entrepreneurship Database. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries in 73 economies. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data - most often due to an absence of digitized registration systems - the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and private vendors or publicly available data.\n\nThe data includes all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.WEF.LLCO.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Measuring women’s entrepreneurial activity is critically important for a better understanding of how female entrepreneurs contribute to the economy and society. The lack of comprehensive sex-disaggregated data on business entry and ownership presents a significant obstacle to the global and diversified analysis of female entrepreneurship. Due to insufficient standardized and country-comparable data, the diagnostics of gender gaps in entrepreneurship are limited.\nIn here, to measure female entrepreneurial activity, annual data is collected directly from 73 company registrars on the number of female/male business owners of LLCs, female/ male sole proprietors and female/ male directors of LLCs, over the past four years.\n\nThe importance of female entrepreneurship for economic development is widely recognized. Numerous studies demonstrate the positive impact of female entrepreneurs on economic growth and development, as well as sustainable and durable peace. Moreover, economies characterized by high levels of female entrepreneurial activity are more resilient to financial crises and experience economic slowdowns less frequently. Despite different methodologies, these studies find significant socioeconomic benefits of female entrepreneurship." }, { "id": "Generalcomments", "value": "For cross-country comparability, only limited liability corporations that operate in the formal sector are included." }, { "id": "IndicatorName", "value": "Share of female business owners (% of total business owners)" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. The Entrepreneurship Database facilitates the analysis of the growth of the formal private sector and the identification of factors that encourage firms to begin operations in or transition to the formal sector. Data is collected all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured.\n\nThe data itself only provides a snapshot of a given economy's business demographics, and cannot by itself explain the factors that affect the business creation cycle. However, when the Entrepreneurship Database is combined with other data such as the Doing Business Report, Investment Climate Assessments, and/or OECD Entrepreneurship Indicators, researchers and policymakers can better understand the dynamics of the business creation process." }, { "id": "Longdefinition", "value": "Share of female business is the proportion of female newly registered limited liability company owners out of the total number of newly registered limited liability company owners in the economy in the calendar year." }, { "id": "Source", "value": "World Bank's Entrepreneurship Survey and database (https://www.worldbank.org/en/programs/entrepreneurship). Downloaded on November 29, 2023." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group’s Entrepreneurship Database. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries in 73 economies. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data - most often due to an absence of digitized registration systems - the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and private vendors or publicly available data.\n\nThe data includes all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.WEF.LLCO.MA", "metatype": [ { "id": "Developmentrelevance", "value": "Measuring women’s entrepreneurial activity is critically important for a better understanding of how female entrepreneurs contribute to the economy and society. The lack of comprehensive sex-disaggregated data on business entry and ownership presents a significant obstacle to the global and diversified analysis of female entrepreneurship. Due to insufficient standardized and country-comparable data, the diagnostics of gender gaps in entrepreneurship are limited.\nIn here, to measure female entrepreneurial activity, annual data is collected directly from 73 company registrars on the number of female/male business owners of LLCs, female/ male sole proprietors and female/ male directors of LLCs, over the past four years.\n\nThe importance of female entrepreneurship for economic development is widely recognized. Numerous studies demonstrate the positive impact of female entrepreneurs on economic growth and development, as well as sustainable and durable peace. Moreover, economies characterized by high levels of female entrepreneurial activity are more resilient to financial crises and experience economic slowdowns less frequently. Despite different methodologies, these studies find significant socioeconomic benefits of female entrepreneurship." }, { "id": "Generalcomments", "value": "For cross-country comparability, only limited liability corporations that operate in the formal sector are included." }, { "id": "IndicatorName", "value": "Number of male business owners" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. The Entrepreneurship Database facilitates the analysis of the growth of the formal private sector and the identification of factors that encourage firms to begin operations in or transition to the formal sector. Data is collected on sole proprietorship. Data on the number of total or closed sole proprietorships are not included.\n\nThe data itself only provides a snapshot of a given economy's business demographics, and cannot by itself explain the factors that affect the business creation cycle. However, when the Entrepreneurship Database is combined with other data such as the Doing Business Report, Investment Climate Assessments, and/or OECD Entrepreneurship Indicators, researchers and policymakers can better understand the dynamics of the business creation process." }, { "id": "Longdefinition", "value": "Number of male business owners is the number of male individuals that own at least one share of a limited liability company that was newly registered in the calendar year." }, { "id": "Source", "value": "World Bank's Entrepreneurship Survey and database (https://www.worldbank.org/en/programs/entrepreneurship). Downloaded on November 29, 2023." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group’s Entrepreneurship Database. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries in 73 economies. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data - most often due to an absence of digitized registration systems - the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and private vendors or publicly available data.\n\nThe data includes all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.WEF.LLCO.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Measuring women’s entrepreneurial activity is critically important for a better understanding of how female entrepreneurs contribute to the economy and society. The lack of comprehensive sex-disaggregated data on business entry and ownership presents a significant obstacle to the global and diversified analysis of female entrepreneurship. Due to insufficient standardized and country-comparable data, the diagnostics of gender gaps in entrepreneurship are limited.\nIn here, to measure female entrepreneurial activity, annual data is collected directly from 73 company registrars on the number of female/male business owners of LLCs, female/ male sole proprietors and female/ male directors of LLCs, over the past four years.\n\nThe importance of female entrepreneurship for economic development is widely recognized. Numerous studies demonstrate the positive impact of female entrepreneurs on economic growth and development, as well as sustainable and durable peace. Moreover, economies characterized by high levels of female entrepreneurial activity are more resilient to financial crises and experience economic slowdowns less frequently. Despite different methodologies, these studies find significant socioeconomic benefits of female entrepreneurship." }, { "id": "Generalcomments", "value": "For cross-country comparability, only limited liability corporations that operate in the formal sector are included." }, { "id": "IndicatorName", "value": "Share of male business owners (% of total business owners)" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. The Entrepreneurship Database facilitates the analysis of the growth of the formal private sector and the identification of factors that encourage firms to begin operations in or transition to the formal sector. Data is collected all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured.\n\nThe data itself only provides a snapshot of a given economy's business demographics, and cannot by itself explain the factors that affect the business creation cycle. However, when the Entrepreneurship Database is combined with other data such as the Doing Business Report, Investment Climate Assessments, and/or OECD Entrepreneurship Indicators, researchers and policymakers can better understand the dynamics of the business creation process." }, { "id": "Longdefinition", "value": "Share of male business is the proportion of male newly registered limited liability company owners out of the total number of newly registered limited liability company owners in the economy in the calendar year." }, { "id": "Source", "value": "World Bank's Entrepreneurship Survey and database (https://www.worldbank.org/en/programs/entrepreneurship). Downloaded on November 29, 2023." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group’s Entrepreneurship Database. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries in 73 economies. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data - most often due to an absence of digitized registration systems - the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and private vendors or publicly available data.\n\nThe data includes all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.WEF.SOLO.FE", "metatype": [ { "id": "Developmentrelevance", "value": "Measuring women’s entrepreneurial activity is critically important for a better understanding of how female entrepreneurs contribute to the economy and society. The lack of comprehensive sex-disaggregated data on business entry and ownership presents a significant obstacle to the global and diversified analysis of female entrepreneurship. Due to insufficient standardized and country-comparable data, the diagnostics of gender gaps in entrepreneurship are limited.\nIn here, to measure female entrepreneurial activity, annual data is collected directly from 73 company registrars on the number of female/male business owners of LLCs, female/ male sole proprietors and female/ male directors of LLCs, over the past four years.\n\nThe importance of female entrepreneurship for economic development is widely recognized. Numerous studies demonstrate the positive impact of female entrepreneurs on economic growth and development, as well as sustainable and durable peace. Moreover, economies characterized by high levels of female entrepreneurial activity are more resilient to financial crises and experience economic slowdowns less frequently. Despite different methodologies, these studies find significant socioeconomic benefits of female entrepreneurship." }, { "id": "Generalcomments", "value": "Cross-country comparability is not applicable to sole proprietorships due to differences in definition of sole proprietors across countries." }, { "id": "IndicatorName", "value": "Number of female sole proprietors" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. The Entrepreneurship Database facilitates the analysis of the growth of the formal private sector and the identification of factors that encourage firms to begin operations in or transition to the formal sector. Data is collected on sole proprietorship. Data on the number of total or closed sole proprietorships are not included.\n The data itself only provides a snapshot of a given economy's business demographics, and cannot by itself explain the factors that affect the business creation cycle. However, when the Entrepreneurship Database is combined with other data such as the Doing Business Report, Investment Climate Assessments, and/or OECD Entrepreneurship Indicators, researchers and policymakers can better understand the dynamics of the business creation process." }, { "id": "Longdefinition", "value": "Number of female sole proprietors is the number of newly registered sole proprietors owned by female individuals in the calendar year. A sole proprietorship is a business entity owned and managed by a single individual who is indistinguishable from the business and personally liable." }, { "id": "Source", "value": "World Bank's Entrepreneurship Survey and database (https://www.worldbank.org/en/programs/entrepreneurship). Downloaded on November 29, 2023." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group’s Entrepreneurship Database. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries in 73 economies. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data - most often due to an absence of digitized registration systems - the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and private vendors or publicly available data. \n While sole proprietorships data is collected and presented, due to differences in the definition of sole proprietors across countries, cross-country comparability is applicable only to limited liability corporations that operate in the formal sector." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.WEF.SOLO.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Measuring women’s entrepreneurial activity is critically important for a better understanding of how female entrepreneurs contribute to the economy and society. The lack of comprehensive sex-disaggregated data on business entry and ownership presents a significant obstacle to the global and diversified analysis of female entrepreneurship. Due to insufficient standardized and country-comparable data, the diagnostics of gender gaps in entrepreneurship are limited.\nIn here, to measure female entrepreneurial activity, annual data is collected directly from 73 company registrars on the number of female/male business owners of LLCs, female/ male sole proprietors and female/ male directors of LLCs, over the past four years.\n\nThe importance of female entrepreneurship for economic development is widely recognized. Numerous studies demonstrate the positive impact of female entrepreneurs on economic growth and development, as well as sustainable and durable peace. Moreover, economies characterized by high levels of female entrepreneurial activity are more resilient to financial crises and experience economic slowdowns less frequently. Despite different methodologies, these studies find significant socioeconomic benefits of female entrepreneurship." }, { "id": "Generalcomments", "value": "Cross-country comparability is not applicable to sole proprietorships due to differences in definition of sole proprietors across countries." }, { "id": "IndicatorName", "value": "Share of female sole proprietors (% of sole proprietors)" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. The Entrepreneurship Database facilitates the analysis of the growth of the formal private sector and the identification of factors that encourage firms to begin operations in or transition to the formal sector. Data is collected on sole proprietorship. Data on the number of total or closed sole proprietorships are not included. \n The data itself only provides a snapshot of a given economy's business demographics, and cannot by itself explain the factors that affect the business creation cycle. However, when the Entrepreneurship Database is combined with other data such as the Doing Business Report, Investment Climate Assessments, and/or OECD Entrepreneurship Indicators, researchers and policymakers can better understand the dynamics of the business creation process." }, { "id": "Longdefinition", "value": "Share of female sole proprietors is the proportion of female newly registered sole proprietors out of the total number of newly registered sole proprietors in the economy in the calendar year. A sole proprietorship is a business entity owned and managed by a single individual who is indistinguishable from the business and personally liable." }, { "id": "Source", "value": "World Bank's Entrepreneurship Survey and database (https://www.worldbank.org/en/programs/entrepreneurship). Downloaded on November 29, 2023." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group’s Entrepreneurship Database. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries in 73 economies. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data - most often due to an absence of digitized registration systems - the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and private vendors or publicly available data. \n While sole proprietorships data is collected and presented, due to differences in the definition of sole proprietors across countries, cross-country comparability is applicable only to limited liability corporations that operate in the formal sector." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.WEF.SOLO.MA", "metatype": [ { "id": "Developmentrelevance", "value": "Measuring women’s entrepreneurial activity is critically important for a better understanding of how female entrepreneurs contribute to the economy and society. The lack of comprehensive sex-disaggregated data on business entry and ownership presents a significant obstacle to the global and diversified analysis of female entrepreneurship. Due to insufficient standardized and country-comparable data, the diagnostics of gender gaps in entrepreneurship are limited.\nIn here, to measure female entrepreneurial activity, annual data is collected directly from 73 company registrars on the number of female/male business owners of LLCs, female/ male sole proprietors and female/ male directors of LLCs, over the past four years.\n\nThe importance of female entrepreneurship for economic development is widely recognized. Numerous studies demonstrate the positive impact of female entrepreneurs on economic growth and development, as well as sustainable and durable peace. Moreover, economies characterized by high levels of female entrepreneurial activity are more resilient to financial crises and experience economic slowdowns less frequently. Despite different methodologies, these studies find significant socioeconomic benefits of female entrepreneurship." }, { "id": "Generalcomments", "value": "Cross-country comparability is not applicable to sole proprietorships due to differences in definition of sole proprietors across countries." }, { "id": "IndicatorName", "value": "Number of male sole proprietors" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. The Entrepreneurship Database facilitates the analysis of the growth of the formal private sector and the identification of factors that encourage firms to begin operations in or transition to the formal sector. Data is collected on sole proprietorship. Data on the number of total or closed sole proprietorships are not included.\n The data itself only provides a snapshot of a given economy's business demographics, and cannot by itself explain the factors that affect the business creation cycle. However, when the Entrepreneurship Database is combined with other data such as the Doing Business Report, Investment Climate Assessments, and/or OECD Entrepreneurship Indicators, researchers and policymakers can better understand the dynamics of the business creation process." }, { "id": "Longdefinition", "value": "Number of male sole proprietors is the number of newly registered sole proprietors owned by female individuals in the calendar year. A sole proprietorship is a business entity owned and managed by a single individual who is indistinguishable from the business and personally liable." }, { "id": "Source", "value": "World Bank's Entrepreneurship Survey and database (https://www.worldbank.org/en/programs/entrepreneurship). Downloaded on November 29, 2023." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group’s Entrepreneurship Database. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries in 73 economies. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data - most often due to an absence of digitized registration systems - the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and private vendors or publicly available data.\n While sole proprietorships data is collected and presented, due to differences in the definition of sole proprietors across countries, cross-country comparability is applicable only to limited liability corporations that operate in the formal sector." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "IC.WEF.SOLO.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Measuring women’s entrepreneurial activity is critically important for a better understanding of how female entrepreneurs contribute to the economy and society. The lack of comprehensive sex-disaggregated data on business entry and ownership presents a significant obstacle to the global and diversified analysis of female entrepreneurship. Due to insufficient standardized and country-comparable data, the diagnostics of gender gaps in entrepreneurship are limited.\nIn here, to measure female entrepreneurial activity, annual data is collected directly from 73 company registrars on the number of female/male business owners of LLCs, female/ male sole proprietors and female/ male directors of LLCs, over the past four years.\n\nThe importance of female entrepreneurship for economic development is widely recognized. Numerous studies demonstrate the positive impact of female entrepreneurs on economic growth and development, as well as sustainable and durable peace. Moreover, economies characterized by high levels of female entrepreneurial activity are more resilient to financial crises and experience economic slowdowns less frequently. Despite different methodologies, these studies find significant socioeconomic benefits of female entrepreneurship." }, { "id": "Generalcomments", "value": "Cross-country comparability is not applicable to sole proprietorships due to differences in definition of sole proprietors across countries." }, { "id": "IndicatorName", "value": "Share of male sole proprietors (% of sole proprietors)" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. The Entrepreneurship Database facilitates the analysis of the growth of the formal private sector and the identification of factors that encourage firms to begin operations in or transition to the formal sector. Data is collected on sole proprietorship. Data on the number of total or closed sole proprietorships are not included.\n The data itself only provides a snapshot of a given economy's business demographics, and cannot by itself explain the factors that affect the business creation cycle. However, when the Entrepreneurship Database is combined with other data such as the Doing Business Report, Investment Climate Assessments, and/or OECD Entrepreneurship Indicators, researchers and policymakers can better understand the dynamics of the business creation process." }, { "id": "Longdefinition", "value": "Share of male sole proprietors is the proportion of male newly registered sole proprietors out of the total number of newly registered sole proprietors in the economy in the calendar year. A sole proprietorship is a business entity owned and managed by a single individual who is indistinguishable from the business and personally liable." }, { "id": "Source", "value": "World Bank's Entrepreneurship Survey and database (https://www.worldbank.org/en/programs/entrepreneurship). Downloaded on November 29, 2023." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank Group’s Entrepreneurship Database. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries in 73 economies. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data - most often due to an absence of digitized registration systems - the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and private vendors or publicly available data.\n While sole proprietorships data is collected and presented, due to differences in the definition of sole proprietors across countries, cross-country comparability is applicable only to limited liability corporations that operate in the formal sector." }, { "id": "Topic", "value": "Entrepreneurship" } ], "source_id": "14" }, { "id": "ID.OWN.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account or activate a SIM card. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "ID ownership, female (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics. For a full list of exclusions, see The Global Findex Database 2021 report and methodology: https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Longdefinition", "value": "The percentage of female respondents age 15 and above who report owning a primary foundational ID (national ID or similar credential). If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded." }, { "id": "Otherweblinks", "value": "https://id4d.worldbank.org/" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Statisticalconceptandmethodology", "value": "ID ownership is calculated based on a survey conducted on representative samples of the non-institutionalized civilian population over age 15. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s primary foundational ID, using the actual term for the foundational ID in the local language. A foundational ID system is primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. The name of the credentials referenced in the survey is included in the 2021 ID4D Global ID Coverage Estimates report, available at: https://id4d.worldbank.org/global-dataset. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "ID.OWN.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account or activate a SIM card. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "ID ownership, male (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics. For a full list of exclusions, see The Global Findex Database 2021 report and methodology: https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Longdefinition", "value": "The percentage of male respondents age 15 and above who report owning a primary foundational ID (national ID or similar credential). If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded." }, { "id": "Otherweblinks", "value": "https://id4d.worldbank.org/" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Statisticalconceptandmethodology", "value": "ID ownership is calculated based on a survey conducted on representative samples of the non-institutionalized civilian population over age 15. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s primary foundational ID, using the actual term for the foundational ID in the local language. A foundational ID system is primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. The name of the credentials referenced in the survey is included in the 2021 ID4D Global ID Coverage Estimates report, available at: https://id4d.worldbank.org/global-dataset. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "internet", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used the internet in the past three months (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they used the internet in the past three months, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "internet.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used the internet in the past three months, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they used the internet in the past three months, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "internet.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Used the internet in the past three months, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report that they used the internet in the past three months, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "IT.NET.USER.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nToday's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains." }, { "id": "IndicatorName", "value": "Individuals using the Internet, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "This indicator refers to female individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "IT.NET.USER.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nToday's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains." }, { "id": "IndicatorName", "value": "Individuals using the Internet, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "This indicator refers to male individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "IT.NET.USER.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nToday's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains." }, { "id": "IndicatorName", "value": "Individuals using the Internet (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU), uri: https://datahub.itu.int/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): The number of in-scope individuals using the Internet is calculated by aggregating the weighted responses. The proportion of individuals using the Internet is expressed as a percentage and is calculated by dividing the total number of in-scope individuals using the Internet by the total number of in-scope individuals, and then multiplying the result by 100." }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "14" }, { "id": "merchant.pay", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a digital merchant payment (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a debit or credit card, or a mobile phone, to make a purchase in-store or to pay online for an internet purchase, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "merchant.pay.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a digital merchant payment, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a debit or credit card, or a mobile phone, to make a purchase in-store or to pay online for an internet purchase, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "merchant.pay.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made a digital merchant payment, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a debit or credit card, or a mobile phone, to make a purchase in-store or to pay online for an internet purchase, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Technology" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "mobileaccount.t.d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Mobile money account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally using a mobile money service in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "mobileaccount.t.d.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Mobile money account, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally using a mobile money service in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "mobileaccount.t.d.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Mobile money account, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally using a mobile money service in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "NY.GDP.MKTP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Gross domestic product (GDP), though widely tracked, may not always be the most relevant summary of aggregated economic performance for all economies, especially when production occurs at the expense of consuming capital stock.\n\n\n\n\n\nWhile GDP estimates based on the production approach are generally more reliable than estimates compiled from the income or expenditure side, different countries use different definitions, methods, and reporting standards. World Bank staff review the quality of national accounts data and sometimes make adjustments to improve consistency with international guidelines. Nevertheless, significant discrepancies remain between international standards and actual practice. Many statistical offices, especially those in developing countries, face severe limitations in the resources, time, training, and budgets required to produce reliable and comprehensive series of national accounts statistics.\n\n\n\n\n\nAmong the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money." }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic and Social Context" }, { "id": "Unitofmeasure", "value": "current US$" } ], "source_id": "14" }, { "id": "NY.GDP.MKTP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Each industry's contribution to growth in the economy's output is measured by growth in the industry's value added. In principle, value added in constant prices can be estimated by measuring the quantity of goods and services produced in a period, valuing them at an agreed set of base year prices, and subtracting the cost of intermediate inputs, also in constant prices. This double-deflation method requires detailed information on the structure of prices of inputs and outputs.\n\n\n\n\n\nIn many industries, however, value added is extrapolated from the base year using single volume indexes of outputs or, less commonly, inputs. Particularly in the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult.\n\n\n\n\n\nMoreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in quality lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of inflation.\n\n\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\n\n\n\n\nRebasing of national accounts can alter the measured growth rate of an economy and lead to breaks in series that affect the consistency of data over time. When countries rebase their national accounts, they update the weights assigned to various components to better reflect current patterns of production or uses of output. The new base year should represent normal operation of the economy - it should be a year without major shocks or distortions. Some developing countries have not rebased their national accounts for many years. Using an old base year can be misleading because implicit price and volume weights become progressively less relevant and useful.\n\n\n\n\n\nTo obtain comparable series of constant price data for computing aggregates, the World Bank rescales GDP and value added by industrial origin to a common reference year. Because rescaling changes the implicit weights used in forming regional and income group aggregates, aggregate growth rates are not comparable with those from earlier editions with different base years. Rescaling may result in a discrepancy between the rescaled GDP and the sum of the rescaled components. To avoid distortions in the growth rates, the discrepancy is left unallocated. As a result, the weighted average of the growth rates of the components generally does not equal the GDP growth rate." }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic and Social Context" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "14" }, { "id": "NY.GDP.PCAP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic and Social Context" }, { "id": "Unitofmeasure", "value": "current US$" } ], "source_id": "14" }, { "id": "NY.GDP.PCAP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP per capita (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic and Social Context" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "14" }, { "id": "NY.GNP.ATLS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI, Atlas method (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This figure is converted to U.S. dollars using the World Bank Atlas method. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic and Social Context" }, { "id": "Unitofmeasure", "value": "current US$" } ], "source_id": "14" }, { "id": "NY.GNP.PCAP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita, Atlas method (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This figure is converted to U.S. dollars using the World Bank Atlas method, and divided by the midyear population. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank's official estimates of the size of economies and country classifications by income level are based on Gross National Income (GNI) per capita. For cross-national comparisons, estimates are converted from local currency units (LCU) to current U.S. dollars using the Atlas method, referring to a former World Bank publication called the Atlas of Global Development. The Atlas method smooths exchange rate fluctuations using a three-year moving average, price-adjusted conversion factor. The USD estimate of GNI per capita is derived by applying the Atlas conversion factor to estimates measured in LCU.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic and Social Context" }, { "id": "Unitofmeasure", "value": "current US$" } ], "source_id": "14" }, { "id": "NY.GNP.PCAP.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross national income (GNI) per person expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic and Social Context" }, { "id": "Unitofmeasure", "value": "Current international $" } ], "source_id": "14" }, { "id": "save.any.t.d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved any money (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally saving or setting aside any money for any reason and using any mode of saving in the past year, (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "save.any.t.d.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved any money, women (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally saving or setting aside any money for any reason and using any mode of saving in the past year, women (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "save.any.t.d.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average: For aggregate data, each economy is classified according to the World Bank Group's fiscal year 2024, which runs from July 1, 2023, to June 30, 2024. The Russian Federation is excluded from the Europe and Central Asia (ECA) averages for all indicators, with the exception of account ownership (account.t.d), bank or similar financial institution account ownership (fiaccount.t.d), mobile phone ownership (con1), mobile money account use (mobileaccount.t.d), and internet use (internet)." }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved any money, men (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally saving or setting aside any money for any reason and using any mode of saving in the past year, men (% age 15+)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database" }, { "id": "Topic", "value": "Assets" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SE.ADT.1524.LT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth female (% of females ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate youths divided by the total number of youths, excluding youths with unknown literacy status. \n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of females ages 15-24" } ], "source_id": "14" }, { "id": "SE.ADT.1524.LT.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEliminating gender disparities in education would help increase the status and capabilities of women. Literate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth (ages 15-24), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Gender parity index for youth literacy rate is the ratio of females to males ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female youth literacy rate by male youth literacy rate. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiteracy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "ratio" } ], "source_id": "14" }, { "id": "SE.ADT.1524.LT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth male (% of males ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate youths divided by the total number of youths, excluding youths with unknown literacy status. \n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of males ages 15-24" } ], "source_id": "14" }, { "id": "SE.ADT.1524.LT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth total (% of people ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data is calculated by dividing the number of literate persons by the total number of persons in the same age group, excluding persons with unknown literacy status.\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "Percent of people ages 15-24" } ], "source_id": "14" }, { "id": "SE.ADT.LITR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, adult female (% of females ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status. \n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of females ages 15 and above" } ], "source_id": "14" }, { "id": "SE.ADT.LITR.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, adult male (% of males ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status. \n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of males ages 15 and above" } ], "source_id": "14" }, { "id": "SE.ADT.LITR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, adult total (% of people ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status. \n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of people ages 15 and above" } ], "source_id": "14" }, { "id": "SE.ENR.PRIM.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "IndicatorName", "value": "School enrollment, primary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in primary education is the ratio of girls to boys enrolled at primary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in primary education by male gross enrollment ratio in primary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.ENR.PRSC.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "IndicatorName", "value": "School enrollment, primary and secondary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in primary and secondary education is the ratio of girls to boys enrolled at primary and secondary levels in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in primary and secondary education by male gross enrollment ratio in primary and secondary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.ENR.SECO.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "IndicatorName", "value": "School enrollment, secondary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in secondary education is the ratio of girls to boys enrolled at secondary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in secondary education by male gross enrollment ratio in secondary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.ENR.TERT.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Education is a basic human entitlement, and it is crucial that both girls and boys are afforded equal chances to learn. The Sustainable Development Goal (SDG) Target 4.5 focuses on eliminating gender disparities in education and ensuring equal access to all levels of education for both girls and boys. This target is part of a broader commitment to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all, as outlined in SDG 4. The pursuit of gender equality in education is not only a matter of fairness and equity but also has significant implications for economic development, empowerment, and the well-being of communities and nations." }, { "id": "IndicatorName", "value": "School enrollment, tertiary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The gross enrolment ratio is a general measure of participation in tertiary education. However, it does not account for variations in the duration of programs between countries or across different levels of education and fields of study. While it is somewhat standardized by measuring it relative to a 5-year age group for all countries, it may still underestimate participation, particularly in countries with underdeveloped tertiary education systems or where offerings are limited to initial tertiary programs, which are typically shorter than 5 years in duration." }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in tertiary education is the ratio of women to men enrolled at tertiary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in tertiary education by male gross enrollment ratio in tertiary education. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "ratio" } ], "source_id": "14" }, { "id": "SE.LPV.PRIM", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Learning poverty: Share of Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The construct of “all children reading by age 10” is an ideal that embodies normative statements about both learning and access. To achieve it, not only should all children be reading proficiently after 3 full years in primary education, but they should also have entered school at age 6 or 7. \n\nBy contrast, the actual indicators used to measure learning poverty are based on grade rather than age. Since the assessments are of 4th- through 6th-graders, the children tested will have had at least 3 to 5 years in school to reach what, according to the ideal, should 10 be an age-10 minimum proficiency, or even the entire primary-school-age segment for the out-of-school indicator.\n\nDue to different assessment availability within and between countries, data comparability, both within countries over time and across countries still poses a significant challenge. The additional out of school component further limits comparability.\n\nThe learning poverty indicator is based on data covering four-fifths of children at the end of primary school. In other words, a little more than 80 percent of children in low- and middle-income countries live in a country with at least one learning assessment at the end of primary, carried out in the past 9 years. For regional and global aggregates, weighted imputations affect regions with less data coverage. The major gaps are concentrated in countries where the learning crisis is most acute. Less than half of children in Sub-Saharan Africa live in a country with a National Large-Scale Learning Assessment (NLSA) or a international of regional large-scale learning assessment (ILSA or RLSA) of adequate quality to be used for this purpose.\n\nThis extensive coverage became possible only in recent years, with the progress in measuring learning in countries and the GAML’s efforts to establish comparability, which has made possible the construction of a global indicator based on harmonized proficiency levels. Future efforts by coalition organizations are also ensuring more flexible assessment options are available for expanding data availability for countries, such as the Assessment of Minimum Proficiency Levels (AMPL) and policy linking exercises led by UIS." }, { "id": "Longdefinition", "value": "The share of 10-year-olds who cannot read and understand a short passage of age-appropriate material—in other words, those who are below the “minimum proficiency” threshold for reading. This measure is defined as the union of two deprivations: 1) schooling deprivation and 2) learning deprivation. A child is considered schooling-deprived (SD) if he or she is of primary school age and out-of-school. The dimension of learning deprivation (LD) applies only for children in school, and identifies those pupils who are below the minimum proficiency level (MPL) for reading, as defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. This “union approach” to measurement reflects the choice that, as presented in the SDGs, all age 10 children must be both in school and learning. The final learning poverty measure combines the two dimensions in a single indicator using the following formula: LP = SD + [(1-SD) x LD]" }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The learning poverty indicator brings together schooling and learning indicators. It starts with the share of children in school who haven’t achieved minimum reading proficiency (Learning Deprived) and adjusts it by the proportion of children who are out of school (Schooling Deprived). \n\n\nFormally, Learning Poverty is calculated as: [LD* (1-SD)] + [1 * SD]\n\n\nwhere LP = Learning poverty; LD = Learning deprivation or the share of children at the end of primary who read at below the minimum proficiency level, as defined by the Global Alliance to Monitor Learning (GAML) in the context of the SDG 4.1.1 monitoring; SD = Schooling deprivation or the share of primary-school-age children who are out-of-school (OOS) and in which all OOS are regarded as being below the minimum proficiency level.\n\n\nBecause out-of-school children are treated as non-proficient in reading, learning poverty will always be higher than the share of children in school who haven't achieved minimum reading proficiency. For countries with a very low schooling deprivation, the learning deprivation value will be very close to Learning Poverty. \n\n\nEstimating the current level of global and regional learning poverty requires deciding how to define “current.” We include results of assessments within four years before or after a set anchor year. This decision is driven by data availability. International and regional large-scale learning assessments used for SDG 4.1.1b reporting are carried out only every 3 to 4 years. And even where assessments have been carried out recently, there is a lag of a couple of years before the data are available. This band is intended as a moving window. In the original 2019 release, the anchor year used was 2015 (Assessments between 2011 and 2019 are included in the learning poverty estimate). In the 2022 Global Update, the anchor year was moved to 2019 (assessments between 2015 and 2023 are included).\n\n\nAggregations for each region comprise the average learning poverty of countries with available data, weighted by their population ages 10–14 years old. To obtain a global estimate, we weight the regional aggregations by the 10–14-year-old population regardless of data availability. This is equivalent to imputing missing country data using regional values.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "14" }, { "id": "SE.LPV.PRIM.BMP", "metatype": [ { "id": "IndicatorName", "value": "Pupils below minimum reading proficiency at end of primary (%). Low GAML threshold" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator uses on Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments." }, { "id": "Source", "value": "Word Bank and UIS" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.LPV.PRIM.BMP.FE", "metatype": [ { "id": "IndicatorName", "value": "Female pupils below minimum reading proficiency at end of primary (%). Low GAML threshold" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator uses on Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments." }, { "id": "Notesfromoriginalsource", "value": "For more information please see Azevedo, Joao Pedro, and others. 2019. \"Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.\" World Bank Policy Research Working Paper series. Washington, DC: World Bank. https://datacatalog.worldbank.org/dataset/learning-poverty" }, { "id": "Source", "value": "Word Bank and UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.LPV.PRIM.BMP.MA", "metatype": [ { "id": "IndicatorName", "value": "Male pupils below minimum reading proficiency at end of primary (%). Low GAML threshold" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator uses on Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments." }, { "id": "Notesfromoriginalsource", "value": "For more information please see Azevedo, Joao Pedro, and others. 2019. \"Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.\" World Bank Policy Research Working Paper series. Washington, DC: World Bank. https://datacatalog.worldbank.org/dataset/learning-poverty" }, { "id": "Source", "value": "Word Bank and UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.LPV.PRIM.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Learning poverty: Share of Female Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The construct of “all children reading by age 10” is an ideal that embodies normative statements about both learning and access. To achieve it, not only should all children be reading proficiently after 3 full years in primary education, but they should also have entered school at age 6 or 7. \n\nBy contrast, the actual indicators used to measure learning poverty are based on grade rather than age. Since the assessments are of 4th- through 6th-graders, the children tested will have had at least 3 to 5 years in school to reach what, according to the ideal, should 10 be an age-10 minimum proficiency, or even the entire primary-school-age segment for the out-of-school indicator.\n\nDue to different assessment availability within and between countries, data comparability, both within countries over time and across countries still poses a significant challenge. The additional out of school component further limits comparability.\n\nThe learning poverty indicator is based on data covering four-fifths of children at the end of primary school. In other words, a little more than 80 percent of children in low- and middle-income countries live in a country with at least one learning assessment at the end of primary, carried out in the past 9 years. For regional and global aggregates, weighted imputations affect regions with less data coverage. The major gaps are concentrated in countries where the learning crisis is most acute. Less than half of children in Sub-Saharan Africa live in a country with a National Large-Scale Learning Assessment (NLSA) or a international of regional large-scale learning assessment (ILSA or RLSA) of adequate quality to be used for this purpose.\n\nThis extensive coverage became possible only in recent years, with the progress in measuring learning in countries and the GAML’s efforts to establish comparability, which has made possible the construction of a global indicator based on harmonized proficiency levels. Future efforts by coalition organizations are also ensuring more flexible assessment options are available for expanding data availability for countries, such as the Assessment of Minimum Proficiency Levels (AMPL) and policy linking exercises led by UIS." }, { "id": "Longdefinition", "value": "The share of female 10-year-olds who cannot read and understand a short passage of age-appropriate material—in other words, those who are below the “minimum proficiency” threshold for reading. This measure is defined as the union of two deprivations: 1) schooling deprivation and 2) learning deprivation. A child is considered schooling-deprived (SD) if he or she is of primary school age and out-of-school. The dimension of learning deprivation (LD) applies only for children in school, and identifies those pupils who are below the minimum proficiency level (MPL) for reading, as defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. This “union approach” to measurement reflects the choice that, as presented in the SDGs, all age 10 children must be both in school and learning. The final learning poverty measure combines the two dimensions in a single indicator using the following formula: LP = SD + [(1-SD) x LD]" }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The learning poverty indicator brings together schooling and learning indicators. It starts with the share of children in school who haven’t achieved minimum reading proficiency (Learning Deprived) and adjusts it by the proportion of children who are out of school (Schooling Deprived). \n\n\nFormally, Learning Poverty is calculated as: [LD* (1-SD)] + [1 * SD]\n\n\nwhere LP = Learning poverty; LD = Learning deprivation or the share of children at the end of primary who read at below the minimum proficiency level, as defined by the Global Alliance to Monitor Learning (GAML) in the context of the SDG 4.1.1 monitoring; SD = Schooling deprivation or the share of primary-school-age children who are out-of-school (OOS) and in which all OOS are regarded as being below the minimum proficiency level.\n\n\nBecause out-of-school children are treated as non-proficient in reading, learning poverty will always be higher than the share of children in school who haven't achieved minimum reading proficiency. For countries with a very low schooling deprivation, the learning deprivation value will be very close to Learning Poverty. \n\n\nEstimating the current level of global and regional learning poverty requires deciding how to define “current.” We include results of assessments within four years before or after a set anchor year. This decision is driven by data availability. International and regional large-scale learning assessments used for SDG 4.1.1b reporting are carried out only every 3 to 4 years. And even where assessments have been carried out recently, there is a lag of a couple of years before the data are available. This band is intended as a moving window. In the original 2019 release, the anchor year used was 2015 (Assessments between 2011 and 2019 are included in the learning poverty estimate). In the 2022 Global Update, the anchor year was moved to 2019 (assessments between 2015 and 2023 are included).\n\n\nAggregations for each region comprise the average learning poverty of countries with available data, weighted by their population ages 10–14 years old. To obtain a global estimate, we weight the regional aggregations by the 10–14-year-old population regardless of data availability. This is equivalent to imputing missing country data using regional values.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "14" }, { "id": "SE.LPV.PRIM.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Learning poverty: Share of Male Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The construct of “all children reading by age 10” is an ideal that embodies normative statements about both learning and access. To achieve it, not only should all children be reading proficiently after 3 full years in primary education, but they should also have entered school at age 6 or 7. \n\nBy contrast, the actual indicators used to measure learning poverty are based on grade rather than age. Since the assessments are of 4th- through 6th-graders, the children tested will have had at least 3 to 5 years in school to reach what, according to the ideal, should 10 be an age-10 minimum proficiency, or even the entire primary-school-age segment for the out-of-school indicator.\n\nDue to different assessment availability within and between countries, data comparability, both within countries over time and across countries still poses a significant challenge. The additional out of school component further limits comparability.\n\nThe learning poverty indicator is based on data covering four-fifths of children at the end of primary school. In other words, a little more than 80 percent of children in low- and middle-income countries live in a country with at least one learning assessment at the end of primary, carried out in the past 9 years. For regional and global aggregates, weighted imputations affect regions with less data coverage. The major gaps are concentrated in countries where the learning crisis is most acute. Less than half of children in Sub-Saharan Africa live in a country with a National Large-Scale Learning Assessment (NLSA) or a international of regional large-scale learning assessment (ILSA or RLSA) of adequate quality to be used for this purpose.\n\nThis extensive coverage became possible only in recent years, with the progress in measuring learning in countries and the GAML’s efforts to establish comparability, which has made possible the construction of a global indicator based on harmonized proficiency levels. Future efforts by coalition organizations are also ensuring more flexible assessment options are available for expanding data availability for countries, such as the Assessment of Minimum Proficiency Levels (AMPL) and policy linking exercises led by UIS." }, { "id": "Longdefinition", "value": "The share of male 10-year-olds who cannot read and understand a short passage of age-appropriate material—in other words, those who are below the “minimum proficiency” threshold for reading. This measure is defined as the union of two deprivations: 1) schooling deprivation and 2) learning deprivation. A child is considered schooling-deprived (SD) if he or she is of primary school age and out-of-school. The dimension of learning deprivation (LD) applies only for children in school, and identifies those pupils who are below the minimum proficiency level (MPL) for reading, as defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. This “union approach” to measurement reflects the choice that, as presented in the SDGs, all age 10 children must be both in school and learning. The final learning poverty measure combines the two dimensions in a single indicator using the following formula: LP = SD + [(1-SD) x LD]" }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The learning poverty indicator brings together schooling and learning indicators. It starts with the share of children in school who haven’t achieved minimum reading proficiency (Learning Deprived) and adjusts it by the proportion of children who are out of school (Schooling Deprived). \n\n\nFormally, Learning Poverty is calculated as: [LD* (1-SD)] + [1 * SD]\n\n\nwhere LP = Learning poverty; LD = Learning deprivation or the share of children at the end of primary who read at below the minimum proficiency level, as defined by the Global Alliance to Monitor Learning (GAML) in the context of the SDG 4.1.1 monitoring; SD = Schooling deprivation or the share of primary-school-age children who are out-of-school (OOS) and in which all OOS are regarded as being below the minimum proficiency level.\n\n\nBecause out-of-school children are treated as non-proficient in reading, learning poverty will always be higher than the share of children in school who haven't achieved minimum reading proficiency. For countries with a very low schooling deprivation, the learning deprivation value will be very close to Learning Poverty. \n\n\nEstimating the current level of global and regional learning poverty requires deciding how to define “current.” We include results of assessments within four years before or after a set anchor year. This decision is driven by data availability. International and regional large-scale learning assessments used for SDG 4.1.1b reporting are carried out only every 3 to 4 years. And even where assessments have been carried out recently, there is a lag of a couple of years before the data are available. This band is intended as a moving window. In the original 2019 release, the anchor year used was 2015 (Assessments between 2011 and 2019 are included in the learning poverty estimate). In the 2022 Global Update, the anchor year was moved to 2019 (assessments between 2015 and 2023 are included).\n\n\nAggregations for each region comprise the average learning poverty of countries with available data, weighted by their population ages 10–14 years old. To obtain a global estimate, we weight the regional aggregations by the 10–14-year-old population regardless of data availability. This is equivalent to imputing missing country data using regional values.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "14" }, { "id": "SE.LPV.PRIM.OOS.FE", "metatype": [ { "id": "IndicatorName", "value": "Female primary school age children out-of-school (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by UIS." }, { "id": "Notesfromoriginalsource", "value": "For more information please see Azevedo, Joao Pedro, and others. 2019. \"Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.\" World Bank Policy Research Working Paper series. Washington, DC: World Bank. https://datacatalog.worldbank.org/dataset/learning-poverty" }, { "id": "Source", "value": "Word Bank and UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.LPV.PRIM.OOS.MA", "metatype": [ { "id": "IndicatorName", "value": "Male primary school age children out-of-school (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by UIS." }, { "id": "Notesfromoriginalsource", "value": "For more information please see Azevedo, Joao Pedro, and others. 2019. \"Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.\" World Bank Policy Research Working Paper series. Washington, DC: World Bank. https://datacatalog.worldbank.org/dataset/learning-poverty" }, { "id": "Source", "value": "Word Bank and UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRE.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, preprimary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for pre-primary school is calculated by dividing the number of students enrolled in pre-primary education regardless of age by the population of the age group which officially corresponds to pre-primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRE.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, preprimary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for pre-primary school is calculated by dividing the number of students enrolled in pre-primary education regardless of age by the population of the age group which officially corresponds to pre-primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRE.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, preprimary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for pre-primary school is calculated by dividing the number of students enrolled in pre-primary education regardless of age by the population of the age group which officially corresponds to pre-primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRM.CMPL.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Primary completion rate, female, based on completers" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Primary completion rate is the percentage of students completing the last year of primary school. The rate based on completers is calculated by taking the total number of completers in the last grade of primary school divided by the total number of children of official graduation age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRM.CMPL.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Primary completion rate, male, based on completers" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Primary completion rate is the percentage of students completing the last year of primary school. The rate based on completers is calculated by taking the total number of completers in the last grade of primary school divided by the total number of children of official graduation age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRM.CMPL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Primary completion rate, total, based on completers" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Primary completion rate is the percentage of students completing the last year of primary school. The rate based on completers is calculated by taking the total number of completers in the last grade of primary school divided by the total number of children of official graduation age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRM.CMPT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education." }, { "id": "IndicatorName", "value": "Primary completion rate, female (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "14" }, { "id": "SE.PRM.CMPT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education." }, { "id": "IndicatorName", "value": "Primary completion rate, male (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "14" }, { "id": "SE.PRM.CMPT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education." }, { "id": "IndicatorName", "value": "Primary completion rate, total (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "14" }, { "id": "SE.PRM.CUAT.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital?" }, { "id": "IndicatorName", "value": "Educational attainment, at least completed primary, population 25+ years, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed primary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "14" }, { "id": "SE.PRM.CUAT.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed primary, population 25+ years, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed primary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "14" }, { "id": "SE.PRM.CUAT.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed primary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "14" }, { "id": "SE.PRM.ENRL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of girls allows an assessment on gender composition in school enrollment. A value greater than 50% indicates participation of more girls at a specific level or programme of education." }, { "id": "IndicatorName", "value": "Primary education, pupils (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The percentage of female enrollment is limited in assessing gender parity, because it's affected by the gender composition of population. Ratio of female to male in enrollment rate provides a population adjusted measure of gender parity." }, { "id": "Longdefinition", "value": "Female pupils as a percentage of total pupils at primary level include enrollments in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentage of female enrollment is calculated by dividing the total number of female students at a given level of education by the total enrollment at the same level, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRM.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education is fundamental to future educational success and opens pathways for continued advancement. This indicator measures the overall rate of participation in primary education, signifying the education system's ability to enroll students within a specific age cohort." }, { "id": "IndicatorName", "value": "School enrollment, primary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for primary school is calculated by dividing the number of students enrolled in primary education regardless of age by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of population in the 5-year age group immediately following preprimary education" } ], "source_id": "14" }, { "id": "SE.PRM.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education is fundamental to future educational success and opens pathways for continued advancement. This indicator measures the overall rate of participation in primary education, signifying the education system's ability to enroll students within a specific age cohort." }, { "id": "IndicatorName", "value": "School enrollment, primary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for primary school is calculated by dividing the number of students enrolled in primary education regardless of age by the population of the age group which officially corresponds to primary education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of female population in the 5-year age group immediately following preprimary education" } ], "source_id": "14" }, { "id": "SE.PRM.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education is fundamental to future educational success and opens pathways for continued advancement. This indicator measures the overall rate of participation in primary education, signifying the education system's ability to enroll students within a specific age cohort." }, { "id": "IndicatorName", "value": "School enrollment, primary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for primary school is calculated by dividing the number of students enrolled in primary education regardless of age by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of male population in the 5-year age group immediately following preprimary education" } ], "source_id": "14" }, { "id": "SE.PRM.GINT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The gross intake ratio in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A low gross intake ratio in the first grade of primary education reflects the fact that many children do not enter primary education even though school attendance, at least through the primary level, is mandatory in most countries. Because the gross intake ratio includes all new entrants regardless of age, it can exceed 100 percent in some situations, such as immediately after fees have been abolished or when the number of reenrolled children is large." }, { "id": "IndicatorName", "value": "Gross intake ratio in first grade of primary education, total (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Gross intake ratio in first grade of primary education is the number of new entrants in the first grade of primary education regardless of age, expressed as a percentage of the population of the official primary entrance age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross intake ratio in the first grade of primary education is calculated by dividing the number of new entrants (enrollments minus repeaters) in the first grade of primary education, regardless of age, by the population of the official primary entrance age and multiplying the result by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRM.NENR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, primary (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRM.NENR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, primary, female (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "UNESCO Institute for Statistics (UIS), UN Educational, Scientific and Cultural Organization (UNESCO), uri: http://uis.unesco.org/, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRM.NENR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, primary, male (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRM.NINT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The net intake rate in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A high net intake rate indicates a high degree of access to primary education for the official primary school entrance age children." }, { "id": "IndicatorName", "value": "Net intake rate in grade 1, female (% of official school-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Net intake rate in grade 1 is the number of new entrants in the first grade of primary education who are of official primary school entrance age, expressed as a percentage of the population of the corresponding age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net intake rate in the first grade of primary education is calculated by dividing the number of children of official primary school entrance age who enter grade 1 of primary education for the first time by the population of the same age, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRM.NINT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The net intake rate in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A high net intake rate indicates a high degree of access to primary education for the official primary school entrance age children." }, { "id": "IndicatorName", "value": "Net intake rate in grade 1, male (% of official school-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Net intake rate in grade 1 is the number of new entrants in the first grade of primary education who are of official primary school entrance age, expressed as a percentage of the population of the corresponding age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net intake rate in the first grade of primary education is calculated by dividing the number of children of official primary school entrance age who enter grade 1 of primary education for the first time by the population of the same age, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRM.NINT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The net intake rate in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A high net intake rate indicates a high degree of access to primary education for the official primary school entrance age children." }, { "id": "IndicatorName", "value": "Net intake rate in grade 1 (% of official school-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Net intake rate in grade 1 is the number of new entrants in the first grade of primary education who are of official primary school entrance age, expressed as a percentage of the population of the corresponding age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net intake rate in the first grade of primary education is calculated by dividing the number of children of official primary school entrance age who enter grade 1 of primary education for the first time by the population of the same age, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRM.PRS5.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to grade 5, female (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to grade 5 (percentage of cohort reaching grade 5) is the share of children enrolled in the first grade of primary school who eventually reach grade 5. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "14" }, { "id": "SE.PRM.PRS5.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to grade 5, male (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to grade 5 (percentage of cohort reaching grade 5) is the share of children enrolled in the first grade of primary school who eventually reach grade 5. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "14" }, { "id": "SE.PRM.TCHR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although there have been advancements, girls in low-income countries continue to face significant barriers to accessing secondary education. The presence of female teachers is crucial in this context, as they act as role models, inspiring and motivating girls to pursue their education. These educators play a pivotal role in attracting and retaining girls in schools, challenging deep-seated gender stereotypes within communities, elevating parental expectations for their daughters, and contributing to the narrowing of the educational achievement gap between boys and girls." }, { "id": "IndicatorName", "value": "Primary education, teachers (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator measures the level of gender representation in the teaching profession, rather than the effectiveness and quality of teaching." }, { "id": "Longdefinition", "value": "Female teachers as a percentage of total primary education teachers includes full-time and part-time teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of female teachers in primary education is calculated by dividing the total number of female teachers at primary level of education by the total number of teachers at the same level, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of female teachers shows the level of gender representation in the teaching force. A value of greater than 50% indicates more opportunities or preference for women to participate in teaching activities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of total primary education teachers" } ], "source_id": "14" }, { "id": "SE.PRM.TENR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Relevance to gender indicator: Women teachers are important as they serve as role models to girls and help to attract and retain girls in school." }, { "id": "IndicatorName", "value": "Adjusted net enrollment rate, primary (% of primary school age children)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Adjusted net enrollment rate in primary education is calculated by dividing the number of children in the official primary school age who are enrolled in primary or secondary education by the population of the same age group and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRM.TENR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Relevance to gender indicator: Women teachers are important as they serve as role models to girls and help to attract and retain girls in school." }, { "id": "IndicatorName", "value": "Adjusted net enrollment rate, primary, female (% of primary school age children)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Adjusted net enrollment rate in primary education is calculated by dividing the number of children in the official primary school age who are enrolled in primary or secondary education by the population of the same age group and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRM.TENR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments. The adjusted net enrollment rate in primary education captures primary school-age children who have progressed to secondary education faster than their peers have and who are not counted in the traditional net enrollment rate." }, { "id": "IndicatorName", "value": "Adjusted net enrollment rate, primary, male (% of primary school age children)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Adjusted net enrollment rate in primary education is calculated by dividing the number of children in the official primary school age who are enrolled in primary or secondary education by the population of the same age group and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.PRM.UNER", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Children out of school, primary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to different data sources for enrollment and population data, the number may not capture the actual number of children not attending in primary school." }, { "id": "Longdefinition", "value": "Children out of school are the number of primary-school-age children not enrolled in primary or secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of out-of-school children is calculated by subtracting the number of primary school-age children enrolled in primary or secondary school from the total population of the official primary school-age children. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "14" }, { "id": "SE.PRM.UNER.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Children out of school, primary, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to different data sources for enrollment and population data, the number may not capture the actual number of children not attending in primary school." }, { "id": "Longdefinition", "value": "Children out of school are the number of primary-school-age children not enrolled in primary or secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of out-of-school children is calculated by subtracting the number of primary school-age children enrolled in primary or secondary school from the total population of the official primary school-age children. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "14" }, { "id": "SE.PRM.UNER.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Children out of school, primary, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to different data sources for enrollment and population data, the number may not capture the actual number of children not attending in primary school." }, { "id": "Longdefinition", "value": "Children out of school are the number of primary-school-age children not enrolled in primary or secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of out-of-school children is calculated by subtracting the number of primary school-age children enrolled in primary or secondary school from the total population of the official primary school-age children. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "14" }, { "id": "SE.PRM.UNER.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Children out of school (% of primary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The rate of out-of-school children allows to compare across countries with different population sizes. It shows the share of official primary-school-age children who never attended school or dropped out to the population of official primary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of children in primary school age" } ], "source_id": "14" }, { "id": "SE.SCH.LIFE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Expected years of schooling" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Expected years of schooling is the number of years a child of school entrance age is expected to spend at school, or university, including years spent on repetition. It is the sum of the age-specific enrolment ratios for primary, secondary, post-secondary non-tertiary and tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "The school life expectancy is calculated as the sum of the age specific enrollment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.SCH.LIFE.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "School life expectancy indicates the average number of years of schooling that the education system can offer. A high value indicates a probability for children to spend more years in education. Note that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition." }, { "id": "IndicatorName", "value": "Expected years of schooling, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The expected number of years of schooling may not be comparable across countries due to different lengths of the school year and policies on repetition and promotion. It is also affected by the magnitude of children who never go to school." }, { "id": "Longdefinition", "value": "Expected years of schooling is the number of years a child of school entrance age is expected to spend at school, or university, including years spent on repetition. It is the sum of the age-specific enrolment ratios for primary, secondary, post-secondary non-tertiary and tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "The school life expectancy is calculated as the sum of the age specific enrollment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.SCH.LIFE.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "School life expectancy indicates the average number of years of schooling that the education system can offer. A high value indicates a probability for children to spend more years in education. Note that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition." }, { "id": "IndicatorName", "value": "Expected years of schooling, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The expected number of years of schooling may not be comparable across countries due to different lengths of the school year and policies on repetition and promotion. It is also affected by the magnitude of children who never go to school." }, { "id": "Longdefinition", "value": "Expected years of schooling is the number of years a child of school entrance age is expected to spend at school, or university, including years spent on repetition. It is the sum of the age-specific enrolment ratios for primary, secondary, post-secondary non-tertiary and tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "The school life expectancy is calculated as the sum of the age specific enrollment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.SEC.CMPT.LO.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Lower secondary education serves as a critical platform for lifelong learning and human development, providing a foundation for further educational pursuits. In certain systems, it includes vocational education programs that equip individuals with skills pertinent to the workforce. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator is particularly important for policymakers who are committed to improving children's access to and participation in education. It measures the ability of the education system to enroll and retain students from the designated starting age through to the completion of all levels of lower secondary education." }, { "id": "IndicatorName", "value": "Lower secondary completion rate, female (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "14" }, { "id": "SE.SEC.CMPT.LO.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Lower secondary education serves as a critical platform for lifelong learning and human development, providing a foundation for further educational pursuits. In certain systems, it includes vocational education programs that equip individuals with skills pertinent to the workforce. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator is particularly important for policymakers who are committed to improving children's access to and participation in education. It measures the ability of the education system to enroll and retain students from the designated starting age through to the completion of all levels of lower secondary education." }, { "id": "IndicatorName", "value": "Lower secondary completion rate, male (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "14" }, { "id": "SE.SEC.CMPT.LO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Lower secondary education serves as a critical platform for lifelong learning and human development, providing a foundation for further educational pursuits. In certain systems, it includes vocational education programs that equip individuals with skills pertinent to the workforce. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator is particularly important for policymakers who are committed to improving children's access to and participation in education. It measures the ability of the education system to enroll and retain students from the designated starting age through to the completion of all levels of lower secondary education." }, { "id": "IndicatorName", "value": "Lower secondary completion rate, total (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "14" }, { "id": "SE.SEC.CUAT.LO.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed lower secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "14" }, { "id": "SE.SEC.CUAT.LO.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed lower secondary, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed lower secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "14" }, { "id": "SE.SEC.CUAT.LO.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed lower secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "14" }, { "id": "SE.SEC.CUAT.PO.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed post-secondary, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed post-secondary non-tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "14" }, { "id": "SE.SEC.CUAT.PO.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed post-secondary, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed post-secondary non-tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "14" }, { "id": "SE.SEC.CUAT.PO.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed post-secondary non-tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "14" }, { "id": "SE.SEC.CUAT.UP.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed upper secondary, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed upper secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed upper secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "14" }, { "id": "SE.SEC.CUAT.UP.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed upper secondary, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed upper secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed upper secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "14" }, { "id": "SE.SEC.CUAT.UP.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed upper secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed upper secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "14" }, { "id": "SE.SEC.ENRL.FE.VO.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vocational and Technical enrolment (% of total secondary enrolment), female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.SEC.ENRL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of girls allows an assessment on gender composition in school enrollment. A value greater than 50% indicates participation of more girls at a specific level or programme of education." }, { "id": "IndicatorName", "value": "Secondary education, pupils (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The percentage of female enrollment is limited in assessing gender parity, because it's affected by the gender composition of population. Ratio of female to male in enrollment rate provides a population adjusted measure of gender parity." }, { "id": "Longdefinition", "value": "Female pupils as a percentage of total pupils at secondary level includes enrollments in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentage of female enrollment is calculated by dividing the total number of female students at a given level of education by the total enrollment at the same level, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.SEC.ENRL.MA.VO.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of male students in secondary education enrolled in vocational programmes, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total number of male students enrolled in vocational programmes at the secondary education level, expressed as a percentage of the total number of male students enrolled in all programmes (vocational and general) at the secondary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.SEC.ENRL.VO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Secondary education, vocational pupils" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Secondary vocational pupils are the number of secondary students enrolled in technical and vocational education programs, including teacher training." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Enrollment includes Individuals officially registered in a given educational programme, or stage or module thereof, regardless of age.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.SEC.ENRL.VO.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of girls allows an assessment on gender composition in school enrollment. A value greater than 50% indicates participation of more girls at a specific level or programme of education." }, { "id": "IndicatorName", "value": "Secondary education, vocational pupils (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The percentage of female enrollment is limited in assessing gender parity, because it's affected by the gender composition of population. Ratio of female to male in enrollment rate provides a population adjusted measure of gender parity." }, { "id": "Longdefinition", "value": "Secondary vocational pupils are the number of secondary students enrolled in technical and vocational education programs, including teacher training." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentage of female enrollment is calculated by dividing the total number of female students at a given level of education by the total enrollment at the same level, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.SEC.ENRL.VO.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vocational and Technical enrolment (% of total secondary enrolment), total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.SEC.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Secondary education acts as a critical intermediary that not only builds upon the foundational knowledge acquired in primary education but also equips students for various pathways, including immediate entry into the workforce, further education in postsecondary non-tertiary institutions, or advancement to higher education. This indicator assesses the aggregate participation rate in secondary education, reflecting the education system's capacity to enroll students within a designated age group." }, { "id": "IndicatorName", "value": "School enrollment, secondary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of population in the 5-year age group immediately following primary education" } ], "source_id": "14" }, { "id": "SE.SEC.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Secondary education acts as a critical intermediary that not only builds upon the foundational knowledge acquired in primary education but also equips students for various pathways, including immediate entry into the workforce, further education in postsecondary non-tertiary institutions, or advancement to higher education. This indicator assesses the aggregate participation rate in secondary education, reflecting the education system's capacity to enroll students within a designated age group." }, { "id": "IndicatorName", "value": "School enrollment, secondary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of female population in the 5-year age group immediately following primary education" } ], "source_id": "14" }, { "id": "SE.SEC.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Secondary education acts as a critical intermediary that not only builds upon the foundational knowledge acquired in primary education but also equips students for various pathways, including immediate entry into the workforce, further education in postsecondary non-tertiary institutions, or advancement to higher education. This indicator assesses the aggregate participation rate in secondary education, reflecting the education system's capacity to enroll students within a designated age group." }, { "id": "IndicatorName", "value": "School enrollment, secondary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of male population in the 5-year age group immediately following primary education" } ], "source_id": "14" }, { "id": "SE.SEC.NENR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, secondary (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for secondary school is calculated by dividing the number of students of official school age enrolled in secondary education by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.SEC.NENR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, secondary, female (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for secondary school is calculated by dividing the number of students of official school age enrolled in secondary education by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.SEC.NENR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, secondary, male (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for secondary school is calculated by dividing the number of students of official school age enrolled in secondary education by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.SEC.PROG.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The effective transition rate from primary to secondary education conveys the degree of access or transition between the two levels. As completing primary education is a prerequisite for participating in lower secondary education, growing numbers of primary completers will inevitably create pressure for more available places at the secondary level. A low effective transition rate can signal such problems as an inadequate examination and promotion system or insufficient secondary education capacity." }, { "id": "IndicatorName", "value": "Progression to secondary school, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data on the transition rate is affected when new entrants and repeaters are not correctly distinguished. Students who interrupt their studies after completing primary education could also affect data quality." }, { "id": "Longdefinition", "value": "Progression to secondary school refers to the number of new entrants to the first grade of secondary school in a given year as a percentage of the number of students enrolled in the final grade of primary school in the previous year (minus the number of repeaters from the last grade of primary education in the given year)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Effective transition rate is calculated by dividing the number of new entrants in the first grade of secondary education in a given year (t) by the number of students who enrolled in the final grade of primary education in the previous school year (t-1) minus the number of repeaters from the last grade of primary education in the given year (t), and multiplying by 100. \nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.SEC.PROG.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The effective transition rate from primary to secondary education conveys the degree of access or transition between the two levels. As completing primary education is a prerequisite for participating in lower secondary education, growing numbers of primary completers will inevitably create pressure for more available places at the secondary level. A low effective transition rate can signal such problems as an inadequate examination and promotion system or insufficient secondary education capacity." }, { "id": "IndicatorName", "value": "Progression to secondary school, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data on the transition rate is affected when new entrants and repeaters are not correctly distinguished. Students who interrupt their studies after completing primary education could also affect data quality." }, { "id": "Longdefinition", "value": "Progression to secondary school refers to the number of new entrants to the first grade of secondary school in a given year as a percentage of the number of students enrolled in the final grade of primary school in the previous year (minus the number of repeaters from the last grade of primary education in the given year)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Effective transition rate is calculated by dividing the number of new entrants in the first grade of secondary education in a given year (t) by the number of students who enrolled in the final grade of primary education in the previous school year (t-1) minus the number of repeaters from the last grade of primary education in the given year (t), and multiplying by 100. \nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.SEC.PROG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The effective transition rate from primary to secondary education conveys the degree of access or transition between the two levels. As completing primary education is a prerequisite for participating in lower secondary education, growing numbers of primary completers will inevitably create pressure for more available places at the secondary level. A low effective transition rate can signal such problems as an inadequate examination and promotion system or insufficient secondary education capacity." }, { "id": "IndicatorName", "value": "Progression to secondary school (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data on the transition rate is affected when new entrants and repeaters are not correctly distinguished. Students who interrupt their studies after completing primary education could also affect data quality." }, { "id": "Longdefinition", "value": "Progression to secondary school refers to the number of new entrants to the first grade of secondary school in a given year as a percentage of the number of students enrolled in the final grade of primary school in the previous year (minus the number of repeaters from the last grade of primary education in the given year)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Effective transition rate is calculated by dividing the number of new entrants in the first grade of secondary education in a given year (t) by the number of students who enrolled in the final grade of primary education in the previous school year (t-1) minus the number of repeaters from the last grade of primary education in the given year (t), and multiplying by 100. \nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.SEC.TCHR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although there have been advancements, girls in low-income countries continue to face significant barriers to accessing secondary education. The presence of female teachers is crucial in this context, as they act as role models, inspiring and motivating girls to pursue their education. These educators play a pivotal role in attracting and retaining girls in schools, challenging deep-seated gender stereotypes within communities, elevating parental expectations for their daughters, and contributing to the narrowing of the educational achievement gap between boys and girls." }, { "id": "IndicatorName", "value": "Secondary education, teachers (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator measures the level of gender representation in the teaching profession, rather than the effectiveness and quality of teaching." }, { "id": "Longdefinition", "value": "Female teachers as a percentage of total secondary education teachers includes full-time and part-time teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of female teachers in secondary education is calculated by dividing the total number of female teachers at secondary level of education by the total number of teachers at the same level, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of female teachers shows the level of gender representation in the teaching force. A value of greater than 50% indicates more opportunities or preference for women to participate in teaching activities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of total secondary education teachers" } ], "source_id": "14" }, { "id": "SE.TER.CMPL.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Gross graduation ratio, tertiary, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Number of graduates from first degree programmes (at ISCED 6 and 7) expressed as a percentage of the population of the theoretical graduation age of the most common first degree programme." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds." }, { "id": "Statisticalconceptandmethodology", "value": "Gross graduation ratio is calculated by dividing the number of graduates from first degree programmes (at ISCED 6 and 7) by the population of the theoretical graduation age of the most common first degree programme and multiplying by 100. Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.TER.CMPL.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Gross graduation ratio, tertiary, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Number of graduates from first degree programmes (at ISCED 6 and 7) expressed as a percentage of the population of the theoretical graduation age of the most common first degree programme." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds." }, { "id": "Statisticalconceptandmethodology", "value": "Gross graduation ratio is calculated by dividing the number of graduates from first degree programmes (at ISCED 6 and 7) by the population of the theoretical graduation age of the most common first degree programme and multiplying by 100. Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.TER.CMPL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Gross graduation ratio, tertiary, total (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Number of graduates from first degree programmes (at ISCED 6 and 7) expressed as a percentage of the population of the theoretical graduation age of the most common first degree programme." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds." }, { "id": "Statisticalconceptandmethodology", "value": "Gross graduation ratio is calculated by dividing the number of graduates from first degree programmes (at ISCED 6 and 7) by the population of the theoretical graduation age of the most common first degree programme and multiplying by 100. Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.TER.CUAT.BA.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Bachelor's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "14" }, { "id": "SE.TER.CUAT.BA.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Bachelor's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "14" }, { "id": "SE.TER.CUAT.BA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Bachelor's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "14" }, { "id": "SE.TER.CUAT.DO.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, Doctoral or equivalent, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Doctoral or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Doctoral or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "14" }, { "id": "SE.TER.CUAT.DO.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, Doctoral or equivalent, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Doctoral or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Doctoral or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero..\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "14" }, { "id": "SE.TER.CUAT.DO.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Doctoral or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Doctoral or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "14" }, { "id": "SE.TER.CUAT.MS.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Master's or equivalent, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Master's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Master's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "14" }, { "id": "SE.TER.CUAT.MS.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Master's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Master's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "14" }, { "id": "SE.TER.CUAT.MS.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Master's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Master's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "14" }, { "id": "SE.TER.CUAT.ST.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed short-cycle tertiary, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed short-cycle tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed short-cycle tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "14" }, { "id": "SE.TER.CUAT.ST.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed short-cycle tertiary, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed short-cycle tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed short-cycle tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "14" }, { "id": "SE.TER.CUAT.ST.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed short-cycle tertiary, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed short-cycle tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed short-cycle tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "14" }, { "id": "SE.TER.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.3 is committed to providing equitable access to affordable and high-quality technical, vocational, and tertiary education, including university, for both women and men. This particular indicator reflects the overall capacity of the educational infrastructure to support enrolment within a specified age demographic at the tertiary level." }, { "id": "IndicatorName", "value": "School enrollment, tertiary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of population in the 5-year age group immediately following upper secondary education" } ], "source_id": "14" }, { "id": "SE.TER.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.3 is committed to providing equitable access to affordable and high-quality technical, vocational, and tertiary education, including university, for both women and men. This particular indicator reflects the overall capacity of the educational infrastructure to support enrolment within a specified age demographic at the tertiary level." }, { "id": "IndicatorName", "value": "School enrollment, tertiary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of female population in the 5-year age group immediately following upper secondary education" } ], "source_id": "14" }, { "id": "SE.TER.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.3 is committed to providing equitable access to affordable and high-quality technical, vocational, and tertiary education, including university, for both women and men. This particular indicator reflects the overall capacity of the educational infrastructure to support enrolment within a specified age demographic at the tertiary level." }, { "id": "IndicatorName", "value": "School enrollment, tertiary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of male population in the 5-year age group immediately following upper secondary education" } ], "source_id": "14" }, { "id": "SE.TER.GRAD.FE.AG.ZS", "metatype": [ { "id": "Generalcomments", "value": "The indicator has been changed from \"Female share of graduates in Agriculture programmes, tertiary (%)\" to \"Female share of graduates in Agriculture, Forestry, Fisheries and Veterinary programmes, tertiary (%)\" as of January 2021." }, { "id": "IndicatorName", "value": "Female share of graduates in Agriculture, Forestry, Fisheries and Veterinary programmes (%, tertiary)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female share of graduates in the given field of education, tertiary is the number of female graduates expressed as a percentage of the total number of graduates in the given field of education from tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Percentage of female graduates by field of study in tertiary education is calculated by dividing the number of female graduates in a given field of education from tertiary education by the total number of graduates in the same field, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.TER.GRAD.FE.ED.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female share of graduates in education (%, tertiary)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female share of graduates in the given field of education, tertiary is the number of female graduates expressed as a percentage of the total number of graduates in the given field of education from tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Percentage of female graduates by field of study in tertiary education is calculated by dividing the number of female graduates in a given field of education from tertiary education by the total number of graduates in the same field, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.TER.GRAD.FE.EN.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female share of graduates in engineering, manufacturing and construction (%, tertiary)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female share of graduates in the given field of education, tertiary is the number of female graduates expressed as a percentage of the total number of graduates in the given field of education from tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Percentage of female graduates by field of study in tertiary education is calculated by dividing the number of female graduates in a given field of education from tertiary education by the total number of graduates in the same field, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.TER.GRAD.FE.HL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female share of graduates in health and welfare (%, tertiary)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female share of graduates in the given field of education, tertiary is the number of female graduates expressed as a percentage of the total number of graduates in the given field of education from tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Percentage of female graduates by field of study in tertiary education is calculated by dividing the number of female graduates in a given field of education from tertiary education by the total number of graduates in the same field, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.TER.GRAD.FE.HU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female share of graduates in Arts and Humanities programmes (%, tertiary)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female share of graduates in the given field of education, tertiary is the number of female graduates expressed as a percentage of the total number of graduates in the given field of education from tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Percentage of female graduates by field of study in tertiary education is calculated by dividing the number of female graduates in a given field of education from tertiary education by the total number of graduates in the same field, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.TER.GRAD.FE.OT.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female share of graduates in unknown or unspecified fields (%, tertiary)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female share of graduates in the given field of education, tertiary is the number of female graduates expressed as a percentage of the total number of graduates in the given field of education from tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Percentage of female graduates by field of study in tertiary education is calculated by dividing the number of female graduates in a given field of education from tertiary education by the total number of graduates in the same field, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.TER.GRAD.FE.SC.ZS", "metatype": [ { "id": "Generalcomments", "value": "The indicator has been changed from \"Female share of graduates in Science programmes, tertiary (%)\" to \"Female share of graduates in Natural Sciences, Mathematics and Statisticsprogrammes, tertiary (%)\" as of January 2021." }, { "id": "IndicatorName", "value": "Female share of graduates in Natural Sciences, Mathematics and Statistics programmes (%, tertiary)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female share of graduates in the given field of education, tertiary is the number of female graduates expressed as a percentage of the total number of graduates in the given field of education from tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Percentage of female graduates by field of study in tertiary education is calculated by dividing the number of female graduates in a given field of education from tertiary education by the total number of graduates in the same field, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.TER.GRAD.FE.SI.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female share of graduates from Science, Technology, Engineering and Mathematics (STEM) programmes, tertiary (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Female share of graduates in the given field of education, tertiary is the number of female graduates expressed as a percentage of the total number of graduates in the given field of education from tertiary education." }, { "id": "Statisticalconceptandmethodology", "value": "Percentage of female graduates by field of study in tertiary education is calculated by dividing the number of female graduates in a given field of education from tertiary education by the total number of graduates in the same field, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Technology" } ], "source_id": "14" }, { "id": "SE.TER.GRAD.FE.SS.ZS", "metatype": [ { "id": "Generalcomments", "value": "The indicator has been changed from \"Female share of graduates in Social Science, Business and Law programmes, tertiary (%)\" to \"Female share of graduates in Social Sciences, Journalism and Information programmes, tertiary (%)\" as of January 2021." }, { "id": "IndicatorName", "value": "Female share of graduates in Social Sciences, Journalism and Information programmes (%, tertiary)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female share of graduates in the given field of education, tertiary is the number of female graduates expressed as a percentage of the total number of graduates in the given field of education from tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Percentage of female graduates by field of study in tertiary education is calculated by dividing the number of female graduates in a given field of education from tertiary education by the total number of graduates in the same field, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.TER.GRAD.FE.SV.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female share of graduates in services (%, tertiary)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female share of graduates in the given field of education, tertiary is the number of female graduates expressed as a percentage of the total number of graduates in the given field of education from tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Percentage of female graduates by field of study in tertiary education is calculated by dividing the number of female graduates in a given field of education from tertiary education by the total number of graduates in the same field, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.TER.TCHR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator reflects the gender distribution within the teaching profession. It serves as a tool for evaluating the necessity of creating opportunities and incentives to promote female participation in educational instruction at various levels. According to UNESCO, there is a global trend of women being disproportionately represented in the teaching workforce. Nonetheless, this representation declines at the tertiary education level, where men are more prevalent, and women are less likely to attain senior and leadership roles within higher education institutions." }, { "id": "IndicatorName", "value": "Tertiary education, academic staff (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator measures the level of gender representation in the teaching profession, rather than the effectiveness and quality of teaching." }, { "id": "Longdefinition", "value": "Tertiary education, academic staff (% female) is the share of female academic staff in tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of female academic staffs in tertiary education is calculated by dividing the total number of female academic staffs at tertiary level of education by the total number of academic staffs at the same level, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of female teachers shows the level of gender representation in the teaching force. A value of greater than 50% indicates more opportunities or preference for women to participate in teaching activities." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of academic staff in tertiary education" } ], "source_id": "14" }, { "id": "SE.XPD.PRIM.PC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Government expenditure per student, primary (% of GDP per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: General government expenditure per student in primary education is calculated by dividing total government expenditure on primary education by the number of students at primary level, expressed as a percentage of GDP per capita. Aggregate data are World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Data on GDP per capita come from the World Bank. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.XPD.SECO.PC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Government expenditure per student, secondary (% of GDP per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: General government expenditure per student in secondary education is calculated by dividing total government expenditure on secondary education by the number of students at secondary level, expressed as a percentage of GDP per capita. Aggregate data are World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Data on GDP per capita come from the World Bank. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "SE.XPD.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investment in education acts as a driving force for economic growth, enhances productivity, and promotes the betterment of individual and collective welfare. This indicator evaluates the extent to which a government prioritizes education in relation to its overall economic prosperity." }, { "id": "IndicatorName", "value": "Government expenditure on education, total (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data may refer to spending by the ministry of education only (excluding spending on educational activities by other ministries)." }, { "id": "Longdefinition", "value": "General government expenditure on education (current, capital, and transfers) is expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government expenditure on education, total (% of GDP) is calculated by dividing total government expenditure for all levels of education by the GDP, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nInformation pertaining to educational expenditures is sourced from national governments, which provide the data in response to the annual survey conducted by the UNESCO Institute for Statistics (UIS) or through the joint UNESCO-OECD-Eurostat (UOE) data collection initiative. The responses to the questionnaire regarding educational spending are typically derived from the annual financial statements issued by either the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office. Additionally, data concerning GDP and overall government expenditure are accessible via the IMF’s World Economic Outlook database, which is updated annually.\nStatistical concept(s): Generally, elevated levels of the indicator suggest that a government places a high priority on educational policy. Values ranging from 4% to 6% are indicative of a country achieving the benchmark set forth by the Education 2030 Framework for Action (https://uis.unesco.org/sites/default/files/documents/education-2030-incheon-framework-for-action-implementation-of-sdg4-2016-en_2.pdf).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEducational expenditure encompasses spending on fundamental educational goods and services, including teaching personnel, school infrastructure, textbooks, and instructional materials, as well as on ancillary educational goods and services such as support services, general administration, and other related activities.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFunding for education may originate from public sources, encompassing all government ministries and agencies that finance or support educational programs within the country, as well as from international and private sources, such as household contributions." }, { "id": "Topic", "value": "Education" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "14" }, { "id": "SG.COK.CHCO.ZS", "metatype": [ { "id": "IndicatorName", "value": "Main cooking fuel: charcoal (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of households who use charcoal as thier main cooking fuel" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Economic and Social Context" } ], "source_id": "14" }, { "id": "SG.COK.CROP.ZS", "metatype": [ { "id": "IndicatorName", "value": "Main cooking fuel: agricultural crop (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of households who use agricultural crop as thier main cooking fuel" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Economic and Social Context" } ], "source_id": "14" }, { "id": "SG.COK.DUNG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Main cooking fuel: dung (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of households who use dung as thier main cooking fuel" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Economic and Social Context" } ], "source_id": "14" }, { "id": "SG.COK.ELEC.ZS", "metatype": [ { "id": "IndicatorName", "value": "Main cooking fuel: electricity (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of households who use electricity as thier main cooking fuel" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Economic and Social Context" } ], "source_id": "14" }, { "id": "SG.COK.HOUS.ZS", "metatype": [ { "id": "IndicatorName", "value": "Location of cooking: inside the house (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of households who do their cooking inside the house" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Economic and Social Context" } ], "source_id": "14" }, { "id": "SG.COK.LPGN.ZS", "metatype": [ { "id": "IndicatorName", "value": "Main cooking fuel: LPG/natural gas/biogas (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of households who use Liquified Petroleum Gas (LPG) or natural gas or biogas as thier main cooking fuel" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Economic and Social Context" } ], "source_id": "14" }, { "id": "SG.COK.OTHR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Location of cooking: other places (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of households who do their cooking in other places" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Economic and Social Context" } ], "source_id": "14" }, { "id": "SG.COK.OUTD.ZS", "metatype": [ { "id": "IndicatorName", "value": "Location of cooking: outdoors (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of households who do their cooking outdoors" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Economic and Social Context" } ], "source_id": "14" }, { "id": "SG.COK.SBLD.ZS", "metatype": [ { "id": "IndicatorName", "value": "Location of cooking: separate building (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of households who do their cooking in a separate building" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Economic and Social Context" } ], "source_id": "14" }, { "id": "SG.COK.STRW.ZS", "metatype": [ { "id": "IndicatorName", "value": "Main cooking fuel: straw/shrubs/grass (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of households who use straw/shrubs/grass as thier main cooking fuel" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Economic and Social Context" } ], "source_id": "14" }, { "id": "SG.COK.WOOD.ZS", "metatype": [ { "id": "IndicatorName", "value": "Main cooking fuel: wood (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of households who use wood as thier main cooking fuel" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Economic and Social Context" } ], "source_id": "14" }, { "id": "SG.DMK.ALLD.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Women participating in the three decisions (own health care, major household purchases, and visiting family) (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women participating in the three decisions (own health care, major household purchases, and visiting family) is the percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in all of the three decisions (own health care, large purchases and visits to family, relatives, and friends)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_DMAK_W_3DC; \tIndicator name from the original source: Final say in all of the decisions [Women], publisher: The DHS program (ICF), type: API, date accessed: 2023-08-18" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Women participating in the three decisions (own health care, major household purchases, and visiting family) is the number of currently married women aged 15-49 who say they alone or jointly have the final say in the three decisions, expressed as percentage of currently married women age 15-49 who have been interviewed and It’s derived by dividing the number of currently married women aged 15-49 who responded they alone or jointly have the final say in the three decisions by total number of currently married women age 15-49 who have been interviewed.\nStatistical concept(s): This indicator assesses the level of women's participation in household decision-making. It emphasizes the importance of decisions regarding their own health care, which are deemed essential to women's self-interest. The indicator also evaluates women's involvement in making substantial economic decisions, such as those related to significant purchases, to gauge their economic decision-making power within the household. Additionally, it measures women's autonomy in deciding on visits to family or friends, which can indicate their freedom of movement and the ability to engage with their birth family." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "14" }, { "id": "SG.DMK.DPCH.FN.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Women participating in making daily purchase decisions (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women participating in making daily purchase decisions is Percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in making daily purchases" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women participating in making daily purchase decisions is the number of currently married women aged 15-49 who say they alone or jointly have the final say in making daily purchases, expressed as percentage of currently married women aged 15-49 who have been interviewed and it's derived by dividing the number of currently married women aged 15-49 who responded they alone or jointly have the final say in making daily purchases by total number of currently married women age 15-49 who have been interviewed." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.FOOD.FN.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Women participating in decision of what food to cook daily (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women participating in decision of what food to cook daily is Percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in what food to cook daily" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women participating in decisions of what food to cook daily is the number of currently married women aged 15-49 who say they alone or jointly have the final say in what food to cook daily, expressed as percentage of currently married women aged 15-49 who have been interviewed and it's derived by dividing the number of currently married women aged 15-49 who responded they alone or jointly have the final say in what food to cook daily by total number of currently married women age 15-49 who have been interviewed." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.HLTH.FN.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Women participating in own health care decisions (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women Participating in own health care decisions is Percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in own health care" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women participating in own health care decisions is the number of currently married women aged 15-49 who say they alone or jointly with thier husband have the final say in own health care, expressed as percentage of currently married women aged 15-49 who have been interviewed and it's derived by dividing the number of currently married women aged 15-49 who responded they alone or jointly with thier husband have the final say in own health care by total number of currently married women age 15-49 who have been interviewed." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.HLTH.HB.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Decision maker about a woman's own health care: mainly husband (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Decision maker about women own health care: mainly husband is Percentage of currently married women aged 15-49 for whom the decision maker for their own health care is mainly the husband" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Decision maker about Own health care: mainly husband is the number of currently married women aged 15-49 for whom the decision maker for their own health care is mainly the husband, expressed as percentage of currently married women aged 15-49 who have been interviewed ." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.HLTH.OT.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Decision maker about a woman's own health care: other (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Decision maker about women own health care: other is Percentage of currently married women aged 15-49 for whom the decision maker for their own health care is recorded as 'other'" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Decision maker about Own health care: other is the number of currently married women aged 15-49 for whom the decision maker for their own health care is registered as others, expressed as percentage of all interviewed currently married women aged 15-49." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.HLTH.SE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Decision maker about a woman's own health care: someone else (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Decision maker about women own health care: someone else is Percentage of currently married women aged 15-49 for whom the decision maker for their own health care is mainly someone else" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Decision maker about Own health care: someone else is the number of currently married women aged 15-49 for whom the decision maker for their own health care is someone esle, expressed as percentage of all interviewed currently married women aged 15-49." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.HLTH.WF.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Decision maker about a woman's own health care: mainly wife  (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Decision maker about women own health care: mainly wife is Percentage of currently married women aged 15-49 for whom the decision maker for their own health care is mainly the respondent" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Decision maker about Own health care: mainly wife is the number of currently married women aged 15-49 for whom the decision maker for their own health care is mainly the respondent, expressed as percentage of currently married women aged 15-49 who have been interviewed ." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.HLTH.WH.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Decision maker about a woman's own health care: wife and husband jointly (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Decision maker about women own health care: wife and husband jointly is Percentage of currently married women aged 15-49 for whom the decision maker for their own health care is mainly the respondent and her husband" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Decision maker about Own health care: wife and husband jointly is the number of currently married women aged 15-49 for whom the decision maker for their own health care is mainly the respondent and her husband, expressed as percentage of currently married women aged 15-49 who have been interviewed." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.NONE.FN.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Women participating in none of the three decisions (own health care, major household purchases, and visiting family) (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women participating in none of the three decisions (own health care, major household purchases, and visiting family) is Percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in none of the three decisions (own health care, major household purchases, and visiting family)" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women participating in none of the three decisions (own health care, major household purchases, and visiting family) is the number of currently married women aged 15-49 who say they alone or jointly have the final say in none of the three decisions, expressed as percentage decisions by total number of currently married women age 15-49 who have been interviewed." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.PRCH.FN.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Women participating in making major household purchase decisions (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women participating in making major household purchase decisions is Percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in making major household purchases" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women participating in making major household purchase decision is the number of currently married women aged 15-49 who say that they alone or jointly with thier husband have the final say in making major household purchases, expressed as percentage of currently married women aged 15-49 who have been interviewed and it's derived by dividing the number of currently married women aged 15-49 who responded they alone or jointly have the final say in making major household purchases by total number of currently married women age 15-49 who have been interviewed." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.PRCH.HB.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Decision maker about major household purchases: mainly husband (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Decision maker about ajor household purchases: mainly husband is Percentage of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly the husband" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Decision maker about major household purchases: mainly husband is the number of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly the husband, expressed as percentage of currently married women aged 15-49 who have been interviewed." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.PRCH.OT.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Decision maker about major household purchases: other (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Decision maker about major household purchases: other is Percentage of currently married women aged 15-49 for whom the decision maker for major household purchases is recorded as 'other'" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Decision maker about major household purchases: other is the number of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly recorded as 'other', expressed as percentage of currently married women aged 15-49 who have been interviewed." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.PRCH.SE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Decision maker about major household purchases: someone else (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Decision maker about Major household purchases: someone else is Percentage of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly someone else" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Decision maker about major household purchases: someone else is the number of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly someone else, expressed as percentage of currently married women aged 15-49 who have been interviewed." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.PRCH.WF.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Decision maker about major household purchases: mainly wife (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Decision maker about major household purchases: mainly wife is Percentage of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly the respondent" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Decision maker about major household purchases: mainly wife is the number of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly the respondent, expressed as percentage of all interviewed currently married women aged 15-49." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.PRCH.WH.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Decision maker about major household purchases: wife and husband jointly (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Decision maker about major household purchases: wife and husband jointly is Percentage of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly the respondent and her husband" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Decision maker about major household purchases: wife and husband jointly is the number of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly the respondent and her husband, expressed as percentage of currently married women aged 15-49 who have been interviewed." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.SRCR.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Women making their own informed decisions regarding sexual relations, contraceptive use and reproductive health care (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Current estimates of the indicator are based on currently married or in union women of reproductive age (15-49 years old) who are using any type of contraception. In the current Demographic and Health Surveys (DHS), the question on decision-making on use of contraception is only asked to women who are currently using contraception. Because the questions on decision- making on sexual relations and health care are restricted to women (15-49) currently married or in union, the denominator for Indicator 5.6.1 is women 15-49, who are currently married or in union and currently using contraception. However, agreement has been reached with Macro/ICF for upcoming DHS surveys to ask the question on decision on use of contraception to all married/ in union women aged 15-49 years, whether they are currently using any contraception or not. The DHS model questionnaire for Phase 7 already includes the question on decision-making for women who are not currently using any contraception." }, { "id": "Longdefinition", "value": "Proportion of women ages 15-49 years (married or in union) who make their own decision on all three selected areas i.e. can say no to sexual intercourse with their husband or partner if they do not want; decide on use of contraception; and decide on their own health care. Only women who provide a “yes” answer to all three components are considered as women who “make her own decisions regarding sexual and reproductive”." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.6.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2022" }, { "id": "Source", "value": "Demographic and Health Surveys compiled by United Nations Population Fund, United Nations (UN), uri: https://unstats.un.org/sdgs/UNSDGAPIV5/swagger/index.html, note: Indicator code from the original source: SH_FPL_INFM; \tIndicator name from the original source: Proportion of women aged 15–49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care, publisher: UN Statistics Division, type: API" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Numerator of the indicator is number of married or in union women ages 15-49 who have been interviewed and satisfy all three empowerment criteria: 1)who can say 'no' to sex; and 2)for whom the decision on contraception is not mainly made by the husband/partner; and 3) for whom decision on health care for themselves ins not usually made by the husband/partner or someone else. Denominator of the indicator is the total number of women ages 15-49 who are married or in union and who have been interviewed. \n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data for this indicator are primarily sourced from nationally representative Demographic and Health Surveys (DHS).\nStatistical concept(s): A woman is deemed to possess autonomy in reproductive health decision-making and to be empowered to assert her reproductive rights when she has the ability to: (1) make decisions regarding her own health care, independently or in conjunction with her husband or partner, (2) determine the use or non-use of contraception, on her own or together with her husband or partner, and (3) refuse sexual relations with her husband or partner if she chooses." }, { "id": "Topic", "value": "Norms and Decision-making" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "14" }, { "id": "SG.DMK.VISI.FN.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Women participating in decision of visits to family, relatives, friends (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women participating in decision of visits to family, relatives, friends is Percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in visits to family, relatives, friends" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women participating in decision of visits to family, relatives, friends is the number of currently married women aged 15-49 who say they alone or jointly with thier husband have the final say in visits to family, relatives, friends, expressed as percentage of currently married women aged 15-49 who have been interviewed and it's derived by dividing the number of currently married women aged 15-49 who responded they alone or jointly have the final say in visits to family, relatives, friends by total number of currently married women age 15-49 who have been interviewed." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.VISI.HB.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Decision maker about a woman's visits to her family or relatives: mainly husband (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Decision maker about a woman's visits to her family or relatives: mainly husband is Percentage of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is mainly the husband" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Decision maker about Visits to her family or relatives: mainly husband is the number of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is mainly the husband, expressed as percentage of currently married women aged 15-49 who have been interviewed." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.VISI.OT.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Decision maker about a woman's visits to her family or relatives: other (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Decision maker about a woman's visits to her family or relatives: other is Percentage of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is recorded as 'other'" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Decision maker about Visits to her family or relatives: other is the number of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is recorded as 'other', expressed as percentage of currently married women aged 15-49 who have been interviewed." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.VISI.SE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Decision maker about a woman's visits to her family or relatives: someone else (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Decision maker about a woman's visits to her family or relatives: someone else is Percentage of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is mainly someone else" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Decision maker about Visits to her family or relatives: someone else is the number of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is mainly someone else, expressed as percentage of currently married women aged 15-49 who have been interviewed." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.VISI.WF.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Decision maker about a woman's visits to her family or relatives: mainly wife (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Decision maker about a woman's visits to her family or relatives: mainly wife is Percentage of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is mainly the respondent" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Decision maker about Visits to her family or relatives: mainly wife is the number of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is mainly the respondent, expressed as percentage of currently married women aged 15-49 who have been interviewed." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.DMK.VISI.WH.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Decision maker about Visits to her family or relatives: wife and husband jointly (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Decision maker about Visits to her family or relatives: wife and husband jointly is Percentage of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is mainly the respondent and her husband" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Decision maker about Visits to her family or relatives: wife and husband jointly is the number of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is mainly the respondent and her husband, expressed as percentage of currently married women aged 15-49 who have been interviewed." }, { "id": "Topic", "value": "Norms and Decision-making" } ], "source_id": "14" }, { "id": "SG.GEN.MNST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Proportion of women in ministerial level positions (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women in ministerial level positions is the proportion of women in ministerial or equivalent positions (including deputy prime ministers) in the government. Prime Ministers/Heads of Government are included when they hold ministerial portfolios. Vice-Presidents and heads of governmental or public agencies are excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Inter-Parliamentary Union. United Nations Entity for Gender Equality and the Empowerment of Women (UN Women). Women in Politics." }, { "id": "Topic", "value": "Leadership" } ], "source_id": "14" }, { "id": "SG.GEN.PARL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite much progress in recent decades, gender inequalities remain pervasive in many dimensions of life - worldwide. But while disparities exist throughout the world, they are most prevalent in developing countries. Gender inequalities in the allocation of such resources as education, health care, nutrition, and political voice matter because of the strong association with well-being, productivity, and economic growth. These patterns of inequality begin at an early age, with boys routinely receiving a larger share of education and health spending than do girls, for example.\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen are vastly underrepresented in decision-making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.\n\n\n\n\n\n\n\n\n\n\n\n\n\nA strong and vibrant democracy is possible only when parliament is fully inclusive of the population it represents. Parliaments cannot consider themselves inclusive, however, until they can boast the full participation of women. This is not just about women's right to equality and their contribution to the conduct of public affairs, but also about using women's resources and potential to determine political and development priorities that benefit societies and the global community." }, { "id": "IndicatorName", "value": "Proportion of seats held by women in national parliaments (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The number of countries covered varies with suspensions or dissolutions of parliaments. There can be difficulties in obtaining information on by-election results and replacements due to death or resignation. These changes are ad hoc events which are more difficult to keep track of. By-elections, for instance, are often not announced internationally as general elections are. Parliaments vary considerably in their internal workings and procedures, however, generally legislate, oversee government and represent the electorate. In terms of measuring women's contribution to political decision making, this indicator may not be sufficient because some women may face obstacles in fully and efficiently carrying out their parliamentary mandate.\n\n\n\n\n\n\n\n\n\n\n\nThe data is compiled by the Inter-Parliamentary Union on the basis of information provided by National Parliaments. The percentages do not take into account the case of parliaments for which no data was available at that date. Information is available in all countries where a national legislature exists and therefore does not include parliaments that have been dissolved or suspended for an indefinite period." }, { "id": "Longdefinition", "value": "Women in parliaments are the percentage of parliamentary seats in a single or lower chamber held by women." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Women are vastly underrepresented in decision making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.\n\nThis is the Sustainable Development Goal indicator 5.5.1 (a). [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2024" }, { "id": "Source", "value": "Monthly ranking of women in national parliaments, Inter-Parliamentary Union (IPU), uri: https://data.ipu.org/women-ranking/, note: For the year of 1998, the data is as of August 10, 1998., type: Excel, date accessed: 2024-03-21" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of seats held by women in national parliaments is the number of seats held by women members in single or lower chambers of national parliaments, expressed as a percentage of all occupied seats; it is derived by dividing the total number of seats occupied by women by the total number of seats in parliament.\nStatistical concept(s): This indicator assesses the extent to which women are provided with equal opportunities to participate in parliamentary decision-making processes. It applies to the sole chamber of unicameral national parliaments and the lower chamber in the case of bicameral systems. The upper chamber in bicameral parliaments is not included in this measure. Parliamentary seats are typically occupied by individuals who are victorious in general elections, though they can also be acquired through nomination, appointment, indirect election, member rotation, or by-elections. The term 'seats' refers to the total count of parliamentary mandates or the total number of parliament members." }, { "id": "Topic", "value": "Leadership" }, { "id": "Unitofmeasure", "value": "% of single or lower chamber seats" } ], "source_id": "14" }, { "id": "SG.GEN.TECH.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female professional and technical workers (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female professional and technical workers refers to the share of professionals and technical workers who are female. Women's share of positions are defined according to the International Standard Classification of Occupations (ISCO-88) to include physical, mathematical and engineering science professionals (and associate professionals), life science and health professionals (and associate professionals), teaching professionals (and associate professionals) and other professionals and associate professionals." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Gender, Institutions and Development Database, Organization for Economic Co-operation and Development (OECD), web site: http://www.oecd.org/document/16/0,3343,en_2649_33935_39323280_1_1_1_1,00.html." }, { "id": "Topic", "value": "Employment and Time Use" } ], "source_id": "14" }, { "id": "SG.H2O.PRMS.HH.ZS", "metatype": [ { "id": "IndicatorName", "value": "Households with water on the premises (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of households who have a water source on their premises" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Economic and Social Context" } ], "source_id": "14" }, { "id": "SG.H2O.TL30.HH.ZS", "metatype": [ { "id": "IndicatorName", "value": "Households with water less than 30 minutes away round trip (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of households who have a water source in less than 30 minutes away round trip" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Economic and Social Context" } ], "source_id": "14" }, { "id": "SG.H2O.TM30.HH.ZS", "metatype": [ { "id": "IndicatorName", "value": "Households with water 30 minutes or longer away round trip (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of households who have a water source 30 minutes or longer away round trip" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Economic and Social Context" } ], "source_id": "14" }, { "id": "SG.MHG.PADP.RU.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "While menstruation can be a taboo topic, menstrual health has been recognized as essential to improve public health and advance gender equality and human rights. Menstrual health is central to achieving multiple Sustainable Development Goals (SDGs), and is directly linked to SDG target 6.2, which calls for ‘special attention to the needs of women and girls’." }, { "id": "IndicatorName", "value": "Women and girls who participate in activities during menstrual period, rural (% of women and girls ages 15-49 living in rural areas who had a menstrual period within the last year)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The JMP does not currently use a service ladder for menstrual health, as norms and standards relating to menstrual health and associated water, sanitation, and hygiene needs are still evolving. Further work is needed to refine the menstrual health indicators and evaluate if others may be more relevant. National data are not available for high-income countries." }, { "id": "Longdefinition", "value": "The percentage of women and girls (ages 15-49 who had a menstrual period within the last year) who participated in activities (such as school, work, and social activities for those who typically participate in) during their last menstrual period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on menstrual health are produced by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) based on household surveys. The data on each of the menstrual health indicators have been compiled and harmonized across countries and surveys, to the extent possible, to support cross-country comparison." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.MHG.PADP.UR.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "While menstruation can be a taboo topic, menstrual health has been recognized as essential to improve public health and advance gender equality and human rights. Menstrual health is central to achieving multiple Sustainable Development Goals (SDGs), and is directly linked to SDG target 6.2, which calls for ‘special attention to the needs of women and girls’." }, { "id": "IndicatorName", "value": "Women and girls who participate in activities during menstrual period, urban (% of women and girls ages 15-49 living in urban areas who had a menstrual period within the last year)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The JMP does not currently use a service ladder for menstrual health, as norms and standards relating to menstrual health and associated water, sanitation, and hygiene needs are still evolving. Further work is needed to refine the menstrual health indicators and evaluate if others may be more relevant. National data are not available for high-income countries." }, { "id": "Longdefinition", "value": "The percentage of women and girls (ages 15-49 who had a menstrual period within the last year) who participated in activities (such as school, work, and social activities for those who typically participate in) during their last menstrual period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on menstrual health are produced by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) based on household surveys. The data on each of the menstrual health indicators have been compiled and harmonized across countries and surveys, to the extent possible, to support cross-country comparison." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.MHG.PADP.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "While menstruation can be a taboo topic, menstrual health has been recognized as essential to improve public health and advance gender equality and human rights. Menstrual health is central to achieving multiple Sustainable Development Goals (SDGs), and is directly linked to SDG target 6.2, which calls for ‘special attention to the needs of women and girls’." }, { "id": "IndicatorName", "value": "Women and girls who participate in activities during menstrual period (% of women and girls ages 15-49 who had a menstrual period within the last year)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The JMP does not currently use a service ladder for menstrual health, as norms and standards relating to menstrual health and associated water, sanitation, and hygiene needs are still evolving. Further work is needed to refine the menstrual health indicators and evaluate if others may be more relevant. National data are not available for high-income countries." }, { "id": "Longdefinition", "value": "The percentage of women and girls (ages 15-49 who had a menstrual period within the last year) who participated in activities (such as school, work, and social activities for those who typically participate in) during their last menstrual period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on menstrual health are produced by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) based on household surveys. The data on each of the menstrual health indicators have been compiled and harmonized across countries and surveys, to the extent possible, to support cross-country comparison." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.MHG.PPDP.RU.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "While menstruation can be a taboo topic, menstrual health has been recognized as essential to improve public health and advance gender equality and human rights. Menstrual health is central to achieving multiple Sustainable Development Goals (SDGs), and is directly linked to SDG target 6.2, which calls for ‘special attention to the needs of women and girls’." }, { "id": "IndicatorName", "value": "Women and girls who have private places to wash and change during menstrual period, rural (% of women and girls ages 15-49 living in rural areas who had a menstrual period within the last year)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The JMP does not currently use a service ladder for menstrual health, as norms and standards relating to menstrual health and associated water, sanitation, and hygiene needs are still evolving. Further work is needed to refine the menstrual health indicators and evaluate if others may be more relevant. National data are not available for high-income countries." }, { "id": "Longdefinition", "value": "The percentage of women and girls (ages 15-49 who had a menstrual period within the last year) who had a private place to wash their bodies and change their menstrual materials at home during their last menstrual period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on menstrual health are produced by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) based on household surveys. The data on each of the menstrual health indicators have been compiled and harmonized across countries and surveys, to the extent possible, to support cross-country comparison." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.MHG.PPDP.UR.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "While menstruation can be a taboo topic, menstrual health has been recognized as essential to improve public health and advance gender equality and human rights. Menstrual health is central to achieving multiple Sustainable Development Goals (SDGs), and is directly linked to SDG target 6.2, which calls for ‘special attention to the needs of women and girls’." }, { "id": "IndicatorName", "value": "Women and girls who have private places to wash and change during menstrual period, urban (% of women and girls ages 15-49 living in urban areas who had a menstrual period within the last year)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The JMP does not currently use a service ladder for menstrual health, as norms and standards relating to menstrual health and associated water, sanitation, and hygiene needs are still evolving. Further work is needed to refine the menstrual health indicators and evaluate if others may be more relevant. National data are not available for high-income countries." }, { "id": "Longdefinition", "value": "The percentage of women and girls (ages 15-49 who had a menstrual period within the last year) who had a private place to wash their bodies and change their menstrual materials at home during their last menstrual period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on menstrual health are produced by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) based on household surveys. The data on each of the menstrual health indicators have been compiled and harmonized across countries and surveys, to the extent possible, to support cross-country comparison." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.MHG.PPDP.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "While menstruation can be a taboo topic, menstrual health has been recognized as essential to improve public health and advance gender equality and human rights. Menstrual health is central to achieving multiple Sustainable Development Goals (SDGs), and is directly linked to SDG target 6.2, which calls for ‘special attention to the needs of women and girls’." }, { "id": "IndicatorName", "value": "Women and girls who have private places to wash and change during menstrual period (% of women and girls ages 15-49 who had a menstrual period within the last year)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The JMP does not currently use a service ladder for menstrual health, as norms and standards relating to menstrual health and associated water, sanitation, and hygiene needs are still evolving. Further work is needed to refine the menstrual health indicators and evaluate if others may be more relevant. National data are not available for high-income countries." }, { "id": "Longdefinition", "value": "The percentage of women and girls (ages 15-49 who had a menstrual period within the last year) who had a private place to wash their bodies and change their menstrual materials at home during their last menstrual period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on menstrual health are produced by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) based on household surveys. The data on each of the menstrual health indicators have been compiled and harmonized across countries and surveys, to the extent possible, to support cross-country comparison." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.MHG.UMDP.RU.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "While menstruation can be a taboo topic, menstrual health has been recognized as essential to improve public health and advance gender equality and human rights. Menstrual health is central to achieving multiple Sustainable Development Goals (SDGs), and is directly linked to SDG target 6.2, which calls for ‘special attention to the needs of women and girls’." }, { "id": "IndicatorName", "value": "Women and girls who use menstrual materials, rural (% of women and girls ages 15-49 living in rural areas who had a menstrual period within the last year)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The JMP does not currently use a service ladder for menstrual health, as norms and standards relating to menstrual health and associated water, sanitation, and hygiene needs are still evolving. Further work is needed to refine the menstrual health indicators and evaluate if others may be more relevant. National data are not available for high-income countries." }, { "id": "Longdefinition", "value": "The percentage of the women and girls (ages 15-49 who had a menstrual period with the last year) who used menstrual materials such as sanitary pads, tampons, menstrual cups, cloth, or cotton wool, to capture and contain menstrual blood during their last menstrual period. Those who used paper, underwear alone, or nothing are not classified as using menstrual materials." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on menstrual health are produced by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) based on household surveys. The data on each of the menstrual health indicators have been compiled and harmonized across countries and surveys, to the extent possible, to support cross-country comparison." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.MHG.UMDP.UR.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "While menstruation can be a taboo topic, menstrual health has been recognized as essential to improve public health and advance gender equality and human rights. Menstrual health is central to achieving multiple Sustainable Development Goals (SDGs), and is directly linked to SDG target 6.2, which calls for ‘special attention to the needs of women and girls’." }, { "id": "IndicatorName", "value": "Women and girls who use menstrual materials, urban (% of women and girls ages 15-49 living in urban areas who had a menstrual period within the last year)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The JMP does not currently use a service ladder for menstrual health, as norms and standards relating to menstrual health and associated water, sanitation, and hygiene needs are still evolving. Further work is needed to refine the menstrual health indicators and evaluate if others may be more relevant. National data are not available for high-income countries." }, { "id": "Longdefinition", "value": "The percentage of the women and girls (ages 15-49 who had a menstrual period with the last year) who used menstrual materials such as sanitary pads, tampons, menstrual cups, cloth, or cotton wool, to capture and contain menstrual blood during their last menstrual period. Those who used paper, underwear alone, or nothing are not classified as using menstrual materials." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on menstrual health are produced by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) based on household surveys. The data on each of the menstrual health indicators have been compiled and harmonized across countries and surveys, to the extent possible, to support cross-country comparison." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.MHG.UMDP.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "While menstruation can be a taboo topic, menstrual health has been recognized as essential to improve public health and advance gender equality and human rights. Menstrual health is central to achieving multiple Sustainable Development Goals (SDGs), and is directly linked to SDG target 6.2, which calls for ‘special attention to the needs of women and girls’." }, { "id": "IndicatorName", "value": "Women and girls who use menstrual materials (% of women and girls ages 15-49 who had a menstrual period within the last year)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The JMP does not currently use a service ladder for menstrual health, as norms and standards relating to menstrual health and associated water, sanitation, and hygiene needs are still evolving. Further work is needed to refine the menstrual health indicators and evaluate if others may be more relevant. National data are not available for high-income countries." }, { "id": "Longdefinition", "value": "The percentage of the women and girls (ages 15-49 who had a menstrual period with the last year) who used menstrual materials such as sanitary pads, tampons, menstrual cups, cloth, or cotton wool, to capture and contain menstrual blood during their last menstrual period. Those who used paper, underwear alone, or nothing are not classified as using menstrual materials." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on menstrual health are produced by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) based on household surveys. The data on each of the menstrual health indicators have been compiled and harmonized across countries and surveys, to the extent possible, to support cross-country comparison." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.OWN.HSAJ.FE.Q1.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who own a house both alone and jointly (% of women age 15-49): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who own house both alone and jointly (% of women age 15-49): Q1 (lowest) is the percentage of women age 15-49 who alone as well as jointly with someone else own a house which is legally registered with their name or cannot be sold without their signature. \"Both alone and jointly\" Implies a woman owns a house alone and another house jointly with someone else. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who own house both alone and jointly is the number of women age 15-49 who say the type of houses they own are the one they own alone as well as the one they own jointly with someone else, expressed as percentage of women age 15-49 who have been interviewed. ‘Ownership’ implies that the house is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the house is recognized as hers and cannot be sold without her signature or equivalent. ‘House’ includes all dwelling types including apartments, duplexes, and houses that are semi-detached or detached, etc., as well as other types of dwellings that are specific to country." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.HSAJ.FE.Q2.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who own a house both alone and jointly (% of women age 15-49): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who own house both alone and jointly (% of women age 15-49): Q2 is the percentage of women age 15-49 who alone as well as jointly with someone else own a house which is legally registered with their name or cannot be sold without their signature. \"Both alone and jointly\" Implies a woman owns a house alone and another house jointly with someone else. 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For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who own a house jointly (% of men): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who own house jointly (% of men): Q1 (lowest) is the percentage of men who only jointly own a house, which legally registered with their name or cannot be sold without their signature, with someone else. \"Only jointly\" implies the man doesn’t own a house on his own, but instead jointly owns one with someone else. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who own house jointly is the number of men who say the only type of house they own is the one they own with someone else, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the house is legally registered in the man's name or, since official property records do not always exist or are not maintained, the house is recognized as his and cannot be sold without his signature or equivalent. ‘House’ includes all dwelling types including apartments, duplexes, and houses that are semi-detached or detached, etc., as well as other types of dwellings that are specific to country." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.HSJT.MA.Q2.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. 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Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who do not own house is the number of men who say they don’t own any house either alone or jointly with someone else or both, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the house is legally registered in the man's name or, since official property records do not always exist or are not maintained, the house is recognized as his and cannot be sold without his signature or equivalent. ‘House’ includes all dwelling types including apartments, duplexes, and houses that are semi-detached or detached, etc., as well as other types of dwellings that are specific to country." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.HSNO.MA.Q3.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. 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Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who do not own house is the number of men who say they don’t own any house either alone or jointly with someone else or both, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the house is legally registered in the man's name or, since official property records do not always exist or are not maintained, the house is recognized as his and cannot be sold without his signature or equivalent. ‘House’ includes all dwelling types including apartments, duplexes, and houses that are semi-detached or detached, etc., as well as other types of dwellings that are specific to country." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.HSNO.MA.Q4.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who do not own a house (% of men): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who do not own house (% of men): Q4 is the percentage of men who don’t own any house, which legally registered with their name or cannot be sold without their signature, either solely or jointly with someone else or both. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who do not own house is the number of men who say they don’t own any house either alone or jointly with someone else or both, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the house is legally registered in the man's name or, since official property records do not always exist or are not maintained, the house is recognized as his and cannot be sold without his signature or equivalent. ‘House’ includes all dwelling types including apartments, duplexes, and houses that are semi-detached or detached, etc., as well as other types of dwellings that are specific to country." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.HSNO.MA.Q5.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who do not own a house (% of men): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who do not own house (% of men): Q5 (highest) is the percentage of men who don’t own any house, which legally registered with their name or cannot be sold without their signature, either solely or jointly with someone else or both. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who do not own house is the number of men who say they don’t own any house either alone or jointly with someone else or both, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the house is legally registered in the man's name or, since official property records do not always exist or are not maintained, the house is recognized as his and cannot be sold without his signature or equivalent. ‘House’ includes all dwelling types including apartments, duplexes, and houses that are semi-detached or detached, etc., as well as other types of dwellings that are specific to country." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.HSNO.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who do not own a house (% of men)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who do not own house (% of men) is the percentage of men who don’t own any house, which legally registered with their name or cannot be sold without their signature, either solely or jointly with someone else or both." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who do not own house is the number of men who say they don’t own any house either alone or jointly with someone else or both, expressed as percentage of men who have been interviewed and It’s derived by dividing the number of men who responded they don’t own any house which legally registered with their name or can’t be sold without their signature by total number of men who have been interviewed. ‘Ownership’ implies that the house is legally registered in the man's name or, since official property records do not always exist or are not maintained, the house is recognized as his and cannot be sold without his signature or equivalent. ‘House’ includes all dwelling types including apartments, duplexes, and houses that are semi-detached or detached, etc., as well as other types of dwellings that are specific to country." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAJ.FE.Q1.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who own land both alone and jointly (% of women age 15-49): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who own land both alone and jointly (% of women age 15-49): Q1 (lowest) is the percentage of women age 15-49 who both solely and jointly with someone else own a land which is legally registered with their name or cannot be sold without their signature. \"Both alone and jointly\" Implies a woman owns a land alone and another land jointly with someone else. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who own land both alone and jointly is the number of women age 15-49 who say the type of lands they own are the one they own alone as well as the one they own jointly with someone else, expressed as percentage of women age 15-49 who have been interviewed. ‘Ownership’ implies that the land is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the land is recognized as hers and cannot be sold without her signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAJ.FE.Q2.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who own land both alone and jointly (% of women age 15-49): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who own land both alone and jointly (% of women age 15-49): Q2 is the percentage of women age 15-49 who both solely and jointly with someone else own a land which is legally registered with their name or cannot be sold without their signature. \"Both alone and jointly\" Implies a woman owns a land alone and another land jointly with someone else. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who own land both alone and jointly is the number of women age 15-49 who say the type of lands they own are the one they own alone as well as the one they own jointly with someone else, expressed as percentage of women age 15-49 who have been interviewed. ‘Ownership’ implies that the land is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the land is recognized as hers and cannot be sold without her signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAJ.FE.Q3.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who own land both alone and jointly (% of women age 15-49): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who own land both alone and jointly (% of women age 15-49): Q3 is the percentage of women age 15-49 who both solely and jointly with someone else own a land which is legally registered with their name or cannot be sold without their signature. \"Both alone and jointly\" Implies a woman owns a land alone and another land jointly with someone else. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who own land both alone and jointly is the number of women age 15-49 who say the type of lands they own are the one they own alone as well as the one they own jointly with someone else, expressed as percentage of women age 15-49 who have been interviewed. ‘Ownership’ implies that the land is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the land is recognized as hers and cannot be sold without her signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAJ.FE.Q4.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who own land both alone and jointly (% of women age 15-49): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who own land both alone and jointly (% of women age 15-49): Q4 is the percentage of women age 15-49 who both solely and jointly with someone else own a land which is legally registered with their name or cannot be sold without their signature. \"Both alone and jointly\" Implies a woman owns a land alone and another land jointly with someone else. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who own land both alone and jointly is the number of women age 15-49 who say the type of lands they own are the one they own alone as well as the one they own jointly with someone else, expressed as percentage of women age 15-49 who have been interviewed. ‘Ownership’ implies that the land is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the land is recognized as hers and cannot be sold without her signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAJ.FE.Q5.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who own land both alone and jointly (% of women age 15-49): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who own land both alone and jointly (% of women age 15-49): Q5 (highest) is the percentage of women age 15-49 who both solely and jointly with someone else own a land which is legally registered with their name or cannot be sold without their signature. \"Both alone and jointly\" Implies a woman owns a land alone and another land jointly with someone else. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who own land both alone and jointly is the number of women age 15-49 who say the type of lands they own are the one they own alone as well as the one they own jointly with someone else, expressed as percentage of women age 15-49 who have been interviewed. ‘Ownership’ implies that the land is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the land is recognized as hers and cannot be sold without her signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAJ.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who own land both alone and jointly (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who own land both alone and jointly (% of women age 15-49) is the percentage of women age 15-49 who both solely and jointly with someone else own a land which is legally registered with their name or cannot be sold without their signature. \"Both alone and jointly\" Implies a woman owns a land alone and another land jointly with someone else." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who own land both alone and jointly is the number of women age 15-49 who say the type of lands they own are the one they own alone as well as the one they own jointly with someone else, expressed as percentage of women age 15-49 who have been interviewed and It’s derived by dividing the number of women age 15-49 who responded they both alone and jointly with someone else own a land which legally registered with their name or can’t be sold without their signature by total number of women age 15-49 who have been interviewed. ‘Ownership’ implies that the land is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the land is recognized as hers and cannot be sold without her signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAJ.MA.Q1.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who own land both alone and jointly (% of men): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who own land both alone and jointly (% of men): Q1 (lowest) is the percentage of men who both solely and jointly with someone else own a land which is legally registered with their name or cannot be sold without their signature. \"Both alone and jointly\" Implies a man owns a land alone and another land jointly with someone else. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who own land both alone and jointly is the number of men who say the type of lands they own are the one they own alone as well as the one they own jointly with someone else, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAJ.MA.Q2.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who own land both alone and jointly (% of men): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who own land both alone and jointly (% of men): Q2 is the percentage of men who both solely and jointly with someone else own a land which is legally registered with their name or cannot be sold without their signature. \"Both alone and jointly\" Implies a man owns a land alone and another land jointly with someone else. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who own land both alone and jointly is the number of men who say the type of lands they own are the one they own alone as well as the one they own jointly with someone else, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAJ.MA.Q3.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who own land both alone and jointly (% of men): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who own land both alone and jointly (% of men): Q3 is the percentage of men who both solely and jointly with someone else own a land which is legally registered with their name or cannot be sold without their signature. \"Both alone and jointly\" Implies a man owns a land alone and another land jointly with someone else. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who own land both alone and jointly is the number of men who say the type of lands they own are the one they own alone as well as the one they own jointly with someone else, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAJ.MA.Q4.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who own land both alone and jointly (% of men): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who own land both alone and jointly (% of men): Q4 is the percentage of men who both solely and jointly with someone else own a land which is legally registered with their name or cannot be sold without their signature. \"Both alone and jointly\" Implies a man owns a land alone and another land jointly with someone else. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who own land both alone and jointly is the number of men who say the type of lands they own are the one they own alone as well as the one they own jointly with someone else, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAJ.MA.Q5.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who own land both alone and jointly (% of men): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who own land both alone and jointly (% of men): Q5 (highest) is the percentage of men who both solely and jointly with someone else own a land which is legally registered with their name or cannot be sold without their signature. \"Both alone and jointly\" Implies a man owns a land alone and another land jointly with someone else. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who own land both alone and jointly is the number of men who say the type of lands they own are the one they own alone as well as the one they own jointly with someone else, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAJ.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who own land both alone and jointly (% of men)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who own land both alone and jointly (% of men) is the percentage of men who both solely and jointly with someone else own a land which is legally registered with their name or cannot be sold without their signature. \"Both alone and jointly\" Implies a man owns a land alone and another land jointly with someone else." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who own land both alone and jointly is the number of men who say the type of lands they own are the one they own alone as well as the one they own jointly with someone else, expressed as percentage of men who have been interviewed and It’s derived by dividing the number of men who responded they both alone and jointly with someone else own a land which legally registered with their name or can’t be sold without their signature by total number of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAL.FE.Q1.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who own land alone (% of women age 15-49): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who own land alone (% of women age 15-49): Q1 (lowest) is the percentage of women age 15-49 who only solely own a land which is legally registered with their name or cannot be sold without their signature. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who own land alone is the number of women age 15-49 who say the only type of land they own is the one they own alone or don't share ownership with anyone, expressed as percentage of women age 15-49 who have been interviewed. ‘Ownership’ implies that the land is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the land is recognized as hers and cannot be sold without her signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAL.FE.Q2.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who own land alone (% of women age 15-49): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who own land alone (% of women age 15-49): Q2 is the percentage of women age 15-49 who only solely own a land which is legally registered with their name or cannot be sold without their signature. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who own land alone is the number of women age 15-49 who say the only type of land they own is the one they own alone or don't share ownership with anyone, expressed as percentage of women age 15-49 who have been interviewed. ‘Ownership’ implies that the land is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the land is recognized as hers and cannot be sold without her signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAL.FE.Q3.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who own land alone (% of women age 15-49): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who own land alone (% of women age 15-49): Q3 is the percentage of women age 15-49 who only solely own a land which is legally registered with their name or cannot be sold without their signature. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who own land alone is the number of women age 15-49 who say the only type of land they own is the one they own alone or don't share ownership with anyone, expressed as percentage of women age 15-49 who have been interviewed. ‘Ownership’ implies that the land is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the land is recognized as hers and cannot be sold without her signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAL.FE.Q4.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who own land alone (% of women age 15-49): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who own land alone (% of women age 15-49): Q4 is the percentage of women age 15-49 who only solely own a land which is legally registered with their name or cannot be sold without their signature. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who own land alone is the number of women age 15-49 who say the only type of land they own is the one they own alone or don't share ownership with anyone, expressed as percentage of women age 15-49 who have been interviewed. ‘Ownership’ implies that the land is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the land is recognized as hers and cannot be sold without her signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAL.FE.Q5.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who own land alone (% of women age 15-49): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who own land alone (% of women age 15-49): Q5 (highest) is the percentage of women age 15-49 who only solely own a land which is legally registered with their name or cannot be sold without their signature. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who own land alone is the number of women age 15-49 who say the only type of land they own is the one they own alone or don't share ownership with anyone, expressed as percentage of women age 15-49 who have been interviewed. ‘Ownership’ implies that the land is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the land is recognized as hers and cannot be sold without her signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAL.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who own land alone (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who own land alone (% of women age 15-49) is the percentage of women age 15-49 who only solely own a land which is legally registered with their name or cannot be sold without their signature." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who own land alone is the number of women age 15-49 who say the only type of land they own is the one they own alone or don't share ownership with anyone, expressed as percentage of women age 15-49 who have been interviewed and It’s derived by dividing the number of women age 15-49 who responded they alone own a land, which legally registered with their name or can’t be sold without their signature, by total number of women age 15-49 who have been interviewed. ‘Ownership’ implies that the land is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the land is recognized as hers and cannot be sold without her signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAL.MA.Q1.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who own land alone (% of men): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who own land alone (% of men): Q1 (lowest) is the percentage of men who solely own a land which is legally registered with their name or cannot be sold without their signature. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who own land alone is the number of men who say the only type of land they own is the one they own alone or don't share ownership with anyone, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAL.MA.Q2.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who own land alone (% of men): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who own land alone (% of men): Q2 is the percentage of men who solely own a land which is legally registered with their name or cannot be sold without their signature. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who own land alone is the number of men who say the only type of land they own is the one they own alone or don't share ownership with anyone, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAL.MA.Q3.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who own land alone (% of men): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who own land alone (% of men): Q3 is the percentage of men who solely own a land which is legally registered with their name or cannot be sold without their signature. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who own land alone is the number of men who say the only type of land they own is the one they own alone or don't share ownership with anyone, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAL.MA.Q4.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who own land alone (% of men): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who own land alone (% of men): Q4 is the percentage of men who solely own a land which is legally registered with their name or cannot be sold without their signature. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who own land alone is the number of men who say the only type of land they own is the one they own alone or don't share ownership with anyone, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAL.MA.Q5.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who own land alone (% of men): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who own land alone (% of men): Q5 (highest) is the percentage of men who solely own a land which is legally registered with their name or cannot be sold without their signature. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who own land alone is the number of men who say the only type of land they own is the one they own alone or don't share ownership with anyone, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDAL.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who own land alone (% of men)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who own land alone (% of men) is the percentage of men who solely own a land which is legally registered with their name or cannot be sold without their signature." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who own land alone is the number of men who say the only type of land they own is the one they own alone or don't share ownership with anyone, expressed as percentage of men who have been interviewed and It’s derived by dividing the number of men who responded they alone own a land, which legally registered with their name or can’t be sold without their signature, by total number of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDJT.FE.Q1.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who own land jointly (% of women age 15-49): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who own land jointly (% of women age 15-49): Q1 (lowest) is the percentage of women age 15-49 who only jointly own a land, which legally registered with their name or cannot be sold without their signature, with someone else. \"Only jointly\" implies the woman doesn’t own a land on her own, but instead jointly owns one with someone else. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who own land jointly is the number of women age 15-49 who say the only type of land they own is the one they own with someone esle, expressed as percentage of women age 15-49 who have been interviewed. ‘Ownership’ implies that the land is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the land is recognized as hers and cannot be sold without her signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDJT.FE.Q2.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who own land jointly (% of women age 15-49): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who own land jointly (% of women age 15-49): Q2 is the percentage of women age 15-49 who only jointly own a land, which legally registered with their name or cannot be sold without their signature, with someone else. \"Only jointly\" implies the woman doesn’t own a land on her own, but instead jointly owns one with someone else. 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Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who do not own land is the number of women age 15-49 who say they don’t own any land either alone or jointly with someone else or both, expressed as percentage of women age 15-49 who have been interviewed. ‘Ownership’ implies that the land is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the land is recognized as hers and cannot be sold without her signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDNO.FE.Q5.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who do not own land (% of women age 15-49): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who do not own land (% of women age 15-49): Q5 (highest) is the percentage of women age 15-49 who don’t own any land, which legally registered with their name or cannot be sold without their signature, either solely or jointly with someone else or both. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who do not own land is the number of women age 15-49 who say they don’t own any land either alone or jointly with someone else or both, expressed as percentage of women age 15-49 who have been interviewed. ‘Ownership’ implies that the land is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the land is recognized as hers and cannot be sold without her signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDNO.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Women who do not own land (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who do not own land (% of women age 15-49) is the percentage of women age 15-49 who don’t own any land, which legally registered with their name or cannot be sold without their signature, either solely or jointly with someone else or both." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Women who do not own land is the number of women age 15-49 who say they don’t own any land either alone or jointly with someone else or both, expressed as percentage of women age 15-49 who have been interviewed and It’s derived by dividing the number of women age 15-49 who responded they don’t own any land which legally registered with their name or can’t be sold without their signature by total number of women age 15-49 who have been interviewed. ‘Ownership’ implies that the land is legally registered in the woman’s name or, since official property records do not always exist or are not maintained, the land is recognized as hers and cannot be sold without her signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDNO.MA.Q1.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who do not own land (% of men): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who do not own land (% of men): Q1 (lowest) is the percentage of men who don’t own any land, which legally registered with their name or cannot be sold without their signature, either solely or jointly with someone else or both. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who do not own land is the number of men who say they don’t own any land either alone or jointly with someone else or both, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDNO.MA.Q2.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who do not own land (% of men): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who do not own land (% of men): Q2 is the percentage of men who don’t own any land, which legally registered with their name or cannot be sold without their signature, either solely or jointly with someone else or both. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who do not own land is the number of men who say they don’t own any land either alone or jointly with someone else or both, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDNO.MA.Q3.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who do not own land (% of men): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who do not own land (% of men): Q3 is the percentage of men who don’t own any land, which legally registered with their name or cannot be sold without their signature, either solely or jointly with someone else or both. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who do not own land is the number of men who say they don’t own any land either alone or jointly with someone else or both, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDNO.MA.Q4.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who do not own land (% of men): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who do not own land (% of men): Q4 is the percentage of men who don’t own any land, which legally registered with their name or cannot be sold without their signature, either solely or jointly with someone else or both. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who do not own land is the number of men who say they don’t own any land either alone or jointly with someone else or both, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDNO.MA.Q5.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who do not own land (% of men): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who do not own land (% of men): Q5 (highest) is the percentage of men who don’t own any land, which legally registered with their name or cannot be sold without their signature, either solely or jointly with someone else or both. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who do not own land is the number of men who say they don’t own any land either alone or jointly with someone else or both, expressed as percentage of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.OWN.LDNO.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Ownership of assets, particularly high-value assets, has many beneficial effects for households, including protection against financial ruin. For women in particular, asset ownership is a source of economic empowerment and provides protection in the case of marital dissolution or abandonment. There is increasing evidence that ownership of property by women has positive consequences for women’s empowerment, nutritional and health outcomes, and children’s schooling." }, { "id": "IndicatorName", "value": "Men who do not own land (% of men)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Men who do not own land (% of men) is the percentage of men who don’t own any land, which legally registered with their name or cannot be sold without their signature, either solely or jointly with someone else or both." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Men who do not own land is the number of men who say they don’t own any land either alone or jointly with someone else or both, expressed as percentage of men who have been interviewed and It’s derived by dividing the number of men who responded they don’t own any land which legally registered with their name or can’t be sold without their signature by total number of men who have been interviewed. ‘Ownership’ implies that the land is legally registered in the man's name or, since official property records do not always exist or are not maintained, the land is recognized as his and cannot be sold without his signature or equivalent. ‘land’ includes all agricultural or non-agricultural land. Non-agricultural land refers to rural land that is not used for growing crops, and most land in urban areas." }, { "id": "Topic", "value": "Assets" } ], "source_id": "14" }, { "id": "SG.POP.MIGR.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female migrants (% of international migrant stock)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In estimating the international migrant stock, international migrants have been equated with the foreign-born population whenever this information is available, which is the case in most countries or areas. In most countries lacking data on place of birth, information on the country of citizenship of those enumerated was available, and was used as the basis for the identification of international migrants, thus effectively equating, in these cases, international migrants with foreign citizens. Equating international migrants with foreign citizens when estimating the migrant stock has important shortcomings. In countries where citizenship is conferred on the basis of jus sanguinis, people who were born in the country of residence may be included in the number of international migrants even though they may have never lived abroad. Conversely, persons who were born abroad and who naturalized in their country of residence are excluded from the stock of international migrants when using citizenship as the criterion to define international migrants. Using country of citizenship as the basis for the identification of international migrants has also an impact on the age distribution of international migrants. In countries where citizenship is conferred mainly on the basis of jus sanguinis, children born to international migrants tend to be considered foreign citizens and are thus included in the count of international migrants. Conversely, in countries where citizenship is conferred mainly on the basis of jus soli, children born to international migrants are granted citizenship upon birth and are thus excluded from the migrant stock." }, { "id": "Longdefinition", "value": "Percentage of female migrants out of total international migrant stock. International migrant stock is the number of people born in a country other than that in which they live. It also includes refugees." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Percentage of female migrants out of total international migrant stock" }, { "id": "Source", "value": "United Nations Population Division, Trends in International Migrant Stock: 2020 Revision. Data downloaded as of December 6, 2024." }, { "id": "Statisticalconceptandmethodology", "value": "Most of the data used to estimate the international migrant stock by country or area were obtained from population censuses. Additionally, population registers and nationally representative surveys provided information on the number and composition of international migrants. In estimating the international migrant stock, international migrants have been equated with the foreign-born population whenever this information is available, which is the case in most countries or areas. In most countries lacking data on place of birth, information on the country of citizenship of those enumerated was available, and was used as the basis for the identification of international migrants, thus effectively equating, in these cases, international migrants with foreign citizens. Among the 232 countries or areas included in the 2015 revision of International Migrant Stock, 92 per cent had at least one data source on the total migrant stock since the 2000 census round. For countries or areas with at least two data points, interpolation or extrapolation was used to estimate the migrant stock for the six reference years from 1990 to 2015. To estimate the total migrant stock for countries or areas with only one data source, the growth rates of the total migrant stock in the relevant major area or region were used, where appropriate, to estimate changes in migrant stock levels." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SG.RSX.BRTH.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she has recently given birth (%): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she has recently given birth. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.BRTH.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she has recently given birth (%): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she has recently given birth. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.BRTH.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she has recently given birth (%): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she has recently given birth. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.BRTH.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she has recently given birth (%): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she has recently given birth. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.BRTH.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she has recently given birth (%): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she has recently given birth. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.BRTH.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she has recently given birth (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she has recently given birth." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.NORS.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband for none of the reasons (%): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband for none of the reasons: husband has sexually transmitted disease, husband has sex with other women, recently given birth, tired or not in the mood. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.NORS.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband for none of the reasons (%): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband for none of the reasons: husband has sexually transmitted disease, husband has sex with other women, recently given birth, tired or not in the mood. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.NORS.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband for none of the reasons (%): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband for none of the reasons: husband has sexually transmitted disease, husband has sex with other women, recently given birth, tired or not in the mood. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.NORS.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband for none of the reasons (%): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband for none of the reasons: husband has sexually transmitted disease, husband has sex with other women, recently given birth, tired or not in the mood. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.NORS.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband for none of the reasons (%): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband for none of the reasons: husband has sexually transmitted disease, husband has sex with other women, recently given birth, tired or not in the mood. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.NORS.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband for none of the reasons (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband for none of the reasons: husband has sexually transmitted disease, husband has sex with other women, recently given birth, tired or not in the mood." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.REAS.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband for all of the reasons (%): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband for all of the reasons: husband has sexually transmitted disease, husband has sex with other women, recently given birth, tired or not in the mood. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.REAS.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband for all of the reasons (%): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband for all of the reasons: husband has sexually transmitted disease, husband has sex with other women, recently given birth, tired or not in the mood. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.REAS.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband for all of the reasons (%): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband for all of the reasons: husband has sexually transmitted disease, husband has sex with other women, recently given birth, tired or not in the mood. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.REAS.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband for all of the reasons (%): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband for all of the reasons: husband has sexually transmitted disease, husband has sex with other women, recently given birth, tired or not in the mood. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.REAS.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband for all of the reasons (%): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband for all of the reasons: husband has sexually transmitted disease, husband has sex with other women, recently given birth, tired or not in the mood. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.REAS.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband for all of the reasons (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband for all of the reasons: husband has sexually transmitted disease, husband has sex with other women, recently given birth, tired or not in the mood." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.SXOT.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she knows he has sex with other women (%): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she knows husband has sex with other women. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.SXOT.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she knows he has sex with other women (%): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she knows husband has sex with other women. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.SXOT.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she knows he has sex with other women (%): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she knows husband has sex with other women. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.SXOT.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she knows he has sex with other women (%): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she knows husband has sex with other women. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.SXOT.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she knows he has sex with other women (%): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she knows husband has sex with other women. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.SXOT.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she knows he has sex with other women (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she knows husband has sex with other women." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.TIRD.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she is tired or not in the mood (%): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she is tired or not in the mood. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.TIRD.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she is tired or not in the mood (%): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she is tired or not in the mood. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.TIRD.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she is tired or not in the mood (%): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she is tired or not in the mood. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.TIRD.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she is tired or not in the mood (%): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she is tired or not in the mood. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.TIRD.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she is tired or not in the mood (%): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she is tired or not in the mood. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.TIRD.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she is tired or not in the mood (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she is tired or not in the mood." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.TMDS.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she knows he has sexually transmitted disease (%): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she knows husband has sexually transmitted disease. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.TMDS.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she knows he has sexually transmitted disease (%): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she knows husband has sexually transmitted disease. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.TMDS.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she knows he has sexually transmitted disease (%): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she knows husband has sexually transmitted disease. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.TMDS.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she knows he has sexually transmitted disease (%): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she knows husband has sexually transmitted disease. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.TMDS.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she knows he has sexually transmitted disease (%): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she knows husband has sexually transmitted disease. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.RSX.TMDS.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a wife is justified refusing sex with her husband if she knows he has sexually transmitted disease (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15-49 who believe that a wife is justified in refusing to have sex with her husband if she knows husband has sexually transmitted disease." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.SXL.SX15.OL.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women who initiated sexual intercourse by age 15 (% of women ages 20-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of women ages 20-24 who initiated sexual intercourse by age 15." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.SXL.SX15.OL.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Men who initiated sexual intercourse by age 15 (% of men ages 20-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of men ages 20-24 who initiated sexual intercourse by age 15." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.SXL.SX15.YG.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women who initiated sexual intercourse before age 15 (% of women ages 15-19)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of women ages 15-19 who initiated sexual intercourse before age 15." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.SXL.SX15.YG.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Men who initiated sexual intercourse before age 15 (% of men ages 15-19)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of men ages 15-19 who initiated sexual intercourse before age 15." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.TIM.UWRK.FE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Women often spend disproportionately more time on unpaid domestic and care work than men. This unequal division of responsibilities is correlated with gender differences in economic opportunities, includign low female labor force participation, occupational sex segregation, and earnings diffrentials. The need for a gender balance in the distribution of unpaid domestic and care work has been increasingly recognized and the Sustainable Development Goals address the issue in the target 5.4." }, { "id": "IndicatorName", "value": "Proportion of time spent on unpaid domestic and care work, female (% of 24 hour day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data may not be strictly comparable across countries as the methods and sampling involved for data collection may differ." }, { "id": "Longdefinition", "value": "The average time women spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.4.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "National statistical offices or national database and publications, United Nations (UN), uri: https://unstats.un.org/sdgs/dataportal/database, note: Indicator code from the original source: SH_FPL_INFM; \tIndicator name from the original source: Proportion of time spent on unpaid domestic chores and care work, by sex, age and location (%), publisher: UN Statistics Division, type: Excel" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of time allocated to unpaid domestic and caregiving tasks is determined by dividing the daily average time spent on such activities by the total number of hours in a day (24 hours). The data for this indicator are presented as a daily time proportion. For instance, if women ages 15+ dedicate 10% of their day to unpaid domestic and caregiving work, while men of the same age bracket allocate 1%, this translates to women spending an average of 2.4 hours (or 2 hours and 24 minutes) and men spending 14.4 minutes per day on these tasks.\n\n\n\n\n\n\nTo ascertain the daily average, weekly data are averaged across all seven days.\nStatistical concept(s): This indicator measures the average amount of on unpaid domestic and care work as a proportion in a day. The objective of this indicator is to quantify the time allocation of both women and men to unpaid tasks, thereby recognizing the value of all forms of work, irrespective of monetary compensation. Furthermore, it serves as a gauge for gender equality by revealing the disparity in time spent by women and men on unpaid activities, such as household chores, caregiving, and childcare.\n\n\n\n\n\n\n\n\n\n\n\nThe daily average is derived from a mean calculated over the data collection reference period, which does not imply that individuals allocate the specified amounts of time to these activities every day." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of a 24 hour day" } ], "source_id": "14" }, { "id": "SG.TIM.UWRK.MA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Women often spend disproportionately more time on unpaid domestic and care work than men. This unequal division of responsibilities is correlated with gender differences in economic opportunities, includign low female labor force participation, occupational sex segregation, and earnings diffrentials. The need for a gender balance in the distribution of unpaid domestic and care work has been increasingly recognized and the Sustainable Development Goals address the issue in the target 5.4." }, { "id": "IndicatorName", "value": "Proportion of time spent on unpaid domestic and care work, male (% of 24 hour day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data may not be strictly comparable across countries as the methods and sampling involved for data collection may differ." }, { "id": "Longdefinition", "value": "The average time men spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.4.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "National statistical offices or national database and publications compiled by United Nations Statistics Division., United Nations (UN), uri: https://unstats.un.org/sdgs/dataportal/database, note: Indicator code from the original source: SH_FPL_INFM; \tIndicator name from the original source: Proportion of time spent on unpaid domestic chores and care work, by sex, age and location (%), publisher: UN Statistics Division, type: Excel" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of time allocated to unpaid domestic and caregiving tasks is determined by dividing the daily average time spent on such activities by the total number of hours in a day (24 hours). The data for this indicator are presented as a daily time proportion. For instance, if women ages 15+ dedicate 10% of their day to unpaid domestic and caregiving work, while men of the same age bracket allocate 1%, this translates to women spending an average of 2.4 hours (or 2 hours and 24 minutes) and men spending 14.4 minutes per day on these tasks.\n\n\n\n\n\n\nTo ascertain the daily average, weekly data are averaged across all seven days.\nStatistical concept(s): This indicator measures the average amount of on unpaid domestic and care work as a proportion in a day. The objective of this indicator is to quantify the time allocation of both women and men to unpaid tasks, thereby recognizing the value of all forms of work, irrespective of monetary compensation. Furthermore, it serves as a gauge for gender equality by revealing the disparity in time spent by women and men on unpaid activities, such as household chores, caregiving, and childcare.\n\n\n\n\n\n\n\n\n\n\n\nThe daily average is derived from a mean calculated over the data collection reference period, which does not imply that individuals allocate the specified amounts of time to these activities every day." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of a 24 hour day" } ], "source_id": "14" }, { "id": "SG.VAW.1519.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women who have ever experienced intimate partner violence (modeled estimate, % of ever partnered women ages 15-19)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 15-19 who have ever experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in their lifetime. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.1519.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (modeled estimate, % of ever partnered women ages 15-19)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 15-19 who experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in the last 12 months. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.1549.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women who have ever experienced intimate partner violence (modeled estimate, % of ever partnered women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 15-49 who have ever experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in their lifetime. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.1549.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (modeled estimate, % of ever partnered women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 15-49 who experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in the last 12 months. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.1549.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (% of ever-partnered women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months is the percentage of ever partnered women age 15-49 who are subjected to physical violence, sexual violence or both by a current or former intimate partner in the last 12 months." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.2.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2017" }, { "id": "Source", "value": "United Nations (UN), uri: https://unstats.un.org/sdgs/dataportal/database, note: Indicator code from the original source: VC_VAW_MARR; \tIndicator name from the original source: Proportion of ever-partnered women and girls subjected to physical and/or sexual violence by a current or former intimate partner in the previous 12 months, by age (%)\n\n\n\n\n\n\n\n\n, publisher: UN Statistics Division, type: API;\nGlobal Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/proportion-of-ever-partnered-women-and-girls-aged-15-49-years-subjected-to-physical-and-or-sexual-violence-by-a-current-or-former-intimate-partner-in-the-previous-12-months, publisher: WHO" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of ever-partnered women (ages 15 and above) subjected to any act of physical violece, sexual violence or both divided by a current or former intimate partner in the previous 12 months divided by the number of ever-partnered women (aged 15 years and above) in the population multiplied by 100. The main source of data are from the Demographic and Health Surveys (DHS), specialized surveys on violence against women and crime victimasation surveys.\nStatistical concept(s): Physical violence is defined as acts that can physically hurt the victim, including, but not limited to: being slapped or having something thrown at you that could hurt you; being pushed or shoved; being hit with a fist or something else that could hurt; being kicked, dragged or beaten up; being choked or burnt on purpose; and/or being threatened with or actually having a gun, knife or other weapon used on you. Sexual violence is operationalized as: being physically forced to have sexual intercourse when you do not want to; having sexual intercourse out of fear for what your partner might do or through coercion; and/or being forced to do something sexual that you consider humiliating or degrading (Reference: WHO, Violence Against Women Prevalence Estimates, 2018. https://www.who.int/publications/i/item/9789240022256). \n\n\n\n\n\n\n\n\n\n\n\nCurrent intimate partner includes current or most recent husbands of ever-married women (and men they live with as if married) and the current intimate partner of never-married women. Former husband/intimate partner is a husband (or partner she is living with as if married) other than the current husband (or man she is living with as if married) for currently married women, any intimate partner for never-married women who do not currently have an intimate partner, and a husband/partner other than the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "% of ever-partnered women and girls ages 15 years and older" } ], "source_id": "14" }, { "id": "SG.VAW.15PL.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women who have ever experienced intimate partner violence (modeled estimate, % of ever partnered women ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 15+ who have ever experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in their lifetime. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.15PL.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (modeled estimate, % of ever partnered women ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 15+ who experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in the last 12 months. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.2024.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women who have ever experienced intimate partner violence (modeled estimate, % of ever partnered women ages 20-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 20-24 who have ever experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in their lifetime. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.2024.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (modeled estimate, % of ever partnered women ages 20-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 20-24 who experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in the last 12 months. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.2529.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women who have ever experienced intimate partner violence (modeled estimate, % of ever partnered women ages 25-29)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 25-29 who have ever experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in their lifetime. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.2529.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (modeled estimate, % of ever partnered women ages 25-29)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 25-29 who experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in the last 12 months. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.3034.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women who have ever experienced intimate partner violence (modeled estimate, % of ever partnered women ages 30-34)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 30-34 who have ever experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in their lifetime. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.3034.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (modeled estimate, % of ever partnered women ages 30-34)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 30-34 who experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in the last 12 months. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.3539.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women who have ever experienced intimate partner violence (modeled estimate, % of ever partnered women ages 35-39)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 35-39 who have ever experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in their lifetime. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.3539.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (modeled estimate, % of ever partnered women ages 35-39)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 35-39 who experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in the last 12 months. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.4044.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women who have ever experienced intimate partner violence (modeled estimate, % of ever partnered women ages 40-44)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 40-44 who have ever experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in their lifetime. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.4044.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (modeled estimate, % of ever partnered women ages 40-44)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 40-44 who experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in the last 12 months. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.4549.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women who have ever experienced intimate partner violence (modeled estimate, % of ever partnered women ages 45-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 45-49 who have ever experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in their lifetime. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.4549.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (modeled estimate, % of ever partnered women ages 45-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 45-49 who experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in the last 12 months. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.5054.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women who have ever experienced intimate partner violence (modeled estimate, % of ever partnered women ages 50-54)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 50-54 who have ever experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in their lifetime. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.5054.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (modeled estimate, % of ever partnered women ages 50-54)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 50-54 who experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in the last 12 months. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.5559.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women who have ever experienced intimate partner violence (modeled estimate, % of ever partnered women ages 55-59)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 55-59 who have ever experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in their lifetime. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.5559.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (modeled estimate, % of ever partnered women ages 55-59)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 55-59 who experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in the last 12 months. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.6064.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women who have ever experienced intimate partner violence (modeled estimate, % of ever partnered women ages 60-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 60-64 who have ever experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in their lifetime. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.6064.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (modeled estimate, % of ever partnered women ages 60-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 60-64 who experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in the last 12 months. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.65PL.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women who have ever experienced intimate partner violence (modeled estimate, % of ever partnered women ages 65+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 65+ who have ever experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in their lifetime. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.65PL.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (modeled estimate, % of ever partnered women ages 65+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of ever-partnered women ages 65+ who experienced one or more acts of physical and/or sexual violence by a current or former intimate partner in the last 12 months. Physical violence includes acts such as being slapped, pushed, hit, kicked, dragged, choked, burnt, or threatened with or assaulted using a weapon by an intimate partner. Sexual violence refers to being physically forced or coerced into sexual intercourse or other sexual acts, including when unable to consent, by an intimate partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.AFSX.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Proportion of women who have ever experienced any form of sexual violence (% of women ages 15-49)" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Proportion of women who have ever experienced any form of sexual violence is the percentage of women ages 15-49 who ever experienced sexual violence." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS) Statcompiler (https://www.statcompiler.com/)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview." }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.ARGU.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she argues with him (%): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she argues with him. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.ARGU.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she argues with him (%): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she argues with him. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.ARGU.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she argues with him (%): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she argues with him. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.ARGU.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she argues with him (%): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she argues with him. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.ARGU.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she argues with him (%): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she argues with him. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.ARGU.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she argues with him (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she argues with him." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_ARG; \tIndicator name from the original source: Wife beating justified if she argues with him [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "14" }, { "id": "SG.VAW.BURN.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she burns the food (%): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she burns the food. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.BURN.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she burns the food (%): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she burns the food. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.BURN.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she burns the food (%): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she burns the food. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.BURN.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she burns the food (%): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she burns the food. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.BURN.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she burns the food (%): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she burns the food. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.BURN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she burns the food (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she burns the food." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_BFD; \tIndicator name from the original source: Wife beating justified if she burns the food [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "14" }, { "id": "SG.VAW.GOES.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she goes out without telling him (%): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she goes out without telling him. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.GOES.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she goes out without telling him (%): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she goes out without telling him. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.GOES.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she goes out without telling him (%): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she goes out without telling him. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.GOES.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she goes out without telling him (%): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she goes out without telling him. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.GOES.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she goes out without telling him (%): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she goes out without telling him. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.GOES.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she goes out without telling him (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she goes out without telling him." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_OUT; \tIndicator name from the original source: Wife beating justified if she goes out without telling him [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "14" }, { "id": "SG.VAW.HLPV.NV.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women who never sought help to stop violence, and never told anyone (% of ever-married women ages 15-49 who have ever experienced any physical or sexual violence)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of ever-married women ages 15-49 who have ever experienced any physical or sexual violence who never sought help to stop violence, and never told anyone" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.HLPV.TD.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women who never sought help to stop violence, but told someone (% of ever-married women ages 15-49 who have ever experienced any physical or sexual violence)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of ever-married women ages 15-49 who have ever experienced any physical or sexual violence who never sought help to stop violence, but told someone" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.HLPV.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Proportion of women who have sought help to stop physical or sexual violence (% of ever-married women ages 15-49)" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Proportion of women who have sought help to stop physical or sexual violence is the percentage of ever-married women ages 15-49 who have ever experienced any physical or sexual violence who have sought help to stop violence." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS) Statcompiler (https://www.statcompiler.com/)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview." }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.INJR.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women who have experienced injuries resulting from spousal violence (% of ever-married women ages 15-49 who have ever experienced any physical or sexual violence)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of ever-married women ages 15-49 who have ever experienced spousal physical or sexual violence suffered one or more of these injuries: deep wounds, broken bones, broken teeth, eye injuries, sprains, dislocations, or burns, cuts, bruises or aches." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.IPCB.NV.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women whose husband or partner has never demonstrated controlling behaviors (% of ever-married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of ever-married women ages 15-49 whose husband or partner never demonstrated controlling behaviors" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.IPCB.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women whose husband or partner has ever demonstrated controlling behaviors (% of ever-married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of ever-married women ages 15-49 whose husband or partner has ever demonstrated 3 or more of the specific behaviors from the following: jealous or angry if she talks to other men; accuses her of being unfaithful; not permit her to meet her female friends; tries to limit her contact with her family; insists on knowing where she is at all times; does not trust her with any money" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.IPEV.LT.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women who have ever experienced emotional violence committed by their husband/partner (% of ever-married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of ever-married women ages 15-49 who have ever experienced emotional violence committed by their husband or partner" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.IPEV.LY.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women who have experienced emotional violence committed by their husband/partner in the 12 months (% of ever-married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of ever-married women ages 15-49 who have experienced emotional violence committed by their husband/partner in the 12 months preceding the survey" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.IPPV.LT.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women who have ever experienced physical violence committed by their husband/partner (% of ever-married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of ever-married women ages 15-49 who have ever experienced physical violence committed by their husband or partner" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.IPPV.LY.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women who have experienced physical violence committed by their husband/partner in the 12 months (% of ever-married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of ever-married women ages 15-49 who have experienced physical violence committed by their husband/partner in the 12 months preceding the survey" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.IPSV.LT.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women who have ever experienced sexual violence committed by their husband/partner (% of ever-married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of ever-married women ages 15-49 who have ever experienced sexual violence committed by their husband or partner" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.IPSV.LY.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women who have experienced sexual violence committed by their husband/partner in the 12 months (% of ever-married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of ever-married women ages 15-49 who have experienced sexual violence committed by their husband/partner in the 12 months preceding the survey" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.IPVE.BM.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women whose first experience of spousal physical or sexual violence was before marriage (% of currently married women age 15-49 who have been married only once)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of currently married women ages 15-49 who have been married only once whose first experience of spousal physical or sexual violence was before marriage" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.IPVE.M10.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women whose first experience of spousal physical or sexual violence was within ten years of marriage (% of currently married women age 15-49 who have been married only once)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of currently married women ages 15-49 who have been married only once whose first experience of spousal physical or sexual violence was within ten years of marriage" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.IPVE.M2.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women whose first experience of spousal physical or sexual violence was within two years of marriage (% of currently married women age 15-49 who have been married only once)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of currently married women ages 15-49 who have been married only once whose first experience of spousal physical or sexual violence was within two years of marriage" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.IPVE.M5.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women whose first experience of spousal physical or sexual violence was within five years of marriage (% of currently married women age 15-49 who have been married only once)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of currently married women ages 15-49 who have been married only once whose first experience of spousal physical or sexual violence was within five years of marriage" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.IPVE.NV.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women who have not experienced spousal physical or sexual violence (% of currently married women age 15-49 who have been married only once)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of currently married women ages 15-49 who have been married only once and have not experienced spousal physical or sexual violence" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.IPVE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Proportion of women who have ever experienced intimate partner violence (% of ever-partnered women ages 15-49)" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Proportion of women who have ever experienced intimate partner violence is the percentage of ever-married women (ages 15 - 49) who have ever experienced physical or sexual violence committed by their husband or partner." }, { "id": "Source", "value": "Demographic and Health Surveys (DHS) Statcompiler (https://www.statcompiler.com/)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.NEGL.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she neglects the children (%): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she neglects the children. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.NEGL.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she neglects the children (%): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she neglects the children. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.NEGL.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she neglects the children (%): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she neglects the children. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.NEGL.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she neglects the children (%): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she neglects the children. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.NEGL.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she neglects the children (%): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she neglects the children. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.NEGL.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "test" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she neglects the children (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she neglects the children." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_NEG; \tIndicator name from the original source: Wife beating justified if she neglects the children [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.1519.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in their lifetime (modeled estimate, % of women ages 15-19)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 15-19 who have ever experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) since age 15, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.1519.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in the last 12 months (modeled estimate, % of women ages 15-19)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 15-19 who experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) in the last 12 months, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.1549.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in their lifetime (modeled estimate, % of women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 15-49 who have ever experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) since age 15, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.1549.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in the last 12 months (modeled estimate, % of women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 15-49 who experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) in the last 12 months, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.15PL.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in their lifetime (modeled estimate, % of women ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 15+ who have ever experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) since age 15, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.15PL.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in the last 12 months (modeled estimate, % of women ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 15+ who experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) in the last 12 months, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.2024.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in their lifetime (modeled estimate, % of women ages 20-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 20-24 who have ever experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) since age 15, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.2024.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in the last 12 months (modeled estimate, % of women ages 20-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 20-24 who experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) in the last 12 months, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.2529.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in their lifetime (modeled estimate, % of women ages 25-29)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 25-29 who have ever experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) since age 15, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.2529.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in the last 12 months (modeled estimate, % of women ages 25-29)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 25-29 who experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) in the last 12 months, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.3034.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in their lifetime (modeled estimate, % of women ages 30-34)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 30-34 who have ever experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) since age 15, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.3034.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in the last 12 months (modeled estimate, % of women ages 30-34)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 30-34 who experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) in the last 12 months, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.3539.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in their lifetime (modeled estimate, % of women ages 35-39)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 35-39 who have ever experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) since age 15, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.3539.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in the last 12 months (modeled estimate, % of women ages 35-39)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 35-39 who experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) in the last 12 months, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.4044.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in their lifetime (modeled estimate, % of women ages 40-44)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 40-44 who have ever experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) since age 15, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.4044.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in the last 12 months (modeled estimate, % of women ages 40-44)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 40-44 who experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) in the last 12 months, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.4549.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in their lifetime (modeled estimate, % of women ages 45-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 45-49 who have ever experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) since age 15, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.4549.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in the last 12 months (modeled estimate, % of women ages 45-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 45-49 who experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) in the last 12 months, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.5054.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in their lifetime (modeled estimate, % of women ages 50-54)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 50-54 who have ever experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) since age 15, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.5054.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in the last 12 months (modeled estimate, % of women ages 50-54)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 50-54 who experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) in the last 12 months, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.5559.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in their lifetime (modeled estimate, % of women ages 55-59)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 55-59 who have ever experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) since age 15, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.5559.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in the last 12 months (modeled estimate, % of women ages 55-59)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 55-59 who experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) in the last 12 months, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.6064.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in their lifetime (modeled estimate, % of women ages 60-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 60-64 who have ever experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) since age 15, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.6064.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in the last 12 months (modeled estimate, % of women ages 60-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 60-64 who experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) in the last 12 months, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.65PL.LT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in their lifetime (modeled estimate, % of women ages 65+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 65+ who have ever experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) since age 15, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.NPSV.65PL.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is a major violation of human rights and a critical public health and development challenge. Both intimate partner violence and non-partner sexual violence have severe and lasting consequences for women’s physical and mental health, reproductive health, educational attainment, and economic participation, and impose significant social and economic costs on households and societies. These forms of violence undermine gender equality and women’s empowerment and hinder progress toward inclusive and sustainable development. Monitoring the prevalence of violence against women is essential for tracking progress toward Sustainable Development Goal (SDG) target 5.2, which calls for the elimination of all forms of violence against women and girls in public and private spheres, and for informing evidence-based policies, legal reforms, prevention strategies, and survivor support services." }, { "id": "IndicatorName", "value": "Proportion of women subjected to sexual violence by persons other than an intimate partner in the last 12 months (modeled estimate, % of women ages 65+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of intimate partner violence and non-partner sexual violence are subject to substantial underreporting due to stigma, fear of retaliation, social norms, and concerns about confidentiality and safety. Survey instruments and question wording vary across countries and over time, which may affect comparability despite harmonization efforts. Cultural differences in the understanding and reporting of violence further complicate cross-country comparisons. Data availability is uneven, particularly in low-income and fragile settings, necessitating the use of statistical modeling; modeled estimates depend on the quality and coverage of underlying data and the assumptions of the modeling approach. Recall bias may affect lifetime prevalence estimates, while shorter reference periods may miss episodic or infrequent experiences. These indicators capture prevalence but do not reflect the severity, frequency, or contextual circumstances of violence." }, { "id": "Longdefinition", "value": "Proportion of women ages 65+ who experienced one or more acts of sexual violence by a non-partner (that is, someone other than a current or former husband or male intimate partner) in the last 12 months, calculated as the share of women reporting such experience among all women in the same age group (all women at risk). Sexual violence includes being forced, coerced, threatened, or intimidated to perform any unwanted sexual act. This may include rape, attempted rape, unwanted sexual touching, or non-contact forms of sexual violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Data on intimate partner violence (IPV) and non-partner sexual violence (NPSV) are compiled by the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) using a multilevel regression modeling framework. The main sources of data include specialized surveys on violence against women (such as those using the WHO multi-country study instrument and methodology and the European Union-wide survey on violence against women) and modules on violence against women included in larger national health surveys, such as Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS). A smaller number of data points are drawn from other surveys, including national crime victimization surveys and Multiple Indicator Cluster Surveys (MICS).\n\nAvailable survey data are harmonized using standardized inclusion criteria, including population-based sampling, representativeness at national or subnational level, and acts-based measures of violence. Adjustments are made for differences in age groups and definitions of violence to improve comparability across sources. Modeled estimates are produced to address gaps in data availability and to generate comparable country, regional, and global estimates over time. Estimates are updated as new survey data become available.\n\nFor details on the estimation methodology, see: Violence against women prevalence estimates, 2023: global, regional and national prevalence estimates for intimate partner violence against women and non-partner sexual violence against women (World Health Organization, 2025)." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.REAS.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife (any of five reasons) (%): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner for any of the following five reasons: argues with him; refuses to have sex; burns the food; goes out without telling him; or when she neglects the children. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.REAS.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife (any of five reasons) (%): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner for any of the following five reasons: argues with him; refuses to have sex; burns the food; goes out without telling him; or when she neglects the children. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.REAS.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife (any of five reasons) (%): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner for any of the following five reasons: argues with him; refuses to have sex; burns the food; goes out without telling him; or when she neglects the children. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.REAS.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife (any of five reasons) (%): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner for any of the following five reasons: argues with him; refuses to have sex; burns the food; goes out without telling him; or when she neglects the children. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.REAS.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife (any of five reasons) (%): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner for any of the following five reasons: argues with him; refuses to have sex; burns the food; goes out without telling him; or when she neglects the children. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.REAS.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife (any of five reasons) (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner for any of the following five reasons: argues with him; refuses to have sex; burns the food; goes out without telling him; or when she neglects the children." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_AGR; \tIndicator name from the original source: Wife beating justified for at least one specific reason [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "14" }, { "id": "SG.VAW.REFU.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she refuses sex with him (%): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she refuses sex with him. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.REFU.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she refuses sex with him (%): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she refuses sex with him. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.REFU.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she refuses sex with him (%): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she refuses sex with him. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.REFU.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she refuses sex with him (%): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she refuses sex with him. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.REFU.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she refuses sex with him (%): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she refuses sex with him. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys" }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SG.VAW.REFU.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she refuses sex with him (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she refuses sex with him." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_REF; \tIndicator name from the original source: Wife beating justified if she refuses to have sex with him [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "14" }, { "id": "SG.VAW.SX15.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women who experienced first sexual violence before age 15 (% of women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who first experienced sexual violence before exact age 15" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.SX18.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women who experienced first sexual violence before age 18 (% of women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who first experienced sexual violence before exact age 18" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SG.VAW.SX22.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Women who experienced first sexual violence before age 22 (% of women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who first experienced sexual violence before exact age 22" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.ALC.PCAP.FE.LI", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions." }, { "id": "IndicatorName", "value": "Total alcohol consumption per capita, female (liters of pure alcohol, projected estimates, female 15+ years of age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.5.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2020" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.ALC.PCAP.LI", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions." }, { "id": "IndicatorName", "value": "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.5.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2020" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.ALC.PCAP.MA.LI", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions." }, { "id": "IndicatorName", "value": "Total alcohol consumption per capita, male (liters of pure alcohol, projected estimates, male 15+ years of age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.5.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2020" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.ANM.ALLW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of anemia among women of reproductive age (% of women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of anemia among women of reproductive age refers to the combined prevalence of both non-pregnant with haemoglobin levels below 12 g/dL and pregnant women with haemoglobin levels below 11 g/dL." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on the prevalence of anaemia and/or mean haemoglobin levels in women of reproductive age, collected between 1995 and 2019, were obtained from 408 population-representative data sources across 124 countries worldwide. A Bayesian hierarchical mixture model was employed to estimate haemoglobin distributions, systematically addressing missing data, non-linear time trends, and the representativeness of data sources. Full details on data sources are available on the GHO Anaemia page. Detailed information on the statistical methods can be found in the following reference: Finucane MM, Paciorek CJ, Stevens GA EM. Semiparametric Bayesian density estimation with disparate data sources: a meta-analysis of global childhood undernutrition. J Am Stat Assoc. 2015;110(511):889–901." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent of women ages 15-49" } ], "source_id": "14" }, { "id": "SH.ANM.NPRG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of anemia among non-pregnant women (% of women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of anemia, non-pregnant women, is the percentage of non-pregnant women whose hemoglobin level is less than 120 grams per liter at sea level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on the prevalence of anaemia and/or mean haemoglobin levels in women of reproductive age, collected between 1995 and 2019, were obtained from 408 population-representative data sources across 124 countries worldwide. A Bayesian hierarchical mixture model was employed to estimate haemoglobin distributions, systematically addressing missing data, non-linear time trends, and the representativeness of data sources. Full details on data sources are available on the GHO Anaemia page. Detailed information on the statistical methods can be found in the following reference: Finucane MM, Paciorek CJ, Stevens GA EM. Semiparametric Bayesian density estimation with disparate data sources: a meta-analysis of global childhood undernutrition. J Am Stat Assoc. 2015;110(511):889–901." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent of women ages 15-49" } ], "source_id": "14" }, { "id": "SH.CON.AIDS.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Condom use at last high-risk sex, adult female (% ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Condom use at last high-risk sex, female is the percentage of the female population ages 15-49 who used a condom at last intercourse with a non-marital and non-cohabiting sexual partner in the last 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys, and UNAIDS." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.CON.AIDS.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Condom use at last high-risk sex, adult male (% ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Condom use at last high-risk sex, male is the percentage of the male population ages 15-49 who used a condom at last intercourse with a non-marital and non-cohabiting sexual partner in the last 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys, and UNAIDS." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.COMM.0004.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 0-4, female (% of female population ages 0-4)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of female deaths ages 0-4 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all female deaths ages 0-4, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.COMM.0004.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 0-4, male (% of male population ages 0-4)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of male deaths ages 0-4 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all male deaths ages 0-4, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.COMM.0004.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 0-4 (% of population ages 0-4)" }, { "id": "Longdefinition", "value": "Number of deaths ages 0-4 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all deaths ages 0-4, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.COMM.0514.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 5-14, female (% of female population ages 5-14)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of female deaths ages 5-14 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all female deaths ages 5-14, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.COMM.0514.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 5-14, male (% of male population ages 5-14)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of male deaths ages 5-14 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all male deaths ages 5-14, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.COMM.0514.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 5-14 (% of population ages 5-14)" }, { "id": "Longdefinition", "value": "Number of deaths ages 5-14 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all deaths ages 5-14, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.COMM.1559.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 15-59, female (% of female population ages 15-59)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of female deaths ages 15-59 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all female deaths ages 15-59, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.COMM.1559.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 15-59, male (% of male population ages 15-59)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of male deaths ages 15-59 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all male deaths ages 15-59, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.COMM.1559.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 15-59 (% of population ages 15-59)" }, { "id": "Longdefinition", "value": "Number of deaths ages 15-59 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all deaths ages 15-59, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.COMM.60UP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 60+, female (% of female population ages 60+)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of female deaths ages 60+ due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all female deaths ages 60+, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.COMM.60UP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 60+, male (% of male population ages 60+)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of male deaths ages 60+ due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all male deaths ages 60+, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.COMM.60UP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 60+ (% of population ages 60+)" }, { "id": "Longdefinition", "value": "Number of deaths ages 60+ due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all deaths ages 60+, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.COMM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, female (% of female population)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of female deaths due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all female deaths, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.COMM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, male (% of male population)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of male deaths due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all male deaths, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.COMM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Cause of death refers to the share of all deaths for all ages by underlying causes. Communicable diseases and maternal, prenatal and nutrition conditions include infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2019" }, { "id": "Source", "value": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates: Deaths by Cause, Age, Sex, by Country and by Region" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.IMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of infant deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of infants dying before reaching one year of age." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "14" }, { "id": "SH.DTH.IMRT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of infant deaths, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of female infants dying before reaching one year of age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.IMRT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of infant deaths, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of male infants dying before reaching one year of age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.INJR.0004.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by injury, ages 0-4, female (% of female population ages 0-4)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of female deaths ages 0-4 due to injury divided by number of all female deaths ages 0-4, expressed by percentage. Injury includes unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.INJR.0004.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by injury, ages 0-4, male (% of male population ages 0-4)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of male deaths ages 0-4 due to injury divided by number of all male deaths ages 0-4, expressed by percentage. Injury includes unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.INJR.0004.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by injury, ages 0-4 (% of population ages 0-4)" }, { "id": "Longdefinition", "value": "Number of deaths ages 0-4 due to injury divided by number of all deaths ages 0-4, expressed by percentage. Injury includes unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.INJR.0514.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by injury, ages 5-14, female (% of female population ages 5-14)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of female deaths ages 5-14 due to injury divided by number of all female deaths ages 5-14, expressed by percentage. Injury includes unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.INJR.0514.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by injury, ages 5-14, male (% of male population ages 5-14)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of male deaths ages 5-14 due to injury divided by number of all male deaths ages 5-14, expressed by percentage. Injury includes unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.INJR.0514.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by injury, ages 5-14 (% of population ages 5-14)" }, { "id": "Longdefinition", "value": "Number of deaths ages 5-14 due to injury divided by number of all deaths ages 5-14, expressed by percentage. Injury includes unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.INJR.1559.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by injury, ages 15-59, female (% of female population ages 15-59)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of female deaths ages 15-59 due to injury divided by number of all female deaths ages 15-59, expressed by percentage. Injury includes unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.INJR.1559.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by injury, ages 15-59, male (% of male population ages 15-59)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of male deaths ages 15-59 due to injury divided by number of all male deaths ages 15-59, expressed by percentage. Injury includes unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.INJR.1559.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by injury, ages 15-59 (% of population ages 15-59)" }, { "id": "Longdefinition", "value": "Number of deaths ages 15-59 due to injury divided by number of all deaths ages 15-59, expressed by percentage. Injury includes unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.INJR.60UP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by injury, ages 60+, female (% of female population ages 60+)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in low-and middle-income countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of female deaths ages 60+ due to injury divided by number of all female deaths ages 60+, expressed by percentage. Injury includes unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.INJR.60UP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by injury, ages 60+, male (% of male population ages 60+)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in low-and middle-income countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of male deaths ages 60+ due to injury divided by number of all male deaths ages 60+, expressed by percentage. Injury includes unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.INJR.60UP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by injury, ages 60+ (% of population ages 60+)" }, { "id": "Longdefinition", "value": "Number of deaths ages 60+ due to injury divided by number of all deaths ages 60+, expressed by percentage. Injury includes unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.INJR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by injury, female (% of female population)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in low-and middle-income countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of female deaths due to injury divided by number of all female deaths, expressed by percentage. Injury includes unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.INJR.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by injury, male (% of male population)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in low-and middle-income countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of male deaths ages due to injury divided by number of all male deaths ages, expressed by percentage. Injury includes unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.INJR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Cause of death, by injury (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Cause of death refers to the share of all deaths for all ages by underlying causes. Injuries include unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2019" }, { "id": "Source", "value": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates: Deaths by Cause, Age, Sex, by Country and by Region" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in under-covered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.MORT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of under-five deaths, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of female children dying before reaching age five." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.MORT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of under-five deaths, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of male children dying before reaching age five." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.NCOM.0004.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases, ages 0-4, female (% of female population ages 0-4)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in low-and middle-income countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of female deaths ages 0-4 due to non-communicable diseases divided by number of all female deaths ages 0-4, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.NCOM.0004.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases, ages 0-4, male (% of male population ages 0-4)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in low-and middle-income countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of male deaths ages 0-4 due to non-communicable diseases divided by number of all male deaths ages 0-4, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.NCOM.0004.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases, ages 0-4 (% of population ages 0-4)" }, { "id": "Longdefinition", "value": "Number of deaths ages 0-4 due to non-communicable diseases divided by number of all deaths ages 0-4, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.NCOM.0514.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases, ages 5-14, female (% of female population ages 5-14)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in low-and middle-income countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of female deaths ages 5-14 due to non-communicable diseases divided by number of all female deaths ages 5-14, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.NCOM.0514.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases, ages 5-14, male (% of male population ages 5-14)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in low-and middle-income countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of male deaths ages 5-14 due to non-communicable diseases divided by number of all male deaths ages 5-14, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.NCOM.0514.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases, ages 5-14 (% of population ages 5-14)" }, { "id": "Longdefinition", "value": "Number of deaths ages 5-14 due to non-communicable diseases divided by number of all deaths ages 5-14, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.NCOM.1559.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases, ages 15-59, female (% of female population ages 15-59)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in low-and middle-income countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of female deaths ages 15-59 due to non-communicable diseases divided by number of all female deaths ages 15-59, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.NCOM.1559.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases, ages 15-59, male (% of male population ages 15-59)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in low-and middle-income countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of male deaths ages 15-59 due to non-communicable diseases divided by number of all male deaths ages 15-59, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.NCOM.1559.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases, ages 15-59 (% of population ages 15-59)" }, { "id": "Longdefinition", "value": "Number of deaths ages 15-59 due to non-communicable diseases divided by number of all deaths ages 15-59 expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.NCOM.60UP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases, ages 60+, female (% of female population ages 60+)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in low-and middle-income countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of female deaths ages 60+ due to non-communicable diseases divided by number of all female deaths ages 60+, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.NCOM.60UP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases, ages 60+, male (% of male population ages 60+)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in low-and middle-income countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of male deaths ages 60+ due to non-communicable diseases divided by number of all male deaths ages 60+, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.NCOM.60UP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases, ages 60+ (% of population ages 60+)" }, { "id": "Longdefinition", "value": "Number of deaths ages 60+ due to non-communicable diseases divided by number of all deaths ages 60+, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.NCOM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases, female (% of female population)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in low-and middle-income countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of female deaths due to non-communicable diseases divided by number of all female deaths, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.NCOM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Measuring the number of deaths and their causes helps to improve health services and reduce preventable deaths.  Specifically, these data are used to monitor progress towards the health-related targets within the Sustainable Development Goals (SDGs). For example, SDG Target 3.4 calls for reducing premature mortality from non-communicable diseases. Disease burden from non-communicable diseases is becoming a serious problem in many low-and middle-income countries . Assessing the cause of deaths data, disaggregated by age, sex and geographic location, is essential to identify inequities in health." }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases, male (% of male population)" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in low-and middle-income countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Number of male deaths ages due to non-communicable diseases divided by number of all male deaths ages, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death" }, { "id": "Statisticalconceptandmethodology", "value": "Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.NCOM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Cause of death refers to the share of all deaths for all ages by underlying causes. Non-communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2019" }, { "id": "Source", "value": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates: Deaths by Cause, Age, Sex, by Country and by Region" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DTH.STLB", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Stillbirths are an important but often neglected global health problem. Sub-Saharan Africa and South Asia bear the greatest burden of these deaths. \n\nThe costs of stillbirths go beyond the loss of life and include psychological cost such as maternal depression, financial costs to parents as well as long-term economic costs to society. The burden on families, especially women, is severe and long lasting, yet stigma and taboo hide this burden even in high-income countries. The progress in lowering the stillbirth rate in the past two decades is much slower than the reduction in mortality of children aged under 5 years old. This could be due to a variety of reasons including absence or poor quality of care during pregnancy and birth, lack of investment in preventative interventions and the health workforce, lack of social recognition of stillbirths as a burden on families, challenges with measurements and major data gaps, absence of global and national leadership, and no established global targets." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of stillbirths" }, { "id": "Limitationsandexceptions", "value": "Data availability and data quality is uneven among countries. Adequate stillbirth data are lacking in many low and middle-income countries. These countries do not have a functioning health information system or civil registration vital statistics (CRVS) system to count or capture stillbirths, and in others stillbirths are excluded from routine registration despite the existence of functioning systems. In such settings, household surveys provide important information on child mortality, but most surveys have substantial data quality issues for stillbirth." }, { "id": "Longdefinition", "value": "Number of fetal deaths at 28 weeks or more of gestation" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The UN IGME’s approach to estimate stillbirth rates (SBR) includes the following steps:\n1.\tCompile all available stillbirth data at a country level, derived from administrative sources, household surveys or population-based studies. \n2.\tEvaluate data in accordance with the data quality criteria and produce adjustment or recalculation by applying standardized definitions. \n3.\tEstimate global and country-specific trends of stillbirth rates using a smoothing time series model, supplemented with covariates associated with stillbirth rates. This process averages empirical data on stillbirths derived from the different sources for a given country. In the case of countries with sparse or no data, the identified covariates associated with stillbirth will inform the trend in stillbirth rate." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DYN.AIDS.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Women's share of population ages 15+ living with HIV (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV is the percentage of people who are infected with HIV. Female rate is as a percentage of the total population ages 15+ who are living with HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated as the number of women aged 15 and older living with HIV divided by the total number of people aged 15 and older living with HIV. Estimates of people living with HIV are produced by UNAIDS using a common modelling framework (Spectrum), which integrates country-reported HIV surveillance data, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators.\n\n\n\nReference: Annex 1. Methods for deriving UNAIDS HIV estimates, 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform:\n\nhttps://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.DYN.MORT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, under-5 (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is the Sustainable Development Goal indicator 3.2.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org, publisher: UNICEF, WHO, World Bank, United Nations Population Division;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "14" }, { "id": "SH.DYN.MORT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, under-5, female (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "14" }, { "id": "SH.DYN.MORT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, under-5, male (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "14" }, { "id": "SH.DYN.NCOM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates.\n\n\nThe current estimates for 2020 and 2021 are likely underestimated in countries where high-quality vital registration data was lacking at the time of GHE2021 production. This is because the modeled estimates cannot fully account for deaths from the four major NCDs indirectly attributed to the COVID-19 pandemic. Therefore, the data for 2020 and 2021 should be interpreted with caution." }, { "id": "Longdefinition", "value": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of death between the exact ages of 30 and 70 is calculated using cause-specific mortality rates for each 5-year age group, applying standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE). These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent of females ages 30 years old" } ], "source_id": "14" }, { "id": "SH.DYN.NCOM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of death between the exact ages of 30 and 70 is calculated using cause-specific mortality rates for each 5-year age group, applying standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE). These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent of males ages 30 years old" } ], "source_id": "14" }, { "id": "SH.DYN.NCOM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS)." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.4.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of death between the exact ages of 30 and 70 is calculated using cause-specific mortality rates for each 5-year age group, applying standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE). These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent of people ages 30 years old" } ], "source_id": "14" }, { "id": "SH.DYN.STLB", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Stillbirths are an important but often neglected global health problem. Sub-Saharan Africa and South Asia bear the greatest burden of these deaths. \n\nThe costs of stillbirths go beyond the loss of life and include psychological cost such as maternal depression, financial costs to parents as well as long-term economic costs to society. The burden on families, especially women, is severe and long lasting, yet stigma and taboo hide this burden even in high-income countries. The progress in lowering the stillbirth rate in the past two decades is much slower than the reduction in mortality of children aged under 5 years old. This could be due to a variety of reasons including absence or poor quality of care during pregnancy and birth, lack of investment in preventative interventions and the health workforce, lack of social recognition of stillbirths as a burden on families, challenges with measurements and major data gaps, absence of global and national leadership, and no established global targets." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Stillbirth rate (per 1,000 total births)" }, { "id": "Limitationsandexceptions", "value": "Data availability and data quality is uneven among countries. Adequate stillbirth data are lacking in many low and middle-income countries. These countries do not have a functioning health information system or civil registration vital statistics (CRVS) system to count or capture stillbirths, and in others stillbirths are excluded from routine registration despite the existence of functioning systems. In such settings, household surveys provide important information on child mortality, but most surveys have substantial data quality issues for stillbirth." }, { "id": "Longdefinition", "value": "Stillbirth rate is the number of fetal deaths at 28 weeks or more of gestation per 1,000 total births. Total birth is the sum of stillbirths (as just defined) and live births." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The UN IGME’s approach to estimate stillbirth rates (SBR) includes the following steps:\n1.\tCompile all available stillbirth data at a country level, derived from administrative sources, household surveys or population-based studies. \n2.\tEvaluate data in accordance with the data quality criteria and produce adjustment or recalculation by applying standardized definitions. \n3.\tEstimate global and country-specific trends of stillbirth rates using a smoothing time series model, supplemented with covariates associated with stillbirth rates. This process averages empirical data on stillbirths derived from the different sources for a given country. In the case of countries with sparse or no data, the identified covariates associated with stillbirth will inform the trend in stillbirth rate." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.FPL.KNMD.AL.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "To decide freely and responsibly the number of and spacing between their children and to have the information on family planning has been internationally recognized as the basic right for couples and individuals. Having knowledge about contraceptive methods helps couples and individuals to choose safe and effective methods." }, { "id": "IndicatorName", "value": "Knowledge of any modern method of contraception (% of all women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of all women ages 15-49 who know at least one of modern methods of contraception. Modern methods of contraception include female and male sterilization, contraceptive pills, intra-uterine device (IUD), injectables, implants, male and female condoms, diaphragm, contraceptive foam and contraceptive jelly female condom, lactational amenorrhea method (LAM), Standard days method (SDM), and emergency contraception." }, { "id": "Notesfromoriginalsource", "value": "Breastfeeding, prolonged breastfeeding, prolonged abstinence are not considered contraceptive methods in themselves." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS) (https://www.statcompiler.com/)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.FPL.KNMD.AL.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "To decide freely and responsibly the number of and spacing between their children and to have the information on family planning has been internationally recognized as the basic right for couples and individuals. Having knowledge about contraceptive methods helps couples and individuals to choose safe and effective methods." }, { "id": "IndicatorName", "value": "Knowledge of any modern method of contraception (% of all men)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of all men who know at least one of modern methods of contraception. Modern methods of contraception include female and male sterilization, contraceptive pills, intra-uterine device (IUD), injectables, implants, male and female condoms, diaphragm, contraceptive foam and contraceptive jelly female condom, lactational amenorrhea method (LAM), Standard days method (SDM), and emergency contraception. The age range of men depends on surveys. In most DHS surveys, men ages 15-49, 15-54, or 15-59 are eligible for individual interviews." }, { "id": "Notesfromoriginalsource", "value": "Breastfeeding, prolonged breastfeeding, prolonged abstinence are not considered contraceptive methods in themselves." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS) (https://www.statcompiler.com/)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.FPL.KNMD.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "To decide freely and responsibly the number of and spacing between their children and to have the information on family planning has been internationally recognized as the basic right for couples and individuals. Having knowledge about contraceptive methods helps couples and individuals to choose safe and effective methods." }, { "id": "IndicatorName", "value": "Knowledge of any modern method of contraception (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of currently married or in union women ages 15-49 who know at least one of modern methods of contraception. Modern methods of contraception include female and male sterilization, contraceptive pills, intra-uterine device (IUD), injectables, implants, male and female condoms, diaphragm, contraceptive foam and contraceptive jelly female condom, lactational amenorrhea method (LAM), Standard days method (SDM), and emergency contraception." }, { "id": "Notesfromoriginalsource", "value": "Breastfeeding, prolonged breastfeeding, prolonged abstinence are not considered contraceptive methods in themselves." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS) (https://www.statcompiler.com/)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.FPL.KNMD.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "To decide freely and responsibly the number of and spacing between their children and to have the information on family planning has been internationally recognized as the basic right for couples and individuals. Having knowledge about contraceptive methods helps couples and individuals to choose safe and effective methods." }, { "id": "IndicatorName", "value": "Knowledge of any modern method of contraception (% of married men)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of currently married or in union men who know at least one of modern methods of contraception. Modern methods of contraception include female and male sterilization, contraceptive pills, intra-uterine device (IUD), injectables, implants, male and female condoms, diaphragm, contraceptive foam and contraceptive jelly female condom, lactational amenorrhea method (LAM), Standard days method (SDM), and emergency contraception. In most DHS surveys, men ages 15-49, 15-54, or 15-59 are eligible for individual interviews." }, { "id": "Notesfromoriginalsource", "value": "Breastfeeding, prolonged breastfeeding, prolonged abstinence are not considered contraceptive methods in themselves." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS) (https://www.statcompiler.com/)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.FPL.KNOW.AL.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "To decide freely and responsibly the number of and spacing between their children and to have the information on family planning has been internationally recognized as the basic right for couples and individuals. Having knowledge about contraceptive methods helps couples and individuals to choose safe and effective methods." }, { "id": "IndicatorName", "value": "Knowledge of any method of contraception (% of all women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of all women ages 15-49 who know any method of contraception. Modern methods of contraception include female and male sterilization, contraceptive pills, intra-uterine device (IUD), injectables, implants, male and female condoms, diaphragm, contraceptive foam and contraceptive jelly female condom, lactational amenorrhea method (LAM), Standard days method (SDM), and emergency contraception. Traditional methods includes periodic abstinence (rhythm, calendar method) and withdrawal." }, { "id": "Notesfromoriginalsource", "value": "Breastfeeding, prolonged breastfeeding, prolonged abstinence are not considered contraceptive methods in themselves." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS) (https://www.statcompiler.com/)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.FPL.KNOW.AL.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "To decide freely and responsibly the number of and spacing between their children and to have the information on family planning has been internationally recognized as the basic right for couples and individuals. Having knowledge about contraceptive methods helps couples and individuals to choose safe and effective methods." }, { "id": "IndicatorName", "value": "Knowledge of any method of contraception (% of all men)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of all men who know any method of contraception. Modern methods of contraception include female and male sterilization, contraceptive pills, intra-uterine device (IUD), injectables, implants, male and female condoms, diaphragm, contraceptive foam and contraceptive jelly female condom, lactational amenorrhea method (LAM), Standard days method (SDM), and emergency contraception. Traditional methods includes periodic abstinence (rhythm, calendar method) and withdrawal. The age range of men depends on surveys. In most DHS surveys, men ages 15-49, 15-54, or 15-59 are eligible for individual interviews." }, { "id": "Notesfromoriginalsource", "value": "Breastfeeding, prolonged breastfeeding, prolonged abstinence are not considered contraceptive methods in themselves." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS) (https://www.statcompiler.com/)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.FPL.KNOW.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "To decide freely and responsibly the number of and spacing between their children and to have the information on family planning has been internationally recognized as the basic right for couples and individuals. Having knowledge about contraceptive methods helps couples and individuals to choose safe and effective methods." }, { "id": "IndicatorName", "value": "Knowledge of any method of contraception (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of currently married or in union women ages 15-49 who know any method of contraception. Modern methods of contraception include female and male sterilization, contraceptive pills, intra-uterine device (IUD), injectables, implants, male and female condoms, diaphragm, contraceptive foam and contraceptive jelly female condom, lactational amenorrhea method (LAM), Standard days method (SDM), and emergency contraception. Traditional methods includes periodic abstinence (rhythm, calendar method) and withdrawal." }, { "id": "Notesfromoriginalsource", "value": "Breastfeeding, prolonged breastfeeding, prolonged abstinence are not considered contraceptive methods in themselves." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS) (https://www.statcompiler.com/)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.FPL.KNOW.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "To decide freely and responsibly the number of and spacing between their children and to have the information on family planning has been internationally recognized as the basic right for couples and individuals. Having knowledge about contraceptive methods helps couples and individuals to choose safe and effective methods." }, { "id": "IndicatorName", "value": "Knowledge of any method of contraception (% of married men)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of currently married or in union men who know any method of contraception. Modern methods of contraception include female and male sterilization, contraceptive pills, intra-uterine device (IUD), injectables, implants, male and female condoms, diaphragm, contraceptive foam and contraceptive jelly female condom, lactational amenorrhea method (LAM), Standard days method (SDM), and emergency contraception. Traditional methods includes periodic abstinence (rhythm, calendar method) and withdrawal. The age range of men depends on surveys. In most DHS surveys, men ages 15-49, 15-54, or 15-59 are eligible for individual interviews." }, { "id": "Notesfromoriginalsource", "value": "Breastfeeding, prolonged breastfeeding, prolonged abstinence are not considered contraceptive methods in themselves." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS) (https://www.statcompiler.com/)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.FPL.MSTM.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Fertility planning status categorizes births according to whether women reported wanting a pregnancy at the time of conceiving, wanting a pregnancy later, or not wanting any births. The data can help us to understand the level of contraceptive needs. Access for all couples and individuals to their preferred contraceptive methods is a basic human right. Also, use of contraception prevents pregnancy-related risks for women, especially for adolescent girls." }, { "id": "IndicatorName", "value": "Fertility planning status: mistimed pregnancy (% of births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The proportion of women ages 15-49 who reported the last birth in the five years preceding the survery was mistimed (wanted later)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.FPL.SATI.ZS", "metatype": [ { "id": "IndicatorName", "value": "Demand for family planning satisfied by any methods (% of married women with demand for family planning)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Demand for family planning satisfied by any methods refers to the percentage of married women ages 15-49 whose need for family planning is satisfied." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.FPL.SATM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Demand for family planning satisfied by modern methods (% of married women with demand for family planning)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Demand for family planning satisfied by modern methods refers to the percentage of married women ages 15-49 years whose need for family planning is satisfied with modern methods." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.7.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated from nationally-representative household survey data. Relevant data for this indicator are collected through various multi-country survey programs, including Contraceptive Prevalence Surveys (CPS), Demographic and Health Surveys (DHS), Fertility and Family Surveys (FFS), Reproductive Health Surveys (RHS), Multiple Indicator Cluster Surveys (MICS), Performance Monitoring and Accountability 2020 surveys (PMA), World Fertility Surveys (WFS), other international survey programs, and national surveys.\n\n\n\n\n\nData compilation involves systematic searches of websites of international survey programs, survey databases (e.g., the Integrated Household Survey Network (IHSN) database), websites of national statistical offices, SDG national reporting platforms, and ad hoc queries. Additionally, country-specific information from UNFPA country offices is utilized." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.FPL.UWTD.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Fertility planning status categorizes births according to whether women reported wanting a pregnancy at the time of conceiving, wanting a pregnancy later, or not wanting any births. The data can help us to understand the level of contraceptive needs. Access for all couples and individuals to their preferred contraceptive methods is a basic human right. Also, use of contraception prevents pregnancy-related risks for women, especially for adolescent girls." }, { "id": "IndicatorName", "value": "Fertility planning status: unwanted pregnancy (% of births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The proportion of women ages 15-49 who reported the last birth in the five years preceding the survery was unwanted." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.FPL.WNTD.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Fertility planning status categorizes births according to whether women reported wanting a pregnancy at the time of conceiving, wanting a pregnancy later, or not wanting any births. The data can help us to understand the level of contraceptive needs. Access for all couples and individuals to their preferred contraceptive methods is a basic human right. Also, use of contraception prevents pregnancy-related risks for women, especially for adolescent girls." }, { "id": "IndicatorName", "value": "Fertility planning status: planned pregnancy (% of births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The proportion of women ages 15-49 who reported the last birth in the five years preceding the survery was planned (wanted then)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.HIV.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Prevalence of HIV, female (% ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV, female is the percentage of females who are infected with HIV. Youth rates are as a percentage of the relevant age group." }, { "id": "Othernotes", "value": "In many developing countries most new infections occur in young adults, with young women especially vulnerable." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.HIV.1524.KW.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Comprehensive correct knowledge of HIV/AIDS, ages 15-24, female (2 prevent ways and reject 3 misconceptions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percent of female respondents ages 15-24 who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can have HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.HIV.1524.KW.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Comprehensive correct knowledge of HIV/AIDS, ages 15-24, male (2 prevent ways and reject 3 misconceptions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percent of male respondents ages 15-24 who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can have HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.HIV.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Prevalence of HIV, male (% ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV, male is the percentage of males who are infected with HIV. Youth rates are as a percentage of the relevant age group." }, { "id": "Othernotes", "value": "In many developing countries most new infections occur in young adults, with young women being especially vulnerable." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.HIV.ARTC.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Antiretroviral therapy coverage (% of adult females living with HIV)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "The percentage of adult females living with HIV who are receiving antiretroviral therapy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Statisticalconceptandmethodology", "value": "Data on HIV are from the Joint United Nations Programme on HIV/AIDS (UNAIDS). Changes in procedures and assumptions for estimating the data and better coordination with countries have resulted in improved estimates of HIV and AIDS. For example, improved software was used to model the course of HIV epidemics and their impacts, making full use of information on HIV prevalence trends from surveillance data as well as survey data. The software explicitly includes the effect of antiretroviral therapy when calculating HIV incidence and models reduced infectivity among people receiving antiretroviral therapy, which is having a larger impact on HIV prevalence and allowing HIV-positive people to live longer. The software also allows for changes in urbanization over time - important because prevalence is higher in urban areas and because many countries have seen rapid urbanization over the past two decades.\n\nAntiretroviral therapy has led to huge reductions in death and suffering of people with advanced HIV infection. Standard antiretroviral therapy consists of the use of at least three antiretroviral drugs to maximally suppress HIV and stop the progression of HIV disease. Data are collected through three international monitoring and reporting processes: country responses to the WHO; research by the Interagency Task Team on Prevention of HIV Infection in Women, Mothers and their Children; and country report to UNAIDS through the United Nations General Assembly Special Session Declaration of Commitment on HIV/AIDS." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.HIV.ARTC.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Antiretroviral therapy coverage (% of adult males living with HIV)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "The percentage of adult males living with HIV who are receiving antiretroviral therapy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Statisticalconceptandmethodology", "value": "Data on HIV are from the Joint United Nations Programme on HIV/AIDS (UNAIDS). Changes in procedures and assumptions for estimating the data and better coordination with countries have resulted in improved estimates of HIV and AIDS. For example, improved software was used to model the course of HIV epidemics and their impacts, making full use of information on HIV prevalence trends from surveillance data as well as survey data. The software explicitly includes the effect of antiretroviral therapy when calculating HIV incidence and models reduced infectivity among people receiving antiretroviral therapy, which is having a larger impact on HIV prevalence and allowing HIV-positive people to live longer. The software also allows for changes in urbanization over time - important because prevalence is higher in urban areas and because many countries have seen rapid urbanization over the past two decades.\n\nAntiretroviral therapy has led to huge reductions in death and suffering of people with advanced HIV infection. Standard antiretroviral therapy consists of the use of at least three antiretroviral drugs to maximally suppress HIV and stop the progression of HIV disease. Data are collected through three international monitoring and reporting processes: country responses to the WHO; research by the Interagency Task Team on Prevention of HIV Infection in Women, Mothers and their Children; and country report to UNAIDS through the United Nations General Assembly Special Session Declaration of Commitment on HIV/AIDS." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.HIV.INCD.FE.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV is still a leading cause of death and public health threat in the world. In Sub-Saharan Africa, women and girls are disproportinally affected by HIV. The incidence rate provides a measure of progress toward preventing onward transmission of HIV." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated Sustainable Development Goal inidcator 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Incidence of HIV, ages 15-49, female (per 1,000 uninfected female population ages 15-49)" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected female populations ages 15-49 expressed per 1,000 uninfected female population ages 15-49 in the year before the period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Statisticalconceptandmethodology", "value": "Data on incidence of HIV are from the Joint United Nations Programme on HIV/AIDS. Because of challenges in collecting direct measures of HIV incidence, modelled estimates are used (the Spectrum software). The models incorporate data on HIV prevalence from surveys of the general population, antenatal clinic attendees, and populations at increased risk of contracting HIV (such as sex workers, men who have sex with men, and people who inject drugs) and on the number of people receiving antiretroviral therapy, which will increase the prevalence of HIV because people living with HIV now survive longer. In countries with high-quality health information systems the models are also informed by case reporting and vital registration data." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.HIV.INCD.MA.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV is still a leading cause of death and public health threat in the world. In Sub-Saharan Africa, women and girls are disproportinally affected by HIV. The incidence rate provides a measure of progress toward preventing onward transmission of HIV." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated Sustainable Development Goal inidcator 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Incidence of HIV, ages 15-49, male (per 1,000 uninfected male population ages 15-49)" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected male populations ages 15-49 expressed per 1,000 uninfected male population ages 15-49 in the year before the period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Statisticalconceptandmethodology", "value": "Data on incidence of HIV are from the Joint United Nations Programme on HIV/AIDS. Because of challenges in collecting direct measures of HIV incidence, modelled estimates are used (the Spectrum software). The models incorporate data on HIV prevalence from surveys of the general population, antenatal clinic attendees, and populations at increased risk of contracting HIV (such as sex workers, men who have sex with men, and people who inject drugs) and on the number of people receiving antiretroviral therapy, which will increase the prevalence of HIV because people living with HIV now survive longer. In countries with high-quality health information systems the models are also informed by case reporting and vital registration data." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.HIV.INCD.YG.FE.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV is still a leading cause of death and public health threat in the world. In Sub-Saharan Africa, women and girls are disproportinally affected by HIV. The incidence rate provides a measure of progress toward preventing onward transmission of HIV." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated Sustainable Development Goal inidcator 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Incidence of HIV, ages 15-24, female (per 1,000 uninfected female population ages 15-24)" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected male populations ages 15-24 expressed per 1,000 uninfected male population ages 15-24 in the year before the period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Statisticalconceptandmethodology", "value": "Data on incidence of HIV are from the Joint United Nations Programme on HIV/AIDS. Because of challenges in collecting direct measures of HIV incidence, modelled estimates are used (the Spectrum software). The models incorporate data on HIV prevalence from surveys of the general population, antenatal clinic attendees, and populations at increased risk of contracting HIV (such as sex workers, men who have sex with men, and people who inject drugs) and on the number of people receiving antiretroviral therapy, which will increase the prevalence of HIV because people living with HIV now survive longer. In countries with high-quality health information systems the models are also informed by case reporting and vital registration data." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.HIV.INCD.YG.MA.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV is still a leading cause of death and public health threat in the world. In Sub-Saharan Africa, women and girls are disproportinally affected by HIV. The incidence rate provides a measure of progress toward preventing onward transmission of HIV." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated Sustainable Development Goal inidcator 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Incidence of HIV, ages 15-24, male (per 1,000 uninfected male population ages 15-24)" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected female populations ages 15-24 expressed per 1,000 uninfected female population ages 15-24 in the year before the period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Statisticalconceptandmethodology", "value": "Data on incidence of HIV are from the Joint United Nations Programme on HIV/AIDS. Because of challenges in collecting direct measures of HIV incidence, modelled estimates are used (the Spectrum software). The models incorporate data on HIV prevalence from surveys of the general population, antenatal clinic attendees, and populations at increased risk of contracting HIV (such as sex workers, men who have sex with men, and people who inject drugs) and on the number of people receiving antiretroviral therapy, which will increase the prevalence of HIV because people living with HIV now survive longer. In countries with high-quality health information systems the models are also informed by case reporting and vital registration data." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.HIV.INCD.YG.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected populations ages 15-24 expressed per 1,000 uninfected population ages 15-24 in the year before the period." }, { "id": "Othernotes", "value": "This is an age-disaggregated indicator for Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.HIV.INCD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Incidence of HIV, ages 15-49 (per 1,000 uninfected population ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected populations ages 15-49 expressed per 1,000 uninfected population in the year before the period." }, { "id": "Othernotes", "value": "This is an age-disaggregated indicator for Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.HIV.KNOW.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Comprehensive correct knowledge of HIV/AIDS, ages 15-49, female (2 prevent ways and reject 3 misconceptions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Knowledge of HIV, female, is the percentage of female respondents who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can have HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.HIV.KNOW.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Comprehensive correct knowledge of HIV/AIDS, ages 15-49, male (2 prevent ways and reject 3 misconceptions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Knowledge of HIV, male, is the percentage of male respondents who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can have HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.HIV.PMTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "HIV can be transmitted through sexual contact, blood transfusions, and the sharing of contaminated needles, and it can also be transmitted from mother to child during pregnancy, childbirth, or breastfeeding. Although the number of children acquiring HIV has decreased over time, mother-to-child transmission remains a significant public health concern.\n\nPrevention of mother-to-child transmission (PMTCT) is critical for reducing new pediatric HIV infections. However, many pregnant and breastfeeding women still do not begin ART or discontinue treatment during this period, contributing to continued transmission risks. Strengthening PMTCT services and ensuring continuity of care are essential for achieving global HIV prevention goals. (Reference: https://www.unaids.org/sites/default/files/2025-07/2025-global-aids-update-JC3153_en.pdf)" }, { "id": "IndicatorName", "value": "Antiretroviral therapy coverage for PMTCT (% of pregnant women living with HIV)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of pregnant women with HIV who receive antiretroviral medicine for prevention of mother-to-child transmission (PMTCT)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The coverage of antiretrovirals for PMTCT is calculated by dividing the number of pregnant women living with HIV who received antiretrovirals for PMTCT by the estimated number of pregnant women living with HIV who need antiretrovirals for PMTCT in the country. \n\n\n\n\nEstimating the Numerator: The number of pregnant women living with HIV receiving antiretrovirals for PMTCT is derived from national program data aggregated from facilities or other service delivery sites and reported by the country.\n\n\n\n\nEstimating the Denominator: The number of pregnant women living with HIV who need antiretroviral medicine for PMTCT is estimated using standardized statistical modeling based on UNAIDS/WHO methods. These methods consider various epidemic and demographic parameters, such as HIV prevalence among women of reproductive age, the effect of HIV on fertility, and national program coverage of antiretroviral therapy. These statistical modeling procedures provide a comprehensive population-based estimate of the number of pregnant women living with HIV who need antiretrovirals for PMTCT in the country." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent of pregnant women living with HIV" } ], "source_id": "14" }, { "id": "SH.IMM.IDPT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, DPT (% of children ages 12-23 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization, DPT, measures the percentage of children ages 12-23 months who received DPT vaccinations before 12 months or at any time before the survey. A child is considered adequately immunized against diphtheria, pertussis (or whooping cough), and tetanus (DPT) after receiving three doses of vaccine." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.b.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2024" }, { "id": "Source", "value": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year. Notes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package." }, { "id": "Topic", "value": "Economic and Social Context" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.IMM.MEAS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, measles (% of children ages 12-23 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization, measles, measures the percentage of children ages 12-23 months who received the measles vaccination before 12 months or at any time before the survey. A child is considered adequately immunized against measles after receiving one dose of vaccine." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2024" }, { "id": "Source", "value": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF), uri: https://data.unicef.org/topic/child-health/immunization/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year. Notes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package." }, { "id": "Topic", "value": "Economic and Social Context" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.MLR.IPTP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Malaria infection during pregnancy is a serious public health concern. It carries substantial risks for the mother, her fetus and the neonate including anaemia, severe malaria, spontaneous abortion, stillbirth, prematurity, neonatal mortality and low birthweight. Intermittent preventive treatment of malaria in pregnancy is a full therapeutic course of antimalarial medicine given to pregnant women at routine antenatal care visits, regardless of whether the recipient is infected with malaria. IPTp reduces maternal malaria episodes, maternal and fetal anaemia, placental parasitaemia, low birth weight, and neonatal mortality." }, { "id": "IndicatorName", "value": "Intermittent preventive treatment (IPT) of malaria in pregnancy (% of pregnant women)" }, { "id": "Longdefinition", "value": "Percentage of women aged 15 - 49 with a live birth in the recent years preceding the survey who received 3+ doses of sulfadoxine-pyrimethamine (SP/Fansidar), at least one during an antenatal care visit. Intermittent Preventive Treatment (IPT) is preventive treatment with SP/Fansidar during an antenatal care (ANC) visit treatment with a dose of sulfadoxine-pyrimethamine (SP/Fansidar) to pregnant women at each scheduled antenatal visit after the first trimester, but not more frequently than once a month." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Percentage of women aged 15 - 49 with a live birth in the recent years preceding the survey who received 3+ doses of sulfadoxine-pyrimethamine (SP/Fansidar), at least one during an antenatal care visit." }, { "id": "Source", "value": "UNICEF Global Databases [data.unicef.org], Multiple Indicator Cluster Surveys, Demographic and Health Surveys." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.MMR.DTHS", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Number of maternal deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The figures cannot be assumed to provide exact estimates." }, { "id": "Longdefinition", "value": "A maternal death refers to the death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management but not from accidental or incidental causes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO);\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "14" }, { "id": "SH.MMR.RISK", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Lifetime risk of maternal death (1 in: rate varies by country)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The probability cannot be assumed to provide an exact estimate of risk of maternal death." }, { "id": "Longdefinition", "value": "Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO);\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Number of 15-year old women for which 1 maternal death occurs assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future" } ], "source_id": "14" }, { "id": "SH.MMR.RISK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Lifetime risk of maternal death (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The probability cannot be assumed to provide an exact estimate of risk of maternal death." }, { "id": "Longdefinition", "value": "Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO);\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.PRG.ANEM", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of anemia among pregnant women (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data should be used with caution because surveys differ in quality, coverage, age group interviewed, and treatment of missing values across countries and over time.\n\n\n\nData on anemia are compiled by the WHO based mainly on nationally representative surveys, which measure hemoglobin in the blood. WHO's hemoglobin thresholds are then used to determine anemia status based on age, sex, and physiological status." }, { "id": "Longdefinition", "value": "Prevalence of anemia, pregnant women, is the percentage of pregnant women whose hemoglobin level is less than 110 grams per liter at sea level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Anemia is a condition in which the number of red blood cells or their oxygen-carrying capacity is insufficient to meet physiologic needs, which vary by age, sex, altitude, smoking status, and pregnancy status. In its severe form it is associated with fatigue, weakness, dizziness, and drowsiness. Children under age 5 and pregnant women have the highest risk for anemia." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.PRG.SYPH.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of syphilis (% of women attending antenatal care)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women attending antenatal care seropositive for syphilis" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Percentage of women attending antenatal care seropositive for syphilis" }, { "id": "Source", "value": "World Health Organization's Global Health Observatory Data Repository" }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.PRV.SMOK", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of current tobacco use (% of adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution." }, { "id": "Longdefinition", "value": "The percentage of the population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, total (ages 15+)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes. The indicator name and definition were updated in December, 2020." }, { "id": "Periodicity", "value": "Biennial" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nA statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. \n\n\n\nThe model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. \n\nDepending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015)." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.PRV.SMOK.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of current tobacco use, females (% of female adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution." }, { "id": "Longdefinition", "value": "The percentage of the female population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, females (% of adults)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes it. The indicator name and definition were updated in December, 2020." }, { "id": "Periodicity", "value": "Biennial" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nA statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. \n\n\n\nThe model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. \n\nDepending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015)." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.PRV.SMOK.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of current tobacco use, males (% of male adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution." }, { "id": "Longdefinition", "value": "The percentage of the male population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, males (% of adults)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes it. The indicator name and definition were updated in December, 2020." }, { "id": "Periodicity", "value": "Biennial" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nSmoking is the most common form of tobacco use and the prevalence of smoking is therefore a good measure of the tobacco epidemic. (Corrao MA, Guindon GE, Sharma N, Shokoohi DF (eds). Tobacco Control Country Profiles, 2000, American Cancer Society, Atlanta.) Tobacco use causes heart and other vascular diseases and cancers of the lung and other organs. Given the long delay between starting to smoke and the onset of disease, the health impact of smoking will increase rapidly only in the next few decades. The data presented are age-standardized rates for adults ages 15 and older from the WHO." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.STA.AIRP.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries." }, { "id": "IndicatorName", "value": "Mortality rate attributed to household and ambient air pollution, age-standardized, female (per 100,000 female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions" }, { "id": "Longdefinition", "value": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2019" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003)." }, { "id": "Topic", "value": "Environment" } ], "source_id": "14" }, { "id": "SH.STA.AIRP.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries." }, { "id": "IndicatorName", "value": "Mortality rate attributed to household and ambient air pollution, age-standardized, male (per 100,000 male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions" }, { "id": "Longdefinition", "value": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2019" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003)." }, { "id": "Topic", "value": "Environment" } ], "source_id": "14" }, { "id": "SH.STA.AIRP.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries." }, { "id": "IndicatorName", "value": "Mortality rate attributed to household and ambient air pollution, age-standardized (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions" }, { "id": "Longdefinition", "value": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years)." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2019" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003)." }, { "id": "Topic", "value": "Environment" } ], "source_id": "14" }, { "id": "SH.STA.ANV4.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Good prenatal and postnatal care improve maternal health and reduce maternal and infant mortality." }, { "id": "IndicatorName", "value": "Pregnant women receiving prenatal care of at least four visits (% of pregnant women)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources." }, { "id": "Longdefinition", "value": "Pregnant women receiving prenatal care, at least four times, are the percentage of women attended at least four times during pregnancy by skilled health personnel for reasons related to pregnancy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\nGood prenatal and postnatal care improves maternal health and reduces maternal and infant mortality. However, indicators on use of antenatal care services provide no information on the content or quality of the services. Data on antenatal care are obtained mostly from household surveys, which ask women who have had a live birth whether and from whom they received antenatal care." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.STA.ANVC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Pregnant women receiving prenatal care (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources." }, { "id": "Longdefinition", "value": "Pregnant women receiving prenatal care are the percentage of women attended at least once during pregnancy by skilled health personnel for reasons related to pregnancy." }, { "id": "Othernotes", "value": "Good prenatal and postnatal care improve maternal health and reduce maternal and infant mortality." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nGood prenatal and postnatal care improves maternal health and reduces maternal and infant mortality. However, indicators on use of antenatal care services provide no information on the content or quality of the services. Data on antenatal care are obtained mostly from household surveys, which ask women who have had a live birth whether and from whom they received antenatal care." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.STA.BRTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\n\n\nThe share of births attended by skilled health staff is an indicator of a health system's ability to provide adequate care for pregnant women." }, { "id": "IndicatorName", "value": "Births attended by skilled health staff (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources." }, { "id": "Longdefinition", "value": "Births attended by skilled health staff are the percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliveries on their own; and to care for newborns." }, { "id": "Othernotes", "value": "Assistance by trained professionals during birth reduces the incidence of maternal deaths during childbirth. The share of births attended by skilled health staff is an indicator of a health system’s ability to provide adequate care for pregnant women.\n\nThis is the Sustainable Development Goal indicator 3.1.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2022" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National-level household surveys are the primary sources for collecting data on skilled health personnel providing childbirth care. These surveys include Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), Reproductive Health Surveys (RHS), and other national surveys based on similar methodologies. Respondents in these surveys are asked about their last live birth and who assisted during delivery, covering a period of up to five years before the interview.\n\n\nAs part of the data harmonization process and interaction with countries, UNICEF conducts an annual country consultation. During this consultation, SDG country focal points are contacted to update and verify values included in the database and to obtain new data sources. These new data sources are reviewed and assessed jointly with WHO. Additionally, the national categories or occupational titles of skilled health personnel are verified. The reported data for some countries may include additional categories of trained personnel beyond doctors, nurses, and midwives." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percent of live births" } ], "source_id": "14" }, { "id": "SH.STA.FGMO.NO.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Female genital mutilation (FGM) is defined as all procedures involving partial or total removal of the female external genitalia or other injury to the female genital organs for non-medical reasons. It doesn’t provide any health benefits, but rather causes serious risks on women’s health including chronic infections and pain, menstrual problems, and complications in childbirth. FGM has been criticized as a violation of women’s basic human rights in the international community, and the elimination of FGM has been set as one of Sustainable Development Goals targets (5.3). However, the United Nations Children's Fund (UNICEF) estimates that at least 200 million women and girls have undergone FGM in the world. The attitudes about FGM among people where FGM has been practiced vary widely across countries." }, { "id": "IndicatorName", "value": "Women who believe that female genital mutilation should not be continued (% of women ages 15-49 who have heard about FGM)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who have heard about female genital mutilation and think the practice should end." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF DATA (http://www.data.unicef.org/); Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS), and other surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.STA.FGMO.NO.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Female genital mutilation (FGM) is defined as all procedures involving partial or total removal of the female external genitalia or other injury to the female genital organs for non-medical reasons. It doesn’t provide any health benefits, but rather causes serious risks on women’s health including chronic infections and pain, menstrual problems, and complications in childbirth. FGM has been criticized as a violation of women’s basic human rights in the international community, and the elimination of FGM has been set as one of Sustainable Development Goals targets (5.3). However, the United Nations Children's Fund (UNICEF) estimates that at least 200 million women and girls have undergone FGM in the world. The attitudes about FGM among people where FGM has been practiced vary widely across countries." }, { "id": "IndicatorName", "value": "Men who believe that female genital mutilation should not be continued (% of men who have heard about FGM)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of men who have heard about female genital mutilation and think the practice should end." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF DATA (http://www.data.unicef.org/); Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS), and other surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.STA.FGMO.RE.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Female genital mutilation (FGM) is defined as all procedures involving partial or total removal of the female external genitalia or other injury to the female genital organs for non-medical reasons. It doesn’t provide any health benefits, but rather causes serious risks on women’s health including chronic infections and pain, menstrual problems, and complications in childbirth. FGM has been criticized as a violation of women’s basic human rights in the international community, and the elimination of FGM has been set as one of Sustainable Development Goals targets (5.3). However, the United Nations Children's Fund (UNICEF) estimates that at least 200 million women and girls have undergone FGM in the world. The attitudes about FGM among people where FGM has been practiced vary widely across countries." }, { "id": "IndicatorName", "value": "Women who believe religion requires female genital mutilation (% of women ages 15-49 who have heard about FGM)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who have heard about female genital mutilation (FGM) and whose opinion is that religion requires FGM." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF DATA (http://www.data.unicef.org/); Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS), and other surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.STA.FGMO.RE.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Female genital mutilation (FGM) is defined as all procedures involving partial or total removal of the female external genitalia or other injury to the female genital organs for non-medical reasons. It doesn’t provide any health benefits, but rather causes serious risks on women’s health including chronic infections and pain, menstrual problems, and complications in childbirth. FGM has been criticized as a violation of women’s basic human rights in the international community, and the elimination of FGM has been set as one of Sustainable Development Goals targets (5.3). However, the United Nations Children's Fund (UNICEF) estimates that at least 200 million women and girls have undergone FGM in the world. The attitudes about FGM among people where FGM has been practiced vary widely across countries." }, { "id": "IndicatorName", "value": "Men who believe religion requires female genital mutilation (% of men who have heard about FGM)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of men who have heard about female genital mutilation (FGM) and whose opinion is that religion requires FGM." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF DATA (http://www.data.unicef.org/); Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS), and other surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months. \n\nData on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.\n\n A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SH.STA.FGMS.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "This is a disaggregated indicator for Sustainable Development Goal 5.3.2\n[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Female genital mutilation prevalence (%): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15–49 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF DATA (http://www.data.unicef.org/); Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS), and other surveys." }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SH.STA.FGMS.Q2.ZS", "metatype": [ { "id": "Generalcomments", "value": "This is a disaggregated indicator for Sustainable Development Goal 5.3.2\n[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Female genital mutilation prevalence (%): Q2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15–49 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF DATA (http://www.data.unicef.org/); Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS), and other surveys." }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SH.STA.FGMS.Q3.ZS", "metatype": [ { "id": "Generalcomments", "value": "This is a disaggregated indicator for Sustainable Development Goal 5.3.2\n[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Female genital mutilation prevalence (%): Q3" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15–49 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF DATA (http://www.data.unicef.org/); Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS), and other surveys." }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SH.STA.FGMS.Q4.ZS", "metatype": [ { "id": "Generalcomments", "value": "This is a disaggregated indicator for Sustainable Development Goal 5.3.2\n[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Female genital mutilation prevalence (%): Q4" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15–49 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF DATA (http://www.data.unicef.org/); Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS), and other surveys." }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SH.STA.FGMS.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "This is a disaggregated indicator for Sustainable Development Goal 5.3.2\n[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Female genital mutilation prevalence (%): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15–49 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF DATA (http://www.data.unicef.org/); Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS), and other surveys." }, { "id": "Topic", "value": "Violence" } ], "source_id": "14" }, { "id": "SH.STA.FGMS.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "FGM is a harmful practice involving the cutting or removal of the external female genitalia. It does not have any health benefits but rather causes serious risks to women’s physical and psychological health, including chronic infections, pain, menstrual problems, and complications during childbirth. FGM has been practiced mainly in the western, eastern, and north-eastern regions of Africa and some countries in the Middle East and Asia. It is reported that FGM is also found in western countries such as United Kingdom, United States, and Canada.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFGM is a violation of girls’ and women’s human rights, as well as a violation of women’s rights to health, security, and physical integrity. However, its eradication is now becoming a global concern and has even been set as one the SDGs, specifically as SDG target 5.3." }, { "id": "IndicatorName", "value": "Female genital mutilation prevalence (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on FGM should be interpreted with caution for several reasons. Women may be reluctant to disclose undergoing FGM due to its sensitivity or illegal status, and some may be unaware of the procedure, especially if performed at an early age. The data is retrospective, not reflecting recent changes, with reports from girls aged 15 to 19 years referring to events 14 to 18 years earlier. Surveys like MICS and DHS only include FGM questions in countries where the practice is prevalent, meaning FGM may still exist in countries without data, including high-income countries with migrant populations and certain low- and middle-income countries. National-level estimates may be misleading as FGM is often practiced by specific ethnic groups in certain locations, thus not accurately representing the prevalence. Reference: A Generation to Protect: Monitoring violence exploitation and abuse of children within the SDG framework (UNICEF 2020). https://data.unicef.org/wp-content/uploads/2020/06/A-Generation-to-Protect-publication-English_2020.pdf" }, { "id": "Longdefinition", "value": "Percentage of women aged 15–49 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.3.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UNICEF DATA, UN Children's Fund (UNICEF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_15-49_FGM; \tIndicator name from the original source: Percentage of girls and women (aged 15-49 years) who have undergone female genital mutilation (FGM), type: API, date accessed: 2023-12-07;\nDemographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and other surverys, DHS Program (ICF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_15-49_FGM; \tIndicator name from the original source: Percentage of girls and women (aged 15-49 years) who have undergone female genital mutilation (FGM), publisher: The DHS Program (ICF), type: API, date accessed: 2023-12-07;\nDHS API, DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: PT_F_15-49_FGM; \tIndicator name from the original source: Percentage of girls and women (aged 15-49 years) who have undergone female genital mutilation (FGM), date accessed: 2023-12-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of women ages 15-49 who have undergone FGM divided by the total number of women ages 15-49 in the population multiplied by 100. The primary sources for this indicator are the Multiple Indicator Cluster Surveys (MICS) and the Demographic and Health Surveys (DHS). The majority of the data are compiled by UNICEF, which coordinates with countries to gather the information.\nStatistical concept(s): Female genital mutilation (FGM) encompasses all practices that involve the partial or total removal of the external female genitalia, or other injury to the female genital organs, for non-medical reasons. Typically, this procedure is carried out on minors." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "14" }, { "id": "SH.STA.MALN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of underweight, weight for age, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of underweight, female, is the percentage of girls under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child underweight belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.STA.MALN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of underweight, weight for age, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of underweight, male, is the percentage of boys under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child underweight belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.STA.MALN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of underweight, weight for age (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of underweight children is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child underweight belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.STA.MMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Maternal mortality ratio (modeled estimate, per 100,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality." }, { "id": "Longdefinition", "value": "Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs)." }, { "id": "Othernotes", "value": "This indicator represents the risk associated with each pregnancy and is also a Sustainable Development Goal Indicator (3.1.1) for monitoring maternal health." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO), uri: https://www.who.int/news/item/23-02-2023-a-woman-dies-every-two-minutes-due-to-pregnancy-or-childbirth--un-agencies;\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 100,000 live births" } ], "source_id": "14" }, { "id": "SH.STA.MMRT.NE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Maternal mortality ratio (national estimate, per 100,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality.\n\nMaternal mortality ratios collected directly from Demographic and Health Surveys are presented in the survey year, but reference time of these maternal mortality ratios is for the seven years preceding the survey." }, { "id": "Longdefinition", "value": "Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Maternal Mortality Estimation Inter-Agency Group (MMEIG), World Health Organization (WHO), note: The country data compiled, adjusted and used in the estimation model by the Maternal Mortality Estimation Inter-Agency Group (MMEIG). The country data were compiled from the following sources: civil registration and vital statistics; specialized studies on maternal mortality; population based surveys and censuses; other available data sources including data from surveillance sites.;\nUN Children's Fund (UNICEF), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nUN Population Fund (UNFPA), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nWorld Bank Group (WBG), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nUnited Nations (UN), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nPAHO, note: Core Indicators Portal;\nICF, note: The DHS Program, Demographic and Health Surveys" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The national estimates of maternal mortality ratios are based on national surveys, vital registration records, and surveillance data or are derived from community and hospital records.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.STA.OB18.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of obesity, female (% of female population ages 18+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of obesity adult is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is 30 kg/m² or higher. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization (WHO):Global Health Observatory Data Repository" }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.STA.OB18.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of obesity, male (% of male population ages 18+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of obesity adult is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is 30 kg/m² or higher. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization (WHO): Global Health Observatory Data Repository" }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.STA.ODFC.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to sanitation is a fundamental human right. Open defecation, which is the practice of relieving oneself outside without proper facilities, is a violation of human dignity and poses a significant threat to public health and nutrition. Poor sanitation is a leading cause of infectious diseases globally, and enhancing sanitation services has been proven to have a substantial positive effect on health outcomes. The provision of basic and safely managed sanitation can decrease the incidence of diarrheal diseases and mitigate the health consequences of other serious illnesses that cause widespread morbidity and mortality among children. Diarrheal conditions and parasitic infections debilitate children, increasing their vulnerability to malnutrition and secondary infections such as pneumonia, measles, and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe absence of adequate sanitation is especially harmful to women who are forced to defecate in the open, as it compromises their privacy and exposes them to a greater risk of assault and violence. The elimination of open defecation is a specific target within the Sustainable Development Goals (SDG target 6.2), underscoring the international commitment to addressing this critical issue." }, { "id": "IndicatorName", "value": "People practicing open defecation, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Othernotes", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Economic and Social Context" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "14" }, { "id": "SH.STA.ODFC.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to sanitation is a fundamental human right. Open defecation, which is the practice of relieving oneself outside without proper facilities, is a violation of human dignity and poses a significant threat to public health and nutrition. Poor sanitation is a leading cause of infectious diseases globally, and enhancing sanitation services has been proven to have a substantial positive effect on health outcomes. The provision of basic and safely managed sanitation can decrease the incidence of diarrheal diseases and mitigate the health consequences of other serious illnesses that cause widespread morbidity and mortality among children. Diarrheal conditions and parasitic infections debilitate children, increasing their vulnerability to malnutrition and secondary infections such as pneumonia, measles, and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe absence of adequate sanitation is especially harmful to women who are forced to defecate in the open, as it compromises their privacy and exposes them to a greater risk of assault and violence. The elimination of open defecation is a specific target within the Sustainable Development Goals (SDG target 6.2), underscoring the international commitment to addressing this critical issue." }, { "id": "IndicatorName", "value": "People practicing open defecation, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Othernotes", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Economic and Social Context" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "14" }, { "id": "SH.STA.ODFC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to sanitation is a fundamental human right. Open defecation, which is the practice of relieving oneself outside without proper facilities, is a violation of human dignity and poses a significant threat to public health and nutrition. Poor sanitation is a leading cause of infectious diseases globally, and enhancing sanitation services has been proven to have a substantial positive effect on health outcomes. The provision of basic and safely managed sanitation can decrease the incidence of diarrheal diseases and mitigate the health consequences of other serious illnesses that cause widespread morbidity and mortality among children. Diarrheal conditions and parasitic infections debilitate children, increasing their vulnerability to malnutrition and secondary infections such as pneumonia, measles, and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe absence of adequate sanitation is especially harmful to women who are forced to defecate in the open, as it compromises their privacy and exposes them to a greater risk of assault and violence. The elimination of open defecation is a specific target within the Sustainable Development Goals (SDG target 6.2), underscoring the international commitment to addressing this critical issue." }, { "id": "IndicatorName", "value": "People practicing open defecation (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Economic and Social Context" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "14" }, { "id": "SH.STA.OWAD.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of overweight, female (% of female adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of overweight female adults is the percentage of females ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Prevalence of overweight female adults is the percentage of females ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters." }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.STA.OWAD.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of overweight, male (% of male adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of overweight male adults is the percentage of males ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Prevalence of overweight male adults is the percentage of males ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters." }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.STA.OWAD.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of overweight (% of adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of overweight adults is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Prevalence of overweight adults is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters." }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.STA.OWGH.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight, weight for height, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.STA.OWGH.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight, weight for height, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of overweight, male, is the percentage of boys under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.STA.OWGH.ME.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of overweight, female (modeled estimate, % of children under 5)" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.STA.OWGH.ME.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of overweight, male (modeled estimate, % of children under 5)" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.STA.OWGH.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "UNICEF, WHO and the World Bank undertake a joint review for each potential primary data source used as to generate the JME global estimates. The group conducts a review when (at minimum) a final report with full methodological details and results are available, as well as (ideally) a data quality assessment flagging potential limitations. When the raw data are available, they are analysed using the Anthro Survey Analyzer software to produce a standard set of results and data quality outputs against which the review is conducted. Comments are documented in a standard review template extracting methodological details (e.g., sampling procedures, description of anthropometrical equipment), data quality outputs (e.g., weight and height distributions, percentage of cases that were flagged as implausible according to the WHO Child Growth Standards) and the malnutrition prevalence estimates from the data source under review generated based on the standard recommended methodology. These estimates are compared against the reported values, as well as against those from other data sources already included in the JME dataset, to assess the plausibility of the trend before including the new point. Reports that are preliminary, or that lack key details on methodology or results, cannot be reviewed and are left pending until full information is available. The methods used to generate the JME global estimates for stunting and overweight were cross validated to ensure estimates produced by the method are closely aligned to national data points." }, { "id": "Derivationmethod", "value": "National estimates from primary sources (e.g., from household surveys) used to generate the JME global estimates are based on standardized methodology using the WHO Child Growth Standards as described in Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old and WHO Anthro Survey Analyser (WHO, 2019). The JME global estimates are generated using smoothing techniques and covariates applied to quality-assured national data to derive trends and up-to-date estimates. Worldwide and regional estimates are derived as the respective country averages weighted by the countries’ under-five population estimates (UNPD-WPP latest available edition) using annual JME global estimates for 205\nCountries and areas." }, { "id": "Generalcomments", "value": "Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates." }, { "id": "IndicatorName", "value": "Prevalence of overweight (modeled estimate, % of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time." }, { "id": "Longdefinition", "value": "Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Otherweblinks", "value": "McLain AC, Frongillo EA, Feng J, Borghi E. Prediction intervals for penalized longitudinal models with multisource summary measures: An application to childhood malnutrition. Stat Med. 2019 Mar 15;38(6):1002-1012. doi: 10.1002/sim.8024. Epub 2018 Nov 14. PMID: 30430613.\n\nRecommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: World Health Organization and the United Nations Children’s Fund (UNICEF), 2019. Licence: CC BY-NC-SA 3.0 IGO. \nhttps://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/\nhttps://www.who.int/publications/i/item/9789241515559\n\nWHO Anthro Survey Analyzer - https://www.who.int/tools/child-growth-standards/software" }, { "id": "Periodicity", "value": "Every two years" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME)." }, { "id": "Statisticalconceptandmethodology", "value": "Country estimates are based on anthropometric data primarily derived from household surveys, national administrative sources, and surveillance systems. To track changes over time, a statistical technique known as a penalized longitudinal mixed model is applied. This model captures non-linear trends using a flexible approach called B-splines, while also accounting for variations in data quality—such as sampling error and incomplete age coverage among children under five. The model also generates confidence intervals to reflect the precision of the estimates. For further details, refer to the following document: The UNICEF-WHO-World Bank Joint Child Malnutrition Estimates (JME) Standard Methodology. New York: United Nations Children’s Fund (UNICEF), World Health Organization (WHO), and World Bank, 2024. Licence: CC BY-NC-SA 3.0 IGO." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "14" }, { "id": "SH.STA.OWGH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "See SH.STA.OWGH.ME.ZS for aggregation" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight, weight for height (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.STA.POIS.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unintentional poisoning (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unintentional poisonings is the number of deaths from unintentional poisonings in a year per 100,000 population. Unintentional poisoning can\n\n\nbe caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.9.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for unintentional poisoning mortality are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database are used, with necessary adjustments for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. For countries lacking high-quality death registration data, cause of death estimates are calculated using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates. The complete methodology can be found at the following: https://www.who.int/docs/defaultsource/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf" }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "14" }, { "id": "SH.STA.POIS.P5.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unintentional poisoning, female (per 100,000 female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unintentional poisonings is the number of female deaths from unintentional poisonings in a year per 100,000 female population. Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for unintentional poisoning mortality are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database are used, with necessary adjustments for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. For countries lacking high-quality death registration data, cause of death estimates are calculated using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates. The complete methodology can be found at the following: https://www.who.int/docs/defaultsource/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf" }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 100,000 female population" } ], "source_id": "14" }, { "id": "SH.STA.POIS.P5.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unintentional poisoning, male (per 100,000 male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unintentional poisonings is the number of male deaths from unintentional poisonings in a year per 100,000 male population. Unintentional poisoning can\n\n\nbe caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for unintentional poisoning mortality are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database are used, with necessary adjustments for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. For countries lacking high-quality death registration data, cause of death estimates are calculated using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates. The complete methodology can be found at the following: https://www.who.int/docs/defaultsource/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf" }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 100,000 male population" } ], "source_id": "14" }, { "id": "SH.STA.STNT.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of stunting, female, is the percentage of girls under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.STA.STNT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.STA.STNT.ME.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, female (modeled estimate, % of children under 5)" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.STA.STNT.ME.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, male (modeled estimate, % of children under 5)" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.STA.STNT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "UNICEF, WHO and the World Bank undertake a joint review for each potential primary data source used as to generate the JME global estimates. The group conducts a review when (at minimum) a final report with full methodological details and results are available, as well as (ideally) a data quality assessment flagging potential limitations. When the raw data are available, they are analysed using the Anthro Survey Analyzer software to produce a standard set of results and data quality outputs against which the review is conducted. Comments are documented in a standard review template extracting methodological details (e.g., sampling procedures, description of anthropometrical equipment), data quality outputs (e.g., weight and height distributions, percentage of cases that were flagged as implausible according to the WHO Child Growth Standards) and the malnutrition prevalence estimates from the data source under review generated based on the standard recommended methodology. These estimates are compared against the reported values, as well as against those from other data sources already included in the JME dataset, to assess the plausibility of the trend before including the new point. Reports that are preliminary, or that lack key details on methodology or results, cannot be reviewed and are left pending until full information is available. The methods used to generate the JME global estimates for stunting and overweight were cross validated to ensure estimates produced by the method are closely aligned to national data points." }, { "id": "Derivationmethod", "value": "National estimates from primary sources (e.g., from household surveys) used to generate the JME global estimates are based on standardized methodology using the WHO Child Growth Standards as described in Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old and WHO Anthro Survey Analyser (WHO, 2019). The JME global estimates are generated using smoothing techniques and covariates applied to quality-assured national data to derive trends and up-to-date estimates. Worldwide and regional estimates are derived as the respective country averages weighted by the countries’ under-five population estimates (UNPD-WPP latest available edition) using annual JME global estimates for 205\nCountries and areas." }, { "id": "Generalcomments", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age (modeled estimate, % of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time." }, { "id": "Longdefinition", "value": "Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Otherweblinks", "value": "McLain AC, Frongillo EA, Feng J, Borghi E. Prediction intervals for penalized longitudinal models with multisource summary measures: An application to childhood malnutrition. Stat Med. 2019 Mar 15;38(6):1002-1012. doi: 10.1002/sim.8024. Epub 2018 Nov 14. PMID: 30430613.\n\nRecommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: World Health Organization and the United Nations Children’s Fund (UNICEF), 2019. Licence: CC BY-NC-SA 3.0 IGO. \nhttps://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/\nhttps://www.who.int/publications/i/item/9789241515559\n\nWHO Anthro Survey Analyzer - https://www.who.int/tools/child-growth-standards/software" }, { "id": "Periodicity", "value": "Every two years" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME)." }, { "id": "Statisticalconceptandmethodology", "value": "Country estimates are based on anthropometric data primarily derived from household surveys, national administrative sources, and surveillance systems. To track changes over time, a statistical technique known as a penalized longitudinal mixed model is applied. This model captures non-linear trends using a flexible approach called B-splines, while also accounting for variations in data quality—such as sampling error and incomplete age coverage among children under five. The model also generates confidence intervals to reflect the precision of the estimates. For further details, refer to the following document: The UNICEF-WHO-World Bank Joint Child Malnutrition Estimates (JME) Standard Methodology. New York: United Nations Children’s Fund (UNICEF), World Health Organization (WHO), and World Bank, 2024. Licence: CC BY-NC-SA 3.0 IGO." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "14" }, { "id": "SH.STA.STNT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "See SH.STA.STNT.ME.ZS for aggregation" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.STA.SUIC.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Suicide mortality rate, female (per 100,000 female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of suicide deaths in a year by the mid-year population for the same calendar year, then multiplying by 100,000. The estimates are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the vital registration data submitted by member states to the WHO Mortality Database are used, with adjustments made where necessary (e.g., for under-reporting of deaths, unknown age and sex, and ill-defined causes of death). For countries without high-quality death registration data, cause of death estimates are calculated using other sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not be identical to official national estimates." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 100,000 female population" } ], "source_id": "14" }, { "id": "SH.STA.SUIC.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Suicide mortality rate, male (per 100,000 male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of suicide deaths in a year by the mid-year population for the same calendar year, then multiplying by 100,000. The estimates are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the vital registration data submitted by member states to the WHO Mortality Database are used, with adjustments made where necessary (e.g., for under-reporting of deaths, unknown age and sex, and ill-defined causes of death). For countries without high-quality death registration data, cause of death estimates are calculated using other sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not be identical to official national estimates." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 100,000 male population" } ], "source_id": "14" }, { "id": "SH.STA.SUIC.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Suicide mortality rate (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted)." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.4.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of suicide deaths in a year by the mid-year population for the same calendar year, then multiplying by 100,000. The estimates are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the vital registration data submitted by member states to the WHO Mortality Database are used, with adjustments made where necessary (e.g., for under-reporting of deaths, unknown age and sex, and ill-defined causes of death). For countries without high-quality death registration data, cause of death estimates are calculated using other sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not be identical to official national estimates." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "14" }, { "id": "SH.STA.TRAF.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Road traffic injuries and deaths is a major global public health problem. Road traffic crashes are currently the leading cause of death for children and young adults in the world. There is a strong association between the risk of road traffic death and the income level of countries. The burden of road traffic deaths is disproportionately high among low- and middle-income countries." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.6.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality caused by road traffic injury, female (per 100,000 female population)" }, { "id": "Longdefinition", "value": "Mortality caused by road traffic injury is estimated road traffic fatal injury deaths per 100,000 population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.STA.TRAF.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Road traffic injuries and deaths is a major global public health problem. Road traffic crashes are currently the leading cause of death for children and young adults in the world. There is a strong association between the risk of road traffic death and the income level of countries. The burden of road traffic deaths is disproportionately high among low- and middle-income countries." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.6.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality caused by road traffic injury, male (per 100,000 male population)" }, { "id": "Longdefinition", "value": "Mortality caused by road traffic injury is estimated road traffic fatal injury deaths per 100,000 population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SH.STA.TRAF.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Road traffic injuries and deaths is a major global public health problem. Road traffic crashes are currently the leading cause of death for children and young adults in the world. There is a strong association between the risk of road traffic death and the income level of countries. The burden of road traffic deaths is disproportionately high among low- and middle-income countries." }, { "id": "IndicatorName", "value": "Mortality caused by road traffic injury (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mortality caused by road traffic injury is estimated road traffic fatal injury deaths per 100,000 population." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.6.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2019" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The methods used for analyzing causes of death vary based on the type of data available from different countries. For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database is utilized, with necessary adjustments made for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. In contrast, for countries lacking high-quality death registration data, cause of death estimates are derived using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "14" }, { "id": "SH.STA.WASH.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unsafe drinking water, unsafe sanitation and lack of hygiene are important causes of death. Most diarrheal deaths in the world are caused by unsafe water, sanitation or hygiene. According to the World Health Organization, in addition to diarrea, the following diseases could be prevented if adequate WASH services are provided: malnutrition, intestinal nematode infections, lymphatic filariasis, trachoma, schistosomiasis and malaria." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene, female (per 100,000 female population)" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Environment" } ], "source_id": "14" }, { "id": "SH.STA.WASH.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unsafe drinking water, unsafe sanitation and lack of hygiene are important causes of death. Most diarrheal deaths in the world are caused by unsafe water, sanitation or hygiene. According to the World Health Organization, in addition to diarrea, the following diseases could be prevented if adequate WASH services are provided: malnutrition, intestinal nematode infections, lymphatic filariasis, trachoma, schistosomiasis and malaria." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene, male (per 100,000 male population)" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Environment" } ], "source_id": "14" }, { "id": "SH.STA.WASH.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unsafe drinking water, unsafe sanitation and lack of hygiene are important causes of death. Most diarrheal deaths in the world are caused by unsafe water, sanitation or hygiene. According to the World Health Organization, in addition to diarrea, the following diseases could be prevented if adequate WASH services are provided: malnutrition, intestinal nematode infections, lymphatic filariasis, trachoma, schistosomiasis and malaria." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.9.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2019" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: To estimate the portion of deaths from diarrhea and acute respiratory infections attributable to unsafe water, sanitation, and hygiene (WASH), a comparative risk assessment approach is used. This involves calculating the attributable disease deaths by combining information on the increased (or relative) risk of a disease resulting from exposure with the prevalence of that exposure in the population. This calculation yields the 'population attributable fraction' (PAF), which represents the fraction of disease in a population that can be attributed to the exposure, in this case, unsafe WASH.\n\n\nBy applying the PAF to the total deaths from diarrhea or acute respiratory infections, the number of deaths resulting from inadequate WASH can be determined. Additionally, deaths from protein-energy malnutrition attributable to inadequate WASH are estimated by evaluating the impacts of repeated infectious diarrhea episodes on nutritional status, particularly stunting. All deaths from intestinal nematode infections are attributed to inadequate WASH due to their transmission pathway." }, { "id": "Topic", "value": "Environment" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "14" }, { "id": "SH.STA.WAST.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of wasting, weight for height, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of wasting, female, is the proportion of girls under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.STA.WAST.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of wasting, weight for height, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of wasting, male, is the proportion of boys under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.STA.WAST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of wasting, weight for height (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of wasting is the proportion of children under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.SVR.WAST.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of severe wasting, weight for height, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of severe wasting, female, is the proportion of girls under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.SVR.WAST.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of severe wasting, weight for height, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of severe wasting, male, is the proportion of boys under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SH.SVR.WAST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of severe wasting, weight for height (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of severe wasting is the proportion of children under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SI.POV.DDAY", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. The World Bank defines extreme poverty as living on less than $3.00 a day (adjusted for purchasing power differences across countries). The value of $3.00 is the typical poverty line of low-income countries, which is the minimum amount of money people in low-income countries need to cover their daily basic needs, including food, clothing, and shelter. The share of population living on less than $3.00 a day is the first indicator the World Bank tracks in its Bank’s expanded vision indicators to create a world free of poverty in a livable planet. It is also the indicator the UN tracks for SDG 1.1. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at $3.00 a day (2021 PPP) (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty headcount ratio at $3.00 a day is the percentage of the population living on less than $3.00 a day at 2021 purchasing power adjusted prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2024" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\n\n\n\n\n\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.\n\n\n\n\n\n\n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\n\n\n\n\n\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): Poverty headcount ratio at $3.00 a day refers to the percentage of a population whose consumption or income per day falls short of the international poverty line of $3.00 a day (adjusted for purchasing power parity differences across countries), the poverty line typical of low-income countries." }, { "id": "Topic", "value": "Economic and Social Context" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "14" }, { "id": "SI.POV.GINI", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group's vision of promoting shared prosperity includes a measure that tracks the number of economies with high inequality, defined as those with a Gini index greater than 0.4" }, { "id": "IndicatorName", "value": "Gini index" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Gini coefficients are not unique. It is possible for two different Lorenz curves to give rise to the same Gini coefficient. Furthermore it is possible for the Gini coefficient of a developing country to rise (due to increasing inequality of income) while the number of people in absolute poverty decreases. This is because the Gini coefficient measures relative, not absolute, wealth.\n\n\n\n\n\n\n\nAnother limitation of the Gini coefficient is that it is not additive across groups, i.e. the total Gini of a society is not equal to the sum of the Gini's for its sub-groups. Thus, country-level Gini coefficients cannot be aggregated into regional or global Gini's, although a Gini coefficient can be computed for the aggregate.\n\n\n\n\n\n\n\nBecause the underlying household surveys differ in methods and types of welfare measures collected, data are not strictly comparable across countries or even across years within a country. Two sources of non-comparability should be noted for distributions of income in particular. First, the surveys can differ in many respects, including whether they use income or consumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much better welfare indicator, particularly in developing countries. Second, households differ in size (number of members) and in the extent of income sharing among members. And individuals differ in age and consumption needs. Differences among countries in these respects may bias comparisons of distribution. \n\n\n\n\n\n\n\nWorld Bank staff have made an effort to ensure that the data are as comparable as possible. Wherever possible, consumption has been used rather than income. Income distribution and Gini indexes for high-income economies are calculated directly from the Luxembourg Income Study database, using an estimation method consistent with that applied for developing countries." }, { "id": "Longdefinition", "value": "Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2024" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.\n\n\n\n\n\n\n\nThe Gini index provides a convenient summary measure of the degree of inequality. Data on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\nStatistical concept(s): The Gini index is the average of all pairwise absolute differences between individual consumption or income, normalized by twice the mean. More intuitively, the Gini index is the average share of mean consumption or income that needs to be transferred between two randomly selected individuals to achieve equality. A Gini index of 1 represents perfect inequality, in which total consumption or income goes to one individual. A Gini index of 0 indicates represents perfect equality, in which all individuals have the same level of consumption or income." }, { "id": "Topic", "value": "Economic and Social Context" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "14" }, { "id": "SI.POV.NAHC", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The poverty rate as defined by national poverty lines reflects the share of the population that fails to meet the standard a country thinks is necessary to cover basic needs (typically in low- and middle-income countries) or afford a decent lifestyle (typically in high-income countries). SDG 1.2 aims to reduce by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions, by 2030." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at national poverty lines (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "National poverty headcount ratio is the percentage of the population living below the national poverty line(s). National estimates are based on population-weighted subgroup estimates from household surveys. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year." }, { "id": "Othernotes", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2024" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines., World Bank (WB), note: Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Poverty headcount ratio among the population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\n\n\n\n\n\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income. \n\n\n\n\n\n\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies. \n\n\n\n\n\n\n\nAlmost all national poverty lines in developing economies are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. \n\n\n\n\n\n\n\nThis series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year. For all other economies, the year reported is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which data collection started.\nStatistical concept(s): National poverty headcount ratio refers to the percentage of a population whose consumption or income per day falls short of the national poverty line. National poverty lines vary by country and over time. In low- and middle-income countries, national poverty lines tend to be absolute poverty lines, thus reflecting the estimated minimum amount of money needed to cover basic needs. In high-income countries, national poverty lines tend to be relative poverty lines, thus reflecting the typical amount of money needed for an individual to afford the typical standard of living and without any restraints to participating fully in the societies in which they live. National poverty lines tend to grow with economic growth, especially in high-income or upper-middle-income countries." }, { "id": "Topic", "value": "Economic and Social Context" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "14" }, { "id": "SL.AGR.EMPL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in agriculture, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "14" }, { "id": "SL.AGR.EMPL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in agriculture, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "14" }, { "id": "SL.AGR.EMPL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in agriculture (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "14" }, { "id": "SL.EMP.1524.SP.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, female (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\nEPR (%) = 100 x Persons employed / Working-age population\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\nEPRw (%) = 100 x Employed women / Working-age women\n\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female population ages 15-24" } ], "source_id": "14" }, { "id": "SL.EMP.1524.SP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, female (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\nEPR (%) = 100 x Persons employed / Working-age population\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\nEPRw (%) = 100 x Employed women / Working-age women\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female population ages 15-24" } ], "source_id": "14" }, { "id": "SL.EMP.1524.SP.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, male (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\nEPR (%) = 100 x Persons employed / Working-age population\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\nEPRw (%) = 100 x Employed women / Working-age women\n\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male population ages 15-24" } ], "source_id": "14" }, { "id": "SL.EMP.1524.SP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, male (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\nEPR (%) = 100 x Persons employed / Working-age population\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\nEPRw (%) = 100 x Employed women / Working-age women\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male population ages 15-24" } ], "source_id": "14" }, { "id": "SL.EMP.1524.SP.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, total (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\nEPR (%) = 100 x Persons employed / Working-age population\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\nEPRw (%) = 100 x Employed women / Working-age women\n\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-24" } ], "source_id": "14" }, { "id": "SL.EMP.1524.SP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, total (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\nEPR (%) = 100 x Persons employed / Working-age population\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\nEPRw (%) = 100 x Employed women / Working-age women\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-24" } ], "source_id": "14" }, { "id": "SL.EMP.MPYR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Employers, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a \"self-employment jobs\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of employers to the total employed is calculated as follows: Employers/Total employment x 100. \nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "14" }, { "id": "SL.EMP.MPYR.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Employers, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a \"self-employment jobs\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of employers to the total employed is calculated as follows: Employers/Total employment x 100. \nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "14" }, { "id": "SL.EMP.MPYR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Employers, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a \"self-employment jobs\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of employers to the total employed is calculated as follows: Employers/Total employment x 100. \nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "14" }, { "id": "SL.EMP.SELF.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Self-employed, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include sub-categories of employers, own-account workers and members of producers' cooperatives and contributing family workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of self-employed workers to the total employed is calculated as follows: Self-employed workers/Total employment x 100. \nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "14" }, { "id": "SL.EMP.SELF.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Self-employed, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include sub-categories of employers, own-account workers and members of producers' cooperatives and contributing family workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of self-employed workers to the total employed is calculated as follows: Self-employed workers/Total employment x 100. \nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "14" }, { "id": "SL.EMP.SELF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Self-employed, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include sub-categories of employers, own-account workers and members of producers' cooperatives and contributing family workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of self-employed workers to the total employed is calculated as follows: Self-employed workers/Total employment x 100. \nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "14" }, { "id": "SL.EMP.SMGT.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator provides a meaningful measure of the percentage of females who are employed in decision-making and management roles in government, large enterprises and institutions, thus providing some insight into women’s power in decision-making and in the economy, relative to men’s power." }, { "id": "IndicatorName", "value": "Female share of employment in senior and middle management (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The main limitation of this indicator is that it fails to capture the differences in the levels of responsibility of women in their respective managerial position, or the importance of the enterprises and organizations in which they are employed. Its quality is also significantly impacted by the reliability of the employment statistics by occupation at the two-digit level of the ISCO. Whenever data at the two-digit level of the ISCO are not available, data at the one-digit level could be used as a proxy, referring only to major group 1 of ISCO-08 or ISCO-88, rather than to also refer to major group 1 minus category 14 of ISCO-08 or major group 1 minus category 13 of ISCO-88. This implies referring to the female share in total management, rather than also to the female share in senior and middle management exclusively. This proxy should be used only in case of lack of availability of data at the two-digit level of the ISCO, as total management includes junior management, and women tend to be more represented in junior management positions than in senior or middle management positions, and thus, by referring only to total management one may over-estimate women’s impact in high-level decision-making roles." }, { "id": "Longdefinition", "value": "The female share of employment in senior and middle management conveys the number of women in management as a percentage of employment in management. Employment in management is defined based on the International Standard Classification of Occupations. This series refers to senior and middle management only, thus excluding junior management (category 1 in both ISCO-08 and ISCO-88 minus category 14 in ISCO-08 and minus category 13 in ISCO-88)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Labour Market-related SDG Indicators database (ILOSDG), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data for this indicator is collected through labor force surveys or any other household survey which collects such data through a module on employment. Establishment/firm surveys or administrative records can also provide useful data on female-occupied management positions by ISCO groups. Surveys are conducted by national statistical offices or ministries of labor in countries.\n\nStatistical concept(s): Employment comprises all persons of working age who, during a short reference period (one week), were engaged in any activity to produce goods or provide services for pay or profit. For further clarification, see: Resolution concerning statistics of work, employment and labor underutilization (2013).\n\n\n\n\n\nEmployment in management is determined according to the categories of the latest version of the International Standard Classification of Occupations (ISCO-08), which organizes jobs into a clearly defined set of groups based on the tasks and duties undertaken in the job. For the purposes of this indicator, it is preferable to refer separately to senior and middle management only on one hand, and to total management (including junior management) on the other. Senior and middle management correspond to sub-major groups 11, 12 and 13 in ISCO-08 and sub-major groups 11 and 12 in ISCO-88. If statistics are not available disaggregated at the sub-major group level (two-digit level of ISCO), then major group 1 of ISCO-88 and ISCO-08 can be used as a proxy and the indicator would then refer only to total management (including junior management)." }, { "id": "Topic", "value": "Leadership" }, { "id": "Unitofmeasure", "value": "% of total employment in senior and middle management" } ], "source_id": "14" }, { "id": "SL.EMP.TOTL.SP.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, female (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\nEPR (%) = 100 x Persons employed / Working-age population\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\nEPRw (%) = 100 x Employed women / Working-age women\n\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female population ages 15+" } ], "source_id": "14" }, { "id": "SL.EMP.TOTL.SP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, female (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\nEPR (%) = 100 x Persons employed / Working-age population\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\nEPRw (%) = 100 x Employed women / Working-age women\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female population ages 15+" } ], "source_id": "14" }, { "id": "SL.EMP.TOTL.SP.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, male (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\nEPR (%) = 100 x Persons employed / Working-age population\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\nEPRw (%) = 100 x Employed women / Working-age women\n\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male population ages 15+" } ], "source_id": "14" }, { "id": "SL.EMP.TOTL.SP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, male (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\nEPR (%) = 100 x Persons employed / Working-age population\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\nEPRw (%) = 100 x Employed women / Working-age women\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male population ages 15+" } ], "source_id": "14" }, { "id": "SL.EMP.TOTL.SP.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "With the aim of promoting international comparability, statistics presented on ILOSTAT are based on standard international definitions wherever feasible and may differ from official national figures. This series is based on the 13th ICLS definitions. For time series comparability, it includes countries that have implemented the 19th ICLS standards." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, total (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Comparability of employment-to-population ratios across countries is affected most significantly by variations in the definitions used for the employment and population figures. Differences result from age coverage, such as the lower and upper bounds for labor force activity. Estimates of both employment and population are also likely to vary according to whether members of the armed forces are included. Another area with scope for measurement differences has to do with the national treatment of particular groups of workers. The international definition of employment calls for inclusion of all persons who worked for at least one hour during the reference period. Workers could be in paid employment or in self-employment, including in less obvious forms of work, some of which are dealt with in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeship or non-market production. The majority of exceptions to coverage of all persons employed in a labor force survey have to do with national variations to the international recommendation applicable to the alternate employment statuses. For example, some countries measure persons employed in paid employment only and some countries measure “all persons engaged”, meaning paid employees plus working proprietors who receive some remuneration based on corporate shares. Other possible variations to the norms pertaining to measurement of total employment include hours limits (beyond one hour) placed on contributing family members for inclusion in employment. Comparisons can also be problematic when the frequency of data collection varies. The range of information collection can run from one month to 12 months in a year. Given the fact that seasonality of various kinds is undoubtedly present in all countries, employment-to-population ratios can vary for this reason alone. Countries with employment-to-population ratios based on less than full-year survey periods can be expected to have ratios that are not directly comparable with those from full-year, month-by-month collections. For example, an annual average based on 12 months of observations, all other things being equal, is likely to be different from an annual average based on four (quarterly) observations." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\n\nEPR (%) = 100 x Persons employed / Working-age population\n\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\n\nEPRw (%) = 100 x Employed women / Working-age women\n\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total population ages 15+" } ], "source_id": "14" }, { "id": "SL.EMP.TOTL.SP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, total (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\nEPR (%) = 100 x Persons employed / Working-age population\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\nEPRw (%) = 100 x Employed women / Working-age women\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total population ages 15+" } ], "source_id": "14" }, { "id": "SL.EMP.UNDR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Time-related underemployment, female (% of employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Time-related underemployment refers to all persons in employment who (i) wanted to work additional hours, (ii) had worked less than a specified hours threshold (working time in all jobs), and (iii) were available to work additional hours given an opportunity for more work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization. “Labour Force Statistics database (LFS)” ILOSTAT. Accessed January 07, 2025. https://ilostat.ilo.org/data/." }, { "id": "Topic", "value": "Employment and Time Use" } ], "source_id": "14" }, { "id": "SL.EMP.UNDR.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Time-related underemployment, male (% of employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Time-related underemployment refers to all persons in employment who (i) wanted to work additional hours, (ii) had worked less than a specified hours threshold (working time in all jobs), and (iii) were available to work additional hours given an opportunity for more work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization. “Labour Force Statistics database (LFS)” ILOSTAT. Accessed January 07, 2025. https://ilostat.ilo.org/data/." }, { "id": "Topic", "value": "Employment and Time Use" } ], "source_id": "14" }, { "id": "SL.EMP.VULN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks. The vulnerable employment rate, which is the share of vulnerable employment in total employment, was an indicator of the (now finished) Millennium Development Goals, under the employment, target on decent work." }, { "id": "IndicatorName", "value": "Vulnerable employment, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of vulnerable employment to the total employed is calculated as follows: (Contributing family workers + own-account workers)/Total employment x 100. \nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "14" }, { "id": "SL.EMP.VULN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks. The vulnerable employment rate, which is the share of vulnerable employment in total employment, was an indicator of the (now finished) Millennium Development Goals, under the employment, target on decent work." }, { "id": "IndicatorName", "value": "Vulnerable employment, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of vulnerable employment to the total employed is calculated as follows: (Contributing family workers + own-account workers)/Total employment x 100. \nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "14" }, { "id": "SL.EMP.VULN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks. The vulnerable employment rate, which is the share of vulnerable employment in total employment, was an indicator of the (now finished) Millennium Development Goals, under the employment, target on decent work." }, { "id": "IndicatorName", "value": "Vulnerable employment, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of vulnerable employment to the total employed is calculated as follows: (Contributing family workers + own-account workers)/Total employment x 100. \nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "14" }, { "id": "SL.EMP.WORK.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Data disaggregated by status in employment are provided according to the latest version of the International Standard Classification of Status in Employment (ICSE-93). Data may have been regrouped from the national classifications, which may not be strictly compatible with ICSE." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Wage and salaried workers, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of wage and salaried workers to the total employed is calculated as follows: Wage and salaried workers /Total employment x 100. \n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "14" }, { "id": "SL.EMP.WORK.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Wage and salaried workers, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of wage and salaried workers to the total employed is calculated as follows: Wage and salaried workers /Total employment x 100. \nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "14" }, { "id": "SL.EMP.WORK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Wage and salaried workers, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of wage and salaried workers to the total employed is calculated as follows: Wage and salaried workers /Total employment x 100. \nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "14" }, { "id": "SL.IND.EMPL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in industry, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "14" }, { "id": "SL.IND.EMPL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in industry, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "14" }, { "id": "SL.IND.EMPL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in industry (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "14" }, { "id": "SL.ISV.IFRM.FE.ZS", "metatype": [ { "id": "Generalcomments", "value": "Harmonized series" }, { "id": "IndicatorName", "value": "Informal employment, female (% of total non-agricultural employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are limitations for comparing data across countries and over time even within a country, due to differences in definitions and methodology of data collection. For example, informal sector enterprises refer to non-registered enterprises in some countries but registration requirements can vary from country to country. Others apply the employment size criterion only (which may vary from country to country). For detailed information on definitions and coverage, see footnotes." }, { "id": "Longdefinition", "value": "Employment in the informal economy as a percentage of total non-agricultural employment. It basically includes all jobs in unregistered and/or small-scale private unincorporated enterprises that produce goods or services meant for sale or barter. Self-employed street vendors, taxi drivers and home-base workers, regardless of size, are all considered enterprises. However, agricultural and related activities, households producing goods exclusively for their own use (e.g. subsistence farming, domestic housework, care work, and employment of paid domestic workers), and volunteer services rendered to the community are excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of September 2020." }, { "id": "Statisticalconceptandmethodology", "value": "There are wide variations in definitions and methodology of data collection. In addition to employment in the informal economy, informal employment within the formal sector should be also taken into account. Casual, short term, and seasonal workers, for example, could be informally employed — lacking social protection, health benefits, legal status, rights and freedom of association. Some countries now provide data according to the guidelines, adopted by the 17th International Conference of Labour Statisticians (2003); Informal employment as the total number of informal jobs, whether carried out in formal sector enterprises, informal sector enterprises, or households, during a given reference period." }, { "id": "Topic", "value": "Employment and Time Use" } ], "source_id": "14" }, { "id": "SL.ISV.IFRM.MA.ZS", "metatype": [ { "id": "Generalcomments", "value": "Harmonized series" }, { "id": "IndicatorName", "value": "Informal employment, male (% of total non-agricultural employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are limitations for comparing data across countries and over time even within a country, due to differences in definitions and methodology of data collection. For example, informal sector enterprises refer to non-registered enterprises in some countries but registration requirements can vary from country to country. Others apply the employment size criterion only (which may vary from country to country). For detailed information on definitions and coverage, see footnotes." }, { "id": "Longdefinition", "value": "Employment in the informal economy as a percentage of total non-agricultural employment. It basically includes all jobs in unregistered and/or small-scale private unincorporated enterprises that produce goods or services meant for sale or barter. Self-employed street vendors, taxi drivers and home-base workers, regardless of size, are all considered enterprises. However, agricultural and related activities, households producing goods exclusively for their own use (e.g. subsistence farming, domestic housework, care work, and employment of paid domestic workers), and volunteer services rendered to the community are excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of September 2020." }, { "id": "Statisticalconceptandmethodology", "value": "There are wide variations in definitions and methodology of data collection. In addition to employment in the informal economy, informal employment within the formal sector should be also taken into account. Casual, short term, and seasonal workers, for example, could be informally employed — lacking social protection, health benefits, legal status, rights and freedom of association. Some countries now provide data according to the guidelines, adopted by the 17th International Conference of Labour Statisticians (2003); Informal employment as the total number of informal jobs, whether carried out in formal sector enterprises, informal sector enterprises, or households, during a given reference period." }, { "id": "Topic", "value": "Employment and Time Use" } ], "source_id": "14" }, { "id": "SL.SRV.EMPL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in services, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "14" }, { "id": "SL.SRV.EMPL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in services, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "14" }, { "id": "SL.SRV.EMPL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in services (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "14" }, { "id": "SL.TLF.0714.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, female (% of female children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SL.TLF.0714.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, male (% of male children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SL.TLF.0714.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, total (% of children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "14" }, { "id": "SL.TLF.ACTI.1524.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, female (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female population ages 15-24" } ], "source_id": "14" }, { "id": "SL.TLF.ACTI.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female population ages 15-24" } ], "source_id": "14" }, { "id": "SL.TLF.ACTI.1524.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, male (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male population ages 15-24" } ], "source_id": "14" }, { "id": "SL.TLF.ACTI.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, male (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male population ages 15-24" } ], "source_id": "14" }, { "id": "SL.TLF.ACTI.1524.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, total (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-24" } ], "source_id": "14" }, { "id": "SL.TLF.ACTI.1524.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, total (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-24" } ], "source_id": "14" }, { "id": "SL.TLF.ACTI.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, female (% of female population ages 15-64) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female population ages 15-64" } ], "source_id": "14" }, { "id": "SL.TLF.ACTI.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, male (% of male population ages 15-64) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male population ages 15-64" } ], "source_id": "14" }, { "id": "SL.TLF.ACTI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, total (% of total population ages 15-64) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-64" } ], "source_id": "14" }, { "id": "SL.TLF.ADVN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with advanced education, female (% of female working-age population with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with advanced education to the working-age population with advanced education. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female working-age population with advanced education" } ], "source_id": "14" }, { "id": "SL.TLF.ADVN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with advanced education, male (% of male working-age population with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with advanced education to the working-age population with advanced education. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male working-age population with advanced education" } ], "source_id": "14" }, { "id": "SL.TLF.ADVN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with advanced education (% of total working-age population with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with advanced education to the working-age population with advanced education. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total working-age population with advanced education" } ], "source_id": "14" }, { "id": "SL.TLF.BASC.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with basic education, female (% of female working-age population with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with basic education to the working-age population with basic education. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female working-age population with basic education" } ], "source_id": "14" }, { "id": "SL.TLF.BASC.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with basic education, male (% of male working-age population with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with basic education to the working-age population with basic education. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male working-age population with basic education" } ], "source_id": "14" }, { "id": "SL.TLF.BASC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with basic education (% of total working-age population with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with basic education to the working-age population with basic education. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total working-age population with basic education" } ], "source_id": "14" }, { "id": "SL.TLF.CACT.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, female (% of female population ages 15+) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female population ages 15+" } ], "source_id": "14" }, { "id": "SL.TLF.CACT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, female (% of female population ages 15+) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\n\n\n\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\n\n\n\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "The labor force participation rate is the labor force as a percent of the population ages 15 and older. The labor force is the sum of all persons of working age who are employed and those who are unemployed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate (LFPR) is calculated as follows: \n\nLFPR (%) = 100 x Labor force / population of a given age group, where the labor force is equal to employment plus unemployment.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Population censuses are another major source of data on the labor force and its components.\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female population ages 15+" } ], "source_id": "14" }, { "id": "SL.TLF.CACT.FM.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "SL.TLF.CACT.FE.NE.ZS'/'SL.TLF.CACT.MA.NE.ZS'*100" }, { "id": "Developmentrelevance", "value": "Estimates of women in the labor force and employment are generally lower than those of men and are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic. In many low-income countries women often work on farms or in other family enterprises without pay, and others work in or near their homes, mixing work and family activities during the day. In many high-income economies, women have been increasingly acquiring higher education that has led to better-compensated, longer-term careers rather than lower-skilled, shorter-term jobs. However, access to good- paying occupations for women remains unequal in many occupations and countries around the world. Labor force statistics by gender is important to monitor gender disparities in employment and unemployment patterns." }, { "id": "IndicatorName", "value": "Ratio of female to male labor force participation rate (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\n\n\n\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\n\n\n\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Ratio of female to male labor force participation rate is the proportion of female labor force participation relative to male labor force participation. The labor force participation rate is the labor force as a percent of the population ages 15 and older. The labor force is the sum of all persons of working age who are employed and those who are unemployed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100. The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\n\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "14" }, { "id": "SL.TLF.CACT.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Estimates of women in the labor force and employment are generally lower than those of men and are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic. In many low-income countries women often work on farms or in other family enterprises without pay, and others work in or near their homes, mixing work and family activities during the day. In many high-income economies, women have been increasingly acquiring higher education that has led to better-compensated, longer-term careers rather than lower-skilled, shorter-term jobs. However, access to good- paying occupations for women remains unequal in many occupations and countries around the world. Labor force statistics by gender is important to monitor gender disparities in employment and unemployment patterns." }, { "id": "IndicatorName", "value": "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100. The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "14" }, { "id": "SL.TLF.CACT.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, male (% of male population ages 15+) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male population ages 15+" } ], "source_id": "14" }, { "id": "SL.TLF.CACT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, male (% of male population ages 15+) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male population ages 15+" } ], "source_id": "14" }, { "id": "SL.TLF.CACT.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, total (% of total population ages 15+) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total population ages 15+" } ], "source_id": "14" }, { "id": "SL.TLF.CACT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, total (% of total population ages 15+) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total population ages 15+" } ], "source_id": "14" }, { "id": "SL.TLF.INTM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with intermediate education, female (% of female working-age population with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with intermediate education to the working-age population with intermediate education. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female working-age population with intermediate education" } ], "source_id": "14" }, { "id": "SL.TLF.INTM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with intermediate education, male (% of male working-age population with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with intermediate education to the working-age population with intermediate education. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male working-age population with intermediate education" } ], "source_id": "14" }, { "id": "SL.TLF.INTM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with intermediate education (% of total working-age population with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with intermediate education to the working-age population with intermediate education. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total working-age population with intermediate education" } ], "source_id": "14" }, { "id": "SL.TLF.PART.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Part-time employment has been seen as an instrument to increase labor supply. Indeed, as part-time work may offer the chance of a better balance between working life and family responsibilities, and suits workers who prefer shorter working hours and more time for their private life, it may allow more working-age persons to actually join the labor force. Also, policy-makers have promoted part-time work in an attempt to redistribute working time in countries of high unemployment, thus lowering politically sensitive unemployment rates without requiring an increase in the total number of hours worked.\n\nPart-time employment, however, is not always a choice. While flexibility may be one advantage of part-time work, disadvantages may exist in comparison with colleagues who work full time. Since the early 1990s, most OECD countries have introduced measures to improve the quality of part-time work, for example with respect to social benefits for part-time workers in line with those of full-time workers. Nevertheless, occupational segregation between part-time and full-time work remains an issue in most countries as it limits the occupational choices of part-time workers.\n\nLooking at part-time employment by sex is useful to see the extent to which the female labor force is more likely to work part time than the male labor force." }, { "id": "IndicatorName", "value": "Part time employment, female (% of total female employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Part-time employment rate represents the percentage of employment that is part time. Part time employment in this series is based on a common definition of less than 35 actual weekly hours worked." }, { "id": "Othernotes", "value": "Relevance to gender indicator: More and more women are working part-time and one of the concern is that part time work does not provide the stability that full time work does." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1976-2024" }, { "id": "Source", "value": "Wages and Working Time Statistics database (COND), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are typically the preferred source of information on hours of work. Such surveys can be designed to cover virtually the entire non-institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders.\nOther types of household surveys could also be used as sources of data on hours of work, if they have an appropriate module on the topic.\nIn the absence of a labor force survey or other types of household surveys with a module on working time, an establishment survey can be used as a source of statistics on hours of work. However, the statistics derived from establishments surveys would typically not refer to the whole employed population but only to employees (and often only to formal sector employees or non-agricultural formal sector employees).\n\nStatistical concept(s): Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work)." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total female employment" } ], "source_id": "14" }, { "id": "SL.TLF.PART.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Part-time employment has been seen as an instrument to increase labor supply. Indeed, as part-time work may offer the chance of a better balance between working life and family responsibilities, and suits workers who prefer shorter working hours and more time for their private life, it may allow more working-age persons to actually join the labor force. Also, policy-makers have promoted part-time work in an attempt to redistribute working time in countries of high unemployment, thus lowering politically sensitive unemployment rates without requiring an increase in the total number of hours worked.\n\nPart-time employment, however, is not always a choice. While flexibility may be one advantage of part-time work, disadvantages may exist in comparison with colleagues who work full time. Since the early 1990s, most OECD countries have introduced measures to improve the quality of part-time work, for example with respect to social benefits for part-time workers in line with those of full-time workers. Nevertheless, occupational segregation between part-time and full-time work remains an issue in most countries as it limits the occupational choices of part-time workers.\n\nLooking at part-time employment by sex is useful to see the extent to which the female labor force is more likely to work part time than the male labor force." }, { "id": "IndicatorName", "value": "Part time employment, male (% of total male employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Part-time employment rate represents the percentage of employment that is part time. Part time employment in this series is based on a common definition of less than 35 actual weekly hours worked." }, { "id": "Othernotes", "value": "Relevance to gender indicator: More and more women are working part-time and one of the concern is that part time work does not provide the stability that full time work does." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1976-2024" }, { "id": "Source", "value": "Wages and Working Time Statistics database (COND), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are typically the preferred source of information on hours of work. Such surveys can be designed to cover virtually the entire non-institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders.\nOther types of household surveys could also be used as sources of data on hours of work, if they have an appropriate module on the topic.\nIn the absence of a labor force survey or other types of household surveys with a module on working time, an establishment survey can be used as a source of statistics on hours of work. However, the statistics derived from establishments surveys would typically not refer to the whole employed population but only to employees (and often only to formal sector employees or non-agricultural formal sector employees).\n\nStatistical concept(s): Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work)." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total male employment" } ], "source_id": "14" }, { "id": "SL.TLF.PART.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Part-time employment has been seen as an instrument to increase labor supply. Indeed, as part-time work may offer the chance of a better balance between working life and family responsibilities, and suits workers who prefer shorter working hours and more time for their private life, it may allow more working-age persons to actually join the labor force. Also, policy-makers have promoted part-time work in an attempt to redistribute working time in countries of high unemployment, thus lowering politically sensitive unemployment rates without requiring an increase in the total number of hours worked.\n\nPart-time employment, however, is not always a choice. While flexibility may be one advantage of part-time work, disadvantages may exist in comparison with colleagues who work full time. Since the early 1990s, most OECD countries have introduced measures to improve the quality of part-time work, for example with respect to social benefits for part-time workers in line with those of full-time workers. Nevertheless, occupational segregation between part-time and full-time work remains an issue in most countries as it limits the occupational choices of part-time workers.\n\nLooking at part-time employment by sex is useful to see the extent to which the female labor force is more likely to work part time than the male labor force." }, { "id": "IndicatorName", "value": "Part time employment, total (% of total employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Part-time employment rate represents the percentage of employment that is part time. Part time employment in this series is based on a common definition of less than 35 actual weekly hours worked." }, { "id": "Othernotes", "value": "Relevance to gender indicator: More and more women are working part-time and one of the concern is that part time work does not provide the stability that full time work does." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1976-2024" }, { "id": "Source", "value": "Wages and Working Time Statistics database (COND), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are typically the preferred source of information on hours of work. Such surveys can be designed to cover virtually the entire non-institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders.\nOther types of household surveys could also be used as sources of data on hours of work, if they have an appropriate module on the topic.\nIn the absence of a labor force survey or other types of household surveys with a module on working time, an establishment survey can be used as a source of statistics on hours of work. However, the statistics derived from establishments surveys would typically not refer to the whole employed population but only to employees (and often only to formal sector employees or non-agricultural formal sector employees).\n\nStatistical concept(s): Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work)." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "14" }, { "id": "SL.TLF.TOTL.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Labor force, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female labor force comprises women ages 15 and older who supply labor for the production of goods and services during a specified period. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Notesfromoriginalsource", "value": "Given the exceptional situation, including the scarcity of relevant data, the ILO modeled estimates and projections from 2020 onwards are subject to substantial uncertainty." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, World Development Indicators database. Estimates are based on data obtained from International Labour Organization and United Nations Population Division." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or voluntarily left work. In addition, persons who did not look for work but have an arrangement for a future job are also counted as unemployed. Still, some unemployment is unavoidable—at any time, some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. The labor force or the economically active portion of the population serves as the base for this indicator, not the total population.\n\nEstimates are based on labor force participation rates and population data from International Labour Organization and United Nations Population Division. The labor force participation rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/" }, { "id": "Topic", "value": "Employment and Time Use" } ], "source_id": "14" }, { "id": "SL.TLF.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force, female (% of total labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB), International Labour Organization (ILO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are based on labor force participation rates and population data from International Labour Organization and United Nations Population Division. The labor force participation rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or voluntarily left work. In addition, persons who did not look for work but have an arrangement for a future job are also counted as unemployed. Still, some unemployment is unavoidable—at any time, some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. The labor force or the economically active portion of the population serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total labor force" } ], "source_id": "14" }, { "id": "SL.TLF.TOTL.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "ROUND(('SP.POP.1564.TO' + 'SP.POP.65UP.TO') * 'SL.TLF.CACT.ZS' / 100,0)" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB), International Labour Organization (ILO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are based on labor force participation rates and population data from International Labour Organization and United Nations Population Division. The labor force participation rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or voluntarily left work. In addition, persons who did not look for work but have an arrangement for a future job are also counted as unemployed. Still, some unemployment is unavoidable—at any time, some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. The labor force or the economically active portion of the population serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Persons" } ], "source_id": "14" }, { "id": "SL.TLF.TOTL.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Labor force, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male labor force comprises men ages 15 and older who supply labor for the production of goods and services during a specified period. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Notesfromoriginalsource", "value": "Given the exceptional situation, including the scarcity of relevant data, the ILO modeled estimates and projections from 2020 onwards are subject to substantial uncertainty." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, World Development Indicators database. Estimates are based on data obtained from International Labour Organization and United Nations Population Division." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or voluntarily left work. In addition, persons who did not look for work but have an arrangement for a future job are also counted as unemployed. Still, some unemployment is unavoidable—at any time, some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. The labor force or the economically active portion of the population serves as the base for this indicator, not the total population.\n\nEstimates are based on labor force participation rates and population data from International Labour Organization and United Nations Population Division. The labor force participation rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/" }, { "id": "Topic", "value": "Employment and Time Use" } ], "source_id": "14" }, { "id": "SL.UEM.1524.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth female (% of female labor force ages 15-24) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\n\n\n\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\n\n\n\n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\n\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female labor force ages 15-24" } ], "source_id": "14" }, { "id": "SL.UEM.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\n\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth female (% of female labor force ages 15-24) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\n\n\n\n\n\n\n\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\n\n\n\n\n\n\n\n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available.\n\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female labor force ages 15-24" } ], "source_id": "14" }, { "id": "SL.UEM.1524.FM.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Youth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nUnemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nIn many developing countries women work on farms or in other family enterprises without pay and others work in or near their homes, mixing work and family activities during the day. Labor force statistics by gender is important to monitor gender disparities in unemployment patterns. In many developed economies, women have been increasingly acquiring higher education that has led to better-compensated, longer-term careers rather than lower-skilled, shorter-term jobs. However, access to good- paying occupations for women remains unequal in many occupations and countries around the world." }, { "id": "Generalcomments", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Ratio of female to male youth unemployment rate (% ages 15-24) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on youth unemployment are drawn from labor force sample surveys and general household sample surveys, censuses, and official estimates, which are generally based on information from different sources and can be combined in many ways. Administrative records, such as social insurance statistics and employment office statistics, need to be treated with care because of their limitations in coverage.\n\nLabor force surveys generally yield the most comprehensive data because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These surveys generally use a definition of unemployment that follows the international recommendations more closely than that used by other sources and therefore generate statistics that are more comparable internationally. But the age group, geographic coverage, and collection methods could differ by country or change over time within a country. For detailed information, consult the original source.\n\nThe \"youth\" is defined as ages 15-24, but the lower age limit for young people in a country could be determined by the minimum age for leaving school, so age groups could differ across countries. Also, since this age group is likely to include school leavers, the level of youth unemployment varies considerably over the year as a result of different school opening and closing dates.\n\nThe ILO definition of unemployment notwithstanding, reference periods, the criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time vary across countries. In many developing countries it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey, for example, can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the count of both women and men although women may have a higher probability of being excluded from the count of unemployed because they suffer more from social barriers overall that impede them from meeting this criterion. There are situations where the conventional means of seeking work are of limited relevance - for example, in developing economies where the informal economy is rampant and where the labour force is largely self-employed. In such cases, the standard definition of unemployment would greatly undercount the untapped human resources of a country and would give a picture of the labour market that was more positive than reality would warrant." }, { "id": "Longdefinition", "value": "Ratio of female to male youth unemployment is the percentage of female to male youth unemployment rates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, World Development Indicators database. Estimates are based on data obtained from International Labour Organization, ILOSTAT at https://ilostat.ilo.org/data/." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work. Persons who did not look for work but have an arrangements for a future job are counted as unemployed. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population. Data are based on labor force sample surveys, general household sample surveys, censuses, official estimates, and administrative records." }, { "id": "Topic", "value": "Employment and Time Use" } ], "source_id": "14" }, { "id": "SL.UEM.1524.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Youth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nUnemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nIn many developing countries women work on farms or in other family enterprises without pay and others work in or near their homes, mixing work and family activities during the day. Labor force statistics by gender is important to monitor gender disparities in unemployment patterns. In many developed economies, women have been increasingly acquiring higher education that has led to better-compensated, longer-term careers rather than lower-skilled, shorter-term jobs. However, access to good- paying occupations for women remains unequal in many occupations and countries around the world." }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Ratio of female to male youth unemployment rate (% ages 15-24) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There may be persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the count of both women and men although women may have a higher probability of being excluded from the count of unemployed because they suffer more from social barriers overall that impede them from meeting this criterion. There are situations where the conventional means of seeking work are of limited relevance - for example, in developing economies where the informal economy is rampant and where the labour force is largely self-employed. In such cases, the standard definition of unemployment would greatly undercount the untapped human resources of a country and would give a picture of the labour market that was more positive than reality would warrant." }, { "id": "Longdefinition", "value": "Ratio of female to male youth unemployment is the percentage of female to male youth unemployment rates." }, { "id": "Notesfromoriginalsource", "value": "Given the exceptional situation, including the scarcity of relevant data, the ILO modeled estimates and projections from 2020 onwards are subject to substantial uncertainty." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, World Development Indicators database. Estimates are based on data obtained from International Labour Organization, ILOSTAT at https://ilostat.ilo.org/data/." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or voluntarily left work. In addition, persons who did not look for work but have an arrangement for a future job are also counted as unemployed. Still, some unemployment is unavoidable—at any time, some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. The labor force or the economically active portion of the population serves as the base for this indicator, not the total population.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/" }, { "id": "Topic", "value": "Employment and Time Use" } ], "source_id": "14" }, { "id": "SL.UEM.1524.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth male (% of male labor force ages 15-24) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\n\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male labor force ages 15-24" } ], "source_id": "14" }, { "id": "SL.UEM.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth male (% of male labor force ages 15-24) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male labor force ages 15-24" } ], "source_id": "14" }, { "id": "SL.UEM.1524.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth total (% of total labor force ages 15-24) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\n\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total labor force ages 15-24" } ], "source_id": "14" }, { "id": "SL.UEM.1524.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth total (% of total labor force ages 15-24) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total labor force ages 15-24" } ], "source_id": "14" }, { "id": "SL.UEM.ADVN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with advanced education, female (% of female labor force with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female labor force with advanced education" } ], "source_id": "14" }, { "id": "SL.UEM.ADVN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with advanced education, male (% of male labor force with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male labor force with advanced education" } ], "source_id": "14" }, { "id": "SL.UEM.ADVN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with advanced education (% of total labor force with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total labor force with advanced education" } ], "source_id": "14" }, { "id": "SL.UEM.BASC.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with basic education, female (% of female labor force with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with a basic level of education who are unemployed. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female labor force with basic education" } ], "source_id": "14" }, { "id": "SL.UEM.BASC.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with basic education, male (% of male labor force with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with a basic level of education who are unemployed. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male labor force with basic education" } ], "source_id": "14" }, { "id": "SL.UEM.BASC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with basic education (% of total labor force with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with a basic level of education who are unemployed. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total labor force with basic education" } ], "source_id": "14" }, { "id": "SL.UEM.INTM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with intermediate education, female (% of female labor force with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female labor force with intermediate education" } ], "source_id": "14" }, { "id": "SL.UEM.INTM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with intermediate education, male (% of male labor force with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male labor force with intermediate education" } ], "source_id": "14" }, { "id": "SL.UEM.INTM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with intermediate education (% of total labor force with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\n\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total labor force with intermediate education" } ], "source_id": "14" }, { "id": "SL.UEM.NEET.FE.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, female (% of female youth population) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\n\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of youth population" } ], "source_id": "14" }, { "id": "SL.UEM.NEET.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, female (% of female youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\n\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female youth population" } ], "source_id": "14" }, { "id": "SL.UEM.NEET.MA.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, male (% of male youth population) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\n\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male youth population" } ], "source_id": "14" }, { "id": "SL.UEM.NEET.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, male (% of male youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\n\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male youth population" } ], "source_id": "14" }, { "id": "SL.UEM.NEET.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, total (% of youth population) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\n\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female youth population" } ], "source_id": "14" }, { "id": "SL.UEM.NEET.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, total (% of youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\n\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of youth population" } ], "source_id": "14" }, { "id": "SL.UEM.TOTL.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, female (% of female labor force) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\n\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female labor force" } ], "source_id": "14" }, { "id": "SL.UEM.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, female (% of female labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of female labor force" } ], "source_id": "14" }, { "id": "SL.UEM.TOTL.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "With the aim of promoting international comparability, statistics presented on ILOSTAT are based on standard international definitions wherever feasible and may differ from official national figures. This series is based on the 13th ICLS definitions. For time series comparability, it includes countries that have implemented the 19th ICLS standards, for which data are also available in the Work Statistics -- 19th ICLS (WORK) database." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, male (% of male labor force) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While the unemployment rate may be considered the most informative labour market indicator, reflecting the general performance of the labour market and the economy as a whole, it should not be interpreted as a measure of economic hardship or of well-being. When based on the internationally-recommended standards, the unemployment rate simply reflects the proportion of the labour force that does not have a job but is available and actively looking for work. It says nothing about the economic resources of unemployed workers or their family members. Its use should, therefore, be limited to serving as a measurement of the utilization of labour and an indication of the failure to find work. Other measures, including income-related indicators, would be needed to evaluate economic hardship. An additional criticism of the aggregate unemployment measure is that it masks information on the composition of the jobless population and therefore misses out on the particularities of the education level, ethnic origin, socio-economic background, work experience, etc. of the unemployed. Moreover, the unemployment rate says nothing about the type of unemployment – whether it is cyclical and short-term or structural and long-term – which is a critical issue for policy makers in the development of their policy responses, especially given that structural unemployment cannot be addressed by boosting market demand only." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\n\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male labor force" } ], "source_id": "14" }, { "id": "SL.UEM.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, male (% of male labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of male labor force" } ], "source_id": "14" }, { "id": "SL.UEM.TOTL.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, total (% of total labor force) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\n\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total labor force" } ], "source_id": "14" }, { "id": "SL.UEM.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, total (% of total labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While the unemployment rate may be considered the most informative labour market indicator, reflecting the general performance of the labour market and the economy as a whole, it should not be interpreted as a measure of economic hardship or of well-being. When based on the internationally-recommended standards, the unemployment rate simply reflects the proportion of the labour force that does not have a job but is available and actively looking for work. It says nothing about the economic resources of unemployed workers or their family members. Its use should, therefore, be limited to serving as a measurement of the utilization of labour and an indication of the failure to find work. Other measures, including income-related indicators, would be needed to evaluate economic hardship. An additional criticism of the aggregate unemployment measure is that it masks information on the composition of the jobless population and therefore misses out on the particularities of the education level, ethnic origin, socio-economic background, work experience, etc. of the unemployed. Moreover, the unemployment rate says nothing about the type of unemployment – whether it is cyclical and short-term or structural and long-term – which is a critical issue for policy makers in the development of their policy responses, especially given that structural unemployment cannot be addressed by boosting market demand only." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "% of total labor force" } ], "source_id": "14" }, { "id": "SP.ADO.TFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Adolescent childbearing is associated with a wide range of risks for young mothers. Women who become pregnant and give birth very early in their lives as well as their newborns are subject to elevated health risks." }, { "id": "IndicatorName", "value": "Adolescent fertility rate (births per 1,000 women ages 15-19)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adolescent fertility rate is the number of births per 1,000 women ages 15-19." }, { "id": "Othernotes", "value": "Figures from 1950 to 2023 are estimates based on available data. From 2024 onward, the figures are projections that use current trends to anticipate future changes. Estimates reflect the past; projections incorporate assumptions about the future and are updated as new information emerges. Please interpret these figures carefully, as they are subject to change. For details, see the WPP methodology: https://population.un.org/wpp/methodology. This is the Sustainable Development Goal indicator 3.7.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Adolescent fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without vital registration systems fertility rates are generally based on censuses or surveys.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 1000 women" } ], "source_id": "14" }, { "id": "SP.DYN.AMRT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "If available, derived from life tables of Human Mortality Database (HMD) by Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France)." }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, adult, female (per 1,000 female adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data from United Nations Population Division's World Populaton Prospects are originally 5-year period data and the presented are linearly interpolated by the World Bank for annual series. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Adult mortality rate, female, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old female dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages." }, { "id": "Othernotes", "value": "Figures from 1950 to 2023 are estimates based on available data. From 2024 onward, the figures are projections that use current trends to anticipate future changes. Estimates reflect the past; projections incorporate assumptions about the future and are updated as new information emerges. Please interpret these figures carefully, as they are subject to change. For details, see the WPP methodology: https://population.un.org/wpp/methodology" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nHuman Mortality Database, Max Planck Institute for Demographic Research, uri: www.mortality.org;\nUniversity of California, Berkeley, uri: www.mortality.org, note: Human Mortality Database;\nFrench Institute for Demographic Studies, uri: www.mortality.org, note: Human Mortality Database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors, l(x), at exact age x in a female period life table. The formula is: (l(60)-l(15))/(l(15))*1000.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data. Where reliable age-specific mortality data are available, life tables can be constructed from age-specific mortality data, and adult mortality rates can be calculated from life tables." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 1000 female adults" } ], "source_id": "14" }, { "id": "SP.DYN.AMRT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "If available, derived from life tables of Human Mortality Database (HMD) by Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France)." }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, adult, male (per 1,000 male adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data from United Nations Population Division's World Populaton Prospects are originally 5-year period data and the presented are linearly interpolated by the World Bank for annual series. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Adult mortality rate, male, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old male dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages." }, { "id": "Othernotes", "value": "Figures from 1950 to 2023 are estimates based on available data. From 2024 onward, the figures are projections that use current trends to anticipate future changes. Estimates reflect the past; projections incorporate assumptions about the future and are updated as new information emerges. Please interpret these figures carefully, as they are subject to change. For details, see the WPP methodology: https://population.un.org/wpp/methodology" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nHuman Mortality Database, Max Planck Institute for Demographic Research, uri: www.mortality.org;\nUniversity of California, Berkeley, uri: www.mortality.org, note: Human Mortality Database;\nFrench Institute for Demographic Studies, uri: www.mortality.org, note: Human Mortality Database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors, l(x), at exact age x in a male period life table. The formula is: (l(60)-l(15))/(l(15))*1000.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data. Where reliable age-specific mortality data are available, life tables can be constructed from age-specific mortality data, and adult mortality rates can be calculated from life tables." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 1000 male adults" } ], "source_id": "14" }, { "id": "SP.DYN.CBRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The crude birth rate is not appropriate for comparison of different populations or areas with large differences in age-distributions. When the crude death rate is subtracted from the crude birth rate, the result is the rate of natural increase, which is the rate of population change in the absence of migration." }, { "id": "IndicatorName", "value": "Birth rate, crude (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Vital registers are the preferred source for these data, but in many developing countries systems for registering births and deaths are absent or incomplete because of deficiencies in the coverage of events or geographic areas. Many developing countries carry out special household surveys that ask respondents about recent births and deaths. Estimates derived in this way are subject to sampling errors and recall errors." }, { "id": "Longdefinition", "value": "Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT);\nPopulation and Vital Statistics Report (various years), United Nations (UN), publisher: UN Statistical Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The crude birth rate is calculated as the number of births in a given period divided by the average population in that period. For human populations the period is usually one year and, if the population changes in size over the year, the divisor is taken as the population at the mid-year. The rate is usually expressed in terms of 1,000 people: for example, a crude birth rate of 9.5 (per 1000 people) in a population of 1 million would imply 9500 births per year in the entire population.\nStatistical concept(s): Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration. Vital rates are based on data from birth and death registration systems, censuses, and sample surveys by national statistical offices and other organizations, or on demographic analysis. Data for the most recent year for some high-income countries are provisional estimates based on vital registers. The estimates for many other countries are from the United Nations Population Division." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "14" }, { "id": "SP.DYN.CDRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The crude death rate is a good indicator of the general health status of a geographic area or population. The crude death rate is not appropriate for comparison of different populations or areas with large differences in age-distributions. Higher crude death rates can be found in some developed countries, despite high life expectancy, because typically these countries have a much higher proportion of older people, due to lower recent birth rates and lower age-specific mortality rates." }, { "id": "IndicatorName", "value": "Death rate, crude (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Vital registers are the preferred source for these data, but in many developing countries systems for registering births and deaths are absent or incomplete because of deficiencies in the coverage of events or geographic areas. Many developing countries carry out special household surveys that ask respondents about recent births and deaths. Estimates derived in this way are subject to sampling errors and recall errors." }, { "id": "Longdefinition", "value": "Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT);\nPopulation and Vital Statistics Report (various years), United Nations (UN), publisher: UN Statistical Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The crude death rate is calculated as the number of deaths in a given period divided by the population exposed to risk of death in that period. For human populations the period is usually one year and, if the population changes in size over the year, the divisor is taken as the population at the mid-year. The rate is usually expressed in terms of 1,000 people: for example, a crude death rate of 9.5 (per 1000 people) in a population of 1 million would imply 9500 deaths per year in the entire population.\nStatistical concept(s): Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration. Vital rates are based on data from birth and death registration systems, censuses, and sample surveys by national statistical offices and other organizations, or on demographic analysis. Data for the most recent year for some high-income countries are provisional estimates based on vital registers. The estimates for many other countries are from the United Nations Population Division." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "14" }, { "id": "SP.DYN.CONM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Contraceptive prevalence among women of reproductive age is related to maternal and child health, as well as gender equality and HIV/AIDS. Contraceptives enable women and men to make informed decisions on family planning – whether, when, and how many children they would have. \n\n\n\nPreventing unwanted pregnancies is essential to reducing maternal deaths, especially in low- and middle- income countries where maternal mortality rate is high. With effective contraception, life-threatening pregnancy complications can be reduced, and thus maternal deaths can be averted. \n\n\n\nUsing condoms (one of the modern contraceptive methods) can prevent pregnancy as well as sexually transmitted diseases, including HIV." }, { "id": "IndicatorName", "value": "Contraceptive prevalence, any modern method (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While the data availability on contraceptive use has increased, in many countries the contraceptive use data are available only for married women. \n\n\n\nThe time frame used to assess contraceptive prevalence may vary. In many surveys, it is left to the respondent to determine what is meant by “currently using” a method of contraception." }, { "id": "Longdefinition", "value": "Contraceptive prevalence, any modern method is the percentage of married women ages 15-49 who are practicing, or whose sexual partners are practicing, at least one modern method of contraception. Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Contraceptive prevalence rates are obtained mainly from nationally representative household surveys, including: Demographic and Health Surveys; Multiple Indicator Cluster Surveys; Contraceptive Prevalence Surveys; Gender and Generations Survey; Reproductive Health Surveys; and World Fertility Surveys. Additional information was provided by other international survey programs and national surveys. \n\n\n\nMarried women refer to women who are married (defined in relation to the marriage laws or customs of a country) and to women in a union, which refers to women living with their partner in the same household (also referred to as cohabiting unions, consensual unions, unmarried unions, or “living together”)." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SP.DYN.CONU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Contraceptive prevalence among women of reproductive age is related to maternal and child health, as well as gender equality and HIV/AIDS. Contraceptives enable women and men to make informed decisions on family planning – whether, when, and how many children they would have. \n\n\n\nPreventing unwanted pregnancies is essential to reducing maternal deaths, especially in low- and middle- income countries where maternal mortality rate is high. With effective contraception, life-threatening pregnancy complications can be reduced, and thus maternal deaths can be averted. \n\n\n\nUsing condoms (one of the modern contraceptive methods) can prevent pregnancy as well as sexually transmitted diseases, including HIV." }, { "id": "IndicatorName", "value": "Contraceptive prevalence, any method (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While the data availability on contraceptive use has increased, in many countries the contraceptive use data are available only for married women. \n\n\n\nThe time frame used to assess contraceptive prevalence may vary. In many surveys, it is left to the respondent to determine what is meant by “currently using” a method of contraception." }, { "id": "Longdefinition", "value": "Contraceptive prevalence, any method is the percentage of married women ages 15-49 who are practicing, or whose sexual partners are practicing, any method of contraception (modern or traditional). Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception. Traditional methods of contraception include rhythm (e.g., fertility awareness based methods, periodic abstinence), withdrawal and other traditional methods." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Contraceptive prevalence rates are obtained mainly from nationally representative household surveys, including: Demographic and Health Surveys; Multiple Indicator Cluster Surveys; Contraceptive Prevalence Surveys; Gender and Generations Survey; Reproductive Health Surveys; and World Fertility Surveys. Additional information was provided by other international survey programs and national surveys. \n\n\n\nMarried women refer to women who are married (defined in relation to the marriage laws or customs of a country) and to women in a union, which refers to women living with their partner in the same household (also referred to as cohabiting unions, consensual unions, unmarried unions, or “living together”)." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "SP.DYN.IMRT.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, infant, female (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Infant mortality rate, female is the number of female infants dying before reaching one year of age, per 1,000 female live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "14" }, { "id": "SP.DYN.IMRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, infant (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "14" }, { "id": "SP.DYN.IMRT.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, infant, male (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Infant mortality rate, male is the number of male infants dying before reaching one year of age, per 1,000 male live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "14" }, { "id": "SP.DYN.LE00.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, female (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "14" }, { "id": "SP.DYN.LE00.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, total (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), uri: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices, note: Derived from male and female life expectancy at birth from sources such as statistical databases and publications from national statistical offices.;\nDemographic Statistics, Eurostat (ESTAT), note: Derived from male and female life expectancy at birth from sources such as Eurostat: Demographic Statistics." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates, or derived from male and female life expectancy at birth.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "14" }, { "id": "SP.DYN.LE00.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, male (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "14" }, { "id": "SP.DYN.LE60.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Life expectancy at age 60, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Life expectancy at age 60, female is the average number of years that a female at age 60 would live if prevailing patterns of mortality at the time of age 60 were to stay the same throughout her life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "2022 World Population Prospects, United Nations Population Division: Retrieved August 31, 2023." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.DYN.LE60.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Life expectancy at age 60, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Life expectancy at age 60, male is the average number of years that a male at age 60 would live if prevailing patterns of mortality at the time of age 60 were to stay the same throughout his life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "2022 World Population Prospects, United Nations Population Division: Retrieved August 31, 2023." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.DYN.SMAM.FE", "metatype": [ { "id": "IndicatorName", "value": "Mean age at first marriage, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates by age may be affected by age misreporting. Marital status may be misreported, particularly in societies where divorce or separation is not socially acceptable. The differences in marital status categories included over time and their definitions limit comparability of data across time and countries. Data derived from surveys with small samples are subject to sampling error." }, { "id": "Longdefinition", "value": "Mean age at marriage, female shows the average length of single life expressed in years among those females who marry before age 50. It is a synthetic indicator calculated from marital status categories of men and women aged 15 to 54 at the census or survey date." }, { "id": "Notesfromoriginalsource", "value": "Note that the SMAM takes a single point in time and calculates the age at marriage from the marital status of the population aged between 15 and 50. This value is different from the mean age of marriage that is calculated from first marriage rates in a respective period (commonly used in countries with complete marriage registration systems) or cohort measures of entry into first marriage or union (based on retrospective survey questions on age at first marriage or union formation). The retrospective nature of the SMAM means that values are influenced by age and marital status specific mortality and migration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations, Department of Economic and Social Affairs, Population Division. World Marriage Data 2019." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.DYN.SMAM.MA", "metatype": [ { "id": "IndicatorName", "value": "Mean age at first marriage, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates by age may be affected by age misreporting. Marital status may be misreported, particularly in societies where divorce or separation is not socially acceptable. The differences in marital status categories included over time and their definitions limit comparability of data across time and countries. Data derived from surveys with small samples are subject to sampling error." }, { "id": "Longdefinition", "value": "Mean age at marriage, male shows the average length of single life expressed in years among those males who marry before age 50. It is a synthetic indicator calculated from marital status categories of men and women aged 15 to 54 at the census or survey date." }, { "id": "Notesfromoriginalsource", "value": "Note that the SMAM takes a single point in time and calculates the age at marriage from the marital status of the population aged between 15 and 50. This value is different from the mean age of marriage that is calculated from first marriage rates in a respective period (commonly used in countries with complete marriage registration systems) or cohort measures of entry into first marriage or union (based on retrospective survey questions on age at first marriage or union formation). The retrospective nature of the SMAM means that values are influenced by age and marital status specific mortality and migration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations, Department of Economic and Social Affairs, Population Division. World Marriage Data." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.DYN.TFRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries." }, { "id": "IndicatorName", "value": "Fertility rate, total (births per woman)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year." }, { "id": "Othernotes", "value": "Relevance to gender indicator: it can indicate the status of women within households and a woman’s decision about the number and spacing of children." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Total fertility rate is the sum of the age-specific fertility rates (multiplied by five, if the age-specific fertility rates are for 5-year age groups).\nStatistical concept(s): Total fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without reliable vital registration systems fertility rates are generally based on extrapolations from trends observed in censuses or surveys from earlier years." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Births per woman" } ], "source_id": "14" }, { "id": "SP.DYN.TO65.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. The lower the age specific mortality rates before age 65, the higher the proportion of people survive to age 65." }, { "id": "IndicatorName", "value": "Survival to age 65, female (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Survival to age 65 refers to the percentage of a cohort of newborn infants that would survive to age 65, if subject to age specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Figures from 1950 to 2023 are estimates based on available data. From 2024 onward, the figures are projections that use current trends to anticipate future changes. Estimates reflect the past; projections incorporate assumptions about the future and are updated as new information emerges. Please interpret these figures carefully, as they are subject to change. For details, see the WPP methodology: https://population.un.org/wpp/methodology" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors, l(x), at exact age x in a female period life table. The formula is: (l(65))/(l(0))*100.\nStatistical concept(s): Survival to age 65 is calculated in a period life table. It provides a population's mortality level up to age 65 at a given time. It therefore does not reflect the mortality level that a person actually experiences during his/her life, which can be calculated in a cohort life table." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SP.DYN.TO65.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. The lower the age specific mortality rates before age 65, the higher the proportion of people survive to age 65." }, { "id": "IndicatorName", "value": "Survival to age 65, male (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Survival to age 65 refers to the percentage of a cohort of newborn infants that would survive to age 65, if subject to age specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Figures from 1950 to 2023 are estimates based on available data. From 2024 onward, the figures are projections that use current trends to anticipate future changes. Estimates reflect the past; projections incorporate assumptions about the future and are updated as new information emerges. Please interpret these figures carefully, as they are subject to change. For details, see the WPP methodology: https://population.un.org/wpp/methodology" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors l(x) at exact age x, in a male period life table. The formula is: (l(65))/(l(0))*100.\nStatistical concept(s): Survival to age 65 is calculated in a period life table. It provides a population's mortality level up to age 65 at a given time. It therefore does not reflect the mortality level that a person actually experiences during his/her life, which can be calculated in a cohort life table." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SP.DYN.WFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Wanted fertility rate (births per woman)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Wanted fertility rate is an estimate of what the total fertility rate would be if all unwanted births were avoided." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Demographic and Health Surveys" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data is calculated by summing the seven age-specific wanted fertility rates, multiplying the result by five, and dividing by 1000. A birth is considered wanted if the number of living children at the time of conception is less than the ideal number of children as reported by the respondent. Special responses such as \"don't know,\" \"up to God,\" or other non-numeric responses for the ideal number of children are assumed to indicate a high ideal number of children. For more details, please refer to the DHS website: https://dhsprogram.com/data/Guide-to-DHS-Statistics/Wanted_Fertility.htm" }, { "id": "Topic", "value": "Health" }, { "id": "Unitofmeasure", "value": "Births per woman" } ], "source_id": "14" }, { "id": "SP.HOU.FEMA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The composition of households plays a pivotal role in determining the well-being of families and individuals. Research from multiple developed countries indicates that female-headed households, especially those with single mothers, face a higher risk of poverty than those with two parents (United Nations, \"Patterns and trends in household size and composition: Evidence from a United Nations dataset,\" 2019). Understanding the diversity in household structures across various populations is essential for achieving Sustainable Development Goal 1, which is dedicated to eradicating poverty in all its forms." }, { "id": "IndicatorName", "value": "Female headed households (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The definition of female-headed household differs greatly across countries, making cross-country comparison difficult. In some cases it is assumed that a woman cannot be the head of any household with an adult male, because of sex-biased stereotype. Caution should be used in interpreting the data." }, { "id": "Longdefinition", "value": "Female headed households refers to the percentage of households that are headed by females." }, { "id": "Othernotes", "value": "The composition of a household plays a role in the determining other characteristics of a household, such as how many children are sent to school and the distribution of family income." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "DHS API, DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: HC_HHHD_H_FEM; \tIndicator name from the original source: Female-headed households, publisher: DHS Program (ICF), type: API, date accessed: 2024-06-14" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of households headed by women divided by the total number of households. \n\n\n\n\n\n\n\n\n\n\n\nThe definition of a household is a person or group of related or unrelated persons who live together in the same dwelling unit(s), who acknowledge one adult male or female as the head of the household, who share the same housekeeping arrangements and who are considered a single unit.\nStatistical concept(s): The information on the characteristics of household head (e.g., sex, age) is collected the household questionnaire in the Demographic and Health Surveys (DHS). Typically, this data is obtained by detailing the connection of each member of the household to a designated central figure, who is considered the primary reference for the household." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "% of households" } ], "source_id": "14" }, { "id": "SP.M15.2024.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "test" }, { "id": "Developmentrelevance", "value": "Although the legal age of marriage is defined as 18 years in most countries, the practice of child marriage remains widespread. A women’s access to education and later her employment opportunities as well as the nature and terms of her work are often compromised by this practice. Young married girls whose schooling is cut short often lack the knowledge and skills for formal work and are limited to occupations with lower incomes and inferior working conditions. Sustainable Development Goal 5 commits to eliminate the practice of child marriage." }, { "id": "IndicatorName", "value": "Women who were first married by age 15 (% of women ages 20-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The measure of child marriage is designed to be retrospective, focusing on the age at first marriage among adult women who have already passed the risk period. Although it is feasible to assess the current marital status of girls under 15, this approach could underestimate the true extent of child marriage. This is because girls who are not married at the time of survey may still marry before reaching 15." }, { "id": "Longdefinition", "value": "Women who were first married by age 15 refers to the percentage of women ages 20-24 who were first married by age 15." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.3.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "UNICEF Data, UN Children's Fund (UNICEF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_20-24_MRD_U15; \tIndicator name from the original source: Percentage of women (aged 20-24 years) married or in union before age 15, type: API;\nDHS API, DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: MA_MBAG_W_B15; \tIndicator name from the original source: Women first married by exact age 15, type: API" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Number of women aged 20-24 who were first married or in union before age 15 divided by the total number of women aged 20-24 in the population multiplied by 100. The primary sources for this indicator are the Multiple Indicator Cluster Surveys (MICS) and the Demographic and Health Surveys (DHS). Additionally, other national household surveys and censuses contribute to the data. These figures are compiled by UNICEF, which coordinates with countries to gather the information.\nStatistical concept(s): This indicator includes both formal marriages and informal cohabiting relationships. Informal relationships are usually defined as those where a couple lives together with the intention of a long-term relationship but without a formal civil or religious ceremony. The incidence of child marriage is assessed retrospectively among women who are past the risk of marrying as children. The age range of 20 to 24 years is conventionally used to reflect the current prevalence of child marriage." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "% of women ages 20-24" } ], "source_id": "14" }, { "id": "SP.M18.2024.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although the legal age of marriage is defined as 18 years in most countries, the practice of child marriage remains widespread. A women’s access to education and later her employment opportunities as well as the nature and terms of her work are often compromised by this practice. Young married girls whose schooling is cut short often lack the knowledge and skills for formal work and are limited to occupations with lower incomes and inferior working conditions. Sustainable Development Goal 5 commits to eliminate the practice of child marriage." }, { "id": "IndicatorName", "value": "Women who were first married by age 18 (% of women ages 20-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The measure of child marriage is designed to be retrospective, focusing on the age at first marriage among adult women who have already passed the risk period. Although it is feasible to assess the current marital status of girls under 18, this approach could underestimate the true extent of child marriage. This is because girls who are not married at the time of survey may still marry before reaching 18." }, { "id": "Longdefinition", "value": "Women who were first married by age 18 refers to the percentage of women ages 20-24 who were first married by age 18." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.3.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "UNICEF Data, UN Children's Fund (UNICEF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_20-24_MRD_U18; \tIndicator name from the original source: Percentage of women (aged 20-24 years) married or in union before age 18, type: API;\nDHS API, DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: MA_MBAG_W_B18; \tIndicator name from the original source: Women first married by exact age 18, type: API" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Number of women aged 20-24 who were first married or in union before age 18 divided by the total number of women aged 20-24 in the population multiplied by 100. The primary sources for this indicator are the Multiple Indicator Cluster Surveys (MICS) and the Demographic and Health Surveys (DHS). Additionally, other national household surveys and censuses contribute to the data. These figures are compiled by UNICEF, which coordinates with countries to gather the information.\nStatistical concept(s): This indicator includes both formal marriages and informal cohabiting relationships. Informal relationships are usually defined as those where a couple lives together with the intention of a long-term relationship but without a formal civil or religious ceremony. The incidence of child marriage is assessed retrospectively among women who are past the risk of marrying as children. The age range of 20 to 24 years is conventionally used to reflect the current prevalence of child marriage." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "% of women ages 20-24" } ], "source_id": "14" }, { "id": "SP.MTR.1519.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Having a child during the teenage years limits girls' opportunities for better education, jobs, and income. Pregnancy is more likely to be unintended during the teenage years, and births are more likely to be premature and are associated with greater risks of complications during delivery and of death. In many countries maternal mortality is a leading cause of death among women of reproductive age, although most of those deaths are preventable. Infants of adolescent mothers are also more likely to have low birth weight, which can have a long-term impact on their health and development. Complications from pregnancy and childbirth are the leading cause of death among girls aged 15-19 years in many low- and middle-income countries." }, { "id": "IndicatorName", "value": "Teenage mothers (% of women ages 15-19 who have had children or are currently pregnant)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Teenage mothers are the percentage of women ages 15-19 who already have children or are currently pregnant." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Demographic and Health Surveys" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data represents the combined percentage of women aged 15-19 who are mothers and those who are pregnant with their first child. This information is gathered through individual interviews with women of reproductive age during household surveys, including Demographic and Health Surveys. For more details, please refer to the DHS website: https://dhsprogram.com/data/Guide-to-DHS-Statistics/Teenage_Pregnancy_and_Motherhood.htm" }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Percent of women ages 15-19 who have had children or are currently pregnant" } ], "source_id": "14" }, { "id": "SP.POP.0004.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 00-04, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.0004.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 00-04, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.0014.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "14" }, { "id": "SP.POP.0014.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "14" }, { "id": "SP.POP.0014.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB), note: Staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects., publisher: World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data.;\nWorld Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "14" }, { "id": "SP.POP.0014.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14 (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Population between the ages 0 to 14 as a percentage of the total population. Population is based on the de facto definition of population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects., United Nations Population Division, uri: https://population.un.org/wpp/, publisher: United Nations Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SP.POP.0509.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 05-09, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 5 to 9." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.0509.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 05-09, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 5 to 9." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.1014.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 10-14, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 10 to 14." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.1014.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 10-14, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 10 to 14." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.1519.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 15-19, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 15 to 19." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.1519.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 15-19, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 15 to 19." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.1564.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "14" }, { "id": "SP.POP.1564.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "14" }, { "id": "SP.POP.1564.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "14" }, { "id": "SP.POP.1564.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64 (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Total population between the ages 15 to 64 as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SP.POP.2024.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 20-24, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 20 to 24." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.2024.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 20-24, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 20 to 24." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.2529.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 25-29, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 25 to 29." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.2529.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 25-29, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 25 to 29." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.3034.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 30-34, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 30 to 34." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.3034.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 30-34, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 30 to 34." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.3539.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 35-39, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 35 to 39." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.3539.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 35-39, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 35 to 39." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.4044.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 40-44, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 40 to 44." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.4044.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 40-44, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 40 to 44." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.4549.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 45-49, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 45 to 49." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.4549.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 45-49, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 45 to 49." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.5054.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 50-54, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 50 to 54." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.5054.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 50-54, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 50 to 54." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.5559.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 55-59, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 55 to 59." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.5559.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 55-59, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 55 to 59." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.6064.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 60-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 60 to 64." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.6064.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 60-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 60 to 64." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.6569.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 65-69, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 65 to 69." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.6569.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 65-69, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 65 to 69." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.65UP.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "14" }, { "id": "SP.POP.65UP.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "14" }, { "id": "SP.POP.65UP.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "14" }, { "id": "SP.POP.65UP.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Population ages 65 and above as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SP.POP.7074.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 70-74, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 70 to 74." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.7074.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 70-74, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 70 to 74." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.7579.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 75-79, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 75 to 79." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.7579.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 75-79, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 75 to 79." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.80UP.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 80 and above, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 80 and above." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.80UP.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 80 and above, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 80 and above." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.AG00.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 00, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.AG00.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 00, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.AG01.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 01, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.AG01.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 01, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.AG02.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 02, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.AG02.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 02, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.AG03.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 03, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.AG03.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 03, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.AG04.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 04, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.AG04.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 04, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.AG05.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 05, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.AG05.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 05, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.POP.BRTH.MF", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In the absence of interference, it is expected that the sex ratio at birth is fairly stable within the range of 1.03 to 1.07 boys born per 1.00 girls. However, in some populations, the observed sex ratio at birth is well above this range because of sex-selection driven by the preference for sons over daughters." }, { "id": "IndicatorName", "value": "Sex ratio at birth (male births per female births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Sex ratio at birth refers to male births per female births." }, { "id": "Othernotes", "value": "Figures from 1950 to 2023 are estimates based on available data. From 2024 onward, the figures are projections that use current trends to anticipate future changes. Estimates reflect the past; projections incorporate assumptions about the future and are updated as new information emerges. Please interpret these figures carefully, as they are subject to change. For details, see the WPP methodology: https://population.un.org/wpp/methodology" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Sex ratio at birth is calculated as number of male births divided by number of female births.\nStatistical concept(s): If the sex ratio at birth is greater than 1, it indicates more boys born that year than girls." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Male births per female births" } ], "source_id": "14" }, { "id": "SP.POP.DPND", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Age dependency ratio (% of working-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Othernotes", "value": "Relevance to gender indicator: this indicator implies the dependency burden that the working-age population bears in relation to children and the elderly. Many times single or widowed women who are the sole caregiver of a household have a high dependency ratio." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Age dependency ratio is calculated as 100 x (Population (0-14) + Population (65+)) / Population (15-64). Data are shown as the proportion of dependents per 100 working-age population.\nStatistical concept(s): Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But dependency ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.\n\n\n\nAge structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SP.POP.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Increases in human population, whether as a result of immigration or more births than deaths, can impact natural resources and social infrastructure. This can place pressure on a country's sustainability. A significant growth in population will negatively impact the availability of land for agricultural production, and will aggravate demand for food, energy, water, social services, and infrastructure. On the other hand, decreasing population size - a result of fewer births than deaths, and people moving out of a country - can impact a government's commitment to maintain services and infrastructure." }, { "id": "IndicatorName", "value": "Population, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Current population estimates for developing countries that lack (i) reliable recent census data, and (ii) pre- and post-census estimates for countries with census data, are provided by the United Nations Population Division and other agencies. \n\n\n\nThe cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in both the model and the data.\n\n\n\nBecause future trends cannot be known with certainty, population projections have a wide range of uncertainty." }, { "id": "Longdefinition", "value": "Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates." }, { "id": "Othernotes", "value": "Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices, uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population estimates are usually based on national population censuses, and estimates of fertility, mortality and migration.\n\n\n\nErrors and undercounting in census occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census.\n\n\n\nThe quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data.\n\n\n\nThe currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census.\n\n\n\nThe United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems.\n\n\n\nInternational migration is the only other factor besides birth and death rates that directly determines a country's population change. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect.\n\n\n\nOne of the major data sources of this indicator is UN Population Division's World Population Prospects, which use the cohort component method to produce population estimates and projections.\n\n\n\nPopulation projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant.\nStatistical concept(s): Estimates of total population describe the size of total population. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "14" }, { "id": "SP.POP.TOTL.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Females comprise almost one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population is based on the de facto definition of population, which counts all female residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "14" }, { "id": "SP.POP.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age/sex distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Females comprise almost one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, female (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population is the percentage of the population that is female. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2022 Revision" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SP.POP.TOTL.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Males comprise about one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population is based on the de facto definition of population, which counts all male residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "14" }, { "id": "SP.REG.BRTH.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 16.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2003-2021" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.REG.BRTH.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 16.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2003-2021" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.REG.BRTH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 16.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.RUR.TOTL.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Rural population, female (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the urban and rural population by sex is based on the 2014 revision of World Urbanization Prospects (WUP) for urban and rural population and the 2012 revision of World Population Prospects (WPP) for total population by age and sex, the data for urban and rural population by sex is not comparable and consistent with the total population data presented in the World Development Indicators database which is based on the 2015 World Population Prospects" }, { "id": "Longdefinition", "value": "Female rural population is the percentage of females who live in rural areas to total population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Population Division's World Urbanization Prospects." }, { "id": "Statisticalconceptandmethodology", "value": "Estimates of the United Nations urban and rural population by sex is based on the 2014 revision of World Urbanization Prospects (WUP) for urban and rural population and the 2012 revision of World Population Prospects (WPP) for total population by age and sex for all countries or territories in the world." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.RUR.TOTL.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Rural population, male (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the urban and rural population by sex is based on the 2014 revision of World Urbanization Prospects (WUP) for urban and rural population and the 2012 revision of World Population Prospects (WPP) for total population by age and sex, the data for urban and rural population by sex is not comparable and consistent with the total population data presented in the World Development Indicators database which is based on the 2015 World Population Prospects" }, { "id": "Longdefinition", "value": "Male rural population is the percentage males who live in rural areas to total population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Population Division's World Urbanization Prospects." }, { "id": "Statisticalconceptandmethodology", "value": "Estimates of the United Nations urban and rural population by sex is based on the 2014 revision of World Urbanization Prospects (WUP) for urban and rural population and the 2012 revision of World Population Prospects (WPP) for total population by age and sex for all countries or territories in the world." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.RUR.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The rural population is calculated using the urban share reported by the United Nations Population Division.\n\nThe two distinct images - isolated farm, thriving metropolis - represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income.\n\nA 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population density and remoteness together define a set of rural areas that face special development challenges.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\"\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nRural population methodology is defined by various national statistical offices. In the United States, for example, the US Census Bureau's urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. \"Rural\" encompasses all population, housing, and territory not included within an urban area." }, { "id": "IndicatorName", "value": "Rural population (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentages rural are calculated as the difference between 100 and the proportion of urban population in percentage.\nStatistical concept(s): Rural population is calculated as the difference between the total population and the urban population. Rural population is approximated as the midyear nonurban population. While a practical means of identifying the rural population, it is not a precise measure." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SP.URB.TOTL.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Urban population, female (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the urban and rural population by sex is based on the 2014 revision of World Urbanization Prospects (WUP) for urban and rural population and the 2012 revision of World Population Prospects (WPP) for total population by age and sex, the data for urban and rural population by sex is not comparable and consistent with the total population data presented in the World Development Indicators database which is based on the 2015 World Population Prospects" }, { "id": "Longdefinition", "value": "Female urban population is the percentage of females who live in urban areas to total population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Population Division's World Urbanization Prospects." }, { "id": "Statisticalconceptandmethodology", "value": "Estimates of the United Nations urban and rural population by sex is based on the 2014 revision of World Urbanization Prospects (WUP) for urban and rural population and the 2012 revision of World Population Prospects (WPP) for total population by age and sex for all countries or territories in the world." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.URB.TOTL.IN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage.\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentages urban are the numbers of persons residing in an area defined as ''urban'' per 100 total population.\nStatistical concept(s): Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Population" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "14" }, { "id": "SP.URB.TOTL.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Urban population, male (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the urban and rural population by sex is based on the 2014 revision of World Urbanization Prospects (WUP) for urban and rural population and the 2012 revision of World Population Prospects (WPP) for total population by age and sex, the data for urban and rural population by sex is not comparable and consistent with the total population data presented in the World Development Indicators database which is based on the 2015 World Population Prospects" }, { "id": "Longdefinition", "value": "Male urban population is the percentage of males who live in urban areas to total population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Population Division's World Urbanization Prospects." }, { "id": "Statisticalconceptandmethodology", "value": "Estimates of the United Nations urban and rural population by sex is based on the 2014 revision of World Urbanization Prospects (WUP) for urban and rural population and the 2012 revision of World Population Prospects (WPP) for total population by age and sex for all countries or territories in the world." }, { "id": "Topic", "value": "Population" } ], "source_id": "14" }, { "id": "SP.UWT.TFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Unmet need for contraception (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Unmet need for contraception is the percentage of fertile, married women of reproductive age who do not want to become pregnant and are not using contraception." }, { "id": "Othernotes", "value": "Unmet need for contraception measures the capacity women have in achieving their desired family size and birth spacing. Many couples in developing countries want to limit or postpone childbearing but are not using effective contraception. These couples have an unmet need for contraception. Common reasons are lack of knowledge about contraceptive methods and concerns about possible side effects." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2024" }, { "id": "Source", "value": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMany couples in developing countries want to limit or postpone childbearing but are not using effective contraception. These couples have an unmet need for contraception. Common reasons are lack of knowledge about contraceptive methods and concerns about possible side effects. This indicator excludes women not exposed to the risk of unintended pregnancy because of menopause, infertility, or postpartum anovulation." }, { "id": "Topic", "value": "Health" } ], "source_id": "14" }, { "id": "UIS.FEP.2.V", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in lower secondary vocational education who are female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of female vocational education students at the lower secondary level expressed as a percentage of the total number of vocational education students (male and female) at the lower secondary level in a given school year." }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.FEP.3.V", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in upper secondary vocational education who are female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of female vocational education students at the upper secondary level expressed as a percentage of the total number of vocational education students (male and female) at the upper secondary level in a given school year." }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.FEP.4.V", "metatype": [ { "id": "IndicatorName", "value": "Percentage of students in post-secondary non-tertiary vocational education who are female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of female vocational education students at the post-secondary non-tertiary level expressed as a percentage of the total number of vocational education students (male and female) at the post-secondary non-tertiary level in a given school year." }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.FGP.5T8.F400", "metatype": [ { "id": "IndicatorName", "value": "Female share of graduates in Business, Administration and Law programmes, tertiary (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female share of graduates in the given field of education, tertiary is the number of female graduates expressed as a percentage of the total number of graduates in the given field of education from tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Percentage of female graduates by field of study in tertiary education is calculated by dividing the number of female graduates in a given field of education from tertiary education by the total number of graduates in the same field, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.FGP.5T8.F600", "metatype": [ { "id": "IndicatorName", "value": "Female share of graduates in Information and Communication Technologies programmes, tertiary (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female share of graduates in the given field of education, tertiary is the number of female graduates expressed as a percentage of the total number of graduates in the given field of education from tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Percentage of female graduates by field of study in tertiary education is calculated by dividing the number of female graduates in a given field of education from tertiary education by the total number of graduates in the same field, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.FGP.5T8.FNON500600700", "metatype": [ { "id": "IndicatorName", "value": "Female share of graduates in other fields than Science, Technology, Engineering and Mathematics programmes, tertiary (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female share of graduates in the given field of education, tertiary is the number of female graduates expressed as a percentage of the total number of graduates in the given field of education from tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Percentage of female graduates by field of study in tertiary education is calculated by dividing the number of female graduates in a given field of education from tertiary education by the total number of graduates in the same field, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.GTVP.2.V", "metatype": [ { "id": "IndicatorName", "value": "Share of all students in lower secondary education enrolled in vocational programmes (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total number of students enrolled in vocational programmes at the lower secondary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the lower secondary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.GTVP.2.V.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of female students in lower secondary education enrolled in vocational programmes, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total number of female students enrolled in vocational programmes at the lower secondary education level, expressed as a percentage of the total number of female students enrolled in all programmes (vocational and general) at the lower secondary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.GTVP.2.V.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of male students in lower secondary education enrolled in vocational programmes, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total number of male students enrolled in vocational programmes at the lower secondary education level, expressed as a percentage of the total number of male students enrolled in all programmes (vocational and general) at the lower secondary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.GTVP.3.V", "metatype": [ { "id": "IndicatorName", "value": "Share of all students in upper secondary education enrolled in vocational programmes (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total number of students enrolled in vocational programmes at the upper secondary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the upper secondary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.GTVP.3.V.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of female students in upper secondary education enrolled in vocational programmes, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total number of female students enrolled in vocational programmes at the upper secondary education level, expressed as a percentage of the total number of female students enrolled in all programmes (vocational and general) at the upper secondary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.GTVP.3.V.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of male students in upper secondary education enrolled in vocational programmes, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total number of male students enrolled in vocational programmes at the upper secondary education level, expressed as a percentage of the total number of male students enrolled in all programmes (vocational and general) at the upper secondary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.GTVP.4.V", "metatype": [ { "id": "IndicatorName", "value": "Share of all students in post-secondary non-tertiary education enrolled in vocational programmes (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total number of students enrolled in vocational programmes at the post-secondary non-tertiary education level, expressed as a percentage of the total number of students enrolled in all programmes (vocational and general) at the post-secondary non-tertiary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.GTVP.4.V.F", "metatype": [ { "id": "IndicatorName", "value": "Percentage of female students in post-secondary non-tertiary education enrolled in vocational programmes, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total number of female students enrolled in vocational programmes at the post-secondary non-tertiary education level, expressed as a percentage of the total number of female students enrolled in all programmes (vocational and general) at the post-secondary non-tertiary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.GTVP.4.V.M", "metatype": [ { "id": "IndicatorName", "value": "Percentage of male students in post-secondary non-tertiary education enrolled in vocational programmes, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total number of male students enrolled in vocational programmes at the post-secondary non-tertiary education level, expressed as a percentage of the total number of male students enrolled in all programmes (vocational and general) at the post-secondary non-tertiary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market." }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of March 2020." }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.LP.AG15T24", "metatype": [ { "id": "IndicatorName", "value": "Youth illiterate population, 15-24 years, both sexes (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total number of youth between age 15 and age 24 who cannot both read and write with understanding a short simple statement on their everyday life." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.LP.AG15T24.F", "metatype": [ { "id": "IndicatorName", "value": "Youth illiterate population, 15-24 years, female (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total number of females between age 15 and age 24 who cannot both read and write with understanding a short simple statement on their everyday life." }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/)" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.LP.AG15T24.M", "metatype": [ { "id": "IndicatorName", "value": "Youth illiterate population, 15-24 years, male (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total number of males between age 15 and age 24 who cannot both read and write with understanding a short simple statement on their everyday life." }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.LPP.AG15T24", "metatype": [ { "id": "IndicatorName", "value": "Youth illiterate population, 15-24 years, % female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Share of the youth illiterate population that is female." }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/)" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.ROFST.3.F.CP", "metatype": [ { "id": "IndicatorName", "value": "Rate of out-of-school youth of upper secondary school age, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of females of official upper secondary school age who are not enrolled in upper secondary school expressed as a percentage of the female population of official upper secondary school age." }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/)" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "UIS.ROFST.3.M.CP", "metatype": [ { "id": "IndicatorName", "value": "Rate of out-of-school youth of upper secondary school age, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of males of official upper secondary school age who are not enrolled in upper secondary school expressed as a percentage of the male population of official upper secondary school age." }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/)" }, { "id": "Topic", "value": "Education" } ], "source_id": "14" }, { "id": "VC.IHR.PSRC.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Aggregate values are computed by UNODC. For additional information, please see the UNODC website: https://dataunodc.un.org/sites/dataunodc.un.org/files/metadata_intentional_homicide.pdf" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In some regions, organized crime, drug trafficking and the violent cultures of youth gangs are predominantly responsible for the high levels of homicide. There has been a sharp increase in homicides in some countries, particularly in Central America, are making the activities of organized crime and drug trafficking more visible. Greater use of firearms is often associated with the illicit activities of organized criminal groups, which are often linked to drug trafficking.\n\nKnowledge of the patterns and causes of violent crime are crucial to forming preventive strategies. Young males are the group most affected by violent crime in all regions, particularly in the Americas. Yet women of all ages are the victims of intimate partner and family-related violence in all regions and countries. Indeed, in many of them, it is within the home where a woman is most likely to be killed.\n\nData on intentional homicides are from the United Nations Office on Drugs and Crime (UNODC), which uses a variety of national and international sources on homicides - primarily criminal justice sources as well as public health data from the World Health Organization (WHO) and the Pan American Health Organization - and the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems to present accurate and comparable statistics. The UNODC defines homicide as \"unlawful death purposefully inflicted on a person by another person.\" This definition excludes deaths arising from armed conflict." }, { "id": "IndicatorName", "value": "Intentional homicides, female (per 100,000 female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Statistics reported to the United Nations in the context of its various surveys on crime levels and criminal justice trends are incidents of victimization that have been reported to the authorities in any given country. That means that this data is subject to the problems of accuracy of all official crime data. The survey results provide an overview of trends and interrelationships between various parts of the criminal justice system to promote informed decision-making in administration, nationally and internationally.\n\nThe degree to which different societies apportion the level of culpability to acts resulting in death is also subject to variation. Consequently, the comparison between countries and regions of \"intentional homicide\", or unlawful death purposefully inflicted on a person by another person, is also a comparison of the extent to which different countries deem that a killing be classified as such, as well as the capacity of their legal systems to record it. Caution should therefore be applied when evaluating and comparing homicide data." }, { "id": "Longdefinition", "value": "An intentional homicide is defined as an unlawful death inflicted upon a person with the intent to cause death or serious injury." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UNODC Research - Data Portal – Intentional Homicide, UN Office on Drugs and Crime (UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data are sourced by UNODC from either criminal justice or public health systems. In the former, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event, whereas in the latter, data are produced by health authorities certifying the cause of death of an individual. \n\nThese data are collected from national authorities with the annual United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS). National focal points working in national agencies responsible for statistics on crime and the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the UN-CTS to UNODC. Following the submission, UNODC checks for consistency and coherence with other data sources. Member States which are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union are sending their response to the UN-CTS to Eurostat for validation. \n\nData submitted by Member States through other means or taken from other sources are added to the dataset after review by Member States. \n\nThe population data is sourced from the World Population Prospect, Population Division, United Nations Department of Economic and Social Affairs. \nStatistical concept(s): The International Classification of Crime for Statistical Purposes (ICCS) is the source of the definition of intentional homicide. The definitions of the disaggregation of victims of intentional homicide included in these tables (by situational context, by relationship to perpetrator and by mechanisms) are also from the ICCS. \n\nThe ICCS includes more information on what is included and excluded in these offences. Intentional homicide (ICCS 0101): Unlawful death inflicted upon a person with the intent to cause death or serious injury. \n\nThe statistical definition contains three elements that characterize the killing of a person as “intentional homicide”: \n1. The killing of a person by another person (objective element) \n2. The intent of the perpetrator to kill or seriously injure the victim (subjective element) \n3. The unlawfulness of the killing (legal element) \n\nFor recording purposes, all killings that meet the criteria listed above are to be considered intentional homicides, irrespective of definitions provided by national legislations or practices. Killings as a result of terrorist activities are also to be classified as a form of intentional homicide." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Rate per 100,000 population" } ], "source_id": "14" }, { "id": "VC.IHR.PSRC.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Aggregate values are computed by UNODC. For additional information, please see the UNODC website: https://dataunodc.un.org/sites/dataunodc.un.org/files/metadata_intentional_homicide.pdf" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In some regions, organized crime, drug trafficking and the violent cultures of youth gangs are predominantly responsible for the high levels of homicide. There has been a sharp increase in homicides in some countries, particularly in Central America, are making the activities of organized crime and drug trafficking more visible. Greater use of firearms is often associated with the illicit activities of organized criminal groups, which are often linked to drug trafficking.\n\nKnowledge of the patterns and causes of violent crime are crucial to forming preventive strategies. Young males are the group most affected by violent crime in all regions, particularly in the Americas. Yet women of all ages are the victims of intimate partner and family-related violence in all regions and countries. Indeed, in many of them, it is within the home where a woman is most likely to be killed.\n\nData on intentional homicides are from the United Nations Office on Drugs and Crime (UNODC), which uses a variety of national and international sources on homicides - primarily criminal justice sources as well as public health data from the World Health Organization (WHO) and the Pan American Health Organization - and the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems to present accurate and comparable statistics. The UNODC defines homicide as \"unlawful death purposefully inflicted on a person by another person.\" This definition excludes deaths arising from armed conflict." }, { "id": "IndicatorName", "value": "Intentional homicides, male (per 100,000 male)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Statistics reported to the United Nations in the context of its various surveys on crime levels and criminal justice trends are incidents of victimization that have been reported to the authorities in any given country. That means that this data is subject to the problems of accuracy of all official crime data. The survey results provide an overview of trends and interrelationships between various parts of the criminal justice system to promote informed decision-making in administration, nationally and internationally.\n\nThe degree to which different societies apportion the level of culpability to acts resulting in death is also subject to variation. Consequently, the comparison between countries and regions of \"intentional homicide\", or unlawful death purposefully inflicted on a person by another person, is also a comparison of the extent to which different countries deem that a killing be classified as such, as well as the capacity of their legal systems to record it. Caution should therefore be applied when evaluating and comparing homicide data." }, { "id": "Longdefinition", "value": "An intentional homicide is defined as an unlawful death inflicted upon a person with the intent to cause death or serious injury." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UNODC Research - Data Portal – Intentional Homicide, UN Office on Drugs and Crime (UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data are sourced by UNODC from either criminal justice or public health systems. In the former, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event, whereas in the latter, data are produced by health authorities certifying the cause of death of an individual. \n\nThese data are collected from national authorities with the annual United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS). National focal points working in national agencies responsible for statistics on crime and the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the UN-CTS to UNODC. Following the submission, UNODC checks for consistency and coherence with other data sources. Member States which are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union are sending their response to the UN-CTS to Eurostat for validation. \n\nData submitted by Member States through other means or taken from other sources are added to the dataset after review by Member States. \n\nThe population data is sourced from the World Population Prospect, Population Division, United Nations Department of Economic and Social Affairs. \nStatistical concept(s): The International Classification of Crime for Statistical Purposes (ICCS) is the source of the definition of intentional homicide. The definitions of the disaggregation of victims of intentional homicide included in these tables (by situational context, by relationship to perpetrator and by mechanisms) are also from the ICCS. \n\nThe ICCS includes more information on what is included and excluded in these offences. Intentional homicide (ICCS 0101): Unlawful death inflicted upon a person with the intent to cause death or serious injury. \n\nThe statistical definition contains three elements that characterize the killing of a person as “intentional homicide”: \n1. The killing of a person by another person (objective element) \n2. The intent of the perpetrator to kill or seriously injure the victim (subjective element) \n3. The unlawfulness of the killing (legal element) \n\nFor recording purposes, all killings that meet the criteria listed above are to be considered intentional homicides, irrespective of definitions provided by national legislations or practices. Killings as a result of terrorist activities are also to be classified as a form of intentional homicide." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Rate per 100,000 population" } ], "source_id": "14" }, { "id": "VC.IHR.PSRC.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Aggregate values are computed by UNODC. For additional information, please see the UNODC website: https://dataunodc.un.org/sites/dataunodc.un.org/files/metadata_intentional_homicide.pdf" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In some regions, organized crime, drug trafficking and the violent cultures of youth gangs are predominantly responsible for the high levels of homicide. There has been a sharp increase in homicides in some countries, particularly in Central America, are making the activities of organized crime and drug trafficking more visible. Greater use of firearms is often associated with the illicit activities of organized criminal groups, which are often linked to drug trafficking.\n\nKnowledge of the patterns and causes of violent crime are crucial to forming preventive strategies. Young males are the group most affected by violent crime in all regions, particularly in the Americas. Yet women of all ages are the victims of intimate partner and family-related violence in all regions and countries. Indeed, in many of them, it is within the home where a woman is most likely to be killed.\n\nData on intentional homicides are from the United Nations Office on Drugs and Crime (UNODC), which uses a variety of national and international sources on homicides - primarily criminal justice sources as well as public health data from the World Health Organization (WHO) and the Pan American Health Organization - and the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems to present accurate and comparable statistics. The UNODC defines homicide as \"unlawful death purposefully inflicted on a person by another person.\" This definition excludes deaths arising from armed conflict." }, { "id": "IndicatorName", "value": "Intentional homicides (per 100,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Statistics reported to the United Nations in the context of its various surveys on crime levels and criminal justice trends are incidents of victimization that have been reported to the authorities in any given country. That means that this data is subject to the problems of accuracy of all official crime data. The survey results provide an overview of trends and interrelationships between various parts of the criminal justice system to promote informed decision-making in administration, nationally and internationally.\n\nThe degree to which different societies apportion the level of culpability to acts resulting in death is also subject to variation. Consequently, the comparison between countries and regions of \"intentional homicide\", or unlawful death purposefully inflicted on a person by another person, is also a comparison of the extent to which different countries deem that a killing be classified as such, as well as the capacity of their legal systems to record it. Caution should therefore be applied when evaluating and comparing homicide data." }, { "id": "Longdefinition", "value": "An intentional homicide is defined as an unlawful death inflicted upon a person with the intent to cause death or serious injury." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UNODC Research - Data Portal – Intentional Homicide, UN Office on Drugs and Crime (UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data are sourced by UNODC from either criminal justice or public health systems. In the former, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event, whereas in the latter, data are produced by health authorities certifying the cause of death of an individual. \n\nThese data are collected from national authorities with the annual United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS). National focal points working in national agencies responsible for statistics on crime and the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the UN-CTS to UNODC. Following the submission, UNODC checks for consistency and coherence with other data sources. Member States which are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union are sending their response to the UN-CTS to Eurostat for validation. \n\nData submitted by Member States through other means or taken from other sources are added to the dataset after review by Member States. \n\nThe population data is sourced from the World Population Prospect, Population Division, United Nations Department of Economic and Social Affairs. \nStatistical concept(s): The International Classification of Crime for Statistical Purposes (ICCS) is the source of the definition of intentional homicide. The definitions of the disaggregation of victims of intentional homicide included in these tables (by situational context, by relationship to perpetrator and by mechanisms) are also from the ICCS. \n\nThe ICCS includes more information on what is included and excluded in these offences. Intentional homicide (ICCS 0101): Unlawful death inflicted upon a person with the intent to cause death or serious injury. \n\nThe statistical definition contains three elements that characterize the killing of a person as “intentional homicide”: \n1. The killing of a person by another person (objective element) \n2. The intent of the perpetrator to kill or seriously injure the victim (subjective element) \n3. The unlawfulness of the killing (legal element) \n\nFor recording purposes, all killings that meet the criteria listed above are to be considered intentional homicides, irrespective of definitions provided by national legislations or practices. Killings as a result of terrorist activities are also to be classified as a form of intentional homicide." }, { "id": "Topic", "value": "Violence" }, { "id": "Unitofmeasure", "value": "Rate per 100,000 population" } ], "source_id": "14" }, { "id": "CPTOTNSXN", "metatype": [ { "id": "Longdefinition", "value": "The consumer price index reflects the change in prices for the average consumer of a constant basket of consumer goods. Data is not seasonally adjusted." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream data." } ], "source_id": "15" }, { "id": "CPTOTSAXMZGY", "metatype": [ { "id": "Longdefinition", "value": "Median inflation rate calculated for geographical aggregates (regions, world, etc) of the annual percent change of the CPI. Data is seasonally adjusted." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream data." } ], "source_id": "15" }, { "id": "CPTOTSAXN", "metatype": [ { "id": "Longdefinition", "value": "The consumer price index reflects the change in prices for the average consumer of a constant basket of consumer goods. Data is in nominal terms and seasonally adjusted." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream data." } ], "source_id": "15" }, { "id": "CPTOTSAXNZGY", "metatype": [ { "id": "Longdefinition", "value": "The consumer price index reflects the change in prices for the average consumer of a constant basket of consumer goods. Data is in nominal percentage terms, measured on a year-on-year basis, and seasonally adjusted." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream data." } ], "source_id": "15" }, { "id": "DMGSRMRCHNSCD", "metatype": [ { "id": "Longdefinition", "value": "Merchandise (goods) imports, cost, insurance and freight basis (c.i.f.), in current US$ millions, not seasonally adjusted." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "DMGSRMRCHNSKD", "metatype": [ { "id": "Longdefinition", "value": "Merchandise (goods) imports, cost, insurance and freight basis (c.i.f.), in constant US$ millions, not seasonally adjusted. The base year is 2005." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "DMGSRMRCHNSXD", "metatype": [ { "id": "Longdefinition", "value": "The price index of Merchandise (goods) imports, cost, insurance and freight basis (c.i.f.), in constant US$ millions, not seasonally adjusted. The base year is 2005." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "DMGSRMRCHSACD", "metatype": [ { "id": "Longdefinition", "value": "Merchandise (goods) imports, cost, insurance and freight basis (c.i.f.), in current US$ millions, seasonally adjusted." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "DMGSRMRCHSAKD", "metatype": [ { "id": "Longdefinition", "value": "Merchandise (goods) exports, cost, insurance and freight basis (c.i.f.), in constant US$ millions, seasonally adjusted. The base year is 2005." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "DMGSRMRCHSAXD", "metatype": [ { "id": "Longdefinition", "value": "The price index of Merchandise (goods) imports, cost, insurance and freight basis (c.i.f.), in constant US$ millions, seasonally adjusted. The base year is 2005." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "DPANUSLCU", "metatype": [ { "id": "Longdefinition", "value": "Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar)." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "DPANUSSPB", "metatype": [ { "id": "Longdefinition", "value": "Local currency units (LCU) per U.S. dollar, with values prior to the currency's introduction presented in the new currency's terms" }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "DPANUSSPF", "metatype": [ { "id": "Longdefinition", "value": "Local currency units (LCU) per U.S. dollar, with values after a new currency's introduction presented in the old currency's terms" }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "DSTKMKTXD", "metatype": [ { "id": "Longdefinition", "value": "Local equity market index valued in US$ terms" }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "DSTKMKTXN", "metatype": [ { "id": "Longdefinition", "value": "Local equity market index valued in local currency unit (LCU) terms" }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "DXGSRMRCHNSCD", "metatype": [ { "id": "Longdefinition", "value": "Merchandise (goods) exports, free on board (f.o.b.), in current US$ millions, not seasonally adjusted." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "DXGSRMRCHNSKD", "metatype": [ { "id": "Longdefinition", "value": "Merchandise (goods) exports, free on board (f.o.b.), in constant US$ millions not seasonally adjusted. The base year is 2005." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "DXGSRMRCHNSXD", "metatype": [ { "id": "Longdefinition", "value": "The price index of Merchandise (goods) exports, free on board (f.o.b.), in currrent US$ millions. Not seasonally adjusted." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "DXGSRMRCHSACD", "metatype": [ { "id": "Longdefinition", "value": "Merchandise (goods) exports, free on board (f.o.b.), in current US$ millions, seasonally adjusted." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "DXGSRMRCHSAKD", "metatype": [ { "id": "Longdefinition", "value": "Merchandise (goods) exports, free on board (f.o.b.), in constant US$ millions, seasonally adjusted. The base year is 2005." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "DXGSRMRCHSAXD", "metatype": [ { "id": "Longdefinition", "value": "The price index of Merchandise (goods) exports, free on board (f.o.b.), in US$ seasonally adjusted." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "IMPCOV", "metatype": [ { "id": "Longdefinition", "value": "The stock of international reserves is expressed as the number of months of financing-coverage it represents for the given country's imports of merchandise goods." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream data." } ], "source_id": "15" }, { "id": "IPTOTNSKD", "metatype": [ { "id": "Longdefinition", "value": "An economic indicator that measures changes in output for the industrial sector of the economy. The industrial sector includes manufacturing, mining, and utilities. Data is in constant US$, and not seasonally adjusted. The base year is 2005." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream data." } ], "source_id": "15" }, { "id": "IPTOTSAKD", "metatype": [ { "id": "Longdefinition", "value": "An economic indicator that measures changes in output for the industrial sector of the economy. The industrial sector includes manufacturing, mining, and utilities. Data is in constant US$, seasonally adjusted. The base year is 2005." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream data." } ], "source_id": "15" }, { "id": "NEER", "metatype": [ { "id": "Longdefinition", "value": "A measure of the value of a currency against a weighted average of several foreign currencies" }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "REER", "metatype": [ { "id": "Longdefinition", "value": "Real effective exchange rate is the nominal effective exchange rate (a measure of the value of a currency against a weighted average of several foreign currencies) divided by a price deflator or index of costs." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream and IMF International Finance Statistics data." } ], "source_id": "15" }, { "id": "TOT", "metatype": [ { "id": "Longdefinition", "value": "The terms of trade effect equals capacity to import less exports of goods and services." }, { "id": "Source", "value": "World Bank staff calculations based on Datastream data." } ], "source_id": "15" }, { "id": "TOTRESV", "metatype": [ { "id": "Longdefinition", "value": "Total reserves comprise holdings of monetary gold, special drawing rights, reserves of IMF members held by the IMF, and holdings of foreign exchange under the control of monetary authorities. " }, { "id": "Source", "value": "World Bank staff calculations based on Datastream data." } ], "source_id": "15" }, { "id": "HD.HCI.OVRL", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI) (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "16" }, { "id": "HD.HCI.OVRL.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI), female (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "16" }, { "id": "HD.HCI.OVRL.LB", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI), lower bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI lower bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "16" }, { "id": "HD.HCI.OVRL.LB.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI), female, lower bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI lower bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "16" }, { "id": "HD.HCI.OVRL.LB.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI), male, lower bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI lower bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "16" }, { "id": "HD.HCI.OVRL.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI), male (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "16" }, { "id": "HD.HCI.OVRL.UB", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI), upper bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI upper bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "16" }, { "id": "HD.HCI.OVRL.UB.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI), female, upper bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI upper bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "16" }, { "id": "HD.HCI.OVRL.UB.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI), male, upper bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI upper bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "16" }, { "id": "NY.GNP.PCAP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita, Atlas method (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This figure is converted to U.S. dollars using the World Bank Atlas method, and divided by the midyear population. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank's official estimates of the size of economies and country classifications by income level are based on Gross National Income (GNI) per capita. For cross-national comparisons, estimates are converted from local currency units (LCU) to current U.S. dollars using the Atlas method, referring to a former World Bank publication called the Atlas of Global Development. The Atlas method smooths exchange rate fluctuations using a three-year moving average, price-adjusted conversion factor. The USD estimate of GNI per capita is derived by applying the Atlas conversion factor to estimates measured in LCU.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Atlas GNI & GNI per capita" }, { "id": "Unitofmeasure", "value": "current US$" } ], "source_id": "16" }, { "id": "SE.ADT.1524.LT.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEliminating gender disparities in education would help increase the status and capabilities of women. Literate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth (ages 15-24), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Gender parity index for youth literacy rate is the ratio of females to males ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female youth literacy rate by male youth literacy rate. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiteracy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "ratio" } ], "source_id": "16" }, { "id": "SE.ADT.1524.LT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth male (% of males ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate youths divided by the total number of youths, excluding youths with unknown literacy status. \n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of males ages 15-24" } ], "source_id": "16" }, { "id": "SE.ADT.1524.LT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth total (% of people ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data is calculated by dividing the number of literate persons by the total number of persons in the same age group, excluding persons with unknown literacy status.\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "Percent of people ages 15-24" } ], "source_id": "16" }, { "id": "SE.ADT.LITR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, adult female (% of females ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status. \n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of females ages 15 and above" } ], "source_id": "16" }, { "id": "SE.ADT.LITR.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, adult male (% of males ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status. \n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of males ages 15 and above" } ], "source_id": "16" }, { "id": "SE.ADT.LITR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, adult total (% of people ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status. \n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of people ages 15 and above" } ], "source_id": "16" }, { "id": "SE.ENR.ORPH", "metatype": [ { "id": "IndicatorName", "value": "Ratio of school attendance of orphans to school attendance of non-orphans ages 10-14" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Ratio of school attendance of orphans to school attendance of non orphans is the ratio of school attendance of orphans to school attendance of non orphans ages 10-14." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys such as Demographic and Health Surveys (DHS) , Multiple Indicator Cluster Surveys (MICS), Reproductive Health Surveys (RHS) and AIDS Indicator Surveys (AIS), maintained in UNICEF Global Databases." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "16" }, { "id": "SE.PRM.CMPT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education." }, { "id": "IndicatorName", "value": "Primary completion rate, female (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "16" }, { "id": "SE.PRM.CMPT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education." }, { "id": "IndicatorName", "value": "Primary completion rate, male (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "16" }, { "id": "SE.PRM.CMPT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education." }, { "id": "IndicatorName", "value": "Primary completion rate, total (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "16" }, { "id": "SE.PRM.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education is fundamental to future educational success and opens pathways for continued advancement. This indicator measures the overall rate of participation in primary education, signifying the education system's ability to enroll students within a specific age cohort." }, { "id": "IndicatorName", "value": "School enrollment, primary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for primary school is calculated by dividing the number of students enrolled in primary education regardless of age by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of population in the 5-year age group immediately following preprimary education" } ], "source_id": "16" }, { "id": "SE.PRM.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education is fundamental to future educational success and opens pathways for continued advancement. This indicator measures the overall rate of participation in primary education, signifying the education system's ability to enroll students within a specific age cohort." }, { "id": "IndicatorName", "value": "School enrollment, primary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for primary school is calculated by dividing the number of students enrolled in primary education regardless of age by the population of the age group which officially corresponds to primary education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of female population in the 5-year age group immediately following preprimary education" } ], "source_id": "16" }, { "id": "SE.PRM.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education is fundamental to future educational success and opens pathways for continued advancement. This indicator measures the overall rate of participation in primary education, signifying the education system's ability to enroll students within a specific age cohort." }, { "id": "IndicatorName", "value": "School enrollment, primary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for primary school is calculated by dividing the number of students enrolled in primary education regardless of age by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of male population in the 5-year age group immediately following preprimary education" } ], "source_id": "16" }, { "id": "SE.PRM.NENR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, primary (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "16" }, { "id": "SE.PRM.NENR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, primary, female (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "UNESCO Institute for Statistics (UIS), UN Educational, Scientific and Cultural Organization (UNESCO), uri: http://uis.unesco.org/, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "16" }, { "id": "SE.PRM.NENR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, primary, male (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "16" }, { "id": "SE.SEC.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Secondary education acts as a critical intermediary that not only builds upon the foundational knowledge acquired in primary education but also equips students for various pathways, including immediate entry into the workforce, further education in postsecondary non-tertiary institutions, or advancement to higher education. This indicator assesses the aggregate participation rate in secondary education, reflecting the education system's capacity to enroll students within a designated age group." }, { "id": "IndicatorName", "value": "School enrollment, secondary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of population in the 5-year age group immediately following primary education" } ], "source_id": "16" }, { "id": "SE.SEC.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Secondary education acts as a critical intermediary that not only builds upon the foundational knowledge acquired in primary education but also equips students for various pathways, including immediate entry into the workforce, further education in postsecondary non-tertiary institutions, or advancement to higher education. This indicator assesses the aggregate participation rate in secondary education, reflecting the education system's capacity to enroll students within a designated age group." }, { "id": "IndicatorName", "value": "School enrollment, secondary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of female population in the 5-year age group immediately following primary education" } ], "source_id": "16" }, { "id": "SE.SEC.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Secondary education acts as a critical intermediary that not only builds upon the foundational knowledge acquired in primary education but also equips students for various pathways, including immediate entry into the workforce, further education in postsecondary non-tertiary institutions, or advancement to higher education. This indicator assesses the aggregate participation rate in secondary education, reflecting the education system's capacity to enroll students within a designated age group." }, { "id": "IndicatorName", "value": "School enrollment, secondary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of male population in the 5-year age group immediately following primary education" } ], "source_id": "16" }, { "id": "SE.SEC.NENR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, secondary (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for secondary school is calculated by dividing the number of students of official school age enrolled in secondary education by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "16" }, { "id": "SE.SEC.NENR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, secondary, female (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for secondary school is calculated by dividing the number of students of official school age enrolled in secondary education by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "16" }, { "id": "SE.SEC.NENR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, secondary, male (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for secondary school is calculated by dividing the number of students of official school age enrolled in secondary education by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "16" }, { "id": "SE.TER.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.3 is committed to providing equitable access to affordable and high-quality technical, vocational, and tertiary education, including university, for both women and men. This particular indicator reflects the overall capacity of the educational infrastructure to support enrolment within a specified age demographic at the tertiary level." }, { "id": "IndicatorName", "value": "School enrollment, tertiary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of population in the 5-year age group immediately following upper secondary education" } ], "source_id": "16" }, { "id": "SE.TER.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.3 is committed to providing equitable access to affordable and high-quality technical, vocational, and tertiary education, including university, for both women and men. This particular indicator reflects the overall capacity of the educational infrastructure to support enrolment within a specified age demographic at the tertiary level." }, { "id": "IndicatorName", "value": "School enrollment, tertiary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of female population in the 5-year age group immediately following upper secondary education" } ], "source_id": "16" }, { "id": "SE.XPD.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investment in education acts as a driving force for economic growth, enhances productivity, and promotes the betterment of individual and collective welfare. This indicator evaluates the extent to which a government prioritizes education in relation to its overall economic prosperity." }, { "id": "IndicatorName", "value": "Government expenditure on education, total (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data may refer to spending by the ministry of education only (excluding spending on educational activities by other ministries)." }, { "id": "Longdefinition", "value": "General government expenditure on education (current, capital, and transfers) is expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-09-22, date published: 2025-09" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government expenditure on education, total (% of GDP) is calculated by dividing total government expenditure for all levels of education by the GDP, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nInformation pertaining to educational expenditures is sourced from national governments, which provide the data in response to the annual survey conducted by the UNESCO Institute for Statistics (UIS) or through the joint UNESCO-OECD-Eurostat (UOE) data collection initiative. The responses to the questionnaire regarding educational spending are typically derived from the annual financial statements issued by either the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office. Additionally, data concerning GDP and overall government expenditure are accessible via the IMF’s World Economic Outlook database, which is updated annually.\nStatistical concept(s): Generally, elevated levels of the indicator suggest that a government places a high priority on educational policy. Values ranging from 4% to 6% are indicative of a country achieving the benchmark set forth by the Education 2030 Framework for Action (https://uis.unesco.org/sites/default/files/documents/education-2030-incheon-framework-for-action-implementation-of-sdg4-2016-en_2.pdf).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEducational expenditure encompasses spending on fundamental educational goods and services, including teaching personnel, school infrastructure, textbooks, and instructional materials, as well as on ancillary educational goods and services such as support services, general administration, and other related activities.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFunding for education may originate from public sources, encompassing all government ministries and agencies that finance or support educational programs within the country, as well as from international and private sources, such as household contributions." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "16" }, { "id": "SH.ALC.PCAP.FE.LI", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions." }, { "id": "IndicatorName", "value": "Total alcohol consumption per capita, female (liters of pure alcohol, projected estimates, female 15+ years of age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.5.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2020" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.ALC.PCAP.LI", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions." }, { "id": "IndicatorName", "value": "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.5.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2020" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.ALC.PCAP.MA.LI", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions." }, { "id": "IndicatorName", "value": "Total alcohol consumption per capita, male (liters of pure alcohol, projected estimates, male 15+ years of age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.5.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2020" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.ANM.ALLW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of anemia among women of reproductive age (% of women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Prevalence of anemia among women of reproductive age refers to the combined prevalence of both non-pregnant with haemoglobin levels below 12 g/dL and pregnant women with haemoglobin levels below 11 g/dL." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on the prevalence of anaemia and/or mean haemoglobin levels in women of reproductive age, collected between 1995 and 2019, were obtained from 408 population-representative data sources across 124 countries worldwide. A Bayesian hierarchical mixture model was employed to estimate haemoglobin distributions, systematically addressing missing data, non-linear time trends, and the representativeness of data sources. Full details on data sources are available on the GHO Anaemia page. Detailed information on the statistical methods can be found in the following reference: Finucane MM, Paciorek CJ, Stevens GA EM. Semiparametric Bayesian density estimation with disparate data sources: a meta-analysis of global childhood undernutrition. J Am Stat Assoc. 2015;110(511):889–901." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percent of women ages 15-49" } ], "source_id": "16" }, { "id": "SH.ANM.CHLD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of anemia among children (% of children ages 6-59 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data for blood haemoglobin concentrations are still limited, compared to other nutritional indicators such as hild anthropometry. As a result, the estimates may not capture the full variation across countries and regions." }, { "id": "Longdefinition", "value": "Prevalence of anemia, children ages 6-59 months, is the percentage of children ages 6-59 months whose hemoglobin level is less than 110 grams per liter, adjusted for altitude." }, { "id": "Othernotes", "value": "Anemia is defined as a low blood haemoglobin concentration. Anaemia may result from a number of causes, with the most significant contributor being iron deficiency. Anaemia resulting from iron deficiency adversely affects cognitive and motor development and causes fatigue and low productivity. Children under age 5 and pregnant women have the highest risk for anemia." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2019" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on anemia are compiled by the WHO, and a statistical model was used to estimate trends. WHO’s hemoglobin threshold concentration in blood was used." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "16" }, { "id": "SH.ANM.NPRG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of anemia among non-pregnant women (% of women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Prevalence of anemia, non-pregnant women, is the percentage of non-pregnant women whose hemoglobin level is less than 120 grams per liter at sea level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on the prevalence of anaemia and/or mean haemoglobin levels in women of reproductive age, collected between 1995 and 2019, were obtained from 408 population-representative data sources across 124 countries worldwide. A Bayesian hierarchical mixture model was employed to estimate haemoglobin distributions, systematically addressing missing data, non-linear time trends, and the representativeness of data sources. Full details on data sources are available on the GHO Anaemia page. Detailed information on the statistical methods can be found in the following reference: Finucane MM, Paciorek CJ, Stevens GA EM. Semiparametric Bayesian density estimation with disparate data sources: a meta-analysis of global childhood undernutrition. J Am Stat Assoc. 2015;110(511):889–901." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percent of women ages 15-49" } ], "source_id": "16" }, { "id": "SH.CON.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "According to UNAIDS estimates, HIV incidence has fallen in many of the most severely affected countries because adolescents and young people are adopting safer sexual practices and more young people living with HIV are accessing treatment to lower their viral load. When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs)." }, { "id": "IndicatorName", "value": "Condom use, population ages 15-24, female (% of females ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Condom use, female is the percentage of the female population ages 15-24 who used a condom at last intercourse in the last 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2015" }, { "id": "Source", "value": "Demographic and Health Surveys;\nUNAIDS, Joint United Nations Programme on HIV/AIDS (UNAIDS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Household Surveys\nStatistical concept(s): When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.CON.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "According to UNAIDS estimates, HIV incidence has fallen in many of the most severely affected countries because adolescents and young people are adopting safer sexual practices and more young people living with HIV are accessing treatment to lower their viral load. When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs)." }, { "id": "IndicatorName", "value": "Condom use, population ages 15-24, male (% of males ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Condom use, male is the percentage of the male population ages 15-24 who used a condom at last intercourse in the last 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2014" }, { "id": "Source", "value": "Demographic and Health Surveys;\nUNAIDS, Joint United Nations Programme on HIV/AIDS (UNAIDS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Household Surveys\nStatistical concept(s): When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.CON.AIDS.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Condom use at last high-risk sex, adult female (% ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Condom use at last high-risk sex, female is the percentage of the female population ages 15-49 who used a condom at last intercourse with a non-marital and non-cohabiting sexual partner in the last 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys, and UNAIDS." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "16" }, { "id": "SH.CON.AIDS.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Condom use at last high-risk sex, adult male (% ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Condom use at last high-risk sex, male is the percentage of the male population ages 15-49 who used a condom at last intercourse with a non-marital and non-cohabiting sexual partner in the last 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys, and UNAIDS." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "16" }, { "id": "SH.DTH.0509", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of deaths ages 5-9 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of children ages 5-9 years" }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SH.DTH.0509.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of deaths ages 5-9 years, female" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of female children ages 5-9 years" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DTH.0509.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of deaths ages 5-9 years, male" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of male children ages 5-9 years" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DTH.1014", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of deaths ages 10-14 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of adolescents ages 10-14 years" }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SH.DTH.1014.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of deaths ages 10-14 years, female" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of female adolescents ages 10-14 years" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DTH.1014.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of deaths ages 10-14 years, male" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of male adolescents ages 10-14 years" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DTH.1019", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of deaths ages 10-19 years" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of adolescents ages 10-19 years" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DTH.1019.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of deaths ages 10-19 years, female" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of female adolescents ages 10-19 years" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DTH.1019.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of deaths ages 10-19 years, male" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of male adolescents ages 10-19 years" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DTH.1519", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of deaths ages 15-19 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of adolescents ages 15-19 years" }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SH.DTH.1519.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of deaths ages 15-19 years, female" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of female adolescents ages 15-19 years" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DTH.1519.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of deaths ages 15-19 years, male" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of male adolescents ages 15-19 years" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DTH.2024", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of deaths ages 20-24 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of youths ages 20-24 years" }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SH.DTH.2024.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of deaths ages 20-24 years, female" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of female youths ages 20-24 years" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DTH.2024.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of deaths ages 20-24 years, male" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of male youths ages 20-24 years" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DTH.COMM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Cause of death refers to the share of all deaths for all ages by underlying causes. Communicable diseases and maternal, prenatal and nutrition conditions include infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2019" }, { "id": "Source", "value": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates: Deaths by Cause, Age, Sex, by Country and by Region" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.DTH.IMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of infant deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of infants dying before reaching one year of age." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SH.DTH.IMRT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of infant deaths, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of female infants dying before reaching one year of age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DTH.IMRT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of infant deaths, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of male infants dying before reaching one year of age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DTH.INJR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Cause of death, by injury (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Cause of death refers to the share of all deaths for all ages by underlying causes. Injuries include unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2019" }, { "id": "Source", "value": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates: Deaths by Cause, Age, Sex, by Country and by Region" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in under-covered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.DTH.MORT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of under-five deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of children dying before reaching age five." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SH.DTH.MORT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of under-five deaths, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of female children dying before reaching age five." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DTH.MORT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of under-five deaths, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of male children dying before reaching age five." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DTH.NCOM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Cause of death refers to the share of all deaths for all ages by underlying causes. Non-communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2019" }, { "id": "Source", "value": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates: Deaths by Cause, Age, Sex, by Country and by Region" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.DTH.NMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of neonatal deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of neonates dying before reaching 28 days of age." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis indicator is related to Sustainable Development Goal 3.2.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SH.DTH.STLB", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Stillbirths are an important but often neglected global health problem. Sub-Saharan Africa and South Asia bear the greatest burden of these deaths. \n\nThe costs of stillbirths go beyond the loss of life and include psychological cost such as maternal depression, financial costs to parents as well as long-term economic costs to society. The burden on families, especially women, is severe and long lasting, yet stigma and taboo hide this burden even in high-income countries. The progress in lowering the stillbirth rate in the past two decades is much slower than the reduction in mortality of children aged under 5 years old. This could be due to a variety of reasons including absence or poor quality of care during pregnancy and birth, lack of investment in preventative interventions and the health workforce, lack of social recognition of stillbirths as a burden on families, challenges with measurements and major data gaps, absence of global and national leadership, and no established global targets." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of stillbirths" }, { "id": "Limitationsandexceptions", "value": "Data availability and data quality is uneven among countries. Adequate stillbirth data are lacking in many low and middle-income countries. These countries do not have a functioning health information system or civil registration vital statistics (CRVS) system to count or capture stillbirths, and in others stillbirths are excluded from routine registration despite the existence of functioning systems. In such settings, household surveys provide important information on child mortality, but most surveys have substantial data quality issues for stillbirth." }, { "id": "Longdefinition", "value": "Number of fetal deaths at 28 weeks or more of gestation" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The UN IGME’s approach to estimate stillbirth rates (SBR) includes the following steps:\n1.\tCompile all available stillbirth data at a country level, derived from administrative sources, household surveys or population-based studies. \n2.\tEvaluate data in accordance with the data quality criteria and produce adjustment or recalculation by applying standardized definitions. \n3.\tEstimate global and country-specific trends of stillbirth rates using a smoothing time series model, supplemented with covariates associated with stillbirth rates. This process averages empirical data on stillbirths derived from the different sources for a given country. In the case of countries with sparse or no data, the identified covariates associated with stillbirth will inform the trend in stillbirth rate." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DYN.0509", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Probability of dying among children ages 5-9 years (per 1,000)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 5-9 years of age expressed per 1,000 children aged 5, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000" } ], "source_id": "16" }, { "id": "SH.DYN.0509.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Probability of dying among children ages 5-9 years, female (per 1,000)" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying among female children between age 5-9 years of age expressed per 1,000 female children aged 5, if subject to female age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DYN.0509.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Probability of dying among children ages 5-9 years, male (per 1,000)" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying among male children between age 5-9 years of age expressed per 1,000 male children aged 5, if subject to male age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DYN.1014", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Probability of dying among adolescents ages 10-14 years (per 1,000)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 10-14 years of age expressed per 1,000 adolescents age 10, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000" } ], "source_id": "16" }, { "id": "SH.DYN.1014.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Probability of dying among adolescents ages 10-14 years, female (per 1,000)" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying among female adolescents between age 10-14 years of age expressed per 1,000 female adolescents age 10, if subject to female age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DYN.1014.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Probability of dying among adolescents ages 10-14 years, male (per 1,000)" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying among male adolescents between age 10-14 years of age expressed per 1,000 male adolescents age 10, if subject to male age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DYN.1019", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Probability of dying among adolescents ages 10-19 years (per 1,000)" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 10-19 years of age expressed per 1,000 adolescents age 10, if subject to age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DYN.1019.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Probability of dying among adolescents ages 10-19 years, female (per 1,000)" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying among female adolescents between age 10-19 years of age expressed per 1,000 female adolescents age 10, if subject to female age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DYN.1019.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Probability of dying among adolescents ages 10-19 years, male (per 1,000)" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying among male adolescents between age 10-19 years of age expressed per 1,000 male adolescents age 10, if subject to male age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DYN.1519", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Probability of dying among adolescents ages 15-19 years (per 1,000)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 15-19 years of age expressed per 1,000 adolescents age 15, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000" } ], "source_id": "16" }, { "id": "SH.DYN.1519.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Probability of dying among adolescents ages 15-19 years, female (per 1,000)" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying female adolescents between age 15-19 years of age expressed per 1,000 female adolescents age 15, if subject to female age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DYN.1519.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Probability of dying among adolescents ages 15-19 years, male (per 1,000)" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying male adolescents between age 15-19 years of age expressed per 1,000 male adolescents age 15, if subject to male age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DYN.2024", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Probability of dying among youth ages 20-24 years (per 1,000)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 20-24 years of age expressed per 1,000 youths age 20, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000" } ], "source_id": "16" }, { "id": "SH.DYN.2024.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Probability of dying among youth ages 20-24 years, female (per 1,000)" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying among female youths between age 20-24 years of age expressed per 1,000 female youths age 20, if subject to female age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DYN.2024.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Probability of dying among youth ages 20-24 years, male (per 1,000)" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying among male youths between age 20-24 years of age expressed per 1,000 male youths age 20, if subject to male age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.DYN.AIDS", "metatype": [ { "id": "IndicatorName", "value": "Adults (ages 15+) living with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Adults living with HIV refers to the number of people ages 15-49 who are infected with HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.DYN.AIDS.DH", "metatype": [ { "id": "IndicatorName", "value": "AIDS estimated deaths (UNAIDS estimates)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "AIDS deaths are the estimated number of adults and children who died due to AIDS-related causes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.DYN.AIDS.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Women's share of population ages 15+ living with HIV (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV is the percentage of people who are infected with HIV. Female rate is as a percentage of the total population ages 15+ who are living with HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated as the number of women aged 15 and older living with HIV divided by the total number of people aged 15 and older living with HIV. Estimates of people living with HIV are produced by UNAIDS using a common modelling framework (Spectrum), which integrates country-reported HIV surveillance data, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators.\n\n\n\nReference: Annex 1. Methods for deriving UNAIDS HIV estimates, 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform:\n\nhttps://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.DYN.AIDS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS." }, { "id": "IndicatorName", "value": "Prevalence of HIV, total (% of population ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\nStatistical concept(s): HIV prevalence rates reflect the rate of HIV infection in each country's population. Low national prevalence rates can be misleading, however. They often disguise epidemics that are initially concentrated in certain localities or population groups and threaten to spill over into the wider population. In many developing countries most new infections occur in young adults, with young women especially vulnerable." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.DYN.MORT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, under-5 (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is the Sustainable Development Goal indicator 3.2.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org, publisher: UNICEF, WHO, World Bank, United Nations Population Division;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "16" }, { "id": "SH.DYN.MORT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, under-5, female (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "16" }, { "id": "SH.DYN.MORT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, under-5, male (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "16" }, { "id": "SH.DYN.NCOM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates.\n\n\nThe current estimates for 2020 and 2021 are likely underestimated in countries where high-quality vital registration data was lacking at the time of GHE2021 production. This is because the modeled estimates cannot fully account for deaths from the four major NCDs indirectly attributed to the COVID-19 pandemic. Therefore, the data for 2020 and 2021 should be interpreted with caution." }, { "id": "Longdefinition", "value": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of death between the exact ages of 30 and 70 is calculated using cause-specific mortality rates for each 5-year age group, applying standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE). These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Percent of females ages 30 years old" } ], "source_id": "16" }, { "id": "SH.DYN.NCOM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of death between the exact ages of 30 and 70 is calculated using cause-specific mortality rates for each 5-year age group, applying standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE). These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Percent of males ages 30 years old" } ], "source_id": "16" }, { "id": "SH.DYN.NCOM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS)." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.4.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of death between the exact ages of 30 and 70 is calculated using cause-specific mortality rates for each 5-year age group, applying standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE). These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Percent of people ages 30 years old" } ], "source_id": "16" }, { "id": "SH.DYN.NMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, neonatal (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Neonatal mortality rate is the number of neonates dying before reaching 28 days of age, per 1,000 live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\n\nThis is the Sustainable Development Goal indicator 3.2.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "16" }, { "id": "SH.DYN.STLB", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Stillbirths are an important but often neglected global health problem. Sub-Saharan Africa and South Asia bear the greatest burden of these deaths. \n\nThe costs of stillbirths go beyond the loss of life and include psychological cost such as maternal depression, financial costs to parents as well as long-term economic costs to society. The burden on families, especially women, is severe and long lasting, yet stigma and taboo hide this burden even in high-income countries. The progress in lowering the stillbirth rate in the past two decades is much slower than the reduction in mortality of children aged under 5 years old. This could be due to a variety of reasons including absence or poor quality of care during pregnancy and birth, lack of investment in preventative interventions and the health workforce, lack of social recognition of stillbirths as a burden on families, challenges with measurements and major data gaps, absence of global and national leadership, and no established global targets." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Stillbirth rate (per 1,000 total births)" }, { "id": "Limitationsandexceptions", "value": "Data availability and data quality is uneven among countries. Adequate stillbirth data are lacking in many low and middle-income countries. These countries do not have a functioning health information system or civil registration vital statistics (CRVS) system to count or capture stillbirths, and in others stillbirths are excluded from routine registration despite the existence of functioning systems. In such settings, household surveys provide important information on child mortality, but most surveys have substantial data quality issues for stillbirth." }, { "id": "Longdefinition", "value": "Stillbirth rate is the number of fetal deaths at 28 weeks or more of gestation per 1,000 total births. Total birth is the sum of stillbirths (as just defined) and live births." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The UN IGME’s approach to estimate stillbirth rates (SBR) includes the following steps:\n1.\tCompile all available stillbirth data at a country level, derived from administrative sources, household surveys or population-based studies. \n2.\tEvaluate data in accordance with the data quality criteria and produce adjustment or recalculation by applying standardized definitions. \n3.\tEstimate global and country-specific trends of stillbirth rates using a smoothing time series model, supplemented with covariates associated with stillbirth rates. This process averages empirical data on stillbirths derived from the different sources for a given country. In the case of countries with sparse or no data, the identified covariates associated with stillbirth will inform the trend in stillbirth rate." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.FPL.SATI.ZS", "metatype": [ { "id": "IndicatorName", "value": "Demand for family planning satisfied by any methods (% of married women with demand for family planning)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Demand for family planning satisfied by any methods refers to the percentage of married women ages 15-49 whose need for family planning is satisfied." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "16" }, { "id": "SH.FPL.SATM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Demand for family planning satisfied by modern methods (% of married women with demand for family planning)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Demand for family planning satisfied by modern methods refers to the percentage of married women ages 15-49 years whose need for family planning is satisfied with modern methods." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.7.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated from nationally-representative household survey data. Relevant data for this indicator are collected through various multi-country survey programs, including Contraceptive Prevalence Surveys (CPS), Demographic and Health Surveys (DHS), Fertility and Family Surveys (FFS), Reproductive Health Surveys (RHS), Multiple Indicator Cluster Surveys (MICS), Performance Monitoring and Accountability 2020 surveys (PMA), World Fertility Surveys (WFS), other international survey programs, and national surveys.\n\n\n\n\n\nData compilation involves systematic searches of websites of international survey programs, survey databases (e.g., the Integrated Household Survey Network (IHSN) database), websites of national statistical offices, SDG national reporting platforms, and ad hoc queries. Additionally, country-specific information from UNFPA country offices is utilized." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "16" }, { "id": "SH.H2O.BASW.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using at least basic drinking water services, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water). This indicator encompasses both people using basic water services as well as those using safely managed water services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "16" }, { "id": "SH.H2O.BASW.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using at least basic drinking water services, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water). This indicator encompasses both people using basic water services as well as those using safely managed water services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "16" }, { "id": "SH.H2O.BASW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using at least basic drinking water services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water). This indicator encompasses both people using basic water services as well as those using safely managed water services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "16" }, { "id": "SH.H2O.SMDW.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using safely managed drinking water services, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality). Many countries lack data on one or more elements of safely managed drinking water. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability). Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.1.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed drinking water services are defined as the water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "16" }, { "id": "SH.H2O.SMDW.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using safely managed drinking water services, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality). Many countries lack data on one or more elements of safely managed drinking water. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability). Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.1.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed drinking water services are defined as the water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "16" }, { "id": "SH.H2O.SMDW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using safely managed drinking water services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality). Many countries lack data on one or more elements of safely managed drinking water. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability). Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis indicator (Total) is calculated as a population-weighted average of the RURAL and URBAN aggregates when sufficient data are available for both domains. When coverage at either the RURAL or URBAN level is insufficient, but country-level TOTAL data meet the minimum population coverage threshold (30%), TOTAL aggregates are calculated directly from country-level TOTAL estimates. Because these two aggregation approaches may be applied in different years as data availability improves, methodological switches can occur and may result in discontinuities in the time series.\n\n\n\nThis indicator corresponds to Sustainable Development Goal indicator 6.1.1 (see UN SDG metadata: https://unstats.un.org/sdgs/metadata/)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed drinking water services are defined as the water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "16" }, { "id": "SH.HIV.0014", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Children (0-14) living with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Children living with HIV refers to the number of children ages 0-14 who are infected with HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "16" }, { "id": "SH.HIV.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Prevalence of HIV, female (% ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV, female is the percentage of females who are infected with HIV. Youth rates are as a percentage of the relevant age group." }, { "id": "Othernotes", "value": "In many developing countries most new infections occur in young adults, with young women especially vulnerable." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.HIV.1524.KW.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Comprehensive correct knowledge of HIV/AIDS, ages 15-24, female (2 prevent ways and reject 3 misconceptions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percent of female respondents ages 15-24 who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can have HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "16" }, { "id": "SH.HIV.1524.KW.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Comprehensive correct knowledge of HIV/AIDS, ages 15-24, male (2 prevent ways and reject 3 misconceptions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percent of male respondents ages 15-24 who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can have HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "16" }, { "id": "SH.HIV.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Prevalence of HIV, male (% ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV, male is the percentage of males who are infected with HIV. Youth rates are as a percentage of the relevant age group." }, { "id": "Othernotes", "value": "In many developing countries most new infections occur in young adults, with young women being especially vulnerable." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.HIV.ARTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Antiretroviral therapy (ART) is central to the global HIV response and has significantly improved both survival and quality of life for people living with HIV. Effective ART suppresses viral load to undetectable levels, preventing progression to AIDS. When viral load remains undetectable, HIV is not sexually transmitted to HIV-negative partners. \n\nDespite this progress, gaps in treatment persist. Nearly 10 million people living with HIV are not receiving ART, and according to UNAIDS, about half of them reside in Africa. Expanding access to ART remains essential for reducing HIV-related morbidity and mortality and for achieving global targets for ending AIDS as a public health threat. (Reference: https://www.unaids.org/sites/default/files/2025-07/2025-global-aids-update-JC3153_en.pdf)" }, { "id": "IndicatorName", "value": "Antiretroviral therapy coverage (% of people living with HIV)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Antiretroviral therapy coverage indicates the percentage of all people living with HIV who are receiving antiretroviral therapy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.HIV.INCD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Adults (ages 15-49) newly infected with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of adults (ages 15-49) newly infected with HIV." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "16" }, { "id": "SH.HIV.INCD.14", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Children (ages 0-14) newly infected with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of children (ages 0-14) newly infected with HIV." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "16" }, { "id": "SH.HIV.INCD.50.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "Generalcomments", "value": "This is an age-disaggregated indicator for Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Incidence of HIV, ages 50+ (per 1,000 uninfected population ages 50+)" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected populations ages 50+ expressed per 1,000 uninfected population ages 50+ in the year before the period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Statisticalconceptandmethodology", "value": "Data on incidence of HIV are from the Joint United Nations Programme on HIV/AIDS. Because of challenges in collecting direct measures of HIV incidence, modelled estimates are used (the Spectrum software). The models incorporate data on HIV prevalence from surveys of the general population, antenatal clinic attendees, and populations at increased risk of contracting HIV (such as sex workers, men who have sex with men, and people who inject drugs) and on the number of people receiving antiretroviral therapy, which will increase the prevalence of HIV because people living with HIV now survive longer. In countries with high-quality health information systems the models are also informed by case reporting and vital registration data." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.HIV.INCD.TL", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Adults (ages 15+) and children (ages 0-14) newly infected with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of adults (ages 15+) and children (ages 0-14) newly infected with HIV." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "16" }, { "id": "SH.HIV.INCD.TL.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Incidence of HIV, all (per 1,000 uninfected population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected populations expressed per 1,000 uninfected population in the year before the period." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.HIV.INCD.YG", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Young people (ages 15-24) newly infected with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of young people (ages 15-24) newly infected with HIV." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "16" }, { "id": "SH.HIV.INCD.YG.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected populations ages 15-24 expressed per 1,000 uninfected population ages 15-24 in the year before the period." }, { "id": "Othernotes", "value": "This is an age-disaggregated indicator for Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.HIV.INCD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Incidence of HIV, ages 15-49 (per 1,000 uninfected population ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected populations ages 15-49 expressed per 1,000 uninfected population in the year before the period." }, { "id": "Othernotes", "value": "This is an age-disaggregated indicator for Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.HIV.KNOW.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Comprehensive correct knowledge of HIV/AIDS, ages 15-49, female (2 prevent ways and reject 3 misconceptions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Knowledge of HIV, female, is the percentage of female respondents who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can have HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "16" }, { "id": "SH.HIV.KNOW.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Comprehensive correct knowledge of HIV/AIDS, ages 15-49, male (2 prevent ways and reject 3 misconceptions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Knowledge of HIV, male, is the percentage of male respondents who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can have HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "16" }, { "id": "SH.HIV.ORPH", "metatype": [ { "id": "IndicatorName", "value": "Children orphaned by HIV/AIDS" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of children orphaned by HIV/AIDS is the estimated number of children who have lost their mother or both parents to AIDS before age 15 since the epidemic began. Some of the orphaned children included in this cumulative total are no longer alive; others are no longer under age 15." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.HIV.PMTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "HIV can be transmitted through sexual contact, blood transfusions, and the sharing of contaminated needles, and it can also be transmitted from mother to child during pregnancy, childbirth, or breastfeeding. Although the number of children acquiring HIV has decreased over time, mother-to-child transmission remains a significant public health concern.\n\nPrevention of mother-to-child transmission (PMTCT) is critical for reducing new pediatric HIV infections. However, many pregnant and breastfeeding women still do not begin ART or discontinue treatment during this period, contributing to continued transmission risks. Strengthening PMTCT services and ensuring continuity of care are essential for achieving global HIV prevention goals. (Reference: https://www.unaids.org/sites/default/files/2025-07/2025-global-aids-update-JC3153_en.pdf)" }, { "id": "IndicatorName", "value": "Antiretroviral therapy coverage for PMTCT (% of pregnant women living with HIV)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of pregnant women with HIV who receive antiretroviral medicine for prevention of mother-to-child transmission (PMTCT)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The coverage of antiretrovirals for PMTCT is calculated by dividing the number of pregnant women living with HIV who received antiretrovirals for PMTCT by the estimated number of pregnant women living with HIV who need antiretrovirals for PMTCT in the country. \n\n\n\n\nEstimating the Numerator: The number of pregnant women living with HIV receiving antiretrovirals for PMTCT is derived from national program data aggregated from facilities or other service delivery sites and reported by the country.\n\n\n\n\nEstimating the Denominator: The number of pregnant women living with HIV who need antiretroviral medicine for PMTCT is estimated using standardized statistical modeling based on UNAIDS/WHO methods. These methods consider various epidemic and demographic parameters, such as HIV prevalence among women of reproductive age, the effect of HIV on fertility, and national program coverage of antiretroviral therapy. These statistical modeling procedures provide a comprehensive population-based estimate of the number of pregnant women living with HIV who need antiretrovirals for PMTCT in the country." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Percent of pregnant women living with HIV" } ], "source_id": "16" }, { "id": "SH.HIV.TOTL", "metatype": [ { "id": "IndicatorName", "value": "Adults (ages 15+) and children (0-14 years) living with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Adults and children living with HIV refers to the number of people ages 0-49 (adult ages 15-49 and children ages 0-14) who are infected with HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.HTN.PREV.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Hypertension significantly increases the risk of heart, brain, and kidney diseases and is one of the leading causes of death and disease worldwide. While it can be easily detected by measuring blood pressure and can often be treated effectively with medications at low cost, the number of people with hypertension has increased for the past decades. Many people with hypertension are not aware of their condition or are not receiving the treatment that they need. \n\nThe WHO points out that the burden of hypertension has shifted from high-income countries to low- and middle-income countries. The prevalence rate of hypertension has decreased in high-income countries, while it has increased in many low- or middle-income countries." }, { "id": "IndicatorName", "value": "Prevalence of hypertension, female (% of female adults ages 30-79)" }, { "id": "Longdefinition", "value": "Prevalence of hypertension, female, is the percentage of female adults ages 30-79 with hypertension (defined as having systolic blood pressure =140 mmHg, diastolic blood pressure =90 mmHg, or taking medication for hypertension).\tThe data is age-standardized." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization's Global Health Observatory Data Repository" }, { "id": "Statisticalconceptandmethodology", "value": "Based on the population-representative studies with measurement of blood pressure and data on blood pressure treatment, a Bayesian hierarchical model was used to estimate the prevalence of hypertension and the proportion of people who were taking medication for hypertension." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.HTN.PREV.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Hypertension significantly increases the risk of heart, brain, and kidney diseases and is one of the leading causes of death and disease worldwide. While it can be easily detected by measuring blood pressure and can often be treated effectively with medications at low cost, the number of people with hypertension has increased for the past decades. Many people with hypertension are not aware of their condition or are not receiving the treatment that they need. \n\nThe WHO points out that the burden of hypertension has shifted from high-income countries to low- and middle-income countries. The prevalence rate of hypertension has decreased in high-income countries, while it has increased in many low- or middle-income countries." }, { "id": "IndicatorName", "value": "Prevalence of hypertension, male (% of male adults ages 30-79)" }, { "id": "Longdefinition", "value": "Prevalence of hypertension, male, is the percentage of male adults ages 30-79 with hypertension (defined as having systolic blood pressure =140 mmHg, diastolic blood pressure =90 mmHg, or taking medication for hypertension).\tThe data is age-standardized." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization's Global Health Observatory Data Repository" }, { "id": "Statisticalconceptandmethodology", "value": "Based on the population-representative studies with measurement of blood pressure and data on blood pressure treatment, a Bayesian hierarchical model was used to estimate the prevalence of hypertension and the proportion of people who were taking medication for hypertension." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.HTN.PREV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Hypertension significantly increases the risk of heart, brain, and kidney diseases and is one of the leading causes of death and disease worldwide. While it can be easily detected by measuring blood pressure and can often be treated effectively with medications at low cost, the number of people with hypertension has increased for the past decades. Many people with hypertension are not aware of their condition or are not receiving the treatment that they need. \n\nThe WHO points out that the burden of hypertension has shifted from high-income countries to low- and middle-income countries. The prevalence rate of hypertension has decreased in high-income countries, while it has increased in many low- or middle-income countries." }, { "id": "IndicatorName", "value": "Prevalence of hypertension (% of adults ages 30-79)" }, { "id": "Longdefinition", "value": "Prevalence of hypertension is the percentage of adults ages 30-79 with hypertension (defined as having systolic blood pressure =140 mmHg, diastolic blood pressure =90 mmHg, or taking medication for hypertension).\tThe data is age-standardized." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization's Global Health Observatory Data Repository" }, { "id": "Statisticalconceptandmethodology", "value": "Based on the population-representative studies with measurement of blood pressure and data on blood pressure treatment, a Bayesian hierarchical model was used to estimate the prevalence of hypertension and the proportion of people who were taking medication for hypertension." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.HTN.TRET.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Hypertension significantly increases the risk of heart, brain, and kidney diseases and is one of the leading causes of death and disease worldwide. While it can be easily detected by measuring blood pressure and can often be treated effectively with medications at low cost, the number of people with hypertension has increased for the past decades. Many people with hypertension are not aware of their condition or are not receiving the treatment that they need. \n\nThe WHO points out that the burden of hypertension has shifted from high-income countries to low- and middle-income countries. The prevalence rate of hypertension has decreased in high-income countries, while it has increased in many low- or middle-income countries." }, { "id": "IndicatorName", "value": "Treatment for hypertension, female (% of female adults ages 30-79 with hypertension)" }, { "id": "Longdefinition", "value": "Treatment for hypertension is the percentage of adults ages 30-79 with hypertension who were taking medicine for hypertension. Hypertension is defined as having systolic blood pressure =140 mmHg, diastolic blood pressure =90 mmHg, or taking medication for hypertension." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization's Global Health Observatory Data Repository" }, { "id": "Statisticalconceptandmethodology", "value": "Based on the population-representative studies with measurement of blood pressure and data on blood pressure treatment, a Bayesian hierarchical model was used to estimate the prevalence of hypertension and the proportion of people who were taking medication for hypertension." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.HTN.TRET.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Hypertension significantly increases the risk of heart, brain, and kidney diseases and is one of the leading causes of death and disease worldwide. While it can be easily detected by measuring blood pressure and can often be treated effectively with medications at low cost, the number of people with hypertension has increased for the past decades. Many people with hypertension are not aware of their condition or are not receiving the treatment that they need. \n\nThe WHO points out that the burden of hypertension has shifted from high-income countries to low- and middle-income countries. The prevalence rate of hypertension has decreased in high-income countries, while it has increased in many low- or middle-income countries." }, { "id": "IndicatorName", "value": "Treatment for hypertension, male (% of male adults ages 30-79 with hypertension)" }, { "id": "Longdefinition", "value": "Treatment for hypertension, male, is the percentage of adults ages 30-79 with hypertension who were taking medicine for hypertension. Hypertension is defined as having systolic blood pressure =140 mmHg, diastolic blood pressure =90 mmHg, or taking medication for hypertension." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization's Global Health Observatory Data Repository" }, { "id": "Statisticalconceptandmethodology", "value": "Based on the population-representative studies with measurement of blood pressure and data on blood pressure treatment, a Bayesian hierarchical model was used to estimate the prevalence of hypertension and the proportion of people who were taking medication for hypertension." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.HTN.TRET.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Hypertension significantly increases the risk of heart, brain, and kidney diseases and is one of the leading causes of death and disease worldwide. While it can be easily detected by measuring blood pressure and can often be treated effectively with medications at low cost, the number of people with hypertension has increased for the past decades. Many people with hypertension are not aware of their condition or are not receiving the treatment that they need. \n\nThe WHO points out that the burden of hypertension has shifted from high-income countries to low- and middle-income countries. The prevalence rate of hypertension has decreased in high-income countries, while it has increased in many low- or middle-income countries." }, { "id": "IndicatorName", "value": "Treatment for hypertension (% of adults ages 30-79 with hypertension)" }, { "id": "Longdefinition", "value": "Treatment for hypertension is the percentage of adults ages 30-79 with hypertension who were taking medicine for hypertension. Hypertension is defined as having systolic blood pressure =140 mmHg, diastolic blood pressure =90 mmHg, or taking medication for hypertension." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization's Global Health Observatory Data Repository" }, { "id": "Statisticalconceptandmethodology", "value": "Based on the population-representative studies with measurement of blood pressure and data on blood pressure treatment, a Bayesian hierarchical model was used to estimate the prevalence of hypertension and the proportion of people who were taking medication for hypertension." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.IMM.HEPB", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, HepB3 (% of one-year-old children)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization rate, hepatitis B is the percentage of children ages 12-23 months who received hepatitis B vaccinations before 12 months or at any time before the survey. A child is considered adequately immunized after three doses." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2024" }, { "id": "Source", "value": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year. Notes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.IMM.HIB3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and ??is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, Hib3 (% of children ages 12-23 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization, Hib3, measures the percentage of children ages 12-23 months who received Hib3 vaccinations before 12 months or at any time before the survey. A child is considered adequately immunized against Hib3 after receiving three doses of Haemophilus influenzae type b vaccine." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO and UNICEF (http://www.who.int/immunization/monitoring_surveillance/en/)." }, { "id": "Statisticalconceptandmethodology", "value": "Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package. The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year.\n\nNotes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "16" }, { "id": "SH.IMM.IBCG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and ??is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, BCG (% of one-year-old children)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization rate, BCG is the percentage of children ages 12-23 months who received vaccinations before 12 months or at any time before the survey for BCG. A child is considered adequately immunized after one dose." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO and UNICEF (http://www.who.int/immunization/monitoring_surveillance/en/)." }, { "id": "Statisticalconceptandmethodology", "value": "Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package. The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year.\n\nNotes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "16" }, { "id": "SH.IMM.IDPT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, DPT (% of children ages 12-23 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization, DPT, measures the percentage of children ages 12-23 months who received DPT vaccinations before 12 months or at any time before the survey. A child is considered adequately immunized against diphtheria, pertussis (or whooping cough), and tetanus (DPT) after receiving three doses of vaccine." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.b.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2024" }, { "id": "Source", "value": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year. Notes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.IMM.MEA2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and ??is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.b.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Immunization, measles second dose (% of children by the nationally recommended age)" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization, measles second dose, measures the percentage of children who received two dose of measles containing vaccine according to nationally recommended schedule through routine immunization services in a given year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO and UNICEF (http://www.who.int/immunization/monitoring_surveillance/en/)." }, { "id": "Statisticalconceptandmethodology", "value": "Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package. The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year.\n\nNotes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "16" }, { "id": "SH.IMM.MEAS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, measles (% of children ages 12-23 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization, measles, measures the percentage of children ages 12-23 months who received the measles vaccination before 12 months or at any time before the survey. A child is considered adequately immunized against measles after receiving one dose of vaccine." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2024" }, { "id": "Source", "value": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF), uri: https://data.unicef.org/topic/child-health/immunization/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year. Notes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.IMM.POL3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and ??is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, Pol3 (% of one-year-old children)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization rate, polio, is the percentage of children ages 12-23 months who received polio vaccinations before 12 months or at any time before the survey. A child is considered adequately immunized after three doses." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO and UNICEF (http://www.who.int/immunization/monitoring_surveillance/en/)." }, { "id": "Statisticalconceptandmethodology", "value": "Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package. The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year.\n\nNotes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "16" }, { "id": "SH.MED.BEDS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Hospital beds are used to indicate the availability of inpatient services." }, { "id": "IndicatorName", "value": "Hospital beds (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Depending on the source and means of monitoring, data may not be exactly comparable across countries. For more information, see the original source." }, { "id": "Longdefinition", "value": "Hospital beds include inpatient beds available in public, private, general, and specialized hospitals and rehabilitation centers. In most cases beds for both acute and chronic care are included." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "WHO data, supplemented by country data, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data were compiled from the WHO Regional offices and country sources other (e.g. ministry of health, national statistical office) and modified to standardize the unit of measure of per 10 000 population by WHO.\n\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\nAvailability and use of health services, such as hospital beds per 1,000 people, reflect both demand- and supply-side factors. In the absence of a consistent definition this is a crude indicator of the extent of physical, financial, and other barriers to health care." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "16" }, { "id": "SH.MED.CMHW.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The WHO estimates that at least 2.5 medical staff (physicians, nurses and midwives) per 1,000 people are needed to provide adequate coverage with primary care interventions (WHO, World Health Report 2006)." }, { "id": "IndicatorName", "value": "Community health workers (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The WHO compiles data from household and labor force surveys, censuses, and administrative records. Data comparability is limited by differences in definitions and training of medical personnel varies. In addition, human resources tend to be concentrated in urban areas, so that average densities do not provide a full picture of health personnel available to the entire population." }, { "id": "Longdefinition", "value": "Community health workers include various types of community health aides, many with country-specific occupational titles such as community health officers, community health-education workers, family health workers, lady health visitors and health extension package workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2016" }, { "id": "Source", "value": "Global Health Workforce Statistics, World Health Organization (WHO);\nOrganisation for Economic Co-operation and Development (OECD);\nCountry data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The method of estimation for number of community health workers (including community health officers, community health-education workers, community health aides, family health workers and associated occupations) depends on the nature of the original data source. Enumeration based on population census data is a count of the number of people reporting 'community health worker' as their current occupation (as classified according to the tasks and duties of their job). A similar method is used for estimates based on labour force survey data, with the additional application of a sampling weight to calibrate for national representation. Data from health facility assessments and administrative reporting systems may be based on head counts of employees, staffing records, payroll records, training records, or tallies from other types of routine administrative records on human resources.\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\n\n\nData on health worker (physicians, nurses and midwives, and community health workers) density show the availability of medical personnel." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "16" }, { "id": "SH.MED.NUMW.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The WHO estimates that at least 2.5 medical staff (physicians, nurses and midwives) per 1,000 people are needed to provide adequate coverage with primary care interventions (WHO, World Health Report 2006)." }, { "id": "IndicatorName", "value": "Nurses and midwives (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The WHO compiles data from household and labor force surveys, censuses, and administrative records. Data comparability is limited by differences in definitions and training of medical personnel varies. In addition, human resources tend to be concentrated in urban areas, so that average densities do not provide a full picture of health personnel available to the entire population." }, { "id": "Longdefinition", "value": "Nurses and midwives include professional nurses, professional midwives, auxiliary nurses, auxiliary midwives, enrolled nurses, enrolled midwives and other associated personnel, such as dental nurses and primary care nurses." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.c.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "Global Health Workforce Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National focal points share the data with WHO through the online NHWA data platform. The platform hosted in WHO, is built to facilitate data reporting on the indicators listed in the NHWA Handbook and data sharing across all the 3 levels of WHO.\n\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\nData on health worker (physicians, nurses and midwives, and community health workers) density show the availability of medical personnel." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "16" }, { "id": "SH.MED.PHYS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The WHO estimates that at least 2.5 medical staff (physicians, nurses and midwives) per 1,000 people are needed to provide adequate coverage with primary care interventions (WHO, World Health Report 2006)." }, { "id": "IndicatorName", "value": "Physicians (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The WHO compiles data from household and labor force surveys, censuses, and administrative records. Data comparability is limited by differences in definitions and training of medical personnel varies. In addition, human resources tend to be concentrated in urban areas, so that average densities do not provide a full picture of health personnel available to the entire population." }, { "id": "Longdefinition", "value": "Physicians include generalist and specialist medical practitioners." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.c.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Global Health Workforce Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National focal points share the data with WHO through the online NHWA data platform. The platform hosted in WHO, is built to facilitate data reporting on the indicators listed in the NHWA Handbook and data sharing across all the 3 levels of WHO.\n\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\nData on health worker (physicians, nurses and midwives, and community health workers) density show the availability of medical personnel." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "16" }, { "id": "SH.MED.SAOP.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Billions people lack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked." }, { "id": "IndicatorName", "value": "Specialist surgical workforce (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Prior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that “We can’t manage what we don’t measure”, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups." }, { "id": "Longdefinition", "value": "Specialist surgical workforce is the number of specialist surgical, anaesthetic, and obstetric (SAO) providers who are working in each country per 100,000 population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2008-2018" }, { "id": "Source", "value": "Lancet Commission on Global Surgery, uri: www.lancetglobalsurgery.org, note: Data collected by the Lancet Commission on Global Surgery;\nWHO Collaborating Centre for Surgery and Public Health, note: data collected by WHO Collaborating Centre for Surgery and Public Health at Lund University from various sources including Ministries of Health or equivalent national regulatory bodies, national official entities such as medical councils, Eurostat, OECD, WHO Euro Health For All Database, WHO EURO Technical resources for health Database;\nBMJ Glob Health" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of specialist surgical, anaesthetic, and obstetric (SAO) providers who are working in each country per 100 000 population.\nStatistical concept(s): The Lancet Commission on Global Surgery, assembled in 2013 to assess surgical care around the world. Commissioners engaged in an iterative global consultative process with partners in over 110 countries to develop six core indicators of the strength of a surgical system. Two indicators assess a country’s preparedness to deliver safe surgery and anesthesia, two assess the current delivery of safe care, and two assess the state of financial risk protection for those seeking surgery." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "16" }, { "id": "SH.MLR.INCD.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes. It is preventable and curable. There are 5 parasite species that cause malaria in humans, and 2 of these species – Plasmodium falciparum and Plasmodium vivax – pose the greatest threat." }, { "id": "IndicatorName", "value": "Incidence of malaria (per 1,000 population at risk)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Incidence of malaria is the number of new cases of malaria in a year per 1,000 population at risk." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.3.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO), uri: http://apps.who.int/ghodata/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Confirmed malaria cases for countries and areas outside Africa, and for low-transmission countries and areas in Africa are adjusted for extent of health service use (treatment seeking), underreporting and lack of case confirmation (the likelihood that cases are parasite positive). In high transmission areas in which the quality of surveillance data does not permit a robust estimate from the number of reported cases, but good data on parasite prevalence is available, the number of cases can be estimated from parasite prevalence. The denominator is estimated, using official UN population and population at risk estimates for countries with sub-national endemicity.\n\nStatistical concept(s): Complete data on malaria cases reported through surveillance systems are the best source of data but are rarely available for large populations at high quality and accuracy. Reported data on malaria cases generally need to be adjusted for extent of health service use (treatment seeking), underreporting and lack of case confirmation (the likelihood that cases are parasite positive). WHO compiles data on reported confirmed cases of malaria and suspected cases tested with microscopy or RDT, submitted by national malaria control programmes. Underreporting is reported or estimated by countries. The extent of health service use (treatment seeking) data were obtained from nationally representative household surveys on health service use." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Per 1000 population at risk" } ], "source_id": "16" }, { "id": "SH.MLR.IPTP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Malaria infection during pregnancy is a serious public health concern. It carries substantial risks for the mother, her fetus and the neonate including anaemia, severe malaria, spontaneous abortion, stillbirth, prematurity, neonatal mortality and low birthweight. Intermittent preventive treatment of malaria in pregnancy is a full therapeutic course of antimalarial medicine given to pregnant women at routine antenatal care visits, regardless of whether the recipient is infected with malaria. IPTp reduces maternal malaria episodes, maternal and fetal anaemia, placental parasitaemia, low birth weight, and neonatal mortality." }, { "id": "IndicatorName", "value": "Intermittent preventive treatment (IPT) of malaria in pregnancy (% of pregnant women)" }, { "id": "Longdefinition", "value": "Percentage of women aged 15 - 49 with a live birth in the recent years preceding the survey who received 3+ doses of sulfadoxine-pyrimethamine (SP/Fansidar), at least one during an antenatal care visit. Intermittent Preventive Treatment (IPT) is preventive treatment with SP/Fansidar during an antenatal care (ANC) visit treatment with a dose of sulfadoxine-pyrimethamine (SP/Fansidar) to pregnant women at each scheduled antenatal visit after the first trimester, but not more frequently than once a month." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Percentage of women aged 15 - 49 with a live birth in the recent years preceding the survey who received 3+ doses of sulfadoxine-pyrimethamine (SP/Fansidar), at least one during an antenatal care visit." }, { "id": "Source", "value": "UNICEF Global Databases [data.unicef.org], Multiple Indicator Cluster Surveys, Demographic and Health Surveys." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "16" }, { "id": "SH.MLR.NETS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Use of insecticide-treated bed nets (% of under-5 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Use of insecticide-treated bed nets refers to the percentage of children under age five who slept under an insecticide-treated bednet to prevent malaria." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Malaria is endemic to the poorest countries in the world, mainly in tropical and subtropical regions of Africa, Asia, and the Americas. Insecticide-treated nets, properly used and maintained, are one of the most important malaria-preventive strategies to limit human-mosquito contact." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "16" }, { "id": "SH.MLR.TRET.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Children with fever receiving antimalarial drugs (% of children under age 5 with fever)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Malaria treatment refers to the percentage of children under age five who were ill with fever in the last two weeks and received any appropriate (locally defined) anti-malarial drugs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Malaria is endemic to the poorest countries in the world, mainly in tropical and subtropical regions of Africa, Asia, and the Americas. Prompt and effective treatment of malaria is a critical element of malaria control. It is vital that sufferers, especially children under age 5, start treatment within 24 hours of the onset of symptoms, to prevent progression - often rapid - to severe malaria and death. Data on malaria are from national-level surveys, including Multiple Indicator Cluster Surveys, Demographic and Health Surveys, and Malaria Indicator Surveys." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "16" }, { "id": "SH.MMR.DTHS", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Number of maternal deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The figures cannot be assumed to provide exact estimates." }, { "id": "Longdefinition", "value": "A maternal death refers to the death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management but not from accidental or incidental causes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO);\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SH.MMR.LEVE", "metatype": [ { "id": "Developmentrelevance", "value": "The knowledge and analysis provided by Women, Business and the Law make a strong economic case for laws that empower women. Better performance in the areas measured by the Women, Business and the Law index is associated with more women in the labor force and with higher income and improved development outcomes. Equality before the law and of economic opportunity are not only wise social policy but also good economic policy. The equal participation of women and men will give every economy a chance to achieve its potential. Given the economic significance of women's empowerment, the ultimate goal of Women, Business and the Law is to encourage governments to reform laws that hold women back from working and doing business." }, { "id": "Generalcomments", "value": "For the reference period, WDI and Gender Databases take the data coverage years instead of reporting years used in WBL (https://wbl.worldbank.org/). For example, the data for YR2020 in WBL (report year) corresponds to data for YR2019 in WDI and Gender Databases." }, { "id": "IndicatorName", "value": "Length of paid maternity leave (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The indicator represents the duration of paid leave available to mothers." }, { "id": "Notesfromoriginalsource", "value": "This indicator is additional to the 35 scored indicators, and associated to the following indicator \"Paid leave of at least 14 weeks is available to women (1=yes; 0=no)\" (SH.MMR.LEVE.AL)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress toward legal equality between men and women in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 2,000 respondents with expertise in family, labor, and criminal law, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and regulations. The Women, Business and the Law team collects the texts of these codified sources of national law - constitutions, codes, laws, statutes, rules, regulations, and procedures - and checks questionnaire responses for accuracy. Thirty-five data points are scored across eight indicators of four or five binary questions, with each indicator representing a different phase of a woman’s career. Indicator-level scores are obtained by calculating the unweighted average of the questions within that indicator and scaling the result to 100. Overall scores are then calculated by taking the average of each indicator, with 100 representing the highest possible score." }, { "id": "Topic", "value": "Gender: Public life & decision making" } ], "source_id": "16" }, { "id": "SH.MMR.RISK", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Lifetime risk of maternal death (1 in: rate varies by country)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The probability cannot be assumed to provide an exact estimate of risk of maternal death." }, { "id": "Longdefinition", "value": "Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO);\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Number of 15-year old women for which 1 maternal death occurs assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future" } ], "source_id": "16" }, { "id": "SH.MMR.RISK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Lifetime risk of maternal death (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The probability cannot be assumed to provide an exact estimate of risk of maternal death." }, { "id": "Longdefinition", "value": "Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO);\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.MMR.WAGE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Maternal leave benefits (% of wages paid)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Maternity leave benefits refers to the total percentage of wages covered by all sources during paid maternity leave." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Women, Business and the Law." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "16" }, { "id": "SH.PRG.ANEM", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of anemia among pregnant women (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data should be used with caution because surveys differ in quality, coverage, age group interviewed, and treatment of missing values across countries and over time.\n\n\n\nData on anemia are compiled by the WHO based mainly on nationally representative surveys, which measure hemoglobin in the blood. WHO's hemoglobin thresholds are then used to determine anemia status based on age, sex, and physiological status." }, { "id": "Longdefinition", "value": "Prevalence of anemia, pregnant women, is the percentage of pregnant women whose hemoglobin level is less than 110 grams per liter at sea level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Anemia is a condition in which the number of red blood cells or their oxygen-carrying capacity is insufficient to meet physiologic needs, which vary by age, sex, altitude, smoking status, and pregnancy status. In its severe form it is associated with fatigue, weakness, dizziness, and drowsiness. Children under age 5 and pregnant women have the highest risk for anemia." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "16" }, { "id": "SH.PRG.SYPH.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of syphilis (% of women attending antenatal care)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of women attending antenatal care seropositive for syphilis" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Percentage of women attending antenatal care seropositive for syphilis" }, { "id": "Source", "value": "World Health Organization's Global Health Observatory Data Repository" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.PRV.SMOK", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of current tobacco use (% of adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution." }, { "id": "Longdefinition", "value": "The percentage of the population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, total (ages 15+)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes. The indicator name and definition were updated in December, 2020." }, { "id": "Periodicity", "value": "Biennial" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nA statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. \n\n\n\nThe model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. \n\nDepending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015)." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.PRV.SMOK.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of current tobacco use, females (% of female adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution." }, { "id": "Longdefinition", "value": "The percentage of the female population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, females (% of adults)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes it. The indicator name and definition were updated in December, 2020." }, { "id": "Periodicity", "value": "Biennial" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nA statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. \n\n\n\nThe model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. \n\nDepending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015)." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.PRV.SMOK.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of current tobacco use, males (% of male adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution." }, { "id": "Longdefinition", "value": "The percentage of the male population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, males (% of adults)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes it. The indicator name and definition were updated in December, 2020." }, { "id": "Periodicity", "value": "Biennial" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nSmoking is the most common form of tobacco use and the prevalence of smoking is therefore a good measure of the tobacco epidemic. (Corrao MA, Guindon GE, Sharma N, Shokoohi DF (eds). Tobacco Control Country Profiles, 2000, American Cancer Society, Atlanta.) Tobacco use causes heart and other vascular diseases and cancers of the lung and other organs. Given the long delay between starting to smoke and the onset of disease, the health impact of smoking will increase rapidly only in the next few decades. The data presented are age-standardized rates for adults ages 15 and older from the WHO." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.SGR.CRSK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Billions people lack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked." }, { "id": "IndicatorName", "value": "Risk of catastrophic expenditure for surgical care (% of people at risk)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Prior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that “We can’t manage what we don’t measure”, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups." }, { "id": "Longdefinition", "value": "The proportion of population at risk of catastrophic expenditure when surgical care is required. Catastrophic expenditure is defined as direct out of pocket payments for surgical and anaesthesia care exceeding 10% of total income." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2003-2022" }, { "id": "Source", "value": "Program in Global Surgery and Social Change (PGSSC), Harvard Medical School, uri: https://www.pgssc.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of experiencing impoverishment when surgical care is required and the probability of experiencing catastrophic expenditure (10 percent of total income) when surgical care is required.\nStatistical concept(s): The Lancet Commission on Global Surgery, assembled in 2013 to assess surgical care around the world. Commissioners engaged in an iterative global consultative process with partners in over 110 countries to develop six core indicators of the strength of a surgical system. Two indicators assess a country’s preparedness to deliver safe surgery and anesthesia, two assess the current delivery of safe care, and two assess the state of financial risk protection for those seeking surgery." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.SGR.IRSK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Billions people lack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked." }, { "id": "IndicatorName", "value": "Risk of impoverishing expenditure for surgical care (% of people at risk)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Prior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that “We can’t manage what we don’t measure”, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups." }, { "id": "Longdefinition", "value": "The proportion of population at risk of impoverishing expenditure when surgical care is required. Impoverishing expenditure is defined as direct out of pocket payments for surgical and anaesthesia care which drive people below a poverty threshold (using a threshold of $2.15 PPP/day)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2003-2022" }, { "id": "Source", "value": "Program in Global Surgery and Social Change (PGSSC), Harvard Medical School, uri: https://www.pgssc.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of experiencing impoverishment when surgical care is required and the probability of experiencing catastrophic expenditure (10 percent of total income) when surgical care is required.\nStatistical concept(s): The Lancet Commission on Global Surgery, assembled in 2013 to assess surgical care around the world. Commissioners engaged in an iterative global consultative process with partners in over 110 countries to develop six core indicators of the strength of a surgical system. Two indicators assess a country’s preparedness to deliver safe surgery and anesthesia, two assess the current delivery of safe care, and two assess the state of financial risk protection for those seeking surgery." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.SGR.PROC.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Billions people lack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked." }, { "id": "IndicatorName", "value": "Number of surgical procedures (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Prior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that “We can’t manage what we don’t measure”, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups." }, { "id": "Longdefinition", "value": "The number of procedures undertaken in an operating theatre per 100,000 population per year in each country. A procedure is defined as the incision, excision, or manipulation of tissue that needs regional or general anaesthesia, or profound sedation to control pain." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2023" }, { "id": "Source", "value": "Lancet Commission on Global Surgery, uri: www.lancetglobalsurgery.org, note: Data from various sources compiled by the Lancet Commission on Global Surgery and the Center for Health Equity in Surgery and Anesthesia at UCSF Medical Center;\nCenter for Health Equity in Surgery and Anesthesia, note: Data from various sources compiled by the Lancet Commission on Global Surgery and the Center for Health Equity in Surgery and Anesthesia at UCSF Medical Center" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of procedures undertaken in an operating theatre per 100 000 population per year in each country. A procedure is defined as the incision, excision, or manipulation of tissue that needs regional or general anaesthesia, or profound sedation to control pain.\nStatistical concept(s): The Lancet Commission on Global Surgery, assembled in 2013 to assess surgical care around the world. Commissioners engaged in an iterative global consultative process with partners in over 110 countries to develop six core indicators of the strength of a surgical system. Two indicators assess a country’s preparedness to deliver safe surgery and anesthesia, two assess the current delivery of safe care, and two assess the state of financial risk protection for those seeking surgery." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "16" }, { "id": "SH.STA.AIRP.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries." }, { "id": "IndicatorName", "value": "Mortality rate attributed to household and ambient air pollution, age-standardized, female (per 100,000 female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Estimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions" }, { "id": "Longdefinition", "value": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2019" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.STA.AIRP.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries." }, { "id": "IndicatorName", "value": "Mortality rate attributed to household and ambient air pollution, age-standardized, male (per 100,000 male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Estimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions" }, { "id": "Longdefinition", "value": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2019" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.STA.AIRP.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries." }, { "id": "IndicatorName", "value": "Mortality rate attributed to household and ambient air pollution, age-standardized (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Estimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions" }, { "id": "Longdefinition", "value": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years)." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2019" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.STA.ANV4.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Good prenatal and postnatal care improve maternal health and reduce maternal and infant mortality." }, { "id": "IndicatorName", "value": "Pregnant women receiving prenatal care of at least four visits (% of pregnant women)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "For the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources." }, { "id": "Longdefinition", "value": "Pregnant women receiving prenatal care, at least four times, are the percentage of women attended at least four times during pregnancy by skilled health personnel for reasons related to pregnancy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\nGood prenatal and postnatal care improves maternal health and reduces maternal and infant mortality. However, indicators on use of antenatal care services provide no information on the content or quality of the services. Data on antenatal care are obtained mostly from household surveys, which ask women who have had a live birth whether and from whom they received antenatal care." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "16" }, { "id": "SH.STA.ANVC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Pregnant women receiving prenatal care (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "For the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources." }, { "id": "Longdefinition", "value": "Pregnant women receiving prenatal care are the percentage of women attended at least once during pregnancy by skilled health personnel for reasons related to pregnancy." }, { "id": "Othernotes", "value": "Good prenatal and postnatal care improve maternal health and reduce maternal and infant mortality." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nGood prenatal and postnatal care improves maternal health and reduces maternal and infant mortality. However, indicators on use of antenatal care services provide no information on the content or quality of the services. Data on antenatal care are obtained mostly from household surveys, which ask women who have had a live birth whether and from whom they received antenatal care." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "16" }, { "id": "SH.STA.ARIC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "ARI treatment (% of children under 5 taken to a health provider)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Children with acute respiratory infection (ARI) who are taken to a health provider refers to the percentage of children under age five with ARI in the last two weeks who were taken to an appropriate health provider, including hospital, health center, dispensary, village health worker, clinic, and private physician." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Acute respiratory infection continues to be a leading cause of death among young children. Data are drawn mostly from household health surveys in which mothers report on number of episodes and treatment for acute respiratory infection." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "16" }, { "id": "SH.STA.BASS.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using at least basic sanitation services, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation). This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "16" }, { "id": "SH.STA.BASS.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using at least basic sanitation services, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation). This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "16" }, { "id": "SH.STA.BASS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using at least basic sanitation services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation). This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "16" }, { "id": "SH.STA.BFED.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "For optimal infant and young child feeding, mothers initiate breastfeeding within one hour of birth, breastfeed exclusively for the first six months, and continue to breastfeed for two years or more while providing nutritionally adequate, safe, and age-appropriate solid, semisolid, and soft foods. Breast milk alone contains all the nutrients, antibodies, hormones, and antioxidants an infant needs to thrive. It protects babies from diarrhea and acute respiratory infections, stimulates their immune systems and response to vaccination, and may confer cognitive benefits." }, { "id": "IndicatorName", "value": "Exclusive breastfeeding (% of children under 6 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Most of the data on breastfeeding are derived from household surveys. For the data that are from household surveys, the year refers to the survey year." }, { "id": "Longdefinition", "value": "Exclusive breastfeeding refers to the percentage of children less than six months old who are fed breast milk alone (no other liquids) in the past 24 hours." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2020" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of infants aged 0–5 months who received only breast milk during the previous day by the total number of infants aged 0–5 months, then multiplying the result by 100.\n\n\nData collection involves Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), which include questions about liquids and foods given the previous day, as well as the number of milk feeds the previous day, to determine if the child is being exclusively breastfed. WHO and UNICEF jointly collect data on infant and young child feeding, pooling information from national surveys. Additionally, the WHO Programme of Nutrition, Physical Activity, and Obesity at the Regional Office for Europe independently compiles country-specific information on exclusive breastfeeding." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percent of children under 6 months" } ], "source_id": "16" }, { "id": "SH.STA.BRTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\n\n\nThe share of births attended by skilled health staff is an indicator of a health system's ability to provide adequate care for pregnant women." }, { "id": "IndicatorName", "value": "Births attended by skilled health staff (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "For the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources." }, { "id": "Longdefinition", "value": "Births attended by skilled health staff are the percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliveries on their own; and to care for newborns." }, { "id": "Othernotes", "value": "Assistance by trained professionals during birth reduces the incidence of maternal deaths during childbirth. The share of births attended by skilled health staff is an indicator of a health system’s ability to provide adequate care for pregnant women.\n\nThis is the Sustainable Development Goal indicator 3.1.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2022" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National-level household surveys are the primary sources for collecting data on skilled health personnel providing childbirth care. These surveys include Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), Reproductive Health Surveys (RHS), and other national surveys based on similar methodologies. Respondents in these surveys are asked about their last live birth and who assisted during delivery, covering a period of up to five years before the interview.\n\n\nAs part of the data harmonization process and interaction with countries, UNICEF conducts an annual country consultation. During this consultation, SDG country focal points are contacted to update and verify values included in the database and to obtain new data sources. These new data sources are reviewed and assessed jointly with WHO. Additionally, the national categories or occupational titles of skilled health personnel are verified. The reported data for some countries may include additional categories of trained personnel beyond doctors, nurses, and midwives." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Percent of live births" } ], "source_id": "16" }, { "id": "SH.STA.BRTW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Low birth-weight, which is associated with maternal malnutrition, raises the risk of infant mortality and stunts growth in infancy and childhood. There is also emerging evidence that low-birth-weight babies are more prone to non-communicable diseases such as diabetes and cardiovascular diseases. Low birth-weight can arise as a result of a baby being born too soon or too small for gestational age. Babies born prematurely, who are also small for their gestational age, have the worst prognosis.\n\n\n\nIn low- and middle-income countries low birth-weight stems primarily from poor maternal health and nutrition. Three factors have the most impact: poor maternal nutritional status before conception, mother's short stature (due mostly to under-nutrition and infections during childhood), and poor nutrition during pregnancy (UNICEF Data, https://data.unicef.org/)." }, { "id": "IndicatorName", "value": "Low-birthweight babies (% of births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Low-birthweight babies are newborns weighing less than 2,500 grams, with the measurement taken within the first hour of life, before significant postnatal weight loss has occurred." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2020" }, { "id": "Source", "value": "UNICEF-WHO Low birthweight estimates, UN Children's Fund (UNICEF), uri: data.unicef.org;\nWorld Health Organization (WHO), uri: data.unicef.org, note: UNICEF-WHO Low birthweight estimates" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Household Surveys including DHS, MICS and other national surveys\nStatistical concept(s): Low birthweight babies are more likely to die during their first month of life and those who survived face lifelong consequences including a higher risk of stunted growth, lower IQ ,and adult-onset chronic conditions such as obesity and diabetes." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.STA.DIAB.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Diabetes, an important cause of ill health and a risk factor for other diseases in developed countries, is spreading rapidly in developing countries. Highest among the elderly, prevalence rates are rising among younger and productive populations in developing countries. Economic development has led to the spread of Western lifestyles and diet to developing countries, resulting in a substantial increase in diabetes. Without effective prevention and control programs, diabetes will likely continue to increase." }, { "id": "IndicatorName", "value": "Diabetes prevalence (% of population ages 20 to 79)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes. It is calculated by adjusting to a standard population age-structure." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Diabetes Atlas, International Diabetes Federation" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data used to estimate diabetes prevalence were gathered from various sources. Most of the data were extracted from peer-reviewed publications and national health surveys, including selected WHO STEPwise approach to surveillance (WHO STEPS) studies. Additionally, data from other official sources, such as registries and reports from health regulatory bodies, were utilized, provided there was sufficient information to assess their quality. Data sources with adequate methodological information on key areas of interest, such as the method of diagnosis and sample representativeness, were included. Given the significance of age as a major determinant for diabetes prevalence, only studies with at least three age-specific estimates were considered. After selecting the data sources, the reported age- and sex-specific data in each source were smoothed using a logistic regression model." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Percent of population ages 20 to 79" } ], "source_id": "16" }, { "id": "SH.STA.HYGN.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Generally, data on handwashing facilities are limited in high-income countries due to the infrequent collection of such information. In the early 2000s, even low- and middle-income countries often lacked this data. However, the recent standardization of hygiene-related questions in international surveys has led to an improvement in the availability of data." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Othernotes", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Utilizing national-level data derived from household surveys, mainly the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), the JMP calculates the proportion of the population with access to basic handwashing facilities at home with soap and water for each country by using a simple linear regression.\nStatistical concept(s): This indicator is measured using a straightforward indicator that examines the presence of handwashing facilities with soap within homes mainly through national household surveys. \n\n\n\n\n\n\n\n\n\n\nCollecting accurate information on handwashing practices presents challenges. Self-reported handwashing is an unreliable measure due to the potential for inaccurate reporting. Direct observation of handwashing can lead to observer bias, as individuals may alter their behavior when they know they are being watched, and implementing such observations on a large scale is resource-intensive. A more effective method involves survey enumerators observing the designated handwashing areas in homes and verifying the availability of water and soap, or a local substitute. This approach provides a more dependable and practical measure of handwashing behavior than relying on self-reported data." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "16" }, { "id": "SH.STA.HYGN.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Generally, data on handwashing facilities are limited in high-income countries due to the infrequent collection of such information. In the early 2000s, even low- and middle-income countries often lacked this data. However, the recent standardization of hygiene-related questions in international surveys has led to an improvement in the availability of data." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Othernotes", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Utilizing national-level data derived from household surveys, mainly the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), the JMP calculates the proportion of the population with access to basic handwashing facilities at home with soap and water for each country by using a simple linear regression.\nStatistical concept(s): This indicator is measured using a straightforward indicator that examines the presence of handwashing facilities with soap within homes mainly through national household surveys. \n\n\n\n\n\n\n\n\n\n\nCollecting accurate information on handwashing practices presents challenges. Self-reported handwashing is an unreliable measure due to the potential for inaccurate reporting. Direct observation of handwashing can lead to observer bias, as individuals may alter their behavior when they know they are being watched, and implementing such observations on a large scale is resource-intensive. A more effective method involves survey enumerators observing the designated handwashing areas in homes and verifying the availability of water and soap, or a local substitute. This approach provides a more dependable and practical measure of handwashing behavior than relying on self-reported data." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "16" }, { "id": "SH.STA.HYGN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Generally, data on handwashing facilities are limited in high-income countries due to the infrequent collection of such information. In the early 2000s, even low- and middle-income countries often lacked this data. However, the recent standardization of hygiene-related questions in international surveys has led to an improvement in the availability of data." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Generally, data on handwashing facilities are limited in high-income countries due to the infrequent collection of such information. In the early 2000s, even low- and middle-income countries often lacked this data. However, the recent standardization of hygiene-related questions in international surveys has led to an improvement in the availability of data." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Utilizing national-level data derived from household surveys, mainly the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), the JMP calculates the proportion of the population with access to basic handwashing facilities at home with soap and water for each country by using a simple linear regression.\nStatistical concept(s): This indicator is measured using a straightforward indicator that examines the presence of handwashing facilities with soap within homes mainly through national household surveys. \n\n\n\n\n\n\n\n\n\n\nCollecting accurate information on handwashing practices presents challenges. Self-reported handwashing is an unreliable measure due to the potential for inaccurate reporting. Direct observation of handwashing can lead to observer bias, as individuals may alter their behavior when they know they are being watched, and implementing such observations on a large scale is resource-intensive. A more effective method involves survey enumerators observing the designated handwashing areas in homes and verifying the availability of water and soap, or a local substitute. This approach provides a more dependable and practical measure of handwashing behavior than relying on self-reported data." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "16" }, { "id": "SH.STA.IYCF.ZS", "metatype": [ { "id": "IndicatorName", "value": "Infant and young child feeding practices, all 3 IYCF (% children ages 6-23 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of children age 6-23 months fed in accordance with all three infant and young child feeding (IYCF) practices (food diversity, feeding frequency, and consumption of breast milk or milk)" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Percentage of children age 6-23 months fed in accordance with all three infant and young child feeding (IYCF) practices (food diversity, feeding frequency, and consumption of breast milk or milk)" }, { "id": "Source", "value": "Demographic and Health Surveys" }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "16" }, { "id": "SH.STA.MALN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of underweight, weight for age, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of underweight, female, is the percentage of girls under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child underweight belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.STA.MALN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of underweight, weight for age, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of underweight, male, is the percentage of boys under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child underweight belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.STA.MALN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of underweight, weight for age (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of underweight children is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child underweight belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.STA.MALR", "metatype": [ { "id": "IndicatorName", "value": "Malaria cases reported" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Reported cases of malaria are the number of confirmed cases of malaria (confirmed by slide examination or RDT)." }, { "id": "Notesfromoriginalsource", "value": "In endemic countries where health information system is weak and diagnosis is limited, national malaria control programmes (NMCPs) often collect data on the number of suspected cases, those tested and those confirmed. Probable or unconfirmed cases are calculated by subtracting the number tested from the number suspected." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, World malaria report and Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/ghodata/). WHO compiles data on reported cases of malaria, submitted by the national malaria control programmes (NMCPs)." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "16" }, { "id": "SH.STA.MMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Maternal mortality ratio (modeled estimate, per 100,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality." }, { "id": "Longdefinition", "value": "Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs)." }, { "id": "Othernotes", "value": "This indicator represents the risk associated with each pregnancy and is also a Sustainable Development Goal Indicator (3.1.1) for monitoring maternal health." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO), uri: https://www.who.int/news/item/23-02-2023-a-woman-dies-every-two-minutes-due-to-pregnancy-or-childbirth--un-agencies;\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Per 100,000 live births" } ], "source_id": "16" }, { "id": "SH.STA.MMRT.NE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Maternal mortality ratio (national estimate, per 100,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality.\n\nMaternal mortality ratios collected directly from Demographic and Health Surveys are presented in the survey year, but reference time of these maternal mortality ratios is for the seven years preceding the survey." }, { "id": "Longdefinition", "value": "Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Maternal Mortality Estimation Inter-Agency Group (MMEIG), World Health Organization (WHO), note: The country data compiled, adjusted and used in the estimation model by the Maternal Mortality Estimation Inter-Agency Group (MMEIG). The country data were compiled from the following sources: civil registration and vital statistics; specialized studies on maternal mortality; population based surveys and censuses; other available data sources including data from surveillance sites.;\nUN Children's Fund (UNICEF), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nUN Population Fund (UNFPA), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nWorld Bank Group (WBG), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nUnited Nations (UN), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nPAHO, note: Core Indicators Portal;\nICF, note: The DHS Program, Demographic and Health Surveys" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The national estimates of maternal mortality ratios are based on national surveys, vital registration records, and surveillance data or are derived from community and hospital records.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "16" }, { "id": "SH.STA.ODFC.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to sanitation is a fundamental human right. Open defecation, which is the practice of relieving oneself outside without proper facilities, is a violation of human dignity and poses a significant threat to public health and nutrition. Poor sanitation is a leading cause of infectious diseases globally, and enhancing sanitation services has been proven to have a substantial positive effect on health outcomes. The provision of basic and safely managed sanitation can decrease the incidence of diarrheal diseases and mitigate the health consequences of other serious illnesses that cause widespread morbidity and mortality among children. Diarrheal conditions and parasitic infections debilitate children, increasing their vulnerability to malnutrition and secondary infections such as pneumonia, measles, and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe absence of adequate sanitation is especially harmful to women who are forced to defecate in the open, as it compromises their privacy and exposes them to a greater risk of assault and violence. The elimination of open defecation is a specific target within the Sustainable Development Goals (SDG target 6.2), underscoring the international commitment to addressing this critical issue." }, { "id": "IndicatorName", "value": "People practicing open defecation, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Othernotes", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "16" }, { "id": "SH.STA.ODFC.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to sanitation is a fundamental human right. Open defecation, which is the practice of relieving oneself outside without proper facilities, is a violation of human dignity and poses a significant threat to public health and nutrition. Poor sanitation is a leading cause of infectious diseases globally, and enhancing sanitation services has been proven to have a substantial positive effect on health outcomes. The provision of basic and safely managed sanitation can decrease the incidence of diarrheal diseases and mitigate the health consequences of other serious illnesses that cause widespread morbidity and mortality among children. Diarrheal conditions and parasitic infections debilitate children, increasing their vulnerability to malnutrition and secondary infections such as pneumonia, measles, and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe absence of adequate sanitation is especially harmful to women who are forced to defecate in the open, as it compromises their privacy and exposes them to a greater risk of assault and violence. The elimination of open defecation is a specific target within the Sustainable Development Goals (SDG target 6.2), underscoring the international commitment to addressing this critical issue." }, { "id": "IndicatorName", "value": "People practicing open defecation, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Othernotes", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "16" }, { "id": "SH.STA.ODFC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to sanitation is a fundamental human right. Open defecation, which is the practice of relieving oneself outside without proper facilities, is a violation of human dignity and poses a significant threat to public health and nutrition. Poor sanitation is a leading cause of infectious diseases globally, and enhancing sanitation services has been proven to have a substantial positive effect on health outcomes. The provision of basic and safely managed sanitation can decrease the incidence of diarrheal diseases and mitigate the health consequences of other serious illnesses that cause widespread morbidity and mortality among children. Diarrheal conditions and parasitic infections debilitate children, increasing their vulnerability to malnutrition and secondary infections such as pneumonia, measles, and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe absence of adequate sanitation is especially harmful to women who are forced to defecate in the open, as it compromises their privacy and exposes them to a greater risk of assault and violence. The elimination of open defecation is a specific target within the Sustainable Development Goals (SDG target 6.2), underscoring the international commitment to addressing this critical issue." }, { "id": "IndicatorName", "value": "People practicing open defecation (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "16" }, { "id": "SH.STA.ORCF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Most diarrhea-related deaths are due to dehydration, and many of these deaths can be prevented with the use of oral rehydration salts at home." }, { "id": "IndicatorName", "value": "Diarrhea treatment (% of children under 5 receiving oral rehydration and continued feeding)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Recommendations for the use of oral rehydration therapy have changed over time based on scientific progress, so it is difficult to accurately compare use rates across countries. Until the current recommended method for home management of diarrhea is adopted and applied in all countries, the data should be used with caution. Also, the prevalence of diarrhea may vary by season. Since country surveys are administered at different times, data comparability is further affected." }, { "id": "Longdefinition", "value": "Children with diarrhea who received oral rehydration and continued feeding refer to the percentage of children under age five with diarrhea in the two weeks prior to the survey who received either oral rehydration therapy or increased fluids, with continued feeding." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Mothers or caregivers of children under five years old are asked whether the child experienced diarrhea at any point in the past two weeks. If the child did have diarrhea, they are further asked whether either oral rehydration therapy or increased fluids, with continued feeding was administered. The term \"diarrhea,\" as defined by the DHS, should include all forms of diarrhea, such as bloody stools (indicative of dysentery), watery stools, and other variations. This definition encompasses both the mother's understanding and locally-used terms." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "16" }, { "id": "SH.STA.ORTH", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Diarrhea treatment (% of children under 5 who received ORS packet)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The indicator does not assess the severity of the diarrhea." }, { "id": "Longdefinition", "value": "Percentage of children under age 5 with diarrhea in the two weeks preceding the survey who received oral rehydration salts (ORS packets or pre-packaged ORS fluids)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Mothers or caregivers of children under five years old are asked whether the child experienced diarrhea at any point in the past two weeks. If the child did have diarrhea, they are further asked whether Oral Rehydration Solution (ORS) was administered. The term \"diarrhea,\" as defined by the DHS, should include all forms of diarrhea, such as bloody stools (indicative of dysentery), watery stools, and other variations. This definition encompasses both the mother's understanding and locally-used terms." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "16" }, { "id": "SH.STA.OWAD.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of overweight, female (% of female adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Prevalence of overweight female adults is the percentage of females ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Prevalence of overweight female adults is the percentage of females ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters." }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "16" }, { "id": "SH.STA.OWAD.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of overweight, male (% of male adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Prevalence of overweight male adults is the percentage of males ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Prevalence of overweight male adults is the percentage of males ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters." }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "16" }, { "id": "SH.STA.OWAD.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of overweight (% of adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Prevalence of overweight adults is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Prevalence of overweight adults is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters." }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "16" }, { "id": "SH.STA.OWGH.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight, weight for height, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.STA.OWGH.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight, weight for height, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of overweight, male, is the percentage of boys under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.STA.OWGH.ME.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of overweight, female (modeled estimate, % of children under 5)" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "16" }, { "id": "SH.STA.OWGH.ME.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of overweight, male (modeled estimate, % of children under 5)" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "16" }, { "id": "SH.STA.OWGH.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight (modeled estimate, % of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time." }, { "id": "Longdefinition", "value": "Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates." }, { "id": "Periodicity", "value": "Every two years" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME);\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.STA.OWGH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "See SH.STA.OWGH.ME.ZS for aggregation" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight, weight for height (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.STA.PNVC.ZS", "metatype": [ { "id": "IndicatorName", "value": "Postnatal care coverage (% mothers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of women with a postnatal checkup in the first two days after birth" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Percentage of women with a postnatal checkup in the first two days after birth" }, { "id": "Source", "value": "Demographic and Health Surveys" }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "16" }, { "id": "SH.STA.POIS.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unintentional poisoning (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unintentional poisonings is the number of deaths from unintentional poisonings in a year per 100,000 population. Unintentional poisoning can\n\n\nbe caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.9.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for unintentional poisoning mortality are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database are used, with necessary adjustments for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. For countries lacking high-quality death registration data, cause of death estimates are calculated using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates. The complete methodology can be found at the following: https://www.who.int/docs/defaultsource/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf" }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "16" }, { "id": "SH.STA.POIS.P5.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unintentional poisoning, female (per 100,000 female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unintentional poisonings is the number of female deaths from unintentional poisonings in a year per 100,000 female population. Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for unintentional poisoning mortality are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database are used, with necessary adjustments for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. For countries lacking high-quality death registration data, cause of death estimates are calculated using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates. The complete methodology can be found at the following: https://www.who.int/docs/defaultsource/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf" }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 female population" } ], "source_id": "16" }, { "id": "SH.STA.POIS.P5.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unintentional poisoning, male (per 100,000 male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unintentional poisonings is the number of male deaths from unintentional poisonings in a year per 100,000 male population. Unintentional poisoning can\n\n\nbe caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for unintentional poisoning mortality are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database are used, with necessary adjustments for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. For countries lacking high-quality death registration data, cause of death estimates are calculated using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates. The complete methodology can be found at the following: https://www.who.int/docs/defaultsource/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf" }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 male population" } ], "source_id": "16" }, { "id": "SH.STA.SMSS.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using safely managed sanitation services, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "There are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporality and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site). Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system. If no information is available, it is assumed that 50 percent is safely managed. Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed sanitation facilities are defined as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "16" }, { "id": "SH.STA.SMSS.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using safely managed sanitation services, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "There are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporality and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site). Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system. If no information is available, it is assumed that 50 percent is safely managed. Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed sanitation facilities are defined as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "16" }, { "id": "SH.STA.SMSS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using safely managed sanitation services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "There are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporality and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site). Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system. If no information is available, it is assumed that 50 percent is safely managed. Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis indicator (Total) is calculated as a population-weighted average of the RURAL and URBAN aggregates when sufficient data are available for both domains. When coverage at either the RURAL or URBAN level is insufficient, but country-level TOTAL data meet the minimum population coverage threshold (30%), TOTAL aggregates are calculated directly from country-level TOTAL estimates. Because these two aggregation approaches may be applied in different years as data availability improves, methodological switches can occur and may result in discontinuities in the time series.\n\n\n\nThis indicator corresponds to Sustainable Development Goal indicator 6.2.1 (see UN SDG metadata: https://unstats.un.org/sdgs/metadata/)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed sanitation facilities are defined as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "16" }, { "id": "SH.STA.STNT.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of stunting, female, is the percentage of girls under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.STA.STNT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.STA.STNT.ME.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, female (modeled estimate, % of children under 5)" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "16" }, { "id": "SH.STA.STNT.ME.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, male (modeled estimate, % of children under 5)" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "16" }, { "id": "SH.STA.STNT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age (modeled estimate, % of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time." }, { "id": "Longdefinition", "value": "Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates." }, { "id": "Periodicity", "value": "Every two years" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, publisher: JME;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME), publisher: JME;\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME), publisher: JME" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.STA.STNT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "See SH.STA.STNT.ME.ZS for aggregation" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.STA.SUIC.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Suicide mortality rate, female (per 100,000 female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of suicide deaths in a year by the mid-year population for the same calendar year, then multiplying by 100,000. The estimates are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the vital registration data submitted by member states to the WHO Mortality Database are used, with adjustments made where necessary (e.g., for under-reporting of deaths, unknown age and sex, and ill-defined causes of death). For countries without high-quality death registration data, cause of death estimates are calculated using other sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not be identical to official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 female population" } ], "source_id": "16" }, { "id": "SH.STA.SUIC.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Suicide mortality rate, male (per 100,000 male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of suicide deaths in a year by the mid-year population for the same calendar year, then multiplying by 100,000. The estimates are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the vital registration data submitted by member states to the WHO Mortality Database are used, with adjustments made where necessary (e.g., for under-reporting of deaths, unknown age and sex, and ill-defined causes of death). For countries without high-quality death registration data, cause of death estimates are calculated using other sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not be identical to official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 male population" } ], "source_id": "16" }, { "id": "SH.STA.SUIC.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Suicide mortality rate (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted)." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.4.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of suicide deaths in a year by the mid-year population for the same calendar year, then multiplying by 100,000. The estimates are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the vital registration data submitted by member states to the WHO Mortality Database are used, with adjustments made where necessary (e.g., for under-reporting of deaths, unknown age and sex, and ill-defined causes of death). For countries without high-quality death registration data, cause of death estimates are calculated using other sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not be identical to official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "16" }, { "id": "SH.STA.TRAF.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Road traffic injuries and deaths is a major global public health problem. Road traffic crashes are currently the leading cause of death for children and young adults in the world. There is a strong association between the risk of road traffic death and the income level of countries. The burden of road traffic deaths is disproportionately high among low- and middle-income countries." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.6.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality caused by road traffic injury, female (per 100,000 female population)" }, { "id": "Longdefinition", "value": "Mortality caused by road traffic injury is estimated road traffic fatal injury deaths per 100,000 population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.STA.TRAF.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Road traffic injuries and deaths is a major global public health problem. Road traffic crashes are currently the leading cause of death for children and young adults in the world. There is a strong association between the risk of road traffic death and the income level of countries. The burden of road traffic deaths is disproportionately high among low- and middle-income countries." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.6.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality caused by road traffic injury, male (per 100,000 male population)" }, { "id": "Longdefinition", "value": "Mortality caused by road traffic injury is estimated road traffic fatal injury deaths per 100,000 population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.STA.TRAF.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Road traffic injuries and deaths is a major global public health problem. Road traffic crashes are currently the leading cause of death for children and young adults in the world. There is a strong association between the risk of road traffic death and the income level of countries. The burden of road traffic deaths is disproportionately high among low- and middle-income countries." }, { "id": "IndicatorName", "value": "Mortality caused by road traffic injury (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Mortality caused by road traffic injury is estimated road traffic fatal injury deaths per 100,000 population." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.6.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2019" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The methods used for analyzing causes of death vary based on the type of data available from different countries. For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database is utilized, with necessary adjustments made for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. In contrast, for countries lacking high-quality death registration data, cause of death estimates are derived using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "16" }, { "id": "SH.STA.WASH.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unsafe drinking water, unsafe sanitation and lack of hygiene are important causes of death. Most diarrheal deaths in the world are caused by unsafe water, sanitation or hygiene. According to the World Health Organization, in addition to diarrea, the following diseases could be prevented if adequate WASH services are provided: malnutrition, intestinal nematode infections, lymphatic filariasis, trachoma, schistosomiasis and malaria." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene, female (per 100,000 female population)" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.STA.WASH.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unsafe drinking water, unsafe sanitation and lack of hygiene are important causes of death. Most diarrheal deaths in the world are caused by unsafe water, sanitation or hygiene. According to the World Health Organization, in addition to diarrea, the following diseases could be prevented if adequate WASH services are provided: malnutrition, intestinal nematode infections, lymphatic filariasis, trachoma, schistosomiasis and malaria." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene, male (per 100,000 male population)" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.STA.WASH.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unsafe drinking water, unsafe sanitation and lack of hygiene are important causes of death. Most diarrheal deaths in the world are caused by unsafe water, sanitation or hygiene. According to the World Health Organization, in addition to diarrea, the following diseases could be prevented if adequate WASH services are provided: malnutrition, intestinal nematode infections, lymphatic filariasis, trachoma, schistosomiasis and malaria." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.9.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2019" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: To estimate the portion of deaths from diarrhea and acute respiratory infections attributable to unsafe water, sanitation, and hygiene (WASH), a comparative risk assessment approach is used. This involves calculating the attributable disease deaths by combining information on the increased (or relative) risk of a disease resulting from exposure with the prevalence of that exposure in the population. This calculation yields the 'population attributable fraction' (PAF), which represents the fraction of disease in a population that can be attributed to the exposure, in this case, unsafe WASH.\n\n\nBy applying the PAF to the total deaths from diarrhea or acute respiratory infections, the number of deaths resulting from inadequate WASH can be determined. Additionally, deaths from protein-energy malnutrition attributable to inadequate WASH are estimated by evaluating the impacts of repeated infectious diarrhea episodes on nutritional status, particularly stunting. All deaths from intestinal nematode infections are attributed to inadequate WASH due to their transmission pathway." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "16" }, { "id": "SH.STA.WAST.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of wasting, weight for height, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of wasting, female, is the proportion of girls under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.STA.WAST.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of wasting, weight for height, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of wasting, male, is the proportion of boys under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.STA.WAST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of wasting, weight for height (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of wasting is the proportion of children under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.SVR.WAST.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of severe wasting, weight for height, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of severe wasting, female, is the proportion of girls under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.SVR.WAST.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of severe wasting, weight for height, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of severe wasting, male, is the proportion of boys under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.SVR.WAST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of severe wasting, weight for height (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of severe wasting is the proportion of children under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.TBS.CURE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tuberculosis (TB) is a preventable and usually curable disease. Yet TB is one of the world’s leading causes of death from a single infectious agent. Millions of people continue to fall ill with TB every year." }, { "id": "IndicatorName", "value": "Tuberculosis treatment success rate (% of new cases)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Tuberculosis treatment success rate is the percentage of all new tuberculosis cases (or new and relapse cases for some countries) registered under a national tuberculosis control programme in a given year that successfully completed treatment, with or without bacteriological evidence of success (\"cured\" and \"treatment completed\" respectively)." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Global Tuberculosis Report, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of cases registered in a given year (excluding cases placed on a second-line drug regimen) that successfully completed treatment without bacteriological evidence of failure. All registered cases fall into one of the following five mutually exclusive categories: treatment success, failure, death, lost to follow-up, not evaluated (missing data on the outcome of treatment).\nStatistical concept(s): Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. Data on the success rate of tuberculosis treatment are provided for countries that have submitted data to the WHO. The treatment success rate for tuberculosis provides a useful indicator of the quality of health services. A low rate suggests that infectious patients may not be receiving adequate treatment. An important complement to the tuberculosis treatment success rate is the case detection rate, which indicates whether there is adequate coverage by the recommended case detection and treatment strategy." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.TBS.DTEC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tuberculosis (TB) is a preventable and usually curable disease. Yet TB is one of the world’s leading causes of death from a single infectious agent. Millions of people continue to fall ill with TB every year." }, { "id": "IndicatorName", "value": "Tuberculosis case detection rate (%, all forms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Tuberculosis case detection rate (all forms) is the number of new and relapse tuberculosis cases notified to WHO in a given year, divided by WHO's estimate of the number of incident tuberculosis cases for the same year, expressed as a percentage. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Tuberculosis Report, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of new and relapse TB cases diagnosed and treated in national TB control programmes and notified to WHO, divided by WHO's estimate of the number of incident TB cases for the same year, expressed as a percentage.\nStatistical concept(s): Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. This indicator shows the tuberculosis detection rate for all detection methods. Editions before 2010 included the tuberculosis detection rates by DOTS, the internationally recommended strategy for tuberculosis control. Thus data on the case detection rate from 2010 onward cannot be compared with data in previous editions." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.TBS.INCD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tuberculosis (TB) is a preventable and usually curable disease. Yet TB is one of the world’s leading causes of death from a single infectious agent. Millions of people continue to fall ill with TB every year." }, { "id": "IndicatorName", "value": "Incidence of tuberculosis (per 100,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nUncertainty bounds for the incidence are available at http://data.worldbank.org" }, { "id": "Longdefinition", "value": "Incidence of tuberculosis is the estimated number of new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population. All forms of TB are included, including cases in people living with HIV. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization.\n\nThis is the Sustainable Development Goal indicator 3.3.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Tuberculosis Report, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of TB incidence are produced through a consultative and analytical process led by WHO and are published annually. These estimates are based on annual case notifications, assessments of the quality and coverage of TB notification data, national surveys of the prevalence of TB disease and on information from death (vital) registration systems.\nStatistical concept(s): Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. In developed countries tuberculosis has reemerged largely as a result of cases among immigrants. Since tuberculosis incidence cannot be directly measured, estimates are obtained by eliciting expert opinion or are derived from measurements of prevalence or mortality." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Per 100,000 people" } ], "source_id": "16" }, { "id": "SH.TBS.MORT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization." }, { "id": "IndicatorName", "value": "Tuberculosis death rate (per 100,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Tuberculosis death rate is the estimated number of deaths from tuberculosis among HIV-negative people, expressed as the rate per 100,000 population. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously." }, { "id": "Notesfromoriginalsource", "value": "Estimates are presented with uncertainty intervals (see footnote). When ranges are presented, the lower and higher numbers correspond to the 2.5th and 97.5th centiles of the outcome distributions (generally produced by simulations). For more detailed information, see the original source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Tuberculosis Report." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "16" }, { "id": "SH.UHC.FBP1.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people pushed further below the $2.15 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the number of people living in households whose non-health expenditures are already below the $2.15 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. The “Pushed further below the poverty line by out-of-pocket health payments” indicators were newly introduced in the 2021 edition of the Global Monitoring Report on Financial Protection in Health (GMR). They represent the number and population shares of people who live in households which have both (a) total consumption (including out-of-pocket health payments) beneath the poverty line and (b) any out-of-pocket payments. Because the households are already poor, any out-of-pocket payments are considered financial hardship. The new indicators replace the “Change in poverty-gap due to out-of-pocket health payments”-indicators used to measure the poverty deepening effect of out-of-pocket payments in previous GMR editions (e.g., SH.UHC.NOP1.ZG and SH.UHC.NOP1.CG). For the measurement of overall medical impoverishment, the new indicators are complementary to the “Pushed below the poverty line by out-of-pocket health payments” indicators reported in all GMRs (e.g., SH.UHC.NOP1.TO and SH.UHC.NOP1.ZS), which represent the number and population share of people who live in households which lie above the poverty line when out-of-pocket health payments are included in consumption, but fall below the poverty line when out-of-pocket health payments are subtracted from consumption. \n\n2. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n3. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Further impoverishing health spending, 2.15$" }, { "id": "Source", "value": "Global Health Observatory. Geneva: World Health Organization. (https://www.who.int/data/gho/data/themes/topics/financial-protection)" }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "16" }, { "id": "SH.UHC.FBP1.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed further below the $2.15 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the $2.15 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the $2.15 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.UHC.FBP2.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people pushed further below the $3.65 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the number of people living in households whose non-health expenditures are already below the $3.65 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. The “Pushed further below the poverty line by out-of-pocket health payments” indicators were newly introduced in the 2021 edition of the Global Monitoring Report on Financial Protection in Health (GMR). They represent the number and population shares of people who live in households which have both (a) total consumption (including out-of-pocket health payments) beneath the poverty line and (b) any out-of-pocket payments. Because the households are already poor, any out-of-pocket payments are considered financial hardship. The new indicators replace the “Change in poverty-gap due to out-of-pocket health payments”-indicators used to measure the poverty deepening effect of out-of-pocket payments in previous GMR editions (e.g., SH.UHC.NOP1.ZG and SH.UHC.NOP1.CG). For the measurement of overall medical impoverishment, the new indicators are complementary to the “Pushed below the poverty line by out-of-pocket health payments” indicators reported in all GMRs (e.g., SH.UHC.NOP1.TO and SH.UHC.NOP1.ZS), which represent the number and population share of people who live in households which lie above the poverty line when out-of-pocket health payments are included in consumption, but fall below the poverty line when out-of-pocket health payments are subtracted from consumption. \n\n2. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n3. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Further impoverishing health spending, 3.65$" }, { "id": "Source", "value": "Global Health Observatory. Geneva: World Health Organization. (https://www.who.int/data/gho/data/themes/topics/financial-protection)" }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "16" }, { "id": "SH.UHC.FBP2.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed further below the $3.65 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the $3.65 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the $3.65 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.UHC.FBPR.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people pushed further below the 60% median consumption poverty line by out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the number of people living in households whose non-health expenditures are already below the 60% median consumption poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. The “Pushed further below the poverty line by out-of-pocket health payments” indicators were newly introduced in the 2021 edition of the Global Monitoring Report on Financial Protection in Health (GMR). They represent the number and population shares of people who live in households which have both (a) total consumption (including out-of-pocket health payments) beneath the poverty line and (b) any out-of-pocket payments. Because the households are already poor, any out-of-pocket payments are considered financial hardship. The new indicators replace the “Change in poverty-gap due to out-of-pocket health payments”-indicators used to measure the poverty deepening effect of out-of-pocket payments in previous GMR editions (e.g., SH.UHC.NOP1.ZG and SH.UHC.NOP1.CG). For the measurement of overall medical impoverishment, the new indicators are complementary to the “Pushed below the poverty line by out-of-pocket health payments” indicators reported in all GMRs (e.g., SH.UHC.NOP1.TO and SH.UHC.NOP1.ZS), which represent the number and population share of people who live in households which lie above the poverty line when out-of-pocket health payments are included in consumption, but fall below the poverty line when out-of-pocket health payments are subtracted from consumption. \n\n2. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n3. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Further impoverishing health spending, 60% of median" }, { "id": "Source", "value": "Global Health Observatory. Geneva: World Health Organization. (https://www.who.int/data/gho/data/themes/topics/financial-protection)" }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "16" }, { "id": "SH.UHC.FBPR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed further below the 60% median consumption poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the 60% median consumption poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. \n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the 60% median consumption poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. \n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.UHC.NOP1.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people pushed below the $2.15 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the number of people living in households experiencing impoverishing out-of-pocket health expenditures, defined as expenditures without which the household they live in would have been above the $2.15 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n2. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Impoverishing health spending, 2.15$" }, { "id": "Source", "value": "Global Health Observatory. Geneva: World Health Organization. (https://www.who.int/data/gho/data/themes/topics/financial-protection)" }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "16" }, { "id": "SH.UHC.NOP1.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed below the $2.15 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the $ 2.15 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the $ 2.15 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.UHC.NOP2.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people pushed below the $3.65 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the number of households experiencing impoverishing out-of-pocket health expenditures, defined as expenditures without which the household would have been above the $3.65 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n2. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Impoverishing health spending, 3.65$" }, { "id": "Source", "value": "Global Health Observatory. Geneva: World Health Organization. (https://www.who.int/data/gho/data/themes/topics/financial-protection)" }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "16" }, { "id": "SH.UHC.NOP2.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed below the $3.65 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the $3.65 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the $3.65 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.UHC.NOPR.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people pushed below the 60% median consumption poverty line by out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the number of people living in households experiencing impoverishing expenditures, defined as out-of-pocket health expenditures without which the household they live in would have been above the 60% median consumption poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n2. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Impoverishing health spending, 60% of median" }, { "id": "Source", "value": "Global Health Observatory. Geneva: World Health Organization. (https://www.who.int/data/gho/data/themes/topics/financial-protection)" }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "16" }, { "id": "SH.UHC.NOPR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed below the 60% median consumption poverty line by out-of-pocket health expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the 60% median consumption but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the 60% median consumption but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.UHC.OOPC.10.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people spending more than 10% of household consumption or income on out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of people spending more than 10% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n2. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Catastrophic Health Expenditure, 10% of total expenditure/income" }, { "id": "Source", "value": "Global Health Observatory. Geneva: World Health Organization. (https://www.who.int/data/gho/data/themes/topics/financial-protection)" }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "16" }, { "id": "SH.UHC.OOPC.10.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.8.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.UHC.OOPC.25.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people spending more than 25% of household consumption or income on out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of people spending more than 25% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n2. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Catastrophic Health Expenditure, 25% of total expenditure/income (thousands)" }, { "id": "Source", "value": "Global Health Observatory. Geneva: World Health Organization. (https://www.who.int/data/gho/data/themes/topics/financial-protection)" }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "16" }, { "id": "SH.UHC.OOPC.25.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.8.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.UHC.SRVS.CV.XD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need without facing financial hardship. It is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "UHC service coverage index" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Coverage index for essential health services (based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, noncommunicable diseases and service capacity and access). It is presented on a scale of 0 to 100." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.8.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Shortdefinition", "value": "Coverage index for essential health services (based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, noncommunicable diseases and service capacity and access). It is presented on a scale of 0 to 100." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/service-coverage" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: For each country, the most recent value for each tracer indicators is taken from WHO or other international agencies. The index is computed using geometric means of the tracer indicators.\nStatistical concept(s): The Service Coverage Index used to track SDG 3.8.1 includes four indicator categories, namely (1) reproductive, manternal and newborn and child health, (2) infectious diseases, (3) non-communicable diseases and (4) service capacity and access. Each category contains several tracers. The index is constructed from geometric means of the tracer indicators; first, within each of the four categories, and then across the four category-specific means to obtain the final summary index. See Source for details about methodology." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "index" } ], "source_id": "16" }, { "id": "SH.UHC.TOT1.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people pushed or further pushed below the $2.15 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the number of people who are either (1) living in households whose non-health expenditures are already below the $2.15 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the $2.15 poverty line but fall below the $2.15 poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. The “Pushed further below the poverty line by out-of-pocket health payments” indicators were newly introduced in the 2021 edition of the Global Monitoring Report on Financial Protection in Health (GMR). They represent the number and population shares of people who live in households which have both (a) total consumption (including out-of-pocket health payments) beneath the poverty line and (b) any out-of-pocket payments. Because the households are already poor, any out-of-pocket payments are considered financial hardship. The new indicators replace the “Change in poverty-gap due to out-of-pocket health payments”-indicators used to measure the poverty deepening effect of out-of-pocket payments in previous GMR editions (e.g., SH.UHC.NOP1.ZG and SH.UHC.NOP1.CG). For the measurement of overall medical impoverishment, the new indicators are complementary to the “Pushed below the poverty line by out-of-pocket health payments” indicators reported in all GMRs (e.g., SH.UHC.NOP1.TO and SH.UHC.NOP1.ZS), which represent the number and population share of people who live in households which lie above the poverty line when out-of-pocket health payments are included in consumption, but fall below the poverty line when out-of-pocket health payments are subtracted from consumption. \n\n2. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n3. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Impoverishing or further impoverishing health spending, 2.15$" }, { "id": "Source", "value": "Global Health Observatory. Geneva: World Health Organization. (https://www.who.int/data/gho/data/themes/topics/financial-protection)" }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "16" }, { "id": "SH.UHC.TOT1.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed or further pushed below the $2.15 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country's population who is either (1) living in households whose non-health expenditures are already below the $2.15 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the $2.15 poverty line but fall below the $2.15 poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country's population who is either (1) living in households whose non-health expenditures are already below the $2.15 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the $2.15 poverty line but fall below the $2.15 poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.UHC.TOT2.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people pushed or further pushed below the $3.65 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the number of people who are either (1) living in households whose non-health expenditures are already below the $3.65 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the $3.65 poverty line but fall below the $3.65 poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. The “Pushed further below the poverty line by out-of-pocket health payments” indicators were newly introduced in the 2021 edition of the Global Monitoring Report on Financial Protection in Health (GMR). They represent the number and population shares of people who live in households which have both (a) total consumption (including out-of-pocket health payments) beneath the poverty line and (b) any out-of-pocket payments. Because the households are already poor, any out-of-pocket payments are considered financial hardship. The new indicators replace the “Change in poverty-gap due to out-of-pocket health payments”-indicators used to measure the poverty deepening effect of out-of-pocket payments in previous GMR editions (e.g., SH.UHC.NOP1.ZG and SH.UHC.NOP1.CG). For the measurement of overall medical impoverishment, the new indicators are complementary to the “Pushed below the poverty line by out-of-pocket health payments” indicators reported in all GMRs (e.g., SH.UHC.NOP1.TO and SH.UHC.NOP1.ZS), which represent the number and population share of people who live in households which lie above the poverty line when out-of-pocket health payments are included in consumption, but fall below the poverty line when out-of-pocket health payments are subtracted from consumption. \n\n2. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n3. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Impoverishing or further impoverishing health spending, 3.65$" }, { "id": "Source", "value": "Global Health Observatory. Geneva: World Health Organization. (https://www.who.int/data/gho/data/themes/topics/financial-protection)" }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "16" }, { "id": "SH.UHC.TOT2.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed or further pushed below the $3.65 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country's population who is either (1) living in households whose non-health expenditures are already below the $3.65 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the $3.65 poverty line but fall below the $3.65 poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country's population who is either (1) living in households whose non-health expenditures are already below the $3.65 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the $3.65 poverty line but fall below the $3.65 poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.UHC.TOTR.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people pushed or further pushed below the 60% median consumption poverty line by out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the number of people who are either (1) living in households whose non-health expenditures are already below the relative poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the relative poverty line but fall below the relative poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. The “Pushed further below the poverty line by out-of-pocket health payments” indicators were newly introduced in the 2021 edition of the Global Monitoring Report on Financial Protection in Health (GMR). They represent the number and population shares of people who live in households which have both (a) total consumption (including out-of-pocket health payments) beneath the poverty line and (b) any out-of-pocket payments. Because the households are already poor, any out-of-pocket payments are considered financial hardship. The new indicators replace the “Change in poverty-gap due to out-of-pocket health payments”-indicators used to measure the poverty deepening effect of out-of-pocket payments in previous GMR editions (e.g., SH.UHC.NOP1.ZG and SH.UHC.NOP1.CG). For the measurement of overall medical impoverishment, the new indicators are complementary to the “Pushed below the poverty line by out-of-pocket health payments” indicators reported in all GMRs (e.g., SH.UHC.NOP1.TO and SH.UHC.NOP1.ZS), which represent the number and population share of people who live in households which lie above the poverty line when out-of-pocket health payments are included in consumption, but fall below the poverty line when out-of-pocket health payments are subtracted from consumption. \n\n2. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n3. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Impoverishing or further impoverishing health spending, 60% of median" }, { "id": "Source", "value": "Global Health Observatory. Geneva: World Health Organization. (https://www.who.int/data/gho/data/themes/topics/financial-protection)" }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "16" }, { "id": "SH.UHC.TOTR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed or further pushed below the 60% median consumption poverty line by out-of-pocket health expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country's population who is either (1) living in households whose non-health expenditures are already below the relative poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the relative poverty line but fall below the relative poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country's population who is either (1) living in households whose non-health expenditures are already below the relative poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the relative poverty line but fall below the relative poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.VAC.TTNS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and ??is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Newborns protected against tetanus (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Newborns protected against tetanus are the percentage of births by women of child-bearing age who are immunized against tetanus." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2024" }, { "id": "Source", "value": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.XPD.CHEX.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Current health expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Level of current health expenditure expressed as a percentage of GDP. Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, IT and stocks of vaccines for emergency or outbreaks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.XPD.CHEX.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Current health expenditure per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current expenditures on health per capita in current US dollars. Estimates of current health expenditures include healthcare goods and services consumed during each year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "current US$" } ], "source_id": "16" }, { "id": "SH.XPD.CHEX.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Current health expenditure per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current expenditures on health per capita expressed in international dollars at purchasing power parity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current iternational $" } ], "source_id": "16" }, { "id": "SH.XPD.EHEX.CH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "External health expenditure (% of current health expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Share of current health expenditures funded from external sources. External sources compose of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country. External sources either flow through the government scheme or are channeled through non-governmental organizations or other schemes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.XPD.EHEX.EH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "External health expenditure channeled through government (% of external health expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Share of external donor funding flowing through the government budgets relative to the overall external expenditures on health." }, { "id": "Notesfromoriginalsource", "value": "The World Health Organization (WHO) has revised health expenditure data using the new international classification for health expenditures in the revised System of Health Accounts (SHA 2011). WHO’s Global Health Expenditure Database in this new version is the reference source for health expenditure for international comparison imbedded in a standardized framework. The SHA 2011 clarifies the financing mechanisms and introduces new dimensions which improve the comparability of health expenditures in the perspective of universal health coverage." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Computed using World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database)." }, { "id": "Statisticalconceptandmethodology", "value": "The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011). The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making." }, { "id": "Topic", "value": "Health: Health systems" } ], "source_id": "16" }, { "id": "SH.XPD.EHEX.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "External health expenditure per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current external expenditures on health per capita expressed in current US dollars. External sources are composed of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "current US$" } ], "source_id": "16" }, { "id": "SH.XPD.EHEX.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "External health expenditure per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current external expenditures on health per capita expressed in international dollars at purchasing power parity. External sources are composed of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current iternational $" } ], "source_id": "16" }, { "id": "SH.XPD.GHED.CH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic general government health expenditure (% of current health expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Share of current health expenditures funded from domestic public sources for health. Domestic public sources include domestic revenue as internal transfers and grants, transfers, subsidies to voluntary health insurance beneficiaries, non-profit institutions serving households (NPISH) or enterprise financing schemes as well as compulsory prepayment and social health insurance contributions. They do not include external resources spent by governments on health." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.XPD.GHED.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic general government health expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public expenditure on health from domestic sources as a share of the economy as measured by GDP." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.XPD.GHED.GE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic general government health expenditure (% of general government expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public expenditure on health from domestic sources as a share of total public expenditure. It indicates the priority of the government to spend on health from own domestic public resources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.XPD.GHED.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic general government health expenditure per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public expenditure on health from domestic sources per capita expressed in current US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "current US$" } ], "source_id": "16" }, { "id": "SH.XPD.GHED.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic general government health expenditure per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public expenditure on health from domestic sources per capita expressed in international dollars at purchasing power parity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current iternational $" } ], "source_id": "16" }, { "id": "SH.XPD.KHEX.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Capital health expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Level of capital investments on health expressed as a percentage of GDP. Capital health investments include health infrastructure (buildings, machinery, IT) and stocks of vaccines for emergency or outbreaks." }, { "id": "Notesfromoriginalsource", "value": "The World Health Organization (WHO) has revised health expenditure data using the new international classification for health expenditures in the revised System of Health Accounts (SHA 2011). WHO’s Global Health Expenditure Database in this new version is the reference source for health expenditure for international comparison imbedded in a standardized framework. The SHA 2011 clarifies the financing mechanisms and introduces new dimensions which improve the comparability of health expenditures in the perspective of universal health coverage." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database)." }, { "id": "Statisticalconceptandmethodology", "value": "The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011). The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making." }, { "id": "Topic", "value": "Health: Health systems" } ], "source_id": "16" }, { "id": "SH.XPD.OOPC.CH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Out-of-pocket expenditure (% of current health expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Share of out-of-pocket payments of total current health expenditures. Out-of-pocket payments are spending on health directly out-of-pocket by households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. Aggregations are weighted by total current health expenditure (not by population).\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.XPD.OOPC.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Out-of-pocket expenditure per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Health expenditure through out-of-pocket payments per capita in USD. Out of pocket payments are spending on health directly out of pocket by households in each country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "current US$" } ], "source_id": "16" }, { "id": "SH.XPD.OOPC.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Out-of-pocket expenditure per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Health expenditure through out-of-pocket payments per capita in international dollars at purchasing power parity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current iternational $" } ], "source_id": "16" }, { "id": "SH.XPD.PVTD.CH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic private health expenditure (% of current health expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Share of current health expenditures funded from domestic private sources. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SH.XPD.PVTD.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic private health expenditure per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current private expenditures on health per capita expressed in current US dollars. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "current US$" } ], "source_id": "16" }, { "id": "SH.XPD.PVTD.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic private health expenditure per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current private expenditures on health per capita expressed in international dollars at purchasing power parity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\n\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current iternational $" } ], "source_id": "16" }, { "id": "SI.POV.NAHC", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The poverty rate as defined by national poverty lines reflects the share of the population that fails to meet the standard a country thinks is necessary to cover basic needs (typically in low- and middle-income countries) or afford a decent lifestyle (typically in high-income countries). SDG 1.2 aims to reduce by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions, by 2030." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at national poverty lines (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "National poverty headcount ratio is the percentage of the population living below the national poverty line(s). National estimates are based on population-weighted subgroup estimates from household surveys. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year." }, { "id": "Othernotes", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2024" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines., World Bank (WB), note: Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Poverty headcount ratio among the population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\n\n\n\n\n\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income. \n\n\n\n\n\n\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies. \n\n\n\n\n\n\n\nAlmost all national poverty lines in developing economies are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. \n\n\n\n\n\n\n\nThis series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year. For all other economies, the year reported is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which data collection started.\nStatistical concept(s): National poverty headcount ratio refers to the percentage of a population whose consumption or income per day falls short of the national poverty line. National poverty lines vary by country and over time. In low- and middle-income countries, national poverty lines tend to be absolute poverty lines, thus reflecting the estimated minimum amount of money needed to cover basic needs. In high-income countries, national poverty lines tend to be relative poverty lines, thus reflecting the typical amount of money needed for an individual to afford the typical standard of living and without any restraints to participating fully in the societies in which they live. National poverty lines tend to grow with economic growth, especially in high-income or upper-middle-income countries." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "16" }, { "id": "SI.POV.RUHC", "metatype": [ { "id": "Generalcomments", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "IndicatorName", "value": "Rural poverty headcount ratio at national poverty lines (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Rural poverty headcount ratio is the percentage of the rural population living below the national poverty lines." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Poverty headcount ratio among the rural population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income.\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies.\n\nAlmost all national poverty lines are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. The data is based on the two most recent years for which survey data are available.\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected." }, { "id": "Topic", "value": "Poverty: Poverty rates" } ], "source_id": "16" }, { "id": "SI.POV.URHC", "metatype": [ { "id": "Generalcomments", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "IndicatorName", "value": "Urban poverty headcount ratio at national poverty lines (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Urban poverty headcount ratio is the percentage of the urban population living below the national poverty lines." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Poverty headcount ratio among the urban population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income.\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies.\n\nAlmost all national poverty lines are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. The data is based on the two most recent years for which survey data are available.\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected." }, { "id": "Topic", "value": "Poverty: Poverty rates" } ], "source_id": "16" }, { "id": "SL.EMP.INSV.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Data on women in wage employment in the nonagricultural sector show the extent to which women have access to paid employment - which affects their integration into the monetary economy - and indicate the degree to which labor markets are open to women in industry and services - which affects not only equal employment opportunity for women, but also economic efficiency through flexibility of the labor market and the economy's capacity to adapt to changes over time.\n\nIn many developing countries nonagricultural wage employment accounts for only a small portion of total employment. As a result, the contribution of women to the national economy is underestimated and therefore misrepresented. The indicator is difficult to interpret without additional information on the share of women in total employment, which allows an assessment to be made of whether women are under- or overrepresented in nonagricultural wage employment. The indicator does not reveal differences in the quality of nonagricultural wage employment in terms of earnings, work conditions, or legal and social protection. The indicator also does not reflect whether women reap the economic benefits of such employment. Finally, female employment and the employment share of the agricultural sector for both men and women tend to be underreported.\n\nWomen's wage work is important for economic growth and the well-being of families. But women often face such obstacles as restricted access to credit markets, capital, land, and training and education; time constraints due to traditional family responsibilities; and labor market bias and discrimination. These obstacles force women to limit their full participation in paid economic activities, to be less productive, and to receive lower wages." }, { "id": "Generalcomments", "value": "Relevance to gender indicator: Women’s share in paid employment in the nonagricultural sector has risen marginally in some regions but remains less than 20 percent in South Asia and Sub-Saharan Africa. Women are also clearly segregated in sectors that are generally known to be lower paid. And in the sectors where women dominate, such as health care, women rarely hold upper-level management jobs." }, { "id": "IndicatorName", "value": "Share of women in wage employment in the nonagricultural sector (% of total nonagricultural employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working." }, { "id": "Longdefinition", "value": "Share of women in wage employment in the nonagricultural sector is the share of female workers in wage employment in the nonagricultural sector (industry and services), expressed as a percentage of total employment in the nonagricultural sector. Industry includes mining and quarrying (including oil production), manufacturing, construction, electricity, gas, and water, corresponding to divisions 2-5 (ISIC revision 2) or tabulation categories C-F (ISIC revision 3). Services include wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services-corresponding to divisions 6-9 (ISIC revision 2) or tabulation categories G-Q (ISIC revision 3)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization." }, { "id": "Statisticalconceptandmethodology", "value": "Employment is defined as persons above a specified age who performed any work at all, in the reference period, for pay or profit (or pay in kind), or were temporarily absent from a job for such reasons as illness, maternity or parental leave, holiday, training or industrial dispute. Unpaid family workers who work for at least one hour should be included in the count of employment, although many countries use a higher hour limit in their definition.\n\nLabor force statistics by gender is important to monitor gender disparities in employment patterns. Estimates of women in the labor force and employment are generally lower than those of men and are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "16" }, { "id": "SL.TLF.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force, female (% of total labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB), International Labour Organization (ILO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are based on labor force participation rates and population data from International Labour Organization and United Nations Population Division. The labor force participation rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or voluntarily left work. In addition, persons who did not look for work but have an arrangement for a future job are also counted as unemployed. Still, some unemployment is unavoidable—at any time, some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. The labor force or the economically active portion of the population serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total labor force" } ], "source_id": "16" }, { "id": "SL.TLF.TOTL.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "ROUND(('SP.POP.1564.TO' + 'SP.POP.65UP.TO') * 'SL.TLF.CACT.ZS' / 100,0)" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB), International Labour Organization (ILO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are based on labor force participation rates and population data from International Labour Organization and United Nations Population Division. The labor force participation rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or voluntarily left work. In addition, persons who did not look for work but have an arrangement for a future job are also counted as unemployed. Still, some unemployment is unavoidable—at any time, some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. The labor force or the economically active portion of the population serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "Persons" } ], "source_id": "16" }, { "id": "SL.UEM.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, female (% of female labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female labor force" } ], "source_id": "16" }, { "id": "SL.UEM.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, male (% of male labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male labor force" } ], "source_id": "16" }, { "id": "SL.UEM.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, total (% of total labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "While the unemployment rate may be considered the most informative labour market indicator, reflecting the general performance of the labour market and the economy as a whole, it should not be interpreted as a measure of economic hardship or of well-being. When based on the internationally-recommended standards, the unemployment rate simply reflects the proportion of the labour force that does not have a job but is available and actively looking for work. It says nothing about the economic resources of unemployed workers or their family members. Its use should, therefore, be limited to serving as a measurement of the utilization of labour and an indication of the failure to find work. Other measures, including income-related indicators, would be needed to evaluate economic hardship. An additional criticism of the aggregate unemployment measure is that it masks information on the composition of the jobless population and therefore misses out on the particularities of the education level, ethnic origin, socio-economic background, work experience, etc. of the unemployed. Moreover, the unemployment rate says nothing about the type of unemployment – whether it is cyclical and short-term or structural and long-term – which is a critical issue for policy makers in the development of their policy responses, especially given that structural unemployment cannot be addressed by boosting market demand only." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\n\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of total labor force" } ], "source_id": "16" }, { "id": "SM.POP.NETM", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\n\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. In most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers.\n\n\n\nBut refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom." }, { "id": "IndicatorName", "value": "Net migration" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "International migration is the component of population change most difficult to measure and estimate reliably. Thus, the quality and quantity of the data used in the estimation and projection of net migration varies considerably by country. Furthermore, the movement of people across international boundaries, which is very often a response to changing socio-economic, political and environmental forces, is subject to a great deal of volatility. Refugee movements, for instance, may involve large numbers of people moving across boundaries in a short time. For these reasons, projections of future international migration levels are the least robust part of current population projections and reflect mainly a continuation of recent levels and trends in net migration." }, { "id": "Longdefinition", "value": "Net migration is the net total of migrants during the period, that is, the number of immigrants minus the number of emigrants, including both citizens and noncitizens." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: When there is insufficient data, net migration is derived through the difference between the overall population growth rate and the rate of natural increase (the difference between the birth rate and the death rate) during the same period. Such calculations are usually made for intercensal periods. The estimates are also derived from the data on foreign-born population - people who have residence in one country but were born in another country. When data on the foreign-born population are not available, data on foreign population - that is, people who are citizens of a country other than the country in which they reside - are used as estimates.\nStatistical concept(s): The United Nations Population Division provides data on net migration and migrant stock. Because data on migrant stock is difficult for countries to collect, the United Nations Population Division takes into account the past migration history of a country or area, the migration policy of a country, and the influx of refugees in recent periods when deriving estimates of net migration. The data to calculate these estimates come from a variety of sources, including border statistics, administrative records, surveys, and censuses." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SN.ITK.DEFC", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 2.1.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people who are undernourished" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of people who are undernourished shows the number of people whose habitual food consumption is insufficient to provide the dietary energy levels that are required to maintain a normal active and healthy life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization (http://www.fao.org/faostat/en/#home)." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "16" }, { "id": "SN.ITK.DEFC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good nutrition is the cornerstone for survival, health and development. Well-nourished children perform better in school, grow into healthy adults and in turn give their children a better start in life. Well-nourished women face fewer risks during pregnancy and childbirth, and their children set off on firmer developmental paths, both physically and mentally (UNICEF www.childinfo.org)." }, { "id": "IndicatorName", "value": "Prevalence of undernourishment (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "From a policy and program standpoint, this measure has its limits. First, food insecurity exists even where food availability is not a problem because of inadequate access of poor households to food. Second, food insecurity is an individual or household phenomenon, and the average food available to each person, even corrected for possible effects of low income, is not a good predictor of food insecurity among the population. And third, nutrition security is determined not only by food security but also by the quality of care of mothers and children and the quality of the household's health environment (Smith and Haddad 2000)." }, { "id": "Longdefinition", "value": "Prevalence of undernourishments is the percentage of the population whose habitual food consumption is insufficient to provide the dietary energy levels that are required to maintain a normal active and healthy life. Data showing as 2.5 may signify a prevalence of undernourishment below 2.5%." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 2.1.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "Food and Agriculture Organization of the United Nations (FAO), uri: http://www.fao.org/faostat/en/#home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on undernourishment are from the Food and Agriculture Organization (FAO) of the United Nations and measure food deprivation based on average food available for human consumption per person, the level of inequality in access to food, and the minimum calories required for an average person.\nStatistical concept(s): Data on undernourishment are from the Food and Agriculture Organization (FAO) of the United Nations and measure food deprivation based on average food available for human consumption per person, the level of inequality in access to food, and the minimum calories required for an average person." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SN.ITK.SALT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Iodine deficiency can lead to a variety of health and developmental consequences known as iodine deficiency disorders (IDDs). Iodine deficiency is a major cause of preventable mental retardation. It is especially damaging during pregnancy and in early childhood. In their most severe forms, IDDs can lead to cretinism, stillbirth and miscarriage; even mild deficiency can cause a significant loss of learning ability. Thus, it is crucially important that pregnant women and young children in particular get adequate levels of iodine.\n\nIDD can easily be prevented at low cost, however, with small quantities of iodine. One of the best and least expensive methods of preventing iodine deficiency disorder is by simply iodizing table salt, which is currently done in many countries. It represents one of the easiest and most cost-effective interventions for social and economic development." }, { "id": "IndicatorName", "value": "Consumption of iodized salt (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Percentage of households which have salt they used for cooking that tested positive (>0ppm) for presence of iodine." }, { "id": "Othernotes", "value": "Iodine deficiency is the single most important cause of preventable mental retardation, contributes significantly to the risk of stillbirth and miscarriage, and increases the incidence of infant mortality. A diet low in iodine is the main cause of iodine deficiency. It usually occurs among populations living in areas where the soil has been depleted of iodine. If soil is deficient in iodine, then so are the plants grown in it, including the grains and vegetables that people and animals consume. There are almost no countries in the world where iodine deficiency has not been a public health problem. Many newborns in low- and middle-income countries remain unprotected from the lifelong consequences of brain damage associated with iodine deficiency disorders, which affect a child's ability to learn and to earn a living as an adult, and in turn prevents children, communities, and countries from fulfilling their potential (UNICEF, www.childinfo.org). Widely used and inexpensive, iodized salt is the best source of iodine, and a global campaign to iodize edible salt is significantly reducing the risks associated with iodine deficiency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2020" }, { "id": "Source", "value": "UNICEF Global Databases on Iodized salt, UN Children's Fund (UNICEF), publisher: Division of Data, Analysis, Planning and Monitoring" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Most of the data on consumption of iodized salt are derived from household surveys. For the data that are from household surveys, the year refers to the survey year." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "16" }, { "id": "SN.ITK.VITA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Vitamin A deficiency is the leading cause of preventable childhood blindness and increases the risk of death from common childhood illnesses such as diarrhoea. Periodic, high-dose vitamin A supplementation is a proven, low-cost intervention which has been shown to reduce all-cause mortality." }, { "id": "IndicatorName", "value": "Vitamin A supplementation coverage rate (% of children ages 6-59 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Vitamin A supplementation coverage rate refers to the percentage of children ages 6-59 months old receiving two high-dose vitamin A supplements in a calendar year." }, { "id": "Othernotes", "value": "Vitamin A is essential for optimal functioning of the immune system. Vitamin A deficiency, a leading cause of blindness, also causes a greater risk of dying from a range of childhood ailments such as measles, malaria, and diarrhea. In low- and middle-income countries, where vitamin A is consumed largely in fruits and vegetables, daily per capita intake is often insufficient to meet dietary requirements. Providing young children with two high-dose vitamin A capsules a year is a safe, cost-effective, efficient strategy for eliminating vitamin A deficiency and improving child survival. Giving vitamin A to new breastfeeding mothers helps protect their children during the first few months of life. Food fortification with vitamin A is being introduced in many developing countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "UNICEF Global Databases, UN Children's Fund (UNICEF), uri: https://data.unicef.org/topic/nutrition/vitamin-a-deficiency/, note: based on administrative reports from countries" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Household Surveys including DHS, MICS and other national surveys\nStatistical concept(s): The World Health Organization has classified vitamin A deficiency as a public health problem affecting many children ages 6-59 months, with the highest rates in sub-Saharan Africa and South Asia." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.ADO.TFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Adolescent childbearing is associated with a wide range of risks for young mothers. Women who become pregnant and give birth very early in their lives as well as their newborns are subject to elevated health risks." }, { "id": "IndicatorName", "value": "Adolescent fertility rate (births per 1,000 women ages 15-19)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Adolescent fertility rate is the number of births per 1,000 women ages 15-19." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.7.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Adolescent fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without vital registration systems fertility rates are generally based on censuses or surveys.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Per 1000 women" } ], "source_id": "16" }, { "id": "SP.DYN.AMRT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "If available, derived from life tables of Human Mortality Database (HMD) by Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France)." }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, adult, female (per 1,000 female adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data from United Nations Population Division's World Populaton Prospects are originally 5-year period data and the presented are linearly interpolated by the World Bank for annual series. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Adult mortality rate, female, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old female dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nHuman Mortality Database, Max Planck Institute for Demographic Research, uri: www.mortality.org;\nUniversity of California, Berkeley, uri: www.mortality.org, note: Human Mortality Database;\nFrench Institute for Demographic Studies, uri: www.mortality.org, note: Human Mortality Database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors, l(x), at exact age x in a female period life table. The formula is: (l(60)-l(15))/(l(15))*1000.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data. Where reliable age-specific mortality data are available, life tables can be constructed from age-specific mortality data, and adult mortality rates can be calculated from life tables." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 female adults" } ], "source_id": "16" }, { "id": "SP.DYN.AMRT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "If available, derived from life tables of Human Mortality Database (HMD) by Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France)." }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, adult, male (per 1,000 male adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data from United Nations Population Division's World Populaton Prospects are originally 5-year period data and the presented are linearly interpolated by the World Bank for annual series. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Adult mortality rate, male, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old male dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nHuman Mortality Database, Max Planck Institute for Demographic Research, uri: www.mortality.org;\nUniversity of California, Berkeley, uri: www.mortality.org, note: Human Mortality Database;\nFrench Institute for Demographic Studies, uri: www.mortality.org, note: Human Mortality Database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors, l(x), at exact age x in a male period life table. The formula is: (l(60)-l(15))/(l(15))*1000.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data. Where reliable age-specific mortality data are available, life tables can be constructed from age-specific mortality data, and adult mortality rates can be calculated from life tables." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 male adults" } ], "source_id": "16" }, { "id": "SP.DYN.CBRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The crude birth rate is not appropriate for comparison of different populations or areas with large differences in age-distributions. When the crude death rate is subtracted from the crude birth rate, the result is the rate of natural increase, which is the rate of population change in the absence of migration." }, { "id": "IndicatorName", "value": "Birth rate, crude (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Vital registers are the preferred source for these data, but in many developing countries systems for registering births and deaths are absent or incomplete because of deficiencies in the coverage of events or geographic areas. Many developing countries carry out special household surveys that ask respondents about recent births and deaths. Estimates derived in this way are subject to sampling errors and recall errors." }, { "id": "Longdefinition", "value": "Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT);\nPopulation and Vital Statistics Report (various years), United Nations (UN), publisher: UN Statistical Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The crude birth rate is calculated as the number of births in a given period divided by the average population in that period. For human populations the period is usually one year and, if the population changes in size over the year, the divisor is taken as the population at the mid-year. The rate is usually expressed in terms of 1,000 people: for example, a crude birth rate of 9.5 (per 1000 people) in a population of 1 million would imply 9500 births per year in the entire population.\nStatistical concept(s): Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration. Vital rates are based on data from birth and death registration systems, censuses, and sample surveys by national statistical offices and other organizations, or on demographic analysis. Data for the most recent year for some high-income countries are provisional estimates based on vital registers. The estimates for many other countries are from the United Nations Population Division." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "16" }, { "id": "SP.DYN.CDRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The crude death rate is a good indicator of the general health status of a geographic area or population. The crude death rate is not appropriate for comparison of different populations or areas with large differences in age-distributions. Higher crude death rates can be found in some developed countries, despite high life expectancy, because typically these countries have a much higher proportion of older people, due to lower recent birth rates and lower age-specific mortality rates." }, { "id": "IndicatorName", "value": "Death rate, crude (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Vital registers are the preferred source for these data, but in many developing countries systems for registering births and deaths are absent or incomplete because of deficiencies in the coverage of events or geographic areas. Many developing countries carry out special household surveys that ask respondents about recent births and deaths. Estimates derived in this way are subject to sampling errors and recall errors." }, { "id": "Longdefinition", "value": "Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT);\nPopulation and Vital Statistics Report (various years), United Nations (UN), publisher: UN Statistical Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The crude death rate is calculated as the number of deaths in a given period divided by the population exposed to risk of death in that period. For human populations the period is usually one year and, if the population changes in size over the year, the divisor is taken as the population at the mid-year. The rate is usually expressed in terms of 1,000 people: for example, a crude death rate of 9.5 (per 1000 people) in a population of 1 million would imply 9500 deaths per year in the entire population.\nStatistical concept(s): Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration. Vital rates are based on data from birth and death registration systems, censuses, and sample surveys by national statistical offices and other organizations, or on demographic analysis. Data for the most recent year for some high-income countries are provisional estimates based on vital registers. The estimates for many other countries are from the United Nations Population Division." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "16" }, { "id": "SP.DYN.CONM.AL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Contraceptive prevalence among women of reproductive age is related to maternal and child health, as well as gender equality and HIV/AIDS. Contraceptives enable women and men to make informed decisions on family planning – whether, when, and how many children they would have. \n\nPreventing unwanted pregnancies is essential to reducing maternal deaths, especially in low- and middle- income countries where maternal mortality rate is high. With effective contraception, life-threatening pregnancy complications can be reduced, and thus maternal deaths can be averted. \n\nUsing condoms (one of the modern contraceptive methods) can prevent pregnancy as well as sexually transmitted diseases, including HIV." }, { "id": "IndicatorName", "value": "Contraceptive prevalence, any modern method (% of all women ages 15-49)" }, { "id": "Limitationsandexceptions", "value": "While the data availability on contraceptive use has increased, in many countries the contraceptive use data are available only for married women. \n\nThe time frame used to assess contraceptive prevalence may vary. In many surveys, it is left to the respondent to determine what is meant by “currently using” a method of contraception." }, { "id": "Longdefinition", "value": "Contraceptive prevalence, any modern method is the percentage of all women ages 15-49 who are practicing, or whose sexual partners are practicing, at least one modern method of contraception. Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division." }, { "id": "Statisticalconceptandmethodology", "value": "Contraceptive prevalence rates are obtained mainly from nationally representative household surveys, including: Demographic and Health Surveys; Multiple Indicator Cluster Surveys; Contraceptive Prevalence Surveys; Gender and Generations Survey; Reproductive Health Surveys; and World Fertility Surveys. Additional information was provided by other international survey programs and national surveys. \n\nAll women refer to all women of reproductive age regardless of their marital status." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "16" }, { "id": "SP.DYN.CONM.SA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Contraceptive prevalence among women of reproductive age is related to maternal and child health, as well as gender equality and HIV/AIDS. Contraceptives enable women and men to make informed decisions on family planning – whether, when, and how many children they would have. \n\nPreventing unwanted pregnancies is essential to reducing maternal deaths, especially in low- and middle- income countries where maternal mortality rate is high. With effective contraception, life-threatening pregnancy complications can be reduced, and thus maternal deaths can be averted. \n\nUsing condoms (one of the modern contraceptive methods) can prevent pregnancy as well as sexually transmitted diseases, including HIV." }, { "id": "IndicatorName", "value": "Contraceptive prevalence, any modern method (% of sexually active unmarried women ages 15-49)" }, { "id": "Limitationsandexceptions", "value": "1. While the data availability on contraceptive use has increased, in many countries the contraceptive use data are available only for married women. \n\n2. The time frame used to assess contraceptive prevalence may vary. In many surveys, it is left to the respondent to determine what is meant by “currently using” a method of contraception.\n\n3. To calculate the aggregated data, the total number of women ages 15-49 is used as a weight, because data on number of sexually active unmarried women ages 15-49 are not widely available." }, { "id": "Longdefinition", "value": "Contraceptive prevalence, any modern method is the percentage of sexually active unmarried women ages 15-49 who are practicing, or whose sexual partners are practicing, at least one modern method of contraception. Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys" }, { "id": "Statisticalconceptandmethodology", "value": "Contraceptive prevalence rates are obtained mainly from nationally representative household surveys, including: Demographic and Health Surveys; Multiple Indicator Cluster Surveys; Contraceptive Prevalence Surveys; Gender and Generations Survey; Reproductive Health Surveys; and World Fertility Surveys. Additional information was provided by other international survey programs and national surveys. \n\nSexually active unmarried women refer to unmarried women who are not pregnant or postpartum amenorrheic, and who had intercourse in the four weeks prior to the survey interview." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "16" }, { "id": "SP.DYN.CONM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Contraceptive prevalence among women of reproductive age is related to maternal and child health, as well as gender equality and HIV/AIDS. Contraceptives enable women and men to make informed decisions on family planning – whether, when, and how many children they would have. \n\n\n\nPreventing unwanted pregnancies is essential to reducing maternal deaths, especially in low- and middle- income countries where maternal mortality rate is high. With effective contraception, life-threatening pregnancy complications can be reduced, and thus maternal deaths can be averted. \n\n\n\nUsing condoms (one of the modern contraceptive methods) can prevent pregnancy as well as sexually transmitted diseases, including HIV." }, { "id": "IndicatorName", "value": "Contraceptive prevalence, any modern method (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "While the data availability on contraceptive use has increased, in many countries the contraceptive use data are available only for married women. \n\n\n\nThe time frame used to assess contraceptive prevalence may vary. In many surveys, it is left to the respondent to determine what is meant by “currently using” a method of contraception." }, { "id": "Longdefinition", "value": "Contraceptive prevalence, any modern method is the percentage of married women ages 15-49 who are practicing, or whose sexual partners are practicing, at least one modern method of contraception. Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Contraceptive prevalence rates are obtained mainly from nationally representative household surveys, including: Demographic and Health Surveys; Multiple Indicator Cluster Surveys; Contraceptive Prevalence Surveys; Gender and Generations Survey; Reproductive Health Surveys; and World Fertility Surveys. Additional information was provided by other international survey programs and national surveys. \n\n\n\nMarried women refer to women who are married (defined in relation to the marriage laws or customs of a country) and to women in a union, which refers to women living with their partner in the same household (also referred to as cohabiting unions, consensual unions, unmarried unions, or “living together”)." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "16" }, { "id": "SP.DYN.CONU.AL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Contraceptive prevalence among women of reproductive age is related to maternal and child health, as well as gender equality and HIV/AIDS. Contraceptives enable women and men to make informed decisions on family planning – whether, when, and how many children they would have. \n\nPreventing unwanted pregnancies is essential to reducing maternal deaths, especially in low- and middle- income countries where maternal mortality rate is high. With effective contraception, life-threatening pregnancy complications can be reduced, and thus maternal deaths can be averted. \n\nUsing condoms (one of the modern contraceptive methods) can prevent pregnancy as well as sexually transmitted diseases, including HIV." }, { "id": "IndicatorName", "value": "Contraceptive prevalence, any method (% of all women ages 15-49)" }, { "id": "Limitationsandexceptions", "value": "While the data availability on contraceptive use has increased, in many countries the contraceptive use data are available only for married women. \n\nThe time frame used to assess contraceptive prevalence may vary. In many surveys, it is left to the respondent to determine what is meant by “currently using” a method of contraception." }, { "id": "Longdefinition", "value": "Contraceptive prevalence, any method is the percentage of all women ages 15-49 who are practicing, or whose sexual partners are practicing, any method of contraception (modern or traditional). Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception. Traditional methods of contraception include rhythm (e.g., fertility awareness based methods, periodic abstinence), withdrawal and other traditional methods." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division." }, { "id": "Statisticalconceptandmethodology", "value": "Contraceptive prevalence rates are obtained mainly from nationally representative household surveys, including: Demographic and Health Surveys; Multiple Indicator Cluster Surveys; Contraceptive Prevalence Surveys; Gender and Generations Survey; Reproductive Health Surveys; and World Fertility Surveys. Additional information was provided by other international survey programs and national surveys. \n\nAll women refer to all women of reproductive age regardless of their marital status." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "16" }, { "id": "SP.DYN.CONU.SA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Contraceptive prevalence among women of reproductive age is related to maternal and child health, as well as gender equality and HIV/AIDS. Contraceptives enable women and men to make informed decisions on family planning – whether, when, and how many children they would have. \n\nPreventing unwanted pregnancies is essential to reducing maternal deaths, especially in low- and middle- income countries where maternal mortality rate is high. With effective contraception, life-threatening pregnancy complications can be reduced, and thus maternal deaths can be averted. \n\nUsing condoms (one of the modern contraceptive methods) can prevent pregnancy as well as sexually transmitted diseases, including HIV." }, { "id": "IndicatorName", "value": "Contraceptive prevalence, any method (% of sexually active unmarried women ages 15-49)" }, { "id": "Limitationsandexceptions", "value": "1. While the data availability on contraceptive use has increased, in many countries the contraceptive use data are available only for married women. \n\n2. The time frame used to assess contraceptive prevalence may vary. In many surveys, it is left to the respondent to determine what is meant by “currently using” a method of contraception.\n\n3. To calculate the aggregated data, the total number of women ages 15-49 is used as a weight, because data on number of sexually active unmarried women ages 15-49 are not widely available." }, { "id": "Longdefinition", "value": "Contraceptive prevalence, any method is the percentage of sexually active unmarried women ages 15-49 who are practicing, or whose sexual partners are practicing, any method of contraception (modern or traditional). Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception. Traditional methods of contraception include rhythm (e.g., fertility awareness based methods, periodic abstinence), withdrawal and other traditional methods." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys" }, { "id": "Statisticalconceptandmethodology", "value": "Contraceptive prevalence rates are obtained mainly from nationally representative household surveys, including: Demographic and Health Surveys; Multiple Indicator Cluster Surveys; Contraceptive Prevalence Surveys; Gender and Generations Survey; Reproductive Health Surveys; and World Fertility Surveys. Additional information was provided by other international survey programs and national surveys. \n\nSexually active unmarried women refer to unmarried women who are not pregnant or postpartum amenorrheic, and who had intercourse in the four weeks prior to the survey interview." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "16" }, { "id": "SP.DYN.CONU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Contraceptive prevalence among women of reproductive age is related to maternal and child health, as well as gender equality and HIV/AIDS. Contraceptives enable women and men to make informed decisions on family planning – whether, when, and how many children they would have. \n\n\n\nPreventing unwanted pregnancies is essential to reducing maternal deaths, especially in low- and middle- income countries where maternal mortality rate is high. With effective contraception, life-threatening pregnancy complications can be reduced, and thus maternal deaths can be averted. \n\n\n\nUsing condoms (one of the modern contraceptive methods) can prevent pregnancy as well as sexually transmitted diseases, including HIV." }, { "id": "IndicatorName", "value": "Contraceptive prevalence, any method (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "While the data availability on contraceptive use has increased, in many countries the contraceptive use data are available only for married women. \n\n\n\nThe time frame used to assess contraceptive prevalence may vary. In many surveys, it is left to the respondent to determine what is meant by “currently using” a method of contraception." }, { "id": "Longdefinition", "value": "Contraceptive prevalence, any method is the percentage of married women ages 15-49 who are practicing, or whose sexual partners are practicing, any method of contraception (modern or traditional). Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception. Traditional methods of contraception include rhythm (e.g., fertility awareness based methods, periodic abstinence), withdrawal and other traditional methods." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Contraceptive prevalence rates are obtained mainly from nationally representative household surveys, including: Demographic and Health Surveys; Multiple Indicator Cluster Surveys; Contraceptive Prevalence Surveys; Gender and Generations Survey; Reproductive Health Surveys; and World Fertility Surveys. Additional information was provided by other international survey programs and national surveys. \n\n\n\nMarried women refer to women who are married (defined in relation to the marriage laws or customs of a country) and to women in a union, which refers to women living with their partner in the same household (also referred to as cohabiting unions, consensual unions, unmarried unions, or “living together”)." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "16" }, { "id": "SP.DYN.IMRT.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, infant, female (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Infant mortality rate, female is the number of female infants dying before reaching one year of age, per 1,000 female live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "16" }, { "id": "SP.DYN.IMRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, infant (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "16" }, { "id": "SP.DYN.IMRT.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, infant, male (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Infant mortality rate, male is the number of male infants dying before reaching one year of age, per 1,000 male live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "16" }, { "id": "SP.DYN.LE00.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, female (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "16" }, { "id": "SP.DYN.LE00.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, total (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), uri: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices, note: Derived from male and female life expectancy at birth from sources such as statistical databases and publications from national statistical offices.;\nDemographic Statistics, Eurostat (ESTAT), note: Derived from male and female life expectancy at birth from sources such as Eurostat: Demographic Statistics." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates, or derived from male and female life expectancy at birth.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "16" }, { "id": "SP.DYN.LE00.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, male (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "16" }, { "id": "SP.DYN.SMAM.FE", "metatype": [ { "id": "IndicatorName", "value": "Mean age at first marriage, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The estimates by age may be affected by age misreporting. Marital status may be misreported, particularly in societies where divorce or separation is not socially acceptable. The differences in marital status categories included over time and their definitions limit comparability of data across time and countries. Data derived from surveys with small samples are subject to sampling error." }, { "id": "Longdefinition", "value": "Mean age at marriage, female shows the average length of single life expressed in years among those females who marry before age 50. It is a synthetic indicator calculated from marital status categories of men and women aged 15 to 54 at the census or survey date." }, { "id": "Notesfromoriginalsource", "value": "Note that the SMAM takes a single point in time and calculates the age at marriage from the marital status of the population aged between 15 and 50. This value is different from the mean age of marriage that is calculated from first marriage rates in a respective period (commonly used in countries with complete marriage registration systems) or cohort measures of entry into first marriage or union (based on retrospective survey questions on age at first marriage or union formation). The retrospective nature of the SMAM means that values are influenced by age and marital status specific mortality and migration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations, Department of Economic and Social Affairs, Population Division. World Marriage Data 2019." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "16" }, { "id": "SP.DYN.SMAM.MA", "metatype": [ { "id": "IndicatorName", "value": "Mean age at first marriage, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The estimates by age may be affected by age misreporting. Marital status may be misreported, particularly in societies where divorce or separation is not socially acceptable. The differences in marital status categories included over time and their definitions limit comparability of data across time and countries. Data derived from surveys with small samples are subject to sampling error." }, { "id": "Longdefinition", "value": "Mean age at marriage, male shows the average length of single life expressed in years among those males who marry before age 50. It is a synthetic indicator calculated from marital status categories of men and women aged 15 to 54 at the census or survey date." }, { "id": "Notesfromoriginalsource", "value": "Note that the SMAM takes a single point in time and calculates the age at marriage from the marital status of the population aged between 15 and 50. This value is different from the mean age of marriage that is calculated from first marriage rates in a respective period (commonly used in countries with complete marriage registration systems) or cohort measures of entry into first marriage or union (based on retrospective survey questions on age at first marriage or union formation). The retrospective nature of the SMAM means that values are influenced by age and marital status specific mortality and migration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations, Department of Economic and Social Affairs, Population Division. World Marriage Data." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "16" }, { "id": "SP.DYN.TFRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries." }, { "id": "IndicatorName", "value": "Fertility rate, total (births per woman)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year." }, { "id": "Othernotes", "value": "Relevance to gender indicator: it can indicate the status of women within households and a woman’s decision about the number and spacing of children." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Total fertility rate is the sum of the age-specific fertility rates (multiplied by five, if the age-specific fertility rates are for 5-year age groups).\nStatistical concept(s): Total fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without reliable vital registration systems fertility rates are generally based on extrapolations from trends observed in censuses or surveys from earlier years." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Births per woman" } ], "source_id": "16" }, { "id": "SP.DYN.TO65.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. The lower the age specific mortality rates before age 65, the higher the proportion of people survive to age 65." }, { "id": "IndicatorName", "value": "Survival to age 65, female (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Survival to age 65 refers to the percentage of a cohort of newborn infants that would survive to age 65, if subject to age specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors, l(x), at exact age x in a female period life table. The formula is: (l(65))/(l(0))*100.\nStatistical concept(s): Survival to age 65 is calculated in a period life table. It provides a population's mortality level up to age 65 at a given time. It therefore does not reflect the mortality level that a person actually experiences during his/her life, which can be calculated in a cohort life table." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.DYN.TO65.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. The lower the age specific mortality rates before age 65, the higher the proportion of people survive to age 65." }, { "id": "IndicatorName", "value": "Survival to age 65, male (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Survival to age 65 refers to the percentage of a cohort of newborn infants that would survive to age 65, if subject to age specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors l(x) at exact age x, in a male period life table. The formula is: (l(65))/(l(0))*100.\nStatistical concept(s): Survival to age 65 is calculated in a period life table. It provides a population's mortality level up to age 65 at a given time. It therefore does not reflect the mortality level that a person actually experiences during his/her life, which can be calculated in a cohort life table." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.DYN.WFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Wanted fertility rate (births per woman)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Wanted fertility rate is an estimate of what the total fertility rate would be if all unwanted births were avoided." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Demographic and Health Surveys" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data is calculated by summing the seven age-specific wanted fertility rates, multiplying the result by five, and dividing by 1000. A birth is considered wanted if the number of living children at the time of conception is less than the ideal number of children as reported by the respondent. Special responses such as \"don't know,\" \"up to God,\" or other non-numeric responses for the ideal number of children are assumed to indicate a high ideal number of children. For more details, please refer to the DHS website: https://dhsprogram.com/data/Guide-to-DHS-Statistics/Wanted_Fertility.htm" }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Births per woman" } ], "source_id": "16" }, { "id": "SP.HOU.FEMA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The composition of households plays a pivotal role in determining the well-being of families and individuals. Research from multiple developed countries indicates that female-headed households, especially those with single mothers, face a higher risk of poverty than those with two parents (United Nations, \"Patterns and trends in household size and composition: Evidence from a United Nations dataset,\" 2019). Understanding the diversity in household structures across various populations is essential for achieving Sustainable Development Goal 1, which is dedicated to eradicating poverty in all its forms." }, { "id": "IndicatorName", "value": "Female headed households (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The definition of female-headed household differs greatly across countries, making cross-country comparison difficult. In some cases it is assumed that a woman cannot be the head of any household with an adult male, because of sex-biased stereotype. Caution should be used in interpreting the data." }, { "id": "Longdefinition", "value": "Female headed households refers to the percentage of households that are headed by females." }, { "id": "Othernotes", "value": "The composition of a household plays a role in the determining other characteristics of a household, such as how many children are sent to school and the distribution of family income." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "DHS API, DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: HC_HHHD_H_FEM; \tIndicator name from the original source: Female-headed households, publisher: DHS Program (ICF), type: API, date accessed: 2024-06-14" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of households headed by women divided by the total number of households. \n\n\n\n\n\n\n\n\n\n\n\nThe definition of a household is a person or group of related or unrelated persons who live together in the same dwelling unit(s), who acknowledge one adult male or female as the head of the household, who share the same housekeeping arrangements and who are considered a single unit.\nStatistical concept(s): The information on the characteristics of household head (e.g., sex, age) is collected the household questionnaire in the Demographic and Health Surveys (DHS). Typically, this data is obtained by detailing the connection of each member of the household to a designated central figure, who is considered the primary reference for the household." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "% of households" } ], "source_id": "16" }, { "id": "SP.M15.2024.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "test" }, { "id": "Developmentrelevance", "value": "Although the legal age of marriage is defined as 18 years in most countries, the practice of child marriage remains widespread. A women’s access to education and later her employment opportunities as well as the nature and terms of her work are often compromised by this practice. Young married girls whose schooling is cut short often lack the knowledge and skills for formal work and are limited to occupations with lower incomes and inferior working conditions. Sustainable Development Goal 5 commits to eliminate the practice of child marriage." }, { "id": "IndicatorName", "value": "Women who were first married by age 15 (% of women ages 20-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The measure of child marriage is designed to be retrospective, focusing on the age at first marriage among adult women who have already passed the risk period. Although it is feasible to assess the current marital status of girls under 15, this approach could underestimate the true extent of child marriage. This is because girls who are not married at the time of survey may still marry before reaching 15." }, { "id": "Longdefinition", "value": "Women who were first married by age 15 refers to the percentage of women ages 20-24 who were first married by age 15." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.3.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "UNICEF Data, UN Children's Fund (UNICEF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_20-24_MRD_U15; \tIndicator name from the original source: Percentage of women (aged 20-24 years) married or in union before age 15, type: API;\nDHS API, DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: MA_MBAG_W_B15; \tIndicator name from the original source: Women first married by exact age 15, type: API" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Number of women aged 20-24 who were first married or in union before age 15 divided by the total number of women aged 20-24 in the population multiplied by 100. The primary sources for this indicator are the Multiple Indicator Cluster Surveys (MICS) and the Demographic and Health Surveys (DHS). Additionally, other national household surveys and censuses contribute to the data. These figures are compiled by UNICEF, which coordinates with countries to gather the information.\nStatistical concept(s): This indicator includes both formal marriages and informal cohabiting relationships. Informal relationships are usually defined as those where a couple lives together with the intention of a long-term relationship but without a formal civil or religious ceremony. The incidence of child marriage is assessed retrospectively among women who are past the risk of marrying as children. The age range of 20 to 24 years is conventionally used to reflect the current prevalence of child marriage." }, { "id": "Topic", "value": "Gender: Agency" }, { "id": "Unitofmeasure", "value": "% of women ages 20-24" } ], "source_id": "16" }, { "id": "SP.M18.2024.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although the legal age of marriage is defined as 18 years in most countries, the practice of child marriage remains widespread. A women’s access to education and later her employment opportunities as well as the nature and terms of her work are often compromised by this practice. Young married girls whose schooling is cut short often lack the knowledge and skills for formal work and are limited to occupations with lower incomes and inferior working conditions. Sustainable Development Goal 5 commits to eliminate the practice of child marriage." }, { "id": "IndicatorName", "value": "Women who were first married by age 18 (% of women ages 20-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The measure of child marriage is designed to be retrospective, focusing on the age at first marriage among adult women who have already passed the risk period. Although it is feasible to assess the current marital status of girls under 18, this approach could underestimate the true extent of child marriage. This is because girls who are not married at the time of survey may still marry before reaching 18." }, { "id": "Longdefinition", "value": "Women who were first married by age 18 refers to the percentage of women ages 20-24 who were first married by age 18." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.3.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "UNICEF Data, UN Children's Fund (UNICEF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_20-24_MRD_U18; \tIndicator name from the original source: Percentage of women (aged 20-24 years) married or in union before age 18, type: API;\nDHS API, DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: MA_MBAG_W_B18; \tIndicator name from the original source: Women first married by exact age 18, type: API" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Number of women aged 20-24 who were first married or in union before age 18 divided by the total number of women aged 20-24 in the population multiplied by 100. The primary sources for this indicator are the Multiple Indicator Cluster Surveys (MICS) and the Demographic and Health Surveys (DHS). Additionally, other national household surveys and censuses contribute to the data. These figures are compiled by UNICEF, which coordinates with countries to gather the information.\nStatistical concept(s): This indicator includes both formal marriages and informal cohabiting relationships. Informal relationships are usually defined as those where a couple lives together with the intention of a long-term relationship but without a formal civil or religious ceremony. The incidence of child marriage is assessed retrospectively among women who are past the risk of marrying as children. The age range of 20 to 24 years is conventionally used to reflect the current prevalence of child marriage." }, { "id": "Topic", "value": "Gender: Agency" }, { "id": "Unitofmeasure", "value": "% of women ages 20-24" } ], "source_id": "16" }, { "id": "SP.MTR.1519.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Having a child during the teenage years limits girls' opportunities for better education, jobs, and income. Pregnancy is more likely to be unintended during the teenage years, and births are more likely to be premature and are associated with greater risks of complications during delivery and of death. In many countries maternal mortality is a leading cause of death among women of reproductive age, although most of those deaths are preventable. Infants of adolescent mothers are also more likely to have low birth weight, which can have a long-term impact on their health and development. Complications from pregnancy and childbirth are the leading cause of death among girls aged 15-19 years in many low- and middle-income countries." }, { "id": "IndicatorName", "value": "Teenage mothers (% of women ages 15-19 who have had children or are currently pregnant)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Teenage mothers are the percentage of women ages 15-19 who already have children or are currently pregnant." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Demographic and Health Surveys" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data represents the combined percentage of women aged 15-19 who are mothers and those who are pregnant with their first child. This information is gathered through individual interviews with women of reproductive age during household surveys, including Demographic and Health Surveys. For more details, please refer to the DHS website: https://dhsprogram.com/data/Guide-to-DHS-Statistics/Teenage_Pregnancy_and_Motherhood.htm" }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Percent of women ages 15-19 who have had children or are currently pregnant" } ], "source_id": "16" }, { "id": "SP.POP.0004.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 00-04, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.0004.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 00-04, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 4 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.0004.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 00-04, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.0004.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 00-04, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 4 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.0014.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SP.POP.0014.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 14 as a percentage of the total female population. Population is based on the de facto definition of population." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.0014.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SP.POP.0014.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 14 as a percentage of the total male population. Population is based on the de facto definition of population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.0014.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB), note: Staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects., publisher: World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data.;\nWorld Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SP.POP.0014.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14 (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Population between the ages 0 to 14 as a percentage of the total population. Population is based on the de facto definition of population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects., United Nations Population Division, uri: https://population.un.org/wpp/, publisher: United Nations Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.0509.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 05-09, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 5 to 9." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.0509.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 05-09, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 5 to 9 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.0509.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 05-09, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 5 to 9." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.0509.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 05-09, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 5 to 9 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.1014.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 10-14, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 10 to 14." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.1014.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 10-14, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 10 to 14 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.1014.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 10-14, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 10 to 14." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.1014.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 10-14, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 10 to 14 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.1519.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 15-19, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 15 to 19." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.1519.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 15-19, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 15 to 19 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.1519.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 15-19, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 15 to 19." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.1519.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 15-19, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 15 to 19 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.1564.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SP.POP.1564.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 15 to 64 as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.1564.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SP.POP.1564.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 15 to 64 as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.1564.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SP.POP.1564.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64 (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Total population between the ages 15 to 64 as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.2024.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 20-24, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 20 to 24." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.2024.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 20-24, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 20 to 24 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.2024.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 20-24, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 20 to 24." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.2024.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 20-24, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 20 to 24 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.2529.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 25-29, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 25 to 29." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.2529.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 25-29, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 25 to 29 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.2529.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 25-29, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 25 to 29." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.2529.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 25-29, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 25 to 29 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.3034.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 30-34, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 30 to 34." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.3034.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 30-34, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 30 to 34 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.3034.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 30-34, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 30 to 34." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.3034.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 30-34, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 30 to 34 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.3539.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 35-39, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 35 to 39." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.3539.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 35-39, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 35 to 39 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.3539.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 35-39, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 35 to 39." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.3539.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 35-39, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 35 to 39 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.4044.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 40-44, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 40 to 44." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.4044.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 40-44, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 40 to 44 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.4044.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 40-44, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 40 to 44." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.4044.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 40-44, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 40 to 44 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.4549.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 45-49, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 45 to 49." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.4549.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 45-49, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 45 to 49 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.4549.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 45-49, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 45 to 49." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.4549.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 45-49, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 45 to 49 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.5054.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 50-54, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 50 to 54." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.5054.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 50-54, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 50 to 54 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.5054.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 50-54, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 50 to 54." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.5054.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 50-54, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 50 to 54 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.5559.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 55-59, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 55 to 59." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.5559.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 55-59, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 55 to 59 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.5559.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 55-59, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 55 to 59." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.5559.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 55-59, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 55 to 59 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.6064.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 60-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 60 to 64." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.6064.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 60-64, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 60 to 64 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.6064.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 60-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 60 to 64." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.6064.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 60-64, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 60 to 64 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.6569.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 65-69, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 65 to 69." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.6569.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 65-69, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 65 to 69 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.6569.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 65-69, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 65 to 69." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.6569.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 65-69, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 65 to 69 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.65UP.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SP.POP.65UP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population 65 years of age or older as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.65UP.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SP.POP.65UP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population 65 years of age or older as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.65UP.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SP.POP.65UP.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Population ages 65 and above as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.7074.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 70-74, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 70 to 74." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.7074.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 70-74, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 70 to 74 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.7074.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 70-74, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 70 to 74." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.7074.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 70-74, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 70 to 74 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.7579.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 75-79, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 75 to 79." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.7579.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 75-79, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 75 to 79 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.7579.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 75-79, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 75 to 79." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.7579.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 75-79, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 75 to 79 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.80UP.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 80 and above, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 80 and above." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.80UP.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 80 and above, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 80 and above as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.80UP.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 80 and above, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 80 and above." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.80UP.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 80 and above, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 80 and above as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.AG00.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 00, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.AG00.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 00, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.AG01.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 01, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.AG01.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 01, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.AG02.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 02, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.AG02.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 02, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.AG03.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 03, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. 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World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.AG21.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 21, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.AG22.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 22, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.AG22.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 22, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.AG23.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 23, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.AG23.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 23, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.AG24.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 24, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.AG24.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 24, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.AG25.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 25, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.AG25.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 25, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "16" }, { "id": "SP.POP.BRTH.MF", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In the absence of interference, it is expected that the sex ratio at birth is fairly stable within the range of 1.03 to 1.07 boys born per 1.00 girls. However, in some populations, the observed sex ratio at birth is well above this range because of sex-selection driven by the preference for sons over daughters." }, { "id": "IndicatorName", "value": "Sex ratio at birth (male births per female births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Sex ratio at birth refers to male births per female births." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Sex ratio at birth is calculated as number of male births divided by number of female births.\nStatistical concept(s): If the sex ratio at birth is greater than 1, it indicates more boys born that year than girls." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Male births per female births" } ], "source_id": "16" }, { "id": "SP.POP.DPND", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Age dependency ratio (% of working-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Othernotes", "value": "Relevance to gender indicator: this indicator implies the dependency burden that the working-age population bears in relation to children and the elderly. Many times single or widowed women who are the sole caregiver of a household have a high dependency ratio." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Age dependency ratio is calculated as 100 x (Population (0-14) + Population (65+)) / Population (15-64). Data are shown as the proportion of dependents per 100 working-age population.\nStatistical concept(s): Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But dependency ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.\n\n\n\nAge structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.DPND.OL", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Age dependency ratio, old (% of working-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Age dependency ratio, old is calculated as 100 x (Population (65+)) / Population (15-64). Data are shown as the proportion of old dependents per 100 working-age population.\nStatistical concept(s): Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But dependency ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.\n\n\n\nAge structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.DPND.YG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Age dependency ratio, young (% of working-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Age dependency ratio, young, is the ratio of younger dependents--people younger than 15--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Age dependency ratio, young is calculated as 100 x (Population (0-14)) / Population (15-64). Data are shown as the proportion of young dependents per 100 working-age population.\nStatistical concept(s): Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But dependency ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.\n\n\n\nAge structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.GROW", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "Derived from total population. Population source: United Nations Population Division, National Statistical Offices, Eurostat, United Nations Statistics Division." }, { "id": "Developmentrelevance", "value": "Increases in human population, whether as a result of immigration or more births than deaths, can impact natural resources and social infrastructure. This can place pressure on a country's sustainability. A significant growth in population will negatively impact the availability of land for agricultural production, and will aggravate demand for food, energy, water, social services, and infrastructure. On the other hand, decreasing population size - a result of fewer births than deaths, and people moving out of a country - can impact a government's commitment to maintain services and infrastructure." }, { "id": "IndicatorName", "value": "Population growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), note: Derived from total population, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices, note: Derived from total population;\nDemographic Statistics, Eurostat (ESTAT), note: Derived from total population;\nPopulation and Vital Statistics Report (various years), United Nations (UN), note: Derived from total population, publisher: UN Statistical Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The growth rate is computed using the exponential growth formula:\n\n\n\nr = ln(pn/p0)/n, \n\n\n\nwhere r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods.\n\n\n\nFor information on total population from which the growth rates are calculated, see total population (SP.POP.TOTL).\nStatistical concept(s): Total population growth rates are calculated on the assumption that rate of growth is constant between two points in time." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Increases in human population, whether as a result of immigration or more births than deaths, can impact natural resources and social infrastructure. This can place pressure on a country's sustainability. A significant growth in population will negatively impact the availability of land for agricultural production, and will aggravate demand for food, energy, water, social services, and infrastructure. On the other hand, decreasing population size - a result of fewer births than deaths, and people moving out of a country - can impact a government's commitment to maintain services and infrastructure." }, { "id": "IndicatorName", "value": "Population, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Current population estimates for developing countries that lack (i) reliable recent census data, and (ii) pre- and post-census estimates for countries with census data, are provided by the United Nations Population Division and other agencies. \n\n\n\nThe cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in both the model and the data.\n\n\n\nBecause future trends cannot be known with certainty, population projections have a wide range of uncertainty." }, { "id": "Longdefinition", "value": "Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates." }, { "id": "Othernotes", "value": "Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices, uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population estimates are usually based on national population censuses, and estimates of fertility, mortality and migration.\n\n\n\nErrors and undercounting in census occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census.\n\n\n\nThe quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data.\n\n\n\nThe currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census.\n\n\n\nThe United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems.\n\n\n\nInternational migration is the only other factor besides birth and death rates that directly determines a country's population change. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect.\n\n\n\nOne of the major data sources of this indicator is UN Population Division's World Population Prospects, which use the cohort component method to produce population estimates and projections.\n\n\n\nPopulation projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant.\nStatistical concept(s): Estimates of total population describe the size of total population. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SP.POP.TOTL.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Females comprise almost one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population is based on the de facto definition of population, which counts all female residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SP.POP.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age/sex distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Females comprise almost one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, female (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population is the percentage of the population that is female. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2022 Revision" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.POP.TOTL.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Males comprise about one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population is based on the de facto definition of population, which counts all male residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SP.POP.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age/sex distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Males comprise about one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, male (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population is the percentage of the population that is male. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2022 Revision" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.REG.BRTH.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 16.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2003-2021" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "16" }, { "id": "SP.REG.BRTH.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 16.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2003-2021" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "16" }, { "id": "SP.REG.BRTH.RU.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration, rural (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "16" }, { "id": "SP.REG.BRTH.UR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration, urban (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Othernotes", "value": "This is a disaggregated indicator (residence) for Sustainable Development Goal 16.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "16" }, { "id": "SP.REG.BRTH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 16.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "16" }, { "id": "SP.REG.DTHS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Completeness of death registration with cause-of-death information (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Completeness of death registration is the estimated percentage of deaths that are registered with their cause of death information in the vital registration system of a country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2017" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO), uri: http://apps.who.int/gho/data/node.main.1?lang=en" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The completeness of death registration is calculated by dividing the total number of deaths registered with cause-of-death information in the vital registration system for a given country-year by the total estimated deaths for that year for the national population. The national level of completeness is provided by the National Statistical Offices of all countries and areas to the United Nations Statistics Division as part of the annual data collection for the United Nations Demographic Yearbook. Currently, the threshold used for compiling the data for this indicator is 75 percent for death registration." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percent of completeness of death registration" } ], "source_id": "16" }, { "id": "SP.RUR.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and urban/rural distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "The rural population is calculated using the urban share reported by the United Nations Population Division.\n\nThe two distinct images - isolated farm, thriving metropolis - represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income.\n\nA 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population density and remoteness together define a set of rural areas that face special development challenges.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\"\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nRural population methodology is defined by various national statistical offices. In the United States, for example, the US Census Bureau's urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. \"Rural\" encompasses all population, housing, and territory not included within an urban area." }, { "id": "IndicatorName", "value": "Rural population" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population. Aggregation of urban and rural population may not add up to total population because of different country coverages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using World Bank's total population estimates and rural ratios derived from the United Nations World Urbanization Prospects.\nStatistical concept(s): Rural population is calculated as the difference between the total population and the urban population. Rural population is approximated as the midyear nonurban population. While a practical means of identifying the rural population, it is not a precise measure." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SP.RUR.TOTL.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and urban/rural distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "The rural population is calculated using the urban share reported by the United Nations Population Division.\n\nThe two distinct images - isolated farm, thriving metropolis - represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income.\n\nA 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population density and remoteness together define a set of rural areas that face special development challenges.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\"\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nRural population methodology is defined by various national statistical offices. In the United States, for example, the US Census Bureau's urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. \"Rural\" encompasses all population, housing, and territory not included within an urban area." }, { "id": "IndicatorName", "value": "Rural population growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. \n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Annual rural population growth rate for year t is the exponential rate of growth of midyear rural population from year t-1 to t, expressed as a percentage. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated from rural population estimates. The rural population estimates are calulcated using World Bank's total population estimates and rural ratios derived from the United Nations World Urbanization Prospects.\n\n\n\n\n\n\n\n\n\n\n\nThe growth rate is computed using the exponential growth formula:\n\n\n\n\n\n\n\n\n\n\n\nr = ln(pn/p0)/n, \n\n\n\n\n\n\n\n\n\n\n\nwhere r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods.\nStatistical concept(s): Rural population is calculated as the difference between the total population and the urban population. Rural population is approximated as the midyear nonurban population. While a practical means of identifying the rural population, it is not a precise measure." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.RUR.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The rural population is calculated using the urban share reported by the United Nations Population Division.\n\nThe two distinct images - isolated farm, thriving metropolis - represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income.\n\nA 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population density and remoteness together define a set of rural areas that face special development challenges.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\"\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nRural population methodology is defined by various national statistical offices. In the United States, for example, the US Census Bureau's urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. \"Rural\" encompasses all population, housing, and territory not included within an urban area." }, { "id": "IndicatorName", "value": "Rural population (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentages rural are calculated as the difference between 100 and the proportion of urban population in percentage.\nStatistical concept(s): Rural population is calculated as the difference between the total population and the urban population. Rural population is approximated as the midyear nonurban population. While a practical means of identifying the rural population, it is not a precise measure." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.URB.GROW", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and urban/rural distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Most countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Annual urban population growth rate for year t is the exponential rate of growth of midyear urban population from year t-1 to t, expressed as a percentage. Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank total population estimates and urban ratios from the United Nations World Urbanization Prospects." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated from urban population estimates. The urban population estimates are calulcated using World Bank's total population estimates and urban ratios from the United Nations World Urbanization Prospects.\n\n\n\n\n\n\n\n\n\n\n\nThe growth rate is computed using the exponential growth formula:\n\n\n\n\n\n\n\n\n\n\n\nr = ln(pn/p0)/n, \n\n\n\n\n\n\n\n\n\n\n\nwhere r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods.\nStatistical concept(s): Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.URB.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and urban/rural distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. \n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. Aggregation of urban and rural population may not add up to total population because of different country coverages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using World Bank's total population estimates and urban ratios from the United Nations World Urbanization Prospects.\nStatistical concept(s): Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "16" }, { "id": "SP.URB.TOTL.IN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage.\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentages urban are the numbers of persons residing in an area defined as ''urban'' per 100 total population.\nStatistical concept(s): Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "16" }, { "id": "SP.UWT.TFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Unmet need for contraception (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Unmet need for contraception is the percentage of fertile, married women of reproductive age who do not want to become pregnant and are not using contraception." }, { "id": "Othernotes", "value": "Unmet need for contraception measures the capacity women have in achieving their desired family size and birth spacing. Many couples in developing countries want to limit or postpone childbearing but are not using effective contraception. These couples have an unmet need for contraception. Common reasons are lack of knowledge about contraceptive methods and concerns about possible side effects." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2024" }, { "id": "Source", "value": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMany couples in developing countries want to limit or postpone childbearing but are not using effective contraception. These couples have an unmet need for contraception. Common reasons are lack of knowledge about contraceptive methods and concerns about possible side effects. This indicator excludes women not exposed to the risk of unintended pregnancy because of menopause, infertility, or postpartum anovulation." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "16" }, { "id": "EG.ELC.ACCS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies.\n\nEnergy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indication of a country's energy poverty status.\n\nElectricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification.\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas." }, { "id": "IndicatorName", "value": "Access to electricity (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Access to electricity is the percentage of population with access to electricity. Electrification data are collected from industry, national surveys and international sources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Sustainable Energy for All (SE4ALL) database from the SE4ALL Global Tracking Framework led jointly by the World Bank, International Energy Agency, and the Energy Sector Management Assistance Program." }, { "id": "Statisticalconceptandmethodology", "value": "Data for access to electricity are collected among different sources: mostly data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Given the low frequency and the regional distribution of some surveys, a number of countries have gaps in available data. To develop the historical evolution and starting point of electrification rates, a simple modeling approach was adopted to fill in the missing data points - around 1990, around 2000, and around 2010. Therefore, a country can have a continuum of zero to three data points. There are 42 countries with zero data point and the weighted regional average was used as an estimate for electrification in each of the data periods. 170 countries have between one and three data points and missing data are estimated by using a model with region, country, and time variables. The model keeps the original observation if data is available for any of the time periods. This modeling approach allowed the estimation of electrification rates for 212 countries over these three time periods (Indicated as \"Estimate\"). Notation \"Assumption\" refers to the assumption of universal access in countries classified as developed by the United Nations. Data begins from the year in which the first survey data is available for each country." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "18" }, { "id": "EN.ATM.CO2E.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "CO2 emissions (metric tons per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The U.S. Department of Energy's Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division's World Energy Data Set) and world cement manufacturing (from the U.S. Department of Interior's Geological Survey, USGS 2011). Although estimates of global carbon dioxide emissions are probably accurate within 10 percent (as calculated from global average fuel chemistry and use), country estimates may have larger error bounds. Trends estimated from a consistent time series tend to be more accurate than individual values.\n\nEach year the CDIAC recalculates the entire time series since 1949, incorporating recent findings and corrections. Estimates exclude fuels supplied to ships and aircraft in international transport because of the difficulty of apportioning the fuels among benefiting countries." }, { "id": "Longdefinition", "value": "Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Data for up to 1990 are sourced from Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data from 1990 are CAIT data: Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See SP.POP.TOTL for the denominator's source." }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Data for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as deforestation." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "18" }, { "id": "EN.ATM.PM25.MC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly." }, { "id": "IndicatorName", "value": "PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the WHO guideline value is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 10 micrograms per cubic meter, the guideline value recommended by the World Health Organization as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Brauer, M. et al. 2017, for the Global Burden of Disease Study 2017." }, { "id": "Statisticalconceptandmethodology", "value": "A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762–3772; GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Prüss-Üstün A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "18" }, { "id": "ER.FST.DFST.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Deforestation is the removal of a forest or stand of trees where the land is thereafter is converted to a non-forest use, such as farms, ranches, or urban use. As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity.\n\nDestruction of rainforests remains a significant environmental problem - up to 90 percent of West Africa's coastal rainforests have disappeared since 1900. Much of what remains of the world's rainforests is in the Amazon basin, where the Amazon Rainforest covers approximately 4 million square kilometers. Large-scale planting of trees is significantly reducing the net loss of forest area globally, and afforestation and natural expansion of forests in some countries and regions have reduced the net loss of forest area significantly at the global level.\n\nForests cover more than 31 percent of total land area of the world; the world's total forest area is just over 4 billion hectares. On a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems.\n\nNational parks, game reserves, wilderness areas and other legally established protected areas cover more than 10 percent of the total forest area in most countries and regions. FAO estimates that around 10 million people are employed in forest management and conservation - but many more are directly dependent on forests for their livelihoods. Also, 80 about percent of the world's forests are publicly owned, but ownership and management of forests by communities, individuals and private companies is on the rise.\n\nClose to 1.2 billion hectares of forest are managed primarily for the production of wood and non-wood forest products. An additional 25 percent of forest area is designated for multiple uses - in most cases including the production of wood and non-wood forest products. The area designated primarily for productive purposes has decreased by more than 50 million hectares since 1990 as forests have been designated for other purposes." }, { "id": "Generalcomments", "value": "This is a period growth rate indicator for online table use only." }, { "id": "IndicatorName", "value": "Annual deforestation (% of change)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The national figures in the database are reported by the countries themselves, thus eliminating any discrepancies between global and national figures. The reporting format ensures that countries provide the full reference for original data sources as well as national definitions and terminology. Separate sections in the reporting format (country reports) deal with the analysis of data; calibration of data to the official land area as held by FAO; and reclassification of data to the classes used in FAO's Global Forest Resources Assessments.\n\nFAO has been collecting and analyzing data on forest area since 1946. This is done at intervals of 5-10 years as part of the Global Forest Resources Assessment (FRA). FAO reports data for 229 countries and territories; for the remaining 56 small island states and territories where no information is provided, a report is prepared by FAO using existing information and a literature search. The data are aggregated at sub-regional, regional and global levels by the FRA team at FAO, and estimates are produced by straight summation.\n\nThe lag between the reference year and the actual production of data series as well as the frequency of data production varies between countries. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area." }, { "id": "Longdefinition", "value": "Average annual deforestation refers to the permanent conversion of natural forest area to other uses, including shifting cultivation, permanent agriculture, ranching, settlements, and infrastructure development. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, Global Forest Resources Assessment." }, { "id": "Statisticalconceptandmethodology", "value": "Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 meters are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate.\n\nData includes areas with bamboo and palms; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters; plantations primarily used for forestry or protective purposes, such as rubber-wood plantations and cork oak stands. Data excludes tree stands in agricultural production systems, such as fruit plantations and agroforestry systems. Forest area also excludes trees in urban parks and gardens. The proportion of forest area to total land area is calculated and changes in the proportion are computed to identify trends.\n\nThe Food and Agricultural Organization (FAO) provides detail information on forest cover, and adjusted estimates of forest cover. The current survey uses a uniform definition of forest. Although FAO provides a breakdown of forest cover between natural forest and plantation for developing countries, this indictor data does not reflect that breakdown. Thus the deforestation data may underestimate the rate at which natural forest is disappearing in some countries.\n\nDeforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area." }, { "id": "Topic", "value": "Environment: Land use" } ], "source_id": "18" }, { "id": "ER.H2O.FWTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times)." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, total (% of internal resources)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including withdrawals for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes. Data are for the most recent year available for 1987-2002." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, AQUASTAT data." }, { "id": "Statisticalconceptandmethodology", "value": "Annual freshwater withdrawals are total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes." }, { "id": "Topic", "value": "Environment: Freshwater" } ], "source_id": "18" }, { "id": "IQ.SCI.OVRL", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "Statistical Capacity is a nation’s ability to collect, analyze, and disseminate high-quality data about its population and economy. Quality statistics are essential for all stages of evidence-based decision-making, including: Monitoring social and economic indicators, Allocating political representation and government resources, Guiding private sector investment, as well as Informing the international donor community for program design and policy formulation." }, { "id": "IndicatorName", "value": "Overall level of statistical capacity (scale 0 - 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The Statistical Capacity Indicator is a composite score assessing the capacity of a country’s statistical system. It is based on a diagnostic framework assessing the following areas: methodology; data sources; and periodicity and timeliness. Countries are scored against 25 criteria in these areas, using publicly available information and/or country input. The overall Statistical Capacity score is then calculated as a simple average of all three area scores on a scale of 0-100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Bulletin Board on Statistical Capacity (http://bbsc.worldbank.org)." }, { "id": "Statisticalconceptandmethodology", "value": "The Statistical Capacity Indicator score is calculated as the average of the scores of the 3 dimensions, i.e. Availability, Collection, Practice." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "18" }, { "id": "LP.LPI.OVRL.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce.\n\nAs the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains.\n\nThe tremendous importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices was recently included in the LPI." }, { "id": "IndicatorName", "value": "Logistics performance index: Overall (1=low to 5=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Feedback from operators is supplemented with quantitative data on the performance of key components of the logistics chain in the country of work. Thus, the LPI consists of both qualitative and quantitative measures.\n\nIn addition, despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Logistics Performance Index overall score reflects perceptions of a country's logistics based on efficiency of customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The index ranges from 1 to 5, with a higher score representing better performance. Data are from Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. 2009 round of surveys covered more than 5,000 country assessments by nearly 1,000 international freight forwarders. Respondents evaluate eight markets on six core dimensions on a scale from 1 (worst) to 5 (best). The markets are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are in Arvis and others' Connecting to Compete 2010: Trade Logistics in the Global Economy (2010)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Logistics Performance Index overall score reflects perceptions of a country's logistics based on efficiency of customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The index ranges from 1 to 5, with a higher score representing better performance." }, { "id": "Source", "value": "World Bank and Turku School of Economics, Logistic Performance Index Surveys. Data are available online at : http://www.worldbank.org/lpi. Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report." }, { "id": "Statisticalconceptandmethodology", "value": "The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics.\n\nThe Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and at the main express carriers. The 2012 LPI data are based on the 2011 survey, which was administered to nearly 1,000 respondents at international logistics companies in 143 countries (domestic performance indicators). The international LPI covers 155 countries. The LPI assesses both large companies and small and medium enterprises. Most of the responses are from small and medium enterprises, with large companies (those with 250 employees or more) accounting for roughly 18 percent of responses. The respondents include groups of professionals who are directly involved in day-today operations, from company headquarters and from country offices such as senior executives, area or country managers, and department managers. Many of the respondents are at corporate or regional headquarters or at country branch offices. The rest are at local branch offices or independent firms. The majority of respondents are involved in providing most logistics services as their main line of work such as warehousing and distribution, customer-tailored logistics solutions, courier services, bulk or break bulk cargo transport, and less-than-full container, full-container, or full-trailer load transport.\n\nEach survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\n\nThe LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores on questions 10-15, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "18" }, { "id": "NY.GDP.PCAP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "GDP per capita growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Annual percentage growth rate of GDP per capita based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. GDP per capita is gross domestic product divided by midyear population. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Statisticalconceptandmethodology", "value": "For more information, see the metadata for constant U.S. dollar GDP (NY.GDP.MKTP.KD) and total population (SP.POP.TOTL)." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" } ], "source_id": "18" }, { "id": "SE.SEC.CMPT.LO.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2021 (July 1, 2020-June 30, 2021)." }, { "id": "IndicatorName", "value": "Lower secondary completion rate, female (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data limitations preclude adjusting for students who drop out during the final year of lower secondary education. Thus this rate is a proxy that should be taken as an upper estimate of the actual lower secondary completion rate. \n\nThere are many reasons why the rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of lower secondary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of lower secondary education." }, { "id": "Longdefinition", "value": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of September 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "18" }, { "id": "SE.SEC.CMPT.LO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2021 (July 1, 2020-June 30, 2021)." }, { "id": "IndicatorName", "value": "Lower secondary completion rate, total (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data limitations preclude adjusting for students who drop out during the final year of lower secondary education. Thus this rate is a proxy that should be taken as an upper estimate of the actual lower secondary completion rate. \n\nThere are many reasons why the rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of lower secondary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of lower secondary education." }, { "id": "Longdefinition", "value": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of September 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "18" }, { "id": "SE.SEC.ENRR.LO", "metatype": [ { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2021 (July 1, 2020-June 30, 2021)." }, { "id": "IndicatorName", "value": "Gross enrollment rate (%), lower secondary, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown." }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of September 2020." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "18" }, { "id": "SE.SEC.ENRR.LO.FE", "metatype": [ { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2021 (July 1, 2020-June 30, 2021)." }, { "id": "IndicatorName", "value": "Gross enrollment rate (%), lower secondary, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown." }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of September 2020." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "18" }, { "id": "SH.DYN.MORT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is the Sustainable Development Goal indicator 3.2.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate, under-5 (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "18" }, { "id": "SH.H2O.BASW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using at least basic drinking water services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic drinking water service as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "18" }, { "id": "SH.HIV.INCD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Incidence of HIV, ages 15-49 (per 1,000 uninfected population ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected populations ages 15-49 expressed per 1,000 uninfected population in the year before the period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Statisticalconceptandmethodology", "value": "Data on incidence of HIV are from the Joint United Nations Programme on HIV/AIDS. Because of challenges in collecting direct measures of HIV incidence, modelled estimates are used (the Spectrum software). The models incorporate data on HIV prevalence from surveys of the general population, antenatal clinic attendees, and populations at increased risk of contracting HIV (such as sex workers, men who have sex with men, and people who inject drugs) and on the number of people receiving antiretroviral therapy, which will increase the prevalence of HIV because people living with HIV now survive longer. In countries with high-quality health information systems the models are also informed by case reporting and vital registration data." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "18" }, { "id": "SH.STA.BASS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using at least basic sanitation services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines basic sanitation facilities as improved sanitation facilities that are not shared with other households. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "18" }, { "id": "SH.STA.BRTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\nThe share of births attended by skilled health staff is an indicator of a health system's ability to provide adequate care for pregnant women." }, { "id": "Generalcomments", "value": "Assistance by trained professionals during birth reduces the incidence of maternal deaths during childbirth. The share of births attended by skilled health staff is an indicator of a health system’s ability to provide adequate care for pregnant women.\n\nThis is the Sustainable Development Goal indicator 3.1.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Births attended by skilled health staff (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "For the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources." }, { "id": "Longdefinition", "value": "Births attended by skilled health staff are the percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliveries on their own; and to care for newborns." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "18" }, { "id": "SH.STA.MMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This indicator represents the risk associated with each pregnancy and is also a Sustainable Development Goal Indicator (3.1.1) for monitoring maternal health." }, { "id": "IndicatorName", "value": "Maternal mortality ratio (modeled estimate, per 100,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality." }, { "id": "Longdefinition", "value": "Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs)." }, { "id": "Notesfromoriginalsource", "value": "Estimates of maternal mortality are presented along with upper and lower limits of intervals (see footnote) designed to depict the uncertainty of estimates. The intervals are the product of a detailed probabilistic evaluation of the uncertainty attributable to the various components of the estimation process. For estimates derived from the multilevel regression model, the components of uncertainty were divided into two groups: those reflected within the regression model (internal sources), and those due to assumptions or calculations that occur outside the model (external sources). Estimates of the total uncertainty reflect a combination of these various sources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division. Trends in Maternal Mortality: 2000 to 2017. Geneva, World Health Organization, 2019" }, { "id": "Statisticalconceptandmethodology", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation.\n\nThe estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "18" }, { "id": "SH.STA.STNT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Generalcomments", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Prevalence of stunting, female, is the percentage of girls under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "18" }, { "id": "SH.STA.STNT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "See SH.STA.OWGH.ME.ZS for aggregation" }, { "id": "Generalcomments", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "18" }, { "id": "SL.EMP.1524.SP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Four targets were added to the UN Millennium Declaration at the 2005 World Summit High-Level Plenary Meeting of the 60th Session of the UN General Assembly. One was full and productive employment and decent work for all, which is seen as the main route for people to escape poverty. Employment to population ratio is a key measure to monitor whether a country is on track to achieve the Millennium Development Goal of eradicating extreme poverty and hunger by 2015. And it continues to be a priority in the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all." }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, female (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved on June 15, 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The employment to population ratio indicates how efficiently an economy provides jobs for people who want to work. A high ratio means that a large proportion of the population is employed. But a lower employment to population ratio can be seen as a positive sign, especially for young people, if it is caused by an increase in their education. \n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "18" }, { "id": "SL.EMP.1524.SP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Four targets were added to the UN Millennium Declaration at the 2005 World Summit High-Level Plenary Meeting of the 60th Session of the UN General Assembly. One was full and productive employment and decent work for all, which is seen as the main route for people to escape poverty. Employment to population ratio is a key measure to monitor whether a country is on track to achieve the Millennium Development Goal of eradicating extreme poverty and hunger by 2015. And it continues to be a priority in the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all." }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, male (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved on June 15, 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The employment to population ratio indicates how efficiently an economy provides jobs for people who want to work. A high ratio means that a large proportion of the population is employed. But a lower employment to population ratio can be seen as a positive sign, especially for young people, if it is caused by an increase in their education. \n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "18" }, { "id": "SL.EMP.1524.SP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Four targets were added to the UN Millennium Declaration at the 2005 World Summit High-Level Plenary Meeting of the 60th Session of the UN General Assembly. One was full and productive employment and decent work for all, which is seen as the main route for people to escape poverty. Employment to population ratio is a key measure to monitor whether a country is on track to achieve the Millennium Development Goal of eradicating extreme poverty and hunger by 2015. And it continues to be a priority in the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all." }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, total (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved on June 15, 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The employment to population ratio indicates how efficiently an economy provides jobs for people who want to work. A high ratio means that a large proportion of the population is employed. But a lower employment to population ratio can be seen as a positive sign, especially for young people, if it is caused by an increase in their education. \n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "18" }, { "id": "SL.GDP.PCAP.EM.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2017" }, { "id": "Developmentrelevance", "value": "Labor productivity is used to assess a country's economic ability to create and sustain decent employment opportunities with fair and equitable remuneration. Productivity increases obtained through investment, trade, technological progress, or changes in work organization can increase social protection and reduce poverty, which in turn reduce vulnerable employment and working poverty. Productivity increases do not guarantee these improvements, but without them - and the economic growth they bring - improvements are highly unlikely.\n\nGDP per person employed is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.2.1]" }, { "id": "IndicatorName", "value": "GDP per person employed (constant 2017 PPP $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "For comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output in both national currencies and purchasing power parity dollars is not easily available, especially in developing countries, because the definition, coverage, and methodology are not always consistent across countries. For example, countries employ different methodologies for estimating the missing values for the nonmarket service sectors and use different definitions of the informal sector." }, { "id": "Longdefinition", "value": "GDP per person employed is gross domestic product (GDP) divided by total employment in the economy. Purchasing power parity (PPP) GDP is GDP converted to 2017 constant international dollars using PPP rates. An international dollar has the same purchasing power over GDP that a U.S. dollar has in the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived using data from International Labour Organization, ILOSTAT database. The data retrieved on January 29, 2021." }, { "id": "Statisticalconceptandmethodology", "value": "GDP per person employed represents labor productivity — output per unit of labor input. To compare labor productivity levels across countries, GDP is converted to international dollars using purchasing power parity rates which take account of differences in relative prices between countries." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "18" }, { "id": "SL.TLF.CACT.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Estimates of women in the labor force and employment are generally lower than those of men and are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic. In many low-income countries women often work on farms or in other family enterprises without pay, and others work in or near their homes, mixing work and family activities during the day. In many high-income economies, women have been increasingly acquiring higher education that has led to better-compensated, longer-term careers rather than lower-skilled, shorter-term jobs. However, access to good- paying occupations for women remains unequal in many occupations and countries around the world. Labor force statistics by gender is important to monitor gender disparities in employment and unemployment patterns." }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived using data from International Labour Organization, ILOSTAT database. The data retrieved on June 15, 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave. \n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "18" }, { "id": "SM.POP.REFG", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\nIn most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers. Globally, the number of refugees at end 2010 was 10.55 million, including 597,300 people considered by UNHCR to be in a refugee-like situation; developing countries hosted 8.5 million refugees, or 80 percent of the global refugee population.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights." }, { "id": "IndicatorName", "value": "Refugee population by country or territory of asylum" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "There are difficulties in collecting accurate statistics on refugees. Many refugees may not be aware of the need to register or may choose not to do so, and administrative records tend to overestimate the number of refugees because it is easier to register than to de-register. In addition, most industrialized countries lack a refugee register and are thus not in a position to provide accurate information on the number of refugees residing in their country. Many countries have registries that are only maintained at the local level, so the data is not centralized.\n\nAsylum-seekers are persons who have applied for asylum or refugee status, but who have not yet received a final decision on their application. A distinction should be made between the number of asylum-seekers who have submitted an individual request during a certain period (\"asylum applications submitted\") and the number of asylum-seekers whose individual asylum request has not yet been decided at a certain date (\"backlog of undecided or pending cases\"). Caution should therefore be exercised when interpreting data on asylum-seekers.\n\nThe United Nations High Commissioner for Refugees (UNHCR) collects and maintains data on refugees, except for Palestinian refugees residing in areas under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA). Registration is voluntary, and estimates by the UNRWA are not an accurate count of the Palestinian refugee population. The data shows estimates of refugees collected by the UNHCR, complemented by estimates of Palestinian refugees under the UNRWA mandate. Thus, the aggregates differ from those published by the UNHCR.\n\nStatistics concerning the former USSR have been reported under the Russian Federation, those concerning the former Czechoslovakia have been reported under the Czech Republic and those concerning the former Yugoslavia and 'Serbia and Montenegro' have been reported under Serbia. Since 2006, separate statistics are available for Serbia and for Montenegro. Prior to 2006, no separate statistics are available and both countries have been reported under Serbia." }, { "id": "Longdefinition", "value": "Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees or its 1967 Protocol, the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted refugee-like humanitarian status, and people provided temporary protection. Asylum seekers--people who have applied for asylum or refugee status and who have not yet received a decision or who are registered as asylum seekers--are excluded. Palestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948 and who lost their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict. Country of asylum is the country where an asylum claim was filed and granted." }, { "id": "Notesfromoriginalsource", "value": "The refugee population category from 2007 onwards also includes people in a refugee-like situation, most of who were previously included in the Others of concern group. This sub-category is descriptive in nature and includes groups of persons who are outside their country or territory of origin and who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained.\n\nStatistics concerning the former USSR have been reported under the Russian Federation, those concerning the former Czechoslovakia have been reported under the Czech Republic and those concerning the former Yugoslavia and 'Serbia and Montenegro' have been reported under Serbia. Since 2006, separate statistics are available for Serbia and for Montenegro. Prior to 2006, no separate statistics are available and both countries have been reported under Serbia." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees or its 1967 Protocol, the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted refugee-like humanitarian status, and people provided temporary protection. Asylum seekers -- people who have applied for asylum or refugee status and who have not yet received a decision or who are registered as asylum seekers--are excluded. Palestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948 and who lost their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict. Country of asylum is the country where an asylum claim was filed and granted." }, { "id": "Source", "value": "United Nations High Commissioner for Refugees (UNHCR) and UNRWA through UNHCR's Refugee Data Finder at https://www.unhcr.org/refugee-statistics/." }, { "id": "Statisticalconceptandmethodology", "value": "The United Nations High Commissioner for Refugees (UNHCR) collects and maintains data on refugees in their Statistical Online Population Database. The refugee data does not include Palestinian refugees residing in areas under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA). However, the Palestinian refugees living outside the UNRWA areas of operation do fall under the responsibility of UNHCR and are thus included in the Statistical Online Population Database.\n\nRefugees are an important part of migrant stock. The refugee data refer to people who have crossed an international border to find sanctuary and have been granted refugee or refugee-like status or temporary protection. There are three main providers of refugee data: governmental agencies, UNHCR field offices and NGOs. Registrations, together with other sources - including estimates and surveys - are the main sources of refugee data. In the absence of Government estimates, UNHCR has estimated the refugee population in most industrialized countries, based on recognition of asylum-seekers. Prior to 2007, resettled refugees were included in these estimates.\n\nUp to and including 2006, to ensure that the refugee population in countries that lack a refugee registry is reflected in the global statistics, the number of refugees was estimated by UNHCR based on the arrival of refugees through resettlement programmes and the individual recognition of refugees over a 10-year (Europe and, since 2006, the United States) or 5-year (the United States before 2006, Canada and Oceania) period. Starting with the 2007 data, the cut-off period has been harmonized and now covers a 10-year period for Europe and non-European countries. Resettled refugees, however, are excluded from the refugee estimates in all countries.\n\nThe 2007-2011 refugee population category includes people in a refugee-like situation, most of who were previously included in the Others of concern group. This sub-category is descriptive in nature and includes groups of persons who are outside their country or territory of origin and who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained.\n\nAsylum seekers - people who have applied for asylum or refugee status and who have not yet received a decision or who are registered as asylum seekers - and internally displaced people - who are often confused with refugees - are not included in the data. Unlike refugees, internally displaced people remain under the protection of their own government, even if their reason for fleeing was similar to that of refugees.\n\nPalestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948 and who lost their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" } ], "source_id": "18" }, { "id": "SP.ADO.TFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.7.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Adolescent fertility rate (births per 1,000 women ages 15-19)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Adolescent fertility rate is the number of births per 1,000 women ages 15-19." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Population Division, World Population Prospects." }, { "id": "Statisticalconceptandmethodology", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\nAdolescent fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without vital registration systems fertility rates are generally based on extrapolations from trends observed in censuses or surveys from earlier years." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "18" }, { "id": "SP.DYN.CONM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Contraceptive prevalence, modern methods (% of women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Contraceptive prevalence rate is the percentage of women who are practicing, or whose sexual partners are practicing, at least one modern method of contraception. It is usually measured for women ages 15-49 who are married or in union. Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "18" }, { "id": "VC.IDP.TOCV", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Although all persons affected by conflict and/or human rights violations suffer, displacement from one's place of residence may make the internally displaced particularly vulnerable. Following are some of the factors that are likely to increase the need for protection:\n \n1) Internally displaced persons may be in transit from one place to another, may be in hiding, may be forced toward unhealthy or inhospitable environments, or face other circumstances that make them especially vulnerable.\n \n2) The social organization of displaced communities may have been destroyed or damaged by the act of physical displacement; family groups may be separated or disrupted; women may be forced to assume non-traditional roles or face particular vulnerabilities. Internally displaced populations, and especially groups like children, the elderly, or pregnant women, may experience profound psychosocial distress related to displacement.\n \n3) Removal from sources of income and livelihood may add to physical and psychosocial vulnerability for displaced people.\n \n4) Schooling for children and adolescents may be disrupted.\n \n5) Internal displacement to areas where local inhabitants are of different groups or inhospitable may increase risk to internally displaced communities; internally displaced persons may face language barriers during displacement.\n \n6) The condition of internal displacement may raise the suspicions of or lead to abuse by armed combatants, or other parties to conflict.\n \n7) Internally displaced persons may lack identity documents essential to receiving benefits or legal recognition; in some cases, fearing persecution, displaced persons have sometimes got rid of such documents.\n \n8) According to the Internal Displacement Monitoring Centre (IDMC) tens of millions people around the world are displaced every year within their countries by conflict, human rights violations, natural disasters and climate change. Unlike refugees who cross national borders and benefit from an established system of international protection and assistance, those forcibly uprooted within their own countries, by armed conflict, large-scale development projects, systematic violations of human rights, or natural disasters, lack predictable structures of support. Internal displacement has become one of the more pressing humanitarian, human rights and security problems confronting affected countries and the international community at large.\n \nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights." }, { "id": "IndicatorName", "value": "Internally displaced persons, total displaced by conflict and violence (number of people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Please note that most of the figures are estimates. The definition highlights two issues:\n\n1) The coercive or otherwise involuntary character of movement. The definition mentions some of the most common causes of involuntary movements, such as armed conflict, violence, human rights violations and disasters. These causes have in common that they give no choice to people but to leave their homes and deprive them of the most essential protection mechanisms, such as community networks, access to services, livelihoods. Displacement severely affects the physical, socio-economic and legal safety of people and should be systematically regarded as an indicator of potential vulnerability.\n \n2) The fact that such movement takes place within national borders. Unlike refugees, who have been deprived of the protection of their state of origin, IDPs remain legally under the protection of national authorities of their country of habitual residence. IDPs should therefore enjoy the same rights as the rest of the population. The Guiding Principles on Internal Displacement remind national authorities and other relevant actors of their responsibility to ensure that IDPs' rights are respected and fulfilled, despite the vulnerability generated by their displacement." }, { "id": "Longdefinition", "value": "Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. “People displaced” refers to the number of people living in displacement as of the end of each year, and reflects the stock of people displaced at the end of the previous year, plus inflows of new cases arriving over the year as well as births over the year to those displaced, minus outflows which may include returnees, those who settled elsewhere, those who integrated locally, those who travelled over borders, and deaths." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The Internal Displacement Monitoring Centre (http://www.internal-displacement.org/)" }, { "id": "Statisticalconceptandmethodology", "value": "Internally displaced persons are \"persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized state border.\" Internally displaced people are often confused with refugees. Unlike refugees, internally displaced people remain under the protection of their own government, even if their reason for fleeing was similar to that of refugees. Refugees are people who have crossed an international border to find sanctuary and have been granted refugee or refugee-like status or temporary protection. “People displaced” refers to the number of people living in displacement as of the end of each year, and reflects the stock of people displaced at the end of the previous year, plus inflows of new cases arriving over the year as well as births over the year to those displaced, minus outflows which may include returnees, those who settled elsewhere, those who integrated locally, those who travelled over borders, and deaths." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "18" }, { "id": "AG.LND.FRST.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOn a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems. The decrease of primary forest area, 0.4 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNational parks, game reserves, wilderness areas and other legally established protected areas cover more than 10 percent of the total forest area in most countries and regions. FAO estimates that around 10 million people are employed in forest management and conservation - but many more are directly dependent on forests for their livelihoods. Also, 80 about percent of the world's forests are publicly owned, but ownership and management of forests by communities, individuals and private companies is on the rise.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClose to 1.2 billion hectares of forest are managed primarily for the production of wood and non-wood forest products. An additional 25 percent of forest area is designated for multiple uses - in most cases including the production of wood and non-wood forest products. The area designated primarily for productive purposes has decreased by more than 50 million hectares since 1990 as forests have been designated for other purposes." }, { "id": "IndicatorName", "value": "Forest area (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Food and Agricultural Organization (FAO) has been collecting and analyzing data on forest area since 1946. This is done at intervals of 5-10 years as part of the Global Forest Resources Assessment (FRA). FAO reports data for 229 countries and territories; for the remaining 56 small island states and territories where no information is provided, a report is prepared by FAO using existing information and a literature search. The data are aggregated at sub-regional, regional and global levels by the FRA team at FAO, and estimates are produced by straight summation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe lag between the reference year and the actual production of data series as well as the frequency of data production varies between countries. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData includes areas with bamboo and palms; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters; plantations primarily used for forestry or protective purposes, such as rubber-wood plantations and cork oak stands. Data excludes tree stands in agricultural production systems, such as fruit plantations and agroforestry systems. Forest area also excludes trees in urban parks and gardens. The proportion of forest area to total land area is calculated and changes in the proportion are computed to identify trends." }, { "id": "Longdefinition", "value": "Forest area is land under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "FAOSTAT, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/RL, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 meters are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFAO provides detail information on forest cover, and adjusted estimates of forest cover. The current survey uses a uniform definition of forest. Although FAO provides a breakdown of forest cover between natural forest and plantation for developing countries, this indictor data does not reflect that breakdown. Thus the deforestation data may underestimate the rate at which natural forest is disappearing in some countries.\nStatistical concept(s): Forest - Forests are lands of more than 0.5 hectares, with a tree canopy cover of more than 10 percent, which are not primarily under agricultural or urban land use. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters in situ. Areas under reforestation which have yet to reach a crown density of 10 percent or tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, that are expected to regenerate. The term specifically includes: forest nurseries and seed orchards that constitute an integral part of the forest; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks and shelterbelts of trees with an area of more than 0.5 ha and width of more than 20 m; plantations primarily used for forestry purposes, including rubberwood plantations and cork oak stands. The term specifically excludes trees planted primarily for agricultural production, for example in fruit plantations and agroforestry systems." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers" } ], "source_id": "19" }, { "id": "AG.LND.FRST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOn a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems. The decrease of forest area, .11 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDestruction of rainforests remains a significant environmental problem Much of what remains of the world's rainforests is in the Amazon basin, where the Amazon Rainforest covers approximately 4 million square kilometers. The regions with the highest tropical deforestation rate are in Central America and tropical Asia. FAO estimates that the decrease of primary forest area, 0.4 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions. Large-scale planting of trees is significantly reducing the net loss of forest area globally, and afforestation and natural expansion of forests in some countries and regions have reduced the net loss of forest area significantly at the global level.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nForests cover about 31 percent of total land area of the world; the world's total forest area is just over 4 billion hectares. On a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNational parks, game reserves, wilderness areas and other legally established protected areas cover more than 10 percent of the total forest area in most countries and regions. FAO estimates that around 10 million people are employed in forest management and conservation - but many more are directly dependent on forests for their livelihoods. Close to 1.2 billion hectares of forest are managed primarily for the production of wood and non-wood forest products. An additional 25 percent of forest area is designated for multiple uses - in most cases including the production of wood and non-wood forest products. The area designated primarily for productive purposes has decreased by more than 50 million hectares since 1990 as forests have been designated for other purposes." }, { "id": "IndicatorName", "value": "Forest area (% of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "FAO has been collecting and analyzing data on forest area since 1946. This is done at intervals of 5-10 years as part of the Global Forest Resources Assessment (FRA). FAO reports data for 229 countries and territories; for the remaining 56 small island states and territories where no information is provided, a report is prepared by FAO using existing information and a literature search. The data are aggregated at sub-regional, regional and global levels by the FRA team at FAO, and estimates are produced by straight summation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe lag between the reference year and the actual production of data series as well as the frequency of data production varies between countries. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData includes areas with bamboo and palms; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters; plantations primarily used for forestry or protective purposes, such as rubber-wood plantations and cork oak stands. Data excludes tree stands in agricultural production systems, such as fruit plantations and agroforestry systems. Forest area also excludes trees in urban parks and gardens. The proportion of forest area to total land area is calculated and changes in the proportion are computed to identify trends." }, { "id": "Longdefinition", "value": "Forest area (% of land area) is the share of total land area that is under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "FAOSTAT, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/RL, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 meters are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Food and Agriculture Organization (FAO) provides detail information on forest cover, and adjusted estimates of forest cover. The survey uses a uniform definition of forest. Although FAO provides a breakdown of forest cover between natural forest and plantation for developing countries, forest data used to derive this indictor data does not reflect that breakdown. Total land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe indictor is derived by dividing total area under forest of a country by country's total land area, and multiplying by 100.\nStatistical concept(s): Forest - Forests are lands of more than 0.5 hectares, with a tree canopy cover of more than 10 percent, which are not primarily under agricultural or urban land use. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters in situ. Areas under reforestation which have yet to reach a crown density of 10 percent or tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, that are expected to regenerate. The term specifically includes: forest nurseries and seed orchards that constitute an integral part of the forest; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks and shelterbelts of trees with an area of more than 0.5 ha and width of more than 20 m; plantations primarily used for forestry purposes, including rubberwood plantations and cork oak stands. The term specifically excludes trees planted primarily for agricultural production, for example in fruit plantations and agroforestry systems." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "19" }, { "id": "DC.ODA.COMM.CD", "metatype": [ { "id": "IndicatorName", "value": "Total bilateral ODA commitments (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral official development assistance (ODA) commitments are firm obligations, expressed in writing and backed by the necessary funds, undertaken by official bilateral donors to provide specified assistance to a recipient country or a multilateral organization. Bilateral commitments are recorded in the full amount of expected transfer, irrespective of the time required for completing disbursements. OECD DAC." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: www.oecd.org/dac/stats/idsonline." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DC.ODA.COMM.SA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Total bilateral sector allocable ODA commitments (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral official development assistance (ODA) commitments are firm obligations, expressed in writing and backed by the necessary funds, undertaken by official bilateral donors to provide specified assistance to a recipient country or a multilateral organization. Bilateral commitments are recorded in the full amount of expected transfer, irrespective of the time required for completing disbursements. Total sector-allocable aid is the sum of aid that can be assigned to specific sectors or multisector activities. OECD DAC." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: www.oecd.org/dac/stats/idsonline." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DC.ODA.SOCL.CD", "metatype": [ { "id": "IndicatorName", "value": "Bilateral, sector-allocable ODA to basic social services (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral official development assistance (ODA) commitments are firm obligations, expressed in writing and backed by the necessary funds, undertaken by official bilateral donors to provide specified assistance to a recipient country or a multilateral organization. Bilateral commitments are recorded in the full amount of expected transfer, irrespective of the time required for completing disbursements. Total sector-allocable aid is the sum of aid that can be assigned to specific sectors or multisector activities. Basic social services consists of, primary education, basic life skills for youth and adults and early childhood education, basic health care, basic health infrastructure, basic nutrition, infectious disease control, health education and health personnel development, population policy and administrative management, reproductive health care, family planning, sexually transmitted disease (STD) control including HIV/AIDS, personnel development (population & reproductive health), basic drinking water supply and basic sanitation, and multi-sector aid for basic social services. OECD DAC." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: www.oecd.org/dac/stats/idsonline." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DC.ODA.SOCL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Bilateral, sector-allocable ODA to basic social services (% of bilateral ODA commitments)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral official development assistance (ODA) commitments are firm obligations, expressed in writing and backed by the necessary funds, undertaken by official bilateral donors to provide specified assistance to a recipient country or a multilateral organization. Bilateral commitments are recorded in the full amount of expected transfer, irrespective of the time required for completing disbursements. Total sector-allocable aid is the sum of aid that can be assigned to specific sectors or multisector activities. Basic social services consists of, primary education, basic life skills for youth and adults and early childhood education, basic health care, basic health infrastructure, basic nutrition, infectious disease control, health education and health personnel development, population policy and administrative management, reproductive health care, family planning, sexually transmitted disease (STD) control including HIV/AIDS, personnel development (population & reproductive health), basic drinking water supply and basic sanitation, and multi-sector aid for basic social services." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: www.oecd.org/dac/stats/idsonline." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DC.ODA.TLDC.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net ODA provided, to the least developed countries (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. The list of least developed countries (LDCs) has been agreed by the General Assembly, on the recommendation of the Committee for Development Policy, Economic and Social Council." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DC.ODA.TLDC.GN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net ODA provided to the least developed countries (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. The list of least developed countries (LDCs) has been agreed by the General Assembly, on the recommendation of the Committee for Development Policy, Economic and Social Council. Series is shown as a share of donors' GNI." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is presented as a percentage of Gross National Income (GNI)." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DC.ODA.TOTL.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net ODA provided, total (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DC.ODA.TOTL.GN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "Official development assistance (ODA): Frequently asked questions" }, { "id": "IndicatorName", "value": "Net ODA provided, total (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. It is shown as a share of donors' GNI." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2017" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is presented as a percentage of Gross National Income (GNI)." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DC.ODA.TOTL.KD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net ODA provided, total (constant 2023 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. Data are in constant 2023 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in constant U.S. dollar prices to account for inflation in the donor's currency and the changes in exchange rates with the U.S. dollar." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DC.ODA.UNTD.CD", "metatype": [ { "id": "IndicatorName", "value": "Bilateral ODA commitments that is untied (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral official development assistance (ODA) commitments are firm obligations, expressed in writing and backed by the necessary funds, undertaken by official bilateral donors to provide specified assistance to a recipient country or a multilateral organization. Bilateral commitments are recorded in the full amount of expected transfer, irrespective of the time required for completing disbursements. Untied bilateral official development assistance is assistance from country to country for which the associated goods and services may be fully and freely procured in substantially all countries. OECD DAC." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: www.oecd.org/dac/stats/idsonline." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DC.ODA.UNTD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Bilateral ODA commitments that is untied (% of bilateral ODA commitments)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral official development assistance (ODA) commitments are firm obligations, expressed in writing and backed by the necessary funds, undertaken by official bilateral donors to provide specified assistance to a recipient country or a multilateral organization. Bilateral commitments are recorded in the full amount of expected transfer, irrespective of the time required for completing disbursements. Untied bilateral official development assistance is assistance from country to country for which the associated goods and services may be fully and freely procured in substantially all countries." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: www.oecd.org/dac/stats/idsonline." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DT.HPC.COMR.PV", "metatype": [ { "id": "IndicatorName", "value": "Debt relief committed under HIPC initiative, cumulative US$ in end-2013 NPV terms" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Debt relief is committed as of the decision point (assuming full participation of creditors) under the enhanced HIPC Initiative. It is calculated as the amount needed to bring the net present value (NPV) of the country's debt level to the thresholds established by the HIPC Initiative (150 percent of exports or in certain cases 250 percent of fiscal revenues). Topping-up assistance and assistance provided under the original HIPC Initiative were committed in net present value terms as of the decision point and are converted to end-2012 terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Economic Policy and Debt Department." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DT.HPC.MDRI.PV", "metatype": [ { "id": "IndicatorName", "value": "Debt relief delivered in full under MDRI initiative, cumulative US$ in end-2013 NPV terms" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Debt relief delivered in full under MDRI initiative is the net present value of debt relief from International Development Association, International Monetary Fund, African Development Fund, and Inter-American Development Bank and delivered to countries having reached the HIPC completion point converted to end-2012 terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Economic Policy and Debt Department." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DT.HPC.STTS", "metatype": [ { "id": "IndicatorName", "value": "Status under enhanced HIPC initiative" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Indicator shows the status of heavily indebted poor countries country under the enhanced HIPC initiative. Heavily indebted poor countries reach HIPC decision point if they have a track record of macroeconomic stability, have prepared an Interim Poverty Reduction Strategy through a participatory process, and have cleared or reached an agreement on a process to clear, the outstanding arrears to multilateral creditors. The amount of debt relief necessary to bring countries’ debt indicators to HIPC thresholds is calculated, and countries begin receiving debt relief. Heavily indebted poor countries reach HIPC completion point if they maintain macroeconomic stability under a Poverty Reduction and Growth Facility (PRGF) supported program, carry out key structural and social reforms agreed on at the decision point, and implement satisfactorily Poverty Reduction Strategy for one year. Debt relief is then provided irrevocably by the country’s creditors." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Economic Policy and Debt Department." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DT.HPC.TOTL.PV", "metatype": [ { "id": "IndicatorName", "value": "Debt relief committed under HIPC and MDRI initiatives, cumulative US$ in end-2013 NPV terms" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Debt relief is committed as of the decision point (assuming full participation of creditors) under the enhanced HIPC Initiative. It is calculated as the amount needed to bring the net present value (NPV) of the country's debt level to the thresholds established by the HIPC Initiative (150 percent of exports or in certain cases 250 percent of fiscal revenues). Topping-up assistance and assistance provided under the original HIPC Initiative were committed in net present value terms as of the decision point and are converted to end-2012 terms. Debt relief delivered in full under MDRI initiative is the net present value of debt relief from International Development Association, International Monetary Fund, African Development Fund, and Inter-American Development Bank and delivered to countries having reached the HIPC completion point converted to end-2012 terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Economic Policy and Debt Department." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DT.ODA.ALLD.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official development assistance and official aid received (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions). \n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nNet official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DT.ODA.ODAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "DAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about USD 130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net official development assistance received (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe nominal values may overstate the real value of aid to recipients. Changes in international prices and exchange rates can reduce the purchasing power of aid. Tying aid, still prevalent though declining in importance, also tends to reduce its purchasing power. Tying requires recipients to purchase goods and services from the donor country or from a specified group of countries. Such arrangements prevent a recipient from misappropriating or mismanaging aid receipts, but they may also be motivated by a desire to benefit donor country suppliers.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions). \n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DT.ODA.ODAT.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The ratio of aid to GNI provides a measure of recipient country's dependency on aid. Ratios of aid are generally much higher in Sub-Saharan Africa than in other regions, and they increased in the 1980s. High ratios are due only in part to aid flows. Many African countries saw severe erosion in their terms of trade in the 1980s, along with weak policies, falling incomes, imports, and investment. Thus the increase in aid dependency ratios reflects events affecting both the numerator (aid) and the denominator (GNI).\n\nDAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about US $130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net ODA received (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nRatio of aid to gross national income (GNI) provides measures of recipient country's dependency on aid. But care must be taken in drawing policy conclusions. For foreign policy reasons some countries have traditionally received large amounts of aid. Thus aid dependency ratio may reveal as much about a donor's interests as about a recipient's needs. \n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nWorld Bank GNI estimates, World Bank (WB), note: World Bank GNI estimates are used for the denominator" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Net ODA received as a percent of GNI is calculated using values in U.S. dollars converted at official exchange rates." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DT.ODA.ODAT.PC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The ratio of aid per capita provides a measure of recipient country's dependency on aid. DAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about USD 130 billion. This demonstrates how effective aid pledges can be when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net ODA received per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients; and is calculated by dividing net ODA received by the midyear population estimate. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nWorld Bank population estimates, World Bank (WB), note: World Bank population estimates are used for the denominator" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\nTotal population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship - except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. The values shown are midyear estimates. Net official development assistance per capita is net ODA divided by midyear population.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThis definition excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "19" }, { "id": "DT.TDS.DECT.EX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "Total debt service (% of exports of goods, services and primary income)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total debt service to exports of goods, services and primary income. Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF." }, { "id": "Othernotes", "value": "The denominator for this indicator in previous versions of Global Development Finance included workers' remittances. Workers' remittances are no longer included." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "19" }, { "id": "DT.TDS.DPPF.XP.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Debt service to exports (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Debt service, the sum of principal repayments and interest actually paid in currency, goods, or services, is expressed as a percentage of exports of goods and services--all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, net exports of goods under merchanting, nonmonetary gold, and services. This series differs from the standard debt to exports series in that it covers only long-term public and publicly guaranteed debt and repayments (repurchases and charges) to the IMF." }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "19" }, { "id": "EG.GDP.PUSE.KO.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFossil fuels are non-renewable resources because they take millions of years to form, and reserves are being depleted much faster than new ones are being made. In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis." }, { "id": "IndicatorName", "value": "GDP per unit of energy use (constant 2021 PPP $ per kg of oil equivalent)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "GDP per unit of energy use is the PPP GDP per kilogram of oil equivalent of energy use. PPP GDP is gross domestic product converted to 2021 constant international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The ratio of gross domestic product (GDP) to energy use indicates energy efficiency. To produce comparable and consistent estimates of real GDP across economies relative to physical inputs to GDP - that is, units of energy use - GDP is converted to 2021 international dollars using purchasing power parity (PPP) rates. Differences in this ratio over time and across economies reflect structural changes in an economy, changes in sectoral energy efficiency, and differences in fuel mixes. Total energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. GDP data are from World Bank's national accounts files." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "constant 2021 PPP $ per kg of oil equivalent" } ], "source_id": "19" }, { "id": "EG.USE.COMM.GD.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "\"In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\nFossil fuels are non-renewable resources because they take millions of years to form, and reserves are being depleted much faster than new ones are being made. In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\"" }, { "id": "IndicatorName", "value": "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2021 PPP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Energy use per PPP GDP is the kilogram of oil equivalent of energy use per constant PPP GDP. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport. PPP GDP is gross domestic product converted to 2021 constant international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by dividing the total energy use (in kg of oil equivalent) by the total GDP (in constant 2021 PPP dollars) and then multiplying by 1000, to express the energy use per $1,000 of GDP." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "kg of oil equivalent per $1,000 GDP constant 2021 PPP" } ], "source_id": "19" }, { "id": "EN.ATM.CO2E.KT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "CO2 emissions (kt)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Limitationsandexceptions", "value": "This series excludes Land-use Change & Forestry (LUCF). \n\nThe world data includes international bunker fuel-related emissions and emissions from territories not part of the United Nations Framework Convention on Climate Change (UNFCCC)." }, { "id": "Longdefinition", "value": "Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions" }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Data for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as deforestation. The unit of measurement is kt (kiloton). Carbon dioxide emissions are often calculated and reported as elemental carbon. The were converted to actual carbon dioxide mass by multiplying them by 3.667 (the ratio of the mass of carbon to that of carbon dioxide)." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "19" }, { "id": "EN.ATM.CO2E.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "CO2 emissions (metric tons per capita)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Longdefinition", "value": "Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Emissions data are sourced from Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions" }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Data for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as deforestation." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "19" }, { "id": "EN.ATM.CO2E.PP.GD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "CO2 emissions (kg per PPP $ of GDP)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Longdefinition", "value": "Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.PP.CD for the denominator's source." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "19" }, { "id": "EN.POP.SLUM.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Population living in slums (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, housing durability, and security of tenure, as adopted in the Millennium Development Goal Target 7.D. The successor, the Sustainable Development Goal 11.1.1, considers inadequate housing (housing affordability) to complement the above definition of slums/informal settlements." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Urban Indicators Database, UN Human Settlements Programme (UN-Habitat), uri: https://data.unhabitat.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, housing durability, and security of tenure, as adopted in the Millennium Development Goal Target 7.D. The successor, the Sustainable Development Goal 11.1.1, considers inadequate housing (housing affordability) to complement the above definition of slums/informal settlements." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "% (share) of urban population" } ], "source_id": "19" }, { "id": "ER.H2O.FWTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times)." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, total (% of internal resources)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including withdrawals for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes. Data are for the most recent year available for 1987-2002." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Annual freshwater withdrawals are total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "% (share) of internal resources" } ], "source_id": "19" }, { "id": "ER.H2O.INTR.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "UNESCO estimates that in developing countries in Asia, Africa and Latin America, public water withdrawal represents just 50-100 liters (13 to 26 gallons) per person per day. In regions with insufficient water resources, this figure may be as low as 20-60 (5 to 15 gallons) liters per day. People in developed countries on average consume about 10 times more water daily than those in developing countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations." }, { "id": "IndicatorName", "value": "Renewable internal freshwater resources per capita (cubic meters)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Renewable internal freshwater resources flows refer to internal renewable resources (internal river flows and groundwater from rainfall) in the country. Renewable internal freshwater resources per capita are calculated using the World Bank's population estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Renewable water resources (internal and external) include average annual flow of rivers and recharge of aquifers generated from endogenous precipitation, and those water resources that are not generated in the country, such as inflows from upstream countries (groundwater and surface water), and part of the water of border lakes and/or rivers. Non-renewable water includes groundwater bodies (deep aquifers) that have a negligible rate of recharge on the human time-scale. While renewable water resources are expressed in flows, non-renewable water resources have to be expressed in quantity (stock). Runoff from glaciers where the mass balance is negative is considered non-renewable. Renewable internal freshwater resources per capita are calculated using the World Bank's population estimates. The unit of calculation is m3/year per inhabitant. Internal renewable freshwater resources per capita are calculated using the World Bank's population estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal actual renewable water resources correspond to the maximum theoretical yearly amount of water actually available for a country at a given moment. The unit of calculation is km3/year or 109 m3/year. Calculation Criteria is [Water resources: total renewable (actual)] = [Surface water: total renewable (actual)] + [Groundwater: total renewable (actual)] - [Overlap between surface water and groundwater].*\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFresh water is naturally occurring water on the Earth's surface. It is a renewable but limited natural resource. Fresh water can only be renewed through the process of the water cycle, where water from seas, lakes, forests, land, rivers, and dams evaporates, forms clouds, and returns as precipitation. However, if more fresh water is consumed through human activities than is restored by nature, the result is that the quantity of fresh water available in lakes, rivers, dams and underground waters can be reduced which can cause serious damage to the surrounding environment.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n* http://www.fao.org/nr/water/aquastat/data/glossary/search.html?termId=4188&submitBtn=s&cls=yes\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "cubic meters" } ], "source_id": "19" }, { "id": "ER.LND.PTLD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The International Union for Conservation of Nature (IUCN) defines a protected area as \"a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values.\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTerrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Nationally protected terrestrial are terrestrial areas as a percentage of total territorial area, where all nationally designated protected areas with known location and extent are included.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nProtected areas remain the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. They provide the core of efforts to protect the world's threatened species and are increasingly recognized as essential providers of ecosystem services and biological resources. Some sites are owned and managed by governments, others by private individuals, companies, communities and faith groups.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Sustainable Development Goals (SDGs) address concerns common to all economies. In recognition of the vulnerability of animal and plant species, SDGs include targets 14 and 15 to highlight the importance of marine and terrestorial protected areas. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity." }, { "id": "IndicatorName", "value": "Terrestrial protected areas (% of total land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data source for this indicator is the World Database on Protected Areas (WDPA), the most comprehensive global dataset on marine and terrestrial protected areas available. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe extent to which the land areas, including inland waters, and territorial waters of a country/territory are protected is useful for planning purpose to protect biodiversity. However, it is neither an indication of how well managed the terrestrial and marine protected areas are, nor confirmation that protection measures are effectively enforced. Further, the indicator does not provide information on non-designated or internationally designated protected areas that may also be important for conserving biodiversity. There are known data and knowledge gaps for some countries/regions due to difficulties in reporting national protected area data to the WDPA and/or determining whether a site conforms to the IUCN definition of a protected area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGaps and/or time lags in reporting national protected area data to the WDPA can however result in discrepancies, which are resolved in communication with data providers. The World Conservation Monitoring Centre (WCMC) compiles data on protected areas, numbers of certain species, and numbers of those species under threat from various sources. Because of differences in definitions, reporting practices, and reporting periods, cross-country comparability is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas." }, { "id": "Longdefinition", "value": "Terrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Marine areas, unclassified areas, littoral (intertidal) areas, and sites protected under local or provincial law are excluded." }, { "id": "Othernotes", "value": "Restricted use: Please contact the Protected Planet for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2013-2025" }, { "id": "Source", "value": "Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM), UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516, date published: 202405;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon). \n\nDesignating an area as protected does not mean that protection is in force. And for small countries that have only protected areas smaller than 1,000 hectares, the size limit in the definition leads to an underestimate of protected areas. Nationally protected areas are defined using the six IUCN management categories for areas of at least 1,000 hectares: scientific reserves and strict nature reserves with limited public access; national parks of national or international significance and not materially affected by human activity; natural monuments and natural landscapes with unique aspects; managed nature reserves and wildlife sanctuaries; protected landscapes (which may include cultural landscapes); and areas managed mainly for the sustainable use of natural systems to ensure long-term protection and maintenance of biological diversity. \n\nA GIS analysis is used to calculate terrestrial and marine protection. For this a global protected area layer is created by combining the polygons and points recorded in the WDPA. Circular buffers are created around points based on the known extent of protected areas for which no polygon is available. Annual protected area layers are created by dissolving the global protected area layer by the known year of establishment of protected areas recorded in the WDPA. The annual protected area layers are overlaid with country/territory boundaries, coastlines and buffered coastlines (delineating the territorial waters) to obtain the absolute coverage (in square kilometers) of protected areas by country/territory. The total area of a country's/territory's terrestrial protected areas and marine protected areas in territorial waters is divided by the total area of its land areas (including inland waters) and territorial waters to obtain the relative coverage (percentage) of protected areas.\n\nThe data reported for a given year reflects all protected areas reported until January of the succeeding yer. For example, the value for 2025 is calculated based on the January 2026 version of the WDPA." }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "19" }, { "id": "ER.MRN.PTMR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The International Union for Conservation of Nature (IUCN) defines a protected area as \"a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values.\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMarine protected areas are areas of intertidal or subtidal terrain - and overlying water and associated flora and fauna and historical and cultural features - that have been reserved by law or other effective means to protect part or the entire enclosed environment. Sites protected under local or provincial law are excluded.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nProtected areas remain the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. They provide the core of efforts to protect the world's threatened species and are increasingly recognized as essential providers of ecosystem services and biological resources. Some sites are owned and managed by governments, others by private individuals, companies, communities and faith groups.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Sustainable Development Goals (SDGs) address concerns common to all economies. In recognition of the vulnerability of animal and plant species, SDGs include targets 14 and 15 to highlight the importance of marine and terrestorial protected areas. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity." }, { "id": "IndicatorName", "value": "Marine protected areas (% of territorial waters)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data source for this indicator is the World Database on Protected Areas (WDPA), the most comprehensive global dataset on marine and terrestrial protected areas available. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe extent to which the land areas, including inland waters, and territorial waters of a country/territory are protected is useful for planning purpose to protect biodiversity. However, it is neither an indication of how well managed the terrestrial and marine protected areas are, nor confirmation that protection measures are effectively enforced. Further, the indicator does not provide information on non-designated or internationally designated protected areas that may also be important for conserving biodiversity. There are known data and knowledge gaps for some countries/regions due to difficulties in reporting national protected area data to the WDPA and/or determining whether a site conforms to the IUCN definition of a protected area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGaps and/or time lags in reporting national protected area data to the WDPA can however result in discrepancies, which are resolved in communication with data providers. The World Conservation Monitoring Centre (WCMC) compiles data on protected areas, numbers of certain species, and numbers of those species under threat from various sources. Because of differences in definitions, reporting practices, and reporting periods, cross-country comparability is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas." }, { "id": "Longdefinition", "value": "Marine protected areas are areas of intertidal or subtidal terrain--and overlying water and associated flora and fauna and historical and cultural features--that have been reserved by law or other effective means to protect part or all of the enclosed environment." }, { "id": "Othernotes", "value": "Restricted use: Please contact the Protected Planet for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2013-2025" }, { "id": "Source", "value": "Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM), UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.protectedplanet.net/en, note: Only the latest data can be retrieved from the Protected Planet website. The time series are provided directly to WDI by Protected Planet., publisher: Protected Planet, date accessed: 20240516, date published: 202405;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon). \n\nDesignating an area as protected does not mean that protection is in force. And for small countries that have only protected areas smaller than 1,000 hectares, the size limit in the definition leads to an underestimate of protected areas. Nationally protected areas are defined using the six IUCN management categories for areas of at least 1,000 hectares: scientific reserves and strict nature reserves with limited public access; national parks of national or international significance and not materially affected by human activity; natural monuments and natural landscapes with unique aspects; managed nature reserves and wildlife sanctuaries; protected landscapes (which may include cultural landscapes); and areas managed mainly for the sustainable use of natural systems to ensure long-term protection and maintenance of biological diversity.\n\nA GIS analysis is used to calculate terrestrial and marine protection. For this a global protected area layer is created by combining the polygons and points recorded in the WDPA. Circular buffers are created around points based on the known extent of protected areas for which no polygon is available. Annual protected area layers are created by dissolving the global protected area layer by the known year of establishment of protected areas recorded in the WDPA. The annual protected area layers are overlaid with country/territory boundaries, coastlines and buffered coastlines (delineating the territorial waters) to obtain the absolute coverage (in square kilometers) of protected areas by country/territory per year from 1990 to present. The total area of a country's/territory's terrestrial protected areas and marine protected areas in territorial waters is divided by the total area of its land areas (including inland waters) and territorial waters to obtain the relative coverage (percentage) of protected areas.\n\nThe data reported for a given year reflects all protected areas reported until January of the succeeding year. For example, the value for 2025 is calculated based on the January 2026 version of the WDPA." }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" }, { "id": "Unitofmeasure", "value": "% (share) of territorial waters" } ], "source_id": "19" }, { "id": "IT.CEL.SETS.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. The International Telecommunication Union (ITU) estimates that there were about 6 billion mobile subscriptions globally in the early 2010s. No technology has ever spread faster around the world. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nMobile cellular telephone subscriptions are subscriptions to a public mobile telephone service using cellular technology, which provide access to the public switched telephone network (PSTN) using cellular technology. It includes postpaid and prepaid subscriptions and includes analogue and digital cellular systems.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "IndicatorName", "value": "Mobile cellular subscriptions (per 100 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU.\n\nMobile subscriptions include both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services are also excluded, but all mobile cellular subscriptions that offer voice communications are included. Both postpaid and prepaid subscriptions are included." }, { "id": "Longdefinition", "value": "Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Refers to the subscriptions to a public mobile telephone service and provides access to Public Switched Telephone Network (PSTN) using cellular technology, including number of pre-paid SIM cards active during the past three months. This includes both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services should also be excluded. This should include all mobile cellular subscriptions that offer voice communications.\n\nData on mobile cellular subscribers are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators.\n\nData for this indicator are readily available for approximately 90 percent of countries, either through ITU's World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator's website. For the rest, information can be aggregated through operators' data (mainly through annual reports) and complemented by market research reports.\n\nMobile cellular subscriptions (per 100 people) indicator is derived by all mobile subscriptions divided by the country's population and multiplied by 100. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): Data can be collected from all licensed mobile-cellular operators in the country, and then aggregated at the country level. If retail mobile-cellular services are also provided by nonfacilities-based operators (i.e., mobile virtual network operators), care should be taken to avoid double counting. One difficulty that may arise is that operators may have different definitions of ‘active’ and therefore may not be able to provide the data according to the recommended definition (i.e., used in the last three months). This indicator can be divided by the population and multiplied by 100 to obtain mobile cellular subscriptions per 100 people." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "number of subscriptions*100/population" } ], "source_id": "19" }, { "id": "IT.MLT.MAIN.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout.\n\nFixed telephone lines are those that connect a subscriber's terminal equipment to the public switched telephone network and that have a port on a telephone exchange. This term is synonymous with the term main station or Direct Exchange Line (DEL) that is commonly used in telecommunication documents. Integrated services digital network channels and fixed wireless subscribers are included. A fixed line also refers to a phone which uses a solid medium telephone line such as a metal wire or fiber optic cable for transmission as distinguished from a mobile cellular line which uses radio waves for transmission.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "IndicatorName", "value": "Fixed telephone subscriptions (per 100 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU." }, { "id": "Longdefinition", "value": "Fixed telephone subscriptions refers to the sum of active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A fixed telephone line (previously called main telephone line in operation) is an active line connecting the subscriber's terminal equipment to the public switched telephone network (PSTN) and which has a dedicated port in the telephone exchange equipment. This term is synonymous with the terms main station or Direct Exchange Line (DEL) that are commonly used in telecommunication documents. It may not be the same as an access line or a subscriber. This should include the active number of analog fixed telephone lines, ISDN channels, fixed wireless, public payphones and VoIP subscriptions. Active lines are those that have registered an activity in the past three months.\n\nData on fixed telephone lines are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators. Data are considered to be very reliable, timely, and complete.\n\nData for this indicator are readily available for approximately 90 percent of countries, either through ITU's World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator's website. For the rest, information can be aggregated through operators' data (mainly through annual reports) and complemented by market research reports.\n\nTelephone lines (per 100 people) indicator is derived by all telephone lines divided by the country's population and multiplied by 100. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): Data can be collected and aggregated at the country level by asking all licensed fixed-telephone line operators how many fixed-telephone subscriptions they have. This indicator can be divided by the population and multiplied by 100 to obtain fixed telephone subscriptions per 100 people." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "number of subscriptions*100/population" } ], "source_id": "19" }, { "id": "IT.NET.USER.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nToday's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains." }, { "id": "Generalcomments", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "IndicatorName", "value": "Internet users (per 100 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Statisticalconceptandmethodology", "value": "The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "19" }, { "id": "NE.GDI.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross capital formation (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on capital formation may be estimated from direct surveys of enterprises and administrative records or based on the commodity flow method using data from production, trade, and construction activities. The quality of data on government fixed capital formation depends on the quality of government accounting systems (which tend to be weak in developing countries). Measures of fixed capital formation by households and corporations - particularly capital outlays by small, unincorporated enterprises - are usually unreliable.\n\n\n\n\n\nEstimates of changes in inventories are rarely complete but usually include the most important activities or commodities. In some countries these estimates are derived as a composite residual along with household final consumption expenditure. According to national accounts conventions, adjustments should be made for appreciation of the value of inventory holdings due to price changes, but this is not always done. In highly inflationary economies this element can be substantial." }, { "id": "Longdefinition", "value": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "19" }, { "id": "NE.TRD.GNFS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Trade (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Trade is the sum of exports and imports of goods and services. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "19" }, { "id": "NY.AGR.SUBS.GD.ZS", "metatype": [ { "id": "IndicatorName", "value": "Agricultural support estimate (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Agriculture support is the annual monetary value of all gross transfers from taxpayers and consumers, both domestic and foreign (in the form of subsidies arising from policy measures that support agriculture), net of the associated budgetary receipts, regardless of their objectives and impacts on farm production and income, or consumption of farm products." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development, Producer and Consumer Support Estimates database. Available online at www.oecd.org/tad/support/psecse." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" } ], "source_id": "19" }, { "id": "NY.GNP.ATLS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI, Atlas method (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This figure is converted to U.S. dollars using the World Bank Atlas method. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Atlas GNI & GNI per capita" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "19" }, { "id": "NY.GNP.PCAP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita, Atlas method (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This figure is converted to U.S. dollars using the World Bank Atlas method, and divided by the midyear population. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank's official estimates of the size of economies and country classifications by income level are based on Gross National Income (GNI) per capita. For cross-national comparisons, estimates are converted from local currency units (LCU) to current U.S. dollars using the Atlas method, referring to a former World Bank publication called the Atlas of Global Development. The Atlas method smooths exchange rate fluctuations using a three-year moving average, price-adjusted conversion factor. The USD estimate of GNI per capita is derived by applying the Atlas conversion factor to estimates measured in LCU.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Atlas GNI & GNI per capita" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "19" }, { "id": "PA.NUS.PPP", "metatype": [ { "id": "Aggregationmethod", "value": "No aggregation provided for this indicator." }, { "id": "DataQuality", "value": "The International Comparison Program (ICP) conducts multiple rounds of validation at global, regional, and national levels in the process of producing benchmark PPP estimates. Please refer to its guidelines (“Operational Guidelines and Procedures for Measuring the Real Size of the World Economy”) for details of validation. https://www.worldbank.org/en/programs/icp/brief/2011-operational-guidelines" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nPPPs, PLIs, and the PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress. \n- Recommended uses of PPPs include: to make spatial comparisons of GDP and its expenditure components; to make spatial comparisons of price levels; and to group countries by their per capita volume indexes and price level indexes.\n- Recommended uses of PPPs with limitations include: to analyze changes over time in relative GDP per capita and relative prices; to analyze price convergence; to make spatial comparisons of the cost of living; and to use PPPs calculated for GDP and its expenditure components as deflators for other values." }, { "id": "IndicatorName", "value": "PPP conversion factor, GDP (LCU per international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global PPP estimates provided by ICP are produced by the ICP Global Office and regional implementing agencies, based on data supplied by the national implementing agencies in the participating economies, and in accordance with the methodology recommended by the ICP Technical Advisory Group and approved by the ICP Governing Board. As such, these results are not produced by participating economies as part of their national official statistics.\n\nPPPs are not recommended to be used as: a precise measure to establish strict rankings of countries; a means of constructing national growth rates; a measure to generate output and productivity comparisons by industry; an indicator of the undervaluation or overvaluation of currencies; and as an equilibrium exchange rate." }, { "id": "Longdefinition", "value": "The purchasing power parity (PPP) conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of gross domestic product (GDP) and its expenditure components. This conversion factor is for the level of GDP and the base currency is the US dollar." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, publisher: International Comparison Program, type: International statistical program, date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat, type: International statistical program;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-https://data-explorer.oecd.org/, publisher: OECD" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model.\n\nICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. Description of WDI extrapolation approach is available here: https://datahelpdesk.worldbank.org/knowledgebase/articles/665452-how-do-you-extrapolate-the-ppp-conversion-factors\n\nFor the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. For Eurostat-OECD PPP Programme, please refer to the following websites.\n(http://www.oecd.org/sdd/prices-ppp/)\n(https://ec.europa.eu/eurostat/web/purchasing-power-parities/overview)\n\nFor more information on the ICP and PPPs, please refer to the ICP website at https://www.worldbank.org/en/programs/icp.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. \n\nPPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. See https://www.worldbank.org/en/programs/icp/methodology." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Local currency unit per international dollar" } ], "source_id": "19" }, { "id": "PA.NUS.PRVT.PP", "metatype": [ { "id": "Aggregationmethod", "value": "No aggregation provided for this indicator." }, { "id": "DataQuality", "value": "The International Comparison Program (ICP) conducts multiple rounds of validation at global, regional, and national levels in the process of producing benchmark PPP estimates. Please refer to its guidelines (“Operational Guidelines and Procedures for Measuring the Real Size of the World Economy”) for details of validation. https://www.worldbank.org/en/programs/icp/brief/2011-operational-guidelines" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nPPPs, PLIs, and the PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress. \n- Recommended uses of PPPs include: to make spatial comparisons of GDP and its expenditure components; to make spatial comparisons of price levels; and to group countries by their per capita volume indexes and price level indexes.\n- Recommended uses of PPPs with limitations include: to analyze changes over time in relative GDP per capita and relative prices; to analyze price convergence; to make spatial comparisons of the cost of living; and to use PPPs calculated for GDP and its expenditure components as deflators for other values." }, { "id": "IndicatorName", "value": "PPP conversion factor, households and NPISHs Final consumption expenditure (LCU per international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global PPP estimates provided by ICP are produced by the ICP Global Office and regional implementing agencies, based on data supplied by the national implementing agencies in the participating economies, and in accordance with the methodology recommended by the ICP Technical Advisory Group and approved by the ICP Governing Board. As such, these results are not produced by participating economies as part of their national official statistics.\n\nPPPs are not recommended to be used as: a precise measure to establish strict rankings of countries; a means of constructing national growth rates; a measure to generate output and productivity comparisons by industry; an indicator of the undervaluation or overvaluation of currencies; and as an equilibrium exchange rate." }, { "id": "Longdefinition", "value": "The purchasing power parity (PPP) conversion factor is a currency conversion factor and a spatial price deflator. They convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of gross domestic product (GDP) and its expenditure components. This conversion factor is for households and NPISHs Final consumption expenditure and the base currency is the US dollar." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, publisher: International Comparison Program, type: International statistical program, date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat, type: International statistical program;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-https://data-explorer.oecd.org/, publisher: OECD" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model.\n\nICP-estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. Description of WDI extrapolation approach is available here: https://datahelpdesk.worldbank.org/knowledgebase/articles/665452-how-do-you-extrapolate-the-ppp-conversion-factors\n\nFor the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. For Eurostat-OECD PPP Programme, please refer to the following websites.\n(http://www.oecd.org/sdd/prices-ppp/)\n(https://ec.europa.eu/eurostat/web/purchasing-power-parities/overview)\n\nFor more information on the ICP and PPPs, please refer to the ICP website at https://www.worldbank.org/en/programs/icp.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. \n\nPPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. See https://www.worldbank.org/en/programs/icp/methodology." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Local currency unit per international dollar" } ], "source_id": "19" }, { "id": "SE.ADT.1524.LT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth female (% of females ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate youths divided by the total number of youths, excluding youths with unknown literacy status. \n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of females ages 15-24" } ], "source_id": "19" }, { "id": "SE.ADT.1524.LT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth male (% of males ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate youths divided by the total number of youths, excluding youths with unknown literacy status. \n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of males ages 15-24" } ], "source_id": "19" }, { "id": "SE.ADT.1524.LT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth total (% of people ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data is calculated by dividing the number of literate persons by the total number of persons in the same age group, excluding persons with unknown literacy status.\n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "Percent of people ages 15-24" } ], "source_id": "19" }, { "id": "SE.ADT.LITR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, adult total (% of people ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status. \n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of people ages 15 and above" } ], "source_id": "19" }, { "id": "SE.ENR.ORPH", "metatype": [ { "id": "IndicatorName", "value": "Ratio of school attendance of orphans to school attendance of non-orphans ages 10-14" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Ratio of school attendance of orphans to school attendance of non orphans is the ratio of school attendance of orphans to school attendance of non orphans ages 10-14." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys such as Demographic and Health Surveys (DHS) , Multiple Indicator Cluster Surveys (MICS), Reproductive Health Surveys (RHS) and AIDS Indicator Surveys (AIS), maintained in UNICEF Global Databases." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "19" }, { "id": "SE.ENR.PRIM.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "IndicatorName", "value": "School enrollment, primary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in primary education is the ratio of girls to boys enrolled at primary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in primary education by male gross enrollment ratio in primary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "19" }, { "id": "SE.ENR.PRSC.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "IndicatorName", "value": "School enrollment, primary and secondary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in primary and secondary education is the ratio of girls to boys enrolled at primary and secondary levels in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in primary and secondary education by male gross enrollment ratio in primary and secondary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "19" }, { "id": "SE.ENR.SECO.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "IndicatorName", "value": "School enrollment, secondary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in secondary education is the ratio of girls to boys enrolled at secondary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in secondary education by male gross enrollment ratio in secondary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "19" }, { "id": "SE.ENR.TERT.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Education is a basic human entitlement, and it is crucial that both girls and boys are afforded equal chances to learn. The Sustainable Development Goal (SDG) Target 4.5 focuses on eliminating gender disparities in education and ensuring equal access to all levels of education for both girls and boys. This target is part of a broader commitment to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all, as outlined in SDG 4. The pursuit of gender equality in education is not only a matter of fairness and equity but also has significant implications for economic development, empowerment, and the well-being of communities and nations." }, { "id": "IndicatorName", "value": "School enrollment, tertiary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The gross enrolment ratio is a general measure of participation in tertiary education. However, it does not account for variations in the duration of programs between countries or across different levels of education and fields of study. While it is somewhat standardized by measuring it relative to a 5-year age group for all countries, it may still underestimate participation, particularly in countries with underdeveloped tertiary education systems or where offerings are limited to initial tertiary programs, which are typically shorter than 5 years in duration." }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in tertiary education is the ratio of women to men enrolled at tertiary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in tertiary education by male gross enrollment ratio in tertiary education. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "ratio" } ], "source_id": "19" }, { "id": "SE.PRM.CMPT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education." }, { "id": "IndicatorName", "value": "Primary completion rate, total (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "19" }, { "id": "SE.PRM.NENR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, primary (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "19" }, { "id": "SE.PRM.PRSL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to last grade of primary, female (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to last grade of primary is the percentage of children enrolled in the first grade of primary school who eventually reach the last grade of primary education. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels. Survival rate to the last grade of primary education is of particular interest for monitoring universal primary education." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "19" }, { "id": "SE.PRM.PRSL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to last grade of primary, male (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to last grade of primary is the percentage of children enrolled in the first grade of primary school who eventually reach the last grade of primary education. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels. Survival rate to the last grade of primary education is of particular interest for monitoring universal primary education." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "19" }, { "id": "SE.PRM.PRSL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to last grade of primary, total (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to last grade of primary is the percentage of children enrolled in the first grade of primary school who eventually reach the last grade of primary education. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. \n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels. Survival rate to the last grade of primary education is of particular interest for monitoring universal primary education." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "19" }, { "id": "SE.PRM.TENR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Relevance to gender indicator: Women teachers are important as they serve as role models to girls and help to attract and retain girls in school." }, { "id": "IndicatorName", "value": "Adjusted net enrollment rate, primary (% of primary school age children)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Adjusted net enrollment rate in primary education is calculated by dividing the number of children in the official primary school age who are enrolled in primary or secondary education by the population of the same age group and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "19" }, { "id": "SG.GEN.PARL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite much progress in recent decades, gender inequalities remain pervasive in many dimensions of life - worldwide. But while disparities exist throughout the world, they are most prevalent in developing countries. Gender inequalities in the allocation of such resources as education, health care, nutrition, and political voice matter because of the strong association with well-being, productivity, and economic growth. These patterns of inequality begin at an early age, with boys routinely receiving a larger share of education and health spending than do girls, for example.\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen are vastly underrepresented in decision-making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.\n\n\n\n\n\n\n\n\n\n\n\n\n\nA strong and vibrant democracy is possible only when parliament is fully inclusive of the population it represents. Parliaments cannot consider themselves inclusive, however, until they can boast the full participation of women. This is not just about women's right to equality and their contribution to the conduct of public affairs, but also about using women's resources and potential to determine political and development priorities that benefit societies and the global community." }, { "id": "IndicatorName", "value": "Proportion of seats held by women in national parliaments (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The number of countries covered varies with suspensions or dissolutions of parliaments. There can be difficulties in obtaining information on by-election results and replacements due to death or resignation. These changes are ad hoc events which are more difficult to keep track of. By-elections, for instance, are often not announced internationally as general elections are. Parliaments vary considerably in their internal workings and procedures, however, generally legislate, oversee government and represent the electorate. In terms of measuring women's contribution to political decision making, this indicator may not be sufficient because some women may face obstacles in fully and efficiently carrying out their parliamentary mandate.\n\n\n\n\n\n\n\n\n\n\n\nThe data is compiled by the Inter-Parliamentary Union on the basis of information provided by National Parliaments. The percentages do not take into account the case of parliaments for which no data was available at that date. Information is available in all countries where a national legislature exists and therefore does not include parliaments that have been dissolved or suspended for an indefinite period." }, { "id": "Longdefinition", "value": "Women in parliaments are the percentage of parliamentary seats in a single or lower chamber held by women." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Women are vastly underrepresented in decision making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.\n\nThis is the Sustainable Development Goal indicator 5.5.1 (a). [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2025" }, { "id": "Source", "value": "Monthly ranking of women in national parliaments, Inter-Parliamentary Union (IPU), uri: https://data.ipu.org/women-ranking/, note: For the year of 1998, the data is as of August 10, 1998., type: Excel, date accessed: 2026-03-29" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of seats held by women in national parliaments is the number of seats held by women members in single or lower chambers of national parliaments, expressed as a percentage of all occupied seats; it is derived by dividing the total number of seats occupied by women by the total number of seats in parliament.\nStatistical concept(s): This indicator assesses the extent to which women are provided with equal opportunities to participate in parliamentary decision-making processes. It applies to the sole chamber of unicameral national parliaments and the lower chamber in the case of bicameral systems. The upper chamber in bicameral parliaments is not included in this measure. Parliamentary seats are typically occupied by individuals who are victorious in general elections, though they can also be acquired through nomination, appointment, indirect election, member rotation, or by-elections. The term 'seats' refers to the total count of parliamentary mandates or the total number of parliament members." }, { "id": "Topic", "value": "Gender: Public life & decision making" }, { "id": "Unitofmeasure", "value": "% of single or lower chamber seats" } ], "source_id": "19" }, { "id": "SH.CON.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "According to UNAIDS estimates, HIV incidence has fallen in many of the most severely affected countries because adolescents and young people are adopting safer sexual practices and more young people living with HIV are accessing treatment to lower their viral load. When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs)." }, { "id": "IndicatorName", "value": "Condom use, population ages 15-24, female (% of females ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Condom use, female is the percentage of the female population ages 15-24 who used a condom at last intercourse in the last 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2015" }, { "id": "Source", "value": "Demographic and Health Surveys;\nUNAIDS, Joint United Nations Programme on HIV/AIDS (UNAIDS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Household Surveys\nStatistical concept(s): When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "19" }, { "id": "SH.CON.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "According to UNAIDS estimates, HIV incidence has fallen in many of the most severely affected countries because adolescents and young people are adopting safer sexual practices and more young people living with HIV are accessing treatment to lower their viral load. When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs)." }, { "id": "IndicatorName", "value": "Condom use, population ages 15-24, male (% of males ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Condom use, male is the percentage of the male population ages 15-24 who used a condom at last intercourse in the last 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2014" }, { "id": "Source", "value": "Demographic and Health Surveys;\nUNAIDS, Joint United Nations Programme on HIV/AIDS (UNAIDS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Household Surveys\nStatistical concept(s): When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "19" }, { "id": "SH.CON.AIDS.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Condom use at last high-risk sex, adult female (% ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Condom use at last high-risk sex, female is the percentage of the female population ages 15-49 who used a condom at last intercourse with a non-marital and non-cohabiting sexual partner in the last 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys, and UNAIDS." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "19" }, { "id": "SH.CON.AIDS.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Condom use at last high-risk sex, adult male (% ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Condom use at last high-risk sex, male is the percentage of the male population ages 15-49 who used a condom at last intercourse with a non-marital and non-cohabiting sexual partner in the last 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys, and UNAIDS." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "19" }, { "id": "SH.DYN.AIDS.DH", "metatype": [ { "id": "IndicatorName", "value": "AIDS estimated deaths (UNAIDS estimates)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "AIDS deaths are the estimated number of adults and children who died due to AIDS-related causes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "19" }, { "id": "SH.DYN.AIDS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS." }, { "id": "IndicatorName", "value": "Prevalence of HIV, total (% of population ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\nStatistical concept(s): HIV prevalence rates reflect the rate of HIV infection in each country's population. Low national prevalence rates can be misleading, however. They often disguise epidemics that are initially concentrated in certain localities or population groups and threaten to spill over into the wider population. In many developing countries most new infections occur in young adults, with young women especially vulnerable." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "19" }, { "id": "SH.DYN.MORT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, under-5 (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is the Sustainable Development Goal indicator 3.2.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org, publisher: UNICEF, WHO, World Bank, United Nations Population Division;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "19" }, { "id": "SH.H2O.SAFE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities.\n\nLack of access to adequate water contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and include diarrhea, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improvement of access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity.\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families.\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of improved drinking water include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "Improved water source (% of population with access)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Please note that the data for this indicator have not been updated since 2015. The WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation has introduced updated water and sanitation indicators. For the most recent data on water access, please see the following indicators: People using safely managed drinking water services (% of population) (SH.H2O.SMDW.ZS) and People using basic drinking water services (% of population) (SH.H2O.BASW.ZS).\n\nThe data on access to an improved water source measure the percentage of the population with ready access to water for domestic purposes.\n\nAccess to drinking water from an improved source does not ensure that the water is safe or adequate, as these characteristics are not tested at the time of survey. But improved drinking water technologies are more likely than those characterized as unimproved to provide safe drinking water and to prevent contact with human excreta. While information on access to an improved water source is widely used, it is extremely subjective, and such terms as safe, improved, adequate, and reasonable may have different meanings in different countries despite official WHO definitions (see Definitions). Even in high-income countries treated water may not always be safe to drink. Access to an improved water source is equated with connection to a supply system; it does not take into account variations in the quality and cost (broadly defined) of the service." }, { "id": "Longdefinition", "value": "Access to an improved water source refers to the percentage of the population using an improved drinking water source. The improved drinking water source includes piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), and other improved drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "The data are derived by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on national censuses and nationally representative household surveys. The coverage rates for water and sanitation are based on information from service users on the facilities their households actually use rather than on information from service providers, which may include nonfunctioning systems.\n\nWHO/UNICEF define an improved drinking-water source as one that, by nature of its construction or through active intervention, is protected from outside contamination, in particular from contamination with fecal matter. Improved water sources include piped water into dwelling, plot or yard; piped water into neighbor's plot; public tap/standpipe; tube well/borehole; protected dug well; protected spring; and rainwater." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "19" }, { "id": "SH.HIV.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Prevalence of HIV, female (% ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV, female is the percentage of females who are infected with HIV. Youth rates are as a percentage of the relevant age group." }, { "id": "Othernotes", "value": "In many developing countries most new infections occur in young adults, with young women especially vulnerable." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "19" }, { "id": "SH.HIV.1524.KW.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Comprehensive correct knowledge of HIV/AIDS, ages 15-24, female (2 prevent ways and reject 3 misconceptions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percent of female respondents ages 15-24 who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can have HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "19" }, { "id": "SH.HIV.1524.KW.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Comprehensive correct knowledge of HIV/AIDS, ages 15-24, male (2 prevent ways and reject 3 misconceptions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percent of male respondents ages 15-24 who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can have HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "19" }, { "id": "SH.HIV.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Prevalence of HIV, male (% ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV, male is the percentage of males who are infected with HIV. Youth rates are as a percentage of the relevant age group." }, { "id": "Othernotes", "value": "In many developing countries most new infections occur in young adults, with young women being especially vulnerable." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "19" }, { "id": "SH.HIV.ARTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Antiretroviral therapy (ART) is central to the global HIV response and has significantly improved both survival and quality of life for people living with HIV. Effective ART suppresses viral load to undetectable levels, preventing progression to AIDS. When viral load remains undetectable, HIV is not sexually transmitted to HIV-negative partners. \n\nDespite this progress, gaps in treatment persist. Nearly 10 million people living with HIV are not receiving ART, and according to UNAIDS, about half of them reside in Africa. Expanding access to ART remains essential for reducing HIV-related morbidity and mortality and for achieving global targets for ending AIDS as a public health threat. (Reference: https://www.unaids.org/sites/default/files/2025-07/2025-global-aids-update-JC3153_en.pdf)" }, { "id": "IndicatorName", "value": "Antiretroviral therapy coverage (% of people living with HIV)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Antiretroviral therapy coverage indicates the percentage of all people living with HIV who are receiving antiretroviral therapy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "19" }, { "id": "SH.HIV.KNOW.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Comprehensive correct knowledge of HIV/AIDS, ages 15-49, female (2 prevent ways and reject 3 misconceptions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Knowledge of HIV, female, is the percentage of female respondents who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can have HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "19" }, { "id": "SH.HIV.KNOW.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Comprehensive correct knowledge of HIV/AIDS, ages 15-49, male (2 prevent ways and reject 3 misconceptions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Knowledge of HIV, male, is the percentage of male respondents who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can have HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "19" }, { "id": "SH.HIV.ORPH", "metatype": [ { "id": "IndicatorName", "value": "Children orphaned by HIV/AIDS" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of children orphaned by HIV/AIDS is the estimated number of children who have lost their mother or both parents to AIDS before age 15 since the epidemic began. Some of the orphaned children included in this cumulative total are no longer alive; others are no longer under age 15." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "19" }, { "id": "SH.IMM.MEAS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, measles (% of children ages 12-23 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization, measles, measures the percentage of children ages 12-23 months who received the measles vaccination before 12 months or at any time before the survey. A child is considered adequately immunized against measles after receiving one dose of vaccine." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2024" }, { "id": "Source", "value": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF), uri: https://data.unicef.org/topic/child-health/immunization/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year. Notes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "19" }, { "id": "SH.MLR.NETS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Use of insecticide-treated bed nets (% of under-5 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Use of insecticide-treated bed nets refers to the percentage of children under age five who slept under an insecticide-treated bednet to prevent malaria." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Malaria is endemic to the poorest countries in the world, mainly in tropical and subtropical regions of Africa, Asia, and the Americas. Insecticide-treated nets, properly used and maintained, are one of the most important malaria-preventive strategies to limit human-mosquito contact." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "19" }, { "id": "SH.MLR.TRET.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Children with fever receiving antimalarial drugs (% of children under age 5 with fever)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Malaria treatment refers to the percentage of children under age five who were ill with fever in the last two weeks and received any appropriate (locally defined) anti-malarial drugs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Malaria is endemic to the poorest countries in the world, mainly in tropical and subtropical regions of Africa, Asia, and the Americas. Prompt and effective treatment of malaria is a critical element of malaria control. It is vital that sufferers, especially children under age 5, start treatment within 24 hours of the onset of symptoms, to prevent progression - often rapid - to severe malaria and death. Data on malaria are from national-level surveys, including Multiple Indicator Cluster Surveys, Demographic and Health Surveys, and Malaria Indicator Surveys." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "19" }, { "id": "SH.STA.ACSN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child.\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Improved sanitation can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria.\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements.\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation are rarely aware of either the origin of their ills, or the true costs of their deficit. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world.\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffers as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "Improved sanitation facilities (% of population with access)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Please note that the data for this indicator have not been updated since 2015. The WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation has introduced updated water and sanitation indicators. For the most recent data on access to sanitation facilities, please see the following indicators: People using safely managed sanitation services (% of population) (SH.STA.SMSS.ZS) and People using basic sanitation services (% of population) (SH.STA.BASS.ZS).\n\nThe data are derived by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on national censuses and nationally representative household surveys. The coverage rates for sanitation are based on information from service users on the facilities their households actually use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Access to improved sanitation facilities refers to the percentage of the population using improved sanitation facilities. Improved sanitation facilities are likely to ensure hygienic separation of human excreta from human contact. They include flush/pour flush (to piped sewer system, septic tank, pit latrine), ventilated improved pit (VIP) latrine, pit latrine with slab, and composting toilet." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on access to sanitation are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on national censuses and nationally representative household surveys. The coverage rates for water and sanitation are based on information from service users on the facilities their households actually use rather than on information from service providers, which may include nonfunctioning systems.\n\nAn improved sanitation facility is defined as one that hygienically separates human excreta from human contact. Improved sanitation facilities range from simple but protected pit latrines to flush toilets with a sewerage connection. To be effective, facilities must be correctly constructed and properly maintained." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "19" }, { "id": "SH.STA.ANV4.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Good prenatal and postnatal care improve maternal health and reduce maternal and infant mortality." }, { "id": "IndicatorName", "value": "Pregnant women receiving prenatal care of at least four visits (% of pregnant women)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources." }, { "id": "Longdefinition", "value": "Pregnant women receiving prenatal care, at least four times, are the percentage of women attended at least four times during pregnancy by skilled health personnel for reasons related to pregnancy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\nGood prenatal and postnatal care improves maternal health and reduces maternal and infant mortality. However, indicators on use of antenatal care services provide no information on the content or quality of the services. Data on antenatal care are obtained mostly from household surveys, which ask women who have had a live birth whether and from whom they received antenatal care." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "19" }, { "id": "SH.STA.ANVC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Pregnant women receiving prenatal care (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources." }, { "id": "Longdefinition", "value": "Pregnant women receiving prenatal care are the percentage of women attended at least once during pregnancy by skilled health personnel for reasons related to pregnancy." }, { "id": "Othernotes", "value": "Good prenatal and postnatal care improve maternal health and reduce maternal and infant mortality." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nGood prenatal and postnatal care improves maternal health and reduces maternal and infant mortality. However, indicators on use of antenatal care services provide no information on the content or quality of the services. Data on antenatal care are obtained mostly from household surveys, which ask women who have had a live birth whether and from whom they received antenatal care." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "19" }, { "id": "SH.STA.ARIC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "ARI treatment (% of children under 5 taken to a health provider)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Children with acute respiratory infection (ARI) who are taken to a health provider refers to the percentage of children under age five with ARI in the last two weeks who were taken to an appropriate health provider, including hospital, health center, dispensary, village health worker, clinic, and private physician." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Acute respiratory infection continues to be a leading cause of death among young children. Data are drawn mostly from household health surveys in which mothers report on number of episodes and treatment for acute respiratory infection." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "19" }, { "id": "SH.STA.BRTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\n\n\nThe share of births attended by skilled health staff is an indicator of a health system's ability to provide adequate care for pregnant women." }, { "id": "IndicatorName", "value": "Births attended by skilled health staff (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources." }, { "id": "Longdefinition", "value": "Births attended by skilled health staff are the percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliveries on their own; and to care for newborns." }, { "id": "Othernotes", "value": "Assistance by trained professionals during birth reduces the incidence of maternal deaths during childbirth. The share of births attended by skilled health staff is an indicator of a health system’s ability to provide adequate care for pregnant women.\n\nThis is the Sustainable Development Goal indicator 3.1.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2022" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National-level household surveys are the primary sources for collecting data on skilled health personnel providing childbirth care. These surveys include Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), Reproductive Health Surveys (RHS), and other national surveys based on similar methodologies. Respondents in these surveys are asked about their last live birth and who assisted during delivery, covering a period of up to five years before the interview.\n\n\nAs part of the data harmonization process and interaction with countries, UNICEF conducts an annual country consultation. During this consultation, SDG country focal points are contacted to update and verify values included in the database and to obtain new data sources. These new data sources are reviewed and assessed jointly with WHO. Additionally, the national categories or occupational titles of skilled health personnel are verified. The reported data for some countries may include additional categories of trained personnel beyond doctors, nurses, and midwives." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Percent of live births" } ], "source_id": "19" }, { "id": "SH.STA.MALN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of underweight, weight for age (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of underweight children is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child underweight belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "19" }, { "id": "SH.STA.MALR", "metatype": [ { "id": "IndicatorName", "value": "Malaria cases reported" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Reported cases of malaria are the number of confirmed cases of malaria (confirmed by slide examination or RDT)." }, { "id": "Notesfromoriginalsource", "value": "In endemic countries where health information system is weak and diagnosis is limited, national malaria control programmes (NMCPs) often collect data on the number of suspected cases, those tested and those confirmed. Probable or unconfirmed cases are calculated by subtracting the number tested from the number suspected." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, World malaria report and Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/ghodata/). WHO compiles data on reported cases of malaria, submitted by the national malaria control programmes (NMCPs)." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "19" }, { "id": "SH.STA.MMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Maternal mortality ratio (modeled estimate, per 100,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality." }, { "id": "Longdefinition", "value": "Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs)." }, { "id": "Othernotes", "value": "This indicator represents the risk associated with each pregnancy and is also a Sustainable Development Goal Indicator (3.1.1) for monitoring maternal health." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO), uri: https://www.who.int/news/item/23-02-2023-a-woman-dies-every-two-minutes-due-to-pregnancy-or-childbirth--un-agencies;\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Per 100,000 live births" } ], "source_id": "19" }, { "id": "SH.TBS.CURE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tuberculosis (TB) is a preventable and usually curable disease. Yet TB is one of the world’s leading causes of death from a single infectious agent. Millions of people continue to fall ill with TB every year." }, { "id": "IndicatorName", "value": "Tuberculosis treatment success rate (% of new cases)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Tuberculosis treatment success rate is the percentage of all new tuberculosis cases (or new and relapse cases for some countries) registered under a national tuberculosis control programme in a given year that successfully completed treatment, with or without bacteriological evidence of success (\"cured\" and \"treatment completed\" respectively)." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Global Tuberculosis Report, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of cases registered in a given year (excluding cases placed on a second-line drug regimen) that successfully completed treatment without bacteriological evidence of failure. All registered cases fall into one of the following five mutually exclusive categories: treatment success, failure, death, lost to follow-up, not evaluated (missing data on the outcome of treatment).\nStatistical concept(s): Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. Data on the success rate of tuberculosis treatment are provided for countries that have submitted data to the WHO. The treatment success rate for tuberculosis provides a useful indicator of the quality of health services. A low rate suggests that infectious patients may not be receiving adequate treatment. An important complement to the tuberculosis treatment success rate is the case detection rate, which indicates whether there is adequate coverage by the recommended case detection and treatment strategy." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "19" }, { "id": "SH.TBS.DTEC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tuberculosis (TB) is a preventable and usually curable disease. Yet TB is one of the world’s leading causes of death from a single infectious agent. Millions of people continue to fall ill with TB every year." }, { "id": "IndicatorName", "value": "Tuberculosis case detection rate (%, all forms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Tuberculosis case detection rate (all forms) is the number of new and relapse tuberculosis cases notified to WHO in a given year, divided by WHO's estimate of the number of incident tuberculosis cases for the same year, expressed as a percentage. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Tuberculosis Report, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of new and relapse TB cases diagnosed and treated in national TB control programmes and notified to WHO, divided by WHO's estimate of the number of incident TB cases for the same year, expressed as a percentage.\nStatistical concept(s): Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. This indicator shows the tuberculosis detection rate for all detection methods. Editions before 2010 included the tuberculosis detection rates by DOTS, the internationally recommended strategy for tuberculosis control. Thus data on the case detection rate from 2010 onward cannot be compared with data in previous editions." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "19" }, { "id": "SH.TBS.INCD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tuberculosis (TB) is a preventable and usually curable disease. Yet TB is one of the world’s leading causes of death from a single infectious agent. Millions of people continue to fall ill with TB every year." }, { "id": "IndicatorName", "value": "Incidence of tuberculosis (per 100,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nUncertainty bounds for the incidence are available at http://data.worldbank.org" }, { "id": "Longdefinition", "value": "Incidence of tuberculosis is the estimated number of new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population. All forms of TB are included, including cases in people living with HIV. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization.\n\nThis is the Sustainable Development Goal indicator 3.3.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Tuberculosis Report, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of TB incidence are produced through a consultative and analytical process led by WHO and are published annually. These estimates are based on annual case notifications, assessments of the quality and coverage of TB notification data, national surveys of the prevalence of TB disease and on information from death (vital) registration systems.\nStatistical concept(s): Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. In developed countries tuberculosis has reemerged largely as a result of cases among immigrants. Since tuberculosis incidence cannot be directly measured, estimates are obtained by eliciting expert opinion or are derived from measurements of prevalence or mortality." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Per 100,000 people" } ], "source_id": "19" }, { "id": "SH.TBS.MORT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization." }, { "id": "IndicatorName", "value": "Tuberculosis death rate (per 100,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Tuberculosis death rate is the estimated number of deaths from tuberculosis among HIV-negative people, expressed as the rate per 100,000 population. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously." }, { "id": "Notesfromoriginalsource", "value": "Estimates are presented with uncertainty intervals (see footnote). When ranges are presented, the lower and higher numbers correspond to the 2.5th and 97.5th centiles of the outcome distributions (generally produced by simulations). For more detailed information, see the original source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Tuberculosis Report." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "19" }, { "id": "SI.DST.FRST.20", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Income share held by lowest 20%" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the bottom quintile, meaning the bottom 20% of people when ranked from lowest to highest income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "19" }, { "id": "SI.POV.DDAY", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. The World Bank defines extreme poverty as living on less than $3.00 a day (adjusted for purchasing power differences across countries). The value of $3.00 is the typical poverty line of low-income countries, which is the minimum amount of money people in low-income countries need to cover their daily basic needs, including food, clothing, and shelter. The share of population living on less than $3.00 a day is the first indicator the World Bank tracks in its Bank’s expanded vision indicators to create a world free of poverty in a livable planet. It is also the indicator the UN tracks for SDG 1.1. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at $3.00 a day (2021 PPP) (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty headcount ratio at $3.00 a day is the percentage of the population living on less than $3.00 a day at 2021 purchasing power adjusted prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\n\n\n\n\n\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.\n\n\n\n\n\n\n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\n\n\n\n\n\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): Poverty headcount ratio at $3.00 a day refers to the percentage of a population whose consumption or income per day falls short of the international poverty line of $3.00 a day (adjusted for purchasing power parity differences across countries), the poverty line typical of low-income countries." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "19" }, { "id": "SI.POV.GAPS", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The poverty gap measures the depth of poverty—that is, how far below the poverty line extreme poor are living. The poverty gap measure is used to estimate the total value of monetary transfers that could lift the poor out of poverty, assuming poverty is transitory and there are no administrative costs of transfers." }, { "id": "IndicatorName", "value": "Poverty gap at $3.00 a day (2021 PPP) (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty gap at $3.00 a day (2021 PPP) is the mean shortfall in income or consumption from the poverty line $3.00 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.Since World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in June 2025, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.Early editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, 2011, 2021, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the median of the poverty lines found in 23 low-income countries. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.The statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): The poverty gap measures the average shortfall in income or consumption of individuals living below the international poverty line of $3.00 per day, adjusted to 2021 purchasing power parity (PPP), expressed as a percentage of that poverty line. It essentially measures the depth of poverty—that is, how far the extreme poor are living below the poverty line." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "19" }, { "id": "SI.POV.NAGP", "metatype": [ { "id": "Generalcomments", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "IndicatorName", "value": "Poverty gap at national poverty lines (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Poverty gap at national poverty lines is the mean shortfall from the poverty lines (counting the nonpoor as having zero shortfall) as a percentage of the poverty lines. This measure reflects the depth of poverty as well as its incidence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Poverty headcount ratio among the population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income.\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies.\n\nAlmost all national poverty lines are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. The data is based on the two most recent years for which survey data are available.\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected." }, { "id": "Topic", "value": "Poverty: Poverty rates" } ], "source_id": "19" }, { "id": "SI.POV.NAHC", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The poverty rate as defined by national poverty lines reflects the share of the population that fails to meet the standard a country thinks is necessary to cover basic needs (typically in low- and middle-income countries) or afford a decent lifestyle (typically in high-income countries). SDG 1.2 aims to reduce by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions, by 2030." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at national poverty lines (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "National poverty headcount ratio is the percentage of the population living below the national poverty line(s). National estimates are based on population-weighted subgroup estimates from household surveys. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year." }, { "id": "Othernotes", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines., World Bank (WB), note: Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Poverty headcount ratio among the population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\n\n\n\n\n\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income. \n\n\n\n\n\n\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies. \n\n\n\n\n\n\n\nAlmost all national poverty lines in developing economies are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. \n\n\n\n\n\n\n\nThis series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year. For all other economies, the year reported is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which data collection started.\nStatistical concept(s): National poverty headcount ratio refers to the percentage of a population whose consumption or income per day falls short of the national poverty line. National poverty lines vary by country and over time. In low- and middle-income countries, national poverty lines tend to be absolute poverty lines, thus reflecting the estimated minimum amount of money needed to cover basic needs. In high-income countries, national poverty lines tend to be relative poverty lines, thus reflecting the typical amount of money needed for an individual to afford the typical standard of living and without any restraints to participating fully in the societies in which they live. National poverty lines tend to grow with economic growth, especially in high-income or upper-middle-income countries." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "19" }, { "id": "SI.POV.RUGP", "metatype": [ { "id": "Generalcomments", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "IndicatorName", "value": "Rural poverty gap at national poverty lines (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Rural poverty gap at national poverty lines is the rural population's mean shortfall from the poverty lines (counting the nonpoor as having zero shortfall) as a percentage of the poverty lines. This measure reflects the depth of poverty as well as its incidence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Poverty headcount ratio among the rural population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income.\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies.\n\nAlmost all national poverty lines are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. The data is based on the two most recent years for which survey data are available.\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected." }, { "id": "Topic", "value": "Poverty: Poverty rates" } ], "source_id": "19" }, { "id": "SI.POV.RUHC", "metatype": [ { "id": "Generalcomments", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "IndicatorName", "value": "Rural poverty headcount ratio at national poverty lines (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Rural poverty headcount ratio is the percentage of the rural population living below the national poverty lines." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Poverty headcount ratio among the rural population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income.\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies.\n\nAlmost all national poverty lines are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. The data is based on the two most recent years for which survey data are available.\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected." }, { "id": "Topic", "value": "Poverty: Poverty rates" } ], "source_id": "19" }, { "id": "SI.POV.URGP", "metatype": [ { "id": "Generalcomments", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "IndicatorName", "value": "Urban poverty gap at national poverty lines (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Urban poverty gap at national poverty lines is the urban population's mean shortfall from the poverty lines (counting the nonpoor as having zero shortfall) as a percentage of the poverty lines. This measure reflects the depth of poverty as well as its incidence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Poverty headcount ratio among the urban population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income.\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies.\n\nAlmost all national poverty lines are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. The data is based on the two most recent years for which survey data are available.\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected." }, { "id": "Topic", "value": "Poverty: Poverty rates" } ], "source_id": "19" }, { "id": "SI.POV.URHC", "metatype": [ { "id": "Generalcomments", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "IndicatorName", "value": "Urban poverty headcount ratio at national poverty lines (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Urban poverty headcount ratio is the percentage of the urban population living below the national poverty lines." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Poverty headcount ratio among the urban population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income.\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies.\n\nAlmost all national poverty lines are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. The data is based on the two most recent years for which survey data are available.\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected." }, { "id": "Topic", "value": "Poverty: Poverty rates" } ], "source_id": "19" }, { "id": "SL.EMP.1524.SP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, female (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female population ages 15-24" } ], "source_id": "19" }, { "id": "SL.EMP.1524.SP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, male (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male population ages 15-24" } ], "source_id": "19" }, { "id": "SL.EMP.1524.SP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, total (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-24" } ], "source_id": "19" }, { "id": "SL.EMP.INSV.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Data on women in wage employment in the nonagricultural sector show the extent to which women have access to paid employment - which affects their integration into the monetary economy - and indicate the degree to which labor markets are open to women in industry and services - which affects not only equal employment opportunity for women, but also economic efficiency through flexibility of the labor market and the economy's capacity to adapt to changes over time.\n\nIn many developing countries nonagricultural wage employment accounts for only a small portion of total employment. As a result, the contribution of women to the national economy is underestimated and therefore misrepresented. The indicator is difficult to interpret without additional information on the share of women in total employment, which allows an assessment to be made of whether women are under- or overrepresented in nonagricultural wage employment. The indicator does not reveal differences in the quality of nonagricultural wage employment in terms of earnings, work conditions, or legal and social protection. The indicator also does not reflect whether women reap the economic benefits of such employment. Finally, female employment and the employment share of the agricultural sector for both men and women tend to be underreported.\n\nWomen's wage work is important for economic growth and the well-being of families. But women often face such obstacles as restricted access to credit markets, capital, land, and training and education; time constraints due to traditional family responsibilities; and labor market bias and discrimination. These obstacles force women to limit their full participation in paid economic activities, to be less productive, and to receive lower wages." }, { "id": "Generalcomments", "value": "Relevance to gender indicator: Women’s share in paid employment in the nonagricultural sector has risen marginally in some regions but remains less than 20 percent in South Asia and Sub-Saharan Africa. Women are also clearly segregated in sectors that are generally known to be lower paid. And in the sectors where women dominate, such as health care, women rarely hold upper-level management jobs." }, { "id": "IndicatorName", "value": "Share of women in wage employment in the nonagricultural sector (% of total nonagricultural employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working." }, { "id": "Longdefinition", "value": "Share of women in wage employment in the nonagricultural sector is the share of female workers in wage employment in the nonagricultural sector (industry and services), expressed as a percentage of total employment in the nonagricultural sector. Industry includes mining and quarrying (including oil production), manufacturing, construction, electricity, gas, and water, corresponding to divisions 2-5 (ISIC revision 2) or tabulation categories C-F (ISIC revision 3). Services include wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services-corresponding to divisions 6-9 (ISIC revision 2) or tabulation categories G-Q (ISIC revision 3)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization." }, { "id": "Statisticalconceptandmethodology", "value": "Employment is defined as persons above a specified age who performed any work at all, in the reference period, for pay or profit (or pay in kind), or were temporarily absent from a job for such reasons as illness, maternity or parental leave, holiday, training or industrial dispute. Unpaid family workers who work for at least one hour should be included in the count of employment, although many countries use a higher hour limit in their definition.\n\nLabor force statistics by gender is important to monitor gender disparities in employment patterns. Estimates of women in the labor force and employment are generally lower than those of men and are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "19" }, { "id": "SL.EMP.SELF.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Self-employed, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include sub-categories of employers, own-account workers and members of producers' cooperatives and contributing family workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of self-employed workers to the total employed is calculated as follows: Self-employed workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "19" }, { "id": "SL.EMP.SELF.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Self-employed, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include sub-categories of employers, own-account workers and members of producers' cooperatives and contributing family workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of self-employed workers to the total employed is calculated as follows: Self-employed workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "19" }, { "id": "SL.EMP.SELF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Self-employed, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include sub-categories of employers, own-account workers and members of producers' cooperatives and contributing family workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of self-employed workers to the total employed is calculated as follows: Self-employed workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "19" }, { "id": "SL.EMP.TOTL.SP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, female (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female population ages 15+" } ], "source_id": "19" }, { "id": "SL.EMP.TOTL.SP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, male (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male population ages 15+" } ], "source_id": "19" }, { "id": "SL.EMP.TOTL.SP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, total (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total population ages 15+" } ], "source_id": "19" }, { "id": "SL.EMP.VULN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks. The vulnerable employment rate, which is the share of vulnerable employment in total employment, was an indicator of the (now finished) Millennium Development Goals, under the employment, target on decent work." }, { "id": "IndicatorName", "value": "Vulnerable employment, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of vulnerable employment to the total employed is calculated as follows: (Contributing family workers + own-account workers)/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "19" }, { "id": "SL.EMP.VULN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks. The vulnerable employment rate, which is the share of vulnerable employment in total employment, was an indicator of the (now finished) Millennium Development Goals, under the employment, target on decent work." }, { "id": "IndicatorName", "value": "Vulnerable employment, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of vulnerable employment to the total employed is calculated as follows: (Contributing family workers + own-account workers)/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "19" }, { "id": "SL.EMP.VULN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks. The vulnerable employment rate, which is the share of vulnerable employment in total employment, was an indicator of the (now finished) Millennium Development Goals, under the employment, target on decent work." }, { "id": "IndicatorName", "value": "Vulnerable employment, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of vulnerable employment to the total employed is calculated as follows: (Contributing family workers + own-account workers)/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "19" }, { "id": "SL.FAM.WORK.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Contributing family workers, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of contributing family workers to the total employed is calculated as follows: Contributing family workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "19" }, { "id": "SL.FAM.WORK.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Contributing family workers, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of contributing family workers to the total employed is calculated as follows: Contributing family workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "19" }, { "id": "SL.FAM.WORK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Contributing family workers, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of contributing family workers to the total employed is calculated as follows: Contributing family workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "19" }, { "id": "SL.GDP.PCAP.EM.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Labor productivity is used to assess a country's economic ability to create and sustain decent employment opportunities with fair and equitable remuneration. Productivity increases obtained through investment, trade, technological progress, or changes in work organization can increase social protection and reduce poverty, which in turn reduce vulnerable employment and working poverty. Productivity increases do not guarantee these improvements, but without them - and the economic growth they bring - improvements are highly unlikely.\n\n\n\nGDP per person employed is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.2.1]" }, { "id": "IndicatorName", "value": "GDP per person employed (constant 2021 PPP $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output in both national currencies and purchasing power parity dollars is not easily available, especially in developing countries, because the definition, coverage, and methodology are not always consistent across countries. For example, countries employ different methodologies for estimating the missing values for the nonmarket service sectors and use different definitions of the informal sector." }, { "id": "Longdefinition", "value": "GDP per person employed is gross domestic product (GDP) divided by total employment in the economy. Purchasing power parity (PPP) GDP is GDP converted to 2021 constant international dollars using PPP rates. An international dollar has the same purchasing power over GDP that a U.S. dollar has in the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB), note: Estimates are based on employment, population, GDP, and PPP data obtained from International Labour Organization, United Nations Population Division, Eurostat, OECD, and World Bank., type: estimates based on external database;\nInternational Labour Organization (ILO);\nUnited Nations (UN), publisher: UN Population Division;\nEurostat (ESTAT);\nOrganisation for Economic Co-operation and Development (OECD);\nWorld Development Indicators database, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are based on employment, population, GDP, and PPP data obtained from International Labour Organization, United Nations Population Division, Eurostat, OECD, and World Bank. The employment rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): GDP per person employed represents labor productivity—output per unit of labor input. To compare labor productivity levels across countries, GDP is converted to international dollars using purchasing power parity rates which take account of differences in relative prices between countries." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "constant 2017 PPP $" } ], "source_id": "19" }, { "id": "SN.ITK.DEFC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good nutrition is the cornerstone for survival, health and development. Well-nourished children perform better in school, grow into healthy adults and in turn give their children a better start in life. Well-nourished women face fewer risks during pregnancy and childbirth, and their children set off on firmer developmental paths, both physically and mentally (UNICEF www.childinfo.org)." }, { "id": "IndicatorName", "value": "Prevalence of undernourishment (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "From a policy and program standpoint, this measure has its limits. First, food insecurity exists even where food availability is not a problem because of inadequate access of poor households to food. Second, food insecurity is an individual or household phenomenon, and the average food available to each person, even corrected for possible effects of low income, is not a good predictor of food insecurity among the population. And third, nutrition security is determined not only by food security but also by the quality of care of mothers and children and the quality of the household's health environment (Smith and Haddad 2000)." }, { "id": "Longdefinition", "value": "Prevalence of undernourishments is the percentage of the population whose habitual food consumption is insufficient to provide the dietary energy levels that are required to maintain a normal active and healthy life. Data showing as 2.5 may signify a prevalence of undernourishment below 2.5%." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 2.1.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "Food and Agriculture Organization of the United Nations (FAO), uri: http://www.fao.org/faostat/en/#home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on undernourishment are from the Food and Agriculture Organization (FAO) of the United Nations and measure food deprivation based on average food available for human consumption per person, the level of inequality in access to food, and the minimum calories required for an average person.\nStatistical concept(s): Data on undernourishment are from the Food and Agriculture Organization (FAO) of the United Nations and measure food deprivation based on average food available for human consumption per person, the level of inequality in access to food, and the minimum calories required for an average person." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "19" }, { "id": "SP.ADO.TFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Adolescent childbearing is associated with a wide range of risks for young mothers. Women who become pregnant and give birth very early in their lives as well as their newborns are subject to elevated health risks." }, { "id": "IndicatorName", "value": "Adolescent fertility rate (births per 1,000 women ages 15-19)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adolescent fertility rate is the number of births per 1,000 women ages 15-19." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.7.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Adolescent fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without vital registration systems fertility rates are generally based on censuses or surveys.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Per 1000 women" } ], "source_id": "19" }, { "id": "SP.DYN.CONU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Contraceptive prevalence among women of reproductive age is related to maternal and child health, as well as gender equality and HIV/AIDS. Contraceptives enable women and men to make informed decisions on family planning – whether, when, and how many children they would have. \n\n\n\nPreventing unwanted pregnancies is essential to reducing maternal deaths, especially in low- and middle- income countries where maternal mortality rate is high. With effective contraception, life-threatening pregnancy complications can be reduced, and thus maternal deaths can be averted. \n\n\n\nUsing condoms (one of the modern contraceptive methods) can prevent pregnancy as well as sexually transmitted diseases, including HIV." }, { "id": "IndicatorName", "value": "Contraceptive prevalence, any method (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While the data availability on contraceptive use has increased, in many countries the contraceptive use data are available only for married women. \n\n\n\nThe time frame used to assess contraceptive prevalence may vary. In many surveys, it is left to the respondent to determine what is meant by “currently using” a method of contraception." }, { "id": "Longdefinition", "value": "Contraceptive prevalence, any method is the percentage of married women ages 15-49 who are practicing, or whose sexual partners are practicing, any method of contraception (modern or traditional). Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception. Traditional methods of contraception include rhythm (e.g., fertility awareness based methods, periodic abstinence), withdrawal and other traditional methods." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Contraceptive prevalence rates are obtained mainly from nationally representative household surveys, including: Demographic and Health Surveys; Multiple Indicator Cluster Surveys; Contraceptive Prevalence Surveys; Gender and Generations Survey; Reproductive Health Surveys; and World Fertility Surveys. Additional information was provided by other international survey programs and national surveys. \n\n\n\nMarried women refer to women who are married (defined in relation to the marriage laws or customs of a country) and to women in a union, which refers to women living with their partner in the same household (also referred to as cohabiting unions, consensual unions, unmarried unions, or “living together”)." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "19" }, { "id": "SP.DYN.IMRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, infant (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "19" }, { "id": "SP.DYN.LE00.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, total (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), uri: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices, note: Derived from male and female life expectancy at birth from sources such as statistical databases and publications from national statistical offices.;\nDemographic Statistics, Eurostat (ESTAT), note: Derived from male and female life expectancy at birth from sources such as Eurostat: Demographic Statistics." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates, or derived from male and female life expectancy at birth.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "19" }, { "id": "SP.DYN.TFRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries." }, { "id": "IndicatorName", "value": "Fertility rate, total (births per woman)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year." }, { "id": "Othernotes", "value": "Relevance to gender indicator: it can indicate the status of women within households and a woman’s decision about the number and spacing of children." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Total fertility rate is the sum of the age-specific fertility rates (multiplied by five, if the age-specific fertility rates are for 5-year age groups).\nStatistical concept(s): Total fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without reliable vital registration systems fertility rates are generally based on extrapolations from trends observed in censuses or surveys from earlier years." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Births per woman" } ], "source_id": "19" }, { "id": "SP.POP.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Increases in human population, whether as a result of immigration or more births than deaths, can impact natural resources and social infrastructure. This can place pressure on a country's sustainability. A significant growth in population will negatively impact the availability of land for agricultural production, and will aggravate demand for food, energy, water, social services, and infrastructure. On the other hand, decreasing population size - a result of fewer births than deaths, and people moving out of a country - can impact a government's commitment to maintain services and infrastructure." }, { "id": "IndicatorName", "value": "Population, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Current population estimates for developing countries that lack (i) reliable recent census data, and (ii) pre- and post-census estimates for countries with census data, are provided by the United Nations Population Division and other agencies. \n\n\n\nThe cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in both the model and the data.\n\n\n\nBecause future trends cannot be known with certainty, population projections have a wide range of uncertainty." }, { "id": "Longdefinition", "value": "Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates." }, { "id": "Othernotes", "value": "Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices, uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population estimates are usually based on national population censuses, and estimates of fertility, mortality and migration.\n\n\n\nErrors and undercounting in census occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census.\n\n\n\nThe quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data.\n\n\n\nThe currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census.\n\n\n\nThe United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems.\n\n\n\nInternational migration is the only other factor besides birth and death rates that directly determines a country's population change. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect.\n\n\n\nOne of the major data sources of this indicator is UN Population Division's World Population Prospects, which use the cohort component method to produce population estimates and projections.\n\n\n\nPopulation projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant.\nStatistical concept(s): Estimates of total population describe the size of total population. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "19" }, { "id": "SP.UWT.TFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Unmet need for contraception (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Unmet need for contraception is the percentage of fertile, married women of reproductive age who do not want to become pregnant and are not using contraception." }, { "id": "Othernotes", "value": "Unmet need for contraception measures the capacity women have in achieving their desired family size and birth spacing. Many couples in developing countries want to limit or postpone childbearing but are not using effective contraception. These couples have an unmet need for contraception. Common reasons are lack of knowledge about contraceptive methods and concerns about possible side effects." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2024" }, { "id": "Source", "value": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMany couples in developing countries want to limit or postpone childbearing but are not using effective contraception. These couples have an unmet need for contraception. Common reasons are lack of knowledge about contraceptive methods and concerns about possible side effects. This indicator excludes women not exposed to the risk of unintended pregnancy because of menopause, infertility, or postpartum anovulation." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "19" }, { "id": "TM.MRC.NOTX.DV.ZS", "metatype": [ { "id": "IndicatorName", "value": "Goods (excluding arms) admitted free of tariffs from developing countries (% total merchandise imports excluding arms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "It is the proportion of duty free imports (excluding arms) into developed countries from developing and least developed countries. For the purpose of calculating this indicator, Japan in Asia, Canada and the United States in North America, Australia and New Zealand in Oceania and Iceland, Norway, Switzerland and the EU (25 countries included since 2004) in Europe are considered “developed” regions or areas, following the common accepted practice used for MDG indicators. Developing countries are those not listed as developed or transition countries. The list of least developed countries (LDCs) has been agreed by the General Assembly, on the recommendation of the Committee for Development Policy, Economic and Social Council." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Conference on Trade and Development, World Trade Organization, and International Trade Center. Data are available online at: www.mdg-trade.org." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "19" }, { "id": "TM.MRC.NOTX.LD.ZS", "metatype": [ { "id": "IndicatorName", "value": "Goods (excluding arms) admitted free of tariffs from least developed countries (% total merchandise imports excluding arms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "It is the proportion of duty free imports (excluding arms) into developed countries from developing and least developed countries. For the purpose of calculating this indicator, Japan in Asia, Canada and the United States in North America, Australia and New Zealand in Oceania and Iceland, Norway, Switzerland and the EU (25 countries included since 2004) in Europe are considered “developed” regions or areas, following the common accepted practice used for MDG indicators. Developing countries are those not listed as developed or transition countries. The list of least developed countries (LDCs) has been agreed by the General Assembly, on the recommendation of the Committee for Development Policy, Economic and Social Council." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Conference on Trade and Development, World Trade Organization, and International Trade Center. Data are available online at: www.mdg-trade.org." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "19" }, { "id": "TM.TAX.AGRI.CD.DV", "metatype": [ { "id": "IndicatorName", "value": "Average tariffs imposed by developed countries on agricultural products from developing countries (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "It is the average tariffs imposed by developed countries on subsets of selected items (agricultural products, textile and clothing exports) that are deemed to be of interest to developing countries. For the purpose of calculating this indicator, Japan in Asia, Canada and the United States in North America, Australia and New Zealand in Oceania and Iceland, Norway, Switzerland and the EU (25 countries included since 2004) in Europe are considered “developed” regions or areas, following the common accepted practice used for MDG indicators. Developing countries are those not listed as developed or transition countries. The list of least developed countries (LDCs) has been agreed by the General Assembly, on the recommendation of the Committee for Development Policy, Economic and Social Council. Agricultural, clothing and textile groups follow the definition in WTO agreements based on the Harmonized System 1992, transposed to current versions by WTO Secretariat. Agricultural products correspond to Harmonized System 1992, chapters 01 to 24 less fish and fish products (chap. 03); in addition to parts of chapters 29, 33, 35, 38, 41, 43, 50 to 53. Textile is mainly covered in chapters 50 to 60. The bulk of clothing products are found in chapters 61-63." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Conference on Trade and Development, World Trade Organization, and International Trade Center. Data are available online at: www.mdg-trade.org." }, { "id": "Topic", "value": "Private Sector & Trade: Tariffs" } ], "source_id": "19" }, { "id": "TM.TAX.AGRI.CD.LD", "metatype": [ { "id": "IndicatorName", "value": "Average tariffs imposed by developed countries on agricultural products from least developed countries (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "It is the average tariffs imposed by developed countries on subsets of selected items (agricultural products, textile and clothing exports) that are deemed to be of interest to developing countries. For the purpose of calculating this indicator, Japan in Asia, Canada and the United States in North America, Australia and New Zealand in Oceania and Iceland, Norway, Switzerland and the EU (25 countries included since 2004) in Europe are considered “developed” regions or areas, following the common accepted practice used for MDG indicators. Developing countries are those not listed as developed or transition countries. The list of least developed countries (LDCs) has been agreed by the General Assembly, on the recommendation of the Committee for Development Policy, Economic and Social Council. Agricultural, clothing and textile groups follow the definition in WTO agreements based on the Harmonized System 1992, transposed to current versions by WTO Secretariat. Agricultural products correspond to Harmonized System 1992, chapters 01 to 24 less fish and fish products (chap. 03); in addition to parts of chapters 29, 33, 35, 38, 41, 43, 50 to 53. Textile is mainly covered in chapters 50 to 60. The bulk of clothing products are found in chapters 61-63." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Conference on Trade and Development, World Trade Organization, and International Trade Center. Data are available online at: www.mdg-trade.org." }, { "id": "Topic", "value": "Private Sector & Trade: Tariffs" } ], "source_id": "19" }, { "id": "TM.TAX.CLTH.CD.DV", "metatype": [ { "id": "IndicatorName", "value": "Average tariffs imposed by developed countries on clothing products from developing countries (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "It is the average tariffs imposed by developed countries on subsets of selected items (agricultural products, textile and clothing exports) that are deemed to be of interest to developing countries. For the purpose of calculating this indicator, Japan in Asia, Canada and the United States in North America, Australia and New Zealand in Oceania and Iceland, Norway, Switzerland and the EU (25 countries included since 2004) in Europe are considered “developed” regions or areas, following the common accepted practice used for MDG indicators. Developing countries are those not listed as developed or transition countries. The list of least developed countries (LDCs) has been agreed by the General Assembly, on the recommendation of the Committee for Development Policy, Economic and Social Council. Agricultural, clothing and textile groups follow the definition in WTO agreements based on the Harmonized System 1992, transposed to current versions by WTO Secretariat. Agricultural products correspond to Harmonized System 1992, chapters 01 to 24 less fish and fish products (chap. 03); in addition to parts of chapters 29, 33, 35, 38, 41, 43, 50 to 53. Textile is mainly covered in chapters 50 to 60. The bulk of clothing products are found in chapters 61-63." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Conference on Trade and Development, World Trade Organization, and International Trade Center. Data are available online at: www.mdg-trade.org." }, { "id": "Topic", "value": "Private Sector & Trade: Tariffs" } ], "source_id": "19" }, { "id": "TM.TAX.CLTH.CD.LD", "metatype": [ { "id": "IndicatorName", "value": "Average tariffs imposed by developed countries on clothing products from least developed countries (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "It is the average tariffs imposed by developed countries on subsets of selected items (agricultural products, textile and clothing exports) that are deemed to be of interest to developing countries. For the purpose of calculating this indicator, Japan in Asia, Canada and the United States in North America, Australia and New Zealand in Oceania and Iceland, Norway, Switzerland and the EU (25 countries included since 2004) in Europe are considered “developed” regions or areas, following the common accepted practice used for MDG indicators. Developing countries are those not listed as developed or transition countries. The list of least developed countries (LDCs) has been agreed by the General Assembly, on the recommendation of the Committee for Development Policy, Economic and Social Council. Agricultural, clothing and textile groups follow the definition in WTO agreements based on the Harmonized System 1992, transposed to current versions by WTO Secretariat. Agricultural products correspond to Harmonized System 1992, chapters 01 to 24 less fish and fish products (chap. 03); in addition to parts of chapters 29, 33, 35, 38, 41, 43, 50 to 53. Textile is mainly covered in chapters 50 to 60. The bulk of clothing products are found in chapters 61-63." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Conference on Trade and Development, World Trade Organization, and International Trade Center. Data are available online at: www.mdg-trade.org." }, { "id": "Topic", "value": "Private Sector & Trade: Tariffs" } ], "source_id": "19" }, { "id": "TM.TAX.TXTL.CD.DV", "metatype": [ { "id": "IndicatorName", "value": "Average tariffs imposed by developed countries on textile products from developing countries (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "It is the average tariffs imposed by developed countries on subsets of selected items (agricultural products, textile and clothing exports) that are deemed to be of interest to developing countries. For the purpose of calculating this indicator, Japan in Asia, Canada and the United States in North America, Australia and New Zealand in Oceania and Iceland, Norway, Switzerland and the EU (25 countries included since 2004) in Europe are considered “developed” regions or areas, following the common accepted practice used for MDG indicators. Developing countries are those not listed as developed or transition countries. The list of least developed countries (LDCs) has been agreed by the General Assembly, on the recommendation of the Committee for Development Policy, Economic and Social Council. Agricultural, clothing and textile groups follow the definition in WTO agreements based on the Harmonized System 1992, transposed to current versions by WTO Secretariat. Agricultural products correspond to Harmonized System 1992, chapters 01 to 24 less fish and fish products (chap. 03); in addition to parts of chapters 29, 33, 35, 38, 41, 43, 50 to 53. Textile is mainly covered in chapters 50 to 60. The bulk of clothing products are found in chapters 61-63." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Conference on Trade and Development, World Trade Organization, and International Trade Center. Data are available online at: www.mdg-trade.org." }, { "id": "Topic", "value": "Private Sector & Trade: Tariffs" } ], "source_id": "19" }, { "id": "TM.TAX.TXTL.CD.LD", "metatype": [ { "id": "IndicatorName", "value": "Average tariffs imposed by developed countries on textile products from least developed countries (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "It is the average tariffs imposed by developed countries on subsets of selected items (agricultural products, textile and clothing exports) that are deemed to be of interest to developing countries. For the purpose of calculating this indicator, Japan in Asia, Canada and the United States in North America, Australia and New Zealand in Oceania and Iceland, Norway, Switzerland and the EU (25 countries included since 2004) in Europe are considered “developed” regions or areas, following the common accepted practice used for MDG indicators. Developing countries are those not listed as developed or transition countries. The list of least developed countries (LDCs) has been agreed by the General Assembly, on the recommendation of the Committee for Development Policy, Economic and Social Council. Agricultural, clothing and textile groups follow the definition in WTO agreements based on the Harmonized System 1992, transposed to current versions by WTO Secretariat. Agricultural products correspond to Harmonized System 1992, chapters 01 to 24 less fish and fish products (chap. 03); in addition to parts of chapters 29, 33, 35, 38, 41, 43, 50 to 53. Textile is mainly covered in chapters 50 to 60. The bulk of clothing products are found in chapters 61-63." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Conference on Trade and Development, World Trade Organization, and International Trade Center. Data are available online at: www.mdg-trade.org." }, { "id": "Topic", "value": "Private Sector & Trade: Tariffs" } ], "source_id": "19" }, { "id": "BM.KLT.DINV.WD.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "IndicatorName", "value": "Foreign direct investment, net outflows (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intra-unit flows of goods and services.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available." }, { "id": "Longdefinition", "value": "Foreign direct investment refers to direct investment equity flows in an economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. This series shows net outflows of investment from the reporting economy to the rest of the world, and is divided by GDP." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments database, supplemented by data from the United Nations Conference on Trade and Development and official national sources." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "25" }, { "id": "BM.TRF.PWKR.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "IndicatorName", "value": "Personal remittances, paid (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Remittance transactions have grown in importance over the past decade. In a number of developing economies, receipts of remittances have become an important and stable source of funds that exceeds receipts from exports of goods and services or from financial inflows on foreign direct investment. But the quality of statistical remittance data is not high. Remittances are a challenge to measure because of their nature. They are heterogeneous with numerous small transactions conducted by individuals through a wide variety of channels: formal channels, such as electronic wire, or through informal channels, such as cash or goods carried across borders. The large number of remittance transactions and the multitude of channels pose challenges to the compilation of comprehensive statistics. The small size of individual transactions means that they often go undetected by typical data source systems, although the aggregate level of transactions may be substantial.\n\nBecause of difficulties in obtaining data on informal remittance transactions, the remittance transactions undertaken through informal channels are sometimes not well covered in current balance of payments data. As a result, even though direct measurement of remittances - through transactions reporting or surveys - may be considered preferable if feasible, some countries instead combine different sources and estimation methods to achieve better coverage, by using direct measurements where practical and supplemented estimates where they are not. Model-based approaches are used in some countries as they are flexible. Compilers can design models to fill gaps in data sources or to provide global totals.\n\nHowever, only reliable input data can lead to sound estimates, regardless of the sophistication of an estimation method or econometric model. Indirect data are converted to remittance estimates using a set of assumptions. These assumptions should be plausible, but it is often not possible to test or verify these assumptions and also the results in practice." }, { "id": "Longdefinition", "value": "Personal remittances comprise personal transfers and compensation of employees. Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual: personal transfers and compensation of employees. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on IMF balance of payments data." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "25" }, { "id": "BX.KLT.DINV.WD.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "IndicatorName", "value": "Foreign direct investment, net inflows (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intra-unit flows of goods and services.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available." }, { "id": "Longdefinition", "value": "Foreign direct investment are the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows net inflows (new investment inflows less disinvestment) in the reporting economy from foreign investors, and is divided by GDP." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and Balance of Payments databases, World Bank, International Debt Statistics, and World Bank and OECD GDP estimates." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "25" }, { "id": "BX.TRF.PWKR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Personal transfers, receipts (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "25" }, { "id": "CM.MKT.LCAP.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Stock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology." }, { "id": "IndicatorName", "value": "Market capitalization of listed domestic companies (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards." }, { "id": "Longdefinition", "value": "Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Federation of Exchanges database." }, { "id": "Topic", "value": "Financial Sector: Capital markets" } ], "source_id": "25" }, { "id": "EG.ELC.ACCS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to electricity (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Access to electricity is the percentage of population with access to electricity. Electrification data are collected from industry, national surveys and international sources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Sustainable Energy for All (SE4ALL) database from the SE4ALL Global Tracking Framework led jointly by the World Bank, International Energy Agency, and the Energy Sector Management Assistance Program." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "25" }, { "id": "EG.USE.ELEC.KH.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data." }, { "id": "IndicatorName", "value": "Electric power consumption (kWh per capita)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "Limitationsandexceptions", "value": "Data on electric power production and consumption are collected from national energy agencies by the International Energy Agency (IEA) and adjusted by the IEA to meet international definitions. Data are reported as net consumption as opposed to gross consumption. Net consumption excludes the energy consumed by the generating units. For all countries except the United States, total electric power consumption is equal total net electricity generation plus electricity imports minus electricity exports minus electricity distribution losses.\n\nThe IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Electric power consumption measures the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "25" }, { "id": "EN.POP.DNST", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Population density (people per sq. km of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data are provided by the United Nations Population Division and other agencies. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\nThe quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data." }, { "id": "Longdefinition", "value": "Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization and World Bank population estimates." }, { "id": "Topic", "value": "Environment: Density & urbanization" } ], "source_id": "25" }, { "id": "FB.ATM.TOTL.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "Country-specific metadata can be found on the IMF’s FAS website at  http://fas.imf.org." }, { "id": "IndicatorName", "value": "Automated teller machines (ATMs) (per 100,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Population-based ratios of the number of branches and ATMs assume a uniform distribution of bank outlets within a country's area and across its population, while in most countries bank branches and ATMs are concentrated in urban centers of the country and are accessible only to some individuals." }, { "id": "Longdefinition", "value": "Automated teller machines are computerized telecommunications devices that provide clients of a financial institution with access to financial transactions in a public place." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Financial Access Survey." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "25" }, { "id": "FB.CBK.BRWR.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "Country-specific metadata can be found on the IMF’s FAS website at  http://fas.imf.org." }, { "id": "IndicatorName", "value": "Borrowers from commercial banks (per 1,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "For several countries, data cover all borrowers including commercial banks, credit unions and financial cooperatives, deposit taking microfinance institutions, and other deposit takers. These include all resident financial corporations and quasi-corporations (except the central bank) that are mainly engaged in financial intermediation and that issue liabilities included in the national definition of broad money. These institutions have varying names in different countries, such as savings and loan associations, building societies, rural banks and agricultural banks, post office giro institutions, post office savings banks, savings banks, and money market funds." }, { "id": "Longdefinition", "value": "Borrowers from commercial banks are the reported number of resident customers that are nonfinancial corporations (public and private) and households who obtained loans from commercial banks and other banks functioning as commercial banks. For many countries data cover the total number of loan accounts due to lack of information on loan account holders." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Financial Access Survey." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "25" }, { "id": "FP.CPI.TOTL", "metatype": [ { "id": "BasePeriod", "value": "2010" }, { "id": "IndicatorName", "value": "Consumer price index (2010 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Consumer price indexes should be interpreted with caution. The definition of a household, the basket of goods, and the geographic (urban or rural) and income group coverage of consumer price surveys can vary widely by country. In addition, weights are derived from household expenditure surveys, which, for budgetary reasons, tend to be conducted infrequently in developing countries, impairing comparability over time. Although useful for measuring consumer price inflation within a country, consumer price indexes are of less value in comparing countries." }, { "id": "Longdefinition", "value": "Consumer price index reflects changes in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used. Data are period averages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" } ], "source_id": "25" }, { "id": "FP.CPI.TOTL.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Inflation, consumer prices (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" } ], "source_id": "25" }, { "id": "FS.AST.DOMS.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Domestic credit provided by financial sector (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In a few countries governments may hold international reserves as deposits in the banking system rather than in the central bank. Since claims on the central government are a net item (claims on the central government minus central government deposits), the figure may be negative, resulting in a negative figure for domestic credit provided by the banking sector." }, { "id": "Longdefinition", "value": "Domestic credit provided by the financial sector includes all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net. The financial sector includes monetary authorities and deposit money banks, as well as other financial corporations where data are available (including corporations that do not accept transferable deposits but do incur such liabilities as time and savings deposits). Examples of other financial corporations are finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates." }, { "id": "Topic", "value": "Financial Sector: Assets" } ], "source_id": "25" }, { "id": "FS.AST.PRVT.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Domestic credit to private sector (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Credit to the private sector may sometimes include credit to state-owned or partially state-owned enterprises." }, { "id": "Longdefinition", "value": "Domestic credit to private sector refers to financial resources provided to the private sector by financial corporations, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises. The financial corporations include monetary authorities and deposit money banks, as well as other financial corporations where data are available (including corporations that do not accept transferable deposits but do incur such liabilities as time and savings deposits). Examples of other financial corporations are finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates." }, { "id": "Topic", "value": "Financial Sector: Assets" } ], "source_id": "25" }, { "id": "GB.XPD.RSDV.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "Research and development expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Estimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers. \n\nR&D totals from SNA accounts may differ from these estimates, due in part to the different treatments of software R&D in the totals." }, { "id": "Longdefinition", "value": "Gloss domestic expenditures on research and development (R&D), expressed as a percent of GDP. They include both capital and current expenditures in the four main sectors: Business enterprise, Government, Higher education and Private non-profit. R&D covers basic research, applied research, and experimental development." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "25" }, { "id": "GC.TAX.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Tax revenue (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Tax revenue refers to compulsory transfers to the central government for public purposes. Certain compulsory transfers such as fines, penalties, and most social security contributions are excluded. Refunds and corrections of erroneously collected tax revenue are treated as negative revenue." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Government Finance Statistics Yearbook and data files, and World Bank and OECD GDP estimates." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" } ], "source_id": "25" }, { "id": "GC.TAX.YPKG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Taxes on income, profits and capital gains (% of total taxes)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes on income, profits, and capital gains are levied on the actual or presumptive net income of individuals, on the profits of corporations and enterprises, and on capital gains, whether realized or not, on land, securities, and other assets. Intragovernmental payments are eliminated in consolidation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Government Finance Statistics Yearbook and data files." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" } ], "source_id": "25" }, { "id": "IC.BUS.EASE.XQ", "metatype": [ { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year. Data before 2013 are not comparable with data from 2013 onward due to methodological changes." }, { "id": "IndicatorName", "value": "Ease of doing business index (1=most business-friendly regulations)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Ease of doing business ranks economies from 1 to 190, with first place being the best. A high ranking (a low numerical rank) means that the regulatory environment is conducive to business operation. The index averages the country's percentile rankings on 10 topics covered in the World Bank's Doing Business. The ranking on each topic is the simple average of the percentile rankings on its component indicators." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "25" }, { "id": "IC.BUS.NDNS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "For cross-country comparability, only limited liability corporations that operate in the formal sector are included." }, { "id": "IndicatorName", "value": "New business density (new registrations per 1,000 people ages 15-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. The Entrepreneurship Database facilitates the analysis of the growth of the formal private sector and the identification of factors that encourage firms to begin operations in or transition to the formal sector. Data is collected all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured.\n\nThe data itself only provides a snapshot of a given economy's business demographics, and cannot by itself explain the factors that affect the business creation cycle. However, when the Entrepreneurship Database is combined with other data such as the Doing Business Report, Investment Climate Assessments, and/or OECD Entrepreneurship Indicators, researchers and policymakers can better understand the dynamics of the business creation process.\n\nThe Entrepreneurship Database is a critical source of data that facilitates the measurement of entrepreneurial activity across countries and over time. The data also allows for a deeper understanding of the relationship between new firm registration, the regulatory environment, and economic growth. Previous research using the Entrepreneurship Database has shown a significant relationship between the level of cost, time, and procedures required to start a business and new firm registration." }, { "id": "Longdefinition", "value": "New businesses registered are the number of new limited liability corporations registered in the calendar year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank's Entrepreneurship Survey and database (http://www.doingbusiness.org/data/exploretopics/entrepreneurship)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "25" }, { "id": "IC.BUS.NREG", "metatype": [ { "id": "Generalcomments", "value": "For cross-country comparability, only limited liability corporations that operate in the formal sector are included." }, { "id": "IndicatorName", "value": "New businesses registered (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. The Entrepreneurship Database facilitates the analysis of the growth of the formal private sector and the identification of factors that encourage firms to begin operations in or transition to the formal sector. Data is collected all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured.\n\nThe Entrepreneurship Database is a critical source of data that facilitates the measurement of entrepreneurial activity across countries and over time. The data also allows for a deeper understanding of the relationship between new firm registration, the regulatory environment, and economic growth. Previous research using the Entrepreneurship Database has shown a significant relationship between the level of cost, time, and procedures required to start a business and new firm registration.\n\nTo facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies." }, { "id": "Longdefinition", "value": "New businesses registered are the number of new limited liability corporations registered in the calendar year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank's Entrepreneurship Survey and database (http://www.doingbusiness.org/data/exploretopics/entrepreneurship)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "25" }, { "id": "IC.CRD.INFO.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year. Data before 2013 are not comparable with data from 2013 onward due to methodological changes." }, { "id": "IndicatorName", "value": "Depth of credit information index (0=low to 8=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Depth of credit information index measures rules affecting the scope, accessibility, and quality of credit information available through public or private credit registries. The index ranges from 0 to 8, with higher values indicating the availability of more credit information, from either a public registry or a private bureau, to facilitate lending decisions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "25" }, { "id": "IC.CRD.PRVT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Private credit bureau coverage (% of adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Private credit bureau coverage reports the number of individuals or firms listed by a private credit bureau with current information on repayment history, unpaid debts, or credit outstanding. The number is expressed as a percentage of the adult population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "25" }, { "id": "IC.CRD.PUBL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Public credit registry coverage (% of adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Public credit registry coverage reports the number of individuals and firms listed in a public credit registry with current information on repayment history, unpaid debts, or credit outstanding. The number is expressed as a percentage of the adult population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "25" }, { "id": "IC.FRM.CORR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "Informal payments to public officials (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Longdefinition", "value": "Informal payments to public officials are the percentage of firms expected to make informal payments to public officials to \"get things done\" with regard to customs, taxes, licenses, regulations, services, and the like." }, { "id": "Notesfromoriginalsource", "value": "All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "25" }, { "id": "IC.FRM.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "Time required to obtain an operating license (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Longdefinition", "value": "Time required to obtain operating license is the average wait to obtain an operating license from the day the establishment applied for it to the day it was granted." }, { "id": "Notesfromoriginalsource", "value": "All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "25" }, { "id": "IC.ISV.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time to resolve insolvency (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Time to resolve insolvency is the number of years from the filing for insolvency in court until the resolution of distressed assets." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "25" }, { "id": "IC.LGL.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time required to enforce a contract (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Time required to enforce a contract is the number of calendar days from the filing of the lawsuit in court until the final determination and, in appropriate cases, payment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "25" }, { "id": "IC.PRP.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time required to register property (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Time required to register property is the number of calendar days needed for businesses to secure rights to property." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "25" }, { "id": "IC.REG.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time required to start a business (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Time required to start a business is the number of calendar days needed to complete the procedures to legally operate a business. If a procedure can be speeded up at additional cost, the fastest procedure, independent of cost, is chosen." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "25" }, { "id": "IC.TAX.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time to prepare and pay taxes (hours)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "To make the data comparable across countries, several assumptions are made about businesses. The main assumptions are that they are limited liability companies, they operate in the country's most populous city, they are domestically owned, they perform general industrial or commercial activities, and they have certain levels of start-up capital, employees, and turnover.\n\nThe Doing Business methodology on business taxes is consistent with the Total Tax Contribution framework developed by PricewaterhouseCoopers (now PwC), which measures the taxes that are borne by companies and that affect their income statements. However, PwC bases its calculation on data from the largest companies in the economy, while Doing Business focuses on a standardized medium-size company." }, { "id": "Longdefinition", "value": "Time to prepare and pay taxes is the time, in hours per year, it takes to prepare, file, and pay (or withhold) three major types of taxes: the corporate income tax, the value added or sales tax, and labor taxes, including payroll taxes and social security contributions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "25" }, { "id": "IC.TAX.METG", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "Number of visits or required meetings with tax officials (average for affected firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Longdefinition", "value": "Average number of visits or required meetings with tax officials during the year. The value represents the average number of visits for all firms which reported being visited or required to meet with tax officials (please see indicator IC.FRM.METG.ZS)." }, { "id": "Notesfromoriginalsource", "value": "All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "25" }, { "id": "IC.TAX.PAYM", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Tax payments (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "To make the data comparable across countries, several assumptions are made about businesses. The main assumptions are that they are limited liability companies, they operate in the country's most populous city, they are domestically owned, they perform general industrial or commercial activities, and they have certain levels of start-up capital, employees, and turnover. \n\nThe Doing Business methodology on business taxes is consistent with the Total Tax Contribution framework developed by PricewaterhouseCoopers (now PwC), which measures the taxes that are borne by companies and that affect their income statements. However, PwC bases its calculation on data from the largest companies in the economy, while Doing Business focuses on a standardized medium-size company." }, { "id": "Longdefinition", "value": "Tax payments by businesses are the total number of taxes paid by businesses, including electronic filing. The tax is counted as paid once a year even if payments are more frequent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "25" }, { "id": "IQ.CPA.ECON.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA economic management cluster average (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The economic management cluster includes macroeconomic management, fiscal policy, and debt policy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "25" }, { "id": "IQ.CPA.FINS.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA financial sector rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Financial sector assesses the structure of the financial sector and the policies and regulations that affect it." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "25" }, { "id": "IQ.CPA.GNDR.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA gender equality rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Gender equality assesses the extent to which the country has installed institutions and programs to enforce laws and policies that promote equal access for men and women in education, health, the economy, and protection under law." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "25" }, { "id": "IQ.CPA.MACR.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA macroeconomic management rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Macroeconomic management assesses the monetary, exchange rate, and aggregate demand policy framework." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "25" }, { "id": "IQ.CPA.PROP.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA property rights and rule-based governance rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Property rights and rule-based governance assess the extent to which private economic activity is facilitated by an effective legal system and rule-based governance structure in which property and contract rights are reliably respected and enforced." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "25" }, { "id": "IQ.CPA.PROT.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA social protection rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Social protection and labor assess government policies in social protection and labor market regulations that reduce the risk of becoming poor, assist those who are poor to better manage further risks, and ensure a minimal level of welfare to all people." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "25" }, { "id": "IQ.CPA.SOCI.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA policies for social inclusion/equity cluster average (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The policies for social inclusion and equity cluster includes gender equality, equity of public resource use, building human resources, social protection and labor, and policies and institutions for environmental sustainability." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "25" }, { "id": "IS.AIR.DPRT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Air transport, registered carrier departures worldwide" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Countries submit air transport data to Civil Aviation Organization (ICAO) on the basis of standard instructions and definitions issued by ICAO. In many cases, however, the data include estimates by ICAO for nonreporting carriers. Where possible, these estimates are based on previous submissions supplemented by information published by the air carriers, such as flight schedules.\n\nThe data cover the air traffic carried on scheduled services, but changes in air transport regulations in Europe have made it more difficult to classify traffic as scheduled or nonscheduled. Thus recent increases shown for some European countries may be due to changes in the classification of air traffic rather than actual growth. In the case of multinational air carriers owned by partner States, traffic within each partner State is shown separately as domestic and all other traffic as international.\n\n\"Foreign\" cabotage traffic (i.e. traffic carried between city-pairs in a State other than the one where the reporting carrier has its principal place of business) is shown as international traffic.\n\nA technical stop does not result in any flight stage being classified differently than would have been the case had the technical stop not been made. For countries with few air carriers or only one, the addition or discontinuation of a home-based air carrier may cause significant changes in air traffic.\n\nData for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\"" }, { "id": "Longdefinition", "value": "Registered carrier departures worldwide are domestic takeoffs and takeoffs abroad of air carriers registered in the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Civil Aviation Organization, Civil Aviation Statistics of the World and ICAO staff estimates." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "25" }, { "id": "IT.CEL.SETS.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "IndicatorName", "value": "Mobile cellular subscriptions (per 100 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU.\n\nMobile subscriptions include both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services are also excluded, but all mobile cellular subscriptions that offer voice communications are included. Both postpaid and prepaid subscriptions are included." }, { "id": "Longdefinition", "value": "Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "25" }, { "id": "IT.MLT.MAIN.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "IndicatorName", "value": "Fixed telephone subscriptions (per 100 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU." }, { "id": "Longdefinition", "value": "Fixed telephone subscriptions refers to the sum of active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "25" }, { "id": "IT.NET.BBND.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "IndicatorName", "value": "Fixed broadband subscriptions (per 100 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are collected by national statistics offices through household surveys. Because survey questions and definitions differ, the estimates may not be strictly comparable across countries.\n\nFixed broadband Internet includes cable modem, DSL, fibre and other fixed broadband technology (such as satellite broadband Internet, Ethernet LANs, fixed-wireless access, Wireless Local Area Network, WiMAX etc.). Subscribers with access to data communications (including the Internet) via mobile cellular networks are excluded.\n\nAdvertised and real speeds can differ substantially. In some countries, regulatory authorities monitor the speed and quality of broadband services and oblige operators to provide accurate quality-of-service information to end users. Regional and global totals are calculated as unweighted sums of the country values. Regional and global penetration rates (per 100 inhabitants) are weighted averages of the country values weighted by the population of the countries/regions.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "Fixed broadband subscriptions refers to fixed subscriptions to high-speed access to the public Internet (a TCP/IP connection), at downstream speeds equal to, or greater than, 256 kbit/s. This includes cable modem, DSL, fiber-to-the-home/building, other fixed (wired)-broadband subscriptions, satellite broadband and terrestrial fixed wireless broadband. This total is measured irrespective of the method of payment. It excludes subscriptions that have access to data communications (including the Internet) via mobile-cellular networks. It should include fixed WiMAX and any other fixed wireless technologies. It includes both residential subscriptions and subscriptions for organizations." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "25" }, { "id": "IT.NET.USER.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "IndicatorName", "value": "Individuals using the Internet (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "25" }, { "id": "NE.EXP.GNFS.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Exports of goods and services (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Annual growth rate of exports of goods and services based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Exports of goods and services represent the value of all goods and other market services provided to the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" } ], "source_id": "25" }, { "id": "NE.EXP.GNFS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Exports of goods and services (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics." }, { "id": "Longdefinition", "value": "Exports of goods and services represent the value of all goods and other market services provided to the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" } ], "source_id": "25" }, { "id": "NE.GDI.FPRV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Gross fixed capital formation, private sector (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Private investment covers gross outlays by the private sector (including private nonprofit agencies) on additions to its fixed domestic assets." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" } ], "source_id": "25" }, { "id": "NE.GDI.TOTL.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Gross capital formation (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Annual growth rate of gross capital formation based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Gross capital formation (formerly gross domestic investment) consists of outlays on additions to the fixed assets of the economy plus net changes in the level of inventories. Fixed assets include land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchases; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. Inventories are stocks of goods held by firms to meet temporary or unexpected fluctuations in production or sales, and \"work in progress.\" According to the 1993 SNA, net acquisitions of valuables are also considered capital formation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" } ], "source_id": "25" }, { "id": "NE.GDI.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Gross capital formation (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\nData on capital formation may be estimated from direct surveys of enterprises and administrative records or based on the commodity flow method using data from production, trade, and construction activities. The quality of data on government fixed capital formation depends on the quality of government accounting systems (which tend to be weak in developing countries). Measures of fixed capital formation by households and corporations - particularly capital outlays by small, unincorporated enterprises - are usually unreliable.\n\nEstimates of changes in inventories are rarely complete but usually include the most important activities or commodities. In some countries these estimates are derived as a composite residual along with household final consumption expenditure. According to national accounts conventions, adjustments should be made for appreciation of the value of inventory holdings due to price changes, but this is not always done. In highly inflationary economies this element can be substantial." }, { "id": "Longdefinition", "value": "Gross capital formation (formerly gross domestic investment) consists of outlays on additions to the fixed assets of the economy plus net changes in the level of inventories. Fixed assets include land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchases; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. Inventories are stocks of goods held by firms to meet temporary or unexpected fluctuations in production or sales, and \"work in progress.\" According to the 1993 SNA, net acquisitions of valuables are also considered capital formation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" } ], "source_id": "25" }, { "id": "NE.IMP.GNFS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Imports of goods and services (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics." }, { "id": "Longdefinition", "value": "Imports of goods and services represent the value of all goods and other market services received from the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" } ], "source_id": "25" }, { "id": "NV.AGR.TOTL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "BasePeriod", "value": "2010" }, { "id": "Generalcomments", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "IndicatorName", "value": "Agriculture, value added (constant 2010 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently \"netted out\" using equally crude and ad hoc approximations." }, { "id": "Longdefinition", "value": "Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4. Data are in constant 2010 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2010 prices: Value added" } ], "source_id": "25" }, { "id": "NV.AGR.TOTL.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "IndicatorName", "value": "Agriculture, value added (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently \"netted out\" using equally crude and ad hoc approximations." }, { "id": "Longdefinition", "value": "Annual growth rate for agricultural value added based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" } ], "source_id": "25" }, { "id": "NV.AGR.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "IndicatorName", "value": "Agriculture, value added (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently \"netted out\" using equally crude and ad hoc approximations." }, { "id": "Longdefinition", "value": "Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" } ], "source_id": "25" }, { "id": "NV.IND.MANF.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "BasePeriod", "value": "2010" }, { "id": "Generalcomments", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "IndicatorName", "value": "Manufacturing, value added (constant 2010 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Manufacturing refers to industries belonging to ISIC divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Data are expressed constant 2010 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2010 prices: Value added" } ], "source_id": "25" }, { "id": "NV.IND.MANF.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "IndicatorName", "value": "Manufacturing, value added (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Annual growth rate for manufacturing value added based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Manufacturing refers to industries belonging to ISIC divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" } ], "source_id": "25" }, { "id": "NV.IND.MANF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "IndicatorName", "value": "Manufacturing, value added (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Manufacturing refers to industries belonging to ISIC divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" } ], "source_id": "25" }, { "id": "NV.IND.TOTL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "BasePeriod", "value": "2010" }, { "id": "Generalcomments", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "IndicatorName", "value": "Industry, value added (constant 2010 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Industry corresponds to ISIC divisions 10-45 and includes manufacturing (ISIC divisions 15-37). It comprises value added in mining, manufacturing (also reported as a separate subgroup), construction, electricity, water, and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Data are in constant 2010 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2010 prices: Value added" } ], "source_id": "25" }, { "id": "NV.IND.TOTL.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "IndicatorName", "value": "Industry, value added (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Annual growth rate for industrial value added based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Industry corresponds to ISIC divisions 10-45 and includes manufacturing (ISIC divisions 15-37). It comprises value added in mining, manufacturing (also reported as a separate subgroup), construction, electricity, water, and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" } ], "source_id": "25" }, { "id": "NV.IND.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "IndicatorName", "value": "Industry, value added (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Industry corresponds to ISIC divisions 10-45 and includes manufacturing (ISIC divisions 15-37). It comprises value added in mining, manufacturing (also reported as a separate subgroup), construction, electricity, water, and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" } ], "source_id": "25" }, { "id": "NY.GDP.MKTP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "GDP growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Each industry's contribution to growth in the economy's output is measured by growth in the industry's value added. In principle, value added in constant prices can be estimated by measuring the quantity of goods and services produced in a period, valuing them at an agreed set of base year prices, and subtracting the cost of intermediate inputs, also in constant prices. This double-deflation method requires detailed information on the structure of prices of inputs and outputs.\n\nIn many industries, however, value added is extrapolated from the base year using single volume indexes of outputs or, less commonly, inputs. Particularly in the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult.\n\nMoreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in quality lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of inflation.\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\nRebasing of national accounts can alter the measured growth rate of an economy and lead to breaks in series that affect the consistency of data over time. When countries rebase their national accounts, they update the weights assigned to various components to better reflect current patterns of production or uses of output. The new base year should represent normal operation of the economy - it should be a year without major shocks or distortions. Some developing countries have not rebased their national accounts for many years. Using an old base year can be misleading because implicit price and volume weights become progressively less relevant and useful.\n\nTo obtain comparable series of constant price data for computing aggregates, the World Bank rescales GDP and value added by industrial origin to a common reference year. Because rescaling changes the implicit weights used in forming regional and income group aggregates, aggregate growth rates are not comparable with those from earlier editions with different base years. Rescaling may result in a discrepancy between the rescaled GDP and the sum of the rescaled components. To avoid distortions in the growth rates, the discrepancy is left unallocated. As a result, the weighted average of the growth rates of the components generally does not equal the GDP growth rate." }, { "id": "Longdefinition", "value": "Annual percentage growth rate of GDP at market prices based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" } ], "source_id": "25" }, { "id": "NY.GDP.PCAP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2010" }, { "id": "IndicatorName", "value": "GDP per capita (constant 2010 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2010 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2010 prices: Aggregate indicators" } ], "source_id": "25" }, { "id": "NY.GDP.PCAP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2011" }, { "id": "IndicatorName", "value": "GDP per capita, PPP (constant 2011 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2011 international dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, International Comparison Program database." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "25" }, { "id": "SE.ADT.1524.LT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "Literacy rate, youth female (% of females ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "25" }, { "id": "SE.ADT.1524.LT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "Literacy rate, youth male (% of males ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "25" }, { "id": "SE.ADT.1524.LT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "Literacy rate, youth total (% of people ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "25" }, { "id": "SE.ADT.LITR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "Literacy rate, adult female (% of females ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "25" }, { "id": "SE.ADT.LITR.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "Literacy rate, adult male (% of males ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "25" }, { "id": "SE.ADT.LITR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "Literacy rate, adult total (% of people ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "25" }, { "id": "SE.ENR.SECO.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "School enrollment, secondary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in secondary education is the ratio of girls to boys enrolled at secondary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "25" }, { "id": "SE.ENR.TERT.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "School enrollment, tertiary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in tertiary education is the ratio of women to men enrolled at tertiary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "25" }, { "id": "SE.PRM.CMPT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "Primary completion rate, female (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data limitations preclude adjusting for students who drop out during the final year of primary education. Thus this rate is a proxy that should be taken as an upper estimate of the actual primary completion rate.\n \nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "25" }, { "id": "SE.PRM.CMPT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "Primary completion rate, male (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data limitations preclude adjusting for students who drop out during the final year of primary education. Thus this rate is a proxy that should be taken as an upper estimate of the actual primary completion rate.\n \nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "25" }, { "id": "SE.PRM.CMPT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "Primary completion rate, total (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data limitations preclude adjusting for students who drop out during the final year of primary education. Thus this rate is a proxy that should be taken as an upper estimate of the actual primary completion rate.\n \nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "25" }, { "id": "SE.PRM.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "School enrollment, primary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "25" }, { "id": "SE.PRM.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "School enrollment, primary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "25" }, { "id": "SE.PRM.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "School enrollment, primary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "25" }, { "id": "SE.SEC.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "School enrollment, secondary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "25" }, { "id": "SE.SEC.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "School enrollment, secondary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "25" }, { "id": "SE.SEC.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "School enrollment, secondary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "25" }, { "id": "SE.TER.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "School enrollment, tertiary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "25" }, { "id": "SE.TER.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "School enrollment, tertiary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "25" }, { "id": "SE.TER.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018)." }, { "id": "IndicatorName", "value": "School enrollment, tertiary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "25" }, { "id": "SE.XPD.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Government expenditure on education, total (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data may refer to spending by the ministry of education only (excluding spending on educational activities by other ministries)." }, { "id": "Longdefinition", "value": "General government expenditure on education (current, capital, and transfers) is expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "25" }, { "id": "SH.DYN.AIDS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Prevalence of HIV, total (% of population ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "25" }, { "id": "SH.MMR.RISK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Lifetime risk of maternal death (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The probability cannot be assumed to provide an exact estimate of risk of maternal death." }, { "id": "Longdefinition", "value": "Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division. Trends in Maternal Mortality: 1990 to 2015. Geneva, World Health Organization, 2015" }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "25" }, { "id": "SI.POV.DDAY", "metatype": [ { "id": "Generalcomments", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty headcount ratio at $1.90 a day is the percentage of the population living on less than $1.90 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Poverty headcount ratio at $1.90 a day is the percentage of the population living on less than $1.90 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions." }, { "id": "Source", "value": "World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm)." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "25" }, { "id": "SI.POV.GINI", "metatype": [ { "id": "Generalcomments", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries." }, { "id": "IndicatorName", "value": "GINI index (World Bank estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Gini coefficients are not unique. It is possible for two different Lorenz curves to give rise to the same Gini coefficient. Furthermore it is possible for the Gini coefficient of a developing country to rise (due to increasing inequality of income) while the number of people in absolute poverty decreases. This is because the Gini coefficient measures relative, not absolute, wealth.\n\nAnother limitation of the Gini coefficient is that it is not additive across groups, i.e. the total Gini of a society is not equal to the sum of the Gini's for its sub-groups. Thus, country-level Gini coefficients cannot be aggregated into regional or global Gini's, although a Gini coefficient can be computed for the aggregate.\n\nBecause the underlying household surveys differ in methods and types of welfare measures collected, data are not strictly comparable across countries or even across years within a country. Two sources of non-comparability should be noted for distributions of income in particular. First, the surveys can differ in many respects, including whether they use income or consumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much better welfare indicator, particularly in developing countries. Second, households differ in size (number of members) and in the extent of income sharing among members. And individuals differ in age and consumption needs. Differences among countries in these respects may bias comparisons of distribution. \n\nWorld Bank staff have made an effort to ensure that the data are as comparable as possible. Wherever possible, consumption has been used rather than income. Income distribution and Gini indexes for high-income economies are calculated directly from the Luxembourg Income Study database, using an estimation method consistent with that applied for developing countries." }, { "id": "Longdefinition", "value": "Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm)." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "25" }, { "id": "SL.AGR.EMPL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Employment in agriculture, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.AGR.EMPL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Employment in agriculture, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.AGR.EMPL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Employment in agriculture (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.EMP.MPYR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Employers, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a \"self-employment jobs\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.EMP.MPYR.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Employers, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a \"self-employment jobs\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.EMP.MPYR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Employers, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a \"self-employment jobs\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.EMP.OWAC.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Own-account workers, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Own-account workers are workers who, working on their own account or with one or more more partners, hold the types of jobs defined as \"self-employment jobs\" and have not engaged on a continuous basis any employees to work for them. Own account workers are a subcategory of \"self-employed\"." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.EMP.OWAC.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Own-account workers, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Own-account workers are workers who, working on their own account or with one or more more partners, hold the types of jobs defined as \"self-employment jobs\" and have not engaged on a continuous basis any employees to work for them. Own account workers are a subcategory of \"self-employed\"." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.EMP.OWAC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Own-account workers, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Own-account workers are workers who, working on their own account or with one or more more partners, hold the types of jobs defined as \"self-employment jobs\" and have not engaged on a continuous basis any employees to work for them. Own account workers are a subcategory of \"self-employed\"." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.EMP.SELF.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Self-employed, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include four sub-categories of employers, own-account workers, members of producers' cooperatives, and contributing family workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.EMP.SELF.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Self-employed, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include four sub-categories of employers, own-account workers, members of producers' cooperatives, and contributing family workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.EMP.SELF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Self-employed, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include four sub-categories of employers, own-account workers, members of producers' cooperatives, and contributing family workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.EMP.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Total employment, total (ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total employment shows the total number employed ages 15 and over." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database, using World Bank population estimates. Labor data retrieved in March 2017." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.EMP.VULN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Vulnerable employment, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived using data from International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.EMP.VULN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Vulnerable employment, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived using data from International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.EMP.VULN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Vulnerable employment, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived using data from International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.EMP.WORK.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Wage and salaried workers, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.EMP.WORK.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Wage and salaried workers, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.EMP.WORK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Wage and salaried workers, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.FAM.WORK.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Contributing family workers, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.FAM.WORK.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Contributing family workers, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.FAM.WORK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Contributing family workers, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.GDP.PCAP.EM.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2011" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "GDP per person employed (constant 2011 PPP $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "For comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output in both national currencies and purchasing power parity dollars is not easily available, especially in developing countries, because the definition, coverage, and methodology are not always consistent across countries. For example, countries employ different methodologies for estimating the missing values for the nonmarket service sectors and use different definitions of the informal sector." }, { "id": "Longdefinition", "value": "GDP per person employed is gross domestic product (GDP) divided by total employment in the economy. Purchasing power parity (PPP) GDP is GDP converted to 2011 constant international dollars using PPP rates. An international dollar has the same purchasing power over GDP that a U.S. dollar has in the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.IND.EMPL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Employment in industry, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.IND.EMPL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Employment in industry, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.IND.EMPL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Employment in industry (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.SRV.EMPL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Employment in services, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.SRV.EMPL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Employment in services, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.SRV.EMPL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Employment in services (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "25" }, { "id": "SL.TLF.ADVN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Labor force with advanced education, female (% of female working-age population with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percentage of the working age population with an advanced level of education who are in the labor force. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "25" }, { "id": "SL.TLF.ADVN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Labor force with advanced education, male (% of male working-age population with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percentage of the working age population with an advanced level of education who are in the labor force. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "25" }, { "id": "SL.TLF.ADVN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Labor force with advanced education (% of total working-age population with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percentage of the working age population with an advanced level of education who are in the labor force. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "25" }, { "id": "SL.TLF.BASC.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Labor force with basic education, female (% of female working-age population with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percentage of the working age population with a basic level of education who are in the labor force. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "25" }, { "id": "SL.TLF.BASC.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Labor force with basic education, male (% of male working-age population with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percentage of the working age population with a basic level of education who are in the labor force. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "25" }, { "id": "SL.TLF.BASC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Labor force with basic education (% of total working-age population with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percentage of the working age population with a basic level of education who are in the labor force. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "25" }, { "id": "SL.TLF.CACT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Labor force participation rate, female (% of female population ages 15+) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "25" }, { "id": "SL.TLF.CACT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Labor force participation rate, male (% of male population ages 15+) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "25" }, { "id": "SL.TLF.CACT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Labor force participation rate, total (% of total population ages 15+) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "25" }, { "id": "SL.TLF.INTM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Labor force with intermediate education, female (% of female working-age population with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percentage of the working age population with an intermediate level of education who are in the labor force. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "25" }, { "id": "SL.TLF.INTM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Labor force with intermediate education, male (% of male working-age population with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percentage of the working age population with an intermediate level of education who are in the labor force. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "25" }, { "id": "SL.TLF.INTM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Labor force with intermediate education (% of total working-age population with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percentage of the working age population with an intermediate level of education who are in the labor force. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "25" }, { "id": "SL.TLF.TOTL.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections." }, { "id": "IndicatorName", "value": "Labor force, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived using data from International Labour Organization, ILOSTAT database and World Bank population estimates. Labor data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "25" }, { "id": "SL.UEM.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, youth female (% of female labor force ages 15-24) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "25" }, { "id": "SL.UEM.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, youth male (% of male labor force ages 15-24) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "25" }, { "id": "SL.UEM.1524.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, youth total (% of total labor force ages 15-24) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "25" }, { "id": "SL.UEM.ADVN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Unemployment with advanced education, female (% of female labor force with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "25" }, { "id": "SL.UEM.ADVN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Unemployment with advanced education, male (% of male labor force with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "25" }, { "id": "SL.UEM.ADVN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Unemployment with advanced education (% of total labor force with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "25" }, { "id": "SL.UEM.INTM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Unemployment with intermediate education, female (% of female labor force with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "25" }, { "id": "SL.UEM.INTM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Unemployment with intermediate education, male (% of male labor force with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "25" }, { "id": "SL.UEM.INTM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Unemployment with intermediate education (% of total labor force with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "25" }, { "id": "SL.UEM.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, female (% of female labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "25" }, { "id": "SL.UEM.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, male (% of male labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "25" }, { "id": "SL.UEM.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, total (% of total labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data retrieved in September 2018." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "25" }, { "id": "SM.POP.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "International migrant stock (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "In deriving the estimates, an international migrant was equated to a person living in a country other than that in which he or she was born. That is, the number of international migrants, also called the international migrant stock, would represent the number of foreign-born persons enumerated in the countries or areas constituting the world. However, because several countries lack data on the foreign-born, data on the number of foreigners, if available, were used instead as the basis of estimation. Consequently, the overall number of migrants in world regions or at the global level do not quite represent the overall number of foreign-born persons.\n\nThe disintegration and reunification of countries causes discontinuities in the change of the international migrant stock. Because an international migrant is equated with a person who was born outside the country in which he or she resides, when a country disintegrates, persons who had been internal migrants because they had moved from one part of the country to another may become, overnight, international migrants without having moved at that time. Such changes introduce artificial but unavoidable discontinuities in the trend of the international migrant stock. The reunification of States also introduces discontinuities, but in the opposite direction." }, { "id": "Longdefinition", "value": "International migrant stock is the number of people born in a country other than that in which they live. It also includes refugees. The data used to estimate the international migrant stock at a particular time are obtained mainly from population censuses. The estimates are derived from the data on foreign-born population--people who have residence in one country but were born in another country. When data on the foreign-born population are not available, data on foreign population--that is, people who are citizens of a country other than the country in which they reside--are used as estimates. After the breakup of the Soviet Union in 1991 people living in one of the newly independent countries who were born in another were classified as international migrants. Estimates of migrant stock in the newly independent states from 1990 on are based on the 1989 census of the Soviet Union. For countries with information on the international migrant stock for at least two points in time, interpolation or extrapolation was used to estimate the international migrant stock on July 1 of the reference years. For countries with only one observation, estimates for the reference years were derived using rates of change in the migrant stock in the years preceding or following the single observation available. A model was used to estimate migrants for countries that had no data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "International migrant stock is the number of people born in a country other than that in which they live, including refugees." }, { "id": "Source", "value": "United Nations Population Division, Trends in Total Migrant Stock: 2008 Revision." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" } ], "source_id": "25" }, { "id": "SP.ADO.TFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Adolescent fertility rate (births per 1,000 women ages 15-19)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Adolescent fertility rate is the number of births per 1,000 women ages 15-19." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Population Division, World Population Prospects." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "25" }, { "id": "SP.DYN.LE00.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Life expectancy at birth, female (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "(1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "25" }, { "id": "SP.DYN.LE00.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Life expectancy at birth, male (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "(1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "25" }, { "id": "SP.DYN.TFRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Relevance to gender indicator: it can indicate the status of women within households and a woman’s decision about the number and spacing of children." }, { "id": "IndicatorName", "value": "Fertility rate, total (births per woman)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "(1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "25" }, { "id": "SP.POP.0014.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 0-14, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2017 Revision." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "25" }, { "id": "SP.POP.0014.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Population ages 0-14 (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Population between the ages 0 to 14 as a percentage of the total population. Population is based on the de facto definition of population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "25" }, { "id": "SP.POP.1564.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2017 Revision." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "25" }, { "id": "SP.POP.1564.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Population ages 15-64 (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Total population between the ages 15 to 64 as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "25" }, { "id": "SP.POP.65UP.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 65 and above, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2017 Revision." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "25" }, { "id": "SP.POP.65UP.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Population ages 65 and above (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Population ages 65 and above as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "25" }, { "id": "SP.POP.DPND", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Relevance to gender indicator: this indicator implies the dependency burden that the working-age population bears in relation to children and the elderly. Many times single or widowed women who are the sole caregiver of a household have a high dependency ratio." }, { "id": "IndicatorName", "value": "Age dependency ratio (% of working-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on age distributions of United Nations Population Division's World Population Prospects: 2017 Revision." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "25" }, { "id": "SP.POP.GROW", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Population growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Annual population growth rate. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2017 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "25" }, { "id": "SP.POP.SCIE.RD.P6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Researchers in R&D (per million people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Estimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers." }, { "id": "Longdefinition", "value": "The number of researchers engaged in Research &Development (R&D), expressed as per million. Researchers are professionals who conduct research and improve or develop concepts, theories, models techniques instrumentation, software of operational methods. R&D covers basic research, applied research, and experimental development." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "25" }, { "id": "SP.POP.TECH.RD.P6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Technicians in R&D (per million people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Estimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers." }, { "id": "Longdefinition", "value": "The number of technicians participated in Research & Development (R&D), expressed as per million. Technicians and equivalent staff are people who perform scientific and technical tasks involving the application of concepts and operational methods, normally under the supervision of researchers. R&D covers basic research, applied research, and experimental development." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "25" }, { "id": "SP.POP.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis." }, { "id": "IndicatorName", "value": "Population, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Current population estimates for developing countries that lack (i) reliable recent census data, and (ii) pre- and post-census estimates for countries with census data, are provided by the United Nations Population Division and other agencies. \n\nThe cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in both the model and the data. In the UN estimates the five-year age group is the cohort unit and five-year period data are used; therefore interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\nBecause future trends cannot be known with certainty, population projections have a wide range of uncertainty." }, { "id": "Longdefinition", "value": "Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "(1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "25" }, { "id": "SP.RUR.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Rural population" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population. Aggregation of urban and rural population may not add up to total population because of different country coverages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision." }, { "id": "Topic", "value": "Environment: Density & urbanization" } ], "source_id": "25" }, { "id": "SP.RUR.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Rural population (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision." }, { "id": "Topic", "value": "Environment: Density & urbanization" } ], "source_id": "25" }, { "id": "SP.URB.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Urban population" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. \n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. Aggregation of urban and rural population may not add up to total population because of different country coverages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision." }, { "id": "Topic", "value": "Environment: Density & urbanization" } ], "source_id": "25" }, { "id": "SP.URB.TOTL.IN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Urban population (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. \n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Population Division. World Urbanization Prospects: 2018 Revision." }, { "id": "Topic", "value": "Environment: Density & urbanization" } ], "source_id": "25" }, { "id": "TM.VAL.ICTG.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "ICT goods imports (% total goods imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Detailed trade data are widely available from country trade statistics. These are collected by the UNSD and published in their UN COMTRADE database. The ICT goods trade indicators are usually compiled by interested international and national agencies using COMTRADE data. Concepts are therefore consistent with those applying to the COMTRADE database.\n\nThe main statistical issue associated with this indicator appears to be the different treatment of re-exports and re-imports by countries, depending on whether the Special or General Trade System is used.2 Re-imports are separately reported for some countries and the value of ICT re-imports (which is included in the value of ICT imports for those countries) is generally small." }, { "id": "Longdefinition", "value": "Information and communication technology goods imports include computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components, and other information and technology goods (miscellaneous)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Conference on Trade and Development's UNCTADstat database at http://unctadstat.unctad.org/ReportFolders/reportFolders.aspx." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "25" }, { "id": "TX.QTY.MRCH.XD.WD", "metatype": [ { "id": "BasePeriod", "value": "2000" }, { "id": "IndicatorName", "value": "Export volume index (2000 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Export volume indexes are derived from UNCTAD's volume index series and are the ratio of the export value indexes to the corresponding unit value indexes. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD’s estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, interna­tional and national sources, and UNCTAD secretariat estimates and calculates unit value indexes at the country level using the current year’s trade values as weights. For economies for which UNCTAD does not publish data, the export volume indexes (lines 72) in the IMF's International Financial Statistics are used." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics." }, { "id": "Topic", "value": "Private Sector & Trade: Trade indexes" } ], "source_id": "25" }, { "id": "TX.VAL.FUEL.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Merchandise export shares may not sum to 100 percent because of unclassified trade." }, { "id": "IndicatorName", "value": "Fuel exports (% of merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Fuels comprise the commodities in SITC section 3 (mineral fuels, lubricants and related materials)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates through the WITS platform from the Comtrade database maintained by the United Nations Statistics Division." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "25" }, { "id": "TX.VAL.MRCH.XD.WD", "metatype": [ { "id": "BasePeriod", "value": "2000" }, { "id": "IndicatorName", "value": "Export value index (2000 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Export values are the current value of exports (f.o.b.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2000). UNCTAD's export value indexes are reported for most economies. For selected economies for which UNCTAD does not publish data, the export value indexes are derived from export volume indexes (line 72) and corresponding unit value indexes of exports (line 74) in the IMF's International Financial Statistics." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics." }, { "id": "Topic", "value": "Private Sector & Trade: Trade indexes" } ], "source_id": "25" }, { "id": "TX.VAL.TECH.MF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "High-technology exports (% of manufactured exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because industrial sectors specializing in a few high-technology products may also produce low-technology products, the product approach is more appropriate for international trade. The method takes only R&D intensity into account, but other characteristics of high technology are also important, such as knowhow, scientific personnel, and technology embodied in patents. Considering these characteristics would yield a different list (see Hatzichronoglou 1997)." }, { "id": "Longdefinition", "value": "High-technology exports are products with high R&D intensity, such as in aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations, Comtrade database through the WITS platform." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "25" }, { "id": "NYGDPMKTPKDZ", "metatype": [ { "id": "Aggregationmethod", "value": "Aggregate growth for the world and all sub-groups of countries (such as regions and income groups) is calculated as GDP-weighted average (at average 2010-19 prices and market exchange rates) of country-specific growth rates. Income groups are defined as in the World Bank's classification of country groups." }, { "id": "Derivationmethod", "value": "The forecast process starts with initial assumptions about advanced-economy growth and commodity price forecasts. These are used as conditioning assumptions for the first set of growth forecasts for EMDEs, which are produced using macroeconometric models, accounting frameworks to ensure national account identities and global consistency, estimates of spillovers from major economies, and high-frequency indicators. These forecasts are then evaluated to ensure consistency of treatment across similar EMDEs. This is followed by extensive discussions with World Bank country teams, who conduct continuous macroeconomic monitoring and dialogue with country authorities. Throughout the forecasting process, staff use macroeconometric models that allow the combination of judgement and consistency with model-based insights." }, { "id": "Generalcomments", "value": "Data used to prepare country forecasts are from a variety of sources. National Income Accounts (NIA), Balance of Payments (BOP), and fiscal data are from Haver Analytics; the World Development Indicators by the World Bank; the \"World Economic Outlook\", Balance of Payments Statistics, and International Financial Statistics by the International Monetary Fund. Population data and forecasts are from the \"World Population Prospects\" by the United Nations. Country- and lending-group classifications are from the World Bank. DECPG databases include commodity prices, data on previous forecast vintages, and in-house country classifications. Other internal databases include high-frequency indicators such as industrial production, consumer price indexes, house prices, exchange rates, exports, imports, and stock market indexes, based on data from Bloomberg, Haver Analytics, OECD Analytical House Prices Indicators, IMF Balance of Payments Statistics, and IMF International Financial Statistics." }, { "id": "IndicatorName", "value": "GDP growth, constant (average 2010-19 prices and market exchange rates)" }, { "id": "Limitationsandexceptions", "value": "GDP data are on fiscal year basis for Bangladesh, Botswana, Egypt, Ethiopia, India, Nepal, and Uganda, and are based on factor cost for Pakistan" }, { "id": "Periodicity", "value": "Semi-annual" }, { "id": "Source", "value": "World Bank, Global Economic Prospects database." }, { "id": "Topic", "value": "Macroeconomic forecasts" } ], "source_id": "27" }, { "id": "IQ.CPA.BREG.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA business regulatory environment rating (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Business regulatory environment assesses the extent to which the legal, regulatory, and policy environments help or hinder private businesses in investing, creating jobs, and becoming more productive." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.DEBT.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA debt policy rating (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Debt policy assesses whether the debt management strategy is conducive to minimizing budgetary risks and ensuring long-term debt sustainability." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.ECON.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA economic management cluster average (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The economic management cluster includes macroeconomic management, fiscal policy, and debt policy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.ENVR.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA policy and institutions for environmental sustainability rating (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Policy and institutions for environmental sustainability assess the extent to which environmental policies foster the protection and sustainable use of natural resources and the management of pollution." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.FINQ.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA quality of budgetary and financial management rating (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Quality of budgetary and financial management assesses the extent to which there is a comprehensive and credible budget linked to policy priorities, effective financial management systems, and timely and accurate accounting and fiscal reporting, including timely and audited public accounts." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.FINS.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA financial sector rating (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Financial sector assesses the structure of the financial sector and the policies and regulations that affect it." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.FISP.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA fiscal policy rating (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Fiscal policy assesses the short- and medium-term sustainability of fiscal policy (taking into account monetary and exchange rate policy and the sustainability of the public debt) and its impact on growth." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.GNDR.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA gender equality rating (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Gender equality assesses the extent to which the country has installed institutions and programs to enforce laws and policies that promote equal access for men and women in education, health, the economy, and protection under law." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.HRES.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA building human resources rating (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Building human resources assesses the national policies and public and private sector service delivery that affect the access to and quality of health and education services, including prevention and treatment of HIV/AIDS, tuberculosis, and malaria." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.IRAI.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "IDA resource allocation index (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "IDA Resource Allocation Index is obtained by calculating the average score for each cluster and then by averaging those scores. For each of 16 criteria countries are rated on a scale of 1 (low) to 6 (high)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.MACR.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA macroeconomic management rating (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Macroeconomic management assesses the monetary, exchange rate, and aggregate demand policy framework." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.PADM.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA quality of public administration rating (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Quality of public administration assesses the extent to which civilian central government staff is structured to design and implement government policy and deliver services effectively." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.PRES.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA equity of public resource use rating (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Equity of public resource use assesses the extent to which the pattern of public expenditures and revenue collection affects the poor and is consistent with national poverty reduction priorities." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.PROP.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA property rights and rule-based governance rating (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Property rights and rule-based governance assess the extent to which private economic activity is facilitated by an effective legal system and rule-based governance structure in which property and contract rights are reliably respected and enforced." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.PROT.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA social protection rating (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Social protection and labor assess government policies in social protection and labor market regulations that reduce the risk of becoming poor, assist those who are poor to better manage further risks, and ensure a minimal level of welfare to all people." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.PUBS.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA public sector management and institutions cluster average (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The public sector management and institutions cluster includes property rights and rule-based governance, quality of budgetary and financial management, efficiency of revenue mobilization, quality of public administration, and transparency, accountability, and corruption in the public sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.REVN.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA efficiency of revenue mobilization rating (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Efficiency of revenue mobilization assesses the overall pattern of revenue mobilization--not only the de facto tax structure, but also revenue from all sources as actually collected." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.SOCI.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA policies for social inclusion/equity cluster average (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The policies for social inclusion and equity cluster includes gender equality, equity of public resource use, building human resources, social protection and labor, and policies and institutions for environmental sustainability." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.STRC.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA structural policies cluster average (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The structural policies cluster includes trade, financial sector, and business regulatory environment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.TRAD.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA trade rating (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Trade assesses how the policy framework fosters trade in goods." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "IQ.CPA.TRAN.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high)" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Transparency, accountability, and corruption in the public sector assess the extent to which the executive can be held accountable for its use of funds and for the results of its actions by the electorate and by the legislature and judiciary, and the extent to which public employees within the executive are required to account for administrative decisions, use of resources, and results obtained. The three main dimensions assessed here are the accountability of the executive to oversight institutions and of public employees for their performance, access of civil society to information on public affairs, and state capture by narrow vested interests." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Group, CPIA database (http://www.worldbank.org/ida)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "31" }, { "id": "GFDD.AI.01", "metatype": [ { "id": "IndicatorName", "value": "Bank accounts per 1,000 adults" }, { "id": "Longdefinition", "value": "For each country calculated as: 1,000*reported number of depositors/adult population in the reporting country." }, { "id": "Shortdefinition", "value": "Number of depositors with commercial banks per 1,000 adults." }, { "id": "Source", "value": "Financial Access Survey (FAS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.02", "metatype": [ { "id": "IndicatorName", "value": "Bank branches per 100,000 adults" }, { "id": "Longdefinition", "value": "For each country calculated as: 100,000*reported number of commercial bank branches/adult population in the reporting country." }, { "id": "Shortdefinition", "value": "Number of commercial bank branches per 100,000 adults." }, { "id": "Source", "value": "Financial Access Survey (FAS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.03", "metatype": [ { "id": "IndicatorName", "value": "Firms with a bank loan or line of credit (%)" }, { "id": "Longdefinition", "value": "Percentage of firms in the formal sector with a line of credit or a loan from a formal financial institution, such as a bank, credit union, microfinance institution, or cooperative." }, { "id": "Shortdefinition", "value": "Percentage of firms in the formal sector with a line of credit or a loan from a financial institution." }, { "id": "Source", "value": "Enterprise Surveys, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.04", "metatype": [ { "id": "IndicatorName", "value": "Small firms with a bank loan or line of credit (%)" }, { "id": "Longdefinition", "value": "Percentage of small firms (5-19 workers) in the formal sector with a line of credit or a loan from a (formal) financial institution, such as a bank, credit union, microfinance institution, or cooperative." }, { "id": "Shortdefinition", "value": "Percentage of small firms (5-19 workers) in the formal sector with a line of credit or a loan from a financial institution." }, { "id": "Source", "value": "Enterprise Surveys, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.05", "metatype": [ { "id": "IndicatorName", "value": "Financial institution account (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents with an account (self or together with someone else) at a bank, credit union, another financial institution (e.g., cooperative, microfinance institution), or the post office (if applicable) including respondents who reported having a debit card (% age 15+). The values correspond to Global Findex variable fin1_t_a." }, { "id": "Shortdefinition", "value": "The percentage of respondents with an account (self or together with someone else) at a bank, credit union, another financial institution (e.g., cooperative, microfinance institution), or the post office (if applicable) including respondents who reported having a debit card (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.06", "metatype": [ { "id": "IndicatorName", "value": "Saved at a financial institution (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving or setting aside any money by using an account at a formal financial institution such as a bank, credit union, microfinance institution, or cooperative in the past 12 months (% age 15+). The values correspond to Global Findex variable fin17a_t_a." }, { "id": "Shortdefinition", "value": "The percentage of respondents who report saving or setting aside any money by using an account at a formal financial institution such as a bank, credit union, microfinance institution, or cooperative in the past 12 months (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.07", "metatype": [ { "id": "IndicatorName", "value": "Borrowed from a financial institution (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money from a bank, credit union, microfinance institution, or another financial institution such as a cooperative in the past 12 months (% age 15+). The values correspond to Global Findex variable fin22a_t_a." }, { "id": "Shortdefinition", "value": "The percentage of respondents who report borrowing any money from a bank, credit union, microfinance institution, or another financial institution such as a cooperative in the past 12 months (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.08", "metatype": [ { "id": "IndicatorName", "value": "Borrowed to start, operate, or expand a farm or business (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using their accounts at a formal financial institution for farming/business purposes only or for both farming/business purposes and personal transactions (% age 15+). The values correspond to Global Findex variable fin21_t_a." }, { "id": "Shortdefinition", "value": "The percentage of respondents who report using their accounts at a formal financial institution for farming/business purposes only or for both farming/business purposes and personal transactions (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.09", "metatype": [ { "id": "IndicatorName", "value": "Received government transfers: into a financial institution account (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using their accounts at a formal financial institution to receive money or payments from the government in the past 12 months (% age 15+). The values correspond to Global Findex variable fin39a_t_a." }, { "id": "Shortdefinition", "value": "The percentage of respondents who report using their accounts at a formal financial institution to receive money or payments from the government in the past 12 months (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.10", "metatype": [ { "id": "IndicatorName", "value": "Received domestic remittances: through a financial institution (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using their accounts at a formal financial institution to receive money from family members living elsewhere in the past 12 months (% age 15+). The values correspond to Global Findex variable fin27a_t_a." }, { "id": "Shortdefinition", "value": "The percentage of respondents who report using their accounts at a formal financial institution to receive money from family members living elsewhere in the past 12 months (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.11", "metatype": [ { "id": "IndicatorName", "value": "Received wages: into a financial institution account (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using their accounts at a formal financial institution to receive money or payments for work or from selling goods in the past 12 months (% age 15+). The values correspond to Global Findex variable fin34a_t_a." }, { "id": "Shortdefinition", "value": "The percentage of respondents who report using their accounts at a formal financial institution to receive money or payments for work or from selling goods in the past 12 months (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.12", "metatype": [ { "id": "IndicatorName", "value": "Saved any money in the past year (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving or setting aside any money in the past 12 months (% age 15+). The values correspond to Global Findex variable fin18_t_d." }, { "id": "Shortdefinition", "value": "The percentage of respondents who report saving or setting aside any money in the past 12 months (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.13", "metatype": [ { "id": "IndicatorName", "value": "Saved using a savings club or a person outside the family (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report saving or setting aside any money by using an informal savings club or a person outside the family in the past 12 months (% age 15+). The values correspond to Global Findex variable fin17b_t_a." }, { "id": "Shortdefinition", "value": "The percentage of respondents who report saving or setting aside any money by using an informal savings club or a person outside the family in the past 12 months (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.14", "metatype": [ { "id": "IndicatorName", "value": "Borrowed any money in the past year (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who borrowed any money in the past 12 months from any of the following sources: a formal financial institution, a store by using installment credit, family or friends, employer, or another private lender (% age 15+). (Note that getting a loan does not necessarily require having an account.) The variables correspond to Global Findex variable fin23_t_d." }, { "id": "Shortdefinition", "value": "The percentage of respondents who borrowed any money in the past 12 months from any of the following sources: a formal financial institution, a store by using installment credit, family or friends, employer, or another private lender (% age 15+). (Note that getting a loan does not necessarily require having an account.)" }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.15", "metatype": [ { "id": "IndicatorName", "value": "Loan from a private lender in the past year (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money from a private lender in the past 12 months (% age 15+). [Global Findex legacy series]" }, { "id": "Shortdefinition", "value": "The percentage of respondents who report borrowing any money from a private lender in the past 12 months (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.16", "metatype": [ { "id": "IndicatorName", "value": "Loan from an employer in the past year (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money from an employer in the past 12 months (% age 15+). [Global Findex legacy series]" }, { "id": "Shortdefinition", "value": "The percentage of respondents who report borrowing any money from an employer in the past 12 months (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.17", "metatype": [ { "id": "IndicatorName", "value": "Borrowed from a store by buying on credit (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who borrowed any money in the past 12 months from a store by using installment credit or buying on credit (% age 15+). The variables correspond to Global Findex variable fin21b_t_14_a." }, { "id": "Shortdefinition", "value": "The percentage of respondents who borrowed any money in the past 12 months from a store by using installment credit or buying on credit (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.18", "metatype": [ { "id": "IndicatorName", "value": "Borrowed from family or friends (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report borrowing any money from family or friends in the past 12 months (% age 15+). The variables correspond to Global Findex variable fin22b_t_a." }, { "id": "Shortdefinition", "value": "The percentage of respondents who report borrowing any money from family or friends in the past 12 months (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.19", "metatype": [ { "id": "IndicatorName", "value": "Used checks to make payments in the past year (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who used checks in the past 12 months to make payments on bills or to buy things using money from their accounts (% age 15+). The variables correspond to Global Findex variable fin68a_11_a." }, { "id": "Shortdefinition", "value": "The percentage of respondents who used checks in the past 12 months to make payments on bills or to buy things using money from their accounts (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.20", "metatype": [ { "id": "IndicatorName", "value": "Credit card ownership (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents with a credit card (% age 15+). The variables correspond to Global Findex variable fin7_t_a." }, { "id": "Shortdefinition", "value": "The percentage of respondents with a credit card (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.21", "metatype": [ { "id": "IndicatorName", "value": "Debit card ownership (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents with a debit card (% age 15+). The variables correspond to Global Findex variable fin2_t_a." }, { "id": "Shortdefinition", "value": "The percentage of respondents with a debit card (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.22", "metatype": [ { "id": "IndicatorName", "value": "Made digital payments in the past year (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who used electronic payments (payments that one makes or that are made automatically including wire transfers or payments made online) in the past 12 months to make payments on bills or to buy things using money from their accounts (% age 15+). The variables correspond to Global Findex variable g20_t_made." }, { "id": "Shortdefinition", "value": "The percentage of respondents who used electronic payments (payments that one makes or that are made automatically including wire transfers or payments made online) in the past 12 months to make payments on bills or to buy things using money from their accounts (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.23", "metatype": [ { "id": "IndicatorName", "value": "Paid utility bills: using a mobile phone (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile phone to pay bills in the past 12 months (% age 15+). The variables correspond to Global Findex variable fin31b_t_a." }, { "id": "Shortdefinition", "value": "The percentage of respondents who report using a mobile phone to pay bills in the past 12 months (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.24", "metatype": [ { "id": "IndicatorName", "value": "Sent domestic remittances: through a mobile phone (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using a mobile phone to send money in the past 12 months (% age 15+). The variables correspond to Global Findex variable fin29b_t_a." }, { "id": "Shortdefinition", "value": "The percentage of respondents who report using a mobile phone to send money in the past 12 months (% age 15+)." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.25", "metatype": [ { "id": "IndicatorName", "value": "ATMs per 100,000 adults" }, { "id": "Longdefinition", "value": "For each country calculated as: 100,000*Number of ATMs/adult population in the reporting country." }, { "id": "Shortdefinition", "value": "Number of ATMs per 100,000 adults." }, { "id": "Source", "value": "Financial Access Survey (FAS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.26", "metatype": [ { "id": "IndicatorName", "value": "No deposit and no withdrawal from an account in the past year (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who did not make deposits or withdrawals from a financial institution account in the past year. The values correspond to Global Findex variable fin9_t_d1." }, { "id": "Shortdefinition", "value": "The percentage of respondents who did not make deposits or withdrawals from a financial institution account in the past year." }, { "id": "Source", "value": "Global Financial Inclusion (Global Findex) Database, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.27", "metatype": [ { "id": "IndicatorName", "value": "Firms with a checking or savings account (%)" }, { "id": "Longdefinition", "value": "Percentage of firms with a checking or savings account." }, { "id": "Shortdefinition", "value": "Percentage of firms with a checking or savings account." }, { "id": "Source", "value": "Enterprise Surveys, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.28", "metatype": [ { "id": "IndicatorName", "value": "Firms using banks to finance investments (%)" }, { "id": "Longdefinition", "value": "Percentage of firms using banks to finance purchases of fixed assets." }, { "id": "Shortdefinition", "value": "Percentage of firms using banks to finance purchases of fixed assets." }, { "id": "Source", "value": "Enterprise Surveys, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.29", "metatype": [ { "id": "IndicatorName", "value": "Firms using banks to finance working capital (%)" }, { "id": "Longdefinition", "value": "Percentage of firms using bank loans to finance working capital." }, { "id": "Shortdefinition", "value": "Percentage of firms using bank loans to finance working capital." }, { "id": "Source", "value": "Enterprise Surveys, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.30", "metatype": [ { "id": "IndicatorName", "value": "Loans requiring collateral (%)" }, { "id": "Longdefinition", "value": "Percentage of loans where a formal financial institution requires collateral in order to provide the financing." }, { "id": "Shortdefinition", "value": "Percentage of loans where a formal financial institution requires collateral in order to provide the financing." }, { "id": "Source", "value": "Enterprise Surveys, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.31", "metatype": [ { "id": "IndicatorName", "value": "Value of collateral needed for a loan (% of the loan amount)" }, { "id": "Longdefinition", "value": "Value of collateral needed by a formal financial institution for a loan or line of credit as a percentage of the loan value or the value of the line of credit." }, { "id": "Shortdefinition", "value": "Value of collateral needed by a formal financial institution for a loan or line of credit as a percentage of the loan value or the value of the line of credit." }, { "id": "Source", "value": "Enterprise Surveys, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.32", "metatype": [ { "id": "IndicatorName", "value": "Firms not needing a loan (%)" }, { "id": "Longdefinition", "value": "Percent of firms that did not apply for a loan in the last fiscal year because they did not need a loan. The denominator is the sum of all firms who applied and did not apply for a loan. The numerator is the number of firms who did not apply for a loan and also stated that they did not need a loan." }, { "id": "Shortdefinition", "value": "Percent of firms that did not apply for a loan in the last fiscal year because they did not need a loan. The denominator is the sum of all firms who applied and did not apply for a loan. The numerator is the number of firms who did not apply for a loan and also stated that they did not need a loan." }, { "id": "Source", "value": "Enterprise Surveys, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.33", "metatype": [ { "id": "IndicatorName", "value": "Firms whose recent loan application was rejected (%)" }, { "id": "Longdefinition", "value": "Percent of firms whose most recent loan application was rejected by a formal financial institution." }, { "id": "Shortdefinition", "value": "Percent of firms whose most recent loan application was rejected by a formal financial institution." }, { "id": "Source", "value": "Enterprise Surveys, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.34", "metatype": [ { "id": "IndicatorName", "value": "Investments financed by banks (%)" }, { "id": "Longdefinition", "value": "Estimated proportion of purchases of fixed assets that was financed from bank loans." }, { "id": "Shortdefinition", "value": "Estimated proportion of purchases of fixed assets that was financed from bank loans." }, { "id": "Source", "value": "Enterprise Surveys, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.35", "metatype": [ { "id": "IndicatorName", "value": "Working capital financed by banks (%)" }, { "id": "Longdefinition", "value": "Proportion of the working capital that was financed by bank loans." }, { "id": "Shortdefinition", "value": "Proportion of the working capital that was financed by bank loans." }, { "id": "Source", "value": "Enterprise Surveys, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AI.36", "metatype": [ { "id": "IndicatorName", "value": "Firms identifying access to finance as a major constraint (%)" }, { "id": "Longdefinition", "value": "Percentage of firms identifying access/cost of finance as a \"major\" or \"very severe\" obstacle." }, { "id": "Shortdefinition", "value": "Percentage of firms identifying access/cost of finance as a \"major\" or \"very severe\" obstacle." }, { "id": "Source", "value": "Enterprise Surveys, World Bank" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AM.01", "metatype": [ { "id": "IndicatorName", "value": "Value traded excluding top 10 traded companies to total value traded (%)" }, { "id": "Longdefinition", "value": "Value of all traded shares outside of the top ten largest traded companies as a share of total value of all traded shares in a stock market exchange. WFE provides data on the exchange level. This variable is aggregated up to the country level by taking a simple average over exchanges." }, { "id": "Shortdefinition", "value": "Value of all traded shares outside of the largest ten traded companies as a share of total value of all traded shares in a stock market exchange." }, { "id": "Source", "value": "World Federation of Exchanges" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AM.02", "metatype": [ { "id": "IndicatorName", "value": "Market capitalization excluding top 10 companies to total market capitalization (%)" }, { "id": "Longdefinition", "value": "Value of listed shares outside of the top ten largest companies to total value of all listed shares." }, { "id": "Shortdefinition", "value": "Value of listed shares outside of the largest ten largest companies to total value of all listed shares." }, { "id": "Source", "value": "World Federation of Exchanges" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.AM.03", "metatype": [ { "id": "IndicatorName", "value": "Nonfinancial corporate bonds to total bonds and notes outstanding (%)" }, { "id": "Longdefinition", "value": "Total amount of domestic nonfinancial corporate bonds and notes outstanding to total amount of domestic bonds and notes outstanding, both corporate and noncorporate. BIS DSS Table C1 (domestic debt amount: nonfinancial corporates) / (domestic debt amount: all issuers)." }, { "id": "Shortdefinition", "value": "Total amount of domestic nonfinancial corporate bonds and notes outstanding to total amount of domestic bonds and notes outstanding, both corporate and noncorporate." }, { "id": "Source", "value": "Debt Securities Statistics (DSS), Bank for International Settlements (BIS)" }, { "id": "Topic", "value": "Access" } ], "source_id": "32" }, { "id": "GFDD.DI.01", "metatype": [ { "id": "IndicatorName", "value": "Private credit by deposit money banks to GDP (%)" }, { "id": "Longdefinition", "value": "Private credit by deposit money banks and other financial institutions to GDP. Raw data are from the electronic version of the IMF’s International Financial Statistics. Private credit by deposit money banks (IFS line 22d and FOSAOP); GDP in local currency (IFS line NGDP)." }, { "id": "Shortdefinition", "value": "The financial resources provided to the private sector by domestic money banks as a share of GDP. Domestic money banks comprise commercial banks and other financial institutions that accept transferable deposits, such as demand deposits." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DI.02", "metatype": [ { "id": "IndicatorName", "value": "Deposit money banks' assets to GDP (%)" }, { "id": "Longdefinition", "value": "Claims on domestic real nonfinancial sector by deposit money banks as a share of GDP. Raw data are from the electronic version of the IMF’s International Financial Statistics. Deposit money bank assets (IFS lines 22, a-d, FOSAG, FOSAOG, FOSAON and FOSAOP); GDP in local currency (IFS line NGDP)." }, { "id": "Shortdefinition", "value": "Total assets held by deposit money banks as a share of GDP. Assets include claims on domestic real nonfinancial sector which includes central, state and local governments, nonfinancial public enterprises and private sector. Deposit money banks comprise commercial banks and other financial institutions that accept transferable deposits, such as demand deposits." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DI.03", "metatype": [ { "id": "IndicatorName", "value": "Nonbank financial institutions’ assets to GDP (%)" }, { "id": "Longdefinition", "value": "Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP. Raw data are from the electronic version of the IMF’s International Financial Statistics. Nonbank financial institutions assets (IFS lines 42, a-d, FFSAG, FFSAOG, FFSAON, FFSAP and FFSAP); GDP in local currency (IFS line NGDP)." }, { "id": "Shortdefinition", "value": "Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DI.04", "metatype": [ { "id": "IndicatorName", "value": "Deposit money bank assets to deposit money bank assets and central bank assets (%)" }, { "id": "Longdefinition", "value": "Raw data are from the electronic version of the IMF's International Financial Statistics (IFS lines 12 and 22, a-d, FASAG, FASAOS, FASAON, FASAOP, FOSAG, FOSAOG, FOSAON and FOSAOP)." }, { "id": "Shortdefinition", "value": "Total assets held by deposit money banks as a share of sum of deposit money bank and Central Bank claims on domestic nonfinancial real sector. Assets include claims on domestic real nonfinancial sector which includes central, state and local governments, nonfinancial public enterprises and private sector. Deposit money banks comprise commercial banks and other financial institutions that accept transferable deposits, such as demand deposits." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DI.05", "metatype": [ { "id": "IndicatorName", "value": "Liquid liabilities to GDP (%)" }, { "id": "Longdefinition", "value": "Ratio of liquid liabilities to GDP. Raw data are from the electronic version of the IMF's International Financial Statistics. Liquid liabilities (IFS lines 55L, FFCD or, if not available, line 35L, FDSB); GDP in local currency (IFS line NGDP). For Eurocurrency area countries liquid liabilities are estimated by summing IFS items 34a, 34b and 35, or alternatively FDSBC, FDSBT, and FDSBO." }, { "id": "Shortdefinition", "value": "Ratio of liquid liabilities to GDP. Liquid liabilities are also known as broad money, or M3. They are the sum of currency and deposits in the central bank (M0), plus transferable deposits and electronic currency (M1), plus time and savings deposits, foreign currency transferable deposits, certificates of deposit, and securities repurchase agreements (M2), plus travelers checks, foreign currency time deposits, commercial paper, and shares of mutual funds or market funds held by residents." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DI.06", "metatype": [ { "id": "IndicatorName", "value": "Central bank assets to GDP (%)" }, { "id": "Longdefinition", "value": "Claims on domestic real nonfinancial sector by the Central Bank as a share of GDP. Raw data are from the electronic version of the IMF's International Financial Statistics. Central Bank claims (IFS lines 12, a-d, FASAG, FASAOS, FASAON and FASAOP); GDP in local currency (IFS line NGDP)." }, { "id": "Shortdefinition", "value": "Ratio of central bank assets to GDP. Central bank assets are claims on domestic real nonfinancial sector by the Central Bank." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DI.07", "metatype": [ { "id": "IndicatorName", "value": "Mutual fund assets to GDP (%)" }, { "id": "Longdefinition", "value": "Data taken from a variety of sources such as Investment Company Institute and national sources. Due to differences in sources these data are not strictly comparable across countries." }, { "id": "Shortdefinition", "value": "Ratio of assets of mutual funds to GDP. A mutual fund is a type of managed collective investment scheme that pools money from many investors to purchase securities." }, { "id": "Source", "value": "Nonbanking financial database, World Bank" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DI.08", "metatype": [ { "id": "IndicatorName", "value": "Financial system deposits to GDP (%)" }, { "id": "Longdefinition", "value": "Demand, time and saving deposits in deposit money banks and other financial institutions as a share of GDP. Raw data are from the electronic version of the IMF’s International Financial Statistics. Financial system deposits (IFS lines 24, 25, 44, 45, FOST and FOSD); GDP in local currency (IFS line NGDP)." }, { "id": "Shortdefinition", "value": "Demand, time and saving deposits in deposit money banks and other financial institutions as a share of GDP." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DI.09", "metatype": [ { "id": "IndicatorName", "value": "Life insurance premium volume to GDP (%)" }, { "id": "Longdefinition", "value": "Premium data is taken from various issues of Sigma reports (Swiss Re). Data on GDP in US dollars is from the electronic version of the World Development Indicators." }, { "id": "Shortdefinition", "value": "Ratio of life insurance premium volume to GDP. Premium volume is the insurer's direct premiums earned (if Property/Casualty) or received (if Life/Health) during the previous calendar year." }, { "id": "Source", "value": "Sigma Reports, Swiss Re" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DI.10", "metatype": [ { "id": "IndicatorName", "value": "Nonlife insurance premium volume to GDP (%)" }, { "id": "Longdefinition", "value": "Premium data is taken from various issues of Sigma reports (Swiss Re). Data on GDP in US dollars is from the electronic version of the World Development Indicators." }, { "id": "Shortdefinition", "value": "Ratio of nonlife insurance premium volume to GDP. Premium volume is the insurer's direct premiums earned (if Property/Casualty) or received (if Life/Health) during the previous calendar year." }, { "id": "Source", "value": "Sigma Reports, Swiss Re" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DI.11", "metatype": [ { "id": "IndicatorName", "value": "Insurance company assets to GDP (%)" }, { "id": "Longdefinition", "value": "Data taken from a variety of sources such as AXCO and national sources." }, { "id": "Shortdefinition", "value": "Ratio of assets of insurance companies to GDP." }, { "id": "Source", "value": "Nonbanking financial database, World Bank" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DI.12", "metatype": [ { "id": "IndicatorName", "value": "Private credit by deposit money banks and other financial institutions to GDP (%)" }, { "id": "Longdefinition", "value": "Private credit by deposit money banks and other financial institutions to GDP. Private credit by deposit money banks and other financial institutions (IFS lines 22d, 42d, FOSAOP and FFSAP); GDP in local currency (IFS line NGDP)." }, { "id": "Shortdefinition", "value": "Private credit by deposit money banks and other financial institutions to GDP." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DI.13", "metatype": [ { "id": "IndicatorName", "value": "Pension fund assets to GDP (%)" }, { "id": "Longdefinition", "value": "Ratio of assets of pension funds to GDP. A pension fund is any plan, fund, or scheme that provides retirement income. Data taken from a variety of sources such as OECD, AIOS, FIAP and national sources." }, { "id": "Shortdefinition", "value": "Ratio of assets of pension funds to GDP. A pension fund is any plan, fund, or scheme that provides retirement income." }, { "id": "Source", "value": "Nonbanking financial database, World Bank" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DI.14", "metatype": [ { "id": "IndicatorName", "value": "Domestic credit to private sector (% of GDP)" }, { "id": "Longdefinition", "value": "Domestic credit to private sector refers to financial resources provided to the private sector, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises." }, { "id": "Shortdefinition", "value": "Domestic credit to private sector refers to financial resources provided to the private sector." }, { "id": "Source", "value": "World Development Indicators (WDI), World Bank" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DM.01", "metatype": [ { "id": "IndicatorName", "value": "Stock market capitalization to GDP (%)" }, { "id": "Longdefinition", "value": "Total value of all listed shares in a stock market as a percentage of GDP." }, { "id": "Shortdefinition", "value": "Total value of all listed shares in a stock market as a percentage of GDP." }, { "id": "Source", "value": "World Federation of Exchanges; Global Stock Markets Factbook and supplemental S&P data, Standard & Poor's" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DM.02", "metatype": [ { "id": "IndicatorName", "value": "Stock market total value traded to GDP (%)" }, { "id": "Longdefinition", "value": "Total value of all traded shares in a stock market exchange as a percentage of GDP." }, { "id": "Shortdefinition", "value": "Total value of all traded shares in a stock market exchange as a percentage of GDP." }, { "id": "Source", "value": "World Federation of Exchanges; Global Stock Markets Factbook and supplemental S&P data, Standard & Poor's" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DM.03", "metatype": [ { "id": "IndicatorName", "value": "Outstanding domestic private debt securities to GDP (%)" }, { "id": "Longdefinition", "value": "Total amount of domestic private debt securities (amounts outstanding) issued in domestic markets as a share of GDP. It covers data on long-term bonds and notes, commercial paper and other short-term notes. BIS DSS Table C1 (domestic debt amount: all issuers) - (domestic debt amount: governments) / GDP. End of year data (i.e. December data) are considered for debt securities. GDP is from World Development Indicators." }, { "id": "Shortdefinition", "value": "Total amount of domestic private debt securities (amount outstanding) issued in domestic markets as a share of GDP. It covers data on long-term bonds and notes, commercial paper and other short-term notes." }, { "id": "Source", "value": "Debt Securities Statistics (DSS), Bank for International Settlements (BIS)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DM.04", "metatype": [ { "id": "IndicatorName", "value": "Outstanding domestic public debt securities to GDP (%)" }, { "id": "Longdefinition", "value": "Total amount of domestic public debt securities (amounts outstanding) issued in domestic markets as a share of GDP. It covers long-term bonds and notes, treasury bills, commercial paper and other short-term notes. BIS DSS Table C1 (domestic debt amount: governments) / GDP. End of year data (i.e. December data) are considered for debt securities. GDP is from World Development Indicators." }, { "id": "Shortdefinition", "value": "Total amount of domestic public debt securities (amount outstanding) issued in domestic markets as a share of GDP. It covers long-term bonds and notes, treasury bills, commercial paper and other short-term notes." }, { "id": "Source", "value": "Debt Securities Statistics (DSS), Bank for International Settlements (BIS)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DM.05", "metatype": [ { "id": "IndicatorName", "value": "Outstanding international private debt securities to GDP (%)" }, { "id": "Longdefinition", "value": "Amount of private international debt securities (amounts outstanding), as a share of GDP. It covers long-term bonds and notes and money market instruments placed on international markets. BIS DSS Table C1 (international debt amount: all issuers) - (international debt amount: governments) / GDP. End of year data (i.e. December data) are considered for debt securities. GDP is from World Development Indicators." }, { "id": "Shortdefinition", "value": "Amount of private international debt securities (amount outstanding), as a share of GDP. It covers long-term bonds and notes and money market instruments placed on international markets." }, { "id": "Source", "value": "Debt Securities Statistics (DSS), Bank for International Settlements (BIS)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DM.06", "metatype": [ { "id": "IndicatorName", "value": "Outstanding international public debt securities to GDP (%)" }, { "id": "Longdefinition", "value": "Amount of public international debt securities (amounts outstanding), as a share of GDP. It covers long-term bonds and notes and money market instruments placed on international markets. BIS DSS Table C1 (international debt amount: governments) / GDP. End of year data (i.e. December data) are considered for debt securities. GDP is from World Development Indicators." }, { "id": "Shortdefinition", "value": "Amount of public international debt securities (amount outstanding), as a share of GDP. It covers long-term bonds and notes and money market instruments placed on international markets." }, { "id": "Source", "value": "Debt Securities Statistics (DSS), Bank for International Settlements (BIS)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DM.07", "metatype": [ { "id": "IndicatorName", "value": "Outstanding total international debt securities / GDP (%)" }, { "id": "Longdefinition", "value": "Amount of international debt securities (amounts outstanding), as a share of GDP. It covers long-term bonds and notes and money market instruments placed on international markets. BIS DSS Table C1 (international debt amount: all issuers) / GDP. End of year data (i.e. December data) are considered for debt securities. GDP is from World Development Indicators." }, { "id": "Shortdefinition", "value": "Amount of international debt securities (amount outstanding), as a share of GDP. It covers long-term bonds and notes and money market instruments placed on international markets." }, { "id": "Source", "value": "Debt Securities Statistics (DSS), Bank for International Settlements (BIS)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DM.08", "metatype": [ { "id": "IndicatorName", "value": "Gross Portfolio Equity & Investment Fund Shares Liabilities / GDP" }, { "id": "Longdefinition", "value": "Ratio of gross portfolio equity liabilities to GDP. Equity liabilities include shares, stocks, participation, and comparable documents (such as American depository receipts) that usually denote ownership of equity. Raw data are from the electronic version of the IMF's International Financial Statistics. IFS line 8BALAZF / GDP. Local currency GDP from IFS was converted to USD. The exchange rate is from World Development Indicators." }, { "id": "Shortdefinition", "value": "Ratio of gross portfolio equity and investment fund shares liabilities to GDP. Equity liabilities include shares, stocks, participation, and similar documents (such as American depository receipts) that usually denote ownership of equity." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DM.09", "metatype": [ { "id": "IndicatorName", "value": "Gross Portfolio Equity & Investment Fund Shares Assets / GDP" }, { "id": "Longdefinition", "value": "Ratio of gross portfolio equity assets to GDP. Equity assets include shares, stocks, participation, and comparable documents (such as American depository receipts) that usually denote ownership of equity. Raw data are from the electronic version of the IMF's International Financial Statistics. IFS line 8BAAAZF / GDP. Local currency GDP from IFS was converted to USD. The exchange rate is from World Development Indicators." }, { "id": "Shortdefinition", "value": "Ratio of gross portfolio equity and investment fund shares assets to GDP. Equity assets include shares, stocks, participation, and similar documents (such as American depository receipts) that usually denote ownership of equity." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DM.10", "metatype": [ { "id": "IndicatorName", "value": "Gross portfolio debt liabilities to GDP (%)" }, { "id": "Longdefinition", "value": "Ratio of gross portfolio debt liabilities to GDP. Debt liabilities cover (1) bonds, debentures, notes, etc., and (2) money market or negotiable debt instruments. Raw data are from the electronic version of the IMF's International Financial Statistics. IFS line 8BBLAZF / GDP. Local currency GDP is from IFS (line 99B..ZF or, if not available, line 99B.CZF). GDP is from World Development Indicators." }, { "id": "Shortdefinition", "value": "Ratio of gross portfolio debt liabilities to GDP. Debt liabilities cover (1) bonds, debentures, notes, etc., and (2) money market or negotiable debt instruments." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DM.11", "metatype": [ { "id": "IndicatorName", "value": "Gross portfolio debt assets to GDP (%)" }, { "id": "Longdefinition", "value": "Ratio of gross portfolio debt assets to GDP. Debt assets cover (1) bonds, debentures, notes, etc., and (2) money market or negotiable debt instruments. Raw data are from the electronic version of the IMF's International Financial Statistics. IFS line 8BBAAZF / GDP Local currency GDP is from IFS. The exchange rate is from IMF International Financial Statistics." }, { "id": "Shortdefinition", "value": "Ratio of gross portfolio debt assets to GDP." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DM.12", "metatype": [ { "id": "IndicatorName", "value": "Syndicated loan issuance volume to GDP (%)" }, { "id": "Longdefinition", "value": "Total volume of newly issued syndicated loans by private entities in industries other than finance, holding companies and insurance, divided by GDP in current USD. GDP is from World Development Indicators." }, { "id": "Shortdefinition", "value": "Ratio of new syndicated borrowing volume by private entities in industries other than finance, holding companies and insurance to GDP." }, { "id": "Source", "value": "Loan Analytics Database, Dealogic; World Bank Global Syndicated Loans and Bonds Database (FinDebt)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DM.13", "metatype": [ { "id": "IndicatorName", "value": "Corporate bond issuance volume to GDP (%)" }, { "id": "Longdefinition", "value": "Total volume of newly issued corporate bonds by private entities in industries other than finance, holding companies and insurance, divided by GDP in current USD. GDP is from World Development Indicators." }, { "id": "Shortdefinition", "value": "Ratio of new corporate bond issuance volume by private entities in industries other than finance, holding companies and insurance to GDP." }, { "id": "Source", "value": "Debt Capital Market Database, Dealogic; World Bank Global Syndicated Loans and Bonds Database (FinDebt)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DM.14", "metatype": [ { "id": "IndicatorName", "value": "Syndicated loan average maturity (years)" }, { "id": "Longdefinition", "value": "Duration of loans weighted by deal volume. Average maturities are constructed in two steps: i) the maturity of each tranche is weighted by the value of the tranche to estimate the deal level weighted-average maturity, ii) deal-level weighted average maturities are weighted again by the total value of the deal to aggregate to the country-year level." }, { "id": "Shortdefinition", "value": "Volume weighted average maturity of new syndicated borrowing by private entities in industries other than finance, holding companies and insurance in years." }, { "id": "Source", "value": "Loan Analytics Database, Dealogic; World Bank Global Syndicated Loans and Bonds Database (FinDebt)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DM.15", "metatype": [ { "id": "IndicatorName", "value": "Corporate bond average maturity (years)" }, { "id": "Longdefinition", "value": "Duration of bonds weighted by deal volume. Average maturities are constructed in two steps: i) the maturity of each tranche is weighted by the value of the tranche to estimate the deal level weighted-average maturity, ii) deal-level weighted average maturities are weighted again by the total value of the bond to aggregate to the country-year level." }, { "id": "Shortdefinition", "value": "Volume weighted average maturity of new corporate bond issuance by private entities in industries other than finance, holding companies and insurance in years." }, { "id": "Source", "value": "Debt Capital Market Database, Dealogic; World Bank Global Syndicated Loans and Bonds Database (FinDebt)" }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.DM.16", "metatype": [ { "id": "IndicatorName", "value": "Credit flows by fintech and bigtech companies to GDP (%)" }, { "id": "Longdefinition", "value": "New lending provided by fintech and big tech companies over a calendar year, normalized by nominal GDP. Fintech lending were originally built around decentralised platforms where individual lenders choose borrowers or projects to lend to in a market framework. Some platforms have moved to fund loans from institutional investors rather than only individuals, and many use increasingly sophisticated credit models. The core business of fintech credit platforms remains financial services. Big tech firms, in contrast, have a range of business lines, of which lending represents only one part, while their core business activity is typically of a non-financial nature. These firms have an existing user base, which facilitates the process of onboarding borrowers." }, { "id": "Shortdefinition", "value": "New lending provided by fintech and big tech companies over a calendar year, normalized by nominal GDP." }, { "id": "Source", "value": "CORNELLI, G., FROST, J., GAMBACORTA, L., RAU, R., WARDROP, R., and Ziegler, T. (2020) \"Fintech and Big Tech Credit: A New Database.\" BIS Working Papers No. 887." }, { "id": "Topic", "value": "Depth" } ], "source_id": "32" }, { "id": "GFDD.EI.01", "metatype": [ { "id": "IndicatorName", "value": "Bank net interest margin (%)" }, { "id": "Longdefinition", "value": "Accounting value of bank's net interest revenue as a share of its average interest-bearing (total earning) assets. Raw data are from Bankscope and Orbis. The formula applied to Bankscope is data2080[t] / ((data2010[t] + data2010[t-1])/2) and a comparable approach is applied to Orbis. Numerator and denominator are aggregated on the country level before division. Note that banks used in the calculation might differ between indicators. Calculated from underlying bank-by-bank unconsolidated data from Bankscope and Orbis. The result is not reported if a country-year has less than 3 bank-level observations." }, { "id": "Shortdefinition", "value": "Accounting value of bank's net interest revenue as a share of its average interest-bearing (total earning) assets." }, { "id": "Source", "value": "Bankscope (2000-14) and Orbis (2015-21), Bureau van Dijk (BvD)" }, { "id": "Topic", "value": "Efficiency" } ], "source_id": "32" }, { "id": "GFDD.EI.02", "metatype": [ { "id": "IndicatorName", "value": "Bank lending-deposit spread" }, { "id": "Longdefinition", "value": "Raw data are from the electronic version of the IMF’s International Financial Statistics. Difference between lending rate and deposit rate. Lending rate is the rate charged by banks on loans to the private sector and deposit interest rate is the rate offered by commercial banks on three-month deposits. IFS line 60P - line 60L." }, { "id": "Shortdefinition", "value": "Difference between lending rate and deposit rate. Lending rate is the rate charged by banks on loans to the private sector and deposit interest rate is the rate offered by commercial banks on three-month deposits." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Efficiency" } ], "source_id": "32" }, { "id": "GFDD.EI.03", "metatype": [ { "id": "IndicatorName", "value": "Bank noninterest income to total income (%)" }, { "id": "Longdefinition", "value": "Bank’s income that has been generated by noninterest related activities as a percentage of total income (net-interest income plus noninterest income). Noninterest related income includes net gains on trading and derivatives, net gains on other securities, net fees and commissions and other operating income. Raw data are from Bankscope and Orbis. The formula applied to Bankscope is data2085 / (data2080 + data2085) and a comparable approach is applied to Orbis. Number is only calculated when net-interest income is not negative. Note that banks used in the calculation might differ between indicators. Calculated from underlying bank-by-bank unconsolidated data from Bankscope and Orbis. The result is not reported if a country-year has less than 3 bank-level observations." }, { "id": "Shortdefinition", "value": "Bank’s income that has been generated by noninterest related activities as a percentage of total income (net-interest income plus noninterest income). Noninterest related income includes net gains on trading and derivatives, net gains on other securities, net fees and commissions and other operating income." }, { "id": "Source", "value": "Bankscope (2000-14) and Orbis (2015-21), Bureau van Dijk (BvD)" }, { "id": "Topic", "value": "Efficiency" } ], "source_id": "32" }, { "id": "GFDD.EI.04", "metatype": [ { "id": "IndicatorName", "value": "Bank overhead costs to total assets (%)" }, { "id": "Longdefinition", "value": "Operating expenses of a bank as a share of the value of all assets held. Total assets include total earning assets, cash and due from banks, foreclosed real estate, fixed assets, goodwill, other intangibles, current tax assets, deferred tax assets, discontinued operations and other assets. Raw data are from Bankscope and Orbis. The formula applied to Bankscope is data2090[t] / ((data2025[t] + data2025[t-1])/2) and a comparable approach is applied to Orbis. Numerator and denominator are first aggregated on the country level before division. Note that banks used in the calculation might differ between indicators. Calculated from underlying bank-by-bank unconsolidated data from Bankscope and Orbis. The result is not reported if a country-year has less than 3 bank-level observations." }, { "id": "Shortdefinition", "value": "Operating expenses of a bank as a share of the value of all assets held. Total assets include total earning assets, cash and due from banks, foreclosed real estate, fixed assets, goodwill, other intangibles, current tax assets, deferred tax assets, discontinued operations and other assets." }, { "id": "Source", "value": "Bankscope (2000-14) and Orbis (2015-21), Bureau van Dijk (BvD)" }, { "id": "Topic", "value": "Efficiency" } ], "source_id": "32" }, { "id": "GFDD.EI.05", "metatype": [ { "id": "IndicatorName", "value": "Bank return on assets (%, after tax)" }, { "id": "Longdefinition", "value": "Commercial banks’ after-tax net income to yearly averaged total assets. Raw data are from Bankscope and Orbis. The formula applied to Bankscope is data2115[t] / ((data2025[t] + data2025[t-1])/2) and a comparable approach is applied to Orbis. Numerator and denominator are first aggregated on the country level before division. Note that banks used in the calculation might differ between indicators. Calculated from underlying bank-by-bank unconsolidated data from Bankscope and Orbis. The result is not reported if a country-year has less than 3 bank-level observations." }, { "id": "Shortdefinition", "value": "Commercial banks’ after-tax net income to yearly averaged total assets." }, { "id": "Source", "value": "Bankscope (2000-14) and Orbis (2015-21), Bureau van Dijk (BvD)" }, { "id": "Topic", "value": "Efficiency" } ], "source_id": "32" }, { "id": "GFDD.EI.06", "metatype": [ { "id": "IndicatorName", "value": "Bank return on equity (%, after tax)" }, { "id": "Longdefinition", "value": "Commercial banks’ after-tax net income to yearly averaged equity. Raw data are from Bankscope and Orbis. The formula applied to Bankscope is data2115[t] / ((data2055[t] + data2055[t-1])/2) and a comparable approach is applied to Orbis. Numerator and denominator are first aggregated on the country level before division. Note that banks used in the calculation might differ between indicators. Calculated from underlying bank-by-bank unconsolidated data from Bankscope and Orbis. The result is not reported if a country-year has less than 3 bank-level observations." }, { "id": "Shortdefinition", "value": "Commercial banks’ after-tax net income to yearly averaged equity." }, { "id": "Source", "value": "Bankscope (2000-14) and Orbis (2015-21), Bureau van Dijk (BvD)" }, { "id": "Topic", "value": "Efficiency" } ], "source_id": "32" }, { "id": "GFDD.EI.07", "metatype": [ { "id": "IndicatorName", "value": "Bank cost to income ratio (%)" }, { "id": "Longdefinition", "value": "Operating expenses of a bank as a share of sum of net-interest revenue and other operating income. Raw data are from Bankscope and Orbis. The formula applied to Bankscope is data2090 / (data2080 + data2085) and a comparable approach is applied to Orbis. All Numerator and denominator are first aggregated on the country level before division. Note that banks used in the calculation might differ between indicators. Calculated from underlying bank-by-bank unconsolidated data from Bankscope and Orbis. The result is not reported if a country-year has less than 3 bank-level observations." }, { "id": "Shortdefinition", "value": "Operating expenses of a bank as a share of sum of net-interest revenue and other operating income." }, { "id": "Source", "value": "Bankscope (2000-14) and Orbis (2015-21), Bureau van Dijk (BvD)" }, { "id": "Topic", "value": "Efficiency" } ], "source_id": "32" }, { "id": "GFDD.EI.08", "metatype": [ { "id": "IndicatorName", "value": "Credit to government and state owned enterprises to GDP (%)" }, { "id": "Longdefinition", "value": "Raw data are from the electronic version of the IMF’s International Financial Statistics. (IFS line 22a + line 22b + line 22c) / GDP. IFS line FOSAG, FOSAOG and FOSAON are used respectively for line 22a, 22b and 22c when unavailable. Local currency GDP is from IFS (line NGDP)." }, { "id": "Shortdefinition", "value": "Ratio between credit by domestic money banks to the government and state-owned enterprises and GDP." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Efficiency" } ], "source_id": "32" }, { "id": "GFDD.EI.09", "metatype": [ { "id": "IndicatorName", "value": "Bank return on assets (%, before tax)" }, { "id": "Longdefinition", "value": "Commercial banks’ pre-tax income to yearly averaged total assets. Raw data are from Bankscope and Orbis. The formula applied to Bankscope is data10270[t] / ((data2025[t] + data2025[t-1])/2) and a comparable approach is applied to Orbis. Numerator and denominator are first aggregated on the country level before division. Note that banks used in the calculation might differ between indicators. Calculated from underlying bank-by-bank unconsolidated data from Bankscope and Orbis. The result is not reported if a country-year has less than 3 bank-level observations." }, { "id": "Shortdefinition", "value": "Commercial banks’ pre-tax income to yearly averaged total assets." }, { "id": "Source", "value": "Bankscope (2000-14) and Orbis (2015-21), Bureau van Dijk (BvD)" }, { "id": "Topic", "value": "Efficiency" } ], "source_id": "32" }, { "id": "GFDD.EI.10", "metatype": [ { "id": "IndicatorName", "value": "Bank return on equity (%, before tax)" }, { "id": "Longdefinition", "value": "Commercial banks’ pre-tax income to yearly averaged equity. Raw data are from Bankscope and Orbis. The formula applied to Bankscope is data10270[t] / ((data2055[t] + data2055[t-1])/2) and a comparable approach is applied to Orbis. Numerator and denominator are first aggregated on the country level before division. Note that banks used in the calculation might differ between indicators. Calculated from underlying bank-by-bank unconsolidated data from Bankscope and Orbis. The result is not reported if a country-year has less than 3 bank-level observations." }, { "id": "Shortdefinition", "value": "Commercial banks’ pre-tax income to yearly averaged equity." }, { "id": "Source", "value": "Bankscope (2000-14) and Orbis (2015-21), Bureau van Dijk (BvD)" }, { "id": "Topic", "value": "Efficiency" } ], "source_id": "32" }, { "id": "GFDD.EM.01", "metatype": [ { "id": "IndicatorName", "value": "Stock market turnover ratio (%)" }, { "id": "Longdefinition", "value": "Total value of shares traded during the period divided by the average market capitalization for the period." }, { "id": "Shortdefinition", "value": "Total value of shares traded during the period divided by the average market capitalization for the period." }, { "id": "Source", "value": "World Federation of Exchanges; Global Stock Markets Factbook and supplemental S&P data, Standard & Poor's" }, { "id": "Topic", "value": "Efficiency" } ], "source_id": "32" }, { "id": "GFDD.OI.01", "metatype": [ { "id": "IndicatorName", "value": "Bank concentration (%)" }, { "id": "Longdefinition", "value": "Assets of three largest commercial banks as a share of total commercial banking assets. Total assets include total earning assets, cash and due from banks, foreclosed real estate, fixed assets, goodwill, other intangibles, current tax assets, deferred tax assets, discontinued operations and other assets. Raw data are from Bankscope and Orbis. The formula applied to Bankscope is (sum(data2025) for the three largest banks in Bankscope) / (sum(data2025) for all banks) and a comparable approach is applied to Orbis. Calculated from underlying bank-by-bank unconsolidated data from Bankscope and Orbis." }, { "id": "Shortdefinition", "value": "Assets of three largest commercial banks as a share of total commercial banking assets. Total assets include total earning assets, cash and due from banks, foreclosed real estate, fixed assets, goodwill, other intangibles, current tax assets, deferred tax assets, discontinued operations and other assets." }, { "id": "Source", "value": "Bankscope (2000-14) and Orbis (2015-21), Bureau van Dijk (BvD)" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.02", "metatype": [ { "id": "IndicatorName", "value": "Bank deposits to GDP (%)" }, { "id": "Longdefinition", "value": "Demand, time and saving deposits in deposit money banks as a share of GDP. Raw data are from the electronic version of the IMF’s International Financial Statistics. Bank deposits (IFS lines 24, 25, FOST and FOSD); GDP in local currency (IFS line NGDP); end-of period CPI (IFS line PCPI); and average annual CPI is calculated using the monthly CPI values (IFS line PCPI)." }, { "id": "Shortdefinition", "value": "The total value of demand, time and saving deposits at domestic deposit money banks as a share of GDP. Deposit money banks comprise commercial banks and other financial institutions that accept transferable deposits, such as demand deposits." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.06", "metatype": [ { "id": "IndicatorName", "value": "5-bank asset concentration" }, { "id": "Longdefinition", "value": "Assets of five largest banks as a share of total commercial banking assets. Total assets include total earning assets, cash and due from banks, foreclosed real estate, fixed assets, goodwill, other intangibles, current tax assets, deferred tax, discontinued operations and other assets. Raw data are from Bankscope and Orbis. The formula applied to Bankscope is (sum(data2025) for the five largest banks in Bankscope) / (sum(data2025) for all banks) and a comparable approach is applied to Orbis. Calculated from underlying bank-by-bank unconsolidated data from Bankscope and Orbis." }, { "id": "Shortdefinition", "value": "Assets of five largest banks as a share of total commercial banking assets. Total assets include total earning assets, cash and due from banks, foreclosed real estate, fixed assets, goodwill, other intangibles, current tax assets, deferred tax, discontinued operations and other assets." }, { "id": "Source", "value": "Bankscope (2000-14) and Orbis (2015-21), Bureau van Dijk (BvD)" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.07", "metatype": [ { "id": "IndicatorName", "value": "Liquid liabilities in millions USD (2010 constant)" }, { "id": "Longdefinition", "value": "Raw data are from the electronic version of the IMF's International Financial Statistics. Liquid liabilities (IFS lines 55L..ZF, FFCD, or, if not available, line 35L..ZF, FDSB); for Eurocurrency area countries, liquid liabilities are estimated by summing IFS items 34A, 34B and 35, or alternatively FDSBC, FDSBT, and FDSBO." }, { "id": "Shortdefinition", "value": "Absolute value of liquid liabilities in 2010 (in past versions, 2000) US million dollars. Liquid liabilities are also known as broad money, or M3. They are the sum of currency and deposits in the central bank (M0), plus transferable deposits and electronic currency (M1), plus time and savings deposits, foreign currency transferable deposits, certificates of deposit, and securities repurchase agreements (M2), plus travelers checks, foreign currency time deposits, commercial paper, and shares of mutual funds or market funds held by residents." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.08", "metatype": [ { "id": "IndicatorName", "value": "Debt securities by offshore investors (net issuances) to GDP (%)" }, { "id": "Longdefinition", "value": "Ratio of net offshore debt securities issuances to GDP. Offshore debt securities issuances data from BIS DSS Table C3 (net Issues): International debt securities - all issuers." }, { "id": "Shortdefinition", "value": "Ratio of net offshore bank loans to GDP. An offshore bank is a bank located outside the country of residence of the depositor, typically in a low tax jurisdiction (or tax haven) that provides financial and legal advantages." }, { "id": "Source", "value": "Debt Securities Statistics (DSS), Bank for International Settlements (BIS)" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.09", "metatype": [ { "id": "IndicatorName", "value": "Debt securities by offshore investors (amounts outstanding) to GDP (%)" }, { "id": "Longdefinition", "value": "Ratio of outstanding offshore debt securities to GDP. Offshore debt securities from BIS DDS Table C3 (amount outstanding): International debt securities - all issuers." }, { "id": "Shortdefinition", "value": "Ratio of outstanding offshore bank loans to GDP. An offshore bank is a bank located outside the country of residence of the depositor, typically in a low tax jurisdiction (or tax haven) that provides financial and legal advantages." }, { "id": "Source", "value": "Debt Securities Statistics (DSS), Bank for International Settlements (BIS)" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.10", "metatype": [ { "id": "IndicatorName", "value": "External loans and deposits of reporting banks vis-à-vis the banking sector (% of domestic bank deposits)" }, { "id": "Longdefinition", "value": "Data is from BIS LBS Table A6.1: External loans and deposits of reporting banks vis-à-vis the banking sector, on the basis of residence. Bank deposits from IFS (IFS lines 24, 25, FOST and FOSD). End of year data (i.e. December data) are used." }, { "id": "Shortdefinition", "value": "Percentage of loans and deposits of reporting banks vis-à-vis the banking sector to the domestic bank deposits." }, { "id": "Source", "value": "Locational Banking Statistics (LBS), Bank for International Settlements (BIS)" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.11", "metatype": [ { "id": "IndicatorName", "value": "External loans and deposits of reporting banks vis-à-vis the nonbanking sectors (% of domestic bank deposits)" }, { "id": "Longdefinition", "value": "Data is from BIS LBS Table A6.1: External loans and deposits of reporting banks vis-à-vis nonbanking sectors, on the basis of residence. Bank deposits from IFS (IFS lines 24, 25, FOST and FOSD). End of year data (i.e. December data) are used." }, { "id": "Shortdefinition", "value": "Percentage of loans and deposits of reporting banks vis-à-vis the nonbanking sectors to the domestic bank deposits." }, { "id": "Source", "value": "Locational Banking Statistics (LBS), Bank for International Settlements (BIS)" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.12", "metatype": [ { "id": "IndicatorName", "value": "External loans and deposits of reporting banks vis-à-vis all sectors (% of domestic bank deposits)" }, { "id": "Longdefinition", "value": "Data is from BIS LBS Table A6.1: External loans and deposits of reporting banks vis-à-vis all sectors, on the basis of residence. Bank deposits from IFS (IFS lines 24, 25, FOST and FOSD). End of year data (i.e. December data) are used." }, { "id": "Shortdefinition", "value": "Percentage of loans and deposits of reporting banks vis-à-vis all sectors to the domestic bank deposits." }, { "id": "Source", "value": "Locational Banking Statistics (LBS), Bank for International Settlements (BIS)" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.13", "metatype": [ { "id": "IndicatorName", "value": "Remittance inflows to GDP (%)" }, { "id": "Longdefinition", "value": "Workers' remittances and compensation of employees comprise current transfers by migrant workers and wages and salaries earned by nonresident workers. Data are the sum of three items defined in the fifth edition of the IMF's Balance of Payments Manual: workers' remittances, compensation of employees, and migrants' transfers. Remittances are classified as current private transfers from migrant workers resident in the host country for more than a year, irrespective of their immigration status, to recipients in their country of origin. Migrants' transfers are defined as the net worth of migrants who are expected to remain in the host country for more than one year that is transferred from one country to another at the time of migration. Compensation of employees is the income of migrants who have lived in the host country for less than a year." }, { "id": "Shortdefinition", "value": "Workers' remittances and compensation of employees comprise current transfers by migrant workers and wages and salaries earned by nonresident workers. Data are the sum of three items defined in the fifth edition of the IMF's Balance of Payments Manual: workers' remittances, compensation of employees, and migrants' transfers." }, { "id": "Source", "value": "World Development Indicators (WDI), World Bank" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.14", "metatype": [ { "id": "IndicatorName", "value": "Consolidated foreign claims of BIS reporting banks to GDP (%)" }, { "id": "Longdefinition", "value": "The ratio of consolidated foreign claims to GDP of the banks that are reporting to BIS. Foreign claims are defined as the sum of cross-border claims plus foreign offices’ local claims in all currencies. In the consolidated banking statistics claims that are granted or extended to nonresidents are referred to as cross-border claims. In the context of the consolidated banking statistics, local claims refer to claims of domestic banks’ foreign affiliates (branches/subsidiaries) on the residents of the host country (i.e. country of residence of affiliates). The calculation is based on BIS CBS Table B3: cross-border claims + local claims. End-of-year data (i.e. December data) are considered for banks claims. GDP is from World Development Indicators." }, { "id": "Shortdefinition", "value": "The ratio of consolidated foreign claims to GDP of the banks that are reporting to BIS. Foreign claims are defined as the sum of cross-border claims plus foreign offices’ local claims in all currencies. In the consolidated banking statistics claims that are granted or extended to nonresidents are referred to as cross-border claims. In the context of the consolidated banking statistics, local claims refer to claims of domestic banks’ foreign affiliates (branches/subsidiaries) on the residents of the host country (i.e. country of residence of affiliates)." }, { "id": "Source", "value": "Consolidated Banking Statistics (CBS), Bank for International Settlements (BIS)" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.15", "metatype": [ { "id": "IndicatorName", "value": "Foreign banks among total banks (%)" }, { "id": "Longdefinition", "value": "Percentage of the number of foreign owned banks to the number of the total banks in an Economy. A foreign bank is a bank where 50 percent or more of its shares are owned by foreigners." }, { "id": "Shortdefinition", "value": "Percentage of the number of foreign owned banks to the number of the total banks in an Economy. A foreign bank is a bank where 50 percent or more of its shares are owned by foreigners." }, { "id": "Source", "value": "CLAESSENS, S. and VAN HOREN, N. (2014), \"Foreign Banks: Trends and Impact\", Journal of Money, Credit and Banking, 46: 295–326\nCLAESSENS, S. and VAN HOREN, N. (2015), \"The Impact of the Global Financial Crisis on Banking Globalization\", DNB WP No. 459" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.16", "metatype": [ { "id": "IndicatorName", "value": "Foreign bank assets among total bank assets (%)" }, { "id": "Longdefinition", "value": "Percentage of the total banking assets that are held by foreign banks. A foreign bank is a bank where 50 percent or more of its shares are owned by foreigners." }, { "id": "Shortdefinition", "value": "Percentage of the total banking assets that are held by foreign banks. A foreign bank is a bank where 50 percent or more of its shares are owned by foreigners." }, { "id": "Source", "value": "CLAESSENS, S. and VAN HOREN, N. (2014), \"Foreign Banks: Trends and Impact\", Journal of Money, Credit and Banking, 46: 295–326\nCLAESSENS, S. and VAN HOREN, N. (2015), \"The Impact of the Global Financial Crisis on Banking Globalization\", DNB WP No. 459" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.16a", "metatype": [ { "id": "IndicatorName", "value": "Foreign bank assets among total banks assets (%)" }, { "id": "Longdefinition", "value": "Percent of the banking system's assets was in banks that were foreign-controlled (i.e. where foreigners owned 50% or more equity)." }, { "id": "Shortdefinition", "value": "Percent of the banking system's assets was in banks that were foreign-controlled (i.e. where foreigners owned 50% or more equity)." }, { "id": "Source", "value": "World Bank Bank Regulation and Supervision Survey" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.17", "metatype": [ { "id": "IndicatorName", "value": "Global leasing volume to GDP (%)" }, { "id": "Longdefinition", "value": "Ratios calculated by source." }, { "id": "Shortdefinition", "value": "Global leasing volume / GDP." }, { "id": "Source", "value": "White Clarke Global Leasing Report" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.18", "metatype": [ { "id": "IndicatorName", "value": "Total factoring volume to GDP (%)" }, { "id": "Longdefinition", "value": "GDP data provided by IFS and converted into USD using IFS exchange rates." }, { "id": "Shortdefinition", "value": "Total factoring volume / GDP." }, { "id": "Source", "value": "Factors Chain International" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.19", "metatype": [ { "id": "IndicatorName", "value": "Banking crisis dummy (1=banking crisis, 0=none)" }, { "id": "Longdefinition", "value": "A banking crisis is defined as systemic if two conditions are met: a. Significant signs of financial distress in the banking system (as indicated bysignificant bank runs, losses in the banking system, and/or bank liquidations), b. Significant banking policy intervention measures in response to significant losses in the banking system. The first year that both criteria are met is considered as the year when the crisis start becoming systemic. The end of a crisis is defined the year before both real GDP growth and real credit growth are positive for at least two consecutive years." }, { "id": "Shortdefinition", "value": "Dummy variable for the presence of banking crisis (1=banking crisis, 0=none)" }, { "id": "Source", "value": "LAEVEN, L. and VALENCIA, F. (2018), “Systemic Banking Crises Revisited”, IMF WP/18/206" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OI.20a", "metatype": [ { "id": "IndicatorName", "value": "Government bank assets among total bank assets (%)" }, { "id": "Longdefinition", "value": "Percent of the banking system's assets was in banks that were government-controlled (i.e. where government owned 50% or more equity)." }, { "id": "Shortdefinition", "value": "Percent of the banking system's assets was in banks that were government-controlled (i.e. where government owned 50% or more equity)." }, { "id": "Source", "value": "World Bank Bank Regulation and Supervision Survey" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OM.01", "metatype": [ { "id": "IndicatorName", "value": "Number of listed companies per 1,000,000 people" }, { "id": "Longdefinition", "value": "Number of publicly listed companies per 1,000,000 people. Number of listed domestic companies is the domestically incorporated companies listed on the country's stock exchanges at the end of the year. This indicator does not include investment companies, mutual funds, or other collective investment vehicles." }, { "id": "Shortdefinition", "value": "Number of domestically incorporated companies listed on the country's stock exchanges at the end of the year per 1,000,000 people (does not include investment companies, mutual funds, or other collective investment vehicles)." }, { "id": "Source", "value": "World Federation of Exchanges; Global Stock Markets Factbook and supplemental S&P data, Standard & Poor's" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.OM.02", "metatype": [ { "id": "IndicatorName", "value": "Stock market return (%, year-on-year)" }, { "id": "Longdefinition", "value": "Stock market return is the growth rate of annual average stock market index. Annual average stock market index is constructed by taking the average of the daily stock market indexes available at Bloomberg." }, { "id": "Shortdefinition", "value": "Stock market return is the growth rate of annual average stock market index." }, { "id": "Source", "value": "Bloomberg" }, { "id": "Topic", "value": "Other" } ], "source_id": "32" }, { "id": "GFDD.SI.01", "metatype": [ { "id": "IndicatorName", "value": "Bank Z-score" }, { "id": "Longdefinition", "value": "It captures the probability of default of a country's banking system. Z-score compares the buffer of a country's banking system (capitalization and returns) with the volatility of those returns. It is estimated as (ROA+(equity/assets))/sd(ROA); sd(ROA) is the standard deviation of ROA, calculated for country-years with no less than 5 bank-level observations. ROA, equity, and assets are country-level aggregate figures. Calculated from underlying bank-by-bank unconsolidated data from Bankscope and Orbis. The result is not reported if a country-year has less than 3 bank-level observations." }, { "id": "Shortdefinition", "value": "It captures the probability of default of a country's commercial banking system. Z-score compares the buffer of a country's commercial banking system (capitalization and returns) with the volatility of those returns." }, { "id": "Source", "value": "Bankscope (2000-14) and Orbis (2015-21), Bureau van Dijk (BvD)" }, { "id": "Topic", "value": "Stability" } ], "source_id": "32" }, { "id": "GFDD.SI.02", "metatype": [ { "id": "IndicatorName", "value": "Bank nonperforming loans to gross loans (%)" }, { "id": "Longdefinition", "value": "Reported by IMF staff. Note that due to differences in national accounting, taxation, and supervisory regimes, these data are not strictly comparable across countries." }, { "id": "Shortdefinition", "value": "Ratio of defaulting loans (payments of interest and principal past due by 90 days or more) to total gross loans (total value of loan portfolio). The loan amount recorded as nonperforming includes the gross value of the loan as recorded on the balance sheet, not just the amount that is overdue." }, { "id": "Source", "value": "Financial Soundness Indicators Database (fsi.imf.org), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Stability" } ], "source_id": "32" }, { "id": "GFDD.SI.03", "metatype": [ { "id": "IndicatorName", "value": "Bank capital to total assets (%)" }, { "id": "Longdefinition", "value": "Ratio of bank capital and reserves to total assets. Capital and reserves include funds contributed by owners, retained earnings, general and special reserves, provisions, and valuation adjustments. Capital includes tier 1 capital (paid-up shares and common stock), which is a common feature in all countries' banking systems, and total regulatory capital, which includes several specified types of subordinated debt instruments that need not be repaid if the funds are required to maintain minimum capital levels (these comprise tier 2 and tier 3 capital). Total assets include all nonfinancial and financial assets. Reported by IMF staff. Note that due to differences in national accounting, taxation, and supervisory regimes, these data are not strictly comparable across countries." }, { "id": "Shortdefinition", "value": "Ratio of bank capital and reserves to total assets. Capital and reserves include funds contributed by owners, retained earnings, general and special reserves, provisions, and valuation adjustments. Capital includes tier 1 capital (paid-up shares and common stock), which is a common feature in all countries' banking systems, and total regulatory capital, which includes several specified types of subordinated debt instruments that need not be repaid if the funds are required to maintain minimum capital levels (these comprise tier 2 and tier 3 capital). Total assets include all nonfinancial and financial assets." }, { "id": "Source", "value": "Financial Soundness Indicators Database (fsi.imf.org), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Stability" } ], "source_id": "32" }, { "id": "GFDD.SI.04", "metatype": [ { "id": "IndicatorName", "value": "Bank credit to bank deposits (%)" }, { "id": "Longdefinition", "value": "Raw data are from the electronic version of the IMF’s International Financial Statistics. Private credit by deposit money banks (IFS line 22d and FOSAOP); bank deposits (IFS lines 24, 25, FOST and FOSD)." }, { "id": "Shortdefinition", "value": "The financial resources provided to the private sector by domestic money banks as a share of total deposits. Domestic money banks comprise commercial banks and other financial institutions that accept transferable deposits, such as demand deposits. Total deposits include demand, time and saving deposits in deposit money banks." }, { "id": "Source", "value": "International Financial Statistics (IFS), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Stability" } ], "source_id": "32" }, { "id": "GFDD.SI.05", "metatype": [ { "id": "IndicatorName", "value": "Bank regulatory capital to risk-weighted assets (%)" }, { "id": "Longdefinition", "value": "Reported by IMF staff. Note that due to differences in national accounting, taxation, and supervisory regimes, these data are not strictly comparable across countries." }, { "id": "Shortdefinition", "value": "The capital adequacy of deposit takers. It is a ratio of total regulatory capital to its assets held, weighted according to risk of those assets." }, { "id": "Source", "value": "Financial Soundness Indicators Database (fsi.imf.org), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Stability" } ], "source_id": "32" }, { "id": "GFDD.SI.06", "metatype": [ { "id": "IndicatorName", "value": "Liquid assets to deposits and short term funding (%)" }, { "id": "Longdefinition", "value": "The ratio of the value of liquid assets (easily converted to cash) to short-term funding plus total deposits. Liquid assets include cash and due from banks, trading securities and at fair value through income, loans and advances to banks, reverse repos and cash collaterals. Deposits and short term funding includes total customer deposits (current, savings and term) and short term borrowing (money market instruments, CDs and other deposits). Raw data are from Bankscope and Orbis. The formula applied to Bankscope is data2075 / data2030 and a comparable approach is applied to Orbis. Numerator and denominator are first aggregated on the country level before division. Calculated from underlying bank-by-bank unconsolidated data from Bankscope and Orbis. The result is not reported if a country-year has less than 3 bank-level observations." }, { "id": "Shortdefinition", "value": "The ratio of the value of liquid assets (easily converted to cash) to short-term funding plus total deposits. Liquid assets include cash and due from banks, trading securities and at fair value through income, loans and advances to banks, reverse repos and cash collaterals. Deposits and short term funding includes total customer deposits (current, savings and term) and short term borrowing (money market instruments, CDs and other deposits)." }, { "id": "Source", "value": "Bankscope (2000-14) and Orbis (2015-21), Bureau van Dijk (BvD)" }, { "id": "Topic", "value": "Stability" } ], "source_id": "32" }, { "id": "GFDD.SI.07", "metatype": [ { "id": "IndicatorName", "value": "Provisions to nonperforming loans (%)" }, { "id": "Longdefinition", "value": "Provisions to nonperforming loans. Nonperforming Loans are loans for which the contractual payments are delinquent, usually defined as and NPL ratio being overdue for more than a certain number of days (e.g., usually more than 90 days). Reported by IMF staff. Note that due to differences in national accounting, taxation, and supervisory regimes, these data are not strictly comparable across countries." }, { "id": "Shortdefinition", "value": "Provisions to nonperforming loans. Nonperforming loans are loans for which the contractual payments are delinquent, usually defined as and NPL ratio being overdue for more than a certain number of days (e.g., usually more than 90 days)." }, { "id": "Source", "value": "Financial Soundness Indicators Database (fsi.imf.org), International Monetary Fund (IMF)" }, { "id": "Topic", "value": "Stability" } ], "source_id": "32" }, { "id": "GFDD.SM.01", "metatype": [ { "id": "IndicatorName", "value": "Stock price volatility" }, { "id": "Longdefinition", "value": "Stock price volatility is the average of the 360-day volatility of the national stock market index." }, { "id": "Shortdefinition", "value": "Stock price volatility is the average of the 360-day volatility of the national stock market index." }, { "id": "Source", "value": "Bloomberg" }, { "id": "Topic", "value": "Stability" } ], "source_id": "32" }, { "id": "account_t_d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 15+, who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15+, who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "account_t_d_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account, female (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of female respondents, age 15+, who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of female respondents, ages 15+, who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "account_t_d_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account, male (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of male respondents, age 15+, who \nreport having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of male respondents, ages 15+, who \nreport having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "account_t_d_3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account, income, poorest 40% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, age 15+, who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, ages 15+, who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "account_t_d_4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account, income, richest 60% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, age 15+, who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, ages 15+, who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "account_t_d_5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account (% ages 15-34)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "account_t_d_6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account (% ages 35-59)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "account_t_d_7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account (% age 60+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 60+, who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, age 60+, who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin11q1", "metatype": [ { "id": "IndicatorName", "value": "SMEs with at least one female owner with a proportion of loans requiring collateral" }, { "id": "Longdefinition", "value": "Denotes the percentage of SMEs (5-99 employees) with at least one female owner required to provide collateral on their bank loan." }, { "id": "Periodicity", "value": "3-5 years" }, { "id": "Shortdefinition", "value": "Denotes the percentage of SMEs (5-99 employees) with at least one female owner required to provide collateral on their bank loan." }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Barriers to Use" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin11q2", "metatype": [ { "id": "IndicatorName", "value": "SMEs with a proportion of loans requiring collateral" }, { "id": "Longdefinition", "value": "Denotes the percentage of SMEs (5-99 employees) required to provide collateral on their bank loan." }, { "id": "Periodicity", "value": "3-5 years" }, { "id": "Shortdefinition", "value": "Denotes the percentage of SMEs (5-99 employees) required to provide collateral on their bank loan." }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Barriers to Use" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin14q1", "metatype": [ { "id": "IndicatorName", "value": "SMEs with at least one female owner with an outstanding loan or line of credit" }, { "id": "Longdefinition", "value": "Denotes the percentage of SMEs (5-99 employees) with at least one female owner with an outstanding loan or line of credit from a bank or other formal financial institution." }, { "id": "Periodicity", "value": "3-5 years" }, { "id": "Shortdefinition", "value": "Denotes the percentage of SMEs (5-99 employees) with at least one female owner with an outstanding loan or line of credit from a bank or other formal financial institution." }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin14q2", "metatype": [ { "id": "IndicatorName", "value": "SMEs with an outstanding loan or line of credit" }, { "id": "Longdefinition", "value": "Denotes the percentage of SMEs (5-99 employees) with an outstanding loan or line of credit from a bank or other formal financial institution." }, { "id": "Periodicity", "value": "3-5 years" }, { "id": "Shortdefinition", "value": "Denotes the percentage of SMEs (5-99 employees) with an outstanding loan or line of credit from a bank or other formal financial institution." }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin15q1", "metatype": [ { "id": "IndicatorName", "value": "SMEs with at least one female owner with an account at a formal financial institution" }, { "id": "Longdefinition", "value": "Denotes the percentage of SMEs (5-99 employees) with at least one female owner with a checking or savings account at a bank or other financial institution." }, { "id": "Periodicity", "value": "3-5 years" }, { "id": "Shortdefinition", "value": "Denotes the percentage of SMEs (5-99 employees) with at least one female owner with a checking or savings account at a bank or other financial institution." }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin15q2", "metatype": [ { "id": "IndicatorName", "value": "SMEs with an account at a formal financial institution" }, { "id": "Longdefinition", "value": "Denotes the percentage of SMEs (5-99 employees) with a checking or savings account at a bank or other financial institution." }, { "id": "Periodicity", "value": "3-5 years" }, { "id": "Shortdefinition", "value": "Denotes the percentage of SMEs (5-99 employees) with a checking or savings account at a bank or other financial institution." }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin16_t_a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Saved for old age (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 15+, who report saving or setting aside any money in the past 12 months for old age." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15+, who report saving or setting aside any money in the past 12 months for old age." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin16_t_a_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Saved for old age, female (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of female respondents, age 15+, who report saving or setting aside any money in the past 12 months for old age." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of female respondents, ages 15+, who report saving or setting aside any money in the past 12 months for old age." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin16_t_a_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Saved for old age, male (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of male respondents, age 15+, who report saving or setting aside any money in the past 12 months for old age." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of male respondents, ages 15+, who report saving or setting aside any money in the past 12 months for old age." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin16_t_a_3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Saved for old age, income, poorest 40% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, age 15+, who report saving or setting aside any money in the past 12 months for old age." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, ages 15+, who report saving or setting aside any money in the past 12 months for old age." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin16_t_a_4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Saved for old age, income, richest 60% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, age 15+, who report saving or setting aside any money in the past 12 months for old age." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, ages 15+, who report saving or setting aside any money in the past 12 months for old age." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin16_t_a_5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Saved for old age (% ages 15-34)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report saving or setting aside any money in the past 12 months for old age." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report saving or setting aside any money in the past 12 months for old age." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin16_t_a_6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Saved for old age (% ages 35-59)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report saving or setting aside any money in the past 12 months for old age." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report saving or setting aside any money in the past 12 months for old age." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin16_t_a_7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Saved for old age (% age 60+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 60+, who report saving or setting aside any money in the past 12 months for old age." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, age 60+, who report saving or setting aside any money in the past 12 months for old age." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin17a_t_a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Saved at a financial institution (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 15+, who report saving or setting aside any money by using an account at a bank or another type of financial institution in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15+, who report saving or setting aside any money by using an account at a bank or another type of financial institution in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin17a_t_a_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Saved at a financial institution, female (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of female respondents, age 15+, who report saving or setting aside any money by using an account at a bank or another type of financial institution in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of female respondents, ages 15+, who report saving or setting aside any money by using an account at a bank or another type of financial institution in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin17a_t_a_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Saved at a financial institution, male (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of male respondents, age 15+, who report saving or setting aside any money by using an account at a bank or another type of financial institution in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of male respondents, ages 15+, who report saving or setting aside any money by using an account at a bank or another type of financial institution in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin17a_t_a_3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Saved at a financial institution, income, poorest 40% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, age 15+, who report saving or setting aside any money by using an account at a bank or another type of financial institution in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, ages 15+, who report saving or setting aside any money by using an account at a bank or another type of financial institution in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin17a_t_a_4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Saved at a financial institution, income, richest 60% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, age 15+, who report saving or setting aside any money by using an account at a bank or another type of financial institution in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, ages 15+, who report saving or setting aside any money by using an account at a bank or another type of financial institution in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin17a_t_a_5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Saved at a financial institution (% ages 15-34)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report saving or setting aside any money by using an account at a bank or another type of financial institution in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report saving or setting aside any money by using an account at a bank or another type of financial institution in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin17a_t_a_6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Saved at a financial institution (% ages 35-59)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report saving or setting aside any money by using an account at a bank or another type of financial institution in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report saving or setting aside any money by using an account at a bank or another type of financial institution in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin17a_t_a_7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Saved at a financial institution (% age 60+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 60+, who report saving or setting aside any money by using an account at a bank or another type of financial institution in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, age 60+, who report saving or setting aside any money by using an account at a bank or another type of financial institution in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin22a_t_d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Borrowed from a financial institution or used a credit card (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 15+, who report borrowing any money from a bank or another type of financial institution in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15+, who report borrowing any money from a bank or another type of financial institution in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin22a_t_d_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Borrowed from a financial institution or used a credit card, female (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of female respondents, age 15+, who report borrowing any money from a bank or another type of financial institution in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of female respondents, ages 15+, who report borrowing any money from a bank or another type of financial institution in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin22a_t_d_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Borrowed from a financial institution or used a credit card, male (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of male respondents, age 15+, who report borrowing any money from a bank or another type of financial institution in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of male respondents, ages 15+, who report borrowing any money from a bank or another type of financial institution in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin22a_t_d_3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Borrowed from a financial institution or used a credit card, income, poorest 40% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, age 15+, who report borrowing any money from a bank or another type of financial institution in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, ages 15+, who report borrowing any money from a bank or another type of financial institution in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin22a_t_d_4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Borrowed from a financial institution or used a credit card, income, richest 60% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, age 15+, who report borrowing any money from a bank or another type of financial institution in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, ages 15+, who report borrowing any money from a bank or another type of financial institution in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin22a_t_d_5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Borrowed from a financial institution or used a credit card (% ages 15-34)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report borrowing any money from a bank or another type of financial institution in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report borrowing any money from a bank or another type of financial institution in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin22a_t_d_6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Borrowed from a financial institution or used a credit card (% ages 35-59)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report borrowing any money from a bank or another type of financial institution in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report borrowing any money from a bank or another type of financial institution in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin22a_t_d_7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Borrowed from a financial institution or used a credit card (% age 60+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 60+, who report borrowing any money from a bank or another type of financial institution in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, age 60+, who report borrowing any money from a bank or another type of financial institution in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin25a_t_a_s", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Main source of emergency funds: savings (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 15+, who report that in case of an emergency it is possible for them to come up with 1/20 of gross national income (GNI) per capita in local currency, and cite savings as their main source of this money." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15+, who report that in case of an emergency it is possible for them to come up with 1/20 of gross national income (GNI) per capita in local currency, and cite savings as their main source of this money." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Financial Literacy and Capability" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin25a_t_a_s_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Main source of emergency funds: savings, female (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of female respondents, age 15+, who report that in case of an emergency it is possible for them to come up with 1/20 of gross national income (GNI) per capita in local currency, and cite savings as their main source of this money." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of female respondents, ages 15+, who report that in case of an emergency it is possible for them to come up with 1/20 of gross national income (GNI) per capita in local currency, and cite savings as their main source of this money." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Financial Literacy and Capability" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin25a_t_a_s_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Main source of emergency funds: savings, male (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of male respondents, age 15+, who report that in case of an emergency it is possible for them to come up with 1/20 of gross national income (GNI) per capita in local currency, and cite savings as their main source of this money." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of male respondents, ages 15+, who report that in case of an emergency it is possible for them to come up with 1/20 of gross national income (GNI) per capita in local currency, and cite savings as their main source of this money." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Financial Literacy and Capability" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin25a_t_a_s_3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Main source of emergency funds: savings, income, poorest 40% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, age 15+, who report that in case of an emergency it is possible for them to come up with 1/20 of gross national income (GNI) per capita in local currency, and cite savings as their main source of this money." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, ages 15+, who report that in case of an emergency it is possible for them to come up with 1/20 of gross national income (GNI) per capita in local currency, and cite savings as their main source of this money." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Financial Literacy and Capability" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin25a_t_a_s_4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Main source of emergency funds: savings, income, richest 60% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, age 15+, who report that in case of an emergency it is possible for them to come up with 1/20 of gross national income (GNI) per capita in local currency, and cite savings as their main source of this money." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, ages 15+, who report that in case of an emergency it is possible for them to come up with 1/20 of gross national income (GNI) per capita in local currency, and cite savings as their main source of this money." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Financial Literacy and Capability" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin25a_t_a_s_5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Main source of emergency funds: savings (% ages 15-34)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report that in case of an emergency it is possible for them to come up with 1/20 of gross national income (GNI) per capita in local currency, and cite savings as their main source of this money." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report that in case of an emergency it is possible for them to come up with 1/20 of gross national income (GNI) per capita in local currency, and cite savings as their main source of this money." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Financial Literacy and Capability" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin25a_t_a_s_6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Main source of emergency funds: savings (% ages 35-59)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report that in case of an emergency it is possible for them to come up with 1/20 of gross national income (GNI) per capita in local currency, and cite savings as their main source of this money." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report that in case of an emergency it is possible for them to come up with 1/20 of gross national income (GNI) per capita in local currency, and cite savings as their main source of this money." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Financial Literacy and Capability" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin25a_t_a_s_7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Main source of emergency funds: savings (% age 60+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 60+, who report that in case of an emergency it is possible for them to come up with 1/20 of gross national income (GNI) per capita in local currency, and cite savings as their main source of this money." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, age 60+, who report that in case of an emergency it is possible for them to come up with 1/20 of gross national income (GNI) per capita in local currency, and cite savings as their main source of this money." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Financial Literacy and Capability" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin4_t_d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Used a debit or credit card to make a purchase in the past year (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents who report using a debit or credit card to make a purchase in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents who report using a debit or credit card to make a purchase in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin48_a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Has a national identity card (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 15+, who report having a national identity card. (To see the full list of IDs included in the survey by country, visit the Global Findex web page at http://www.worldbank.org/globalfindex.)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15+, who report having a national identity card. (To see the full list of IDs included in the survey by country, visit the Global Findex web page at http://www.worldbank.org/globalfindex.)" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Quality of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin48_a_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Has a national identity card, female (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of female respondents, age 15+, who report having a national identity card. (To see the full list of IDs included in the survey by country, visit the Global Findex web page at http://www.worldbank.org/globalfindex.)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of female respondents, ages 15+, who report having a national identity card. (To see the full list of IDs included in the survey by country, visit the Global Findex web page at http://www.worldbank.org/globalfindex.)" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Quality of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin48_a_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Has a national identity card, male (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of male respondents, age 15+, who report having a national identity card. (To see the full list of IDs included in the survey by country, visit the Global Findex web page at http://www.worldbank.org/globalfindex.)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of male respondents, ages 15+, who report having a national identity card. (To see the full list of IDs included in the survey by country, visit the Global Findex web page at http://www.worldbank.org/globalfindex.)" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Quality of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin48_a_3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Has a national identity card, income, poorest 40% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, age 15+, who report having a national identity card. (To see the full list of IDs included in the survey by country, visit the Global Findex web page at http://www.worldbank.org/globalfindex.)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, ages 15+, who report having a national identity card. (To see the full list of IDs included in the survey by country, visit the Global Findex web page at http://www.worldbank.org/globalfindex.)" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Quality of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin48_a_4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Has a national identity card, income, richest 60% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, age 15+, who report having a national identity card. (To see the full list of IDs included in the survey by country, visit the Global Findex web page at http://www.worldbank.org/globalfindex.)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, ages 15+, who report having a national identity card. (To see the full list of IDs included in the survey by country, visit the Global Findex web page at http://www.worldbank.org/globalfindex.)" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Quality of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin48_a_5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Has a national identity card (% ages 15-34)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report having a national identity card. (To see the full list of IDs included in the survey by country, visit the Global Findex web page at http://www.worldbank.org/globalfindex.)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report having a national identity card. (To see the full list of IDs included in the survey by country, visit the Global Findex web page at http://www.worldbank.org/globalfindex.)" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Quality of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin48_a_6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Has a national identity card (% ages 35-59)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report having a national identity card. (To see the full list of IDs included in the survey by country, visit the Global Findex web page at http://www.worldbank.org/globalfindex.)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report having a national identity card. (To see the full list of IDs included in the survey by country, visit the Global Findex web page at http://www.worldbank.org/globalfindex.)" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Quality of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin48_a_7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Has a national identity card (% age 60+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 60+, who report having a national identity card. (To see the full list of IDs included in the survey by country, visit the Global Findex web page at http://www.worldbank.org/globalfindex.)" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, age 60+, who report having a national identity card. (To see the full list of IDs included in the survey by country, visit the Global Findex web page at http://www.worldbank.org/globalfindex.)" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Quality of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin6_a", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to check account balance in the past year (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 15+, who report using a mobile phone or the internet to check their balance for a financial institution account in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15+, who report using a mobile phone or the internet to check their balance for a financial institution account in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin6_a_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to check account balance in the past year, female (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of female respondents, age 15+, who report using a mobile phone or the internet to check their balance for a financial institution account in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of female respondents, ages 15+, who report using a mobile phone or the internet to check their balance for a financial institution account in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin6_a_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to check account balance in the past year, male (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of male respondents, age 15+, who report using a mobile phone or the internet to check their balance for a financial institution account in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of male respondents, ages 15+, who report using a mobile phone or the internet to check their balance for a financial institution account in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin6_a_3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to check account balance in the past year, income, poorest 40% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, age 15+, who report using a mobile phone or the internet to check their balance for a financial institution account in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, ages 15+, who report using a mobile phone or the internet to check their balance for a financial institution account in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin6_a_4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to check account balance in the past year, income, richest 60% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, age 15+, who report using a mobile phone or the internet to check their balance for a financial institution account in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, ages 15+, who report using a mobile phone or the internet to check their balance for a financial institution account in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin6_a_5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to check account balance in the past year (% ages 15-34)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report using a mobile phone or the internet to check their balance for a financial institution account in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report using a mobile phone or the internet to check their balance for a financial institution account in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin6_a_6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to check account balance in the past year (% ages 35-59)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report using a mobile phone or the internet to check their balance for a financial institution account in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report using a mobile phone or the internet to check their balance for a financial institution account in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "fin6_a_7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Used a mobile phone or the internet to check account balance in the past year (% age 60+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 60+, who report using a mobile phone or the internet to check their balance for a financial institution account in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, age 60+, who report using a mobile phone or the internet to check their balance for a financial institution account in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "g20_t", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Made or received digital payments in the past year (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 15+, who report using mobile money, a debit or credit card, or a mobile phone to receive a payment through an account in the past 12 months. It also includes respondents who report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a financial institution account or through a mobile money account in the past 12 months" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15+, who report using mobile money, a debit or credit card, or a mobile phone to receive a payment through an account in the past 12 months. It also includes respondents who report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a financial institution account or through a mobile money account in the past 12 months" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "g20_t_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Made or received digital payments in the past year, female (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of female respondents, age 15+, who report using mobile money, a debit or credit card, or a mobile phone to receive a payment through an account in the past 12 months. It also includes respondents\nwho report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a financial institution account or through a mobile money account in the past 12 months" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of female respondents, ages 15+, who report using mobile money, a debit or credit card, or a mobile phone to receive a payment through an account in the past 12 months. It also includes respondents\nwho report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a financial institution account or through a mobile money account in the past 12 months" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "g20_t_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Made or received digital payments in the past year, male (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of male respondents, age 15+, who report using a transaction account (with a bank or other formal financial institution or mobile money provider) to make or receive a digital financial payment in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of male respondents, ages 15+, who report using a transaction account (with a bank or other formal financial institution or mobile money provider) to make or receive a digital financial payment in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "g20_t_3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Made or received digital payments in the past year, income, poorest 40% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, age 15+, who report using mobile money, a debit or credit card, or a mobile phone to receive a payment through an account in the past 12 months. It also includes respondents who report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a financial institution account or through a mobile money account in the past 12 months" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, ages 15+, who report using mobile money, a debit or credit card, or a mobile phone to receive a payment through an account in the past 12 months. It also includes respondents who report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a financial institution account or through a mobile money account in the past 12 months" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "g20_t_4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Made or received digital payments in the past year, income, richest 60% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, age 15+, who report using mobile money, a debit or credit card, or a mobile phone to receive a payment through an account in the past 12 months. It also includes respondents who report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a financial institution account or through a mobile money account in the past 12 months" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, ages 15+, who report using mobile money, a debit or credit card, or a mobile phone to receive a payment through an account in the past 12 months. It also includes respondents who report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a financial institution account or through a mobile money account in the past 12 months" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "g20_t_5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Made or received digital payments in the past year (% ages 15-34)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report using mobile money, a debit or credit card, or a mobile phone to receive a payment through an account in the past 12 months. It also includes respondents who report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a financial institution account or through a mobile money account in the past 12 months" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report using mobile money, a debit or credit card, or a mobile phone to receive a payment through an account in the past 12 months. It also includes respondents who report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a financial institution account or through a mobile money account in the past 12 months" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "g20_t_6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Made or received digital payments in the past year (% ages 35-59)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report using mobile money, a debit or credit card, or a mobile phone to receive a payment through an account in the past 12 months. It also includes respondents who report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a financial institution account or through a mobile money account in the past 12 months" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report using mobile money, a debit or credit card, or a mobile phone to receive a payment through an account in the past 12 months. It also includes respondents who report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a financial institution account or through a mobile money account in the past 12 months" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "g20_t_7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Made or received digital payments in the past year (% age 60+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 60+, who report using mobile money, a debit or credit card, or a mobile phone to receive a payment through an account in the past 12 months. It also includes respondents who report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a financial institution account or through a mobile money account in the past 12 months" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, age 60+, who report using mobile money, a debit or credit card, or a mobile phone to receive a payment through an account in the past 12 months. It also includes respondents who report receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly into a financial institution account or through a mobile money account in the past 12 months" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf10_n", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Received wages or government transfers into an account (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 15+, who report personally receiving payments from the government in the past 12 months directly into a financial institution account, into a card, or into a mobile money account. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15+, who report personally receiving payments from the government in the past 12 months directly into a financial institution account, into a card, or into a mobile money account. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf10_n_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Received wages or government transfers into an account, female (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of female respondents, age 15+, who report personally receiving payments from the government in the past 12 months directly into a financial institution account, into a card, or into a mobile money account. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of female respondents, ages 15+, who report personally receiving payments from the government in the past 12 months directly into a financial institution account, into a card, or into a mobile money account. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf10_n_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Received wages or government transfers into an account, male (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of male respondents, age 15+, who report personally receiving payments from the government in the past 12 months directly into a financial institution account, into a card, or into a mobile money account. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of male respondents, ages 15+, who report personally receiving payments from the government in the past 12 months directly into a financial institution account, into a card, or into a mobile money account. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf10_n_3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Received wages or government transfers into an account, income, poorest 40% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, age 15+, who report personally receiving payments from the government in the past 12 months directly into a financial institution account, into a card, or into a mobile money account. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, ages 15+, who report personally receiving payments from the government in the past 12 months directly into a financial institution account, into a card, or into a mobile money account. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf10_n_4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Received wages or government transfers into an account, income, richest 60% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, age 15+, who report personally receiving payments from the government in the past 12 months directly into a financial institution account, into a card, or into a mobile money account. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, ages 15+, who report personally receiving payments from the government in the past 12 months directly into a financial institution account, into a card, or into a mobile money account. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf10_n_5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Received wages or government transfers into an account (% ages 15-34)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report personally receiving payments from the government in the past 12 months directly into a financial institution account, into a card, or into a mobile money account. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report personally receiving payments from the government in the past 12 months directly into a financial institution account, into a card, or into a mobile money account. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf10_n_6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Received wages or government transfers into an account (% ages 35-59)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report personally receiving payments from the government in the past 12 months directly into a financial institution account, into a card, or into a mobile money account. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report personally receiving payments from the government in the past 12 months directly into a financial institution account, into a card, or into a mobile money account. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf10_n_7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Received wages or government transfers into an account (% age 60+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 60+, who report personally receiving payments from the government in the past 12 months directly into a financial institution account, into a card, or into a mobile money account. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, age 60+, who report personally receiving payments from the government in the past 12 months directly into a financial institution account, into a card, or into a mobile money account. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf4_n", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Made payment using a mobile phone or the internet (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 15+, who report using a mobile phone or the internet to make or receive payments, to make a purchase, or to send or receive money through their financial institution account or through the use of a mobile money service in the past 12 months" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15+, who report using a mobile phone or the internet to make or receive payments, to make a purchase, or to send or receive money through their financial institution account or through the use of a mobile money service in the past 12 months" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf4_n_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Made payment using a mobile phone or the internet, female (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of female respondents, age 15+, who report using a mobile phone or the internet to make or receive payments, to make a purchase, or to send or receive money through their financial institution account or through the use of a mobile money service in the past 12 months" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of female respondents, ages 15+, who report using a mobile phone or the internet to make or receive payments, to make a purchase, or to send or receive money through their financial institution account or through the use of a mobile money service in the past 12 months" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf4_n_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Made payment using a mobile phone or the internet, male (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of male respondents, age 15+, who report using a mobile phone or the internet to make or receive payments, to make a purchase, or to send or receive money through their financial institution account or through the use of a mobile money service in the past 12 months" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of male respondents, ages 15+, who report using a mobile phone or the internet to make or receive payments, to make a purchase, or to send or receive money through their financial institution account or through the use of a mobile money service in the past 12 months" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf4_n_3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Made payment using a mobile phone or the internet, income, poorest 40% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, age 15+, who report using a mobile phone or the internet to make or receive payments, to make a purchase, or to send or receive money through their financial institution account or through the use of a mobile money service in the past 12 months" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, ages 15+, who report using a mobile phone or the internet to make or receive payments, to make a purchase, or to send or receive money through their financial institution account or through the use of a mobile money service in the past 12 months" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf4_n_4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Made payment using a mobile phone or the internet, income, richest 60% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, age 15+, who report using a mobile phone or the internet to make or receive payments, to make a purchase, or to send or receive money through their financial institution account or through the use of a mobile money service in the past 12 months" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, ages 15+, who report using a mobile phone or the internet to make or receive payments, to make a purchase, or to send or receive money through their financial institution account or through the use of a mobile money service in the past 12 months" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf4_n_5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Made payment using a mobile phone or the internet (% ages 15-34)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report using a mobile phone or the internet to make or receive payments, to make a purchase, or to send or receive money through their financial institution account or through the use of a mobile money service in the past 12 months" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report using a mobile phone or the internet to make or receive payments, to make a purchase, or to send or receive money through their financial institution account or through the use of a mobile money service in the past 12 months" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf4_n_6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Made payment using a mobile phone or the internet (% ages 35-59)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report using a mobile phone or the internet to make or receive payments, to make a purchase, or to send or receive money through their financial institution account or through the use of a mobile money service in the past 12 months" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report using a mobile phone or the internet to make or receive payments, to make a purchase, or to send or receive money through their financial institution account or through the use of a mobile money service in the past 12 months" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf4_n_7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Made payment using a mobile phone or the internet (% age 60+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 60+, who report using a mobile phone or the internet to make or receive payments, to make a purchase, or to send or receive money through their financial institution account or through the use of a mobile money service in the past 12 months" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, age 60+, who report using a mobile phone or the internet to make or receive payments, to make a purchase, or to send or receive money through their financial institution account or through the use of a mobile money service in the past 12 months" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf7_n", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Active account (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 15+, who report either making a deposit or a withdrawal using their account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15+, who report either making a deposit or a withdrawal using their account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf7_n_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Active account, female (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of female respondents, age 15+, who report either making a deposit or a withdrawal using their account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of female respondents, ages 15+, who report either making a deposit or a withdrawal using their account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf7_n_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Active account, male (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of male respondents, age 15+, who report either making a deposit or a withdrawal using their account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of male respondents, ages 15+, who report either making a deposit or a withdrawal using their account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf7_n_3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Active account, income, poorest 40% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, age 15+, who report either making a deposit or a withdrawal using their account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, ages 15+, who report either making a deposit or a withdrawal using their account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf7_n_4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Active account, income, richest 60% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, age 15+, who report either making a deposit or a withdrawal using their account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, ages 15+, who report either making a deposit or a withdrawal using their account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf7_n_5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Active account (% ages 15-34)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report either making a deposit or a withdrawal using their account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report either making a deposit or a withdrawal using their account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf7_n_6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Active account (% ages 35-59)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report either making a deposit or a withdrawal using their account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report either making a deposit or a withdrawal using their account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gf7_n_7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Active account (% age 60+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 60+, who report either making a deposit or a withdrawal using their account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, age 60+, who report either making a deposit or a withdrawal using their account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "GPFI1_TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Financial knowledge score (0-3)" }, { "id": "Longdefinition", "value": "Denotes the average amount of financial knowledge related questions respondents in the reported country answered correctly." }, { "id": "Periodicity", "value": "Periodic" }, { "id": "Shortdefinition", "value": "Denotes the average amount of financial knowledge related questions respondents in the reported country answered correctly." }, { "id": "Source", "value": "Organisation for Economic Co-operation and Development (OECD), Measuring Financial Literacy and World Bank, Financial Capability Surveys" }, { "id": "Topic", "value": "Financial Literacy and Capability" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "33" }, { "id": "GPFI2", "metatype": [ { "id": "Generalcomments", "value": "Sum of five indicators." }, { "id": "IndicatorName", "value": "Disclosure index (0-5)" }, { "id": "Longdefinition", "value": "the sum of a variety of existing disclosure requirements. These are (a) law specifies disclosure requirements in plain language, (b) law specifies disclosure requirements in local language, (c) law specifies requirement for prescribed standardized disclosure format, (d) law specifies requirement for recourse rights and processes, and (e) law specifies disclosure requirement of annual percentage rate using standard formula for credit products." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "the sum of a variety of existing disclosure requirements. These are (a) law specifies disclosure requirements in plain language, (b) law specifies disclosure requirements in local language, (c) law specifies requirement for prescribed standardized disclosure format, (d) law specifies requirement for recourse rights and processes, and (e) law specifies disclosure requirement of annual percentage rate using standard formula for credit products." }, { "id": "Source", "value": "World Bank, Global Survey on Consumer Protection and Financial Literacy." }, { "id": "Topic", "value": "Market Conduct and Consumer Protection" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "33" }, { "id": "GPFI3", "metatype": [ { "id": "Generalcomments", "value": "Simple average of two indicators. Missing values were ignored (i.e. average of . and 1 was treated as 1)." }, { "id": "IndicatorName", "value": "Dispute resolution index (0-1)" }, { "id": "Longdefinition", "value": "Index reflecting the existence of formal internal and external dispute resolution mechanisms. Takes the value 1 if both resolution mechanisms are available, the value 0.5 if one of the mechanisms is available, and 0 if neither of the mechanisms is available." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Index reflecting the existence of formal internal and external dispute resolution mechanisms. Takes the value 1 if both resolution mechanisms are available, the value 0.5 if one of the mechanisms is available, and 0 if neither of the mechanisms is available." }, { "id": "Source", "value": "World Bank, Global Survey on Consumer Protection and Financial Literacy." }, { "id": "Topic", "value": "Market Conduct and Consumer Protection" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "33" }, { "id": "GPFI4", "metatype": [ { "id": "Generalcomments", "value": "DB changed the methodology in 2015. Earlier data was not included." }, { "id": "IndicatorName", "value": "Getting credit: Distance to frontier (0-100)" }, { "id": "Longdefinition", "value": "Denotes the distance of each economy to the “frontier,” which represents the highest performance observed on the getting credit indicator across all economies included in Doing Business. An economy’s distance to frontier is indicated on a scale from 0 to 100, where 0 represents the lowest performance and 100 the frontier." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Denotes the distance of each economy to the “frontier,” which represents the highest performance observed on the getting credit indicator across all economies included in Doing Business. An economy’s distance to frontier is indicated on a scale from 0 to 100, where 0 represents the lowest performance and 100 the frontier." }, { "id": "Source", "value": "World Bank, Doing Business (http://www.doingbusiness.org/)." }, { "id": "Topic", "value": "Quality of Financial Services" } ], "source_id": "33" }, { "id": "GPSS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Interoperability of ATM networks and interoperability of POS terminals (0-1)" }, { "id": "Longdefinition", "value": "Takes the value 1 if most or all ATM networks (/POS terminals) are interconnected and 0 if they are not interconnected." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Takes the value 1 if most or all ATM networks (/POS terminals) are interconnected and 0 if they are not interconnected." }, { "id": "Source", "value": "World Bank, Global Payments Systems Survey" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Median" } ], "source_id": "33" }, { "id": "GPSS_1", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "Used WDI population data to convert into per 1,000 adults. Five countries' adult population was not available. Kosovo data was calculated by using adult/total ratio from 2011. For smaller countries, total population was used." }, { "id": "IndicatorName", "value": "E-money accounts per 1,000 adults" }, { "id": "Longdefinition", "value": "Denotes the number of e-money accounts per 1,000 adults." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Denotes the number of e-money accounts per 1,000 adults." }, { "id": "Source", "value": "World Bank, Global Payments Systems Survey" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Median" } ], "source_id": "33" }, { "id": "GPSS_2", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "Variable was formerly named \"Retail cashless transactions per 1,000 adults\". Per capita data are used since 2019." }, { "id": "IndicatorName", "value": "Retail cashless transactions per 1,000 adults" }, { "id": "Longdefinition", "value": "Denotes the number of retail cashless transactions per 1,000 adults which includes the number of cheques, credit transfers, direct debits, payment card transactions (debit cards, credit cards), and payment by e-money instruments (card based e-money instruments, mobile money products, and online money products)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Denotes the number of retail cashless transactions per 1,000 adults which includes the number of cheques, credit transfers, direct debits, payment card transactions (debit cards, credit cards), and payment by e-money instruments (card based e-money instruments, mobile money products, and online money products)." }, { "id": "Source", "value": "World Bank, Global Payments Systems Survey" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Median" } ], "source_id": "33" }, { "id": "GPSS_3", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "Used WDI population data to convert into per 1,000 adults. Five countries' adult population was not available. Kosovo data was calculated by using adult/total ratio from 2011. For smaller countries, total population was used." }, { "id": "IndicatorName", "value": "Agents of payment service providers per 100,000 adults" }, { "id": "Longdefinition", "value": "Denotes the number of agents of payment service providers per 100,000 adults. Includes: agents of banks and other deposit taking institutions, as well as specialized entities such as money transfer operators and e-money issuers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Denotes the number of agents of payment service providers per 100,000 adults. Includes: agents of banks and other deposit taking institutions, as well as specialized entities such as money transfer operators and e-money issuers." }, { "id": "Source", "value": "World Bank, Global Payments Systems Survey" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Median" } ], "source_id": "33" }, { "id": "GPSS_4", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "Used WDI population data to convert into per 1,000 adults. Five countries' adult population was not available. Kosovo data was calculated by using adult/total ratio from 2011. For smaller countries, total population was used." }, { "id": "IndicatorName", "value": "POS terminals per 100,000 adults" }, { "id": "Longdefinition", "value": "Denotes the number of point of sale (POS) terminals per 100,000 adults." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Denotes the number of point of sale (POS) terminals per 100,000 adults." }, { "id": "Source", "value": "World Bank, Global Payments Systems Survey" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Median" } ], "source_id": "33" }, { "id": "GPSS_5", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "Used WDI population data to convert into per 1,000 adults. Five countries' adult population was not available. Kosovo data was calculated by using adult/total ratio from 2011. For smaller countries, total population was used." }, { "id": "IndicatorName", "value": "Debit cards per 1,000 adults" }, { "id": "Longdefinition", "value": "Denotes the number of debit cards per 1,000 adults." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Denotes the number of debit cards per 1,000 adults." }, { "id": "Source", "value": "World Bank, Global Payments Systems Survey" }, { "id": "Topic", "value": "Usage of Financial Services" }, { "id": "Unitofmeasure", "value": "Median" } ], "source_id": "33" }, { "id": "gwp1_n", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to a mobile phone (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 15+, who report having access to a mobile phone" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15+, who report having access to a mobile phone" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gwp1_n_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to a mobile phone, female (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of female respondents, age 15+, who report having access to a mobile phone" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of female respondents, ages 15+, who report having access to a mobile phone" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gwp1_n_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to a mobile phone, male (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of male respondents, age 15+, who report having access to a mobile phone" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of male respondents, ages 15+, who report having access to a mobile phone" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gwp1_n_3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to a mobile phone, income, poorest 40% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, age 15+, who report having access to a mobile phone" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, ages 15+, who report having access to a mobile phone" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gwp1_n_4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to a mobile phone, income, richest 60% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, age 15+, who report having access to a mobile phone" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, ages 15+, who report having access to a mobile phone" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gwp1_n_5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to a mobile phone (% ages 15-34)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report having access to a mobile phone" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report having access to a mobile phone" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gwp1_n_6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to a mobile phone (% ages 35-59)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report having access to a mobile phone" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report having access to a mobile phone" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gwp1_n_7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to a mobile phone (% age 60+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 60+, who report having access to a mobile phone" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, age 60+, who report having access to a mobile phone" }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gwp2_n", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to internet (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 15+, who report having access to the internet in the home." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15+, who report having access to the internet in the home." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gwp2_n_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to internet, female (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of female respondents, age 15+, who report having access to the internet in the home." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of female respondents, ages 15+, who report having access to the internet in the home." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gwp2_n_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to internet, male (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of male respondents, age 15+, who report having access to the internet in the home." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of male respondents, ages 15+, who report having access to the internet in the home." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gwp2_n_3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to internet, income, poorest 40% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, age 15+, who report having access to the internet in the home." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the poorest 40% of households, ages 15+, who report having access to the internet in the home." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gwp2_n_4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to internet, income, richest 60% (% age 15+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, age 15+, who report having access to the internet in the home." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents in the richest 60% of households, ages 15+, who report having access to the internet in the home." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gwp2_n_5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to internet (% ages 15-34)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report having access to the internet in the home." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 15-34, who report having access to the internet in the home." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gwp2_n_6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to internet (% ages 35-59)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report having access to the internet in the home." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, ages 35-59, who report having access to the internet in the home." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "gwp2_n_7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to internet (% age 60+)" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents, age 60+, who report having access to the internet in the home." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "Denotes the percentage of respondents, age 60+, who report having access to the internet in the home." }, { "id": "Source", "value": "Global Findex database (http://datatopics.worldbank.org/financialinclusion/)" }, { "id": "Topic", "value": "Physical Points of Service" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "33" }, { "id": "i_ATMs_pop", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "ATMs per 100,000 adults" }, { "id": "Longdefinition", "value": "Denotes the total number of ATMs for every 100,000 adults in the reporting country. Calculated as (number of ATMs)*100,000/adult population in the reporting country. Automated teller machines are computerized telecommunications devices that provide clients of a financial institution with access to financial transactions in a public place." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Denotes the total number of ATMs for every 100,000 adults in the reporting country. Calculated as (number of ATMs)*100,000/adult population in the reporting country. Automated teller machines are computerized telecommunications devices that provide clients of a financial institution with access to financial transactions in a public place." }, { "id": "Source", "value": "International Monetary Fund, Financial Access Survey." }, { "id": "Topic", "value": "Physical Points of Service" } ], "source_id": "33" }, { "id": "i_branches_A1_pop", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Branches per 100,000 adults" }, { "id": "Longdefinition", "value": "Denotes the number of branches of commercial banks for every 100,000 adults in the reporting country. Calculated as (number of institutions + number of branches)*100,000/adult population in the reporting country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Denotes the number of branches of commercial banks for every 100,000 adults in the reporting country. Calculated as (number of institutions + number of branches)*100,000/adult population in the reporting country." }, { "id": "Source", "value": "International Monetary Fund, Financial Access Survey." }, { "id": "Topic", "value": "Physical Points of Service" } ], "source_id": "33" }, { "id": "i_deposit_acc_A1_pop", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Deposit accounts per 1,000 adults" }, { "id": "Longdefinition", "value": "Denotes the total number of deposit accounts that are held by resident nonfinancial corporations (public and private) and households in commercial banks for every 1,000 adults in the reporting country. For several countries, however, data cover the total deposit accounts by all clients. Calculated as: (number of deposit accounts*1,000)/adult population in the reporting country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Denotes the total number of deposit accounts that are held by resident nonfinancial corporations (public and private) and households in commercial banks for every 1,000 adults in the reporting country. For several countries, however, data cover the total deposit accounts by all clients. Calculated as: (number of deposit accounts*1,000)/adult population in the reporting country." }, { "id": "Source", "value": "International Monetary Fund, Financial Access Survey." }, { "id": "Topic", "value": "Usage of Financial Services" } ], "source_id": "33" }, { "id": "i_deposit_acc_A1_sme_perNFC", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "SME deposit accounts (as a % of non-financial corporation borrowers)" }, { "id": "Longdefinition", "value": "Denotes the total number of deposit accounts in commercial banks held by SMEs as a fraction of the total number of deposit accounts in commercial banks held by non-financial corporations. Calculated as: (number of deposit accounts by SMEs with commercial banks)/(number of deposit accounts with commercial banks - number of deposit accounts by households with commercial banks)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Denotes the total number of deposit accounts in commercial banks held by SMEs as a fraction of the total number of deposit accounts in commercial banks held by non-financial corporations. Calculated as: (number of deposit accounts by SMEs with commercial banks)/(number of deposit accounts with commercial banks - number of deposit accounts by households with commercial banks)." }, { "id": "Source", "value": "International Monetary Fund, Financial Access Survey." }, { "id": "Topic", "value": "Usage of Financial Services" } ], "source_id": "33" }, { "id": "i_loan_acc_A1_sme_perNFC", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "SME loan accounts (as a % of non-financial corporation borrowers)" }, { "id": "Longdefinition", "value": "Denotes the total number of loans obtained by SMEs from a commercial bank as a fraction of the total loans obtained by non-financial corporations from commercial banks. Calculated as: (number of loan accounts by SMEs with commercial banks)/(number of loan accounts with commercial banks - number of loan accounts by households with commercial banks)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Denotes the total number of loans obtained by SMEs from a commercial bank as a fraction of the total loans obtained by non-financial corporations from commercial banks. Calculated as: (number of loan accounts by SMEs with commercial banks)/(number of loan accounts with commercial banks - number of loan accounts by households with commercial banks)." }, { "id": "Source", "value": "International Monetary Fund, Financial Access Survey." }, { "id": "Topic", "value": "Usage of Financial Services" } ], "source_id": "33" }, { "id": "i_mob_agent_pop_registered", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Mobile agent outlets per 100,000 adults" }, { "id": "Longdefinition", "value": "Denotes the number of registered mobile agent outlets per 100,000 adults." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Denotes the number of registered mobile agent outlets per 100,000 adults." }, { "id": "Source", "value": "International Monetary Fund, Financial Access Survey." }, { "id": "Topic", "value": "Physical Points of Service" } ], "source_id": "33" }, { "id": "i_mob_transactions_number_pop", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Mobile money transactions per 100,000 adults" }, { "id": "Longdefinition", "value": "Denotes the number of mobile money transactions per 100,000 adults." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Denotes the number of mobile money transactions per 100,000 adults." }, { "id": "Source", "value": "International Monetary Fund, Financial Access Survey." }, { "id": "Topic", "value": "Access to Financial Services" } ], "source_id": "33" }, { "id": "s_loans_A1", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "Used WDI population data to convert into per 1,000 adults. Five countries' adult population was not available. Kosovo data was calculated by using adult/total ratio from 2011. For smaller countries, total population was used." }, { "id": "IndicatorName", "value": "Outstanding loans per 1,000 adults" }, { "id": "Longdefinition", "value": "Denotes the total number of loan accounts that are obtained by resident nonfinancial corporations (public and private) and households from commercial banks for every 1,000 adults in the reporting country. For several countries, however, data cover the total number of loan accounts by all clients. Calculated as: (number of loan accounts*1,000)/adult population in the reporting country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Denotes the total number of loan accounts that are obtained by resident nonfinancial corporations (public and private) and households from commercial banks for every 1,000 adults in the reporting country. For several countries, however, data cover the total number of loan accounts by all clients. Calculated as: (number of loan accounts*1,000)/adult population in the reporting country." }, { "id": "Source", "value": "International Monetary Fund, Financial Access Survey." }, { "id": "Topic", "value": "Usage of Financial Services" } ], "source_id": "33" }, { "id": "s_policyholders_B2_life", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "Used WDI population data to convert into per 1,000 adults. Five countries' adult population was not available. Kosovo data was calculated by using adult/total ratio from 2011. For smaller countries, total population was used." }, { "id": "IndicatorName", "value": "Insurance policy holders per 1,000 adults (life)" }, { "id": "Longdefinition", "value": "Denotes the total number of life insurance policy holders (resident) that are resident nonfinancial corporations (public and private) and households for every 1,000 adults in the reporting country. Calculated as (number of life insurance policy holders)*1,000/adult population in the reporting country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Denotes the total number of life insurance policy holders (resident) that are resident nonfinancial corporations (public and private) and households for every 1,000 adults in the reporting country. Calculated as (number of life insurance policy holders)*1,000/adult population in the reporting country." }, { "id": "Source", "value": "International Monetary Fund, Financial Access Survey." }, { "id": "Topic", "value": "Access to Financial Services" } ], "source_id": "33" }, { "id": "s_policyholders_B2_nonlife", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "Used WDI population data to convert into per 1,000 adults. Five countries' adult population was not available. Kosovo data was calculated by using adult/total ratio from 2011. For smaller countries, total population was used." }, { "id": "IndicatorName", "value": "Insurance policy holders per 1,000 adults (non-life)" }, { "id": "Longdefinition", "value": "Denotes the total number of non-life insurance policy holders (resident) that are resident nonfinancial corporations (public and private) and households for every 1,000 adults in the reporting country. Calculated as (number of non-life insurance policy holders)*1,000/adult population in the reporting country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Denotes the total number of non-life insurance policy holders (resident) that are resident nonfinancial corporations (public and private) and households for every 1,000 adults in the reporting country. Calculated as (number of non-life insurance policy holders)*1,000/adult population in the reporting country." }, { "id": "Source", "value": "International Monetary Fund, Financial Access Survey." }, { "id": "Topic", "value": "Access to Financial Services" } ], "source_id": "33" }, { "id": "1.1_YOUTH.LITERACY.RATE", "metatype": [ { "id": "IndicatorName", "value": "Literacy rate, youth total (% of people ages 15-24)" }, { "id": "Longdefinition", "value": "The number of persons aged 15 to 24 years who can both read and write with understanding a short simple statement on their everyday life, divided by the population in that age group. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. For further country-specific definition details please refer to the source of information, the UNESCO Institute for Statistics (UIS): www.uis.unesco.org" }, { "id": "Topic", "value": "Literacy Rate" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "2.1_PRE.PRIMARY.GER", "metatype": [ { "id": "IndicatorName", "value": "School enrolment, preprimary, national source (% gross)" }, { "id": "Limitationsandexceptions", "value": "Enrolment data for ECE programs can be affected by differences in reporting practices, namely by the extent to which childcare programs with little or no pedagogical component are included in the statistics. The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Pre-Primary Gross Enrolment Rate (GER): The number of pupils enrolled in pre-primary school, regardless of age, expressed as a percentage of the population in the theoretical age group in pre-primary school. The purpose of this indicator is to measure the general level of participation of children in Early Childhood Education (ECE) programs. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Key Outcome Indicators" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "2.2_GIR", "metatype": [ { "id": "IndicatorName", "value": "Gross intake ratio in grade 1, total, national source (% of relevant age group)" }, { "id": "Limitationsandexceptions", "value": "A high GIR indicates a high degree of access to primary education for children of the official primary school entrance age. This indicator can be distorted if repeaters in grade 1 are not distinguished from new entrants. The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Gross intake ratio (GIR): This indicator measures the total number of new entrants in the first grade of primary education, regardless of age, expressed as a percentage of the population at the official primary school-entrance age. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Key Outcome Indicators" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "2.3_GIR.GPI", "metatype": [ { "id": "IndicatorName", "value": "Gender parity index for gross intake ratio in grade 1" }, { "id": "Limitationsandexceptions", "value": "An indication of success should be denoted by a GIR in the parity range (between 0.97 and 1.03). The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Ratio of female to male values of gross intake ratio for primary first grade. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Key Outcome Indicators" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "2.4_OOSC.RATE", "metatype": [ { "id": "IndicatorName", "value": "Rate of out of school children, national source (% of relevant age group)" }, { "id": "Limitationsandexceptions", "value": "The national data used in the calculation of the out-of-school rate (OOSR) are based on enrolment at a specific date, which can over- or underestimate the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school children. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the OOSR. The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Number of children of official primary school age who are not enrolled in primary or secondary school, expressed as a percentage of the population of official primary school age. This indicator is intended to measure the size of the population in the official primary school age range that should be targeted by policies and efforts to achieve universal primary education. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Key Outcome Indicators" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "2.5_PCR", "metatype": [ { "id": "IndicatorName", "value": "Primary completion rate, total, national source (% of relevant age group)" }, { "id": "Limitationsandexceptions", "value": "Under certain circumstances, the computation can under- or overestimate the actual proportion of a given cohort completing primary school and sometimes exceeds 100% due to over-aged and under-aged children who enter primary school late/early and/or repeat grades, or due to the use of a population denominator that is derived from population projections or interpolations, which may under- or over-estimate the real population size. The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "The Primary Completion Rate (PCR) is the percentage of pupils who completed the last year of primary schooling. It is computed by dividing the total number of students in the last grade of primary school minus repeaters in that grade, divided by the total number of children of official completing age. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Key Outcome Indicators" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "2.6_PCR.GPI", "metatype": [ { "id": "IndicatorName", "value": "Gender parity index for primary completion rate" }, { "id": "Limitationsandexceptions", "value": "An indication of success should be denoted by a GIR in the parity range (between 0.97 and 1.03). The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Ratio of female to male values of Primary Completion Rate. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Key Outcome Indicators" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "2.7_PRI.SEC.TRANSITION.RATE", "metatype": [ { "id": "IndicatorName", "value": "Progression to secondary school, national source (%)" }, { "id": "Limitationsandexceptions", "value": "The main issue associated with this indicator is that at the time of data collection schools may have difficulties distinguishing between new entrants and repeaters. The quality of the indicator can also be affected by students who dropped out and changed schools. The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "This indicator measures the number of new entrants to the first grade of secondary education (general programs only) in a given year, expressed as a percentage of the number of pupils enrolled in the final grade of primary education in the previous year. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Key Outcome Indicators" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "2.8_LOW.SEC.COMPLETION.RATE", "metatype": [ { "id": "IndicatorName", "value": "Lower secondary completion rate, total, national source (% of relevant age group)" }, { "id": "Limitationsandexceptions", "value": "Under certain circumstances, the computation can under- or overestimate the actual proportion of a given cohort completing lower secondary school and sometimes exceeds 100% due to over-aged and under-aged children who enter lower secondary school late/early and/or repeat grades, or due to the use of a population denominator that is derived from population projections or interpolations, which may under- or over-estimate the real population size. The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "The lower secondary school completion rate is the percentage of children who are completing the last year of lower secondary education. It is computed by dividing the total number of students in the last grade of lower secondary education school minus repeaters in that grade divided by the total number of children of official completing age. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Key Outcome Indicators" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "3.1_LOW.SEC.NEW.TEACHERS", "metatype": [ { "id": "IndicatorName", "value": "Lower secondary education, new teachers, national source" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Persons employed for the first time in an official capacity to guide and direct the learning experience of pupils in lower secondary school, excluding educational personnel who have no active teaching duties. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Service Delivery" }, { "id": "Unitofmeasure", "value": "Unit numbers" } ], "source_id": "34" }, { "id": "3.1_PRI.NEW.ENTRANTS", "metatype": [ { "id": "IndicatorName", "value": "Primary education, new entrants, national source" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Pupils entering primary school for the first time, excluding repetears. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Service Delivery" }, { "id": "Unitofmeasure", "value": "Unit numbers" } ], "source_id": "34" }, { "id": "3.11_LOW.SEC.CLASSROOMS", "metatype": [ { "id": "IndicatorName", "value": "Lower secondary education, classrooms, national source" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Number of rooms or single accommodations, minimally equipped, in which a class of pupils in lower secondary school is taught. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Service Delivery" }, { "id": "Unitofmeasure", "value": "Unit numbers" } ], "source_id": "34" }, { "id": "3.12_LOW.SEC.NEW.CLASSROOMS", "metatype": [ { "id": "IndicatorName", "value": "Lower secondary education, new classrooms, national source" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Number of rooms or single accommodations, minimally equipped, built for the first time in which a class of pupils in lower secondary school is taught. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Service Delivery" }, { "id": "Unitofmeasure", "value": "Unit numbers" } ], "source_id": "34" }, { "id": "3.13_PRI.MATH.BOOK.PER.PUPIL", "metatype": [ { "id": "IndicatorName", "value": "Ratio of textbooks per pupil, primary education, mathematics" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Number of books used by a single pupil used as a standard work for the study of the mathematics subject. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Service Delivery" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "3.14_PRI.LANGU.BOOK.PER.PUPIL", "metatype": [ { "id": "IndicatorName", "value": "Ratio of textbooks per pupil, primary education, language" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Number of books used by a single pupil used as a standard work for the study of the primary language of instruction. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Service Delivery" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "3.15_LEARN.TIME.TEACHER.STUDY", "metatype": [ { "id": "IndicatorName", "value": "Last study on effective learning time and teacher attendance (year)" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Date (year) of the most recent study carried out by any national or international entity on the effectiveness of the instruction time devoted to learning activities." }, { "id": "Topic", "value": "Service Delivery" }, { "id": "Unitofmeasure", "value": "year" } ], "source_id": "34" }, { "id": "3.2_PRI.STUDENTS", "metatype": [ { "id": "IndicatorName", "value": "Primary education, pupils, national source" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Total population of pupils in primary school, regardless of age. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Service Delivery" }, { "id": "Unitofmeasure", "value": "Unit numbers" } ], "source_id": "34" }, { "id": "3.3_PRI.TEACHERS", "metatype": [ { "id": "IndicatorName", "value": "Primary education, teachers, national source" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Persons employed in an official capacity to guide and direct the learning experience of pupils in primary school, excluding educational personnel who have no active teaching duties. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Service Delivery" }, { "id": "Unitofmeasure", "value": "Unit numbers" } ], "source_id": "34" }, { "id": "3.4_PRI.NEW.TEACHERS", "metatype": [ { "id": "IndicatorName", "value": "Primary education, new teachers, national source" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Persons employed for the first time in an official capacity to guide and direct the learning experience of pupils in primary school. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Service Delivery" }, { "id": "Unitofmeasure", "value": "Unit numbers" } ], "source_id": "34" }, { "id": "3.5_PRI.CLASSROOMS", "metatype": [ { "id": "IndicatorName", "value": "Primary education, classrooms, national source" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Number of rooms or single accommodations, minimally equipped, in which a class of pupils in primary school is taught. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Service Delivery" }, { "id": "Unitofmeasure", "value": "Unit numbers" } ], "source_id": "34" }, { "id": "3.6_PRI.NEW.CLASSROOMS", "metatype": [ { "id": "IndicatorName", "value": "Primary education, new classrooms, national source" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Number of rooms or single accommodations, minimally equipped, built for the first time in which a class of pupils in primary school is taught. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Service Delivery" }, { "id": "Unitofmeasure", "value": "Unit numbers" } ], "source_id": "34" }, { "id": "3.7_LOW.SEC.NEW.ENTRANTS", "metatype": [ { "id": "IndicatorName", "value": "Lower Secondary education, new entrants, national source" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Pupils entering lower secondary school for the first time, excluding repeaters. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Service Delivery" }, { "id": "Unitofmeasure", "value": "Unit numbers" } ], "source_id": "34" }, { "id": "3.8_LOW.SEC.STUDENTS", "metatype": [ { "id": "IndicatorName", "value": "Lower secondary education, pupils, national source" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Total population of pupils in lower secondary school, regardless of age. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Service Delivery" }, { "id": "Unitofmeasure", "value": "Unit numbers" } ], "source_id": "34" }, { "id": "3.9_LOW.SEC.TEACHERS", "metatype": [ { "id": "IndicatorName", "value": "Lower secondary education, teachers, national source" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Persons employed in an official capacity to guide and direct the learning experience of pupils in lower secondary school, excluding educational personnel who have no active teaching duties. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Service Delivery" }, { "id": "Unitofmeasure", "value": "Unit numbers" } ], "source_id": "34" }, { "id": "4.1_TOTAL.EDU.SPENDING", "metatype": [ { "id": "IndicatorName", "value": "Public spending on total education (% of total public spending)" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Public expenses devoted to the education sector, including recurrent and capital expenditures and teacher salaries, expressed as a percentage of the total general government expenses. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Domestic Financing" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "4.2_BASIC.EDU.SPENDING", "metatype": [ { "id": "IndicatorName", "value": "Public spending on basic education (% of public spending on total education)" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Public expenses devoted to the basic education sector, including recurrent and capital expenditures and teacher salaries, expressed as a percentage of the public spending on total education. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Domestic Financing" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "4.3_TOTAL.EDU.RECURRENT", "metatype": [ { "id": "IndicatorName", "value": "Public recurrent spending on total education (% of total public recurrent spending)" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Public recurrent expenses devoted to the education sector, expressed as a percentage of the total general government recurrent expenses. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Domestic Financing" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "4.4_BASIC.EDU.RECURRENT", "metatype": [ { "id": "IndicatorName", "value": "Public recurrent spending on basic education (% of public recurrent spending on total education)" }, { "id": "Limitationsandexceptions", "value": "The data derived from national sources may differ from international sources because of divergences in definitions, methods of calculation, or in some cases due to different underlying data. For these reasons, the data based on national sources in this database should not be used for making comparisons between countries, but rather for assessing the progress of individual countries." }, { "id": "Longdefinition", "value": "Public recurrent expenses devoted to the basic education sector, expressed as a percentage of the public recurrent spending on total education. Country-specific definition, method and targets are determined by countries themselves." }, { "id": "Topic", "value": "Domestic Financing" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "5.1.1_AFG.TOTA.AID.CIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, CIDA to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_ALB.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Albania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_BFA.TOTA.AID.CIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, CIDA to Burkina Faso (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_CAF.TOT.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Global Partnership for Education to Central African Republic (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_CIV.TOTA.AID.AFDB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AfDB to Côte d'Ivoire (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_CMR.TOTA.AID.BAD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AfDB to Cameroun (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_DJI.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank (IDA) to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_ETH.TOTA.AID.ADB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, ADB to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_GEO.TOTA.AID.EC", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, European Commission to Georgia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_GHA.TOTA.AID.DFID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, DFID to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_GIN.TOTA.AID.ADPP.AFD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AFD to Guinea (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_GNB.TOTA.AID.ADPP.EU", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, ADPP (European Union) to Guinea Bissau (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_KGZ.TOTA.AID.ADPP.EU", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, European Commission to Kyrgyzstan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_KHM.TOTA.AID.BAD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AfDB to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_LAO.TOTA.AID.ADB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, ADB to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_LBR.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Liberia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_MDA.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Moldova (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_MDG.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid to total education executed by World Bank (including GPE funds) in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_MOZ.TOTA.AID.CAN", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Canada to Mozambique (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_MRT.TOTA.AID.AFD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AFD to Mauritania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_MWI.TOTA.AID.AFDB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AfDB to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_NER.TOTA.AID.AFD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AFD to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_RWA.TOTA.AID.DFID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, DFID to Rwanda (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_SEN.TOTA.AID.CIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, CIDA to Senegal (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_SLE.TOTA.AID.DFID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, DFID to Sierra Leone (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_VNM.TOTA.AID.BEL", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Belgium to Vietnam (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.1_ZMB.TOTA.AID.DNK", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Denmark to Zambia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.10_AFG.TOTA.AID.SIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Sida to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.10_ETH.TOTA.AID.JPN", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Japan Government to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.10_KHM.TOTA.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, WFP to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.10_LAO.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.10_MDG.TOTA.AID.EC", "metatype": [ { "id": "IndicatorName", "value": "International aid to total education executed by the European Commission in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.10_MOZ.TOTA.AID.JPN", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Japan to Mozambique (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.10_MWI.TOTA.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, WFP to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.10_NER.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.10_TJK.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.11_AFG.TOTA.AID.UNESCO", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNESCO to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.11_ETH.TOTA.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, JICA to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.11_KHM.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.11_LAO.TOTA.AID.INGOS", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, International NGOs to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.11_MOZ.TOTA.AID.NLD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Netherlands to Mozambique (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.11_MWI.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.12_AFG.TOTA.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, USAID to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.12_ETH.TOTA.AID.KFW", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, KfW to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.12_MOZ.TOTA.AID.PRT", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Portugal to Mozambique (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.13_AFG.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.13_ETH.TOTA.AID.NLD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Netherlands to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.13_MOZ.TOTA.AID.ESP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Spain to Mozambique (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.14_ETH.TOTA.AID.SIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Sida to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.14_MOZ.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Mozambique (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.15_ETH.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.15_MOZ.TOTA.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, USAID to Mozambique (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.16_ETH.TOTA.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, USAID to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.16_MOZ.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Mozambique (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.17_ETH.TOTA.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, WFP to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.18_ETH.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_AFG.TOTA.AID.DANIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, DANIDA to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_ALB.TOTA.AID.BEI", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, BEI to Albania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_BFA.TOTA.AID.AFD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AFD to Burkina Faso (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_CIV.TOTA.AID.BADEA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, BADEA to Côte d'Ivoire (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_CMR.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Cameroun (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_DJI.TOTA.AID.FSD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, FSD to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_ETH.TOTA.AID.BEL", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Belgium (VLIR USO) to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_GEO.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Georgia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_GHA.TOTA.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Global Partnership for Education to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_GIN.TOTA.AID.ADPP.AFDB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AfDB to Guinea (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_GNB.TOTA.AID.ADPP.HUM", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, ADPP (Humana People to People) to Guinea Bissau (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_KGZ.TOTA.AID.ADPP.GIZ", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, GIZ to Kyrgyzstan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_KHM.TOTA.AID.BEL", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Belgium to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_LAO.TOTA.AID.AUS", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AusAID to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_LBR.TOTA.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, USAID to Liberia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_MDA.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Moldova (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_MDG.TOTA.AID.ILO", "metatype": [ { "id": "IndicatorName", "value": "International aid to total education executed by ILO in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_MOZ.TOTA.AID.DANIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, DANIDA to Mozambique (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_MRT.TOTA.AID.ISDB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, IsDB to Mauritania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_MWI.TOTA.AID.CIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, CIDA to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_NER.TOTA.AID.BEL", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Belgium to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_RWA.TOTA.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Global Partnership for Education to Rwanda (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_SEN.TOTA.AID.FR", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AFD and French Embassy to Senegal (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_SLE.TOTA.AID.EC", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, European Commission to Sierra Leone (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_TJK.TOTA.AID.AGAK", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Aga Khan to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_VNM.TOTA.AID.CIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, CIDA to Vietnam (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.2_ZMB.TOTA.AID.IRL", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Ireland to Zambia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_AFG.TOTA.AID.FRA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, France to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_ALB.TOTA.AID.CEIB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, CEIB to Albania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_BFA.TOTA.AID.CHE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Switzerland to Burkina Faso (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_CIV.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Côte d'Ivoire (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_CMR.TOTA.AID.FR", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AFD and French Embassy to Cameroun (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_DJI.TOTA.AID.AFD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AFD to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_ETH.TOTA.AID.DFID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, DFID to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_GEO.TOTA.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, USAID to Georgia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_GHA.TOTA.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, JICA to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_GIN.TOTA.AID.ADPP.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Guinea (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_GNB.TOTA.AID.ADPP.OTH", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, ADPP (other donors) to Guinea Bissau (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_KGZ.TOTA.AID.ADPP.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Kyrgyzstan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_KHM.TOTA.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Global Partnership for Education to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_LAO.TOTA.AID.EC", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, European Commission to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_LBR.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Liberia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_MDG.TOTA.AID.FR", "metatype": [ { "id": "IndicatorName", "value": "International aid to total education executed by AFD and French Embassy in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_MOZ.TOTA.AID.DFID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, DFID to Mozambique (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_MRT.TOTA.AID.SP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Spanish Cooperation to Mauritania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_MWI.TOTA.AID.DFID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, DFID to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_NER.TOTA.AID.FR", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, French Embassy to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_RWA.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Rwanda (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_SEN.TOTA.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Global Partnership for Education to Senegal (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_SLE.TOTA.AID.GIZ", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, GIZ to Sierra Leone (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_TJK.TOTA.AID.OPENS", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Open Society Foundations to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_VNM.TOTA.AID.DFID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, DFID to Vietnam (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.3_ZMB.TOTA.AID.ILO", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, ILO to Zambia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_AFG.TOTA.AID.DEU", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Germany to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_BFA.TOTA.AID.DNK", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Denmark to Burkina Faso (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_CIV.TOTA.AID.ISDB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, IsDB to Côte d'Ivoire (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_CMR.TOTA.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, JICA to Cameroun (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_DJI.TOTA.AID.AFDB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AfDB to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_ETH.TOTA.AID.DVV", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, DVV international to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_GEO.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Georgia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_GHA.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_GIN.TOTA.AID.ADPP.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Global Partnership for Education to Guinea (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_GNB.TOTA.AID.EU", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, European Commission to Guinea Bissau (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_KGZ.TOTA.AID.ADPP.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Kyrgyzstan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_KHM.TOTA.AID.EC", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, European Commission to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_LAO.TOTA.AID.DEU", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Germany (GIZ and KfW) to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_MDG.TOTA.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid to total education executed by JICA in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_MOZ.TOTA.AID.FIN", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Finland to Mozambique (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_MRT.TOTA.AID.UNESCO", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNESCO to Mauritania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_MWI.TOTA.AID.GIZ", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, GIZ to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_NER.TOTA.AID.JAPAN", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Japan to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_RWA.TOTA.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, USAID to Rwanda (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_SEN.TOTA.AID.IT", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Italy to Senegal (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_SLE.TOTA.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, JICA to Sierra Leone (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_TJK.TOTA.AID.EC", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, European Commission to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_VNM.TOTA.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, JICA to Vietnam (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.4_ZMB.TOTA.AID.JPN", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Japan to Zambia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_AFG.TOTA.AID.IND", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, India to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_BFA.TOTA.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, JICA to Burkina Faso (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_CIV.TOTA.AID.FSD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, FSD to Côte d'Ivoire (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_CMR.TOTA.AID.UNESCO", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNESCO to Cameroun (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_DJI.TOTA.AID.ISDB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, IsDB to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_ETH.TOTA.AID.EC", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, European Commission to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_GHA.TOTA.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, USAID to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_GIN.TOTA.AID.ADPP.GIZ", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, GIZ to Guinea (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_GNB.TOTA.AID.FR", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AFD and French Embassy to Guinea Bissau (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_KHM.TOTA.AID.JPN", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Japan to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_LAO.TOTA.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Global Partnership for Education to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_MDG.TOTA.AID.NOR", "metatype": [ { "id": "IndicatorName", "value": "International aid to total education executed by Norway in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_MOZ.TOTA.AID.FLAND", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Flanders to Mozambique (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_MRT.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Mauritania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_MWI.TOTA.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Global Partnership for Education to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_NER.TOTA.AID.KFW", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, KfW to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_RWA.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Rwanda (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_SEN.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Senegal (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_SLE.TOTA.AID.SIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Sida to Sierra Leone (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_TJK.TOTA.AID.GIZ", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, GIZ to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_VNM.TOTA.AID.UNESCO", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNESCO to Vietnam (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.5_ZMB.TOTA.AID.ZMB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Netherlands to Zambia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_AFG.TOTA.AID.JPN", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Japan's MoFA to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_BFA.TOTA.AID.NLD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Netherlands to Burkina Faso (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_CIV.TOTA.AID.KFW", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, KfW to Côte d'Ivoire (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_CMR.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Cameroun (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_DJI.TOTA.AID.IMOA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, IMOA to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_ETH.TOTA.AID.FIN", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Finland to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_GHA.TOTA.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, WFP to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_GIN.TOTA.AID.ADPP.KFW", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, KfW to Guinea (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_GNB.TOTA.AID.PORT", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Portuguese Cooperation to Guinea Bissau (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_KHM.TOTA.AID.SWE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Sweden to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_LAO.TOTA.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, JICA to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_MDG.TOTA.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid to total education executed by WFP in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_MOZ.TOTA.AID.DEU", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Germany (GIZ and KfW) to Mozambique (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_MWI.TOTA.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, JICA to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_NER.TOTA.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, WFP to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_SEN.TOTA.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, USAID to Senegal (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_SLE.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Sierra Leone (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_TJK.TOTA.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Global Partnership for Education (CF and EPDF) to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_VNM.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Vietnam (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.6_ZMB.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Zambia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_AFG.TOTA.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, JICA to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_BFA.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Burkina Faso (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_CIV.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Côte d'Ivoire (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_ETH.TOTA.AID.GIZ", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, GIZ/BMZ to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_GHA.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_GIN.TOTA.AID.ADPP.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Guinea (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_GNB.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF (excluding Japan funds) to Guinea Bissau (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_KHM.TOTA.AID.UNESCO", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNESCO to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_LAO.TOTA.AID.UNESCO", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNESCO to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_MDG.TOTA.AID.UNESCO", "metatype": [ { "id": "IndicatorName", "value": "International aid to total education executed by UNESCO in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_MOZ.TOTA.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, GPE to Mozambique (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_MWI.TOTA.AID.KFW", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, KfW to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_NER.TOTA.AID.DFID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, DFID to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_SLE.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank (including the Global Partnership for Education) to Sierra Leone (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_TJK.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_VNM.TOTA.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, USAID to Vietnam (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.7_ZMB.TOTA.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, USAID to Zambia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.8_AFG.TOTA.AID.NLD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Netherlands to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.8_BFA.TOTA.AID.EC", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, European Commission to Burkina Faso (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.8_CIV.TOTA.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, USAID to Côte d'Ivoire (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.8_ETH.TOTA.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, GPE Catalytic Fund to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.8_GNB.TOTA.AID.JAP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Japan (via UNICEF) to Guinea Bissau (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.8_KHM.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.8_LAO.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.8_MDG.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid to total education executed by UNICEF (excluding GPE funds) in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.8_MOZ.TOTA.AID.IRL", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Ireland to Mozambique (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.8_MWI.TOTA.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.8_NER.TOTA.AID.CHE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Switzerland to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.8_SLE.TOTA.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education from WFP to Sierra Leone (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.8_TJK.TOTA.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, USAID to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.8_VNM.TOTA.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank to Vietnam (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.9_AFG.TOTA.AID.NZL", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, New Zealand to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.9_ETH.TOTA.AID.ITA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Italian Cooperation to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.9_KHM.TOTA.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, USAID to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.9_LAO.TOTA.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, WFP to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.9_MDG.TOTA.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education from the Global Partnership for Education (via UNICEF and WB) to Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.9_MOZ.TOTA.AID.ITA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Italy to Mozambique (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.9_MWI.TOTA.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, USAID to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.9_NER.TOTA.AID.LUX", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Luxembourg to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1.9_TJK.TOTA.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, WFP to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.1_TOTAL.EDU.AID", "metatype": [ { "id": "IndicatorName", "value": "International aid for total education, disbursed (up to present year) and scheduled (next years), aggregation of reporting donors (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "Sum of international concessional aid disbursed by reporting development partners (donors) to the total education sector in a specific developing country. Targets indicate the sum of the scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by donors to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_AFG.BAS.AID.CIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, CIDA to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_ALB.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Albania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_BFA.BAS.AID.CIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, CIDA to Burkina Faso (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_CAF.BAS.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Global Partnership for Education to Central African Republic (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_CIV.BAS.AID.AFDB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, AfDB to Côte d'Ivoire (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_CMR.BAS.AID.BAD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, AfDB to Cameroun (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_DJI.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank (IDA) to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_ETH.BAS.AID.ADB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, ADB to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_GEO.BAS.AID.EC", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, European Commission to Georgia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_GHA.BAS.AID.DFID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, DFID to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_GIN.BAS.AID.ADPP.AFD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, AFD to Guinea (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_GNB.BAS.AID.ADPP.EU", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, ADPP (European Union) to Guinea Bissau (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_KGZ.BAS.AID.ADPP.EU", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, European Commission to Kyrgyzstan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_KHM.BAS.AID.BAD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, AfDB to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_LAO.BAS.AID.ADB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, ADB to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_LBR.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Liberia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_MDA.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Moldova (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_MDG.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid to basic education executed by World Bank (GPE funds) in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_MRT.TOTA.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, WFP to Mauritania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_MWI.BAS.AID.AFDB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, AfDB to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_NER.BAS.AID.AFD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, AFD to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_RWA.BAS.AID.DFID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, DFID to Rwanda (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_SEN.BAS.AID.CIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, CIDA to Senegal (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_SLE.BAS.AID.DFID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, DFID to Sierra Leone (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_TJK.BAS.AID.AGAK", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Aga Khan to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_TLS.TOT.AID.AUSAID.CFAUS", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AusAID and ChildFund Australia to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_VNM.BAS.AID.CIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, CIDA to Vietnam (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.1_ZMB.BAS.AID.DNK", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Denmark to Zambia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.10_AFG.BAS.AID.SIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Sida to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.10_ETH.BAS.AID.JPN", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Japan Government to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.10_KHM.BAS.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, WFP to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.10_LAO.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.10_MDG.BAS.AID.EC", "metatype": [ { "id": "IndicatorName", "value": "International aid to basic education executed by the European Commission in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.10_MWI.BAS.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, WFP to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.10_NER.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.10_TLS.TOT.AID.PRIV", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, private donors to Timor-Leste (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.11_AFG.BAS.AID.UNESCO", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNESCO to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.11_ETH.BAS.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, JICA to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.11_KHM.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.11_LAO.BAS.AID.INGOS", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, International NGOs to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.11_MWI.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.11_TLS.TOT.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, UNICEF to Timor-Leste (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.12_AFG.BAS.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, USAID to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.12_ETH.BAS.AID.KFW", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, KfW to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.12_TLS.TOT.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, USAID to Timor-Leste (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.13_AFG.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.13_ETH.BAS.AID.NLD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Netherlands to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.14_ETH.BAS.AID.SIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Sida to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.15_ETH.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.16_ETH.BAS.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, USAID to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.17_ETH.BAS.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, WFP to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.18_ETH.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_AFG.BAS.AID.DANIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, DANIDA to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_ALB.BAS.AID.BEI", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, BEI to Albania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_BFA.BAS.AID.AFD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, AFD to Burkina Faso (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_CIV.BAS.AID.BADEA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, BADEA to Côte d'Ivoire (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_CMR.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Cameroun (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_DJI.BAS.AID.FSD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, FSD to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_ETH.BAS.AID.BEL", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Belgium (VLIR USO) to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_GEO.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Georgia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_GHA.BAS.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Global Partnership for Education to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_GIN.BAS.AID.ADPP.AFDB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, AfDB to Guinea (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_GNB.BAS.AID.ADPP.HUM", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, ADPP (Humana People to People) to Guinea Bissau (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_KGZ.BAS.AID.ADPP.GIZ", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, GIZ to Kyrgyzstan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_KHM.BAS.AID.BEL", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Belgium to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_LAO.BAS.AID.AUS", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, AusAID to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_LBR.BAS.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, USAID to Liberia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_MDA.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Moldova (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_MDG.BAS.AID.ILO", "metatype": [ { "id": "IndicatorName", "value": "International aid to basic education executed by ILO in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_MRT.BAS.AID.AFD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, AFD to Mauritania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_MWI.BAS.AID.CIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, CIDA to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_NER.BAS.AID.BEL", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Belgium to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_RWA.BAS.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Global Partnership for Education to Rwanda (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_SEN.BAS.AID.FR", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, AFD and French Embassy to Senegal (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_SLE.BAS.AID.EC", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, European Commission to Sierra Leone (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_TJK.BAS.AID.OPENS", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Open Society Foundations to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_TLS.TOT.AID.AUSAID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, AusAID (World Bank) to Timor-Leste (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_VNM.BAS.AID.DFID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, DFID to Vietnam (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.2_ZMB.BAS.AID.IRL", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Ireland to Zambia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_AFG.BAS.AID.FRA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, France to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_ALB.BAS.AID.CEIB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, CEIB to Albania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_BFA.BAS.AID.CHE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Switzerland to Burkina Faso (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_CIV.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Côte d'Ivoire (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_CMR.BAS.AID.FR", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, AFD and French Embassy to Cameroun (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_DJI.BAS.AID.AFD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, AFD to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_ETH.BAS.AID.DFID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, DFID to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_GEO.BAS.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, USAID to Georgia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_GHA.BAS.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, JICA to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_GIN.BAS.AID.ADPP.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Guinea (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_GNB.BAS.AID.ADPP.OTH", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, ADPP (other donors) to Guinea Bissau (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_KGZ.BAS.AID.ADPP.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Kyrgyzstan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_KHM.BAS.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Global Partnership for Education to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_LAO.BAS.AID.EC", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, European Commission to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_LBR.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Liberia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_MDG.BAS.AID.FR", "metatype": [ { "id": "IndicatorName", "value": "International aid to basic education executed by AFD and French Embassy in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_MRT.BAS.AID.ISDB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, IsDB to Mauritania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_MWI.BAS.AID.DFID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, DFID to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_NER.BAS.AID.FR", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, French Embassy to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_RWA.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Rwanda (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_SEN.BAS.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Global Partnership for Education to Senegal (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_SLE.BAS.AID.GIZ", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, GIZ to Sierra Leone (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_TJK.BAS.AID.EC", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, European Commission to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_TLS.TOT.AID.AUS", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Australia to Timor-Leste (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_VNM.BAS.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, JICA to Vietnam (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.3_ZMB.BAS.AID.ILO", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, ILO to Zambia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_AFG.BAS.AID.DEU", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Germany to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_BFA.BAS.AID.DNK", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Denmark to Burkina Faso (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_CIV.BAS.AID.ISDB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, IsDB to Côte d'Ivoire (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_CMR.BAS.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, JICA to Cameroun (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_DJI.BAS.AID.AFDB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, AfDB to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_ETH.BAS.AID.DVV", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, DVV international to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_GEO.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Georgia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_GHA.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_GIN.BAS.AID.ADPP.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Global Partnership for Education to Guinea (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_GNB.BAS.AID.EU", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, European Commission to Guinea Bissau (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_KGZ.BAS.AID.ADPP.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Kyrgyzstan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_KHM.BAS.AID.EC", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, European Commission to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_LAO.BAS.AID.DEU", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Germany (GIZ and KfW) to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_MDG.BAS.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid to basic education executed by JICA in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_MRT.BAS.AID.SP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Spanish Cooperation to Mauritania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_MWI.BAS.AID.GIZ", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, GIZ to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_NER.BAS.AID.JAPAN", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Japan to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_RWA.BAS.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, USAID to Rwanda (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_SEN.BAS.AID.IT", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Italy to Senegal (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_SLE.BAS.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, JICA to Sierra Leone (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_TJK.BAS.AID.GIZ", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, GIZ to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_TLS.TOT.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, World Bank (IDA) to Timor-Leste (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_VNM.BAS.AID.UNESCO", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNESCO to Vietnam (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.4_ZMB.BAS.AID.JPN", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Japan to Zambia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_AFG.BAS.AID.IND", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, India to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_BFA.BAS.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, JICA to Burkina Faso (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_CIV.BAS.AID.FSD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, FSD to Côte d'Ivoire (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_CMR.BAS.AID.UNESCO", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNESCO to Cameroun (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_DJI.BAS.AID.ISDB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, IsDB to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_ETH.BAS.AID.EC", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, European Commission to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_GHA.BAS.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, USAID to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_GIN.BAS.AID.ADPP.GIZ", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, GIZ to Guinea (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_GNB.BAS.AID.FR", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, AFD and French Embassy to Guinea Bissau (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_KHM.BAS.AID.JPN", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Japan to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_LAO.BAS.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Global Partnership for Education to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_MDG.BAS.AID.NOR", "metatype": [ { "id": "IndicatorName", "value": "International aid to basic education executed by Norway in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_MRT.BAS.AID.UNESCO", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNESCO to Mauritania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_MWI.BAS.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Global Partnership for Education to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_NER.BAS.AID.KFW", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, KfW to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_RWA.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Rwanda (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_SEN.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Senegal (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_SLE.BAS.AID.SIDA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Sida to Sierra Leone (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_TJK.BAS.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Global Partnership for Education (CF and EPDF) to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_TLS.TOT.AID.JPN", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Japan to Timor-Leste (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_VNM.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Vietnam (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.5_ZMB.BAS.AID.ZMB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Netherlands to Zambia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_AFG.BAS.AID.JPN", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Japan's MoFA to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_BFA.BAS.AID.NLD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Netherlands to Burkina Faso (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_CIV.BAS.AID.KFW", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, KfW to Côte d'Ivoire (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_CMR.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Cameroun (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_DJI.BAS.AID.IMOA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, IMOA to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_ETH.BAS.AID.FIN", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Finland to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_GHA.BAS.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, WFP to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_GIN.BAS.AID.ADPP.KFW", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, KfW to Guinea (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_GNB.BAS.AID.PORT", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Portuguese Cooperation to Guinea Bissau (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_KHM.BAS.AID.SWE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Sweden to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_LAO.BAS.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, JICA to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_MDG.BAS.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid to basic education executed by WFP in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_MRT.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Mauritania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_MWI.BAS.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, JICA to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_NER.BAS.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, WFP to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_SEN.BAS.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, USAID to Senegal (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_SLE.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Sierra Leone (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_TJK.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_TLS.TOT.AID.KOR", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, South Korea to Timor-Leste (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_VNM.BAS.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, USAID to Vietnam (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.6_ZMB.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Zambia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_AFG.BAS.AID.JICA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, JICA to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_BFA.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Burkina Faso (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_CIV.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Côte d'Ivoire (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_ETH.BAS.AID.GIZ", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, GIZ/BMZ to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_GHA.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Djibouti (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_GIN.BAS.AID.ADPP.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Guinea (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_GNB.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF (excluding Japan funds) to Guinea Bissau (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_KHM.BAS.AID.UNESCO", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNESCO to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_LAO.BAS.AID.UNESCO", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNESCO to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_MDG.BAS.AID.UNESCO", "metatype": [ { "id": "IndicatorName", "value": "International aid to basic education executed by UNESCO in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_MRT.BAS.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, WFP to Mauritania (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_MWI.BAS.AID.KFW", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, KfW to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_NER.BAS.AID.DFID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, DFID to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_SLE.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank (including the Global Partnership for Education) to Sierra Leone (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_TJK.BAS.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, USAID to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_TLS.TOT.AID.NZL", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, New Zealand to Timor-Leste (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_VNM.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Vietnam (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.7_ZMB.BAS.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, USAID to Zambia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.8_AFG.BAS.AID.NLD", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Netherlands to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.8_BFA.BAS.AID.EC", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, European Commission to Burkina Faso (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.8_CIV.BAS.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, USAID to Côte d'Ivoire (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.8_ETH.BAS.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, GPE Catalytic Fund to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.8_GNB.BAS.AID.JAP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Japan (via UNICEF) to Guinea Bissau (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.8_KHM.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.8_LAO.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.8_MDG.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid to basic education executed by UNICEF in Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.8_MWI.BAS.AID.UNICEF", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, UNICEF to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.8_NER.BAS.AID.CHE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Switzerland to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.8_SLE.BAS.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, WFP to Sierra Leone (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.8_TJK.BAS.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, WFP to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.8_TLS.TOT.AID.CFNZL", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, ChildFund NZAID and UNICEF to Timor-Leste (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.9_AFG.BAS.AID.NZL", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, New Zealand to Afghanistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.9_ETH.BAS.AID.ITA", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Italian Cooperation to Ethiopia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.9_KHM.BAS.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, AUSAID to Cambodia (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.9_LAO.BAS.AID.WFP", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, WFP to Laos (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.9_MDG.BAS.AID.GPE", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Global Partnership for Education to Madagascar (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.9_MWI.BAS.AID.USAID", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, USAID to Malawi (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.9_NER.BAS.AID.LUX", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, Luxembourg to Niger (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.9_TJK.BAS.AID.WB", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to basic education, World Bank to Tajikistan (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the basic education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2.9_TLS.TOT.AID.PRT", "metatype": [ { "id": "IndicatorName", "value": "International aid disbursed to total education, Portugal to Timor-Leste (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "International concessional aid disbursed by the reporting development partner to the total education sector in the specific developing country. Targets indicate scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by the donor to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "5.2_BASIC.EDU.AID", "metatype": [ { "id": "IndicatorName", "value": "International aid for basic education, disbursed (up to present year) and scheduled (next years), aggregation of reporting donors (USD million)" }, { "id": "Limitationsandexceptions", "value": "The following issues might lead to divergences between the total reported and real disbursed/scheduled aid: the fact that most of donors reported aid in calendar years, but others in fiscal years; when figures were reported in USD it was not specified the exchange rates used and if figures referred to current USD; general budget support (GBS) was not always reported, nor the 20% estimation of GBS going to the education sector; aid to basic education was not always possible to distinguish from the total education aid; not all donors participated in this exercise (although in most of the cases the majority of them participated and in some countries the totality of them reported their aid)." }, { "id": "Longdefinition", "value": "Sum of international concessional aid disbursed by reporting development partners (donors) to the total education sector in a specific developing country. Targets indicate the sum of the scheduled or projected aid. Accounted aid includes activities, projects, technical cooperation and sector and budget support (20%), as it was reported by donors to the Global Partnership for Education." }, { "id": "Topic", "value": "International Aid to Education" }, { "id": "Unitofmeasure", "value": "USD million" } ], "source_id": "34" }, { "id": "6.1_LEG.CA", "metatype": [ { "id": "IndicatorName", "value": "Coordinating agency of Local Education Group (1=text in notes)" }, { "id": "Longdefinition", "value": "Coordinating Agency (CA) or lead donor in the Local Education Group (LEG) coordinates and facilitates partners’ engagement with the Global Partnership for Education, thus serving as the communication link between the LEG and the Secretariat. The CA has a central role in facilitating the work of the LEG and is selected by this group. The LEG is the local structure of the Global Partnership for Education that bring together partners to develop high quality education strategies and programs. They are typically led by the Ministry of Education and include development partners and other education stakeholders such as national government and public entities, local and international civil society organizations (CSOs), CSO coalitions, teachers' and parents' organizations and private sector providers. The specific composition, title, and working arrangements of the LEG will vary from country to country. The LEG should be a collaborative forum for policy dialogue and for alignment and harmonization of technical and financial support to the education sector plan. It seeks to ensure that all parties are kept fully apprised of the progress and challenges in the sector, and it collates and disseminates information on domestic and external funding for the education sector." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "1 = text in note" } ], "source_id": "34" }, { "id": "6.2_LEG.OTHER.DONORS", "metatype": [ { "id": "IndicatorName", "value": "Donor members in Local Education Group (1=text in notes)" }, { "id": "Longdefinition", "value": "Development partners other than the Coordinating Agency or lead donor in the Local Education Group (LEG), excluding civil society organizations (CSOs) and government and public entities. The LEG is the local structure of the Global Partnership for Education that bring together partners to develop high quality education strategies and programs. They are typically led by the Ministry of Education and include development partners and other education stakeholders such as national government and public entities, local and international civil society organizations (CSOs), CSO coalitions, teachers' and parents' organizations and private sector providers. The specific composition, title, and working arrangements of the LEG will vary from country to country. The LEG should be a collaborative forum for policy dialogue and for alignment and harmonization of technical and financial support to the education sector plan. It seeks to ensure that all parties are kept fully apprised of the progress and challenges in the sector, and it collates and disseminates information on domestic and external funding for the education sector." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "1 = text in note" } ], "source_id": "34" }, { "id": "6.3_LEG.CSO", "metatype": [ { "id": "IndicatorName", "value": "Civil society organizations in Local Education Group (1=text in notes)" }, { "id": "Longdefinition", "value": "Local and international civil society organizations (CSO) participating in the Local Education Group (LEG), excluding development partners and government and public entities. The LEG is the local structure of the Global Partnership for Education that brings together partners to develop high quality education strategies and programs. They are typically led by the Ministry of Education and include development partners and other education stakeholders such as national government and public entities, local and international civil society organizations (CSOs), CSO coalitions, teachers' and parents' organizations and private sector providers. The specific composition, title, and working arrangements of the LEG will vary from country to country. The LEG should be a collaborative forum for policy dialogue and for alignment and harmonization of technical and financial support to the education sector plan. It seeks to ensure that all parties are kept fully apprised of the progress and challenges in the sector, and it collates and disseminates information on domestic and external funding for the education sector." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "1 = text in note" } ], "source_id": "34" }, { "id": "6.4_LAST.JSR", "metatype": [ { "id": "IndicatorName", "value": "Date of last Joint Education Sector Review (year=full date in notes)" }, { "id": "Longdefinition", "value": "Date of most recent Joint Sector Review (JSR) carried out by the Local Education Group (LEG). A JSR is a monitoring and review mechanism where LEG members get together to assess progress, challenges and funding of the education sector. They provide an opportunity to measure progress against Education Sector Plans (ESPs) as well as to influence allocations and work-plans. They take place annually or biannually (some countries have more than two JSRs per year). Periodicity, participants and working arrangements are determined by the LEG. An aide-memoire gathering conclusions reached by stakeholders is produced at the end of the JSR." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "year = date in note" } ], "source_id": "34" }, { "id": "6.5_NEXT.JSR", "metatype": [ { "id": "IndicatorName", "value": "Date of next Joint Education Sector Review (year=full date in notes)" }, { "id": "Longdefinition", "value": "Date of next Joint Sector Review (JSR) planned by the Local Education Group (LEG). A JSR is a monitoring and review mechanism where LEG members get together to assess progress, challenges and funding of the education sector. They provide an opportunity to measure progress against Education Sector Plans (ESPs) as well as to influence allocations and work-plans. They take place annually or biannually (some countries have more than two JSRs per year). Periodicity, participants and working arrangements are determined by the LEG. An aide-memoire gathering conclusions reached by stakeholders is produced at the end of the JSR." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "year = date in note" } ], "source_id": "34" }, { "id": "7.1.1_ESP.PERIOD.START", "metatype": [ { "id": "IndicatorName", "value": "Starting year of current Education Sector Plan period (year=full period in notes)" }, { "id": "Longdefinition", "value": "The starting year for the period in which the Education Sector Plan or Transitional Education Plan is in effect." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "year=period in note" } ], "source_id": "34" }, { "id": "7.1.2_ESP.PERIOD.END", "metatype": [ { "id": "IndicatorName", "value": "Ending year of current Education Sector Plan period (year=full period in notes)" }, { "id": "Longdefinition", "value": "The ending year for the period in which the Education Sector Plan or Transitional Education Plan is in effect." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "year=period in note" } ], "source_id": "34" }, { "id": "7.1_CURR.ALLOCATION.SE", "metatype": [ { "id": "IndicatorName", "value": "Current allocation - Supervising or managing entity (1=text in notes)" }, { "id": "Longdefinition", "value": "When a Program Implementation Grant is requested from the Global Partnership, a supervising entity (SE) or managing entity (ME) must be designated by the Local Education Group (LEG). They are a bilateral or multilateral development agency. The key difference between these two roles is that a SE will transfer grant funds to the developing-country government, who will implement the program, whereas a ME will manage program activities directly." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "1 = text in note" } ], "source_id": "34" }, { "id": "7.11_CURR.ALLOCATION.MODALITY", "metatype": [ { "id": "IndicatorName", "value": "Current allocation - Modality (1=text in notes)" }, { "id": "Longdefinition", "value": "The Global Partnership for Education is underpinned by the principles set out in the March 2005 Paris Declaration on Aid Effectiveness. The Global Partnership, therefore, anticipates that Local Education Groups (LEGs) will use the following order of preference when choosing a modality for GPE funding: Budget support (general or sector); pool funding; or stand-alone project. The LEG will need to determine the most appropriate and efficient way to channel the Program Implementation Grant into the education sector, balancing risks with the need to optimize capacity building and country ownership." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "1 = text in note" } ], "source_id": "34" }, { "id": "7.12_CURR.ALLOCATION.2011.DISB", "metatype": [ { "id": "IndicatorName", "value": "Current allocation - Total disbursements as of 12/2011 (USD millions)" }, { "id": "Longdefinition", "value": "Current GPE funding disbursed from the date of the signature of allocation to December 31st, 2011." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "number and decimals" } ], "source_id": "34" }, { "id": "7.13_CURR.ALLOCATION.DISB", "metatype": [ { "id": "IndicatorName", "value": "Current allocation - Annual disbursements (USD million)" }, { "id": "Longdefinition", "value": "Current GPE funding disbursed and projected. Disbursements are indicated up to 2011; projections are labeled as \"targets\" starting in 2012." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "number and decimals" } ], "source_id": "34" }, { "id": "7.2_ESP.ENDORSEMENT", "metatype": [ { "id": "IndicatorName", "value": "Endorsement of Education Sector Plan (year)" }, { "id": "Longdefinition", "value": "Year in which the Education Sector Plan or Transitional Education Plan was endorsed (a first or subsecuent times) by local donor partners, which grants membership into the Global Partnership for Education (GPE) and enable developing countries to apply for GPE funding. Education sector plans should contribute to the Education for All goals. When partners endorse a country’s education sector plan, they signal that the plan contributes to the attainment of those goals, and they commit to aligning their technical and financial support with the plan. This commitment promotes harmonization as well as consistency, coherence, and sustainability in education sector development." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "year" } ], "source_id": "34" }, { "id": "7.3_PREV.ALLOCATION.YEAR", "metatype": [ { "id": "IndicatorName", "value": "Previous allocation - Approval (year)" }, { "id": "Longdefinition", "value": "Year(s) of approval of a Global Partnership funding operation that is now closed." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "year" } ], "source_id": "34" }, { "id": "7.4_PREV.ALLOCATION.AMOUNT", "metatype": [ { "id": "IndicatorName", "value": "Previous allocation - Amount disbursed (USD million)" }, { "id": "Longdefinition", "value": "Amount(s) allocated through a Global Partnership funding operation that is now closed." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "units and decimals" } ], "source_id": "34" }, { "id": "7.5_CURR.ALLOCATION.YEAR", "metatype": [ { "id": "IndicatorName", "value": "Current allocation - Approval (year)" }, { "id": "Longdefinition", "value": "Year of approval of a Global Partnership funding operation that is currently being disbursed." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "year" } ], "source_id": "34" }, { "id": "7.6_CURR.ALLOCATION.AMOUNT", "metatype": [ { "id": "IndicatorName", "value": "Current allocation - Total indicative amount (USD million)" }, { "id": "Longdefinition", "value": "Amount allocated through a Global Partnership funding operation that is currently being disbursed." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "number and decimals" } ], "source_id": "34" }, { "id": "7.7.1_CURR.ALLOCATION.PERIOD.START", "metatype": [ { "id": "IndicatorName", "value": "Current allocation - Starting year of implementation period (year=full period in notes)" }, { "id": "Longdefinition", "value": "The starting year for the disbursement period of the current Global Partnership funding." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "year=period in note" } ], "source_id": "34" }, { "id": "7.7.2_CURR.ALLOCATION.PERIOD.END", "metatype": [ { "id": "IndicatorName", "value": "Current allocation - Ending year of Implementation period (year=full period in notes)" }, { "id": "Longdefinition", "value": "The ending year for the disbursement period of the current Global Partnership funding." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "year=period in note" } ], "source_id": "34" }, { "id": "7.8_CURR.ALLOCATION.SIGNATURE", "metatype": [ { "id": "IndicatorName", "value": "Current allocation - Signature date (year=full date in notes)" }, { "id": "Longdefinition", "value": "Date of GPE grant agreement signature between a recipient government and the supervising entity." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "year = date in note" } ], "source_id": "34" }, { "id": "7.9_CURR.ALLOCATION.CLOSURE", "metatype": [ { "id": "IndicatorName", "value": "Current allocation - Closing date (year=full date in notes)" }, { "id": "Longdefinition", "value": "Date of GPE grant agreement closure, when the supervising or managing entity informs the GPE that there will not be any other disbursements on the grant." }, { "id": "Topic", "value": "Global Partnership Funding" }, { "id": "Unitofmeasure", "value": "year = date in note" } ], "source_id": "34" }, { "id": "8.1_SCH.LEAVING.EXAMS", "metatype": [ { "id": "IndicatorName", "value": "Administration of school leaving exams (yes=1, no=0, see notes if available)" }, { "id": "Longdefinition", "value": "It indicates if school leaving exams are administered for primary and lower secondary levels and in which grade." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "1=yes and text in note; 0=no" } ], "source_id": "34" }, { "id": "8.2_INT.TESTS", "metatype": [ { "id": "IndicatorName", "value": "Participation in international tests (yes=1, no=0, see notes if available)" }, { "id": "Longdefinition", "value": "It indicates in which international learning outcome assessments has the country participated and in which year. Please refer to the subtopic Learning Outcomes and the specific country for details on the scores obtained in these assessments, as reported by the Local Education Group (LEG)." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "1=yes and text in note; 0=no" } ], "source_id": "34" }, { "id": "8.3.1_ALB.LEAR.TEST.9.LANG.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Albania, grade 9, Language (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Scale goes up to 50." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.1_BFA.PASEC.CP2.FR", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Burkina Faso, CP2, French (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.1_CAF.BREVET.SUCC", "metatype": [ { "id": "IndicatorName", "value": "Brevet des colleges in Central African Republic, success rate (%)" }, { "id": "Longdefinition", "value": "Success rate in the Brevet des colleges, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.1_CIV.LEAR.TEST.PRIM.ALL.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Côte d'Ivoire, primary (CEPE), mean score of all subjects" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific education level for French, Mathematics, Sciences and H-G, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.1_CMR.PASEC.25.FRE", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Cameroon, grades 2 and 5, French (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.1_ETH.LEAR.TEST.10.ENG.OPT", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ethiopia, grade 10, English, optimal competency (%)" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Optimal competency are scores above 50%." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.1_GEO.PIRLS.4.READ.MEAN", "metatype": [ { "id": "IndicatorName", "value": "PIRLS in Georgia, grade 4, Reading (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Progress in International Reading Literacy Study (PIRLS) on the reading achievement of fourth grade students, as reported by the Local Education Group (LEG). It was first conduced in 2001 and then every five years by the TIMS & PIRLS International Study Center of Boston College's Lynch School of Education. For further details please refer to this web site: pirls.bc.edu." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.1_GHA.LEAR.TEST.P3.ENG.ABOV.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ghana, P3, English, students above mean (%)" }, { "id": "Longdefinition", "value": "Students with results above the mean competency in the National Education Assessment (NEA) carried out in the specific subject and grade, using multiple choice items with 4 options, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.1_GIN.PASEC.CP2.FR.MEAN", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Guinea, CP2, French (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.1_KGZ.PISA.89.READ1", "metatype": [ { "id": "IndicatorName", "value": "PISA in Kyrgyzstan, grades 8-9, Reading - overall (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme for International Student Assessment (PISA) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PISA is an international study that was launched by the OECD in 1997. It aims to evaluate education systems worldwide every three years by assessing 15-year-olds' competencies in the key subjects: reading, mathematics and science. For further details please refer to this web site: http://www.oecd.org/pisa/. Pupils who were 15 year old participated in this test, some of them were at grade 8 and some at grade 10." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.1_KHM.LEAR.TEST.3.LANG.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Cambodia, grade 3, Language (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.1_LAO.LEAR.TEST.5.LANG.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Laos, grade 5, Language (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.1_MDA.LEAR.TEST.4.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Moldova, grade 4, mean competency (%)" }, { "id": "Longdefinition", "value": "Students with mean competency in the results of the national assessment carried out in the specific grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.1_MDG.PASEC.CM2.FRE", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Madagascar, CM2, French (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.1_MOZ.SACMEQ.TEST.6.READ", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ in Mozambique, grade 5, Reading (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. SACMEQ has completed two major education policy research projects (SACMEQ I and SACMEQ II) between 1995 and 2005. The third project (SACMEQ III) commenced in 2007 and was completed in 2011. For further details please refer to the following web site: www.sacmeq.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.1_MRT.PASEC.5.FR", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Mauritania, grade 5, French (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.1_MWI.SACMEQ.357.READ", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ in Malawi, standards 3,5,7, Reading (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. SACMEQ has completed two major education policy research projects (SACMEQ I and SACMEQ II) between 1995 and 2005. The third project (SACMEQ III) commenced in 2007 and was completed in 2011. For further details please refer to the following web site: www.sacmeq.org. In Malawi SACMEQ I tested learners on English; SACMEQ II on English and Mathematics; and SACMEQ III on English, Mathematics, and HIV/AIDs." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.1_NER.LEAR.TEST.CP.FR.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CP, French (mean score)" }, { "id": "Longdefinition", "value": "Mean scores calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.1_SEN.LEAR.TEST.CE2.MATH.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes (SNERS) in Senegal, CE2, Mathematics, minimal competency (%)" }, { "id": "Longdefinition", "value": "Minimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.1_VNM.LEAR.TEST.5.MAT1", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Vietnam, grade 5, Mathematics - Level 1, scores in indicated level (%)" }, { "id": "Longdefinition", "value": "Students in respective level in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Relative level means that student: Reads, writes and compares natural numbers, fractions and decimals. Uses single operations of +, -, x and : on simple whole numbers; works with simple measures such as time; recognizes simple 3D shapes." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.1_VNM.LEAR.TEST.5.READ1", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Vietnam, grade 5, Reading - Level 1, scores in indicated level (%)" }, { "id": "Longdefinition", "value": "Students in respective level in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Relative level means that student: Matches text at word or sentence level aided by pictures. Restricted to a limited range of vocabulary linked to pictures." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.1_ZMB.LEAR.TEST.5.READ", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Zambia, grade 5, Reading (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.10_ETH.LEAR.TEST.12.CHE.OPT", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ethiopia, grade 12, Chemistry, optimal competency (%)" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Optimal competency are scores above 50%." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.10_GEO.LEAR.TEST.9.LANG.LOWEST", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Georgia, grade 9, Language, students in lowest level (%)" }, { "id": "Longdefinition", "value": "Students with lowest competency in the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.10_GHA.LEAR.TEST.P6.ENG.ABOV.PROF", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ghana, P6, English, students above proficient levels (%)" }, { "id": "Longdefinition", "value": "Students with results above proficient competency in the National Education Assessment (NEA) carried out in the specific subject and grade, using multiple choice items with 4 options, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. % of pupils achieving 55% of results or more in the test." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.10_GIN.PASEC.CM1.FR.MATH.MEAN.BEG", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Guinea, CM1, French and Mathematics, mean score at the end of year (%)" }, { "id": "Longdefinition", "value": "Mean score calculated at the end of the year for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.10_NER.LEAR.TEST.CP.FR.UNDERMIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CP, French, under minimal competency (%)" }, { "id": "Longdefinition", "value": "Students under minimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.11_ETH.LEAR.TEST.12.PHY.OPT", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ethiopia, grade 12, Physics, optimal competency (%)" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Optimal competency are scores above 50%." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.11_GEO.LEAR.TEST.9.MAT.LOWEST", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Georgia, grade 9, Mathematics, students in lowest level (%)" }, { "id": "Longdefinition", "value": "Students with lowest competency in the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.11_GHA.LEAR.TEST.P3.MAT.ABOV.PROF", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ghana, P3, Mathematics, students above proficient levels (%)" }, { "id": "Longdefinition", "value": "Students with results above proficient competency in the National Education Assessment (NEA) carried out in the specific subject and grade, using multiple choice items with 4 options, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. % of pupils achieving 55% of results or more in the test." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.11_GIN.LEAR.TEST.CEPE.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment at the end of primary (CEPE) in Guinea, CM2 (6 grade) (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.11_NER.LEAR.TEST.CE2.FR.UNDERMIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CE2, French, under minimal competency (%)" }, { "id": "Longdefinition", "value": "Students under minimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.12_ETH.LEAR.TEST.12.AVR.OPT", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ethiopia, grade 12, average of all subjects, optimal competency (%)" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Optimal competency are scores above 50%." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.12_GEO.LEAR.TEST.1.ENG.MED", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Georgia, grade 1, English, students in medium level (%)" }, { "id": "Longdefinition", "value": "Students with medium competency in the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.12_GHA.LEAR.TEST.P6.MAT.ABOV.PROF", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ghana, P6, Mathematics, students above proficient levels (%)" }, { "id": "Longdefinition", "value": "Students with results above proficient competency in the National Education Assessment (NEA) carried out in the specific subject and grade, using multiple choice items with 4 options, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. % of pupils achieving 55% of results or more in the test." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.12_GIN.LEAR.TEST.BEPC.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment at the end of lower secondary (BEPC) in Guinea, 10 grade (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.12_NER.LEAR.TEST.CM2.FR.UNDERMIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CM2, French, under minimal competency (%)" }, { "id": "Longdefinition", "value": "Students under minimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.13_GEO.LEAR.TEST.9.LANG.MED", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Georgia, grade 9, Language, students in medium level (%)" }, { "id": "Longdefinition", "value": "Students with medium competency in the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.13_GHA.TIMSS.8.MAT.MEAN", "metatype": [ { "id": "IndicatorName", "value": "TIMSS in Ghana, grade 8, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Trends in International Mathematics and Science Study (TIMSS) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. It was first conduced in 1995 and then every four years by the TIMS & PIRLS International Study Center of Boston College's Lynch School of Education. For further details please refer to this web site: timss.bc.edu." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.13_GIN.LEAR.TEST.BAC.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment at the end of secondary (BAC) in Guinea, Terminale (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.13_NER.LEAR.TEST.CP.MATH.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CP, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean scores calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.14_GEO.LEAR.TEST.9.MAT.MED", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Georgia, grade 9, Mathematics, students in medium level (%)" }, { "id": "Longdefinition", "value": "Students with medium competency in the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.14_GHA.TIMSS.8.SCI.MEAN", "metatype": [ { "id": "IndicatorName", "value": "TIMSS in Ghana, grade 8, Science (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Trends in International Mathematics and Science Study (TIMSS) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. It was first conduced in 1995 and then every four years by the TIMS & PIRLS International Study Center of Boston College's Lynch School of Education. For further details please refer to this web site: timss.bc.edu." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.14_GIN.LEAR.TEST.CEPE.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment at the end of primary (CEPE) in Guinea, CM2 (6 grade), minimal competency" }, { "id": "Longdefinition", "value": "Minimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "?" } ], "source_id": "34" }, { "id": "8.3.14_NER.LEAR.TEST.CE2.MATH.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CE2, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.15_GEO.LEAR.TEST.1.ENG.HIGH", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Georgia, grade 1, English, students in higher level (%)" }, { "id": "Longdefinition", "value": "Students with higher competency in the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.15_GHA.LITERACY.P3.LETTERS", "metatype": [ { "id": "IndicatorName", "value": "Making the Grade Scores in Ghana, P3, Literacy in English, Letters per minute (mean)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.15_GIN.LEAR.TEST.BEPC.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment at the end of lower secondary (BEPC) in Guinea, 10 grade, minimum competency" }, { "id": "Longdefinition", "value": "Minimum competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "?" } ], "source_id": "34" }, { "id": "8.3.15_NER.LEAR.TEST.CM2.MATH.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CM2, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean scores calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.16_GEO.LEAR.TEST.9.LANG.HIGH", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Georgia, grade 9, Language, students in higher level (%)" }, { "id": "Longdefinition", "value": "Students with higher competency in the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.16_GHA.LITERACY.P5.LETTERS", "metatype": [ { "id": "IndicatorName", "value": "Making the Grade Scores in Ghana, P5, Literacy in English, Letters per minute (mean)" }, { "id": "Longdefinition", "value": "Mean letters per minute read in the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "units" } ], "source_id": "34" }, { "id": "8.3.16_GIN.LEAR.TEST.BAC.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment at the end of secondary (BAC) in Guinea, minimal competency" }, { "id": "Longdefinition", "value": "Minimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "?" } ], "source_id": "34" }, { "id": "8.3.16_NER.LEAR.TEST.CP.MATH.OPTIM", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CP, Mathematics, optimal competency (%)" }, { "id": "Longdefinition", "value": "Students with optimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.17_GEO.LEAR.TEST.9.MAT.HIGH", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Georgia, grade 9, Mathematics, students in higher level (%)" }, { "id": "Longdefinition", "value": "Students with higher competency in the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.17_GHA.LITERACY.P3.WORDS", "metatype": [ { "id": "IndicatorName", "value": "Making the Grade Scores in Ghana, P3 Literacy in English, Words per minute (mean)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.17_GIN.LEAR.TEST.CEPE.OPTIM", "metatype": [ { "id": "IndicatorName", "value": "National assessment at the end of primary (CEPE) in Guinea, CM2 (6 grade), optimal competency" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "?" } ], "source_id": "34" }, { "id": "8.3.17_NER.LEAR.TEST.CE2.MATH.OPTIM", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CE2, Mathematics, optimal competency (%)" }, { "id": "Longdefinition", "value": "Students with optimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.18_GEO.LEAR.TEST.9.LAG.HIGHEST", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Georgia, grade 9, Language, students in highest level (%)" }, { "id": "Longdefinition", "value": "Students with highest competency in the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.18_GHA.LITERACY.P5.WORDS", "metatype": [ { "id": "IndicatorName", "value": "Making the Grade Scores in Ghana, P5, Literacy in English, Words per minute (mean)" }, { "id": "Longdefinition", "value": "Mean words per minute read in the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "units" } ], "source_id": "34" }, { "id": "8.3.18_GIN.LEAR.TEST.BEPC.OPTIM", "metatype": [ { "id": "IndicatorName", "value": "National assessment at the end of lower secondary (BEPC) in Guinea, 10 grade, optimal competency" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "?" } ], "source_id": "34" }, { "id": "8.3.18_NER.LEAR.TEST.CM2.MATH.OPTIM", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CM2, Mathematics, optimal competency (%)" }, { "id": "Longdefinition", "value": "Students with optimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.19_GEO.LEAR.TEST.9.MAT.HIGHEST", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Georgia, grade 9, Mathematics, students in highest level (%)" }, { "id": "Longdefinition", "value": "Students with highest competency in the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.19_GHA.LITERACY.P3.ZERO", "metatype": [ { "id": "IndicatorName", "value": "Making the Grade Scores in Ghana, P3, Literacy in English, Zero score" }, { "id": "Longdefinition", "value": "Zero score in results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "?" } ], "source_id": "34" }, { "id": "8.3.19_GIN.LEAR.TEST.BAC.OPTIM", "metatype": [ { "id": "IndicatorName", "value": "National assessment at the end of secondary (BAC) in Guinea, optimal competency" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "?" } ], "source_id": "34" }, { "id": "8.3.19_NER.LEAR.TEST.CP.MATH.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CP, Mathematics, minimal competency (%)" }, { "id": "Longdefinition", "value": "Students with minimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.2_ALB.LEAR.TEST.9.MAT.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Albania, grade 9, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific subject and grade. Country-specific definition and method are determined by country. Scale goes up to 50." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.2_BFA.PASEC.CM1.FR", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Burkina Faso, CM1, French (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.2_CAF.BAC.SUCC", "metatype": [ { "id": "IndicatorName", "value": "Baccalaureate in Central African Republic, exam at the end of secondary education, success rate (%)" }, { "id": "Longdefinition", "value": "Success rate in the exam at the end of secondary education (Baccalaureate), as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.2_CIV.LEAR.TEST.SEC.ALL.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Côte d'Ivoire, lower secondary (BEPC), mean score of all subjects" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific education level for French, Mathematics, Physics, English, SVT, L2, H-G and ECM, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.2_CMR.PASEC.25.MAT", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Cameroon, grades 2 and 5, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.2_ETH.LEAR.TEST.10.MAT.OPT", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ethiopia, grade 10, Mathematics, optimal competency (%)" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Optimal competency are scores above 50%." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.2_GEO.TIMSS.4.MAT.MEAN", "metatype": [ { "id": "IndicatorName", "value": "TIMSS in Georgia, grade 4, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Trends in International Mathematics and Science Study (TIMSS) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. It was first conduced in 1995 and then every four years by the TIMS & PIRLS International Study Center of Boston College's Lynch School of Education. For further details please refer to this web site: timss.bc.edu." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.2_GHA.LEAR.TEST.P6.ENG.ABOV.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ghana, P6, English, students above mean (%)" }, { "id": "Longdefinition", "value": "Students with results above the mean competency in the National Education Assessment (NEA) carried out in the specific subject and grade, using multiple choice items with 4 options, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.2_GIN.PASEC.CP2.MAT.MEAN", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Guinea, CP2, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.2_KGZ.PISA.89.READ2", "metatype": [ { "id": "IndicatorName", "value": "PISA in Kyrgyzstan, grades 8-9, Reading - access and retrieve (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme for International Student Assessment (PISA) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PISA is an international study that was launched by the OECD in 1997. It aims to evaluate education systems worldwide every three years by assessing 15-year-olds' competencies in the key subjects: reading, mathematics and science. For further details please refer to this web site: http://www.oecd.org/pisa/. Pupils who were 15 year old participated in this test, some of them were at grade 8 and some at grade 10." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.2_KHM.LEAR.TEST.3.MAT.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Cambodia, grade 3, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.2_LAO.LEAR.TEST.5.LANG.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Laos, grade 5, Language (minimal competency)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.2_MDA.LEAR.TEST.9.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Moldova, grade 9, mean competency (%)" }, { "id": "Longdefinition", "value": "Students with mean competency in the results of the national assessment carried out in the specific grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.2_MDG.PASEC.CM2.MAT", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Madagascar, CM2, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.2_MOZ.SACMEQ.TEST.6.MAT", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ in Mozambique, grade 5, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. SACMEQ has completed two major education policy research projects (SACMEQ I and SACMEQ II) between 1995 and 2005. The third project (SACMEQ III) commenced in 2007 and was completed in 2011. For further details please refer to the following web site: www.sacmeq.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.2_MRT.PASEC.5.MAT", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Mauritania, grade 5, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.2_MWI.SACMEQ.357.MAT", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ in Malawi, standards 3,5,7, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. SACMEQ has completed two major education policy research projects (SACMEQ I and SACMEQ II) between 1995 and 2005. The third project (SACMEQ III) commenced in 2007 and was completed in 2011. For further details please refer to the following web site: www.sacmeq.org. In Malawi SACMEQ I tested learners on English; SACMEQ II on English and Mathematics; and SACMEQ III on English, Mathematics, and HIV/AIDs." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.2_NER.LEAR.TEST.CE2.FR.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CE2, French (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.2_SEN.LEAR.TEST.CE2.FR.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes (SNERS) in Senegal, CE2, French, minimal competency (%)" }, { "id": "Longdefinition", "value": "Minimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.2_VNM.LEAR.TEST.5.MAT2", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Vietnam, grade 5, Mathematics - Level 1, scores in indicated level (%)" }, { "id": "Longdefinition", "value": "Students in respective level in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Relative level means that student: Converts fractions with denominator of 10 to decimals. Calculates with whole numbers using one operation (x, -, + or ;) in a one step word problem; recognizes 2D and 3D shapes." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.2_VNM.LEAR.TEST.5.READ2", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Vietnam, grade 5, Reading - Level 2, scores in indicated level (%)" }, { "id": "Longdefinition", "value": "Students in respective level in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Relative level means that student: Locates text expressed in short repetitive sentences and can deal with text unaided by pictures. Type of text is limited to short sentences and phrases with repetitive patterns." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.2_ZMB.LEAR.TEST.5.MAT", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Zambia, grade 5, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.20_GHA.LITERACY.P5.ZERO", "metatype": [ { "id": "IndicatorName", "value": "Making the Grade Scores in Ghana, P5, Literacy in English, Zero score" }, { "id": "Longdefinition", "value": "Zero score in results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "?" } ], "source_id": "34" }, { "id": "8.3.20_GIN.LEAR.TEST.CEPE.MAX", "metatype": [ { "id": "IndicatorName", "value": "National assessment at the end of primary (CEPE) in Guinea, CM2 (6 grade), maximal competency" }, { "id": "Longdefinition", "value": "Maximum competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "?" } ], "source_id": "34" }, { "id": "8.3.20_NER.LEAR.TEST.CE2.MATH.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CE2, Mathematics, minimal competency (%)" }, { "id": "Longdefinition", "value": "Students with minimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.21_GHA.NUMERACY.P3.ADDITIO", "metatype": [ { "id": "IndicatorName", "value": "Making the Grade Scores in Ghana, P3, Numeracy, Correct Additions (%)" }, { "id": "Longdefinition", "value": "Students capable of perform correct additions in the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.21_GIN.LEAR.TEST.BEPC.MAX", "metatype": [ { "id": "IndicatorName", "value": "National assessment at the end of lower secondary (BEPC) in Guinea, 10 grade, maximal competency" }, { "id": "Longdefinition", "value": "Maximum competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "?" } ], "source_id": "34" }, { "id": "8.3.21_NER.LEAR.TEST.CM2.MATH.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CM2, Mathematics, minimal competency (%)" }, { "id": "Longdefinition", "value": "Students with minimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.22_GHA.NUMERACY.P5.ADDITIO", "metatype": [ { "id": "IndicatorName", "value": "Making the Grade Scores in Ghana, P5, Numeracy, Correct Additions (%)" }, { "id": "Longdefinition", "value": "Students capable of perform correct additions in the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.22_GIN.LEAR.TEST.BAC.MAX", "metatype": [ { "id": "IndicatorName", "value": "National assessment at the end of secondary (BAC) in Guinea, maximal competency" }, { "id": "Longdefinition", "value": "Maximum competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "?" } ], "source_id": "34" }, { "id": "8.3.22_NER.LEAR.TEST.CP.MATH.UNDERMIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CP, Mathematics, under minimal competency (%)" }, { "id": "Longdefinition", "value": "Students under minimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.23_GHA.NUMERACY.P3.MULTIPLI", "metatype": [ { "id": "IndicatorName", "value": "Making the Grade Scores in Ghana, P3, Numeracy, Correct Multiplications (%)" }, { "id": "Longdefinition", "value": "Students capable of performing correct multiplications in the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.23_GIN.LEAR.TEST.CEPE.SUCC", "metatype": [ { "id": "IndicatorName", "value": "National assessment at the end of primary (CEPE) in Guinea, CM2 (6 grade), success rate (%)" }, { "id": "Longdefinition", "value": "Success rate calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.23_NER.LEAR.TEST.CE2.MATH.UNDERMIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CE2, Mathematics, under minimal competency (%)" }, { "id": "Longdefinition", "value": "Students under minimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.24_GHA.NUMERACY.P5.MULTIPLI", "metatype": [ { "id": "IndicatorName", "value": "Making the Grade Scores in Ghana, P5, Numeracy, Correct Multiplications (%)" }, { "id": "Longdefinition", "value": "Students capable of perform correct multiplications in the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.24_GIN.LEAR.TEST.BEPC.SUCC", "metatype": [ { "id": "IndicatorName", "value": "National assessment at the end of lower secondary (BEPC) in Guinea, 10 grade, success rate (%)" }, { "id": "Longdefinition", "value": "Success rate calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.24_NER.LEAR.TEST.CM2.MATH.UNDERMIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CM2, Mathematics, under minimal competency (%)" }, { "id": "Longdefinition", "value": "Students under minimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.25_GHA.NUMERACY.P3.ZERO", "metatype": [ { "id": "IndicatorName", "value": "Making the Grade Scores in Ghana, P3, Numeracy, Zero score" }, { "id": "Longdefinition", "value": "Zero score in results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "?" } ], "source_id": "34" }, { "id": "8.3.25_GIN.LEAR.TEST.BAC.SUCC", "metatype": [ { "id": "IndicatorName", "value": "National assessment at the end of secondary (BAC) in Guinea, success rate (%)" }, { "id": "Longdefinition", "value": "Success rate calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.25_NER.LEAR.TEST.CERTIFICATE.SUCC", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, end of 1st degree certificate, success rate (%)" }, { "id": "Longdefinition", "value": "Success rate in the end of 1st degree certificate, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.26_GHA.NUMERACY.P5.ZERO", "metatype": [ { "id": "IndicatorName", "value": "Making the Grade Scores in Ghana, P5, Numeracy, Zero score" }, { "id": "Longdefinition", "value": "Zero score in results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "?" } ], "source_id": "34" }, { "id": "8.3.3_ALB.PISA.910.READ", "metatype": [ { "id": "IndicatorName", "value": "PISA in Albania, grade 9 and 10, Reading (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme for International Student Assessment (PISA) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PISA is an international study that was launched by the OECD in 1997. It aims to evaluate education systems worldwide every three years by assessing 15-year-olds' competencies in the key subjects: reading, mathematics and science. For further details please refer to this web site: http://www.oecd.org/pisa/" }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.3_BFA.PASEC.CP2.MAT", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Burkina Faso, CP2, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.3_CIV.LEAR.TEST.PRIM.ALL.MIN.COMP", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Côte d'Ivoire, primary (CEPE), minimal competency (%)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific education level for French, Mathematics, Sciences and H-G, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.3_ETH.LEAR.TEST.10.BIO.OPT", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ethiopia, grade 10, Biology, optimal competency (%)" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Optimal competency are scores above 50%." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.3_GEO.TIMSS.4.SCI.MEAN", "metatype": [ { "id": "IndicatorName", "value": "TIMSS in Georgia, grade 4, Science (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Trends in International Mathematics and Science Study (TIMSS) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. It was first conduced in 1995 and then every four years by the TIMS & PIRLS International Study Center of Boston College's Lynch School of Education. For further details please refer to this web site: timss.bc.edu." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.3_GHA.LEAR.TEST.P3.MAT.ABOV.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ghana, P3, Mathematics, students above mean (%)" }, { "id": "Longdefinition", "value": "Students with results above the mean competency in the National Education Assessment (NEA) carried out in the specific subject and grade, using multiple choice items with 4 options, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.3_GIN.PASEC.CP2.FR.MAT.MEAN", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Guinea, CP2, French and Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.3_KGZ.PISA.89.READ3", "metatype": [ { "id": "IndicatorName", "value": "PISA in Kyrgyzstan, grades 8-9, Reading - integrate and interpret (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme for International Student Assessment (PISA) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PISA is an international study that was launched by the OECD in 1997. It aims to evaluate education systems worldwide every three years by assessing 15-year-olds' competencies in the key subjects: reading, mathematics and science. For further details please refer to this web site: http://www.oecd.org/pisa/. Pupils who were 15 year old participated in this test, some of them were at grade 8 and some at grade 10." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.3_KHM.LEAR.TEST.6.LANG.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Cambodia, grade 6, Language (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.3_LAO.LEAR.TEST.5.LANG.PROF", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Laos, grade 5, Language (proficiency)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.3_MDA.LEAR.TEST.4.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Moldova, grade 4, minimal competency (%)" }, { "id": "Longdefinition", "value": "Students with minimal competency in the results of the national assessment carried out in the specific grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.3_NER.LEAR.TEST.CM2.FR.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CM2, French (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.3_SEN.LEAR.TEST.CE2.MATH.OPT", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes (SNERS) in Senegal, CE2, Mathematics, optimal competency (%)" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.3_VNM.LEAR.TEST.5.MAT3", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Vietnam, grade 5, Mathematics - Level 1, scores in indicated level (%)" }, { "id": "Longdefinition", "value": "Students in respective level in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Relative level means that student: Identifies place value; determines the value of a simple number sentence; understands equivalent fractions; adds and subtracts simple fractions; carries out multiple operations in correct order; converts and estimates common and familiar measurement units in solving problems." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.3_VNM.LEAR.TEST.5.READ3", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Vietnam, grade 5, Reading - Level 3, scores in indicated level (%)" }, { "id": "Longdefinition", "value": "Students in respective level in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Relative level means that student: Reads and understands longer passages. Can search backwards or forwards through text for information. Understands paraphrasing. Expanding vocabulary enables understanding of sentences with some complex structure." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.3_ZMB.SACMEQ.TEST.5.READ", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ in Zambia, grade 5, Reading (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. SACMEQ has completed two major education policy research projects (SACMEQ I and SACMEQ II) between 1995 and 2005. The third project (SACMEQ III) commenced in 2007 and was completed in 2011. For further details please refer to the following web site: www.sacmeq.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.4_ALB.PISA.910.MAT", "metatype": [ { "id": "IndicatorName", "value": "PISA in Albania, grade 9 and 10, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme for International Student Assessment (PISA) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PISA is an international study that was launched by the OECD in 1997. It aims to evaluate education systems worldwide every three years by assessing 15-year-olds' competencies in the key subjects: reading, mathematics and science. For further details please refer to this web site: http://www.oecd.org/pisa/" }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.4_BFA.PASEC.CM1.MAT", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Burkina Faso, CM1, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.4_CIV.LEAR.TEST.SEC.ALL.MIN.COMP", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Côte d'Ivoire, lower secondary (BEPC), minimal competency (%)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific education level for French, Mathematics, Physics, English, SVT, L2, H-G and ECM, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Subjects include: French, Mathematics, Physics, English, SVT, L2, H-G and ECM." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.4_ETH.LEAR.TEST.10.CHE.OPT", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ethiopia, grade 10, Chemistry, optimal competency (%)" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Optimal competency are scores above 50%." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.4_GEO.TIMSS.8.MAT.MEAN", "metatype": [ { "id": "IndicatorName", "value": "TIMSS in Georgia, grade 8, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Trends in International Mathematics and Science Study (TIMSS) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. It was first conduced in 1995 and then every four years by the TIMS & PIRLS International Study Center of Boston College's Lynch School of Education. For further details please refer to this web site: timss.bc.edu." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.4_GHA.LEAR.TEST.P6.MAT.ABOV.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ghana, P6, Mathematics, students above mean (%)" }, { "id": "Longdefinition", "value": "Students with results above the mean competency in the National Education Assessment (NEA) carried out in the specific subject and grade, using multiple choice items with 4 options, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.4_GIN.PASEC.CM1.FR.MEAN", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Guinea, CM1, French (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.4_KGZ.PISA.89.READ4", "metatype": [ { "id": "IndicatorName", "value": "PISA in Kyrgyzstan, grades 8-9, Reading - reflect and evaluate (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme for International Student Assessment (PISA) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PISA is an international study that was launched by the OECD in 1997. It aims to evaluate education systems worldwide every three years by assessing 15-year-olds' competencies in the key subjects: reading, mathematics and science. For further details please refer to this web site: http://www.oecd.org/pisa/. Pupils who were 15 year old participated in this test, some of them were at grade 8 and some at grade 10." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.4_KHM.LEAR.TEST.6.MAT.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Cambodia, grade 6, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.4_LAO.LEAR.TEST.5.MAT.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Laos, grade 5, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.4_MDA.LEAR.TEST.9.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Moldova, grade 9, minimal competency (%)" }, { "id": "Longdefinition", "value": "Students with minimal competency in the results of the national assessment carried out in the specific grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.4_NER.LEAR.TEST.CP.FR.OPTIM", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CP, French, optimal competency (%)" }, { "id": "Longdefinition", "value": "Students with optimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.4_SEN.LEAR.TEST.CE2.FR.OPT", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes (SNERS) in Senegal, CE2, French, optimal competency (%)" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.4_VNM.LEAR.TEST.5.MAT4", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Vietnam, grade 5, Mathematics - Level 1, scores in indicated level (%)" }, { "id": "Longdefinition", "value": "Students in respective level in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Relative level means that student: Reads, writes and compares larger numbers; solves problems involving calendars and currency, area and volume; uses charts and tables for estimation; solves inequalities; transformations with 3D figures; knowledge of angles in regular figures; understands simple transformations with 2D and 3D shapes." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.4_VNM.LEAR.TEST.5.READ4", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Vietnam, grade 5, Reading - Level 4, scores in indicated level (%)" }, { "id": "Longdefinition", "value": "Students in respective level in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Relative level means that student: Links information from different parts of the text. Selects and connects text to derive and infer different possible meanings." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.4_ZMB.SACMEQ.TEST.5.MAT", "metatype": [ { "id": "IndicatorName", "value": "SACMEQ in Zambia, grade 5, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. SACMEQ has completed two major education policy research projects (SACMEQ I and SACMEQ II) between 1995 and 2005. The third project (SACMEQ III) commenced in 2007 and was completed in 2011. For further details please refer to the following web site: www.sacmeq.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.5_ALB.PISA.910.SCIENCE", "metatype": [ { "id": "IndicatorName", "value": "PISA in Albania, grade 9 and 10, Science (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme for International Student Assessment (PISA) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PISA is an international study that was launched by the OECD in 1997. It aims to evaluate education systems worldwide every three years by assessing 15-year-olds' competencies in the key subjects: reading, mathematics and science. For further details please refer to this web site: http://www.oecd.org/pisa/" }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.5_CIV.LEAR.TEST.PRIM.ALL.OPT.COMP", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Côte d'Ivoire, primary (CEPE), optimal competency (%)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific education level for French, Mathematics, Sciences and H-G, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.5_ETH.LEAR.TEST.10.PHY.OPT", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ethiopia, grade 10, Physics, optimal competency (%)" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Optimal competency are scores above 50%." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.5_GEO.TIMSS.8.SCI.MEAN", "metatype": [ { "id": "IndicatorName", "value": "TIMSS in Georgia, grade 8, Science (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Trends in International Mathematics and Science Study (TIMSS) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. It was first conduced in 1995 and then every four years by the TIMS & PIRLS International Study Center of Boston College's Lynch School of Education. For further details please refer to this web site: timss.bc.edu." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.5_GHA.LEAR.TEST.P3.ENG.ABOV.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ghana, P3, English, students above minimal competency (%)" }, { "id": "Longdefinition", "value": "Students with results above the minimal competency in the National Education Assessment (NEA) carried out in the specific subject and grade, using multiple choice items with 4 options, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. % of pupils achieving 35% of results or more in the test." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.5_GIN.PASEC.CM1.MAT.MEAN", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Guinea, CM1, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.5_KGZ.PISA.89.READ5", "metatype": [ { "id": "IndicatorName", "value": "PISA in Kyrgyzstan, grades 8-9, Reading - continuous texts (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme for International Student Assessment (PISA) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PISA is an international study that was launched by the OECD in 1997. It aims to evaluate education systems worldwide every three years by assessing 15-year-olds' competencies in the key subjects: reading, mathematics and science. For further details please refer to this web site: http://www.oecd.org/pisa/. Pupils who were 15 year old participated in this test, some of them were at grade 8 and some at grade 10." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.5_KHM.LEAR.TEST.9.LANG.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Cambodia, grade 9, Language (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.5_LAO.LEAR.TEST.5.MAT.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Laos, grade 5, Mathematics (minimal competency)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.5_MDA.LEAR.TEST.4.PROF", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Moldova, grade 4, proficient competency (%)" }, { "id": "Longdefinition", "value": "Students with proficient competency in the results of the national assessment carried out in the specific grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.5_NER.LEAR.TEST.CE2.FR.OPTIM", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CE2, French, optimal competency (%)" }, { "id": "Longdefinition", "value": "Students with optimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.5_SEN.PASEC.CM1.MATH.MEAN", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Senegal, CM1, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.5_VNM.LEAR.TEST.5.MAT5", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Vietnam, grade 5, Mathematics - Level 1, scores in indicated level (%)" }, { "id": "Longdefinition", "value": "Students in respective level in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Relative level means that student: Calculates with multiple and varied operations; recognizes rules and patterns in number sequences; calculates the perimeter and area of irregular shapes; measurement of irregular objects; recognized transformed figures after reflection; solves problems with multiple operations involving measurement units, percentage and averages." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.5_VNM.LEAR.TEST.5.READ5", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Vietnam, grade 5, Reading - Level 5, scores in indicated level (%)" }, { "id": "Longdefinition", "value": "Students in respective level in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Relative level means that student: Links inferences and identifies an author's intention from information stated in different ways, in different text types and in documents where the message is not explicit." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.6_CIV.LEAR.TEST.SEC.ALL.OPT.COMP", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Côte d'Ivoire, lower secondary (BEPC), optimal competency (%)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific education level for French, Mathematics, Physics, English, SVT, L2, H-G and ECM, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.6_ETH.LEAR.TEST.10.AVR.OPT", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ethiopia, grade 10, average of all subjects, optimal competency (%)" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Optimal competency are scores above 50%." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.6_GEO.PISA.9.READ.MEAN", "metatype": [ { "id": "IndicatorName", "value": "PISA in Georgia, grade 9, Reading (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme for International Student Assessment (PISA) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PISA is an international study that was launched by the OECD in 1997. It aims to evaluate education systems worldwide every three years by assessing 15-year-olds' competencies in the key subjects: reading, mathematics and science. For further details please refer to this web site: http://www.oecd.org/pisa/" }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.6_GHA.LEAR.TEST.P6.ENG.ABOV.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ghana, P6, English, students above minimum competency (%)" }, { "id": "Longdefinition", "value": "Students with results above the mean competency in the National Education Assessment (NEA) carried out in the specific subject and grade, using multiple choice items with 4 options, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. % of pupils achieving 35% of results or more in the test." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.6_GIN.PASEC.CM1.FR.MAT.MEAN", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Guinea, CM1, French and Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.6_KGZ.PISA.89.READ6", "metatype": [ { "id": "IndicatorName", "value": "PISA in Kyrgyzstan, grades 8-9, Reading - non-continuous texts (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme for International Student Assessment (PISA) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PISA is an international study that was launched by the OECD in 1997. It aims to evaluate education systems worldwide every three years by assessing 15-year-olds' competencies in the key subjects: reading, mathematics and science. For further details please refer to this web site: http://www.oecd.org/pisa/. Pupils who were 15 year old participated in this test, some of them were at grade 8 and some at grade 10." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.6_KHM.LEAR.TEST.9.MAT.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Cambodia, grade 9, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.6_LAO.LEAR.TEST.5.MAT.PROF", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Laos, grade 5, Mathematics (proficiency)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.6_MDA.LEAR.TEST.9.PROF", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Moldova, grade 9, proficient competency (%)" }, { "id": "Longdefinition", "value": "Students with proficient competency in the results of the national assessment carried out in the specific grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.6_NER.LEAR.TEST.CM2.FR.OPTIM", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CM2, French, optimal competency (%)" }, { "id": "Longdefinition", "value": "Students with optimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.6_SEN.PASEC.CM1.FR.MEAN", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Senegal, CM1, French (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.6_VNM.LEAR.TEST.5.MAT6", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Vietnam, grade 5, Mathematics - Level 1, scores in indicated level (%)" }, { "id": "Longdefinition", "value": "Students in respective level in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Relative level means that student: Problem solving with periods of time, length, area and volume; embedded and dependent number patterns; develops formulas; recognizes 3D figures after rotation and reflection and embedded figures and right angles in irregular shapes; use data from graphs." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.6_VNM.LEAR.TEST.5.READ6", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Vietnam, grade 5, Reading - Level 6, scores in indicated level (%)" }, { "id": "Longdefinition", "value": "Students in respective level in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Relative level means that student: Combines text with outside knowledge to infer various meanings, including hidden meanings. Identifies an author's purposes, attitudes, values, beliefs, motives, unstated assumptions and arguments." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.7_CIV.PASEC.PRI.FRE.MAT", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Côte d'Ivoire, CP2 and CM1, French and Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.7_ETH.LEAR.TEST.12.ENG.OPT", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ethiopia, grade 12, English, optimal competency (%)" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Optimal competency are scores above 50%." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.7_GEO.PISA.9.MAT.MEAN", "metatype": [ { "id": "IndicatorName", "value": "PISA in Georgia, grade 9, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme for International Student Assessment (PISA) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PISA is an international study that was launched by the OECD in 1997. It aims to evaluate education systems worldwide every three years by assessing 15-year-olds' competencies in the key subjects: reading, mathematics and science. For further details please refer to this web site: http://www.oecd.org/pisa/" }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.7_GHA.LEAR.TEST.P3.MAT.ABOV.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ghana, P3, Mathematics, students above minimal competency (%)" }, { "id": "Longdefinition", "value": "Students with results above the minimal competency in the National Education Assessment (NEA) carried out in the specific subject and grade, using multiple choice items with 4 options, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. % of pupils achieving 35% of results or more in the test." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.7_GIN.PASEC.CP2.FR.MATH.MEAN.END", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Guinea, CP2, French and Mathematics, mean score at the end of year (%)" }, { "id": "Longdefinition", "value": "Mean score calculated at the end of the year for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.7_KGZ.PISA.89.READ7", "metatype": [ { "id": "IndicatorName", "value": "PISA in Kyrgyzstan, grades 8-9, Reading - mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme for International Student Assessment (PISA) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PISA is an international study that was launched by the OECD in 1997. It aims to evaluate education systems worldwide every three years by assessing 15-year-olds' competencies in the key subjects: reading, mathematics and science. For further details please refer to this web site: http://www.oecd.org/pisa/. Pupils who were 15 year old participated in this test, some of them were at grade 8 and some at grade 10." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.7_LAO.LEAR.TEST.5.WORLD.MEAN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Laos, grade 5, world around us (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.7_MDA.PIRLS.READ.4.MEAN", "metatype": [ { "id": "IndicatorName", "value": "PIRLS in Moldova, grade 4, Reading (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Progress in International Reading Literacy Study (PIRLS) on the reading achievement of fourth grade students, as reported by the Local Education Group (LEG). It was first conduced in 2001 and then every five years by the TIMS & PIRLS International Study Center of Boston College's Lynch School of Education. For further details please refer to this web site: pirls.bc.edu." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.7_NER.LEAR.TEST.CP.FR.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CP, French, minimal competency (%)" }, { "id": "Longdefinition", "value": "Students with minimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.7_SEN.PASEC.MATH.MEAN", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Senegal, Mathematics, (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.8_ETH.LEAR.TEST.12.MAT.OPT", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ethiopia, grade 12, Mathematics, optimal competency (%)" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Optimal competency are scores above 50%." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.8_GEO.PISA.9.SCI.MEAN", "metatype": [ { "id": "IndicatorName", "value": "PISA in Georgia, grade 9, Science (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme for International Student Assessment (PISA) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PISA is an international study that was launched by the OECD in 1997. It aims to evaluate education systems worldwide every three years by assessing 15-year-olds' competencies in the key subjects: reading, mathematics and science. For further details please refer to this web site: http://www.oecd.org/pisa/" }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.8_GHA.LEAR.TEST.P6.MAT.ABOV.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ghana, P6, Mathematics, students above minimal competency (%)" }, { "id": "Longdefinition", "value": "Students with results above the minimal competency in the National Education Assessment (NEA) carried out in the specific subject and grade, using multiple choice items with 4 options, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. % of pupils achieving 35% of results or more in the test." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.8_GIN.PASEC.CM1.FR.MATH.MEAN.END", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Guinea, CM1, French and Mathematics, mean score at the end of year (%)" }, { "id": "Longdefinition", "value": "Mean score calculated at the end of the year for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.8_KGZ.PISA.89.READ8", "metatype": [ { "id": "IndicatorName", "value": "PISA in Kyrgyzstan, grades 8-9, Reading - science (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme for International Student Assessment (PISA) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PISA is an international study that was launched by the OECD in 1997. It aims to evaluate education systems worldwide every three years by assessing 15-year-olds' competencies in the key subjects: reading, mathematics and science. For further details please refer to this web site: http://www.oecd.org/pisa/. Pupils who were 15 year old participated in this test, some of them were at grade 8 and some at grade 10." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.8_LAO.LEAR.TEST.5.WORLD.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Laos, grade 5, world around us (minimal competency)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.8_MDA.TIMSS.MAT.MEAN", "metatype": [ { "id": "IndicatorName", "value": "TIMSS in Moldova, Mathematics (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Trends in International Mathematics and Science Study (TIMSS) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. It was first conduced in 1995 and then every four years by the TIMS & PIRLS International Study Center of Boston College's Lynch School of Education. For further details please refer to this web site: timss.bc.edu." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.8_NER.LEAR.TEST.CE2.FR.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CE2, French, minimal competency (%)" }, { "id": "Longdefinition", "value": "Students with minimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.8_SEN.PASEC.FR.MEAN", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Senegal, French (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.9_ETH.LEAR.TEST.12.BIO.OPT", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ethiopia, grade 12, Biology, optimal competency (%)" }, { "id": "Longdefinition", "value": "Optimal competency calculated for the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. Optimal competency are scores above 50%." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.9_GEO.LEAR.TEST.1.ENG.LOWEST", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Georgia, grade 1, English, students in lowest level (%)" }, { "id": "Longdefinition", "value": "Students with lowest competency in the results of the national assessment carried out in the specific subject and grade, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.9_GHA.LEAR.TEST.P3.ENG.ABOV.PROF", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Ghana, P3, English, students above proficient levels (%)" }, { "id": "Longdefinition", "value": "Students with results above the proficient competency in the National Education Assessment (NEA) carried out in the specific subject and grade, using multiple choice items with 4 options, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country. % of pupils achieving 55% of results or more in the test." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.9_GIN.PASEC.CP2.FR.MATH.MEAN.BEG", "metatype": [ { "id": "IndicatorName", "value": "PASEC in Guinea, CP2, French and Mathematics, mean score at the end of year (%)" }, { "id": "Longdefinition", "value": "Mean score calculated at the end of the year for the results of the Programme on the Analysis of Education Systems (PASEC) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. PASEC is an international study created in 1991 by the Conference of Ministers of Education of Francophone countries (CONFEMEN) to assess educational attainments in primary school. For further information please refer to this web site: www.confemen.org." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.9_LAO.LEAR.TEST.5.WORLD.PROF", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Laos, grade 5, world around us (proficiency)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3.9_MDA.TIMSS.SCIEN.MEAN", "metatype": [ { "id": "IndicatorName", "value": "TIMSS in Moldova, Science (mean score)" }, { "id": "Longdefinition", "value": "Mean score calculated for the results of the Trends in International Mathematics and Science Study (TIMSS) in the specific subject and grade, as reported by the Local Education Group (LEG) of the evaluated country. It was first conduced in 1995 and then every four years by the TIMS & PIRLS International Study Center of Boston College's Lynch School of Education. For further details please refer to this web site: timss.bc.edu." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "unit and decimal numbers" } ], "source_id": "34" }, { "id": "8.3.9_NER.LEAR.TEST.CM2.FR.MIN", "metatype": [ { "id": "IndicatorName", "value": "National assessment for learning outcomes in Niger, CM2, French, minimal competency (%)" }, { "id": "Longdefinition", "value": "Students with minimal competency in the results of the national assessment carried out in the specific grade and subject, as reported by the Local Education Group (LEG). Country-specific definition and method are determined by the country." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "8.3_NATIONAL.ASSESSMENTS", "metatype": [ { "id": "IndicatorName", "value": "Realization of national assessments (yes=1, no=0, see notes if available)" }, { "id": "Longdefinition", "value": "It indicates if national assessments for learning outcomes are administered for primary and lower secondary level and in which grade. Please refer to the subtopic Learning Outcomes and the specific country for details on the scores obtained in these assessments, as reported by the Local Education Group (LEG)." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "1=yes and text in note; 0=no" } ], "source_id": "34" }, { "id": "8.4_ORAL.READING.TEST", "metatype": [ { "id": "IndicatorName", "value": "Administration of oral reading fluency tests (yes=1, no=0, see notes if available)" }, { "id": "Longdefinition", "value": "It indicates if oral reading fluency tests are administered for primary and lower secondary level and in which grade. Please refer to the subtopic Learning Outcomes and the specific country for details on the scores obtained in these assessments, as reported by the Local Education Group (LEG)." }, { "id": "Topic", "value": "Learning Outcomes" }, { "id": "Unitofmeasure", "value": "1=yes and text in note; 0=no" } ], "source_id": "34" }, { "id": "9.1_AID.ALIGNMENT", "metatype": [ { "id": "IndicatorName", "value": "Alignment of aid to education (% of total international aid to education)" }, { "id": "Limitationsandexceptions", "value": "This indicator shows the relationship between the country partner budget and the donor partners’ aid contribution. It measures how much of the education aid was disbursed to the government sector in accordance with the estimates in the government budget. The definition of ‘aid on budget’ is contentious, as it can be understood in significantly different ways. The accuracy of this indicator depends on the availability of the government budget estimates of aid flows for each development partner, the availability of the data from development partners about the disbursements to the government sector and a match between recipient government fiscal year and the development partner's fiscal year. This data aims at stimulating, reviving and strengthening dialogue on aid effectiveness among the Local Education Group (LEG) partners. It does not attempt to provide a full and exhaustive picture of the aid effectiveness situation in the education sector in a country. It does not intend to issue a summative judgment on aid effectiveness in a country, or to rank the effectiveness of a country’s education aid in comparison with other countries’ for any high-stakes purpose other than mutual learning from challenges and good practices. It is based on data submitted by the LEG, which was also subsequently reviewed and validated by the LEG." }, { "id": "Longdefinition", "value": "Estimated international education aid reported on the government’s budget and expressed as a percentage of the disbursed education aid for the government sector. Government sector aid includes aid disbursed in the context of an agreement with administrations (ministries, departments, agencies or municipalities) authorized to receive revenue or undertake expenditures on behalf of central government. This is part of the 2011 Monitoring Exercise on Development Effectiveness in the Education Sector, an unprecedented picture of aid effectiveness in the education sector that can be used as the basis for in-country dialogue and debate going forward. This information looks at how education aid is delivered and managed by development partners and governments." }, { "id": "Topic", "value": "Aid Effectiveness in the Education Sector" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "9.2_COORDINATED.TECH.COOP", "metatype": [ { "id": "IndicatorName", "value": "Coordinated technical cooperation (% of total cooperation to education)" }, { "id": "Limitationsandexceptions", "value": "This data aims at stimulating, reviving and strengthening dialogue on aid effectiveness among the Local Education Group (LEG) partners. It does not attempt to provide a full and exhaustive picture of the aid effectiveness situation in the education sector in a country. It does not intend to issue a summative judgment on aid effectiveness in a country, or to rank the effectiveness of a country’s education aid in comparison with other countries’ for any high-stakes purpose other than mutual learning from challenges and good practices. It is based on data submitted by the LEG, which was also subsequently reviewed and validated by the LEG." }, { "id": "Longdefinition", "value": "Technical cooperation from development partners provided through coordinated programs and expressed as a percentage of the total disbursed technical cooperation. Coordinated technical cooperation is consistent with the capacity development priorities of the developing recipient government. This is part of the 2011 Monitoring Exercise on Development Effectiveness in the Education Sector, an unprecedented picture of aid effectiveness in the education sector that can be used as the basis for in-country dialogue and debate going forward. This information looks at how education aid is delivered and managed by development partners and governments." }, { "id": "Topic", "value": "Aid Effectiveness in the Education Sector" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "9.3_PFM.COUNTRY.SYSTEMS", "metatype": [ { "id": "IndicatorName", "value": "Use of public financial management country systems (% of total international aid to education)" }, { "id": "Limitationsandexceptions", "value": "Using country systems to the greatest extent possible is seen as an effective way to build or increase capacity at the institutional, organizational and personnel levels. This data aims at stimulating, reviving and strengthening dialogue on aid effectiveness among the Local Education Group (LEG) partners. It does not attempt to provide a full and exhaustive picture of the aid effectiveness situation in the education sector in a country. It does not intend to issue a summative judgment on aid effectiveness in a country, or to rank the effectiveness of a country’s education aid in comparison with other countries’ for any high-stakes purpose other than mutual learning from challenges and good practices. It is based on data submitted by the LEG, which was also subsequently reviewed and validated by the LEG." }, { "id": "Longdefinition", "value": "International education aid using national public financial management (PFM) systems and expressed as a percentage of the disbursed education aid for the government sector. PFM systems include budge execution procedures, financial reporting procedures and auditing procedures. Donors use national budget execution procedures when the funds they provide are managed according to the national budgeting procedures established in the general legislation and implemented by government. The use of national financial reporting means that donors do not impose additional requirements on governments for financial reporting. The use of national auditing procedures means that donors rely on the audit opinions issued by the country's supreme audit institution and on the government's normal financial reports/statements. Government sector aid includes aid disbursed in the context of an agreement with administrations (ministries, departments, agencies or municipalities) authorized to receive revenue or undertake expenditures on behalf of central government. This is part of the 2011 Monitoring Exercise on Development Effectiveness in the Education Sector, an unprecedented picture of aid effectiveness in the education sector that can be used as the basis for in-country dialogue and debate going forward. This information looks at how education aid is delivered and managed by development partners and governments." }, { "id": "Topic", "value": "Aid Effectiveness in the Education Sector" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "9.4_PROCUREMENT.COUNTRY.SYSTEMS", "metatype": [ { "id": "IndicatorName", "value": "Use of procurement country systems (% of total international aid to education)" }, { "id": "Limitationsandexceptions", "value": "Using country systems to the greatest extent possible is seen as an effective way to build or increase capacity at the institutional, organizational and personnel levels. This data aims at stimulating, reviving and strengthening dialogue on aid effectiveness among the Local Education Group (LEG) partners. It does not attempt to provide a full and exhaustive picture of the aid effectiveness situation in the education sector in a country. It does not intend to issue a summative judgment on aid effectiveness in a country, or to rank the effectiveness of a country’s education aid in comparison with other countries’ for any high-stakes purpose other than mutual learning from challenges and good practices. It is based on data submitted by the LEG, which was also subsequently reviewed and validated by the LEG." }, { "id": "Longdefinition", "value": "International education aid using national procurement systems and procedures expressed as a percentage of the disbursed education aid for the government sector. Donors use national procurement systems when the funds they provide for the implementation of projects and programs are managed according to the national procurement procedures as they were established in the general legislation and implemented by government. Government sector aid includes aid disbursed in the context of an agreement with administrations (ministries, departments, agencies or municipalities) authorized to receive revenue or undertake expenditures on behalf of central government. This is part of the 2011 Monitoring Exercise on Development Effectiveness in the Education Sector, an unprecedented picture of aid effectiveness in the education sector that can be used as the basis for in-country dialogue and debate going forward. This information looks at how education aid is delivered and managed by development partners and governments." }, { "id": "Topic", "value": "Aid Effectiveness in the Education Sector" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "9.5_PIU", "metatype": [ { "id": "IndicatorName", "value": "Number of parallel implementation units, education sector" }, { "id": "Limitationsandexceptions", "value": "There might be a confusion regarding the definition of a PIU. As a result, there are instances where donors are reported to have structures that are the equivalent of a PIU, even if they are not officially considered to be a PIU. This data aims at stimulating, reviving and strengthening dialogue on aid effectiveness among the Local Education Group (LEG) partners. It does not attempt to provide a full and exhaustive picture of the aid effectiveness situation in the education sector in a country. It does not intend to issue a summative judgment on aid effectiveness in a country, or to rank the effectiveness of a country’s education aid in comparison with other countries’ for any high-stakes purpose other than mutual learning from challenges and good practices. It is based on data submitted by the LEG, which was also subsequently reviewed and validated by the LEG." }, { "id": "Longdefinition", "value": "Number of parallel implementation units (PIUs) in the education sector, as reported by the development donors. A project implementation unit (PIU) is parallel when it is created and operates outside existing country institutional and administrative structures at the behest of a donor. This is part of the 2011 Monitoring Exercise on Development Effectiveness in the Education Sector, an unprecedented picture of aid effectiveness in the education sector that can be used as the basis for in-country dialogue and debate going forward. This information looks at how education aid is delivered and managed by development partners and governments." }, { "id": "Topic", "value": "Aid Effectiveness in the Education Sector" }, { "id": "Unitofmeasure", "value": "units" } ], "source_id": "34" }, { "id": "9.6_PBA", "metatype": [ { "id": "IndicatorName", "value": "Aid provided through program based approaches (% of international aid to education)" }, { "id": "Limitationsandexceptions", "value": "This data aims at stimulating, reviving and strengthening dialogue on aid effectiveness among the Local Education Group (LEG) partners. It does not attempt to provide a full and exhaustive picture of the aid effectiveness situation in the education sector in a country. It does not intend to issue a summative judgment on aid effectiveness in a country, or to rank the effectiveness of a country’s education aid in comparison with other countries’ for any high-stakes purpose other than mutual learning from challenges and good practices. It is based on data submitted by the LEG, which was also subsequently reviewed and validated by the LEG." }, { "id": "Longdefinition", "value": "International education aid provided in the context of programme-based approaches (PBAs) and expressed as a percentage of the total disbursed education aid. This indicator is measured by the donors’ use of (general or sector) budget support and/or joint financing mechanisms such as pool funding mechanisms. PBA is a way of engaging in development co-operation based on the principles of coordinated support for a locally owned programme of development, such as a national education plan. Programme-based approaches share the following features: Leadership by the host country; a single comprehensive programme and budget framework; a formalized process for donor coordination and harmonization of donor procedures; efforts to increase the use of country systems. This is part of the 2011 Monitoring Exercise on Development Effectiveness in the Education Sector, an unprecedented picture of aid effectiveness in the education sector that can be used as the basis for in-country dialogue and debate going forward. This information looks at how education aid is delivered and managed by development partners and governments." }, { "id": "Topic", "value": "Aid Effectiveness in the Education Sector" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "34" }, { "id": "1.1_ACCESS.ELECTRICITY.TOT", "metatype": [ { "id": "IndicatorName", "value": "Access to electricity (% of total population)" }, { "id": "Longdefinition", "value": "Access to electricity (% of total population): Percentage of total population with access to electricity" }, { "id": "Topic", "value": "Access to energy" }, { "id": "Unitofmeasure", "value": "Data for access to electricity are collected among different sources: mostly data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Given the low frequency and the regional distribution of some surveys, a number of countries have gaps in available data. To develop the historical evolution and starting point of electrification rates, a simple modeling approach was adopted to fill in the missing data points - around 1990, around 2000, and around 2010. Therefore, a country can have a continuum of zero to three data points. The model keeps the original observation if data is available for any of the time periods. This modeling approach allowed the estimation of electrification rates for 212 countries over these three time periods (Indicated as \"Estimate\"). Notation \"Assumption\" refers to the assumption of universal access in countries classified as developed by the United Nations." } ], "source_id": "35" }, { "id": "1.1_TOTAL.FINAL.ENERGY.CONSUM", "metatype": [ { "id": "IndicatorName", "value": "Total final energy consumption (TFEC) (TJ)" }, { "id": "Longdefinition", "value": "Total final energy consumption (TFEC): This indicator is derived form energy balances statistics and is equivalent to total final consumption excluding non-energy use" }, { "id": "Topic", "value": "Renewable Energy" }, { "id": "Unitofmeasure", "value": "TJ" } ], "source_id": "35" }, { "id": "1.2_ACCESS.ELECTRICITY.RURAL", "metatype": [ { "id": "IndicatorName", "value": "Access to electricity (% of rural population with access)" }, { "id": "Longdefinition", "value": "Access to electricity (% of rural population with access): Percentage of rural population with access to electricity" }, { "id": "Topic", "value": "Access to energy" }, { "id": "Unitofmeasure", "value": "Data for access to electricity are collected among different sources: mostly data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Given the low frequency and the regional distribution of some surveys, a number of countries have gaps in available data. To develop the historical evolution and starting point of electrification rates, a simple modeling approach was adopted to fill in the missing data points - around 1990, around 2000, and around 2010. Therefore, a country can have a continuum of zero to three data points. The model keeps the original observation if data is available for any of the time periods. This modeling approach allowed the estimation of electrification rates for 212 countries over these three time periods (Indicated as \"Estimate\"). Notation \"Assumption\" refers to the assumption of universal access in countries classified as developed by the United Nations." } ], "source_id": "35" }, { "id": "1.3_ACCESS.ELECTRICITY.URBAN", "metatype": [ { "id": "IndicatorName", "value": "Access to electricity (% of urban population with access)" }, { "id": "Longdefinition", "value": "Access to electricity (% of urban population with access): Percentage of urban population with access to electricity" }, { "id": "Topic", "value": "Access to energy" }, { "id": "Unitofmeasure", "value": "Data for access to electricity are collected among different sources: mostly data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Given the low frequency and the regional distribution of some surveys, a number of countries have gaps in available data. To develop the historical evolution and starting point of electrification rates, a simple modeling approach was adopted to fill in the missing data points - around 1990, around 2000, and around 2010. Therefore, a country can have a continuum of zero to three data points. The model keeps the original observation if data is available for any of the time periods. This modeling approach allowed the estimation of electrification rates for 212 countries over these three time periods (Indicated as \"Estimate\"). Notation \"Assumption\" refers to the assumption of universal access in countries classified as developed by the United Nations." } ], "source_id": "35" }, { "id": "2.1_SHARE.TOTAL.RE.IN.TFEC", "metatype": [ { "id": "IndicatorName", "value": "Renewable energy share of TFEC (%)" }, { "id": "Longdefinition", "value": "Renewable energy share of TFEC (%): Share of renewable energy in total final energy consumption" }, { "id": "Topic", "value": "Renewable Energy" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "35" }, { "id": "3.1_RE.CONSUMPTION", "metatype": [ { "id": "IndicatorName", "value": "Renewable energy consumption (TJ)" }, { "id": "Longdefinition", "value": "Renewable energy consumption (TJ): This indicator includes energy consumption from all renewable resources: hydro, solid biofuels, wind, solar, liquid biofuels, biogas, geothermal, marine and waste" }, { "id": "Topic", "value": "Renewable Energy" }, { "id": "Unitofmeasure", "value": "TJ" } ], "source_id": "35" }, { "id": "4.1.1_TOTAL.ELECTRICITY.OUTPUT", "metatype": [ { "id": "IndicatorName", "value": "Total electricity output (GWh)" }, { "id": "Longdefinition", "value": "Total electricity output (GWh): Total number of GWh generated by all power plants" }, { "id": "Topic", "value": "Renewable Energy" }, { "id": "Unitofmeasure", "value": "GWh" } ], "source_id": "35" }, { "id": "4.1.2_REN.ELECTRICITY.OUTPUT", "metatype": [ { "id": "IndicatorName", "value": "Renewable electricity output (GWh)" }, { "id": "Longdefinition", "value": "Renewable electricity output (GWh): Electric output (GWh) of power plants using renewable resources, including wind, solar PV, solar thermal, hydro, marine, geothermal, solid biofuels, renewable municipal waste, liquid biofuels and biogas. Electricity production from hydro pumped storage is excluded." }, { "id": "Topic", "value": "Renewable Energy" }, { "id": "Unitofmeasure", "value": "GWh" } ], "source_id": "35" }, { "id": "4.1_SHARE.RE.IN.ELECTRICITY", "metatype": [ { "id": "IndicatorName", "value": "Renewable electricity share of total electricity output (%)" }, { "id": "Longdefinition", "value": "Renewable electricity share of total electricity output (%): Electricity generated by power plants using renewable resources as a share of total electricity output." }, { "id": "Topic", "value": "Renewable Energy" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "35" }, { "id": "6.1_PRIMARY.ENERGY.INTENSITY", "metatype": [ { "id": "IndicatorName", "value": "Energy intensity level of primary energy (MJ/2011 USD PPP)" }, { "id": "Longdefinition", "value": "Energy intensity level of primary energy (MJ/2011 USD PPP): A ratio between energy supply and gross domestic product measured at purchasing power parity. Energy intensity is an indication of how much energy is used to produce one unit of economic output. A lower ratio indicates that less energy is used to produce one unit of output." }, { "id": "Topic", "value": "Energy efficiency" }, { "id": "Unitofmeasure", "value": "MJ/2011 USD PPP" } ], "source_id": "35" }, { "id": "1.0.HCount.1.90usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Headcount ($1.90 a day)" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income (in 2011 PPP) below the poverty line." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.0.HCount.2.5usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Headcount ($2.50 a day)" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income (in 2005 PPP) below the poverty line." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.0.HCount.Mid10to50", "metatype": [ { "id": "IndicatorName", "value": "Middle Class ($10-50 a day) Headcount" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income (in 2005 PPP) below the poverty line." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.0.HCount.Ofcl", "metatype": [ { "id": "IndicatorName", "value": "Official Moderate Poverty Rate-National" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income below the official poverty line developed by each country." }, { "id": "Source", "value": "LAC Equity Lab tabulations of data from National Statistical Offices." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.0.HCount.Poor4uds", "metatype": [ { "id": "IndicatorName", "value": "Poverty Headcount ($4 a day)" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income (in 2005 PPP) below the poverty line." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.0.HCount.Vul4to10", "metatype": [ { "id": "IndicatorName", "value": "Vulnerable ($4-10 a day) Headcount" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income (in 2005 PPP) below the poverty line." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.0.PGap.1.90usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Gap ($1.90 a day)" }, { "id": "Shortdefinition", "value": "The poverty gap captures the mean aggregate income or consumption shortfall relative to the poverty line across the entire population. It measures the total resources needed to bring all the poor to the level of the poverty line (averaged over the total population)." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.0.PGap.2.5usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Gap ($2.50 a day)" }, { "id": "Shortdefinition", "value": "The poverty gap captures the mean aggregate income or consumption shortfall relative to the poverty line across the entire population. It measures the total resources needed to bring all the poor to the level of the poverty line (averaged over the total population)." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.0.PGap.Poor4uds", "metatype": [ { "id": "IndicatorName", "value": "Poverty Gap ($4 a day)" }, { "id": "Shortdefinition", "value": "The poverty gap captures the mean aggregate income or consumption shortfall relative to the poverty line across the entire population. It measures the total resources needed to bring all the poor to the level of the poverty line (averaged over the total population)." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.0.PSev.1.90usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Severity ($1.90 a day)" }, { "id": "Shortdefinition", "value": "The poverty severity index combines information on both poverty and inequality among the poor by averaging the squares of the poverty gaps relative the poverty line" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.0.PSev.2.5usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Severity ($2.50 a day)" }, { "id": "Shortdefinition", "value": "The poverty severity index combines information on both poverty and inequality among the poor by averaging the squares of the poverty gaps relative the poverty line" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.0.PSev.Poor4uds", "metatype": [ { "id": "IndicatorName", "value": "Poverty Severity ($4 a day)" }, { "id": "Shortdefinition", "value": "The poverty severity index combines information on both poverty and inequality among the poor by averaging the squares of the poverty gaps relative the poverty line" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.1.HCount.1.90usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Headcount ($1.90 a day)-Rural" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income (in 2011 PPP) below the poverty line." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.1.HCount.2.5usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Headcount ($2.50 a day)-Rural" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income (in 2005 PPP) below the poverty line." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.1.HCount.Mid10to50", "metatype": [ { "id": "IndicatorName", "value": "Middle Class ($10-50 a day) Headcount-Rural" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income (in 2005 PPP) below the poverty line." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.1.HCount.Ofcl", "metatype": [ { "id": "IndicatorName", "value": "Official Moderate Poverty Rate- Rural" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income below the official poverty line developed by each country." }, { "id": "Source", "value": "LAC Equity Lab tabulations of data from National Statistical Offices." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.1.HCount.Poor4uds", "metatype": [ { "id": "IndicatorName", "value": "Poverty Headcount ($4 a day)-Rural" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income (in 2005 PPP) below the poverty line." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.1.HCount.Vul4to10", "metatype": [ { "id": "IndicatorName", "value": "Vulnerable ($4-10 a day) Headcount-Rural" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income (in 2005 PPP) below the poverty line." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.1.PGap.1.90usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Gap ($1.90 a day)-Rural" }, { "id": "Shortdefinition", "value": "The poverty gap captures the mean aggregate income or consumption shortfall relative to the poverty line across the entire population. It measures the total resources needed to bring all the poor to the level of the poverty line (averaged over the total population)." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.1.PGap.2.5usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Gap ($2.50 a day)-Rural" }, { "id": "Shortdefinition", "value": "The poverty gap captures the mean aggregate income or consumption shortfall relative to the poverty line across the entire population. It measures the total resources needed to bring all the poor to the level of the poverty line (averaged over the total population)." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.1.PGap.Poor4uds", "metatype": [ { "id": "IndicatorName", "value": "Poverty Gap ($4 a day)-Rural" }, { "id": "Shortdefinition", "value": "The poverty gap captures the mean aggregate income or consumption shortfall relative to the poverty line across the entire population. It measures the total resources needed to bring all the poor to the level of the poverty line (averaged over the total population)." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.1.PSev.1.90usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Severity ($1.90 a day)-Rural" }, { "id": "Shortdefinition", "value": "The poverty severity index combines information on both poverty and inequality among the poor by averaging the squares of the poverty gaps relative the poverty line" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.1.PSev.2.5usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Severity ($2.50 a day)-Rural" }, { "id": "Shortdefinition", "value": "The poverty severity index combines information on both poverty and inequality among the poor by averaging the squares of the poverty gaps relative the poverty line" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.1.PSev.Poor4uds", "metatype": [ { "id": "IndicatorName", "value": "Poverty Severity ($4 a day)-Rural" }, { "id": "Shortdefinition", "value": "The poverty severity index combines information on both poverty and inequality among the poor by averaging the squares of the poverty gaps relative the poverty line" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.2.HCount.1.90usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Headcount ($1.90 a day)-Urban" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income (in 2011 PPP) below the poverty line." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.2.HCount.2.5usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Headcount ($2.50 a day)-Urban" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income (in 2005 PPP) below the poverty line." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.2.HCount.Mid10to50", "metatype": [ { "id": "IndicatorName", "value": "Middle Class ($10-50 a day) Headcount-Urban" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income (in 2005 PPP) below the poverty line." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.2.HCount.Ofcl", "metatype": [ { "id": "IndicatorName", "value": "Official Moderate Poverty Rate-Urban" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income below the official poverty line developed by each country." }, { "id": "Source", "value": "LAC Equity Lab tabulations of data from National Statistical Offices." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.2.HCount.Poor4uds", "metatype": [ { "id": "IndicatorName", "value": "Poverty Headcount ($4 a day)-Urban" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income (in 2005 PPP) below the poverty line." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.2.HCount.Vul4to10", "metatype": [ { "id": "IndicatorName", "value": "Vulnerable ($4-10 a day) Headcount-Urban" }, { "id": "Shortdefinition", "value": "The poverty headcount index measures the proportion of the population with daily per capita income (in 2005 PPP) below the poverty line." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.2.PGap.1.90usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Gap ($1.90 a day)-Urban" }, { "id": "Shortdefinition", "value": "The poverty gap captures the mean aggregate income or consumption shortfall relative to the poverty line across the entire population. It measures the total resources needed to bring all the poor to the level of the poverty line (averaged over the total population)." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.2.PGap.2.5usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Gap ($2.50 a day)-Urban" }, { "id": "Shortdefinition", "value": "The poverty gap captures the mean aggregate income or consumption shortfall relative to the poverty line across the entire population. It measures the total resources needed to bring all the poor to the level of the poverty line (averaged over the total population)." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.2.PGap.Poor4uds", "metatype": [ { "id": "IndicatorName", "value": "Poverty Gap ($4 a day)-Urban" }, { "id": "Shortdefinition", "value": "The poverty gap captures the mean aggregate income or consumption shortfall relative to the poverty line across the entire population. It measures the total resources needed to bring all the poor to the level of the poverty line (averaged over the total population)." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.2.PSev.1.90usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Severity ($1.90 a day)-Urban" }, { "id": "Shortdefinition", "value": "The poverty severity index combines information on both poverty and inequality among the poor by averaging the squares of the poverty gaps relative the poverty line" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.2.PSev.2.5usd", "metatype": [ { "id": "IndicatorName", "value": "Poverty Severity ($2.50 a day)-Urban" }, { "id": "Shortdefinition", "value": "The poverty severity index combines information on both poverty and inequality among the poor by averaging the squares of the poverty gaps relative the poverty line" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "1.2.PSev.Poor4uds", "metatype": [ { "id": "IndicatorName", "value": "Poverty Severity ($4 a day)-Urban" }, { "id": "Shortdefinition", "value": "The poverty severity index combines information on both poverty and inequality among the poor by averaging the squares of the poverty gaps relative the poverty line" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "2.0.cov.Cel", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Mobile Phone" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Ele", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Electricity" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.FPS", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Finished Primary School" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Int", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Internet" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Math.pl_2.all", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Mathematics Proficiency Level 2" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Math.pl_2.prv", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Mathematics Proficiency Level 2, Private schools" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Math.pl_2.pub", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Mathematics Proficiency Level 2, Public schools" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Math.pl_3.all", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Mathematics Proficiency Level 3" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Math.pl_3.prv", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Mathematics Proficiency Level 3, Private schools" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Math.pl_3.pub", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Mathematics Proficiency Level 3, Public schools" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Read.pl_2.all", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Reading Proficiency Level 2" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Read.pl_2.prv", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Reading Proficiency Level 2, Private schools" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Read.pl_2.pub", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Reading Proficiency Level 2, Public schools" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Read.pl_3.all", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Reading Proficiency Level 3" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Read.pl_3.prv", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Reading Proficiency Level 3, Private schools" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Read.pl_3.pub", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Reading Proficiency Level 3, Public schools" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.San", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Sanitation" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Sch", "metatype": [ { "id": "IndicatorName", "value": "Coverage: School Enrollment" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Scie.pl_2.all", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Science Proficiency Level 2" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Scie.pl_2.prv", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Science Proficiency Level 2, Private schools" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Scie.pl_2.pub", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Science Proficiency Level 2, Public schools" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Scie.pl_3.all", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Science Proficiency Level 3" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Scie.pl_3.prv", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Science Proficiency Level 3, Private schools" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Scie.pl_3.pub", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Science Proficiency Level 3, Public schools" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.cov.Wat", "metatype": [ { "id": "IndicatorName", "value": "Coverage: Water" }, { "id": "Shortdefinition", "value": "The coverage rate is the childhood access rate of a given opportunity used in calculating the Human Opportunities Index (HOI). The coverage rate does not take into account inequality of access between different circumstance groups." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Cel", "metatype": [ { "id": "IndicatorName", "value": "HOI: Mobile Phone" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, household per capita income, number of siblings, presence of both parents in the household, gender of the household head, and urban or rural residence.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Ele", "metatype": [ { "id": "IndicatorName", "value": "HOI: Electricity" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, household per capita income, number of siblings, presence of both parents in the household, gender of the household head, and urban or rural residence.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.FPS", "metatype": [ { "id": "IndicatorName", "value": "HOI: Finished Primary School" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, household per capita income, number of siblings, presence of both parents in the household, gender of the household head, and urban or rural residence.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Int", "metatype": [ { "id": "IndicatorName", "value": "HOI: Internet" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, household per capita income, number of siblings, presence of both parents in the household, gender of the household head, and urban or rural residence.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Math.pl_2.all", "metatype": [ { "id": "IndicatorName", "value": "HOI: Mathematics Proficiency Level 2" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Math.pl_2.prv", "metatype": [ { "id": "IndicatorName", "value": "HOI: Mathematics Proficiency Level 2, Private schools" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Math.pl_2.pub", "metatype": [ { "id": "IndicatorName", "value": "HOI: Mathematics Proficiency Level 2, Public schools" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Math.pl_3.all", "metatype": [ { "id": "IndicatorName", "value": "HOI: Mathematics Proficiency Level 3" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Math.pl_3.prv", "metatype": [ { "id": "IndicatorName", "value": "HOI: Mathematics Proficiency Level 3, Private schools" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Math.pl_3.pub", "metatype": [ { "id": "IndicatorName", "value": "HOI: Mathematics Proficiency Level 3, Public schools" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Read.pl_2.all", "metatype": [ { "id": "IndicatorName", "value": "HOI: Reading Proficiency Level 2" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Read.pl_2.prv", "metatype": [ { "id": "IndicatorName", "value": "HOI: Reading Proficiency Level 2, Private schools" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Read.pl_2.pub", "metatype": [ { "id": "IndicatorName", "value": "HOI: Reading Proficiency Level 2, Public schools" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Read.pl_3.all", "metatype": [ { "id": "IndicatorName", "value": "HOI: Reading Proficiency Level 3" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Read.pl_3.prv", "metatype": [ { "id": "IndicatorName", "value": "HOI: Reading Proficiency Level 3, Private schools" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Read.pl_3.pub", "metatype": [ { "id": "IndicatorName", "value": "HOI: Reading Proficiency Level 3, Public schools" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.San", "metatype": [ { "id": "IndicatorName", "value": "HOI: Sanitation" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, household per capita income, number of siblings, presence of both parents in the household, gender of the household head, and urban or rural residence.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Sch", "metatype": [ { "id": "IndicatorName", "value": "HOI: School Enrollment" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, household per capita income, number of siblings, presence of both parents in the household, gender of the household head, and urban or rural residence.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Scie.pl_2.all", "metatype": [ { "id": "IndicatorName", "value": "HOI: Science Proficiency Level 2" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Scie.pl_2.prv", "metatype": [ { "id": "IndicatorName", "value": "HOI: Science Proficiency Level 2, Private schools" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Scie.pl_2.pub", "metatype": [ { "id": "IndicatorName", "value": "HOI: Science Proficiency Level 2, Public schools" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Scie.pl_3.all", "metatype": [ { "id": "IndicatorName", "value": "HOI: Science Proficiency Level 3" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Scie.pl_3.prv", "metatype": [ { "id": "IndicatorName", "value": "HOI: Science Proficiency Level 3, Private schools" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Scie.pl_3.pub", "metatype": [ { "id": "IndicatorName", "value": "HOI: Science Proficiency Level 3, Public schools" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, region of school location, father's occupation, and a household wealth index based on assets.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations using PISA Data." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "2.0.hoi.Wat", "metatype": [ { "id": "IndicatorName", "value": "HOI: Water" }, { "id": "Shortdefinition", "value": "The Human Opportunities Index (HOI) is an economic indicator that captures the degree of inequality of access to an essential service by different circumstance groups. This index takes into account the average access rate (the coverage) of a given opportunity (service) and the inequality of its distribution. The circumstances included in the HOI are the gender of the child, parents' education, household per capita income, number of siblings, presence of both parents in the household, gender of the household head, and urban or rural residence.An increase in the index can be related either to an increase in the coverage or to a more equal distribution of that service." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Equality of Opportunities" } ], "source_id": "37" }, { "id": "3.0.Atkin.0.5", "metatype": [ { "id": "IndicatorName", "value": "Atkinson, A(.5)" }, { "id": "Shortdefinition", "value": "Atkinson (1970) proposed this class of inequality measures with a weighting parameter e which measures aversion to inequality. As e rises, the index becomes more sensitive to transfers at the lower end of the distribution and less sensitive to transfers at the top. The limit case, e?0, the index reflects the Function of Rawls which only takes account of transfers to the very lowest income group; at the other extreme, when e=0, we obtain the linear utility function. This ranks distributions solely according to total income." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.0.Atkin.1", "metatype": [ { "id": "IndicatorName", "value": "Atkinson, A(1)" }, { "id": "Shortdefinition", "value": "Atkinson (1970) proposed this class of inequality measures with a weighting parameter e which measures aversion to inequality. As e rises, the index becomes more sensitive to transfers at the lower end of the distribution and less sensitive to transfers at the top. The limit case, e?0, the index reflects the Function of Rawls which only takes account of transfers to the very lowest income group; at the other extreme, when e=0, we obtain the linear utility function. This ranks distributions solely according to total income." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.0.Atkin.2", "metatype": [ { "id": "IndicatorName", "value": "Atkinson, A(2)" }, { "id": "Shortdefinition", "value": "Atkinson (1970) proposed this class of inequality measures with a weighting parameter e which measures aversion to inequality. As e rises, the index becomes more sensitive to transfers at the lower end of the distribution and less sensitive to transfers at the top. The limit case, e?0, the index reflects the Function of Rawls which only takes account of transfers to the very lowest income group; at the other extreme, when e=0, we obtain the linear utility function. This ranks distributions solely according to total income." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.0.GenEnt-1", "metatype": [ { "id": "IndicatorName", "value": "Generalized Entrophy, GE(-1)" }, { "id": "Shortdefinition", "value": "The parameter a in the GE class represents the weight given to distances between incomes at different parts of the income distribution, and can take any real value. For lower values of a, GE is more sensitive to changes in the lower tail of the distribution, and for higher values GE is more sensitive to changes that affect the upper tail. GE(1) is the Theil index." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.0.GenEnt2", "metatype": [ { "id": "IndicatorName", "value": "Generalized Entrophy, GE(2)" }, { "id": "Shortdefinition", "value": "The parameter a in the GE class represents the weight given to distances between incomes at different parts of the income distribution, and can take any real value. For lower values of a, GE is more sensitive to changes in the lower tail of the distribution, and for higher values GE is more sensitive to changes that affect the upper tail. GE(1) is the Theil index." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.0.Gini", "metatype": [ { "id": "IndicatorName", "value": "Gini Coefficient" }, { "id": "Shortdefinition", "value": "The Gini coefficient is most common measure of inequality. It is based on the Lorenz curve, a cumulative frequency curve that compares the distribution of a specific variable (in this case, income) with the uniform distribution that represents equality. The Gini coefficient is bounded by 0 (indicating perfect equality of income) and 1, which means complete inequality. This calculation includes observations of 0 income." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.0.Gini_nozero", "metatype": [ { "id": "IndicatorName", "value": "Gini Coefficient (No Zero Income)" }, { "id": "Shortdefinition", "value": "The Gini coefficient is most common measure of inequality. It is based on the Lorenz curve, a cumulative frequency curve that compares the distribution of a specific variable (in this case, income) with the uniform distribution that represents equality. The Gini coefficient is bounded by 0 (indicating perfect equality of income) and 1, which means complete inequality. This calculation does not includes observations of 0 income." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.0.IncShr.q1", "metatype": [ { "id": "IndicatorName", "value": "Income Share of First Quintile" }, { "id": "Shortdefinition", "value": "Share of household income held by the bottom quintile (0-20 percent)." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.0.IncShr.q2", "metatype": [ { "id": "IndicatorName", "value": "Income Share of Second Quintile" }, { "id": "Shortdefinition", "value": "Share of household income held by the seocnd quintile (20 - 40 percent)." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.0.IncShr.q3", "metatype": [ { "id": "IndicatorName", "value": "Income Share of Third Quintile" }, { "id": "Shortdefinition", "value": "Share of household income held by the third quintile (40 - 60 percent)." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.0.IncShr.q4", "metatype": [ { "id": "IndicatorName", "value": "Income Share of Fourth Quintile" }, { "id": "Shortdefinition", "value": "Share of household income held by the fourth quintile (60 - 80 percent)." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.0.IncShr.q5", "metatype": [ { "id": "IndicatorName", "value": "Income Share of Fifth Quintile" }, { "id": "Shortdefinition", "value": "Share of household income held by the top quintile (80 - 100 percent)." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.0.MLongDev0", "metatype": [ { "id": "IndicatorName", "value": "Mean Log Deviation, GE(0)" }, { "id": "Shortdefinition", "value": "The parameter a in the GE class represents the weight given to distances between incomes at different parts of the income distribution, and can take any real value. For lower values of a, GE is more sensitive to changes in the lower tail of the distribution, and for higher values GE is more sensitive to changes that affect the upper tail. GE(1) is the Theil index." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.0.Rate75-25", "metatype": [ { "id": "IndicatorName", "value": "Rate 75/25" }, { "id": "Shortdefinition", "value": "The decile dispersion ratio presents the ratio of the average income of the richest 25 percent by that of the poorest 25 percent. This ratio expresses the income of the top quantile as multiples of that of the poorest quantile. However, it ignores information about incomes in the middle of the income distribution and doesn’t use information about the distribution of income within the top and bottom deciles or percentiles." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.0.Rate90-10", "metatype": [ { "id": "IndicatorName", "value": "Rate 90/10" }, { "id": "Shortdefinition", "value": "The decile dispersion ratio presents the ratio of the average income of the richest 10 percent by that of the poorest 10 percent. This ratio expresses the income of the top quantile as multiples of that of the poorest quantile. However, it ignores information about incomes in the middle of the income distribution and doesn’t use information about the distribution of income within the top and bottom deciles or percentiles." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.0.TheilInd1", "metatype": [ { "id": "IndicatorName", "value": "Theil Index, GE(1)" }, { "id": "Shortdefinition", "value": "The Theil index is part of a larger family of measures referred to as the General Entropy class. Compared to the Gini index, it has the advantage of being additive across different subgroups or regions in the country. However, it does not have a straightforward representation and lacks the appealing interpretation of the Gini coefficient." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.1.Gini", "metatype": [ { "id": "IndicatorName", "value": "Gini, Rural" }, { "id": "Shortdefinition", "value": "The Gini coefficient is most common measure of inequality. It is based on the Lorenz curve, a cumulative frequency curve that compares the distribution of a specific variable (in this case, income) with the uniform distribution that represents equality. The Gini coefficient is bounded by 0 (indicating perfect equality of income) and 1, which means complete inequality. This calculation includes observations of 0 income." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.1.MLongDev0", "metatype": [ { "id": "IndicatorName", "value": "Mean Log Deviation, GE(0), Rural" }, { "id": "Shortdefinition", "value": "The parameter a in the GE class represents the weight given to distances between incomes at different parts of the income distribution, and can take any real value. For lower values of a, GE is more sensitive to changes in the lower tail of the distribution, and for higher values GE is more sensitive to changes that affect the upper tail. GE(1) is the Theil index." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.1.TheilInd1", "metatype": [ { "id": "IndicatorName", "value": "Theil Index, GE(1), Rural" }, { "id": "Shortdefinition", "value": "The Theil index is part of a larger family of measures referred to as the General Entropy class. Compared to the Gini index, it has the advantage of being additive across different subgroups or regions in the country. However, it does not have a straightforward representation and lacks the appealing interpretation of the Gini coefficient." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.2.Gini", "metatype": [ { "id": "IndicatorName", "value": "Gini, Urban" }, { "id": "Shortdefinition", "value": "The Gini coefficient is most common measure of inequality. It is based on the Lorenz curve, a cumulative frequency curve that compares the distribution of a specific variable (in this case, income) with the uniform distribution that represents equality. The Gini coefficient is bounded by 0 (indicating perfect equality of income) and 1, which means complete inequality. This calculation includes observations of 0 income." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.2.MLongDev0", "metatype": [ { "id": "IndicatorName", "value": "Mean Log Deviation, GE(0),Urban" }, { "id": "Shortdefinition", "value": "The parameter a in the GE class represents the weight given to distances between incomes at different parts of the income distribution, and can take any real value. For lower values of a, GE is more sensitive to changes in the lower tail of the distribution, and for higher values GE is more sensitive to changes that affect the upper tail. GE(1) is the Theil index." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "3.2.TheilInd1", "metatype": [ { "id": "IndicatorName", "value": "Theil Index, GE(1),Urban" }, { "id": "Shortdefinition", "value": "The Theil index is part of a larger family of measures referred to as the General Entropy class. Compared to the Gini index, it has the advantage of being additive across different subgroups or regions in the country. However, it does not have a straightforward representation and lacks the appealing interpretation of the Gini coefficient." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Income Inequality" } ], "source_id": "37" }, { "id": "4.0.nini.15a18", "metatype": [ { "id": "IndicatorName", "value": "Youth: Neither in School Nor Working (15-18)" }, { "id": "Shortdefinition", "value": "Share of the population ages 15-18 that is neither employed nor in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.0.nini.15a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: Neither in School Nor Working (15-24)" }, { "id": "Shortdefinition", "value": "Share of the population ages 15-24 that is neither employed nor in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.0.nini.19a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: Neither in School Nor Working (19-24)" }, { "id": "Shortdefinition", "value": "Share of the population ages 19-24 that is neither employed nor in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.0.stud.15a18", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School (15-18)" }, { "id": "Shortdefinition", "value": "Share of the population ages 15-18 that is in school and not employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.0.stud.15a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School (15-24)" }, { "id": "Shortdefinition", "value": "Share of the population ages 15-24 that is in school and not employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.0.stud.19a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School (19-24)" }, { "id": "Shortdefinition", "value": "Share of the population ages 19-24 that is in school and not employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.0.studwork.15a18", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School and Employed (15-18)" }, { "id": "Shortdefinition", "value": "Share of the population ages 15-18 that is in school and employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.0.studwork.15a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School and Employed (15-24)" }, { "id": "Shortdefinition", "value": "Share of the population ages 15-24 that is in school and employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.0.studwork.19a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School and Employed (19-24)" }, { "id": "Shortdefinition", "value": "Share of the population ages 19-24 that is in school and employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.0.work.15a18", "metatype": [ { "id": "IndicatorName", "value": "Youth: Employed (15-18)" }, { "id": "Shortdefinition", "value": "Share of the population ages 15-18 that is employed and not in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.0.work.15a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: Employed (15-24)" }, { "id": "Shortdefinition", "value": "Share of the population ages 15-24 that is employed and not in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.0.work.19a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: Employed (19-24)" }, { "id": "Shortdefinition", "value": "Share of the population ages 19-24 that is employed and not in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.1.nini.15a18", "metatype": [ { "id": "IndicatorName", "value": "Youth: Neither in School Nor Working (15-18), Male" }, { "id": "Shortdefinition", "value": "Share of the male population ages 15-18 that is neither employed nor in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.1.nini.15a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: Neither in School Nor Working (15-24), Male" }, { "id": "Shortdefinition", "value": "Share of the male population ages 15-24 that is neither employed nor in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.1.nini.19a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: Neither in School Nor Working (19-24), Male" }, { "id": "Shortdefinition", "value": "Share of the male population ages 19-24 that is neither employed nor in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.1.stud.15a18", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School (15-18), Male" }, { "id": "Shortdefinition", "value": "Share of the male population ages 15-18 that is in school and not employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.1.stud.15a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School (15-24), Male" }, { "id": "Shortdefinition", "value": "Share of the male population ages 15-24 that is in school and not employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.1.stud.19a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School (19-24), Male" }, { "id": "Shortdefinition", "value": "Share of the male population ages 19-24 that is in school and not employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.1.studwork.15a18", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School and Employed (15-18), Male" }, { "id": "Shortdefinition", "value": "Share of the male population ages 15-18 that is in school and employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.1.studwork.15a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School and Employed (15-24), Male" }, { "id": "Shortdefinition", "value": "Share of the male population ages 15-24 that is in school and employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.1.studwork.19a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School and Employed (19-24), Male" }, { "id": "Shortdefinition", "value": "Share of the male population ages 19-24 that is in school and employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.1.work.15a18", "metatype": [ { "id": "IndicatorName", "value": "Youth: Employed (15-18), Male" }, { "id": "Shortdefinition", "value": "Share of the male population ages 15-18 that is employed and not in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.1.work.15a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: Employed (15-24), Male" }, { "id": "Shortdefinition", "value": "Share of the male population ages 15-24 that is employed and not in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.1.work.19a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: Employed (19-24), Male" }, { "id": "Shortdefinition", "value": "Share of the male population ages 19-24 that is employed and not in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.2.nini.15a18", "metatype": [ { "id": "IndicatorName", "value": "Youth: Neither in School Nor Working (15-18), Female" }, { "id": "Shortdefinition", "value": "Share of the female population ages 15-18 that is neither employed nor in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.2.nini.15a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: Neither in School Nor Working (15-24), Female" }, { "id": "Shortdefinition", "value": "Share of the female population ages 15-24 that is neither employed nor in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.2.nini.19a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: Neither in School Nor Working (19-24), Female" }, { "id": "Shortdefinition", "value": "Share of the female population ages 19-24 that is neither employed nor in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.2.stud.15a18", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School (15-18), Female" }, { "id": "Shortdefinition", "value": "Share of the female population ages 15-18 that is in school and not employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.2.stud.15a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School (15-24), Female" }, { "id": "Shortdefinition", "value": "Share of the female population ages 15-24 that is in school and not employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.2.stud.19a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School (19-24), Female" }, { "id": "Shortdefinition", "value": "Share of the female population ages 19-24 that is in school and not employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.2.studwork.15a18", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School and Employed (15-18), Female" }, { "id": "Shortdefinition", "value": "Share of the female population ages 15-18 that is in school and employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.2.studwork.15a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School and Employed (15-24), Female" }, { "id": "Shortdefinition", "value": "Share of the female population ages 15-24 that is in school and employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.2.studwork.19a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: In School and Employed (19-24), Female" }, { "id": "Shortdefinition", "value": "Share of the female population ages 19-24 that is in school and employed." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.2.work.15a18", "metatype": [ { "id": "IndicatorName", "value": "Youth: Employed (15-18), Female" }, { "id": "Shortdefinition", "value": "Share of the female population ages 15-18 that is employed and not in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.2.work.15a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: Employed (15-24), Female" }, { "id": "Shortdefinition", "value": "Share of the female population ages 15-24 that is employed and not in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "4.2.work.19a24", "metatype": [ { "id": "IndicatorName", "value": "Youth: Employed (19-24), Female" }, { "id": "Shortdefinition", "value": "Share of the female population ages 19-24 that is employed and not in school." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank), based on \"Out of School and Out of Work: A Diagnostic of Ninis in Latin America\" by De Hoyos, Popova, and Rogers (2014, World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "5.0.AMeanIncGr.All", "metatype": [ { "id": "IndicatorName", "value": "Annualized Mean Income Growth (2004-2014)" }, { "id": "Shortdefinition", "value": "The official indicator to monitor shared prosperity is the growth in real per capita income (or consumption) of the bottom 40 percent of the income (or consumption) distribution in a country." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank) and World Development Indicators" }, { "id": "Topic", "value": "Shared Prosperity" } ], "source_id": "37" }, { "id": "5.0.AMeanIncGr.B40", "metatype": [ { "id": "IndicatorName", "value": "Annualized Mean Income Growth Bottom 40 Percent (2004-2014)" }, { "id": "Shortdefinition", "value": "The indicator to monitor shared prosperity is the growth in real per capita income (or consumption) of the bottom 40 percent of the income (or consumption) distribution in a country." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank) and World Development Indicators" }, { "id": "Topic", "value": "Shared Prosperity" } ], "source_id": "37" }, { "id": "5.1.AMeanIncGr.All", "metatype": [ { "id": "IndicatorName", "value": "Annualized Mean Income Growth (2004-2009)" }, { "id": "Shortdefinition", "value": "The indicator to monitor shared prosperity is the growth in real per capita income (or consumption) of the bottom 40 percent of the income (or consumption) distribution in a country." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank) and World Development Indicators" }, { "id": "Topic", "value": "Shared Prosperity" } ], "source_id": "37" }, { "id": "5.1.AMeanIncGr.B40", "metatype": [ { "id": "IndicatorName", "value": "Annualized Mean Income Growth Bottom 40 Percent (2004-2009)" }, { "id": "Shortdefinition", "value": "The indicator to monitor shared prosperity is the growth in real per capita income (or consumption) of the bottom 40 percent of the income (or consumption) distribution in a country." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank) and World Development Indicators" }, { "id": "Topic", "value": "Shared Prosperity" } ], "source_id": "37" }, { "id": "5.2.AMeanIncGr.All", "metatype": [ { "id": "IndicatorName", "value": "Annualized Mean Income Growth (2009-2014)" }, { "id": "Shortdefinition", "value": "The indicator to monitor shared prosperity is the growth in real per capita income (or consumption) of the bottom 40 percent of the income (or consumption) distribution in a country." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank) and World Development Indicators" }, { "id": "Topic", "value": "Shared Prosperity" } ], "source_id": "37" }, { "id": "5.2.AMeanIncGr.B40", "metatype": [ { "id": "IndicatorName", "value": "Annualized Mean Income Growth Bottom 40 Percent (2009-2014)" }, { "id": "Shortdefinition", "value": "The indicator to monitor shared prosperity is the growth in real per capita income (or consumption) of the bottom 40 percent of the income (or consumption) distribution in a country." }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank) and World Development Indicators" }, { "id": "Topic", "value": "Shared Prosperity" } ], "source_id": "37" }, { "id": "6.0.Conspc", "metatype": [ { "id": "IndicatorName", "value": "Consumption per capita (2011 $)" }, { "id": "Shortdefinition", "value": "Consumption per capita is the market value of all goods and services, including durable products and payments and fees to governments to obtain permits and licenses, purchased by households. It excludes purchases of dwellings but includes imputed rent for owner-occupied dwellings. It also includes the expenditures of nonprofit institutions serving households, even when reported separately by the country." }, { "id": "Source", "value": "LAC Equity Lab Tablulations of the World Development Indicators (World Bank)." }, { "id": "Topic", "value": "Economic Growth" } ], "source_id": "37" }, { "id": "6.0.GDP_current", "metatype": [ { "id": "IndicatorName", "value": "GDP (current $)" }, { "id": "Shortdefinition", "value": "GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars. Dollar figures for GDP are converted from domestic currencies using single year official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used." }, { "id": "Source", "value": "World Development Indicators (World Bank)" }, { "id": "Topic", "value": "Economic Growth" } ], "source_id": "37" }, { "id": "6.0.GDP_growth", "metatype": [ { "id": "IndicatorName", "value": "GDP growth (annual %)" }, { "id": "Shortdefinition", "value": "Annual percentage growth rate of GDP at market prices based on constant local currency. Aggregates are based on constant 2011 U.S. dollars. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources." }, { "id": "Source", "value": "World Development Indicators (World Bank)" }, { "id": "Topic", "value": "Economic Growth" } ], "source_id": "37" }, { "id": "6.0.GDP_usd", "metatype": [ { "id": "IndicatorName", "value": "GDP (constant 2005 $)" }, { "id": "Shortdefinition", "value": "GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2005 U.S. dollars. Dollar figures for GDP are converted from domestic currencies using 2000 official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used." }, { "id": "Source", "value": "World Development Indicators (World Bank)" }, { "id": "Topic", "value": "Economic Growth" } ], "source_id": "37" }, { "id": "6.0.GDPpc_constant", "metatype": [ { "id": "IndicatorName", "value": "GDP per capita, PPP (constant 2011 international $)" }, { "id": "Shortdefinition", "value": "GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2011 international dollars." }, { "id": "Source", "value": "World Development Indicators (World Bank)" }, { "id": "Topic", "value": "Economic Growth" } ], "source_id": "37" }, { "id": "6.0.GNIpc", "metatype": [ { "id": "IndicatorName", "value": "GNI per capita (2011 $)" }, { "id": "Shortdefinition", "value": "GNI per capita is the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and the Euro area, Japan, the United Kingdom, and the United States." }, { "id": "Source", "value": "LAC Equity Lab Tablulations of the World Development Indicators (World Bank)." }, { "id": "Topic", "value": "Economic Growth" } ], "source_id": "37" }, { "id": "8.0.LIPI", "metatype": [ { "id": "IndicatorName", "value": "Labor Income Poverty Index" }, { "id": "Shortdefinition", "value": "The Labor Income Poverty Index (LIPI) measures changes in the share of households that have per capita labor income below the regional poverty line of $4 per day, relative to a selected reference period. This reference period is the third quarter in 2010 (2010Q3 = 1), except for Chile and Guatemala, where 2010Q4 = 1." }, { "id": "Source", "value": "LAC Equity Lab tabulations of LABLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Poverty Rates" } ], "source_id": "37" }, { "id": "9.0.Employee.All", "metatype": [ { "id": "IndicatorName", "value": "Employees (%)" }, { "id": "Shortdefinition", "value": "Share of the labor force (ages 18-65) that is a wage or salary worker" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.Employee.B40", "metatype": [ { "id": "IndicatorName", "value": "Employees-Bottom 40 Percent (%)" }, { "id": "Shortdefinition", "value": "Share of the labor force (ages 18-65) in the bottom 40 percent that is a wage or salary worker" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.Employee.T60", "metatype": [ { "id": "IndicatorName", "value": "Employees-Top 60 Percent (%)" }, { "id": "Shortdefinition", "value": "Share of the labor force (ages 18-65) in the top 60 percent that is a wage or salary worker" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.Employer.All", "metatype": [ { "id": "IndicatorName", "value": "Employers (%)" }, { "id": "Shortdefinition", "value": "Share of the labor force (ages 18-65) that is an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.Employer.B40", "metatype": [ { "id": "IndicatorName", "value": "Employers-Bottom 40 Percent (%)" }, { "id": "Shortdefinition", "value": "Share of the labor force (ages 18-65) in the bottom 40 percent that is an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.Employer.T60", "metatype": [ { "id": "IndicatorName", "value": "Employers-Top 60 Percent (%)" }, { "id": "Shortdefinition", "value": "Share of the labor force (ages 18-65) in the top 60 percent that is an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.Labor.All", "metatype": [ { "id": "IndicatorName", "value": "Labor Force Participation Rate (%)" }, { "id": "Shortdefinition", "value": "Share of the population (ages 18-65) that is in the labor force" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.Labor.B40", "metatype": [ { "id": "IndicatorName", "value": "Labor Force Participation Rate (%)-Bottom 40 Percent" }, { "id": "Shortdefinition", "value": "Share of the population (ages 18-65) in the Bottom 40 percent that is in the labor force" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.Labor.T60", "metatype": [ { "id": "IndicatorName", "value": "Labor Force Participation Rate (%)-Top 60 Percent" }, { "id": "Shortdefinition", "value": "Share of the population (ages 18-65) in the Top 60 percent that is in the labor force" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.SelfEmp.All", "metatype": [ { "id": "IndicatorName", "value": "Self-Employed (%)" }, { "id": "Shortdefinition", "value": "Share of the labor force (ages 18-65) that is self-employed and not an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.SelfEmp.B40", "metatype": [ { "id": "IndicatorName", "value": "Self-Employed-Bottom 40 Percent (%)" }, { "id": "Shortdefinition", "value": "Share of the labor force (ages 18-65) in the bottom 40 percent that is self-employed and not an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.SelfEmp.T60", "metatype": [ { "id": "IndicatorName", "value": "Self-Employed-Top 60 Percent (%)" }, { "id": "Shortdefinition", "value": "Share of the labor force (ages 18-65) in the top 60 percent that is a self-employed and not an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.Unemp.All", "metatype": [ { "id": "IndicatorName", "value": "Unemployed (%)" }, { "id": "Shortdefinition", "value": "Share of the labor force (ages 18-65) that is unemployed" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.Unemp.B40", "metatype": [ { "id": "IndicatorName", "value": "Unemployed-Bottom 40 Percent (%)" }, { "id": "Shortdefinition", "value": "Share of the labor force (ages 18-65) in the bottom 40 percent that is unemployed" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.Unemp.T60", "metatype": [ { "id": "IndicatorName", "value": "Unemployed-Top 60 Percent (%)" }, { "id": "Shortdefinition", "value": "Share of the labor force (ages 18-65) in the top 60 percent that is unemployed" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.Unpaid.All", "metatype": [ { "id": "IndicatorName", "value": "Unpaid Workers (%)" }, { "id": "Shortdefinition", "value": "Share of the labor force (ages 18-65) that works without pay" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.Unpaid.B40", "metatype": [ { "id": "IndicatorName", "value": "Unpaid Workers-Bottom 40 Percent (%)" }, { "id": "Shortdefinition", "value": "Share of the labor force (ages 18-65) in the bottom 40 percent that works without pay" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.0.Unpaid.T60", "metatype": [ { "id": "IndicatorName", "value": "Unpaid Workers-Top 60 Percent (%)" }, { "id": "Shortdefinition", "value": "Share of the labor force (ages 18-65) in the top 60 percent that works without pay" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.Employee.All", "metatype": [ { "id": "IndicatorName", "value": "Employees (%), Male" }, { "id": "Shortdefinition", "value": "Share of the male labor force (ages 18-65) that is a wage or salary worker" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.Employee.B40", "metatype": [ { "id": "IndicatorName", "value": "Employees-Bottom 40 Percent (%), Male" }, { "id": "Shortdefinition", "value": "Share of the male labor force (ages 18-65) in the bottom 40 percent that is a wage or salary worker" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.Employee.T60", "metatype": [ { "id": "IndicatorName", "value": "Employees-Top 60 Percent (%), Male" }, { "id": "Shortdefinition", "value": "Share of the male labor force (ages 18-65) in the top 60 percent that is a wage or salary worker" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.Employer.All", "metatype": [ { "id": "IndicatorName", "value": "Employers (%), Male" }, { "id": "Shortdefinition", "value": "Share of the male labor force (ages 18-65) that is an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.Employer.B40", "metatype": [ { "id": "IndicatorName", "value": "Employers-Bottom 40 Percent (%), Male" }, { "id": "Shortdefinition", "value": "Share of the male labor force (ages 18-65) in the bottom 40 percent that is an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.Employer.T60", "metatype": [ { "id": "IndicatorName", "value": "Employers-Top 60 Percent (%), Male" }, { "id": "Shortdefinition", "value": "Share of the male labor force (ages 18-65) in the top 60 percent that is an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.Labor.All", "metatype": [ { "id": "IndicatorName", "value": "Labor Force Participation Rate (%), Male" }, { "id": "Shortdefinition", "value": "Share of the male population (ages 18-65) that is in the labor force" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.Labor.B40", "metatype": [ { "id": "IndicatorName", "value": "Labor Force Participation Rate (%)-Bottom 40 Percent, Male" }, { "id": "Shortdefinition", "value": "Share of the male population (ages 18-65) in the Bottom 40 percent that is in the labor force" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.Labor.T60", "metatype": [ { "id": "IndicatorName", "value": "Labor Force Participation Rate (%)-Top 60 Percent, Male" }, { "id": "Shortdefinition", "value": "Share of the male population (ages 18-65) in the Top 60 percent that is in the labor force" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.SelfEmp.All", "metatype": [ { "id": "IndicatorName", "value": "Self-Employed (%), Male" }, { "id": "Shortdefinition", "value": "Share of the male labor force (ages 18-65) that is self-employed and not an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.SelfEmp.B40", "metatype": [ { "id": "IndicatorName", "value": "Self-Employed-Bottom 40 Percent (%), Male" }, { "id": "Shortdefinition", "value": "Share of the male labor force (ages 18-65) in the bottom 40 percent that is self-employed and not an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.SelfEmp.T60", "metatype": [ { "id": "IndicatorName", "value": "Self-Employed-Top 60 Percent (%), Male" }, { "id": "Shortdefinition", "value": "Share of the male labor force (ages 18-65) in the top 60 percent that is a self-employed and not an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.Unemp.All", "metatype": [ { "id": "IndicatorName", "value": "Unemployed (%), Male" }, { "id": "Shortdefinition", "value": "Share of the male labor force (ages 18-65) that is unemployed" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.Unemp.B40", "metatype": [ { "id": "IndicatorName", "value": "Unemployed-Bottom 40 Percent (%), Male" }, { "id": "Shortdefinition", "value": "Share of the male labor force (ages 18-65) in the bottom 40 percent that is unemployed" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.Unemp.T60", "metatype": [ { "id": "IndicatorName", "value": "Unemployed-Top 60 Percent (%), Male" }, { "id": "Shortdefinition", "value": "Share of the male labor force (ages 18-65) in the top 60 percent that is unemployed" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.Unpaid.All", "metatype": [ { "id": "IndicatorName", "value": "Unpaid Workers (%), Male" }, { "id": "Shortdefinition", "value": "Share of the male labor force (ages 18-65) that works without pay" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.Unpaid.B40", "metatype": [ { "id": "IndicatorName", "value": "Unpaid Workers-Bottom 40 Percent (%), Male" }, { "id": "Shortdefinition", "value": "Share of the male labor force (ages 18-65) in the bottom 40 percent that works without pay" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.1.Unpaid.T60", "metatype": [ { "id": "IndicatorName", "value": "Unpaid Workers-Top 60 Percent (%), Male" }, { "id": "Shortdefinition", "value": "Share of the male labor force (ages 18-65) in the top 60 percent that works without pay" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.Employee.All", "metatype": [ { "id": "IndicatorName", "value": "Employees (%), Female" }, { "id": "Shortdefinition", "value": "Share of the female labor force (ages 18-65) that is a wage or salary worker" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.Employee.B40", "metatype": [ { "id": "IndicatorName", "value": "Employees-Bottom 40 Percent (%), Female" }, { "id": "Shortdefinition", "value": "Share of the female labor force (ages 18-65) in the bottom 40 percent that is a wage or salary worker" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.Employee.T60", "metatype": [ { "id": "IndicatorName", "value": "Employees-Top 60 Percent (%), Female" }, { "id": "Shortdefinition", "value": "Share of the female labor force (ages 18-65) in the top 60 percent that is a wage or salary worker" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.Employer.All", "metatype": [ { "id": "IndicatorName", "value": "Employers (%), Female" }, { "id": "Shortdefinition", "value": "Share of the female labor force (ages 18-65) that is an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.Employer.B40", "metatype": [ { "id": "IndicatorName", "value": "Employers-Bottom 40 Percent (%), Female" }, { "id": "Shortdefinition", "value": "Share of the female labor force (ages 18-65) in the bottom 40 percent that is an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.Employer.T60", "metatype": [ { "id": "IndicatorName", "value": "Employers-Top 60 Percent (%), Female" }, { "id": "Shortdefinition", "value": "Share of the female labor force (ages 18-65) in the top 60 percent that is an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.Labor.All", "metatype": [ { "id": "IndicatorName", "value": "Labor Force Participation Rate (%), Female" }, { "id": "Shortdefinition", "value": "Share of the female population (ages 18-65) that is in the labor force" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.Labor.B40", "metatype": [ { "id": "IndicatorName", "value": "Labor Force Participation Rate (%)-Bottom 40 Percent, Female" }, { "id": "Shortdefinition", "value": "Share of the female population (ages 18-65) in the Bottom 40 percent that is in the labor force" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.Labor.T60", "metatype": [ { "id": "IndicatorName", "value": "Labor Force Participation Rate (%)-Top 60 Percent, Female" }, { "id": "Shortdefinition", "value": "Share of the female population (ages 18-65) in the Top 60 percent that is in the labor force" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.SelfEmp.All", "metatype": [ { "id": "IndicatorName", "value": "Self-Employed (%), Female" }, { "id": "Shortdefinition", "value": "Share of the female labor force (ages 18-65) that is self-employed and not an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.SelfEmp.B40", "metatype": [ { "id": "IndicatorName", "value": "Self-Employed-Bottom 40 Percent (%), Female" }, { "id": "Shortdefinition", "value": "Share of the female labor force (ages 18-65) in the bottom 40 percent that is self-employed and not an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.SelfEmp.T60", "metatype": [ { "id": "IndicatorName", "value": "Self-Employed-Top 60 Percent (%), Female" }, { "id": "Shortdefinition", "value": "Share of the female labor force (ages 18-65) in the top 60 percent that is a self-employed and not an employer" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.Unemp.All", "metatype": [ { "id": "IndicatorName", "value": "Unemployed (%), Female" }, { "id": "Shortdefinition", "value": "Share of the female labor force (ages 18-65) that is unemployed" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.Unemp.B40", "metatype": [ { "id": "IndicatorName", "value": "Unemployed-Bottom 40 Percent (%), Female" }, { "id": "Shortdefinition", "value": "Share of the female labor force (ages 18-65) in the bottom 40 percent that is unemployed" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.Unemp.T60", "metatype": [ { "id": "IndicatorName", "value": "Unemployed-Top 60 Percent (%), Female" }, { "id": "Shortdefinition", "value": "Share of the female labor force (ages 18-65) in the top 60 percent that is unemployed" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.Unpaid.All", "metatype": [ { "id": "IndicatorName", "value": "Unpaid Workers (%), Female" }, { "id": "Shortdefinition", "value": "Share of the female labor force (ages 18-65) that works without pay" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.Unpaid.B40", "metatype": [ { "id": "IndicatorName", "value": "Unpaid Workers-Bottom 40 Percent (%), Female" }, { "id": "Shortdefinition", "value": "Share of the female labor force (ages 18-65) in the bottom 40 percent that works without pay" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "9.2.Unpaid.T60", "metatype": [ { "id": "IndicatorName", "value": "Unpaid Workers-Top 60 Percent (%), Female" }, { "id": "Shortdefinition", "value": "Share of the female labor force (ages 18-65) in the top 60 percent that works without pay" }, { "id": "Source", "value": "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." }, { "id": "Topic", "value": "Markets" } ], "source_id": "37" }, { "id": "SH.ACS.ALON.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (not wanting to go alone) (% of women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.ALON.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (not wanting to go alone) (% of women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.ALON.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (not wanting to go alone) (% of women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.ALON.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (not wanting to go alone) (% of women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.ALON.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (not wanting to go alone) (% of women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.DIST.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (distance to health facility) (% of women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.DIST.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (distance to health facility) (% of women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.DIST.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (distance to health facility) (% of women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.DIST.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (distance to health facility) (% of women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.DIST.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (distance to health facility) (% of women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.MONY.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (getting money for treatment) (% of women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.MONY.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (getting money for treatment) (% of women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.MONY.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (getting money for treatment) (% of women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.MONY.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (getting money for treatment) (% of women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.MONY.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (getting money for treatment) (% of women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.NOFP.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (concern there may not be a female provider) (% of women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.NOFP.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (concern there may not be a female provider) (% of women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.NOFP.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (concern there may not be a female provider) (% of women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.NOFP.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (concern there may not be a female provider) (% of women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.NOFP.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (concern there may not be a female provider) (% of women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.PERM.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (getting permission to go for treatment) (% of women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.PERM.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (getting permission to go for treatment) (% of women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.PERM.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (getting permission to go for treatment) (% of women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.PERM.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (getting permission to go for treatment) (% of women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.PERM.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (getting permission to go for treatment) (% of women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.PROB.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Problems in accessing health care (any of the specified problems) (% of women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.PROB.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (any of the specified problems) (% of women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.PROB.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (any of the specified problems) (% of women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.PROB.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (any of the specified problems) (% of women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.PROB.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Problems in accessing health care (any of the specified problems) (% of women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.TRAN.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (having to take transport) (% of women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.TRAN.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (having to take transport) (% of women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.TRAN.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (having to take transport) (% of women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.TRAN.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (having to take transport) (% of women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.TRAN.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (having to take transport) (% of women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.WHER.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (knowing where to go for treatment) (% of women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.WHER.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (knowing where to go for treatment) (% of women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.WHER.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (knowing where to go for treatment) (% of women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.WHER.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (knowing where to go for treatment) (% of women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.ACS.WHER.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Problems in accessing health care (knowing where to go for treatment) (% of women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.DYN.MORT.Q1", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Under-5 mortality rate (per 1,000 live births): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Under-5 mortality rate: Number of deaths to children under age five years per 1000 live births, based on experience during the reference period before the survey. The reference period is ten years preceding the survey for DHS surveys, and the reference period varies for MICS surveys (often three to five years preceding the survey)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.DYN.MORT.Q2", "metatype": [ { "id": "IndicatorName", "value": "Under-5 mortality rate (per 1,000 live births): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Under-5 mortality rate: Number of deaths to children under age five years per 1000 live births, based on experience during the reference period before the survey. The reference period is ten years preceding the survey for DHS surveys, and the reference period varies for MICS surveys (often three to five years preceding the survey)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.DYN.MORT.Q3", "metatype": [ { "id": "IndicatorName", "value": "Under-5 mortality rate (per 1,000 live births): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Under-5 mortality rate: Number of deaths to children under age five years per 1000 live births, based on experience during the reference period before the survey. The reference period is ten years preceding the survey for DHS surveys, and the reference period varies for MICS surveys (often three to five years preceding the survey)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.DYN.MORT.Q4", "metatype": [ { "id": "IndicatorName", "value": "Under-5 mortality rate (per 1,000 live births): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Under-5 mortality rate: Number of deaths to children under age five years per 1000 live births, based on experience during the reference period before the survey. The reference period is ten years preceding the survey for DHS surveys, and the reference period varies for MICS surveys (often three to five years preceding the survey)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.DYN.MORT.Q5", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Under-5 mortality rate (per 1,000 live births): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Under-5 mortality rate: Number of deaths to children under age five years per 1000 live births, based on experience during the reference period before the survey. The reference period is ten years preceding the survey for DHS surveys, and the reference period varies for MICS surveys (often three to five years preceding the survey)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.ACPT.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Acceptability of media messages on family planning (% of women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Acceptability of media messages on family planning: Percentage of all women who believe that it is acceptable to have messages about family planning on the radio or television." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Acceptability of media messages on family planning: Percentage of all women who believe that it is acceptable to have messages about family planning on the radio or television." }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.ACPT.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Acceptability of media messages on family planning (% of women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Acceptability of media messages on family planning: Percentage of all women who believe that it is acceptable to have messages about family planning on the radio or television." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.ACPT.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Acceptability of media messages on family planning (% of women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Acceptability of media messages on family planning: Percentage of all women who believe that it is acceptable to have messages about family planning on the radio or television." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.ACPT.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Acceptability of media messages on family planning (% of women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Acceptability of media messages on family planning: Percentage of all women who believe that it is acceptable to have messages about family planning on the radio or television." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.ACPT.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Acceptability of media messages on family planning (% of women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Acceptability of media messages on family planning: Percentage of all women who believe that it is acceptable to have messages about family planning on the radio or television." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.FBRT.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Median age at first birth (women ages 25-49): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median age at first birth: Median age at first birth among women aged 25-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.FBRT.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Median age at first birth (women ages 25-49): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median age at first birth: Median age at first birth among women aged 25-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.FBRT.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Median age at first birth (women ages 25-49): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median age at first birth: Median age at first birth among women aged 25-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.FBRT.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Median age at first birth (women ages 25-49): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median age at first birth: Median age at first birth among women aged 25-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.FBRT.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Median age at first birth (women ages 25-49): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median age at first birth: Median age at first birth among women aged 25-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.FMAR.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Median age at first marriage (women ages 25-49): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median age at first marriage: Median age at first marriage among women aged 25-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.FMAR.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Median age at first marriage (women ages 25-49): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median age at first marriage: Median age at first marriage among women aged 25-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.FMAR.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Median age at first marriage (women ages 25-49): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median age at first marriage: Median age at first marriage among women aged 25-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.FMAR.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Median age at first marriage (women ages 25-49): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median age at first marriage: Median age at first marriage among women aged 25-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.FMAR.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Median age at first marriage (women ages 25-49): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median age at first marriage: Median age at first marriage among women aged 25-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.FSEX.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Median age at first sexual intercourse (women ages 25-49): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median age at first sexual intercourse: Median age at first sexual intercourse among women aged 25-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.FSEX.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Median age at first sexual intercourse (women ages 25-49): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median age at first sexual intercourse: Median age at first sexual intercourse among women aged 25-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.FSEX.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Median age at first sexual intercourse (women ages 25-49): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median age at first sexual intercourse: Median age at first sexual intercourse among women aged 25-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.FSEX.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Median age at first sexual intercourse (women ages 25-49): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median age at first sexual intercourse: Median age at first sexual intercourse among women aged 25-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.FSEX.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Median age at first sexual intercourse (women ages 25-49): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median age at first sexual intercourse: Median age at first sexual intercourse among women aged 25-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.HEAR.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Heard family planning on radio and television (% of women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Heard family planning on radio and television: Percentage of all women who have heard a radio or television message about family planning in the last few months prior to the interview." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.HEAR.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Heard family planning on radio and television (% of women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Heard family planning on radio and television: Percentage of all women who have heard a radio or television message about family planning in the last few months prior to the interview." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.HEAR.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Heard family planning on radio and television (% of women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Heard family planning on radio and television: Percentage of all women who have heard a radio or television message about family planning in the last few months prior to the interview." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.HEAR.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Heard family planning on radio and television (% of women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Heard family planning on radio and television: Percentage of all women who have heard a radio or television message about family planning in the last few months prior to the interview." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.HEAR.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Heard family planning on radio and television (% of women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Heard family planning on radio and television: Percentage of all women who have heard a radio or television message about family planning in the last few months prior to the interview." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.IDLC.Q1", "metatype": [ { "id": "IndicatorName", "value": "Mean ideal number of children (per woman): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mean ideal number of children: Mean ideal number of children for all women." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.IDLC.Q2", "metatype": [ { "id": "IndicatorName", "value": "Mean ideal number of children (per woman): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mean ideal number of children: Mean ideal number of children for all women." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.IDLC.Q3", "metatype": [ { "id": "IndicatorName", "value": "Mean ideal number of children (per woman): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mean ideal number of children: Mean ideal number of children for all women." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.IDLC.Q4", "metatype": [ { "id": "IndicatorName", "value": "Mean ideal number of children (per woman): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mean ideal number of children: Mean ideal number of children for all women." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.IDLC.Q5", "metatype": [ { "id": "IndicatorName", "value": "Mean ideal number of children (per woman): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mean ideal number of children: Mean ideal number of children for all women." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.KNOW.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Knowledge of contraception (any method) (% of married women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Knowledge of contraception: Percentage of currently married women who know at least one contraceptive method and at least one modern contraceptive method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.KNOW.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Knowledge of contraception (any method) (% of married women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Knowledge of contraception: Percentage of currently married women who know at least one contraceptive method and at least one modern contraceptive method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.KNOW.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Knowledge of contraception (any method) (% of married women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Knowledge of contraception: Percentage of currently married women who know at least one contraceptive method and at least one modern contraceptive method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.KNOW.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Knowledge of contraception (any method) (% of married women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Knowledge of contraception: Percentage of currently married women who know at least one contraceptive method and at least one modern contraceptive method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.KNOW.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Knowledge of contraception (any method) (% of married women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Knowledge of contraception: Percentage of currently married women who know at least one contraceptive method and at least one modern contraceptive method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.KWMD.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Knowledge of contraception (modern method) (% of married women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Knowledge of contraception: Percentage of currently married women who know at least one contraceptive method and at least one modern contraceptive method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.KWMD.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Knowledge of contraception (modern method) (% of married women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Knowledge of contraception: Percentage of currently married women who know at least one contraceptive method and at least one modern contraceptive method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.KWMD.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Knowledge of contraception (modern method) (% of married women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Knowledge of contraception: Percentage of currently married women who know at least one contraceptive method and at least one modern contraceptive method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.KWMD.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Knowledge of contraception (modern method) (% of married women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Knowledge of contraception: Percentage of currently married women who know at least one contraceptive method and at least one modern contraceptive method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.KWMD.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Knowledge of contraception (modern method) (% of married women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Knowledge of contraception: Percentage of currently married women who know at least one contraceptive method and at least one modern contraceptive method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.LIMT.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Desire to stop (limit) childbearing (% of married women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Desire to stop (limit) childbearing: Percentage of currently married women who want no more children. Women who have been sterilized or whose spouses are sterilized, are considered to want no more children." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.LIMT.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Desire to stop (limit) childbearing (% of married women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Desire to stop (limit) childbearing: Percentage of currently married women who want no more children. Women who have been sterilized or whose spouses are sterilized, are considered to want no more children." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.LIMT.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Desire to stop (limit) childbearing (% of married women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Desire to stop (limit) childbearing: Percentage of currently married women who want no more children. Women who have been sterilized or whose spouses are sterilized, are considered to want no more children." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.LIMT.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Desire to stop (limit) childbearing (% of married women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Desire to stop (limit) childbearing: Percentage of currently married women who want no more children. Women who have been sterilized or whose spouses are sterilized, are considered to want no more children." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.LIMT.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Desire to stop (limit) childbearing (% of married women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Desire to stop (limit) childbearing: Percentage of currently married women who want no more children. Women who have been sterilized or whose spouses are sterilized, are considered to want no more children." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.MBRI.Q1", "metatype": [ { "id": "IndicatorName", "value": "Median birth interval (months): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median birth interval: Median duration of the birth interval in months for non-first births in the five years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.MBRI.Q2", "metatype": [ { "id": "IndicatorName", "value": "Median birth interval (months): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median birth interval: Median duration of the birth interval in months for non-first births in the five years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.MBRI.Q3", "metatype": [ { "id": "IndicatorName", "value": "Median birth interval (months): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median birth interval: Median duration of the birth interval in months for non-first births in the five years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.MBRI.Q4", "metatype": [ { "id": "IndicatorName", "value": "Median birth interval (months): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median birth interval: Median duration of the birth interval in months for non-first births in the five years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.MBRI.Q5", "metatype": [ { "id": "IndicatorName", "value": "Median birth interval (months): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Median birth interval: Median duration of the birth interval in months for non-first births in the five years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.MSTM.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Fertility planning status (wanted later) (% of births): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Fertility planning status: Percentage of births in the five years preceding the survey which are planned (wanted then), mistimed (wanted later), and unplanned (wanted no more)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.MSTM.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Fertility planning status (wanted later) (% of births): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Fertility planning status: Percentage of births in the five years preceding the survey which are planned (wanted then), mistimed (wanted later), and unplanned (wanted no more)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.MSTM.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Fertility planning status (wanted later) (% of births): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Fertility planning status: Percentage of births in the five years preceding the survey which are planned (wanted then), mistimed (wanted later), and unplanned (wanted no more)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.MSTM.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Fertility planning status (wanted later) (% of births): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Fertility planning status: Percentage of births in the five years preceding the survey which are planned (wanted then), mistimed (wanted later), and unplanned (wanted no more)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.MSTM.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Fertility planning status (wanted later) (% of births): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Fertility planning status: Percentage of births in the five years preceding the survey which are planned (wanted then), mistimed (wanted later), and unplanned (wanted no more)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.READ.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Family planning messages in print (% of women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Family planning messages in print: Percentage of all women who have received a message about family planning from printed media in the last few months prior to the interview." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.READ.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Family planning messages in print (% of women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Family planning messages in print: Percentage of all women who have received a message about family planning from printed media in the last few months prior to the interview." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.READ.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Family planning messages in print (% of women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Family planning messages in print: Percentage of all women who have received a message about family planning from printed media in the last few months prior to the interview." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.READ.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Family planning messages in print (% of women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Family planning messages in print: Percentage of all women who have received a message about family planning from printed media in the last few months prior to the interview." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.READ.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Family planning messages in print (% of women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Family planning messages in print: Percentage of all women who have received a message about family planning from printed media in the last few months prior to the interview." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.UWTD.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Fertility planning status (wanted no more) (% of births): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Fertility planning status: Percentage of births in the five years preceding the survey which are planned (wanted then), mistimed (wanted later), and unplanned (wanted no more)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.UWTD.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Fertility planning status (wanted no more) (% of births): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Fertility planning status: Percentage of births in the five years preceding the survey which are planned (wanted then), mistimed (wanted later), and unplanned (wanted no more)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.UWTD.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Fertility planning status (wanted no more) (% of births): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Fertility planning status: Percentage of births in the five years preceding the survey which are planned (wanted then), mistimed (wanted later), and unplanned (wanted no more)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.UWTD.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Fertility planning status (wanted no more) (% of births): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Fertility planning status: Percentage of births in the five years preceding the survey which are planned (wanted then), mistimed (wanted later), and unplanned (wanted no more)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.UWTD.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Fertility planning status (wanted no more) (% of births): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Fertility planning status: Percentage of births in the five years preceding the survey which are planned (wanted then), mistimed (wanted later), and unplanned (wanted no more)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.WNTD.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Fertility planning status (wanted then) (% of births): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Fertility planning status: Percentage of births in the five years preceding the survey which are planned (wanted then), mistimed (wanted later), and unplanned (wanted no more)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.WNTD.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Fertility planning status (wanted then) (% of births): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Fertility planning status: Percentage of births in the five years preceding the survey which are planned (wanted then), mistimed (wanted later), and unplanned (wanted no more)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.WNTD.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Fertility planning status (wanted then) (% of births): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Fertility planning status: Percentage of births in the five years preceding the survey which are planned (wanted then), mistimed (wanted later), and unplanned (wanted no more)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.WNTD.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Fertility planning status (wanted then) (% of births): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Fertility planning status: Percentage of births in the five years preceding the survey which are planned (wanted then), mistimed (wanted later), and unplanned (wanted no more)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.FPL.WNTD.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Fertility planning status (wanted then) (% of births): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Fertility planning status: Percentage of births in the five years preceding the survey which are planned (wanted then), mistimed (wanted later), and unplanned (wanted no more)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.H2O.BASW.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic drinking water services (% of population): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.H2O.BASW.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic drinking water services (% of population): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.H2O.BASW.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic drinking water services (% of population): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.H2O.BASW.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic drinking water services (% of population): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.H2O.BASW.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic drinking water services (% of population): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.H2O.BASW.RU.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic drinking water services, rural (% of rural population): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.H2O.BASW.RU.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic drinking water services, rural (% of rural population): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.H2O.BASW.RU.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic drinking water services, rural (% of rural population): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.H2O.BASW.RU.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic drinking water services, rural (% of rural population): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.H2O.BASW.RU.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic drinking water services, rural (% of rural population): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.H2O.BASW.UR.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic drinking water services, urban (% of urban population): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.H2O.BASW.UR.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic drinking water services, urban (% of urban population): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.H2O.BASW.UR.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic drinking water services, urban (% of urban population): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.H2O.BASW.UR.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic drinking water services, urban (% of urban population): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.H2O.BASW.UR.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic drinking water services, urban (% of urban population): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.ALLV.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Vaccinations (all vaccinations) (% of children ages 12-23 months): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.ALLV.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (all vaccinations) (% of children ages 12-23 months): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.ALLV.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (all vaccinations) (% of children ages 12-23 months): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.ALLV.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (all vaccinations) (% of children ages 12-23 months): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.ALLV.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Vaccinations (all vaccinations) (% of children ages 12-23 months): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.IBCG.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (BCG) (% of children ages 12-23 months): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.IBCG.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (BCG) (% of children ages 12-23 months): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.IBCG.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (BCG) (% of children ages 12-23 months): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.IBCG.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (BCG) (% of children ages 12-23 months): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.IBCG.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (BCG) (% of children ages 12-23 months): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.IDPT.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (DPT 3) (% of children ages 12-23 months): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.IDPT.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (DPT 3) (% of children ages 12-23 months): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.IDPT.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (DPT 3) (% of children ages 12-23 months): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.IDPT.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (DPT 3) (% of children ages 12-23 months): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.IDPT.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (DPT 3) (% of children ages 12-23 months): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.MEAS.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (Measles) (% of children ages 12-23 months): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.MEAS.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (Measles) (% of children ages 12-23 months): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.MEAS.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (Measles) (% of children ages 12-23 months): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.MEAS.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (Measles) (% of children ages 12-23 months): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.MEAS.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (Measles) (% of children ages 12-23 months): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.NONE.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (no vaccinations) (% of children ages 12-23 months): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.NONE.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (no vaccinations) (% of children ages 12-23 months): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.NONE.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (no vaccinations) (% of children ages 12-23 months): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.NONE.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (no vaccinations) (% of children ages 12-23 months): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.IMM.NONE.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vaccinations (no vaccinations) (% of children ages 12-23 months): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETA.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by children (any mosquito net) (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by children: Percentage of children under age five years who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETA.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by children (any mosquito net) (% of children under 5): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by children: Percentage of children under age five years who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETA.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by children (any mosquito net) (% of children under 5): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by children: Percentage of children under age five years who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETA.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by children (any mosquito net) (% of children under 5): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by children: Percentage of children under age five years who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETA.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by children (any mosquito net) (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by children: Percentage of children under age five years who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETH.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Household possession of mosquito nets (any type of mosquito net) (% of households): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Household possession of mosquito nets: Percentage of households with at least one any type of mosquito net (treated or untreated), and insecticide-treated net (ITN)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETH.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Household possession of mosquito nets (any type of mosquito net) (% of households): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Household possession of mosquito nets: Percentage of households with at least one any type of mosquito net (treated or untreated), and insecticide-treated net (ITN)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETH.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Household possession of mosquito nets (any type of mosquito net) (% of households): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Household possession of mosquito nets: Percentage of households with at least one any type of mosquito net (treated or untreated), and insecticide-treated net (ITN)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETH.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Household possession of mosquito nets (any type of mosquito net) (% of households): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Household possession of mosquito nets: Percentage of households with at least one any type of mosquito net (treated or untreated), and insecticide-treated net (ITN)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETH.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Household possession of mosquito nets (any type of mosquito net) (% of households): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Household possession of mosquito nets: Percentage of households with at least one any type of mosquito net (treated or untreated), and insecticide-treated net (ITN)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETP.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by pregnant women (any mosquito net) (% of pregnant women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by pregnant women: Percentage of pregnant women who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETP.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by pregnant women (any mosquito net) (% of pregnant women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by pregnant women: Percentage of pregnant women who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETP.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by pregnant women (any mosquito net) (% of pregnant women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by pregnant women: Percentage of pregnant women who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETP.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by pregnant women (any mosquito net) (% of pregnant women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by pregnant women: Percentage of pregnant women who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETP.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by pregnant women (any mosquito net) (% of pregnant women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by pregnant women: Percentage of pregnant women who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETS.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by children (insecticide-treated net) (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by children: Percentage of children under age five years who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETS.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by children (insecticide-treated net) (% of children under 5): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by children: Percentage of children under age five years who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETS.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by children (insecticide-treated net) (% of children under 5): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by children: Percentage of children under age five years who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETS.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by children (insecticide-treated net) (% of children under 5): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by children: Percentage of children under age five years who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NETS.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by children (insecticide-treated net) (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by children: Percentage of children under age five years who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NTHI.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Household possession of mosquito nets (insecticide-treated net) (% of households): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Household possession of mosquito nets: Percentage of households with at least one any type of mosquito net (treated or untreated), and insecticide-treated net (ITN)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NTHI.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Household possession of mosquito nets (insecticide-treated net) (% of households): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Household possession of mosquito nets: Percentage of households with at least one any type of mosquito net (treated or untreated), and insecticide-treated net (ITN)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NTHI.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Household possession of mosquito nets (insecticide-treated net) (% of households): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Household possession of mosquito nets: Percentage of households with at least one any type of mosquito net (treated or untreated), and insecticide-treated net (ITN)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NTHI.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Household possession of mosquito nets (insecticide-treated net) (% of households): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Household possession of mosquito nets: Percentage of households with at least one any type of mosquito net (treated or untreated), and insecticide-treated net (ITN)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NTHI.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Household possession of mosquito nets (insecticide-treated net) (% of households): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Household possession of mosquito nets: Percentage of households with at least one any type of mosquito net (treated or untreated), and insecticide-treated net (ITN)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NTPI.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by pregnant women (insecticide-treated net) (% of pregnant women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by pregnant women: Percentage of pregnant women who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NTPI.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by pregnant women (insecticide-treated net) (% of pregnant women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by pregnant women: Percentage of pregnant women who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NTPI.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by pregnant women (insecticide-treated net) (% of pregnant women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by pregnant women: Percentage of pregnant women who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NTPI.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by pregnant women (insecticide-treated net) (% of pregnant women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by pregnant women: Percentage of pregnant women who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.NTPI.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mosquito net use by pregnant women (insecticide-treated net) (% of pregnant women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mosquito net use by pregnant women: Percentage of pregnant women who slept under any mosquito net (treated or untreated), and an insecticide-treated net (ITN) the night before the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.SPFN.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Antimalarial drug use by pregnant women (SP/Fansidar two or more doses) (% of women with a birth): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Anti-malarial drug use by pregnant women: Percentage of women with a live birth in the two years preceding the survey who during the pregnancy took any antimalarial drug for prevention, and who took SP/Fansidar two or more doses." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.SPFN.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Antimalarial drug use by pregnant women (SP/Fansidar two or more doses) (% of women with a birth): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Anti-malarial drug use by pregnant women: Percentage of women with a live birth in the two years preceding the survey who during the pregnancy took any antimalarial drug for prevention, and who took SP/Fansidar two or more doses." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.SPFN.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Antimalarial drug use by pregnant women (SP/Fansidar two or more doses) (% of women with a birth): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Anti-malarial drug use by pregnant women: Percentage of women with a live birth in the two years preceding the survey who during the pregnancy took any antimalarial drug for prevention, and who took SP/Fansidar two or more doses." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.SPFN.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Antimalarial drug use by pregnant women (SP/Fansidar two or more doses) (% of women with a birth): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Anti-malarial drug use by pregnant women: Percentage of women with a live birth in the two years preceding the survey who during the pregnancy took any antimalarial drug for prevention, and who took SP/Fansidar two or more doses." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.SPFN.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Antimalarial drug use by pregnant women (SP/Fansidar two or more doses) (% of women with a birth): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Anti-malarial drug use by pregnant women: Percentage of women with a live birth in the two years preceding the survey who during the pregnancy took any antimalarial drug for prevention, and who took SP/Fansidar two or more doses." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.TRET.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Treatment of fever (% of children under 5 with fever): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of fever: Percentage of children under age five years with fever in the two weeks preceding the survey who took antimalarial drugs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.TRET.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Treatment of fever (% of children under 5 with fever): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of fever: Percentage of children under age five years with fever in the two weeks preceding the survey who took antimalarial drugs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.TRET.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Treatment of fever (% of children under 5 with fever): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of fever: Percentage of children under age five years with fever in the two weeks preceding the survey who took antimalarial drugs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.TRET.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Treatment of fever (% of children under 5 with fever): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of fever: Percentage of children under age five years with fever in the two weeks preceding the survey who took antimalarial drugs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.MLR.TRET.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Treatment of fever (% of children under 5 with fever): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of fever: Percentage of children under age five years with fever in the two weeks preceding the survey who took antimalarial drugs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.PRV.SMOK.FE.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Smoking (% of women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Smoking: Percentage of all women who smoke cigarettes, pipe or other tobacco." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.PRV.SMOK.FE.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Smoking (% of women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Smoking: Percentage of all women who smoke cigarettes, pipe or other tobacco." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.PRV.SMOK.FE.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Smoking (% of women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Smoking: Percentage of all women who smoke cigarettes, pipe or other tobacco." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.PRV.SMOK.FE.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Smoking (% of women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Smoking: Percentage of all women who smoke cigarettes, pipe or other tobacco." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.PRV.SMOK.FE.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Smoking (% of women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Smoking: Percentage of all women who smoke cigarettes, pipe or other tobacco." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ANCP.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Components of antenatal care (received iron tablets or syrup) (% of women with a birth): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Components of antenatal care: Percentage of women with a live birth in the three years preceding the survey who received iron tablets or syrup during pregnancy before the most recent birth." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ANCP.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Components of antenatal care (received iron tablets or syrup) (% of women with a birth): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Components of antenatal care: Percentage of women with a live birth in the three years preceding the survey who received iron tablets or syrup during pregnancy before the most recent birth." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ANCP.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Components of antenatal care (received iron tablets or syrup) (% of women with a birth): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Components of antenatal care: Percentage of women with a live birth in the three years preceding the survey who received iron tablets or syrup during pregnancy before the most recent birth." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ANCP.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Components of antenatal care (received iron tablets or syrup) (% of women with a birth): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Components of antenatal care: Percentage of women with a live birth in the three years preceding the survey who received iron tablets or syrup during pregnancy before the most recent birth." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ANCP.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Components of antenatal care (received iron tablets or syrup) (% of women with a birth): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Components of antenatal care: Percentage of women with a live birth in the three years preceding the survey who received iron tablets or syrup during pregnancy before the most recent birth." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ANVC.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Antenatal care (any skilled personnel) (% of women with a birth): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Antenatal care: Percentage of women with one or more live births in the three (one, two) years preceding the survey who have received at least one antenatal care during pregnancy before the most recent birth from any skilled personnel and from a doctor. If the respondent mentioned more than one provider, only the most qualified provider is considered. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ANVC.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Antenatal care (any skilled personnel) (% of women with a birth): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Antenatal care: Percentage of women with one or more live births in the three (one, two) years preceding the survey who have received at least one antenatal care during pregnancy before the most recent birth from any skilled personnel and from a doctor. If the respondent mentioned more than one provider, only the most qualified provider is considered. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ANVC.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Antenatal care (any skilled personnel) (% of women with a birth): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Antenatal care: Percentage of women with one or more live births in the three (one, two) years preceding the survey who have received at least one antenatal care during pregnancy before the most recent birth from any skilled personnel and from a doctor. If the respondent mentioned more than one provider, only the most qualified provider is considered. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ANVC.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Antenatal care (any skilled personnel) (% of women with a birth): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Antenatal care: Percentage of women with one or more live births in the three (one, two) years preceding the survey who have received at least one antenatal care during pregnancy before the most recent birth from any skilled personnel and from a doctor. If the respondent mentioned more than one provider, only the most qualified provider is considered. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ANVC.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Antenatal care (any skilled personnel) (% of women with a birth): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Antenatal care: Percentage of women with one or more live births in the three (one, two) years preceding the survey who have received at least one antenatal care during pregnancy before the most recent birth from any skilled personnel and from a doctor. If the respondent mentioned more than one provider, only the most qualified provider is considered. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ANVP.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Antenatal care (doctor) (% of women with a birth): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Antenatal care: Percentage of women with one or more live births in the three (one, two) years preceding the survey who have received at least one antenatal care during pregnancy before the most recent birth from any skilled personnel and from a doctor. If the respondent mentioned more than one provider, only the most qualified provider is considered. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ANVP.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Antenatal care (doctor) (% of women with a birth): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Antenatal care: Percentage of women with one or more live births in the three (one, two) years preceding the survey who have received at least one antenatal care during pregnancy before the most recent birth from any skilled personnel and from a doctor. If the respondent mentioned more than one provider, only the most qualified provider is considered. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ANVP.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Antenatal care (doctor) (% of women with a birth): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Antenatal care: Percentage of women with one or more live births in the three (one, two) years preceding the survey who have received at least one antenatal care during pregnancy before the most recent birth from any skilled personnel and from a doctor. If the respondent mentioned more than one provider, only the most qualified provider is considered. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ANVP.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Antenatal care (doctor) (% of women with a birth): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Antenatal care: Percentage of women with one or more live births in the three (one, two) years preceding the survey who have received at least one antenatal care during pregnancy before the most recent birth from any skilled personnel and from a doctor. If the respondent mentioned more than one provider, only the most qualified provider is considered. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ANVP.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Antenatal care (doctor) (% of women with a birth): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Antenatal care: Percentage of women with one or more live births in the three (one, two) years preceding the survey who have received at least one antenatal care during pregnancy before the most recent birth from any skilled personnel and from a doctor. If the respondent mentioned more than one provider, only the most qualified provider is considered. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ARIC.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Treatment of acute respiratory infection (ARI) (% of children under 5 taken to a health provider): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of acute respiratory infection (ARI): Percentage of children under age five years with acute respiratory infection (ARI) in the two weeks preceding the survey who were taken to a health facility." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ARIC.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Treatment of acute respiratory infection (ARI) (% of children under 5 taken to a health provider): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of acute respiratory infection (ARI): Percentage of children under age five years with acute respiratory infection (ARI) in the two weeks preceding the survey who were taken to a health facility." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ARIC.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Treatment of acute respiratory infection (ARI) (% of children under 5 taken to a health provider): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of acute respiratory infection (ARI): Percentage of children under age five years with acute respiratory infection (ARI) in the two weeks preceding the survey who were taken to a health facility." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ARIC.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Treatment of acute respiratory infection (ARI) (% of children under 5 taken to a health provider): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of acute respiratory infection (ARI): Percentage of children under age five years with acute respiratory infection (ARI) in the two weeks preceding the survey who were taken to a health facility." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ARIC.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Treatment of acute respiratory infection (ARI) (% of children under 5 taken to a health provider): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of acute respiratory infection (ARI): Percentage of children under age five years with acute respiratory infection (ARI) in the two weeks preceding the survey who were taken to a health facility." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ARIF.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Prevalence of acute respiratory infection (ARI) (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Prevalence of acute respiratory infection (ARI): Percentage of children under age five years who were ill with a cough accompanied with rapid breathing in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ARIF.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of acute respiratory infection (ARI) (% of children under 5): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Prevalence of acute respiratory infection (ARI): Percentage of children under age five years who were ill with a cough accompanied with rapid breathing in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ARIF.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of acute respiratory infection (ARI) (% of children under 5): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Prevalence of acute respiratory infection (ARI): Percentage of children under age five years who were ill with a cough accompanied with rapid breathing in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ARIF.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of acute respiratory infection (ARI) (% of children under 5): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Prevalence of acute respiratory infection (ARI): Percentage of children under age five years who were ill with a cough accompanied with rapid breathing in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ARIF.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Prevalence of acute respiratory infection (ARI) (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Prevalence of acute respiratory infection (ARI): Percentage of children under age five years who were ill with a cough accompanied with rapid breathing in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BASS.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic sanitation services (% of population): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BASS.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic sanitation services (% of population): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BASS.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic sanitation services (% of population): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BASS.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic sanitation services (% of population): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BASS.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic sanitation services (% of population): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BASS.RU.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic sanitation services, rural (% of rural population): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BASS.RU.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic sanitation services, rural (% of rural population): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BASS.RU.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic sanitation services, rural (% of rural population): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BASS.RU.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic sanitation services, rural (% of rural population): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BASS.RU.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic sanitation services, rural (% of rural population): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BASS.UR.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic sanitation services, urban (% of urban population): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BASS.UR.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic sanitation services, urban (% of urban population): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BASS.UR.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic sanitation services, urban (% of urban population): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BASS.UR.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic sanitation services, urban (% of urban population): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BASS.UR.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "People using at least basic sanitation services, urban (% of urban population): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BFED.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Breastfeeding (% of children under 6 months): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Breastfeeding: The percentage of children under age 6 months who were breastfed six or more times in the 24 hours preceding the interview." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BFED.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Breastfeeding (% of children under 6 months): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Breastfeeding: The percentage of children under age 6 months who were breastfed six or more times in the 24 hours preceding the interview." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BFED.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Breastfeeding (% of children under 6 months): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Breastfeeding: The percentage of children under age 6 months who were breastfed six or more times in the 24 hours preceding the interview." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BFED.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Breastfeeding (% of children under 6 months): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Breastfeeding: The percentage of children under age 6 months who were breastfed six or more times in the 24 hours preceding the interview." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BFED.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Breastfeeding (% of children under 6 months): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Breastfeeding: The percentage of children under age 6 months who were breastfed six or more times in the 24 hours preceding the interview." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BRTC.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Assistance during delivery (any skilled personnel) (% of births): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Assistance during delivery (Assisted births): Percentage of live births in the three (one, two) years preceding the survey attended by any skilled personnel and by a doctor. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BRTC.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Assistance during delivery (any skilled personnel) (% of births): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Assistance during delivery (Assisted births): Percentage of live births in the three (one, two) years preceding the survey attended by any skilled personnel and by a doctor. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BRTC.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Assistance during delivery (any skilled personnel) (% of births): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Assistance during delivery (Assisted births): Percentage of live births in the three (one, two) years preceding the survey attended by any skilled personnel and by a doctor. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BRTC.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Assistance during delivery (any skilled personnel) (% of births): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Assistance during delivery (Assisted births): Percentage of live births in the three (one, two) years preceding the survey attended by any skilled personnel and by a doctor. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BRTC.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Assistance during delivery (any skilled personnel) (% of births): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Assistance during delivery (Assisted births): Percentage of live births in the three (one, two) years preceding the survey attended by any skilled personnel and by a doctor. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BRTF.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Place of delivery (births at health facility) (% of births): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Place of delivery (Births at health facility): Percentage of live births in the three years preceding the survey which took place at health facility." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BRTF.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Place of delivery (births at health facility) (% of births): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Place of delivery (Births at health facility): Percentage of live births in the three years preceding the survey which took place at health facility." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BRTF.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Place of delivery (births at health facility) (% of births): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Place of delivery (Births at health facility): Percentage of live births in the three years preceding the survey which took place at health facility." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BRTF.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Place of delivery (births at health facility) (% of births): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Place of delivery (Births at health facility): Percentage of live births in the three years preceding the survey which took place at health facility." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BRTF.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Place of delivery (births at health facility) (% of births): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Place of delivery (Births at health facility): Percentage of live births in the three years preceding the survey which took place at health facility." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BRTP.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Assistance during delivery (doctor) (% of births): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Assistance during delivery (Assisted births): Percentage of live births in the three (one, two) years preceding the survey attended by any skilled personnel and by a doctor. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BRTP.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Assistance during delivery (doctor) (% of births): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Assistance during delivery (Assisted births): Percentage of live births in the three (one, two) years preceding the survey attended by any skilled personnel and by a doctor. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BRTP.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Assistance during delivery (doctor) (% of births): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Assistance during delivery (Assisted births): Percentage of live births in the three (one, two) years preceding the survey attended by any skilled personnel and by a doctor. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BRTP.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Assistance during delivery (doctor) (% of births): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Assistance during delivery (Assisted births): Percentage of live births in the three (one, two) years preceding the survey attended by any skilled personnel and by a doctor. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.BRTP.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Assistance during delivery (doctor) (% of births): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Assistance during delivery (Assisted births): Percentage of live births in the three (one, two) years preceding the survey attended by any skilled personnel and by a doctor. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.DIRH.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Prevalence of diarrhea (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Prevalence of diarrhea: Percentage of children under age five years who had diarrhea in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.DIRH.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of diarrhea (% of children under 5): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Prevalence of diarrhea: Percentage of children under age five years who had diarrhea in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.DIRH.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of diarrhea (% of children under 5): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Prevalence of diarrhea: Percentage of children under age five years who had diarrhea in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.DIRH.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of diarrhea (% of children under 5): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Prevalence of diarrhea: Percentage of children under age five years who had diarrhea in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.DIRH.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Prevalence of diarrhea (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Prevalence of diarrhea: Percentage of children under age five years who had diarrhea in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.FEVR.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of children with fever (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Prevalence of children with fever: Percentage of children under age five years with fever in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.FEVR.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of children with fever (% of children under 5): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Prevalence of children with fever: Percentage of children under age five years with fever in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.FEVR.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of children with fever (% of children under 5): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Prevalence of children with fever: Percentage of children under age five years with fever in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.FEVR.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of children with fever (% of children under 5): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Prevalence of children with fever: Percentage of children under age five years with fever in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.FEVR.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of children with fever (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Prevalence of children with fever: Percentage of children under age five years with fever in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.HYGN.Q1.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water (% of population): Q1 (lowest)" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.HYGN.Q2.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water (% of population): Q2" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.HYGN.Q3.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water (% of population): Q3" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.HYGN.Q4.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water (% of population): Q4" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.HYGN.Q5.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water (% of population): Q5 (highest)" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.HYGN.RU.Q1.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, rural (% of rural population): Q1 (lowest)" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.HYGN.RU.Q2.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, rural (% of rural population): Q2" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.HYGN.RU.Q3.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, rural (% of rural population): Q3" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.HYGN.RU.Q4.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, rural (% of rural population): Q4" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.HYGN.RU.Q5.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, rural (% of rural population): Q5 (highest)" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.HYGN.UR.Q1.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, urban (% of urban population): Q1 (lowest)" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.HYGN.UR.Q2.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, urban (% of urban population): Q2" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.HYGN.UR.Q3.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, urban (% of urban population): Q3" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.HYGN.UR.Q4.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, urban (% of urban population): Q4" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.HYGN.UR.Q5.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, urban (% of urban population): Q5 (highest)" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.LBMI.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished women (BMI is less than 18.5) (% of women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished women (BMI is less than 18.5): Percentage of women whose body mass index (BMI) is less than 18.5 for women with births in the three years preceding the survey. The BMI is the ratio of the weight in kilograms to the square of the height in meters (kg/m2). The BMI excludes pregnant women and those who are less than three months postpartum." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.LBMI.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished women (BMI is less than 18.5) (% of women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished women (BMI is less than 18.5): Percentage of women whose body mass index (BMI) is less than 18.5 for women with births in the three years preceding the survey. The BMI is the ratio of the weight in kilograms to the square of the height in meters (kg/m2). The BMI excludes pregnant women and those who are less than three months postpartum." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.LBMI.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished women (BMI is less than 18.5) (% of women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished women (BMI is less than 18.5): Percentage of women whose body mass index (BMI) is less than 18.5 for women with births in the three years preceding the survey. The BMI is the ratio of the weight in kilograms to the square of the height in meters (kg/m2). The BMI excludes pregnant women and those who are less than three months postpartum." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.LBMI.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished women (BMI is less than 18.5) (% of women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished women (BMI is less than 18.5): Percentage of women whose body mass index (BMI) is less than 18.5 for women with births in the three years preceding the survey. The BMI is the ratio of the weight in kilograms to the square of the height in meters (kg/m2). The BMI excludes pregnant women and those who are less than three months postpartum." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.LBMI.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished women (BMI is less than 18.5) (% of women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished women (BMI is less than 18.5): Percentage of women whose body mass index (BMI) is less than 18.5 for women with births in the three years preceding the survey. The BMI is the ratio of the weight in kilograms to the square of the height in meters (kg/m2). The BMI excludes pregnant women and those who are less than three months postpartum." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.MALN.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Malnourished children (underweight, -2SD) (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.MALN.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (underweight, -2SD) (% of children under 5): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.MALN.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (underweight, -2SD) (% of children under 5): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.MALN.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (underweight, -2SD) (% of children under 5): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.MALN.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Malnourished children (underweight, -2SD) (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.MLN3.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (underweight, -3SD) (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.MLN3.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (underweight, -3SD) (% of children under 5): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.MLN3.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (underweight, -3SD) (% of children under 5): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.MLN3.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (underweight, -3SD) (% of children under 5): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.MLN3.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (underweight, -3SD) (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ODFC.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "People practicing open defecation (% of population): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ODFC.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "People practicing open defecation (% of population): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ODFC.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "People practicing open defecation (% of population): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ODFC.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "People practicing open defecation (% of population): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ODFC.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "People practicing open defecation (% of population): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ODFC.RU.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "People practicing open defecation, rural (% of rural population): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ODFC.RU.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "People practicing open defecation, rural (% of rural population): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ODFC.RU.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "People practicing open defecation, rural (% of rural population): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ODFC.RU.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "People practicing open defecation, rural (% of rural population): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ODFC.RU.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "People practicing open defecation, rural (% of rural population): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ODFC.UR.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "People practicing open defecation, urban (% of urban population): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ODFC.UR.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "People practicing open defecation, urban (% of urban population): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ODFC.UR.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "People practicing open defecation, urban (% of urban population): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ODFC.UR.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "People practicing open defecation, urban (% of urban population): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ODFC.UR.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "People practicing open defecation, urban (% of urban population): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ORHF.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Treatment of diarrhea (ORS, RHS or increased fluids) (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of diarrhea (ORS, RHS or increased fluids): Percentage of children under age five years with diarrhea in the two weeks preceding the survey who received oral rehydration solution (ORS), recommended home solution (RHS) or increased fluids." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ORHF.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Treatment of diarrhea (ORS, RHS or increased fluids) (% of children under 5): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of diarrhea (ORS, RHS or increased fluids): Percentage of children under age five years with diarrhea in the two weeks preceding the survey who received oral rehydration solution (ORS), recommended home solution (RHS) or increased fluids." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ORHF.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Treatment of diarrhea (ORS, RHS or increased fluids) (% of children under 5): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of diarrhea (ORS, RHS or increased fluids): Percentage of children under age five years with diarrhea in the two weeks preceding the survey who received oral rehydration solution (ORS), recommended home solution (RHS) or increased fluids." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ORHF.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Treatment of diarrhea (ORS, RHS or increased fluids) (% of children under 5): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of diarrhea (ORS, RHS or increased fluids): Percentage of children under age five years with diarrhea in the two weeks preceding the survey who received oral rehydration solution (ORS), recommended home solution (RHS) or increased fluids." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ORHF.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Treatment of diarrhea (ORS, RHS or increased fluids) (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of diarrhea (ORS, RHS or increased fluids): Percentage of children under age five years with diarrhea in the two weeks preceding the survey who received oral rehydration solution (ORS), recommended home solution (RHS) or increased fluids." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ORHK.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Knowledge of diarrhea care (% of mothers): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Knowledge of diarrhea care: Percentage of mothers with births in the three years preceding the survey who know about oral rehydration salts (ORS) packets." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ORHK.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Knowledge of diarrhea care (% of mothers): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Knowledge of diarrhea care: Percentage of mothers with births in the three years preceding the survey who know about oral rehydration salts (ORS) packets." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ORHK.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Knowledge of diarrhea care (% of mothers): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Knowledge of diarrhea care: Percentage of mothers with births in the three years preceding the survey who know about oral rehydration salts (ORS) packets." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ORHK.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Knowledge of diarrhea care (% of mothers): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Knowledge of diarrhea care: Percentage of mothers with births in the three years preceding the survey who know about oral rehydration salts (ORS) packets." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ORHK.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Knowledge of diarrhea care (% of mothers): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Knowledge of diarrhea care: Percentage of mothers with births in the three years preceding the survey who know about oral rehydration salts (ORS) packets." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ORHS.Q1ZS", "metatype": [ { "id": "IndicatorName", "value": "Treatment of diarrhea (either ORS or RHS) (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of diarrhea (either ORS or RHS): Percentage of children under age five years with diarrhea in the two weeks preceding the survey who received either oral rehydration solution (ORS) or recommended home solution (RHS)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ORHS.Q2ZS", "metatype": [ { "id": "IndicatorName", "value": "Treatment of diarrhea (either ORS or RHS) (% of children under 5): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of diarrhea (either ORS or RHS): Percentage of children under age five years with diarrhea in the two weeks preceding the survey who received either oral rehydration solution (ORS) or recommended home solution (RHS)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ORHS.Q3ZS", "metatype": [ { "id": "IndicatorName", "value": "Treatment of diarrhea (either ORS or RHS) (% of children under 5): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of diarrhea (either ORS or RHS): Percentage of children under age five years with diarrhea in the two weeks preceding the survey who received either oral rehydration solution (ORS) or recommended home solution (RHS)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ORHS.Q4ZS", "metatype": [ { "id": "IndicatorName", "value": "Treatment of diarrhea (either ORS or RHS) (% of children under 5): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of diarrhea (either ORS or RHS): Percentage of children under age five years with diarrhea in the two weeks preceding the survey who received either oral rehydration solution (ORS) or recommended home solution (RHS)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.ORHS.Q5ZS", "metatype": [ { "id": "IndicatorName", "value": "Treatment of diarrhea (either ORS or RHS) (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Treatment of diarrhea (either ORS or RHS): Percentage of children under age five years with diarrhea in the two weeks preceding the survey who received either oral rehydration solution (ORS) or recommended home solution (RHS)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.STN3.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (stunting, -3SD) (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.STN3.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (stunting, -3SD) (% of children under 5): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.STN3.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (stunting, -3SD) (% of children under 5): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.STN3.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (stunting, -3SD) (% of children under 5): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.STN3.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (stunting, -3SD) (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.STNT.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (stunting, -2SD) (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.STNT.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (stunting, -2SD) (% of children under 5): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.STNT.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (stunting, -2SD) (% of children under 5): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.STNT.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (stunting, -2SD) (% of children under 5): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.STNT.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (stunting, -2SD) (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.WAST.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (wasting, -2SD) (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.WAST.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (wasting, -2SD) (% of children under 5): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.WAST.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (wasting, -2SD) (% of children under 5): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.WAST.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (wasting, -2SD) (% of children under 5): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.WAST.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (wasting, -2SD) (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.WST3.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (wasting, -3SD) (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.WST3.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (wasting, -3SD) (% of children under 5): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.WST3.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (wasting, -3SD) (% of children under 5): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.WST3.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (wasting, -3SD) (% of children under 5): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.STA.WST3.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Malnourished children (wasting, -3SD) (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.VAC.TTNS.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Tetanus toxoid vaccination (% of live births): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Percent distribution of last live births in the last three years preceding the survey for tetanus toxoid injections (two doses or more) given to the mother during pregnancy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.VAC.TTNS.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Tetanus toxoid vaccination (% of live births): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Percent distribution of last live births in the last three years preceding the survey for tetanus toxoid injections (two doses or more) given to the mother during pregnancy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.VAC.TTNS.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Tetanus toxoid vaccination (% of live births): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Percent distribution of last live births in the last three years preceding the survey for tetanus toxoid injections (two doses or more) given to the mother during pregnancy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.VAC.TTNS.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Tetanus toxoid vaccination (% of live births): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Percent distribution of last live births in the last three years preceding the survey for tetanus toxoid injections (two doses or more) given to the mother during pregnancy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SH.VAC.TTNS.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Tetanus toxoid vaccination (% of live births): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Percent distribution of last live births in the last three years preceding the survey for tetanus toxoid injections (two doses or more) given to the mother during pregnancy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SN.ITK.VAPP.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vitamin A supplements for postpartum women (% of women with a birth): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vitamin A supplements for postpartum women: Percentage of women with a birth in the five (two) years preceding the survey who received a vitamin A dose in the first two months after delivery. The DHS surveys refer births in the five years preceding the survey, and the MICS surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Nutrition" } ], "source_id": "39" }, { "id": "SN.ITK.VAPP.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vitamin A supplements for postpartum women (% of women with a birth): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vitamin A supplements for postpartum women: Percentage of women with a birth in the five (two) years preceding the survey who received a vitamin A dose in the first two months after delivery. The DHS surveys refer births in the five years preceding the survey, and the MICS surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Nutrition" } ], "source_id": "39" }, { "id": "SN.ITK.VAPP.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vitamin A supplements for postpartum women (% of women with a birth): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vitamin A supplements for postpartum women: Percentage of women with a birth in the five (two) years preceding the survey who received a vitamin A dose in the first two months after delivery. The DHS surveys refer births in the five years preceding the survey, and the MICS surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Nutrition" } ], "source_id": "39" }, { "id": "SN.ITK.VAPP.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vitamin A supplements for postpartum women (% of women with a birth): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vitamin A supplements for postpartum women: Percentage of women with a birth in the five (two) years preceding the survey who received a vitamin A dose in the first two months after delivery. The DHS surveys refer births in the five years preceding the survey, and the MICS surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Nutrition" } ], "source_id": "39" }, { "id": "SN.ITK.VAPP.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vitamin A supplements for postpartum women (% of women with a birth): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vitamin A supplements for postpartum women: Percentage of women with a birth in the five (two) years preceding the survey who received a vitamin A dose in the first two months after delivery. The DHS surveys refer births in the five years preceding the survey, and the MICS surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Nutrition" } ], "source_id": "39" }, { "id": "SN.ITK.VITA.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vitamin A supplements for children (% of children ages 6-59 months): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vitamin A supplements for children: Percentage of children aged 6-59 months who received vitamin A supplements in the six months preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Nutrition" } ], "source_id": "39" }, { "id": "SN.ITK.VITA.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vitamin A supplements for children (% of children ages 6-59 months): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vitamin A supplements for children: Percentage of children aged 6-59 months who received vitamin A supplements in the six months preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Nutrition" } ], "source_id": "39" }, { "id": "SN.ITK.VITA.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vitamin A supplements for children (% of children ages 6-59 months): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vitamin A supplements for children: Percentage of children aged 6-59 months who received vitamin A supplements in the six months preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Nutrition" } ], "source_id": "39" }, { "id": "SN.ITK.VITA.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vitamin A supplements for children (% of children ages 6-59 months): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vitamin A supplements for children: Percentage of children aged 6-59 months who received vitamin A supplements in the six months preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Nutrition" } ], "source_id": "39" }, { "id": "SN.ITK.VITA.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Vitamin A supplements for children (% of children ages 6-59 months): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Vitamin A supplements for children: Percentage of children aged 6-59 months who received vitamin A supplements in the six months preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Nutrition" } ], "source_id": "39" }, { "id": "SP.DYN.CEBN.Q1", "metatype": [ { "id": "IndicatorName", "value": "Mean number of children ever born to women aged 40-49: Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mean number of children ever born to women aged 40-49: Mean number of children ever born (CEB) to women aged 40-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.CEBN.Q2", "metatype": [ { "id": "IndicatorName", "value": "Mean number of children ever born to women aged 40-49: Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mean number of children ever born to women aged 40-49: Mean number of children ever born (CEB) to women aged 40-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.CEBN.Q3", "metatype": [ { "id": "IndicatorName", "value": "Mean number of children ever born to women aged 40-49: Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mean number of children ever born to women aged 40-49: Mean number of children ever born (CEB) to women aged 40-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.CEBN.Q4", "metatype": [ { "id": "IndicatorName", "value": "Mean number of children ever born to women aged 40-49: Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mean number of children ever born to women aged 40-49: Mean number of children ever born (CEB) to women aged 40-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.CEBN.Q5", "metatype": [ { "id": "IndicatorName", "value": "Mean number of children ever born to women aged 40-49: Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Mean number of children ever born to women aged 40-49: Mean number of children ever born (CEB) to women aged 40-49 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.CONM.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Current use of contraception (modern method) (% of married women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Current use of contraception: Percentage of currently married women who are using or whose partners are using any method of contraception and modern method of contraception. Modern method includes female sterilization, male sterilization, pill, IUD, injections, implants, male condom, female condom, diaphragm, foam, and jelly. Traditional method includes periodic abstinence, withdrawal, long term abstinence, folk method, and others." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.CONM.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Current use of contraception (modern method) (% of married women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Current use of contraception: Percentage of currently married women who are using or whose partners are using any method of contraception and modern method of contraception. Modern method includes female sterilization, male sterilization, pill, IUD, injections, implants, male condom, female condom, diaphragm, foam, and jelly. Traditional method includes periodic abstinence, withdrawal, long term abstinence, folk method, and others." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.CONM.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Current use of contraception (modern method) (% of married women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Current use of contraception: Percentage of currently married women who are using or whose partners are using any method of contraception and modern method of contraception. Modern method includes female sterilization, male sterilization, pill, IUD, injections, implants, male condom, female condom, diaphragm, foam, and jelly. Traditional method includes periodic abstinence, withdrawal, long term abstinence, folk method, and others." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.CONM.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Current use of contraception (modern method) (% of married women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Current use of contraception: Percentage of currently married women who are using or whose partners are using any method of contraception and modern method of contraception. Modern method includes female sterilization, male sterilization, pill, IUD, injections, implants, male condom, female condom, diaphragm, foam, and jelly. Traditional method includes periodic abstinence, withdrawal, long term abstinence, folk method, and others." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.CONM.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Current use of contraception (modern method) (% of married women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Current use of contraception: Percentage of currently married women who are using or whose partners are using any method of contraception and modern method of contraception. Modern method includes female sterilization, male sterilization, pill, IUD, injections, implants, male condom, female condom, diaphragm, foam, and jelly. Traditional method includes periodic abstinence, withdrawal, long term abstinence, folk method, and others." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.CONU.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Current use of contraception (any method) (% of married women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Current use of contraception: Percentage of currently married women who are using or whose partners are using any method of contraception and modern method of contraception. Modern method includes female sterilization, male sterilization, pill, IUD, injections, implants, male condom, female condom, diaphragm, foam, and jelly. Traditional method includes periodic abstinence, withdrawal, long term abstinence, folk method, and others." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.CONU.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Current use of contraception (any method) (% of married women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Current use of contraception: Percentage of currently married women who are using or whose partners are using any method of contraception and modern method of contraception. Modern method includes female sterilization, male sterilization, pill, IUD, injections, implants, male condom, female condom, diaphragm, foam, and jelly. Traditional method includes periodic abstinence, withdrawal, long term abstinence, folk method, and others." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.CONU.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Current use of contraception (any method) (% of married women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Current use of contraception: Percentage of currently married women who are using or whose partners are using any method of contraception and modern method of contraception. Modern method includes female sterilization, male sterilization, pill, IUD, injections, implants, male condom, female condom, diaphragm, foam, and jelly. Traditional method includes periodic abstinence, withdrawal, long term abstinence, folk method, and others." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.CONU.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Current use of contraception (any method) (% of married women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Current use of contraception: Percentage of currently married women who are using or whose partners are using any method of contraception and modern method of contraception. Modern method includes female sterilization, male sterilization, pill, IUD, injections, implants, male condom, female condom, diaphragm, foam, and jelly. Traditional method includes periodic abstinence, withdrawal, long term abstinence, folk method, and others." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.CONU.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Current use of contraception (any method) (% of married women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Current use of contraception: Percentage of currently married women who are using or whose partners are using any method of contraception and modern method of contraception. Modern method includes female sterilization, male sterilization, pill, IUD, injections, implants, male condom, female condom, diaphragm, foam, and jelly. Traditional method includes periodic abstinence, withdrawal, long term abstinence, folk method, and others." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.IMRT.Q1", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Infant mortality rate (per 1,000 live births): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Infant mortality rate: Number of deaths to children under age twelve months per 1000 live births, based on experience during the reference period before the survey. The reference period is ten years preceding the survey for DHS surveys, and the reference period varies for MICS surveys (often three to five years preceding the survey)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.IMRT.Q2", "metatype": [ { "id": "IndicatorName", "value": "Infant mortality rate (per 1,000 live births): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Infant mortality rate: Number of deaths to children under age twelve months per 1000 live births, based on experience during the reference period before the survey. The reference period is ten years preceding the survey for DHS surveys, and the reference period varies for MICS surveys (often three to five years preceding the survey)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.IMRT.Q3", "metatype": [ { "id": "IndicatorName", "value": "Infant mortality rate (per 1,000 live births): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Infant mortality rate: Number of deaths to children under age twelve months per 1000 live births, based on experience during the reference period before the survey. The reference period is ten years preceding the survey for DHS surveys, and the reference period varies for MICS surveys (often three to five years preceding the survey)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.IMRT.Q4", "metatype": [ { "id": "IndicatorName", "value": "Infant mortality rate (per 1,000 live births): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Infant mortality rate: Number of deaths to children under age twelve months per 1000 live births, based on experience during the reference period before the survey. The reference period is ten years preceding the survey for DHS surveys, and the reference period varies for MICS surveys (often three to five years preceding the survey)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.IMRT.Q5", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Infant mortality rate (per 1,000 live births): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Infant mortality rate: Number of deaths to children under age twelve months per 1000 live births, based on experience during the reference period before the survey. The reference period is ten years preceding the survey for DHS surveys, and the reference period varies for MICS surveys (often three to five years preceding the survey)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.TFRT.Q1", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Total fertility rate (TFR) (births per woman): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Total fertility rate (TFR): The number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates currently observed. The reference period is three years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.TFRT.Q2", "metatype": [ { "id": "IndicatorName", "value": "Total fertility rate (TFR) (births per woman): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Total fertility rate (TFR): The number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates currently observed. The reference period is three years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.TFRT.Q3", "metatype": [ { "id": "IndicatorName", "value": "Total fertility rate (TFR) (births per woman): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Total fertility rate (TFR): The number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates currently observed. The reference period is three years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.TFRT.Q4", "metatype": [ { "id": "IndicatorName", "value": "Total fertility rate (TFR) (births per woman): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Total fertility rate (TFR): The number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates currently observed. The reference period is three years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.TFRT.Q5", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Total fertility rate (TFR) (births per woman): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Total fertility rate (TFR): The number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates currently observed. The reference period is three years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.WFRT.Q1", "metatype": [ { "id": "IndicatorName", "value": "Total wanted fertility rate (births per woman): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Total wanted fertility rate: Total wanted fertility rate is an estimate what the total fertility rate would be if all unwanted births were avoided. The reference period is three years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.WFRT.Q2", "metatype": [ { "id": "IndicatorName", "value": "Total wanted fertility rate (births per woman): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Total wanted fertility rate: Total wanted fertility rate is an estimate what the total fertility rate would be if all unwanted births were avoided. The reference period is three years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.WFRT.Q3", "metatype": [ { "id": "IndicatorName", "value": "Total wanted fertility rate (births per woman): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Total wanted fertility rate: Total wanted fertility rate is an estimate what the total fertility rate would be if all unwanted births were avoided. The reference period is three years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.WFRT.Q4", "metatype": [ { "id": "IndicatorName", "value": "Total wanted fertility rate (births per woman): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Total wanted fertility rate: Total wanted fertility rate is an estimate what the total fertility rate would be if all unwanted births were avoided. The reference period is three years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.DYN.WFRT.Q5", "metatype": [ { "id": "IndicatorName", "value": "Total wanted fertility rate (births per woman): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Total wanted fertility rate: Total wanted fertility rate is an estimate what the total fertility rate would be if all unwanted births were avoided. The reference period is three years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.MTR.1519.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Teenage pregnancy and motherhood (% of women ages 15-19 who have had children or are currently pregnant): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Teenage pregnancy and motherhood: Percentage of women aged 15-19 years who are mothers or pregnant with their first child." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.MTR.1519.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Teenage pregnancy and motherhood (% of women ages 15-19 who have had children or are currently pregnant): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Teenage pregnancy and motherhood: Percentage of women aged 15-19 years who are mothers or pregnant with their first child." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.MTR.1519.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Teenage pregnancy and motherhood (% of women ages 15-19 who have had children or are currently pregnant): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Teenage pregnancy and motherhood: Percentage of women aged 15-19 years who are mothers or pregnant with their first child." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.MTR.1519.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Teenage pregnancy and motherhood (% of women ages 15-19 who have had children or are currently pregnant): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Teenage pregnancy and motherhood: Percentage of women aged 15-19 years who are mothers or pregnant with their first child." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.MTR.1519.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Teenage pregnancy and motherhood (% of women ages 15-19 who have had children or are currently pregnant): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Teenage pregnancy and motherhood: Percentage of women aged 15-19 years who are mothers or pregnant with their first child." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.REG.BRTH.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Completeness of birth registration (%): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF global databases, based on DHS, MICS, other national household surveys, censuses and vital registration systems." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SP.REG.BRTH.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Completeness of birth registration (%): Q2" }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF global databases, based on DHS, MICS, other national household surveys, censuses and vital registration systems." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SP.REG.BRTH.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Completeness of birth registration (%): Q3" }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF global databases, based on DHS, MICS, other national household surveys, censuses and vital registration systems." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SP.REG.BRTH.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Completeness of birth registration (%): Q4" }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF global databases, based on DHS, MICS, other national household surveys, censuses and vital registration systems." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SP.REG.BRTH.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Completeness of birth registration (%): Q5 (highest)" }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF global databases, based on DHS, MICS, other national household surveys, censuses and vital registration systems." }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "39" }, { "id": "SP.UWT.LMTG.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for family planning (for limiting) (% of married women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Unmet need for family planning: Percentage of currently married women with unmet need for family planning for spacing, for limiting, and the sum of these two (total). Unmet need for spacing includes pregnant women whose pregnancy was mistimed, amenorrheic women who are not using family planning and whose last birth was mistimed, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and say they want to wait 2 or more years for their next birth. Also included in unmet need for spacing are fecund women who are not using any method of family planning and say they are unsure whether they want another child or who want another child but are unsure when to have the birth unless they say it would not be a problem if they discovered they were pregnant in the next few weeks. Unmet need for limiting refers to pregnant women whose pregnancy was unwanted, amenorrheic women whose last child was unwanted, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and who want no more children. Excluded from the unmet need category are pregnant and amenorrheic women who became pregnant while using a method (these women are in need of a better method of contraception)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.UWT.LMTG.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for family planning (for limiting) (% of married women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Unmet need for family planning: Percentage of currently married women with unmet need for family planning for spacing, for limiting, and the sum of these two (total). Unmet need for spacing includes pregnant women whose pregnancy was mistimed, amenorrheic women who are not using family planning and whose last birth was mistimed, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and say they want to wait 2 or more years for their next birth. Also included in unmet need for spacing are fecund women who are not using any method of family planning and say they are unsure whether they want another child or who want another child but are unsure when to have the birth unless they say it would not be a problem if they discovered they were pregnant in the next few weeks. Unmet need for limiting refers to pregnant women whose pregnancy was unwanted, amenorrheic women whose last child was unwanted, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and who want no more children. Excluded from the unmet need category are pregnant and amenorrheic women who became pregnant while using a method (these women are in need of a better method of contraception)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.UWT.LMTG.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for family planning (for limiting) (% of married women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Unmet need for family planning: Percentage of currently married women with unmet need for family planning for spacing, for limiting, and the sum of these two (total). Unmet need for spacing includes pregnant women whose pregnancy was mistimed, amenorrheic women who are not using family planning and whose last birth was mistimed, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and say they want to wait 2 or more years for their next birth. Also included in unmet need for spacing are fecund women who are not using any method of family planning and say they are unsure whether they want another child or who want another child but are unsure when to have the birth unless they say it would not be a problem if they discovered they were pregnant in the next few weeks. Unmet need for limiting refers to pregnant women whose pregnancy was unwanted, amenorrheic women whose last child was unwanted, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and who want no more children. Excluded from the unmet need category are pregnant and amenorrheic women who became pregnant while using a method (these women are in need of a better method of contraception)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.UWT.LMTG.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for family planning (for limiting) (% of married women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Unmet need for family planning: Percentage of currently married women with unmet need for family planning for spacing, for limiting, and the sum of these two (total). Unmet need for spacing includes pregnant women whose pregnancy was mistimed, amenorrheic women who are not using family planning and whose last birth was mistimed, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and say they want to wait 2 or more years for their next birth. Also included in unmet need for spacing are fecund women who are not using any method of family planning and say they are unsure whether they want another child or who want another child but are unsure when to have the birth unless they say it would not be a problem if they discovered they were pregnant in the next few weeks. Unmet need for limiting refers to pregnant women whose pregnancy was unwanted, amenorrheic women whose last child was unwanted, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and who want no more children. Excluded from the unmet need category are pregnant and amenorrheic women who became pregnant while using a method (these women are in need of a better method of contraception)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.UWT.LMTG.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for family planning (for limiting) (% of married women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Unmet need for family planning: Percentage of currently married women with unmet need for family planning for spacing, for limiting, and the sum of these two (total). Unmet need for spacing includes pregnant women whose pregnancy was mistimed, amenorrheic women who are not using family planning and whose last birth was mistimed, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and say they want to wait 2 or more years for their next birth. Also included in unmet need for spacing are fecund women who are not using any method of family planning and say they are unsure whether they want another child or who want another child but are unsure when to have the birth unless they say it would not be a problem if they discovered they were pregnant in the next few weeks. Unmet need for limiting refers to pregnant women whose pregnancy was unwanted, amenorrheic women whose last child was unwanted, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and who want no more children. Excluded from the unmet need category are pregnant and amenorrheic women who became pregnant while using a method (these women are in need of a better method of contraception)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.UWT.SPCG.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for family planning (for spacing) (% of married women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Unmet need for family planning: Percentage of currently married women with unmet need for family planning for spacing, for limiting, and the sum of these two (total). Unmet need for spacing includes pregnant women whose pregnancy was mistimed, amenorrheic women who are not using family planning and whose last birth was mistimed, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and say they want to wait 2 or more years for their next birth. Also included in unmet need for spacing are fecund women who are not using any method of family planning and say they are unsure whether they want another child or who want another child but are unsure when to have the birth unless they say it would not be a problem if they discovered they were pregnant in the next few weeks. Unmet need for limiting refers to pregnant women whose pregnancy was unwanted, amenorrheic women whose last child was unwanted, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and who want no more children. Excluded from the unmet need category are pregnant and amenorrheic women who became pregnant while using a method (these women are in need of a better method of contraception)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.UWT.SPCG.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for family planning (for spacing) (% of married women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Unmet need for family planning: Percentage of currently married women with unmet need for family planning for spacing, for limiting, and the sum of these two (total). Unmet need for spacing includes pregnant women whose pregnancy was mistimed, amenorrheic women who are not using family planning and whose last birth was mistimed, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and say they want to wait 2 or more years for their next birth. Also included in unmet need for spacing are fecund women who are not using any method of family planning and say they are unsure whether they want another child or who want another child but are unsure when to have the birth unless they say it would not be a problem if they discovered they were pregnant in the next few weeks. Unmet need for limiting refers to pregnant women whose pregnancy was unwanted, amenorrheic women whose last child was unwanted, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and who want no more children. Excluded from the unmet need category are pregnant and amenorrheic women who became pregnant while using a method (these women are in need of a better method of contraception)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.UWT.SPCG.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for family planning (for spacing) (% of married women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Unmet need for family planning: Percentage of currently married women with unmet need for family planning for spacing, for limiting, and the sum of these two (total). Unmet need for spacing includes pregnant women whose pregnancy was mistimed, amenorrheic women who are not using family planning and whose last birth was mistimed, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and say they want to wait 2 or more years for their next birth. Also included in unmet need for spacing are fecund women who are not using any method of family planning and say they are unsure whether they want another child or who want another child but are unsure when to have the birth unless they say it would not be a problem if they discovered they were pregnant in the next few weeks. Unmet need for limiting refers to pregnant women whose pregnancy was unwanted, amenorrheic women whose last child was unwanted, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and who want no more children. Excluded from the unmet need category are pregnant and amenorrheic women who became pregnant while using a method (these women are in need of a better method of contraception)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.UWT.SPCG.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for family planning (for spacing) (% of married women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Unmet need for family planning: Percentage of currently married women with unmet need for family planning for spacing, for limiting, and the sum of these two (total). Unmet need for spacing includes pregnant women whose pregnancy was mistimed, amenorrheic women who are not using family planning and whose last birth was mistimed, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and say they want to wait 2 or more years for their next birth. Also included in unmet need for spacing are fecund women who are not using any method of family planning and say they are unsure whether they want another child or who want another child but are unsure when to have the birth unless they say it would not be a problem if they discovered they were pregnant in the next few weeks. Unmet need for limiting refers to pregnant women whose pregnancy was unwanted, amenorrheic women whose last child was unwanted, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and who want no more children. Excluded from the unmet need category are pregnant and amenorrheic women who became pregnant while using a method (these women are in need of a better method of contraception)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.UWT.SPCG.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for family planning (for spacing) (% of married women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Unmet need for family planning: Percentage of currently married women with unmet need for family planning for spacing, for limiting, and the sum of these two (total). Unmet need for spacing includes pregnant women whose pregnancy was mistimed, amenorrheic women who are not using family planning and whose last birth was mistimed, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and say they want to wait 2 or more years for their next birth. Also included in unmet need for spacing are fecund women who are not using any method of family planning and say they are unsure whether they want another child or who want another child but are unsure when to have the birth unless they say it would not be a problem if they discovered they were pregnant in the next few weeks. Unmet need for limiting refers to pregnant women whose pregnancy was unwanted, amenorrheic women whose last child was unwanted, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and who want no more children. Excluded from the unmet need category are pregnant and amenorrheic women who became pregnant while using a method (these women are in need of a better method of contraception)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.UWT.TFRT.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for family planning (total) (% of married women): Q1 (lowest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Unmet need for family planning: Percentage of currently married women with unmet need for family planning for spacing, for limiting, and the sum of these two (total). Unmet need for spacing includes pregnant women whose pregnancy was mistimed, amenorrheic women who are not using family planning and whose last birth was mistimed, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and say they want to wait 2 or more years for their next birth. Also included in unmet need for spacing are fecund women who are not using any method of family planning and say they are unsure whether they want another child or who want another child but are unsure when to have the birth unless they say it would not be a problem if they discovered they were pregnant in the next few weeks. Unmet need for limiting refers to pregnant women whose pregnancy was unwanted, amenorrheic women whose last child was unwanted, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and who want no more children. Excluded from the unmet need category are pregnant and amenorrheic women who became pregnant while using a method (these women are in need of a better method of contraception)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.UWT.TFRT.Q2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for family planning (total) (% of married women): Q2" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Unmet need for family planning: Percentage of currently married women with unmet need for family planning for spacing, for limiting, and the sum of these two (total). Unmet need for spacing includes pregnant women whose pregnancy was mistimed, amenorrheic women who are not using family planning and whose last birth was mistimed, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and say they want to wait 2 or more years for their next birth. Also included in unmet need for spacing are fecund women who are not using any method of family planning and say they are unsure whether they want another child or who want another child but are unsure when to have the birth unless they say it would not be a problem if they discovered they were pregnant in the next few weeks. Unmet need for limiting refers to pregnant women whose pregnancy was unwanted, amenorrheic women whose last child was unwanted, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and who want no more children. Excluded from the unmet need category are pregnant and amenorrheic women who became pregnant while using a method (these women are in need of a better method of contraception)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.UWT.TFRT.Q3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for family planning (total) (% of married women): Q3" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Unmet need for family planning: Percentage of currently married women with unmet need for family planning for spacing, for limiting, and the sum of these two (total). Unmet need for spacing includes pregnant women whose pregnancy was mistimed, amenorrheic women who are not using family planning and whose last birth was mistimed, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and say they want to wait 2 or more years for their next birth. Also included in unmet need for spacing are fecund women who are not using any method of family planning and say they are unsure whether they want another child or who want another child but are unsure when to have the birth unless they say it would not be a problem if they discovered they were pregnant in the next few weeks. Unmet need for limiting refers to pregnant women whose pregnancy was unwanted, amenorrheic women whose last child was unwanted, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and who want no more children. Excluded from the unmet need category are pregnant and amenorrheic women who became pregnant while using a method (these women are in need of a better method of contraception)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.UWT.TFRT.Q4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for family planning (total) (% of married women): Q4" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Unmet need for family planning: Percentage of currently married women with unmet need for family planning for spacing, for limiting, and the sum of these two (total). Unmet need for spacing includes pregnant women whose pregnancy was mistimed, amenorrheic women who are not using family planning and whose last birth was mistimed, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and say they want to wait 2 or more years for their next birth. Also included in unmet need for spacing are fecund women who are not using any method of family planning and say they are unsure whether they want another child or who want another child but are unsure when to have the birth unless they say it would not be a problem if they discovered they were pregnant in the next few weeks. Unmet need for limiting refers to pregnant women whose pregnancy was unwanted, amenorrheic women whose last child was unwanted, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and who want no more children. Excluded from the unmet need category are pregnant and amenorrheic women who became pregnant while using a method (these women are in need of a better method of contraception)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SP.UWT.TFRT.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for family planning (total) (% of married women): Q5 (highest)" }, { "id": "License_Type", "value": "Open" }, { "id": "Longdefinition", "value": "Unmet need for family planning: Percentage of currently married women with unmet need for family planning for spacing, for limiting, and the sum of these two (total). Unmet need for spacing includes pregnant women whose pregnancy was mistimed, amenorrheic women who are not using family planning and whose last birth was mistimed, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and say they want to wait 2 or more years for their next birth. Also included in unmet need for spacing are fecund women who are not using any method of family planning and say they are unsure whether they want another child or who want another child but are unsure when to have the birth unless they say it would not be a problem if they discovered they were pregnant in the next few weeks. Unmet need for limiting refers to pregnant women whose pregnancy was unwanted, amenorrheic women whose last child was unwanted, and fecund women who are neither pregnant nor amenorrheic and who are not using any method of family planning and who want no more children. Excluded from the unmet need category are pregnant and amenorrheic women who became pregnant while using a method (these women are in need of a better method of contraception)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "39" }, { "id": "SH.DTH.0509", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of deaths ages 5-9 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of children ages 5-9 years" }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Shortdefinition", "value": "Number of deaths of children ages 5-9 years" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SH.DTH.1014", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of deaths ages 10-14 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of adolescents ages 10-14 years" }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Shortdefinition", "value": "Number of deaths of adolescents ages 10-14 years" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SH.DTH.1019", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of deaths ages 10-19 years" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of adolescents ages 10-19 years" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "40" }, { "id": "SH.DTH.1519", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of deaths ages 15-19 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of adolescents ages 15-19 years" }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Shortdefinition", "value": "Number of deaths of adolescents ages 15-19 years" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SH.DTH.2024", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of deaths ages 20-24 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of youths ages 20-24 years" }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Shortdefinition", "value": "Number of deaths of youths ages 20-24 years" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SH.DTH.IMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of infant deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of infants dying before reaching one year of age." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Number of infants dying before reaching one year of age." }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SH.DTH.IMRT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of infant deaths, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of female infants dying before reaching one year of age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "40" }, { "id": "SH.DTH.IMRT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of infant deaths, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of male infants dying before reaching one year of age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "40" }, { "id": "SH.DTH.MORT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of under-five deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of children dying before reaching age five." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Number of children dying before reaching age five." }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SH.DTH.MORT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of under-five deaths, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of female children dying before reaching age five." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "40" }, { "id": "SH.DTH.MORT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of under-five deaths, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of male children dying before reaching age five." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "40" }, { "id": "SH.DTH.NMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of neonatal deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of neonates dying before reaching 28 days of age." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis indicator is related to Sustainable Development Goal 3.2.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Number of neonates dying before reaching 28 days of age." }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SH.DYN.0509", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Probability of dying among children ages 5-9 years (per 1,000)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 5-9 years of age expressed per 1,000 children aged 5, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Shortdefinition", "value": "Probability of dying between age 5-9 years of age expressed per 1,000 children aged 5, if subject to age-specific mortality rates of the specified year." }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000" } ], "source_id": "40" }, { "id": "SH.DYN.1014", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Probability of dying among adolescents ages 10-14 years (per 1,000)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 10-14 years of age expressed per 1,000 adolescents age 10, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Shortdefinition", "value": "Probability of dying between age 10-14 years of age expressed per 1,000 adolescents age 10, if subject to age-specific mortality rates of the specified year." }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000" } ], "source_id": "40" }, { "id": "SH.DYN.1019", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Probability of dying among adolescents ages 10-19 years (per 1,000)" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 10-19 years of age expressed per 1,000 adolescents age 10, if subject to age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "40" }, { "id": "SH.DYN.1519", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Probability of dying among adolescents ages 15-19 years (per 1,000)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 15-19 years of age expressed per 1,000 adolescents age 15, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Shortdefinition", "value": "Probability of dying between age 15-19 years of age expressed per 1,000 adolescents age 15, if subject to age-specific mortality rates of the specified year." }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000" } ], "source_id": "40" }, { "id": "SH.DYN.2024", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Probability of dying among youth ages 20-24 years (per 1,000)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 20-24 years of age expressed per 1,000 youths age 20, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Shortdefinition", "value": "Probability of dying between age 20-24 years of age expressed per 1,000 youths age 20, if subject to age-specific mortality rates of the specified year." }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000" } ], "source_id": "40" }, { "id": "SH.DYN.MORT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, under-5 (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is the Sustainable Development Goal indicator 3.2.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year." }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org, publisher: UNICEF, WHO, World Bank, United Nations Population Division;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "40" }, { "id": "SH.DYN.MORT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, under-5, female (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female age-specific mortality rates of the specified year." }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "40" }, { "id": "SH.DYN.MORT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, under-5, male (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male age-specific mortality rates of the specified year." }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "40" }, { "id": "SH.DYN.NMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, neonatal (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Neonatal mortality rate is the number of neonates dying before reaching 28 days of age, per 1,000 live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\n\nThis is the Sustainable Development Goal indicator 3.2.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Neonatal mortality rate is the number of neonates dying before reaching 28 days of age, per 1,000 live births in a given year." }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "40" }, { "id": "SM.POP.NETM", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\n\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. In most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers.\n\n\n\nBut refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom." }, { "id": "IndicatorName", "value": "Net migration" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "International migration is the component of population change most difficult to measure and estimate reliably. Thus, the quality and quantity of the data used in the estimation and projection of net migration varies considerably by country. Furthermore, the movement of people across international boundaries, which is very often a response to changing socio-economic, political and environmental forces, is subject to a great deal of volatility. Refugee movements, for instance, may involve large numbers of people moving across boundaries in a short time. For these reasons, projections of future international migration levels are the least robust part of current population projections and reflect mainly a continuation of recent levels and trends in net migration." }, { "id": "Longdefinition", "value": "Net migration is the net total of migrants during the period, that is, the number of immigrants minus the number of emigrants, including both citizens and noncitizens." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Net migration is the net total of migrants during the period, that is, the number of immigrants minus the number of emigrants, including both citizens and noncitizens." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: When there is insufficient data, net migration is derived through the difference between the overall population growth rate and the rate of natural increase (the difference between the birth rate and the death rate) during the same period. Such calculations are usually made for intercensal periods. The estimates are also derived from the data on foreign-born population - people who have residence in one country but were born in another country. When data on the foreign-born population are not available, data on foreign population - that is, people who are citizens of a country other than the country in which they reside - are used as estimates.\nStatistical concept(s): The United Nations Population Division provides data on net migration and migrant stock. Because data on migrant stock is difficult for countries to collect, the United Nations Population Division takes into account the past migration history of a country or area, the migration policy of a country, and the influx of refugees in recent periods when deriving estimates of net migration. The data to calculate these estimates come from a variety of sources, including border statistics, administrative records, surveys, and censuses." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SP.DYN.AMRT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "If available, derived from life tables of Human Mortality Database (HMD) by Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France)." }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, adult, female (per 1,000 female adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data from United Nations Population Division's World Populaton Prospects are originally 5-year period data and the presented are linearly interpolated by the World Bank for annual series. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Adult mortality rate, female, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old female dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Adult mortality rate, female, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old female dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nHuman Mortality Database, Max Planck Institute for Demographic Research, uri: www.mortality.org;\nUniversity of California, Berkeley, uri: www.mortality.org, note: Human Mortality Database;\nFrench Institute for Demographic Studies, uri: www.mortality.org, note: Human Mortality Database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors, l(x), at exact age x in a female period life table. The formula is: (l(60)-l(15))/(l(15))*1000.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data. Where reliable age-specific mortality data are available, life tables can be constructed from age-specific mortality data, and adult mortality rates can be calculated from life tables." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 female adults" } ], "source_id": "40" }, { "id": "SP.DYN.AMRT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "If available, derived from life tables of Human Mortality Database (HMD) by Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France)." }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, adult, male (per 1,000 male adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Data from United Nations Population Division's World Populaton Prospects are originally 5-year period data and the presented are linearly interpolated by the World Bank for annual series. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Adult mortality rate, male, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old male dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Adult mortality rate, male, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old male dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nHuman Mortality Database, Max Planck Institute for Demographic Research, uri: www.mortality.org;\nUniversity of California, Berkeley, uri: www.mortality.org, note: Human Mortality Database;\nFrench Institute for Demographic Studies, uri: www.mortality.org, note: Human Mortality Database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors, l(x), at exact age x in a male period life table. The formula is: (l(60)-l(15))/(l(15))*1000.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data. Where reliable age-specific mortality data are available, life tables can be constructed from age-specific mortality data, and adult mortality rates can be calculated from life tables." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 male adults" } ], "source_id": "40" }, { "id": "SP.DYN.CBRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The crude birth rate is not appropriate for comparison of different populations or areas with large differences in age-distributions. When the crude death rate is subtracted from the crude birth rate, the result is the rate of natural increase, which is the rate of population change in the absence of migration." }, { "id": "IndicatorName", "value": "Birth rate, crude (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Vital registers are the preferred source for these data, but in many developing countries systems for registering births and deaths are absent or incomplete because of deficiencies in the coverage of events or geographic areas. Many developing countries carry out special household surveys that ask respondents about recent births and deaths. Estimates derived in this way are subject to sampling errors and recall errors." }, { "id": "Longdefinition", "value": "Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT);\nPopulation and Vital Statistics Report (various years), United Nations (UN), publisher: UN Statistical Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The crude birth rate is calculated as the number of births in a given period divided by the average population in that period. For human populations the period is usually one year and, if the population changes in size over the year, the divisor is taken as the population at the mid-year. The rate is usually expressed in terms of 1,000 people: for example, a crude birth rate of 9.5 (per 1000 people) in a population of 1 million would imply 9500 births per year in the entire population.\nStatistical concept(s): Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration. Vital rates are based on data from birth and death registration systems, censuses, and sample surveys by national statistical offices and other organizations, or on demographic analysis. Data for the most recent year for some high-income countries are provisional estimates based on vital registers. The estimates for many other countries are from the United Nations Population Division." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "40" }, { "id": "SP.DYN.CDRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The crude death rate is a good indicator of the general health status of a geographic area or population. The crude death rate is not appropriate for comparison of different populations or areas with large differences in age-distributions. Higher crude death rates can be found in some developed countries, despite high life expectancy, because typically these countries have a much higher proportion of older people, due to lower recent birth rates and lower age-specific mortality rates." }, { "id": "IndicatorName", "value": "Death rate, crude (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Vital registers are the preferred source for these data, but in many developing countries systems for registering births and deaths are absent or incomplete because of deficiencies in the coverage of events or geographic areas. Many developing countries carry out special household surveys that ask respondents about recent births and deaths. Estimates derived in this way are subject to sampling errors and recall errors." }, { "id": "Longdefinition", "value": "Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT);\nPopulation and Vital Statistics Report (various years), United Nations (UN), publisher: UN Statistical Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The crude death rate is calculated as the number of deaths in a given period divided by the population exposed to risk of death in that period. For human populations the period is usually one year and, if the population changes in size over the year, the divisor is taken as the population at the mid-year. The rate is usually expressed in terms of 1,000 people: for example, a crude death rate of 9.5 (per 1000 people) in a population of 1 million would imply 9500 deaths per year in the entire population.\nStatistical concept(s): Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration. Vital rates are based on data from birth and death registration systems, censuses, and sample surveys by national statistical offices and other organizations, or on demographic analysis. Data for the most recent year for some high-income countries are provisional estimates based on vital registers. The estimates for many other countries are from the United Nations Population Division." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "40" }, { "id": "SP.DYN.IMRT.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, infant, female (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Infant mortality rate, female is the number of female infants dying before reaching one year of age, per 1,000 female live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Infant mortality rate, female is the number of female infants dying before reaching one year of age, per 1,000 female live births in a given year." }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "40" }, { "id": "SP.DYN.IMRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, infant (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year." }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "40" }, { "id": "SP.DYN.IMRT.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, infant, male (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Infant mortality rate, male is the number of male infants dying before reaching one year of age, per 1,000 male live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Infant mortality rate, male is the number of male infants dying before reaching one year of age, per 1,000 male live births in a given year." }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "40" }, { "id": "SP.DYN.LE00.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, female (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "40" }, { "id": "SP.DYN.LE00.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, total (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), uri: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices, note: Derived from male and female life expectancy at birth from sources such as statistical databases and publications from national statistical offices.;\nDemographic Statistics, Eurostat (ESTAT), note: Derived from male and female life expectancy at birth from sources such as Eurostat: Demographic Statistics." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates, or derived from male and female life expectancy at birth.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "40" }, { "id": "SP.DYN.LE00.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, male (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "40" }, { "id": "SP.DYN.TFRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries." }, { "id": "IndicatorName", "value": "Fertility rate, total (births per woman)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year." }, { "id": "Othernotes", "value": "Relevance to gender indicator: it can indicate the status of women within households and a woman’s decision about the number and spacing of children." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Total fertility rate is the sum of the age-specific fertility rates (multiplied by five, if the age-specific fertility rates are for 5-year age groups).\nStatistical concept(s): Total fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without reliable vital registration systems fertility rates are generally based on extrapolations from trends observed in censuses or surveys from earlier years." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Births per woman" } ], "source_id": "40" }, { "id": "SP.POP.0004.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 00-04, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.0004.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 00-04, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 4 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 0 to 4 as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.0004.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 00-04, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.0004.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 00-04, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 4 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 0 to 4 as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.0014.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SP.POP.0014.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 14 as a percentage of the total female population. Population is based on the de facto definition of population." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 0 to 14 as a percentage of the total female population. Population is based on the de facto definition of population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.0014.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SP.POP.0014.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 14 as a percentage of the total male population. Population is based on the de facto definition of population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 0 to 14 as a percentage of the total male population. Population is based on the de facto definition of population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.0014.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Total population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "Staff estimates, World Bank (WB), note: Staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects., publisher: World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data.;\nWorld Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SP.POP.0014.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14 (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Population between the ages 0 to 14 as a percentage of the total population. Population is based on the de facto definition of population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Population between the ages 0 to 14 as a percentage of the total population. Population is based on the de facto definition of population." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects., United Nations Population Division, uri: https://population.un.org/wpp/, publisher: United Nations Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.0509.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 05-09, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 5 to 9." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.0509.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 05-09, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 5 to 9 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 5 to 9 as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.0509.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 05-09, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 5 to 9." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.0509.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 05-09, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 5 to 9 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 5 to 9 as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.1014.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 10-14, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 10 to 14." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.1014.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 10-14, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 10 to 14 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 10 to 14 as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.1014.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 10-14, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 10 to 14." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.1014.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 10-14, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 10 to 14 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 10 to 14 as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.1519.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 15-19, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 15 to 19." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.1519.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 15-19, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 15 to 19 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 15 to 19 as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.1519.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 15-19, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 15 to 19." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.1519.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 15-19, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 15 to 19 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 15 to 19 as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.1564.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SP.POP.1564.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 15 to 64 as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 15 to 64 as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.1564.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SP.POP.1564.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 15 to 64 as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 15 to 64 as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.1564.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Total population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SP.POP.1564.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64 (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Total population between the ages 15 to 64 as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Total population between the ages 15 to 64 as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.2024.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 20-24, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 20 to 24." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.2024.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 20-24, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 20 to 24 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 20 to 24 as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.2024.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 20-24, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 20 to 24." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.2024.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 20-24, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 20 to 24 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 20 to 24 as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.2529.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 25-29, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 25 to 29." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.2529.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 25-29, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 25 to 29 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 25 to 29 as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.2529.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 25-29, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 25 to 29." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.2529.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 25-29, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 25 to 29 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 25 to 29 as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.3034.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 30-34, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 30 to 34." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.3034.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 30-34, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 30 to 34 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 30 to 34 as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.3034.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 30-34, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 30 to 34." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.3034.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 30-34, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 30 to 34 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 30 to 34 as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.3539.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 35-39, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 35 to 39." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.3539.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 35-39, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 35 to 39 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 35 to 39 as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.3539.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 35-39, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 35 to 39." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.3539.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 35-39, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 35 to 39 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 35 to 39 as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.4044.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 40-44, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 40 to 44." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.4044.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 40-44, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 40 to 44 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 40 to 44 as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.4044.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 40-44, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 40 to 44." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.4044.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 40-44, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 40 to 44 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 40 to 44 as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.4549.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 45-49, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 45 to 49." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.4549.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 45-49, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 45 to 49 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 45 to 49 as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.4549.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 45-49, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 45 to 49." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.4549.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 45-49, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 45 to 49 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 45 to 49 as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.5054.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 50-54, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 50 to 54." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.5054.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 50-54, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 50 to 54 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 50 to 54 as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.5054.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 50-54, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 50 to 54." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.5054.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 50-54, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 50 to 54 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 50 to 54 as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.5559.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 55-59, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 55 to 59." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.5559.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 55-59, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 55 to 59 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 55 to 59 as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.5559.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 55-59, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 55 to 59." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.5559.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 55-59, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 55 to 59 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 55 to 59 as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.6064.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 60-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 60 to 64." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.6064.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 60-64, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 60 to 64 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 60 to 64 as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.6064.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 60-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 60 to 64." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.6064.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 60-64, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 60 to 64 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 60 to 64 as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.6569.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 65-69, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 65 to 69." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.6569.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 65-69, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 65 to 69 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 65 to 69 as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.6569.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 65-69, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 65 to 69." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.6569.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 65-69, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 65 to 69 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 65 to 69 as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.65UP.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SP.POP.65UP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population 65 years of age or older as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population 65 years of age or older as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.65UP.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SP.POP.65UP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population 65 years of age or older as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population 65 years of age or older as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.65UP.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Total population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SP.POP.65UP.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Population ages 65 and above as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Population ages 65 and above as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.7074.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 70-74, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 70 to 74." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.7074.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 70-74, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 70 to 74 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 70 to 74 as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.7074.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 70-74, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 70 to 74." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.7074.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 70-74, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 70 to 74 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 70 to 74 as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.7579.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 75-79, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 75 to 79." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.7579.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 75-79, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 75 to 79 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 75 to 79 as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.7579.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 75-79, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 75 to 79." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.7579.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 75-79, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 75 to 79 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 75 to 79 as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.80UP.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 80 and above, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 80 and above." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.80UP.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 80 and above, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population between the ages 80 and above as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population between the ages 80 and above as a percentage of the total female population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.80UP.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Population ages 80 and above, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 80 and above." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.80UP.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 80 and above, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population between the ages 80 and above as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population between the ages 80 and above as a percentage of the total male population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.AG00.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 00, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.AG00.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 00, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.AG01.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 01, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.AG01.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 01, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.AG02.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 02, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.AG02.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 02, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.AG03.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 03, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.AG03.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 03, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.AG04.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 04, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.AG04.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 04, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. 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World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.AG23.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 23, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.AG23.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 23, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.AG24.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 24, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.AG24.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 24, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.AG25.FE.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 25, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, female refers to female population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.AG25.MA.IN", "metatype": [ { "id": "IndicatorName", "value": "Age population, age 25, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Age population, male refers to male population at the specified age level." }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "40" }, { "id": "SP.POP.BRTH.MF", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In the absence of interference, it is expected that the sex ratio at birth is fairly stable within the range of 1.03 to 1.07 boys born per 1.00 girls. However, in some populations, the observed sex ratio at birth is well above this range because of sex-selection driven by the preference for sons over daughters." }, { "id": "IndicatorName", "value": "Sex ratio at birth (male births per female births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Sex ratio at birth refers to male births per female births." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Shortdefinition", "value": "Sex ratio at birth refers to male births per female births." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Sex ratio at birth is calculated as number of male births divided by number of female births.\nStatistical concept(s): If the sex ratio at birth is greater than 1, it indicates more boys born that year than girls." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Male births per female births" } ], "source_id": "40" }, { "id": "SP.POP.DPND", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Age dependency ratio (% of working-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Othernotes", "value": "Relevance to gender indicator: this indicator implies the dependency burden that the working-age population bears in relation to children and the elderly. Many times single or widowed women who are the sole caregiver of a household have a high dependency ratio." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Age dependency ratio is calculated as 100 x (Population (0-14) + Population (65+)) / Population (15-64). Data are shown as the proportion of dependents per 100 working-age population.\nStatistical concept(s): Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But dependency ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.\n\n\n\nAge structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.DPND.OL", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Age dependency ratio, old (% of working-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Age dependency ratio, old is calculated as 100 x (Population (65+)) / Population (15-64). Data are shown as the proportion of old dependents per 100 working-age population.\nStatistical concept(s): Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But dependency ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.\n\n\n\nAge structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.DPND.YG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Age dependency ratio, young (% of working-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Age dependency ratio, young, is the ratio of younger dependents--people younger than 15--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Age dependency ratio, young, is the ratio of younger dependents--people younger than 15--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Age dependency ratio, young is calculated as 100 x (Population (0-14)) / Population (15-64). Data are shown as the proportion of young dependents per 100 working-age population.\nStatistical concept(s): Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But dependency ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.\n\n\n\nAge structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.GROW", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "Derived from total population. Population source: United Nations Population Division, National Statistical Offices, Eurostat, United Nations Statistics Division." }, { "id": "Developmentrelevance", "value": "Increases in human population, whether as a result of immigration or more births than deaths, can impact natural resources and social infrastructure. This can place pressure on a country's sustainability. A significant growth in population will negatively impact the availability of land for agricultural production, and will aggravate demand for food, energy, water, social services, and infrastructure. On the other hand, decreasing population size - a result of fewer births than deaths, and people moving out of a country - can impact a government's commitment to maintain services and infrastructure." }, { "id": "IndicatorName", "value": "Population growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Shortdefinition", "value": "Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), note: Derived from total population, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices, note: Derived from total population;\nDemographic Statistics, Eurostat (ESTAT), note: Derived from total population;\nPopulation and Vital Statistics Report (various years), United Nations (UN), note: Derived from total population, publisher: UN Statistical Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The growth rate is computed using the exponential growth formula:\n\n\n\nr = ln(pn/p0)/n, \n\n\n\nwhere r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods.\n\n\n\nFor information on total population from which the growth rates are calculated, see total population (SP.POP.TOTL).\nStatistical concept(s): Total population growth rates are calculated on the assumption that rate of growth is constant between two points in time." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Increases in human population, whether as a result of immigration or more births than deaths, can impact natural resources and social infrastructure. This can place pressure on a country's sustainability. A significant growth in population will negatively impact the availability of land for agricultural production, and will aggravate demand for food, energy, water, social services, and infrastructure. On the other hand, decreasing population size - a result of fewer births than deaths, and people moving out of a country - can impact a government's commitment to maintain services and infrastructure." }, { "id": "IndicatorName", "value": "Population, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Current population estimates for developing countries that lack (i) reliable recent census data, and (ii) pre- and post-census estimates for countries with census data, are provided by the United Nations Population Division and other agencies. \n\n\n\nThe cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in both the model and the data.\n\n\n\nBecause future trends cannot be known with certainty, population projections have a wide range of uncertainty." }, { "id": "Longdefinition", "value": "Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates." }, { "id": "Othernotes", "value": "Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices, uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population estimates are usually based on national population censuses, and estimates of fertility, mortality and migration.\n\n\n\nErrors and undercounting in census occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census.\n\n\n\nThe quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data.\n\n\n\nThe currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census.\n\n\n\nThe United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems.\n\n\n\nInternational migration is the only other factor besides birth and death rates that directly determines a country's population change. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect.\n\n\n\nOne of the major data sources of this indicator is UN Population Division's World Population Prospects, which use the cohort component method to produce population estimates and projections.\n\n\n\nPopulation projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant.\nStatistical concept(s): Estimates of total population describe the size of total population. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SP.POP.TOTL.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Females comprise almost one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population is based on the de facto definition of population, which counts all female residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population is based on the de facto definition of population, which counts all female residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SP.POP.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age/sex distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Females comprise almost one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, female (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Female population is the percentage of the population that is female. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Female population is the percentage of the population that is female. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2022 Revision" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.POP.TOTL.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Males comprise about one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population is based on the de facto definition of population, which counts all male residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population is based on the de facto definition of population, which counts all male residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SP.POP.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age/sex distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Males comprise about one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, male (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Male population is the percentage of the population that is male. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Male population is the percentage of the population that is male. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2022 Revision" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.RUR.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and urban/rural distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "The rural population is calculated using the urban share reported by the United Nations Population Division.\n\nThe two distinct images - isolated farm, thriving metropolis - represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income.\n\nA 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population density and remoteness together define a set of rural areas that face special development challenges.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\"\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nRural population methodology is defined by various national statistical offices. In the United States, for example, the US Census Bureau's urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. \"Rural\" encompasses all population, housing, and territory not included within an urban area." }, { "id": "IndicatorName", "value": "Rural population" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population. Aggregation of urban and rural population may not add up to total population because of different country coverages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population. Aggregation of urban and rural population may not add up to total population because of different country coverages." }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using World Bank's total population estimates and rural ratios derived from the United Nations World Urbanization Prospects.\nStatistical concept(s): Rural population is calculated as the difference between the total population and the urban population. Rural population is approximated as the midyear nonurban population. While a practical means of identifying the rural population, it is not a precise measure." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SP.RUR.TOTL.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and urban/rural distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "The rural population is calculated using the urban share reported by the United Nations Population Division.\n\nThe two distinct images - isolated farm, thriving metropolis - represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income.\n\nA 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population density and remoteness together define a set of rural areas that face special development challenges.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\"\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nRural population methodology is defined by various national statistical offices. In the United States, for example, the US Census Bureau's urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. \"Rural\" encompasses all population, housing, and territory not included within an urban area." }, { "id": "IndicatorName", "value": "Rural population growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. \n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Annual rural population growth rate for year t is the exponential rate of growth of midyear rural population from year t-1 to t, expressed as a percentage. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Shortdefinition", "value": "Annual rural population growth rate for year t is the exponential rate of growth of midyear rural population from year t-1 to t, expressed as a percentage. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population." }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated from rural population estimates. The rural population estimates are calulcated using World Bank's total population estimates and rural ratios derived from the United Nations World Urbanization Prospects.\n\n\n\n\n\n\n\n\n\n\n\nThe growth rate is computed using the exponential growth formula:\n\n\n\n\n\n\n\n\n\n\n\nr = ln(pn/p0)/n, \n\n\n\n\n\n\n\n\n\n\n\nwhere r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods.\nStatistical concept(s): Rural population is calculated as the difference between the total population and the urban population. Rural population is approximated as the midyear nonurban population. While a practical means of identifying the rural population, it is not a precise measure." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.RUR.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The rural population is calculated using the urban share reported by the United Nations Population Division.\n\nThe two distinct images - isolated farm, thriving metropolis - represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income.\n\nA 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population density and remoteness together define a set of rural areas that face special development challenges.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\"\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nRural population methodology is defined by various national statistical offices. In the United States, for example, the US Census Bureau's urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. \"Rural\" encompasses all population, housing, and territory not included within an urban area." }, { "id": "IndicatorName", "value": "Rural population (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population." }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentages rural are calculated as the difference between 100 and the proportion of urban population in percentage.\nStatistical concept(s): Rural population is calculated as the difference between the total population and the urban population. Rural population is approximated as the midyear nonurban population. While a practical means of identifying the rural population, it is not a precise measure." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.URB.GROW", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and urban/rural distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Most countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Annual urban population growth rate for year t is the exponential rate of growth of midyear urban population from year t-1 to t, expressed as a percentage. Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank total population estimates and urban ratios from the United Nations World Urbanization Prospects." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Shortdefinition", "value": "Annual urban population growth rate for year t is the exponential rate of growth of midyear urban population from year t-1 to t, expressed as a percentage. Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank total population estimates and urban ratios from the United Nations World Urbanization Prospects." }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated from urban population estimates. The urban population estimates are calulcated using World Bank's total population estimates and urban ratios from the United Nations World Urbanization Prospects.\n\n\n\n\n\n\n\n\n\n\n\nThe growth rate is computed using the exponential growth formula:\n\n\n\n\n\n\n\n\n\n\n\nr = ln(pn/p0)/n, \n\n\n\n\n\n\n\n\n\n\n\nwhere r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods.\nStatistical concept(s): Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "SP.URB.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and urban/rural distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. \n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. Aggregation of urban and rural population may not add up to total population because of different country coverages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. Aggregation of urban and rural population may not add up to total population because of different country coverages." }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using World Bank's total population estimates and urban ratios from the United Nations World Urbanization Prospects.\nStatistical concept(s): Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "40" }, { "id": "SP.URB.TOTL.IN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage.\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Shortdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division." }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentages urban are the numbers of persons residing in an area defined as ''urban'' per 100 total population.\nStatistical concept(s): Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "40" }, { "id": "NY.ADJ.AEDU.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, education expenditure (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Education expenditure refers to the current operating expenditures in education, including wages and salaries and excluding capital investments in buildings and equipment. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nStatistical Yearbook, United Nations (UN), publisher: UN Statistics Division;\nOnline database, UN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "43" }, { "id": "NY.ADJ.AEDU.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, education expenditure (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Public education expenditures are considered an addition to savings. However, because of the wide variability in the effectiveness of public education expenditures, these figures cannot be construed as the value of investments in human capital. A current expenditure of $1 on education does not necessarily yield $1 of human capital. The calculation should also consider private education expenditure, but data are not available for a large number of countries." }, { "id": "Longdefinition", "value": "Education expenditure refers to the current operating expenditures in education, including wages and salaries and excluding capital investments in buildings and equipment. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nStatistical Yearbook, United Nations (UN), publisher: UN Statistics Division;\nOnline database, UN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "43" }, { "id": "NY.ADJ.DCO2.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, carbon dioxide damage (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Cost of damage due to carbon dioxide emissions from fossil fuel use and the manufacture of cement, estimated to be US$40 per ton of CO2 (the unit damage in 2017 US dollars for CO2 emitted in 2020) times the number of tons of CO2 emitted. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "43" }, { "id": "NY.ADJ.DCO2.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, carbon dioxide damage (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Cost of damage due to carbon dioxide emissions from fossil fuel use and the manufacture of cement, estimated to be US$40 per ton of CO2 (the unit damage in 2017 US dollars for CO2 emitted in 2020) times the number of tons of CO2 emitted. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "43" }, { "id": "NY.ADJ.DFOR.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, net forest depletion (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net forest depletion is calculated as the product of unit resource rents and the excess of roundwood harvest over natural growth. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "43" }, { "id": "NY.ADJ.DFOR.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, net forest depletion (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A positive net depletion figure for forest resources implies that the harvest rate exceeds the rate of natural growth; this is not the same as deforestation, which represents a change in land use. In principle, there should be an addition to savings in countries where growth exceeds harvest, but empirical estimates suggest that most of this net growth is in forested areas that cannot currently be exploited economically. Because the depletion estimates reflect only timber values, they ignore all the external and nontimber benefits associated with standing forests." }, { "id": "Longdefinition", "value": "Net forest depletion is calculated as the product of unit resource rents and the excess of roundwood harvest over natural growth. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "43" }, { "id": "NY.ADJ.DKAP.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, consumption of fixed capital (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Consumption of fixed capital represents the replacement value of capital used up in the process of production. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "43" }, { "id": "NY.ADJ.DKAP.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, consumption of fixed capital (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Consumption of fixed capital represents the replacement value of capital used up in the process of production. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nNational Accounts Statistics, United Nations (UN), publisher: UN Statistics Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "43" }, { "id": "NY.ADJ.DMIN.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, mineral depletion (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mineral depletion is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime (capped at 25 years). It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "43" }, { "id": "NY.ADJ.DMIN.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, mineral depletion (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mineral depletion is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime (capped at 25 years). It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "43" }, { "id": "NY.ADJ.DNGY.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, energy depletion (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Energy depletion is the ratio of the value of the stock of energy resources to the remaining reserve lifetime (capped at 25 years). It covers coal, crude oil, and natural gas. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "43" }, { "id": "NY.ADJ.DNGY.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, energy depletion (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Energy depletion is the ratio of the value of the stock of energy resources to the remaining reserve lifetime (capped at 25 years). It covers coal, crude oil, and natural gas. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "43" }, { "id": "NY.ADJ.DPEM.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, particulate emission damage (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Particulate emissions damage is the damage due to exposure of a country's population to ambient concentrations of particulates measuring less than 2.5 microns in diameter (PM2.5), ambient ozone pollution, and indoor concentrations of PM2.5 in households cooking with solid fuels. Damages are calculated as foregone labor income due to premature death. Estimates of health impacts from the Global Burden of Disease Study 2013 are for 1990, 1995, 2000, 2005, 2010, and 2013. Data for other years have been extrapolated from trends in mortality rates. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Global Burden of Disease 2013 study, Institute for Health Metrics and Evaluation (IHME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "43" }, { "id": "NY.ADJ.DPEM.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, particulate emission damage (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Labor productivity losses, as calculated within the framework of adjusted net savings, represent only part of the economic costs of air pollution and should be interpreted as a lower-end estimate." }, { "id": "Longdefinition", "value": "Particulate emissions damage is the damage due to exposure of a country's population to ambient concentrations of particulates measuring less than 2.5 microns in diameter (PM2.5), ambient ozone pollution, and indoor concentrations of PM2.5 in households cooking with solid fuels. Damages are calculated as foregone labor income due to premature death. Estimates of health impacts from the Global Burden of Disease Study 2013 are for 1990, 1995, 2000, 2005, 2010, and 2013. Data for other years have been extrapolated from trends in mortality rates. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Global Burden of Disease 2013 study, Institute for Health Metrics and Evaluation (IHME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "43" }, { "id": "NY.ADJ.DRES.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, natural resources depletion (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Net forest depletion is not the monetary value of deforestation. Roundwood and fuelwood production are different from deforestation, which represents a permanent change in land use and, thus, is not comparable. Areas logged out but intended for regeneration are not included in deforestation figures; rather, they are counted as producing timber depletion. Net forest depletion includes only timber values and does not include the loss of nontimber forest benefits and nonuse benefits.\n\n\n\n\n\nFor both energy and mineral depletion, unit resource rent is calculated as (unit world price - average cost) / unit world price. Marginal cost should be used instead of average cost in order to calculate the true opportunity cost of extraction; however, marginal cost is difficult to compute and data are not readily available. Unit prices refer to international rather than local prices to reflect the social cost of natural resources depletion. This differs from methodologies of national accounts, which may use local prices to measure energy or mineral GDP. This difference explains eventual discrepancies in the values for energy or mineral depletion, verses energy or mineral GDP." }, { "id": "Longdefinition", "value": "Natural resource depletion is the sum of net forest depletion, energy depletion, and mineral depletion. Net forest depletion is unit resource rents times the excess of roundwood harvest over natural growth. Energy depletion is the ratio of the value of the stock of energy resources to the remaining reserve lifetime (capped at 25 years). It covers coal, crude oil, and natural gas. Mineral depletion is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime (capped at 25 years). It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "43" }, { "id": "NY.ADJ.ICTR.CD", "metatype": [ { "id": "IndicatorName", "value": "Adjusted savings: gross savings (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross savings are the difference between gross national income and public and private consumption, plus net current transfers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" } ], "source_id": "43" }, { "id": "NY.ADJ.ICTR.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, gross savings (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because gross savings is calculated as a residual it includes errors, which may not be offsetting, in its components." }, { "id": "Longdefinition", "value": "Gross savings are the difference between gross national income and public and private consumption, plus net current transfers. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "43" }, { "id": "NY.ADJ.NNAT.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, net national savings (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net national savings are equal to gross national savings less the value of consumption of fixed capital. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "43" }, { "id": "NY.ADJ.NNAT.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, net national savings (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net national savings are equal to gross national savings less the value of consumption of fixed capital. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "43" }, { "id": "NY.ADJ.NNTY.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Adjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators." }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "43" }, { "id": "NY.ADJ.NNTY.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Adjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators." }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "43" }, { "id": "NY.ADJ.NNTY.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Adjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators." }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1971-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "43" }, { "id": "NY.ADJ.NNTY.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "43" }, { "id": "NY.ADJ.NNTY.PC.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income per capita (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Adjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators." }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "43" }, { "id": "NY.ADJ.NNTY.PC.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income per capita (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Adjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators." }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1971-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "43" }, { "id": "NY.ADJ.SVNG.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net savings, including particulate emission damage (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide and particulate emissions damage. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "43" }, { "id": "NY.ADJ.SVNG.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net savings, including particulate emission damage (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The exercise treats public education expenditures as an addition to savings. However, because of the wide variability in the effectiveness of public education expenditures, these figures cannot be construed as the value of investments in human capital. A current expenditure of $1 on education does not necessarily yield $1 of human capital. The calculation should also consider private education expenditure, but data are not available for a large number of countries.\n\n\n\n\n\nWhile extensive, the accounting of natural resource depletion and pollution costs still has some gaps. Key estimates missing on the resource side include the value of fossil water extracted from aquifers, net depletion of fish stocks, and depletion and degradation of soils. Important pollutants affecting human health and economic assets are excluded because no internationally comparable data are widely available on damage from ground-level ozone or sulfur oxides." }, { "id": "Longdefinition", "value": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide and particulate emissions damage. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "43" }, { "id": "NY.ADJ.SVNG.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Adjusted net savings per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide and particulate emissions damage." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on sources and methods in World Bank's \"The Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium\" (2011)." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" } ], "source_id": "43" }, { "id": "NY.ADJ.SVNX.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net savings, excluding particulate emission damage (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide. This series excludes particulate emissions damage. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "43" }, { "id": "NY.ADJ.SVNX.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net savings, excluding particulate emission damage (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide. This series excludes particulate emissions damage. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "43" }, { "id": "NY.GDP.COAL.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Coal rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Coal rents are the difference between the value of both hard and soft coal production at world prices and their total costs of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "43" }, { "id": "NY.GDP.FRST.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Forest rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Forest rents are roundwood harvest times the product of regional prices and a regional rental rate." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "43" }, { "id": "NY.GDP.MINR.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Mineral rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mineral rents are the difference between the value of production for a stock of minerals at world prices and their total costs of production. Minerals included in the calculation are tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "43" }, { "id": "NY.GDP.NGAS.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Natural gas rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Natural gas rents are the difference between the value of natural gas production at regional prices and total costs of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "43" }, { "id": "NY.GDP.PETR.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Oil rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Oil rents are the difference between the value of crude oil production at regional prices and total costs of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "43" }, { "id": "NY.GDP.TOTL.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Total natural resources rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total natural resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "43" }, { "id": "AG.LND.FRST.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity.\n\nOn a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems. The decrease of primary forest area, 0.4 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions.\n\nNational parks, game reserves, wilderness areas and other legally established protected areas cover more than 10 percent of the total forest area in most countries and regions. FAO estimates that around 10 million people are employed in forest management and conservation - but many more are directly dependent on forests for their livelihoods. Also, 80 about percent of the world's forests are publicly owned, but ownership and management of forests by communities, individuals and private companies is on the rise.\n\nClose to 1.2 billion hectares of forest are managed primarily for the production of wood and non-wood forest products. An additional 25 percent of forest area is designated for multiple uses - in most cases including the production of wood and non-wood forest products. The area designated primarily for productive purposes has decreased by more than 50 million hectares since 1990 as forests have been designated for other purposes." }, { "id": "IndicatorName", "value": "Forest area (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Food and Agricultural Organization (FAO) has been collecting and analyzing data on forest area since 1946. This is done at intervals of 5-10 years as part of the Global Forest Resources Assessment (FRA). FAO reports data for 229 countries and territories; for the remaining 56 small island states and territories where no information is provided, a report is prepared by FAO using existing information and a literature search. The data are aggregated at sub-regional, regional and global levels by the FRA team at FAO, and estimates are produced by straight summation.\n\nThe lag between the reference year and the actual production of data series as well as the frequency of data production varies between countries. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area.\n\nData includes areas with bamboo and palms; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters; plantations primarily used for forestry or protective purposes, such as rubber-wood plantations and cork oak stands. Data excludes tree stands in agricultural production systems, such as fruit plantations and agroforestry systems. Forest area also excludes trees in urban parks and gardens. The proportion of forest area to total land area is calculated and changes in the proportion are computed to identify trends." }, { "id": "Longdefinition", "value": "Forest area is land under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, electronic files and web site." }, { "id": "Statisticalconceptandmethodology", "value": "Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 meters are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate.\n\nFAO provides detail information on forest cover, and adjusted estimates of forest cover. The current survey uses a uniform definition of forest. Although FAO provides a breakdown of forest cover between natural forest and plantation for developing countries, this indictor data does not reflect that breakdown. Thus the deforestation data may underestimate the rate at which natural forest is disappearing in some countries." }, { "id": "Topic", "value": "Environment: Land use" } ], "source_id": "46" }, { "id": "AG.LND.FRST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity.\n\nOn a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems. The decrease of forest area, .11 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions.\n\nDestruction of rainforests remains a significant environmental problem Much of what remains of the world's rainforests is in the Amazon basin, where the Amazon Rainforest covers approximately 4 million square kilometers. The regions with the highest tropical deforestation rate are in Central America and tropical Asia. FAO estimates that the decrease of primary forest area, 0.4 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions. Large-scale planting of trees is significantly reducing the net loss of forest area globally, and afforestation and natural expansion of forests in some countries and regions have reduced the net loss of forest area significantly at the global level.\n\nForests cover about 31 percent of total land area of the world; the world's total forest area is just over 4 billion hectares. On a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems.\n\nNational parks, game reserves, wilderness areas and other legally established protected areas cover more than 10 percent of the total forest area in most countries and regions. FAO estimates that around 10 million people are employed in forest management and conservation - but many more are directly dependent on forests for their livelihoods. Close to 1.2 billion hectares of forest are managed primarily for the production of wood and non-wood forest products. An additional 25 percent of forest area is designated for multiple uses - in most cases including the production of wood and non-wood forest products. The area designated primarily for productive purposes has decreased by more than 50 million hectares since 1990 as forests have been designated for other purposes." }, { "id": "IndicatorName", "value": "Forest area (% of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "FAO has been collecting and analyzing data on forest area since 1946. This is done at intervals of 5-10 years as part of the Global Forest Resources Assessment (FRA). FAO reports data for 229 countries and territories; for the remaining 56 small island states and territories where no information is provided, a report is prepared by FAO using existing information and a literature search. The data are aggregated at sub-regional, regional and global levels by the FRA team at FAO, and estimates are produced by straight summation.\n\nThe lag between the reference year and the actual production of data series as well as the frequency of data production varies between countries. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area.\n\nData includes areas with bamboo and palms; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters; plantations primarily used for forestry or protective purposes, such as rubber-wood plantations and cork oak stands. Data excludes tree stands in agricultural production systems, such as fruit plantations and agroforestry systems. Forest area also excludes trees in urban parks and gardens. The proportion of forest area to total land area is calculated and changes in the proportion are computed to identify trends." }, { "id": "Longdefinition", "value": "Forest area is land under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, electronic files and web site." }, { "id": "Statisticalconceptandmethodology", "value": "Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 meters are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate.\n\nThe Food and Agriculture Organization (FAO) provides detail information on forest cover, and adjusted estimates of forest cover. The survey uses a uniform definition of forest. Although FAO provides a breakdown of forest cover between natural forest and plantation for developing countries, forest data used to derive this indictor data does not reflect that breakdown. Total land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area. The indictor is derived by dividing total area under forest of a country by country's total land area, and multiplying by 100." }, { "id": "Topic", "value": "Environment: Land use" } ], "source_id": "46" }, { "id": "AG.YLD.CREL.KG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "In developed countries, cereal crops are universally machine-harvested, typically using a combine harvester, which cuts, threshes, and winnows the grain during a single pass across the field. In many industrialized countries, particularly in the United States and Canada, farmers commonly deliver their newly harvested grain to a grain elevator or a storage facility that consolidates the crops of many farmers. In developing countries, a variety of harvesting methods are used in cereal cultivation, depending on the cost of labor, from small combines to hand tools such as the scythe or cradle.\n\nCrop production systems have evolved rapidly over the past century and have resulted in significantly increased crop yields, but have also created undesirable environmental side-effects such as soil degradation and erosion, pollution from chemical fertilizers and agrochemicals and a loss of bio-diversity. Factors such as the green revolution, has led to impressive progress in increasing cereals yields over the last few decades. This progress, however, is not equal across all regions. Continued progress depends on maintaining agricultural research and education. The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth." }, { "id": "IndicatorName", "value": "Cereal yield (kg per hectare)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Cereals production data relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. The FAO allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year.\n\nThe data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\nData on cereal yield may be affected by a variety of reporting and timing differences. Millet and sorghum, which are grown as feed for livestock and poultry in Europe and North America, are used as food in Africa, Asia, and countries of the former Soviet Union. So some cereal crops are excluded from the data for some countries and included elsewhere, depending on their use.\n\nThe data collected from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Longdefinition", "value": "Cereal yield, measured as kilograms per hectare of harvested land, includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. The FAO allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Cereal yield, measured as kilograms per hectare of harvested land, includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded." }, { "id": "Source", "value": "Food and Agriculture Organization, electronic files and web site." }, { "id": "Statisticalconceptandmethodology", "value": "A cereal is a grass cultivated for the edible components of their grain, composed of the endosperm, germ, and bran. Cereal yield is measured as kilograms per hectare of harvested land. Cereal grains are grown in greater quantities and provide more food energy worldwide than any other type of crop; cereal crops therefore can also be called staple crops Cereals production includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded." }, { "id": "Topic", "value": "Environment: Agricultural production" } ], "source_id": "46" }, { "id": "BN.CAB.XOKA.GD.ZS", "metatype": [ { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Current account balance (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current account balance is the sum of net exports of goods and services, net primary income, and net secondary income." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files, and World Bank and OECD GDP estimates." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Balances" } ], "source_id": "46" }, { "id": "BN.KLT.PTXL.CD", "metatype": [ { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. In BPM6, the headings of the financial account have been changed from credits and debits to net acquisition of financial assets and net incurrence of liabilities; i.e., all changes due to credit and debit entries are recorded on a net basis separately for financial assets and liabilities. Financial account balances are calculated as the change in assets minus the change in liabilities; signs are reversed from previous editions." }, { "id": "IndicatorName", "value": "Portfolio Investment, net (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Portfolio investment covers transactions in equity securities and debt securities. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "46" }, { "id": "BX.KLT.DINV.CD.WD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Private financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits." }, { "id": "Generalcomments", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "IndicatorName", "value": "Foreign direct investment, net inflows (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intra-unit flows of goods and services.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available." }, { "id": "Longdefinition", "value": "Foreign direct investment refers to direct investment equity flows in the reporting economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments database, supplemented by data from the United Nations Conference on Trade and Development and official national sources." }, { "id": "Statisticalconceptandmethodology", "value": "Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Foreign direct investment (FDI) data are supplemented by the World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources.\n\nThe internationally accepted definition of FDI (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results in a direct investment relationship, based on control and influence. Distinguished from other kinds of international investment, FDI is made to establish a lasting interest in or effective management control over an enterprise in another country. A lasting interest in an investment enterprise typically involves establishing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. BoP refers to Balance of Payments." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "46" }, { "id": "BX.KLT.DINV.WD.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Private financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits." }, { "id": "Generalcomments", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "IndicatorName", "value": "Foreign direct investment, net inflows (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intra-unit flows of goods and services.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available." }, { "id": "Longdefinition", "value": "Foreign direct investment are the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows net inflows (new investment inflows less disinvestment) in the reporting economy from foreign investors, and is divided by GDP." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and Balance of Payments databases, World Bank, International Debt Statistics, and World Bank and OECD GDP estimates." }, { "id": "Statisticalconceptandmethodology", "value": "Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Foreign direct investment (FDI) data are supplemented by the World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources.\n\nThe internationally accepted definition of FDI (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results in a direct investment relationship, based on control and influence. Distinguished from other kinds of international investment, FDI is made to establish a lasting interest in or effective management control over an enterprise in another country. A lasting interest in an investment enterprise typically involves establishing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. BoP refers to Balance of Payments." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "46" }, { "id": "BX.TRF.PWKR.DT.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "IndicatorName", "value": "Personal remittances, received (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Personal remittances comprise personal transfers and compensation of employees. Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual: personal transfers and compensation of employees." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on IMF balance of payments data, and World Bank and OECD GDP estimates." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "46" }, { "id": "DT.DOD.DECT.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "External debt stocks (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total external debt stocks to gross national income. Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total external debt stocks to gross national income." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Statisticalconceptandmethodology", "value": "Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "46" }, { "id": "DT.ODA.ALLD.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Net official development assistance and official aid received (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net official development assistance is disbursement flows (net of repayment of principal) that meet the DAC definition of ODA and are made to countries and territories on the DAC list of aid recipients. Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars." }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: https://stats.oecd.org/." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "46" }, { "id": "DT.ODA.ODAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "DAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about USD 130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net official development assistance received (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe nominal values may overstate the real value of aid to recipients. Changes in international prices and exchange rates can reduce the purchasing power of aid. Tying aid, still prevalent though declining in importance, also tends to reduce its purchasing power. Tying requires recipients to purchase goods and services from the donor country or from a specified group of countries. Such arrangements prevent a recipient from misappropriating or mismanaging aid receipts, but they may also be motivated by a desire to benefit donor country suppliers.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net official development assistance is disbursement flows (net of repayment of principal) that meet the DAC definition of ODA and are made to countries and territories on the DAC list of aid recipients. Data are in current U.S. dollars." }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: https://stats.oecd.org/." }, { "id": "Statisticalconceptandmethodology", "value": "The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions). \n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "46" }, { "id": "DT.ODA.ODAT.CD1", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "DAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about USD 130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net official development assistance received (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe nominal values may overstate the real value of aid to recipients. Changes in international prices and exchange rates can reduce the purchasing power of aid. Tying aid, still prevalent though declining in importance, also tends to reduce its purchasing power. Tying requires recipients to purchase goods and services from the donor country or from a specified group of countries. Such arrangements prevent a recipient from misappropriating or mismanaging aid receipts, but they may also be motivated by a desire to benefit donor country suppliers.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net official development assistance is disbursement flows (net of repayment of principal) that meet the DAC definition of ODA and are made to countries and territories on the DAC list of aid recipients. Data are in current U.S. dollars." }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: www.oecd.org/dac/stats/idsonline." }, { "id": "Statisticalconceptandmethodology", "value": "The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions). \n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "46" }, { "id": "DT.ODA.ODAT.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Net official development assistance received (constant 2020 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in constant 2020 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net official development assistance is disbursement flows (net of repayment of principal) that meet the DAC definition of ODA and are made to countries and territories on the DAC list of aid recipients. Data are in constant 2020 U.S. dollars." }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: https://stats.oecd.org/." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "46" }, { "id": "DT.TDS.DPPF.XP.ZS", "metatype": [ { "id": "IndicatorName", "value": "Debt service to exports (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Debt service, the sum of principal repayments and interest actually paid in currency, goods, or services, is expressed as a percentage of exports of goods and services--all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, net exports of goods under merchanting, nonmonetary gold, and services. This series differs from the standard debt to exports series in that it covers only long-term public and publicly guaranteed debt and repayments (repurchases and charges) to the IMF." }, { "id": "Shortdefinition", "value": "Debt service (public and publicly guaranteed and IMF only, % of exports of goods, services and primary income)" }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "46" }, { "id": "EA.PRD.AGRI.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2010" }, { "id": "Developmentrelevance", "value": "Until 2000, agriculture was the mainstay of employment around the world. Since then, the services sector has assumed this mantle and the gap between the two has widened. Although employment growth in agriculture has slowed, the number of workers in this sector reached over one billion in the early 2010s. At the global level, women are more active in the agricultural sector than men - some 38 per cent versus 33 per cent.\n\nAccording to FAO, over 1 billion people are employed in world agriculture, representing 1 in 3 of all workers; in sub-Saharan Africa over 60 percent of the entire labor force is involved in agriculture. Agriculture still accounts for about 45 per cent of the world's labor force. In developing countries, about 55 per cent of the labor force is in agriculture, with the figure being close to two thirds in many parts of Africa and Asia. The level of employment in agriculture is extremely small in most developed countries, and has been declining steadily for many generations; on the other hand, it has been declining considerably in developing countries as well.\n\nThe distribution of economic wealth in the world remains strongly correlated with employment by economic activity. The wealthier economies are those with the largest share of total employment in services, whereas the poorer economies are largely agriculture based.\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Agriculture value added per worker (constant 2010 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Agricultural data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through the questionnaire and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 2 instead of revision 3 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment sector indicator data." }, { "id": "Longdefinition", "value": "Agriculture value added per worker is a measure of agricultural productivity. Value added in agriculture measures the output of the agricultural sector (ISIC divisions 1-5) less the value of intermediate inputs. Agriculture comprises value added from forestry, hunting, and fishing as well as cultivation of crops and livestock production. Data are in constant 2010 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived from World Bank national accounts files and Food and Agriculture Organization, Production Yearbook and data files." }, { "id": "Statisticalconceptandmethodology", "value": "Agriculture comprises value added from forestry, hunting, and fishing as well as cultivation of crops and livestock production. Data are in constant 2000 U.S. dollars.\n\nAgricultural productivity is measured by value added per unit of input. Agricultural value added includes that from forestry and fishing. Thus interpretations of land productivity should be made with caution. Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques.\n\nData on employment are drawn from labor force surveys, household surveys, official estimates, censuses and administrative records of social insurance schemes, and establishment surveys when no other information is available. The concept of employment generally refers to people above a certain age who worked, or who held a job, during a reference period. Employment data include both full-time and part-time workers." }, { "id": "Topic", "value": "Environment: Agricultural production" } ], "source_id": "46" }, { "id": "EG.CFT.ACCS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to clean fuels and technologies for cooking (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to clean fuels and technologies for cooking is the proportion of total population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO Global Health Observatory  (https://www.who.int/data/gho/data/themes/air-pollution/household-air-pollution)" }, { "id": "Statisticalconceptandmethodology", "value": "Data for access to clean fuels and technologies for cooking are based on the World Health Organization’s (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Trends in the proportion of the population using each fuel type are estimated using a single multivariate hierarchical model, with urban and rural disaggregation. Estimates for overall ‘polluting’ fuels (unprocessed biomass, charcoal, coal, and kerosene) and ‘clean’ fuels (gaseous fuels, electricity, as well as an aggregation of any other clean fuels like alcohol) are produced by aggregating estimates of relevant fuel types. The model was used to derive clean fuel use estimates for 191 countries (ref. Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (2020), Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. J. R. Stat. Soc. C, 69: 815-839). Countries classified by the World Bank as high income (57 countries) in the 2022 fiscal year are assumed to have universal access to clean fuels and technologies for cooking." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "46" }, { "id": "EG.EGY.PRIM.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Energy intensity level of primary energy (MJ/$2017 PPP GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Energy intensity level is only an imperfect proxy to energy efficiency indicator and it can be affected by a number of factors not necessarily linked to pure efficiency such as climate." }, { "id": "Longdefinition", "value": "Energy intensity level of primary energy is the ratio between energy supply and gross domestic product measured at purchasing power parity. Energy intensity is an indication of how much energy is used to produce one unit of economic output. Lower ratio indicates that less energy is used to produce one unit of output." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Sustainable Energy for All (SE4ALL) database from the SE4ALL Global Tracking Framework led jointly by the World Bank, International Energy Agency, and the Energy Sector Management Assistance Program." }, { "id": "Statisticalconceptandmethodology", "value": "This indicator is obtained by dividing total primary energy supply over gross domestic product measured in constant 2017 US dollars at purchasing power parity." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "46" }, { "id": "EG.ELC.ACCS.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to electricity, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to electricity, rural is the percentage of rural population with access to electricity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Global Electrification Database from \"Tracking SDG 7: The Energy Progress Report\" led jointly by the custodian agencies: the International Energy Agency (IEA), the International Renewable Energy Agency (IRENA), the United Nations Statistics Division (UNSD), the World Bank and the World Health Organization (WHO)." }, { "id": "Statisticalconceptandmethodology", "value": "Data for access to electricity are collected among different sources: mostly data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Given the low frequency and the regional distribution of some surveys, a number of countries have gaps in available data. To develop the historical evolution and starting point of electrification rates, a simple modeling approach was adopted to fill in the missing data points - around 1990, around 2000, and around 2010. Therefore, a country can have a continuum of zero to three data points. There are 42 countries with zero data point and the weighted regional average was used as an estimate for electrification in each of the data periods. 170 countries have between one and three data points and missing data are estimated by using a model with region, country, and time variables. The model keeps the original observation if data is available for any of the time periods. This modeling approach allowed the estimation of electrification rates for 212 countries over these three time periods (Indicated as \"Estimate\"). Notation \"Assumption\" refers to the assumption of universal access in countries classified as developed by the United Nations. Data begins from the year in which the first survey data is available for each country." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "46" }, { "id": "EG.ELC.ACCS.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to electricity, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to electricity, urban is the percentage of urban population with access to electricity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Global Electrification Database from \"Tracking SDG 7: The Energy Progress Report\" led jointly by the custodian agencies: the International Energy Agency (IEA), the International Renewable Energy Agency (IRENA), the United Nations Statistics Division (UNSD), the World Bank and the World Health Organization (WHO)." }, { "id": "Statisticalconceptandmethodology", "value": "Data for access to electricity are collected among different sources: mostly data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Given the low frequency and the regional distribution of some surveys, a number of countries have gaps in available data. To develop the historical evolution and starting point of electrification rates, a simple modeling approach was adopted to fill in the missing data points - around 1990, around 2000, and around 2010. Therefore, a country can have a continuum of zero to three data points. There are 42 countries with zero data point and the weighted regional average was used as an estimate for electrification in each of the data periods. 170 countries have between one and three data points and missing data are estimated by using a model with region, country, and time variables. The model keeps the original observation if data is available for any of the time periods. This modeling approach allowed the estimation of electrification rates for 212 countries over these three time periods (Indicated as \"Estimate\"). Notation \"Assumption\" refers to the assumption of universal access in countries classified as developed by the United Nations. Data begins from the year in which the first survey data is available for each country." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "46" }, { "id": "EG.ELC.ACCS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies.\n\nEnergy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indication of a country's energy poverty status.\n\nElectricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification.\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas." }, { "id": "IndicatorName", "value": "Access to electricity (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to electricity is the percentage of population with access to electricity. Electrification data are collected from industry, national surveys and international sources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Global Electrification Database from \"Tracking SDG 7: The Energy Progress Report\" led jointly by the custodian agencies: the International Energy Agency (IEA), the International Renewable Energy Agency (IRENA), the United Nations Statistics Division (UNSD), the World Bank and the World Health Organization (WHO)." }, { "id": "Statisticalconceptandmethodology", "value": "Data for access to electricity are collected among different sources: mostly data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Given the low frequency and the regional distribution of some surveys, a number of countries have gaps in available data. To develop the historical evolution and starting point of electrification rates, a simple modeling approach was adopted to fill in the missing data points - around 1990, around 2000, and around 2010. Therefore, a country can have a continuum of zero to three data points. There are 42 countries with zero data point and the weighted regional average was used as an estimate for electrification in each of the data periods. 170 countries have between one and three data points and missing data are estimated by using a model with region, country, and time variables. The model keeps the original observation if data is available for any of the time periods. This modeling approach allowed the estimation of electrification rates for 212 countries over these three time periods (Indicated as \"Estimate\"). Notation \"Assumption\" refers to the assumption of universal access in countries classified as developed by the United Nations. Data begins from the year in which the first survey data is available for each country." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "46" }, { "id": "EG.ELC.RNEW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data." }, { "id": "IndicatorName", "value": "Renewable electricity output (% of total electricity output)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Longdefinition", "value": "Renewable electricity is the share of electrity generated by renewable power plants in total electricity generated by all types of plants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2018 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "46" }, { "id": "EG.FEC.RNEW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Renewable energy consumption (% of total final energy consumption)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Renewable energy consumption is the share of renewables energy in total final energy consumption." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Sustainable Energy for All (SE4ALL) database from the SE4ALL Global Tracking Framework led jointly by the World Bank, International Energy Agency, and the Energy Sector Management Assistance Program." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "46" }, { "id": "EN.ATM.CO2E.KD.GD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2015" }, { "id": "IndicatorName", "value": "CO2 emissions (kg per 2015 US$ of GDP)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Longdefinition", "value": "Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.KD for the denominator's source." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "46" }, { "id": "EN.ATM.CO2E.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "CO2 emissions (metric tons per capita)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Limitationsandexceptions", "value": "The U.S. Department of Energy's Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division's World Energy Data Set) and world cement manufacturing (from the U.S. Department of Interior's Geological Survey, USGS 2011). Although estimates of global carbon dioxide emissions are probably accurate within 10 percent (as calculated from global average fuel chemistry and use), country estimates may have larger error bounds. Trends estimated from a consistent time series tend to be more accurate than individual values.\n\nEach year the CDIAC recalculates the entire time series since 1949, incorporating recent findings and corrections. Estimates exclude fuels supplied to ships and aircraft in international transport because of the difficulty of apportioning the fuels among benefiting countries." }, { "id": "Longdefinition", "value": "Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See SP.POP.TOTL for the denominator's source." }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Data for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as deforestation." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "46" }, { "id": "EN.ATM.CO2E.PP.GD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "CO2 emissions (kg per PPP $ of GDP)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Longdefinition", "value": "Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.PP.CD for the denominator's source." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "46" }, { "id": "EN.ATM.CO2E.PP.GD.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2017" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "CO2 emissions (kg per 2017 PPP $ of GDP)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Limitationsandexceptions", "value": "The U.S. Department of Energy's Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division's World Energy Data Set) and world cement manufacturing (from the U.S. Department of Interior's Geological Survey, USGS 2011). Although estimates of global carbon dioxide emissions are probably accurate within 10 percent (as calculated from global average fuel chemistry and use), country estimates may have larger error bounds. Trends estimated from a consistent time series tend to be more accurate than individual values.\n\nEach year the CDIAC recalculates the entire time series since 1949, incorporating recent findings and corrections. Estimates exclude fuels supplied to ships and aircraft in international transport because of the difficulty of apportioning the fuels among benefiting countries.\n\nData for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as deforestation." }, { "id": "Longdefinition", "value": "Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.PP.KD for the denominator's source." }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Carbon dioxide emissions are often calculated and reported as elemental carbon. The values were converted to actual carbon dioxide mass by multiplying them by 3.667 (the ratio of the mass of carbon to that of carbon dioxide)." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "46" }, { "id": "EN.ATM.PM25.MC.M3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly." }, { "id": "IndicatorName", "value": "PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Population-weighted exposure to ambient PM2.5 pollution is defined as the average level of exposure of a nation's population to concentrations of suspended particles measuring less than 2.5 microns in aerodynamic diameter, which are capable of penetrating deep into the respiratory tract and causing severe health damage. Exposure is calculated by weighting mean annual concentrations of PM2.5 by population in both urban and rural areas." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Brauer, M. et al. 2017, for the Global Burden of Disease Study 2017." }, { "id": "Statisticalconceptandmethodology", "value": "A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762–3772; GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Prüss-Üstün A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Exposure to concentrations of PM2.5 in both urban and rural areas is weighted by population and is aggregated at the national level." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "46" }, { "id": "EN.ATM.PM25.MC.T1.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly. Three interim targets were defined for PM2.5 and have been shown to be achievable with successive and sustained abatement measures. Countries may find these interim targets particularly helpful in gauging progress over time in the difficult process of steadily reducing population exporsure to PM. IT-1 level corresponds to the highest mean concentrations reported in studies of long-term effects, and may also reflect higher but unknown historical concentrations that may have been contributed to observed health effects. IT-1 level has been shown to be associated with significant mortality in the developed world." }, { "id": "IndicatorName", "value": "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-1 value (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 1 (IT-1) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 35 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Brauer, M. et al. 2017, for the Global Burden of Disease Study 2017." }, { "id": "Statisticalconceptandmethodology", "value": "A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762–3772;GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Prüss-Üstün A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "46" }, { "id": "EN.ATM.PM25.MC.T2.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly. Three interim targets were defined for PM2.5 and have been shown to be achievable with successive and sustained abatement measures. Countries may find these interim targets particularly helpful in gauging progress over time in the difficult process of steadily reducing population exporsure to PM. IT-2 level is greater than the mean concentration at which effects have been observed in studies of long-term exposure and mortality and is likely to be associated with significant health impacts from both long-term and daily exposures to PM2.5. Attainment of IT-2 value would reduce the health risks of long-term exposure by about 6% relative to the IT-1 value." }, { "id": "IndicatorName", "value": "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-2 value (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 2 (IT-2) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 25 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Brauer, M. et al. 2017, for the Global Burden of Disease Study 2017." }, { "id": "Statisticalconceptandmethodology", "value": "A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762–3772; GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Prüss-Üstün A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "46" }, { "id": "EN.ATM.PM25.MC.T3.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly. Three interim targets were defined for PM2.5 and have been shown to be achievable with successive and sustained abatement measures. Countries may find these interim targets particularly helpful in gauging progress over time in the difficult process of steadily reducing population exporsure to PM. IT-3 level places greater weight than IT-2 on the likelihood of signifcant effects associated with long-term exposures. IT-3 value is close to the mean concentrations that are reported in studies of long-term exposure and provides an additional 6% reduction in mortality risk relative to the IT-2 value." }, { "id": "IndicatorName", "value": "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-3 value (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 3 (IT-3) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 15 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Brauer, M. et al. 2017, for the Global Burden of Disease Study 2017." }, { "id": "Statisticalconceptandmethodology", "value": "A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762–3772; GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Prüss-Üstün A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "46" }, { "id": "EN.ATM.PM25.MC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly." }, { "id": "IndicatorName", "value": "PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the WHO guideline value is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 10 micrograms per cubic meter, the guideline value recommended by the World Health Organization as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Brauer, M. et al. 2017, for the Global Burden of Disease Study 2017." }, { "id": "Statisticalconceptandmethodology", "value": "A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762–3772; GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Prüss-Üstün A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "46" }, { "id": "EN.BIR.THRD.NO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. The Red List Index for the world's birds shows that there has been a steady and continuing deterioration in the threat status of the world's birds since 1988, when the first complete global assessment was carried out.\n\nThe number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\n Threatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN guides conservation activities of governments, NGOs and scientific institutions. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\nGlobally, threatened birds occur worldwide - nearly all countries support one or more threatened bird species. Small islands hold disproportionately high numbers of Globally Threatened Birds, supporting over half of threatened species. Threatened seabirds are found throughout the world's oceans. The most important threats to the world's birds are the spread of agriculture and an ever increasing human use of biological resources.\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future." }, { "id": "IndicatorName", "value": "Bird species, threatened" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List." }, { "id": "Longdefinition", "value": "Birds are listed for countries included within their breeding or wintering ranges. Threatened species are the number of species classified by the IUCN as endangered, vulnerable, rare, indeterminate, out of danger, or insufficiently known." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Environmental Program and the World Conservation Monitoring Centre, and International Union for Conservation of Nature, Red List of Threatened Species." }, { "id": "Statisticalconceptandmethodology", "value": "Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threated species.\n\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\n\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\n\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\n\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\n\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process." }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" } ], "source_id": "46" }, { "id": "EN.CLC.DRSK.XQ", "metatype": [ { "id": "Developmentrelevance", "value": "The Hyogo Framework's goal is to substantially reduce disaster losses by 2015 - in lives, and in the social, economic, and environmental assets of communities and countries. The Hyogo Framework offers guiding principles, priorities for action, and practical means for achieving disaster resilience for vulnerable communities. Governments around the world have committed to take action to reduce disaster risk, and have adopted a guideline to reduce vulnerabilities to natural hazards, called the Hyogo Framework for Action (HFA). The HFA assists the efforts of nations and communities to become more resilient to, and cope better with the hazards that threaten their development gains.\n\nScientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems." }, { "id": "IndicatorName", "value": "Disaster risk reduction progress score (1-5 scale; 5=best)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Hyogo Framework for Action (FHA) national progress reports assess strategic priorities in the implementation of disaster risk reduction actions and establish baselines on levels of progress achieved in implementing the HFA's five priorities for action. National reporting processes are led by officially designated HFA focal institutions in country, and regional reporting by regional intergovernmental organizations.\n\nHFA's five priorities are:\n1. Making disaster risk reduction a policy priority, institutional strengthening\n2. Risk assessment and early warning systems\n3. Education, information and public awareness\n4. Reducing underlying risk factors\n5. Preparedness for effective response" }, { "id": "Longdefinition", "value": "Disaster risk reduction progress score is an average of self-assessment scores, ranging from 1 to 5, submitted by countries under Priority 1 of the Hyogo Framework National Progress Reports. The Hyogo Framework is a global blueprint for disaster risk reduction efforts that was adopted by 168 countries in 2005. Assessments of \"Priority 1\" include four indicators that reflect the degree to which countries have prioritized disaster risk reduction and the strengthening of relevant institutions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "(UNISDR, 2009-2011 Progress Reports, http://www.preventionweb.net/english/hyogo)." }, { "id": "Statisticalconceptandmethodology", "value": "Resilience is measured by the disaster risk reduction progress score, an average of self-assessment scores submitted by countries under Priority 1 of the Hyogo Framework National Progress Reports. The Hyogo Framework is a global blueprint for disaster risk reduction efforts that was adopted by 168 countries in 2005. Assessments of Priority 1 include four indicators that reflect the degree to which countries have prioritized disaster risk reduction and the strengthening of relevant institutions." }, { "id": "Topic", "value": "Environment: Land use" } ], "source_id": "46" }, { "id": "EN.CLC.MDAT.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Scientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems.\n\nA drought can lead to losses in agriculture, affect inland navigation and hydropower plants, reduce access to drinking water, and cause famines. A flood is a significant rise of water level in a stream, lake, reservoir, or coastal region. Extreme temperature events are either cold waves or heat waves. A cold wave can be both a prolonged period of excessively cold weather and the sudden invasion of very cold air over a large area. Accompanied by frost, it can damage agriculture, infrastructure, and property. A heat wave is a prolonged period of excessively hot and sometimes humid weather. Population affected by these natural disasters is the number of people injured, left homeless, or requiring immediate assistance and can include displaced or evacuated people." }, { "id": "IndicatorName", "value": "Droughts, floods, extreme temperatures (% of population, average 1990-2009)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is \"unequivocal\" and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20-86 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5°C by 2100 (Kattenberg and others 1996) and increase of \"killer\" heat waves during the warm season (Karl and others 1997)." }, { "id": "Longdefinition", "value": "Droughts, floods and extreme temperatures is the annual average percentage of the population that is affected by natural disasters classified as either droughts, floods, or extreme temperature events. A drought is an extended period of time characterized by a deficiency in a region's water supply that is the result of constantly below average precipitation. A drought can lead to losses to agriculture, affect inland navigation and hydropower plants, and cause a lack of drinking water and famine. A flood is a significant rise of water level in a stream, lake, reservoir or coastal region. Extreme temperature events are either cold waves or heat waves. A cold wave can be both a prolonged period of excessively cold weather and the sudden invasion of very cold air over a large area. Along with frost it can cause damage to agriculture, infrastructure, and property. A heat wave is a prolonged period of excessively hot and sometimes also humid weather relative to normal climate patterns of a certain region. Population affected is the number of people injured, left homeless or requiring immediate assistance during a period of emergency resulting from a natural disaster; it can also include displaced or evacuated people. Average percentage of population affected is calculated by dividing the sum of total affected for the period stated by the sum of the annual population figures for the period stated." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "EM-DAT: The OFDA/CRED International Disaster Database: www.emdat.be, Université Catholique de Louvain, Brussels (Belgium), World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "This indicator measures vulnerability of population affected by droughts, floods, and extreme temperature. A drought is an extended period of deficiency in a region's water supply as a result of below average precipitation." }, { "id": "Topic", "value": "Environment: Land use" } ], "source_id": "46" }, { "id": "EN.FSH.THRD.NO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. The Red List Index for the world's birds shows that there has been a steady and continuing deterioration in the threat status of the world's birds since 1988, when the first complete global assessment was carried out.\n\nThe number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\nThreatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN guides conservation activities of governments, NGOs and scientific institutions. The introduction in 1994 of a scientifically rigorous approach to determine risks of extinction that is applicable to all species, has become a world standard. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\nThe freshwater system represents the most threatened of all ecosystems, and many freshwater species have a very high livelihood value for local human communities. IUCN's freshwater focus is on the following taxonomic groups: fish; molluscs; crabs and crayfish; and dragonflies. Global assessment of these groups is being pursued through a series of regional projects, such as one for Africa that is currently being implemented.\n\nThe marine realm is poorly covered in the IUCN Red List, comprising less than 5 percent of the species included. IUCN has identified priority taxonomic groups of marine fish, invertebrates, plants (mangroves and seagrasses) and macro-algae (seaweeds). If these priority groups can be assessed, the number of marine species on the IUCN Red List will be increased more than six-fold.\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future." }, { "id": "IndicatorName", "value": "Fish species, threatened" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List." }, { "id": "Longdefinition", "value": "Fish species are based on Froese, R. and Pauly, D. (eds). 2008. Threatened species are the number of species classified by the IUCN as endangered, vulnerable, rare, indeterminate, out of danger, or insufficiently known." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Froese, R. and Pauly, D. (eds). 2008. FishBase database, www.fishbase.org." }, { "id": "Statisticalconceptandmethodology", "value": "Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threated species.\n\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\n\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\n\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\n\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\n\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process." }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" } ], "source_id": "46" }, { "id": "EN.HPT.THRD.NO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "The number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\nThreatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN guides conservation activities of governments, NGOs and scientific institutions. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\nThe plants and animals assessed for the IUCN Red List are the bearers of genetic diversity and the building blocks of ecosystems, and information on their conservation status and distribution provides the foundation for making informed decisions about conserving biodiversity from local to global levels. Only a small number of the world's plant and animal species have been assessed. In addition to the many thousands of species which have not yet been assessed so far, other species not included on the IUCN Red List are those that went extinct before 1500 AD and the \"Least Concern\" (plants that have been evaluated to have a low risk of extinction) species that have not yet been data based.\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future." }, { "id": "IndicatorName", "value": "Plant species (higher), threatened" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\nAlthough there are over 12,000 plant species on the IUCN Red List, fewer than one thousand of these are properly documented. To help address this gap, IUCN is pursuing global assessments of plant species of value to people including species of high economic value. The conifer and cycad species already on the IUCN Red List need to be fully documented. IUCN is also developing a tool to assist with preliminary assessments of plant species.\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List." }, { "id": "Longdefinition", "value": "Higher plants are native vascular plant species. Threatened species are the number of species classified by the IUCN as endangered, vulnerable, rare, indeterminate, out of danger, or insufficiently known." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Environmental Program and the World Conservation Monitoring Centre, and International Union for Conservation of Nature, Red List of Threatened Species." }, { "id": "Statisticalconceptandmethodology", "value": "Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threated species.\n\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\n\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\n\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\n\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\n\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process." }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" } ], "source_id": "46" }, { "id": "EN.MAM.THRD.NO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. The number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\nThreatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\nthe IUCN Red List covers a comprehensive assessment of the conservation status of the world's 5,488 mammal species, including global summary statistics, individual species accounts/threat category, range map, ecology information, and some other data. Mammal species are found spread across the globe, with the exception of the land mass of Antarctica. Nearly one-quarter of the world's mammal species are known to be globally threatened or extinct, 63 percent are known to not be threatened, and 15 percent have insufficient data to determine their threat status. Habitat loss, affecting over 2,000 mammal species, is the greatest threat globally. The second greatest threat is utilization which is affecting over 900 mammal species, mainly those in Asia.\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future." }, { "id": "IndicatorName", "value": "Mammal species, threatened" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\nSome parts of the world, such as the Andes, Central and West Africa, Angola, parts of South and Southeast Asia, and Melanesia, still have sparse information available of their mammal faunas. In addition, many species' names, especially in the tropics, actually represent complexes of several species that have not yet been resolved. The information on the relative importance of different threatening processes to mammal species is incomplete. IUCN codes all threats that appear to have an important impact, but not their relative importance for each species.\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List." }, { "id": "Longdefinition", "value": "Mammal species are mammals excluding whales and porpoises. Threatened species are the number of species classified by the IUCN as endangered, vulnerable, rare, indeterminate, out of danger, or insufficiently known." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Environmental Program and the World Conservation Monitoring Centre, and International Union for Conservation of Nature, Red List of Threatened Species." }, { "id": "Statisticalconceptandmethodology", "value": "Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threated species.\n\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\n\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\n\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\n\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\n\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process." }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" } ], "source_id": "46" }, { "id": "EN.POP.SLUM.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Population living in slums (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, housing durability, and security of tenure, as adopted in the Millennium Development Goal Target 7.D. The successor, the Sustainable Development Goal 11.1.1, considers inadequate housing (housing affordability) to complement the above definition of slums/informal settlements." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Human Settlements Programme (UN-HABITAT)" }, { "id": "Topic", "value": "Environment: Density & urbanization" } ], "source_id": "46" }, { "id": "ER.FSH.AQUA.MT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Aquaculture is understood to mean the farming of aquatic organisms including fish, molluscs, crustaceans and aquatic plants. Farming implies some form of intervention in the rearing process to enhance production, such as regular stocking, feeding, protection from predators, etc. Farming also implies individual or corporate ownership of the stock being cultivated. For statistical purposes, aquatic organisms which are harvested by an individual of corporate body which has owned them throughout their rearing period contribute to aquaculture while aquatic organisms which are exploitable by public as a common property resource, with or without appropriate licences, are the harvest of fisheries." }, { "id": "IndicatorName", "value": "Aquaculture production (metric tons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Aquaculture is understood to mean the farming of aquatic organisms including fish, molluscs, crustaceans and aquatic plants. Aquaculture production specifically refers to output from aquaculture activities, which are designated for final harvest for consumption." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization." }, { "id": "Statisticalconceptandmethodology", "value": "Aquaculture production specifically refers to output from aquaculture activities, which are designated for final harvest for consumption. At this time, harvest for ornamental purposes is not included." }, { "id": "Topic", "value": "Environment: Agricultural production" } ], "source_id": "46" }, { "id": "ER.FSH.CAPT.MT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Capture fisheries production (metric tons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Capture fisheries production measures the volume of fish catches landed by a country for all commercial, industrial, recreational and subsistence purposes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization." }, { "id": "Statisticalconceptandmethodology", "value": "In 2008, China revised its 2006 production statistics to reduce about 13 percent based on its Second National Agriculture Census conducted in 2007. This implied the downward adjustment of global capture production about 2 percent. Historical statistics of China for the period 1997-2005 were subsequently revised by FAO." }, { "id": "Topic", "value": "Environment: Agricultural production" } ], "source_id": "46" }, { "id": "ER.FSH.PROD.MT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Total fisheries production (metric tons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total fisheries production measures the volume of aquatic species caught by a country for all commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is also included." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization." }, { "id": "Topic", "value": "Environment: Agricultural production" } ], "source_id": "46" }, { "id": "ER.FST.DFST.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Deforestation is the removal of a forest or stand of trees where the land is thereafter is converted to a non-forest use, such as farms, ranches, or urban use. As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity.\n\nDestruction of rainforests remains a significant environmental problem - up to 90 percent of West Africa's coastal rainforests have disappeared since 1900. Much of what remains of the world's rainforests is in the Amazon basin, where the Amazon Rainforest covers approximately 4 million square kilometers. Large-scale planting of trees is significantly reducing the net loss of forest area globally, and afforestation and natural expansion of forests in some countries and regions have reduced the net loss of forest area significantly at the global level.\n\nForests cover more than 31 percent of total land area of the world; the world's total forest area is just over 4 billion hectares. On a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems.\n\nNational parks, game reserves, wilderness areas and other legally established protected areas cover more than 10 percent of the total forest area in most countries and regions. FAO estimates that around 10 million people are employed in forest management and conservation - but many more are directly dependent on forests for their livelihoods. Also, 80 about percent of the world's forests are publicly owned, but ownership and management of forests by communities, individuals and private companies is on the rise.\n\nClose to 1.2 billion hectares of forest are managed primarily for the production of wood and non-wood forest products. An additional 25 percent of forest area is designated for multiple uses - in most cases including the production of wood and non-wood forest products. The area designated primarily for productive purposes has decreased by more than 50 million hectares since 1990 as forests have been designated for other purposes." }, { "id": "Generalcomments", "value": "This is a period growth rate indicator for online table use only." }, { "id": "IndicatorName", "value": "Annual deforestation (% of change)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The national figures in the database are reported by the countries themselves, thus eliminating any discrepancies between global and national figures. The reporting format ensures that countries provide the full reference for original data sources as well as national definitions and terminology. Separate sections in the reporting format (country reports) deal with the analysis of data; calibration of data to the official land area as held by FAO; and reclassification of data to the classes used in FAO's Global Forest Resources Assessments.\n\nFAO has been collecting and analyzing data on forest area since 1946. This is done at intervals of 5-10 years as part of the Global Forest Resources Assessment (FRA). FAO reports data for 229 countries and territories; for the remaining 56 small island states and territories where no information is provided, a report is prepared by FAO using existing information and a literature search. The data are aggregated at sub-regional, regional and global levels by the FRA team at FAO, and estimates are produced by straight summation.\n\nThe lag between the reference year and the actual production of data series as well as the frequency of data production varies between countries. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area." }, { "id": "Longdefinition", "value": "Average annual deforestation refers to the permanent conversion of natural forest area to other uses, including shifting cultivation, permanent agriculture, ranching, settlements, and infrastructure development. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, Global Forest Resources Assessment." }, { "id": "Statisticalconceptandmethodology", "value": "Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 meters are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate.\n\nData includes areas with bamboo and palms; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters; plantations primarily used for forestry or protective purposes, such as rubber-wood plantations and cork oak stands. Data excludes tree stands in agricultural production systems, such as fruit plantations and agroforestry systems. Forest area also excludes trees in urban parks and gardens. The proportion of forest area to total land area is calculated and changes in the proportion are computed to identify trends.\n\nThe Food and Agricultural Organization (FAO) provides detail information on forest cover, and adjusted estimates of forest cover. The current survey uses a uniform definition of forest. Although FAO provides a breakdown of forest cover between natural forest and plantation for developing countries, this indictor data does not reflect that breakdown. Thus the deforestation data may underestimate the rate at which natural forest is disappearing in some countries.\n\nDeforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area." }, { "id": "Topic", "value": "Environment: Land use" } ], "source_id": "46" }, { "id": "ER.GDP.FWTL.M3.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2015" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectoral planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain." }, { "id": "IndicatorName", "value": "Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Water productivity is calculated as GDP in constant prices divided by annual total water withdrawal." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, AQUASTAT data, and World Bank and OECD GDP estimates." }, { "id": "Statisticalconceptandmethodology", "value": "Water productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. GDP data are from World Bank's national accounts files.\n\nWater withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants)." }, { "id": "Topic", "value": "Environment: Freshwater" } ], "source_id": "46" }, { "id": "ER.H2O.FWAG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectoral planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times)." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, agriculture (% of total freshwater withdrawal)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for agriculture are total withdrawals for irrigation and livestock production. Data are for the most recent year available for 1987-2002." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, AQUASTAT data." }, { "id": "Statisticalconceptandmethodology", "value": "This indicator measures the pressure on the renewable water resources of a country caused by irrigation. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\nWater withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants)." }, { "id": "Topic", "value": "Environment: Freshwater" } ], "source_id": "46" }, { "id": "ER.H2O.FWDM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "UNESCO estimates that in developing countries in Asia, Africa and Latin America, public water withdrawal represents just 50-100 liters (13 to 26 gallons) per person per day. In regions with insufficient water resources, this figure may be as low as 20-60 (5 to 15 gallons) liters per day. People in developed countries on average consume about 10 times more water daily than those in developing countries.\n\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, domestic (% of total freshwater withdrawal)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes. Data are for the most recent year available for 1987-2002." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, AQUASTAT data." }, { "id": "Statisticalconceptandmethodology", "value": "Domestic water withdrawal, sometimes used interchangeably with municipal water withdrawal, focuses on human needs (drinking, cooking, cleaning, and sanitation). Data includes renewable freshwater resources, potential over-abstraction of renewable groundwater, withdrawal of fossil groundwater, and the potential use of desalinated water or treated wastewater. It is usually computed as the total water withdrawn by the public distribution network, and includes that part of the industries, which is connected to the municipal network. The ratio between the net consumption and the water withdrawn can vary from 5 to 15 percent in urban areas and from 10 to 50 percent in rural areas.\n\nWater withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes." }, { "id": "Topic", "value": "Environment: Freshwater" } ], "source_id": "46" }, { "id": "ER.H2O.FWIN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture. UNESCO estimates that Industrial uses account for about 20 percent of global freshwater withdrawals. Of this, 57-69 percent is used for hydropower and nuclear power generation, 30-40 percent for industrial processes, and 0.5-3 percent for thermal power generation.\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, industry (% of total freshwater withdrawal)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for industry are total withdrawals for direct industrial use (including withdrawals for cooling thermoelectric plants). Data are for the most recent year available for 1987-2002." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, AQUASTAT data." }, { "id": "Statisticalconceptandmethodology", "value": "Annual industrial freshwater withdrawals include renewable water resources as well as potential over-abstraction of renewable groundwater or potential use of desalinated water or treated wastewater. It includes water for the cooling of thermoelectric plants, but it does not include hydropower.\n\nWater withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for industry are total withdrawals for direct industrial use (including withdrawals for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes." }, { "id": "Topic", "value": "Environment: Freshwater" } ], "source_id": "46" }, { "id": "ER.H2O.FWST.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "The level of water stress can show the degree to which water resources are being exploited to meet the country's water demand. It measures a country's pressure on its water resources and therefore the challenge on the sustainability of its water use. It tracks progress in regard to “withdrawals and supply of freshwater to address water scarcity”, i.e. the environmental component of target 6.4. It also shows to what extent water resources are already used, and signals the importance of effective supply and demand management policies. It indicates the likelihood of increasing competition and conflict between different water uses and users in a situation of increasing water scarcity. Increased water stress, shown by an increase in the value of the indicator, has potentially negative effects on the sustainability of the natural resources and on economic development. On the other hand, low values of water stress indicate that water does not represent a particular challenge for economic development and sustainability." }, { "id": "IndicatorName", "value": "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Water withdrawal as a percentage of water resources is a good indicator of pressure on limited water resources, one of the most important natural resources. However, it only partially addresses the issues related to sustainable water management. Supplementary indicators that capture the multiple dimensions of water management would combine data on water demand management, behavioural changes with regard to water use and the availability of appropriate infrastructure, and measure progress in increasing the efficiency and sustainability of water use, in particular in relation to population and economic growth. They would also recognize the different climatic environments that affect water use in countries, in particular in agriculture, which is the main user of water. Sustainability assessment is also linked to the critical thresholds fixed for this indicator and there is no universal consensus on such threshold.\n\nTrends in water withdrawal show relatively slow patterns of change. Usually, three-five years are a minimum frequency to be able to detect significant changes, as it is unlikely that the indicator would show meaningful variations from one year to the other. Estimation of water withdrawal by sector is the main limitation to the computation of the indicator. Few countries actually publish water use data on a regular basis by sector. Renewable water resources include all surface water and groundwater resources that are available on a yearly basis without consideration of the capacity to harvest and use this resource. Exploitable water resources, which refer to the volume of surface water or groundwater that is available with an occurrence of 90% of the time, are considerably less than renewable water resources, but no universal method exists to assess such exploitable water resources. There is no universally agreed method for the computation of incoming freshwater flows originating outside of a country's borders. Nor is there any standard method to account for return flows, the part of the water withdrawn from its source and which flows back to the river system after use. In countries where return flow represents a substantial part of water withdrawal, the indicator tends to underestimate available water and therefore overestimate the level of water stress.\n\nOther limitations that affect the interpretation of the water stress indicator include: difficulty to obtain accurate, complete and up-to-date data; potentially large variation of sub-national data; lack of account of seasonal variations in water resources; lack of consideration to the distribution among water uses; lack of consideration of water quality and its suitability for use; and the indicator can be higher than 100 per cent when water withdrawal includes secondary freshwater (water withdrawn previously and returned to the system), non-renewable water (fossil groundwater), when annual groundwater withdrawal is higher than annual replenishment (over-abstraction) or when water withdrawal includes part or all of the water set aside for environmental water requirements. Some of these issues can be solved through disaggregation of the index at the level of hydrological units and by distinguishing between different use sectors. However, due to the complexity of water flows, both within a country and between countries, care should be taken not to double-count." }, { "id": "Longdefinition", "value": "The level of water stress: freshwater withdrawal as a proportion of available freshwater resources is the ratio between total freshwater withdrawn by all major sectors and total renewable freshwater resources, after taking into account environmental water requirements. Main sectors, as defined by ISIC standards, include agriculture; forestry and fishing; manufacturing; electricity industry; and services. This indicator is also known as water withdrawal intensity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, AQUASTAT data." }, { "id": "Statisticalconceptandmethodology", "value": "Proportion of total renewable water resources withdrawn is the total volume of groundwater and surface water withdrawn from their sources for human use (in the agricultural, municipal and industrial sectors), expressed as a percentage of the total actual renewable water resources. The terms water resources and water withdrawal are understood as freshwater resources and freshwater withdrawal. Water withdrawal is estimated for the following three main sectors: agriculture, municipalities (including domestic water withdrawal) and industries, at country level and expressed in km3/year. The total actual renewable water resources for a country or region are defined as the sum of internal renewable water resources and the external renewable water resources, also expressed in km3/year. The indicator is computed by dividing total water withdrawal by total actual renewable water resources minus environmental requirements and expressed in percentage points.\n\nTotal freshwater withdrawal is the volume of freshwater extracted from its source (rivers, lakes, aquifers) for agriculture, industries and municipalities. It is estimated at the country level for the following three main sectors: agriculture, municipalities (including domestic water withdrawal) and industries. Freshwater withdrawal includes primary freshwater (not withdrawn before), secondary freshwater (previously withdrawn and returned to rivers and groundwater, such as discharged wastewater and agricultural drainage water) and fossil groundwater. It does not include non-conventional water, i.e. direct use of treated wastewater, direct use of agricultural drainage water and desalinated water. Total freshwater withdrawal is in general calculated as being the sum of total water withdrawal by sector minus direct use of wastewater, direct use of agricultural drainage water and use of desalinated water.\n\nTotal renewable freshwater resources are expressed as the sum of internal and external renewable water resources. The terms “water resources” and “water withdrawal” are understood here as freshwater resources and freshwater withdrawal. Internal renewable water resources are defined as the long-term average annual flow of rivers and recharge of groundwater for a given country generated from endogenous precipitation. External renewable water resources refer to the flows of water entering the country, taking into consideration the quantity of flows reserved to upstream and downstream countries through agreements or treaties.\n\nEnvironmental water requirements (Env.) are the quantities of water required to sustain freshwater and estuarine ecosystems. Water quality and also the resulting ecosystem services are excluded from this formulation which is confined to water volumes. This does not imply that quality and the support to societies which are dependent on environmental flows are not important and should not be taken care of. Methods of computation of Env. are extremely variable and range from global estimates to comprehensive assessments for river reaches. Water volumes can be expressed in the same units as the total freshwater withdrawal, and then as percentages of the available water resources." }, { "id": "Topic", "value": "Environment: Freshwater" } ], "source_id": "46" }, { "id": "ER.H2O.FWTL.K3", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration.\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times)." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, total (billion cubic meters)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including withdrawals for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes. Data are for the most recent year available for 1987-2002." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, AQUASTAT data." }, { "id": "Statisticalconceptandmethodology", "value": "Annual freshwater withdrawals are total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Water withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes." }, { "id": "Topic", "value": "Environment: Freshwater" } ], "source_id": "46" }, { "id": "ER.H2O.FWTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times)." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, total (% of internal resources)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including withdrawals for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes. Data are for the most recent year available for 1987-2002." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, AQUASTAT data." }, { "id": "Statisticalconceptandmethodology", "value": "Annual freshwater withdrawals are total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes." }, { "id": "Topic", "value": "Environment: Freshwater" } ], "source_id": "46" }, { "id": "ER.H2O.INTR.K3", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "UNESCO estimates that in developing countries in Asia, Africa and Latin America, public water withdrawal represents just 50-100 liters (13 to 26 gallons) per person per day. In regions with insufficient water resources, this figure may be as low as 20-60 (5 to 15 gallons) liters per day. People in developed countries on average consume about 10 times more water daily than those in developing countries.\n\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations." }, { "id": "IndicatorName", "value": "Renewable internal freshwater resources, total (billion cubic meters)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Renewable internal freshwater resources flows refer to internal renewable resources (internal river flows and groundwater from rainfall) in the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, AQUASTAT data." }, { "id": "Statisticalconceptandmethodology", "value": "The data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. Renewable water resources (internal and external) include average annual flow of rivers and recharge of aquifers generated from endogenous precipitation, and those water resources that are not generated in the country, such as inflows from upstream countries (groundwater and surface water), and part of the water of border lakes and/or rivers. Non-renewable water includes groundwater bodies (deep aquifers) that have a negligible rate of recharge on the human time-scale. While renewable water resources are expressed in flows, non-renewable water resources have to be expressed in quantity (stock). Runoff from glaciers where the mass balance is negative is considered non-renewable.\n\nTotal actual renewable water resources correspond to the maximum theoretical yearly amount of water actually available for a country at a given moment. The unit of calculation is km3/year or 109 m3/year. Calculation Criteria is [Water resources: total renewable (actual)] = [Surface water: total renewable (actual)] + [Groundwater: total renewable (actual)] - [Overlap between surface water and groundwater].*\n\nFresh water is naturally occurring water on the Earth's surface. It is a renewable but limited natural resource. Fresh water can only be renewed through the process of the water cycle, where water from seas, lakes, forests, land, rivers, and dams evaporates, forms clouds, and returns as precipitation. However, if more fresh water is consumed through human activities than is restored by nature, the result is that the quantity of fresh water available in lakes, rivers, dams and underground waters can be reduced which can cause serious damage to the surrounding environment.\n\n* http://www.fao.org/nr/water/aquastat/data/glossary/search.html?termId=4188&submitBtn=s&cls=yes" }, { "id": "Topic", "value": "Environment: Freshwater" } ], "source_id": "46" }, { "id": "ER.H2O.INTR.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "UNESCO estimates that in developing countries in Asia, Africa and Latin America, public water withdrawal represents just 50-100 liters (13 to 26 gallons) per person per day. In regions with insufficient water resources, this figure may be as low as 20-60 (5 to 15 gallons) liters per day. People in developed countries on average consume about 10 times more water daily than those in developing countries.\n\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations." }, { "id": "IndicatorName", "value": "Renewable internal freshwater resources per capita (cubic meters)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Renewable internal freshwater resources flows refer to internal renewable resources (internal river flows and groundwater from rainfall) in the country. Renewable internal freshwater resources per capita are calculated using the World Bank's population estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, AQUASTAT data." }, { "id": "Statisticalconceptandmethodology", "value": "Renewable water resources (internal and external) include average annual flow of rivers and recharge of aquifers generated from endogenous precipitation, and those water resources that are not generated in the country, such as inflows from upstream countries (groundwater and surface water), and part of the water of border lakes and/or rivers. Non-renewable water includes groundwater bodies (deep aquifers) that have a negligible rate of recharge on the human time-scale. While renewable water resources are expressed in flows, non-renewable water resources have to be expressed in quantity (stock). Runoff from glaciers where the mass balance is negative is considered non-renewable. Renewable internal freshwater resources per capita are calculated using the World Bank's population estimates. The unit of calculation is m3/year per inhabitant. Internal renewable freshwater resources per capita are calculated using the World Bank's population estimates.\n\nTotal actual renewable water resources correspond to the maximum theoretical yearly amount of water actually available for a country at a given moment. The unit of calculation is km3/year or 109 m3/year. Calculation Criteria is [Water resources: total renewable (actual)] = [Surface water: total renewable (actual)] + [Groundwater: total renewable (actual)] - [Overlap between surface water and groundwater].*\n\nFresh water is naturally occurring water on the Earth's surface. It is a renewable but limited natural resource. Fresh water can only be renewed through the process of the water cycle, where water from seas, lakes, forests, land, rivers, and dams evaporates, forms clouds, and returns as precipitation. However, if more fresh water is consumed through human activities than is restored by nature, the result is that the quantity of fresh water available in lakes, rivers, dams and underground waters can be reduced which can cause serious damage to the surrounding environment.\n\n* http://www.fao.org/nr/water/aquastat/data/glossary/search.html?termId=4188&submitBtn=s&cls=yes" }, { "id": "Topic", "value": "Environment: Freshwater" } ], "source_id": "46" }, { "id": "ER.LND.PTLD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The International Union for Conservation of Nature (IUCN) defines a protected area as \"a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values.\"\n\nTerrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Nationally protected terrestrial are terrestrial areas as a percentage of total territorial area, where all nationally designated protected areas with known location and extent are included.\n\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n \nProtected areas remain the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. They provide the core of efforts to protect the world's threatened species and are increasingly recognized as essential providers of ecosystem services and biological resources. Some sites are owned and managed by governments, others by private individuals, companies, communities and faith groups.\n\nThe Sustainable Development Goals (SDGs) address concerns common to all economies. In recognition of the vulnerability of animal and plant species, SDGs include targets 14 and 15 to highlight the importance of marine and terrestorial protected areas. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the Protected Planet for third-party use of these data." }, { "id": "IndicatorName", "value": "Terrestrial protected areas (% of total land area)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to Protected Planet terms and conditions." }, { "id": "License_URL", "value": "https://www.protectedplanet.net/c/terms-and-conditions" }, { "id": "Limitationsandexceptions", "value": "The data source for this indicator is the World Database on Protected Areas (WDPA), the most comprehensive global dataset on marine and terrestrial protected areas available. \n\nThe extent to which the land areas, including inland waters, and territorial waters of a country/territory are protected is useful for planning purpose to protect biodiversity. However, it is neither an indication of how well managed the terrestrial and marine protected areas are, nor confirmation that protection measures are effectively enforced. Further, the indicator does not provide information on non-designated or internationally designated protected areas that may also be important for conserving biodiversity. There are known data and knowledge gaps for some countries/regions due to difficulties in reporting national protected area data to the WDPA and/or determining whether a site conforms to the IUCN definition of a protected area.\n\nGaps and/or time lags in reporting national protected area data to the WDPA can however result in discrepancies, which are resolved in communication with data providers. The World Conservation Monitoring Centre (WCMC) compiles data on protected areas, numbers of certain species, and numbers of those species under threat from various sources. Because of differences in definitions, reporting practices, and reporting periods, cross-country comparability is limited.\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas." }, { "id": "Longdefinition", "value": "Terrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Marine areas, unclassified areas, littoral (intertidal) areas, and sites protected under local or provincial law are excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Database on Protected Areas (WDPA) where the compilation and management is carried out by United Nations Environment World Conservation Monitoring Centre (UNEP-WCMC) in collaboration with governments, non-governmental organizations, academia and industry. The data is available online through the Protected Planet website (https://www.protectedplanet.net/)." }, { "id": "Statisticalconceptandmethodology", "value": "This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon). \n\nDesignating an area as protected does not mean that protection is in force. And for small countries that have only protected areas smaller than 1,000 hectares, the size limit in the definition leads to an underestimate of protected areas. Nationally protected areas are defined using the six IUCN management categories for areas of at least 1,000 hectares: scientific reserves and strict nature reserves with limited public access; national parks of national or international significance and not materially affected by human activity; natural monuments and natural landscapes with unique aspects; managed nature reserves and wildlife sanctuaries; protected landscapes (which may include cultural landscapes); and areas managed mainly for the sustainable use of natural systems to ensure long-term protection and maintenance of biological diversity. \n\nA GIS analysis is used to calculate terrestrial and marine protection. For this a global protected area layer is created by combining the polygons and points recorded in the WDPA. Circular buffers are created around points based on the known extent of protected areas for which no polygon is available. Annual protected area layers are created by dissolving the global protected area layer by the known year of establishment of protected areas recorded in the WDPA. The annual protected area layers are overlaid with country/territory boundaries, coastlines and buffered coastlines (delineating the territorial waters) to obtain the absolute coverage (in square kilometers) of protected areas by country/territory. The total area of a country's/territory's terrestrial protected areas and marine protected areas in territorial waters is divided by the total area of its land areas (including inland waters) and territorial waters to obtain the relative coverage (percentage) of protected areas." }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" } ], "source_id": "46" }, { "id": "ER.MRN.PTMR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The International Union for Conservation of Nature (IUCN) defines a protected area as \"a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values.\"\n\nMarine protected areas are areas of intertidal or subtidal terrain - and overlying water and associated flora and fauna and historical and cultural features - that have been reserved by law or other effective means to protect part or the entire enclosed environment. Sites protected under local or provincial law are excluded.\n\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n \nProtected areas remain the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. They provide the core of efforts to protect the world's threatened species and are increasingly recognized as essential providers of ecosystem services and biological resources. Some sites are owned and managed by governments, others by private individuals, companies, communities and faith groups.\n\nThe Sustainable Development Goals (SDGs) address concerns common to all economies. In recognition of the vulnerability of animal and plant species, SDGs include targets 14 and 15 to highlight the importance of marine and terrestorial protected areas. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the Protected Planet for third-party use of these data." }, { "id": "IndicatorName", "value": "Marine protected areas (% of territorial waters)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to Protected Planet terms and conditions." }, { "id": "License_URL", "value": "https://www.protectedplanet.net/c/terms-and-conditions" }, { "id": "Limitationsandexceptions", "value": "The data source for this indicator is the World Database on Protected Areas (WDPA), the most comprehensive global dataset on marine and terrestrial protected areas available. \n\nThe extent to which the land areas, including inland waters, and territorial waters of a country/territory are protected is useful for planning purpose to protect biodiversity. However, it is neither an indication of how well managed the terrestrial and marine protected areas are, nor confirmation that protection measures are effectively enforced. Further, the indicator does not provide information on non-designated or internationally designated protected areas that may also be important for conserving biodiversity. There are known data and knowledge gaps for some countries/regions due to difficulties in reporting national protected area data to the WDPA and/or determining whether a site conforms to the IUCN definition of a protected area.\n\nGaps and/or time lags in reporting national protected area data to the WDPA can however result in discrepancies, which are resolved in communication with data providers. The World Conservation Monitoring Centre (WCMC) compiles data on protected areas, numbers of certain species, and numbers of those species under threat from various sources. Because of differences in definitions, reporting practices, and reporting periods, cross-country comparability is limited.\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas." }, { "id": "Longdefinition", "value": "Marine protected areas are areas of intertidal or subtidal terrain--and overlying water and associated flora and fauna and historical and cultural features--that have been reserved by law or other effective means to protect part or all of the enclosed environment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Database on Protected Areas (WDPA) where the compilation and management is carried out by United Nations Environment World Conservation Monitoring Centre (UNEP-WCMC) in collaboration with governments, non-governmental organizations, academia and industry. The data is available online through the Protected Planet website (https://www.protectedplanet.net/)." }, { "id": "Statisticalconceptandmethodology", "value": "This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon). \n\nDesignating an area as protected does not mean that protection is in force. And for small countries that have only protected areas smaller than 1,000 hectares, the size limit in the definition leads to an underestimate of protected areas. Nationally protected areas are defined using the six IUCN management categories for areas of at least 1,000 hectares: scientific reserves and strict nature reserves with limited public access; national parks of national or international significance and not materially affected by human activity; natural monuments and natural landscapes with unique aspects; managed nature reserves and wildlife sanctuaries; protected landscapes (which may include cultural landscapes); and areas managed mainly for the sustainable use of natural systems to ensure long-term protection and maintenance of biological diversity.\n\nA GIS analysis is used to calculate terrestrial and marine protection. For this a global protected area layer is created by combining the polygons and points recorded in the WDPA. Circular buffers are created around points based on the known extent of protected areas for which no polygon is available. Annual protected area layers are created by dissolving the global protected area layer by the known year of establishment of protected areas recorded in the WDPA. The annual protected area layers are overlaid with country/territory boundaries, coastlines and buffered coastlines (delineating the territorial waters) to obtain the absolute coverage (in square kilometers) of protected areas by country/territory per year from 1990 to present. The total area of a country's/territory's terrestrial protected areas and marine protected areas in territorial waters is divided by the total area of its land areas (including inland waters) and territorial waters to obtain the relative coverage (percentage) of protected areas." }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" } ], "source_id": "46" }, { "id": "ER.PTD.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The International Union for Conservation of Nature (IUCN) defines a protected area as \"a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values.\"\n\nTerrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use.\n\nMarine protected areas are areas of intertidal or subtidal terrain - and overlying water and associated flora and fauna and historical and cultural features - that have been reserved by law or other effective means to protect part or the entire enclosed environment. Sites protected under local or provincial law are excluded.\n\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n \nProtected areas remain the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. They provide the core of efforts to protect the world's threatened species and are increasingly recognized as essential providers of ecosystem services and biological resources. Some sites are owned and managed by governments, others by private individuals, companies, communities and faith groups.\n\nThe Sustainable Development Goals (SDGs) address concerns common to all economies. In recognition of the vulnerability of animal and plant species, SDGs include targets 14 and 15 to highlight the importance of marine and terrestorial protected areas. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the Protected Planet for third-party use of these data." }, { "id": "IndicatorName", "value": "Terrestrial and marine protected areas (% of total territorial area)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to Protected Planet terms and conditions." }, { "id": "License_URL", "value": "https://www.protectedplanet.net/c/terms-and-conditions" }, { "id": "Limitationsandexceptions", "value": "The data source for this indicator is the World Database on Protected Areas (WDPA), the most comprehensive global dataset on marine and terrestrial protected areas available. \n\nThe extent to which the land areas, including inland waters, and territorial waters of a country/territory are protected is useful for planning purpose to protect biodiversity. However, it is neither an indication of how well managed the terrestrial and marine protected areas are, nor confirmation that protection measures are effectively enforced. Further, the indicator does not provide information on non-designated or internationally designated protected areas that may also be important for conserving biodiversity. There are known data and knowledge gaps for some countries/regions due to difficulties in reporting national protected area data to the WDPA and/or determining whether a site conforms to the IUCN definition of a protected area.\n\nGaps and/or time lags in reporting national protected area data to the WDPA can however result in discrepancies, which are resolved in communication with data providers. The World Conservation Monitoring Centre (WCMC) compiles data on protected areas, numbers of certain species, and numbers of those species under threat from various sources. Because of differences in definitions, reporting practices, and reporting periods, cross-country comparability is limited.\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas." }, { "id": "Longdefinition", "value": "Terrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Marine protected areas are areas of intertidal or subtidal terrain--and overlying water and associated flora and fauna and historical and cultural features--that have been reserved by law or other effective means to protect part or all of the enclosed environment. Sites protected under local or provincial law are excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Database on Protected Areas (WDPA) where the compilation and management is carried out by United Nations Environment World Conservation Monitoring Centre (UNEP-WCMC) in collaboration with governments, non-governmental organizations, academia and industry. The data is available online through the Protected Planet website (https://www.protectedplanet.net/)." }, { "id": "Statisticalconceptandmethodology", "value": "This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon).\n\nA GIS analysis is used to calculate terrestrial and marine protection. For this a global protected area layer is created by combining the polygons and points recorded in the WDPA. Circular buffers are created around points based on the known extent of protected areas for which no polygon is available. Annual protected area layers are created by dissolving the global protected area layer by the known year of establishment of protected areas recorded in the WDPA. The annual protected area layers are overlaid with country/territory boundaries, coastlines and buffered coastlines (delineating the territorial waters) to obtain the absolute coverage (in square kilometers) of protected areas by country/territory per year from 1990 to present. The total area of a country's/territory's terrestrial protected areas and marine protected areas in territorial waters is divided by the total area of its land areas (including inland waters) and territorial waters to obtain the relative coverage (percentage) of protected areas." }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" } ], "source_id": "46" }, { "id": "FB.AST.NPER.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy. The ratio of bank nonperforming loans to total gross loans measures bank health and efficiency by identifying problems with asset quality in the loan portfolio. A high ratio may signal deterioration of the credit portfolio." }, { "id": "IndicatorName", "value": "Bank nonperforming loans to total gross loans (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting countries compile the data using different methodologies, which may also vary for different points in time for the same country. Users are advised to consult the accompanying metadata on the IMF FSI website (data.imf.org) to conduct more meaningful cross-country comparisons or to assess the evolution of the indicator for any of the countries." }, { "id": "Longdefinition", "value": "Bank nonperforming loans to total gross loans are the value of nonperforming loans divided by the total value of the loan portfolio (including nonperforming loans before the deduction of specific loan-loss provisions). The loan amount recorded as nonperforming should be the gross value of the loan as recorded on the balance sheet, not just the amount that is overdue." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Financial Soundness Indicators." }, { "id": "Statisticalconceptandmethodology", "value": "The ratio of bank nonperforming loans to total gross loans is the value of nonperforming loans (gross value of the loan as recorded on the balance sheet) divided by the total value of the loan portfolio (including nonperforming loans before the deduction of loan loss provisions). It measures bank health and efficiency by identifying problems with asset quality in the loan portfolio. International guidelines recommend that loans be classified as nonperforming when payments of principal and interest are 90 days or more past due or when future payments are not expected to be received in full. Data are submitted by national authorities to the IMF following the Financial Soundness Indicators (FSI) Compilation Guide. For country specific metadata, including reporting period, please refer to the GFSR FSI Tables and the Data and Metadata Tables available through FSIs website: http://data.imf.org/." }, { "id": "Topic", "value": "Financial Sector: Assets" } ], "source_id": "46" }, { "id": "FB.BNK.CAPA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy. The ratio of bank capital to assets, a measure of bank solvency and resiliency, shows the extent to which banks can deal with unexpected losses. Capital includes tier 1 capital (paid-up shares and common stock), a common feature in all countries' banking systems, and total regulatory capital, which includes several types of subordinated debt instruments that need not be repaid if the funds are required to maintain minimum capital levels (tier 2 and tier 3 capital). Total assets include all nonfinancial and financial assets. Data are from internally consistent financial statements." }, { "id": "IndicatorName", "value": "Bank capital to assets ratio (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting countries compile the data using different methodologies, which may also vary for different points in time for the same country. Users are advised to consult the accompanying metadata on the IMF FSI website (data.imf.org) to conduct more meaningful cross-country comparisons or to assess the evolution of the indicator for any of the countries." }, { "id": "Longdefinition", "value": "Bank capital to assets is the ratio of bank capital and reserves to total assets. Capital and reserves include funds contributed by owners, retained earnings, general and special reserves, provisions, and valuation adjustments. Capital includes tier 1 capital (paid-up shares and common stock), which is a common feature in all countries' banking systems, and total regulatory capital, which includes several specified types of subordinated debt instruments that need not be repaid if the funds are required to maintain minimum capital levels (these comprise tier 2 and tier 3 capital). Total assets include all nonfinancial and financial assets." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Financial Soundness Indicators." }, { "id": "Statisticalconceptandmethodology", "value": "The ratio of capital to total assets, without the latter being risk weighted. Capital is measured as total capital and reserves as reported in the sectoral balance sheet; for cross-border consolidated data, Tier 1 capital can also be used. It indicates the extent to which assets are funded by other than own funds and is a measure of capital adequacy of the deposit-taking sector. It complements the capital adequacy ratios compiled based on the methodology agreed to by the Basle Committee on Banking Supervision. Also, it measures financial leverage and is sometimes called the leverage ratio. Data are submitted by national authorities to the IMF following the Financial Soundness Indicators (FSI) Compilation Guide. For country specific metadata, including reporting period, please refer to the GFSR FSI Tables and the Data and Metadata Tables available through FSIs website: http://data.imf.org/." }, { "id": "Topic", "value": "Financial Sector: Assets" } ], "source_id": "46" }, { "id": "FB.CBK.BRCH.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Developmentrelevance", "value": "Access to finance can expand opportunities for all with higher levels of access and use of banking services associated with lower financing obstacles for people and businesses. A stable financial system that promotes efficient savings and investment is also crucial for a thriving democracy and market economy. There are several aspects of access to financial services: availability, cost, and quality of services. The development and growth of credit markets depend on access to timely, reliable, and accurate data on borrowers' credit experiences. Access to credit can be improved by making it easy to create and enforce collateral agreements and by increasing information about potential borrowers' creditworthiness. Lenders look at a borrower's credit history and collateral. Where credit registries and effective collateral laws are absent - as in many developing countries - banks make fewer loans. Indicators that cover getting credit include the strength of legal rights index and the depth of credit information index." }, { "id": "Generalcomments", "value": "Country-specific metadata can be found on the IMF’s FAS website (data.imf.org)." }, { "id": "IndicatorName", "value": "Commercial bank branches (per 100,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Population-based ratios of the number of branches and ATMs assume a uniform distribution of bank outlets within a country's area and across its population, while in most countries bank branches and ATMs are concentrated in urban centers of the country and are accessible only to some individuals." }, { "id": "Longdefinition", "value": "Commercial bank branches are retail locations of resident commercial banks and other resident banks that function as commercial banks that provide financial services to customers and are physically separated from the main office but not organized as legally separated subsidiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Financial Access Survey." }, { "id": "Statisticalconceptandmethodology", "value": "Data are shown as the number of branches of commercial banks for every 100,000 adults in the reporting country. It is calculated as (number of institutions + number of branches)*100,000/adult population in the reporting country." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "46" }, { "id": "FI.RES.TOTL.MO", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Total reserves in months of imports" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total reserves comprise holdings of monetary gold, special drawing rights, reserves of IMF members held by the IMF, and holdings of foreign exchange under the control of monetary authorities. The gold component of these reserves is valued at year-end (December 31) London prices. This item shows reserves expressed in terms of the number of months of imports of goods and services they could pay for [Reserves/(Imports/12)]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "46" }, { "id": "FM.LBL.BMNY.IR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Broad money to total reserves ratio" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Broad money (IFS line 35L..ZK) is the sum of currency outside banks; demand deposits other than those of the central government; the time, savings, and foreign currency deposits of resident sectors other than the central government; bank and traveler’s checks; and other securities such as certificates of deposit and commercial paper." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" } ], "source_id": "46" }, { "id": "FM.LBL.BMNY.ZG", "metatype": [ { "id": "IndicatorName", "value": "Broad money growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Monetary accounts are derived from the balance sheets of financial institutions - the central bank, commercial banks, and nonbank financial intermediaries. Although these balance sheets are usually reliable, they are subject to errors of classification, valuation, and timing and to differences in accounting practices. For example, whether interest income is recorded on an accrual or a cash basis can make a substantial difference, as can the treatment of nonperforming assets. Valuation errors typically arise for foreign exchange transactions, particularly in countries with flexible exchange rates or in countries that have undergone currency devaluation during the reporting period. The valuation of financial derivatives and the net liabilities of the banking system can also be difficult. The quality of commercial bank reporting also may be adversely affected by delays in reports from bank branches, especially in countries where branch accounts are not computerized. Thus the data in the balance sheets of commercial banks may be based on preliminary estimates subject to constant revision. This problem is likely to be even more serious for nonbank financial intermediaries." }, { "id": "Longdefinition", "value": "Broad money (IFS line 35L..ZK) is the sum of currency outside banks; demand deposits other than those of the central government; the time, savings, and foreign currency deposits of resident sectors other than the central government; bank and traveler’s checks; and other securities such as certificates of deposit and commercial paper." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Statisticalconceptandmethodology", "value": "Money and the financial accounts that record the supply of money lie at the heart of a country’s financial system. There are several commonly used definitions of the money supply. The narrowest, M1, encompasses currency held by the public and demand deposits with banks. M2 includes M1 plus time and savings deposits with banks that require prior notice for withdrawal. M3 includes M2 as well as various money market instruments, such as certificates of deposit issued by banks, bank deposits denominated in foreign currency, and deposits with financial institutions other than banks. However defined, money is a liability of the banking system, distinguished from other bank liabilities by the special role it plays as a medium of exchange, a unit of account, and a store of value." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" } ], "source_id": "46" }, { "id": "FP.CPI.TOTL.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Inflation, consumer prices (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" } ], "source_id": "46" }, { "id": "FX.OWN.TOTL.40.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, poorest 40% (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (poorest 40%, share of population ages 15+)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex)." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "46" }, { "id": "FX.OWN.TOTL.60.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, richest 60% (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (richest 60%, share of population ages 15+)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex)." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "46" }, { "id": "FX.OWN.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (female, % age 15+)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex)." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "46" }, { "id": "FX.OWN.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, male (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (male, % age 15+)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex)." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "46" }, { "id": "FX.OWN.TOTL.OL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, older adults (% of population ages 25+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (older adults, % of population ages 25+)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex)." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "46" }, { "id": "FX.OWN.TOTL.PL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, primary education or less (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (primary education or less, % of population ages 15+)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex)." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "46" }, { "id": "FX.OWN.TOTL.SO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, secondary education or more (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (secondary education or more, % of population ages 15+)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex)." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "46" }, { "id": "FX.OWN.TOTL.YG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, young adults (% of population ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (young adults, % of population ages 15-24)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex)." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "46" }, { "id": "FX.OWN.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (% age 15+)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex)." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "46" }, { "id": "GB.XPD.RSDV.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Expenditure on research and development (R&D) is a key indicator of government and private sector efforts to obtain competitive advantage in science and technology." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2023 (July 1, 2022-June 30, 2023)." }, { "id": "IndicatorName", "value": "Research and development expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers. \n\nR&D totals from SNA accounts may differ from these estimates, due in part to the different treatments of software R&D in the totals." }, { "id": "Longdefinition", "value": "Gross domestic expenditures on research and development (R&D), expressed as a percent of GDP. They include both capital and current expenditures in the four main sectors: Business enterprise, Government, Higher education and Private non-profit. R&D covers basic research, applied research, and experimental development." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The gross domestic expenditure on R&D indicator consists of the total expenditure (current and capital) on R&D by all resident companies, research institutes, university and government laboratories, etc. It excludes R&D expenditures financed by domestic firms but performed abroad. \n\nThe OECD's Frascati Manual defines research and experimental development as \"creative work undertaken on a systemic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications.\" R&D covers basic research, applied research, and experimental development.\n\n(1) Basic research - Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view\n\n(2) Applied research - Applied research is also original investigation undertaken in order to acquire new knowledge; it is, however, directed primarily towards a specific practical aim or objective.\n\n(3) Experimental development - Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed.\n\nThe fields of science and technology used to classify R&D according to the Revised Fields of Science and Technology Classification are:\n1. Natural sciences;\n2. Engineering and technology;\n3. Medical and health sciences;\n4. Agricultural sciences;\n5. Social sciences;\n6. Humanities and the arts.\n\nThe data are obtained through statistical surveys which are regularly conducted at national level covering R&D performing entities in the private and public sectors." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "46" }, { "id": "GC.TAX.TOTL.CN", "metatype": [ { "id": "IndicatorName", "value": "Tax revenue (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Tax revenue refers to compulsory transfers to the central government for public purposes. Certain compulsory transfers such as fines, penalties, and most social security contributions are excluded. Refunds and corrections of erroneously collected tax revenue are treated as negative revenue." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Government Finance Statistics Yearbook and data files." }, { "id": "Statisticalconceptandmethodology", "value": "The IMF's Government Finance Statistics Manual 2014, harmonized with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, revenues, and expenses, not just those represented by cash transactions. It accounts for all changes in stocks, so stock data at the end of an accounting period equal stock data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks.\n\nGovernment finance statistics are reported in local currency. Many countries report government finance data by fiscal year; see country metadata for information on fiscal year end by country." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" } ], "source_id": "46" }, { "id": "GC.TAX.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Tax revenue (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Tax revenue refers to compulsory transfers to the central government for public purposes. Certain compulsory transfers such as fines, penalties, and most social security contributions are excluded. Refunds and corrections of erroneously collected tax revenue are treated as negative revenue." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Government Finance Statistics Yearbook and data files, and World Bank and OECD GDP estimates." }, { "id": "Statisticalconceptandmethodology", "value": "The IMF's Government Finance Statistics Manual 2014, harmonized with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, revenues, and expenses, not just those represented by cash transactions. It accounts for all changes in stocks, so stock data at the end of an accounting period equal stock data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks.\n\nGovernment finance statistics are reported in local currency. Many countries report government finance data by fiscal year; see country metadata for information on fiscal year end by country." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" } ], "source_id": "46" }, { "id": "GF.XPD.BUDG.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "The indicator attempts to capture the reliability of government budgets: do governments spend what they intend to and do they collect what they set out to collect. The ability to implement the enacted budget is an important factor in government’s ability to deliver public services and achieve development objectives. The deviation between approved and actual spending is measured over a 12-month period (the budget year) and may have important implications for macroeconomic stability, public service delivery, and social welfare. A credibly implemented budget has only small deviations from the approved one. If expenditure is under-executed, beneficiaries may not receive crucial services. Over-executed budgets may result in budget deficits and increased public debt levels and can influence the macroeconomic stability. In both cases, lack of budget credibility undermines the usefulness of the budget process for policy making and implementation and erodes public trust in government." }, { "id": "IndicatorName", "value": "Primary government expenditures as a proportion of original approved budget (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Primary government expenditures as a proportion of original approved budget measures the extent to which aggregate budget expenditure outturn reflects the amount originally approved, as defined in government budget documentation and fiscal reports. The coverage is budgetary central government (BCG) and the time period covered is the last three completed fiscal years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Public Expenditure and Financial Accountability (PEFA). Ministry of Finance (MoF)." }, { "id": "Topic", "value": "Public Sector: Government finance" } ], "source_id": "46" }, { "id": "IC.BUS.NDNS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "Entrepreneurship is a critical part of economic development and growth and important for the continued dynamism of the modern economy. To measure entrepreneurial activity, annual data is collected directly from 170 company registrars on the number of newly registered firms over the past seven years. The data shows the trends in new firm creation across regions, the relationship between entrepreneurship and the business environment and financial development, and the financial crisis' effect on the entrepreneurial activity in the formal sector.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure." }, { "id": "Generalcomments", "value": "For cross-country comparability, only limited liability corporations that operate in the formal sector are included." }, { "id": "IndicatorName", "value": "New business density (new registrations per 1,000 people ages 15-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. The Entrepreneurship Database facilitates the analysis of the growth of the formal private sector and the identification of factors that encourage firms to begin operations in or transition to the formal sector. Data is collected all limited liability corporations regardless of size. Partnerships and sole proprietorships are not considered in the analysis due to the differences with respect to their definition and regulation worldwide. Data on the number of total or closed firms are not included due to heterogeneity in how these entities are defined and measured.\n\nThe Entrepreneurship Database is a critical source of data that facilitates the measurement of entrepreneurial activity across countries and over time. The data also allows for a deeper understanding of the relationship between new firm registration, the regulatory environment, and economic growth. Previous research using the Entrepreneurship Database has shown a significant relationship between the level of cost, time, and procedures required to start a business and new firm registration.\n\nTo facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies." }, { "id": "Longdefinition", "value": "New businesses registered are the number of new limited liability corporations (or its equivalent) registered in the calendar year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank's Entrepreneurship Database (https://www.worldbank.org/en/programs/entrepreneurship)." }, { "id": "Statisticalconceptandmethodology", "value": "To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries in 170 economies. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data - most often due to an absence of digitized registration systems - the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and private vendors or publicly available data.\n\nThe units of measurement are private, formal sector companies with limited liability." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "46" }, { "id": "IC.FRM.BRIB.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "Firms evaluating investment options, governments interested in improving business conditions, and economists seeking to explain economic performance have all grappled with defining and measuring the business environment. The firm-level data from Enterprise Surveys provide a useful tool for benchmarking economies across a large number of indicators measured at the firm level.\n\nCorruption by public officials may present a major administrative and financial burden on firms. Corruption creates an unfavorable business environment by undermining the operational efficiency of firms and raising the costs and risks associated with doing business.\n\nIn some countries doing business requires informal payments to \"get things done\" in customs, taxes, licenses, regulations, services, and the like. Such corruption can harm the business environment by distorting policymaking, undermining government credibility, and diverting public resources." }, { "id": "IndicatorName", "value": "Bribery incidence (% of firms experiencing at least one bribe payment request)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Longdefinition", "value": "Bribery incidence is the percentage of firms experiencing at least one bribe payment request across 6 public transactions dealing with utilities access, permits, licenses, and taxes." }, { "id": "Notesfromoriginalsource", "value": "All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Firm-level surveys have been conducted since the 1990's by different units within the World Bank. Since 2005-06, most data collection efforts have been centralized within the Enterprise Analysis Unit. Surveys implemented by the Enterprise Analysis Unit follow the Global Methodology.\n\nPrivate contractors conduct the Enterprise Surveys on behalf of the World Bank. Due to sensitive survey questions addressing business-government relations and bribery-related topics, private contractors, rather than any government agency or an organization/institution associated with government, are hired by the World Bank to collect the data.\n\nConfidentiality of the survey respondents and the sensitive information they provide is necessary to ensure the greatest degree of survey participation, integrity and confidence in the quality of the data. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but confidentiality is never compromised.\n\nThe Enterprise Survey is answered by business owners and top managers. Sometimes the survey respondent calls company accountants and human resource managers into the interview to answer questions in the sales and labor sections of the survey. Typically 1200-1800 interviews are conducted in larger economies, 360 interviews are conducted in medium-sized economies, and for smaller economies, 150 interviews take place.\n\nThe manufacturing and services sectors are the primary business sectors of interest. This corresponds to firms classified with ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies with 5 or more employees are targeted for interview. Services firms include construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government/state ownership are not eligible to participate in an Enterprise Survey. Occasionally, for a few surveyed countries, other sectors are included in the companies surveyed such as education or health-related businesses. In each country, businesses in the cities/regions of major economic activity are interviewed.\n\nIn some countries, other surveys, which depart from the usual Enterprise Survey methodology, are conducted. Examples include 1) Informal Surveys- surveys of informal (unregistered) enterprises, 2) Micro Surveys- surveys fielded to registered firms with less than five employees, and 3) Financial Crisis Assessment Surveys- short surveys administered by telephone to assess the effects of the global financial crisis of 2008-09.\n\nThe Enterprise Surveys Unit uses two instruments: the Manufacturing Questionnaire and the Services Questionnaire. Although many questions overlap, some are only applicable to one type of business. For example, retail firms are not asked about production and nonproduction workers.\n\nThe standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.\n\nOver 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "46" }, { "id": "IC.FRM.FEMM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "Firms evaluating investment options, governments interested in improving business conditions, and economists seeking to explain economic performance have all grappled with defining and measuring the business environment. The firm-level data from Enterprise Surveys provide a useful tool for benchmarking economies across a large number of indicators measured at the firm level.\n\nFirms with female top manager measures women's integration as decision makers. Benchmarking firms with female top manager is important to achieving gender equality promotion and empowerment of women. The gender topic provides information about women's entrepreneurship and economic participation in the labor force." }, { "id": "Generalcomments", "value": "Relevance to gender indicator: Women are vastly underrepresented in decision making positions at the top level in the private sector and this indicator monitors progress that has been made." }, { "id": "IndicatorName", "value": "Firms with female top manager (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Longdefinition", "value": "Firms with female top manager refers to the percentage of firms in the private sector who have females as top managers. Top manager refers to the highest ranking manager or CEO of the establishment. This person may be the owner if he/she works as the manager of the firm. The results are based on surveys of more than 100,000 private firms." }, { "id": "Notesfromoriginalsource", "value": "All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Firm-level surveys have been conducted since the 1990's by different units within the World Bank. Since 2005-06, most data collection efforts have been centralized within the Enterprise Analysis Unit. Surveys implemented by the Enterprise Analysis Unit follow the Global Methodology.\n\nPrivate contractors conduct the Enterprise Surveys on behalf of the World Bank. Due to sensitive survey questions addressing business-government relations and bribery-related topics, private contractors, rather than any government agency or an organization/institution associated with government, are hired by the World Bank to collect the data.\n\nConfidentiality of the survey respondents and the sensitive information they provide is necessary to ensure the greatest degree of survey participation, integrity and confidence in the quality of the data. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but confidentiality is never compromised.\n\nThe Enterprise Survey is answered by business owners and top managers. Sometimes the survey respondent calls company accountants and human resource managers into the interview to answer questions in the sales and labor sections of the survey. Typically 1200-1800 interviews are conducted in larger economies, 360 interviews are conducted in medium-sized economies, and for smaller economies, 150 interviews take place.\n\nThe manufacturing and services sectors are the primary business sectors of interest. This corresponds to firms classified with ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies with 5 or more employees are targeted for interview. Services firms include construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government/state ownership are not eligible to participate in an Enterprise Survey. Occasionally, for a few surveyed countries, other sectors are included in the companies surveyed such as education or health-related businesses. In each country, businesses in the cities/regions of major economic activity are interviewed.\n\nIn some countries, other surveys, which depart from the usual Enterprise Survey methodology, are conducted. Examples include 1) Informal Surveys- surveys of informal (unregistered) enterprises, 2) Micro Surveys- surveys fielded to registered firms with less than five employees, and 3) Financial Crisis Assessment Surveys- short surveys administered by telephone to assess the effects of the global financial crisis of 2008-09.\n\nThe Enterprise Surveys Unit uses two instruments: the Manufacturing Questionnaire and the Services Questionnaire. Although many questions overlap, some are only applicable to one type of business. For example, retail firms are not asked about production and nonproduction workers.\n\nThe standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.\n\nOver 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "46" }, { "id": "IC.FRM.FEMO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "Firms evaluating investment options, governments interested in improving business conditions, and economists seeking to explain economic performance have all grappled with defining and measuring the business environment. The firm-level data from Enterprise Surveys provide a useful tool for benchmarking economies across a large number of indicators measured at the firm level.\n\nFemale participation in firm ownership and in management measures women's integration as decision makers. Benchmarking female participation in firm ownership, management, and the workforce is important to achieving gender equality promotion and empowerment of women. The gender topic provides information about women's entrepreneurship and economic participation in the labor force." }, { "id": "IndicatorName", "value": "Firms with female participation in ownership (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Longdefinition", "value": "Firms with female participation in ownership are the percentage of firms with a woman among the principal owners." }, { "id": "Notesfromoriginalsource", "value": "All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Firm-level surveys have been conducted since the 1990's by different units within the World Bank. Since 2005-06, most data collection efforts have been centralized within the Enterprise Analysis Unit. Surveys implemented by the Enterprise Analysis Unit follow the Global Methodology.\n\nPrivate contractors conduct the Enterprise Surveys on behalf of the World Bank. Due to sensitive survey questions addressing business-government relations and bribery-related topics, private contractors, rather than any government agency or an organization/institution associated with government, are hired by the World Bank to collect the data.\n\nConfidentiality of the survey respondents and the sensitive information they provide is necessary to ensure the greatest degree of survey participation, integrity and confidence in the quality of the data. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but confidentiality is never compromised.\n\nThe Enterprise Survey is answered by business owners and top managers. Sometimes the survey respondent calls company accountants and human resource managers into the interview to answer questions in the sales and labor sections of the survey. Typically 1200-1800 interviews are conducted in larger economies, 360 interviews are conducted in medium-sized economies, and for smaller economies, 150 interviews take place.\n\nThe manufacturing and services sectors are the primary business sectors of interest. This corresponds to firms classified with ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies with 5 or more employees are targeted for interview. Services firms include construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government/state ownership are not eligible to participate in an Enterprise Survey. Occasionally, for a few surveyed countries, other sectors are included in the companies surveyed such as education or health-related businesses. In each country, businesses in the cities/regions of major economic activity are interviewed.\n\nIn some countries, other surveys, which depart from the usual Enterprise Survey methodology, are conducted. Examples include 1) Informal Surveys- surveys of informal (unregistered) enterprises, 2) Micro Surveys- surveys fielded to registered firms with less than five employees, and 3) Financial Crisis Assessment Surveys- short surveys administered by telephone to assess the effects of the global financial crisis of 2008-09.\n\nThe Enterprise Surveys Unit uses two instruments: the Manufacturing Questionnaire and the Services Questionnaire. Although many questions overlap, some are only applicable to one type of business. For example, retail firms are not asked about production and nonproduction workers.\n\nThe standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.\n\nOver 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "46" }, { "id": "IC.TAX.GIFT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "Firms evaluating investment options, governments interested in improving business conditions, and economists seeking to explain economic performance have all grappled with defining and measuring the business environment. The firm-level data from Enterprise Surveys provide a useful tool for benchmarking economies across a large number of indicators measured at the firm level.\n\nThe reliability and availability of infrastructure benefit households and support development. Firms with access to modern and efficient infrastructure - telecommunications, electricity, and transport - can be more productive.\n\nA strong infrastructure enhances the competitiveness of an economy and generates a business environment conducive to firm growth and development. Good infrastructure efficiently connects firms to their customers and suppliers, and enables the use of modern production technologies. Conversely, deficiencies in infrastructure, such as loss of electricity on regular basis, create barriers to productive opportunities and increase costs for all firms, from micro enterprises to large multinational corporations." }, { "id": "IndicatorName", "value": "Firms expected to give gifts in meetings with tax officials (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Longdefinition", "value": "Firms expected to give gifts in meetings with tax officials is the percentage of firms that answered positively to the question \"was a gift or informal payment expected or requested during a meeting with tax officials?\"" }, { "id": "Notesfromoriginalsource", "value": "All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Firm-level surveys have been conducted since the 1990's by different units within the World Bank. Since 2005-06, most data collection efforts have been centralized within the Enterprise Analysis Unit. Surveys implemented by the Enterprise Analysis Unit follow the Global Methodology.\n\nPrivate contractors conduct the Enterprise Surveys on behalf of the World Bank. Due to sensitive survey questions addressing business-government relations and bribery-related topics, private contractors, rather than any government agency or an organization/institution associated with government, are hired by the World Bank to collect the data.\n\nConfidentiality of the survey respondents and the sensitive information they provide is necessary to ensure the greatest degree of survey participation, integrity and confidence in the quality of the data. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but confidentiality is never compromised.\n\nThe Enterprise Survey is answered by business owners and top managers. Sometimes the survey respondent calls company accountants and human resource managers into the interview to answer questions in the sales and labor sections of the survey. Typically 1200-1800 interviews are conducted in larger economies, 360 interviews are conducted in medium-sized economies, and for smaller economies, 150 interviews take place.\n\nThe manufacturing and services sectors are the primary business sectors of interest. This corresponds to firms classified with ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies with 5 or more employees are targeted for interview. Services firms include construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government/state ownership are not eligible to participate in an Enterprise Survey. Occasionally, for a few surveyed countries, other sectors are included in the companies surveyed such as education or health-related businesses. In each country, businesses in the cities/regions of major economic activity are interviewed.\n\nIn some countries, other surveys, which depart from the usual Enterprise Survey methodology, are conducted. Examples include 1) Informal Surveys- surveys of informal (unregistered) enterprises, 2) Micro Surveys- surveys fielded to registered firms with less than five employees, and 3) Financial Crisis Assessment Surveys- short surveys administered by telephone to assess the effects of the global financial crisis of 2008-09.\n\nThe Enterprise Surveys Unit uses two instruments: the Manufacturing Questionnaire and the Services Questionnaire. Although many questions overlap, some are only applicable to one type of business. For example, retail firms are not asked about production and nonproduction workers.\n\nThe standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.\n\nOver 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "46" }, { "id": "IE.PPI.ENGY.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Investment in infrastructure projects with private participation has made important contributions to easing fiscal constraints, improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, pioneering better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure." }, { "id": "IndicatorName", "value": "Investment in energy with private participation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nInvestment commitments are the sum of investments in physical assets and payments to the government. Investments in physical assets are resources the project company commits to invest during the contract period in new facilities or in expansion and modernization of existing facilities. Payments to the government are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "Investment in energy projects with private participation refers to commitments to infrastructure projects in energy (electricity and natural gas: generation, transmission and distribution) that have reached financial closure and directly or indirectly serve the public. Movable assets and small projects such as windmills are excluded. The types of projects included are management and lease contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Investment in energy with private participation is the value of commitments to energy projects that have reached financial closure and directly or indirectly serve the public, including lease and management contracts, operation and management contracts with major capital expenditure, greenfield projects (in which a private entity or public-private joint venture builds and operates a new facility), and divestitures. Movable assets and small projects such as windmills are excluded." }, { "id": "Source", "value": "World Bank, Private Participation in Infrastructure Project Database (http://ppi.worldbank.org)." }, { "id": "Statisticalconceptandmethodology", "value": "The data are from the World Bank's Private Participation in Infrastructure (PPI) Project database, which tracks infrastructure projects with private participation in developing countries. It provides information on more than 5,000 infrastructure projects in 139 developing economies from 1984. The database contains more than 30 fields per project record, including country, financial closure year, infrastructure services provided, type of private participation, investment, technology, capacity, project location, contract duration, private sponsors, bidding process, and development bank support.\n\nThe database is a joint product of the World Bank's Finance, Economics, and Urban Development Department and the Public-Private Infrastructure Advisory Facility. Geographic and income aggregates are calculated by the World Bank's Development Data Group.\n\nData are in current U.S. dollars." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" } ], "source_id": "46" }, { "id": "IE.PPI.TELE.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Investment in infrastructure projects with private participation has made important contributions to easing fiscal constraints, improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, pioneering better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure." }, { "id": "IndicatorName", "value": "Investment in telecoms with private participation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nInvestment commitments are the sum of investments in physical assets and payments to the government. Investments in physical assets are resources the project company commits to invest during the contract period in new facilities or in expansion and modernization of existing facilities. Payments to the government are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "Investment in telecom projects with private participation refers to commitments to infrastructure projects in telecommunications that have reached financial closure and directly or indirectly serve the public. Movable assets and small projects are excluded. The types of projects included are management and lease contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Investment in telecoms with private participation is the value of committments to telecom projects that have reached financial closure and directly or indirectly serve the public, including lease and management contracts, operation and management contracts with major capital expenditure, greenfield projects (in which a private entity or public-private joint venture builds and operates a new facility), and divestitures. Movable assets and small projects are excluded." }, { "id": "Source", "value": "World Bank, Private Participation in Infrastructure Project Database (http://ppi.worldbank.org)." }, { "id": "Statisticalconceptandmethodology", "value": "The data are from the World Bank's Private Participation in Infrastructure (PPI) Project database, which tracks infrastructure projects with private participation in developing countries. It provides information on more than 5,000 infrastructure projects in 139 developing economies from 1984. The database contains more than 30 fields per project record, including country, financial closure year, infrastructure services provided, type of private participation, investment, technology, capacity, project location, contract duration, private sponsors, bidding process, and development bank support.\n\nThe database is a joint product of the World Bank's Finance, Economics, and Urban Development Department and the Public-Private Infrastructure Advisory Facility. Geographic and income aggregates are calculated by the World Bank's Development Data Group.\n\nData are in current U.S. dollars." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" } ], "source_id": "46" }, { "id": "IE.PPI.TRAN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Investment in infrastructure projects with private participation has made important contributions to easing fiscal constraints, improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, pioneering better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure." }, { "id": "IndicatorName", "value": "Investment in transport with private participation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "Investment in transport projects with private participation refers to commitments to infrastructure projects in transport that have reached financial closure and directly or indirectly serve the public. Movable assets and small projects are excluded. The types of projects included are management and lease contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Investment in transport with private participation is the value of commitments to transportation projects that have reached financial closure and directly or indirectly serve the public, including management and lease contracts, operation and management contracts with major capital expenditure, greenfield projects (in which a private entity or public-private joint venture builds and operates a new facility), and divestitures. Movable assets and small projects are excluded." }, { "id": "Source", "value": "World Bank, Private Participation in Infrastructure Project Database (http://ppi.worldbank.org)." }, { "id": "Statisticalconceptandmethodology", "value": "The data are from the World Bank's Private Participation in Infrastructure (PPI) Project database, which tracks infrastructure projects with private participation in developing countries. It provides information on more than 5,000 infrastructure projects in 139 developing economies from 1984. The database contains more than 30 fields per project record, including country, financial closure year, infrastructure services provided, type of private participation, investment, technology, capacity, project location, contract duration, private sponsors, bidding process, and development bank support.\n\nThe database is a joint product of the World Bank's Finance, Economics, and Urban Development Department and the Public-Private Infrastructure Advisory Facility. Geographic and income aggregates are calculated by the World Bank's Development Data Group.\n\nData are in current U.S. dollars." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" } ], "source_id": "46" }, { "id": "IE.PPI.WATR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Investment in infrastructure projects with private participation has made important contributions to easing fiscal constraints, improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, pioneering better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure." }, { "id": "IndicatorName", "value": "Investment in water and sanitation with private participation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nInvestment commitments are the sum of investments in physical assets and payments to the government. Investments in physical assets are resources the project company commits to invest during the contract period in new facilities or in expansion and modernization of existing facilities. Payments to the government are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "Investment in water and sanitation projects with private participation refers to commitments to infrastructure projects in water and sanitation that have reached financial closure and directly or indirectly serve the public. Movable assets, incinerators, standalone solid waste projects, and small projects are excluded. The types of projects included are management and lease contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Investment in water and sanitation with private participation is the commitments to value of water and sanitation projects that have reached financial closure and directly or indirectly serve the public, including operation and management contracts with major capital expenditure, greenfield projects (in which a private entity or public-private joint venture builds and operates a new facility), and divestitures. Incinerators, movable assets, standalone solid waste projects, and small projects are excluded." }, { "id": "Source", "value": "World Bank, Private Participation in Infrastructure Project Database (http://ppi.worldbank.org)." }, { "id": "Statisticalconceptandmethodology", "value": "The data are from the World Bank's Private Participation in Infrastructure (PPI) Project database, which tracks infrastructure projects with private participation in developing countries. It provides information on more than 5,000 infrastructure projects in 139 developing economies from 1984. The database contains more than 30 fields per project record, including country, financial closure year, infrastructure services provided, type of private participation, investment, technology, capacity, project location, contract duration, private sponsors, bidding process, and development bank support.\n\nThe database is a joint product of the World Bank's Finance, Economics, and Urban Development Department and the Public-Private Infrastructure Advisory Facility. Geographic and income aggregates are calculated by the World Bank's Development Data Group.\n\nData are in current U.S. dollars." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" } ], "source_id": "46" }, { "id": "IP.PAT.NRES", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "The Patent Cooperation Treaty (www.wipo.int/pct) provides a two phase system for filing patent. International applications under the treaty provide for a national patent grant only - there is no international patent. The national filing represents the applicant's seeking of patent protection for a given territory, whereas international filings, while representing a legal right, do not accurately reflect where patent protection is sought. Resident filings are those from residents of the country concerned. Nonresident filings are from applicants abroad. For regional offices applications from residents of any member state of the regional patent convention are considered nonresident filings. Some offices (notably the U.S. Patent and Trademark Office) use the residence of the inventor rather than the applicant to classify filings.\n\nPatent data are a great resource for the study of technical change in a country or region. Patent data provide a uniquely detailed source of information on inventive activity and the multiple dimensions of the inventive process (e.g. geographical location, technical and institutional origin, individuals and networks). Furthermore, patent data form a consistent basis for comparisons across time and across countries.\n\nPatent data can be used in the analysis of a wide array of topics related to technical change and patenting activity including industry-science linkages, patenting strategies by companies, internationalization of research, and indicators on the value of patents. Patent-based statistics reflect the inventive performance of countries, regions and firms, as well as other aspects of the dynamics of the innovation process such as co-operation in innovation or technology paths." }, { "id": "IndicatorName", "value": "Patent applications, nonresidents" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A patent is an exclusive right granted for a specified period (generally 20 years) for a new way of doing something or a new technical solution to a problem - an invention. The invention must be of practical use and display a characteristic unknown in the existing body of knowledge in its field. Most countries have systems to protect patentable inventions.\n\nUnless otherwise stated, statistics on the number of resident and non-resident patent applications include those filed via the PCT system as PCT national/regional phase entries." }, { "id": "Longdefinition", "value": "Patent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention--a product or process that provides a new way of doing something or offers a new technical solution to a problem. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Patent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office." }, { "id": "Source", "value": "World Intellectual Property Organization (WIPO), WIPO Patent Report: Statistics on Worldwide Patent Activity. The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Statisticalconceptandmethodology", "value": "Non-resident patent applications are from applicants outside the relevant State or region. Patent data cover applications and grants classified by field of technology. International applications series distinguish four subcategories: a) patents taken out by residents of a country in that country; b) patents taken out in a country by non-residents of that country; c) total patents registered in the country or naming it; d) patents taken out outside a country by its residents. Data on patents granted only distinguish between patents awarded to residents and to non-residents. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years.\n\nPatent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention - a product or process that provides a new way of doing something or offers a new technical solution to a problem." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "46" }, { "id": "IP.PAT.RESD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "The Patent Cooperation Treaty (www.wipo.int/pct) provides a two phase system for filing patent. International applications under the treaty provide for a national patent grant only - there is no international patent. The national filing represents the applicant's seeking of patent protection for a given territory, whereas international filings, while representing a legal right, do not accurately reflect where patent protection is sought. Resident filings are those from residents of the country concerned. Nonresident filings are from applicants abroad. For regional offices applications from residents of any member state of the regional patent convention are considered nonresident filings. Some offices (notably the U.S. Patent and Trademark Office) use the residence of the inventor rather than the applicant to classify filings.\n\nPatent data are a great resource for the study of technical change in a country or region. Patent data provide a uniquely detailed source of information on inventive activity and the multiple dimensions of the inventive process (e.g. geographical location, technical and institutional origin, individuals and networks). Furthermore, patent data form a consistent basis for comparisons across time and across countries.\n\nPatent data can be used in the analysis of a wide array of topics related to technical change and patenting activity including industry-science linkages, patenting strategies by companies, internationalization of research, and indicators on the value of patents. Patent-based statistics reflect the inventive performance of countries, regions and firms, as well as other aspects of the dynamics of the innovation process such as co-operation in innovation or technology paths." }, { "id": "IndicatorName", "value": "Patent applications, residents" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A patent is an exclusive right granted for a specified period (generally 20 years) for a new way of doing something or a new technical solution to a problem - an invention. The invention must be of practical use and display a characteristic unknown in the existing body of knowledge in its field. Most countries have systems to protect patentable inventions." }, { "id": "Longdefinition", "value": "Patent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention--a product or process that provides a new way of doing something or offers a new technical solution to a problem. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Patent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office." }, { "id": "Source", "value": "World Intellectual Property Organization (WIPO), WIPO Patent Report: Statistics on Worldwide Patent Activity. The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Statisticalconceptandmethodology", "value": "Resident patent applications are those for which the first-named applicant or assignee is a resident of the State or region concerned. In the case of regional offices such as the European Patent Office, a resident is an applicant from any of the member States of the regional patent convention.\n\nPatent data cover applications and grants classified by field of technology. International applications series distinguish four subcategories: a) patents taken out by residents of a country in that country; b) patents taken out in a country by non-residents of that country; c) total patents registered in the country or naming it; d) patents taken out outside a country by its residents. Data on patents granted only distinguish between patents awarded to residents and to non-residents. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years.\n\nPatent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention - a product or process that provides a new way of doing something or offers a new technical solution to a problem." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "46" }, { "id": "IQ.SCI.MTHD", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "Methodology assessment of statistical capacity (scale 0 - 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The methodology indicator measures a country’s ability to adhere to internationally recommended standards and methods. The methodology score is calculated as the weighted average of 10 underlying indicator scores. The final methodology score contributes 1/3 of the overall Statistical Capacity Indicator score." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Bulletin Board on Statistical Capacity (http://bbsc.worldbank.org)." }, { "id": "Statisticalconceptandmethodology", "value": "The Practice score is calculated as weighted average of all 10 Practice indicator scores." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "46" }, { "id": "IQ.SCI.OVRL", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "Statistical Capacity is a nation’s ability to collect, analyze, and disseminate high-quality data about its population and economy. Quality statistics are essential for all stages of evidence-based decision-making, including: Monitoring social and economic indicators, Allocating political representation and government resources, Guiding private sector investment, as well as Informing the international donor community for program design and policy formulation." }, { "id": "IndicatorName", "value": "Statistical Capacity Score (Overall Average) (scale 0 - 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The Statistical Capacity Indicator is a composite score assessing the capacity of a country’s statistical system. It is based on a diagnostic framework assessing the following areas: methodology; data sources; and periodicity and timeliness. Countries are scored against 25 criteria in these areas, using publicly available information and/or country input. The overall Statistical Capacity score is then calculated as a simple average of all three area scores on a scale of 0-100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Bulletin Board on Statistical Capacity (http://bbsc.worldbank.org)." }, { "id": "Statisticalconceptandmethodology", "value": "The Statistical Capacity Indicator score is calculated as the average of the scores of the 3 dimensions, i.e. Availability, Collection, Practice." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "46" }, { "id": "IQ.SCI.PRDC", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "Periodicity and timeliness assessment of statistical capacity (scale 0 - 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The periodicity and timeliness indicator assesses the availability and periodicity of key socioeconomic indicators. It measures the extent to which data are made accessible to users through transformation of source data into timely statistical outputs. The periodicity score is calculated as the weighted average of 10 underlying indicator scores. The final periodicity score contributes 1/3 of the overall Statistical Capacity Indicator score." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Bulletin Board on Statistical Capacity (http://bbsc.worldbank.org)." }, { "id": "Statisticalconceptandmethodology", "value": "The Availability score is calculated as weighted average of all 10 Availability indicator scores." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "46" }, { "id": "IQ.SCI.SRCE", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "Source data assessment of statistical capacity (scale 0 - 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The source data indicator reflects whether a country conducts data collection activities in line with internationally recommended periodicity, and whether data from administrative systems are available. The source data score is calculated as the weighted average of 5 underlying indicator scores. The final source data score contributes 1/3 of the overall Statistical Capacity Indicator score." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Bulletin Board on Statistical Capacity (http://bbsc.worldbank.org)." }, { "id": "Statisticalconceptandmethodology", "value": "The Collection score is calculated as weighted average of all 5 Collection indicator scores." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "46" }, { "id": "IS.AIR.GOOD.MT.K1", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Transport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe air transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses, and stimulating foreign investment and international trade. Economic growth, technological change, market liberalization, the growth of low cost carriers, airport congestion, oil prices and other trends affect commercial aviation throughout the world." }, { "id": "IndicatorName", "value": "Air transport, freight (million ton-km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The air transport data represent the total (international and domestic) scheduled traffic carried by the air carriers registered in a country. Countries submit air transport data to International Civil Aviation Organization (ICAO) on the basis of standard instructions and definitions issued by ICAO. In many cases, however, the data include estimates by ICAO for nonreporting carriers. Where possible, these estimates are based on previous submissions supplemented by information published by the air carriers, such as flight schedules.\n\nThe data cover the air traffic carried on scheduled services, but changes in air transport regulations in Europe have made it more difficult to classify traffic as scheduled or nonscheduled. Thus recent increases shown for some European countries may be due to changes in the classification of air traffic rather than actual growth. In the case of multinational air carriers owned by partner States, traffic within each partner State is shown separately as domestic and all other traffic as international.\n\n\"Foreign\" cabotage traffic (i.e. traffic carried between city-pairs in a State other than the one where the reporting carrier has its principal place of business) is shown as international traffic.\n\nA technical stop does not result in any flight stage being classified differently than would have been the case had the technical stop not been made. For countries with few air carriers or only one, the addition or discontinuation of a home-based air carrier may cause significant changes in air traffic.\n\nData for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\"" }, { "id": "Longdefinition", "value": "Air freight is the volume of freight, express, and diplomatic bags carried on each flight stage (operation of an aircraft from takeoff to its next landing), measured in metric tons times kilometers traveled." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Civil Aviation Organization, Civil Aviation Statistics of the World and ICAO staff estimates." }, { "id": "Statisticalconceptandmethodology", "value": "For statistical uses, departures are equal to the number of landings made or flight stages flown. A flight stage is the operation of an aircraft from take-off to its next landing. A flight stage is classified as either international or domestic. International flight stage is one or both terminals in the territory of a State, other than the State in which the air carrier has its principal place of business.\n\nDomestic flight stage is not classifiable as international. Domestic flight stages include all flight stages flown between points within the domestic boundaries of a State by an air carrier whose principal place of business is in that State. Flight stages between a State and territories belonging to it, as well as any flight stages between two such territories, should be classified as domestic. This applies even though a stage may cross international waters or over the territory of another State.\n\nFreight tonne-kilometres performed measures a metric tonne of freight carried one kilometre. Freight tonne-kilometres equal the sum of the products obtained by multiplying the number of tonnes of freight, express, diplomatic bags carried on each flight stage by the stage distance. For ICAO statistical purposes freight includes express and diplomatic bags but not passenger baggage." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "46" }, { "id": "IS.AIR.PSGR", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Transport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe air transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses, and stimulating foreign investment and international trade. Economic growth, technological change, market liberalization, the growth of low cost carriers, airport congestion, oil prices and other trends affect commercial aviation throughout the world." }, { "id": "IndicatorName", "value": "Air transport, passengers carried" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The air transport data represent the total (international and domestic) scheduled traffic carried by the air carriers registered in a country. Countries submit air transport data to International Civil Aviation Organization (ICAO) on the basis of standard instructions and definitions issued by ICAO. In many cases, however, the data include estimates by ICAO for nonreporting carriers. Where possible, these estimates are based on previous submissions supplemented by information published by the air carriers, such as flight schedules.\n\nThe data cover the air traffic carried on scheduled services, but changes in air transport regulations in Europe have made it more difficult to classify traffic as scheduled or nonscheduled. Thus recent increases shown for some European countries may be due to changes in the classification of air traffic rather than actual growth. In the case of multinational air carriers owned by partner States, traffic within each partner State is shown separately as domestic and all other traffic as international.\n \n\"Foreign\" cabotage traffic (i.e. traffic carried between city-pairs in a State other than the one where the reporting carrier has its principal place of business) is shown as international traffic.\n\nA technical stop does not result in any flight stage being classified differently than would have been the case had the technical stop not been made. For countries with few air carriers or only one, the addition or discontinuation of a home-based air carrier may cause significant changes in air traffic.\n\nData for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\"" }, { "id": "Longdefinition", "value": "Air passengers carried include both domestic and international aircraft passengers of air carriers registered in the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Civil Aviation Organization, Civil Aviation Statistics of the World and ICAO staff estimates." }, { "id": "Statisticalconceptandmethodology", "value": "For statistical uses, departures are equal to the number of landings made or flight stages flown. A flight stage is the operation of an aircraft from take-off to its next landing. A flight stage is classified as either international or domestic. International flight stage is one or both terminals in the territory of a State, other than the State in which the air carrier has its principal place of business.\n\nDomestic flight stage is not classifiable as international. Domestic flight stages include all flight stages flown between points within the domestic boundaries of a State by an air carrier whose principal place of business is in that State. Flight stages between a State and territories belonging to it, as well as any flight stages between two such territories, should be classified as domestic. This applies even though a stage may cross international waters or over the territory of another State.\n\nThe number of passengers carried is obtained by counting each passenger on a particular flight (with one flight number) once only and not repeatedly on each individual stage of that flight, with a single exception that a passenger flying on both the international and domestic stages of the same flight should be counted as both a domestic and an international passenger." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "46" }, { "id": "IS.RRS.GOOD.MT.K6", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Developmentrelevance", "value": "Transport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe railway transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. Economic growth, technological change, and market liberalization affect road transport throughout the world.\n\nRailways have helped in the industrialization process of a country by easy transportation of coal and raw-materials at a cheaper rate. As railways require huge capital outlay, they may give rise to monopolies and work against public interest at large. Even if controlled and managed by the government, lack of competition sometimes results in inefficiency and high costs. Also, many times it is not economical to operate railways in sparsely settled rural areas. Thus, in many developing countries large rural areas have no railway even today.\n\nRail transport is a major form of passenger and freight transport in many countries. It is ubiquitous in Europe, with an integrated network covering virtually the whole continent. In India, China, South Korea and Japan, many millions use trains as regular transport. In the North America, freight rail transport is widespread and heavily used in for transporting gods. The western Europe region has the highest railway density in the world and has many individual trains which operate through several countries despite technical and organizational differences in each national network. Australia has a generally sparse network, mostly along its densely populated urban centers.\n\nBulk freight handling is a key advantage for rail transport. Low or even zero transshipment costs combined with energy efficiency and low inventory costs allow trains to handle bulk much cheaper than by road. Typical bulk cargo includes coal, ore, grains and liquids. Bulk goods can be transported in open-topped cars, hopper cars and tank cars. Container trains have become the dominant type in the US for non-bulk haulage." }, { "id": "IndicatorName", "value": "Railways, goods transported (million ton-km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Unlike the road sector, where numerous qualified motor vehicle operators can operate anywhere on the road network, railways are a restricted transport system with vehicles confined to a fixed guideway. Considering the cost and service characteristics, railways generally are best suited to carry - and can effectively compete for - bulk commodities and containerized freight for distances of 500-5,000 kilometers, and passengers for distances of 50-1,000 kilometers. Below these limits road transport tends to be more competitive, while above these limits air transport for passengers and freight and sea transport for freight tend to be more competitive. \n\nData for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\"" }, { "id": "Longdefinition", "value": "Goods transported by railway are the volume of goods transported by railway, measured in metric tons times kilometers traveled." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Internation Union of Railways (UIC), OECD Statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Freight traffic on any mode is typically measured in tons and ton-kilometers. A ton-kilometer equals cargo weight transported times distance transported. For railways, an important measure of work performed is gross ton-kilometers, this measure includes rail wagons' empty weight for both empty and loaded movements. This measure of gross ton-kilometers is also called ‘trailing tons' or the total tons being hauled. Sometimes gross ton-kilometer measures include the weight of locomotives used to haul freight trains." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "46" }, { "id": "IS.RRS.PASG.KM", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Developmentrelevance", "value": "Transport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe railway transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. Economic growth, technological change, and market liberalization affect road transport throughout the world.\n\nRailways have helped in the industrialization process of a country by easy transportation of coal and raw-materials at a cheaper rate. As railways require huge capital outlay, they may give rise to monopolies and work against public interest at large. Even if controlled and managed by the government, lack of competition sometimes results in inefficiency and high costs. Also, many times it is not economical to operate railways in sparsely settled rural areas. Thus, in many developing countries large rural areas have no railway even today.\n\nRail transport is a major form of passenger and freight transport in many countries. Passenger trains can involve a variety of functions including long distance travel, daily commuter trips, or local urban transit services. Railways are very popular mode of transportation in Europe, with an integrated network covering virtually the whole continent. In India, China, South Korea and Japan, many millions use trains as regular transport. In the North America, freight rail transport is widespread and heavily used in for transporting gods. The western Europe region has the highest railway density in the world and has many individual trains which operate through several countries despite technical and organizational differences in each national network. Australia has a generally sparse network, mostly along its densely populated urban centers." }, { "id": "IndicatorName", "value": "Railways, passengers carried (million passenger-km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Unlike the road sector, where numerous qualified motor vehicle operators can operate anywhere on the road network, railways are a restricted transport system with vehicles confined to a fixed guideway. Considering the cost and service characteristics, railways generally are best suited to carry - and can effectively compete for - bulk commodities and containerized freight for distances of 500-5,000 kilometers, and passengers for distances of 50-1,000 kilometers. Below these limits road transport tends to be more competitive, while above these limits air transport for passengers and freight and sea transport for freight tend to be more competitive. \n\nData for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\"" }, { "id": "Longdefinition", "value": "Passengers carried by railway are the number of passengers transported by rail times kilometers traveled." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Internation Union of Railways (UIC), OECD Statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Passenger-kilometers are usually measured on the basis of the rail travel distance between origin and destination multiplied by the number of passengers traveling between each origin and destination." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "46" }, { "id": "IT.NET.USER.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nToday's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains." }, { "id": "Generalcomments", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "IndicatorName", "value": "Individuals using the Internet (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union (ITU) World Telecommunication/ICT Indicators Database" }, { "id": "Statisticalconceptandmethodology", "value": "The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx" }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "46" }, { "id": "NE.CON.GOVT.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "General government final consumption expenditure (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Annual percentage growth of general government final consumption expenditure based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. General government final consumption expenditure (general government consumption) includes all government current expenditures for purchases of goods and services (including compensation of employees). It also includes most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" } ], "source_id": "46" }, { "id": "NE.CON.PRVT.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Household and NPISHs Final consumption expenditure (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Annual percentage growth of household and NPISHs final consumption expenditure based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. Household and NPISHs final consumption expenditure (formerly private consumption) is the market value of all goods and services, including durable products (such as cars, washing machines, and home computers), purchased by households. It excludes purchases of dwellings but includes imputed rent for owner-occupied dwellings. It also includes payments and fees to governments to obtain permits and licenses. This indicator includes the expenditures of nonprofit institutions serving households even when reported separately by the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" } ], "source_id": "46" }, { "id": "NE.EXP.GNFS.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Exports of goods and services (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Annual growth rate of exports of goods and services based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. Exports of goods and services represent the value of all goods and other market services provided to the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" } ], "source_id": "46" }, { "id": "NE.EXP.GNFS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Exports of goods and services (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics." }, { "id": "Longdefinition", "value": "Exports of goods and services represent the value of all goods and other market services provided to the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Statisticalconceptandmethodology", "value": "Gross domestic product (GDP) from the expenditure side is made up of household final consumption expenditure, general government final consumption expenditure, gross capital formation (private and public investment in fixed assets, changes in inventories, and net acquisitions of valuables), and net exports (exports minus imports) of goods and services. Such expenditures are recorded in purchaser prices and include net taxes on products." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" } ], "source_id": "46" }, { "id": "NE.GDI.TOTL.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Gross capital formation (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Annual growth rate of gross capital formation based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. Gross capital formation (formerly gross domestic investment) consists of outlays on additions to the fixed assets of the economy plus net changes in the level of inventories. Fixed assets include land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchases; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. Inventories are stocks of goods held by firms to meet temporary or unexpected fluctuations in production or sales, and \"work in progress.\" According to the 2008 SNA, net acquisitions of valuables are also considered capital formation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" } ], "source_id": "46" }, { "id": "NE.IMP.GNFS.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Imports of goods and services (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Annual growth rate of imports of goods and services based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. Imports of goods and services represent the value of all goods and other market services received from the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" } ], "source_id": "46" }, { "id": "NV.AGR.EMPL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2015" }, { "id": "Developmentrelevance", "value": "Labor productivity is used to assess a country's economic ability to create and sustain decent employment opportunities with fair and equitable remuneration. Productivity increases obtained through investment, trade, technological progress, or changes in work organization can increase social protection and reduce poverty, which in turn reduce vulnerable employment and working poverty. Productivity increases do not guarantee these improvements, but without them—and the economic growth they bring—improvements are highly unlikely. Please also see GDP per person employed (constant 2011 PPP $) [SL.GDP.PCAP.EM.KD], which is a key measure for monitoring the Sustainable Development Goal 8 of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all." }, { "id": "IndicatorName", "value": "Agriculture, forestry, and fishing, value added per worker (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output is not easily available, especially in low- and middle-income countries, because the definition, coverage, and methodology are not always consistent across countries. For more details, see Agriculture, forestry, and fishing, value added (constant 2015 US$) [NV.AGR.TOTL.KD], Industry (including construction), value added (constant 2015 US$) [NV.IND.TOTL.KD], and Services, value added (constant 2015 US$) [NV.SRV.TOTL.KD]." }, { "id": "Longdefinition", "value": "Value added per worker is a measure of labor productivity—value added per unit of input. Value added denotes the net output of a sector after adding up all outputs and subtracting intermediate inputs. Data are in constant 2015 U.S. dollars. Agriculture corresponds to the International Standard Industrial Classification (ISIC) tabulation categories A and B (revision 3) or tabulation category A (revision 4), and includes forestry, hunting, and fishing as well as cultivation of crops and livestock production." }, { "id": "Othernotes", "value": "Caution should be used for aggregates (population-weighted averages); world totals can be presented without a large economy such as USA." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived using World Bank national accounts data and OECD National Accounts data files, and employment data from International Labour Organization, ILOSTAT database." }, { "id": "Statisticalconceptandmethodology", "value": "Value added per worker is calculated by dividing value added of a sector by the number employed in the sector. \nGross domestic product (GDP) represents the sum of value added by all producers. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers but excluding sales or value added taxes). Both valuations exclude transport charges that are invoiced separately by producers. Value added by industry is normally measured at basic prices, while total GDP is measured at purchaser prices. \nData on employment are modeled estimates by the International Labour Organization (ILO) ILOSTAT database. The concept of employment generally refers to people above a certain age who worked, or who held a job, during a reference period. Employment data include both full-time and part-time workers." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" } ], "source_id": "46" }, { "id": "NV.IND.EMPL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2015" }, { "id": "Developmentrelevance", "value": "Labor productivity is used to assess a country's economic ability to create and sustain decent employment opportunities with fair and equitable remuneration. Productivity increases obtained through investment, trade, technological progress, or changes in work organization can increase social protection and reduce poverty, which in turn reduce vulnerable employment and working poverty. Productivity increases do not guarantee these improvements, but without them—and the economic growth they bring—improvements are highly unlikely. Please also see GDP per person employed (constant 2011 PPP $) [SL.GDP.PCAP.EM.KD], which is a key measure for monitoring the Sustainable Development Goal 8 of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all." }, { "id": "IndicatorName", "value": "Industry (including construction), value added per worker (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output is not easily available, especially in low- and middle-income countries, because the definition, coverage, and methodology are not always consistent across countries. For more details, see Agriculture, forestry, and fishing, value added (constant 2015 US$) [NV.AGR.TOTL.KD], Industry (including construction), value added (constant 2015 US$) [NV.IND.TOTL.KD], and Services, value added (constant 2015 US$) [NV.SRV.TOTL.KD]." }, { "id": "Longdefinition", "value": "Value added per worker is a measure of labor productivity—value added per unit of input. Value added denotes the net output of a sector after adding up all outputs and subtracting intermediate inputs. Data are in constant 2015 U.S. dollars. Industry corresponds to the International Standard Industrial Classification (ISIC) tabulation categories C-F (revision 3) or tabulation categories B-F (revision 4), and includes mining and quarrying (including oil production), manufacturing, construction, and public utilities (electricity, gas, and water)." }, { "id": "Othernotes", "value": "Caution should be used for aggregates (population-weighted averages); world totals can be presented without a large economy such as USA." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived using World Bank national accounts data and OECD National Accounts data files, and employment data from International Labour Organization, ILOSTAT database." }, { "id": "Statisticalconceptandmethodology", "value": "Value added per worker is calculated by dividing value added of a sector by the number employed in the sector. \nGross domestic product (GDP) represents the sum of value added by all producers. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers but excluding sales or value added taxes). Both valuations exclude transport charges that are invoiced separately by producers. Value added by industry is normally measured at basic prices, while total GDP is measured at purchaser prices. \nData on employment are modeled estimates by the International Labour Organization (ILO) ILOSTAT database. The concept of employment generally refers to people above a certain age who worked, or who held a job, during a reference period. Employment data include both full-time and part-time workers." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" } ], "source_id": "46" }, { "id": "NV.IND.MANF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Developmentrelevance", "value": "Firms typically use multiple processes to produce a product. For example, an automobile manufacturer engages in forging, welding, and painting as well as advertising, accounting, and other service activities. Collecting data at such a detailed level is not practical, nor is it useful to record production data at the highest level of a large, multiplant, multiproduct firm. The ISIC has therefore adopted as the definition of an establishment \"an enterprise or part of an enterprise which independently engages in one, or predominantly one, kind of economic activity at or from one location . . . for which data are available . . .\" (United Nations 1990). By design, this definition matches the reporting unit required for the production accounts of the United Nations System of National Accounts.\n\nThe ISIC system is described in the United Nations' International Standard Industrial Classification of All Economic Activities, Third Revision (1990). The discussion of the ISIC draws on Ryten (1998)." }, { "id": "Generalcomments", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "IndicatorName", "value": "Manufacturing, value added (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"" }, { "id": "Longdefinition", "value": "Manufacturing refers to industries belonging to ISIC divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Statisticalconceptandmethodology", "value": "The data on manufacturing value added in U.S. dollars are from the World Bank's national accounts files and may differ from those UNIDO uses to calculate shares of value added by industry, in part because of differences in exchange rates. Thus value added in a particular industry estimated by applying the shares to total manufacturing value added will not match those from UNIDO sources. Classification of manufacturing industries accords with the United Nations International Standard Industrial Classification (ISIC) revision 3.\n\nData prior to 2008 used revision 2, first published in 1948. Revision 3 was completed in 1989, and many countries now use it. But revision 2 is still widely used for compiling cross-country data. UNIDO has converted these data to accord with revision 3. Concordances matching ISIC categories to national classification systems and to related systems such as the Standard International Trade Classification are available." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Value added" } ], "source_id": "46" }, { "id": "NV.IND.MANF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "IndicatorName", "value": "Manufacturing, value added (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Manufacturing refers to industries belonging to ISIC divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Statisticalconceptandmethodology", "value": "Gross domestic product (GDP) represents the sum of value added by all its producers. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers but excluding sales or value added taxes). Both valuations exclude transport charges that are invoiced separately by producers. Total GDP is measured at purchaser prices. Value added by industry is normally measured at basic prices." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" } ], "source_id": "46" }, { "id": "NV.MNF.TECH.ZS.UN", "metatype": [ { "id": "Developmentrelevance", "value": "Industrial development generally entails a structural transition from resource-based and low technology activities to medium and high-tech industry (MHT) activities. A modern, highly complex production structure offers better opportunities for skills development and technological innovation. MHT activities are also the high value addition industries of manufacturing with higher technological intensity and labour productivity. Increasing the share of MHT sectors also reflects the impact of innovation" }, { "id": "IndicatorName", "value": "Medium and high-tech manufacturing value added (% manufacturing value added)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Value added by economic activity should be reported at least at 3-digit ISIC for compiling MHT values. Missing values at country level are imputed based on the methodology from Competitive Industrial Performance Report (UNIDO, 2017. Conversion to USD or difference in ISIC combinations may cause discrepancy between national and international figures. For additional information please see UNIDO (2017): http://stat.unido.org/content/publications/volume-i%252c-competitive-industrial-performance-report-2016" }, { "id": "Longdefinition", "value": "The proportion of medium and high-tech industry value added in total value added of manufacturing" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Industrial Development Organization (UNIDO), Competitive Industrial Performance (CIP) database" }, { "id": "Statisticalconceptandmethodology", "value": "The indicator is calculated as the share of the sum of the value added from medium and high-tech industry economic activities to manufacturing value added. The medium and high-tech industry is defined using OECD classification as the following by International Standard Industrial Classification of All Economic Activities (ISIC) Revision 3 and Revision 4 Division respectively: ISIC Rev. 3 (24, 29, 30, 31, 32, 33, 34, 35 excluding 351). Manufacturing value added is the value added of manufacturing industry, which is Section C of ISIC Rev.4, and Section D of ISIC Rev.3. Data can be found in UNIDO INDSTAT4 Database by ISIC Revision 3 and ISIC Revision 4 respectively. Data are collected using General Industrial Statistics Questionnaire which is filled by NSOs and submitted to UNIDO annually. Data for OECD countries are obtained directly from OECD. Country data are also collected from official publications and official web-sites. For additional information please see Table B.2.2 in Appendix B of UNIDO (2017): http://stat.unido.org/content/publications/volume-i%252c-competitive-industrial-performance-report-2016" }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" } ], "source_id": "46" }, { "id": "NV.SRV.EMPL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2015" }, { "id": "Developmentrelevance", "value": "Labor productivity is used to assess a country's economic ability to create and sustain decent employment opportunities with fair and equitable remuneration. Productivity increases obtained through investment, trade, technological progress, or changes in work organization can increase social protection and reduce poverty, which in turn reduce vulnerable employment and working poverty. Productivity increases do not guarantee these improvements, but without them—and the economic growth they bring—improvements are highly unlikely. Please also see GDP per person employed (constant 2011 PPP $) [SL.GDP.PCAP.EM.KD], which is a key measure for monitoring the Sustainable Development Goal 8 of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all." }, { "id": "IndicatorName", "value": "Services, value added per worker (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output is not easily available, especially in low- and middle-income countries, because the definition, coverage, and methodology are not always consistent across countries. For more details, see Agriculture, forestry, and fishing, value added (constant 2015 US$) [NV.AGR.TOTL.KD], Industry (including construction), value added (constant 2015 US$) [NV.IND.TOTL.KD], and Services, value added (constant 2015 US$) [NV.SRV.TOTL.KD]." }, { "id": "Longdefinition", "value": "Value added per worker is a measure of labor productivity—value added per unit of input. Value added denotes the net output of a sector after adding up all outputs and subtracting intermediate inputs. Data are in constant 2015 U.S. dollars. Services corresponds to the International Standard Industrial Classification (ISIC) tabulation categories G-P (revision 3) or tabulation categories G-U (revision 4), and includes wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social and personal services." }, { "id": "Othernotes", "value": "Caution should be used for aggregates (population-weighted averages); world totals can be presented without a large economy such as USA." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived using World Bank national accounts data and OECD National Accounts data files, and employment data from International Labour Organization, ILOSTAT database." }, { "id": "Statisticalconceptandmethodology", "value": "Value added per worker is calculated by dividing value added of a sector by the number employed in the sector. \nGross domestic product (GDP) represents the sum of value added by all producers. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers but excluding sales or value added taxes). Both valuations exclude transport charges that are invoiced separately by producers. Value added by industry is normally measured at basic prices, while total GDP is measured at purchaser prices. \nData on employment are modeled estimates by the International Labour Organization (ILO) ILOSTAT database. The concept of employment generally refers to people above a certain age who worked, or who held a job, during a reference period. Employment data include both full-time and part-time workers." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" } ], "source_id": "46" }, { "id": "NY.ADJ.SVNX.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Adjusted net savings, excluding particulate emission damage (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide. This series excludes particulate emissions damage." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on sources and methods in World Bank's \"The Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium\" (2011)." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" } ], "source_id": "46" }, { "id": "NY.GDP.COAL.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Coal rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Coal rents are the difference between the value of both hard and soft coal production at world prices and their total costs of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" } ], "source_id": "46" }, { "id": "NY.GDP.FRST.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Forest rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Forest rents are roundwood harvest times the product of regional prices and a regional rental rate." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" } ], "source_id": "46" }, { "id": "NY.GDP.MINR.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Mineral rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mineral rents are the difference between the value of production for a stock of minerals at world prices and their total costs of production. Minerals included in the calculation are tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" } ], "source_id": "46" }, { "id": "NY.GDP.MKTP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "IndicatorName", "value": "GDP (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Gross domestic product (GDP), though widely tracked, may not always be the most relevant summary of aggregated economic performance for all economies, especially when production occurs at the expense of consuming capital stock.\n\nWhile GDP estimates based on the production approach are generally more reliable than estimates compiled from the income or expenditure side, different countries use different definitions, methods, and reporting standards. World Bank staff review the quality of national accounts data and sometimes make adjustments to improve consistency with international guidelines. Nevertheless, significant discrepancies remain between international standards and actual practice. Many statistical offices, especially those in developing countries, face severe limitations in the resources, time, training, and budgets required to produce reliable and comprehensive series of national accounts statistics.\n\nAmong the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money." }, { "id": "Longdefinition", "value": "GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars. Dollar figures for GDP are converted from domestic currencies using single year official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Statisticalconceptandmethodology", "value": "Gross domestic product (GDP) represents the sum of value added by all its producers. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers but excluding sales or value added taxes). Both valuations exclude transport charges that are invoiced separately by producers. Total GDP is measured at purchaser prices. Value added by industry is normally measured at basic prices." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" } ], "source_id": "46" }, { "id": "NY.GDP.MKTP.CN", "metatype": [ { "id": "IndicatorName", "value": "GDP (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current local currency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" } ], "source_id": "46" }, { "id": "NY.GDP.MKTP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "BasePeriod", "value": "2015" }, { "id": "Developmentrelevance", "value": "An economy's growth is measured by the change in the volume of its output or in the real incomes of its residents. The 2008 United Nations System of National Accounts (2008 SNA) offers three plausible indicators for calculating growth: the volume of gross domestic product (GDP), real gross domestic income, and real gross national income. The volume of GDP is the sum of value added, measured at constant prices, by households, government, and industries operating in the economy. GDP accounts for all domestic production, regardless of whether the income accrues to domestic or foreign institutions." }, { "id": "IndicatorName", "value": "GDP (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Each industry's contribution to growth in the economy's output is measured by growth in the industry's value added. In principle, value added in constant prices can be estimated by measuring the quantity of goods and services produced in a period, valuing them at an agreed set of base year prices, and subtracting the cost of intermediate inputs, also in constant prices. This double-deflation method requires detailed information on the structure of prices of inputs and outputs.\n\nIn many industries, however, value added is extrapolated from the base year using single volume indexes of outputs or, less commonly, inputs. Particularly in the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult.\n\nMoreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in quality lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of inflation.\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\nRebasing of national accounts can alter the measured growth rate of an economy and lead to breaks in series that affect the consistency of data over time. When countries rebase their national accounts, they update the weights assigned to various components to better reflect current patterns of production or uses of output. The new base year should represent normal operation of the economy - it should be a year without major shocks or distortions. Some developing countries have not rebased their national accounts for many years. Using an old base year can be misleading because implicit price and volume weights become progressively less relevant and useful.\n\nTo obtain comparable series of constant price data for computing aggregates, the World Bank rescales GDP and value added by industrial origin to a common reference year. Because rescaling changes the implicit weights used in forming regional and income group aggregates, aggregate growth rates are not comparable with those from earlier editions with different base years. Rescaling may result in a discrepancy between the rescaled GDP and the sum of the rescaled components. To avoid distortions in the growth rates, the discrepancy is left unallocated. As a result, the weighted average of the growth rates of the components generally does not equal the GDP growth rate." }, { "id": "Longdefinition", "value": "GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 prices, expressed in U.S. dollars. Dollar figures for GDP are converted from domestic currencies using 2015 official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Statisticalconceptandmethodology", "value": "Gross domestic product (GDP) represents the sum of value added by all its producers. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers but excluding sales or value added taxes). Both valuations exclude transport charges that are invoiced separately by producers. Total GDP is measured at purchaser prices. Value added by industry is normally measured at basic prices. When value added is measured at producer prices.\n\nGrowth rates of GDP and its components are calculated using the least squares method and constant price data in the local currency. Constant price U.S. dollar series are used to calculate regional and income group growth rates. Local currency series are converted to constant U.S. dollars using an exchange rate in the common reference year." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Aggregate indicators" } ], "source_id": "46" }, { "id": "NY.GDP.MKTP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "An economy's growth is measured by the change in the volume of its output or in the real incomes of its residents. The 2008 United Nations System of National Accounts (2008 SNA) offers three plausible indicators for calculating growth: the volume of gross domestic product (GDP), real gross domestic income, and real gross national income. The volume of GDP is the sum of value added, measured at constant prices, by households, government, and industries operating in the economy. GDP accounts for all domestic production, regardless of whether the income accrues to domestic or foreign institutions." }, { "id": "IndicatorName", "value": "GDP growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Each industry's contribution to growth in the economy's output is measured by growth in the industry's value added. In principle, value added in constant prices can be estimated by measuring the quantity of goods and services produced in a period, valuing them at an agreed set of base year prices, and subtracting the cost of intermediate inputs, also in constant prices. This double-deflation method requires detailed information on the structure of prices of inputs and outputs.\n\nIn many industries, however, value added is extrapolated from the base year using single volume indexes of outputs or, less commonly, inputs. Particularly in the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult.\n\nMoreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in quality lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of inflation.\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\nRebasing of national accounts can alter the measured growth rate of an economy and lead to breaks in series that affect the consistency of data over time. When countries rebase their national accounts, they update the weights assigned to various components to better reflect current patterns of production or uses of output. The new base year should represent normal operation of the economy - it should be a year without major shocks or distortions. Some developing countries have not rebased their national accounts for many years. Using an old base year can be misleading because implicit price and volume weights become progressively less relevant and useful.\n\nTo obtain comparable series of constant price data for computing aggregates, the World Bank rescales GDP and value added by industrial origin to a common reference year. Because rescaling changes the implicit weights used in forming regional and income group aggregates, aggregate growth rates are not comparable with those from earlier editions with different base years. Rescaling may result in a discrepancy between the rescaled GDP and the sum of the rescaled components. To avoid distortions in the growth rates, the discrepancy is left unallocated. As a result, the weighted average of the growth rates of the components generally does not equal the GDP growth rate." }, { "id": "Longdefinition", "value": "Annual percentage growth rate of GDP at market prices based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Statisticalconceptandmethodology", "value": "Gross domestic product (GDP) represents the sum of value added by all its producers. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers but excluding sales or value added taxes). Both valuations exclude transport charges that are invoiced separately by producers. Total GDP is measured at purchaser prices. Value added by industry is normally measured at basic prices. When value added is measured at producer prices.\n\nGrowth rates of GDP and its components are calculated using the least squares method and constant price data in the local currency. Constant price in U.S. dollar series are used to calculate regional and income group growth rates. Local currency series are converted to constant U.S. dollars using an exchange rate in the common reference year." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" } ], "source_id": "46" }, { "id": "NY.GDP.MKTP.KN", "metatype": [ { "id": "BasePeriod", "value": "varies by country" }, { "id": "IndicatorName", "value": "GDP (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant local currency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" } ], "source_id": "46" }, { "id": "NY.GDP.MKTP.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "IndicatorName", "value": "GDP, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross domestic product (GDP) expressed in current international dollars, converted by purchasing power parity (PPP) conversion factor. \nGDP is the sum of gross value added by all resident producers in the country plus any product taxes and minus any subsidies not included in the value of the products. PPP conversion factor is a spatial price deflator and currency converter that eliminates the effects of the differences in price levels between countries. \nFrom April 2020, “GDP: linked series (current LCU)” [NY.GDP.MKTP.CN.AD] is used as underlying GDP in local currency unit so that it’s in line with time series of PPP conversion factors for GDP, which are extrapolated with linked GDP deflators." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme." }, { "id": "Statisticalconceptandmethodology", "value": "Typically, higher income countries have higher price levels, while lower income countries have lower price levels (Balassa-Samuelson effect). Market exchange rate-based cross-country comparisons of GDP at its expenditure components reflect both differences in economic outputs (volumes) and prices. Given the differences in price levels, the size of higher income countries is inflated, while the size of lower income countries is depressed in the comparison. PPP-based cross-country comparisons of GDP at its expenditure components only reflect differences in economic outputs (volume), as PPPs control for price level differences between the countries. Hence, the comparison reflects the real size of the countries.\n\nFor more information on underlying GDP in local currency, please refer to the metadata for “GDP: linked series (current LCU)” [NY.GDP.MKTP.CN.AD]. For more information on underlying PPP conversion factor, please refer to the metadata for \"PPP conversion factor, GDP (LCU per international $)\" [PA.NUS.PPP]. \n\nFor the concept and methodology of PPP, please refer to the International Comparison Program (ICP)’s website (https://www.worldbank.org/en/programs/icp)." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "46" }, { "id": "NY.GDP.MKTP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "BasePeriod", "value": "2017" }, { "id": "IndicatorName", "value": "GDP, PPP (constant 2017 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the country plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2017 international dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme." }, { "id": "Statisticalconceptandmethodology", "value": "For the concept and methodology of 2017 PPP, please refer to the International Comparison Program (ICP)’s website (https://www.worldbank.org/en/programs/icp)." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "46" }, { "id": "NY.GDP.NGAS.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Natural gas rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Natural gas rents are the difference between the value of natural gas production at regional prices and total costs of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" } ], "source_id": "46" }, { "id": "NY.GDP.PCAP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "GDP per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Statisticalconceptandmethodology", "value": "For more information, see the metadata for current U.S. dollar GDP (NY.GDP.MKTP.CD) and total population (SP.POP.TOTL)." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" } ], "source_id": "46" }, { "id": "NY.GDP.PCAP.CN", "metatype": [ { "id": "IndicatorName", "value": "GDP per capita (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current local currency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" } ], "source_id": "46" }, { "id": "NY.GDP.PCAP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2015" }, { "id": "IndicatorName", "value": "GDP per capita (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Statisticalconceptandmethodology", "value": "For more information, see the metadata for constant U.S. dollar GDP (NY.GDP.MKTP.KD) and total population (SP.POP.TOTL)." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Aggregate indicators" } ], "source_id": "46" }, { "id": "NY.GDP.PCAP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "GDP per capita growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Annual percentage growth rate of GDP per capita based on constant local currency. GDP per capita is gross domestic product divided by midyear population. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Statisticalconceptandmethodology", "value": "For more information, see the metadata for constant U.S. dollar GDP (NY.GDP.MKTP.KD) and total population (SP.POP.TOTL)." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" } ], "source_id": "46" }, { "id": "NY.GDP.PCAP.KN", "metatype": [ { "id": "BasePeriod", "value": "varies by country" }, { "id": "IndicatorName", "value": "GDP per capita (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GDP per capita is gross domestic product divided by midyear population. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant local currency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" } ], "source_id": "46" }, { "id": "NY.GDP.PCAP.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "GDP per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides per capita values for gross domestic product (GDP) expressed in current international dollars converted by purchasing power parity (PPP) conversion factor. \n\nGDP is the sum of gross value added by all resident producers in the country plus any product taxes and minus any subsidies not included in the value of the products. conversion factor is a spatial price deflator and currency converter that controls for price level differences between countries. Total population is a mid-year population based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme." }, { "id": "Statisticalconceptandmethodology", "value": "Typically, higher income countries have higher price levels, while lower income countries have lower price levels (Balassa-Samuelson effect). Market exchange rate-based cross-country comparisons of GDP at its expenditure components reflect both differences in economic outputs (volumes) and prices. Given the differences in price levels, the size of higher income countries is inflated, while the size of lower income countries is depressed in the comparison. PPP-based cross-country comparisons of GDP at its expenditure components only reflect differences in economic outputs (volume), as PPPs control for price level differences between the countries. Hence, the comparison reflects the real size of the countries.\n\nFor more information on underlying GDP in current international dollar, please refer to the metadata for \"GDP, PPP (current international $)\" [NY.GDP.MKTP.PP.CD].\nFor more information on underlying population, please refer to the metadata for \"total population” [SP.POP.TOTL]. \nFor the concept and methodology of PPP, please refer to the International Comparison Program (ICP)’s website (https://www.worldbank.org/en/programs/icp)." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "46" }, { "id": "NY.GDP.PCAP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2017" }, { "id": "IndicatorName", "value": "GDP per capita, PPP (constant 2017 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP at purchaser's prices is the sum of gross value added by all resident producers in the country plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2017 international dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme." }, { "id": "Statisticalconceptandmethodology", "value": "For the concept and methodology of 2017 PPP, please refer to the International Comparison Program (ICP)’s website (https://www.worldbank.org/en/programs/icp)." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "46" }, { "id": "NY.GDP.PETR.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Oil rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Oil rents are the difference between the value of crude oil production at regional prices and total costs of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" } ], "source_id": "46" }, { "id": "NY.GDP.TOTL.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Total natural resources rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total natural resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" } ], "source_id": "46" }, { "id": "NY.GNP.MKTP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "BasePeriod", "value": "2015" }, { "id": "Developmentrelevance", "value": "Because development encompasses many factors - economic, environmental, cultural, educational, and institutional - no single measure gives a complete picture. However, the total earnings of the residents of an economy, measured by its gross national income (GNI), is a good measure of its capacity to provide for the well-being of its people." }, { "id": "IndicatorName", "value": "GNI (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in constant 2015 prices, expressed in U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Aggregate indicators" } ], "source_id": "46" }, { "id": "NY.GNP.MKTP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "GNI growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" } ], "source_id": "46" }, { "id": "NY.GNP.MKTP.KN", "metatype": [ { "id": "IndicatorName", "value": "GNI (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in constant local currency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" } ], "source_id": "46" }, { "id": "NY.GNP.MKTP.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "GNI per capita (US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GNI per capita is gross national income divided by midyear population. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" } ], "source_id": "46" }, { "id": "NY.GNP.MKTP.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Developmentrelevance", "value": "Because development encompasses many factors - economic, environmental, cultural, educational, and institutional - no single measure gives a complete picture. However, the total earnings of the residents of an economy, measured by its gross national income (GNI), is a good measure of its capacity to provide for the well-being of its people." }, { "id": "IndicatorName", "value": "GNI, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross national income (GNI. Formerly GNP) expressed in current international dollars converted by purchasing power parity (PPP) conversion factor. \nGross national income is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. PPP conversion factor is a spatial price deflator and currency converter that eliminates the effects of the differences in price levels between countries.\n\nFrom July 2020, “GNI: linked series (current LCU)” [NY.GNP.MKTP.CN.AD] is used as underlying GNI in local currency unit so that it’s in line with time series of PPP conversion factors, which are extrapolated with linked deflators." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme." }, { "id": "Statisticalconceptandmethodology", "value": "Typically, higher income countries have higher price levels, while lower income countries have lower price levels (Balassa-Samuelson effect). Market exchange rate-based cross-country comparisons of GDP at its expenditure components reflect both differences in economic outputs (volumes) and prices. Given the differences in price levels, the size of higher income countries is inflated, while the size of lower income countries is depressed in the comparison. PPP-based cross-country comparisons of GDP at its expenditure components only reflect differences in economic outputs (volume), as PPPs control for price level differences between the countries. Hence, the comparison reflects the real size of the countries.\n\nFor more information on underlying GNI in local currency, please refer to the metadata for \"GNI (current LCU)\" [NY.GNP.MKTP.CN]. For more information on underlying PPP conversion factor, please refer to the metadata for \"PPP conversion factor, GDP (LCU per international $)\" [PA.NUS.PPP]. \n\nFor the concept and methodology of PPP, please refer to the International Comparison Program (ICP)’s website (https://www.worldbank.org/en/programs/icp)." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "46" }, { "id": "NY.GNP.MKTP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "BasePeriod", "value": "2017" }, { "id": "IndicatorName", "value": "GNI, PPP (constant 2017 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "PPP GNI (formerly PPP GNP) is gross national income (GNI) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNI as a U.S. dollar has in the United States. Gross national income is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in constant 2017 international dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme." }, { "id": "Statisticalconceptandmethodology", "value": "For the concept and methodology of 2017 PPP, please refer to the International Comparison Program (ICP)’s website (https://www.worldbank.org/en/programs/icp)." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "46" }, { "id": "NY.GNP.PCAP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "GNI per capita, Atlas method (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GNI per capita (formerly GNP per capita) is the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Statisticalconceptandmethodology", "value": "The World Bank uses Atlas method GNI per capita in U.S. dollars to classify countries for analytical purposes and to determine borrowing eligibility. For more information, see the metadata for Atlas method GNI in current U.S. dollars (NY.GNP.ATLS.CD) and total population (SP.POP.TOTL)." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Atlas GNI & GNI per capita" } ], "source_id": "46" }, { "id": "NY.GNP.PCAP.CN", "metatype": [ { "id": "IndicatorName", "value": "GNI per capita (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GNI per capita is gross national income divided by midyear population. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current local currency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" } ], "source_id": "46" }, { "id": "NY.GNP.PCAP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2015" }, { "id": "IndicatorName", "value": "GNI per capita (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GNI per capita is gross national income divided by midyear population. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in constant 2015 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Aggregate indicators" } ], "source_id": "46" }, { "id": "NY.GNP.PCAP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "GNI per capita growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Annual percentage growth rate of GNI per capita based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. GNI per capita is gross national income divided by midyear population. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" } ], "source_id": "46" }, { "id": "NY.GNP.PCAP.KN", "metatype": [ { "id": "IndicatorName", "value": "GNI per capita (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GNI per capita is gross national income divided by midyear population. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in constant local currency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" } ], "source_id": "46" }, { "id": "NY.GNP.PCAP.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "GNI per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides per capita values for gross national income (GNI. Formerly GNP) expressed in current international dollars converted by purchasing power parity (PPP) conversion factor. \nGNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. PPP conversion factor is a spatial price deflator and currency converter that eliminates the effects of the differences in price levels between countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme." }, { "id": "Statisticalconceptandmethodology", "value": "Typically, higher income countries have higher price levels, while lower income countries have lower price levels (Balassa-Samuelson effect). Market exchange rate-based cross-country comparisons of GDP at its expenditure components reflect both differences in economic outputs (volumes) and prices. Given the differences in price levels, the size of higher income countries is inflated, while the size of lower income countries is depressed in the comparison. PPP-based cross-country comparisons of GDP at its expenditure components only reflect differences in economic outputs (volume), as PPPs control for price level differences between the countries. Hence, the comparison reflects the real size of the countries.\n\nFor more information on underlying GNI in current international dollar, please refer to the metadata for \"GNI, PPP (current international $)\" [NY.GNP.MKTP.PP.CD]. For more information on underlying population, please refer to the metadata for \"total population\" [SP.POP.TOTL]. \nFor the concept and methodology of PPP, please refer to the International Comparison Program (ICP)’s website (https://www.worldbank.org/en/programs/icp)." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "46" }, { "id": "NY.GNP.PCAP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2017" }, { "id": "IndicatorName", "value": "GNI per capita, PPP (constant 2017 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GNI per capita based on purchasing power parity (PPP). PPP GNI is gross national income (GNI) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNI as a U.S. dollar has in the United States. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in constant 2017 international dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme." }, { "id": "Statisticalconceptandmethodology", "value": "For the concept and methodology of 2017 PPP, please refer to the International Comparison Program (ICP)’s website (https://www.worldbank.org/en/programs/icp)." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "46" }, { "id": "PA.NUS.ATLS", "metatype": [ { "id": "Generalcomments", "value": "In the WDI database, the DEC alternative conversion factor is used to convert data in local currency units (LCU) into U.S. dollars." }, { "id": "IndicatorName", "value": "DEC alternative conversion factor (LCU per US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The DEC alternative conversion factor is the underlying annual exchange rate used for the World Bank Atlas method. As a rule, it is the official exchange rate reported in the IMF's International Financial Statistics (line rf). Exceptions arise where further refinements are made by World Bank staff. It is expressed in local currency units per U.S. dollar." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics, supplemented by World Bank staff estimates." }, { "id": "Statisticalconceptandmethodology", "value": "The World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. In certain countries, multiple or dual exchange rate activity exists and must be accounted for appropriately in underlying statistics. Doing so better reflects economic reality and leads to more accurate cross-country comparisons and country classifications by income level. Consequently, an alternative conversion factor is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate effectively applied to domestic transactions of foreign currencies and traded products. This applies to only a small number of countries, as shown in the country-level metadata. An alternative conversion factor is also used when the period covered by national accounts differs from the calendar year and the alternative conversion factor will then cover the same period. Alternative conversion factors are used in the Atlas methodology and elsewhere in World Development Indicators as single-year conversion factors." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" } ], "source_id": "46" }, { "id": "PA.NUS.PPP", "metatype": [ { "id": "Developmentrelevance", "value": "PPP can be used to convert national accounts data, like GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. They can also be used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\n\nPPPs and the PLIs and real (or PPP-adjusted) expenditures to which they give rise allow for many use-cases, but they are particularly valuable for empirical work involving comparisons of per capita consumption or levels of GDP (or other GDP aggregates) across countries and for the measurement of global poverty and global income inequality. The breadth and depth of ICP data allows its use-cases to cover other areas of economics, including empirical analyses of economic growth, productivity and trade, and even beyond, for instance, to help track global targets such as the UN Sustainable Development Goals related to health, education, energy and emissions and labor. Other applications of ICP data include their use in the construction of indexes, for example cost-of-living measures. Uses-cases can even be extended into the policymaking domain at all levels (global, regional and national) given the increased importance of cross-country benchmarking, among other possibilities.\n\nRecommended uses of PPPs include: To make spatial comparisons of GDP and its expenditure components | To make spatial comparisons of price levels | To group countries by their per capita volume indexes and price level indexes\n\nRecommended uses of PPPs with limitations include: To analyze changes over time in relative GDP per capita and relative prices | To analyze price convergence | To make spatial comparisons of the cost of living | To use PPPs calculated for GDP and its expenditure components as deflators for other values." }, { "id": "IndicatorName", "value": "PPP conversion factor, GDP (LCU per international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global PPP estimates provided by ICP are produced by the ICP Global Office and regional implementing agencies, based on data supplied by participating countries, and in accordance with the methodology recommended by the ICP Technical Advisory Group and approved by the ICP Governing Board. As such, these results are not produced by participating countries as part of their national official statistics.\n\nPPPs are not recommended use: As a precise measure to establish strict rankings of countries | As a means of constructing national growth rates | As a measure to generate output and productivity comparisons by industry | As an indicator of the undervaluation or overvaluation of currencies | As an equilibrium exchange rate." }, { "id": "Longdefinition", "value": "Purchasing power parity (PPP) conversion factor is a spatial price deflator and currency converter that controls for price level differences between countries, thereby allowing volume comparisons of gross domestic product (GDP) and its expenditure components. This conversion factor is for GDP." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme." }, { "id": "Statisticalconceptandmethodology", "value": "PPPs are both currency conversion factors and spatial price indexes. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by controlling differences in price levels between countries.\n\nTypically, higher income countries have higher price levels, while lower income countries have lower price levels (Balassa-Samuelson effect). Market exchange rate-based cross-country comparisons of GDP at its expenditure components reflect both differences in economic outputs (volumes) and prices. Given the differences in price levels, the size of higher income countries is inflated, while the size of lower income countries is depressed in the comparison. PPP-based cross-country comparisons of GDP at its expenditure components only reflect differences in economic outputs (volume), as PPPs control for price level differences between the countries. Hence, the comparison reflects the real size of the countries.\n\nThe International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The most recent 2017 ICP comparison covered 176 countries, including 47 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model.\n\nICP estimated PPPs cover years from 2011 to 2017. WDI extrapolates 2011 PPPs for years earlier years, and 2017 PPPs for later years. Description of WDI extrapolation approach is available here: https://datahelpdesk.worldbank.org/knowledgebase/articles/665452-how-do-you-extrapolate-the-ppp-conversion-factors\n\nFor the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. For Eurostat-OECD PPP Programme, please refer to the following websites.\n(http://www.oecd.org/sdd/prices-ppp/)\n(https://ec.europa.eu/eurostat/web/purchasing-power-parities/overview)\n\nFor more information on the ICP and PPPs, please refer to the ICP website at https://www.worldbank.org/en/programs/icp." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "46" }, { "id": "PA.NUS.PRVT.PP", "metatype": [ { "id": "Developmentrelevance", "value": "PPP can be used to convert national accounts data, like GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. They can also be used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\n\nPPPs and the PLIs and real (or PPP-adjusted) expenditures to which they give rise allow for many use-cases, but they are particularly valuable for empirical work involving comparisons of per capita consumption or levels of GDP (or other GDP aggregates) across countries and for the measurement of global poverty and global income inequality. The breadth and depth of ICP data allows its use-cases to cover other areas of economics, including empirical analyses of economic growth, productivity and trade, and even beyond, for instance, to help track global targets such as the UN Sustainable Development Goals related to health, education, energy and emissions and labor. Other applications of ICP data include their use in the construction of indexes, for example cost-of-living measures. Uses-cases can even be extended into the policymaking domain at all levels (global, regional and national) given the increased importance of cross-country benchmarking, among other possibilities.\n\nRecommended uses of PPPs include: To make spatial comparisons of GDP and its expenditure components | To make spatial comparisons of price levels | To group countries by their per capita volume indexes and price level indexes\n\nRecommended uses of PPPs with limitations include: To analyze changes over time in relative GDP per capita and relative prices | To analyze price convergence | To make spatial comparisons of the cost of living | To use PPPs calculated for GDP and its expenditure components as deflators for other values." }, { "id": "IndicatorName", "value": "PPP conversion factor, private consumption (LCU per international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global PPP estimates provided by ICP are produced by the ICP Global Office and regional implementing agencies, based on data supplied by participating countries, and in accordance with the methodology recommended by the ICP Technical Advisory Group and approved by the ICP Governing Board. As such, these results are not produced by participating countries as part of their national official statistics.\n\nPPPs are not recommended use: As a precise measure to establish strict rankings of countries | As a means of constructing national growth rates | As a measure to generate output and productivity comparisons by industry | As an indicator of the undervaluation or overvaluation of currencies | As an equilibrium exchange rate." }, { "id": "Longdefinition", "value": "Purchasing power parity (PPP) conversion factor is a spatial price deflator and currency converter that controls for price level differences between countries, thereby allowing volume comparisons of gross domestic product (GDP) and its expenditure components. This conversion factor is for household final consumption expenditure." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme." }, { "id": "Statisticalconceptandmethodology", "value": "PPPs are both currency conversion factors and spatial price indexes. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by controlling differences in price levels between countries.\n\nTypically, higher income countries have higher price levels, while lower income countries have lower price levels (Balassa-Samuelson effect). Market exchange rate-based cross-country comparisons of GDP at its expenditure components reflect both differences in economic outputs (volumes) and prices. Given the differences in price levels, the size of higher income countries is inflated, while the size of lower income countries is depressed in the comparison. PPP-based cross-country comparisons of GDP at its expenditure components only reflect differences in economic outputs (volume), as PPPs control for price level differences between the countries. Hence, the comparison reflects the real size of the countries.\n\nThe International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The most recent 2017 ICP comparison covered 176 countries, including 47 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model.\n\nICP estimated PPPs cover years from 2011 to 2017. WDI extrapolates 2011 PPPs for years earlier years, and 2017 PPPs for later years. Description of WDI extrapolation approach is available here: https://datahelpdesk.worldbank.org/knowledgebase/articles/665452-how-do-you-extrapolate-the-ppp-conversion-factors\n\nFor the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. For Eurostat-OECD PPP Programme, please refer to the following websites.\n(http://www.oecd.org/sdd/prices-ppp/)\n(https://ec.europa.eu/eurostat/web/purchasing-power-parities/overview)\n\nFor more information on the ICP and PPPs, please refer to the ICP website at https://www.worldbank.org/en/programs/icp." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" } ], "source_id": "46" }, { "id": "per_lm_alllm.cov_pop_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_lm_alllm.cov_q1_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP in poorest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_lm_alllm.cov_q2_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP in 2nd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_lm_alllm.cov_q3_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP in 3rd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_lm_alllm.cov_q4_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP in 4th quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_lm_alllm.cov_q5_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP in richest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_sa_allsa.cov_pop_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of social safety net programs (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_sa_allsa.cov_q1_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of social safety net programs in poorest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_sa_allsa.cov_q2_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of social safety net programs in 2nd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_sa_allsa.cov_q3_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of social safety net programs in 3rd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_sa_allsa.cov_q4_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of social safety net programs in 4th quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_sa_allsa.cov_q5_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of social safety net programs in richest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_si_allsi.cov_pop_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of social insurance programs (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_si_allsi.cov_q1_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of social insurance programs in poorest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_si_allsi.cov_q2_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of social insurance programs in 2nd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_si_allsi.cov_q3_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of social insurance programs in 3rd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_si_allsi.cov_q4_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of social insurance programs in 4th quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "per_si_allsi.cov_q5_tot", "metatype": [ { "id": "IndicatorName", "value": "Coverage of social insurance programs in richest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" } ], "source_id": "46" }, { "id": "SE.ADT.1524.LT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Literacy rate, youth female (% of females ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.ADT.1524.LT.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\nThe Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women. Literate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Literacy rate, youth (ages 15-24), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Gender parity index for youth literacy rate is the ratio of females to males ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "This indicator is calculated by dividing female youth literacy rate by male youth literacy rate. \n\nLiteracy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.ADT.1524.LT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Literacy rate, youth male (% of males ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.ADT.1524.LT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Literacy rate, youth total (% of people ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.ADT.LITR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Literacy rate, adult female (% of females ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.ADT.LITR.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Literacy rate, adult male (% of males ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.ADT.LITR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Literacy rate, adult total (% of people ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.COM.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2023 (July 1, 2022-June 30, 2023)." }, { "id": "IndicatorName", "value": "Compulsory education, duration (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Duration of compulsory education is the number of years that children are legally obliged to attend school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Aggregate data are based on World Bank estimates.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.ENR.PRIM.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "School enrollment, primary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in primary education is the ratio of girls to boys enrolled at primary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "This indicator is calculated by dividing female gross enrollment ratio in primary education by male gross enrollment ratio in primary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.ENR.PRSC.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "School enrollment, primary and secondary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in primary and secondary education is the ratio of girls to boys enrolled at primary and secondary levels in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "This indicator is calculated by dividing female gross enrollment ratio in primary and secondary education by male gross enrollment ratio in primary and secondary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.ENR.SECO.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "School enrollment, secondary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in secondary education is the ratio of girls to boys enrolled at secondary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "This indicator is calculated by dividing female gross enrollment ratio in secondary education by male gross enrollment ratio in secondary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.ENR.TERT.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "School enrollment, tertiary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in tertiary education is the ratio of women to men enrolled at tertiary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "This indicator is calculated by dividing female gross enrollment ratio in tertiary education by male gross enrollment ratio in tertiary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.PRE.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2023 (July 1, 2022-June 30, 2023)." }, { "id": "IndicatorName", "value": "Preprimary education, duration (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Preprimary duration refers to the number of grades (years) in preprimary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Aggregate data are based on World Bank estimates.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.PRE.ENRL.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, preprimary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Preprimary school pupil-teacher ratio is the average number of pupils per teacher in preprimary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.PRE.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "School enrollment, preprimary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Gross enrollment ratio for pre-primary school is calculated by dividing the number of students enrolled in pre-primary education regardless of age by the population of the age group which officially corresponds to pre-primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.PRE.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "School enrollment, preprimary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Gross enrollment ratio for pre-primary school is calculated by dividing the number of students enrolled in pre-primary education regardless of age by the population of the age group which officially corresponds to pre-primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.PRE.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "School enrollment, preprimary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Gross enrollment ratio for pre-primary school is calculated by dividing the number of students enrolled in pre-primary education regardless of age by the population of the age group which officially corresponds to pre-primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.PRE.TCAQ.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Trained teachers refer to teaching force with the necessary pedagogical skills to teach and use teaching materials in an effective manner. The share of trained teachers reveals a country's commitment to investing in the development of its human capital engaged in teaching.\n\nTeachers are important resource, especially for children who are the first-generation of receiving education in their families and heavily rely on teachers in acquiring basic literacy skills. However, rapid increase in enrollments may cause the shortage of trained teachers. Education finance is a key for appropriate teacher allocations, since teacher salaries account for a large share of education budgets. The shortage of trained teacher may result in low qualified teachers in more disadvantaged area." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Trained teachers in preprimary education, female (% of female teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in preprimary education are the percentage of preprimary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.PRE.TCAQ.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Trained teachers refer to teaching force with the necessary pedagogical skills to teach and use teaching materials in an effective manner. The share of trained teachers reveals a country's commitment to investing in the development of its human capital engaged in teaching.\n\nTeachers are important resource, especially for children who are the first-generation of receiving education in their families and heavily rely on teachers in acquiring basic literacy skills. However, rapid increase in enrollments may cause the shortage of trained teachers. Education finance is a key for appropriate teacher allocations, since teacher salaries account for a large share of education budgets. The shortage of trained teacher may result in low qualified teachers in more disadvantaged area." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Trained teachers in preprimary education, male (% of male teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in preprimary education are the percentage of preprimary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.PRE.TCAQ.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Trained teachers refer to teaching force with the necessary pedagogical skills to teach and use teaching materials in an effective manner. The share of trained teachers reveals a country's commitment to investing in the development of its human capital engaged in teaching.\n\nTeachers are important resource, especially for children who are the first-generation of receiving education in their families and heavily rely on teachers in acquiring basic literacy skills. However, rapid increase in enrollments may cause the shortage of trained teachers. Education finance is a key for appropriate teacher allocations, since teacher salaries account for a large share of education budgets. The shortage of trained teacher may result in low qualified teachers in more disadvantaged area." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Trained teachers in preprimary education (% of total teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in preprimary education are the percentage of preprimary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.PRM.CMPT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The World Bank and the UNESCO Institute for Statistics jointly developed the primary completion rate indicator. Increasingly used as a core indicator of an education system's performance, it reflects an education system's coverage and the educational attainment of students." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Primary completion rate, female (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data limitations preclude adjusting for students who drop out during the final year of primary education. Thus this rate is a proxy that should be taken as an upper estimate of the actual primary completion rate.\n \nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.PRM.CMPT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The World Bank and the UNESCO Institute for Statistics jointly developed the primary completion rate indicator. Increasingly used as a core indicator of an education system's performance, it reflects an education system's coverage and the educational attainment of students." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Primary completion rate, male (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data limitations preclude adjusting for students who drop out during the final year of primary education. Thus this rate is a proxy that should be taken as an upper estimate of the actual primary completion rate.\n \nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.PRM.CMPT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The World Bank and the UNESCO Institute for Statistics jointly developed the primary completion rate indicator. Increasingly used as a core indicator of an education system's performance, it reflects an education system's coverage and the educational attainment of students." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Primary completion rate, total (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data limitations preclude adjusting for students who drop out during the final year of primary education. Thus this rate is a proxy that should be taken as an upper estimate of the actual primary completion rate.\n \nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.PRM.CUAT.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed primary, population 25+ years, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed primary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.PRM.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2023 (July 1, 2022-June 30, 2023)." }, { "id": "IndicatorName", "value": "Primary education, duration (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Primary duration refers to the number of grades (years) in primary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Aggregate data are based on World Bank estimates.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.PRM.ENRL.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, primary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Primary school pupil-teacher ratio is the average number of pupils per teacher in primary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.PRM.OENR.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Over-age students, primary, female (% of female enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Over-age students are the percentage of those enrolled who are older than the official school-age range for primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020." }, { "id": "Statisticalconceptandmethodology", "value": "The percentage of over-age students is calculated by dividing the number of students who are older than the official school-age range for primary education by primary school enrollment, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "46" }, { "id": "SE.PRM.OENR.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Over-age students, primary, male (% of male enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Over-age students are the percentage of those enrolled who are older than the official school-age range for primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020." }, { "id": "Statisticalconceptandmethodology", "value": "The percentage of over-age students is calculated by dividing the number of students who are older than the official school-age range for primary education by primary school enrollment, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "46" }, { "id": "SE.PRM.OENR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Over-age students, primary (% of enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Over-age students are the percentage of those enrolled who are older than the official school-age range for primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020." }, { "id": "Statisticalconceptandmethodology", "value": "The percentage of over-age students is calculated by dividing the number of students who are older than the official school-age range for primary education by primary school enrollment, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "46" }, { "id": "SE.PRM.TCAQ.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Trained teachers refer to teaching force with the necessary pedagogical skills to teach and use teaching materials in an effective manner. The share of trained teachers reveals a country's commitment to investing in the development of its human capital engaged in teaching.\n\nTeachers are important resource, especially for children who are the first-generation of receiving education in their families and heavily rely on teachers in acquiring basic literacy skills. However, rapid increase in enrollments may cause the shortage of trained teachers. Education finance is a key for appropriate teacher allocations, since teacher salaries account for a large share of education budgets. The shortage of trained teacher may result in low qualified teachers in more disadvantaged area." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Trained teachers in primary education, female (% of female teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in primary education are the percentage of primary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.PRM.TCAQ.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Trained teachers refer to teaching force with the necessary pedagogical skills to teach and use teaching materials in an effective manner. The share of trained teachers reveals a country's commitment to investing in the development of its human capital engaged in teaching.\n\nTeachers are important resource, especially for children who are the first-generation of receiving education in their families and heavily rely on teachers in acquiring basic literacy skills. However, rapid increase in enrollments may cause the shortage of trained teachers. Education finance is a key for appropriate teacher allocations, since teacher salaries account for a large share of education budgets. The shortage of trained teacher may result in low qualified teachers in more disadvantaged area." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Trained teachers in primary education, male (% of male teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in primary education are the percentage of primary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.PRM.TCAQ.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Trained teachers refer to teaching force with the necessary pedagogical skills to teach and use teaching materials in an effective manner. The share of trained teachers reveals a country's commitment to investing in the development of its human capital engaged in teaching.\n\nTeachers are important resource, especially for children who are the first-generation of receiving education in their families and heavily rely on teachers in acquiring basic literacy skills. However, rapid increase in enrollments may cause the shortage of trained teachers. Education finance is a key for appropriate teacher allocations, since teacher salaries account for a large share of education budgets. The shortage of trained teacher may result in low qualified teachers in more disadvantaged area." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Trained teachers in primary education (% of total teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in primary education are the percentage of primary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.PRM.UNER", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Large numbers of children out of school create pressure to enroll children and provide classrooms, teachers, and educational materials, a task made difficult in many countries by limited education budgets. However, getting children into school is a high priority for countries and crucial for achieving universal primary education." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Children out of school, primary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to different data sources for enrollment and population data, the number may not capture the actual number of children not attending in primary school." }, { "id": "Longdefinition", "value": "Children out of school are the number of primary-school-age children not enrolled in primary or secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The number of out-of-school children is calculated by subtracting the number of primary school-age children enrolled in primary or secondary school from the total population of the official primary school-age children. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.PRM.UNER.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Large numbers of children out of school create pressure to enroll children and provide classrooms, teachers, and educational materials, a task made difficult in many countries by limited education budgets. However, getting children into school is a high priority for countries and crucial for achieving universal primary education." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Children out of school, primary, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to different data sources for enrollment and population data, the number may not capture the actual number of children not attending in primary school." }, { "id": "Longdefinition", "value": "Children out of school are the number of primary-school-age children not enrolled in primary or secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The number of out-of-school children is calculated by subtracting the number of primary school-age children enrolled in primary or secondary school from the total population of the official primary school-age children. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.PRM.UNER.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Children out of school, female (% of female primary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The rate of out-of-school children allows to compare across countries with different population sizes. It shows the share of official primary-school-age children who never attended school or dropped out to the population of official primary school age.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.PRM.UNER.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Large numbers of children out of school create pressure to enroll children and provide classrooms, teachers, and educational materials, a task made difficult in many countries by limited education budgets. However, getting children into school is a high priority for countries and crucial for achieving universal primary education." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Children out of school, primary, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to different data sources for enrollment and population data, the number may not capture the actual number of children not attending in primary school." }, { "id": "Longdefinition", "value": "Children out of school are the number of primary-school-age children not enrolled in primary or secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The number of out-of-school children is calculated by subtracting the number of primary school-age children enrolled in primary or secondary school from the total population of the official primary school-age children. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.PRM.UNER.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Children out of school, male (% of male primary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The rate of out-of-school children allows to compare across countries with different population sizes. It shows the share of official primary-school-age children who never attended school or dropped out to the population of official primary school age.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.PRM.UNER.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Children out of school (% of primary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The rate of out-of-school children allows to compare across countries with different population sizes. It shows the share of official primary-school-age children who never attended school or dropped out to the population of official primary school age.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.SEC.CMPT.LO.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Lower secondary completion rate, female (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data limitations preclude adjusting for students who drop out during the final year of lower secondary education. Thus this rate is a proxy that should be taken as an upper estimate of the actual lower secondary completion rate. \n\nThere are many reasons why the rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of lower secondary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of lower secondary education." }, { "id": "Longdefinition", "value": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.SEC.CMPT.LO.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Lower secondary completion rate, male (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data limitations preclude adjusting for students who drop out during the final year of lower secondary education. Thus this rate is a proxy that should be taken as an upper estimate of the actual lower secondary completion rate. \n\nThere are many reasons why the rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of lower secondary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of lower secondary education." }, { "id": "Longdefinition", "value": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.SEC.CMPT.LO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Lower secondary completion rate, total (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data limitations preclude adjusting for students who drop out during the final year of lower secondary education. Thus this rate is a proxy that should be taken as an upper estimate of the actual lower secondary completion rate. \n\nThere are many reasons why the rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of lower secondary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of lower secondary education." }, { "id": "Longdefinition", "value": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.SEC.CUAT.LO.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed lower secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.SEC.CUAT.LO.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed lower secondary, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed lower secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.SEC.CUAT.LO.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed lower secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.SEC.CUAT.PO.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed post-secondary, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed post-secondary non-tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.SEC.CUAT.PO.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed post-secondary, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed post-secondary non-tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.SEC.CUAT.PO.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed post-secondary non-tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.SEC.CUAT.UP.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed upper secondary, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed upper secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed upper secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.SEC.CUAT.UP.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed upper secondary, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed upper secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed upper secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.SEC.CUAT.UP.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed upper secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed upper secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.SEC.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2023 (July 1, 2022-June 30, 2023)." }, { "id": "IndicatorName", "value": "Secondary education, duration (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Secondary duration refers to the number of grades (years) in secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Aggregate data are based on World Bank estimates.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.SEC.ENRL.LO.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, lower secondary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Lower secondary school pupil-teacher ratio is the average number of pupils per teacher in lower secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.SEC.ENRL.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, secondary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Secondary school pupil-teacher ratio is the average number of pupils per teacher in secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.SEC.ENRL.UP.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, upper secondary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Upper secondary school pupil-teacher ratio is the average number of pupils per teacher in upper secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.SEC.TCAQ.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Trained teachers refer to teaching force with the necessary pedagogical skills to teach and use teaching materials in an effective manner. The share of trained teachers reveals a country's commitment to investing in the development of its human capital engaged in teaching.\n\nTeachers are important resource, especially for children who are the first-generation of receiving education in their families and heavily rely on teachers in acquiring basic literacy skills. However, rapid increase in enrollments may cause the shortage of trained teachers. Education finance is a key for appropriate teacher allocations, since teacher salaries account for a large share of education budgets. The shortage of trained teacher may result in low qualified teachers in more disadvantaged area." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Trained teachers in secondary education, female (% of female teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in secondary education are the percentage of secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.SEC.TCAQ.LO.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Trained teachers refer to teaching force with the necessary pedagogical skills to teach and use teaching materials in an effective manner. The share of trained teachers reveals a country's commitment to investing in the development of its human capital engaged in teaching.\n\nTeachers are important resource, especially for children who are the first-generation of receiving education in their families and heavily rely on teachers in acquiring basic literacy skills. However, rapid increase in enrollments may cause the shortage of trained teachers. Education finance is a key for appropriate teacher allocations, since teacher salaries account for a large share of education budgets. The shortage of trained teacher may result in low qualified teachers in more disadvantaged area." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Trained teachers in lower secondary education, female (% of female teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in lower secondary education are the percentage of lower secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.SEC.TCAQ.LO.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Trained teachers refer to teaching force with the necessary pedagogical skills to teach and use teaching materials in an effective manner. The share of trained teachers reveals a country's commitment to investing in the development of its human capital engaged in teaching.\n\nTeachers are important resource, especially for children who are the first-generation of receiving education in their families and heavily rely on teachers in acquiring basic literacy skills. However, rapid increase in enrollments may cause the shortage of trained teachers. Education finance is a key for appropriate teacher allocations, since teacher salaries account for a large share of education budgets. The shortage of trained teacher may result in low qualified teachers in more disadvantaged area." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Trained teachers in lower secondary education, male (% of male teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in lower secondary education are the percentage of lower secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.SEC.TCAQ.LO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Trained teachers refer to teaching force with the necessary pedagogical skills to teach and use teaching materials in an effective manner. The share of trained teachers reveals a country's commitment to investing in the development of its human capital engaged in teaching.\n\nTeachers are important resource, especially for children who are the first-generation of receiving education in their families and heavily rely on teachers in acquiring basic literacy skills. However, rapid increase in enrollments may cause the shortage of trained teachers. Education finance is a key for appropriate teacher allocations, since teacher salaries account for a large share of education budgets. The shortage of trained teacher may result in low qualified teachers in more disadvantaged area." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Trained teachers in lower secondary education (% of total teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in lower secondary education are the percentage of lower secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.SEC.TCAQ.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Trained teachers refer to teaching force with the necessary pedagogical skills to teach and use teaching materials in an effective manner. The share of trained teachers reveals a country's commitment to investing in the development of its human capital engaged in teaching.\n\nTeachers are important resource, especially for children who are the first-generation of receiving education in their families and heavily rely on teachers in acquiring basic literacy skills. However, rapid increase in enrollments may cause the shortage of trained teachers. Education finance is a key for appropriate teacher allocations, since teacher salaries account for a large share of education budgets. The shortage of trained teacher may result in low qualified teachers in more disadvantaged area." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Trained teachers in secondary education, male (% of male teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in secondary education are the percentage of secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.SEC.TCAQ.UP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Trained teachers refer to teaching force with the necessary pedagogical skills to teach and use teaching materials in an effective manner. The share of trained teachers reveals a country's commitment to investing in the development of its human capital engaged in teaching.\n\nTeachers are important resource, especially for children who are the first-generation of receiving education in their families and heavily rely on teachers in acquiring basic literacy skills. However, rapid increase in enrollments may cause the shortage of trained teachers. Education finance is a key for appropriate teacher allocations, since teacher salaries account for a large share of education budgets. The shortage of trained teacher may result in low qualified teachers in more disadvantaged area." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Trained teachers in upper secondary education, female (% of female teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in upper secondary education are the percentage of upper secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.SEC.TCAQ.UP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Trained teachers refer to teaching force with the necessary pedagogical skills to teach and use teaching materials in an effective manner. The share of trained teachers reveals a country's commitment to investing in the development of its human capital engaged in teaching.\n\nTeachers are important resource, especially for children who are the first-generation of receiving education in their families and heavily rely on teachers in acquiring basic literacy skills. However, rapid increase in enrollments may cause the shortage of trained teachers. Education finance is a key for appropriate teacher allocations, since teacher salaries account for a large share of education budgets. The shortage of trained teacher may result in low qualified teachers in more disadvantaged area." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Trained teachers in upper secondary education, male (% of male teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in upper secondary education are the percentage of upper secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.SEC.TCAQ.UP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Trained teachers refer to teaching force with the necessary pedagogical skills to teach and use teaching materials in an effective manner. The share of trained teachers reveals a country's commitment to investing in the development of its human capital engaged in teaching.\n\nTeachers are important resource, especially for children who are the first-generation of receiving education in their families and heavily rely on teachers in acquiring basic literacy skills. However, rapid increase in enrollments may cause the shortage of trained teachers. Education finance is a key for appropriate teacher allocations, since teacher salaries account for a large share of education budgets. The shortage of trained teacher may result in low qualified teachers in more disadvantaged area." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Trained teachers in upper secondary education (% of total teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in upper secondary education are the percentage of upper secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.SEC.TCAQ.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Trained teachers refer to teaching force with the necessary pedagogical skills to teach and use teaching materials in an effective manner. The share of trained teachers reveals a country's commitment to investing in the development of its human capital engaged in teaching.\n\nTeachers are important resource, especially for children who are the first-generation of receiving education in their families and heavily rely on teachers in acquiring basic literacy skills. However, rapid increase in enrollments may cause the shortage of trained teachers. Education finance is a key for appropriate teacher allocations, since teacher salaries account for a large share of education budgets. The shortage of trained teacher may result in low qualified teachers in more disadvantaged area." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Trained teachers in secondary education (% of total teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in secondary education are the percentage of secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.SEC.UNER.LO.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Adolescents out of school, female (% of female lower secondary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Adolescents out of school are the percentage of lower secondary school age adolescents who are not enrolled in school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The rate of out-of-school adolescents allows to compare across countries with different population sizes. It shows the share of official lower secondary age adolescents who never attended school or dropped out to the population of official lower secondary school age.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.SEC.UNER.LO.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Adolescents out of school, male (% of male lower secondary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Adolescents out of school are the percentage of lower secondary school age adolescents who are not enrolled in school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The rate of out-of-school adolescents allows to compare across countries with different population sizes. It shows the share of official lower secondary age adolescents who never attended school or dropped out to the population of official lower secondary school age.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.SEC.UNER.LO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Adolescents out of school (% of lower secondary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Adolescents out of school are the percentage of lower secondary school age adolescents who are not enrolled in school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The rate of out-of-school adolescents allows to compare across countries with different population sizes. It shows the share of official lower secondary age adolescents who never attended school or dropped out to the population of official lower secondary school age.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.TER.CUAT.BA.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed Bachelor's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.TER.CUAT.BA.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed Bachelor's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.TER.CUAT.BA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed Bachelor's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.TER.CUAT.DO.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, Doctoral or equivalent, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Doctoral or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed Doctoral or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.TER.CUAT.DO.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, Doctoral or equivalent, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Doctoral or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed Doctoral or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.TER.CUAT.DO.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Doctoral or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed Doctoral or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.TER.CUAT.MS.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Master's or equivalent, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Master's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed Master's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.TER.CUAT.MS.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Master's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed Master's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.TER.CUAT.MS.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Master's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed Master's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.TER.CUAT.ST.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed short-cycle tertiary, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed short-cycle tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed short-cycle tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.TER.CUAT.ST.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed short-cycle tertiary, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed short-cycle tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed short-cycle tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.TER.CUAT.ST.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed short-cycle tertiary, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed short-cycle tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "It is calculated by dividing the number of population ages 25 and older who attained or completed short-cycle tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "46" }, { "id": "SE.TER.ENRL.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, tertiary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Tertiary school pupil-teacher ratio is the average number of pupils per teacher in tertiary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020." }, { "id": "Statisticalconceptandmethodology", "value": "Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "46" }, { "id": "SE.TER.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "School enrollment, tertiary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.TER.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "School enrollment, tertiary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SE.TER.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "School enrollment, tertiary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "46" }, { "id": "SG.DMK.SRCR.FN.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 5.6.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Women making their own informed decisions regarding sexual relations, contraceptive use and reproductive health care (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Current estimates of the indicator are based on currently married or in union women of reproductive age (15-49 years old) who are using any type of contraception. In the current Demographic and Health Surveys (DHS), the question on decision-making on use of contraception is only asked to women who are currently using contraception. Because the questions on decision- making on sexual relations and health care are restricted to women (15-49) currently married or in union, the denominator for Indicator 5.6.1 is women 15-49, who are currently married or in union and currently using contraception. However, agreement has been reached with Macro/ICF for upcoming DHS surveys to ask the question on decision on use of contraception to all married/ in union women aged 15-49 years, whether they are currently using any contraception or not. The DHS model questionnaire for Phase 7 already includes the question on decision-making for women who are not currently using any contraception." }, { "id": "Longdefinition", "value": "Proportion of women ages 15-49 years (married or in union) who make their own decision on all three selected areas i.e. can say no to sexual intercourse with their husband or partner if they do not want; decide on use of contraception; and decide on their own health care. Only women who provide a “yes” answer to all three components are considered as women who “make her own decisions regarding sexual and reproductive”." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys compiled by United Nations Population Fund" }, { "id": "Statisticalconceptandmethodology", "value": "Numerator of the indicator is number of married or in union women ages 15-49 who have been interviewed and satisfy all three empowerment criteria: 1)who can say \"no\" to sex; and 2)for whom the decision on contraception is not mainly made by the husband/partner; and 3) for whom decision on health care for themselves ins not usually made by the husband/partner or someone else. Denominator of the indicator is the total number of women ages 15-49 who are married or in union and who have been interviewed." }, { "id": "Topic", "value": "Gender: Public life & decision making" } ], "source_id": "46" }, { "id": "SG.GEN.PARL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Despite much progress in recent decades, gender inequalities remain pervasive in many dimensions of life - worldwide. But while disparities exist throughout the world, they are most prevalent in developing countries. Gender inequalities in the allocation of such resources as education, health care, nutrition, and political voice matter because of the strong association with well-being, productivity, and economic growth. These patterns of inequality begin at an early age, with boys routinely receiving a larger share of education and health spending than do girls, for example.\n\nWomen are vastly underrepresented in decision-making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.\n\nA strong and vibrant democracy is possible only when parliament is fully inclusive of the population it represents. Parliaments cannot consider themselves inclusive, however, until they can boast the full participation of women. This is not just about women's right to equality and their contribution to the conduct of public affairs, but also about using women's resources and potential to determine political and development priorities that benefit societies and the global community." }, { "id": "Generalcomments", "value": "Relevance to gender indicator: Women are vastly underrepresented in decision making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.\n\nThis is the Sustainable Development Goal indicator 5.5.1 (a). [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Proportion of seats held by women in national parliaments (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The number of countries covered varies with suspensions or dissolutions of parliaments. There can be difficulties in obtaining information on by-election results and replacements due to death or resignation. These changes are ad hoc events which are more difficult to keep track of. By-elections, for instance, are often not announced internationally as general elections are. Parliaments vary considerably in their internal workings and procedures, however, generally legislate, oversee government and represent the electorate. In terms of measuring women's contribution to political decision making, this indicator may not be sufficient because some women may face obstacles in fully and efficiently carrying out their parliamentary mandate.\n\nThe data is compiled by the Inter-Parliamentary Union on the basis of information provided by National Parliaments. The percentages do not take into account the case of parliaments for which no data was available at that date. Information is available in all countries where a national legislature exists and therefore does not include parliaments that have been dissolved or suspended for an indefinite period." }, { "id": "Longdefinition", "value": "Women in parliaments are the percentage of parliamentary seats in a single or lower chamber held by women." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Inter-Parliamentary Union (IPU) (www.ipu.org). For the year of 1998, the data is as of August 10, 1998." }, { "id": "Statisticalconceptandmethodology", "value": "The proportion of seats held by women in national parliaments is the number of seats held by women members in single or lower chambers of national parliaments, expressed as a percentage of all occupied seats; it is derived by dividing the total number of seats occupied by women by the total number of seats in parliament.\n\nNational parliaments can be bicameral or unicameral. This indicator covers the single chamber in unicameral parliaments and the lower chamber in bicameral parliaments. It does not cover the upper chamber of bicameral parliaments. Seats are usually won by members in general parliamentary elections. Seats may also be filled by nomination, appointment, indirect election, rotation of members and by-election. Seats refer to the number of parliamentary mandates, or the number of members of parliament." }, { "id": "Topic", "value": "Gender: Public life & decision making" } ], "source_id": "46" }, { "id": "SG.LAW.INDX", "metatype": [ { "id": "Developmentrelevance", "value": "The knowledge and analysis provided by Women, Business and the Law make a strong economic case for laws that empower women. Better performance in the areas measured by the Women, Business and the Law index is associated with more women in the labor force and with higher income and improved development outcomes. Equality before the law and of economic opportunity are not only wise social policy but also good economic policy. The equal participation of women and men will give every economy a chance to achieve its potential. Given the economic significance of women's empowerment, the ultimate goal of Women, Business and the Law is to encourage governments to reform laws that hold women back from working and doing business." }, { "id": "Generalcomments", "value": "For the reference period, WDI and Gender Databases take the data coverage years instead of reporting years used in WBL (https://wbl.worldbank.org/). For example, the data for YR2020 in WBL (report year) corresponds to data for YR2019 in WDI and Gender Databases." }, { "id": "IndicatorName", "value": "Women Business and the Law Index Score (scale 1-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Women, Business and the Law methodology has limitations that should be considered when interpreting the data. All eight indicators are based on standardized assumptions to ensure comparability across economies. Comparability is one of the strengths of the data, but the assumptions can also be limitations as they may not capture all restrictions or represent all particularities in a country. It is assumed that the woman resides in the economy's main business city of the economy. In federal economies, laws affecting women can vary by state or province. Even in nonfederal economies, women in rural areas and small towns could face more restrictive local legislation. Such restrictions are not captured by Women, Business and the Law unless they are also found in the main business city. The woman has reached the legal age of majority and is capable of making decisions as an adult, is in good health and has no criminal record. She is a lawful citizen of the economy being examined, and she works as a cashier in the food retail sector in a supermarket or grocery store that has 60 employees. She is a cisgender, heterosexual woman in a monogamous first marriage registered with the appropriate authorities (de facto marriages and customary unions are not measured), she is of the same religion as her husband, and is in a marriage under the rules of the default marital property regime, or the most common regime for that jurisdiction, which will not change during the course of the marriage. She is not a member of a union, unless membership is mandatory. Membership is considered mandatory when collective bargaining agreements cover more than 50 percent of the workforce in the food retail sector and when they apply to individuals who were not party to the original collective bargaining agreement. Where personal law prescribes different rights and obligations for different groups of women, the data focus on the most populous group, which may mean that restrictions that apply only to minority populations are missed. Women, Business and the Law focuses solely on the ways in which the formal legal and regulatory environment determines whether women can work or open their own businesses. The data set is constructed using laws and regulations that are codified (de jure) and currently in force, therefore implementation of laws (de facto) is not measured. The data looks only at laws that apply to the private sector. These assumptions can limit the representativeness of the data for the entire population in each country. Finally, Women, Business and the Law recognizes that the laws it measures do not apply to all women in the same way. Women face intersectional forms of discrimination based on gender, sex, sexuality, race, gender identity, religion, family status, ethnicity, nationality, disability, and a myriad of other grounds. Women, Business and the Law therefore encourages readers to interpret the data in conjunction with other available research." }, { "id": "Longdefinition", "value": "The index measures how laws and regulations affect women’s economic opportunity. Overall scores are calculated by taking the average score of each index (Mobility, Workplace, Pay, Marriage, Parenthood, Entrepreneurship, Assets and Pension), with 100 representing the highest possible score." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress toward legal equality between men and women in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 2,000 respondents with expertise in family, labor, and criminal law, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and regulations. The Women, Business and the Law team collects the texts of these codified sources of national law - constitutions, codes, laws, statutes, rules, regulations, and procedures - and checks questionnaire responses for accuracy. Thirty-five data points are scored across eight indicators of four or five binary questions, with each indicator representing a different phase of a woman’s career. Indicator-level scores are obtained by calculating the unweighted average of the questions within that indicator and scaling the result to 100. Overall scores are then calculated by taking the average of each indicator, with 100 representing the highest possible score." }, { "id": "Topic", "value": "Gender: Public life & decision making" } ], "source_id": "46" }, { "id": "SG.NOD.CONS", "metatype": [ { "id": "IndicatorName", "value": "Nondiscrimination clause mentions gender in the constitution (1=yes; 0=no)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nondiscrimination clause mentions gender in the constitution is whether there is a nondiscrimination clause in the constitution which mentions gender. For the answer to be “Yes,” the constitution must use either the word discrimination or the word nondiscrimination or even when there is a “clawback” provision granting exceptions to the nondiscrimination clause for certain areas of the law, such as inheritance, family and customary law. The answer is “No” if there is no nondiscrimination provision, or the nondiscrimination language is present in the preamble but not in an article of the constitution, or there is a provision that merely stipulates that the sexes are equal, or the sexes have equal rights and obligations. The answer is \"N/A\" if there is no nondiscrimination provision." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank: Women, Business and the Law." }, { "id": "Topic", "value": "Gender: Public life & decision making" } ], "source_id": "46" }, { "id": "SG.TIM.UWRK.FE", "metatype": [ { "id": "Developmentrelevance", "value": "Women often spend disproportionately more time on unpaid domestic and care work than men. This unequal division of responsibilities is correlated with gender differences in economic opportunities, includign low female labor force participation, occupational sex segregation, and earnings diffrentials. The need for a gender balance in the distribution of unpaid domestic and care work has been increasingly recognized and the Sustainable Development Goals address the issue in the target 5.4." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 5.4.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Proportion of time spent on unpaid domestic and care work, female (% of 24 hour day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data may not be strictly comparable across countries as the methods and sampling involved for data collection may differ." }, { "id": "Longdefinition", "value": "The average time women spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "National statistical offices or national database and publications compiled by United Nations Statistics Division. The data were downloaded on December 3 from the Global SDG Indicators Database: \nhttps://unstats.un.org/sdgs/indicators/database/" }, { "id": "Statisticalconceptandmethodology", "value": "Proportion of time spent on unpaid domestic and care work is calculated by dividing the daily average number of hours spent on unpaid domestic and care work by 24 hours. Data presented for this indicator are expressed as a proportion of time in a day. Weekly data is averaged over seven days of the week to obtain the daily average time." }, { "id": "Topic", "value": "Gender: Participation & access" } ], "source_id": "46" }, { "id": "SG.TIM.UWRK.MA", "metatype": [ { "id": "Developmentrelevance", "value": "Women often spend disproportionately more time on unpaid domestic and care work than men. This unequal division of responsibilities is correlated with gender differences in economic opportunities, includign low female labor force participation, occupational sex segregation, and earnings diffrentials. The need for a gender balance in the distribution of unpaid domestic and care work has been increasingly recognized and the Sustainable Development Goals address the issue in the target 5.4." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 5.4.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Proportion of time spent on unpaid domestic and care work, male (% of 24 hour day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data may not be strictly comparable across countries as the methods and sampling involved for data collection may differ." }, { "id": "Longdefinition", "value": "The average time men spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "National statistical offices or national database and publications compiled by United Nations Statistics Division" }, { "id": "Statisticalconceptandmethodology", "value": "Proportion of time spent on unpaid domestic and care work is calculated by dividing the daily average number of hours spent on unpaid domestic and care work by 24 hours. Data presented for this indicator are expressed as a proportion of time in a day. Weekly data is averaged over seven days of the week to obtain the daily average time." }, { "id": "Topic", "value": "Gender: Participation & access" } ], "source_id": "46" }, { "id": "SG.VAW.1549.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children" }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 5.2.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (% of ever-partnered women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months is the percentage of ever partnered women age 15-49 who are subjected to physical violence, sexual violence or both by a current or former intimate partner in the last 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Statistics Division (UNSD)" }, { "id": "Topic", "value": "Gender: Health" } ], "source_id": "46" }, { "id": "SH.ALC.PCAP.FE.LI", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Acoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.5.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Total alcohol consumption per capita, female (liters of pure alcohol, projected estimates, female 15+ years of age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Statisticalconceptandmethodology", "value": "The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "46" }, { "id": "SH.ALC.PCAP.LI", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Acoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.5.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Statisticalconceptandmethodology", "value": "The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "46" }, { "id": "SH.ALC.PCAP.MA.LI", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Acoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.5.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Total alcohol consumption per capita, male (liters of pure alcohol, projected estimates, male 15+ years of age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Statisticalconceptandmethodology", "value": "The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "46" }, { "id": "SH.ANM.ALLW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Prevalence of anemia among women of reproductive age (% of women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of anemia among women of reproductive age refers to the combined prevalence of both non-pregnant with haemoglobin levels below 12 g/dL and pregnant women with haemoglobin levels below 11 g/dL." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository/World Health Statistics." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.DYN.AIDS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Prevalence of HIV, total (% of population ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Statisticalconceptandmethodology", "value": "HIV prevalence rates reflect the rate of HIV infection in each country's population. Low national prevalence rates can be misleading, however. They often disguise epidemics that are initially concentrated in certain localities or population groups and threaten to spill over into the wider population. In many developing countries most new infections occur in young adults, with young women especially vulnerable.\n\nData on HIV are from the Joint United Nations Programme on HIV/AIDS (UNAIDS). Changes in procedures and assumptions for estimating the data and better coordination with countries have resulted in improved estimates of HIV and AIDS. The models, which are routinely updated, track the course of HIV epidemics and their impact, making full use of information in HIV prevalence trends from surveillance data as well as survey data. The models take into account reduced infectivity among people receiving antiretroviral therapy (which is having a larger impact on HIV prevalence and allowing HIV-positive people to live longer) and allow for changes in urbanization over time in generalized epidemics. The estimates include plausibility bounds, which reflect the certainty associated with each of the estimates." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "46" }, { "id": "SH.DYN.MORT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is the Sustainable Development Goal indicator 3.2.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate, under-5 (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.DYN.MORT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate, under-5, female (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.DYN.MORT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate, under-5, male (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.DYN.NCOM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.DYN.NCOM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.DYN.NCOM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.4.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.DYN.NMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\n\nThis is the Sustainable Development Goal indicator 3.2.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate, neonatal (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Neonatal mortality rate is the number of neonates dying before reaching 28 days of age, per 1,000 live births in a given year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.FPL.SATM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.7.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Demand for family planning satisfied by modern methods (% of married women with demand for family planning)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Demand for family planning satisfied by modern methods refers to the percentage of married women ages 15-49 years whose need for family planning is satisfied with modern methods." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "46" }, { "id": "SH.H2O.BASW.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using at least basic drinking water services, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic drinking water service as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.H2O.BASW.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using at least basic drinking water services, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic drinking water service as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.H2O.BASW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using at least basic drinking water services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic drinking water service as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.H2O.SMDW.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "Generalcomments", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.1.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "People using safely managed drinking water services, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality). Many countries lack data on one or more elements of safely managed drinking water. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability). Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a safely managed drinking water as an improved water source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include: piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.H2O.SMDW.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "Generalcomments", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.1.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "People using safely managed drinking water services, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality). Many countries lack data on one or more elements of safely managed drinking water. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability). Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a safely managed drinking water as an improved water source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include: piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.H2O.SMDW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "Generalcomments", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\nThis is the Sustainable Development Goal indicator 6.1.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "People using safely managed drinking water services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality). Many countries lack data on one or more elements of safely managed drinking water. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability). Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a safely managed drinking water as an improved water source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include: piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.HIV.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "In many developing countries most new infections occur in young adults, with young women especially vulnerable." }, { "id": "IndicatorName", "value": "Prevalence of HIV, female (% ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV, female is the percentage of females who are infected with HIV. Youth rates are as a percentage of the relevant age group." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Statisticalconceptandmethodology", "value": "HIV prevalence rates reflect the rate of HIV infection in each country's population. Low national prevalence rates can be misleading, however. They often disguise epidemics that are initially concentrated in certain localities or population groups and threaten to spill over into the wider population. In many developing countries most new infections occur in young adults, with young women especially vulnerable.\n\nData on HIV are from the Joint United Nations Programme on HIV/AIDS (UNAIDS). Changes in procedures and assumptions for estimating the data and better coordination with countries have resulted in improved estimates of HIV and AIDS. The models, which are routinely updated, track the course of HIV epidemics and their impact, making full use of information in HIV prevalence trends from surveillance data as well as survey data. The models take into account reduced infectivity among people receiving antiretroviral therapy (which is having a larger impact on HIV prevalence and allowing HIV-positive people to live longer) and allow for changes in urbanization over time in generalized epidemics. The estimates include plausibility bounds, which reflect the certainty associated with each of the estimates." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "46" }, { "id": "SH.HIV.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "In many developing countries most new infections occur in young adults, with young women being especially vulnerable." }, { "id": "IndicatorName", "value": "Prevalence of HIV, male (% ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV, male is the percentage of males who are infected with HIV. Youth rates are as a percentage of the relevant age group." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Statisticalconceptandmethodology", "value": "HIV prevalence rates reflect the rate of HIV infection in each country's population. Low national prevalence rates can be misleading, however. They often disguise epidemics that are initially concentrated in certain localities or population groups and threaten to spill over into the wider population. In many developing countries most new infections occur in young adults, with young women especially vulnerable.\n\nData on HIV are from the Joint United Nations Programme on HIV/AIDS (UNAIDS). Changes in procedures and assumptions for estimating the data and better coordination with countries have resulted in improved estimates of HIV and AIDS. The models, which are routinely updated, track the course of HIV epidemics and their impact, making full use of information in HIV prevalence trends from surveillance data as well as survey data. The models take into account reduced infectivity among people receiving antiretroviral therapy (which is having a larger impact on HIV prevalence and allowing HIV-positive people to live longer) and allow for changes in urbanization over time in generalized epidemics. The estimates include plausibility bounds, which reflect the certainty associated with each of the estimates." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "46" }, { "id": "SH.HIV.INCD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Incidence of HIV, ages 15-49 (per 1,000 uninfected population ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected populations ages 15-49 expressed per 1,000 uninfected population in the year before the period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNAIDS estimates." }, { "id": "Statisticalconceptandmethodology", "value": "Data on incidence of HIV are from the Joint United Nations Programme on HIV/AIDS. Because of challenges in collecting direct measures of HIV incidence, modelled estimates are used (the Spectrum software). The models incorporate data on HIV prevalence from surveys of the general population, antenatal clinic attendees, and populations at increased risk of contracting HIV (such as sex workers, men who have sex with men, and people who inject drugs) and on the number of people receiving antiretroviral therapy, which will increase the prevalence of HIV because people living with HIV now survive longer. In countries with high-quality health information systems the models are also informed by case reporting and vital registration data." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "46" }, { "id": "SH.IMM.HEPB", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and ??is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, HepB3 (% of one-year-old children)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization rate, hepatitis B is the percentage of children ages 12-23 months who received hepatitis B vaccinations before 12 months or at any time before the survey. A child is considered adequately immunized after three doses." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO and UNICEF (http://www.who.int/immunization/monitoring_surveillance/en/)." }, { "id": "Statisticalconceptandmethodology", "value": "Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package. The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year.\n\nNotes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.IMM.IDPT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and ??is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.b.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Immunization, DPT (% of children ages 12-23 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization, DPT, measures the percentage of children ages 12-23 months who received DPT vaccinations before 12 months or at any time before the survey. A child is considered adequately immunized against diphtheria, pertussis (or whooping cough), and tetanus (DPT) after receiving three doses of vaccine." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO and UNICEF (http://www.who.int/immunization/monitoring_surveillance/en/)." }, { "id": "Statisticalconceptandmethodology", "value": "Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package. The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year.\n\nNotes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.IMM.MEAS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and ??is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, measles (% of children ages 12-23 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization, measles, measures the percentage of children ages 12-23 months who received the measles vaccination before 12 months or at any time before the survey. A child is considered adequately immunized against measles after receiving one dose of vaccine." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO and UNICEF (http://www.who.int/immunization/monitoring_surveillance/en/)." }, { "id": "Statisticalconceptandmethodology", "value": "Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package. The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year.\n\nNotes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.MED.NUMW.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The WHO estimates that at least 2.5 medical staff (physicians, nurses and midwives) per 1,000 people are needed to provide adequate coverage with primary care interventions (WHO, World Health Report 2006)." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.c.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Nurses and midwives (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The WHO compiles data from household and labor force surveys, censuses, and administrative records. Data comparability is limited by differences in definitions and training of medical personnel varies. In addition, human resources tend to be concentrated in urban areas, so that average densities do not provide a full picture of health personnel available to the entire population." }, { "id": "Longdefinition", "value": "Nurses and midwives include professional nurses, professional midwives, auxiliary nurses, auxiliary midwives, enrolled nurses, enrolled midwives and other associated personnel, such as dental nurses and primary care nurses." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data." }, { "id": "Statisticalconceptandmethodology", "value": "Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\nData on health worker (physicians, nurses and midwives, and community health workers) density show the availability of medical personnel." }, { "id": "Topic", "value": "Health: Health systems" } ], "source_id": "46" }, { "id": "SH.MED.PHYS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The WHO estimates that at least 2.5 medical staff (physicians, nurses and midwives) per 1,000 people are needed to provide adequate coverage with primary care interventions (WHO, World Health Report 2006)." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.c.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Physicians (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The WHO compiles data from household and labor force surveys, censuses, and administrative records. Data comparability is limited by differences in definitions and training of medical personnel varies. In addition, human resources tend to be concentrated in urban areas, so that average densities do not provide a full picture of health personnel available to the entire population." }, { "id": "Longdefinition", "value": "Physicians include generalist and specialist medical practitioners." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data." }, { "id": "Statisticalconceptandmethodology", "value": "Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\nData on health worker (physicians, nurses and midwives, and community health workers) density show the availability of medical personnel." }, { "id": "Topic", "value": "Health: Health systems" } ], "source_id": "46" }, { "id": "SH.MLR.INCD.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.3.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Incidence of malaria (per 1,000 population at risk)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Incidence of malaria is the number of new cases of malaria in a year per 1,000 population at risk." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "46" }, { "id": "SH.PRV.SMOK.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, females (% of adults)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes it. The indicator name and definition were updated in December, 2020." }, { "id": "IndicatorName", "value": "Prevalence of current tobacco use, females (% of female adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution." }, { "id": "Longdefinition", "value": "The percentage of the female population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population." }, { "id": "Periodicity", "value": "Biennial" }, { "id": "Previous_Indicator_Name", "value": "Previous indicator name: Smoking prevalence, females (% of adults)\n\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes it. The indicator name and definition were updated in December, 2020." }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Statisticalconceptandmethodology", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\nA statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. \n\nThe model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. \nDepending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015)." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "46" }, { "id": "SH.PRV.SMOK.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, males (% of adults)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes it. The indicator name and definition were updated in December, 2020." }, { "id": "IndicatorName", "value": "Prevalence of current tobacco use, males (% of male adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution." }, { "id": "Longdefinition", "value": "The percentage of the male population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population." }, { "id": "Periodicity", "value": "Biennial" }, { "id": "Previous_Indicator_Name", "value": "Previous indicator name: Smoking prevalence, males (% of adults)\n\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes it. The indicator name and definition were updated in December, 2020." }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Statisticalconceptandmethodology", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\nSmoking is the most common form of tobacco use and the prevalence of smoking is therefore a good measure of the tobacco epidemic. (Corrao MA, Guindon GE, Sharma N, Shokoohi DF (eds). Tobacco Control Country Profiles, 2000, American Cancer Society, Atlanta.) Tobacco use causes heart and other vascular diseases and cancers of the lung and other organs. Given the long delay between starting to smoke and the onset of disease, the health impact of smoking will increase rapidly only in the next few decades. The data presented are age-standardized rates for adults ages 15 and older from the WHO." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "46" }, { "id": "SH.STA.AIRP.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Air pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate attributed to household and ambient air pollution, age-standardized, female (per 100,000 female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions" }, { "id": "Longdefinition", "value": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Statisticalconceptandmethodology", "value": "Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.STA.AIRP.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Air pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate attributed to household and ambient air pollution, age-standardized, male (per 100,000 male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions" }, { "id": "Longdefinition", "value": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Statisticalconceptandmethodology", "value": "Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.STA.AIRP.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Air pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate attributed to household and ambient air pollution, age-standardized (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions" }, { "id": "Longdefinition", "value": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Statisticalconceptandmethodology", "value": "Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.STA.BASS.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using at least basic sanitation services, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines basic sanitation facilities as improved sanitation facilities that are not shared with other households. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.STA.BASS.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using at least basic sanitation services, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines basic sanitation facilities as improved sanitation facilities that are not shared with other households. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.STA.BASS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using at least basic sanitation services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines basic sanitation facilities as improved sanitation facilities that are not shared with other households. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.STA.BFED.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "For optimal infant and young child feeding, mothers initiate breastfeeding within one hour of birth, breastfeed exclusively for the first six months, and continue to breastfeed for two years or more while providing nutritionally adequate, safe, and age-appropriate solid, semisolid, and soft foods. Breast milk alone contains all the nutrients, antibodies, hormones, and antioxidants an infant needs to thrive. It protects babies from diarrhea and acute respiratory infections, stimulates their immune systems and response to vaccination, and may confer cognitive benefits." }, { "id": "IndicatorName", "value": "Exclusive breastfeeding (% of children under 6 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Most of the data on breastfeeding are derived from household surveys. For the data that are from household surveys, the year refers to the survey year." }, { "id": "Longdefinition", "value": "Exclusive breastfeeding refers to the percentage of children less than six months old who are fed breast milk alone (no other liquids) in the past 24 hours." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.STA.BRTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\nThe share of births attended by skilled health staff is an indicator of a health system's ability to provide adequate care for pregnant women." }, { "id": "Generalcomments", "value": "Assistance by trained professionals during birth reduces the incidence of maternal deaths during childbirth. The share of births attended by skilled health staff is an indicator of a health system’s ability to provide adequate care for pregnant women.\n\nThis is the Sustainable Development Goal indicator 3.1.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Births attended by skilled health staff (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources." }, { "id": "Longdefinition", "value": "Births attended by skilled health staff are the percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliveries on their own; and to care for newborns." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "46" }, { "id": "SH.STA.FGMS.ZS", "metatype": [ { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 5.3.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Female genital mutilation prevalence (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women aged 15–49 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF DATA (http://www.data.unicef.org/); Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS), and other surveys." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "46" }, { "id": "SH.STA.HYGN.RU.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "Generalcomments", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.STA.HYGN.UR.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "Generalcomments", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.STA.HYGN.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.STA.MALN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Generalcomments", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "IndicatorName", "value": "Prevalence of underweight, weight for age, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of underweight, female, is the percentage of girls under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's new child growth standards released in 2006." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.STA.MALN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Generalcomments", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "IndicatorName", "value": "Prevalence of underweight, weight for age, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of underweight, male, is the percentage of boys under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's new child growth standards released in 2006." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.STA.MALN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Generalcomments", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "IndicatorName", "value": "Prevalence of underweight, weight for age (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of underweight children is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's child growth standards released in 2006." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.STA.MMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This indicator represents the risk associated with each pregnancy and is also a Sustainable Development Goal Indicator (3.1.1) for monitoring maternal health." }, { "id": "IndicatorName", "value": "Maternal mortality ratio (modeled estimate, per 100,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality." }, { "id": "Longdefinition", "value": "Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs)." }, { "id": "Notesfromoriginalsource", "value": "Estimates of maternal mortality are presented along with upper and lower limits of intervals (see footnote) designed to depict the uncertainty of estimates. The intervals are the product of a detailed probabilistic evaluation of the uncertainty attributable to the various components of the estimation process. For estimates derived from the multilevel regression model, the components of uncertainty were divided into two groups: those reflected within the regression model (internal sources), and those due to assumptions or calculations that occur outside the model (external sources). Estimates of the total uncertainty reflect a combination of these various sources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division. Trends in Maternal Mortality: 2000 to 2017. Geneva, World Health Organization, 2019" }, { "id": "Statisticalconceptandmethodology", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation.\n\nThe estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "46" }, { "id": "SH.STA.ODFC.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "People practicing open defecation, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "46" }, { "id": "SH.STA.ODFC.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "People practicing open defecation, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "46" }, { "id": "SH.STA.ODFC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "People practicing open defecation (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "46" }, { "id": "SH.STA.OWGH.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Generalcomments", "value": "Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues." }, { "id": "IndicatorName", "value": "Prevalence of overweight, weight for height, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.STA.OWGH.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Generalcomments", "value": "Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues." }, { "id": "IndicatorName", "value": "Prevalence of overweight, weight for height, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of overweight, male, is the percentage of boys under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.STA.OWGH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "See SH.STA.OWGH.ME.ZS for aggregation" }, { "id": "Generalcomments", "value": "Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues." }, { "id": "IndicatorName", "value": "Prevalence of overweight, weight for height (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.STA.POIS.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.9.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unintentional poisoning (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unintentional poisonings is the number of deaths from unintentional poisonings in a year per 100,000 population. Unintentional poisoning can\nbe caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.STA.POIS.P5.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unintentional poisoning, female (per 100,000 female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unintentional poisonings is the number of female deaths from unintentional poisonings in a year per 100,000 female population. Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.STA.POIS.P5.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system." }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unintentional poisoning, male (per 100,000 male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unintentional poisonings is the number of male deaths from unintentional poisonings in a year per 100,000 male population. Unintentional poisoning can\nbe caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.STA.SMSS.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "Generalcomments", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "People using safely managed sanitation services, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporality and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site). Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system. If no information is available, it is assumed that 50 percent is safely managed. Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines safely managed sanitation facilities as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.STA.SMSS.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "Generalcomments", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "People using safely managed sanitation services, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporality and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site). Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system. If no information is available, it is assumed that 50 percent is safely managed. Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines safely managed sanitation facilities as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.STA.SMSS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "Generalcomments", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\nThis is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "People using safely managed sanitation services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporality and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site). Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system. If no information is available, it is assumed that 50 percent is safely managed. Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines safely managed sanitation facilities as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "46" }, { "id": "SH.STA.STNT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Generalcomments", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of stunting, female, is the percentage of girls under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.STA.STNT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Generalcomments", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.STA.STNT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "See SH.STA.STNT.ME.ZS for aggregation" }, { "id": "Generalcomments", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.STA.SUIC.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Suicide mortality rate, female (per 100,000 female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.STA.SUIC.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Suicide mortality rate, male (per 100,000 male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.STA.SUIC.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.4.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Suicide mortality rate (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.STA.TRAF.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Road traffic injuries and deaths is a major global public health problem. Road traffic crashes are currently the leading cause of death for children and young adults in the world. There is a strong association between the risk of road traffic death and the income level of countries. The burden of road traffic deaths is disproportionately high among low- and middle-income countries." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.6.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality caused by road traffic injury (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mortality caused by road traffic injury is estimated road traffic fatal injury deaths per 100,000 population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.STA.WASH.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unsafe drinking water, unsafe sanitation and lack of hygiene are important causes of death. Most diarrheal deaths in the world are caused by unsafe water, sanitation or hygiene. According to the World Health Organization, in addition to diarrea, the following diseases could be prevented if adequate WASH services are provided: malnutrition, intestinal nematode infections, lymphatic filariasis, trachoma, schistosomiasis and malaria." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.9.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "46" }, { "id": "SH.STA.WAST.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Generalcomments", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "IndicatorName", "value": "Prevalence of wasting, weight for height, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of wasting, female, is the proportion of girls under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.STA.WAST.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Generalcomments", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "IndicatorName", "value": "Prevalence of wasting, weight for height, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of wasting, male,is the proportion of boys under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.STA.WAST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Generalcomments", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "IndicatorName", "value": "Prevalence of wasting, weight for height (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of wasting is the proportion of children under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.SVR.WAST.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Generalcomments", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "IndicatorName", "value": "Prevalence of severe wasting, weight for height, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of severe wasting, female, is the proportion of girls under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.SVR.WAST.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Generalcomments", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "IndicatorName", "value": "Prevalence of severe wasting, weight for height, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of severe wasting, male, is the proportion of boys under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.SVR.WAST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Generalcomments", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "IndicatorName", "value": "Prevalence of severe wasting, weight for height (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of severe wasting is the proportion of children under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SH.TBS.INCD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization.\n\nThis is the Sustainable Development Goal indicator 3.3.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Incidence of tuberculosis (per 100,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\nUncertainty bounds for the incidence are available at http://data.worldbank.org" }, { "id": "Longdefinition", "value": "Incidence of tuberculosis is the estimated number of new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population. All forms of TB are included, including cases in people living with HIV. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously." }, { "id": "Notesfromoriginalsource", "value": "Estimates are presented with uncertainty intervals (see footnote). When ranges are presented, the lower and higher numbers correspond to the 2.5th and 97.5th centiles of the outcome distributions (generally produced by simulations). For more detailed information, see the original source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Tuberculosis Report." }, { "id": "Statisticalconceptandmethodology", "value": "Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. In developed countries tuberculosis has reemerged largely as a result of cases among immigrants. Since tuberculosis incidence cannot be directly measured, estimates are obtained by eliciting expert opinion or are derived from measurements of prevalence or mortality." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "46" }, { "id": "SH.UHC.OOPC.10.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.1, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people spending more than 10% of household consumption or income on out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of people spending more than 10% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n2. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Catastrophic Health Expenditure, 10% of total expenditure/income" }, { "id": "Source", "value": "World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $1.90 or $3.20 ($ 2011 PPP) per day poverty lines, or if they are incurred by households already living under the $1.90 or $3.20 ($ 2011 PPP) per day poverty lines." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "46" }, { "id": "SH.UHC.OOPC.10.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.1, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.8.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n2. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Catastrophic Health Expenditure, 10% of total expenditure/income (%)" }, { "id": "Source", "value": "World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $1.90 or $3.20 ($ 2011 PPP) per day poverty lines, or if they are incurred by households already living under the $1.90 or $3.20 ($ 2011 PPP) per day poverty lines." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "46" }, { "id": "SH.UHC.OOPC.25.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.1, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people spending more than 25% of household consumption or income on out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of people spending more than 25% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n2. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Catastrophic Health Expenditure, 25% of total expenditure/income (thousands)" }, { "id": "Source", "value": "World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $1.90 or $3.20 ($ 2011 PPP) per day poverty lines, or if they are incurred by households already living under the $1.90 or $3.20 ($ 2011 PPP) per day poverty lines." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "46" }, { "id": "SH.UHC.OOPC.25.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.1, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.8.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n2. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Catastrophic Health Expenditure, 25% of total expenditure/income (%)" }, { "id": "Source", "value": "World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $1.90 or $3.20 ($ 2011 PPP) per day poverty lines, or if they are incurred by households already living under the $1.90 or $3.20 ($ 2011 PPP) per day poverty lines." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "46" }, { "id": "SI.DST.50MD", "metatype": [ { "id": "Developmentrelevance", "value": "The World Bank Group's goal of promoting shared prosperity has been defined as fostering income growth of the bottom 40 per cent of the welfare distribution in every country. Income distribution measures are important background indicators for shared prosperity. The share living below half the median income is Sustainable Development Goal indicator 10.2.1." }, { "id": "Generalcomments", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from around 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "IndicatorName", "value": "Proportion of people living below 50 percent of median income (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2011 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "World Bank, Poverty and Inequality Platform: https://pip.worldbank.org/" }, { "id": "Shortdefinition", "value": "The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita." }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org." }, { "id": "Statisticalconceptandmethodology", "value": "Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\nPercentage shares by quintile may not sum to 100 because of rounding." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "46" }, { "id": "SI.POV.DDAY", "metatype": [ { "id": "Developmentrelevance", "value": "The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The World Bank produced its first global poverty estimates for developing countries for World Development Report 1990: Poverty (World Bank 1990) using household survey data for 22 countries (Ravallion, Datt, and van de Walle 1991). Since then there has been considerable expansion in the number of countries that field household income and expenditure surveys. The World Bank maintains a database that is updated annually as new survey data become available (and thus may contain more recent data or revisions) and conducts a major reassessment of progress against poverty every year. The Poverty and Inequality Platform (PIP) is an interactive computational tool that allows users to replicate these internationally comparable $1.90, $3.20 and $5.50 a day global, regional and country-level poverty estimates and to compute poverty measures for custom country groupings and for different poverty lines. PIP also provides access to user-friendly dashboards with graphs and interactive maps that visualize trends in key poverty and inequality indicators for different regions and countries." }, { "id": "Generalcomments", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from around 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty headcount ratio at $1.90 a day is the percentage of the population living on less than $1.90 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "World Bank, Poverty and Inequality Platform: https://pip.worldbank.org/" }, { "id": "Shortdefinition", "value": "Poverty headcount ratio at $1.90 a day is the percentage of the population living on less than $1.90 a day at 2011 international prices." }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org." }, { "id": "Statisticalconceptandmethodology", "value": "International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in October 2015, when we adopted $1.90 as the international poverty line using the 2011 PPP. Prior to that, the 2008 update set the international poverty line at $1.25 using the 2005 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $3.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $5.50 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.\n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2011 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $1.90 a day in 2011 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "46" }, { "id": "SI.POV.MDIM", "metatype": [ { "id": "Derivationmethod", "value": "Official multidimensional poverty headcount is calculated by each country using different methodologies. The most commonly used method is Alkire Foster (AF) methodology which identifies dimensions, typically health, education and living standards and several indicators in each dimension. The individuals are considered as multidimensionally poor if they are deprived in multiple indicators exceeding certain thresholds. Details of the methodology can be found here (https://ophi.org.uk/research/multidimensional-poverty/). It is also important to note that a country-specific analysis is carried out by adjusting the dimensions, indicators and thresholds to better reflect each country’s context and so the results presented in one country are not comparable with the findings of other countries on multidimensional poverty headcount. On the other hand, EU countries and North Macedonia use a completely different approach to measure the multidimensional poverty using the concept of “people at risk of poverty or social exclusion” (AROPE). AROPE consists of three indicators, and people will be considered as “at risk of poverty or social exclusion” if they are “at risk of poverty” or “severely materially deprived” or “living in a household with a very low work intensity”. Details of the methodology can be found here (https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:At_risk_of_poverty_or_social_exclusion_(AROPE)).\nOfficial multidimensional poverty headcount is calculated by each country using different methodologies. The most commonly used method is Alkire Foster (AF) methodology which identifies dimensions, typically health, education and living standards and several indicators in each dimension. The individuals are considered as multidimensionally poor if they are deprived in multiple indicators exceeding certain thresholds. Details of the methodology can be found here (https://ophi.org.uk/research/multidimensional-poverty/). It is also important to note that a country-specific analysis is carried out by adjusting the dimensions, indicators and thresholds to better reflect each country’s context and so the results presented in one country are not comparable with the findings of other countries on multidimensional poverty headcount. \n\nOn the other hand, EU countries and North Macedonia use a slightly different approach to measure the multidimensional poverty using the concept of “people at risk of poverty or social exclusion” (AROPE). AROPE consists of three indicators, and people will be considered as “at risk of poverty or social exclusion” if they are “at risk of poverty” or “severely materially deprived” or “living in a household with a very low work intensity”. Details of the methodology can be found here (https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:At_risk_of_poverty_or_social_exclusion_(AROPE))." }, { "id": "IndicatorName", "value": "Multidimensional poverty headcount ratio (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "It should be clearly noted that these multidimensional indicators are not comparable across countries. For instance, AF methodology and AROPE are different, and although both produce some headcount ratio of people who are considered as “multidimensionally poor”, their definition of multidimensionality of poverty is utterly different, and so should not be compared. \n\nBesides, even when they use the same approach, the numbers are not comparable across countries, as the important parameters to calculate the figures such as the number of indicators and the weight allocated to each indicator are tailored depending on the country specific context." }, { "id": "Shortdefinition", "value": "The percentage of people who are multidimensionally poor" }, { "id": "Source", "value": "Government statistical agencies. Data for EU countires are from the EUROSTAT" }, { "id": "Topic", "value": "Poverty: Multidimensional poverty" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "46" }, { "id": "SI.POV.MDIM.17", "metatype": [ { "id": "Derivationmethod", "value": "Official multidimensional poverty headcount is calculated by each country using different methodologies. The most commonly used method is Alkire Foster (AF) methodology which identifies dimensions, typically health, education and living standards and several indicators in each dimension. The individuals are considered as multidimensionally poor if they are deprived in multiple indicators exceeding certain thresholds. Details of the methodology can be found here (https://ophi.org.uk/research/multidimensional-poverty/). It is also important to note that a country-specific analysis is carried out by adjusting the dimensions, indicators and thresholds to better reflect each country’s context and so the results presented in one country are not comparable with the findings of other countries on multidimensional poverty headcount. \n\nOn the other hand, EU countries and North Macedonia use a slightly different approach to measure the multidimensional poverty using the concept of “people at risk of poverty or social exclusion” (AROPE). AROPE consists of three indicators, and people will be considered as “at risk of poverty or social exclusion” if they are “at risk of poverty” or “severely materially deprived” or “living in a household with a very low work intensity”. Details of the methodology can be found here (https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:At_risk_of_poverty_or_social_exclusion_(AROPE))." }, { "id": "IndicatorName", "value": "Multidimensional poverty headcount ratio, children (% of child population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "It should be clearly noted that these multidimensional indicators are not comparable across countries. For instance, AF methodology and AROPE are different, and although both produce some headcount ratio of people who are considered as “multidimensionally poor”, their definition of multidimensionality of poverty is utterly different, and so should not be compared. \n\nBesides, even when they use the same approach, the numbers are not comparable across countries, as the important parameters to calculate the figures such as the number of indicators and the weight allocated to each indicator are tailored depending on the country specific context." }, { "id": "Shortdefinition", "value": "The percentage of children who are multidimensionally poor" }, { "id": "Source", "value": "Government statistical agencies. Data for EU countires are from the EUROSTAT" }, { "id": "Topic", "value": "Poverty: Multidimensional poverty" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "46" }, { "id": "SI.POV.MDIM.17.XQ", "metatype": [ { "id": "Derivationmethod", "value": "The Multidimensional poverty index for children is calculated by multiplying the multidimensional poverty headcount and the average share of weighted deprivations (intensity) of children." }, { "id": "IndicatorName", "value": "Multidimensional poverty index, children (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The MPI here is a national MPI reported by each country and differs from the global MPI collected by the UNDP. Unlike the global MPI which uses the same dimensional and indicators, the national MPI is calculated using different dimensions and indicators by each country, therefore, it is not comparable across countries." }, { "id": "Shortdefinition", "value": "Proportion of the child population that is multidimensionally poor adjusted by the intensity of the deprivations" }, { "id": "Source", "value": "Government statistical agencies. Data for EU countires are from the EUROSTAT" }, { "id": "Topic", "value": "Poverty: Multidimensional poverty" } ], "source_id": "46" }, { "id": "SI.POV.MDIM.FE", "metatype": [ { "id": "Derivationmethod", "value": "Official multidimensional poverty headcount is calculated by each country using different methodologies. The most commonly used method is Alkire Foster (AF) methodology which identifies dimensions, typically health, education and living standards and several indicators in each dimension. The individuals are considered as multidimensionally poor if they are deprived in multiple indicators exceeding certain thresholds. Details of the methodology can be found here (https://ophi.org.uk/research/multidimensional-poverty/). It is also important to note that a country-specific analysis is carried out by adjusting the dimensions, indicators and thresholds to better reflect each country’s context and so the results presented in one country are not comparable with the findings of other countries on multidimensional poverty headcount. \n\nOn the other hand, EU countries and North Macedonia use a slightly different approach to measure the multidimensional poverty using the concept of “people at risk of poverty or social exclusion” (AROPE). AROPE consists of three indicators, and people will be considered as “at risk of poverty or social exclusion” if they are “at risk of poverty” or “severely materially deprived” or “living in a household with a very low work intensity”. Details of the methodology can be found here (https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:At_risk_of_poverty_or_social_exclusion_(AROPE))." }, { "id": "IndicatorName", "value": "Multidimensional poverty headcount ratio, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "It should be clearly noted that these multidimensional indicators are not comparable across countries. For instance, AF methodology and AROPE are different, and although both produce some headcount ratio of people who are considered as “multidimensionally poor”, their definition of multidimensionality of poverty is utterly different, and so should not be compared. \n\nBesides, even when they use the same approach, the numbers are not comparable across countries, as the important parameters to calculate the figures such as the number of indicators and the weight allocated to each indicator are tailored depending on the country specific context." }, { "id": "Shortdefinition", "value": "The percentage of female population who are multidimensionally poor" }, { "id": "Source", "value": "Government statistical agencies. Data for EU countires are from the EUROSTAT" }, { "id": "Topic", "value": "Poverty: Multidimensional poverty" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "46" }, { "id": "SI.POV.MDIM.HH", "metatype": [ { "id": "Derivationmethod", "value": "Official multidimensional poverty headcount for houesholds is calculated by each country using different methodologies. The most commonly used method is Alkire Foster (AF) methodology which identifies dimensions, typically health, education and living standards and several indicators in each dimension. The households are considered as multidimensionally poor if they are deprived in multiple indicators exceeding certain thresholds. Details can be found here (https://ophi.org.uk/research/multidimensional-poverty/). It is also important to note that a country-specific analysis is carried out by adjusting the dimensions, indicators and thresholds to better reflect each country’s context and so the results presented in one country are not comparable with the findings of other countries on multidimensional poverty headcount." }, { "id": "IndicatorName", "value": "Multidimensional poverty headcount ratio, household (% of total households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "It should be clearly noted that these multidimensional indicators are not comparable across countries as the important parameters to calculate the figures such as the number of indicators and the weight allocated to each indicator are tailored accoding to each country specific context." }, { "id": "Shortdefinition", "value": "The percentage of households who are multidimensionally poor" }, { "id": "Source", "value": "Government statistical agencies. Data for EU countires are from the EUROSTAT" }, { "id": "Topic", "value": "Poverty: Multidimensional poverty" } ], "source_id": "46" }, { "id": "SI.POV.MDIM.IT", "metatype": [ { "id": "Derivationmethod", "value": "The multidimensional headcount is a useful measure, but it does not increase if poor people become more deprived. Because of that, we need a different set of measures, which is the average share of weighted deprivations, also known as intensity. Intensity is calculated by adding up the proportion of total deprivations each person suffers and dividing by the total number of poor persons." }, { "id": "IndicatorName", "value": "Multidimensional poverty intensity" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "It should be noted that the intensity is not comparable across diffferent countries as it depends on the parameters used in the methodology such as what kind of dimensions and indicators are used, and how the weight is constructed, which varies significantly across countries." }, { "id": "Shortdefinition", "value": "The average share of weighted deprivations (intensity)" }, { "id": "Source", "value": "Government statistical agencies. Data for EU countires are from the EUROSTAT" }, { "id": "Topic", "value": "Poverty: Multidimensional poverty" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "46" }, { "id": "SI.POV.MDIM.MA", "metatype": [ { "id": "Derivationmethod", "value": "Official multidimensional poverty headcount is calculated by each country using different methodologies. The most commonly used method is Alkire Foster (AF) methodology which identifies dimensions, typically health, education and living standards and several indicators in each dimension. The individuals are considered as multidimensionally poor if they are deprived in multiple indicators exceeding certain thresholds. Details can be found here (https://ophi.org.uk/research/multidimensional-poverty/). It is also important to note that a country-specific analysis is carried out by adjusting the dimensions, indicators and thresholds to better reflect each country’s context and so the results presented in one country are not comparable with the findings of other countries on multidimensional poverty headcount. \n\nOn the other hand, EU countries and North Macedonia use a completely different approach to measure the multidimensional poverty using the concept of “people at risk of poverty or social exclusion” (AROPE). AROPE consists of three indicators, and people will be considered as “at risk of poverty or social exclusion” if they are “at risk of poverty” or “severely materially deprived” or “living in a household with a very low work intensity”. Details can be found here (https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:At_risk_of_poverty_or_social_exclusion_(AROPE))." }, { "id": "IndicatorName", "value": "Multidimensional poverty headcount ratio, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "It should be clearly noted that these multidimensional indicators are not comparable across countries. For instance, AF methodology and AROPE are fundamentally very different, and although both produce some headcount ratio of people who are considered as “multidimensionally poor”, their definition of multidimensionality of poverty is utterly different, and so should not be compared. \n\nBesides, even when they use the same approach, the numbers are not comparable across countries, as the important parameters to calculate the figures such as the number of indicators and the weight allocated to each indicator are tailored accoding to the country specific context." }, { "id": "Shortdefinition", "value": "The percentage of male population who are multidimensionally poor" }, { "id": "Source", "value": "Government statistical agencies. Data for EU countires are from the EUROSTAT" }, { "id": "Topic", "value": "Poverty: Multidimensional poverty" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "46" }, { "id": "SI.POV.MDIM.XQ", "metatype": [ { "id": "Derivationmethod", "value": "The Multidimensional poverty index is calculated by multiplying multidimensional poverty headcount and average number of deprivations (intensity)." }, { "id": "IndicatorName", "value": "Multidimensional poverty index (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The MPI here is a national MPI reported by each country and differs from the global MPI collected by the UNDP. Unlike the global MPI which uses the same dimensions and indicators, the national MPI is calculated using different dimensions and indicators by each country, therefore, it is not comparable across countries." }, { "id": "Shortdefinition", "value": "Proportion of the population that is multidimensionally poor adjusted by the intensity of the deprivations" }, { "id": "Source", "value": "Government statistical agencies. Data for EU countires are from the EUROSTAT" }, { "id": "Topic", "value": "Poverty: Multidimensional poverty" } ], "source_id": "46" }, { "id": "SI.POV.NAHC", "metatype": [ { "id": "Developmentrelevance", "value": "The poverty rate as defined by national poverty lines reflects the share of the population that fails to meet the standard a country thinks is necessarty to cover basic needs." }, { "id": "Generalcomments", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at national poverty lines (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "National poverty headcount ratio is the percentage of the population living below the national poverty line(s). National estimates are based on population-weighted subgroup estimates from household surveys. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "World Bank, Poverty and Inequality Platform: https://pip.worldbank.org/" }, { "id": "Shortdefinition", "value": "National poverty headcount ratio is the percentage of the population living below the national poverty line(s)." }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Poverty headcount ratio among the population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income. \n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies. \n\nAlmost all national poverty lines in developing economies are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. \n\nThis series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year. For all other economies, the year reported is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which data collection started." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "46" }, { "id": "SI.POV.RUHC", "metatype": [ { "id": "Generalcomments", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "IndicatorName", "value": "Rural poverty headcount ratio at national poverty lines (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Rural poverty headcount ratio is the percentage of the rural population living below the national poverty lines." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Poverty headcount ratio among the rural population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income.\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies.\n\nAlmost all national poverty lines are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. The data is based on the two most recent years for which survey data are available.\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected." }, { "id": "Topic", "value": "Poverty: Poverty rates" } ], "source_id": "46" }, { "id": "SI.POV.URHC", "metatype": [ { "id": "Generalcomments", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "IndicatorName", "value": "Urban poverty headcount ratio at national poverty lines (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Urban poverty headcount ratio is the percentage of the urban population living below the national poverty lines." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Poverty headcount ratio among the urban population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income.\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies.\n\nAlmost all national poverty lines are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. The data is based on the two most recent years for which survey data are available.\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected." }, { "id": "Topic", "value": "Poverty: Poverty rates" } ], "source_id": "46" }, { "id": "SI.RMT.COST.IB.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Reducing the cost of remittance transactions has a direct impact on development by freeing additional resources that, instead of being paid as transaction cost, will remain with the senders and receivers of the flows. Remittance cost is highlighted in Sustainable Development Goal 10. Target 10.c calls for reducing to less than 3 percent the transaction costs of migrant remittances and ensure that in no corridor remittance senders are required to pay more than 5 percent by 2030." }, { "id": "IndicatorName", "value": "Average transaction cost of sending remittances to a specific country (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Remittance service providers (RSPs) are excluded when they do not disclose the exchange rate applied to the transaction" }, { "id": "Longdefinition", "value": "Average transaction cost of sending remittance to a specific country is the average of the total transaction cost in percentage of the amount sent for sending USD 200 charged by each single remittance service provider (RSP) included in the Remittance Prices Worldwide (RPW) database to a specific country." }, { "id": "Periodicity", "value": "Quarterly (represented as Annual)" }, { "id": "Source", "value": "World Bank, Remittance Prices Worldwide, available at http://remittanceprices.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "The World Bank calculates and tracks the global average cost for sending remittances following each iteration of Remittance Prices Worldwide (RPW). This is intended to provide a tool to track the trend of remittance prices by various policy makers, including measuring progress towards the commitment by the G8 member countries to reduce the cost of remittances by five percentage points over five years (the “5x5 Objective”), as well as the commitment by the G20 member countries to also reduce the global average to 5 percent. The Global Average Total Cost is calculated as the average total cost for sending USD 200 with all remittance service providers (RSPs) worldwide. In other terms, the global average total cost is the simple average of the total cost for sending USD 200 charged by each single RSP included in the RPW database, expressed as the percentage of the amount sent. The regional and national average total costs are calculated using the same methodology used to calculate the Global Average Total Cost. These represent the simple average total cost for sending USD 200 with every single RSP to a specific region of the world (regional), or to a specific country (national). The reference years reflect third quarter data here; for example, data for 2016 refers to data in the third quarter of the year. For all quarterly data, visit http://remittanceprices.worldbank.org." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "46" }, { "id": "SI.RMT.COST.OB.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Reducing the cost of remittance transactions has a direct impact on development by freeing additional resources that, instead of being paid as transaction cost, will remain with the senders and receivers of the flows. Remittance cost is highlighted in Sustainable Development Goal 10. Target 10.c calls for reducing to less than 3 percent the transaction costs of migrant remittances and ensure that in no corridor remittance senders are required to pay more than 5 percent by 2030." }, { "id": "IndicatorName", "value": "Average transaction cost of sending remittances from a specific country (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Remittance service providers (RSPs) are excluded when they do not disclose the exchange rate applied to the transaction." }, { "id": "Longdefinition", "value": "Average transaction cost of sending remittance from a specific country is the average of the total transaction cost in percentage of the amount sent for sending USD 200 charged by each single remittance service provider (RSP) included in the Remittance Prices Worldwide (RPW) database from a specific country." }, { "id": "Periodicity", "value": "Quarterly (represented as Annual)" }, { "id": "Source", "value": "World Bank, Remittance Prices Worldwide, available at http://remittanceprices.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "The World Bank calculates and tracks the global average cost for sending remittances following each iteration of Remittance Prices Worldwide (RPW). This is intended to provide a tool to track the trend of remittance prices by various policy makers, including measuring progress towards the commitment by the G8 member countries to reduce the cost of remittances by five percentage points over five years (the “5x5 Objective”), as well as the commitment by the G20 member countries to also reduce the global average to 5 percent. The Global Average Total Cost is calculated as the average total cost for sending USD 200 with all remittance service providers (RSPs) worldwide. In other terms, the global average total cost is the simple average of the total cost for sending USD 200 charged by each single RSP included in the RPW database, expressed as the percentage of the amount sent. The regional and national average total costs are calculated using the same methodology used to calculate the Global Average Total Cost. These represent the simple average total cost for sending USD 200 with every single RSP from a specific region of the world (regional), or from a specific country (national). The same applies to other averages such as the G8 average, which calculates the average cost of sending USD 200 from the G8 member countries, or the bank average, which represent the average cost of sending USD 200 with a bank worldwide. The reference years reflect third quarter data here; for example, data for 2016 refers to data in the third quarter of the year. For all quarterly data, visit http://remittanceprices.worldbank.org." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "46" }, { "id": "SI.SPR.PC40.ZG", "metatype": [ { "id": "Developmentrelevance", "value": "The World Bank Group's goal of promoting shared prosperity has been defined as fostering income growth of the bottom 40 per cent of the welfare distribution in every country." }, { "id": "Generalcomments", "value": "The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes." }, { "id": "IndicatorName", "value": "Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See the Poverty and Inequality Platform (http://www.pip.worldbank.org) for detailed explanations.\n\nBecause household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Longdefinition", "value": "The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered.\n\nThe coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See the Poverty and Inequality Platform for detailed explanations." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "World Bank, Poverty and Inequality Platform: https://pip.worldbank.org/" }, { "id": "Shortdefinition", "value": "The growth rate in the welfare aggregate of bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the income distribution in a country from household surveys over a roughly 5-year period." }, { "id": "Source", "value": "World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity)." }, { "id": "Topic", "value": "Poverty: Shared prosperity" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "46" }, { "id": "SI.SPR.PCAP.ZG", "metatype": [ { "id": "Developmentrelevance", "value": "The World Bank Group's goal of promoting shared prosperity has been defined as fostering income growth of the bottom 40 per cent of the welfare distribution in every country." }, { "id": "Generalcomments", "value": "The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes." }, { "id": "IndicatorName", "value": "Annualized average growth rate in per capita real survey mean consumption or income, total population (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See the Poverty and Inequality Platform (http://www.pip.worldbank.org) for detailed explanations.\n\nBecause household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Longdefinition", "value": "The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. \n\nThe coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See the Poverty and Inequality Platform for detailed explanations." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "World Bank, Poverty and Inequality Platform: https://pip.worldbank.org/" }, { "id": "Shortdefinition", "value": "The growth rate in the welfare aggregate of total population is computed as annualized average growth rate in per capita real consumption or income of total population from household surveys over a roughly 5-year period." }, { "id": "Source", "value": "World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity)." }, { "id": "Topic", "value": "Poverty: Shared prosperity" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "46" }, { "id": "SL.AGR.EMPL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labour flows from agriculture and other labour-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in agriculture, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of January 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.AGR.EMPL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labour flows from agriculture and other labour-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in agriculture, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of January 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.AGR.EMPL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labour flows from agriculture and other labour-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in agriculture (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of January 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.EMP.SMGT.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "The indicator provides information on the proportion of women who are employed in decision-making and management roles in government, large enterprises and institutions." }, { "id": "IndicatorName", "value": "Female share of employment in senior and middle management (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The proportion of females in total employment in senior and middle management. It corresponds to major group 1 in both ISCO-08 and ISCO-88 minus category 14 in ISCO-08 (hospitality, retail and other services managers) and minus category 13 in ISCO-88 (general managers), since these comprise mainly managers of small enterprises." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.EMP.WORK.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Wage and salaried workers, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of January 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The indicator of status in employment distinguishes between two categories of the total employed. These are: (a) wage and salaried workers (also known as employees); and (b) self-employed workers. Self-employed group is broken down in the subcategories: self-employed workers with employees (employers), self-employed workers without employees (own-account workers), members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of contributing family workers and own-account workers.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.EMP.WORK.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Wage and salaried workers, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of January 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The indicator of status in employment distinguishes between two categories of the total employed. These are: (a) wage and salaried workers (also known as employees); and (b) self-employed workers. Self-employed group is broken down in the subcategories: self-employed workers with employees (employers), self-employed workers without employees (own-account workers), members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of contributing family workers and own-account workers.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.EMP.WORK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Wage and salaried workers, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of January 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The indicator of status in employment distinguishes between two categories of the total employed. These are: (a) wage and salaried workers (also known as employees); and (b) self-employed workers. Self-employed group is broken down in the subcategories: self-employed workers with employees (employers), self-employed workers without employees (own-account workers), members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of contributing family workers and own-account workers.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.FAM.WORK.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Contributing family workers, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of January 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The indicator of status in employment distinguishes between two categories of the total employed. These are: (a) wage and salaried workers (also known as employees); and (b) self-employed workers. Self-employed group is broken down in the subcategories: self-employed workers with employees (employers), self-employed workers without employees (own-account workers), members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of contributing family workers and own-account workers.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.FAM.WORK.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Contributing family workers, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of January 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The indicator of status in employment distinguishes between two categories of the total employed. These are: (a) wage and salaried workers (also known as employees); and (b) self-employed workers. Self-employed group is broken down in the subcategories: self-employed workers with employees (employers), self-employed workers without employees (own-account workers), members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of contributing family workers and own-account workers.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.GDP.PCAP.EM.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2017" }, { "id": "Developmentrelevance", "value": "Labor productivity is used to assess a country's economic ability to create and sustain decent employment opportunities with fair and equitable remuneration. Productivity increases obtained through investment, trade, technological progress, or changes in work organization can increase social protection and reduce poverty, which in turn reduce vulnerable employment and working poverty. Productivity increases do not guarantee these improvements, but without them - and the economic growth they bring - improvements are highly unlikely.\n\nGDP per person employed is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.2.1]" }, { "id": "IndicatorName", "value": "GDP per person employed (constant 2017 PPP $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output in both national currencies and purchasing power parity dollars is not easily available, especially in developing countries, because the definition, coverage, and methodology are not always consistent across countries. For example, countries employ different methodologies for estimating the missing values for the nonmarket service sectors and use different definitions of the informal sector." }, { "id": "Longdefinition", "value": "GDP per person employed is gross domestic product (GDP) divided by total employment in the economy. Purchasing power parity (PPP) GDP is GDP converted to 2017 constant international dollars using PPP rates. An international dollar has the same purchasing power over GDP that a U.S. dollar has in the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived using data from International Labour Organization, ILOSTAT database. The data retrieved on June 15, 2021." }, { "id": "Statisticalconceptandmethodology", "value": "GDP per person employed represents labor productivity — output per unit of labor input. To compare labor productivity levels across countries, GDP is converted to international dollars using purchasing power parity rates which take account of differences in relative prices between countries." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.IND.EMPL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labour flows from agriculture and other labour-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in industry, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of January 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.IND.EMPL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labour flows from agriculture and other labour-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in industry, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of January 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.IND.EMPL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labour flows from agriculture and other labour-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in industry (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of January 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.ISV.IFRM.FE.ZS", "metatype": [ { "id": "Generalcomments", "value": "Harmonized series" }, { "id": "IndicatorName", "value": "Informal employment, female (% of total non-agricultural employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are limitations for comparing data across countries and over time even within a country, due to differences in definitions and methodology of data collection. For example, informal sector enterprises refer to non-registered enterprises in some countries but registration requirements can vary from country to country. Others apply the employment size criterion only (which may vary from country to country). For detailed information on definitions and coverage, see footnotes." }, { "id": "Longdefinition", "value": "Employment in the informal economy as a percentage of total non-agricultural employment. It basically includes all jobs in unregistered and/or small-scale private unincorporated enterprises that produce goods or services meant for sale or barter. Self-employed street vendors, taxi drivers and home-base workers, regardless of size, are all considered enterprises. However, agricultural and related activities, households producing goods exclusively for their own use (e.g. subsistence farming, domestic housework, care work, and employment of paid domestic workers), and volunteer services rendered to the community are excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of September 2020." }, { "id": "Statisticalconceptandmethodology", "value": "There are wide variations in definitions and methodology of data collection. In addition to employment in the informal economy, informal employment within the formal sector should be also taken into account. Casual, short term, and seasonal workers, for example, could be informally employed — lacking social protection, health benefits, legal status, rights and freedom of association. Some countries now provide data according to the guidelines, adopted by the 17th International Conference of Labour Statisticians (2003); Informal employment as the total number of informal jobs, whether carried out in formal sector enterprises, informal sector enterprises, or households, during a given reference period." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.ISV.IFRM.MA.ZS", "metatype": [ { "id": "Generalcomments", "value": "Harmonized series" }, { "id": "IndicatorName", "value": "Informal employment, male (% of total non-agricultural employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are limitations for comparing data across countries and over time even within a country, due to differences in definitions and methodology of data collection. For example, informal sector enterprises refer to non-registered enterprises in some countries but registration requirements can vary from country to country. Others apply the employment size criterion only (which may vary from country to country). For detailed information on definitions and coverage, see footnotes." }, { "id": "Longdefinition", "value": "Employment in the informal economy as a percentage of total non-agricultural employment. It basically includes all jobs in unregistered and/or small-scale private unincorporated enterprises that produce goods or services meant for sale or barter. Self-employed street vendors, taxi drivers and home-base workers, regardless of size, are all considered enterprises. However, agricultural and related activities, households producing goods exclusively for their own use (e.g. subsistence farming, domestic housework, care work, and employment of paid domestic workers), and volunteer services rendered to the community are excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of September 2020." }, { "id": "Statisticalconceptandmethodology", "value": "There are wide variations in definitions and methodology of data collection. In addition to employment in the informal economy, informal employment within the formal sector should be also taken into account. Casual, short term, and seasonal workers, for example, could be informally employed — lacking social protection, health benefits, legal status, rights and freedom of association. Some countries now provide data according to the guidelines, adopted by the 17th International Conference of Labour Statisticians (2003); Informal employment as the total number of informal jobs, whether carried out in formal sector enterprises, informal sector enterprises, or households, during a given reference period." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.ISV.IFRM.ZS", "metatype": [ { "id": "Generalcomments", "value": "Harmonized series" }, { "id": "IndicatorName", "value": "Informal employment (% of total non-agricultural employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are limitations for comparing data across countries and over time even within a country, due to differences in definitions and methodology of data collection. For example, informal sector enterprises refer to non-registered enterprises in some countries but registration requirements can vary from country to country. Others apply the employment size criterion only (which may vary from country to country). For detailed information on definitions and coverage, see footnotes." }, { "id": "Longdefinition", "value": "Employment in the informal economy as a percentage of total non-agricultural employment. It basically includes all jobs in unregistered and/or small-scale private unincorporated enterprises that produce goods or services meant for sale or barter. Self-employed street vendors, taxi drivers and home-base workers, regardless of size, are all considered enterprises. However, agricultural and related activities, households producing goods exclusively for their own use (e.g. subsistence farming, domestic housework, care work, and employment of paid domestic workers), and volunteer services rendered to the community are excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of September 2020." }, { "id": "Statisticalconceptandmethodology", "value": "There are wide variations in definitions and methodology of data collection. In addition to employment in the informal economy, informal employment within the formal sector should be also taken into account. Casual, short term, and seasonal workers, for example, could be informally employed — lacking social protection, health benefits, legal status, rights and freedom of association. Some countries now provide data according to the guidelines, adopted by the 17th International Conference of Labour Statisticians (2003); Informal employment as the total number of informal jobs, whether carried out in formal sector enterprises, informal sector enterprises, or households, during a given reference period." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.SRV.EMPL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labour flows from agriculture and other labour-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in services, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of January 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.SRV.EMPL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labour flows from agriculture and other labour-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in services, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of January 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.SRV.EMPL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labour flows from agriculture and other labour-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in services (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of January 2021." }, { "id": "Statisticalconceptandmethodology", "value": "The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.TLF.0714.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, female (% of female children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Understanding Children's Work project based on data from ILO, UNICEF and the World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.TLF.0714.MA.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, male (% of male children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Understanding Children's Work project based on data from ILO, UNICEF and the World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.TLF.0714.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, total (% of children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Understanding Children's Work project based on data from ILO, UNICEF and the World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "46" }, { "id": "SL.UEM.1524.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Paradoxically, low unemployment rates can disguise substantial poverty in a country, while high unemployment rates can occur in countries with a high level of economic development and low rates of poverty. In countries without unemployment or welfare benefits people eke out a living in vulnerable employment. In countries with well-developed safety nets workers can afford to wait for suitable or desirable jobs. But high and sustained unemployment indicates serious inefficiencies in resource allocation.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nUnemployment is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.5.2]" }, { "id": "Generalcomments", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, youth female (% of female labor force ages 15-24) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or who have voluntarily left work. Persons who did not look for work but have an arrangements for a future job are also counted as unemployed. \n\nSome unemployment is unavoidable. At any time some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "46" }, { "id": "SL.UEM.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Paradoxically, low unemployment rates can disguise substantial poverty in a country, while high unemployment rates can occur in countries with a high level of economic development and low rates of poverty. In countries without unemployment or welfare benefits people eke out a living in vulnerable employment. In countries with well-developed safety nets workers can afford to wait for suitable or desirable jobs. But high and sustained unemployment indicates serious inefficiencies in resource allocation.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nUnemployment is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.5.2]" }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, youth female (% of female labor force ages 15-24) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or who have voluntarily left work. Persons who did not look for work but have an arrangements for a future job are also counted as unemployed. \n\nSome unemployment is unavoidable. At any time some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "46" }, { "id": "SL.UEM.1524.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Paradoxically, low unemployment rates can disguise substantial poverty in a country, while high unemployment rates can occur in countries with a high level of economic development and low rates of poverty. In countries without unemployment or welfare benefits people eke out a living in vulnerable employment. In countries with well-developed safety nets workers can afford to wait for suitable or desirable jobs. But high and sustained unemployment indicates serious inefficiencies in resource allocation.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nUnemployment is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.5.2]" }, { "id": "Generalcomments", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, youth male (% of male labor force ages 15-24) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or who have voluntarily left work. Persons who did not look for work but have an arrangements for a future job are also counted as unemployed. \n\nSome unemployment is unavoidable. At any time some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "46" }, { "id": "SL.UEM.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Paradoxically, low unemployment rates can disguise substantial poverty in a country, while high unemployment rates can occur in countries with a high level of economic development and low rates of poverty. In countries without unemployment or welfare benefits people eke out a living in vulnerable employment. In countries with well-developed safety nets workers can afford to wait for suitable or desirable jobs. But high and sustained unemployment indicates serious inefficiencies in resource allocation.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nUnemployment is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.5.2]" }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, youth male (% of male labor force ages 15-24) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or who have voluntarily left work. Persons who did not look for work but have an arrangements for a future job are also counted as unemployed. \n\nSome unemployment is unavoidable. At any time some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "46" }, { "id": "SL.UEM.1524.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Paradoxically, low unemployment rates can disguise substantial poverty in a country, while high unemployment rates can occur in countries with a high level of economic development and low rates of poverty. In countries without unemployment or welfare benefits people eke out a living in vulnerable employment. In countries with well-developed safety nets workers can afford to wait for suitable or desirable jobs. But high and sustained unemployment indicates serious inefficiencies in resource allocation.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nUnemployment is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.5.2]" }, { "id": "Generalcomments", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, youth total (% of total labor force ages 15-24) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or who have voluntarily left work. Persons who did not look for work but have an arrangements for a future job are also counted as unemployed. \n\nSome unemployment is unavoidable. At any time some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "46" }, { "id": "SL.UEM.1524.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Paradoxically, low unemployment rates can disguise substantial poverty in a country, while high unemployment rates can occur in countries with a high level of economic development and low rates of poverty. In countries without unemployment or welfare benefits people eke out a living in vulnerable employment. In countries with well-developed safety nets workers can afford to wait for suitable or desirable jobs. But high and sustained unemployment indicates serious inefficiencies in resource allocation.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nUnemployment is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.5.2]" }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, youth total (% of total labor force ages 15-24) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or who have voluntarily left work. Persons who did not look for work but have an arrangements for a future job are also counted as unemployed. \n\nSome unemployment is unavoidable. At any time some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "46" }, { "id": "SL.UEM.NEET.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, female (% of female youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data should be used cautiously because of differences in age coverage." }, { "id": "Longdefinition", "value": "Share of youth not in education, employment or training (NEET) is the proportion of young people who are not in education, employment, or training to the population of the corresponding age group: youth (ages 15 to 24); persons ages 15 to 29; or both age groups." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work in a recent past period, and currently available for and seeking for employment. But there may be persons who do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. NEET rates capture more broadly untapped potential youth, including such individuals who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\").\n\nYouth are defined as persons ages 15 to 24; young adults are those ages 25 to 29; and adults are those ages 25 and above. However, countries vary somewhat in their operational definitions. In particular, the lower age limit for young people is usually determined by the minimum age for leaving school, where this exists. When data are available for more than two age groups in a given year, one value for persons ages 15 to 29 is taken, considering that not all people complete their education by the age of 24." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "46" }, { "id": "SL.UEM.NEET.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, male (% of male youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data should be used cautiously because of differences in age coverage." }, { "id": "Longdefinition", "value": "Share of youth not in education, employment or training (NEET) is the proportion of young people who are not in education, employment, or training to the population of the corresponding age group: youth (ages 15 to 24); persons ages 15 to 29; or both age groups." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work in a recent past period, and currently available for and seeking for employment. But there may be persons who do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. NEET rates capture more broadly untapped potential youth, including such individuals who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\").\n\nYouth are defined as persons ages 15 to 24; young adults are those ages 25 to 29; and adults are those ages 25 and above. However, countries vary somewhat in their operational definitions. In particular, the lower age limit for young people is usually determined by the minimum age for leaving school, where this exists. When data are available for more than two age groups in a given year, one value for persons ages 15 to 29 is taken, considering that not all people complete their education by the age of 24." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "46" }, { "id": "SL.UEM.NEET.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, total (% of youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data should be used cautiously because of differences in age coverage." }, { "id": "Longdefinition", "value": "Share of youth not in education, employment or training (NEET) is the proportion of young people who are not in education, employment, or training to the population of the corresponding age group: youth (ages 15 to 24); persons ages 15 to 29; or both age groups." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work in a recent past period, and currently available for and seeking for employment. But there may be persons who do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. NEET rates capture more broadly untapped potential youth, including such individuals who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\").\n\nYouth are defined as persons ages 15 to 24; young adults are those ages 25 to 29; and adults are those ages 25 and above. However, countries vary somewhat in their operational definitions. In particular, the lower age limit for young people is usually determined by the minimum age for leaving school, where this exists. When data are available for more than two age groups in a given year, one value for persons ages 15 to 29 is taken, considering that not all people complete their education by the age of 24." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "46" }, { "id": "SL.UEM.TOTL.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Paradoxically, low unemployment rates can disguise substantial poverty in a country, while high unemployment rates can occur in countries with a high level of economic development and low rates of poverty. In countries without unemployment or welfare benefits people eke out a living in vulnerable employment. In countries with well-developed safety nets workers can afford to wait for suitable or desirable jobs. But high and sustained unemployment indicates serious inefficiencies in resource allocation.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nUnemployment is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.5.2]" }, { "id": "Generalcomments", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, female (% of female labor force) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or who have voluntarily left work. Persons who did not look for work but have an arrangements for a future job are also counted as unemployed. \n\nSome unemployment is unavoidable. At any time some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "46" }, { "id": "SL.UEM.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Paradoxically, low unemployment rates can disguise substantial poverty in a country, while high unemployment rates can occur in countries with a high level of economic development and low rates of poverty. In countries without unemployment or welfare benefits people eke out a living in vulnerable employment. In countries with well-developed safety nets workers can afford to wait for suitable or desirable jobs. But high and sustained unemployment indicates serious inefficiencies in resource allocation.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nUnemployment is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.5.2]" }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, female (% of female labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or who have voluntarily left work. Persons who did not look for work but have an arrangements for a future job are also counted as unemployed. \n\nSome unemployment is unavoidable. At any time some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "46" }, { "id": "SL.UEM.TOTL.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Paradoxically, low unemployment rates can disguise substantial poverty in a country, while high unemployment rates can occur in countries with a high level of economic development and low rates of poverty. In countries without unemployment or welfare benefits people eke out a living in vulnerable employment. In countries with well-developed safety nets workers can afford to wait for suitable or desirable jobs. But high and sustained unemployment indicates serious inefficiencies in resource allocation.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nUnemployment is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.5.2]" }, { "id": "Generalcomments", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, male (% of male labor force) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or who have voluntarily left work. Persons who did not look for work but have an arrangements for a future job are also counted as unemployed. \n\nSome unemployment is unavoidable. At any time some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "46" }, { "id": "SL.UEM.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Paradoxically, low unemployment rates can disguise substantial poverty in a country, while high unemployment rates can occur in countries with a high level of economic development and low rates of poverty. In countries without unemployment or welfare benefits people eke out a living in vulnerable employment. In countries with well-developed safety nets workers can afford to wait for suitable or desirable jobs. But high and sustained unemployment indicates serious inefficiencies in resource allocation.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nUnemployment is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.5.2]" }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, male (% of male labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or who have voluntarily left work. Persons who did not look for work but have an arrangements for a future job are also counted as unemployed. \n\nSome unemployment is unavoidable. At any time some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "46" }, { "id": "SL.UEM.TOTL.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Paradoxically, low unemployment rates can disguise substantial poverty in a country, while high unemployment rates can occur in countries with a high level of economic development and low rates of poverty. In countries without unemployment or welfare benefits people eke out a living in vulnerable employment. In countries with well-developed safety nets workers can afford to wait for suitable or desirable jobs. But high and sustained unemployment indicates serious inefficiencies in resource allocation.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nUnemployment is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.5.2]" }, { "id": "Generalcomments", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, total (% of total labor force) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or who have voluntarily left work. Persons who did not look for work but have an arrangements for a future job are also counted as unemployed. \n\nSome unemployment is unavoidable. At any time some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "46" }, { "id": "SL.UEM.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Paradoxically, low unemployment rates can disguise substantial poverty in a country, while high unemployment rates can occur in countries with a high level of economic development and low rates of poverty. In countries without unemployment or welfare benefits people eke out a living in vulnerable employment. In countries with well-developed safety nets workers can afford to wait for suitable or desirable jobs. But high and sustained unemployment indicates serious inefficiencies in resource allocation.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nUnemployment is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.5.2]" }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, total (% of total labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or who have voluntarily left work. Persons who did not look for work but have an arrangements for a future job are also counted as unemployed. \n\nSome unemployment is unavoidable. At any time some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "46" }, { "id": "SN.ITK.DEFC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Good nutrition is the cornerstone for survival, health and development. Well-nourished children perform better in school, grow into healthy adults and in turn give their children a better start in life. Well-nourished women face fewer risks during pregnancy and childbirth, and their children set off on firmer developmental paths, both physically and mentally (UNICEF www.childinfo.org)." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 2.1.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Prevalence of undernourishment (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "From a policy and program standpoint, this measure has its limits. First, food insecurity exists even where food availability is not a problem because of inadequate access of poor households to food. Second, food insecurity is an individual or household phenomenon, and the average food available to each person, even corrected for possible effects of low income, is not a good predictor of food insecurity among the population. And third, nutrition security is determined not only by food security but also by the quality of care of mothers and children and the quality of the household's health environment (Smith and Haddad 2000)." }, { "id": "Longdefinition", "value": "Prevalence of undernourishments is the percentage of the population whose habitual food consumption is insufficient to provide the dietary energy levels that are required to maintain a normal active and healthy life. Data showing as 2.5 may signify a prevalence of undernourishment below 2.5%." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization (http://www.fao.org/faostat/en/#home)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on undernourishment are from the Food and Agriculture Organization (FAO) of the United Nations and measure food deprivation based on average food available for human consumption per person, the level of inequality in access to food, and the minimum calories required for an average person." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "46" }, { "id": "SN.ITK.MSFI.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Food insecurity at moderate levels of severity is typically associated with the inability to regularly eat healthy, balanced diets. As such, high prevalence of food insecurity at moderate levels can be considered a predictor of various forms of diet-related health conditions in the population, associated with micronutrient deficiency and unbalanced diets. Severe levels of food insecurity, on the other hand, imply a high probability of reduced food intake and therefore can lead to more severe forms of undernutrition, including hunger. FAO has identified the FIES as the tool with the greatest potential for becoming a global standard capable of providing comparable information on food insecurity experience across countries and population groups to track progress on reducing food insecurity and\nhunger" }, { "id": "IndicatorName", "value": "Prevalence of moderate or severe food insecurity in the population (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of people in the population who live in households classified as moderately or severely food insecure. A household is classified as moderately or severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to low quality diets and might have been forced to also reduce the quantity of food they would normally eat because of a lack of money or other resources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "The assessment is conducted using data collected with the Food Insecurity Experience Scale or a compatible experience-based food security measurement questionnaire (such as the HFSSM). The probability to be food insecure is estimated using the one-parameter logistic Item Response Theory model (the Rasch model) and thresholds for classification are made cross country comparable by calibrating the metrics obtained in each country against the FIES global reference scale, maintained by FAO. The threshold to classify \"moderate or severe\" food insecurity corresponds to the severity associated with the item \"having to eat less\" on the global FIES scale. It is an indicator of lack of food access.The indicator is calculated as an average over 3 years (eg. data for 2015 is the average of 2014-2016 data)." }, { "id": "Topic", "value": "Health" } ], "source_id": "46" }, { "id": "SN.ITK.SVFI.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Food insecurity at moderate levels of severity is typically associated with the inability to regularly eat healthy, balanced diets. As such, high prevalence of food insecurity at moderate levels can be considered a predictor of various forms of diet-related health conditions in the population, associated with micronutrient deficiency and unbalanced diets. Severe levels of food insecurity, on the other hand, imply a high probability of reduced food intake and therefore can lead to more severe forms of undernutrition, including hunger. FAO has identified the FIES as the tool with the greatest potential for becoming a global standard capable of providing comparable information on food insecurity experience across countries and population groups to track progress on reducing food insecurity and\nhunger" }, { "id": "IndicatorName", "value": "Prevalence of severe food insecurity in the population (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of people in the population who live in households classified as severely food insecure. A household is classified as severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to several of the most severe experiences described in the FIES questions, such as to have been forced to reduce the quantity of the food, to have skipped meals, having gone hungry, or having to go for a whole day without eating because of a lack of money or other resources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "The assessment is conducted using data collected with the Food Insecurity Experience Scale or a compatible experience-based food security measurement questionnaire (such as the HFSSM). The probability to be food insecure is estimated using the one-parameter logistic Item Response Theory model (the Rasch model) and thresholds for classification are made cross country comparable by calibrating the metrics obtained in each country against the FIES global reference scale, maintained by FAO. The threshold to classify \"severe\" food insecurity corresponds to the severity associated with the item \"having not eaten for an entire day\" on the global FIES scale. It is an indicator of lack of food access.The indicator is calculated as an average over 3 years (eg. data for 2015 is the average of 2014-2016 data)." }, { "id": "Topic", "value": "Health" } ], "source_id": "46" }, { "id": "SP.ADO.TFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 3.7.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Adolescent fertility rate (births per 1,000 women ages 15-19)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adolescent fertility rate is the number of births per 1,000 women ages 15-19." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Population Division, World Population Prospects." }, { "id": "Statisticalconceptandmethodology", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\nAdolescent fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without vital registration systems fertility rates are generally based on extrapolations from trends observed in censuses or surveys from earlier years." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "46" }, { "id": "SP.M15.2024.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Although the legal age of marriage is defined as 18 years in most countries, the practice of child marriage remains widespread. A women’s access to education and later her employment opportunities as well as the nature and terms of her work are often compromised by this practice. Young married girls whose schooling is cut short often lack the knowledge and skills for formal work and are limited to occupations with lower incomes and inferior working conditions. Sustainable Development Goal 5 commits to eliminate the practice of child marriage." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 5.3.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Women who were first married by age 15 (% of women ages 20-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who were first married by age 15 refers to the percentage of women ages 20-24 who were first married by age 15." }, { "id": "Source", "value": "UNICEF Data; Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), AIDS Indicator Surveys(AIS), Reproductive Health Survey(RHS), and other household surveys." }, { "id": "Topic", "value": "Gender: Agency" } ], "source_id": "46" }, { "id": "SP.M18.2024.FE.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Although the legal age of marriage is defined as 18 years in most countries, the practice of child marriage remains widespread. A women’s access to education and later her employment opportunities as well as the nature and terms of her work are often compromised by this practice. Young married girls whose schooling is cut short often lack the knowledge and skills for formal work and are limited to occupations with lower incomes and inferior working conditions. Sustainable Development Goal 5 commits to eliminate the practice of child marriage." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 5.3.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Women who were first married by age 18 (% of women ages 20-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women who were first married by age 18 refers to the percentage of women ages 20-24 who were first married by age 18." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF Data; Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), AIDS Indicator Surveys(AIS), Reproductive Health Survey(RHS), and other household surveys." }, { "id": "Topic", "value": "Gender: Agency" } ], "source_id": "46" }, { "id": "SP.POP.SCIE.RD.P6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2023 (July 1, 2022-June 30, 2023)." }, { "id": "IndicatorName", "value": "Researchers in R&D (per million people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers." }, { "id": "Longdefinition", "value": "The number of researchers engaged in Research &Development (R&D), expressed as per million. Researchers are professionals who conduct research and improve or develop concepts, theories, models techniques instrumentation, software of operational methods. R&D covers basic research, applied research, and experimental development." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Researchers are professionals engaged in the conception or creation of new knowledge, products, processes, methods and systems, as well as in the management of these projects. Students studying at the master’s or doctoral level (ISCED2011 level 7 or 8) engaged in R&D are included. \n\nThe OECD's Frascati Manual defines research and experimental development as \"creative work undertaken on a systemic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications.\" R&D covers basic research, applied research, and experimental development.\n\n(1) Basic research - Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view.\n\n(2) Applied research - Applied research is also original investigation undertaken in order to acquire new knowledge; it is, however, directed primarily towards a specific practical aim or objective.\n\n(3) Experimental development - Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed.\n\nThe fields of science and technology used to classify R&D according to the Revised Fields of Science and Technology Classification are:\n1. Natural sciences;\n2. Engineering and technology;\n3. Medical and health sciences;\n4. Agricultural sciences;\n5. Social sciences;\n6. Humanities and the arts.\n\nData are for full-time equivalent (FTE); the FTE of R&D personnel is defined as the ratio of working hours actually spent on R&D during a specific reference period (usually a calendar year) divided by the total number of hours conventionally worked in the same period by an individual or by a group. \n\nThe data are obtained through statistical surveys which are regularly conducted at national level covering R&D performing entities in the private and public sectors." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "46" }, { "id": "SP.REG.BRTH.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Completeness of birth registration, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF's State of the World's Children based mostly on household surveys and ministry of health data." }, { "id": "Statisticalconceptandmethodology", "value": "Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\nNumerous indicators have been proposed to assess a country's health information system.They can be grouped into two broad types: indicators related to data generation using core sources and methods (health surveys, civil registration, censuses, facility reporting, health system resource tracking) and indicators related to capacity for data synthesis, analysis, and validation. Indicators related to data generation reflect a country's capacity to collect relevant data at suitable intervals using the most appropriate data sources. Benchmarks include periodicity, timeliness, contents, and availability. Indicators related to capacity for synthesis, analysis, and validation measure the dimensions of the institutional frameworks needed to ensure data quality, including independence, transparency, and access. Benchmarks include the availability of independent coordination mechanisms and micro- and meta-data. Indicators related to data generation include completeness of birth registration.\n\nBirth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\nCompleteness of birth registration indicator is related to the group of indictors of data generation." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "46" }, { "id": "SP.REG.BRTH.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Completeness of birth registration, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF's State of the World's Children based mostly on household surveys and ministry of health data." }, { "id": "Statisticalconceptandmethodology", "value": "Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\nNumerous indicators have been proposed to assess a country's health information system.They can be grouped into two broad types: indicators related to data generation using core sources and methods (health surveys, civil registration, censuses, facility reporting, health system resource tracking) and indicators related to capacity for data synthesis, analysis, and validation. Indicators related to data generation reflect a country's capacity to collect relevant data at suitable intervals using the most appropriate data sources. Benchmarks include periodicity, timeliness, contents, and availability. Indicators related to capacity for synthesis, analysis, and validation measure the dimensions of the institutional frameworks needed to ensure data quality, including independence, transparency, and access. Benchmarks include the availability of independent coordination mechanisms and micro- and meta-data. Indicators related to data generation include completeness of birth registration.\n\nBirth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\nCompleteness of birth registration indicator is related to the group of indictors of data generation." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "46" }, { "id": "SP.REG.BRTH.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Completeness of birth registration, rural (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF's State of the World's Children based mostly on household surveys and ministry of health data." }, { "id": "Statisticalconceptandmethodology", "value": "Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\nNumerous indicators have been proposed to assess a country's health information system.They can be grouped into two broad types: indicators related to data generation using core sources and methods (health surveys, civil registration, censuses, facility reporting, health system resource tracking) and indicators related to capacity for data synthesis, analysis, and validation. Indicators related to data generation reflect a country's capacity to collect relevant data at suitable intervals using the most appropriate data sources. Benchmarks include periodicity, timeliness, contents, and availability. Indicators related to capacity for synthesis, analysis, and validation measure the dimensions of the institutional frameworks needed to ensure data quality, including independence, transparency, and access. Benchmarks include the availability of independent coordination mechanisms and micro- and meta-data. Indicators related to data generation include completeness of birth registration.\n\nBirth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\nCompleteness of birth registration indicator is related to the group of indictors of data generation." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "46" }, { "id": "SP.REG.BRTH.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Completeness of birth registration, urban (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF's State of the World's Children based mostly on household surveys and ministry of health data." }, { "id": "Statisticalconceptandmethodology", "value": "Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\nNumerous indicators have been proposed to assess a country's health information system.They can be grouped into two broad types: indicators related to data generation using core sources and methods (health surveys, civil registration, censuses, facility reporting, health system resource tracking) and indicators related to capacity for data synthesis, analysis, and validation. Indicators related to data generation reflect a country's capacity to collect relevant data at suitable intervals using the most appropriate data sources. Benchmarks include periodicity, timeliness, contents, and availability. Indicators related to capacity for synthesis, analysis, and validation measure the dimensions of the institutional frameworks needed to ensure data quality, including independence, transparency, and access. Benchmarks include the availability of independent coordination mechanisms and micro- and meta-data. Indicators related to data generation include completeness of birth registration.\n\nBirth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\nCompleteness of birth registration indicator is related to the group of indictors of data generation." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "46" }, { "id": "SP.REG.BRTH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Completeness of birth registration (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF's State of the World's Children based mostly on household surveys and ministry of health data." }, { "id": "Statisticalconceptandmethodology", "value": "Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\nNumerous indicators have been proposed to assess a country's health information system.They can be grouped into two broad types: indicators related to data generation using core sources and methods (health surveys, civil registration, censuses, facility reporting, health system resource tracking) and indicators related to capacity for data synthesis, analysis, and validation. Indicators related to data generation reflect a country's capacity to collect relevant data at suitable intervals using the most appropriate data sources. Benchmarks include periodicity, timeliness, contents, and availability. Indicators related to capacity for synthesis, analysis, and validation measure the dimensions of the institutional frameworks needed to ensure data quality, including independence, transparency, and access. Benchmarks include the availability of independent coordination mechanisms and micro- and meta-data. Indicators related to data generation include completeness of birth registration.\n\nBirth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\nCompleteness of birth registration indicator is related to the group of indictors of data generation." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "46" }, { "id": "SP.URB.GROW", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision." }, { "id": "Statisticalconceptandmethodology", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Environment: Density & urbanization" } ], "source_id": "46" }, { "id": "SP.URB.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. \n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. Aggregation of urban and rural population may not add up to total population because of different country coverages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision." }, { "id": "Statisticalconceptandmethodology", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Environment: Density & urbanization" } ], "source_id": "46" }, { "id": "SP.URB.TOTL.IN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. \n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Population Division. World Urbanization Prospects: 2018 Revision." }, { "id": "Statisticalconceptandmethodology", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects.\n\nPercentages urban are the numbers of persons residing in an area defined as ''urban'' per 100 total population. They are calculated by the Statistics Division of the United Nations Department of Economic and Social Affairs. Particular caution should be used in interpreting the figures for percentage urban for different countries.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Environment: Density & urbanization" } ], "source_id": "46" }, { "id": "TG.VAL.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Merchandise trade (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise trade as a share of GDP is the sum of merchandise exports and imports divided by the value of GDP, all in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Trade Organization, and World Bank GDP estimates." }, { "id": "Topic", "value": "Private Sector & Trade: Total merchandise trade" } ], "source_id": "46" }, { "id": "TM.TAX.MANF.SM.AR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Tariff rate, applied, simple mean, manufactured products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Source", "value": "World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database." }, { "id": "Topic", "value": "Private Sector & Trade: Tariffs" } ], "source_id": "46" }, { "id": "TM.TAX.MANF.WM.AR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Tariff rate, applied, weighted mean, manufactured products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Source", "value": "World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database." }, { "id": "Topic", "value": "Private Sector & Trade: Tariffs" } ], "source_id": "46" }, { "id": "TM.TAX.MRCH.SM.AR.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Poor people in developing countries work primarily in agriculture and labor-intensive manufactures, sectors that confront the greatest trade barriers. Removing barriers to merchandise trade could increase growth in these countries - even more if trade in services were also liberalized.\n\nIn general, tariffs in high-income countries on imports from developing countries, though low, are twice those collected from other high-income countries. But protection is also an issue for developing countries, which maintain high tariffs on agricultural commodities, labor-intensive manufactures, and other products and services.\n\nCountries use a combination of tariff and nontariff measures to regulate imports. The most common form of tariff is an ad valorem duty, based on the value of the import, but tariffs may also be levied on a specific, or per unit, basis or may combine ad valorem and specific rates. Tariffs may be used to raise fiscal revenues or to protect domestic industries from foreign competition - or both. Nontariff barriers, which limit the quantity of imports of a particular good, include quotas, prohibitions, licensing schemes, export restraint arrangements, and health and quarantine measures. Because of the difficulty of combining nontariff barriers into an aggregate indicator, they are not included in the data.\n\nSome countries set fairly uniform tariff rates across all imports. Others are selective, setting high tariffs to protect favored domestic industries. The effective rate of protection - the degree to which the value added in an industry is protected - may exceed the nominal rate if the tariff system systematically differentiates among imports of raw materials, intermediate products, and finished goods." }, { "id": "IndicatorName", "value": "Tariff rate, applied, simple mean, all products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods." }, { "id": "Source", "value": "World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database." }, { "id": "Statisticalconceptandmethodology", "value": "Simple averages are often a better indicator of tariff protection than weighted averages, which are biased downward because higher tariffs discourage trade and reduce the weights applied to these tariffs." }, { "id": "Topic", "value": "Private Sector & Trade: Tariffs" } ], "source_id": "46" }, { "id": "TM.TAX.MRCH.WM.AR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Tariff rate, applied, weighted mean, all products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country." }, { "id": "Source", "value": "World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database." }, { "id": "Topic", "value": "Private Sector & Trade: Tariffs" } ], "source_id": "46" }, { "id": "TM.TAX.TCOM.SM.AR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Tariff rate, applied, simple mean, primary products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Source", "value": "World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database." }, { "id": "Topic", "value": "Private Sector & Trade: Tariffs" } ], "source_id": "46" }, { "id": "TM.TAX.TCOM.WM.AR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Tariff rate, applied, weighted mean, primary products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Source", "value": "World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database." }, { "id": "Topic", "value": "Private Sector & Trade: Tariffs" } ], "source_id": "46" }, { "id": "VC.BTL.DETH", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "According to the Geneva Declaration on Armed Violence and Development, more than 526,000 people die each year because of the violence associated with armed conflict and large- and small-scale criminality. Recovery and rebuilding can take years, and the challenges are numerous: infrastructure to be rebuilt, persistently high crime, widespread health problems, education systems in disrepair, and unexploded ordnance to be cleared.\n\nMost countries emerging from conflict lack the capacity to rebuild the economy. Thus, capacity building is one of the first tasks for restoring growth and is linked to building peace and creating the conditions that lead to sustained poverty reduction. UN Peacekeepers serve in some of the most difficult and dangerous situations around the globe. United Nations Peacekeeping force, comprised of civilian, police and military personnel, helps countries torn by conflict create the conditions for lasting peace. In addition to maintaining peace and security, peacekeepers are increasingly charged with assisting in political processes; reforming judicial systems; training law enforcement and police forces; disarming and reintegrating former combatants; supporting the return of internally displaced persons and refugees.\n\nThe World Bank and other international development agencies can help, but countries with fragile situations have to build their own institutions tailored to their own needs. Peacekeeping operations in post-conflict situations have been effective in reducing the risks of reversion to conflict." }, { "id": "IndicatorName", "value": "Battle-related deaths (number of people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "An armed conflict is a contested incompatibility that concerns a government or territory where the use of armed force between two parties (one of them the government) results in at least 25 battle related deaths in a calendar year. \n\nData is from the Uppsala Conflict Data Program (UCDP) Battle-Related Deaths Dataset which focuses on the incompatibility and lists the country, as well as the battle location and territory where battle-related deaths are reported. When more than one country is listed in the dataset, the assignment of battle-related deaths is determined by the battle location. User can refer to the ICDP dataset where they have split the deaths for the actual location of the fighting when the fighting occurred on the disputed border." }, { "id": "Longdefinition", "value": "Battle-related deaths are deaths in battle-related conflicts between warring parties in the conflict dyad (two conflict units that are parties to a conflict). Typically, battle-related deaths occur in warfare involving the armed forces of the warring parties. This includes traditional battlefield fighting, guerrilla activities, and all kinds of bombardments of military units, cities, and villages, etc. The targets are usually the military itself and its installations or state institutions and state representatives, but there is often substantial collateral damage in the form of civilians being killed in crossfire, in indiscriminate bombings, etc. All deaths--military as well as civilian--incurred in such situations, are counted as battle-related deaths." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Battle-related deaths are deaths in battle-related conflicts between warring parties, usually involving armed forces. This includes traditional battlefield fighting, guerrilla activities, and all kinds of bombardments of military units, cities, and villages, etc. All deaths--military as well as civilian--incurred in such situations, are counted as battle-related deaths." }, { "id": "Source", "value": "Uppsala Conflict Data Program, http://www.pcr.uu.se/research/ucdp/." }, { "id": "Statisticalconceptandmethodology", "value": "During warfare, targets are usually the military and its installations or state institutions and state representatives, but there is often substantial collateral damage of civilians killed in crossfire, indiscriminate bombings, and other military activities. All deaths - civilian as well as military - incurred in such situations are counted as battle-related deaths." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "46" }, { "id": "VC.IHR.PSRC.FE.P5", "metatype": [ { "id": "Developmentrelevance", "value": "In some regions, organized crime, drug trafficking and the violent cultures of youth gangs are predominantly responsible for the high levels of homicide. There has been a sharp increase in homicides in some countries, particularly in Central America, are making the activities of organized crime and drug trafficking more visible. Greater use of firearms is often associated with the illicit activities of organized criminal groups, which are often linked to drug trafficking.\n\nKnowledge of the patterns and causes of violent crime are crucial to forming preventive strategies. Young males are the group most affected by violent crime in all regions, particularly in the Americas. Yet women of all ages are the victims of intimate partner and family-related violence in all regions and countries. Indeed, in many of them, it is within the home where a woman is most likely to be killed.\n\nData on intentional homicides are from the United Nations Office on Drugs and Crime (UNODC), which uses a variety of national and international sources on homicides - primarily criminal justice sources as well as public health data from the World Health Organization (WHO) and the Pan American Health Organization - and the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems to present accurate and comparable statistics. The UNODC defines homicide as \"unlawful death purposefully inflicted on a person by another person.\" This definition excludes deaths arising from armed conflict." }, { "id": "IndicatorName", "value": "Intentional homicides, female (per 100,000 female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Statistics reported to the United Nations in the context of its various surveys on crime levels and criminal justice trends are incidents of victimization that have been reported to the authorities in any given country. That means that this data is subject to the problems of accuracy of all official crime data. The survey results provide an overview of trends and interrelationships between various parts of the criminal justice system to promote informed decision-making in administration, nationally and internationally.\n\nThe degree to which different societies apportion the level of culpability to acts resulting in death is also subject to variation. Consequently, the comparison between countries and regions of \"intentional homicide\", or unlawful death purposefully inflicted on a person by another person, is also a comparison of the extent to which different countries deem that a killing be classified as such, as well as the capacity of their legal systems to record it. Caution should therefore be applied when evaluating and comparing homicide data." }, { "id": "Longdefinition", "value": "Intentional homicides, female are estimates of unlawful female homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UN Office on Drugs and Crime's International Homicide Statistics database." }, { "id": "Statisticalconceptandmethodology", "value": "The definitions used to produce data are in line with the homicide definition used in the UNODC Homicide Statistics dataset. On the basis of these selection criteria and subject to data availability, a long and continuous time series including recent data on homicide counts and rates has been identified or created at country level. Data are adjusted to conform to the total number of victims of intentional homicide. The adjustment is carried out by applying the sex ratio of reported victims to the total number of victims of intentional homicide.\n\nThe intentional killing of a human being by another is the ultimate crime. Its indisputable physical consequences manifested in the form of a dead body also make it the most categorical and calculable. All existing data sources on intentional homicides, both at national and international level, stem from either criminal justice or public health systems. In the former case, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event. In the latter, data are produced by health authorities certifying the cause of death of an individual.\n \nCriminal justice data were collected through UNODC regular collections of crime data from Member States, through publicly available data produced by national government sources and from data compiled by other international and regional agencies, including from Interpol, Eurostat, the Organization of American States and UNICEF. Public health data on homicides were mainly derived from databases on deaths by cause disseminated by the World Health Organization (WHO).\n\nThe inclusion of recent data was given a higher priority in the selection process than the length of the time series (number of years covered). An analysis of official reports and research literature is regularly carried out to verify homicide data used by government agencies and the scientific community.\n\nAs a result of the data collection and validation process, in many countries several homicide datasets have become available from different or multiple sources. Therefore, data series have been selected to provide the most appropriate reference counts." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "46" }, { "id": "VC.IHR.PSRC.MA.P5", "metatype": [ { "id": "Developmentrelevance", "value": "In some regions, organized crime, drug trafficking and the violent cultures of youth gangs are predominantly responsible for the high levels of homicide. There has been a sharp increase in homicides in some countries, particularly in Central America, are making the activities of organized crime and drug trafficking more visible. Greater use of firearms is often associated with the illicit activities of organized criminal groups, which are often linked to drug trafficking.\n\nKnowledge of the patterns and causes of violent crime are crucial to forming preventive strategies. Young males are the group most affected by violent crime in all regions, particularly in the Americas. Yet women of all ages are the victims of intimate partner and family-related violence in all regions and countries. Indeed, in many of them, it is within the home where a woman is most likely to be killed.\n\nData on intentional homicides are from the United Nations Office on Drugs and Crime (UNODC), which uses a variety of national and international sources on homicides - primarily criminal justice sources as well as public health data from the World Health Organization (WHO) and the Pan American Health Organization - and the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems to present accurate and comparable statistics. The UNODC defines homicide as \"unlawful death purposefully inflicted on a person by another person.\" This definition excludes deaths arising from armed conflict." }, { "id": "IndicatorName", "value": "Intentional homicides, male (per 100,000 male)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Statistics reported to the United Nations in the context of its various surveys on crime levels and criminal justice trends are incidents of victimization that have been reported to the authorities in any given country. That means that this data is subject to the problems of accuracy of all official crime data. The survey results provide an overview of trends and interrelationships between various parts of the criminal justice system to promote informed decision-making in administration, nationally and internationally.\n\nThe degree to which different societies apportion the level of culpability to acts resulting in death is also subject to variation. Consequently, the comparison between countries and regions of \"intentional homicide\", or unlawful death purposefully inflicted on a person by another person, is also a comparison of the extent to which different countries deem that a killing be classified as such, as well as the capacity of their legal systems to record it. Caution should therefore be applied when evaluating and comparing homicide data." }, { "id": "Longdefinition", "value": "Intentional homicides, male are estimates of unlawful male homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UN Office on Drugs and Crime's International Homicide Statistics database." }, { "id": "Statisticalconceptandmethodology", "value": "The definitions used to produce data are in line with the homicide definition used in the UNODC Homicide Statistics dataset. On the basis of these selection criteria and subject to data availability, a long and continuous time series including recent data on homicide counts and rates has been identified or created at country level. Data are adjusted to conform to the total number of victims of intentional homicide. The adjustment is carried out by applying the sex ratio of reported victims to the total number of victims of intentional homicide.\n\nThe intentional killing of a human being by another is the ultimate crime. Its indisputable physical consequences manifested in the form of a dead body also make it the most categorical and calculable. All existing data sources on intentional homicides, both at national and international level, stem from either criminal justice or public health systems. In the former case, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event. In the latter, data are produced by health authorities certifying the cause of death of an individual.\n \nCriminal justice data were collected through UNODC regular collections of crime data from Member States, through publicly available data produced by national government sources and from data compiled by other international and regional agencies, including from Interpol, Eurostat, the Organization of American States and UNICEF. Public health data on homicides were mainly derived from databases on deaths by cause disseminated by the World Health Organization (WHO).\n\nThe inclusion of recent data was given a higher priority in the selection process than the length of the time series (number of years covered). An analysis of official reports and research literature is regularly carried out to verify homicide data used by government agencies and the scientific community.\n\nAs a result of the data collection and validation process, in many countries several homicide datasets have become available from different or multiple sources. Therefore, data series have been selected to provide the most appropriate reference counts." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "46" }, { "id": "VC.IHR.PSRC.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "In some regions, organized crime, drug trafficking and the violent cultures of youth gangs are predominantly responsible for the high levels of homicide. There has been a sharp increase in homicides in some countries, particularly in Central America, are making the activities of organized crime and drug trafficking more visible. Greater use of firearms is often associated with the illicit activities of organized criminal groups, which are often linked to drug trafficking.\n\nKnowledge of the patterns and causes of violent crime are crucial to forming preventive strategies. Young males are the group most affected by violent crime in all regions, particularly in the Americas. Yet women of all ages are the victims of intimate partner and family-related violence in all regions and countries. Indeed, in many of them, it is within the home where a woman is most likely to be killed.\n\nData on intentional homicides are from the United Nations Office on Drugs and Crime (UNODC), which uses a variety of national and international sources on homicides - primarily criminal justice sources as well as public health data from the World Health Organization (WHO) and the Pan American Health Organization - and the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems to present accurate and comparable statistics. The UNODC defines homicide as \"unlawful death purposefully inflicted on a person by another person.\" This definition excludes deaths arising from armed conflict." }, { "id": "IndicatorName", "value": "Intentional homicides (per 100,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Statistics reported to the United Nations in the context of its various surveys on crime levels and criminal justice trends are incidents of victimization that have been reported to the authorities in any given country. That means that this data is subject to the problems of accuracy of all official crime data. The survey results provide an overview of trends and interrelationships between various parts of the criminal justice system to promote informed decision-making in administration, nationally and internationally.\n\nThe degree to which different societies apportion the level of culpability to acts resulting in death is also subject to variation. Consequently, the comparison between countries and regions of \"intentional homicide\", or unlawful death purposefully inflicted on a person by another person, is also a comparison of the extent to which different countries deem that a killing be classified as such, as well as the capacity of their legal systems to record it. Caution should therefore be applied when evaluating and comparing homicide data." }, { "id": "Longdefinition", "value": "Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UN Office on Drugs and Crime's International Homicide Statistics database." }, { "id": "Statisticalconceptandmethodology", "value": "The definitions used to produce data are in line with the homicide definition used in the UNODC Homicide Statistics dataset. On the basis of these selection criteria and subject to data availability, a long and continuous time series including recent data on homicide counts and rates has been identified or created at country level. Data included in the dataset correspond to the original value provided by the source of origin, since no statistical procedure or modeling was used to change collected values or to create new or revised figures.\n\nThe intentional killing of a human being by another is the ultimate crime. Its indisputable physical consequences manifested in the form of a dead body also make it the most categorical and calculable. All existing data sources on intentional homicides, both at national and international level, stem from either criminal justice or public health systems. In the former case, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event. In the latter, data are produced by health authorities certifying the cause of death of an individual.\n \nCriminal justice data were collected through UNODC regular collections of crime data from Member States, through publicly available data produced by national government sources and from data compiled by other international and regional agencies, including from Interpol, Eurostat, the Organization of American States and UNICEF. Public health data on homicides were mainly derived from databases on deaths by cause disseminated by the World Health Organization (WHO).\n\nThe inclusion of recent data was given a higher priority in the selection process than the length of the time series (number of years covered). An analysis of official reports and research literature is regularly carried out to verify homicide data used by government agencies and the scientific community.\n\nAs a result of the data collection and validation process, in many countries several homicide datasets have become available from different or multiple sources. Therefore, data series have been selected to provide the most appropriate reference counts." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "46" }, { "id": "AG.AGR.TRAC.NO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Agricultural land covers more than one-third of the world's land area. In many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\nA substantial contribution to agriculture in the last century has been the escalation from manual and stock-animal farm work to gas-powered farm equipment. Globally, steel plows, mowers, mechanical reapers, seed drills, and threshers contributed to the development of mechanized agriculture, tractors enabled the farmer to sow and harvest large agricultural lands with less manpower. In modern times, powered machinery such as tractors, has replaced many jobs formerly carried out by men or animals such as oxen, horses and mules. FAO estimates that most farmers in developing countries experience a greater annual expenditure on farm power inputs than on fertilizer, seeds or agrochemicals.\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources as poor farming practices cause soil erosion and loss of soil fertility.\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Agricultural machinery, tractors" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. The data collected from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Longdefinition", "value": "Agricultural machinery refers to the number of wheel and crawler tractors (excluding garden tractors) in use in agriculture at the end of the calendar year specified or during the first quarter of the following year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, electronic files and web site." }, { "id": "Statisticalconceptandmethodology", "value": "A tractor provides the power and traction to mechanize agricultural tasks, especially tillage. Agricultural implements may be towed behind or mounted on the tractor, and the tractor may also provide a source of power if the implement is mechanized. The most common use of the term \"tractor\" is for the vehicles used on farms. The farm tractor is used for pulling or pushing agricultural machinery or trailers, for plowing, tilling, disking, harrowing, planting, and similar tasks. Planting, tending and harvesting a crop requires both a significant amount of power and a suitable range of tools and equipment. Mechanization of farming has allowed an increase to the area that can be planted and has contributed towards increased yields, mainly due to the precision with which the farming tasks can be accomplished." }, { "id": "Topic", "value": "Environment: Agricultural production" } ], "source_id": "57" }, { "id": "AG.CON.FERT.PT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Factors, such as the green revolution, have led to impressive progress in increasing crop yields over the last few decades. This progress, however, is not equal across all regions. Continued progress depends on maintaining agricultural research and education. The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth.\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\nIn many developed countries, excessive nitrogen fertilizer applications have sometimes led to pest problems by increasing the birth rate, longevity and overall fitness of certain agricultural pests, such as aphids. Further, excessive use of fertilizers emits significant quantities of greenhouse gases into the atmosphere. Over-fertilization of a vital nutrient can be detrimental, as \"fertilizer burn\" can occur when too much fertilizer is applied, resulting in drying out of the leaves and damage or even death of the plant. In many industrialized countries, overuse of fertilizers has resulted in contamination of surface water and groundwater.\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Fertilizer consumption (% of fertilizer production)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The FAO has revised the time series for fertilizer consumption and irrigation for 2002 onward. FAO collects fertilizer statistics for production, imports, exports, and consumption through the new FAO fertilizer resources questionnaire. In the previous release, the data were based on total consumption of fertilizers, but the data in the recent release are based on the nutrients in fertilizers. Some countries compile fertilizer data on a calendar year basis, while others compile on a crop year basis (July-June). Previous editions of this indicator, Fertilizer consumption (100 grams per hectare of arable land), reported data on a crop year basis, but this edition uses the calendar year, as adopted by the FAO. Caution should thus be used when comparing data over time.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Longdefinition", "value": "Fertilizer consumption measures the quantity of plant nutrients and is calculated as production plus imports minus exports. Fertilizer products cover nitrogenous, potash, and phosphate fertilizers (including ground rock phosphate). Traditional nutrients--animal and plant manures--are not included. Because some chemical compounds used for fertilizers have other industrial applications, the consumption data may overstate the quantity available for crops. Fertilizer consumption as a share of production shows the agriculture sector's vulnerability to import and energy price fluctuation. Most fertilizers that are commonly used in agriculture contain the three basic plant nutrients-nitrogen, phosphorus, and potassium. Some fertilizers also contain certain micronutrients such as zinc and other metals that are necessary for plant growth. Materials that are applied to the land primarily to enhance soil characteristics (rather than as plant food) are commonly referred to as soil amendments." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Fertilizer consumption measures the quantity of plant nutrients, and is calculated as production plus imports minus exports. Because some chemical compounds used for fertilizers have other industrial applications, the consumption data may overstate the quantity available for crops. Fertilizer consumption as a share of production shows the agriculture sector's vulnerability to import and energy price fluctuation.\n\n\n\nFor the purpose of data dissemination, FAO has adopted the concept of a calendar year (January to December). Some countries compile fertilizer data on a calendar year basis, while others are on a split-year basis.\n\n\n\nFAO has revised the time series for fertilizer consumption and irrigation from 2002 onward. FAO collects fertilizer statistics for production, imports, exports, and consumption through the new FAO fertilizer resources questionnaire. In the previous release, the data were based on the total consumption of fertilizers, but the data in the recent release are based on the nutrients in fertilizers. Some countries compile fertilizer data on a calendar year basis, while others compile on a crop year basis (July-June). Previous editions of this indicator, Fertilizer consumption (100 g per ha of arable land), reported data on a crop year basis, but this edition uses the calendar year, as adopted by the FAO. Caution should thus be used when comparing data over time. The data are collected by FAO through annual questionnaires. FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.\n\n\n\nMost fertilizers that are commonly used in agriculture contain the three basic plant nutrients - nitrogen, phosphorus, and potassium. Some fertilizers also contain certain \"micronutrients,\" such as zinc and other metals that are necessary for plant growth. Materials that are applied to the land primarily to enhance soil characteristics (rather than as plant food) are commonly referred to as soil amendments. Fertilizers and soil amendments are largely derived from raw material, composts and other organic matter, and wastes, such as sewage sludge and certain industrial wastes." }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "% (ratio)" } ], "source_id": "57" }, { "id": "AG.CON.FERT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Factors, such as the green revolution, have led to impressive progress in increasing crop yields over the last few decades. This progress, however, is not equal across all regions. Continued progress depends on maintaining agricultural research and education. The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many developed countries, excessive nitrogen fertilizer applications have sometime lead to pest problems by increasing the birth rate, longevity and overall fitness of certain agricultural pests, such as aphids. Further, excessive use of fertilizers emits significant quantities of greenhouse gas into the atmosphere. Over-fertilization of a vital nutrient can be detrimental, as \"fertilizer burn\" can occur when too much fertilizer is applied, resulting in drying out of the leaves and damage or even death of the plant. In many industrialized countries, overuse of fertilizers has resulted in contamination of surface water and groundwater.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Fertilizer consumption (kilograms per hectare of arable land)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The FAO has revised the time series for fertilizer consumption and irrigation for 2002 onward. FAO collects fertilizer statistics for production, imports, exports, and consumption through the new FAO fertilizer resources questionnaire. In the previous release, the data were based on total consumption of fertilizers, but the data in the recent release are based on the nutrients in fertilizers. Some countries compile fertilizer data on a calendar year basis, while others compile on a crop year basis (July-June). Previous editions of this indicator, Fertilizer consumption (100 grams per hectare of arable land), reported data on a crop year basis, but this edition uses the calendar year, as adopted by the FAO. Caution should thus be used when comparing data over time.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Longdefinition", "value": "Fertilizer consumption measures the quantity of plant nutrients used per unit of arable land. Fertilizer products cover nitrogenous, potash, and phosphate fertilizers (including ground rock phosphate). Traditional nutrients--animal and plant manures--are not included. For the purpose of data dissemination, FAO has adopted the concept of a calendar year (January to December). Some countries compile fertilizer data on a calendar year basis, while others are on a split-year basis. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Fertilizer consumption measures the quantity of plant nutrients, and is calculated as production plus imports minus exports. Because some chemical compounds used for fertilizers have other industrial applications, the consumption data may overstate the quantity available for crops. Fertilizer consumption as a share of production shows the agriculture sector's vulnerability to import and energy price fluctuation.\n\n\n\nMost fertilizers that are commonly used in agriculture contain the three basic plant nutrients - nitrogen, phosphorus, and potassium. Some fertilizers also contain certain \"micronutrients,\" such as zinc and other metals that are necessary for plant growth. Materials that are applied to the land primarily to enhance soil characteristics (rather than as plant food) are commonly referred to as soil amendments. Fertilizers and soil amendments are largely derived from raw material, composts and other organic matter, and wastes, such as sewage sludge and certain industrial wastes.\n\n\n\nFAO defines arable land as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow; land abandoned as a result of shifting cultivation is excluded." }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "kg per hectare of arable land" } ], "source_id": "57" }, { "id": "AG.LND.AGRI.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Agricultural land covers more than one-third of the world's land area. In many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFAO's agricultural land data contains a wide range of information on variables that are significant for understanding the structure of a country's agricultural sector; making economic plans and policies for food security; and deriving environmental indicators, including those related to investment in agriculture and data on gross crop area and net crop area which are useful for policy formulation and monitoring.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Agricultural land (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Longdefinition", "value": "Agricultural land refers to the land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies. Three components of the agricultural land are a) arable land - land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow, b) permanent pasture - land used for five or more years for forage, including natural and cultivated crops, and c) and under permanent crops - land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber; land under flowering shrubs, fruit trees, nut trees, and vines is included, but land under trees grown for wood or timber is not.\n\n\n\nAgricultural land is also sometimes classified as irrigated and non-irrigated land. In arid and semi-arid countries agriculture is often confined to irrigated land, with very little farming possible in non-irrigated areas. Land abandoned as a result of shifting cultivation is excluded from arable land.\n\n\n\nData on agricultural land are valuable for conducting studies on various perspectives concerning agricultural production, food security and for deriving cropping intensity among other uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers (sq. km)" } ], "source_id": "57" }, { "id": "AG.LND.AGRI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Agricultural land covers more than one-third of the world's land area, with arable land representing less than one-third of agricultural land (about 10 percent of the world's land area). Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFAO's agricultural land data contains a wide range of information on variables that are significant for: understanding the structure of a country's agricultural sector; making economic plans and policies for food security; deriving environmental indicators, including those related to investment in agriculture and data on gross crop area and net crop area which are useful for policy formulation and monitoring.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Agricultural land (% of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through annual questionnaires and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage." }, { "id": "Longdefinition", "value": "Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Agriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgricultural land is also sometimes classified as irrigated and non-irrigated land. In arid and semi-arid countries agriculture is often confined to irrigated land, with very little farming possible in non-irrigated areas. Land abandoned as a result of shifting cultivation is excluded from Arable land.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "57" }, { "id": "AG.LND.ARBL.HA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Agricultural land covers more than one-third of the world's land area. Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is significant geographic variation in the availability of land considered suitable for agriculture. Increasing population and demand from other sectors place growing pressure on available resources. According to FAO, the world's cultivated area has grown by 12 percent over the last 50 years. The global irrigated area has doubled over the same period, accounting for most of the net increase in cultivated land. Agriculture already uses 11 percent of the world's land surface for crop production. It also makes use of 70 percent of all water withdrawn from aquifers, streams and lakes. Agricultural policies have primarily benefitted farmers with productive land and access to water, bypassing the majority of small-scale producers who are still locked in a poverty trap of high vulnerability, land degradation and climatic uncertainty.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLand resources are central to agriculture and rural development, and are intrinsically linked to global challenges of food insecurity and poverty, climate change adaptation and mitigation, as well as degradation and depletion of natural resources that affect the livelihoods of millions of rural people across the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land." }, { "id": "IndicatorName", "value": "Arable land (hectares)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Food and Agriculture Organization (FAO) tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. Data on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTrue comparability of the data is limited, however, by variations in definitions, statistical methods, and quality of data. Countries use different definitions land use. The Food and Agriculture Organization of the United Nations (FAO), the primary compiler of the data, occasionally adjusts its definitions of land use categories and revises earlier data. Because the data reflect changes in reporting procedures as well as actual changes in land use, apparent trends should be interpreted cautiously.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSatellite images show land use that differs from that of ground-based measures in area under cultivation and type of land use. Moreover, land use data in some countries (India is an example) are based on reporting systems designed for collecting tax revenue. With land taxes no longer a major source of government revenue, the quality and coverage of land use data have declined." }, { "id": "Longdefinition", "value": "Arable land (in hectares) includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Temporary fallow land refers to land left fallow for less than five years. The abandoned land resulting from shifting cultivation is not included in this category. Data for \"Arable land\" are not meant to indicate the amount of land that is potentially cultivable." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "hectares" } ], "source_id": "57" }, { "id": "AG.LND.ARBL.HA.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Agricultural land covers about one-third of the world's land area, with arable land representing less than one-third of agricultural land (about 10 percent of the world's land area). Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is significant geographic variation in the availability of land considered suitable for agriculture. Increasing population and demand from other sectors place growing pressure on available resources. According to FAO, the world's cultivated area has grown by 12 percent over the last 50 years. The global irrigated area has doubled over the same period, accounting for most of the net increase in cultivated land. Agriculture already uses 11 percent of the world's land surface for crop production. It also makes use of 70 percent of all water withdrawn from aquifers, streams and lakes. Agricultural policies have primarily benefitted farmers with productive land and access to water, bypassing the majority of small-scale producers who are still locked in a poverty trap of high vulnerability, land degradation and climatic uncertainty.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLand resources are central to agriculture and rural development, and are intrinsically linked to global challenges of food insecurity and poverty, climate change adaptation and mitigation, as well as degradation and depletion of natural resources that affect the livelihoods of millions of rural people across the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land." }, { "id": "IndicatorName", "value": "Arable land (hectares per person)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Food and Agriculture Organization (FAO) tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTrue comparability of the data is limited, by variations in definitions, statistical methods, and quality of data. Countries use different definitions land use. The Food and Agriculture Organization of the United Nations (FAO), the primary compiler of the data, occasionally adjusts its definitions of land use categories and revises earlier data. Because the data reflect changes in reporting procedures as well as actual changes in land use, apparent trends should be interpreted cautiously.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSatellite images show land use that differs from that of ground-based measures in area under cultivation and type of land use. Moreover, land use data in some countries (India is an example) are based on reporting systems designed for collecting tax revenue. With land taxes no longer a major source of government revenue, the quality and coverage of land use data have declined." }, { "id": "Longdefinition", "value": "Arable land (hectares per person) includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Temporary fallow land refers to land left fallow for less than five years. The abandoned land resulting from shifting cultivation is not included in this category. Data for \"Arable land\" are not meant to indicate the amount of land that is potentially cultivable. The data collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "hectares per person" } ], "source_id": "57" }, { "id": "AG.LND.ARBL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Agricultural land covers more than one-third of the world's land area. Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is significant geographic variation in the availability of land considered suitable for agriculture. Increasing population and demand from other sectors place growing pressure on available resources. According to FAO, the world's cultivated area has grown by 12 percent over the last 50 years. The global irrigated area has doubled over the same period, accounting for most of the net increase in cultivated land. Agriculture already uses 11 percent of the world's land surface for crop production. It also makes use of 70 percent of all water withdrawn from aquifers, streams and lakes. Agricultural policies have primarily benefitted farmers with productive land and access to water, bypassing the majority of small-scale producers who are still locked in a poverty trap of high vulnerability, land degradation and climatic uncertainty.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLand resources are central to agriculture and rural development, and are intrinsically linked to global challenges of food insecurity and poverty, climate change adaptation and mitigation, as well as degradation and depletion of natural resources that affect the livelihoods of millions of rural people across the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFAO's agricultural land data contains a wide range of information on variables that are significant for: understanding the structure of a country's agricultural sector; making economic plans and policies for food security; deriving environmental indicators, including those related to investment in agriculture and data on gross crop area and net crop area which are useful for policy formulation and monitoring." }, { "id": "IndicatorName", "value": "Arable land (% of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Food and Agriculture Organization (FAO) tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\nThe data collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. Data on agricultural land are valuable for conducting studies on various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\nTrue comparability of the data is limited, by variations in definitions, statistical methods, and quality of data. Countries use different definitions of land use. The Food and Agriculture Organization of the United Nations (FAO), the primary compiler of the data, occasionally adjusts its definitions of land use categories and revises earlier data. Because the data reflect changes in reporting procedures as well as actual changes in land use, apparent trends should be interpreted cautiously.\n\nSatellite images show land use that differs from that of ground-based measures in area under cultivation and type of land use. Moreover, land use data in some countries (India is an example) are based on reporting systems designed for collecting tax revenue. With land taxes no longer a major source of government revenue, the quality and coverage of land use data have declined." }, { "id": "Longdefinition", "value": "Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and website, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Temporary fallow land refers to land left fallow for less than five years. The abandoned land resulting from shifting cultivation is not included in this category. Data for \"Arable land\" are not meant to indicate the amount of land that is potentially cultivable. Total land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area. The data collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "57" }, { "id": "AG.LND.CREL.HA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality od seeds, and the techniques applied to promote growth.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn developed countries, cereal crops are universally machine-harvested, typically using a combine harvester, which cuts, threshes, and winnows the grain during a single pass across the field. In many industrialized countries, particularly in the United States and Canada, farmers commonly deliver their newly harvested grain to a grain elevator or a storage facility that consolidates the crops of many farmers. In developing countries, a variety of harvesting methods are used in cereal cultivation, depending on the cost of labor, from small combines to hand tools such as the scythe or cradle.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production systems have evolved rapidly over the past century and have resulted in significantly increased crop yields, but have also created undesirable environmental side-effects such as soil degradation and erosion, pollution from chemical fertilizers and agrochemicals and a loss of bio-diversity. Factors such as the green revolution, has led to impressive progress in increasing cereals yields over the last few decades. This progress, however, is not equal across all regions. Continued progress depends on maintaining agricultural research and education. The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Land under cereal production (hectares)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. They are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses." }, { "id": "Longdefinition", "value": "Land under cereal production refers to harvested area, although some countries report only sown or cultivated area. Cereals include wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/QCL, note: Item code F1717, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cereals production includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nA cereal is a grass cultivated for the edible components of their grain, composed of the endosperm, germ, and bran. Cereal grains are grown in greater quantities and provide more food energy worldwide than any other type of crop; cereal crops therefore can also be called staple crops." }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "hectares" } ], "source_id": "57" }, { "id": "AG.LND.CROP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Agricultural land covers more than one-third of the world's land area. Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrops are divided into temporary and permanent crops. Permanent crops are sown or planted once, and then occupy the land for some years and need not be replanted after each annual harvest, such as cocoa, coffee and rubber. This category includes flowering shrubs, fruit trees, nut trees and vines, but excludes trees grown for wood or timber. Temporary crops are those which are both sown and harvested during the same agricultural year, sometimes more than once. Temporary crop land is used for crops with a less than one-year growing cycle and which must be newly sown or planted for further production after the harvest.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is significant geographic variation in the availability of land considered suitable for agriculture. Increasing population and demand from other sectors place growing pressure on available resources. According to FAO, the world's cultivated area has grown by 12 percent over the last 50 years. The global irrigated area has doubled over the same period, accounting for most of the net increase in cultivated land. Agriculture already uses 11 percent of the world's land surface for crop production. It also makes use of 70 percent of all water withdrawn from aquifers, streams and lakes. Agricultural policies have primarily benefitted farmers with productive land and access to water, bypassing the majority of small-scale producers who are still locked in a poverty trap of high vulnerability, land degradation and climatic uncertainty.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLand resources are central to agriculture and rural development, and are intrinsically linked to global challenges of food insecurity and poverty, climate change adaptation and mitigation, as well as degradation and depletion of natural resources that affect the livelihoods of millions of rural people across the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land." }, { "id": "IndicatorName", "value": "Permanent cropland (% of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Food and Agriculture Organization (FAO) tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\nTrue comparability of the data is limited by variations in definitions, statistical methods, and quality of data. Countries use different definitions of land use. The Food and Agriculture Organization of the United Nations (FAO), the primary compiler of the data, occasionally adjusts its definitions of land use categories and revises earlier data. Because the data reflect changes in reporting procedures as well as actual changes in land use, apparent trends should be interpreted cautiously.\n\nSatellite images show land use that differs from that of ground-based measures in area under cultivation and type of land use. Moreover, land use data in some countries (India is an example) are based on reporting systems designed for collecting tax revenue. With land taxes no longer a major source of government revenue, the quality and coverage of land use data have declined." }, { "id": "Longdefinition", "value": "Permanent cropland is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data on Permanent cropland and land area are collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through a questionnaire and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "57" }, { "id": "AG.LND.EL5M.RU.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Scientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems." }, { "id": "IndicatorName", "value": "Rural land area where elevation is below 5 meters (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is “unequivocal” and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20–86 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5°C by 2100 (Kattenberg and others 1996) and increase of “killer” heat waves during the warm season (Karl and others 1997)." }, { "id": "Longdefinition", "value": "Rural land area below 5m is the total rural land area in square kilometers where the elevation is 5 meters or less." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev3-3.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Elevation data used to generate the low elevation coastal zones come from the SRTM3 Enhanced Global Map developed by ISCIENCES. The ISCIENCES digital elevation model was created using NASA’s Jet Propulsion Laboratory Shuttle Radar Topography Mission data processed to 3 arc-seconds (SRTM3).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers" } ], "source_id": "57" }, { "id": "AG.LND.EL5M.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Scientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems." }, { "id": "IndicatorName", "value": "Rural land area where elevation is below 5 meters (% of total land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is “unequivocal” and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20–86 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5°C by 2100 (Kattenberg and others 1996) and increase of “killer” heat waves during the warm season (Karl and others 1997)." }, { "id": "Longdefinition", "value": "Rural land area below 5m is the percentage of total land where the rural land elevation is 5 meters or less." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev3-3.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Elevation data used to generate the low elevation coastal zones come from the SRTM3 Enhanced Global Map developed by ISCIENCES. The ISCIENCES digital elevation model was created using NASA’s Jet Propulsion Laboratory Shuttle Radar Topography Mission data processed to 3 arc-seconds (SRTM3).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "57" }, { "id": "AG.LND.EL5M.UR.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Scientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems." }, { "id": "IndicatorName", "value": "Urban land area where elevation is below 5 meters (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Urban land area below 5m is the total urban land area in square kilometers where the elevation is 5 meters or less." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev3-3.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Elevation data used to generate the low elevation coastal zones come from the SRTM3 Enhanced Global Map developed by ISCIENCES. The ISCIENCES digital elevation model was created using NASA’s Jet Propulsion Laboratory Shuttle Radar Topography Mission data processed to 3 arc-seconds (SRTM3).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers" } ], "source_id": "57" }, { "id": "AG.LND.EL5M.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Scientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems." }, { "id": "IndicatorName", "value": "Urban land area where elevation is below 5 meters (% of total land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Urban land area below 5m is the percentage of total land where the urban land elevation is 5 meters or less." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev3-3.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Elevation data used to generate the low elevation coastal zones come from the SRTM3 Enhanced Global Map developed by ISCIENCES. The ISCIENCES digital elevation model was created using NASA’s Jet Propulsion Laboratory Shuttle Radar Topography Mission data processed to 3 arc-seconds (SRTM3).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "57" }, { "id": "AG.LND.EL5M.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Scientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems." }, { "id": "IndicatorName", "value": "Land area where elevation is below 5 meters (% of total land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is “unequivocal” and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20–86 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5°C by 2100 (Kattenberg and others 1996) and increase of “killer” heat waves during the warm season (Karl and others 1997)." }, { "id": "Longdefinition", "value": "Land area below 5m is the percentage of total land where the elevation is 5 meters or less." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev3-3.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Elevation data used to generate the low elevation coastal zones come from the SRTM3 Enhanced Global Map developed by ISCIENCES. The ISCIENCES digital elevation model was created using NASA’s Jet Propulsion Laboratory Shuttle Radar Topography Mission data processed to 3 arc-seconds (SRTM3).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "57" }, { "id": "AG.LND.FRST.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOn a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems. The decrease of primary forest area, 0.4 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNational parks, game reserves, wilderness areas and other legally established protected areas cover more than 10 percent of the total forest area in most countries and regions. FAO estimates that around 10 million people are employed in forest management and conservation - but many more are directly dependent on forests for their livelihoods. Also, 80 about percent of the world's forests are publicly owned, but ownership and management of forests by communities, individuals and private companies is on the rise.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClose to 1.2 billion hectares of forest are managed primarily for the production of wood and non-wood forest products. An additional 25 percent of forest area is designated for multiple uses - in most cases including the production of wood and non-wood forest products. The area designated primarily for productive purposes has decreased by more than 50 million hectares since 1990 as forests have been designated for other purposes." }, { "id": "IndicatorName", "value": "Forest area (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Food and Agricultural Organization (FAO) has been collecting and analyzing data on forest area since 1946. This is done at intervals of 5-10 years as part of the Global Forest Resources Assessment (FRA). FAO reports data for 229 countries and territories; for the remaining 56 small island states and territories where no information is provided, a report is prepared by FAO using existing information and a literature search. The data are aggregated at sub-regional, regional and global levels by the FRA team at FAO, and estimates are produced by straight summation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe lag between the reference year and the actual production of data series as well as the frequency of data production varies between countries. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData includes areas with bamboo and palms; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters; plantations primarily used for forestry or protective purposes, such as rubber-wood plantations and cork oak stands. Data excludes tree stands in agricultural production systems, such as fruit plantations and agroforestry systems. Forest area also excludes trees in urban parks and gardens. The proportion of forest area to total land area is calculated and changes in the proportion are computed to identify trends." }, { "id": "Longdefinition", "value": "Forest area is land under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "FAOSTAT, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/RL, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 meters are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFAO provides detail information on forest cover, and adjusted estimates of forest cover. The current survey uses a uniform definition of forest. Although FAO provides a breakdown of forest cover between natural forest and plantation for developing countries, this indictor data does not reflect that breakdown. Thus the deforestation data may underestimate the rate at which natural forest is disappearing in some countries.\nStatistical concept(s): Forest - Forests are lands of more than 0.5 hectares, with a tree canopy cover of more than 10 percent, which are not primarily under agricultural or urban land use. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters in situ. Areas under reforestation which have yet to reach a crown density of 10 percent or tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, that are expected to regenerate. The term specifically includes: forest nurseries and seed orchards that constitute an integral part of the forest; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks and shelterbelts of trees with an area of more than 0.5 ha and width of more than 20 m; plantations primarily used for forestry purposes, including rubberwood plantations and cork oak stands. The term specifically excludes trees planted primarily for agricultural production, for example in fruit plantations and agroforestry systems." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers" } ], "source_id": "57" }, { "id": "AG.LND.FRST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOn a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems. The decrease of forest area, .11 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDestruction of rainforests remains a significant environmental problem Much of what remains of the world's rainforests is in the Amazon basin, where the Amazon Rainforest covers approximately 4 million square kilometers. The regions with the highest tropical deforestation rate are in Central America and tropical Asia. FAO estimates that the decrease of primary forest area, 0.4 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions. Large-scale planting of trees is significantly reducing the net loss of forest area globally, and afforestation and natural expansion of forests in some countries and regions have reduced the net loss of forest area significantly at the global level.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nForests cover about 31 percent of total land area of the world; the world's total forest area is just over 4 billion hectares. On a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNational parks, game reserves, wilderness areas and other legally established protected areas cover more than 10 percent of the total forest area in most countries and regions. FAO estimates that around 10 million people are employed in forest management and conservation - but many more are directly dependent on forests for their livelihoods. Close to 1.2 billion hectares of forest are managed primarily for the production of wood and non-wood forest products. An additional 25 percent of forest area is designated for multiple uses - in most cases including the production of wood and non-wood forest products. The area designated primarily for productive purposes has decreased by more than 50 million hectares since 1990 as forests have been designated for other purposes." }, { "id": "IndicatorName", "value": "Forest area (% of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "FAO has been collecting and analyzing data on forest area since 1946. This is done at intervals of 5-10 years as part of the Global Forest Resources Assessment (FRA). FAO reports data for 229 countries and territories; for the remaining 56 small island states and territories where no information is provided, a report is prepared by FAO using existing information and a literature search. The data are aggregated at sub-regional, regional and global levels by the FRA team at FAO, and estimates are produced by straight summation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe lag between the reference year and the actual production of data series as well as the frequency of data production varies between countries. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData includes areas with bamboo and palms; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters; plantations primarily used for forestry or protective purposes, such as rubber-wood plantations and cork oak stands. Data excludes tree stands in agricultural production systems, such as fruit plantations and agroforestry systems. Forest area also excludes trees in urban parks and gardens. The proportion of forest area to total land area is calculated and changes in the proportion are computed to identify trends." }, { "id": "Longdefinition", "value": "Forest area (% of land area) is the share of total land area that is under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "FAOSTAT, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/RL, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 meters are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Food and Agriculture Organization (FAO) provides detail information on forest cover, and adjusted estimates of forest cover. The survey uses a uniform definition of forest. Although FAO provides a breakdown of forest cover between natural forest and plantation for developing countries, forest data used to derive this indictor data does not reflect that breakdown. Total land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe indictor is derived by dividing total area under forest of a country by country's total land area, and multiplying by 100.\nStatistical concept(s): Forest - Forests are lands of more than 0.5 hectares, with a tree canopy cover of more than 10 percent, which are not primarily under agricultural or urban land use. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters in situ. Areas under reforestation which have yet to reach a crown density of 10 percent or tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, that are expected to regenerate. The term specifically includes: forest nurseries and seed orchards that constitute an integral part of the forest; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks and shelterbelts of trees with an area of more than 0.5 ha and width of more than 20 m; plantations primarily used for forestry purposes, including rubberwood plantations and cork oak stands. The term specifically excludes trees planted primarily for agricultural production, for example in fruit plantations and agroforestry systems." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "57" }, { "id": "AG.LND.IRIG.AG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Worldwide, irrigated agriculture accounts for about four-fifths of global water withdrawals. The share of irrigated land ranges widely, from 4 percent of the total area cropped in Africa to 42 percent in South Asia. The leading countries are India and China with about 30 percent and 52 percent of all cropland irrigated, respectively. Without irrigation and drainage, much of the increases in agricultural output that has fed the world's growing population and stabilized food production would not have been possible.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn the dry sub-humid countries, irrigation is critical for crop production. Due to highly variable rainfall, long dry seasons, and recurrent droughts, dry spells and floods, water management is a key determinant for agricultural production in these regions and is increasingly becoming more important with climate change. World Bank estimates that rainfed agriculture is most significant in Sub-Saharan Africa where it accounts for about 96 percent of the cropland.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIrrigation and drainage continue to be an important source of productivity growth, especially in Sub-Saharan Africa and parts of Latin America that still have large untapped water resources for agriculture. In other regions where the scope for further expanding irrigated agriculture is limited, more efforts are needed to enhance the policy, technical, and governance aspects of agricultural water use.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgricultural land covers more than one-third of the world's land area. In many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land. Data on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Agricultural irrigated land (% of total agricultural land)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through annual questionnaires and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries." }, { "id": "Longdefinition", "value": "Agricultural irrigated land refers to agricultural areas purposely provided with water, including land irrigated by controlled flooding." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/RL, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Irrigated agricultural area refers to area equipped to provide water (via artificial means of irrigation such as by diverting streams, flooding, or spraying) to the crops. In non-irrigated agricultural areas, production of crops is dependent on rain-fed irrigation. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies. Agricultural land can also be classified as irrigated and non-irrigated land. In arid and semi-arid countries agriculture is often confined to irrigated land, with very little farming possible in non-irrigated areas." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of total agricultural land" } ], "source_id": "57" }, { "id": "AG.LND.PRCP.MM", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The agriculture sector is the most water-intensive sector, and water delivery in agriculture is increasingly important. Data on irrigated agricultural land and data on average precipitation illustrate how countries obtain water for agricultural use." }, { "id": "IndicatorName", "value": "Average precipitation in depth (mm per year)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible." }, { "id": "Longdefinition", "value": "Average precipitation is the long-term average in depth (over space and time) of annual precipitation in the country. Precipitation is defined as any kind of water that falls from clouds as a liquid or a solid." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2022" }, { "id": "Source", "value": "FAOSTAT, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimation of Areal Precipitation\n\n\n\n\n\nA single point precipitation measurement is quite often not representative of the volume of precipitation falling over a given catchment area. A dense network of point measurements and/or radar estimates can provide a better representation of the true volume over a given area. A network of precipitation measurements is converted to areal estimates using the arithmentic mean. This technique calculates areal precipitation using the arithmetic mean of all the point or areal measurements considered in the analysis.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "milimeter (mm) per year" } ], "source_id": "57" }, { "id": "AG.LND.TOTL.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Land area is particularly important for understanding an economy's agricultural capacity and the environmental effects of human activity. Innovations in satellite mapping and computer databases have resulted in more precise measurements of land and water areas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPopulation, land area, income, and output are basic measures of the size of an economy. They also provide a broad indication of actual and potential resources. Land area is therefore used as one of the major indicator to normalize other indicators." }, { "id": "IndicatorName", "value": "Land area (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization (FAO) of the United Nations through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data collected from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Longdefinition", "value": "Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAOSTAT, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/RL, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Total land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area. Including areas of former states; for example, the areas of the Union of Soviet Socialist Republics (USSR) are counted in Russian Federation and other successor states." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers" } ], "source_id": "57" }, { "id": "AG.LND.TOTL.RU.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Rural land area (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is “unequivocal” and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20–86 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5°C by 2100 (Kattenberg and others 1996) and increase of “killer” heat waves during the warm season (Karl and others 1997)." }, { "id": "Longdefinition", "value": "Rural land area in square kilometers, derived from urban extent grids which distinguish urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev2-2.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2013" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) urban extent grid distinguishes urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights . Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT)\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers" } ], "source_id": "57" }, { "id": "AG.LND.TOTL.UR.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Urban land area (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Urban land area in square kilometers, based on a combination of population counts (persons), settlement points, and the presence of nighttime lights. Areas are defined as urban where contiguous lighted cells from the nighttime lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev2-2.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2013" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) urban extent grid distinguishes urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights . Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT)" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers" } ], "source_id": "57" }, { "id": "AG.LND.TRAC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Agricultural land covers more than one-third of the world's land area. In many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\nA substantial contribution to agriculture in the last century has been the escalation from manual and stock-animal farm work to gas-powered farm equipment. Globally, steel plows, mowers, mechanical reapers, seed drills, and threshers contributed to the development of mechanized agriculture, tractors enabled the farmer to sow and harvest large agricultural lands with less manpower. In modern times, powered machinery such as tractors, has replaced many jobs formerly carried out by men or animals such as oxen, horses and mules. FAO estimates that most farmers in developing countries experience a greater annual expenditure on farm power inputs than on fertilizer, seeds or agrochemicals.\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources as poor farming practices cause soil erosion and loss of soil fertility.\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Agricultural machinery, tractors per 100 sq. km of arable land" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage.\n\nThe data collected from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Longdefinition", "value": "Agricultural machinery refers to the number of wheel and crawler tractors (excluding garden tractors) in use in agriculture at the end of the calendar year specified or during the first quarter of the following year. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, electronic files and web site." }, { "id": "Statisticalconceptandmethodology", "value": "A tractor provides the power and traction to mechanize agricultural tasks, especially tillage. Agricultural implements may be towed behind or mounted on the tractor, and the tractor may also provide a source of power if the implement is mechanized. The most common use of the term \"tractor\" is for the vehicles used on farms. The farm tractor is used for pulling or pushing agricultural machinery or trailers, for plowing, tilling, disking, harrowing, planting, and similar tasks. Planting, tending and harvesting a crop requires both a significant amount of power and a suitable range of tools and equipment. Mechanization of farming has allowed an increase to the area that can be planted and has contributed towards increased yields, mainly due to the precision with which the farming tasks can be accomplished.\n\nAgricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies. Data on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices." }, { "id": "Topic", "value": "Environment: Agricultural production" } ], "source_id": "57" }, { "id": "AG.PRD.CREL.MT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Food and Agriculture Organization (FAO) estimates that cereals supply 51 percent of Calories and 47 percent of protein in the average diet. The total annual cereal production globally is about 2,500 million tons.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFAO estimates that maize (corn), wheat and rice together account for more than three-fourths of all grain production worldwide. In developed countries, cereal crops are universally machine-harvested, typically using a combine harvester, which cuts, threshes, and winnows the grain during a single pass across the field. In many industrialized countries, particularly in the United States and Canada, farmers commonly deliver their newly harvested grain to a grain elevator or a storage facility that consolidates the crops of many farmers. In developing countries, a variety of harvesting methods are used in cereal cultivation, depending on the cost of labor, from small combines to hand tools such as the scythe or cradle.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production systems have evolved rapidly over the past century and have resulted in significantly increased crop yields, but have also created undesirable environmental side-effects such as soil degradation and erosion, pollution from chemical fertilizers and agrochemicals and a loss of bio-diversity. Factors such as the green revolution, has led to impressive progress in increasing cereals yields over the last few decades. This progress, however, is not equal across all regions. Continued progress depends on maintaining agricultural research and education. The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth." }, { "id": "IndicatorName", "value": "Cereal production (metric tons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on cereal production may be affected by a variety of reporting and timing differences. Millet and sorghum, which are grown as feed for livestock and poultry in Europe and North America, are used as food in Africa, Asia, and countries of the former Soviet Union. So some cereal crops are excluded from the data for some countries and included elsewhere, depending on their use.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are collected by the Food and Agriculture Organization (FAO) of the United Nations through annual questionnaires and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data collected from official national sources." }, { "id": "Longdefinition", "value": "Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/QCL, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A cereal is a grass cultivated for the edible components of their grain, composed of the endosperm, germ, and bran. Cereal grains are grown in greater quantities and provide more food energy worldwide than any other type of crop; cereal crops therefore can also be called staple crops. Cereals production data relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. The Food and Agriculture Organization (FAO) allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year." }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "metric tons" } ], "source_id": "57" }, { "id": "AG.PRD.CROP.XD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. The category of food production includes commodities that are considered edible and that contain nutrients. Accordingly, coffee and tea are excluded along with inedible commodities because, although edible, they have practically no nutritive value.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption)." }, { "id": "IndicatorName", "value": "Crop production index (2014-2016 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The FAO indices may differ from those produced by the countries themselves because of differences in concepts of production, coverage, time periods, weights, time reference of data, methods of calculation, and use of international prices.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgricultural data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through annual questionnaires and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage." }, { "id": "Longdefinition", "value": "Crop production index shows agricultural production for each year relative to the base period 2014-2016. It includes all crops except fodder crops. Regional and income group aggregates for the FAO's production indexes are calculated from the underlying values in international dollars, normalized to the base period 2014-2016." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2022" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The agricultural production index is prepared by the Food and Agriculture Organization of the United Nations (FAO). The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 2014-2016. They are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. All the indices at the country, regional and world levels are calculated by the Laspeyres formula*. Production quantities of each commodity are weighted by 2014-2016 average international commodity prices and summed for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period 2014-2016. Since the FAO indices are based on the concept of agriculture as a single enterprise, amounts of seed and feed are subtracted from the production data to avoid double counting, once in the production data and once with the crops or livestock produced from them. Deductions for seed (in the case of eggs, for hatching) and for livestock and poultry feed apply to both domestically produced and imported commodities. They cover only primary agricultural products destined to animal feed (e.g. maize, potatoes, milk, etc.). Processed and semi-processed feed items such as bran, oilcakes, meals and molasses have been completely excluded from the calculations at all stages. It should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed. Indices which take into account deductions for feed and seed are referred to as ''net''. Indices calculated without any deductions for feed and seed are referred to as ''gross\". The \"international commodity prices\" are used in order to avoid the use of exchange rates for obtaining continental and world aggregates, and also to improve and facilitate international comparative analysis of productivity at the national level. These\" international prices,\" expressed in so-called \"international dollars,\" are derived using a Geary-Khamis formula** for the agricultural sector. This method assigns a single \"price\" to each commodity. For example, one metric ton of wheat has the same price regardless of the country where it was produced. The currency unit in which the prices are expressed has no influence on the indices published. The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. \n* A Laspeyres Index is known as a \"base-weighted\" or \"fixed-weighted\" index because the price increases are weighted by the quantities in the base period. The Consumer Price Index is an example of a Laspeyres Index. http://www.usna.edu/Users/econ/rbrady/312%20Materials/LaspeyresCalc.pdf\n** Geary-Khamis formula is an aggregation method in which category \"international prices\" (reflecting relative category values) and country purchasing power parities (PPPs), (depicting relative country price levels) are estimated simultaneously from a system of linear equations. http://stats.oecd.org/glossary/detail.asp?ID=5528" }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "index (2014-2016=100)" } ], "source_id": "57" }, { "id": "AG.PRD.FOOD.XD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. The category of food production includes commodities that are considered edible and that contain nutrients. Accordingly, coffee and tea are excluded along with inedible commodities because, although edible, they have practically no nutritive value.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption)." }, { "id": "IndicatorName", "value": "Food production index (2014-2016 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Agricultural data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through the questionnaire and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage." }, { "id": "Longdefinition", "value": "Food production index covers food crops that are considered edible and that contain nutrients. Coffee and tea are excluded because, although edible, they have no nutritive value." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2022" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The agricultural production index is prepared by the Food and Agriculture Organization of the United Nations (FAO). The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 2014-2016. They are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. All the indices at the country, regional and world levels are calculated by the Laspeyres formula*. Production quantities of each commodity are weighted by 2014-2016 average international commodity prices and summed for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period 2014-2016. Since the FAO indices are based on the concept of agriculture as a single enterprise, amounts of seed and feed are subtracted from the production data to avoid double counting, once in the production data and once with the crops or livestock produced from them. Deductions for seed (in the case of eggs, for hatching) and for livestock and poultry feed apply to both domestically produced and imported commodities. They cover only primary agricultural products destined to animal feed (e.g. maize, potatoes, milk, etc.). Processed and semi-processed feed items such as bran, oilcakes, meals and molasses have been completely excluded from the calculations at all stages. It should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed. Indices which take into account deductions for feed and seed are referred to as ''net''. Indices calculated without any deductions for feed and seed are referred to as ''gross\". The \"international commodity prices\" are used in order to avoid the use of exchange rates for obtaining continental and world aggregates, and also to improve and facilitate international comparative analysis of productivity at the national level. These\" international prices,\" expressed in so-called \"international dollars,\" are derived using a Geary-Khamis formula** for the agricultural sector. This method assigns a single \"price\" to each commodity. For example, one metric ton of wheat has the same price regardless of the country where it was produced. The currency unit in which the prices are expressed has no influence on the indices published. The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. \n* A Laspeyres Index is known as a \"base-weighted\" or \"fixed-weighted\" index because the price increases are weighted by the quantities in the base period. The Consumer Price Index is an example of a Laspeyres Index. http://www.usna.edu/Users/econ/rbrady/312%20Materials/LaspeyresCalc.pdf\n** Geary-Khamis formula is an aggregation method in which category \"international prices\" (reflecting relative category values) and country purchasing power parities (PPPs), (depicting relative country price levels) are estimated simultaneously from a system of linear equations. http://stats.oecd.org/glossary/detail.asp?ID=5528" }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "index (2014-2016=100)" } ], "source_id": "57" }, { "id": "AG.PRD.LVSK.XD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. The category of food production includes commodities that are considered edible and that contain nutrients. Accordingly, coffee and tea are excluded along with inedible commodities because, although edible, they have practically no nutritive value.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption)." }, { "id": "IndicatorName", "value": "Livestock production index (2014-2016 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Agricultural data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through the questionnaire and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage." }, { "id": "Longdefinition", "value": "Livestock production index includes meat and milk from all sources, dairy products such as cheese, and eggs, honey, raw silk, wool, and hides and skins." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2022" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The agricultural production index is prepared by the Food and Agriculture Organization of the United Nations (FAO). The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 2014-2016. They are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. All the indices at the country, regional and world levels are calculated by the Laspeyres formula*. Production quantities of each commodity are weighted by 2014-2016 average international commodity prices and summed for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period 2014-2016. Since the FAO indices are based on the concept of agriculture as a single enterprise, amounts of seed and feed are subtracted from the production data to avoid double counting, once in the production data and once with the crops or livestock produced from them. Deductions for seed (in the case of eggs, for hatching) and for livestock and poultry feed apply to both domestically produced and imported commodities. They cover only primary agricultural products destined to animal feed (e.g. maize, potatoes, milk, etc.). Processed and semi-processed feed items such as bran, oilcakes, meals and molasses have been completely excluded from the calculations at all stages. It should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed. Indices which take into account deductions for feed and seed are referred to as ''net''. Indices calculated without any deductions for feed and seed are referred to as ''gross\". The \"international commodity prices\" are used in order to avoid the use of exchange rates for obtaining continental and world aggregates, and also to improve and facilitate international comparative analysis of productivity at the national level. These\" international prices,\" expressed in so-called \"international dollars,\" are derived using a Geary-Khamis formula** for the agricultural sector. This method assigns a single \"price\" to each commodity. For example, one metric ton of wheat has the same price regardless of the country where it was produced. The currency unit in which the prices are expressed has no influence on the indices published. The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. \n* A Laspeyres Index is known as a \"base-weighted\" or \"fixed-weighted\" index because the price increases are weighted by the quantities in the base period. The Consumer Price Index is an example of a Laspeyres Index. http://www.usna.edu/Users/econ/rbrady/312%20Materials/LaspeyresCalc.pdf\n** Geary-Khamis formula is an aggregation method in which category \"international prices\" (reflecting relative category values) and country purchasing power parities (PPPs), (depicting relative country price levels) are estimated simultaneously from a system of linear equations. http://stats.oecd.org/glossary/detail.asp?ID=5528" }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "index (2014-2016=100)" } ], "source_id": "57" }, { "id": "AG.SRF.TOTL.K2", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Total surface area is particularly important for understanding an economy's agricultural capacity and the environmental effects of human activity. Innovations in satellite mapping and computer databases have resulted in more precise measurements of land and water areas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPopulation, surface area, income, and output are basic measures of the size of an economy. They also provide a broad indication of actual and potential resources. Land area is therefore used as one of the major indicator to normalize other indicators." }, { "id": "IndicatorName", "value": "Surface area (sq. km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization (FAO) of the United Nations through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data collected from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Longdefinition", "value": "Surface area is a country's total area, including areas under inland bodies of water and some coastal waterways." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Total land area includes inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area. Including areas of former states; for example, the areas of the Union of Soviet Socialist Republics (USSR) are counted in Russian Federationand other successor states." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "square kilometers" } ], "source_id": "57" }, { "id": "AG.YLD.CREL.KG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In developed countries, cereal crops are universally machine-harvested, typically using a combine harvester, which cuts, threshes, and winnows the grain during a single pass across the field. In many industrialized countries, particularly in the United States and Canada, farmers commonly deliver their newly harvested grain to a grain elevator or a storage facility that consolidates the crops of many farmers. In developing countries, a variety of harvesting methods are used in cereal cultivation, depending on the cost of labor, from small combines to hand tools such as the scythe or cradle.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production systems have evolved rapidly over the past century and have resulted in significantly increased crop yields, but have also created undesirable environmental side-effects such as soil degradation and erosion, pollution from chemical fertilizers and agrochemicals and a loss of bio-diversity. Factors such as the green revolution, has led to impressive progress in increasing cereals yields over the last few decades. This progress, however, is not equal across all regions. Continued progress depends on maintaining agricultural research and education. The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth." }, { "id": "IndicatorName", "value": "Cereal yield (kg per hectare)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Cereals production data relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. The FAO allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on cereal yield may be affected by a variety of reporting and timing differences. Millet and sorghum, which are grown as feed for livestock and poultry in Europe and North America, are used as food in Africa, Asia, and countries of the former Soviet Union. So some cereal crops are excluded from the data for some countries and included elsewhere, depending on their use.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data collected from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Longdefinition", "value": "Cereal yield, measured as kilograms per hectare of harvested land, includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. The FAO allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/QCL, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A cereal is a grass cultivated for the edible components of their grain, composed of the endosperm, germ, and bran. Cereal yield is measured as kilograms per hectare of harvested land. Cereal grains are grown in greater quantities and provide more food energy worldwide than any other type of crop; cereal crops therefore can also be called staple crops Cereals production includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded." }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "kg per hectare" } ], "source_id": "57" }, { "id": "BG.GSR.NFSV.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Trade in services (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total trade in services includes services provided by residents to non-residents plus services provided by non-residents to residents. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "BM.GSR.CMCP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Communications, computer, etc. (% of service imports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Communications, computer, information, and other services cover international telecommunications; computer data; news-related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; personal, cultural, and recreational services; manufacturing services on physical inputs owned by others; and maintenance and repair services and government services not included elsewhere. This indicator is expressed as a percentage of service imports which are services provided by non-residents to residents." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "BM.GSR.COMM.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Communications services, imports (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" } ], "source_id": "57" }, { "id": "BM.GSR.FCTY.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Primary income payments (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Primary income payments refer to employee compensation paid to nonresident workers and investment income (payments on direct investment, portfolio investment, other investments).This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BM.GSR.FINS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Financial services, imports (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" } ], "source_id": "57" }, { "id": "BM.GSR.GNFS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Imports of goods and services (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Discrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded." }, { "id": "Longdefinition", "value": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BM.GSR.INSF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Insurance and financial services (% of service imports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Insurance and financial services cover various types of insurance provided to nonresidents by resident insurance enterprises and vice versa, and financial intermediary and auxiliary services (except those of insurance enterprises and pension funds) exchanged between residents and nonresidents. This indicator is expressed as a percentage of service imports which are services provided by non-residents to residents." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "BM.GSR.INSU.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Insurance services, imports (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" } ], "source_id": "57" }, { "id": "BM.GSR.MRCH.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Goods imports (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Imports of goods occur when there are changes in the economic ownership of goods from non-residents to residents of the compiling economy, irrespective of physical movement of goods across national borders. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BM.GSR.NFSV.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Service imports (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Imports of services are services provided by non-residents to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. Manufacturing services on physical inputs owned by others (goods for processing in BPM5) and maintenance and repair services n.i.e. are reclassified from goods to services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BM.GSR.ROYL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Charges for the use of intellectual property, payments (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Charges for the use of proprietary rights (such as patents, trademarks, copyrights, industrial processes and designs including trade secrets, franchises), and charges for licenses to reproduce or distribute (or both) intellectual property embodied in produced originals or prototypes (such as copyrights on books and manuscripts, computer software, cinematographic works, and sound recordings) and related rights (such as for live performances and television, cable, or satellite broadcast). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BM.GSR.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Imports of goods, services and primary income (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. Primary income represents the return that accrues to institutional units for their contribution to the production process or for the provision of financial assets and renting natural resources to other institutional units. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BM.GSR.TRAN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Transport services, imports (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Transport covers all transport services (sea, air, land, internal waterway, pipeline, space and electricity transmission) performed by residents of one economy for those of another and involving the carriage of passengers, the movement of goods (freight), rental of carriers with crew, and related support and auxiliary services. Also included are postal and courier services. Excluded are freight insurance (included in insurance services); goods procured in ports by nonresident carriers (included in goods); maintenance and repairs on transport equipment (included in maintenance and repair services n.i.e.); and repairs of railway facilities, harbors, and airfield facilities (included in construction). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" } ], "source_id": "57" }, { "id": "BM.GSR.TRAN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Transport services (% of service imports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Transport services covers the process of carriage of people and objects from one location to another as well as related supporting and auxiliary services. Also included are postal and courier services. This indicator is expressed as a percentage of service imports which are services provided by non-residents to residents." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "BM.GSR.TRVL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Travel services, imports (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Travel covers goods and services acquired from an economy by travelers for their own use during visits of less than one year in that economy for either business or personal purposes. Travel includes local transport (i.e., transport within the economy being visited and provided by a resident of that economy), but excludes international transport (which is included in passenger transport. Travel also excludes goods for resale, which are included in general merchandise. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" } ], "source_id": "57" }, { "id": "BM.GSR.TRVL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Travel services (% of service imports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Travel services cover goods and services for own use or to give away acquired from an economy by nonresidents during visits to that economy, or acquired from other economies by residents during visits to these other economies. This indicator is expressed as a percentage of service imports which are services provided by non-residents to residents." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "BM.KLT.DINV.CD.WD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits." }, { "id": "IndicatorName", "value": "Foreign direct investment, net outflows (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intra-unit flows of goods and services.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available." }, { "id": "Longdefinition", "value": "Foreign direct investment refers to direct investment equity flows in an economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. This series shows net outflows of investment from the reporting economy to the rest of the world. Data are in current U.S. dollars." }, { "id": "Othernotes", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Balance of Payments database, International Monetary Fund (IMF), note: International Monetary Fund, Balance of Payments database, supplemented by data from the United Nations Conference on Trade and Development and official national sources.;\nUN Conference on Trade and Development (UNCTAD);\nOfficial national sources" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Foreign direct investment (FDI) data are supplemented by the World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources.\n\nThe internationally accepted definition of FDI (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results in a direct investment relationship, based on control and influence. Distinguished from other kinds of international investment, FDI is made to establish a lasting interest in or effective management control over an enterprise in another country. A lasting interest in an investment enterprise typically involves establishing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. BoP refers to Balance of Payments." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "57" }, { "id": "BM.KLT.DINV.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Foreign direct investment, net outflows (IMF-BoP, % of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Foreign direct investment are the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows net outflows of investment from the reporting economy to the rest of the world and is divided by GDP." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and Balance of Payments databases, World Bank, International Debt Statistics, and World Bank and OECD GDP estimates." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "57" }, { "id": "BM.KLT.DINV.WD.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits." }, { "id": "IndicatorName", "value": "Foreign direct investment, net outflows (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intra-unit flows of goods and services.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available." }, { "id": "Longdefinition", "value": "Foreign direct investment refers to direct investment equity flows in an economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. This series shows net outflows of investment from the reporting economy to the rest of the world, and is divided by GDP." }, { "id": "Othernotes", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Balance of Payments database, International Monetary Fund (IMF), note: International Monetary Fund, Balance of Payments database, supplemented by data from the United Nations Conference on Trade and Development and official national sources.;\nUN Conference on Trade and Development (UNCTAD);\nOfficial national sources" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Foreign direct investment (FDI) data are supplemented by the World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources.\n\nThe internationally accepted definition of FDI (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results in a direct investment relationship, based on control and influence. Distinguished from other kinds of international investment, FDI is made to establish a lasting interest in or effective management control over an enterprise in another country. A lasting interest in an investment enterprise typically involves establishing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. BoP refers to Balance of Payments." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "57" }, { "id": "BM.TRF.OFDC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Secondary income, general government, payments (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Secondary income refers to transfers recorded in the balance of payments whenever an economy provides or receives goods, services, income, or financial items without a quid pro quo. All transfers not considered to be capital are current. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "57" }, { "id": "BM.TRF.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies." }, { "id": "IndicatorName", "value": "Secondary income, other sectors, payments (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Secondary income refers to transfers recorded in the balance of payments whenever an economy provides or receives goods, services, income, or financial items without a quid pro quo. All transfers not considered to be capital are current. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BM.TRF.PWKR.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\nIn most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers. Globally, the number of refugees at end 2010 was 10.55 million, including 597,300 people considered by the United Nations High Commissioner for Refugees (UNHCR) to be in a refugee-like situation; developing countries hosted 8.5 million refugees, or 80 percent of the global refugee population.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights." }, { "id": "IndicatorName", "value": "Personal remittances, paid (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Remittance transactions have grown in importance over the past decade. In a number of developing economies, receipts of remittances have become an important and stable source of funds that exceeds receipts from exports of goods and services or from financial inflows on foreign direct investment. But the quality of statistical remittance data is not high. Remittances are a challenge to measure because of their nature. They are heterogeneous with numerous small transactions conducted by individuals through a wide variety of channels: formal channels, such as electronic wire, or through informal channels, such as cash or goods carried across borders. The large number of remittance transactions and the multitude of channels pose challenges to the compilation of comprehensive statistics. The small size of individual transactions means that they often go undetected by typical data source systems, although the aggregate level of transactions may be substantial.\n\nBecause of difficulties in obtaining data on informal remittance transactions, the remittance transactions undertaken through informal channels are sometimes not well covered in current balance of payments data. As a result, even though direct measurement of remittances - through transactions reporting or surveys - may be considered preferable if feasible, some countries instead combine different sources and estimation methods to achieve better coverage, by using direct measurements where practical and supplemented estimates where they are not. Model-based approaches are used in some countries as they are flexible. Compilers can design models to fill gaps in data sources or to provide global totals.\n\nHowever, only reliable input data can lead to sound estimates, regardless of the sophistication of an estimation method or econometric model. Indirect data are converted to remittance estimates using a set of assumptions. These assumptions should be plausible, but it is often not possible to test or verify these assumptions and also the results in practice." }, { "id": "Longdefinition", "value": "Personal remittances comprise personal transfers and compensation of employees. Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual: personal transfers and compensation of employees. Data are in current U.S. dollars." }, { "id": "Othernotes", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1966-2024" }, { "id": "Source", "value": "IMF balance of payments data, International Monetary Fund (IMF);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The two main components of personal remittances, \"personal transfers\" and \"compensation of employees\", are items in the balance of payments (BPM6) framework. Both of these standard components are recorded in the current account. \n\"Personal transfers,\" a new item in the Balance of Payments (BPM6) represents a broader definition of worker remittances. Personal transfers include all current transfers in cash or in kind between resident and nonresident individuals, independent of the source of income of the sender (irrespective of whether the sender receives income from labor, entrepreneurial or property income, social benefits, and any other types of transfers; or disposes assets) and the relationship between the households (irrespective of whether they are related or unrelated individuals).\n\nCompensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Compensation of employees represents remuneration in return for the labor input to the production process contributed by an individual in an employer-employee relationship with the enterprise. Compensation of employees is recorded gross and includes amounts paid by the employee as taxes or for other purposes in the economy where the work is performed. Compensation of employees has three main components: wages and salaries in cash, wages and salaries in kind, and employers' social contributions." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "57" }, { "id": "BN.CAB.XOKA.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the capital account of the balance of payments. The capital account records acquisitions and disposals of nonproduced nonfinancial assets, such as sales of leases and licenses, crypto assets without a corresponding liability designed as a medium of exchange, as well as capital transfers. These transactions can have a profound impact on a country's economy and are an essential part of understanding the overall balance of payments." }, { "id": "IndicatorName", "value": "Current account balance (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Discrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded." }, { "id": "Longdefinition", "value": "Balance of current transactions (transactions in goods and services, earned income and transfer income) between residents and non-residents. The term current account balance is used in the external accounts and is expressed from the perspective of resident units. The term current external balance is used in the national accounts and is expressed from the perspective of the non-resident units, and therefore with the opposite sign. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Balances" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BN.CAB.XOKA.GD.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the capital account of the balance of payments. The capital account records acquisitions and disposals of nonproduced nonfinancial assets, such as sales of leases and licenses, crypto assets without a corresponding liability designed as a medium of exchange, as well as capital transfers. These transactions can have a profound impact on a country's economy and are an essential part of understanding the overall balance of payments." }, { "id": "IndicatorName", "value": "Current account balance (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Balance of current transactions (transactions in goods and services, earned income and transfer income) between residents and non-residents. The term current account balance is used in the external accounts and is expressed from the perspective of resident units. The term current external balance is used in the national accounts and is expressed from the perspective of the non-resident units, and therefore with the opposite sign. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Balances" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "BN.FIN.TOTL.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the financial account of the balance of payments, which shows net acquisition and disposal of financial assets and liabilities. It is crucial for assessing, among other things (i) capital mobility, reflecting the degree of international capital mobility and the country's integration into the global financial system; (ii) investor confidence and country's creditworthiness; (iii) pressures on a country's currency and central bank's actions in the foreign exchange market; (iv) effectiveness of a country's economic policies, including interest rate and exchange rate policies; or (v) country's vulnerability to external economic shocks and its ability to finance current account deficits. Overall, the financial transactions section is a critical source of information for central banks to formulate and adjust monetary policy to achieve objectives like price stability, full employment, and economic growth." }, { "id": "IndicatorName", "value": "Net financial account (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The net financial account shows net acquisition and disposal of financial assets and liabilities. It measures how net lending to or borrowing from nonresidents is financed, and is conceptually equal to the sum of the balances on the current and capital accounts. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. In BPM6, the headings of the financial account have been changed from credits and debits to net acquisition of financial assets and net incurrence of liabilities; i.e., all changes due to credit and debit entries are recorded on a net basis separately for financial assets and liabilities. Financial account balances are calculated as the change in assets minus the change in liabilities; signs are reversed from previous editions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BN.GSR.FCTY.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Net primary income (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Discrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded." }, { "id": "Longdefinition", "value": "Net primary income includes the net labor income and net property and entrepreneurial income components of the SNA. Labor income covers compensation of employees paid to nonresident workers. Property and entrepreneurial income covers investment income from the ownership of foreign financial claims (interest, dividends, rent, etc.) and nonfinancial property income (patents, copyrights, etc.). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BN.GSR.GNFS.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Net trade in goods and services (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The balance of international trade in goods and services is the difference between the exports and imports of goods and services. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Balances" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BN.GSR.MRCH.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Net trade in goods (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The balance of international trade in goods is the difference between the exports and imports of goods. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Balances" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BN.KAC.EOMS.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth." }, { "id": "IndicatorName", "value": "Net errors and omissions (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net errors and omissions constitute a residual category needed to ensure that accounts in the balance of payments statement sum to zero. Net errors and omissions are derived as the balance on the financial account minus the balances on the current and capital accounts. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BN.KLT.DINV.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth." }, { "id": "IndicatorName", "value": "Foreign direct investment, net (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Foreign direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the voting power is evidence of a direct investment relationship. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. In BPM6, the headings of the financial account have been changed from credits and debits to net acquisition of financial assets and net incurrence of liabilities; i.e., all changes due to credit and debit entries are recorded on a net basis separately for financial assets and liabilities. Financial account balances are calculated as the change in assets minus the change in liabilities; signs are reversed from previous editions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BN.KLT.PRVT.CD", "metatype": [ { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. In BPM6, the headings of the financial account have been changed from credits and debits to net acquisition of financial assets and net incurrence of liabilities; i.e., all changes due to credit and debit entries are recorded on a net basis separately for financial assets and liabilities. Financial account balances are calculated as the change in assets minus the change in liabilities; signs are reversed from previous editions." }, { "id": "IndicatorName", "value": "Private capital flows, net (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Private capital flows consist of net foreign direct investment and portfolio investment. Foreign direct investment is net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. The FDI included here is total net. Portfolio investment covers transactions in equity securities and debt securities. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "57" }, { "id": "BN.KLT.PRVT.GD.ZS", "metatype": [ { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. In BPM6, the headings of the financial account have been changed from credits and debits to net acquisition of financial assets and net incurrence of liabilities; i.e., all changes due to credit and debit entries are recorded on a net basis separately for financial assets and liabilities. Financial account balances are calculated as the change in assets minus the change in liabilities; signs are reversed from previous editions." }, { "id": "IndicatorName", "value": "Private capital flows, net (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Private capital flows consist of net foreign direct investment and portfolio investment. Foreign direct investment is net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. The FDI included here is total net. Portfolio investment covers transactions in equity securities and debt securities." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files, and World Bank and OECD GDP estimates." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "57" }, { "id": "BN.KLT.PTXL.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth." }, { "id": "IndicatorName", "value": "Portfolio investment, net (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Portfolio investment includes cross-border flows and positions involving debt or equity securities, other than those included in direct investment or reserve assets. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. In BPM6, the headings of the financial account have been changed from credits and debits to net acquisition of financial assets and net incurrence of liabilities; i.e., all changes due to credit and debit entries are recorded on a net basis separately for financial assets and liabilities. Financial account balances are calculated as the change in assets minus the change in liabilities; signs are reversed from previous editions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BN.RES.INCL.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth." }, { "id": "IndicatorName", "value": "Reserves and related items (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Reserves and related items is the net change in a country's holdings of international reserves resulting from transactions on the current, capital, and financial accounts. Reserve assets are external assets, including monetary gold, that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency.Also included are net credit and loans from the IMF (excluding reserve position) and total exceptional financing. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. In BPM6, the headings of the financial account have been changed from credits and debits to net acquisition of financial assets and net incurrence of liabilities; i.e., all changes due to credit and debit entries are recorded on a net basis separately for financial assets and liabilities. Financial account balances are calculated as the change in assets minus the change in liabilities; signs are reversed from previous editions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BN.TRF.CURR.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies." }, { "id": "IndicatorName", "value": "Net secondary income (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Discrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded." }, { "id": "Longdefinition", "value": "Net secondary income (from abroad) comprises transfers of income between residents of the reporting country and the rest of the world that carry no provisions for repayment. Net secondary income is equal to the unrequited transfers of income from nonresidents to residents minus the unrequited transfers from residents to nonresidents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BN.TRF.KOGT.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies." }, { "id": "IndicatorName", "value": "Net capital account (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net capital account records acquisitions and disposals of nonproduced nonfinancial assets, such as land sold to embassies and sales of leases and licenses, as well as capital transfers, including government debt forgiveness. The use of the term capital account in this context is designed to be consistent with the System of National Accounts, which distinguishes between capital transactions and financial transactions. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BN.TRF.OFDC.CD", "metatype": [ { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Secondary income, general government, net (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current transfers are recorded in the balance of payments whenever an economy provides or receives goods, services, income, or financial items without a quid pro quo. All transfers not considered to be capital are current. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "57" }, { "id": "BN.TRF.PRVT.CD", "metatype": [ { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Secondary income, other sectors, net (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current transfers are recorded in the balance of payments whenever an economy provides or receives goods, services, income, or financial items without a quid pro quo. All transfers not considered to be capital are current. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "57" }, { "id": "BN.TRF.PWKR.CD", "metatype": [ { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Personal transfers, net (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "57" }, { "id": "BN.TRF.PWKR.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\nIn most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers. Globally, the number of refugees at end 2010 was 10.55 million, including 597,300 people considered by the United Nations High Commissioner for Refugees (UNHCR) to be in a refugee-like situation; developing countries hosted 8.5 million refugees, or 80 percent of the global refugee population.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights." }, { "id": "Generalcomments", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "IndicatorName", "value": "Personal remittances, net (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Remittance transactions have grown in importance over the past decade. In a number of developing economies, receipts of remittances have become an important and stable source of funds that exceeds receipts from exports of goods and services or from financial inflows on foreign direct investment. But the quality of statistical remittance data is not high. Remittances are a challenge to measure because of their nature. They are heterogeneous with numerous small transactions conducted by individuals through a wide variety of channels: formal channels, such as electronic wire, or through informal channels, such as cash or goods carried across borders. The large number of remittance transactions and the multitude of channels pose challenges to the compilation of comprehensive statistics. The small size of individual transactions means that they often go undetected by typical data source systems, although the aggregate level of transactions may be substantial.\n\nBecause of difficulties in obtaining data on informal remittance transactions, the remittance transactions undertaken through informal channels are sometimes not well covered in current balance of payments data. As a result, even though direct measurement of remittances - through transactions reporting or surveys - may be considered preferable if feasible, some countries instead combine different sources and estimation methods to achieve better coverage, by using direct measurements where practical and supplemented estimates where they are not. Model-based approaches are used in some countries as they are flexible. Compilers can design models to fill gaps in data sources or to provide global totals.\n\nHowever, only reliable input data can lead to sound estimates, regardless of the sophistication of an estimation method or econometric model. Indirect data are converted to remittance estimates using a set of assumptions. These assumptions should be plausible, but it is often not possible to test or verify these assumptions and also the results in practice." }, { "id": "Longdefinition", "value": "Personal remittances comprise personal transfers and compensation of employees. Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual: personal transfers and compensation of employees. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on IMF balance of payments data." }, { "id": "Statisticalconceptandmethodology", "value": "The two main components of personal remittances, \"personal transfers\" and \"compensation of employees\", are items in the balance of payments (BPM6) framework. Both of these standard components are recorded in the current account. \n\"Personal transfers\", a new item in the Balance of Payments (BPM6) represents a broader definition of worker remittances. Personal transfers include all current transfers in cash or in kind between resident and nonresident individuals, independent of the source of income of the sender (irrespective of whether the sender receives income from labor, entrepreneurial or property income, social benefits, and any other types of transfers; or disposes assets) and the relationship between the households (irrespective of whether they are related or unrelated individuals).\n\nCompensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Compensation of employees represents remuneration in return for the labor input to the production process contributed by an individual in an employer-employee relationship with the enterprise. Compensation of employees is recorded gross and includes amounts paid by the employee as taxes or for other purposes in the economy where the work is performed. Compensation of employees has three main components: wages and salaries in cash, wages and salaries in kind, and employers' social contributions." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "57" }, { "id": "BX.GRT.EXTA.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Developmentrelevance", "value": "DAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nOECD's IDS database provides a set of readily available basic data that enables analysis on where aid goes, what purposes it serves and what policies it aims to implement, on a comparable basis for all DAC members. The aid data is most commonly used to analyze the sectoral and geographical breakdown of aid for selected years and donors or groups of donors. The data can also be used to target specific policy issues (e.g. tying status of aid) and monitor donors' compliance with various international recommendations in the field of development co-operation." }, { "id": "IndicatorName", "value": "Grants, excluding technical cooperation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Grants are defined as legally binding commitments that obligate a specific value of funds available for disbursement for which there is no repayment requirement. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Grants are defined as legally binding commitments that obligate a specific value of funds available for disbursement for which there is no repayment requirement. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Statisticalconceptandmethodology", "value": "Grants are transfers made in cash, goods or services for which no repayment is required. Data excludes technical cooperation grants. The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat. A network of statistical correspondents collects data from aid agencies and government departments (central, state and local) on an ongoing basis. Their task is also to ensure that reporting conforms to the Reporting Directives (definitions and classifications) agreed by the DAC.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Data are in current U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "57" }, { "id": "BX.GRT.EXTA.CD.WD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The OECD’s aid statistics are completely transparent and publicly available to users. They seek to do the following: 1) Inform taxpayers in OECD members and other countries about what is being spent on aid overseas which enables the public to see what governments are doing with their money, 2) Allow users to understand the key characteristics of ODA to help inform the policies and programs of development co-operation providers in low- and middle-income countries, 3) Provides a comprehensive perspective on what ODA is doing worldwide, 4) Hold governments accountable for their international commitments and legal obligations on where and how to spend their aid to maximize results or benefit the neediest countries, 5) Showcase the contributions of providers outside of the members of the Development Assistance Committee (DAC) where many bilateral providers and multilateral agencies outside of the DAC have been reporting their statistics to the OECD on a voluntary basis, and 6) improve OECD’s ability to provide a comprehensive perspective on development finance flows to partner countries." }, { "id": "IndicatorName", "value": "Grants, excluding technical cooperation (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Grants are transfers made in cash, goods or services for which no repayment is required. For ODA reporting purposes, they also include forgiveness of non-military debt, support to non-governmental organisations, certain interest subsidies, and certain costs incurred in the implementation of aid. Grants to multilateral agencies intended to soften the terms of the latter’s lending are a direct resource outflow and should also be recorded as ODA grants. For OOF reporting purposes, grants for commercial purposes such as subsidies to national private investors, and grants to forgive military debt, are also included. Grant-like flows are assimilated to grants. They comprise a) loans for which the service payments are to be made into an account in the borrowing country and used in the borrowing country for its own benefit, and b) provision of commodities for sale in the recipient’s currency the proceeds of which are used in the recipient country for its own benefit. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "DAC2A: Aid (ODA) disbursements to countries and regions, Organisation for Economic Co-operation and Development (OECD), uri: DSD_DAC2@DF_DAC2A, note: Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows, Development Co-operation Report, and OECD Data Explorer database. Data are available online at: https://data-explorer.oecd.org/., publisher: Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Grants are transfers in cash or in kind for which no legal debt is incurred by the recipient. For ODA reporting purposes, they also include forgiveness of non-military debt, support to non-governmental organisations, certain interest subsidies, and certain costs incurred in the implementation of aid. Grants to multilateral agencies intended to soften the terms of the latter’s lending are a direct resource outflow and should also be recorded as ODA grants. For OOF reporting purposes, grants for commercial purposes such as subsidies to national private investors, and grants to forgive military debt, are also included. Grant-like flows are assimilated to grants. They comprise a) loans for which the service payments are to be made into an account in the borrowing country and used in the borrowing country for its own benefit, and b) provision of commodities for sale in the recipient’s currency the proceeds of which are used in the recipient country for its own benefit. \n\nData are in current U.S. dollars.\n\nFor more information, please refer to the Converged Statistical Reporting Directives for the Creditor Reporting System (CRS) and the Annual DAC Questionnaire at https://one.oecd.org/document/DCD/DAC/STAT(2023)9/FINAL/en/pdf.\n\nStatistical concept(s): Grants are wholly concessional by definition. All grants are reported as flows from the sector providing the funds for development or relief purposes. For their grant equivalents to be counted as official development assistance (ODA), the loans must be concessional i.e. bear a grant element of at least:\n\n• 45 per cent in the case of bilateral loans to the official sector of least developed countries (LDCs) and other low-income countries (LICs).\n• 15 per cent in the case of bilateral loans to the official sector of lower middle-income countries (LMICs).\n• 10 per cent in the case of bilateral loans to the official sector of upper middle-income countries (UMICs), loans to multilateral institutions and loans to international non-governmental organizations (INGOs).\n\nLoans whose terms are not consistent with the IMF Debt Limits Policy and/or the World Bank’s Non-Concessional Borrowing Policy/Sustainable Development Finance Policy are not reportable as ODA.\n\nLoans committed before 2018, qualifying under the rules valid at the time (25% threshold calculated at a 10% discount rate) but not qualifying under the new rules (new concessionality thresholds calculated using the new discount rates) is reportable as ODA. Loans committed before 2018, not qualifying under the rules valid at the time but qualifying under the new rules are reportable as OOF over their life time. Future reporting on repayments of those loans are recorded as OOF too, not as ODA." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "57" }, { "id": "BX.GRT.TECH.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Developmentrelevance", "value": "DAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nOECD's IDS database provides a set of readily available basic data that enables analysis on where aid goes, what purposes it serves and what policies it aims to implement, on a comparable basis for all DAC members. The aid data is most commonly used to analyze the sectoral and geographical breakdown of aid for selected years and donors or groups of donors. The data can also be used to target specific policy issues (e.g. tying status of aid) and monitor donors' compliance with various international recommendations in the field of development co-operation." }, { "id": "IndicatorName", "value": "Technical cooperation grants (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Technical cooperation grants include free-standing technical cooperation grants, which are intended to finance the transfer of technical and managerial skills or of technology for the purpose of building up general national capacity without reference to any specific investment projects; and investment-related technical cooperation grants, which are provided to strengthen the capacity to execute specific investment projects. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Technical cooperation grants include free-standing technical cooperation grants, which are intended to finance the transfer of technical and managerial skills or of technology for the purpose of building up general national capacity without reference to any specific investment projects; and investment-related technical cooperation grants, which are provided to strengthen the capacity to execute specific investment projects. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Statisticalconceptandmethodology", "value": "Technical cooperation contributions take the form mainly of the supply of human resources from donors or action directed to human resources (such as training or advice). Also included are aid for promoting development awareness and aid provided to refugees in the donor economy. Assistance specifically to facilitate a capital project is not included. Technical cooperation ncludes both grants to nationals of aid recipient countries receiving education or training at home or abroad and payments to consultants, advisers and similar personnel as well as teachers and administrators serving in recipient countries (including the cost of associated equipment).\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat. A network of statistical correspondents collects data from aid agencies and government departments (central, state and local) on an ongoing basis. Their task is also to ensure that reporting conforms to the Reporting Directives (definitions and classifications) agreed by the DAC.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Data are in current U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "57" }, { "id": "BX.GRT.TECH.CD.WD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The OECD’s aid statistics are completely transparent and publicly available to users. They seek to do the following: 1) Inform taxpayers in OECD members and other countries about what is being spent on aid overseas which enables the public to see what governments are doing with their money, 2) Allow users to understand the key characteristics of ODA to help inform the policies and programs of development co-operation providers in low- and middle-income countries, 3) Provides a comprehensive perspective on what ODA is doing worldwide, 4) Hold governments accountable for their international commitments and legal obligations on where and how to spend their aid to maximize results or benefit the neediest countries, 5) Showcase the contributions of providers outside of the members of the Development Assistance Committee (DAC) where many bilateral providers and multilateral agencies outside of the DAC have been reporting their statistics to the OECD on a voluntary basis, and 6) improve OECD’s ability to provide a comprehensive perspective on development finance flows to partner countries." }, { "id": "IndicatorName", "value": "Technical cooperation grants (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Technical cooperation grants include free-standing technical cooperation grants, which are intended to finance the transfer of technical and managerial skills or of technology for the purpose of building up general national capacity without reference to any specific investment projects; and investment-related technical cooperation grants, which are provided to strengthen the capacity to execute specific investment projects. Data are in current U.S. dollars." }, { "id": "Othernotes", "value": "For more information, please refer to the Converged Statistical Reporting Directives for the Creditor Reporting System (CRS) and the Annual DAC Questionnaire at https://one.oecd.org/document/DCD/DAC/STAT(2023)9/FINAL/en/pdf.\nStatistical concept(s): Grants are wholly concessional by definition. All grants are reported as flows from the sector providing the funds for development or relief purposes. For their grant equivalents to be counted as official development assistance (ODA), the loans must be concessional i.e. bear a grant element of at least:\n\n• 45 per cent in the case of bilateral loans to the official sector of least developed countries (LDCs) and other low-income countries (LICs).\n• 15 per cent in the case of bilateral loans to the official sector of lower middle-income countries (LMICs).\n• 10 per cent in the case of bilateral loans to the official sector of upper middle-income countries (UMICs), loans to multilateral institutions and loans to international non-governmental organizations (INGOs).\n\nLoans whose terms are not consistent with the IMF Debt Limits Policy and/or the World Bank’s Non-Concessional Borrowing Policy/Sustainable Development Finance Policy are not reportable as ODA.\n\nLoans committed before 2018, qualifying under the rules valid at the time (25% threshold calculated at a 10% discount rate) but not qualifying under the new rules (new concessionality thresholds calculated using the new discount rates) is reportable as ODA. Loans committed before 2018, not qualifying under the rules valid at the time but qualifying under the new rules are reportable as OOF over their life time. Future reporting on repayments of those loans are recorded as OOF too, not as ODA." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "DAC2A: Aid (ODA) disbursements to countries and regions, Organisation for Economic Co-operation and Development (OECD), uri: DSD_DAC2@DF_DAC2A, note: Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows, Development Co-operation Report, and OECD Data Explorer database. Data are available online at: https://data-explorer.oecd.org/." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The term technical co-operation covers a large variety of aid activities. Some technical co-operation is extended in the form of projects.\n\nNon-project technical co-operation comprises activities such as the supply of volunteers or experts, other technical assistance, provision of scholarships and imputed student costs. Many of these activities are funded through specific TC budget lines, which may or may not be administered by the main aid agency. The exact use of funds is seldom known at the commitment stage. Consequently, data on the sectoral and geographical breakdown of such programmes are often collected on a disbursement basis only. As disbursement data can be very detailed (one “activity” corresponding to one individual expert or student), aggregation by recipient and sector (purpose code) is recommended prior to reporting to the CRS++. Grants of technical co-operation to individual countries include the actual or imputed costs of tuition in the reporting country of nationals of developing countries concerned. \n\nFree-standing technical co-operation comprises activities financed by a donor country whose primary purpose is to augment the level of knowledge, skills, technical know-how or productive aptitudes of the population of developing countries, i.e. increasing their stock of human intellectual capital, or their capacity for more effective use of their existing factor endowment. This relates essentially to activities that either enhance or supply human resources. It includes financing of students and trainees who are nationals of developing countries; experts, teachers, and volunteers; equipment and materials for training; research; development-oriented social and cultural programmes, etc. Associated supplies are also classified as technical co-operation.\n\nGrants are transfers in cash or in kind for which no legal debt is incurred by the recipient. For ODA reporting purposes, they also include forgiveness of non-military debt, support to non-governmental organisations, certain interest subsidies, and certain costs incurred in the implementation of aid. Grants to multilateral agencies intended to soften the terms of the latter’s lending are a direct resource outflow and should also be recorded as ODA grants. For OOF reporting purposes, grants for commercial purposes such as subsidies to national private investors, and grants to forgive military debt, are also included. Grant-like flows are assimilated to grants. They comprise a) loans for which the service payments are to be made into an account in the borrowing country and used in the borrowing country for its own benefit, and b) provision of commodities for sale in the recipient’s currency the proceeds of which are used in the recipient country for its own benefit. \n\nData are in current U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "57" }, { "id": "BX.GSR.CCIS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "ICT service exports (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Information and communication technology service exports include computer and communications services (telecommunications and postal and courier services) and information services (computer data and news-related service transactions). Data are in current U.S. dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The balance of payments (BoP) is a double-entry accounting system that shows all flows of goods and services into and out of an economy; all transfers that are the counterpart of real resources or financial claims provided to or by the rest of the world without a quid pro quo, such as donations and grants; and all changes in residents' claims on and liabilities to nonresidents that arise from economic transactions. All transactions are recorded twice - once as a credit and once as a debit. In principle the net balance should be zero, but in practice the accounts often do not balance, requiring inclusion of a balancing item, net errors and omissions.\nThe concepts and definitions underlying the data are based on the sixth edition of the International Monetary Fund's (IMF) Balance of Payments Manual." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "BX.GSR.CCIS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The balance of payments records an economy's transactions with the rest of the world. Balance of payments accounts are divided into two groups: the current account, which records transactions in goods, services, income, and current transfers, and the capital and financial account, which records capital transfers, acquisition or disposal of non-produced, nonfinancial assets, and transactions in financial assets and liabilities." }, { "id": "IndicatorName", "value": "ICT service exports (% of service exports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Discrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded." }, { "id": "Longdefinition", "value": "Information and communication technology service exports include computer and communications services (telecommunications and postal and courier services) and information services (computer data and news-related service transactions)." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The balance of payments (BoP) is a double-entry accounting system that shows all flows of goods and services into and out of an economy; all transfers that are the counterpart of real resources or financial claims provided to or by the rest of the world without a quid pro quo, such as donations and grants; and all changes in residents' claims on and liabilities to nonresidents that arise from economic transactions. All transactions are recorded twice - once as a credit and once as a debit. In principle the net balance should be zero, but in practice the accounts often do not balance, requiring inclusion of a balancing item, net errors and omissions.\n\nThe concepts and definitions underlying the data are based on the sixth edition of the International Monetary Fund's (IMF) Balance of Payments Manual." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "BX.GSR.CMCP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Communications, computer, etc. (% of service exports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Communications, computer, information, and other services cover international telecommunications; computer data; news-related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; personal, cultural, and recreational services; manufacturing services on physical inputs owned by others; and maintenance and repair services and government services not included elsewhere. This indicator is expressed as a percentage of service exports which are services provided by residents to non-residents." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "BX.GSR.COMM.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Communications services, exports (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" } ], "source_id": "57" }, { "id": "BX.GSR.FCTY.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Primary income receipts (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Primary income receipts refer to employee compensation paid to resident workers working abroad and investment income (receipts on direct investment, portfolio investment, other investments, and receipts on reserve assets). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BX.GSR.FINS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Financial services, exports (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" } ], "source_id": "57" }, { "id": "BX.GSR.GNFS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Exports of goods and services (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Discrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded." }, { "id": "Longdefinition", "value": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BX.GSR.INCL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Exports of goods, services, primary income and personal transfers (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Exports of goods, services, primary income and personal transfers are the total value of goods and services exported as well as primary income receipts and personal transfers received. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" } ], "source_id": "57" }, { "id": "BX.GSR.INSF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Insurance and financial services (% of service exports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Insurance and financial services cover various types of insurance provided to nonresidents by resident insurance enterprises and vice versa, and financial intermediary and auxiliary services (except those of insurance enterprises and pension funds) exchanged between residents and nonresidents. This indicator is expressed as a percentage of service exports which are services provided by residents to non-residents." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "BX.GSR.INSU.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Insurance services, exports (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" } ], "source_id": "57" }, { "id": "BX.GSR.MRCH.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Goods exports (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Exports of goods occur when there are changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. Merchanting is reclassified from services to goods." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BX.GSR.NFSV.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Service exports (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Exports of services are services provided by residents to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. Manufacturing services on physical inputs owned by others (goods for processing in BPM5) and maintenance and repair services n.i.e. are reclassified from goods to services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BX.GSR.ROYL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Charges for the use of intellectual property, receipts (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Charges for the use of proprietary rights (such as patents, trademarks, copyrights, industrial processes and designs including trade secrets, franchises), and charges for licenses to reproduce or distribute (or both) intellectual property embodied in produced originals or prototypes (such as copyrights on books and manuscripts, computer software, cinematographic works, and sound recordings) and related rights (such as for live performances and television, cable, or satellite broadcast). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BX.GSR.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Exports of goods, services and primary income (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. Primary income represents the return that accrues to institutional units for their contribution to the production process or for the provision of financial assets and renting natural resources to other institutional units. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BX.GSR.TRAN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Transport services, exports (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Transport covers all transport services (sea, air, land, internal waterway, pipeline, space and electricity transmission) performed by residents of one economy for those of another and involving the carriage of passengers, the movement of goods (freight), rental of carriers with crew, and related support and auxiliary services. Also included are postal and courier services. Excluded are freight insurance (included in insurance services); goods procured in ports by nonresident carriers (included in goods); maintenance and repairs on transport equipment (included in maintenance and repair services n.i.e.); and repairs of railway facilities, harbors, and airfield facilities (included in construction). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" } ], "source_id": "57" }, { "id": "BX.GSR.TRAN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Transport services (% of service exports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Transport services covers the process of carriage of people and objects from one location to another as well as related supporting and auxiliary services. Also included are postal and courier services. This indicator is expressed as a percentage of service exports which are services provided by residents to non-residents." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "BX.GSR.TRVL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Travel services, exports (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Travel covers goods and services acquired from an economy by travelers for their own use during visits of less than one year in that economy for either business or personal purposes. Travel includes local transport (i.e., transport within the economy being visited and provided by a resident of that economy), but excludes international transport (which is included in passenger transport. Travel also excludes goods for resale, which are included in general merchandise. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" } ], "source_id": "57" }, { "id": "BX.GSR.TRVL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Travel services (% of service exports, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Travel services cover goods and services for own use or to give away acquired from an economy by nonresidents during visits to that economy, or acquired from other economies by residents during visits to these other economies. This indicator is expressed as a percentage of service exports which are services provided by residents to non-residents." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "BX.KLT.DINV.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "Note: Data starting from 2005 are based the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "IndicatorName", "value": "Foreign direct investment, net inflows in reporting economy (DRS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Foreign direct investment (net) shows the net change in foreign investment in the reporting country. Foreign direct investment is defined as investment that is made to acquire a lasting management interest (usually of 10 percent of voting stock) in an enterprise operating in a country other than that of the investor (defined according to residency), the investor's purpose being an effective voice in the management of the enterprise. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows net inflows in the reporting economy. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Foreign direct investment is net inflows of investment to acquire a lasting interest in or management control over an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvested earnings, other long-term capital, and short-term capital, as shown in the balance of payments. Data are in current U.S. dollars." }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments, supplemented by data from United Nations Conference on Trade and Development and official national sources. Data starting from 2005 are based the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "57" }, { "id": "BX.KLT.DINV.CD.WD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits." }, { "id": "IndicatorName", "value": "Foreign direct investment, net inflows (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intra-unit flows of goods and services.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available." }, { "id": "Longdefinition", "value": "Foreign direct investment refers to direct investment equity flows in the reporting economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. Data are in current U.S. dollars." }, { "id": "Othernotes", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Balance of Payments database, International Monetary Fund (IMF), note: International Monetary Fund, Balance of Payments database, supplemented by data from the United Nations Conference on Trade and Development and official national sources.;\nUN Conference on Trade and Development (UNCTAD);\nOfficial national sources" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Foreign direct investment (FDI) data are supplemented by the World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources.\n\nThe internationally accepted definition of FDI (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results in a direct investment relationship, based on control and influence. Distinguished from other kinds of international investment, FDI is made to establish a lasting interest in or effective management control over an enterprise in another country. A lasting interest in an investment enterprise typically involves establishing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. BoP refers to Balance of Payments." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "57" }, { "id": "BX.KLT.DINV.WD.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits." }, { "id": "IndicatorName", "value": "Foreign direct investment, net inflows (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intra-unit flows of goods and services.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available." }, { "id": "Longdefinition", "value": "Foreign direct investment are the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows net inflows (new investment inflows less disinvestment) in the reporting economy from foreign investors, and is divided by GDP." }, { "id": "Othernotes", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Financial Statistics and Balance of Payments databases, International Monetary Fund (IMF);\nInternational Debt Statistics, World Bank (WB);\nWorld Bank GDP estimates, World Bank (WB);\nOECD GDP estimates, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Foreign direct investment (FDI) data are supplemented by the World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources.\n\nThe internationally accepted definition of FDI (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results in a direct investment relationship, based on control and influence. Distinguished from other kinds of international investment, FDI is made to establish a lasting interest in or effective management control over an enterprise in another country. A lasting interest in an investment enterprise typically involves establishing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. BoP refers to Balance of Payments." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "57" }, { "id": "BX.KLT.DREM.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Primary income on FDI (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Primary income on foreign direct investment covers payments of direct investment income (debit side), which consist of income on equity (dividends, branch profits, and reinvested earnings) and income on the intercompany debt (interest). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Primary income on foreign direct investment covers payments of direct investment income (debit side), which consist of income on equity (dividends, branch profits, and reinvested earnings) and income on the intercompany debt (interest). Data are in current U.S. dollars." }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "57" }, { "id": "BX.PEF.TOTL.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Developmentrelevance", "value": "Private financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits." }, { "id": "Generalcomments", "value": "Note: Data starting from 2005 are based the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "IndicatorName", "value": "Portfolio investment, equity (DRS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Portfolio equity includes net inflows from equity securities other than those recorded as direct investment and including shares, stocks, depository receipts (American or global), and direct purchases of shares in local stock markets by foreign investors. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Portfolio equity includes net inflows from equity securities other than those recorded as direct investment and including shares, stocks, depository receipts (American or global), and direct purchases of shares in local stock markets by foreign investors. Data are in current U.S. dollars." }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook. Data starting from 2005 are based the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF).\n\nPortfolio equity investment is defined as cross-border transactions and positions involving equity securities, other than those included in direct investment or reserve assets. Equity securities are equity instruments that are negotiable and designed to be traded, usually on organized exchanges or \"over the counter.\" The negotiability of securities facilitates trading, allowing securities to be held by different parties during their lives. Negotiability allows investors to diversify their portfolios and to withdraw their investment readily. Included in portfolio investment are investment fund shares or units (that is, those issued by investment funds) that are evidenced by securities and that are not reserve assets or direct investment. Although they are negotiable instruments, exchange-traded financial derivatives are not included in portfolio investment because they are in their own category." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "57" }, { "id": "BX.PEF.TOTL.CD.WD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits." }, { "id": "IndicatorName", "value": "Portfolio equity, net inflows (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Portfolio investors typically have less of a role in the decision making of the enterprise with potentially important implications for future flows and for the volatility of the price and volume of positions. Portfolio investment differs from other investment in that it provides a direct way to access financial markets, and thus it can provide liquidity and flexibility. It is associated with financial markets and with their specialized service providers, such as exchanges, dealers, and regulators. The nature of financial derivatives as instruments through which risk is traded in its own right in financial markets sets them apart from other types of investment. Whereas other instruments may also have risk transfer elements, these other instruments also provide financial or other resources.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available." }, { "id": "Longdefinition", "value": "Portfolio equity includes net inflows from equity securities other than those recorded as direct investment and including shares, stocks, depository receipts (American or global), and direct purchases of shares in local stock markets by foreign investors. Data are in current U.S. dollars." }, { "id": "Othernotes", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments database, International Monetary Fund (IMF);\nInternational Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF).\n\nPortfolio equity investment is defined as cross-border transactions and positions involving equity securities, other than those included in direct investment or reserve assets. Equity securities are equity instruments that are negotiable and designed to be traded, usually on organized exchanges or \"over the counter.\" The negotiability of securities facilitates trading, allowing securities to be held by different parties during their lives. Negotiability allows investors to diversify their portfolios and to withdraw their investment readily. Included in portfolio investment are investment fund shares or units (that is, those issued by investment funds) that are evidenced by securities and that are not reserve assets or direct investment. Although they are negotiable instruments, exchange-traded financial derivatives are not included in portfolio investment because they are in their own category." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "57" }, { "id": "BX.TRF.CURR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies." }, { "id": "IndicatorName", "value": "Secondary income receipts (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Secondary income refers to transfers recorded in the balance of payments whenever an economy provides or receives goods, services, income, or financial items without a quid pro quo. All transfers not considered to be capital are current. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BX.TRF.OFDC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "IndicatorName", "value": "Secondary income, general government, receipts (BoP, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Secondary income refers to transfers recorded in the balance of payments whenever an economy provides or receives goods, services, income, or financial items without a quid pro quo. All transfers not considered to be capital are current. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "57" }, { "id": "BX.TRF.PWKR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies." }, { "id": "IndicatorName", "value": "Personal transfers, receipts (current US$, BoP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Personal transfers are current transfers, in cash or in kind, received by resident households from non-resident households. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1968-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n(a) the international investment position (IIP)—a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n(b) the balance of payments—a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n(c) the other changes in financial assets and liabilities accounts—a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "BX.TRF.PWKR.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\nIn most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers. Globally, the number of refugees at end 2010 was 10.55 million, including 597,300 people considered by the United Nations High Commissioner for Refugees (UNHCR) to be in a refugee-like situation; developing countries hosted 8.5 million refugees, or 80 percent of the global refugee population.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights." }, { "id": "IndicatorName", "value": "Personal remittances, received (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Remittance transactions have grown in importance over the past decade. In a number of developing economies, receipts of remittances have become an important and stable source of funds that exceeds receipts from exports of goods and services or from financial inflows on foreign direct investment. But the quality of statistical remittance data is not high. Remittances are a challenge to measure because of their nature. They are heterogeneous with numerous small transactions conducted by individuals through a wide variety of channels: formal channels, such as electronic wire, or through informal channels, such as cash or goods carried across borders. The large number of remittance transactions and the multitude of channels pose challenges to the compilation of comprehensive statistics. The small size of individual transactions means that they often go undetected by typical data source systems, although the aggregate level of transactions may be substantial.\n\nBecause of difficulties in obtaining data on informal remittance transactions, the remittance transactions undertaken through informal channels are sometimes not well covered in current balance of payments data. As a result, even though direct measurement of remittances - through transactions reporting or surveys - may be considered preferable if feasible, some countries instead combine different sources and estimation methods to achieve better coverage, by using direct measurements where practical and supplemented estimates where they are not. Model-based approaches are used in some countries as they are flexible. Compilers can design models to fill gaps in data sources or to provide global totals.\n\nHowever, only reliable input data can lead to sound estimates, regardless of the sophistication of an estimation method or econometric model. Indirect data are converted to remittance estimates using a set of assumptions. These assumptions should be plausible, but it is often not possible to test or verify these assumptions and also the results in practice." }, { "id": "Longdefinition", "value": "Personal remittances comprise personal transfers and compensation of employees. Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual: personal transfers and compensation of employees. Data are in current U.S. dollars." }, { "id": "Othernotes", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "IMF balance of payments data, International Monetary Fund (IMF);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The two main components of personal remittances, \"personal transfers\" and \"compensation of employees\", are items in the balance of payments (BPM6) framework. Both of these standard components are recorded in the current account. \n\"Personal transfers\", a new item in the Balance of Payments (BPM6) represents a broader definition of worker remittances. Personal transfers include all current transfers in cash or in kind between resident and nonresident individuals, independent of the source of income of the sender (irrespective of whether the sender receives income from labor, entrepreneurial or property income, social benefits, and any other types of transfers; or disposes assets) and the relationship between the households (irrespective of whether they are related or unrelated individuals).\n\nCompensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Compensation of employees represents remuneration in return for the labor input to the production process contributed by an individual in an employer-employee relationship with the enterprise. Compensation of employees is recorded gross and includes amounts paid by the employee as taxes or for other purposes in the economy where the work is performed. Compensation of employees has three main components: wages and salaries in cash, wages and salaries in kind, and employers' social contributions." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "57" }, { "id": "BX.TRF.PWKR.DT.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Personal remittances, received (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Personal remittances comprise personal transfers and compensation of employees. Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual: personal transfers and compensation of employees." }, { "id": "Othernotes", "value": "Note: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nIMF balance of payments data, International Monetary Fund (IMF);\nWorld Bank GDP estimates, World Bank (WB);\nOECD GDP estimates, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Balance of Payments (BOP) from the International Monetary Fund (IMF) serves as the primary source of information for personal transfers, which are categorized under secondary income, and for the compensation of employees, classified as primary income of the current account. Depending on data availability, references may be made to quarterly or annual figures.\n\nInformation from government agencies such as central banks and national statistical offices further complements the BOP data. When countries have missing data for certain years, this is addressed using methods like Last Observation Carried Forward (LOCF) and Next Observation Carried Backward (NOCB). If disaggregated data is unavailable, estimates are created based on historical ratios and trends.\n\nThe data is presented as a percentage of \"GDP (current US$)\" (NY.GDP.MKTP.CD), which is sourced from the World Bank's national accounts data and the OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "57" }, { "id": "CC.EST", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Control of Corruption: Estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them. The WGI measures six dimensions of governance: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. \n\nControl of Corruption captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as \"capture\" of the state by elites and private interests.\n\nEstimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5." }, { "id": "Othernotes", "value": "The UCM assigns greater weight to data sources that tend to be more strongly correlated with each other. While this weighting improves the statistical precision of the aggregate indicators, it typically does not affect very much the ranking of countries on the aggregate indicators. The composite measures of governance generated by the UCM are in units of a standard normal distribution, with mean zero, standard deviation of one, and running from approximately -2.5 to 2.5, with higher values corresponding to better governance. The data is also reported in percentile rank terms, ranging from 0 (lowest rank) to 100 (highest rank).\n\nStatistical concept(s): The six aggregate indicators are reported in two ways: (1) in their standard normal units, ranging from approximately -2.5 to 2.5, and (2) in percentile rank terms from 0 to 100, with higher values corresponding to better outcomes.\n\nA key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). A key feature of the methodology is that it generates margins of error for each governance estimate. These margins of error need to be taken into account when making comparisons across countries and over time. \n\nEach of the six aggregate WGI measures are constructed by averaging together data from the underlying sources that correspond to the concept of governance being measured. This is done in the three steps:\n\nSTEP 1: Assigning data from individual sources to the six aggregate indicators. Individual questions from the underlying data sources are assigned to each of the six aggregate indicators. For example, a firm survey question on the regulatory environment would be assigned to Regulatory Quality, or a measure of press freedom would be assigned to Voice and Accountability. The individual variables used in the WGI and how they are assigned to the six aggregate indicators, can be found on the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators]. Note that not all of the data sources cover all countries, and so the aggregate governance scores are based on different sets of underlying data for different countries.\n\nSTEP 2: Rescaling of the individual source data to run from 0 to 1. The questions from the individual data sources are first rescaled to range from 0 to 1, with higher values corresponding to better outcomes. If, for example, a survey question asks for responses on a scale from a minimum of 1 to a maximum of 4, we rescale a score of 2 as (2-min)/(max-min)=(2-1)/3=0.33. When an individual data source provides more than one question relating to a particular dimension of governance, the rescaled scores are averaged together.\nThe 0-1 rescaled data from the individual sources are available interactively through the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators/interactive-data-access] and in the data files for each individual source [https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2]. Although nominally in the same 0-1 units, this rescaled data is not necessarily comparable across sources. For example, one data source might use a 0-10 scale but in practice most scores are clustered between 6 and 10, while another data source might also use a 0-10 scale but have responses spread out over the entire range. While the max-min rescaling above does not correct for this source of non-comparability, the procedure used to construct the aggregate indicators does (see below).\n\nSTEP 3: Using an Unobserved Components Model (UCM) to construct a weighted average of the individual indicators for each source. A statistical tool known as an Unobserved Components Model (UCM) is used to make the 0-1 rescaled data comparable across sources, and then to construct a weighted average of the data from each source for each country. The UCM assumes that the observed data from each source are a linear function of the unobserved level of governance, plus an error term. This linear function is different for different data sources, and so corrects for the remaining non-comparability of units of the rescaled data noted above. The resulting estimates of governance are a weighted average of the data from each source, with weights reflecting the pattern of correlation among data sources. The weights applied to the component indicators." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Units of a standard normal distribution (between -2.5 and 2.5, approximately)" } ], "source_id": "57" }, { "id": "CC.NO.SRC", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Control of Corruption: Number of Sources" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data sources based on expert assessments have advantages and disadvantages relative to surveys. One advantage is that they lend themselves well to cross-country comparisons, as their methodologies are explicitly designed for this purpose. Expert assessments can also provide more granular technical assessments, for example on the quality of specific types of public institutions, that would be more difficult for a typical household or firm survey respondent to provide and informed view on. Expert assessments also are less likely to be affected by respondent reticence, a concern in household and firm surveys where respondents may be unwilling to give candid responses to sensitive questions about corruption or other dimensions of governance, particularly in countries where governance is weak. \n\nOn the other hand, a shortcoming of expert assessments is that they reflect the views of a narrower set of respondents than household or firm surveys. It also is possible that the ratings provided by one expert assessment to some extent reflect the views of other expert assessments, so that each assessment does not bring completely independent information on the underlying governance concept of interest. To guard against this, the WGI do not use expert assessments that are explicitly based on other existing data sources.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nNumber of sources indicates the number of underlying data sources on which the aggregate estimate is based.\n\nThe WGI are based on a large number of different data sources, capturing the views and experiences of survey respondents and experts in the public and private sectors, as well as various NGOs. These data sources include: (a) surveys of households and firms (e.g. Afrobarometer surveys, Gallup World Poll, and Global Competitiveness Report survey), (b) NGOs (e.g. Global Integrity, Freedom House, Reporters Without Borders), (c) commercial business information providers (e.g. Economist Intelligence Unit, S&P Global, Political Risk Services), and (d) public sector organizations (e.g. CPIA assessments of World Bank and regional development banks). \n\nControl of corruption captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as \"capture\" of the state by elites and private interests." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI compile and summarize information from over 30 existing data sources that report the views and experiences of citizens, entrepreneurs, and experts in the public, private and NGO sectors from around the world, on the quality of various aspects of governance.\n\n•\tThe data sources must provide subjective perceptions of relevant dimensions of governance, as the WGI are based exclusively on this type of data.\n•\tThe sources must provide original primary data produced using a well-defined methodology.\n•\tThe data sources must cover multiple countries, so that cross-country comparisons are possible.\n•\tThe data sources must be updated regularly, ideally every year, although some WGI data sources are updated once every two or three years.\n\nThe WGI draw on four different types of source data:\n\n•\tSurveys of households and firms, including the Afrobarometer surveys, Gallup World Poll, and Global Competitiveness Report survey,\n•\tCommercial business information providers, including the Economist Intelligence Unit, S&P Global, and Political Risk Services,\n•\tNon-governmental organizations, including Global Integrity, Freedom House, Reporters Without Borders, and\n•\tPublic sector organizations, including the Country Policy and Institutional Assessments (CPIA) assessments of World Bank and regional development banks.\n\nFor the detailed list of sources, please refer to: https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2 \nStatistical concept(s): Number of sources indicates the number of underlying data sources on which the aggregate estimate is based.\n\nVariables from the data sources are assigned to each of these six governance dimensions. For example, an assessment of the quality of the bureaucracy would be assigned to Government Effectiveness, a question about confidence in the police or the courts would be assigned to Rule of Law, and a question about the perceived likelihood of having to pay a bribe would be assigned to Control of Corruption. In some cases, a single data source will have multiple questions that can be assigned to the same dimension. In this case, the WGI use the average across all relevant questions from that data source. In addition each question from each data source is assigned to only one of the six governance dimensions, selecting the dimension that best matches the content of the question." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Number [NUMBER]" } ], "source_id": "57" }, { "id": "CC.PER.RNK", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Control of Corruption: Percentile Rank" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nControl of Corruption captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as \"capture\" of the state by elites and private interests. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI." }, { "id": "Othernotes", "value": "The UCM assigns greater weight to data sources that tend to be more strongly correlated with each other. While this weighting improves the statistical precision of the aggregate indicators, it typically does not affect very much the ranking of countries on the aggregate indicators. The composite measures of governance generated by the UCM are in units of a standard normal distribution, with mean zero, standard deviation of one, and running from approximately -2.5 to 2.5, with higher values corresponding to better governance. The data is also reported in percentile rank terms, ranging from 0 (lowest rank) to 100 (highest rank).\n\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). A key feature of the methodology is that it generates margins of error for each governance estimate. These margins of error need to be taken into account when making comparisons across countries and over time. \n\nEach of the six aggregate WGI measures are constructed by averaging together data from the underlying sources that correspond to the concept of governance being measured. This is done in the three steps:\n\nSTEP 1: Assigning data from individual sources to the six aggregate indicators. Individual questions from the underlying data sources are assigned to each of the six aggregate indicators. For example, a firm survey question on the regulatory environment would be assigned to Regulatory Quality, or a measure of press freedom would be assigned to Voice and Accountability. The individual variables used in the WGI and how they are assigned to the six aggregate indicators, can be found on the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators]. Note that not all of the data sources cover all countries, and so the aggregate governance scores are based on different sets of underlying data for different countries.\n\nSTEP 2: Rescaling of the individual source data to run from 0 to 1. The questions from the individual data sources are first rescaled to range from 0 to 1, with higher values corresponding to better outcomes. If, for example, a survey question asks for responses on a scale from a minimum of 1 to a maximum of 4, we rescale a score of 2 as (2-min)/(max-min)=(2-1)/3=0.33. When an individual data source provides more than one question relating to a particular dimension of governance, the rescaled scores are averaged together.\nThe 0-1 rescaled data from the individual sources are available interactively through the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators/interactive-data-access] and in the data files for each individual source [https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2]. Although nominally in the same 0-1 units, this rescaled data is not necessarily comparable across sources. For example, one data source might use a 0-10 scale but in practice most scores are clustered between 6 and 10, while another data source might also use a 0-10 scale but have responses spread out over the entire range. While the max-min rescaling above does not correct for this source of non-comparability, the procedure used to construct the aggregate indicators does (see below).\n\nSTEP 3: Using an Unobserved Components Model (UCM) to construct a weighted average of the individual indicators for each source. A statistical tool known as an Unobserved Components Model (UCM) is used to make the 0-1 rescaled data comparable across sources, and then to construct a weighted average of the data from each source for each country. The UCM assumes that the observed data from each source are a linear function of the unobserved level of governance, plus an error term. This linear function is different for different data sources, and so corrects for the remaining non-comparability of units of the rescaled data noted above. The resulting estimates of governance are a weighted average of the data from each source, with weights reflecting the pattern of correlation among data sources. The weights applied to the component indicators." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile" } ], "source_id": "57" }, { "id": "CC.PER.RNK.LOWER", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Control of Corruption: Percentile Rank, Lower Bound of 90% Confidence Interval" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nControl of Corruption captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as \"capture\" of the state by elites and private interests. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Lower refers to lower bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether.\n\nThe standard deviations are essential to the interpretation of the WGI. It often is more appropriate to think of the WGI methodology as identifying a statistically likely range of values for the unobserved “true” level of governance in a country. For example, the assumption of normality tells us that there is a 90 percent probability that the true unobserved level of governance conditional on the available data for a country is in a range given by plus or minus 1.64 standard deviations around the estimate of governance. These confidence intervals are informally referred to as the “margin of error” around the estimates of governance.\n\nThese 90 percent confidence interval are also reported in percentile rank terms (the percentile rank among all country estimates of governance, of the upper and lower bounds of the 90 percent confidence interval for each country).\n\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Lower refers to lower bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile" } ], "source_id": "57" }, { "id": "CC.PER.RNK.UPPER", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Control of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nControl of Corruption captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as \"capture\" of the state by elites and private interests. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Upper refers to upper bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether.\n\nThe standard deviations are essential to the interpretation of the WGI. It often is more appropriate to think of the WGI methodology as identifying a statistically likely range of values for the unobserved “true” level of governance in a country. For example, the assumption of normality tells us that there is a 90 percent probability that the true unobserved level of governance conditional on the available data for a country is in a range given by plus or minus 1.64 standard deviations around the estimate of governance. These confidence intervals are informally referred to as the “margin of error” around the estimates of governance.\n\nThese 90 percent confidence interval are also reported in percentile rank terms (the percentile rank among all country estimates of governance, of the upper and lower bounds of the 90 percent confidence interval for each country).\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Upper refers to upper bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile Rank" } ], "source_id": "57" }, { "id": "CC.STD.ERR", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Control of Corruption: Standard Error" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data sources based on expert assessments have advantages and disadvantages relative to surveys. One advantage is that they lend themselves well to cross-country comparisons, as their methodologies are explicitly designed for this purpose. Expert assessments can also provide more granular technical assessments, for example on the quality of specific types of public institutions, that would be more difficult for a typical household or firm survey respondent to provide and informed view on. Expert assessments also are less likely to be affected by respondent reticence, a concern in household and firm surveys where respondents may be unwilling to give candid responses to sensitive questions about corruption or other dimensions of governance, particularly in countries where governance is weak. \n\nOn the other hand, a shortcoming of expert assessments is that they reflect the views of a narrower set of respondents than household or firm surveys. It also is possible that the ratings provided by one expert assessment to some extent reflect the views of other expert assessments, so that each assessment does not bring completely independent information on the underlying governance concept of interest. To guard against this, the WGI do not use expert assessments that are explicitly based on other existing data sources.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nStandard error indicates the precision of the estimate of governance. Larger values of the standard error indicate less precise estimates. \n\nA 90 percent confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error.\n\nControl of Corruption captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as \"capture\" of the state by elites and private interests." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). The six aggregate indicators are reported in two ways : (1) in their standard normal units, ranging from approximately -2.5 to 2.5, and (2) in percentile rank terms from 0 to 100, with higher values corresponding to better outcomes.\n\nA key feature of the WGI is that all country scores are accompanied by standard errors. These margins of error need to be taken into account when making comparisons across countries and over time. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether. \n\nPlease see more information at: https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#1\nStatistical concept(s): Standard error indicates the precision of the estimate of governance. Larger values of the standard error indicate less precise estimates. A 90 percent confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Standard error" } ], "source_id": "57" }, { "id": "CM.MKT.INDX.ZG", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information. This lowers transaction costs, which in turn improves resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks. Open economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets." }, { "id": "IndicatorName", "value": "S&P Global Equity Indices (annual % change)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Markets included in Standard & Poor's emerging markets category vary widely in level of development. Thus, it is best to look at the entire category to identify the most significant market trends. It is also useful to remember that stock market trends may be distorted by currency conversions, especially when a currency has registered a significant devaluation.\nIndex methodology details are available on the S&P Global website: https://www.spglobal.com/spdji/en/indices/equity/sp-global-bmi/#overview" }, { "id": "Longdefinition", "value": "S&P Global Equity Indices measure the U.S. dollar price change in the stock markets covered by the S&P BMI country indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2022" }, { "id": "Source", "value": "S&P Global BMI, S&P Dow Jones Indices, uri: https://www.spglobal.com/spdji/en/index-family/equity/global-equity/sp-global-bmi/#overview" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The S&P Global Broad Market Index (BMI) series is a global index suite with a transparent, modular structure that has been fully float-adjusted since 1989. It includes more than 14,000 stocks from 25 developed and 24 emerging markets. It is a rules-based index that measures global stock market performance. The index covers all publicly listed equities with float-adjusted market values of US$ 100 million or more and that meet minimum liquidity criteria measured by median daily value traded figures. The S&P Global BMI is made up of the S&P Developed BMI and S&P Emerging BMI. Additional information about methodology can be found on the S&P Global website: https://www.spglobal.com/spdji/en/documents/methodologies/methodology-sp-global-bmi-sp-ifci-indices.pdf\n\nThe percentage changes for France, Germany, Japan, the United Kingdom, and the United States refer to local stock market prices: CAC40, DAX, Nikkei, FTSE, S&P500.\nStatistical concept(s): Ratios of end-of-period levels in U.S. dollars over previous end-of-period values in U.S. dollars times 100." }, { "id": "Topic", "value": "Financial Sector: Capital markets" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "CM.MKT.LCAP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Stock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations." }, { "id": "IndicatorName", "value": "Market capitalization of listed domestic companies (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards." }, { "id": "Longdefinition", "value": "Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates." }, { "id": "Othernotes", "value": "Stock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1975-2025" }, { "id": "Source", "value": "World Federation of Exchanges database, World Federation of Exchanges (WFE)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Market capitalization figures include: shares of listed domestic companies; shares of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange); common and preferred shares of domestic companies; and shares without voting rights. Market capitalization figures exclude: collective investment funds ; rights, warrants, ETFs, convertible instruments ; options, futures ; foreign listed shares other than exclusively listed ones; companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status; and companies admitted to trading (i.e., companies whose shares are traded at the exchange but not listed at the exchange).\nStatistical concept(s): Market capitalization figures include: shares of listed domestic companies; shares of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange); common and preferred shares of domestic companies; and shares without voting rights. Market capitalization figures exclude: collective investment funds ; rights, warrants, ETFs, convertible instruments ; options, futures ; foreign listed shares other than exclusively listed ones; companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status; and companies admitted to trading (i.e., companies whose shares are traded at the exchange but not listed at the exchange)." }, { "id": "Topic", "value": "Financial Sector: Capital markets" }, { "id": "Unitofmeasure", "value": "stocks, bonds, options contracts, futures contracts and commodities." } ], "source_id": "57" }, { "id": "CM.MKT.LCAP.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Stock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations." }, { "id": "IndicatorName", "value": "Market capitalization of listed domestic companies (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards." }, { "id": "Longdefinition", "value": "Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values." }, { "id": "Othernotes", "value": "Stock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1975-2024" }, { "id": "Source", "value": "World Federation of Exchanges database, World Federation of Exchanges (WFE)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Market capitalization figures include: shares of listed domestic companies; shares of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange); common and preferred shares of domestic companies; and shares without voting rights. Market capitalization figures exclude: collective investment funds ; rights, warrants, ETFs, convertible instruments ; options, futures ; foreign listed shares other than exclusively listed ones; companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status; and companies admitted to trading (i.e., companies whose shares are traded at the exchange but not listed at the exchange).\nStatistical concept(s): Market capitalization figures include: shares of listed domestic companies; shares of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange); common and preferred shares of domestic companies; and shares without voting rights. Market capitalization figures exclude: collective investment funds ; rights, warrants, ETFs, convertible instruments ; options, futures ; foreign listed shares other than exclusively listed ones; companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status; and companies admitted to trading (i.e., companies whose shares are traded at the exchange but not listed at the exchange)." }, { "id": "Topic", "value": "Financial Sector: Capital markets" }, { "id": "Unitofmeasure", "value": "End-of-year total market capitalization of listed domestic companies, divided by gross domestic product, expressed as a percentage." } ], "source_id": "57" }, { "id": "CM.MKT.LDOM.NO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Stock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations." }, { "id": "IndicatorName", "value": "Listed domestic companies, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards." }, { "id": "Longdefinition", "value": "Listed domestic companies, including foreign companies which are exclusively listed, are those which have shares listed on an exchange at the end of the year. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status, are excluded. A company with several classes of shares is counted once. Only companies admitted to listing on the exchange are included." }, { "id": "Othernotes", "value": "Stock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1975-2025" }, { "id": "Source", "value": "World Federation of Exchanges database, World Federation of Exchanges (WFE)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A company is considered domestic when it is incorporated in the same country as where the exchange is located. The only exception is the case of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange as defined in the domestic market capitalization definition).\nStatistical concept(s): A company is considered domestic when it is incorporated in the same country as where the exchange is located. The only exception is the case of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange as defined in the domestic market capitalization definition)." }, { "id": "Topic", "value": "Financial Sector: Capital markets" }, { "id": "Unitofmeasure", "value": "End-of-year total market capitalization of listed domestic companies, divided by gross domestic product, expressed as a percentage. (percentile)" } ], "source_id": "57" }, { "id": "CM.MKT.TRAD.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Stock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations." }, { "id": "IndicatorName", "value": "Stocks traded, total value (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards. Only EOB trades are included in the total value of shares traded." }, { "id": "Longdefinition", "value": "The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates." }, { "id": "Othernotes", "value": "Stock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1975-2025" }, { "id": "Source", "value": "World Federation of Exchanges database, World Federation of Exchanges (WFE)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The value of shares traded represent the transfer of ownership effected automatically through the exchange's electronic order book (EOB), where orders placed by trading members are usually exposed to all market users and automatically matched according to precise rules set up by the exchange, generally on a price/time priority basis. For data before 2001, the WFE used two different approaches for the collection of trading data, depending on the individual stock exchange's market organization and rules. The first approach is the Trading System View (TSV). Stock exchanges adopting this view count only those transactions which pass through their trading system or trading floor. The TSV is generally adopted by exchanges which operate a centralized order book (order-driven market). Trades done by their members off the exchange are not included. The second approach is the Regulated Environment View (REV). Stock exchanges in this category include all transactions subject to supervision by the market authority, including transactions made by members, and sometimes non-members, on outside trading systems and transactions into foreign markets. Figures reported under the REV approach will be higher than those reported under the TSV approach.\nStatistical concept(s): The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates. The value of shares traded represent the transfer of ownership effected automatically through the exchange's electronic order book (EOB), where orders placed by trading members are usually exposed to all market users and automatically matched according to precise rules set up by the exchange, generally on a price/time priority basis. https://data.worldbank.org/indicator/CM.MKT.TRAD.CD" }, { "id": "Topic", "value": "Financial Sector: Capital markets" }, { "id": "Unitofmeasure", "value": "stocks, bonds, options contracts, futures contracts and commodities" } ], "source_id": "57" }, { "id": "CM.MKT.TRAD.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Stock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations." }, { "id": "IndicatorName", "value": "Stocks traded, total value (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards. Only EOB trades are included in the total value of shares traded." }, { "id": "Longdefinition", "value": "The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values." }, { "id": "Othernotes", "value": "Stock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1975-2024" }, { "id": "Source", "value": "World Federation of Exchanges database, World Federation of Exchanges (WFE)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The value of shares traded represent the transfer of ownership effected automatically through the exchange's electronic order book (EOB), where orders placed by trading members are usually exposed to all market users and automatically matched according to precise rules set up by the exchange, generally on a price/time priority basis. For data before 2001, the WFE used two different approaches for the collection of trading data, depending on the individual stock exchange's market organization and rules. The first approach is the Trading System View (TSV). Stock exchanges adopting this view count only those transactions which pass through their trading system or trading floor. The TSV is generally adopted by exchanges which operate a centralized order book (order-driven market). Trades done by their members off the exchange are not included. The second approach is the Regulated Environment View (REV). Stock exchanges in this category include all transactions subject to supervision by the market authority, including transactions made by members, and sometimes non-members, on outside trading systems and transactions into foreign markets. Figures reported under the REV approach will be higher than those reported under the TSV approach.\nStatistical concept(s): The value of shares traded represent the transfer of ownership effected automatically through the exchange's electronic order book (EOB), where orders placed by trading members are usually exposed to all market users and automatically matched according to precise rules set up by the exchange, generally on a price/time priority basis. For data before 2001, the WFE used two different approaches for the collection of trading data, depending on the individual stock exchange's market organization and rules. The first approach is the Trading System View (TSV). Stock exchanges adopting this view count only those transactions which pass through their trading system or trading floor. The TSV is generally adopted by exchanges which operate a centralized order book (order-driven market). Trades done by their members off the exchange are not included. The second approach is the Regulated Environment View (REV). Stock exchanges in this category include all transactions subject to supervision by the market authority, including transactions made by members, and sometimes non-members, on outside trading systems and transactions into foreign markets. Figures reported under the REV approach will be higher than those reported under the TSV approach." }, { "id": "Topic", "value": "Financial Sector: Capital markets" }, { "id": "Unitofmeasure", "value": "stocks, bonds, options contracts, futures contracts and commodities. (total number of shares traded, both domestic and foreign, multiplied by respective prices)" } ], "source_id": "57" }, { "id": "CM.MKT.TRNR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Stock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations." }, { "id": "IndicatorName", "value": "Stocks traded, turnover ratio of domestic shares (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards. Only domestic shares are used in order to be consistent with domestic market capitalization." }, { "id": "Longdefinition", "value": "Turnover ratio is the value of domestic shares traded divided by their market capitalization. The value is annualized by multiplying the monthly average by 12." }, { "id": "Othernotes", "value": "Stock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1975-2024" }, { "id": "Source", "value": "World Federation of Exchanges database, World Federation of Exchanges (WFE)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Turnover ratio is the value of electronic order book (EOB) domestic shares traded divided by their market capitalization. The value is annualized by multiplying the monthly average by 12, according to the following formula: (Monthly EOB domestic shares traded / Month-end domestic market capitalization) x 12.\nStatistical concept(s): Statistical Concept and Methodology: Turnover ratio is the value of electronic order book (EOB) domestic shares traded divided by their market capitalization. The value is annualized by multiplying the monthly average by 12, according to the following formula: (Monthly EOB domestic shares traded / Month-end domestic market capitalization) x 12." }, { "id": "Topic", "value": "Financial Sector: Capital markets" } ], "source_id": "57" }, { "id": "DC.DAC.AUSL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Australia (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1965-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.AUTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Austria (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.BELL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Belgium (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.CANL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Canada (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.CECL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, European Union institutions (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.CHEL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Switzerland (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.CZEL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Czech Republic (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.DEUL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Germany (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.DNKL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Denmark (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.ESPL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Spain (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.ESTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Estonia (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Lithuania, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 32 members - 31 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.FINL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Finland (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.FRAL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, France (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.GBRL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, United Kingdom (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.GRCL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Greece (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.HUNL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Hungary (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.IRLL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Ireland (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1974-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.ISLL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Iceland (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.ITAL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Italy (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.JPNL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Japan (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.KORL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Korea, Rep. (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.LTUL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Lithuania (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Lithuania, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 32 members - 31 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.LUXL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Luxembourg (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.NLDL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Netherlands (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.NORL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Norway (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.NZLL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, New Zealand (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.POLL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Poland (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.PRTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Portugal (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.SVKL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Slovak Republic (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.SVNL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Slovenia (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.SWEL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Sweden (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, Total (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.DAC.USAL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net bilateral aid flows from DAC donors, United States (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude DAC members’ multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total." }, { "id": "Longdefinition", "value": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.ODA.TLDC.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net ODA provided, to the least developed countries (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. The list of least developed countries (LDCs) has been agreed by the General Assembly, on the recommendation of the Committee for Development Policy, Economic and Social Council." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.ODA.TLDC.GN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net ODA provided to the least developed countries (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. The list of least developed countries (LDCs) has been agreed by the General Assembly, on the recommendation of the Committee for Development Policy, Economic and Social Council. Series is shown as a share of donors' GNI." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is presented as a percentage of Gross National Income (GNI)." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.ODA.TOTL.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net ODA provided, total (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.ODA.TOTL.GN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "Official development assistance (ODA): Frequently asked questions" }, { "id": "IndicatorName", "value": "Net ODA provided, total (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. It is shown as a share of donors' GNI." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2017" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is presented as a percentage of Gross National Income (GNI)." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DC.ODA.TOTL.KD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net ODA provided, total (constant 2023 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. Data are in constant 2023 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in constant U.S. dollar prices to account for inflation in the donor's currency and the changes in exchange rates with the U.S. dollar." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.AMT.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "IMF repurchases (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.DLTF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, long-term + IMF (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. This item includes principal repayments on long-term debt and IMF repurchases. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. To maintain comparability between data on transactions with the IMF and data on long-term debt, use of IMF credit outstanding at the end of year (stock) is converted to dollars at the SDR exchange rate in effect at the end of year. Repurchases (flows) are converted at the average SDR exchange rate for the year in which transactions take place. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. This item includes principal repayments on long-term debt and IMF repurchases. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. To maintain comparability between data on transactions with the IMF and data on long-term debt, use of IMF credit outstanding at the end of year (stock) is converted to dollars at the SDR exchange rate in effect at the end of year. Repurchases (flows) are converted at the average SDR exchange rate for the year in which transactions take place. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, long-term (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Principal repayments on long-term debt are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal repayments on long-term debt are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, private nonguaranteed (PNG) (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, public and publicly guaranteed (PPG) (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 25 percent or more. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 25 percent or more. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, bonds (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, other private creditors (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AMT.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, private creditors (AMT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "57" }, { "id": "DT.AXA.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal arrears, public and publicly guaranteed (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.AXA.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal arrears, official creditors (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.AXA.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal arrears, private creditors (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.AXF.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal forgiven (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Principal forgiven is the amount of principal due or in arrears that was written off or forgiven in any given year. It includes debt forgiven within and outside Paris Club agreements, principal forgiven and principal arrears forgiven. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal forgiven is the amount of principal due or in arrears that was written off or forgiven in any given year. It includes debt forgiven within and outside Paris Club agreements, principal forgiven and principal arrears forgiven. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.AXR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal rescheduled (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.AXR.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal rescheduled, official (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). 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Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.COM.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, public and publicly guaranteed (COM, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Commitments are the total amount of long-term loans for which contracts were signed in the year specified; data for private nonguaranteed debt are not available. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Commitments are the total amount of long-term loans for which contracts were signed in the year specified; data for private nonguaranteed debt are not available. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "57" }, { "id": "DT.COM.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, IBRD (COM, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Commitments (IBRD) are the sum of new commitments on public and publicly guaranteed loans from the International Bank for Reconstruction and Development (IBRD). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Commitments (IBRD) are the sum of new commitments on public and publicly guaranteed loans from the International Bank for Reconstruction and Development (IBRD). Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "57" }, { "id": "DT.COM.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, IDA (COM, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Commitments (IDA) are the sum of new commitments on public and publicly guaranteed loans from the International Development Association (IDA). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Commitments (IDA) are the sum of new commitments on public and publicly guaranteed loans from the International Development Association (IDA). Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "57" }, { "id": "DT.COM.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, official creditors (COM, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Commitments are the amount of long-term loans for which contracts were signed in the year specified. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Commitments are the amount of long-term loans for which contracts were signed in the year specified. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "57" }, { "id": "DT.COM.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, private creditors (COM, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Commitments are the amount of long-term loans for which contracts were signed in the year specified; data for private nonguaranteed debt are not available. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Commitments are the amount of long-term loans for which contracts were signed in the year specified; data for private nonguaranteed debt are not available. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "57" }, { "id": "DT.CUR.DMAK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Deutsche mark (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Deutsche marks for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Deutsche marks for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "57" }, { "id": "DT.CUR.EURO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Euro (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Euros for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Euros for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "57" }, { "id": "DT.CUR.FFRC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, French franc (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in French francs for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in French francs for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "57" }, { "id": "DT.CUR.JYEN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Japanese yen (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Japanese yen for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Japanese yen for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "57" }, { "id": "DT.CUR.MULC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Multiple currencies (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in multiple currencies for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in multiple currencies for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "57" }, { "id": "DT.CUR.OTHC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, all other currencies (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in all other currencies not specified for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in all other currencies not specified for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "57" }, { "id": "DT.CUR.SDRW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, SDR (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in special drawing rights for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in special drawing rights for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "57" }, { "id": "DT.CUR.SWFR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Swiss franc (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Swiss francs for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Swiss francs for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "57" }, { "id": "DT.CUR.UKPS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Pound sterling (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in U.K. pound sterling for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in U.K. pound sterling for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "57" }, { "id": "DT.CUR.USDL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, U.S. dollars (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in U.S. dollars for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in U.S. dollars for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "57" }, { "id": "DT.DFR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt forgiveness or reduction (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Debt forgiveness or reduction shows the change in debt stock due to debt forgiveness or reduction. It is derived by subtracting debt forgiven and debt stock reduction from debt buyback. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt forgiveness or reduction shows the change in debt stock due to debt forgiveness or reduction. It is derived by subtracting debt forgiven and debt stock reduction from debt buyback. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.DIS.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "IMF purchases (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "IMF purchases are total drawings on the General Resources Account of the IMF during the year specified, excluding drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "IMF purchases are total drawings on the General Resources Account of the IMF during the year specified, excluding drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.DLTF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, long-term + IMF (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Disbursements are drawings by the borrower on loan commitments during the year specified. This item includes disbursements on long-term debt and IMF purchases. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. IMF purchases are total drawings on the General Resources Account of the IMF during the year specified, excluding drawings in the reserve tranche. To maintain comparability between data on transactions with the IMF and data on long-term debt, use of IMF credit outstanding at the end of year (stock) is converted to dollars at the SDR exchange rate in effect at the end of year. Purchases are converted at the average SDR exchange rate for the year in which transactions take place. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Disbursements are drawings by the borrower on loan commitments during the year specified. This item includes disbursements on long-term debt and IMF purchases. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. IMF purchases are total drawings on the General Resources Account of the IMF during the year specified, excluding drawings in the reserve tranche. To maintain comparability between data on transactions with the IMF and data on long-term debt, use of IMF credit outstanding at the end of year (stock) is converted to dollars at the SDR exchange rate in effect at the end of year. Purchases are converted at the average SDR exchange rate for the year in which transactions take place. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, long-term (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Disbursements on long-term debt are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Disbursements on long-term debt are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, private nonguaranteed (PNG) (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, public and publicly guaranteed (PPG) (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.IDAG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "IDA grants (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "IDA grants are net disbursements of grants from the International Development Association (IDA). Data are in current U.S. dollars. Regional allocations are included in aggregate data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "IDA grants are net disbursements of grants from the International Development Association (IDA). Data are in current U.S. dollars. Regional allocations are included in aggregate data." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.DIS.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 25 percent or more. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 25 percent or more. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, bonds (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, other private creditors (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DIS.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, private creditors (DIS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "57" }, { "id": "DT.DOD.ALLC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, concessional (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Concessional external debt conveys information about the borrower's receipt of aid from official lenders at concessional terms as defined by the Development Assistance Committee (DAC) of the OECD. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Loans from major regional development banks--African Development Bank, Asian Development Bank, and the Inter-American Development Bank--and from the World Bank are classified as concessional according to each institution's classification and not according to the DAC definition, as was the practice in earlier reports. Long-term debt outstanding and disbursed is the total outstanding long-term debt at year end. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Concessional external debt conveys information about the borrower's receipt of aid from official lenders at concessional terms as defined by the Development Assistance Committee (DAC) of the OECD. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Loans from major regional development banks--African Development Bank, Asian Development Bank, and the Inter-American Development Bank--and from the World Bank are classified as concessional according to each institution's classification and not according to the DAC definition, as was the practice in earlier reports. Long-term debt outstanding and disbursed is the total outstanding long-term debt at year end. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.ALLC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Concessional debt (% of total external debt)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Concessional debt to total external debt stocks. Concessional debt is defined as loans with an original grant element of 25 percent or more." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Concessional debt to total external debt stocks. Concessional debt is defined as loans with an original grant element of 25 percent or more." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "57" }, { "id": "DT.DOD.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels." }, { "id": "IndicatorName", "value": "External debt stocks, total (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\nStatistical concept(s): Disbursed and outstanding debt definition (??)\nDebt stock definition (??)" }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "DT.DOD.DECT.CD.CG", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Total change in external debt stocks (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total change in debt stocks shows the variation in debt stock between two consecutive years. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total change in debt stocks shows the variation in debt stock between two consecutive years. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.DOD.DECT.EX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "The denominator for this indicator in previous versions of Global Development Finance included workers' remittances. Workers' remittances are no longer included." }, { "id": "IndicatorName", "value": "External debt stocks (% of exports of goods, services and primary income)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total external debt stocks to exports of goods, services and primary income." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total external debt stocks to exports of goods, services and primary income." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "57" }, { "id": "DT.DOD.DECT.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "External debt stocks (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total external debt stocks to gross national income. Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "57" }, { "id": "DT.DOD.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels." }, { "id": "IndicatorName", "value": "Use of IMF credit (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Use of IMF Credit: Data related to the operations of the IMF are provided by the IMF Treasurer’s Department. They are converted from special drawing rights into dollars using end-of-period exchange rates for stocks and average-over-the-period exchange rates for flows. IMF trust fund operations under the Enhanced Structural Adjustment Facility, Extended Fund Facility, Poverty Reduction and Growth Facility, and Structural Adjustment Facility (Enhanced Structural Adjustment Facility in 1999) are presented together with all of the IMF’s special facilities (buffer stock, supplemental reserve, compensatory and contingency facilities, oil facilities, and other facilities). SDR allocations are also included in this category. According to the BPM6, SDR allocations are recorded as the incurrence of a debt liability of the member receiving them (because of a requirement to repay the allocation in certain circumstances, and also because interest accrues). This debt item is introduced for the first time this year with historical data starting in 1999." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data related to the operations of the IMF come from the IMF Treasurer's Department and are converted from special drawing rights (SDRs) into dollars using end-of-period exchange rates for stocks and average over the period exchange rates for converting flows. DOD refers to disbursed and outstanding debt; data are in current U.S. dollars.\n\nData on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels." }, { "id": "IndicatorName", "value": "External debt stocks, long-term (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Long-term debt is debt that has an original or extended maturity of more than one year. It has three components: public, publicly guaranteed, and private nonguaranteed debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels." }, { "id": "IndicatorName", "value": "External debt stocks, private nonguaranteed (PNG) (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt comprises long-term external obligations of private debtors that are not guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels." }, { "id": "IndicatorName", "value": "External debt stocks, public and publicly guaranteed (PPG) (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt comprises long-term external obligations of public debtors, including the national government, Public Corporations, State Owned Enterprises, Development Banks and Other Mixed Enterprises, political subdivisions (or an agency of either), autonomous public bodies, and external obligations of private debtors that are guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.DSTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels." }, { "id": "IndicatorName", "value": "External debt stocks, short-term (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.DSTC.IR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "Short-term debt (% of total reserves)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The DRS encourages debtor countries to voluntarily provide information on their short-term external obligations. By its nature, short-term external debt is difficult to monitor: loan-by-loan registration is normally impractical, and monitoring systems typically rely on information requested periodically by the central bank from the banking sector. The World Bank regards the debtor country as the authoritative source of information on its short-term debt. Where such information is not available from the debtor country, data are derived from BIS data on international bank lending based on time remaining to original maturity. The data are reported based on residual maturity, but an estimate of short-term external liabilities by original maturity can be derived by deducting from claims due in one year those that have a maturity of between one and two years. However, BIS data include liabilities reported only by banks within the BIS reporting area. The results should thus be interpreted with caution. Because short-term debt poses an immediate burden and is particularly important for monitoring vulnerability, it is compared with total debt and foreign exchange reserves, which are instrumental in providing coverage for such obligations.\n\nA country's external debt burden, both debt outstanding and debt service, affects its creditworthiness and vulnerability. While data related to public and publicly guaranteed debt are reported to the DRS on a loan-by-loan basis, aggregate data on long-term private nonguaranteed debt are reported annually and are reported by the country or estimated by World Bank staff for countries where this type of external debt is known to be significant. Estimates are based on national data from the World Bank's Quarterly External Debt Statistics." }, { "id": "Longdefinition", "value": "Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Total reserves includes gold." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "World Development Indicators, World Bank (WB);\nInternational Monetary Fund (IMF), type: Balance of Payments Statistics Yearbook and data files;\nWorld Bank (WB), type: GDP estimates;\nOrganisation for Economic Co-operation and Development (OECD), type: GDP estimates" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "57" }, { "id": "DT.DOD.DSTC.XP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "Short-term debt (% of exports of goods, services and primary income)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Exports of goods, services and primary income is the sum of goods (merchandise) exports, exports of (nonfactor) services and income (factor) receipts." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value. \n\nThe data is presented as a percentage of \"Exports of goods, services and primary income (BoP, current US$)\" (BX.GSR.TOTL.CD), sourced from the International Monetary Fund's Balance of Payments Statistics Yearbook and data files." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "57" }, { "id": "DT.DOD.DSTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "Short-term debt (% of total external debt)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "57" }, { "id": "DT.DOD.MDRI.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "The aggregate figure for all developing countries is sourced from OECD and includes all OECD countries and regions." }, { "id": "IndicatorName", "value": "Debt forgiveness grants (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Debt forgiveness grants data cover both debt cancelled by agreement between debtor and creditor and a reduction in the net present value of non-ODA debt achieved by concessional rescheduling or refinancing. The data are on a disbursement basis and cover flows from all bilateral and multilateral donors. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt forgiveness grants data cover both debt cancelled by agreement between debtor and creditor and a reduction in the net present value of non-ODA debt achieved by concessional rescheduling or refinancing. The data are on a disbursement basis and cover flows from all bilateral and multilateral donors. Data are in current U.S. dollars." }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.DOD.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PPG, IBRD (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PPG, IDA (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.MLAT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Multilateral debt (% of total external debt)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Multilateral debt to total external debt stocks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Multilateral debt to total external debt stocks." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "57" }, { "id": "DT.DOD.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.MWBG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "IBRD loans and IDA credits (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "IBRD loans and IDA credits are public and publicly guaranteed debt extended by the World Bank Group. The International Bank for Reconstruction and Development (IBRD) lends at market rates. Credits from the International Development Association (IDA) are at concessional rates. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, bonds (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Private nonguaranteed long-term debt outstanding and disbursed is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Private nonguaranteed long-term debt outstanding and disbursed is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Private nonguaranteed long-term debt outstanding and disbursed is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Private nonguaranteed long-term debt outstanding and disbursed is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, other private creditors (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.PRVS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, long-term private sector (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Long-term private sector external debt conveys information about the distribution of long-term debt for DRS countries by type of debtor (private banks and private entities). Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Long-term private sector external debt conveys information about the distribution of long-term debt for DRS countries by type of debtor (private banks and private entities). Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, private creditors (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.PUBS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, long-term public sector (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Long-term public sector external debt conveys information about the distribution of long-term debt for DRS countries by type of debtor (central government, state and local government, central bank, public and mixed enterprises, and official development banks). Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Long-term public sector external debt conveys information about the distribution of long-term debt for DRS countries by type of debtor (central government, state and local government, central bank, public and mixed enterprises, and official development banks). Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DOD.PVLX.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nPresent value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. The PV of external debt is a better measure for debt burden's of countries that have access to concessional financing. The IMF/World Bank's Low-Income Countries (LICs) Debt Sustainability Framework (DSF) uses the present value of external debt as a means to assess a country's risk of external and overall debt distress.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "Present value of external debt (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Present value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "DT.DOD.PVLX.EX.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nPresent value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. The PV of external debt is a better measure for debt burden's of countries that have access to concessional financing. The IMF/World Bank's Low-Income Countries (LICs) Debt Sustainability Framework (DSF) uses the present value of external debt as a means to assess a country's risk of external and overall debt distress.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "Present value of external debt (% of exports of goods, services and income)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Present value of external debt to exports of goods, services and income. Present value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan. Exports of goods, services and primary income is the sum of goods (merchandise) exports, exports of (nonfactor) services and income (factor) receipts. The exports denominator is a three-year average." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "DT.DOD.PVLX.GN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nPresent value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. The PV of external debt is a better measure for debt burden's of countries that have access to concessional financing. The IMF/World Bank's Low-Income Countries (LICs) Debt Sustainability Framework (DSF) uses the present value of external debt as a means to assess a country's risk of external and overall debt distress.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "Present value of external debt (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Present value of external debt to gross national income. Present value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. The GNI denominator is a three-year average." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "DT.DOD.RSDL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Residual, debt stock-flow reconciliation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The residual difference, i.e. the change in stock not explained by any of the factors identified under debt stock-flow reconciliation, is calculated as the sum of identified accounts minus the change in stock. Where the latter is large it can, in some cases, serve as an illustration of the inconsistencies in the reported data. More often however, it can be explained by specific borrowing phenomenon in individual countries. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The residual difference, i.e. the change in stock not explained by any of the factors identified under debt stock-flow reconciliation, is calculated as the sum of identified accounts minus the change in stock. Where the latter is large it can, in some cases, serve as an illustration of the inconsistencies in the reported data. More often however, it can be explained by specific borrowing phenomenon in individual countries. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.DOD.VTOT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, variable rate (DOD, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Variable interest rate is long-term external debt with interest rates that float with movements in a key market rate; for example, the London interbank offered rate (LIBOR) or the U.S. prime rate. This item conveys information about the borrower's exposure to changes in international interest rates. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Variable interest rate is long-term external debt with interest rates that float with movements in a key market rate; for example, the London interbank offered rate (LIBOR) or the U.S. prime rate. This item conveys information about the borrower's exposure to changes in international interest rates. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "57" }, { "id": "DT.DSB.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt buyback (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Debt buyback is the repurchase by a debtor of its own debt, discounted or at par. In the event of a buyback of long-term debt, the face value of the debt bought back will be recorded as a decline in the long-term debt stock, and the cash amount received by creditors will be recorded as a principal repayment. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt buyback is the repurchase by a debtor of its own debt, discounted or at par. In the event of a buyback of long-term debt, the face value of the debt bought back will be recorded as a decline in the long-term debt stock, and the cash amount received by creditors will be recorded as a principal repayment. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.DSF.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt stock reduction (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Debt stock reductions show the amount that has been netted out of the stock of debt using debt conversion schemes such as buybacks and equity swaps or the discounted value of long-term bonds that were issued in exchange for outstanding debt. It includes the effect of any financial operation that will reduce the debt stock other than debt stock restructuring, repayment of principal and debt forgiven. In particular, debt stock reduction will include the face value of debt bought back, the face value of debt swapped for equity (or \"nature\" or \"development\"), any face value reduction that might result as the consequence of a bond exchange, and any face value reduction resulting from an exchange of debt for discount bonds. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt stock reductions show the amount that has been netted out of the stock of debt using debt conversion schemes such as buybacks and equity swaps or the discounted value of long-term bonds that were issued in exchange for outstanding debt. It includes the effect of any financial operation that will reduce the debt stock other than debt stock restructuring, repayment of principal and debt forgiven. In particular, debt stock reduction will include the face value of debt bought back, the face value of debt swapped for equity (or \"nature\" or \"development\"), any face value reduction that might result as the consequence of a bond exchange, and any face value reduction resulting from an exchange of debt for discount bonds. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.DXR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt stock rescheduled (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Debt stocks rescheduled is the amount of debt outstanding rescheduled in any given year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt stocks rescheduled is the amount of debt outstanding rescheduled in any given year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.GPA.DPPG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grace period on new external debt commitments (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "57" }, { "id": "DT.GPA.OFFT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grace period on new external debt commitments, official (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "57" }, { "id": "DT.GPA.PRVT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grace period on new external debt commitments, private (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "57" }, { "id": "DT.GRE.DPPG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grant element on new external debt commitments (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Data for private nonguaranteed debt are not available." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Data for private nonguaranteed debt are not available." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "57" }, { "id": "DT.GRE.OFFT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grant element on new external debt commitments, official (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "57" }, { "id": "DT.GRE.PRVT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grant element on new external debt commitments, private (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "57" }, { "id": "DT.INR.DPPG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average interest on new external debt commitments (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "57" }, { "id": "DT.INR.OFFT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average interest on new external debt commitments, official (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "57" }, { "id": "DT.INR.PRVT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average interest on new external debt commitments, private (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "57" }, { "id": "DT.INT.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, total (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. This item includes interest paid on long-term debt, IMF charges, and interest paid on short-term debt. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. This item includes interest paid on long-term debt, IMF charges, and interest paid on short-term debt. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.DECT.EX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "The denominator for this indicator in previous versions of Global Development Finance included workers' remittances. Workers' remittances are no longer included." }, { "id": "IndicatorName", "value": "Interest payments on external debt (% of exports of goods, services and primary income)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total interest payments to exports of goods, services and primary income. Total interest payment is the sum of interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and charges to the IMF." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total interest payments to exports of goods, services and primary income. Total interest payment is the sum of interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and charges to the IMF." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "57" }, { "id": "DT.INT.DECT.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total interest payments to gross national income." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total interest payments to gross national income." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "57" }, { "id": "DT.INT.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "IMF charges (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "IMF charges cover interest payments with respect to all uses of IMF resources, excluding those resulting from drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "IMF charges cover interest payments with respect to all uses of IMF resources, excluding those resulting from drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, long-term (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Interest payments on long-term debt are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest payments on long-term debt are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, private nonguaranteed (PNG) (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, public and publicly guaranteed (PPG) (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.DSTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, short-term (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Interest payments on short-term debt are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. This item includes interest paid on long-term debt, IMF charges, and interest paid on short-term debt. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest payments on short-term debt are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. This item includes interest paid on long-term debt, IMF charges, and interest paid on short-term debt. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 25 percent or more. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 25 percent or more. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, bonds (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, other private creditors (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.INT.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, private creditors (INT, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "57" }, { "id": "DT.IXA.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest arrears, public and publicly guaranteed (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Interest in arrears on long-term debt is defined as interest payment due but not paid, on a cumulative basis. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest in arrears on long-term debt is defined as interest payment due but not paid, on a cumulative basis. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.IXA.DPPG.CD.CG", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net change in interest arrears (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net change in interest arrears is the variation in the total amount of interest in arrears between two consecutive years. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net change in interest arrears is the variation in the total amount of interest in arrears between two consecutive years. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.IXA.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest arrears, official creditors (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Interest in arrears on long-term debt is defined as interest payment due but not paid, on a cumulative basis. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest in arrears on long-term debt is defined as interest payment due but not paid, on a cumulative basis. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.IXA.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest arrears, private creditors (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Interest in arrears on long-term debt is defined as interest payment due but not paid, on a cumulative basis. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest in arrears on long-term debt is defined as interest payment due but not paid, on a cumulative basis. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.IXF.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest forgiven (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Interest forgiven is the amount of interest due or in arrears that was written off or forgiven in any given year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest forgiven is the amount of interest due or in arrears that was written off or forgiven in any given year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.IXR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest rescheduled (capitalized) (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. (Interest capitalized is the interest that became part of the stock of debt due to a rescheduling operation.) Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. (Interest capitalized is the interest that became part of the stock of debt due to a rescheduling operation.) Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.IXR.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest rescheduled, official (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organizations include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organizations include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.IXR.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest rescheduled, private (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.MAT.DPPG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average maturity on new external debt commitments (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "57" }, { "id": "DT.MAT.OFFT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average maturity on new external debt commitments, official (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "57" }, { "id": "DT.MAT.PRVT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average maturity on new external debt commitments, private (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "57" }, { "id": "DT.NFL.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net financial flows, bilateral (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data show concessional and nonconcessional financial flows from official bilateral sources. The Organisation for Economic Co-operation and Development's (OECD) Development Assistance Committee (DAC) defines concessional flows from bilateral donors as flows with a grant element of at least 25 percent; they are evaluated assuming a 10 percent nominal discount rate." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.BOND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels." }, { "id": "IndicatorName", "value": "Portfolio investment, bonds (PPG + PNG) (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The DRS encourages debtor countries to voluntarily provide information on their short-term external obligations. By its nature, short-term external debt is difficult to monitor: loan-by-loan registration is normally impractical, and monitoring systems typically rely on information requested periodically by the central bank from the banking sector. The World Bank regards the debtor country as the authoritative source of information on its short-term debt. Where such information is not available from the debtor country, data are derived from BIS data on international bank lending based on time remaining to original maturity. The data are reported based on residual maturity, but an estimate of short-term external liabilities by original maturity can be derived by deducting from claims due in one year those that have a maturity of between one and two years. However, BIS data include liabilities reported only by banks within the BIS reporting area. The results should thus be interpreted with caution. Because short-term debt poses an immediate burden and is particularly important for monitoring vulnerability, it is compared with total debt and foreign exchange reserves, which are instrumental in providing coverage for such obligations.\n\nA country's external debt burden, both debt outstanding and debt service, affects its creditworthiness and vulnerability. While data related to public and publicly guaranteed debt are reported to the DRS on a loan-by-loan basis, aggregate data on long-term private nonguaranteed debt are reported annually and are reported by the country or estimated by World Bank staff for countries where this type of external debt is known to be significant. Estimates are based on national data from the World Bank's Quarterly External Debt Statistics." }, { "id": "Longdefinition", "value": "Bonds are securities issued with a fixed rate of interest for a period of more than one year. They include net flows through cross-border public and publicly guaranteed and private nonguaranteed bond issues. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Bonds are debt instruments issued by public and publicly guaranteed or private debtors with durations of one year or longer. Bonds usually give the holder the unconditional right to fixed money income or contractually determined, variable money income.\n\nData on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.CERF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Official development assistance (ODA) is defined as government aid that promotes and specifically targets the economic development and welfare of developing countries. The DAC adopted ODA as the “gold standard” of foreign aid in 1969 and it remains the main source of financing for development aid. ODA data is collected, verified and made publicly available by the OECD. The DAC has measured resource flows to developing countries since 1961. Special attention has been given to the official and concessional part of this flow, defined as “official development assistance” (ODA). The DAC first defined ODA in 1969, and tightened the definition in 1972. ODA is the key measure used in practically all aid targets and assessments of aid performance." }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, CERF (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2017-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "DT.NFL.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, total (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net flows on external debt are disbursements on long-term external debt and IMF purchases minus principal repayments on long-term external debt and IMF repurchases up to 1984. Beginning in 1985 this line includes the change in stock of short-term debt (including interest arrears for long-term debt). Thus, if the change in stock is positive, a disbursement is assumed to have taken place; if negative, a repayment is assumed to have taken place. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net flows on external debt are disbursements on long-term external debt and IMF purchases minus principal repayments on long-term external debt and IMF repurchases up to 1984. Beginning in 1985 this line includes the change in stock of short-term debt (including interest arrears for long-term debt). Thus, if the change in stock is positive, a disbursement is assumed to have taken place; if negative, a repayment is assumed to have taken place. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, long-term (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator tells us the net flows (disbursements minus principal payments) of private debtor's external debt that is not guaranteed for repayment by a public entity." }, { "id": "IndicatorName", "value": "Net flows on external debt, private nonguaranteed (PNG) (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Countries that report to the World Bank’s Debtor Reporting System (DRS) submit their private nonguaranteed (PNG) on an aggregate basis. The Debt Data Team then compiles this information to produce this indicator." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" }, { "id": "Unitofmeasure", "value": "current US$" } ], "source_id": "57" }, { "id": "DT.NFL.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, public and publicly guaranteed (PPG) (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.DSTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, short-term (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.FAOG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, FAO (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2013-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.IAEA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, IAEA (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.IFAD.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, IFAD (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1979-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.ILOG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, ILO (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2012-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.IMFC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net financial flows, IMF concessional (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IMF is the International Monetary Fund, which provides concessional lending through the Poverty Reduction and Growth Facility and the IMF Trust Fund. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Monetary Fund (IMF) makes concessional funds available through its Extended Credit Facility (which replaced the Poverty Reduction and Growth Facility in 2010), the Standby Credit Facility, and the Rapid Credit Facility. Eligibility is based principally on a country's per capita income and eligibility under IDA." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.IMFN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net financial flows, IMF nonconcessional (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IMF is the International Monetary Fund, which provides nonconcessional lending through the credit it provides to its members, mainly to meet balance of payments needs. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Nonconcessional lending from the IMF is provided mainly through Stand-by Arrangements, the Flexible Credit Line, and the Extended Fund Facility." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net financial flows, IBRD (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IBRD is the International Bank for Reconstruction and Development, the founding and largest member of the World Bank Group. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank's International Bank for Reconstruction and Development (IBRD) lends to creditworthy countries at a variable base rate of six-month LIBOR plus a spread, either variable or fixed, for the life of the loan. The rate is reset every six months and applies to the interest period beginning on that date." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net financial flows, IDA (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IDA is the International Development Association, the concessional loan window of the World Bank Group. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: World Bank concessional lending is done by the International Development Association (IDA) based on gross national income (GNI) per capita and performance standards assessed by World Bank staff." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net financial flows, multilateral (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data show concessional and nonconcessional financial flows from international financial institutions. International financial institutions fund nonconcessional lending operations primarily by selling low-interest, highly rated bonds backed by prudent lending and financial policies and the strong financial support of their members. Funds are then on-lent to developing countries at slightly higher interest rates with 15- to 20-year maturities. Lending terms vary with market conditions and institutional policies. Concessional flows from international financial institutions are credits provided through concessional lending facilities. Subsidies from donors or other resources reduce the cost of these loans. Grants are not included in net flows." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.MOTH.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net financial flows, others (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Others is a residual category in the World Bank's Debtor Reporting System. It includes such institutions as the Caribbean Development Fund, Council of Europe, European Development Fund, Islamic Development Bank, Nordic Development Fund, and the like. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.NEBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "EBRD, private nonguaranteed (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt privately placed from the European Bank for Reconstruction and Development (EBRD). Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt privately placed from the European Bank for Reconstruction and Development (EBRD). Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.NIFC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "IFC, private nonguaranteed (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt privately placed from the International Finance Corporation (IFC). Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PPG, official creditors (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PPG, bonds (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PPG, commercial banks (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.PCBO.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nProposed: External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the governments, corporations or private households. The debt includes money owed to governments or public agencies (bilateral), to international organizations (multilateral) or to exporters, commercial banks or other financial institutions.\nExternal indebtedness affects a country's creditworthiness and investor perceptions. When used effectively, a reasonable level of external debt can help a country finance productive investments, such as building infrastructure and investing in education and health, that can increase growth." }, { "id": "IndicatorName", "value": "Commercial banks and other lending (PPG + PNG) (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Longdefinition", "value": "Commercial bank and other lending includes net commercial bank lending (public and publicly guaranteed and private non- guaranteed) and other private credits. Data are in current U.S. dollars.\n\nProposed: Commercial bank and other lending includes net commercial bank and other private creditors lending excluding bonds (public and publicly guaranteed + private nonguaranteed)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Commercial banks include all commercial banks, whether or not publicly owned, that provide loans and other financial services. Private creditors include commercial banks, bondholders, and other private creditors. This indicator includes only publicly guaranteed creditors. Nonguaranteed private creditors are shown separately. Bonds include publicly issued or privately placed bonds. Commercial bank loans are loans from private banks and other private financial institutions. Credits of other private creditors include credits from manufacturers, exporters, and other suppliers of goods, plus bank credits covered by a guarantee of an export credit agency.\n\nChanged to:\nCountries that report to the World Bank’s Debtor Reporting System (DRS) submit their 1- public and publicly guaranteed debt (PPG) and private debt with a public guarantee on a loan-by-loan basis and 2- private nonguaranteed (PNG) debt on an aggregate basis. The World Bank Debt Data Team then compiles this information to produce this indicator.\n\n\nStatistical concept(s): Commercial banks include all commercial banks, whether or not publicly owned, that provide loans and other financial services. Private creditors include commercial banks, bondholders, and other private creditors. This indicator includes only publicly guaranteed creditors. Nonguaranteed private creditors are shown separately. Bonds include publicly issued or privately placed bonds. Commercial bank loans are loans from private banks and other private financial institutions. Credits of other private creditors include credits from manufacturers, exporters, and other suppliers of goods, plus bank credits covered by a guarantee of an export credit agency.\n\nproposed: Commercial banks include all commercial banks, whether or not publicly owned, that provide loans and other financial services. Credits of other private creditors include credits from manufacturers, exporters, and other suppliers of goods, plus bank credits covered by a guarantee of an export credit agency." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "DT.NFL.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PNG, bonds (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics, note: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Countries that report to the World Bank’s Debtor Reporting System (DRS) submit their private nonguaranteed (PNG) on an aggregate basis. The World Bank Debt Data Team then compiles this information to produce this indicator." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "DT.NFL.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics, note: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Countries that report to the World Bank’s Debtor Reporting System (DRS) submit their private nonguaranteed (PNG) on an aggregate basis. The World Bank Debt Data Team then compiles this information to produce this indicator." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "DT.NFL.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PPG, other private creditors (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "PPG, private creditors (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "57" }, { "id": "DT.NFL.RDBC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Concessional finance is below market rate finance provided by major financial institutions, such as development banks and multilateral funds, to developing countries to accelerate development objectives. The term concessional finance does not represent a single mechanism or type of financial support but comprises a range of below market rate products used to accelerate development objective.\n\nConcessional finance often targets high-impact projects responding to globally significant development challenges – from climate change mitigation and resilience to vaccine deployment, water sanitation and education - that otherwise could not go ahead without specialized financial support.\n\nNet financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal." }, { "id": "IndicatorName", "value": "Net financial flows, RDB concessional (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Concessional financial flows cover disbursements made through concessional lending facilities. Regional development banks are the African Development Bank, in Tunis, Tunisia, which serves all of Africa, including North Africa; the Asian Development Bank, in Manila, Philippines, which serves South and Central Asia and East Asia and Pacific; the European Bank for Reconstruction and Development, in London, United Kingdom, which serves Europe and Central Asia; and the Inter-American Development Bank, in Washington, D.C., which serves the Americas. Aggregates include amounts for economies not specified elsewhere. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), note: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Regional Development Banks (RDB) share their lending information on a loan by loan basis with the World Bank through the World Bank's Debtor Reporting System (DRS). The Debt Data Team then compiles this information to produce this indicator.\nStatistical concept(s): Regional development banks also maintain concessional windows. Their loans are recorded according to each institution's classification and not according to the Organisation for Economic Co-operation and Development's (OECD) Development Assistance Committee (DAC) definition." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "DT.NFL.RDBN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The financial flows from regional development banks (RDB) that are not made through concessional lending facilities. Concessional finance is below market rate finance provided by major financial institutions." }, { "id": "IndicatorName", "value": "Net financial flows, RDB nonconcessional (NFL, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Nonconcessional financial flows cover all disbursements except those made through concessional lending facilities. Regional development banks are the African Development Bank, in Tunis, Tunisia, which serves all of Africa, including North Africa; the Asian Development Bank, in Manila, Philippines, which serves South and Central Asia and East Asia and Pacific; the European Bank for Reconstruction and Development, in London, United Kingdom, which serves Europe and Central Asia; and the Inter-American Development Bank, in Washington, D.C., which serves the Americas. Aggregates include amounts for economies not specified elsewhere. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Regional Development Banks (RDB) share their lending information on a loan by loan basis with the World Bank through the World Bank's Debtor Reporting System (DRS). The Debt Data Team then compiles this information to produce this indicator.\nStatistical concept(s): Regional development banks also maintain concessional windows. Their loans are recorded according to each institution's classification and not according to the Organisation for Economic Co-operation and Development's (OECD) Development Assistance Committee (DAC) definition." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "DT.NFL.SDGF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, SDGFUND (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2021-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.SPRP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, SPRP (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2021-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNAI.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNAIDS (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNCD.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNCDF (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2020-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNCF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNICEF (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNCR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNHCR (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1969-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNCTAD.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNCTAD (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2020-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNCV.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNCOVID (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2021-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNDP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNDP (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1968-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNEC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNECE (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Othernotes", "value": "Data for net official flows from UNECE at present are reported at the regional level only. A more detailed breakdown by recipient country will be available in the future." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2008-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNEP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNEP (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2015-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNFP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNFPA (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1977-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNID.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNIDIR (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNIDO.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNIDO (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2020-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNPB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNPBF (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2007-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNRW.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNRWA (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1969-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNTA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNTA (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1969-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNWN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNWOMEN (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2021-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.UNWT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, UNWTO (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2016-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.WFPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, WFP (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1969-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.WHOL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, WHO (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2009-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NFL.WITC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official flows from UN agencies, WTO-ITC (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2020-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.NTR.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.NTR.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.NTR.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, total (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.NTR.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, long-term (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.NTR.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, private nonguaranteed (PNG) (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.NTR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, public and publicly guaranteed (PPG) (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.NTR.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 25 percent or more. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 25 percent or more. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.NTR.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.NTR.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.NTR.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.NTR.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.NTR.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.NTR.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.NTR.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, bonds (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.NTR.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.NTR.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, other private creditors (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.NTR.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, private creditors (NTR, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "57" }, { "id": "DT.ODA.ALLD.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official development assistance and official aid received (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions). \n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nNet official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.ODA.ALLD.GI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Net official development assistance and official aid received (% of gross capital formation)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Aid includes both official development assistance (ODA) and official aid. Ratios are computed using values in U.S. dollars converted at official exchange rates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development, and World Bank GCF estimates." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.ODA.ALLD.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Net official development assistance and official aid received (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Aid includes both official development assistance (ODA) and official aid. Ratios are computed using values in U.S. dollars converted at official exchange rates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development, and World Bank GNI estimates." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.ODA.ALLD.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official development assistance and official aid received (constant 2023 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in constant 2023 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions). \n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in constant U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nNet official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.ODA.ALLD.MP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Net official development assistance and official aid received (% of imports of goods, services and primary income)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Aid includes both official development assistance (ODA) and official aid. Ratios are computed using values in U.S. dollars converted at official exchange rates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development, and World Bank imports of goods and services estimates." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.ODA.ALLD.PC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Net official development assistance and official aid received (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Aid per capita includes both official development assistance (ODA) and official aid, and is calculated by dividing total aid by the midyear population estimate." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development, and World Bank population estimates." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.ODA.ALLD.XP.ZS", "metatype": [ { "id": "IndicatorName", "value": "Net official development assistance and official aid received (% of central government expense)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Aid includes both official development assistance (ODA) and official aid. Ratios are computed using values in U.S. dollars converted at official exchange rates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development, and IMF central government expense estimates." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.ODA.OATL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "DAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about US $130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net official aid received (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe nominal values may overstate the real value of aid to recipients. Changes in international prices and exchange rates can reduce the purchasing power of aid. Tying aid, still prevalent though declining in importance, also tends to reduce its purchasing power. Tying requires recipients to purchase goods and services from the donor country or from a specified group of countries. Such arrangements prevent a recipient from misappropriating or mismanaging aid receipts, but they may also be motivated by a desire to benefit donor country suppliers.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions). \n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nNet official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Data are in current U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.ODA.OATL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official aid received (constant 2023 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in constant 2023 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in constant U.S. dollars." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.ODA.ODAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "DAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about USD 130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net official development assistance received (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe nominal values may overstate the real value of aid to recipients. Changes in international prices and exchange rates can reduce the purchasing power of aid. Tying aid, still prevalent though declining in importance, also tends to reduce its purchasing power. Tying requires recipients to purchase goods and services from the donor country or from a specified group of countries. Such arrangements prevent a recipient from misappropriating or mismanaging aid receipts, but they may also be motivated by a desire to benefit donor country suppliers.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions). \n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.ODA.ODAT.GI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The ratio of aid to gross capital formation provides a measure of recipient country's dependency on aid. Ratios of aid are generally much higher in Sub-Saharan Africa than in other regions, particularly in the 1980s. High ratios are due only in part to aid flows. Many African countries saw severe erosion in their terms of trade in the 1980s, along with weak policies, falling incomes, imports, and investment. Thus the increase in aid dependency ratios reflects events affecting both the numerator (aid) and the denominator (gross capital formation).\n\nDAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about US $130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net ODA received (% of gross capital formation)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nRatio of aid to gross capital formation provides measures of recipient country's dependency on aid. But care must be taken in drawing policy conclusions. For foreign policy reasons some countries have traditionally received large amounts of aid. Thus aid dependency ratio may reveal as much about a donor's interests as about a recipient's needs. The quality of data on government fixed capital formation depends on the quality of government accounting systems which tend to be weak in developing countries. Measures of fixed capital formation by households and corporations - particularly capital outlays by small, unincorporated enterprises - are usually unreliable. Estimates of changes in inventories are rarely complete but usually include the most important activities of commodities.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nWorld Bank gross capital formation estimates, World Bank (WB), note: World Bank gross capital formation estimates are used for the denominator" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).Gross capital formation consists of outlays on additions to the conomy's fixed assets plus net changes in the level of inventories. It is generally obtained from industry reports of acquisitions and distinguishes only the broad categories of capital formation. Data on capital formation may be estimated from direct surveys of enterprises and administrative records or based on the commodity flow methods using data from production, trade and construction activities.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Net ODA received as a percent of gross capital formation is calculated using values in U.S. dollars converted at official exchange rates." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.ODA.ODAT.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The ratio of aid to GNI provides a measure of recipient country's dependency on aid. Ratios of aid are generally much higher in Sub-Saharan Africa than in other regions, and they increased in the 1980s. High ratios are due only in part to aid flows. Many African countries saw severe erosion in their terms of trade in the 1980s, along with weak policies, falling incomes, imports, and investment. Thus the increase in aid dependency ratios reflects events affecting both the numerator (aid) and the denominator (GNI).\n\nDAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about US $130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net ODA received (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nRatio of aid to gross national income (GNI) provides measures of recipient country's dependency on aid. But care must be taken in drawing policy conclusions. For foreign policy reasons some countries have traditionally received large amounts of aid. Thus aid dependency ratio may reveal as much about a donor's interests as about a recipient's needs. \n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nWorld Bank GNI estimates, World Bank (WB), note: World Bank GNI estimates are used for the denominator" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Net ODA received as a percent of GNI is calculated using values in U.S. dollars converted at official exchange rates." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.ODA.ODAT.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net official development assistance received (constant 2023 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in constant 2023 U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions). \n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in constant U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.ODA.ODAT.MP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The ratio of aid to imports of goods and services provides a measure of recipient country's dependency on aid. Ratios of aid are generally much higher in Sub-Saharan Africa than in other regions, and they increased in the 1980s. High ratios are due only in part to aid flows. Many African countries saw severe erosion in their terms of trade in the 1980s, along with weak policies, falling incomes, imports, and investment. Thus the increase in aid dependency ratios reflects events affecting both the numerator (aid) and the denominator (imports of goods and services).\n\nDAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about US $130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net ODA received (% of imports of goods, services and primary income)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nRatio of aid to imports of goods and services provides measures of recipient country's dependency on aid. But care must be taken in drawing policy conclusions. For foreign policy reasons some countries have traditionally received large amounts of aid. Thus aid dependency ratio may reveal as much about a donor's interests as about a recipient's needs. Data on imports are compiled from customs reports and balance of payments data. Although data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or correspond to the change of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in league but unreported shuttle trade may further distort trade statistics.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nWorld Bank imports of good and services estimates, World Bank (WB), note: World Bank imports of good and services estimates are used for the denominator." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\nData on imports are compiled from customs reports and balance of payments data. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services. They exclude compensation of employees and investment income (factor services in the 1969 SNA) and transfer payments.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Net ODA received as a percent of imports of goods and services is calculated using values in U.S. dollars converted at official exchange rates." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.ODA.ODAT.PC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The ratio of aid per capita provides a measure of recipient country's dependency on aid. DAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about USD 130 billion. This demonstrates how effective aid pledges can be when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net ODA received per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients; and is calculated by dividing net ODA received by the midyear population estimate. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nWorld Bank population estimates, World Bank (WB), note: World Bank population estimates are used for the denominator" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\nTotal population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship - except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. The values shown are midyear estimates. Net official development assistance per capita is net ODA divided by midyear population.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThis definition excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.ODA.ODAT.XP.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ratio of aid to central government expense provides measures of recipient country's dependency on aid. Ratios of aid are generally much higher in Sub-Saharan Africa than in other regions, and they increased in the 1980s. High ratios are due only in part to aid flows. Many African countries saw severe erosion in their terms of trade in the 1980salong with weak policies, falling incomes, imports, and investment. Thus the increase in aid dependency ratios reflects events affecting both the numerator (aid) and the denominator (central government expense).\n\nDAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about US $130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will." }, { "id": "IndicatorName", "value": "Net ODA received (% of central government expense)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nRatio of aid to central government expense provides measures of recipient country's dependency on aid. But care must be taken in drawing policy conclusions. For foreign policy reasons some countries have traditionally received large amounts of aid. Thus aid dependency ratio may reveal as much about a donor's interests as about a recipient's needs.\n\nThe nominal values used here may overstate the real value of aid to recipients. Changes in international prices and exchange rates can reduce the purchasing power of aid. Tying aid, still prevalent though declining in importance, also tends to reduce its purchasing power. Tying requires recipients to purchase goods and services from the donor country or from a specified group of countries. Such arrangements prevent a recipient from misappropriating or mismanaging aid receipts, but they may also be motivated by a desire to benefit donor country suppliers.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD." }, { "id": "Longdefinition", "value": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2023" }, { "id": "Source", "value": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nIMF central government expense estimates, International Monetary Fund (IMF), note: IMF central government expense estimates are used for the denominator" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\nCentral government expense is cash payments for operating activities of the government in providing goods and services. It includes compensation of employees (such as wages and salaries), interest and subsidies, grants, social benefits, and other expenses such as rent and dividends.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Net ODA received as a percent of central government expense is calculated using values in U.S. dollars converted using the DEC alternative conversion factor which is the underlying annual exchange rate used for the World Bank Atlas method." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "57" }, { "id": "DT.TDS.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TDS.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TDS.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the governments, corporations or private households. The debt includes money owed to governments or public agencies (bilateral), to international organizations (multilateral) or to exporters, commercial banks or other financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. When used effectively, a reasonable level of external debt can help a country finance productive investments, such as building infrastructure and investing in education and health, that can increase growth." }, { "id": "IndicatorName", "value": "Debt service on external debt, total (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "DT.TDS.DECT.EX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio \nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock" }, { "id": "IndicatorName", "value": "Total debt service (% of exports of goods, services and primary income)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total debt service to exports of goods, services and primary income. Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF." }, { "id": "Othernotes", "value": "The denominator for this indicator in previous versions of Global Development Finance included workers' remittances. Workers' remittances are no longer included." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "57" }, { "id": "DT.TDS.DECT.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Total debt service (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "57" }, { "id": "DT.TDS.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "IMF repurchases and charges (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. IMF charges cover interest payments with respect to all uses of IMF resources, excluding those resulting from drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TDS.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, long-term (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TDS.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, private nonguaranteed (PNG) (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Private nonguaranteed debt service is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed debt service is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TDS.DPPF.XP.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Debt service to exports (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Debt service, the sum of principal repayments and interest actually paid in currency, goods, or services, is expressed as a percentage of exports of goods and services--all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, net exports of goods under merchanting, nonmonetary gold, and services. This series differs from the standard debt to exports series in that it covers only long-term public and publicly guaranteed debt and repayments (repurchases and charges) to the IMF." }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TDS.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the governments, corporations or private households. The debt includes money owed to governments or public agencies (bilateral), to international organizations (multilateral) or to exporters, commercial banks or other financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. When used effectively, a reasonable level of external debt can help a country finance productive investments, such as building infrastructure and investing in education and health, that can increase growth." }, { "id": "IndicatorName", "value": "Debt service on external debt, public and publicly guaranteed (PPG) (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Countries that report to the World Bank’s Debtor Reporting System (DRS) submit their public and publicly guaranteed debt (PPG) and private debt with a public guarantee on a loan-by-loan basis. The World Bank Debt Data Team then compiles this information to produce this indicator." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "DT.TDS.DPPG.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Public and publicly guaranteed debt service (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt service to gross national income. Public and publicly guaranteed debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Countries that report to the World Bank’s Debtor Reporting System (DRS) submit their public and publicly guaranteed debt (PPG) and private debt with a public guarantee on a loan-by-loan basis. The World Bank Debt Data Team then compiles this information to produce this indicator." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "DT.TDS.DPPG.XP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Public and publicly guaranteed debt service (% of exports of goods, services and primary income)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt service to exports of goods, services, and income. Public and publicly guaranteed debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Exports of goods, services and primary income is the sum of goods (merchandise) exports, exports of (nonfactor) services and income (factor) receipts." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Countries that report to the World Bank’s Debtor Reporting System (DRS) submit their public and publicly guaranteed debt (PPG) and private debt with a public guarantee on a loan-by-loan basis. The World Bank Debt Data Team then compiles this information to produce this indicator." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "DT.TDS.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 25 percent or more. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 25 percent or more. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TDS.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TDS.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Multilateral debt service (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TDS.MLAT.PG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Multilateral debt service (% of public and publicly guaranteed debt service)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Multilateral debt service is the repayment of principal and interest to the World Bank, regional development banks, and other multilateral agencies. public and publicly guaranteed debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "57" }, { "id": "DT.TDS.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TDS.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TDS.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TDS.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TDS.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, bonds (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TDS.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TDS.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, other private creditors (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TDS.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, private creditors (TDS, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "57" }, { "id": "DT.TXR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Total amount of debt rescheduled (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total amount of debt rescheduled includes the debt stock, principal, interest, charges and penalties rescheduled. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total amount of debt rescheduled includes the debt stock, principal, interest, charges and penalties rescheduled. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "57" }, { "id": "DT.UND.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Undisbursed external debt, total (UND, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Undisbursed debt" } ], "source_id": "57" }, { "id": "DT.UND.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Undisbursed external debt, official creditors (UND, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Undisbursed debt" } ], "source_id": "57" }, { "id": "DT.UND.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Undisbursed external debt, private creditors (UND, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Undisbursed debt" } ], "source_id": "57" }, { "id": "EE.BOD.CGLS.ZS", "metatype": [ { "id": "IndicatorName", "value": "Water pollution, clay and glass industry (% of total BOD emissions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Industry shares of emissions of organic water pollutants refer to emissions from manufacturing activities as defined by two-digit divisions of the International Standard Industrial Classification (ISIC), revision 2: stone, ceramics, and glass (36). Emissions of organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "1998 study by Hemamala Hettige, Muthukumara Mani, and David Wheeler, \"Industrial Pollution in Economic Development: Kuznets Revisited\" (available at www.worldbank.org/nipr). The data were updated through 2005 by the World Bank's Development Research Group using the same methodology as the initial study." }, { "id": "Topic", "value": "Environment: Water pollution" } ], "source_id": "57" }, { "id": "EE.BOD.CHEM.ZS", "metatype": [ { "id": "IndicatorName", "value": "Water pollution, chemical industry (% of total BOD emissions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Industry shares of emissions of organic water pollutants refer to emissions from manufacturing activities as defined by two-digit divisions of the International Standard Industrial Classification (ISIC), revision 2: chemicals (35). Emissions of organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "1998 study by Hemamala Hettige, Muthukumara Mani, and David Wheeler, \"Industrial Pollution in Economic Development: Kuznets Revisited\" (available at www.worldbank.org/nipr). The data were updated through 2005 by the World Bank's Development Research Group using the same methodology as the initial study." }, { "id": "Topic", "value": "Environment: Water pollution" } ], "source_id": "57" }, { "id": "EE.BOD.FOOD.ZS", "metatype": [ { "id": "IndicatorName", "value": "Water pollution, food industry (% of total BOD emissions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Industry shares of emissions of organic water pollutants refer to emissions from manufacturing activities as defined by two-digit divisions of the International Standard Industrial Classification (ISIC), revision 2: food and beverages (31). Emissions of organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "1998 study by Hemamala Hettige, Muthukumara Mani, and David Wheeler, \"Industrial Pollution in Economic Development: Kuznets Revisited\" (available at www.worldbank.org/nipr). The data were updated through 2005 by the World Bank's Development Research Group using the same methodology as the initial study." }, { "id": "Topic", "value": "Environment: Water pollution" } ], "source_id": "57" }, { "id": "EE.BOD.MTAL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Water pollution, metal industry (% of total BOD emissions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Industry shares of emissions of organic water pollutants refer to emissions from manufacturing activities as defined by two-digit divisions of the International Standard Industrial Classification (ISIC), revision 2: primary metals (ISIC division 37). Emissions of organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "1998 study by Hemamala Hettige, Muthukumara Mani, and David Wheeler, \"Industrial Pollution in Economic Development: Kuznets Revisited\" (available at www.worldbank.org/nipr). The data were updated through 2005 by the World Bank's Development Research Group using the same methodology as the initial study." }, { "id": "Topic", "value": "Environment: Water pollution" } ], "source_id": "57" }, { "id": "EE.BOD.OTHR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Water pollution, other industry (% of total BOD emissions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Industry shares of emissions of organic water pollutants refer to emissions from manufacturing activities as defined by two-digit divisions of the International Standard Industrial Classification (ISIC), revision 2: other (38 and 39). Emissions of organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "1998 study by Hemamala Hettige, Muthukumara Mani, and David Wheeler, \"Industrial Pollution in Economic Development: Kuznets Revisited\" (available at www.worldbank.org/nipr). The data were updated through 2005 by the World Bank's Development Research Group using the same methodology as the initial study." }, { "id": "Topic", "value": "Environment: Water pollution" } ], "source_id": "57" }, { "id": "EE.BOD.PAPR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Water pollution, paper and pulp industry (% of total BOD emissions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Industry shares of emissions of organic water pollutants refer to emissions from manufacturing activities as defined by two-digit divisions of the International Standard Industrial Classification (ISIC), revision 2: paper and pulp (34). Emissions of organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "1998 study by Hemamala Hettige, Muthukumara Mani, and David Wheeler, \"Industrial Pollution in Economic Development: Kuznets Revisited\" (available at www.worldbank.org/nipr). The data were updated through 2005 by the World Bank's Development Research Group using the same methodology as the initial study." }, { "id": "Topic", "value": "Environment: Water pollution" } ], "source_id": "57" }, { "id": "EE.BOD.TOTL.KG", "metatype": [ { "id": "Developmentrelevance", "value": "Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations." }, { "id": "IndicatorName", "value": "Organic water pollutant (BOD) emissions (kg per day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.\n\nThe data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.\n\nUnlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD." }, { "id": "Longdefinition", "value": "Emissions of organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "1998 study by Hemamala Hettige, Muthukumara Mani, and David Wheeler, \"Industrial Pollution in Economic Development: Kuznets Revisited\" (available at www.worldbank.org/nipr). The data were updated through 2005 by the World Bank's Development Research Group using the same methodology as the initial study." }, { "id": "Statisticalconceptandmethodology", "value": "Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.\n\nThe estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.\n\nBOD refers to biochemical oxygen demand." }, { "id": "Topic", "value": "Environment: Water pollution" } ], "source_id": "57" }, { "id": "EE.BOD.TXTL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Water pollution, textile industry (% of total BOD emissions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Industry shares of emissions of organic water pollutants refer to emissions from manufacturing activities as defined by two-digit divisions of the International Standard Industrial Classification (ISIC), revision 2: textiles (32). Emissions of organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "1998 study by Hemamala Hettige, Muthukumara Mani, and David Wheeler, \"Industrial Pollution in Economic Development: Kuznets Revisited\" (available at www.worldbank.org/nipr). The data were updated through 2005 by the World Bank's Development Research Group using the same methodology as the initial study." }, { "id": "Topic", "value": "Environment: Water pollution" } ], "source_id": "57" }, { "id": "EE.BOD.WOOD.ZS", "metatype": [ { "id": "IndicatorName", "value": "Water pollution, wood industry (% of total BOD emissions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Industry shares of emissions of organic water pollutants refer to emissions from manufacturing activities as defined by two-digit divisions of the International Standard Industrial Classification (ISIC), revision 2: wood (33). Emissions of organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "1998 study by Hemamala Hettige, Muthukumara Mani, and David Wheeler, \"Industrial Pollution in Economic Development: Kuznets Revisited\" (available at www.worldbank.org/nipr). The data were updated through 2005 by the World Bank's Development Research Group using the same methodology as the initial study." }, { "id": "Topic", "value": "Environment: Water pollution" } ], "source_id": "57" }, { "id": "EE.BOD.WRKR.KG", "metatype": [ { "id": "Developmentrelevance", "value": "Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations." }, { "id": "IndicatorName", "value": "Organic water pollutant (BOD) emissions (kg per day per worker)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.\n\nThe data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.\n\nUnlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD." }, { "id": "Longdefinition", "value": "Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank and UNIDO's industry database." }, { "id": "Statisticalconceptandmethodology", "value": "Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time. \n\nThe estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.\n\nBOD refers to biochemical oxygen demand." }, { "id": "Topic", "value": "Environment: Water pollution" } ], "source_id": "57" }, { "id": "EG.CFT.ACCS.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Access to clean fuels and technologies for cooking, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to clean fuels and technologies for cooking, rural is the proportion of rural population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Tracking SDG 7: The Energy Progress Report, International Energy Agency (IEA), note: License: Creative Commons Attribution—NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO);\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data for access to clean fuels and technologies for cooking are based on the World Health Organization’s (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Trends in the proportion of the population using each fuel type are estimated using a single multivariate hierarchical model, with urban and rural disaggregation. Estimates for overall ‘polluting’ fuels (unprocessed biomass, charcoal, coal, and kerosene) and ‘clean’ fuels (gaseous fuels, electricity, as well as an aggregation of any other clean fuels like alcohol) are produced by aggregating estimates of relevant fuel types. The model was used to derive clean fuel use estimates for 191 countries (ref. Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (2020), Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. J. R. Stat. Soc. C, 69: 815-839). Countries classified by the World Bank as high income (57 countries) in the 2022 fiscal year are assumed to have universal access to clean fuels and technologies for cooking." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of rural population" } ], "source_id": "57" }, { "id": "EG.CFT.ACCS.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Access to clean fuels and technologies for cooking, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to clean fuels and technologies for cooking, urban is the proportion of urban population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Tracking SDG 7: The Energy Progress Report, International Energy Agency (IEA), note: License: Creative Commons Attribution—NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO);\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data for access to clean fuels and technologies for cooking are based on the World Health Organization’s (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Trends in the proportion of the population using each fuel type are estimated using a single multivariate hierarchical model, with urban and rural disaggregation. Estimates for overall ‘polluting’ fuels (unprocessed biomass, charcoal, coal, and kerosene) and ‘clean’ fuels (gaseous fuels, electricity, as well as an aggregation of any other clean fuels like alcohol) are produced by aggregating estimates of relevant fuel types. The model was used to derive clean fuel use estimates for 191 countries (ref. Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (2020), Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. J. R. Stat. Soc. C, 69: 815-839). Countries classified by the World Bank as high income (57 countries) in the 2022 fiscal year are assumed to have universal access to clean fuels and technologies for cooking." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of urban population" } ], "source_id": "57" }, { "id": "EG.CFT.ACCS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Access to clean fuels and technologies for cooking (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to clean fuels and technologies for cooking is the proportion of total population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Tracking SDG 7: The Energy Progress Report, International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistical Division (UNSD), World Bank, World Health Organization (WHO), uri: https://trackingsdg7.esmap.org/, note: License: Creative Commons Attribution—NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO), publisher: International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistical Division (UNSD), World Bank, World Health Organization (WHO), date published: 2025-06;\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data for access to clean fuels and technologies for cooking are based on the World Health Organization’s (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Trends in the proportion of the population using each fuel type are estimated using a single multivariate hierarchical model, with urban and rural disaggregation. Estimates for overall ‘polluting’ fuels (unprocessed biomass, charcoal, coal, and kerosene) and ‘clean’ fuels (gaseous fuels, electricity, as well as an aggregation of any other clean fuels like alcohol) are produced by aggregating estimates of relevant fuel types. The model was used to derive clean fuel use estimates for 191 countries (ref. Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (2020), Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. J. R. Stat. Soc. C, 69: 815-839). Countries classified by the World Bank as high income are assumed to have universal access to clean fuels and technologies for cooking." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "57" }, { "id": "EG.EGY.PRIM.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Energy intensity level of primary energy (MJ/$2021 PPP GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Energy intensity level is only an imperfect proxy to energy efficiency indicator and it can be affected by a number of factors not necessarily linked to pure efficiency such as climate." }, { "id": "Longdefinition", "value": "Energy intensity level of primary energy is the ratio between energy supply and gross domestic product measured at purchasing power parity. Energy intensity is an indication of how much energy is used to produce one unit of economic output. Lower ratio indicates that less energy is used to produce one unit of output." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Tracking SDG 7: The Energy Progress Report, International Energy Agency (IEA), note: License: Creative Commons Attribution—NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO);\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is obtained by dividing total primary energy supply over gross domestic product measured in constant 2021 US dollars at purchasing power parity." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "MJ per 2021 USD PPP GDP" } ], "source_id": "57" }, { "id": "EG.EGY.PROD.KT.OE", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Developmentrelevance", "value": "Total energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity.\n\nIn developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Energy production (kt of oil equivalent)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Energy production refers to forms of primary energy--petroleum (crude oil, natural gas liquids, and oil from nonconventional sources), natural gas, solid fuels (coal, lignite, and other derived fuels), and combustible renewables and waste--and primary electricity, all converted into oil equivalents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Statisticalconceptandmethodology", "value": "Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\n\nData for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences. All forms of energy - primary energy and primary electricity - are converted into oil equivalents. A notional thermal efficiency of 33 percent is assumed for converting nuclear electricity into oil equivalents and 100 percent efficiency for converting hydroelectric power." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "57" }, { "id": "EG.ELC.ACCS.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnergy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indication of a country's energy poverty status.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nElectricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas." }, { "id": "IndicatorName", "value": "Access to electricity, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to electricity, rural is the percentage of rural population with access to electricity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "SDG 7.1.1 Electrification Dataset, World Bank (WB), uri: https://trackingsdg7.esmap.org/downloads, note: Data is downloaded from ESMAP website. Data is released when a new Tracking SDG7 report is released., publisher: World Bank (WB), date accessed: 2024-05-16, date published: 2023" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank’s Global Electrification Database (GED) compiles nationally representative household survey data, and occasionally census data, from sources going back as far as 1990. The database also incorporates data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), Middle East and North Africa Poverty Database (MNAPOV) and the Europe and Central Asia Poverty Database (ECAPOV), which are based on similar surveys. At the time of this analysis, the GED contained 1,375 surveys for 149 countries in 1990-2021.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of rural population" } ], "source_id": "57" }, { "id": "EG.ELC.ACCS.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnergy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indication of a country's energy poverty status.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nElectricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas." }, { "id": "IndicatorName", "value": "Access to electricity, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to electricity, urban is the percentage of urban population with access to electricity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "SDG 7.1.1 Electrification Dataset, World Bank (WB), uri: https://trackingsdg7.esmap.org/downloads, note: Data is downloaded from ESMAP website. Data is released when a new Tracking SDG7 report is released., publisher: World Bank (WB), date accessed: 2024-05-16, date published: 2023" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank’s Global Electrification Database (GED) compiles nationally representative household survey data, and occasionally census data, from sources going back as far as 1990. The database also incorporates data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), Middle East and North Africa Poverty Database (MNAPOV) and the Europe and Central Asia Poverty Database (ECAPOV), which are based on similar surveys. At the time of this analysis, the GED contained 1,375 surveys for 149 countries in 1990-2021.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of urban population" } ], "source_id": "57" }, { "id": "EG.ELC.ACCS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnergy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indication of a country's energy poverty status.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nElectricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas." }, { "id": "IndicatorName", "value": "Access to electricity (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to electricity is the percentage of population with access to electricity. Electrification data are collected from industry, national surveys and international sources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "SDG 7.1.1 Electrification Dataset, World Bank (WB), uri: https://trackingsdg7.esmap.org/downloads, note: Data is downloaded from ESMAP website. Data is released when a new Tracking SDG7 report is released., publisher: World Bank (WB), date accessed: 2024-05-16, date published: 2023" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank’s Global Electrification Database (GED) compiles nationally representative household survey data, and occasionally census data, from sources going back as far as 1990. The database also incorporates data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), Middle East and North Africa Poverty Database (MNAPOV) and the Europe and Central Asia Poverty Database (ECAPOV), which are based on similar surveys. At the time of this analysis, the GED contained 1,375 surveys for 149 countries in 1990-2021." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "57" }, { "id": "EG.ELC.COAL.KH", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Since the beginning of the 21st century, coal has been the fastest-growing global energy source; it currently provides about 40 percent of the world's electricity needs. Coal is the second source of primary energy in the world after oil, and the first source of electricity generation. The last decade's growth in coal use has been driven by the economic growth of developing economies, mainly China. Irrespective of its economic benefits for the countries, the environmental impact of coal use, especially that coming from carbon dioxide emissions, is significant, and efforts are underway globally to build more efficient plants, to retrofit old plants and to decommission the oldest and least efficient coal plants.\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Electricity production from coal sources (kWh)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Sources of electricity refer to the inputs used to generate electricity. Coal refers to all coal and brown coal, both primary (including hard coal and lignite-brown coal) and derived fuels (including patent fuel, coke oven coke, gas coke, coke oven gas, and blast furnace gas). Peat is also included in this category." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Statisticalconceptandmethodology", "value": "Electricity production is total number of kWh generated by power plants separated into electricity plants and CHP plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "57" }, { "id": "EG.ELC.COAL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Since the beginning of the 21st century, coal has been the fastest-growing global energy source; it currently provides about 40 percent of the world's electricity needs. Coal is the second source of primary energy in the world after oil, and the first source of electricity generation.. The last decade's growth in coal use has been driven by the economic growth of developing economies, mainly China. Irrespective of its economic benefits for the countries, the environmental impact of coal use, especially that coming from carbon dioxide emissions, is significant, and efforts are underway globally to build more efficient plants, to retrofit old plants and to decommission the oldest and least efficient coal plants.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "IndicatorName", "value": "Electricity production from coal sources (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "The share of electricity production from coal sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Coal refers to all coal and brown coal, both primary (including hard coal and lignite-brown coal) and derived fuels (including patent fuel, coke oven coke, gas coke, coke oven gas, and blast furnace gas). Peat is also included in this category." }, { "id": "Othernotes", "value": "Electricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production is total number of kilowatt-hours (kWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity production" } ], "source_id": "57" }, { "id": "EG.ELC.FOSL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Oil, gas and coal, which are fossil fuels, account for well over 70% of the World's electricity generation. Irrespective of its economic benefits for the countries, the environmental impact of fossil fuels use, especially that coming from carbon dioxide emissions, is significant, and efforts are underway globally to build more efficient plants, to retrofit old plants and to decommission the oldest and least efficient plants as well as to transition to renewable energy sources.\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "IndicatorName", "value": "Electricity production from oil, gas and coal sources (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The share of electricity production from oil. gas and coal sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Oil refers to crude oil and petroleum products. Gas refers to natural gas but excludes natural gas liquids. Coal refers to all coal and brown coal, both primary (including hard coal and lignite-brown coal) and derived fuels (including patent fuel, coke oven coke, gas coke, coke oven gas, and blast furnace gas). Peat is also included in this category." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production from oil, gas and coal sources (% of total) is the share of electricity produced by oil and petroleum products, natural gas, which is natural gas but not natural gas liquids, and coal in total electricity production which is the total number of Gigawattt-hours (GWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity production" } ], "source_id": "57" }, { "id": "EG.ELC.HYRO.KH", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Electrical energy from hydropower is derived from turbines being driven by flowing water in rivers, with or without man-made dams forming reservoirs. Presently, hydropower is the world's largest source of renewable electricity. Hydropower represents the largest share of renewable electricity production. It was second only to wind power for new-built capacities between 2005 and 2010. IEA estimates that hydropower could produce up to 6,000 terawatt-hours in 2050, roughly twice as much as today.\n\nHydropower's storage capacity and fast response characteristics are especially valuable to meet sudden fluctuations in electricity demand and to match supply from less flexible electricity sources and variable renewable sources, such as solar photovoltaic (PV) and wind power.\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Electricity production from hydroelectric sources (kWh)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Sources of electricity refer to the inputs used to generate electricity. Hydropower refers to electricity produced by hydroelectric power plants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Statisticalconceptandmethodology", "value": "Electricity production is total number of kWh generated by power plants separated into electricity plants and CHP plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "57" }, { "id": "EG.ELC.HYRO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Electrical energy from hydropower is derived from turbines being driven by flowing water in rivers, with or without man-made dams forming reservoirs. Presently, hydropower is the world's largest source of renewable electricity. Hydropower represents the largest share of renewable electricity production. It was second only to wind power for new-built capacities between 2005 and 2010. IEA estimates that hydropower could produce up to 6,000 terawatt-hours in 2050, roughly twice as much as today.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHydropower's storage capacity and fast response characteristics are especially valuable to meet sudden fluctuations in electricity demand and to match supply from less flexible electricity sources and variable renewable sources, such as solar photovoltaic (PV) and wind power.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "IndicatorName", "value": "Electricity production from hydroelectric sources (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "The share of electricity production from hydroelectric sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Hydropower refers to electricity produced by hydroelectric power plants." }, { "id": "Othernotes", "value": "Electricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production is total number of kilowatt-hours (kWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity production" } ], "source_id": "57" }, { "id": "EG.ELC.LOSS.KH", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "An economy's production and consumption of electricity are basic indicators of its size and level of development. Although a few countries export electric power, most production is for domestic consumption. Expanding the supply of electricity to meet the growing demand of increasingly urbanized and industrialized economies without incurring unacceptable social, economic, and environmental costs is one of the great challenges facing developing countries.\n\nModern societies are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies. Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world.\n\nIn developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Electric power transmission and distribution losses (kWh)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Limitationsandexceptions", "value": "Electricity consumption is equivalent to production less power plants' own use and transmission, distribution, and transformation losses less exports plus imports. It includes consumption by auxiliary stations, losses in transformers that are considered integral parts of those stations, and electricity produced by pumping installations. Where data are available, it covers electricity generated by primary sources of energy - coal, oil, gas, nuclear, hydro, geothermal, wind, tide and wave, and combustible renewables. Neither production nor consumption data capture the reliability of supplies, including breakdowns, load factors, and frequency of outages." }, { "id": "Longdefinition", "value": "Electric power transmission and distribution losses include losses in transmission between sources of supply and points of distribution and in the distribution to consumers, including pilferage." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Statisticalconceptandmethodology", "value": "Data on electric power production and consumption are collected from national energy agencies by the International Energy Agency (IEA) and adjusted by the IEA to meet international definitions. Electric power transmission and distribution losses percentage of output is the share of electric power transmission and distribution losses to electricity production which is the total number of GWh generated by power plants separated into electricity plants and CHP plants." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "57" }, { "id": "EG.ELC.LOSS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "An economy's production and consumption of electricity are basic indicators of its size and level of development. Although a few countries export electric power, most production is for domestic consumption. Expanding the supply of electricity to meet the growing demand of increasingly urbanized and industrialized economies without incurring unacceptable social, economic, and environmental costs is one of the great challenges facing developing countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nModern societies are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies. Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions." }, { "id": "IndicatorName", "value": "Electric power transmission and distribution losses (% of output)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Electricity consumption is equivalent to production less power plants' own use and transmission, distribution, and transformation losses less exports plus imports. It includes consumption by auxiliary stations, losses in transformers that are considered integral parts of those stations, and electricity produced by pumping installations. Where data are available, it covers electricity generated by primary sources of energy - coal, oil, gas, nuclear, hydro, geothermal, wind, tide and wave, and combustible renewables. Neither production nor consumption data capture the reliability of supplies, including breakdowns, load factors, and frequency of outages." }, { "id": "Longdefinition", "value": "Electric power transmission and distribution losses include losses in transmission between sources of supply and points of distribution and in the distribution to consumers, including pilferage. The losses are expressed as a share of the total output." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on electric power production and consumption are collected from national energy agencies by the International Energy Agency (IEA) and adjusted by the IEA to meet international definitions. Electric power transmission and distribution losses percentage of output is the share of electric power transmission and distribution losses to electricity production which is the total number of GWh generated by power plants separated into electricity plants and combined heat and power (CHP) plants." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity output" } ], "source_id": "57" }, { "id": "EG.ELC.NGAS.KH", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Natural gas is considered a good source of electricity supply for a number of economic, operational and environmental reasons, such as:\n\n1) it is technically and financially of low-risk;\n2) lower carbon relative to other fossil fuels;\n3) gas plants can be built relatively quickly in around two years, unlike nuclear facilities, which can take much longer.\n\nAlso, gas plants are flexible both in technical and economic terms, so they can react quickly to demand peaks, and are ideally twinned with intermittent renewable options such as wind power.\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Electricity production from natural gas sources (kWh)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Sources of electricity refer to the inputs used to generate electricity. Gas refers to natural gas but excludes natural gas liquids." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Statisticalconceptandmethodology", "value": "Electricity production from natural gas sources (kWh) is the share of natutal gas, which is natural gas but not natural gas liquids, in total electricity production which is the total number of GWh generated by power plants separated into electricity plants and CHP plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "57" }, { "id": "EG.ELC.NGAS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Natural gas is considered a good source of electricity supply for a number of economic, operational and environmental reasons, such as:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n1) it is technically and financially of low-risk;\n\n\n\n\n\n\n\n\n\n2) lower carbon relative to other fossil fuels;\n\n\n\n\n\n\n\n\n\n3) gas plants can be built relatively quickly in around two years, unlike nuclear facilities, which can take much longer.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAlso, gas plants are flexible both in technical and economic terms, so they can react quickly to demand peaks, and are ideally twinned with intermittent renewable options such as wind power.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "IndicatorName", "value": "Electricity production from natural gas sources (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "The share of electricity production from natural gas sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Gas refers to natural gas but excludes natural gas liquids." }, { "id": "Othernotes", "value": "Electricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production from natural gas sources (% of total) is the share of natural gas, which is natural gas but not natural gas liquids, in total electricity production which is the total number of Gigawattt-hours (GWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity production" } ], "source_id": "57" }, { "id": "EG.ELC.NUCL.KH", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "The generation of electricity using nuclear energy was first demonstrated in the 1950s, and the first commercial nuclear power plants entered operation in the early 1960s. Nuclear capacity grew rapidly in the 1970s and 1980s as countries sought to reduce dependence on fossil fuels, especially after the oil crises of the 1970s. There was a renewed interest in nuclear energy from 2000, and 60 new countries expressed interest in launching a nuclear program to the International Atomic Energy Agency (IAEA). However, after the earthquake and tsunami devastation of the Pacific coast of northern Japan, most nuclear countries announced safety reviews of their nuclear reactors (stress tests) and the revision/improvement of their plans to address similar emergency situations; countries such as Germany and Italy decided to eventually phase out nuclear power or to abandon their nuclear plant projects.\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Electricity production from nuclear sources (kWh)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Sources of electricity refer to the inputs used to generate electricity. Nuclear power refers to electricity produced by nuclear power plants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Statisticalconceptandmethodology", "value": "Electricity production from nuclear sources is the electricity produced by nuclear power plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "57" }, { "id": "EG.ELC.NUCL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The generation of electricity using nuclear energy was first demonstrated in the 1950s, and the first commercial nuclear power plants entered operation in the early 1960s. Nuclear capacity grew rapidly in the 1970s and 1980s as countries sought to reduce dependence on fossil fuels, especially after the oil crises of the 1970s. There was a renewed interest in nuclear energy from 2000, and 60 new countries expressed interest in launching a nuclear program to the International Atomic Energy Agency (IAEA). However, after the earthquake and tsunami devastation of the Pacific coast of northern Japan, most nuclear countries announced safety reviews of their nuclear reactors (stress tests) and the revision/improvement of their plans to address similar emergency situations; countries such as Germany and Italy decided to eventually phase out nuclear power or to abandon their nuclear plant projects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "IndicatorName", "value": "Electricity production from nuclear sources (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "The share of electricity production from nuclear sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Nuclear power refers to electricity produced by nuclear power plants." }, { "id": "Othernotes", "value": "Electricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production from nuclear sources (% of total) is the share of electricity produced by nuclear power plants in total electricity production which is the total number of Gigawattt-hours (GWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity production" } ], "source_id": "57" }, { "id": "EG.ELC.PETR.KH", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Oil includes crude oil, condensates, natural gas liquids, refinery feedstocks and additives, other hydrocarbons (including emulsified oils, synthetic crude oil, mineral oils extracted from bituminous minerals such as oil shale, and bituminous sand) and petroleum products (refinery gas, ethane, LPG, aviation gasoline, motor gasoline, jet fuels, kerosene, gas/diesel oil, heavy fuel oil, naphtha, white spirit, lubricants, bitumen, paraffin waxes and petroleum coke).\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Electricity production from oil sources (kWh)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Sources of electricity refer to the inputs used to generate electricity. Oil refers to crude oil and petroleum products." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Statisticalconceptandmethodology", "value": "Electricity production from oil sources (kWh) is the electricity produced from crude oil and petroleum products. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "57" }, { "id": "EG.ELC.PETR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Oil includes crude oil, condensates, natural gas liquids, refinery feedstocks and additives, other hydrocarbons (including emulsified oils, synthetic crude oil, mineral oils extracted from bituminous minerals such as oil shale, and bituminous sand) and petroleum products (refinery gas, ethane, LPG, aviation gasoline, motor gasoline, jet fuels, kerosene, gas/diesel oil, heavy fuel oil, naphtha, white spirit, lubricants, bitumen, paraffin waxes and petroleum coke).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "IndicatorName", "value": "Electricity production from oil sources (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on access to electricity are collected by the IEA from industry, national surveys, and international sources." }, { "id": "Longdefinition", "value": "The share of electricity production from oil sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Oil refers to crude oil and petroleum products." }, { "id": "Othernotes", "value": "Electricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production from oil sources (% of total) is the share of electricity produced by oil and petroleum products in total electricity production which is the total number of Gigawattt-hours (GWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity production" } ], "source_id": "57" }, { "id": "EG.ELC.PROD.KH", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Developmentrelevance", "value": "Use of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Electricity production (kWh)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Electricity production is measured at the terminals of all alternator sets in a station. In addition to hydropower, coal, oil, gas, and nuclear power generation, it covers generation by geothermal, solar, wind, and tide and wave energy, as well as that from combustible renewables and waste. Production includes the output of electricity plants that are designed to produce electricity only as well as that of combined heat and power plants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Statisticalconceptandmethodology", "value": "Electricity production includes the output of electric plants designed to produce electricity only, as well as that of combined heat and power plants. It is the total number of GWh generated by power plants separated into electricity plants and CHP plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "57" }, { "id": "EG.ELC.RNEW.KH", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Electricity production from renewable sources (kWh)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Longdefinition", "value": "Electricity production from renewable sources includes hydropower, geothermal, solar, tides, wind, biomass, and biofuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "57" }, { "id": "EG.ELC.RNEW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Renewable energy sources are essential for reducing greenhouse gas emissions and combating climate change. They help decrease dependence on fossil fuels, enhancing energy security and price stability. The sector also drives economic growth by creating jobs and attracting investment. Technological advancements in renewables support innovation in storage, smart grids, and sustainable infrastructure. Additionally, they improve energy access in remote areas, promoting social and economic development worldwide." }, { "id": "IndicatorName", "value": "Renewable electricity output (% of total electricity output)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Renewable electricity is the share of electrity generated by renewable power plants in total electricity generated by all types of plants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of renewable electricity output is calculated using the formula:\n\n\n\n\n\nRenewable Electricity Share(%)=(Electricity from Renewable Sources (MWh) / Total Electricity Output (MWh))×100\n\n\n\n\n\nWhere:\n\n\n\n\n\nElectricity from Renewable Sources = Total electricity generated from hydropower, wind, solar, biomass, geothermal, and ocean energy.\n\n\n\n\n\nTotal Electricity Output = Sum of electricity generated from all sources, including fossil fuels, nuclear, and renewables." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of electricity output" } ], "source_id": "57" }, { "id": "EG.ELC.RNWX.KH", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Renewable energy sources are essential for reducing greenhouse gas emissions and combating climate change. They help decrease dependence on fossil fuels, enhancing energy security and price stability. The sector also drives economic growth by creating jobs and attracting investment. Technological advancements in renewables support innovation in storage, smart grids, and sustainable infrastructure. Additionally, they improve energy access in remote areas, promoting social and economic development worldwide." }, { "id": "IndicatorName", "value": "Electricity production from renewable sources, excluding hydroelectric (kWh)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Electricity production from renewable sources in kilowatt-hour (kWh), excluding hydroelectric, includes geothermal, solar, tides, wind, biomass, and biofuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production from renewable sources in kilowatt-hour (kWh) is the amount of electricity produced by geothermal, solar photovoltaic, solar thermal, tide, wind, industrial waste, municipal waste, primary solid biofuels, biogases, biogasoline, biodiesels, other liquid biofuels, nonspecified primary biofuels and waste, and charcoal in total electricity production which is the total number of GWh generated by power plants separated into electricity plants and combined heat power (CHP) plants. Hydropower is excluded. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "kilowatt-hour (kWh)" } ], "source_id": "57" }, { "id": "EG.ELC.RNWX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Renewable energy sources are essential for reducing greenhouse gas emissions and combating climate change. They help decrease dependence on fossil fuels, enhancing energy security and price stability. The sector also drives economic growth by creating jobs and attracting investment. Technological advancements in renewables support innovation in storage, smart grids, and sustainable infrastructure. Additionally, they improve energy access in remote areas, promoting social and economic development worldwide." }, { "id": "IndicatorName", "value": "Electricity production from renewable sources, excluding hydroelectric (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "The share of electricity production from renewable sources of total electricity production. Electricity production from renewable sources, excluding hydroelectric, includes geothermal, solar, tides, wind, biomass, and biofuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production from renewable sources (% of total) is the share of electricity produced by geothermal, solar photovoltaic, solar thermal, tide, wind, industrial waste, municipal waste, primary solid biofuels, biogases, biogasoline, biodiesels, other liquid biofuels, nonspecified primary biofuels and waste, and charcoal in total electricity production which is the total number of Gigawattt-hours (GWh) generated by power plants separated into electricity plants and CHP plants. Hydropower is excluded. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity production" } ], "source_id": "57" }, { "id": "EG.FEC.RNEW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Renewable energy consumption (% of total final energy consumption)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Renewable energy consumption is the share of renewables energy in total final energy consumption." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2022" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The numerator includes the direct consumption of renewable energy sources plus the final consumption of gross electricity and heat estimated to have come from renewable sources, while the denominator is the total final energy consumption of all energy products.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of final energy consumption" } ], "source_id": "57" }, { "id": "EG.GDP.PUSE.KO.PP", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis." }, { "id": "IndicatorName", "value": "GDP per unit of energy use (PPP $ per kg of oil equivalent)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GDP per unit of energy use is the PPP GDP per kilogram of oil equivalent of energy use. PPP GDP is gross domestic product converted to current international dollars using purchasing power parity rates based on the 2017 ICP round. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: GDP per unit of energy use, measured as PPP $ per kg of oil equivalent, is calculated by dividing the gross domestic product (PPP) by the total energy consumption, expressed in kilograms of oil equivalent." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "PPP $ per kg of oil equivalent" } ], "source_id": "57" }, { "id": "EG.GDP.PUSE.KO.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFossil fuels are non-renewable resources because they take millions of years to form, and reserves are being depleted much faster than new ones are being made. In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis." }, { "id": "IndicatorName", "value": "GDP per unit of energy use (constant 2021 PPP $ per kg of oil equivalent)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "GDP per unit of energy use is the PPP GDP per kilogram of oil equivalent of energy use. PPP GDP is gross domestic product converted to 2021 constant international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The ratio of gross domestic product (GDP) to energy use indicates energy efficiency. To produce comparable and consistent estimates of real GDP across economies relative to physical inputs to GDP - that is, units of energy use - GDP is converted to 2021 international dollars using purchasing power parity (PPP) rates. Differences in this ratio over time and across economies reflect structural changes in an economy, changes in sectoral energy efficiency, and differences in fuel mixes. Total energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. GDP data are from World Bank's national accounts files." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "constant 2021 PPP $ per kg of oil equivalent" } ], "source_id": "57" }, { "id": "EG.IMP.CONS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Modern energy services are crucial to a country's economic development. Access to modern energy is essential for the provision of clean water, sanitation and healthcare and for the provision of reliable and efficient lighting, heating, cooking, mechanical power, and transport and telecommunications services.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions." }, { "id": "IndicatorName", "value": "Energy imports, net (% of energy use)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Net energy imports are estimated as gross imports less gross exports, both measured in tons of oil equivalents (toe). A negative value indicates that the country is a net exporter. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nA negative value in energy imports indicates that the country is a net exporter. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of energy use" } ], "source_id": "57" }, { "id": "EG.NSF.ACCS.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to non-solid fuel, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to non-solid fuel, rural is the percentage of rural population with access to non-solid fuel." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Sustainable Energy for All (SE4ALL) database from WHO Global Household Energy database." }, { "id": "Statisticalconceptandmethodology", "value": "Data for access to Non-Solid Fuel are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). To develop the historical evolution of Non-Solid Fuel Use rates, a multi-level non-parametrical mixed model, using both fixed and random effects, was used to derive solid fuel use estimates for 150 countries (ref. Bonjour S, Adair-Rohani H, Wolf J, Bruce NG, Mehta S, Prüss-Ustün A, Lahiff M, Rehfuess EA, Mishra V, Smith KR. Solid Fuel Use for Household Cooking: Country and Regional Estimates for 1980-2010. Environ Health Perspect (): .doi:10.1289/ehp.1205987.). For a country with no data, estimates are derived by using regional trends or assumed to be universal access if a country is classified as developed by the United Nations." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "57" }, { "id": "EG.NSF.ACCS.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to non-solid fuel, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to non-solid fuel, urban is the percentage of urban population with access to non-solid fuel." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Sustainable Energy for All (SE4ALL) database from WHO Global Household Energy database." }, { "id": "Statisticalconceptandmethodology", "value": "Data for access to Non-Solid Fuel are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). To develop the historical evolution of Non-Solid Fuel Use rates, a multi-level non-parametrical mixed model, using both fixed and random effects, was used to derive solid fuel use estimates for 150 countries (ref. Bonjour S, Adair-Rohani H, Wolf J, Bruce NG, Mehta S, Prüss-Ustün A, Lahiff M, Rehfuess EA, Mishra V, Smith KR. Solid Fuel Use for Household Cooking: Country and Regional Estimates for 1980-2010. Environ Health Perspect (): .doi:10.1289/ehp.1205987.). For a country with no data, estimates are derived by using regional trends or assumed to be universal access if a country is classified as developed by the United Nations." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "57" }, { "id": "EG.NSF.ACCS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Access to non-solid fuel (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to non-solid fuel is the percentage of population with access to non-solid fuel." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Sustainable Energy for All (SE4ALL) database from WHO Global Household Energy database." }, { "id": "Statisticalconceptandmethodology", "value": "Data for access to Non-Solid Fuel are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). To develop the historical evolution of Non-Solid Fuel Use rates, a multi-level non-parametrical mixed model, using both fixed and random effects, was used to derive solid fuel use estimates for 150 countries (ref. Bonjour S, Adair-Rohani H, Wolf J, Bruce NG, Mehta S, Prüss-Ustün A, Lahiff M, Rehfuess EA, Mishra V, Smith KR. Solid Fuel Use for Household Cooking: Country and Regional Estimates for 1980-2010. Environ Health Perspect (): .doi:10.1289/ehp.1205987.). For a country with no data, estimates are derived by using regional trends or assumed to be universal access if a country is classified as developed by the United Nations." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "57" }, { "id": "EG.USE.COMM.CL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Alternative energy is produced without the undesirable consequences of the burning of fossil fuels, such as high carbon dioxide emissions, which is considered to be the major contributing factor of global warming.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPast few decade have seen a rise in global investment in renewable energy, led by wind and solar. In transport, major car companies are adding hybrid and full-electric vehicles to their product lines and many governments have launched plans to encourage consumers to buy these vehicles Fossil fuels continue to outpace alternative and renewable energy growth. Coal has been the fastest-growing global energy source, meeting about one-half of new electricity demand.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions." }, { "id": "IndicatorName", "value": "Alternative and nuclear energy (% of total energy use)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Clean energy is noncarbohydrate energy that does not produce carbon dioxide when generated. It includes hydropower and nuclear, geothermal, and solar power, among others. This is the share of total energy supply that is non-fossil." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of energy use" } ], "source_id": "57" }, { "id": "EG.USE.COMM.FO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Fossil fuels are non-renewable resources because they take millions of years to form, and reserves are being depleted much faster than new ones are being made. In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity." }, { "id": "IndicatorName", "value": "Fossil fuel energy consumption (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Fossil fuel comprises coal, oil, petroleum, and natural gas products." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences. All forms of energy - primary energy and primary electricity - are converted into oil equivalents. A notional thermal efficiency of 33 percent is assumed for converting nuclear electricity into oil equivalents and 100 percent efficiency for converting hydroelectric power." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of energy consumption" } ], "source_id": "57" }, { "id": "EG.USE.COMM.GD.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "\"In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\nFossil fuels are non-renewable resources because they take millions of years to form, and reserves are being depleted much faster than new ones are being made. In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\"" }, { "id": "IndicatorName", "value": "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2021 PPP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Energy use per PPP GDP is the kilogram of oil equivalent of energy use per constant PPP GDP. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport. PPP GDP is gross domestic product converted to 2021 constant international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by dividing the total energy use (in kg of oil equivalent) by the total GDP (in constant 2021 PPP dollars) and then multiplying by 1000, to express the energy use per $1,000 of GDP." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "kg of oil equivalent per $1,000 GDP constant 2021 PPP" } ], "source_id": "57" }, { "id": "EG.USE.COMM.KT.OE", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Developmentrelevance", "value": "In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\nFossil fuels are non-renewable resources because they take millions of years to form, and reserves are being depleted much faster than new ones are being made. In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Energy use (kt of oil equivalent)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Statisticalconceptandmethodology", "value": "Total energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. Data for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences. All forms of energy - primary energy and primary electricity - are converted into oil equivalents. A notional thermal efficiency of 33 percent is assumed for converting nuclear electricity into oil equivalents and 100 percent efficiency for converting hydroelectric power.\n\nUnit kt refers to kilotonnes." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "57" }, { "id": "EG.USE.CRNW.KT.OE", "metatype": [ { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Combustible renewables and waste (metric tons of oil equivalent)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Longdefinition", "value": "Combustible renewables and waste comprise solid biomass, liquid biomass, biogas, industrial waste, and municipal waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "57" }, { "id": "EG.USE.CRNW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Total energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRenewable energy is derived from natural processes (e.g. sunlight and wind) that are replenished at a higher rate than they are consumed. Solar, wind, geothermal, hydro, and biomass are common sources of renewable energy. Majority of renewable energy in the world is from solid biofuels and hydroelectricity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRenewable sources of energy have been the driver of much of the growth in the global clean energy sector in the past few decades. Recent years have seen a major scale-up of wind and solar photovoltaic (PV) technologies. Other renewable technologies - including hydropower, geothermal and biomass - continued to grow from a strong established base, adding hundreds of gigawatts of new capacity worldwide.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions." }, { "id": "IndicatorName", "value": "Combustible renewables and waste (% of total energy)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Combustible renewables and waste comprise solid biomass, liquid biomass, biogas, industrial waste, and municipal waste, measured as a percentage of total energy use. The indicator expresses the share of total energy supply." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\n\n\n\n\n\n\n\nThe indicator is calculated as the share of biofuels and waste in the total energy supply of the country.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences. All forms of energy - primary energy and primary electricity - are converted into oil equivalents. A notional thermal efficiency of 33 percent is assumed for converting nuclear electricity into oil equivalents and 100 percent efficiency for converting hydroelectric power." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "% (share) of total energy" } ], "source_id": "57" }, { "id": "EG.USE.ELEC.KH", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "An economy's production and consumption of electricity are basic indicators of its size and level of development. Although a few countries export electric power, most production is for domestic consumption. Expanding the supply of electricity to meet the growing demand of increasingly urbanized and industrialized economies without incurring unacceptable social, economic, and environmental costs is one of the great challenges facing developing countries.\n\nModern societies are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies. Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world.\n\nIn developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Electric power consumption (kWh)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Limitationsandexceptions", "value": "Data on electric power production and consumption are collected from national energy agencies by the International Energy Agency (IEA) and adjusted by the IEA to meet international definitions. Data are reported as net consumption as opposed to gross consumption. Net consumption excludes the energy consumed by the generating units. For all countries except the United States, total electric power consumption is equal total net electricity generation plus electricity imports minus electricity exports minus electricity distribution losses. IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Electric power consumption measures the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Statisticalconceptandmethodology", "value": "Electric power consumption (kWh ) is the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants. Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. Electricity consumption is equivalent to production less power plants' own use and transmission, distribution, and transformation losses less exports plus imports. It includes consumption by auxiliary stations, losses in transformers that are considered integral parts of those stations, and electricity produced by pumping installations. Where data are available, it covers electricity generated by primary sources of energy - coal, oil, gas, nuclear, hydro, geothermal, wind, tide and wave, and combustible renewables. Neither production nor consumption data capture the reliability of supplies, including breakdowns, load factors, and frequency of outages." }, { "id": "Topic", "value": "Environment: Energy production & use" } ], "source_id": "57" }, { "id": "EG.USE.ELEC.KH.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "An economy's production and consumption of electricity are basic indicators of its size and level of development. Although a few countries export electric power, most production is for domestic consumption. Expanding the supply of electricity to meet the growing demand of increasingly urbanized and industrialized economies without incurring unacceptable social, economic, and environmental costs is one of the great challenges facing developing countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nModern societies are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies. Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions." }, { "id": "IndicatorName", "value": "Electric power consumption (kWh per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on electric power production and consumption are collected from national energy agencies by the International Energy Agency (IEA) and adjusted by the IEA to meet international definitions. Data are reported as net consumption as opposed to gross consumption. Net consumption excludes the energy consumed by the generating units. For all countries except the United States, total electric power consumption is equal total net electricity generation plus electricity imports minus electricity exports minus electricity distribution losses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Electric power consumption measures the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electric power consumption per capita (kWh ) is the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants, divided by midyear population. Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. Electricity consumption is equivalent to production less power plants' own use and transmission, distribution, and transformation losses less exports plus imports. It includes consumption by auxiliary stations, losses in transformers that are considered integral parts of those stations, and electricity produced by pumping installations. Where data are available, it covers electricity generated by primary sources of energy - coal, oil, gas, nuclear, hydro, geothermal, wind, tide and wave, and combustible renewables. Neither production nor consumption data capture the reliability of supplies, including breakdowns, load factors, and frequency of outages." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "kilowatt-hour (kWh) per capita" } ], "source_id": "57" }, { "id": "EG.USE.PCAP.KG.OE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions." }, { "id": "IndicatorName", "value": "Energy use (kg of oil equivalent per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Total energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. World Bank population estimates are used to calculate per capita data.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnergy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences. All forms of energy - primary energy and primary electricity - are converted into oil equivalents. A notional thermal efficiency of 33 percent is assumed for converting nuclear electricity into oil equivalents and 100 percent efficiency for converting hydroelectric power." }, { "id": "Topic", "value": "Environment: Energy production & use" }, { "id": "Unitofmeasure", "value": "kg of oil equivalent per capita" } ], "source_id": "57" }, { "id": "EN.AGR.EMPL", "metatype": [ { "id": "IndicatorName", "value": "Economically active population in agriculture (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Agricultural employment shows the number of workers in the agricultural sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, Production Yearbook and data files." }, { "id": "Topic", "value": "Environment: Agricultural production" } ], "source_id": "57" }, { "id": "EN.ATM.CO2E.EG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "CO2 intensity (kg per kg of oil equivalent energy use)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The U.S. Department of Energy's Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division's World Energy Data Set) and world cement manufacturing (from the U.S. Department of Interior's Geological Survey, USGS 2011). Although estimates of global carbon dioxide emissions are probably accurate within 10 percent (as calculated from global average fuel chemistry and use), country estimates may have larger error bounds. Trends estimated from a consistent time series tend to be more accurate than individual values.\n\nEach year the CDIAC recalculates the entire time series since 1949, incorporating recent findings and corrections. Estimates exclude fuels supplied to ships and aircraft in international transport because of the difficulty of apportioning the fuels among benefiting countries.\n\nData for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as deforestation." }, { "id": "Longdefinition", "value": "Carbon dioxide emissions from solid fuel consumption refer mainly to emissions from use of coal as an energy source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States." }, { "id": "Statisticalconceptandmethodology", "value": "Carbon intensity is the ratio of carbon dioxide per unit of energy, or the amount of carbon dioxide emitted as a result of using one unit of energy in production. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nCarbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Carbon dioxide emissions are often calculated and reported as elemental carbon. The values were converted to actual carbon dioxide mass by multiplying them by 3.667 (the ratio of the mass of carbon to that of carbon dioxide)." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.CO2E.GF.KT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nAn emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "CO2 emissions from gaseous fuel consumption (kt)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The U.S. Department of Energy's Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division's World Energy Data Set) and world cement manufacturing (from the U.S. Department of Interior's Geological Survey, USGS 2011). Although estimates of global carbon dioxide emissions are probably accurate within 10 percent (as calculated from global average fuel chemistry and use), country estimates may have larger error bounds. Trends estimated from a consistent time series tend to be more accurate than individual values.\n\nEach year the CDIAC recalculates the entire time series since 1949, incorporating recent findings and corrections. Estimates exclude fuels supplied to ships and aircraft in international transport because of the difficulty of apportioning the fuels among benefiting countries." }, { "id": "Longdefinition", "value": "Carbon dioxide emissions from liquid fuel consumption refer mainly to emissions from use of natural gas as an energy source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States." }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Data for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as deforestation. Carbon dioxide emissions are often calculated and reported as elemental carbon. The values were converted to actual carbon dioxide mass by multiplying them by 3.667 (the ratio of the mass of carbon to that of carbon dioxide)." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.CO2E.GF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nAn emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "CO2 emissions from gaseous fuel consumption (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The U.S. Department of Energy's Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division's World Energy Data Set) and world cement manufacturing (from the U.S. Department of Interior's Geological Survey, USGS 2011). Although estimates of global carbon dioxide emissions are probably accurate within 10 percent (as calculated from global average fuel chemistry and use), country estimates may have larger error bounds. Trends estimated from a consistent time series tend to be more accurate than individual values.\n\nEach year the CDIAC recalculates the entire time series since 1949, incorporating recent findings and corrections. Estimates exclude fuels supplied to ships and aircraft in international transport because of the difficulty of apportioning the fuels among benefiting countries." }, { "id": "Longdefinition", "value": "Carbon dioxide emissions from liquid fuel consumption refer mainly to emissions from use of natural gas as an energy source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States." }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Data for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as deforestation. Carbon dioxide emissions are often calculated and reported as elemental carbon. The values were converted to actual carbon dioxide mass by multiplying them by 3.667 (the ratio of the mass of carbon to that of carbon dioxide)." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.CO2E.KD.GD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2015" }, { "id": "IndicatorName", "value": "CO2 emissions (kg per 2015 US$ of GDP)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Longdefinition", "value": "Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.KD for the denominator's source." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.CO2E.KT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "CO2 emissions (kt)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Limitationsandexceptions", "value": "This series excludes Land-use Change & Forestry (LUCF). \n\nThe world data includes international bunker fuel-related emissions and emissions from territories not part of the United Nations Framework Convention on Climate Change (UNFCCC)." }, { "id": "Longdefinition", "value": "Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions" }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Data for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as deforestation. The unit of measurement is kt (kiloton). Carbon dioxide emissions are often calculated and reported as elemental carbon. The were converted to actual carbon dioxide mass by multiplying them by 3.667 (the ratio of the mass of carbon to that of carbon dioxide)." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.CO2E.LF.KT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nAn emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "CO2 emissions from liquid fuel consumption (kt)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The U.S. Department of Energy's Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division's World Energy Data Set) and world cement manufacturing (from the U.S. Department of Interior's Geological Survey, USGS 2011). Although estimates of global carbon dioxide emissions are probably accurate within 10 percent (as calculated from global average fuel chemistry and use), country estimates may have larger error bounds. Trends estimated from a consistent time series tend to be more accurate than individual values.\n\nEach year the CDIAC recalculates the entire time series since 1949, incorporating recent findings and corrections. Estimates exclude fuels supplied to ships and aircraft in international transport because of the difficulty of apportioning the fuels among benefiting countries." }, { "id": "Longdefinition", "value": "Carbon dioxide emissions from liquid fuel consumption refer mainly to emissions from use of petroleum-derived fuels as an energy source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States." }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Data for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as deforestation. Carbon dioxide emissions are often calculated and reported as elemental carbon. The values were converted to actual carbon dioxide mass by multiplying them by 3.667 (the ratio of the mass of carbon to that of carbon dioxide)." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.CO2E.LF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nAn emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "CO2 emissions from liquid fuel consumption (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The U.S. Department of Energy's Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division's World Energy Data Set) and world cement manufacturing (from the U.S. Department of Interior's Geological Survey, USGS 2011). Although estimates of global carbon dioxide emissions are probably accurate within 10 percent (as calculated from global average fuel chemistry and use), country estimates may have larger error bounds. Trends estimated from a consistent time series tend to be more accurate than individual values.\n\nEach year the CDIAC recalculates the entire time series since 1949, incorporating recent findings and corrections. Estimates exclude fuels supplied to ships and aircraft in international transport because of the difficulty of apportioning the fuels among benefiting countries." }, { "id": "Longdefinition", "value": "Carbon dioxide emissions from liquid fuel consumption refer mainly to emissions from use of petroleum-derived fuels as an energy source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States." }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Data for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as deforestation. Carbon dioxide emissions are often calculated and reported as elemental carbon. The values were converted to actual carbon dioxide mass by multiplying them by 3.667 (the ratio of the mass of carbon to that of carbon dioxide)." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.CO2E.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "CO2 emissions (metric tons per capita)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Longdefinition", "value": "Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Emissions data are sourced from Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions" }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Data for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as deforestation." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.CO2E.PP.GD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "CO2 emissions (kg per PPP $ of GDP)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Longdefinition", "value": "Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.PP.CD for the denominator's source." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.CO2E.PP.GD.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2021" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "CO2 emissions (kg per 2021 PPP $ of GDP)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Limitationsandexceptions", "value": "The U.S. Department of Energy's Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division's World Energy Data Set) and world cement manufacturing (from the U.S. Department of Interior's Geological Survey, USGS 2011). Although estimates of global carbon dioxide emissions are probably accurate within 10 percent (as calculated from global average fuel chemistry and use), country estimates may have larger error bounds. Trends estimated from a consistent time series tend to be more accurate than individual values.\n\nEach year the CDIAC recalculates the entire time series since 1949, incorporating recent findings and corrections. Estimates exclude fuels supplied to ships and aircraft in international transport because of the difficulty of apportioning the fuels among benefiting countries.\n\nData for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as deforestation." }, { "id": "Longdefinition", "value": "Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.PP.KD for the denominator's source." }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Carbon dioxide emissions are often calculated and reported as elemental carbon. The values were converted to actual carbon dioxide mass by multiplying them by 3.667 (the ratio of the mass of carbon to that of carbon dioxide)." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.CO2E.SF.KT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nAn emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "CO2 emissions from solid fuel consumption (kt)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The U.S. Department of Energy's Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division's World Energy Data Set) and world cement manufacturing (from the U.S. Department of Interior's Geological Survey, USGS 2011). Although estimates of global carbon dioxide emissions are probably accurate within 10 percent (as calculated from global average fuel chemistry and use), country estimates may have larger error bounds. Trends estimated from a consistent time series tend to be more accurate than individual values.\n\nEach year the CDIAC recalculates the entire time series since 1949, incorporating recent findings and corrections. Estimates exclude fuels supplied to ships and aircraft in international transport because of the difficulty of apportioning the fuels among benefiting countries." }, { "id": "Longdefinition", "value": "Carbon dioxide emissions from solid fuel consumption refer mainly to emissions from use of coal as an energy source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States." }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Data for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as deforestation. The unit of measurement is kt (kiloton). Carbon dioxide emissions are often calculated and reported as elemental carbon. The values were converted to actual carbon dioxide mass by multiplying them by 3.667 (the ratio of the mass of carbon to that of carbon dioxide)." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.CO2E.SF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nAn emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "CO2 emissions from solid fuel consumption (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The U.S. Department of Energy's Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division's World Energy Data Set) and world cement manufacturing (from the U.S. Department of Interior's Geological Survey, USGS 2011). Although estimates of global carbon dioxide emissions are probably accurate within 10 percent (as calculated from global average fuel chemistry and use), country estimates may have larger error bounds. Trends estimated from a consistent time series tend to be more accurate than individual values.\n\nEach year the CDIAC recalculates the entire time series since 1949, incorporating recent findings and corrections. Estimates exclude fuels supplied to ships and aircraft in international transport because of the difficulty of apportioning the fuels among benefiting countries." }, { "id": "Longdefinition", "value": "Carbon dioxide emissions from solid fuel consumption refer mainly to emissions from use of coal as an energy source." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States." }, { "id": "Statisticalconceptandmethodology", "value": "The U.S. Department of Energy's Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division's World Energy Data Set) and world cement manufacturing (from the U.S. Department of Interior's Geological Survey (USGS 2011)). Although estimates of global carbon dioxide emissions are probably accurate within 10 percent (as calculated from global average fuel chemistry and use), country estimates may have larger error bounds. Trends estimated from a consistent time series tend to be more accurate than individual values.\n\nEach year the CDIAC recalculates the entire time series since 1949, incorporating recent findings and corrections. Estimates exclude fuels supplied to ships and aircraft in international transport because of the difficulty of apportioning the fuels among benefiting countries." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.GHGO.KT.CE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "The addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), Sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National reporting to the United Nations Framework Convention on Climate Change that follows the Intergovernmental Panel on Climate Change guidelines is based on national emission inventories and covers all sources of anthropogenic carbon dioxide emissions as well as carbon sinks (such as forests). To estimate emissions, the countries that are Parties to the Climate Change Convention (UNFCCC) use complex, state-of-the-art methodologies recommended by the Intergovernmental Panel on Climate Change (IPCC)." }, { "id": "Longdefinition", "value": "Other greenhouse gas emissions are by-product emissions of hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates from original source: European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR): http://edgar.jrc.ec.europa.eu/." }, { "id": "Statisticalconceptandmethodology", "value": "Other greenhouse gas emissions are by-product emissions of hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride (F-gases (c-C4F8 GWP=8700, C2F6 GWP=9200, C3F8 GWP=7000, C4F10 GWP=7000, C5F12 GWP=7500, C6F14 GWP=7400, C7F16 GWP=7820, CF4 GWP=6500, HFC-125 GWP=2800, HFC-134a GWP=1300, HFC-143a GWP=3800, HFC-152a GWP=140, HFC-227ea GWP=2900, HFC-23 GWP=11700, HFC-236fa GWP=6300, HFC-245fa GWP=858, HFC-32 GWP=650, HFC-365mfc GWP=804, HFC-43-10-mee GWP=1300, SF6 GWP=23900). Derived as residuals from total GHG emissions, CO2 emissions, CH4 emissions, and N2O emissions in kt of CO equivalent. Other greenhouse gases covered under the Kyoto Protocol are hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. Although emissions of these artificial gases are small, they are more powerful greenhouse gases than carbon dioxide, with much higher atmospheric lifetimes and high global warming potential. The emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the effective contributions of different gases to be compared." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.GHGO.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), Sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "Other greenhouse gas emissions (% change from 1990)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National reporting to the United Nations Framework Convention on Climate Change that follows the Intergovernmental Panel on Climate Change guidelines is based on national emission inventories and covers all sources of anthropogenic carbon dioxide emissions as well as carbon sinks (such as forests). To estimate emissions, the countries that are Parties to the Climate Change Convention (UNFCCC) use complex, state-of-the-art methodologies recommended by the Intergovernmental Panel on Climate Change (IPCC)." }, { "id": "Longdefinition", "value": "Other greenhouse gas emissions are by-product emissions of hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. Each year of data shows the percentage change to that year from 1990." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates from original source: European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR): http://edgar.jrc.ec.europa.eu/." }, { "id": "Statisticalconceptandmethodology", "value": "Other greenhouse gas emissions are by-product emissions of hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride (F-gases (c-C4F8 GWP=8700, C2F6 GWP=9200, C3F8 GWP=7000, C4F10 GWP=7000, C5F12 GWP=7500, C6F14 GWP=7400, C7F16 GWP=7820, CF4 GWP=6500, HFC-125 GWP=2800, HFC-134a GWP=1300, HFC-143a GWP=3800, HFC-152a GWP=140, HFC-227ea GWP=2900, HFC-23 GWP=11700, HFC-236fa GWP=6300, HFC-245fa GWP=858, HFC-32 GWP=650, HFC-365mfc GWP=804, HFC-43-10-mee GWP=1300, SF6 GWP=23900). Derived as residuals from total GHG emissions, CO2 emissions, CH4 emissions, and N2O emissions in kt of CO equivalent. Other greenhouse gases covered under the Kyoto Protocol are hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. Although emissions of these artificial gases are small, they are more powerful greenhouse gases than carbon dioxide, with much higher atmospheric lifetimes and high global warming potential. The emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the effective contributions of different gases to be compared." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.GHGT.KT.CE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "The addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), Sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "Total greenhouse gas emissions (kt of CO2 equivalent)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Limitationsandexceptions", "value": "This series excludes Land-use Change & Forestry (LUCF). \n\nThe world data includes international bunker fuel-related emissions and emissions from territories not part of the United Nations Framework Convention on Climate Change (UNFCCC)." }, { "id": "Longdefinition", "value": "Total greenhouse gas emissions in kt of CO2 equivalent are composed of CO2 totals excluding short-cycle biomass burning (such as agricultural waste burning and savanna burning) but including other biomass burning (such as forest fires, post-burn decay, peat fires and decay of drained peatlands), all anthropogenic CH4 sources, N2O sources and F-gases (HFCs, PFCs and SF6)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions" }, { "id": "Statisticalconceptandmethodology", "value": "The GHG totals are expressed in CO2 equivalent using the GWP100 metric of the Second Assessment Report of IPCC and include CO2 (GWP100=1), CH4 (GWP100=21), N2O (GWP100=310) and F-gases (c-C4F8 GWP=8700, C2F6 GWP=9200, C3F8 GWP=7000, C4F10 GWP=7000, C5F12 GWP=7500, C6F14 GWP=7400, C7F16 GWP=7820, CF4 GWP=6500, HFC-125 GWP=2800, HFC-134a GWP=1300, HFC-143a GWP=3800, HFC-152a GWP=140, HFC-227ea GWP=2900, HFC-23 GWP=11700, HFC-236fa GWP=6300, HFC-245fa GWP=858, HFC-32 GWP=650, HFC-365mfc GWP=804, HFC-43-10-mee GWP=1300, SF6 GWP=23900)." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.GHGT.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), Sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "Total greenhouse gas emissions (% change from 1990)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National reporting to the United Nations Framework Convention on Climate Change that follows the Intergovernmental Panel on Climate Change guidelines is based on national emission inventories and covers all sources of anthropogenic carbon dioxide emissions as well as carbon sinks (such as forests). To estimate emissions, the countries that are Parties to the Climate Change Convention (UNFCCC) use complex, state-of-the-art methodologies recommended by the Intergovernmental Panel on Climate Change (IPCC)." }, { "id": "Longdefinition", "value": "Total greenhouse gas emissions are composed of CO2 totals excluding short-cycle biomass burning (such as agricultural waste burning and savanna burning) but including other biomass burning (such as forest fires, post-burn decay, peat fires and decay of drained peatlands), all anthropogenic CH4 sources, N2O sources and F-gases (HFCs, PFCs and SF6). Each year of data shows the percentage change to that year from 1990." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates from original source: European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR): http://edgar.jrc.ec.europa.eu/." }, { "id": "Statisticalconceptandmethodology", "value": "The GHG totals are expressed in CO2 equivalent using the GWP100 metric of the Second Assessment Report of IPCC and include CO2 (GWP100=1), CH4 (GWP100=21), N2O (GWP100=310) and F-gases (c-C4F8 GWP=8700, C2F6 GWP=9200, C3F8 GWP=7000, C4F10 GWP=7000, C5F12 GWP=7500, C6F14 GWP=7400, C7F16 GWP=7820, CF4 GWP=6500, HFC-125 GWP=2800, HFC-134a GWP=1300, HFC-143a GWP=3800, HFC-152a GWP=140, HFC-227ea GWP=2900, HFC-23 GWP=11700, HFC-236fa GWP=6300, HFC-245fa GWP=858, HFC-32 GWP=650, HFC-365mfc GWP=804, HFC-43-10-mee GWP=1300, SF6 GWP=23900)." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.HFCG.KT.CE", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "IndicatorName", "value": "HFC gas emissions (thousand metric tons of CO2 equivalent)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Hydrofluorocarbons, used as a replacement for chlorofluorocarbons, are used mainly in refrigeration and semiconductor manufacturing." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR): http://edgar.jrc.ec.europa.eu/" }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.METH.AG.KT.CE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "The addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), Sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "Agricultural methane emissions (thousand metric tons of CO2 equivalent)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Limitationsandexceptions", "value": "This series excludes Land-use Change & Forestry (LUCF). \n\nThe world data includes international bunker fuel-related emissions and emissions from territories not part of the United Nations Framework Convention on Climate Change (UNFCCC)." }, { "id": "Longdefinition", "value": "Agricultural methane emissions are emissions from animals, animal waste, rice production, agricultural waste burning (nonenergy, on-site), and savanna burning." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions" }, { "id": "Statisticalconceptandmethodology", "value": "IPCC category 4 = Agriculture. Expressed in CO2 equivalent using the GWP100 metric of the Second Assessment Report of IPCC and include CH4 (GWP100=21). Methane emissions result largely from agricultural activities, industrial production landfills and wastewater treatment, and other sources such as tropical forest and other vegetation fires. The emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the effective contributions of different gases to be compared. A kilogram of methane is 21 times as effective at trapping heat in the earth's atmosphere as a kilogram of carbon dioxide within 100 years. The emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the effective contributions of different gases to be compared." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.METH.AG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), Sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "Agricultural methane emissions (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National reporting to the United Nations Framework Convention on Climate Change that follows the Intergovernmental Panel on Climate Change guidelines is based on national emission inventories and covers all sources of anthropogenic carbon dioxide emissions as well as carbon sinks (such as forests). To estimate emissions, the countries that are Parties to the Climate Change Convention (UNFCCC) use complex, state-of-the-art methodologies recommended by the Intergovernmental Panel on Climate Change (IPCC)." }, { "id": "Longdefinition", "value": "Agricultural methane emissions are emissions from animals, animal waste, rice production, agricultural waste burning (nonenergy, on-site), and savanna burning." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates from original source: European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR): http://edgar.jrc.ec.europa.eu/." }, { "id": "Statisticalconceptandmethodology", "value": "IPCC category 4 = Agriculture. Methane emissions result largely from agricultural activities, industrial production landfills and wastewater treatment, and other sources such as tropical forest and other vegetation fires. The emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the effective contributions of different gases to be compared. A kilogram of methane is 21 times as effective at trapping heat in the earth's atmosphere as a kilogram of carbon dioxide within 100 years." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.METH.EG.KT.CE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "The addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), Sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Limitationsandexceptions", "value": "This series excludes Land-use Change & Forestry (LUCF). \n\nThe world data includes international bunker fuel-related emissions and emissions from territories not part of the United Nations Framework Convention on Climate Change (UNFCCC)." }, { "id": "Longdefinition", "value": "Methane emissions from energy processes are emissions from the production, handling, transmission, and combustion of fossil fuels and biofuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions" }, { "id": "Statisticalconceptandmethodology", "value": "IPCC category 1 = Energy. Expressed in CO2 equivalent using the GWP100 metric of the Second Assessment Report of IPCC and include CH4 (GWP100=21). Methane emissions are those stemming from human activities such as agriculture and from industrial methane production. The emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the effective contributions of different gases to be compared. A kilogram of methane is 21 times as effective at trapping heat in the earth's atmosphere as a kilogram of carbon dioxide within 100 years. The unit of measurement is kt (kiloton) of carbon dioxide equivalent." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.METH.EG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), Sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "Energy related methane emissions (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National reporting to the United Nations Framework Convention on Climate Change that follows the Intergovernmental Panel on Climate Change guidelines is based on national emission inventories and covers all sources of anthropogenic carbon dioxide emissions as well as carbon sinks (such as forests). To estimate emissions, the countries that are Parties to the Climate Change Convention (UNFCCC) use complex, state-of-the-art methodologies recommended by the Intergovernmental Panel on Climate Change (IPCC)." }, { "id": "Longdefinition", "value": "Methane emissions from energy processes are emissions from the production, handling, transmission, and combustion of fossil fuels and biofuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates from original source: European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR): http://edgar.jrc.ec.europa.eu/." }, { "id": "Statisticalconceptandmethodology", "value": "IPCC category 1 = Energy. Methane emissions result largely from agricultural activities, industrial production landfills and wastewater treatment, and other sources such as tropical forest and other vegetation fires. The emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the effective contributions of different gases to be compared. A kilogram of methane is 21 times as effective at trapping heat in the earth's atmosphere as a kilogram of carbon dioxide within 100 years. The emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the effective contributions of different gases to be compared." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.METH.KT.CE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "The addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), Sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "Methane emissions (kt of CO2 equivalent)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Limitationsandexceptions", "value": "This series excludes Land-use Change & Forestry (LUCF). \n\nThe world data includes international bunker fuel-related emissions and emissions from territories not part of the United Nations Framework Convention on Climate Change (UNFCCC)." }, { "id": "Longdefinition", "value": "Methane emissions are those stemming from human activities such as agriculture and from industrial methane production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions" }, { "id": "Statisticalconceptandmethodology", "value": "Methane emissions are those stemming from human activities such as agriculture and from industrial methane production. Expressed in CO2 equivalent using the GWP100 metric of the Second Assessment Report of IPCC and include CH4 (GWP100=21). The emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the effective contributions of different gases to be compared. A kilogram of methane is 21 times as effective at trapping heat in the earth's atmosphere as a kilogram of carbon dioxide within 100 years." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.METH.PC", "metatype": [ { "id": "IndicatorName", "value": "Methane emissions (metric tons of CO2 equivalent per capita)" }, { "id": "License_Type", "value": "Data from CAIT carry a Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0)." }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Longdefinition", "value": "Methane emissions are those stemming from human activities such as agriculture and from industrial methane production." }, { "id": "Source", "value": "Emissions data are sourced from Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions" }, { "id": "Topic", "value": "Environment: Emissions & pollution" } ], "source_id": "57" }, { "id": "EN.ATM.METH.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), Sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "Methane emissions (% change from 1990)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National reporting to the United Nations Framework Convention on Climate Change that follows the Intergovernmental Panel on Climate Change guidelines is based on national emission inventories and covers all sources of anthropogenic carbon dioxide emissions as well as carbon sinks (such as forests). To estimate emissions, the countries that are Parties to the Climate Change Convention (UNFCCC) use complex, state-of-the-art methodologies recommended by the Intergovernmental Panel on Climate Change (IPCC)." }, { "id": "Longdefinition", "value": "Methane emissions are those stemming from human activities such as agriculture and from industrial methane production. Each year of data shows the percentage change to that year from 1990." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates from original source: European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR): http://edgar.jrc.ec.europa.eu/." }, { "id": "Statisticalconceptandmethodology", "value": "Methane emissions are those stemming from human activities such as agriculture and from industrial methane production. Expressed in CO2 equivalent using the GWP100 metric of the Second Assessment Report of IPCC and include CH4 (GWP100=21). The emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the effective contributions of different gases to be compared. A kilogram of methane is 21 times as effective at trapping heat in the earth's atmosphere as a kilogram of carbon dioxide within 100 years." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.NOXE.AG.KT.CE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "The addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), Sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Limitationsandexceptions", "value": "This series excludes Land-use Change & Forestry (LUCF). \n\nThe world data includes international bunker fuel-related emissions and emissions from territories not part of the United Nations Framework Convention on Climate Change (UNFCCC)." }, { "id": "Longdefinition", "value": "Agricultural nitrous oxide emissions are emissions produced through fertilizer use (synthetic and animal manure), animal waste management, agricultural waste burning (nonenergy, on-site), and savanna burning." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions" }, { "id": "Statisticalconceptandmethodology", "value": "Agricultural nitrous oxide emissions are emissions produced through fertilizer use (synthetic and animal manure), animal waste management, agricultural waste burning (nonenergy, on-site), and savannah burning. IPCC category 4 = Agriculture. Expressed in CO2 equivalent using the GWP100 metric of the Second Assessment Report of IPCC and include N2O (GWP100=310)." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.NOXE.AG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), Sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "Agricultural nitrous oxide emissions (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National reporting to the United Nations Framework Convention on Climate Change that follows the Intergovernmental Panel on Climate Change guidelines is based on national emission inventories and covers all sources of anthropogenic carbon dioxide emissions as well as carbon sinks (such as forests). To estimate emissions, the countries that are Parties to the Climate Change Convention (UNFCCC) use complex, state-of-the-art methodologies recommended by the Intergovernmental Panel on Climate Change (IPCC)." }, { "id": "Longdefinition", "value": "Agricultural nitrous oxide emissions are emissions produced through fertilizer use (synthetic and animal manure), animal waste management, agricultural waste burning (nonenergy, on-site), and savanna burning." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates from original source: European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR): http://edgar.jrc.ec.europa.eu/." }, { "id": "Statisticalconceptandmethodology", "value": "Agricultural nitrous oxide emissions are emissions produced through fertilizer use (synthetic and animal manure), animal waste management, agricultural waste burning (nonenergy, on-site), and savannah burning. IPCC category 4 = Agriculture. Expressed in CO2 equivalent using the GWP100 metric of the Second Assessment Report of IPCC and include N2O (GWP100=310)." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.NOXE.EG.KT.CE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "The addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), Sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Limitationsandexceptions", "value": "This series excludes Land-use Change & Forestry (LUCF). \n\nThe world data includes international bunker fuel-related emissions and emissions from territories not part of the United Nations Framework Convention on Climate Change (UNFCCC)." }, { "id": "Longdefinition", "value": "Nitrous oxide emissions from energy processes are emissions produced by the combustion of fossil fuels and biofuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions" }, { "id": "Statisticalconceptandmethodology", "value": "IPCC category 1 = Energy. Expressed in CO2 equivalent using the GWP100 metric of the Second Assessment Report of IPCC and include N2O (GWP100=310). Nitrous oxide emissions are mainly from fossil fuel combustion, fertilizers, rainforest fires, and animal waste. Nitrous oxide is a powerful greenhouse gas, with an estimated atmospheric lifetime of 114 years, compared with 12 years for methane. The per kilogram global warming potential of nitrous oxide is nearly 310 times that of carbon dioxide within 100 years. The emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the effective contributions of different gases to be compared." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.NOXE.EG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), Sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "Nitrous oxide emissions in energy sector (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National reporting to the United Nations Framework Convention on Climate Change that follows the Intergovernmental Panel on Climate Change guidelines is based on national emission inventories and covers all sources of anthropogenic carbon dioxide emissions as well as carbon sinks (such as forests). To estimate emissions, the countries that are Parties to the Climate Change Convention (UNFCCC) use complex, state-of-the-art methodologies recommended by the Intergovernmental Panel on Climate Change (IPCC)." }, { "id": "Longdefinition", "value": "Nitrous oxide emissions from energy processes are emissions produced by the combustion of fossil fuels and biofuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates from original source: European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR): http://edgar.jrc.ec.europa.eu/." }, { "id": "Statisticalconceptandmethodology", "value": "Nitrous oxide emissions are mainly from fossil fuel combustion, fertilizers, rainforest fires, and animal waste. Nitrous oxide is a powerful greenhouse gas, with an estimated atmospheric lifetime of 114 years, compared with 12 years for methane. The per kilogram global warming potential of nitrous oxide is nearly 310 times that of carbon dioxide within 100 years. The emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the effective contributions of different gases to be compared." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.NOXE.KT.CE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "The addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), Sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Limitationsandexceptions", "value": "This series excludes Land-use Change & Forestry (LUCF). \n\nThe world data includes international bunker fuel-related emissions and emissions from territories not part of the United Nations Framework Convention on Climate Change (UNFCCC)." }, { "id": "Longdefinition", "value": "Nitrous oxide emissions are emissions from agricultural biomass burning, industrial activities, and livestock management." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions" }, { "id": "Statisticalconceptandmethodology", "value": "Nitrous oxide emissions are mainly from fossil fuel combustion, fertilizers, rainforest fires, and animal waste. Nitrous oxide is a powerful greenhouse gas, with an estimated atmospheric lifetime of 114 years, compared with 12 years for methane. The per kilogram global warming potential of nitrous oxide is nearly 310 times that of carbon dioxide within 100 years. The emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the effective contributions of different gases to be compared." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.NOXE.PC", "metatype": [ { "id": "IndicatorName", "value": "Nitrous oxide emissions (metric tons of CO2 equivalent per capita)" }, { "id": "License_Type", "value": "Data from CAIT carry a Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0)." }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Longdefinition", "value": "Nitrous oxide emissions are emissions from agricultural biomass burning, industrial activities, and livestock management." }, { "id": "Source", "value": "Emissions data are sourced from Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions" }, { "id": "Topic", "value": "Environment: Emissions & pollution" } ], "source_id": "57" }, { "id": "EN.ATM.NOXE.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), Sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "Nitrous oxide emissions (% change from 1990)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National reporting to the United Nations Framework Convention on Climate Change that follows the Intergovernmental Panel on Climate Change guidelines is based on national emission inventories and covers all sources of anthropogenic carbon dioxide emissions as well as carbon sinks (such as forests). To estimate emissions, the countries that are Parties to the Climate Change Convention (UNFCCC) use complex, state-of-the-art methodologies recommended by the Intergovernmental Panel on Climate Change (IPCC)." }, { "id": "Longdefinition", "value": "Nitrous oxide emissions are emissions from agricultural biomass burning, industrial activities, and livestock management. Each year of data shows the percentage change to that year from 1990." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates from original source: European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR): http://edgar.jrc.ec.europa.eu/." }, { "id": "Statisticalconceptandmethodology", "value": "Nitrous oxide emissions are mainly from fossil fuel combustion, fertilizers, rainforest fires, and animal waste. Nitrous oxide is a powerful greenhouse gas, with an estimated atmospheric lifetime of 114 years, compared with 12 years for methane. The per kilogram global warming potential of nitrous oxide is nearly 310 times that of carbon dioxide within 100 years. The emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the effective contributions of different gases to be compared." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.PFCG.KT.CE", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "IndicatorName", "value": "PFC gas emissions (thousand metric tons of CO2 equivalent)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Perfluorocarbons, used as a replacement for chlorofluorocarbons in manufacturing semiconductors, are a byproduct of aluminum smelting and uranium enrichment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR): http://edgar.jrc.ec.europa.eu/" }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.PM10.MC.M3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Indoor and outdoor air pollution place a major burden on world health. More than half the world’s people rely on dung, wood, crop waste, or coal to meet basic energy needs. Cooking and heating with these fuels on open fires or stoves without chimneys lead to indoor air pollution, which is responsible for 1.6 million deaths a year - one every 20 seconds. In many urban areas air pollution exposure is the main environmental threat to health. Long-term exposure to high levels of soot and small particles contributes to such health effects as respiratory diseases, lung cancer, and heart disease. Particulate pollution, alone or with sulfur dioxide, creates an enormous burden of ill health." }, { "id": "IndicatorName", "value": "PM10, country level (micrograms per cubic meter)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Thus these data should be considered only a general indication of air quality, and comparisons should be made with caution. They allow for cross-country comparisons of the relative risk of particulate matter pollution facing urban residents. Major sources of urban outdoor particulate matter pollution are traffic and industrial emissions, but nonanthropogenic sources such as dust storms may also be a substantial contributor for some cities. Country technology and pollution controls are important determinants of particulate matter. Current WHO air quality guidelines are annual mean concentrations of 20 micrograms per cubic meter for particulate matter less than 10 microns in diameter." }, { "id": "Longdefinition", "value": "Particulate matter concentrations refer to fine suspended particulates less than 10 microns in diameter (PM10) that are capable of penetrating deep into the respiratory tract and causing significant health damage. Data for countries and aggregates for regions and income groups are urban-population weighted PM10 levels in residential areas of cities with more than 100,000 residents. The estimates represent the average annual exposure level of the average urban resident to outdoor particulate matter. The state of a country's technology and pollution controls is an important determinant of particulate matter concentrations." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Kiran Dev Pandey, David Wheeler, Bart Ostro, Uwe Deichmann, Kirk Hamilton, and Katherine Bolt. \"Ambient Particulate Matter Concentrations in Residential and Pollution Hotspot Areas of World Cities: New Estimates Based on the Global Model of Ambient Particulates (GMAPS),\" World Bank, Development Research Group and Environment Department (2006)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on particulate matter are estimated average annual concentrations in residential areas away from air pollution \"hotspots,\" such as industrial districts and transport corridors. Data are estimates of annual ambient concentrations of particulate matter in cities of more than 100,000 people by the World Bank’s Agriculture and Environmental Services Department." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.PM25.MC.M3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly." }, { "id": "IndicatorName", "value": "PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Population-weighted exposure to ambient PM2.5 pollution is defined as the average level of exposure of a nation's population to concentrations of suspended particles measuring less than 2.5 microns in aerodynamic diameter, which are capable of penetrating deep into the respiratory tract and causing severe health damage. Exposure is calculated by weighting mean annual concentrations of PM2.5 by population in both urban and rural areas." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2020" }, { "id": "Source", "value": "Global Burden of Disease Study 2023 (GBD 2023) Air Pollution Exposure Estimates and Risk Curves 1990-2023, Global Burden of Disease Collaborative Network, uri: https://ghdx.healthdata.org/record/ihme-data/gbd-2023-air-pollution-exposure-estimates-1990-2023, note: Need to create account to retrieve data., publisher: Institute for Health Metrics and Evaluation (IHME), date accessed: 2026-04-03, date published: 2026-01-23" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population exposure to ambient PM2.5 air pollution is estimated using an integrated modeling approach developed for the Global Burden of Disease (GBD) study by the Institute for Health Metrics and Evaluation (IHME). Annual mean concentrations of fine particulate matter (PM2.5) are derived by combining satellite-based aerosol optical depth measurements, chemical transport models, and available ground-level air quality monitoring data. These data sources are fused using geophysical–statistical models to generate globally consistent, high-resolution gridded estimates of PM2.5 concentrations, including areas without direct monitoring.\n\nPopulation exposure is calculated by weighting annual mean PM2.5 concentrations by the spatial distribution of population in both urban and rural areas. National estimates represent the population-weighted average annual concentration of PM2.5 to which a country’s population is exposed. Estimates are produced annually using a consistent methodology to allow comparison across countries and over time. Values represent modeled exposure levels and are intended for comparative risk assessment rather than regulatory compliance monitoring.\nStatistical concept(s): Fine particulate matter (PM2.5) refers to airborne particles with an aerodynamic diameter of 2.5 micrometers or less, which are small enough to penetrate deeply into the human respiratory system. Exposure to PM2.5 is associated with adverse health outcomes, including cardiovascular and respiratory diseases and premature mortality." }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "microgram per cubic meter" } ], "source_id": "57" }, { "id": "EN.ATM.PM25.MC.T1.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly. Three interim targets were defined for PM2.5 and have been shown to be achievable with successive and sustained abatement measures. Countries may find these interim targets particularly helpful in gauging progress over time in the difficult process of steadily reducing population exporsure to PM. IT-1 level corresponds to the highest mean concentrations reported in studies of long-term effects, and may also reflect higher but unknown historical concentrations that may have been contributed to observed health effects. IT-1 level has been shown to be associated with significant mortality in the developed world." }, { "id": "IndicatorName", "value": "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-1 value (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 1 (IT-1) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 35 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2017" }, { "id": "Source", "value": "Global Burden of Disease Study 2017 (GBD 2017), Institute for Health Metrics and Evaluation (IHME), uri: https://ghdx.healthdata.org/gbd-2017, publisher: Institute for Health Metrics and Evaluation (IHME), date published: 202112" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762–3772;GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Prüss-Üstün A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population." }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "57" }, { "id": "EN.ATM.PM25.MC.T2.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly. Three interim targets were defined for PM2.5 and have been shown to be achievable with successive and sustained abatement measures. Countries may find these interim targets particularly helpful in gauging progress over time in the difficult process of steadily reducing population exporsure to PM. IT-2 level is greater than the mean concentration at which effects have been observed in studies of long-term exposure and mortality and is likely to be associated with significant health impacts from both long-term and daily exposures to PM2.5. Attainment of IT-2 value would reduce the health risks of long-term exposure by about 6% relative to the IT-1 value." }, { "id": "IndicatorName", "value": "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-2 value (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 2 (IT-2) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 25 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2017" }, { "id": "Source", "value": "Brauer, M. et al. 2017, for the Global Burden of Disease Study 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762–3772; GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Prüss-Üstün A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.ATM.PM25.MC.T3.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly. Three interim targets were defined for PM2.5 and have been shown to be achievable with successive and sustained abatement measures. Countries may find these interim targets particularly helpful in gauging progress over time in the difficult process of steadily reducing population exporsure to PM. IT-3 level places greater weight than IT-2 on the likelihood of signifcant effects associated with long-term exposures. IT-3 value is close to the mean concentrations that are reported in studies of long-term exposure and provides an additional 6% reduction in mortality risk relative to the IT-2 value." }, { "id": "IndicatorName", "value": "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-3 value (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 3 (IT-3) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 15 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2017" }, { "id": "Source", "value": "Global Burden of Disease Study 2017 (GBD 2017), Institute for Health Metrics and Evaluation (IHME), uri: https://ghdx.healthdata.org/gbd-2017, publisher: Institute for Health Metrics and Evaluation (IHME), date published: 202112" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762–3772; GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Prüss-Üstün A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population." }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "57" }, { "id": "EN.ATM.PM25.MC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly." }, { "id": "IndicatorName", "value": "PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the WHO guideline value is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 10 micrograms per cubic meter, the guideline value recommended by the World Health Organization as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2017" }, { "id": "Source", "value": "Global Burden of Disease Study 2017 (GBD 2017), Institute for Health Metrics and Evaluation (IHME), uri: https://ghdx.healthdata.org/gbd-2017, publisher: Institute for Health Metrics and Evaluation (IHME), date published: 202112" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762–3772; GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Prüss-Üstün A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "57" }, { "id": "EN.ATM.SF6G.KT.CE", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "IndicatorName", "value": "SF6 gas emissions (thousand metric tons of CO2 equivalent)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Sulfur hexafluoride is used largely to insulate high-voltage electric power equipment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR): http://edgar.jrc.ec.europa.eu/" }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.BIR.THRD.NO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. The Red List Index for the world's birds shows that there has been a steady and continuing deterioration in the threat status of the world's birds since 1988, when the first complete global assessment was carried out.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Threatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN guides conservation activities of governments, NGOs and scientific institutions. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGlobally, threatened birds occur worldwide - nearly all countries support one or more threatened bird species. Small islands hold disproportionately high numbers of Globally Threatened Birds, supporting over half of threatened species. Threatened seabirds are found throughout the world's oceans. The most important threats to the world's birds are the spread of agriculture and an ever increasing human use of biological resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future." }, { "id": "IndicatorName", "value": "Bird species, threatened" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List." }, { "id": "Longdefinition", "value": "Birds are listed for countries included within their breeding or wintering ranges. Threatened species are the number of species classified by the IUCN as endangered, vulnerable, rare, indeterminate, out of danger, or insufficiently known." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2017-2022" }, { "id": "Source", "value": "The IUCN Red List of Threatened Species, UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.iucnredlist.org/;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.iucnredlist.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threated species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" }, { "id": "Unitofmeasure", "value": "species" } ], "source_id": "57" }, { "id": "EN.CLC.DRSK.XQ", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Hyogo Framework's goal is to substantially reduce disaster losses by 2015 - in lives, and in the social, economic, and environmental assets of communities and countries. The Hyogo Framework offers guiding principles, priorities for action, and practical means for achieving disaster resilience for vulnerable communities. Governments around the world have committed to take action to reduce disaster risk, and have adopted a guideline to reduce vulnerabilities to natural hazards, called the Hyogo Framework for Action (HFA). The HFA assists the efforts of nations and communities to become more resilient to, and cope better with the hazards that threaten their development gains.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nScientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems." }, { "id": "IndicatorName", "value": "Disaster risk reduction progress score (1-5 scale; 5=best)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Hyogo Framework for Action (FHA) national progress reports assess strategic priorities in the implementation of disaster risk reduction actions and establish baselines on levels of progress achieved in implementing the HFA's five priorities for action. National reporting processes are led by officially designated HFA focal institutions in country, and regional reporting by regional intergovernmental organizations.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHFA's five priorities are:\n\n\n\n\n\n\n\n1. Making disaster risk reduction a policy priority, institutional strengthening\n\n\n\n\n\n\n\n2. Risk assessment and early warning systems\n\n\n\n\n\n\n\n3. Education, information and public awareness\n\n\n\n\n\n\n\n4. Reducing underlying risk factors\n\n\n\n\n\n\n\n5. Preparedness for effective response" }, { "id": "Longdefinition", "value": "Disaster risk reduction progress score is an average of self-assessment scores, ranging from 1 to 5, submitted by countries under Priority 1 of the Hyogo Framework National Progress Reports. The Hyogo Framework is a global blueprint for disaster risk reduction efforts that was adopted by 168 countries in 2005. Assessments of \"Priority 1\" include four indicators that reflect the degree to which countries have prioritized disaster risk reduction and the strengthening of relevant institutions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2011-2011" }, { "id": "Source", "value": "2009-2011 Progress Reports, UN Office for Disaster Risk Reduction (UNDRR), uri: http://www.preventionweb.net/english/hyogo" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Resilience is measured by the disaster risk reduction progress score, an average of self-assessment scores submitted by countries under Priority 1 of the Hyogo Framework National Progress Reports. The Hyogo Framework is a global blueprint for disaster risk reduction efforts that was adopted by 168 countries in 2005. Assessments of Priority 1 include four indicators that reflect the degree to which countries have prioritized disaster risk reduction and the strengthening of relevant institutions.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "index (1-5)" } ], "source_id": "57" }, { "id": "EN.CLC.GHGR.MT.CE", "metatype": [ { "id": "IndicatorName", "value": "GHG net emissions/removals by LUCF (Mt of CO2 equivalent)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GHG net emissions/removals by LUCF refers to changes in atmospheric levels of all greenhouse gases attributable to forest and land-use change activities, including but not limited to (1) emissions and removals of CO2 from decreases or increases in biomass stocks due to forest management, logging, fuelwood collection, etc.; (2) conversion of existing forests and natural grasslands to other land uses; (3) removal of CO2 from the abandonment of formerly managed lands (e.g. croplands and pastures); and (4) emissions and removals of CO2 in soil associated with land-use change and management. For Annex-I countries under the UNFCCC, these data are drawn from the annual GHG inventories submitted to the UNFCCC by each country; for non-Annex-I countries, data are drawn from the most recently submitted National Communication where available. Because of differences in reporting years and methodologies, these data are not generally considered comparable across countries. Data are in million metric tons." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Framework Convention on Climate Change." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.CLC.MDAT.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Scientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nA drought can lead to losses in agriculture, affect inland navigation and hydropower plants, reduce access to drinking water, and cause famines. A flood is a significant rise of water level in a stream, lake, reservoir, or coastal region. Extreme temperature events are either cold waves or heat waves. A cold wave can be both a prolonged period of excessively cold weather and the sudden invasion of very cold air over a large area. Accompanied by frost, it can damage agriculture, infrastructure, and property. A heat wave is a prolonged period of excessively hot and sometimes humid weather. Population affected by these natural disasters is the number of people injured, left homeless, or requiring immediate assistance and can include displaced or evacuated people." }, { "id": "IndicatorName", "value": "Droughts, floods, extreme temperatures (% of population, average 1990-2009)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is \"unequivocal\" and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20-86 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5°C by 2100 (Kattenberg and others 1996) and increase of \"killer\" heat waves during the warm season (Karl and others 1997)." }, { "id": "Longdefinition", "value": "Droughts, floods and extreme temperatures is the annual average percentage of the population that is affected by natural disasters classified as either droughts, floods, or extreme temperature events. A drought is an extended period of time characterized by a deficiency in a region's water supply that is the result of constantly below average precipitation. A drought can lead to losses to agriculture, affect inland navigation and hydropower plants, and cause a lack of drinking water and famine. A flood is a significant rise of water level in a stream, lake, reservoir or coastal region. Extreme temperature events are either cold waves or heat waves. A cold wave can be both a prolonged period of excessively cold weather and the sudden invasion of very cold air over a large area. Along with frost it can cause damage to agriculture, infrastructure, and property. A heat wave is a prolonged period of excessively hot and sometimes also humid weather relative to normal climate patterns of a certain region. Population affected is the number of people injured, left homeless or requiring immediate assistance during a period of emergency resulting from a natural disaster; it can also include displaced or evacuated people. Average percentage of population affected is calculated by dividing the sum of total affected for the period stated by the sum of the annual population figures for the period stated." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2009-2009" }, { "id": "Source", "value": "EM-DAT The International Disaster Database, Centre for Research on the Epidemiology of Disasters (CRED) - Université Catholique de Louvain, uri: https://www.emdat.be/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures vulnerability of population affected by droughts, floods, and extreme temperature. A drought is an extended period of deficiency in a region's water supply as a result of below average precipitation." }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of population, average 1990-2009" } ], "source_id": "57" }, { "id": "EN.CO2.BLDG.MT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nGlobal emissions of carbon dioxide have risen by 99%, or on average 2.0% per year, since 1971, and are projected to rise by another 45% by 2030, or by 1.6% per year. It is estimated that emissions in China have risen by 5.7 percent per annum between 1971 and 2006 - the use of coal in China increased levels of CO2 by 4.8 billion tonnes over this period.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "CO2 emissions from residential buildings and commercial and public services (million metric tons)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Limitationsandexceptions", "value": "As a response to the objectives of the UNFCCC, the IEA Secretariat, together with the IPCC, the OECD and umerous international experts, has helped to develop and refine an internationally-agreed methodology for the calculation and reporting of national greenhouse-gas emissions from fuel combustion. This methodology was published in 1995 in the IPCC Guidelines for National Greenhouse Gas Inventories. After the initial dissemination of the methodology, revisions were added to several chapters, and published as the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 IPCC Guidelines). In April 2006, the IPCC approved the 2006 Guidelines at the 25th session of the IPCC in Mauritius. For now, most countries (as well as the IEA Secretariat) are still calculating their inventories using the 1996 IPCC Guidelines.1. Both the 1996 IPCC Guidelines and the 2006 IPCC Guidelines are available from the IPCC Greenhouse Gas Inventories Programme (www.ipcc-nggip.iges.or.jp).\n\nSince the IPCC methodology for fuel combustion is largely based on energy balances, the IEA estimates for CO2 from fuel combustion have been calculated using the IEA energy balances and the default IPCC methodology. However, other possibly more detailed methodologies may be used by Parties to calculate their inventories. This may lead to different estimates of emissions.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies." }, { "id": "Longdefinition", "value": "CO2 emissions from residential buildings and commercial and public services contains all emissions from fuel combustion in households. This corresponds to IPCC Source/Sink Category 1 A 4 b. Commercial and public services includes emissions from all activities of ISIC Divisions 41, 50-52, 55, 63-67, 70-75, 80, 85, 90-93 and 99." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions , largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. In 2010 the International Energy Agency (IEA) released data on carbon dioxide emissions by sector for the first time, allowing a more comprehensive understanding of each sector's contribution to total emissions. The sectoral approach yields data on carbon dioxide emissions from fuel combustion (Intergovernmental Panel on Climate Change [IPCC] source/sink category 1A) as calculated using the IPCC tier 1 sectoral approach.\n\nCarbon emissions from residential buildings and commercial and public services are the sum of emissions from fuel combustion in households (IPCC source/sink category 1A4b) and emissions from all activities of International Standard Industrial Classification divisions 41, 50-52, 55, 63-67, 70-75, 80, 85, 90-93, and 99.\n\n Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.CO2.BLDG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nGlobal emissions of carbon dioxide have risen by 99%, or on average 2.0% per year, since 1971, and are projected to rise by another 45% by 2030, or by 1.6% per year. It is estimated that emissions in China have risen by 5.7 percent per annum between 1971 and 2006 - the use of coal in China increased levels of CO2 by 4.8 billion tonnes over this period.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data." }, { "id": "IndicatorName", "value": "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "https://www.iea.org/terms" }, { "id": "Limitationsandexceptions", "value": "As a response to the objectives of the UNFCCC, the IEA Secretariat, together with the IPCC, the OECD and umerous international experts, has helped to develop and refine an internationally-agreed methodology for the calculation and reporting of national greenhouse-gas emissions from fuel combustion. This methodology was published in 1995 in the IPCC Guidelines for National Greenhouse Gas Inventories. After the initial dissemination of the methodology, revisions were added to several chapters, and published as the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 IPCC Guidelines). In April 2006, the IPCC approved the 2006 Guidelines at the 25th session of the IPCC in Mauritius. For now, most countries (as well as the IEA Secretariat) are still calculating their inventories using the 1996 IPCC Guidelines.1. Both the 1996 IPCC Guidelines and the 2006 IPCC Guidelines are available from the IPCC Greenhouse Gas Inventories Programme (www.ipcc-nggip.iges.or.jp).\n\nSince the IPCC methodology for fuel combustion is largely based on energy balances, the IEA estimates for CO2 from fuel combustion have been calculated using the IEA energy balances and the default IPCC methodology. However, other possibly more detailed methodologies may be used by Parties to calculate their inventories. This may lead to different estimates of emissions.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies." }, { "id": "Longdefinition", "value": "CO2 emissions from residential buildings and commercial and public services contains all emissions from fuel combustion in households. This corresponds to IPCC Source/Sink Category 1 A 4 b. Commercial and public services includes emissions from all activities of ISIC Divisions 41, 50-52, 55, 63-67, 70-75, 80, 85, 90-93 and 99." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (https://www.iea.org/data-and-statistics), subject to https://www.iea.org/terms/" }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions , largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. In 2010 the International Energy Agency (IEA) released data on carbon dioxide emissions by sector for the first time, allowing a more comprehensive understanding of each sector's contribution to total emissions. The sectoral approach yields data on carbon dioxide emissions from fuel combustion (Intergovernmental Panel on Climate Change [IPCC] source/sink category 1A) as calculated using the IPCC tier 1 sectoral approach.\n\nCarbon emissions from residential buildings and commercial and public services are the sum of emissions from fuel combustion in households (IPCC source/sink category 1A4b) and emissions from all activities of International Standard Industrial Classification divisions 41, 50-52, 55, 63-67, 70-75, 80, 85, 90-93, and 99.\n\n Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.CO2.ETOT.MT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nGlobal emissions of carbon dioxide have risen by 99%, or on average 2.0% per year, since 1971, and are projected to rise by another 45% by 2030, or by 1.6% per year. It is estimated that emissions in China have risen by 5.7 percent per annum between 1971 and 2006 - the use of coal in China increased levels of CO2 by 4.8 billion tonnes over this period.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "CO2 emissions from electricity and heat production, total (million metric tons)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Limitationsandexceptions", "value": "As a response to the objectives of the UNFCCC, the IEA Secretariat, together with the IPCC, the OECD and umerous international experts, has helped to develop and refine an internationally-agreed methodology for the calculation and reporting of national greenhouse-gas emissions from fuel combustion. This methodology was published in 1995 in the IPCC Guidelines for National Greenhouse Gas Inventories. After the initial dissemination of the methodology, revisions were added to several chapters, and published as the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 IPCC Guidelines). In April 2006, the IPCC approved the 2006 Guidelines at the 25th session of the IPCC in Mauritius. For now, most countries (as well as the IEA Secretariat) are still calculating their inventories using the 1996 IPCC Guidelines.1. Both the 1996 IPCC Guidelines and the 2006 IPCC Guidelines are available from the IPCC Greenhouse Gas Inventories Programme (www.ipcc-nggip.iges.or.jp).\n\nSince the IPCC methodology for fuel combustion is largely based on energy balances, the IEA estimates for CO2 from fuel combustion have been calculated using the IEA energy balances and the default IPCC methodology. However, other possibly more detailed methodologies may be used by Parties to calculate their inventories. This may lead to different estimates of emissions.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies." }, { "id": "Longdefinition", "value": "CO2 emissions from electricity and heat production is the sum of three IEA categories of CO2 emissions: (1) Main Activity Producer Electricity and Heat which contains the sum of emissions from main activity producer electricity generation, combined heat and power generation and heat plants. Main activity producers (formerly known as public utilities) are defined as those undertakings whose primary activity is to supply the public. They may be publicly or privately owned. This corresponds to IPCC Source/Sink Category 1 A 1 a. For the CO2 emissions from fuel combustion (summary) file, emissions from own on-site use of fuel in power plants (EPOWERPLT) are also included. (2) Unallocated Autoproducers which contains the emissions from the generation of electricity and/or heat by autoproducers. Autoproducers are defined as undertakings that generate electricity and/or heat, wholly or partly for their own use as an activity which supports their primary activity. They may be privately or publicly owned. In the 1996 IPCC Guidelines, these emissions would normally be distributed between industry, transport and \"other\" sectors. (3) Other Energy Industries contains emissions from fuel combusted in petroleum refineries, for the manufacture of solid fuels, coal mining, oil and gas extraction and other energy-producing industries. This corresponds to the IPCC Source/Sink Categories 1 A 1 b and 1 A 1 c. According to the 1996 IPCC Guidelines, emissions from coke inputs to blast furnaces can either be counted here or in the Industrial Processes source/sink category. Within detailed sectoral calculations, certain non-energy processes can be distinguished. In the reduction of iron in a blast furnace through the combustion of coke, the primary purpose of the coke oxidation is to produce pig iron and the emissions can be considered as an industrial process. Care must be taken not to double count these emissions in both Energy and Industrial Processes. In the IEA estimations, these emissions have been included in this category." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions account for the largest share of greenhouse gases, which are associated with global warming. In 2010 the International Energy Agency (IEA) released data on carbon dioxide emissions by sector for the first time, allowing a more comprehensive understanding of each sector's contribution to total emissions. The sectoral approach yields data on carbon dioxide emissions from fuel combustion (Intergovernmental Panel on Climate Change [IPCC] source/sink category 1A) as calculated using the IPCC tier 1 sectoral approach.\n\nCarbon dioxide emissions from electricity and heat production are the sum of emissions from main activity producers of electricity and heat, unallocated autoproducers, and other energy industries. Main activity producers (formerly known as public supply undertakings) generate electricity or heat for sale to third parties as their primary activity and may be privately or publicly owned. Emissions from own onsite use of fuel in power plants are also included in this category. Unallocated autoproducers are undertakings that generate electricity or heat, wholly or partly for their own use as an activity that supports their primary activity and may be privately or publicly owned. In the 1996 IPCC guidelines these emissions were allocated among industry, transport, and \"other\" sectors. Emissions from other energy industries are emissions from fuel combusted in petroleum refineries, the manufacture of solid fuels, coal mining, oil and gas extraction, and other energy-producing industries.\n\nCarbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.CO2.ETOT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nGlobal emissions of carbon dioxide have risen by 99%, or on average 2.0% per year, since 1971, and are projected to rise by another 45% by 2030, or by 1.6% per year. It is estimated that emissions in China have risen by 5.7 percent per annum between 1971 and 2006 - the use of coal in China increased levels of CO2 by 4.8 billion tonnes over this period.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data." }, { "id": "IndicatorName", "value": "CO2 emissions from electricity and heat production, total (% of total fuel combustion)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "https://www.iea.org/terms" }, { "id": "Limitationsandexceptions", "value": "As a response to the objectives of the UNFCCC, the IEA Secretariat, together with the IPCC, the OECD and umerous international experts, has helped to develop and refine an internationally-agreed methodology for the calculation and reporting of national greenhouse-gas emissions from fuel combustion. This methodology was published in 1995 in the IPCC Guidelines for National Greenhouse Gas Inventories. After the initial dissemination of the methodology, revisions were added to several chapters, and published as the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 IPCC Guidelines). In April 2006, the IPCC approved the 2006 Guidelines at the 25th session of the IPCC in Mauritius. For now, most countries (as well as the IEA Secretariat) are still calculating their inventories using the 1996 IPCC Guidelines.1. Both the 1996 IPCC Guidelines and the 2006 IPCC Guidelines are available from the IPCC Greenhouse Gas Inventories Programme (www.ipcc-nggip.iges.or.jp).\n\nSince the IPCC methodology for fuel combustion is largely based on energy balances, the IEA estimates for CO2 from fuel combustion have been calculated using the IEA energy balances and the default IPCC methodology. However, other possibly more detailed methodologies may be used by Parties to calculate their inventories. This may lead to different estimates of emissions.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies." }, { "id": "Longdefinition", "value": "CO2 emissions from electricity and heat production is the sum of three IEA categories of CO2 emissions: (1) Main Activity Producer Electricity and Heat which contains the sum of emissions from main activity producer electricity generation, combined heat and power generation and heat plants. Main activity producers (formerly known as public utilities) are defined as those undertakings whose primary activity is to supply the public. They may be publicly or privately owned. This corresponds to IPCC Source/Sink Category 1 A 1 a. For the CO2 emissions from fuel combustion (summary) file, emissions from own on-site use of fuel in power plants (EPOWERPLT) are also included. (2) Unallocated Autoproducers which contains the emissions from the generation of electricity and/or heat by autoproducers. Autoproducers are defined as undertakings that generate electricity and/or heat, wholly or partly for their own use as an activity which supports their primary activity. They may be privately or publicly owned. In the 1996 IPCC Guidelines, these emissions would normally be distributed between industry, transport and \"other\" sectors. (3) Other Energy Industries contains emissions from fuel combusted in petroleum refineries, for the manufacture of solid fuels, coal mining, oil and gas extraction and other energy-producing industries. This corresponds to the IPCC Source/Sink Categories 1 A 1 b and 1 A 1 c. According to the 1996 IPCC Guidelines, emissions from coke inputs to blast furnaces can either be counted here or in the Industrial Processes source/sink category. Within detailed sectoral calculations, certain non-energy processes can be distinguished. In the reduction of iron in a blast furnace through the combustion of coke, the primary purpose of the coke oxidation is to produce pig iron and the emissions can be considered as an industrial process. Care must be taken not to double count these emissions in both Energy and Industrial Processes. In the IEA estimations, these emissions have been included in this category." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (https://www.iea.org/data-and-statistics), subject to https://www.iea.org/terms/" }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions account for the largest share of greenhouse gases, which are associated with global warming. In 2010 the International Energy Agency (IEA) released data on carbon dioxide emissions by sector for the first time, allowing a more comprehensive understanding of each sector's contribution to total emissions. The sectoral approach yields data on carbon dioxide emissions from fuel combustion (Intergovernmental Panel on Climate Change [IPCC] source/sink category 1A) as calculated using the IPCC tier 1 sectoral approach.\n\nCarbon dioxide emissions from electricity and heat production are the sum of emissions from main activity producers of electricity and heat, unallocated autoproducers, and other energy industries. Main activity producers (formerly known as public supply undertakings) generate electricity or heat for sale to third parties as their primary activity and may be privately or publicly owned. Emissions from own onsite use of fuel in power plants are also included in this category. Unallocated autoproducers are undertakings that generate electricity or heat, wholly or partly for their own use as an activity that supports their primary activity and may be privately or publicly owned. In the 1996 IPCC guidelines these emissions were allocated among industry, transport, and \"other\" sectors. Emissions from other energy industries are emissions from fuel combusted in petroleum refineries, the manufacture of solid fuels, coal mining, oil and gas extraction, and other energy-producing industries.\n\nCarbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.CO2.MANF.MT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nGlobal emissions of carbon dioxide have risen by 99%, or on average 2.0% per year, since 1971, and are projected to rise by another 45% by 2030, or by 1.6% per year. It is estimated that emissions in China have risen by 5.7 percent per annum between 1971 and 2006 - the use of coal in China increased levels of CO2 by 4.8 billion tonnes over this period.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "CO2 emissions from manufacturing industries and construction (million metric tons)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Limitationsandexceptions", "value": "As a response to the objectives of the UNFCCC, the IEA Secretariat, together with the IPCC, the OECD and umerous international experts, has helped to develop and refine an internationally-agreed methodology for the calculation and reporting of national greenhouse-gas emissions from fuel combustion. This methodology was published in 1995 in the IPCC Guidelines for National Greenhouse Gas Inventories. After the initial dissemination of the methodology, revisions were added to several chapters, and published as the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 IPCC Guidelines). In April 2006, the IPCC approved the 2006 Guidelines at the 25th session of the IPCC in Mauritius. For now, most countries (as well as the IEA Secretariat) are still calculating their inventories using the 1996 IPCC Guidelines.1. Both the 1996 IPCC Guidelines and the 2006 IPCC Guidelines are available from the IPCC Greenhouse Gas Inventories Programme (www.ipcc-nggip.iges.or.jp).\n\nSince the IPCC methodology for fuel combustion is largely based on energy balances, the IEA estimates for CO2 from fuel combustion have been calculated using the IEA energy balances and the default IPCC methodology. However, other possibly more detailed methodologies may be used by Parties to calculate their inventories. This may lead to different estimates of emissions.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies." }, { "id": "Longdefinition", "value": "CO2 emissions from manufacturing industries and construction contains the emissions from combustion of fuels in industry. The IPCC Source/Sink Category 1 A 2 includes these emissions. However, in the 1996 IPCC Guidelines, the IPCC category also includes emissions from industry autoproducers that generate electricity and/or heat. The IEA data are not collected in a way that allows the energy consumption to be split by specific end-use and therefore, autoproducers are shown as a separate item (Unallocated Autoproducers). Manufacturing industries and construction also includes emissions from coke inputs into blast furnaces, which may be reported either in the transformation sector, the industry sector or the separate IPCC Source/Sink Category 2, Industrial Processes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions account for the largest share of greenhouse gases, which are associated with global warming. In 2010 the International Energy Agency (IEA) released data on carbon dioxide emissions by sector for the first time, allowing a more comprehensive understanding of each sector's contribution to total emissions. The sectoral approach yields data on carbon dioxide emissions from fuel combustion (Intergovernmental Panel on Climate Change [IPCC] source/sink category 1A) as calculated using the IPCC tier 1 sectoral approach.\n\nCarbon dioxide emissions from manufacturing industries and construction are the emissions from fuel combustion in industry (IPCC source/sink Category 1A2). Although in the 1996 IPCC guidelines, this category included emissions from industry autoproducers that generate electricity or heat, the IEA data do not allow energy consumption to be categorized by end-use, and thus emissions from autoproducers are listed separately under unallocated autoproducers. Emissions from manufacturing industries and construction include those from coke inputs into blast furnaces, which may be reported under the transformation sector, the industry sector, or industrial processes (IPCC source/sink category 2).\n\nCarbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.CO2.MANF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nGlobal emissions of carbon dioxide have risen by 99%, or on average 2.0% per year, since 1971, and are projected to rise by another 45% by 2030, or by 1.6% per year. It is estimated that emissions in China have risen by 5.7 percent per annum between 1971 and 2006 - the use of coal in China increased levels of CO2 by 4.8 billion tonnes over this period.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data." }, { "id": "IndicatorName", "value": "CO2 emissions from manufacturing industries and construction (% of total fuel combustion)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "https://www.iea.org/terms" }, { "id": "Limitationsandexceptions", "value": "As a response to the objectives of the UNFCCC, the IEA Secretariat, together with the IPCC, the OECD and umerous international experts, has helped to develop and refine an internationally-agreed methodology for the calculation and reporting of national greenhouse-gas emissions from fuel combustion. This methodology was published in 1995 in the IPCC Guidelines for National Greenhouse Gas Inventories. After the initial dissemination of the methodology, revisions were added to several chapters, and published as the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 IPCC Guidelines). In April 2006, the IPCC approved the 2006 Guidelines at the 25th session of the IPCC in Mauritius. For now, most countries (as well as the IEA Secretariat) are still calculating their inventories using the 1996 IPCC Guidelines.1. Both the 1996 IPCC Guidelines and the 2006 IPCC Guidelines are available from the IPCC Greenhouse Gas Inventories Programme (www.ipcc-nggip.iges.or.jp).\n\nSince the IPCC methodology for fuel combustion is largely based on energy balances, the IEA estimates for CO2 from fuel combustion have been calculated using the IEA energy balances and the default IPCC methodology. However, other possibly more detailed methodologies may be used by Parties to calculate their inventories. This may lead to different estimates of emissions.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies." }, { "id": "Longdefinition", "value": "CO2 emissions from manufacturing industries and construction contains the emissions from combustion of fuels in industry. The IPCC Source/Sink Category 1 A 2 includes these emissions. However, in the 1996 IPCC Guidelines, the IPCC category also includes emissions from industry autoproducers that generate electricity and/or heat. The IEA data are not collected in a way that allows the energy consumption to be split by specific end-use and therefore, autoproducers are shown as a separate item (Unallocated Autoproducers). Manufacturing industries and construction also includes emissions from coke inputs into blast furnaces, which may be reported either in the transformation sector, the industry sector or the separate IPCC Source/Sink Category 2, Industrial Processes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (https://www.iea.org/data-and-statistics), subject to https://www.iea.org/terms/" }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions account for the largest share of greenhouse gases, which are associated with global warming. In 2010 the International Energy Agency (IEA) released data on carbon dioxide emissions by sector for the first time, allowing a more comprehensive understanding of each sector's contribution to total emissions. The sectoral approach yields data on carbon dioxide emissions from fuel combustion (Intergovernmental Panel on Climate Change [IPCC] source/sink category 1A) as calculated using the IPCC tier 1 sectoral approach.\n\nCarbon dioxide emissions from manufacturing industries and construction are the emissions from fuel combustion in industry (IPCC source/sink Category 1A2). Although in the 1996 IPCC guidelines, this category included emissions from industry autoproducers that generate electricity or heat, the IEA data do not allow energy consumption to be categorized by end-use, and thus emissions from autoproducers are listed separately under unallocated autoproducers. Emissions from manufacturing industries and construction include those from coke inputs into blast furnaces, which may be reported under the transformation sector, the industry sector, or industrial processes (IPCC source/sink category 2).\n\nCarbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.CO2.OTHX.MT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nGlobal emissions of carbon dioxide have risen by 99%, or on average 2.0% per year, since 1971, and are projected to rise by another 45% by 2030, or by 1.6% per year. It is estimated that emissions in China have risen by 5.7 percent per annum between 1971 and 2006 - the use of coal in China increased levels of CO2 by 4.8 billion tonnes over this period.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "CO2 emissions from other sectors, excluding residential buildings and commercial and public services (million metric tons)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Limitationsandexceptions", "value": "As a response to the objectives of the UNFCCC, the IEA Secretariat, together with the IPCC, the OECD and umerous international experts, has helped to develop and refine an internationally-agreed methodology for the calculation and reporting of national greenhouse-gas emissions from fuel combustion. This methodology was published in 1995 in the IPCC Guidelines for National Greenhouse Gas Inventories. After the initial dissemination of the methodology, revisions were added to several chapters, and published as the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 IPCC Guidelines). In April 2006, the IPCC approved the 2006 Guidelines at the 25th session of the IPCC in Mauritius. For now, most countries (as well as the IEA Secretariat) are still calculating their inventories using the 1996 IPCC Guidelines.1. Both the 1996 IPCC Guidelines and the 2006 IPCC Guidelines are available from the IPCC Greenhouse Gas Inventories Programme (www.ipcc-nggip.iges.or.jp).\n\nSince the IPCC methodology for fuel combustion is largely based on energy balances, the IEA estimates for CO2 from fuel combustion have been calculated using the IEA energy balances and the default IPCC methodology. However, other possibly more detailed methodologies may be used by Parties to calculate their inventories. This may lead to different estimates of emissions.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies." }, { "id": "Longdefinition", "value": "CO2 emissions from other sectors, less residential buildings and commercial and public services, contains the emissions from commercial/institutional activities, residential, agriculture/forestry, fishing and other emissions not specified elsewhere that are included in the IPCC Source/Sink Categories 1 A 4 and 1 A 5. In the 1996 IPCC Guidelines, the category also includes emissions from autoproducers in the commercial/residential/agricultural sectors that generate electricity and/or heat. The IEA data are not collected in a way that allows the energy consumption to be split by specific end-use and therefore, autoproducers are shown as a separate item (Unallocated Autoproducers)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions account for the largest share of greenhouse gases, which are associated with global warming. In 2010 the International Energy Agency (IEA) released data on carbon dioxide emissions by sector for the first time, allowing a more comprehensive understanding of each sector's contribution to total emissions. The sectoral approach yields data on carbon dioxide emissions from fuel combustion (Intergovernmental Panel on Climate Change [IPCC] source/sink category 1A) as calculated using the IPCC tier 1 sectoral approach.\n\nCarbon dioxide emissions from other sectors are emissions from commercial and institutional activities and from residential, agriculture and forestry, fishing, and other processes not specified elsewhere that are included in IPCC source/sink categories 1A4 and 1A5. Although in the 1996 IPCC guidelines, this category included emissions from autoproducers in the commercial, residential, and agricultural sectors that generate electricity or heat, the IEA data do not allow energy consumption to be classified by end-use, and thus emissions from autoproducers are listed separately under unallocated autoproducers.\n\nCarbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.CO2.OTHX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nGlobal emissions of carbon dioxide have risen by 99%, or on average 2.0% per year, since 1971, and are projected to rise by another 45% by 2030, or by 1.6% per year. It is estimated that emissions in China have risen by 5.7 percent per annum between 1971 and 2006 - the use of coal in China increased levels of CO2 by 4.8 billion tonnes over this period.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data." }, { "id": "IndicatorName", "value": "CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "https://www.iea.org/terms" }, { "id": "Limitationsandexceptions", "value": "As a response to the objectives of the UNFCCC, the IEA Secretariat, together with the IPCC, the OECD and umerous international experts, has helped to develop and refine an internationally-agreed methodology for the calculation and reporting of national greenhouse-gas emissions from fuel combustion. This methodology was published in 1995 in the IPCC Guidelines for National Greenhouse Gas Inventories. After the initial dissemination of the methodology, revisions were added to several chapters, and published as the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 IPCC Guidelines). In April 2006, the IPCC approved the 2006 Guidelines at the 25th session of the IPCC in Mauritius. For now, most countries (as well as the IEA Secretariat) are still calculating their inventories using the 1996 IPCC Guidelines.1. Both the 1996 IPCC Guidelines and the 2006 IPCC Guidelines are available from the IPCC Greenhouse Gas Inventories Programme (www.ipcc-nggip.iges.or.jp).\n\nSince the IPCC methodology for fuel combustion is largely based on energy balances, the IEA estimates for CO2 from fuel combustion have been calculated using the IEA energy balances and the default IPCC methodology. However, other possibly more detailed methodologies may be used by Parties to calculate their inventories. This may lead to different estimates of emissions.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies." }, { "id": "Longdefinition", "value": "CO2 emissions from other sectors, less residential buildings and commercial and public services, contains the emissions from commercial/institutional activities, residential, agriculture/forestry, fishing and other emissions not specified elsewhere that are included in the IPCC Source/Sink Categories 1 A 4 and 1 A 5. In the 1996 IPCC Guidelines, the category also includes emissions from autoproducers in the commercial/residential/agricultural sectors that generate electricity and/or heat. The IEA data are not collected in a way that allows the energy consumption to be split by specific end-use and therefore, autoproducers are shown as a separate item (Unallocated Autoproducers)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (https://www.iea.org/data-and-statistics), subject to https://www.iea.org/terms/" }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions account for the largest share of greenhouse gases, which are associated with global warming. In 2010 the International Energy Agency (IEA) released data on carbon dioxide emissions by sector for the first time, allowing a more comprehensive understanding of each sector's contribution to total emissions. The sectoral approach yields data on carbon dioxide emissions from fuel combustion (Intergovernmental Panel on Climate Change [IPCC] source/sink category 1A) as calculated using the IPCC tier 1 sectoral approach.\n\nCarbon dioxide emissions from other sectors are emissions from commercial and institutional activities and from residential, agriculture and forestry, fishing, and other processes not specified elsewhere that are included in IPCC source/sink categories 1A4 and 1A5. Although in the 1996 IPCC guidelines, this category included emissions from autoproducers in the commercial, residential, and agricultural sectors that generate electricity or heat, the IEA data do not allow energy consumption to be classified by end-use, and thus emissions from autoproducers are listed separately under unallocated autoproducers.\n\nCarbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.CO2.TRAN.MT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nGlobal emissions of carbon dioxide have risen by 99%, or on average 2.0% per year, since 1971, and are projected to rise by another 45% by 2030, or by 1.6% per year. It is estimated that emissions in China have risen by 5.7 percent per annum between 1971 and 2006 - the use of coal in China increased levels of CO2 by 4.8 billion tonnes over this period.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "CO2 emissions from transport (million metric tons)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "http://www.iea.org/t&c/termsandconditions" }, { "id": "Limitationsandexceptions", "value": "As a response to the objectives of the UNFCCC, the IEA Secretariat, together with the IPCC, the OECD and umerous international experts, has helped to develop and refine an internationally-agreed methodology for the calculation and reporting of national greenhouse-gas emissions from fuel combustion. This methodology was published in 1995 in the IPCC Guidelines for National Greenhouse Gas Inventories. After the initial dissemination of the methodology, revisions were added to several chapters, and published as the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 IPCC Guidelines). In April 2006, the IPCC approved the 2006 Guidelines at the 25th session of the IPCC in Mauritius. For now, most countries (as well as the IEA Secretariat) are still calculating their inventories using the 1996 IPCC Guidelines.1. Both the 1996 IPCC Guidelines and the 2006 IPCC Guidelines are available from the IPCC Greenhouse Gas Inventories Programme (www.ipcc-nggip.iges.or.jp).\n\nSince the IPCC methodology for fuel combustion is largely based on energy balances, the IEA estimates for CO2 from fuel combustion have been calculated using the IEA energy balances and the default IPCC methodology. However, other possibly more detailed methodologies may be used by Parties to calculate their inventories. This may lead to different estimates of emissions.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies." }, { "id": "Longdefinition", "value": "CO2 emissions from transport contains emissions from the combustion of fuel for all transport activity, regardless of the sector, except for international marine bunkers and international aviation. This includes domestic aviation, domestic navigation, road, rail and pipeline transport, and corresponds to IPCC Source/Sink Category 1 A 3. In addition, the IEA data are not collected in a way that allows the autoproducer consumption to be split by specific end-use and therefore, autoproducers are shown as a separate item (Unallocated Autoproducers)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/" }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions account for the largest share of greenhouse gases, which are associated with global warming. In 2010 the International Energy Agency (IEA) released data on carbon dioxide emissions by sector for the first time, allowing a more comprehensive understanding of each sector's contribution to total emissions. The sectoral approach yields data on carbon dioxide emissions from fuel combustion (Intergovernmental Panel on Climate Change [IPCC] source/sink category 1A) as calculated using the IPCC tier 1 sectoral approach.\n\nCarbon dioxide emissions from transport are emissions from fuel combustion for all transport activity (IPCC source/sink category 1A3), including domestic aviation, domestic navigation, road, rail, and pipeline transport but excluding international marine bunkers and international aviation. The IEA data do not allow energy consumption to be categorized by end-use, and thus emissions from autoproducers are listed separately under unallocated autoproducers.\n\nCarbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.CO2.TRAN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nGlobal emissions of carbon dioxide have risen by 99%, or on average 2.0% per year, since 1971, and are projected to rise by another 45% by 2030, or by 1.6% per year. It is estimated that emissions in China have risen by 5.7 percent per annum between 1971 and 2006 - the use of coal in China increased levels of CO2 by 4.8 billion tonnes over this period.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data." }, { "id": "IndicatorName", "value": "CO2 emissions from transport (% of total fuel combustion)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IEA terms and conditions." }, { "id": "License_URL", "value": "https://www.iea.org/terms" }, { "id": "Limitationsandexceptions", "value": "As a response to the objectives of the UNFCCC, the IEA Secretariat, together with the IPCC, the OECD and umerous international experts, has helped to develop and refine an internationally-agreed methodology for the calculation and reporting of national greenhouse-gas emissions from fuel combustion. This methodology was published in 1995 in the IPCC Guidelines for National Greenhouse Gas Inventories. After the initial dissemination of the methodology, revisions were added to several chapters, and published as the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 IPCC Guidelines). In April 2006, the IPCC approved the 2006 Guidelines at the 25th session of the IPCC in Mauritius. For now, most countries (as well as the IEA Secretariat) are still calculating their inventories using the 1996 IPCC Guidelines.1. Both the 1996 IPCC Guidelines and the 2006 IPCC Guidelines are available from the IPCC Greenhouse Gas Inventories Programme (www.ipcc-nggip.iges.or.jp).\n\nSince the IPCC methodology for fuel combustion is largely based on energy balances, the IEA estimates for CO2 from fuel combustion have been calculated using the IEA energy balances and the default IPCC methodology. However, other possibly more detailed methodologies may be used by Parties to calculate their inventories. This may lead to different estimates of emissions.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies." }, { "id": "Longdefinition", "value": "CO2 emissions from transport contains emissions from the combustion of fuel for all transport activity, regardless of the sector, except for international marine bunkers and international aviation. This includes domestic aviation, domestic navigation, road, rail and pipeline transport, and corresponds to IPCC Source/Sink Category 1 A 3. In addition, the IEA data are not collected in a way that allows the autoproducer consumption to be split by specific end-use and therefore, autoproducers are shown as a separate item (Unallocated Autoproducers)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Statistics © OECD/IEA 2014 (https://www.iea.org/data-and-statistics), subject to https://www.iea.org/terms/" }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions account for the largest share of greenhouse gases, which are associated with global warming. In 2010 the International Energy Agency (IEA) released data on carbon dioxide emissions by sector for the first time, allowing a more comprehensive understanding of each sector's contribution to total emissions. The sectoral approach yields data on carbon dioxide emissions from fuel combustion (Intergovernmental Panel on Climate Change [IPCC] source/sink category 1A) as calculated using the IPCC tier 1 sectoral approach.\n\nCarbon dioxide emissions from transport are emissions from fuel combustion for all transport activity (IPCC source/sink category 1A3), including domestic aviation, domestic navigation, road, rail, and pipeline transport but excluding international marine bunkers and international aviation. The IEA data do not allow energy consumption to be categorized by end-use, and thus emissions from autoproducers are listed separately under unallocated autoproducers.\n\nCarbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced." }, { "id": "Topic", "value": "Environment: Emissions" } ], "source_id": "57" }, { "id": "EN.FSH.THRD.NO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. The Red List Index for the world's birds shows that there has been a steady and continuing deterioration in the threat status of the world's birds since 1988, when the first complete global assessment was carried out.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThreatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN guides conservation activities of governments, NGOs and scientific institutions. The introduction in 1994 of a scientifically rigorous approach to determine risks of extinction that is applicable to all species, has become a world standard. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe freshwater system represents the most threatened of all ecosystems, and many freshwater species have a very high livelihood value for local human communities. IUCN's freshwater focus is on the following taxonomic groups: fish; molluscs; crabs and crayfish; and dragonflies. Global assessment of these groups is being pursued through a series of regional projects, such as one for Africa that is currently being implemented.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe marine realm is poorly covered in the IUCN Red List, comprising less than 5 percent of the species included. IUCN has identified priority taxonomic groups of marine fish, invertebrates, plants (mangroves and seagrasses) and macro-algae (seaweeds). If these priority groups can be assessed, the number of marine species on the IUCN Red List will be increased more than six-fold.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future." }, { "id": "IndicatorName", "value": "Fish species, threatened" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List." }, { "id": "Longdefinition", "value": "Fish species are based on Froese, R. and Pauly, D. (eds). 2008. Threatened species are the number of species classified by the IUCN as endangered, vulnerable, rare, indeterminate, out of danger, or insufficiently known." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2017-2022" }, { "id": "Source", "value": "FishBase database, Froese, R. and Pauly, D. (eds)., uri: https://www.fishbase.org/, date published: 2008" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threated species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" }, { "id": "Unitofmeasure", "value": "species" } ], "source_id": "57" }, { "id": "EN.GHG.ALL.LU.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Total greenhouse gas emissions including LULUCF (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of the six greenhouse gases (GHG) covered by the Kyoto Protocol (carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphurhexafluoride (SF6)) from the energy, industry, waste, agriculture, and land use, land use changes, and forestry (LULUCF) sectors, standardized to carbon dioxide equivalent values. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nEuropean Forest Observatory, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.ALL.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Total greenhouse gas emissions excluding LULUCF (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of the six greenhouse gases (GHG) covered by the Kyoto Protocol (carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphurhexafluoride (SF6)) from the energy, industry, waste, and agriculture sectors, standardized to carbon dioxide equivalent values. This measure excludes GHG fluxes caused by Land Use Change Land Use and Forestry (LULUCF), as these fluxes have larger uncertainties. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.ALL.PC.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using population as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Total greenhouse gas emissions per capita excluding LULUCF (t CO2e/capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "Total annual emissions of the six greenhouse gases (GHG) covered by the Kyoto Protocol (carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphurhexafluoride (SF6)) from the energy, industry, waste, and agriculture sectors, standardized to carbon dioxide equivalent values divided by the economy's population. This measure excludes GHG fluxes caused by Land Use Change Land Use and Forestry (LULUCF), as these fluxes have larger uncertainties." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n?? =??×????\n\nWhere:\n\nE = Total emissions (kg, tons, or CO2-equivalent)\nA = Activity data (e.g., fuel consumption, production levels)\nEF = Emission factor (kg of pollutant per unit of activity)\n\nDepending on the pollutant, different tiers of complexity are used:\n\nTier 1 – Default IPCC emission factors (simple estimation)\nTier 2 – Country/region-specific emission factors\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "t CO2e/capita" } ], "source_id": "57" }, { "id": "EN.GHG.CH4.AG.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions from Agriculture (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the agricultural sector. This includes emissions from livestock (IPCC 2006 codes 3.A.1 (enteric fermentation, 3.a.2 (manure management) and crops (IPCC 2006 codes 3.C.1 Emissions from biomass burning, 3.C.2 Liming, 3.C.3 Urea application, 3.C.4 Direct N2O Emissions from managed soils, 3.C.5 Indirect N2O Emissions from managed soils, 3.C.6 Indirect N2O Emissions from manure management, 3.C.7 Rice cultivations). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CH4.BU.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions from Building (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the building sector (subsector of the energy sector) including IPCC 2006 codes 1.A.4 Residential and other sectors, 1.A.5 Non-Specified. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CH4.FE.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions from Fugitive Emissions (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from fugitive emissions (subsector of the energy sector) including IPCC 2006 codes 1.A.1.bc Petroleum Refining - Manufacture of Solid Fuels and Other Energy Industries, 1.B.1 Solid Fuels, 1.B.2 Oil and Natural Gas, 5.B. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CH4.IC.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions from Industrial Combustion (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from industrial combustion (subsector of the energy sector) including IPCC 2006 code 1.A.2 Manufacturing Industries and Construction. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CH4.IP.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions from Industrial Processes (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from industrial processes including IPCC 2006 codes 2.A.1 Cement production, 2.A.2 Lime production, 2.A.3 Glass Production, 2.A.4 Other Process Uses of Carbonates, 2.B Chemical Industry, 2.C Metal Industry, 2.D Non-Energy Products from Fuels and Solvent Use, 2.E Electronics Industry, 2.F Product Uses as Substitutes for Ozone Depleting Substances, 2.G Other Product Manufacture and Use and 5.A Indirect N2O emissions from the atmospheric deposition of nitrogen in NOx and NH3). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CH4.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions (total) excluding LULUCF (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CH4.PI.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions from Power Industry (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from electricity and heat generation (subsector of the energy sector) including IPCC 2006 code 1.A.1.a. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CH4.TR.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions from Transport (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the transportation sector (subsector of the energy sector) including IPCC 2006 codes 1.A.3.a Civil Aviation, 1.A.3.b_noRES Road Transportation no resuspension, 1.A.3.c Railways, 1.A.3.d Water-borne Navigation, 1.A.3.e Other Transportation. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CH4.WA.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions from Waste (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the waste sector. This includes emissions from solid waste (IPCC 2006 codes 4.A Solid Waste Disposal, 4.B Biological Treatment of Solid Waste, 4.C Incineration and Open Burning of Waste) and wastewater treatment (IPCC 2006 code 4.D Wastewater Treatment and Discharge). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CH4.ZG.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using 1990 emission levels as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Methane (CH4) emissions (total) excluding LULUCF (% change from 1990)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "Change of emissions (as %) of current year with respect to emissions in baseline year 1990 emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5). Negative values indicate that the emission level for that year is lower than the emissions level in 1990." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.AG.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions from Agriculture (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agricultural sector. This includes emissions from livestock (IPCC 2006 codes 3.A.1 (enteric fermentation, 3.a.2 (manure management) and crops (IPCC 2006 codes 3.C.1 Emissions from biomass burning, 3.C.2 Liming, 3.C.3 Urea application, 3.C.4 Direct N2O Emissions from managed soils, 3.C.5 Indirect N2O Emissions from managed soils, 3.C.6 Indirect N2O Emissions from manure management, 3.C.7 Rice cultivations). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.BU.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions from Building (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the building sector (subsector of the energy sector) including IPCC 2006 codes 1.A.4 Residential and other sectors, 1.A.5 Non-Specified. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.FE.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions from Fugitive Emissions (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from fugitive emissions (subsector of the energy sector) including IPCC 2006 codes 1.A.1.bc Petroleum Refining - Manufacture of Solid Fuels and Other Energy Industries, 1.B.1 Solid Fuels, 1.B.2 Oil and Natural Gas, 5.B. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.IC.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions from Industrial Combustion (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from industrial combustion (subsector of the energy sector) including IPCC 2006 code 1.A.2 Manufacturing Industries and Construction. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.IP.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions from Industrial Processes (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from industrial processes including IPCC 2006 codes 2.A.1 Cement production, 2.A.2 Lime production, 2.A.3 Glass Production, 2.A.4 Other Process Uses of Carbonates, 2.B Chemical Industry, 2.C Metal Industry, 2.D Non-Energy Products from Fuels and Solvent Use, 2.E Electronics Industry, 2.F Product Uses as Substitutes for Ozone Depleting Substances, 2.G Other Product Manufacture and Use and 5.A Indirect N2O emissions from the atmospheric deposition of nitrogen in NOx and NH3). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.LU.DF.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) net fluxes from LULUCF - Deforestation (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net flux of carbon dioxide (CO2) in the category \"Deforestation\"." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "Carbon fluxes from land 2000–2020: bringing clarity on countries’ reporting, uri: https://doi.org/10.5194/essd-14-4643-2022, note: Data available from https://doi.org/10.5281/zenodo.7190605, publisher: Earth System Science Data (ESSD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.LU.FL.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) net fluxes from LULUCF - Forest Land (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net flux of carbon dioxide (CO2) in the category \"Forest land\"." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "Carbon fluxes from land 2000–2020: bringing clarity on countries’ reporting, uri: https://doi.org/10.5194/essd-14-4643-2022, note: Data available from https://doi.org/10.5281/zenodo.7190605, publisher: Earth System Science Data (ESSD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.LU.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) net fluxes from LULUCF - Total excluding non-tropical fires (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net flux of carbon dioxide (CO2) from Land Use, Land Use Change and Forestry LULUCF, excluding non-ropical fires at the country level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Carbon fluxes from land 2000–2020: bringing clarity on countries’ reporting, uri: https://doi.org/10.5194/essd-14-4643-2022, note: Data available from https://doi.org/10.5281/zenodo.7190605, publisher: Earth System Science Data (ESSD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.LU.OL.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) net fluxes from LULUCF - Other Land (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net flux of carbon dioxide (CO2) in the category \"Other land\"." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "Carbon fluxes from land 2000–2020: bringing clarity on countries’ reporting, uri: https://doi.org/10.5194/essd-14-4643-2022, note: Data available from https://doi.org/10.5281/zenodo.7190605, publisher: Earth System Science Data (ESSD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.LU.OS.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) net fluxes from LULUCF - Organic Soil (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net flux of carbon dioxide (CO2) in the category \"Organic soil\"." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "Carbon fluxes from land 2000–2020: bringing clarity on countries’ reporting, uri: https://doi.org/10.5194/essd-14-4643-2022, note: Data available from https://doi.org/10.5281/zenodo.7190605, publisher: Earth System Science Data (ESSD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions (total) excluding LULUCF (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: International Energy Agency (IEA), date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.PC.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using population as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions excluding LULUCF per capita (t CO2e/capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "Total annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF, standardized to carbon dioxide equivalent values divided by the economy's population. This measure excludes GHG fluxes caused by Land Use Change Land Use and Forestry (LULUCF), as these fluxes have larger uncertainties." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "t CO2e/capita" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.PI.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions from Power Industry (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from electricity and heat generation (subsector of the energy sector) including IPCC 2006 code 1.A.1.a. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.RT.GDP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using GDP as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon intensity of GDP (kg CO2e per constant 2021 US$ of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "Annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF divided by the GDP in constant 2021 US$." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "kg CO2e per 2021 constant US$ of GDP" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.RT.GDP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using GDP as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon intensity of GDP (kg CO2e per 2021 PPP $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "Annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF divided by the GDP in 2021 PPP $." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??×????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "kg CO2e per 2021 PPP $" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.TR.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions from Transport (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the transportation sector (subsector of the energy sector) including IPCC 2006 codes 1.A.3.a Civil Aviation, 1.A.3.b_noRES Road Transportation no resuspension, 1.A.3.c Railways, 1.A.3.d Water-borne Navigation, 1.A.3.e Other Transportation. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.WA.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions from Waste (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the waste sector. This includes emissions from solid waste (IPCC 2006 codes 4.A Solid Waste Disposal, 4.B Biological Treatment of Solid Waste, 4.C Incineration and Open Burning of Waste) and wastewater treatment (IPCC 2006 code 4.D Wastewater Treatment and Discharge). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.CO2.ZG.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using 1990 emission levels as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Carbon dioxide (CO2) emissions (total) excluding LULUCF (% change from 1990)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "Change of emissions (as %) of current year with respect to emissions in baseline year 1990 emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5). Negative values indicate that the emission level for that year is lower than the emissions level in 1990." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "EN.GHG.FGAS.IP.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Fluorinated greenhouse gases (F-gases) emissions from Industrial Processes (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of fluorinated gases (hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphurhexafluoride (SF6)), from industrial processes including IPCC 2006 codes 2.B Chemical Industry, 2.C Metal Industry, 2.E Electronics Industry, 2.F Product Uses as Substitutes for Ozone Depleting Substances, 2.G Other Product Manufacture and Use The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.N2O.AG.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions from Agriculture (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the agricultural sector. This includes emissions from livestock (IPCC 2006 codes 3.A.1 (enteric fermentation, 3.a.2 (manure management) and crops (IPCC 2006 codes 3.C.1 Emissions from biomass burning, 3.C.2 Liming, 3.C.3 Urea application, 3.C.4 Direct N2O Emissions from managed soils, 3.C.5 Indirect N2O Emissions from managed soils, 3.C.6 Indirect N2O Emissions from manure management, 3.C.7 Rice cultivations). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.N2O.BU.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions from Building (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the building sector (subsector of the energy sector) including IPCC 2006 codes 1.A.4 Residential and other sectors, 1.A.5 Non-Specified. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.N2O.FE.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions from Fugitive Emissions (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from fugitive emissions (subsector of the energy sector) including IPCC 2006 codes 1.A.1.bc Petroleum Refining - Manufacture of Solid Fuels and Other Energy Industries, 1.B.1 Solid Fuels, 1.B.2 Oil and Natural Gas, 5.B. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.N2O.IC.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions from Industrial Combustion (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from industrial combustion (subsector of the energy sector) including IPCC 2006 code 1.A.2 Manufacturing Industries and Construction. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.N2O.IP.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions from Industrial Processes (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from industrial processes including IPCC 2006 codes 2.A.1 Cement production, 2.A.2 Lime production, 2.A.3 Glass Production, 2.A.4 Other Process Uses of Carbonates, 2.B Chemical Industry, 2.C Metal Industry, 2.D Non-Energy Products from Fuels and Solvent Use, 2.E Electronics Industry, 2.F Product Uses as Substitutes for Ozone Depleting Substances, 2.G Other Product Manufacture and Use and 5.A Indirect N2O emissions from the atmospheric deposition of nitrogen in NOx and NH3). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.N2O.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions (total) excluding LULUCF (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.N2O.PI.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions from Power Industry (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from electricity and heat generation (subsector of the energy sector) including IPCC 2006 code 1.A.1.a. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.N2O.TR.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions from Transport (Energy) (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the transportation sector (subsector of the energy sector) including IPCC 2006 codes 1.A.3.a Civil Aviation, 1.A.3.b_noRES Road Transportation no resuspension, 1.A.3.c Railways, 1.A.3.d Water-borne Navigation, 1.A.3.e Other Transportation. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.N2O.WA.MT.CE.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions from Waste (Mt CO2e)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the waste sector. This includes emissions from solid waste (IPCC 2006 codes 4.A Solid Waste Disposal, 4.B Biological Treatment of Solid Waste, 4.C Incineration and Open Burning of Waste) and wastewater treatment (IPCC 2006 code 4.D Wastewater Treatment and Discharge). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "Mt CO2eq" } ], "source_id": "57" }, { "id": "EN.GHG.N2O.ZG.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using 1990 emission levels as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Nitrous oxide (N2O) emissions (total) excluding LULUCF (% change from 1990)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "Change of emissions (as %) of current year with respect to emissions in baseline year 1990 emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5). Negative values indicate that the emission level for that year is lower than the emissions level in 1990." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nEDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, International Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "% change from 1990" } ], "source_id": "57" }, { "id": "EN.GHG.TOT.ZG.AR5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using 1990 emission levels as weights" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Anthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank’s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries’ economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries’ GDP." }, { "id": "IndicatorName", "value": "Total greenhouse gas emissions excluding LULUCF (% change from 1990)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower—around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6)." }, { "id": "Longdefinition", "value": "Change of emissions (as %) of current year with respect to emissions in baseline year 1990 emissions of the six greenhouse gases (GHG) covered by the Kyoto Protocol (carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphurhexafluoride (SF6)) from the energy, industry, waste, and agriculture sectors, standardized to carbon dioxide equivalent values. This measure excludes GHG fluxes caused by Land Use Change Land Use and Forestry (LULUCF), as these fluxes have larger uncertainties. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5) to combine different GHGs. Negative values indicate that the emission level for that year is lower than the emissions level in 1990." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) – Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) – National GHG inventories, Food and Agriculture Organization (FAO) – Agricultural emissions, World Bank & National Statistics Offices – Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines – Emission factors and methodologies. \n\n\n\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n\n\n\n?? =??×????\n\n\n\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\n\n\n\nTier 1 – Default IPCC emission factors (simple estimation)\n\n\n\n\nTier 2 – Country/region-specific emission factors\n\n\n\n\nTier 3 – Uses detailed modeling, facility-level data, or continuous emissions monitoring\n\n\n\n\nTier 3 – Detailed process-based models (highest accuracy)" }, { "id": "Topic", "value": "Environment: Emissions" }, { "id": "Unitofmeasure", "value": "% change from 1990" } ], "source_id": "57" }, { "id": "EN.HPT.THRD.NO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThreatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN guides conservation activities of governments, NGOs and scientific institutions. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe plants and animals assessed for the IUCN Red List are the bearers of genetic diversity and the building blocks of ecosystems, and information on their conservation status and distribution provides the foundation for making informed decisions about conserving biodiversity from local to global levels. Only a small number of the world's plant and animal species have been assessed. In addition to the many thousands of species which have not yet been assessed so far, other species not included on the IUCN Red List are those that went extinct before 1500 AD and the \"Least Concern\" (plants that have been evaluated to have a low risk of extinction) species that have not yet been data based.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future." }, { "id": "IndicatorName", "value": "Plant species (higher), threatened" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAlthough there are over 12,000 plant species on the IUCN Red List, fewer than one thousand of these are properly documented. To help address this gap, IUCN is pursuing global assessments of plant species of value to people including species of high economic value. The conifer and cycad species already on the IUCN Red List need to be fully documented. IUCN is also developing a tool to assist with preliminary assessments of plant species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List." }, { "id": "Longdefinition", "value": "Higher plants are native vascular plant species. Threatened species are the number of species classified by the IUCN as endangered, vulnerable, rare, indeterminate, out of danger, or insufficiently known." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2017-2022" }, { "id": "Source", "value": "The IUCN Red List of Threatened Species, UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.iucnredlist.org/;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.iucnredlist.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threated species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" }, { "id": "Unitofmeasure", "value": "species" } ], "source_id": "57" }, { "id": "EN.MAM.THRD.NO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. The number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThreatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nthe IUCN Red List covers a comprehensive assessment of the conservation status of the world's 5,488 mammal species, including global summary statistics, individual species accounts/threat category, range map, ecology information, and some other data. Mammal species are found spread across the globe, with the exception of the land mass of Antarctica. Nearly one-quarter of the world's mammal species are known to be globally threatened or extinct, 63 percent are known to not be threatened, and 15 percent have insufficient data to determine their threat status. Habitat loss, affecting over 2,000 mammal species, is the greatest threat globally. The second greatest threat is utilization which is affecting over 900 mammal species, mainly those in Asia.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future." }, { "id": "IndicatorName", "value": "Mammal species, threatened" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSome parts of the world, such as the Andes, Central and West Africa, Angola, parts of South and Southeast Asia, and Melanesia, still have sparse information available of their mammal faunas. In addition, many species' names, especially in the tropics, actually represent complexes of several species that have not yet been resolved. The information on the relative importance of different threatening processes to mammal species is incomplete. IUCN codes all threats that appear to have an important impact, but not their relative importance for each species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List." }, { "id": "Longdefinition", "value": "Mammal species are mammals excluding whales and porpoises. Threatened species are the number of species classified by the IUCN as endangered, vulnerable, rare, indeterminate, out of danger, or insufficiently known." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2017-2022" }, { "id": "Source", "value": "The IUCN Red List of Threatened Species, UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.iucnredlist.org/;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.iucnredlist.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threated species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" }, { "id": "Unitofmeasure", "value": "species" } ], "source_id": "57" }, { "id": "EN.POP.DNST", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries; in developing countries errors may be substantial because of limits in the transport, communications, and other resources required conducting and analyzing a full census.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPopulation density is a measure of the intensity of land-use, and can be calculated for a block, city, county, state, country, continent or the entire world. Considering that over half of the Earth's land mass consists of areas inhospitable to human inhabitation, such as deserts and high mountains, and that population tends to cluster around seaports and fresh water sources, a simple number of population density by itself does not give any meaningful measurement of human population density.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSeveral of the most densely populated territories in the world are city-states, microstates, or dependencies.[6][7] These territories share a relatively small area and a high urbanization level, with an economically specialized city population drawing also on rural resources outside the area, illustrating the difference between high population density and overpopulation." }, { "id": "IndicatorName", "value": "Population density (people per sq. km of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data are provided by the United Nations Population Division and other agencies. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data." }, { "id": "Longdefinition", "value": "Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2023" }, { "id": "Source", "value": "FAO population estimates, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO);\nWorld Bank population estimates, World Bank (WB), publisher: World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population density is midyear population divided by land area in square kilometers. This ratio can be calculated for any territorial unit for any point in time, depending on the source of the population data. Populationestimates are prepared by World Bank staff from variety of sources. They are based on the de facto definition of population and include all residents regardless of legal status or citizenship, within the physical boundaries of a country and under the jurisdiction of that country's political control. Refugees not permanently settled in the country of asylum are considered part of the population of their country of origin. Population numbers are either current census data or historical census data extrapolated through demographic methods. The count also excludes visitors from overseas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPopulation density is calculated by dividing midyear population by land area in a country. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship - except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "people per square kilometer of land area" } ], "source_id": "57" }, { "id": "EN.POP.EL5M.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Rural population living in areas where elevation is below 5 meters (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Rural population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112, date published: 202112;\nCUNY Institute for Demographic Research (CIDR) - City University of New York, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population counts in low elevation zones in the year 1990 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2000 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2010 derived from the application of United Nations 2000-2010 national growth rates to year 2000 population data from GRUMPv1 ( see documentation for full description of methodologies ).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "57" }, { "id": "EN.POP.EL5M.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Urban population living in areas where elevation is below 5 meters (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Urban population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112, date published: 202112;\nCUNY Institute for Demographic Research (CIDR) - City University of New York, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population counts in low elevation zones in the year 1990 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2000 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2010 derived from the application of United Nations 2000-2010 national growth rates to year 2000 population data from GRUMPv1 ( see documentation for full description of methodologies ).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "57" }, { "id": "EN.POP.EL5M.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Scientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems." }, { "id": "IndicatorName", "value": "Population living in areas where elevation is below 5 meters (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is “unequivocal” and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20–86 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5°C by 2100 (Kattenberg and others 1996) and increase of “killer” heat waves during the warm season (Karl and others 1997)." }, { "id": "Longdefinition", "value": "Population below 5m is the percentage of the total population living in areas where the elevation is 5 meters or less." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2015" }, { "id": "Source", "value": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112, date published: 202112;\nCUNY Institute for Demographic Research (CIDR) - City University of New York, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population counts in low elevation zones in the year 1990 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2000 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2010 derived from the application of United Nations 2000-2010 national growth rates to year 2000 population data from GRUMPv1 (see documentation for full description of methodologies).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Land use" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "57" }, { "id": "EN.POP.SLUM.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Population living in slums (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, housing durability, and security of tenure, as adopted in the Millennium Development Goal Target 7.D. The successor, the Sustainable Development Goal 11.1.1, considers inadequate housing (housing affordability) to complement the above definition of slums/informal settlements." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Urban Indicators Database, UN Human Settlements Programme (UN-Habitat), uri: https://data.unhabitat.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, housing durability, and security of tenure, as adopted in the Millennium Development Goal Target 7.D. The successor, the Sustainable Development Goal 11.1.1, considers inadequate housing (housing affordability) to complement the above definition of slums/informal settlements." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "% (share) of urban population" } ], "source_id": "57" }, { "id": "EN.RUR.DNST", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Rural population density (rural population per sq. km of arable land)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Rural population density is the rural population divided by the arable land area. Rural population is calculated as the difference between the total population and the urban population. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization and World Bank population estimates." }, { "id": "Topic", "value": "Environment: Density & urbanization" } ], "source_id": "57" }, { "id": "EN.URB.LCTY", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A metropolitan area includes the urban area, and its satellite cities plus intervening rural land that is socio-economically connected to the urban core city, typically by employment ties through commuting, with the urban core city being the primary labor market. According to the United Nations' definition, a metropolitan area includes both the contiguous territory inhabited at urban levels of residential density and additional surrounding areas of lower settlement density that are also under the direct influence of the city (e.g., through frequent transport, road linkages, commuting facilities etc.).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nExplosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service. For the first time ever, the majority of the world's population lives in a city, and this proportion continues to grow. One hundred years ago, 2 out of every 10 people lived in an urban area. By 1990, less than 40 percent of the global population lived in a city, but as of early 2010s, more than half of all people live in an urban area. By 2030, 6 out of every 10 people will live in a city, and by 2050, this proportion will increase to 7 out of 10 people. About half of all urban dwellers live in cities with between 100,000-500,000 people, and fewer than 10% of urban dwellers live in megacities (a city with a population of more than 10 million, as defined by UN HABITAT). Currently, the number of urban residents is growing by nearly 60 million every year.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBy the middle of the 21st century, the urban population will almost double, reaching 6.4 billion in 2050. Almost all urban population growth in the next 30 years will occur in cities of developing countries. By the middle of the 21st century, it is estimated that the urban population of developing counties will more than double, reaching almost 5.2 billion in 2050. In high-income countries, the urban population is expected to remain largely unchanged over the next two decades, reaching to just over 1 billion by 2025. In these countries, immigration (legal and illegal) will account for more than two-thirds of urban growth. Without immigration, the urban population in these countries would most likely decline or remain static.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment. Poverty is growing faster in urban than in rural areas. According to UN one billion people live in urban slums, which are typically overcrowded, polluted and dangerous, and lack basic services such as clean water and sanitation." }, { "id": "IndicatorName", "value": "Population in largest city" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Most countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries. For example, in Botswana, agglomeration of 5,000 or more inhabitants where 75 per cent of the economic activity is non-agricultural is considered \"urban\" while in Iceland localities of 200 or more inhabitants, and in Peru population centers with 100 or more dwellings, are considered \"urban.\" In the United States places of 2,500 or more inhabitants, generally having population densities of 1,000 persons per square mile or more are considered \"urban\".\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. According to China's State Statistical Bureau, by the end of 1996 urban residents accounted for about 43 percent of China's population, more than double the 20 percent considered urban in 1994. In addition to the continuous migration of people from rural to urban areas, one of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as cities in recent years.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Population in largest city is the urban population living in the country's largest metropolitan area." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Urbanization Prospects 2018, United Nations (UN), uri: https://population.un.org/wup/, publisher: UN Population Division, date published: 2018" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. The United Nations Population Division and other agencies provide current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\" Typically, a community or settlement with a population of 2,000 or more is considered urban, but national definitions are most commonly based on size of locality. Eurostat defines urban areas as clusters of contiguous grid cells of 1 km2 with a density of at least 300 inhabitants per km2 and a minimum population of 5,000. Further it defines high-density cluster as contiguous grid cells of 1 km2 with a density of at least 1,500 inhabitants per km2 and a minimum population of 50,000.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe population of a city or metropolitan area depends on the boundaries chosen. For example, in 1990 Beijing, China, contained 2.3 million people in 87 square kilometers of \"inner city\" and 5.4 million in 158 square kilometers of \"core city.\" The population of \"inner city and inner suburban districts\" was 6.3 million and that of \"inner city, inner and outer suburban districts, and inner and outer counties\" was 10.8 million. (Most countries use the last definition.)" }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "people" } ], "source_id": "57" }, { "id": "EN.URB.LCTY.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A metropolitan area includes the urban area, and its satellite cities plus intervening rural land that is socio-economically connected to the urban core city, typically by employment ties through commuting, with the urban core city being the primary labor market. According to the United Nations' definition, a metropolitan area includes both the contiguous territory inhabited at urban levels of residential density and additional surrounding areas of lower settlement density that are also under the direct influence of the city (e.g., through frequent transport, road linkages, commuting facilities etc.).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nExplosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service. For the first time ever, the majority of the world's population lives in a city, and this proportion continues to grow. One hundred years ago, 2 out of every 10 people lived in an urban area. By 1990, less than 40 percent of the global population lived in a city, but as of early 2010s, more than half of all people live in an urban area. By 2030, 6 out of every 10 people will live in a city, and by 2050, this proportion will increase to 7 out of 10 people. About half of all urban dwellers live in cities with between 100,000-500,000 people, and fewer than 10% of urban dwellers live in megacities (a city with a population of more than 10 million, as defined by UN HABITAT). Currently, the number of urban residents is growing by nearly 60 million every year.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBy the middle of the 21st century, the urban population will almost double, reaching 6.4 billion in 2050. Almost all urban population growth in the next 30 years will occur in cities of developing countries. By the middle of the 21st century, it is estimated that the urban population of developing counties will more than double, reaching almost 5.2 billion in 2050. In high-income countries, the urban population is expected to remain largely unchanged over the next two decades, reaching to just over 1 billion by 2025. In these countries, immigration (legal and illegal) will account for more than two-thirds of urban growth. Without immigration, the urban population in these countries would most likely decline or remain static.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment. Poverty is growing faster in urban than in rural areas. According to UN one billion people live in urban slums, which are typically overcrowded, polluted and dangerous, and lack basic services such as clean water and sanitation." }, { "id": "IndicatorName", "value": "Population in the largest city (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Most countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries. For example, in Botswana, agglomeration of 5,000 or more inhabitants where 75 per cent of the economic activity is non-agricultural is considered \"urban\" while in Iceland localities of 200 or more inhabitants, and in Peru population centers with 100 or more dwellings, are considered \"urban.\" In the United States places of 2,500 or more inhabitants, generally having population densities of 1,000 persons per square mile or more are considered \"urban\".\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. According to China's State Statistical Bureau, by the end of 1996 urban residents accounted for about 43 percent of China's population, more than double the 20 percent considered urban in 1994. In addition to the continuous migration of people from rural to urban areas, one of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as cities in recent years.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Urbanization Prospects 2018, United Nations (UN), uri: https://population.un.org/wup/, publisher: UN Population Division, date published: 2018" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. The United Nations Population Division and other agencies provide current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\" Typically, a community or settlement with a population of 2,000 or more is considered urban, but national definitions are most commonly based on size of locality. Eurostat defines urban areas as clusters of contiguous grid cells of 1 km2 with a density of at least 300 inhabitants per km2 and a minimum population of 5,000. Further it defines high-density cluster as contiguous grid cells of 1 km2 with a density of at least 1,500 inhabitants per km2 and a minimum population of 50,000.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe population of a city or metropolitan area depends on the boundaries chosen. For example, in 1990 Beijing, China, contained 2.3 million people in 87 square kilometers of \"inner city\" and 5.4 million in 158 square kilometers of \"core city.\" The population of \"inner city and inner suburban districts\" was 6.3 million and that of \"inner city, inner and outer suburban districts, and inner and outer counties\" was 10.8 million. (Most countries use the last definition.)" }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "% (share) of urban population" } ], "source_id": "57" }, { "id": "EN.URB.MCTY", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "According to the United Nations, an Urban Agglomeration refers to the de facto population contained within the contours of a contiguous territory inhabited at urban density levels without regard to administrative boundaries. It usually incorporates the population in a city or town plus that in the sub-urban areas lying outside of but being adjacent to the city boundaries. In general, an urban agglomeration is an extended city or town area comprising the built-up area of a central place and any suburbs linked by continuous urban area. INSEE, the French Statistical Institute, uses the term unité urbaine, which means continuous urbanized area. There are differences in definitions of what does and does not constitute an \"agglomeration\", as well as differenced in statistical and geographical methodology. Some of the well-known urban agglomerations of the world are Tokyo, New York City, Mexico City, New Delhi, and Seoul.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nA metropolitan area includes the urban area, and its satellite cities plus intervening rural land that is socio-economically connected to the urban core city, typically by employment ties through commuting, with the urban core city being the primary labor market. According to the United Nations' definition, a metropolitan area includes both the contiguous territory inhabited at urban levels of residential density and additional surrounding areas of lower settlement density that are also under the direct influence of the city (e.g., through frequent transport, road linkages, commuting facilities etc.).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nExplosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service. For the first time ever, the majority of the world's population lives in a city, and this proportion continues to grow. One hundred years ago, 2 out of every 10 people lived in an urban area. By 1990, less than 40 percent of the global population lived in a city, but as of early 2010s, more than half of all people live in an urban area. By 2030, 6 out of every 10 people will live in a city, and by 2050, this proportion will increase to 7 out of 10 people. About half of all urban dwellers live in cities with between 100,000-500,000 people, and fewer than 10% of urban dwellers live in megacities (a city with a population of more than 10 million, as defined by UN HABITAT). Currently, the number of urban residents is growing by nearly 60 million every year.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBy the middle of the 21st century, the urban population will almost double, reaching 6.4 billion in 2050. Almost all urban population growth in the next 30 years will occur in cities of developing countries. By the middle of the 21st century, it is estimated that the urban population of developing counties will more than double, reaching almost 5.2 billion in 2050. In high-income countries, the urban population is expected to remain largely unchanged over the next two decades, reaching to just over 1 billion by 2025. In these countries, immigration (legal and illegal) will account for more than two-thirds of urban growth. Without immigration, the urban population in these countries would most likely decline or remain static.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment. Poverty is growing faster in urban than in rural areas. According to UN one billion people live in urban slums, which are typically overcrowded, polluted and dangerous, and lack basic services such as clean water and sanitation." }, { "id": "IndicatorName", "value": "Population in urban agglomerations of more than 1 million" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to varying definitions, it is not possible to compare different agglomerations around the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Most countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries. For example, in Botswana, agglomeration of 5,000 or more inhabitants where 75 per cent of the economic activity is non-agricultural is considered \"urban\" while in Iceland localities of 200 or more inhabitants, and in Peru population centers with 100 or more dwellings, are considered \"urban.\" In the United States places of 2,500 or more inhabitants, generally having population densities of 1,000 persons per square mile or more are considered \"urban\".\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. According to China's State Statistical Bureau, by the end of 1996 urban residents accounted for about 43 percent of China's population, more than double the 20 percent considered urban in 1994. In addition to the continuous migration of people from rural to urban areas, one of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as cities in recent years.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Population in urban agglomerations of more than one million is the country's population living in metropolitan areas that in 2018 had a population of more than one million people." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Urbanization Prospects 2018, United Nations (UN), uri: https://population.un.org/wup/, publisher: UN Population Division, date published: 2018" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. The United Nations Population Division and other agencies provide current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\" Typically, a community or settlement with a population of 2,000 or more is considered urban, but national definitions are most commonly based on size of locality. Eurostat defines urban areas as clusters of contiguous grid cells of 1 km2 with a density of at least 300 inhabitants per km2 and a minimum population of 5,000. Further it defines high-density cluster as contiguous grid cells of 1 km2 with a density of at least 1,500 inhabitants per km2 and a minimum population of 50,000.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe population of a city or metropolitan area depends on the boundaries chosen. For example, in 1990 Beijing, China, contained 2.3 million people in 87 square kilometers of \"inner city\" and 5.4 million in 158 square kilometers of \"core city.\" The population of \"inner city and inner suburban districts\" was 6.3 million and that of \"inner city, inner and outer suburban districts, and inner and outer counties\" was 10.8 million. (Most countries use the last definition.)\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "people" } ], "source_id": "57" }, { "id": "EN.URB.MCTY.TL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "According to the United Nations, an Urban Agglomeration refers to the de facto population contained within the contours of a contiguous territory inhabited at urban density levels without regard to administrative boundaries. It usually incorporates the population in a city or town plus that in the sub-urban areas lying outside of but being adjacent to the city boundaries. In general, an urban agglomeration is an extended city or town area comprising the built-up area of a central place and any suburbs linked by continuous urban area. INSEE, the French Statistical Institute, uses the term unité urbaine, which means continuous urbanized area. There are differences in definitions of what does and does not constitute an \"agglomeration\", as well as differenced in statistical and geographical methodology. Some of the well-known urban agglomerations of the world are Tokyo, New York City, Mexico City, New Delhi, and Seoul.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nA metropolitan area includes the urban area, and its satellite cities plus intervening rural land that is socio-economically connected to the urban core city, typically by employment ties through commuting, with the urban core city being the primary labor market. According to the United Nations' definition, a metropolitan area includes both the contiguous territory inhabited at urban levels of residential density and additional surrounding areas of lower settlement density that are also under the direct influence of the city (e.g., through frequent transport, road linkages, commuting facilities etc.).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nExplosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service. For the first time ever, the majority of the world's population lives in a city, and this proportion continues to grow. One hundred years ago, 2 out of every 10 people lived in an urban area. By 1990, less than 40 percent of the global population lived in a city, but as of early 2010s, more than half of all people live in an urban area. By 2030, 6 out of every 10 people will live in a city, and by 2050, this proportion will increase to 7 out of 10 people. About half of all urban dwellers live in cities with between 100,000-500,000 people, and fewer than 10% of urban dwellers live in megacities (a city with a population of more than 10 million, as defined by UN HABITAT). Currently, the number of urban residents is growing by nearly 60 million every year.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBy the middle of the 21st century, the urban population will almost double, reaching 6.4 billion in 2050. Almost all urban population growth in the next 30 years will occur in cities of developing countries. By the middle of the 21st century, it is estimated that the urban population of developing counties will more than double, reaching almost 5.2 billion in 2050. In high-income countries, the urban population is expected to remain largely unchanged over the next two decades, reaching to just over 1 billion by 2025. In these countries, immigration (legal and illegal) will account for more than two-thirds of urban growth. Without immigration, the urban population in these countries would most likely decline or remain static.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment. Poverty is growing faster in urban than in rural areas. According to UN one billion people live in urban slums, which are typically overcrowded, polluted and dangerous, and lack basic services such as clean water and sanitation." }, { "id": "IndicatorName", "value": "Population in urban agglomerations of more than 1 million (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to varying definitions, it is not possible to compare different agglomerations around the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Most countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries. For example, in Botswana, agglomeration of 5,000 or more inhabitants where 75 per cent of the economic activity is non-agricultural is considered \"urban\" while in Iceland localities of 200 or more inhabitants, and in Peru population centers with 100 or more dwellings, are considered \"urban.\" In the United States places of 2,500 or more inhabitants, generally having population densities of 1,000 persons per square mile or more are considered \"urban\".\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. According to China's State Statistical Bureau, by the end of 1996 urban residents accounted for about 43 percent of China's population, more than double the 20 percent considered urban in 1994. In addition to the continuous migration of people from rural to urban areas, one of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as cities in recent years.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Population in urban agglomerations of more than one million is the percentage of a country's population living in metropolitan areas that in 2018 had a population of more than one million people." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Urbanization Prospects 2018, United Nations (UN), uri: https://population.un.org/wup/, publisher: UN Population Division, date published: 2018" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. The United Nations Population Division and other agencies provide current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\" Typically, a community or settlement with a population of 2,000 or more is considered urban, but national definitions are most commonly based on size of locality. Eurostat defines urban areas as clusters of contiguous grid cells of 1 km2 with a density of at least 300 inhabitants per km2 and a minimum population of 5,000. Further it defines high-density cluster as contiguous grid cells of 1 km2 with a density of at least 1,500 inhabitants per km2 and a minimum population of 50,000.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe population of a city or metropolitan area depends on the boundaries chosen. For example, in 1990 Beijing, China, contained 2.3 million people in 87 square kilometers of \"inner city\" and 5.4 million in 158 square kilometers of \"core city.\" The population of \"inner city and inner suburban districts\" was 6.3 million and that of \"inner city, inner and outer suburban districts, and inner and outer counties\" was 10.8 million. (Most countries use the last definition.)" }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "57" }, { "id": "EP.PMP.DESL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Pump price for diesel fuel (US$ per liter)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Fuel prices refer to the pump prices of the most widely sold grade of diesel fuel. Prices have been converted from the local currency to U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "German Agency for International Cooperation (GIZ)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on fuel prices are compiled by the German Agency for International Cooperation (GIZ), from its global network and other sources, including the Allgemeiner Deutscher Automobile Club (for Europe) and the Latin American Energy Organization (for Latin America). Local prices are converted to U.S. dollars using the exchange rate in the Financial Times international monetary table on the survey date. When multiple exchange rates exist, the market, parallel, or black market rate is used." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "57" }, { "id": "EP.PMP.SGAS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Pump price for gasoline (US$ per liter)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Fuel prices refer to the pump prices of the most widely sold grade of gasoline. Prices have been converted from the local currency to U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "German Agency for International Cooperation (GIZ)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on fuel prices are compiled by the German Agency for International Cooperation (GIZ), from its global network and other sources, including the Allgemeiner Deutscher Automobile Club (for Europe) and the Latin American Energy Organization (for Latin America). Local prices are converted to U.S. dollars using the exchange rate in the Financial Times international monetary table on the survey date. When multiple exchange rates exist, the market, parallel, or black market rate is used." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "57" }, { "id": "ER.BDV.TOTL.XQ", "metatype": [ { "id": "Developmentrelevance", "value": "Biodiversity is defined as \"the variability among living organisms from all sources including, inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within species, between species, and of ecosystems.\" In simple terms, it can be described as the \"diversity of life on Earth.\" 'Biodiversity' can be used as a synonym for living nature, with an emphasis on its complexity, at genetic, species and ecosystem levels. This complexity is vital in the maintenance of healthy ecosystem functioning, and it is important to recognize this when developing policies to protect the natural environment.\n\nThe direct drivers of biodiversity loss and degradation of ecosystem are habitat change, climate change, invasive alien species, overexploitation, and pollution. These elements are influenced by a series of indirect drivers of change, including governance, institutions and legal frameworks, science and technology.\n\nAccording to UN Environment Programme, although much debate on biodiversity focuses on 'species', it is important to note that this is not a standard unit. The way species are defined differs between groups and between taxonomists. However, despite these ambiguities, measures of species richness and distribution are important tools in assessing the state of the environment and the direction and speed of change. The number of described species is now around 1.7 million. The estimated total number of species in existence ranges in order of magnitude from around 10 million to 100 million.\n\nThe greatest threats to biodiversity are the unsustainable harvesting of natural resources, including plants, animals and marine species, and the loss, degradation or fragmentation of ecosystems through land conversion for agriculture, forest clearing etc., pollution, and climate change.\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN guides conservation activities of governments, NGOs and scientific institutions. The introduction in 1994 of a scientifically rigorous approach to determine risks of extinction that is applicable to all species, has become a world standard. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future." }, { "id": "IndicatorName", "value": "GEF benefits index for biodiversity (0 = no biodiversity potential to 100 = maximum)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GEF benefits index for biodiversity is a composite index of relative biodiversity potential for each country based on the species represented in each country, their threat status, and the diversity of habitat types in each country. The index has been normalized so that values run from 0 (no biodiversity potential) to 100 (maximum biodiversity potential)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Kiran Dev Pandey, Piet Buys, Ken Chomitz, and David Wheeler's, \"Biodiversity Conservation Indicators: New Tools for Priority Setting at the Global Environment Facility\" (2006)." }, { "id": "Statisticalconceptandmethodology", "value": "The Global Environment Facility's (GEF) benefits index for biodiversity is a comprehensive indicator of national biodiversity status and is used to guide its biodiversity priorities. \n\nFor each country the biodiversity indicator incorporates the best available and comparable information in four relevant dimensions: represented species, threatened species, represented ecoregions, and threatened ecoregions. To combine these dimensions into one measure, the indicator uses dimensional weights that reflect the consensus of conservation scientists at the GEF, and International Union for Conservation of Nature (IUCN), WWF International, and other nongovernmental organizations.\n\nReporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\nThe index has been normalized so that values run from 0 (no biodiversity potential) to 100 (maximum biodiversity potential)." }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" } ], "source_id": "57" }, { "id": "ER.FSH.AQUA.MT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Aquaculture is understood to mean the farming of aquatic organisms including fish, molluscs, crustaceans and aquatic plants. Farming implies some form of intervention in the rearing process to enhance production, such as regular stocking, feeding, protection from predators, etc. Farming also implies individual or corporate ownership of the stock being cultivated. For statistical purposes, aquatic organisms which are harvested by an individual of corporate body which has owned them throughout their rearing period contribute to aquaculture while aquatic organisms which are exploitable by public as a common property resource, with or without appropriate licences, are the harvest of fisheries." }, { "id": "IndicatorName", "value": "Aquaculture production (metric tons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Aquaculture is understood to mean the farming of aquatic organisms including fish, molluscs, crustaceans and aquatic plants. Aquaculture production specifically refers to output from aquaculture activities, which are designated for final harvest for consumption." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Food and Agriculture Organization., Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Aquaculture production specifically refers to output from aquaculture activities, which are designated for final harvest for consumption. At this time, harvest for ornamental purposes is not included." }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "metric tons" } ], "source_id": "57" }, { "id": "ER.FSH.CAPT.MT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Capture fisheries production (metric tons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Capture fisheries production measures the volume of fish catches landed by a country for all commercial, industrial, recreational and subsistence purposes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Food and Agriculture Organization., Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Information on capture production is collected annually from relevant national offices concerned with fishery statistics, by means of a system of standardized forms, which list for each country the relative species items and fishing areas breakdown.\n\n\n\n\n\n\n\n\nIn the case of some \"aquatic products\", data are also obtained from trade associations or other specialized international organizations to which data are also submitted. In this way the statistics are reviewed by subject matter specialists.\n\n\n\n\n\n\n\n\nData concerning the nominal catch of certain major groups are generally reviewed in collaboration with the regional agency concerned. For example, for ISSCAAP group 36 (Tunas, bonitos and billfishes) data provided by the national correspondents are often replaced by the \"best scientific estimates\" produced by regional bodies collecting tuna catch statistics (i.e. ICCAT, IOTC, SPC and IATTC.)\n\n\n\n\n\n\n\n\nSee https://www.fao.org/fishery/en/collection/capture" }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "metric tons" } ], "source_id": "57" }, { "id": "ER.FSH.PROD.MT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Total fisheries production (metric tons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total fisheries production measures the volume of aquatic species caught by a country for all commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is also included." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Food and Agriculture Organization., Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Information on capture production is collected annually from relevant national offices concerned with fishery statistics, by means of a system of standardized forms, which list for each country the relative species items and fishing areas breakdown.\n\n\n\n\n\n\n\n\nIn the case of some \"aquatic products\", data are also obtained from trade associations or other specialized international organizations to which data are also submitted. In this way the statistics are reviewed by subject matter specialists.\n\n\n\n\n\n\n\n\nData concerning the nominal catch of certain major groups are generally reviewed in collaboration with the regional agency concerned. For example, for ISSCAAP group 36 (Tunas, bonitos and billfishes) data provided by the national correspondents are often replaced by the \"best scientific estimates\" produced by regional bodies collecting tuna catch statistics (i.e. ICCAT, IOTC, SPC and IATTC.)\n\n\n\n\n\n\n\n\nSee https://www.fao.org/fishery/en/collection/capture" }, { "id": "Topic", "value": "Environment: Agricultural production" }, { "id": "Unitofmeasure", "value": "metric tons" } ], "source_id": "57" }, { "id": "ER.GDP.FWTL.M3.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectoral planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain." }, { "id": "IndicatorName", "value": "Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Water productivity is calculated as GDP in constant prices divided by annual total water withdrawal." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO);\nWorld Bank GDP estimates, World Bank (WB), publisher: World Bank (WB);\nOECD GDP estimates, Organisation for Economic Co-operation and Development (OECD), publisher: Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Water productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. GDP data are from World Bank's national accounts files.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "constant 2015 US$ GDP per cubic meter of total freshwater withdrawal" } ], "source_id": "57" }, { "id": "ER.H2O.FWAG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectoral planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times)." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, agriculture (% of total freshwater withdrawal)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for agriculture are total withdrawals for irrigation and livestock production. Data are for the most recent year available for 1987-2002." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1965-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the pressure on the renewable water resources of a country caused by irrigation. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants).\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "% (share) of total freshwater withdrawal" } ], "source_id": "57" }, { "id": "ER.H2O.FWDM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "UNESCO estimates that in developing countries in Asia, Africa and Latin America, public water withdrawal represents just 50-100 liters (13 to 26 gallons) per person per day. In regions with insufficient water resources, this figure may be as low as 20-60 (5 to 15 gallons) liters per day. People in developed countries on average consume about 10 times more water daily than those in developing countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, domestic (% of total freshwater withdrawal)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes. Data are for the most recent year available for 1987-2002." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1965-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Domestic water withdrawal, sometimes used interchangeably with municipal water withdrawal, focuses on human needs (drinking, cooking, cleaning, and sanitation). Data includes renewable freshwater resources, potential over-abstraction of renewable groundwater, withdrawal of fossil groundwater, and the potential use of desalinated water or treated wastewater. It is usually computed as the total water withdrawn by the public distribution network, and includes that part of the industries, which is connected to the municipal network. The ratio between the net consumption and the water withdrawn can vary from 5 to 15 percent in urban areas and from 10 to 50 percent in rural areas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "% (share) of total freshwater withdrawal" } ], "source_id": "57" }, { "id": "ER.H2O.FWIN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture. UNESCO estimates that Industrial uses account for about 20 percent of global freshwater withdrawals. Of this, 57-69 percent is used for hydropower and nuclear power generation, 30-40 percent for industrial processes, and 0.5-3 percent for thermal power generation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, industry (% of total freshwater withdrawal)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for industry are total withdrawals for direct industrial use (including withdrawals for cooling thermoelectric plants). Data are for the most recent year available for 1987-2002." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1965-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Annual industrial freshwater withdrawals include renewable water resources as well as potential over-abstraction of renewable groundwater or potential use of desalinated water or treated wastewater. It includes water for the cooling of thermoelectric plants, but it does not include hydropower.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for industry are total withdrawals for direct industrial use (including withdrawals for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "% (share) of total freshwater withdrawal" } ], "source_id": "57" }, { "id": "ER.H2O.FWST.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The level of water stress can show the degree to which water resources are being exploited to meet the country's water demand. It measures a country's pressure on its water resources and therefore the challenge on the sustainability of its water use. It tracks progress in regard to “withdrawals and supply of freshwater to address water scarcity”, i.e. the environmental component of target 6.4. It also shows to what extent water resources are already used, and signals the importance of effective supply and demand management policies. It indicates the likelihood of increasing competition and conflict between different water uses and users in a situation of increasing water scarcity. Increased water stress, shown by an increase in the value of the indicator, has potentially negative effects on the sustainability of the natural resources and on economic development. On the other hand, low values of water stress indicate that water does not represent a particular challenge for economic development and sustainability." }, { "id": "IndicatorName", "value": "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Water withdrawal as a percentage of water resources is a good indicator of pressure on limited water resources, one of the most important natural resources. However, it only partially addresses the issues related to sustainable water management. Supplementary indicators that capture the multiple dimensions of water management would combine data on water demand management, behavioural changes with regard to water use and the availability of appropriate infrastructure, and measure progress in increasing the efficiency and sustainability of water use, in particular in relation to population and economic growth. They would also recognize the different climatic environments that affect water use in countries, in particular in agriculture, which is the main user of water. Sustainability assessment is also linked to the critical thresholds fixed for this indicator and there is no universal consensus on such threshold.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTrends in water withdrawal show relatively slow patterns of change. Usually, three-five years are a minimum frequency to be able to detect significant changes, as it is unlikely that the indicator would show meaningful variations from one year to the other. Estimation of water withdrawal by sector is the main limitation to the computation of the indicator. Few countries actually publish water use data on a regular basis by sector. Renewable water resources include all surface water and groundwater resources that are available on a yearly basis without consideration of the capacity to harvest and use this resource. Exploitable water resources, which refer to the volume of surface water or groundwater that is available with an occurrence of 90% of the time, are considerably less than renewable water resources, but no universal method exists to assess such exploitable water resources. There is no universally agreed method for the computation of incoming freshwater flows originating outside of a country's borders. Nor is there any standard method to account for return flows, the part of the water withdrawn from its source and which flows back to the river system after use. In countries where return flow represents a substantial part of water withdrawal, the indicator tends to underestimate available water and therefore overestimate the level of water stress.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOther limitations that affect the interpretation of the water stress indicator include: difficulty to obtain accurate, complete and up-to-date data; potentially large variation of sub-national data; lack of account of seasonal variations in water resources; lack of consideration to the distribution among water uses; lack of consideration of water quality and its suitability for use; and the indicator can be higher than 100 per cent when water withdrawal includes secondary freshwater (water withdrawn previously and returned to the system), non-renewable water (fossil groundwater), when annual groundwater withdrawal is higher than annual replenishment (over-abstraction) or when water withdrawal includes part or all of the water set aside for environmental water requirements. Some of these issues can be solved through disaggregation of the index at the level of hydrological units and by distinguishing between different use sectors. However, due to the complexity of water flows, both within a country and between countries, care should be taken not to double-count." }, { "id": "Longdefinition", "value": "The level of water stress: freshwater withdrawal as a proportion of available freshwater resources is the ratio between total freshwater withdrawn by all major sectors and total renewable freshwater resources, after taking into account environmental water requirements. Main sectors, as defined by ISIC standards, include agriculture; forestry and fishing; manufacturing; electricity industry; and services. This indicator is also known as water withdrawal intensity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Proportion of total renewable water resources withdrawn is the total volume of groundwater and surface water withdrawn from their sources for human use (in the agricultural, municipal and industrial sectors), expressed as a percentage of the total actual renewable water resources. The terms water resources and water withdrawal are understood as freshwater resources and freshwater withdrawal. Water withdrawal is estimated for the following three main sectors: agriculture, municipalities (including domestic water withdrawal) and industries, at country level and expressed in km3/year. The total actual renewable water resources for a country or region are defined as the sum of internal renewable water resources and the external renewable water resources, also expressed in km3/year. The indicator is computed by dividing total water withdrawal by total actual renewable water resources minus environmental requirements and expressed in percentage points.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal freshwater withdrawal is the volume of freshwater extracted from its source (rivers, lakes, aquifers) for agriculture, industries and municipalities. It is estimated at the country level for the following three main sectors: agriculture, municipalities (including domestic water withdrawal) and industries. Freshwater withdrawal includes primary freshwater (not withdrawn before), secondary freshwater (previously withdrawn and returned to rivers and groundwater, such as discharged wastewater and agricultural drainage water) and fossil groundwater. It does not include non-conventional water, i.e. direct use of treated wastewater, direct use of agricultural drainage water and desalinated water. Total freshwater withdrawal is in general calculated as being the sum of total water withdrawal by sector minus direct use of wastewater, direct use of agricultural drainage water and use of desalinated water.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal renewable freshwater resources are expressed as the sum of internal and external renewable water resources. The terms “water resources” and “water withdrawal” are understood here as freshwater resources and freshwater withdrawal. Internal renewable water resources are defined as the long-term average annual flow of rivers and recharge of groundwater for a given country generated from endogenous precipitation. External renewable water resources refer to the flows of water entering the country, taking into consideration the quantity of flows reserved to upstream and downstream countries through agreements or treaties.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnvironmental water requirements (Env.) are the quantities of water required to sustain freshwater and estuarine ecosystems. Water quality and also the resulting ecosystem services are excluded from this formulation which is confined to water volumes. This does not imply that quality and the support to societies which are dependent on environmental flows are not important and should not be taken care of. Methods of computation of Env. are extremely variable and range from global estimates to comprehensive assessments for river reaches. Water volumes can be expressed in the same units as the total freshwater withdrawal, and then as percentages of the available water resources." }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "% (ratio)" } ], "source_id": "57" }, { "id": "ER.H2O.FWTL.K3", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times)." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, total (billion cubic meters)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including withdrawals for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Annual freshwater withdrawals are total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Water withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "billion cubic meters" } ], "source_id": "57" }, { "id": "ER.H2O.FWTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times)." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, total (% of internal resources)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including withdrawals for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes. Data are for the most recent year available for 1987-2002." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Annual freshwater withdrawals are total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "% (share) of internal resources" } ], "source_id": "57" }, { "id": "ER.H2O.INTR.K3", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "UNESCO estimates that in developing countries in Asia, Africa and Latin America, public water withdrawal represents just 50-100 liters (13 to 26 gallons) per person per day. In regions with insufficient water resources, this figure may be as low as 20-60 (5 to 15 gallons) liters per day. People in developed countries on average consume about 10 times more water daily than those in developing countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations." }, { "id": "IndicatorName", "value": "Renewable internal freshwater resources, total (billion cubic meters)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Renewable internal freshwater resources flows refer to internal renewable resources (internal river flows and groundwater from rainfall) in the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. Renewable water resources (internal and external) include average annual flow of rivers and recharge of aquifers generated from endogenous precipitation, and those water resources that are not generated in the country, such as inflows from upstream countries (groundwater and surface water), and part of the water of border lakes and/or rivers. Non-renewable water includes groundwater bodies (deep aquifers) that have a negligible rate of recharge on the human time-scale. While renewable water resources are expressed in flows, non-renewable water resources have to be expressed in quantity (stock). Runoff from glaciers where the mass balance is negative is considered non-renewable.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal actual renewable water resources correspond to the maximum theoretical yearly amount of water actually available for a country at a given moment. The unit of calculation is km3/year or 109 m3/year. Calculation Criteria is [Water resources: total renewable (actual)] = [Surface water: total renewable (actual)] + [Groundwater: total renewable (actual)] - [Overlap between surface water and groundwater].*\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFresh water is naturally occurring water on the Earth's surface. It is a renewable but limited natural resource. Fresh water can only be renewed through the process of the water cycle, where water from seas, lakes, forests, land, rivers, and dams evaporates, forms clouds, and returns as precipitation. However, if more fresh water is consumed through human activities than is restored by nature, the result is that the quantity of fresh water available in lakes, rivers, dams and underground waters can be reduced which can cause serious damage to the surrounding environment.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n* http://www.fao.org/nr/water/aquastat/data/glossary/search.html?termId=4188&submitBtn=s&cls=yes\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "billion cubic meters" } ], "source_id": "57" }, { "id": "ER.H2O.INTR.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "UNESCO estimates that in developing countries in Asia, Africa and Latin America, public water withdrawal represents just 50-100 liters (13 to 26 gallons) per person per day. In regions with insufficient water resources, this figure may be as low as 20-60 (5 to 15 gallons) liters per day. People in developed countries on average consume about 10 times more water daily than those in developing countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations." }, { "id": "IndicatorName", "value": "Renewable internal freshwater resources per capita (cubic meters)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Renewable internal freshwater resources flows refer to internal renewable resources (internal river flows and groundwater from rainfall) in the country. Renewable internal freshwater resources per capita are calculated using the World Bank's population estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2022" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Renewable water resources (internal and external) include average annual flow of rivers and recharge of aquifers generated from endogenous precipitation, and those water resources that are not generated in the country, such as inflows from upstream countries (groundwater and surface water), and part of the water of border lakes and/or rivers. Non-renewable water includes groundwater bodies (deep aquifers) that have a negligible rate of recharge on the human time-scale. While renewable water resources are expressed in flows, non-renewable water resources have to be expressed in quantity (stock). Runoff from glaciers where the mass balance is negative is considered non-renewable. Renewable internal freshwater resources per capita are calculated using the World Bank's population estimates. The unit of calculation is m3/year per inhabitant. Internal renewable freshwater resources per capita are calculated using the World Bank's population estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal actual renewable water resources correspond to the maximum theoretical yearly amount of water actually available for a country at a given moment. The unit of calculation is km3/year or 109 m3/year. Calculation Criteria is [Water resources: total renewable (actual)] = [Surface water: total renewable (actual)] + [Groundwater: total renewable (actual)] - [Overlap between surface water and groundwater].*\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFresh water is naturally occurring water on the Earth's surface. It is a renewable but limited natural resource. Fresh water can only be renewed through the process of the water cycle, where water from seas, lakes, forests, land, rivers, and dams evaporates, forms clouds, and returns as precipitation. However, if more fresh water is consumed through human activities than is restored by nature, the result is that the quantity of fresh water available in lakes, rivers, dams and underground waters can be reduced which can cause serious damage to the surrounding environment.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n* http://www.fao.org/nr/water/aquastat/data/glossary/search.html?termId=4188&submitBtn=s&cls=yes\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Freshwater" }, { "id": "Unitofmeasure", "value": "cubic meters" } ], "source_id": "57" }, { "id": "ER.LND.PTLD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The International Union for Conservation of Nature (IUCN) defines a protected area as \"a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values.\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTerrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Nationally protected terrestrial are terrestrial areas as a percentage of total territorial area, where all nationally designated protected areas with known location and extent are included.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nProtected areas remain the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. They provide the core of efforts to protect the world's threatened species and are increasingly recognized as essential providers of ecosystem services and biological resources. Some sites are owned and managed by governments, others by private individuals, companies, communities and faith groups.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Sustainable Development Goals (SDGs) address concerns common to all economies. In recognition of the vulnerability of animal and plant species, SDGs include targets 14 and 15 to highlight the importance of marine and terrestorial protected areas. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity." }, { "id": "IndicatorName", "value": "Terrestrial protected areas (% of total land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data source for this indicator is the World Database on Protected Areas (WDPA), the most comprehensive global dataset on marine and terrestrial protected areas available. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe extent to which the land areas, including inland waters, and territorial waters of a country/territory are protected is useful for planning purpose to protect biodiversity. However, it is neither an indication of how well managed the terrestrial and marine protected areas are, nor confirmation that protection measures are effectively enforced. Further, the indicator does not provide information on non-designated or internationally designated protected areas that may also be important for conserving biodiversity. There are known data and knowledge gaps for some countries/regions due to difficulties in reporting national protected area data to the WDPA and/or determining whether a site conforms to the IUCN definition of a protected area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGaps and/or time lags in reporting national protected area data to the WDPA can however result in discrepancies, which are resolved in communication with data providers. The World Conservation Monitoring Centre (WCMC) compiles data on protected areas, numbers of certain species, and numbers of those species under threat from various sources. Because of differences in definitions, reporting practices, and reporting periods, cross-country comparability is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas." }, { "id": "Longdefinition", "value": "Terrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Marine areas, unclassified areas, littoral (intertidal) areas, and sites protected under local or provincial law are excluded." }, { "id": "Othernotes", "value": "Restricted use: Please contact the Protected Planet for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2013-2025" }, { "id": "Source", "value": "Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM), UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516, date published: 202405;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon). \n\nDesignating an area as protected does not mean that protection is in force. And for small countries that have only protected areas smaller than 1,000 hectares, the size limit in the definition leads to an underestimate of protected areas. Nationally protected areas are defined using the six IUCN management categories for areas of at least 1,000 hectares: scientific reserves and strict nature reserves with limited public access; national parks of national or international significance and not materially affected by human activity; natural monuments and natural landscapes with unique aspects; managed nature reserves and wildlife sanctuaries; protected landscapes (which may include cultural landscapes); and areas managed mainly for the sustainable use of natural systems to ensure long-term protection and maintenance of biological diversity. \n\nA GIS analysis is used to calculate terrestrial and marine protection. For this a global protected area layer is created by combining the polygons and points recorded in the WDPA. Circular buffers are created around points based on the known extent of protected areas for which no polygon is available. Annual protected area layers are created by dissolving the global protected area layer by the known year of establishment of protected areas recorded in the WDPA. The annual protected area layers are overlaid with country/territory boundaries, coastlines and buffered coastlines (delineating the territorial waters) to obtain the absolute coverage (in square kilometers) of protected areas by country/territory. The total area of a country's/territory's terrestrial protected areas and marine protected areas in territorial waters is divided by the total area of its land areas (including inland waters) and territorial waters to obtain the relative coverage (percentage) of protected areas.\n\nThe data reported for a given year reflects all protected areas reported until January of the succeeding yer. For example, the value for 2025 is calculated based on the January 2026 version of the WDPA." }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "57" }, { "id": "ER.MRN.PTMR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The International Union for Conservation of Nature (IUCN) defines a protected area as \"a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values.\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMarine protected areas are areas of intertidal or subtidal terrain - and overlying water and associated flora and fauna and historical and cultural features - that have been reserved by law or other effective means to protect part or the entire enclosed environment. Sites protected under local or provincial law are excluded.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nProtected areas remain the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. They provide the core of efforts to protect the world's threatened species and are increasingly recognized as essential providers of ecosystem services and biological resources. Some sites are owned and managed by governments, others by private individuals, companies, communities and faith groups.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Sustainable Development Goals (SDGs) address concerns common to all economies. In recognition of the vulnerability of animal and plant species, SDGs include targets 14 and 15 to highlight the importance of marine and terrestorial protected areas. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity." }, { "id": "IndicatorName", "value": "Marine protected areas (% of territorial waters)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data source for this indicator is the World Database on Protected Areas (WDPA), the most comprehensive global dataset on marine and terrestrial protected areas available. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe extent to which the land areas, including inland waters, and territorial waters of a country/territory are protected is useful for planning purpose to protect biodiversity. However, it is neither an indication of how well managed the terrestrial and marine protected areas are, nor confirmation that protection measures are effectively enforced. Further, the indicator does not provide information on non-designated or internationally designated protected areas that may also be important for conserving biodiversity. There are known data and knowledge gaps for some countries/regions due to difficulties in reporting national protected area data to the WDPA and/or determining whether a site conforms to the IUCN definition of a protected area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGaps and/or time lags in reporting national protected area data to the WDPA can however result in discrepancies, which are resolved in communication with data providers. The World Conservation Monitoring Centre (WCMC) compiles data on protected areas, numbers of certain species, and numbers of those species under threat from various sources. Because of differences in definitions, reporting practices, and reporting periods, cross-country comparability is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas." }, { "id": "Longdefinition", "value": "Marine protected areas are areas of intertidal or subtidal terrain--and overlying water and associated flora and fauna and historical and cultural features--that have been reserved by law or other effective means to protect part or all of the enclosed environment." }, { "id": "Othernotes", "value": "Restricted use: Please contact the Protected Planet for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2013-2025" }, { "id": "Source", "value": "Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM), UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.protectedplanet.net/en, note: Only the latest data can be retrieved from the Protected Planet website. The time series are provided directly to WDI by Protected Planet., publisher: Protected Planet, date accessed: 20240516, date published: 202405;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon). \n\nDesignating an area as protected does not mean that protection is in force. And for small countries that have only protected areas smaller than 1,000 hectares, the size limit in the definition leads to an underestimate of protected areas. Nationally protected areas are defined using the six IUCN management categories for areas of at least 1,000 hectares: scientific reserves and strict nature reserves with limited public access; national parks of national or international significance and not materially affected by human activity; natural monuments and natural landscapes with unique aspects; managed nature reserves and wildlife sanctuaries; protected landscapes (which may include cultural landscapes); and areas managed mainly for the sustainable use of natural systems to ensure long-term protection and maintenance of biological diversity.\n\nA GIS analysis is used to calculate terrestrial and marine protection. For this a global protected area layer is created by combining the polygons and points recorded in the WDPA. Circular buffers are created around points based on the known extent of protected areas for which no polygon is available. Annual protected area layers are created by dissolving the global protected area layer by the known year of establishment of protected areas recorded in the WDPA. The annual protected area layers are overlaid with country/territory boundaries, coastlines and buffered coastlines (delineating the territorial waters) to obtain the absolute coverage (in square kilometers) of protected areas by country/territory per year from 1990 to present. The total area of a country's/territory's terrestrial protected areas and marine protected areas in territorial waters is divided by the total area of its land areas (including inland waters) and territorial waters to obtain the relative coverage (percentage) of protected areas.\n\nThe data reported for a given year reflects all protected areas reported until January of the succeeding year. For example, the value for 2025 is calculated based on the January 2026 version of the WDPA." }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" }, { "id": "Unitofmeasure", "value": "% (share) of territorial waters" } ], "source_id": "57" }, { "id": "ER.PTD.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The International Union for Conservation of Nature (IUCN) defines a protected area as \"a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values.\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTerrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMarine protected areas are areas of intertidal or subtidal terrain - and overlying water and associated flora and fauna and historical and cultural features - that have been reserved by law or other effective means to protect part or the entire enclosed environment. Sites protected under local or provincial law are excluded.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nProtected areas remain the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. They provide the core of efforts to protect the world's threatened species and are increasingly recognized as essential providers of ecosystem services and biological resources. Some sites are owned and managed by governments, others by private individuals, companies, communities and faith groups.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Sustainable Development Goals (SDGs) address concerns common to all economies. In recognition of the vulnerability of animal and plant species, SDGs include targets 14 and 15 to highlight the importance of marine and terrestorial protected areas. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity." }, { "id": "IndicatorName", "value": "Terrestrial and marine protected areas (% of total territorial area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data source for this indicator is the World Database on Protected Areas (WDPA), the most comprehensive global dataset on marine and terrestrial protected areas available. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe extent to which the land areas, including inland waters, and territorial waters of a country/territory are protected is useful for planning purpose to protect biodiversity. However, it is neither an indication of how well managed the terrestrial and marine protected areas are, nor confirmation that protection measures are effectively enforced. Further, the indicator does not provide information on non-designated or internationally designated protected areas that may also be important for conserving biodiversity. There are known data and knowledge gaps for some countries/regions due to difficulties in reporting national protected area data to the WDPA and/or determining whether a site conforms to the IUCN definition of a protected area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGaps and/or time lags in reporting national protected area data to the WDPA can however result in discrepancies, which are resolved in communication with data providers. The World Conservation Monitoring Centre (WCMC) compiles data on protected areas, numbers of certain species, and numbers of those species under threat from various sources. Because of differences in definitions, reporting practices, and reporting periods, cross-country comparability is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas." }, { "id": "Longdefinition", "value": "Terrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Marine protected areas are areas of intertidal or subtidal terrain--and overlying water and associated flora and fauna and historical and cultural features--that have been reserved by law or other effective means to protect part or all of the enclosed environment. Sites protected under local or provincial law are excluded." }, { "id": "Othernotes", "value": "Restricted use: Please contact the Protected Planet for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2013-2025" }, { "id": "Source", "value": "Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM), UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516, date published: 202405;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon).\n\nA GIS analysis is used to calculate terrestrial and marine protection. For this a global protected area layer is created by combining the polygons and points recorded in the WDPA. Circular buffers are created around points based on the known extent of protected areas for which no polygon is available. Annual protected area layers are created by dissolving the global protected area layer by the known year of establishment of protected areas recorded in the WDPA. The annual protected area layers are overlaid with country/territory boundaries, coastlines and buffered coastlines (delineating the territorial waters) to obtain the absolute coverage (in square kilometers) of protected areas by country/territory per year from 1990 to present. The total area of a country's/territory's terrestrial protected areas and marine protected areas in territorial waters is divided by the total area of its land areas (including inland waters) and territorial waters to obtain the relative coverage (percentage) of protected areas.\n\nThe data reported for a given year reflects all protected areas reported until January of the succeeding yer. For example, the value for 2025 is calculated based on the January 2026 version of the WDPA." }, { "id": "Topic", "value": "Environment: Biodiversity & protected areas" }, { "id": "Unitofmeasure", "value": "% (share) of total territorial area" } ], "source_id": "57" }, { "id": "FB.AST.LOAN.CB.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Loan accounts, commercial banks (per 1,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Loan accounts at commercial banks include loans from banks to individuals, businesses, and others, including home mortgages, consumer loans, business loans, trade loans, student loans, emergency loans, agricultural loans, and the like. Commercial banks are banks with a full banking license. In some countries, the term \"universal banks\" or other terms may be used. Majority government- and state-owned banks are included in this category to the extent that they perform a broad set of retail banking functions and are regulated and supervised in the same manner as privately owned banks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Loan accounts at commercial banks include loans from banks to individuals, businesses, and others, including home mortgages, consumer loans, business loans, trade loans, student loans, emergency loans, agricultural loans, and the like." }, { "id": "Source", "value": "Consultative Group to Assist the Poor and the World Bank Group’s \"Financial Access 2010.\"" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "FB.AST.LOAN.CO.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Loan accounts, cooperatives (per 1,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Cooperatives, credit unions, and mutuals are financial institutions that are owned and controlled by their members (customers), regardless of whether they do business exclusively with their members or with members and nonmembers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Consultative Group to Assist the Poor and the World Bank Group’s \"Financial Access 2010.\"" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "FB.AST.LOAN.MF.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Loan accounts, microfinance institutions (per 1,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Microfinance institutions are institutions whose primary business model is to lend to (and possibly take deposits from) the poor, often using specialized methodologies such as group lending. The data collected using this institutional classification necessarily understate the scale of microfinance because many banks, cooperatives, and specialized state-owned institutions provide microfinance services as part of their activities." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Microfinance institutions are institutions whose primary business model is to lend to (and possibly take deposits from) the poor, often using specialized methodologies such as group lending." }, { "id": "Source", "value": "Consultative Group to Assist the Poor and the World Bank Group’s \"Financial Access 2010.\"" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "FB.AST.LOAN.SF.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Loan accounts, specialized state financial institutions (per 1,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Specialized state-owned financial institutions are extensions of the government whose main purpose is to lend support to economic development and/or to provide savings, payment, and deposit services to the public. They include postal banks, government savings banks, SME lending facilities, agriculture banks, and development banks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Consultative Group to Assist the Poor and the World Bank Group’s \"Financial Access 2010.\"" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "FB.AST.NPER.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Ratio of non-performing/total loan portfolio" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator serves as a key measure of asset quality within the banking sector. A higher ratio indicates a greater share of impaired loans, which can signal deteriorating credit risk and financial health of lending institutions. Conversely, a lower ratio reflects better asset quality and lower credit risk. Importantly, total gross loans include the full book value of loans before provisioning and do not net out collateral or guarantees. This indicator helps assess potential vulnerabilities in a financial system, especially when tracked over time or compared across institutions and jurisdictions." }, { "id": "IndicatorName", "value": "Bank nonperforming loans to total gross loans (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting countries compile the data using different methodologies, which may also vary for different points in time for the same country. Users are advised to consult the accompanying metadata on the IMF FSI website (data.imf.org) to conduct more meaningful cross-country comparisons or to assess the evolution of the indicator for any of the countries." }, { "id": "Longdefinition", "value": "The indicator measures the proportion of a deposit taker’s loan portfolio that is impaired or at risk of default. It is calculated as the ratio of non-performing loans (NPLs) to total gross loans, where NPLs are defined as loans that are past due by 90 days or more or are otherwise considered unlikely to be repaid in full without the realization of collateral. Both non-performing loans and total gross loans should be reported at their gross book value, without deducting for loan-loss provisions or collateral. This indicator provides a key measure of asset quality and potential credit risk in the banking system." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Financial Soundness Indicators, International Monetary Fund (IMF), uri: https://data.imf.org/en/datasets/IMF:EXTERNAL_DATASET_CARDS/IMF.STA:LFSI" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The ratio is calculated by dividing the value of non-performing loans (NPLs) by the total value of gross loans, expressed as a percentage. Both NPLs and total gross loans should be reported at their gross book value, meaning before any deductions for provisions, write-offs, or collateral. Gross loans include all outstanding loans on the balance sheet, including those classified as non-performing. The classification of NPLs should be based on either quantitative criteria (e.g., days past due) or qualitative assessments (e.g., evidence of financial difficulties), following supervisory standards aligned with Basel guidance. Data should be compiled using a consistent consolidation basis, typically the cross-border, cross-sector, domestically incorporated consolidation approach (CBCSDI), to ensure comparability across reporting entities and jurisdictions. \nStatistical concept(s): The Non-performing Loans (NPLs) to Total Gross Loans ratio is a core indicator of asset quality within the banking sector. It measures the proportion of a deposit taker’s loan portfolio that is impaired or at significant risk of default. A loan is classified as non-performing when payments of principal or interest are overdue by 90 days or more, or when it is assessed that full repayment is unlikely without the realization of collateral, even if the loan is not yet past due. Data are submitted by national authorities to the IMF following the Financial Soundness Indicators (FSI) Compilation Guide." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "FB.ATM.TOTL.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion acts as a powerful driver not only of economic growth but, more importantly, of inclusive growth. It ensures that diverse segments of society—especially low-income households and small enterprises—have access to and can effectively use financial services, allowing them to share in the benefits of economic development. By promoting savings and investment, stabilizing consumption, and reducing the financial vulnerability of individuals and businesses, financial inclusion supports broader economic expansion. Accessible and affordable financial tools—such as savings accounts, credit, and insurance—enable people, particularly those traditionally underserved or excluded, to invest in their futures, manage spending more effectively, and mitigate financial risks. These benefits can translate into higher income levels and contribute to reducing poverty and inequality. Ultimately, financial inclusion aims to expand economic opportunity and participation, helping to build a more equitable and prosperous society." }, { "id": "IndicatorName", "value": "Automated teller machines (ATMs) (per 100,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Population-based ratios of the number of branches and ATMs assume a uniform distribution of bank outlets within a country's area and across its population, while in most countries bank branches and ATMs are concentrated in urban centers of the country and are accessible only to some individuals." }, { "id": "Longdefinition", "value": "Automated teller machines (ATMs) are electromechanical devices which enable customers of financial institutions to perform financial transactions such as cash withdrawals, balance inquiries, deposits, transfer of funds, and obtaining account information, using an electronic card." }, { "id": "Othernotes", "value": "Country-specific metadata can be found on the IMF’s FAS website (data.imf.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2023" }, { "id": "Source", "value": "Financial Access Survey, International Monetary Fund (IMF), uri: https://data.imf.org/?sk=E5DCAB7E-A5CA-4892-A6EA-598B5463A34C" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are shown as the total number of ATMs for every 100,000 adults in the reporting country. Calculated as (number of ATMs)*100,000/adult population in the reporting country.\nStatistical concept(s): Data are shown as the total number of ATMs for every 100,000 adults in the reporting country. Calculated as (number of ATMs)*100,000/adult population in the reporting country." }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "(number of ATMs)*100,000/adult population" } ], "source_id": "57" }, { "id": "FB.BNK.BRCH.CB.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Branches, commercial banks (per 100,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Commercial bank branches are retail locations offering a wide array of face-to-face and automated financial services. Commercial banks are banks with a full banking license. In some countries, the term \"universal banks\" or other terms may be used. Majority government- and state-owned banks are included in this category to the extent that they perform a broad set of retail banking functions and are regulated and supervised in the same manner as privately owned banks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Commercial bank branches are retail locations offering a wide array of face-to-face and automated financial services." }, { "id": "Source", "value": "Consultative Group to Assist the Poor and the World Bank Group’s \"Financial Access 2010.\"" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "FB.BNK.BRCH.CO.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Branches, cooperatives (per 100,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Cooperatives, credit unions, and mutuals are financial institutions that are owned and controlled by their members (customers), regardless of whether they do business exclusively with their members or with members and nonmembers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Consultative Group to Assist the Poor and the World Bank Group’s \"Financial Access 2010.\"" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "FB.BNK.BRCH.MF.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Branches, microfinance institutions (per 100,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Microfinance institutions are institutions whose primary business model is to lend to (and possibly take deposits from) the poor, often using specialized methodologies such as group lending. The data collected using this institutional classification necessarily understate the scale of microfinance because many banks, cooperatives, and specialized state-owned institutions provide microfinance services as part of their activities." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Microfinance institutions are institutions whose primary business model is to lend to (and possibly take deposits from) the poor, often using specialized methodologies such as group lending." }, { "id": "Source", "value": "Consultative Group to Assist the Poor and the World Bank Group’s \"Financial Access 2010.\"" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "FB.BNK.BRCH.SF.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Branches, specialized state financial institutions (per 100,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Specialized state-owned financial institutions are extensions of the government whose main purpose is to lend support to economic development and/or to provide savings, payment, and deposit services to the public. They include postal banks, government savings banks, SME lending facilities, agriculture banks, and development banks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Consultative Group to Assist the Poor and the World Bank Group’s \"Financial Access 2010.\"" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "FB.BNK.CAPA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy. \n\nTotal assets in this context refer to the gross value of all assets on the balance sheet, without deducting for provisions or reserves. By calculating the ratio of Tier 1 capital to total assets, this indicator provides a non-risk-weighted measure of leverage, offering insight into the overall capital buffer relative to the size of a deposit taker’s balance sheet. A higher ratio indicates a stronger capital position and lower leverage, while a lower ratio may suggest vulnerability in times of financial stress. This metric is useful for analyzing systemic stability and comparing institutions that may have different levels of risk exposure." }, { "id": "IndicatorName", "value": "Bank capital to assets ratio (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Reporting countries compile the data using different methodologies, which may also vary for different points in time for the same country. Users are advised to consult the accompanying metadata on the IMF FSI website (data.imf.org) to conduct more meaningful cross-country comparisons or to assess the evolution of the indicator for any of the countries." }, { "id": "Longdefinition", "value": "The indicator is a measure of capital adequacy that evaluates the financial strength of deposit takers by comparing Tier 1 capital to total assets. Tier 1 capital, often referred to as core capital, includes the most stable and readily available forms of capital, such as common equity, disclosed reserves, retained earnings, and certain other instruments that meet regulatory requirements under the Basel framework. This capital is considered the highest quality because it is fully available to cover losses and does not need to be repaid." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Financial Soundness Indicators, International Monetary Fund (IMF), uri: https://data.imf.org/en/datasets/IMF:EXTERNAL_DATASET_CARDS/IMF.STA:LFSI" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The ratio is calculated by dividing Tier 1 capital by total assets, expressed as a percentage. Tier 1 capital should be measured in accordance with the Basel regulatory framework as adopted by national supervisory authorities, ensuring consistency with international standards. Total assets are recorded at gross book value and include all financial and non-financial assets, without deduction for provisions, write-downs, or collateral. The data are compiled on a consolidated basis, typically using the cross-border, cross-sector, domestically incorporated consolidation approach (CBCSDI), which includes all relevant financial entities under common control, regardless of location or legal form.\nStatistical concept(s): The Tier 1 Capital to Assets ratio is a core financial soundness indicator that assesses the capital adequacy and financial resilience of deposit takers by measuring the proportion of high-quality, loss-absorbing capital relative to their total balance sheet size. Tier 1 capital, often referred to as core capital, comprises components such as common equity, retained earnings, and other instruments that meet Basel criteria for being fully available to cover losses." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "FB.CBK.BRCH.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion acts as a powerful driver not only of economic growth but, more importantly, of inclusive growth. It ensures that diverse segments of society—especially low-income households and small enterprises—have access to and can effectively use financial services, allowing them to share in the benefits of economic development. By promoting savings and investment, stabilizing consumption, and reducing the financial vulnerability of individuals and businesses, financial inclusion supports broader economic expansion. Accessible and affordable financial tools—such as savings accounts, credit, and insurance—enable people, particularly those traditionally underserved or excluded, to invest in their futures, manage spending more effectively, and mitigate financial risks. These benefits can translate into higher income levels and contribute to reducing poverty and inequality. Ultimately, financial inclusion aims to expand economic opportunity and participation, helping to build a more equitable and prosperous society." }, { "id": "IndicatorName", "value": "Commercial bank branches (per 100,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Population-based ratios of the number of branches and ATMs assume a uniform distribution of bank outlets within a country's area and across its population, while in most countries bank branches and ATMs are concentrated in urban centers of the country and are accessible only to some individuals." }, { "id": "Longdefinition", "value": "Commercial bank branches are retail locations of resident commercial banks and other resident banks that function as commercial banks that provide financial services to customers and are physically separated from the main office but not organized as legally separated subsidiaries." }, { "id": "Othernotes", "value": "Country-specific metadata can be found on the IMF’s FAS website (data.imf.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2023" }, { "id": "Source", "value": "Financial Access Survey, International Monetary Fund (IMF), uri: https://data.imf.org/en/datasets/IMF.STA:FAS" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Number of branches refers to all units in the country that are physically separated from the main offices, but not incorporated as separate subsidiaries. The number of main offices is excluded. Typically, a branch provides a wide range of services to its customers including cash withdrawals, deposits in an account with a teller, financial advice, safe deposit box rentals, and currency exchange. Units, with or without human staff, which offer only automated services for cash withdrawal and deposits, or computer terminals for online banking and check depositing machines should also be counted as branches.\nStatistical concept(s): Data are shown as the number of branches of commercial banks for every 100,000 adults in the reporting country. It is calculated as (number of institutions + number of branches)*100,000/adult population in the reporting country." }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "(number of branches)*100,000/adult population" } ], "source_id": "57" }, { "id": "FB.CBK.BRWR.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion acts as a powerful driver not only of economic growth but, more importantly, of inclusive growth. It ensures that diverse segments of society—especially low-income households and small enterprises—have access to and can effectively use financial services, allowing them to share in the benefits of economic development. By promoting savings and investment, stabilizing consumption, and reducing the financial vulnerability of individuals and businesses, financial inclusion supports broader economic expansion. Accessible and affordable financial tools—such as savings accounts, credit, and insurance—enable people, particularly those traditionally underserved or excluded, to invest in their futures, manage spending more effectively, and mitigate financial risks. These benefits can translate into higher income levels and contribute to reducing poverty and inequality. Ultimately, financial inclusion aims to expand economic opportunity and participation, helping to build a more equitable and prosperous society." }, { "id": "IndicatorName", "value": "Borrowers from commercial banks (per 1,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For many countries data cover the total number of loan accounts due to lack of information on loan account holders. For several countries, data cover all borrowers including commercial banks, credit unions and financial cooperatives, deposit taking microfinance institutions, and other deposit takers. These include all resident financial corporations and quasi-corporations (except the central bank) that are mainly engaged in financial intermediation and that issue liabilities included in the national definition of broad money. These institutions have varying names in different countries, such as savings and loan associations, building societies, rural banks and agricultural banks, post office giro institutions, post office savings banks, savings banks, and money market funds." }, { "id": "Longdefinition", "value": "Borrowers from commercial banks are the reported number of resident customers that are nonfinancial corporations (public and private) and households who obtained loans from commercial banks and other banks functioning as commercial banks." }, { "id": "Othernotes", "value": "Country-specific metadata can be found on the IMF’s FAS website (data.imf.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2023" }, { "id": "Source", "value": "Financial Access Survey, International Monetary Fund (IMF), uri: https://data.imf.org/en/datasets/IMF.STA:FAS" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Number of borrowers refers to the number of resident nonfinancial corporations (public and private) and individuals in the household sector that have obtained credit (loans) from each type of financial institution.\n\n• A corporate entity must be counted as one borrower, irrespective of the number of loans extended to that corporate borrower.\n• An individual from the household sector must be counted as one borrower, irrespective of the number of loan accounts held.\n• If a loan is extended to a group of borrowers, all borrowers must be counted individually rather than as one borrower.\n\nStatistical concept(s): Borrowers from commercial banks denotes the total number of resident customers that are nonfinancial corporations (public and private) and households who obtained loans from commercial banks for every 1,000 adults in the reporting country. It is calculated as (number of borrowers)*1,000/adult population in the reporting country." }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "(number of borrowers)*1,000/adult population" } ], "source_id": "57" }, { "id": "FB.CBK.DPTR.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion acts as a powerful driver not only of economic growth but, more importantly, of inclusive growth. It ensures that diverse segments of society—especially low-income households and small enterprises—have access to and can effectively use financial services, allowing them to share in the benefits of economic development. By promoting savings and investment, stabilizing consumption, and reducing the financial vulnerability of individuals and businesses, financial inclusion supports broader economic expansion. Accessible and affordable financial tools—such as savings accounts, credit, and insurance—enable people, particularly those traditionally underserved or excluded, to invest in their futures, manage spending more effectively, and mitigate financial risks. These benefits can translate into higher income levels and contribute to reducing poverty and inequality. Ultimately, financial inclusion aims to expand economic opportunity and participation, helping to build a more equitable and prosperous society." }, { "id": "IndicatorName", "value": "Depositors with commercial banks (per 1,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For many countries data cover the total number of deposit accounts due to lack of information on account holders." }, { "id": "Longdefinition", "value": "Depositors with commercial banks are the reported number of deposit account holders, including both resident non-financial corporations (both public and private) and individuals from the household sector, at commercial banks within the reporting jurisdiction for every 1,000 adults. The major types of deposits are checking accounts, savings accounts, and time deposits." }, { "id": "Othernotes", "value": "Country-specific metadata can be found on the IMF’s FAS website (data.imf.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2023" }, { "id": "Source", "value": "Financial Access Survey, International Monetary Fund (IMF), uri: https://data.imf.org/en/datasets/IMF.STA:FAS" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: While calculating the number of depositors at each type of financial institution, the following are recommended: (a) Corporate accounts are counted as only one depositor, irrespective of the number of deposit accounts (checking, demand, saving, time deposits, etc.) held; (b) Individual accounts are counted as only one depositor, irrespective of the number of deposit accounts (checking, demand, saving, time deposits, etc.) held; (c) For joint accounts, all depositors are counted individually rather than as one depositor.\nStatistical concept(s): Depositors with commercial banks are deposit account holders at commercial banks and other resident banks functioning as commercial banks that are resident nonfinancial corporations (public and private) and households. It is calculated as (number of depositors)*1,000/adult population in the reporting country. The major types of deposits are checking accounts, savings accounts, and time deposits." }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "(number of depositors)*1,000/adult population" } ], "source_id": "57" }, { "id": "FB.LBL.DDPT.CB.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Deposit accounts, commercial banks (per 1,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Deposit accounts are accounts at commercial banks that allow money to be deposited and withdrawn by the account holder. The major types of deposits are checking accounts, savings accounts, and time deposits. Commercial banks are banks with a full banking license. In some countries, the term \"universal banks\" or other terms may be used. Majority government- and state-owned banks are included in this category to the extent that they perform a broad set of retail banking functions and are regulated and supervised in the same manner as privately owned banks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Deposit accounts are accounts at commercial banks that allow money to be deposited and withdrawn by the account holder. The major types of deposits are checking accounts, savings accounts, and time deposits." }, { "id": "Source", "value": "Consultative Group to Assist the Poor and the World Bank Group’s \"Financial Access 2010.\"" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "FB.LBL.DDPT.CO.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Deposit accounts, cooperatives (per 1,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Cooperatives, credit unions, and mutuals are financial institutions that are owned and controlled by their members (customers), regardless of whether they do business exclusively with their members or with members and nonmembers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Consultative Group to Assist the Poor and the World Bank Group’s \"Financial Access 2010.\"" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "FB.LBL.DDPT.MF.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Deposit accounts, microfinance institutions (per 1,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Microfinance institutions are institutions whose primary business model is to lend to (and possibly take deposits from) the poor, often using specialized methodologies such as group lending. The data collected using this institutional classification necessarily understate the scale of microfinance because many banks, cooperatives, and specialized state-owned institutions provide microfinance services as part of their activities." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Microfinance institutions are institutions whose primary business model is to lend to (and possibly take deposits from) the poor, often using specialized methodologies such as group lending." }, { "id": "Source", "value": "Consultative Group to Assist the Poor and the World Bank Group’s \"Financial Access 2010.\"" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "FB.LBL.DDPT.SF.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Deposit accounts, specialized state financial institutions (per 1,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Specialized state-owned financial institutions are extensions of the government whose main purpose is to lend support to economic development and/or to provide savings, payment, and deposit services to the public. They include postal banks, government savings banks, SME lending facilities, agriculture banks, and development banks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Consultative Group to Assist the Poor and the World Bank Group’s \"Financial Access 2010.\"" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "FB.POS.TOTL.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Point-of-sale terminals (per 100,000 adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Point-of-sale terminals are the equipment used to manage the selling process by a salesperson-accessible interface in the location where a transaction takes place." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Consultative Group to Assist the Poor and the World Bank Group’s \"Financial Access 2010.\"" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "FD.AST.PRVT.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Domestic credit to private sector by banks (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Credit to the private sector may sometimes include credit to state-owned or partially state-owned enterprises." }, { "id": "Longdefinition", "value": "Domestic credit to private sector by banks refers to financial resources provided to the private sector by other depository corporations (deposit taking corporations except central banks), such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FD.RES.LIQU.AS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Bank liquid reserves to bank assets ratio (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Ratio of bank liquid reserves to bank assets is the ratio of domestic currency holdings and deposits with the monetary authorities to claims on other governments, nonfinancial public enterprises, the private sector, and other banking institutions. This indicator is expressed as a percentage (a÷b)*100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2001-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FI.RES.TOTL.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Total reserves, including gold (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Discrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded." }, { "id": "Longdefinition", "value": "Reserve assets are external assets, including monetary gold, that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "FI.RES.TOTL.DT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Total reserves (% of total external debt)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Reserve assets are external assets, including monetary gold, that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency. This indicator is expressed as a percentage of total external debt which are all liabilities that require payment(s) of interest and/or principal by the debtor at some point(s) in the future and that are owed to non-residents by residents of an economy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1971-2024" }, { "id": "Source", "value": "International Debt Statistics, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FI.RES.TOTL.MO", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Total reserves in months of imports" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Reserve assets are external assets, including monetary gold, that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency. This item is expressed in terms of the number of months of imports of goods and services they could pay for [X/(Imports/12)]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" }, { "id": "Unitofmeasure", "value": "months" } ], "source_id": "57" }, { "id": "FI.RES.XGLD.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Total reserves, excluding gold (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This series includes external assets (excluding monetary gold) that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "FM.AST.CGOV.ZG.M3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Claims on central government (annual growth as % of broad money)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Monetary accounts are derived from the balance sheets of financial institutions - the central bank, commercial banks, and nonbank financial intermediaries. Although these balance sheets are usually reliable, they are subject to errors of classification, valuation, and timing and to differences in accounting practices. For example, whether interest income is recorded on an accrual or a cash basis can make a substantial difference, as can the treatment of nonperforming assets. Valuation errors typically arise for foreign exchange transactions, particularly in countries with flexible exchange rates or in countries that have undergone currency devaluation during the reporting period. The valuation of financial derivatives and the net liabilities of the banking system can also be difficult. The quality of commercial bank reporting also may be adversely affected by delays in reports from bank branches, especially in countries where branch accounts are not computerized. Thus the data in the balance sheets of commercial banks may be based on preliminary estimates subject to constant revision. This problem is likely to be even more serious for nonbank financial intermediaries." }, { "id": "Longdefinition", "value": "Claims on central government include loans to central government institutions net of deposits. Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator represents the annual percentage growth in the ratio of claims to broad money." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FM.AST.DOMO.ZG.M3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Claims on other sectors of the domestic economy (annual growth as % of broad money)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Monetary accounts are derived from the balance sheets of financial institutions - the central bank, commercial banks, and nonbank financial intermediaries. Although these balance sheets are usually reliable, they are subject to errors of classification, valuation, and timing and to differences in accounting practices. For example, whether interest income is recorded on an accrual or a cash basis can make a substantial difference, as can the treatment of nonperforming assets. Valuation errors typically arise for foreign exchange transactions, particularly in countries with flexible exchange rates or in countries that have undergone currency devaluation during the reporting period. The valuation of financial derivatives and the net liabilities of the banking system can also be difficult. The quality of commercial bank reporting also may be adversely affected by delays in reports from bank branches, especially in countries where branch accounts are not computerized. Thus the data in the balance sheets of commercial banks may be based on preliminary estimates subject to constant revision. This problem is likely to be even more serious for nonbank financial intermediaries." }, { "id": "Longdefinition", "value": "Claims on other sectors of the domestic economy include gross credit from the financial system to households, nonprofit institutions serving households, nonfinancial corporations, state and local governments, and social security funds. Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator represents the annual percentage growth in the ratio of claims to broad money." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FM.AST.DOMS.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Net domestic credit (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net domestic credit is the sum of net claims on the central government and claims on other sectors of the domestic economy. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "FM.AST.GOVT.CN", "metatype": [ { "id": "IndicatorName", "value": "Claims on governments and other public entities (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Claims on governments and other public entities (IFS line 32an + 32b + 32bx + 32c) usually comprise direct credit for specific purposes such as financing of the government budget deficit or loans to state enterprises, advances against future credit authorizations, and purchases of treasury bills and bonds, net of deposits by the public sector. Public sector deposits with the banking system also include sinking funds for the service of debt and temporary deposits of government revenues. Data are in current local currency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Financial Sector: Assets" } ], "source_id": "57" }, { "id": "FM.AST.GOVT.ZG.M2", "metatype": [ { "id": "IndicatorName", "value": "Claims on governments, etc. (annual growth as % of M2)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Claims on governments and other public entities (IFS line 32an + 32b + 32bx + 32c) usually comprise direct credit for specific purposes such as financing of the government budget deficit or loans to state enterprises, advances against future credit authorizations, and purchases of treasury bills and bonds, net of deposits by the public sector. Public sector deposits with the banking system also include sinking funds for the service of debt and temporary deposits of government revenues. Money and quasi money (M2) comprise the sum of currency outside banks, demand deposits other than those of the central government, and the time, savings, and foreign currency deposits of resident sectors other than the central government." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Financial Sector: Assets" } ], "source_id": "57" }, { "id": "FM.AST.NFRG.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Net foreign assets (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net foreign assets are the sum of foreign assets held by monetary authorities and deposit money banks, less their foreign liabilities. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "FM.AST.PRVT.CN", "metatype": [ { "id": "IndicatorName", "value": "Banking survey: claims on private sector (net) (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Claims on private sector (IFS line 32D..ZK or 32D..ZF) include gross credit from the financial system to individuals, enterprises, nonfinancial public entities not included under net domestic credit, and financial institutions not included elsewhere." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Financial Sector: Assets" } ], "source_id": "57" }, { "id": "FM.AST.PRVT.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Monetary sector credit to private sector (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Domestic credit to private sector refers to financial resources provided to the private sector, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FM.AST.PRVT.ZG.M2", "metatype": [ { "id": "IndicatorName", "value": "Claims on private sector (annual growth as % of M2)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Claims on private sector (IFS line 32d) include gross credit from the financial system to individuals, enterprises, nonfinancial public entities not included under net domestic credit, and financial institutions not included elsewhere. Money and quasi money (M2) comprise the sum of currency outside banks, demand deposits other than those of the central government, and the time, savings, and foreign currency deposits of resident sectors other than the central government." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Financial Sector: Assets" } ], "source_id": "57" }, { "id": "FM.AST.PRVT.ZG.M3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Claims on private sector (annual growth as % of broad money)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Claims on private sector include gross credit from the financial system to individuals, enterprises, nonfinancial public entities not included under net domestic credit, and financial institutions not included elsewhere. Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator represents the annual percentage growth in the ratio of claims to broad money." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FM.LBL.BMNY.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Broad money (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "FM.LBL.BMNY.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Broad money (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Othernotes", "value": "The derivation of this indicator was simplified in September 2012 to be current-year broad money divided by current-year GDP times 100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FM.LBL.BMNY.IR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Broad money to total reserves ratio" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. Reserve assets are external assets, including monetary gold, that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency. This indicator is expressed as a ratio (a÷b)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" }, { "id": "Unitofmeasure", "value": "ratio" } ], "source_id": "57" }, { "id": "FM.LBL.BMNY.ZG", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Broad money (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Monetary accounts are derived from the balance sheets of financial institutions - the central bank, commercial banks, and nonbank financial intermediaries. Although these balance sheets are usually reliable, they are subject to errors of classification, valuation, and timing and to differences in accounting practices. For example, whether interest income is recorded on an accrual or a cash basis can make a substantial difference, as can the treatment of nonperforming assets. Valuation errors typically arise for foreign exchange transactions, particularly in countries with flexible exchange rates or in countries that have undergone currency devaluation during the reporting period. The valuation of financial derivatives and the net liabilities of the banking system can also be difficult. The quality of commercial bank reporting also may be adversely affected by delays in reports from bank branches, especially in countries where branch accounts are not computerized. Thus the data in the balance sheets of commercial banks may be based on preliminary estimates subject to constant revision. This problem is likely to be even more serious for nonbank financial intermediaries." }, { "id": "Longdefinition", "value": "Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FM.LBL.MONY.CN", "metatype": [ { "id": "IndicatorName", "value": "Money (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Money is the sum of currency outside banks and demand deposits other than those of central government. This series, frequently referred to as M1 is a narrower definition of money than M2. Data are in current local currency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" } ], "source_id": "57" }, { "id": "FM.LBL.MQMY.CN", "metatype": [ { "id": "IndicatorName", "value": "Money and quasi money (M2) (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Money and quasi money comprise the sum of currency outside banks, demand deposits other than those of the central government, and the time, savings, and foreign currency deposits of resident sectors other than the central government. This definition of money supply is frequently called M2; it corresponds to lines 34 and 35 in the International Monetary Fund's (IMF) International Financial Statistics (IFS). Data are in current local currency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" } ], "source_id": "57" }, { "id": "FM.LBL.MQMY.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "The derivation of this indicator was simplified in September 2012 to be current-year M2 divided by current-year GDP times 100." }, { "id": "IndicatorName", "value": "Money and quasi money (M2) as % of GDP" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Money and quasi money comprise the sum of currency outside banks, demand deposits other than those of the central government, and the time, savings, and foreign currency deposits of resident sectors other than the central government. This definition of money supply is frequently called M2; it corresponds to lines 34 and 35 in the International Monetary Fund's (IMF) International Financial Statistics (IFS)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" } ], "source_id": "57" }, { "id": "FM.LBL.MQMY.IR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Money and quasi money (M2) to total reserves ratio" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Money and quasi money comprise the sum of currency outside banks, demand deposits other than those of the central government, and the time, savings, and foreign currency deposits of resident sectors other than the central government. This definition is frequently called M2; it corresponds to lines 34 and 35 in the International Monetary Fund's (IMF) International Financial Statistics (IFS). Total reserves comprise holdings of monetary gold, special drawing rights, reserves of IMF members held by the IMF, and holdings of foreign exchange under the control of monetary authorities. The gold component of these reserves is valued at year-end (December 31) London prices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" } ], "source_id": "57" }, { "id": "FM.LBL.MQMY.ZG", "metatype": [ { "id": "IndicatorName", "value": "Money and quasi money growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average annual growth rate in money and quasi money. Money and quasi money comprise the sum of currency outside banks, demand deposits other than those of the central government, and the time, savings, and foreign currency deposits of resident sectors other than the central government. This definition is frequently called M2; it corresponds to lines 34 and 35 in the International Monetary Fund's (IMF) International Financial Statistics (IFS). The change in the money supply is measured as the difference in end-of-year totals relative to the level of M2 in the preceding year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" } ], "source_id": "57" }, { "id": "FM.LBL.QMNY.CN", "metatype": [ { "id": "IndicatorName", "value": "Quasi money (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Quasi money refers to time, savings, and foreign currency deposits of resident sectors other than the central government." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" } ], "source_id": "57" }, { "id": "FP.CPI.TOTL", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A general and continuing increase in an economy’s price level is called inflation. The increase in the average prices of goods and services in the economy should be distinguished from a change in the relative prices of individual goods and services. Generally accompanying an overall increase in the price level is a change in the structure of relative prices, but it is only the average increase, not the relative price changes, that constitutes inflation. A commonly used measure of inflation is the consumer price index, which measures the prices of a representative basket of goods and services purchased by a typical household. The consumer price index is usually calculated on the basis of periodic surveys of consumer prices. Other price indices are derived implicitly from indexes of current and constant price series." }, { "id": "IndicatorName", "value": "Consumer price index (2010 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Consumer price indexes should be interpreted with caution. The definition of a household, the basket of goods, and the geographic (urban or rural) and income group coverage of consumer price surveys can vary widely by country. In addition, weights are derived from household expenditure surveys, which, for budgetary reasons, tend to be conducted infrequently in developing countries, impairing comparability over time. Although useful for measuring consumer price inflation within a country, consumer price indexes are of less value in comparing countries." }, { "id": "Longdefinition", "value": "Index of the prices of consumption goods and services, as compared to a certain reference period (2010=100)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Consumer Prices Indices are compiled in accordance with international standards: Consumer Price Index Manual, 2020 or 2004 version. Specific information on how countries compile their CPI statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of a consumer price index series is to measure the rate at which prices of consumption goods and services are changing from one period to another." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "index (2010 = 100)" } ], "source_id": "57" }, { "id": "FP.CPI.TOTL.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A general and continuing increase in an economy’s price level is called inflation. The increase in the average prices of goods and services in the economy should be distinguished from a change in the relative prices of individual goods and services. Generally accompanying an overall increase in the price level is a change in the structure of relative prices, but it is only the average increase, not the relative price changes, that constitutes inflation. A commonly used measure of inflation is the consumer price index, which measures the prices of a representative basket of goods and services purchased by a typical household. The consumer price index is usually calculated on the basis of periodic surveys of consumer prices. Other price indices are derived implicitly from indexes of current and constant price series." }, { "id": "IndicatorName", "value": "Inflation, consumer prices (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Consumer Prices Indices are compiled in accordance with international standards: Consumer Price Index Manual, 2020 or 2004 version. Specific information on how countries compile their CPI statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of a consumer price index series is to measure the rate at which prices of consumption goods and services are changing from one period to another." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FP.FPI.TOTL", "metatype": [ { "id": "BasePeriod", "value": "2000" }, { "id": "IndicatorName", "value": "Food price index (2000 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Food price index is a subindex of the consumer price index." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations, Statistical Yearbook and Monthly Bulletin of Statistics." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" } ], "source_id": "57" }, { "id": "FP.WPI.TOTL", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A general and continuing increase in an economy’s price level is called inflation. The increase in the average prices of goods and services in the economy should be distinguished from a change in the relative prices of individual goods and services. Generally accompanying an overall increase in the price level is a change in the structure of relative prices, but it is only the average increase, not the relative price changes, that constitutes inflation." }, { "id": "IndicatorName", "value": "Wholesale price index (2010 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Index of prices of a mix of agricultural and industrial goods at various stages of production and distribution, including import duties, as compared to a certain reference period (2010=100)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Wholesale Prices Indices are compiled in accordance with international standards: Producer Price Index Manual, 2004 version. Specific information on how countries compile their WPI statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of a wholesale price index series is to measure the rate at which prices of goods and services are changing from one period to another, for products that flow from a wholesaler to a retailer." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "index (2010 = 100)" } ], "source_id": "57" }, { "id": "FR.INR.DPST", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Both banking and financial systems enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient. The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy." }, { "id": "IndicatorName", "value": "Deposit interest rate (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Countries use a variety of reporting formats, sample designs, interest compounding formulas, averaging methods, and data presentations for indices and other data series on interest rates. The IMF's Monetary and Financial Statistics Manual does not provide guidelines beyond the general recommendation that such data should reflect market prices and effective (rather than nominal) interest rates and should be representative of the financial assets and markets to be covered. For more information, please see http://www.imf.org/external/pubs/ft/mfs/manual/index.htm." }, { "id": "Longdefinition", "value": "Deposit interest rate is the rate paid by commercial or similar banks for demand, time, or savings deposits. The terms and conditions attached to these rates differ by country, however, limiting their comparability. This indicator is expressed as a percentage (a÷b)*100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Interest rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FR.INR.LEND", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Both banking and financial systems enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient. The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy." }, { "id": "IndicatorName", "value": "Lending interest rate (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Countries use a variety of reporting formats, sample designs, interest compounding formulas, averaging methods, and data presentations for indices and other data series on interest rates. The IMF's Monetary and Financial Statistics Manual does not provide guidelines beyond the general recommendation that such data should reflect market prices and effective (rather than nominal) interest rates and should be representative of the financial assets and markets to be covered. For more information, please see http://www.imf.org/external/pubs/ft/mfs/manual/index.htm." }, { "id": "Longdefinition", "value": "Lending rate is the bank rate that usually meets the short- and medium-term financing needs of the private sector. This rate is normally differentiated according to creditworthiness of borrowers and objectives of financing. The terms and conditions attached to these rates differ by country, however, limiting their comparability. This indicator is expressed as a percentage (a÷b)*100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Interest rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FR.INR.LNDP", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Both banking and financial systems enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient. The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy." }, { "id": "IndicatorName", "value": "Interest rate spread (lending rate minus deposit rate, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Countries use a variety of reporting formats, sample designs, interest compounding formulas, averaging methods, and data presentations for indices and other data series on interest rates. The IMF's Monetary and Financial Statistics Manual does not provide guidelines beyond the general recommendation that such data should reflect market prices and effective (rather than nominal) interest rates and should be representative of the financial assets and markets to be covered. For more information, please see http://www.imf.org/external/pubs/ft/mfs/manual/index.htm." }, { "id": "Longdefinition", "value": "Interest rate spread is the interest rate charged by banks on loans to private sector customers minus the interest rate paid by commercial or similar banks for demand, time, or savings deposits. The terms and conditions attached to these rates differ by country, however, limiting their comparability. This indicator is expressed as a percentage (a÷b)*100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1967-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Interest rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FR.INR.LNLB", "metatype": [ { "id": "IndicatorName", "value": "Interest rate spread (lending rate minus LIBOR, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Financial Sector: Interest rates" } ], "source_id": "57" }, { "id": "FR.INR.RINR", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Both banking and financial systems enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient. The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy." }, { "id": "IndicatorName", "value": "Real interest rate (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "An interest rate is the amount charged, expressed as a percentage of the principal over a period of time, by the owners of certain kinds of financial assets for putting the financial assets at the disposal of another institutional unit. The real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator. The terms and conditions attached to lending rates differ by country, however, limiting their comparability. This indicator is expressed as a percentage (a÷b)*100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Interest rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FR.INR.RISK", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Both banking and financial systems enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient. The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy." }, { "id": "IndicatorName", "value": "Risk premium on lending (lending rate minus treasury bill rate, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Countries use a variety of reporting formats, sample designs, interest compounding formulas, averaging methods, and data presentations for indices and other data series on interest rates. The IMF's Monetary and Financial Statistics Manual does not provide guidelines beyond the general recommendation that such data should reflect market prices and effective (rather than nominal) interest rates and should be representative of the financial assets and markets to be covered. For more information, please see http://www.imf.org/external/pubs/ft/mfs/manual/index.htm." }, { "id": "Longdefinition", "value": "Risk premium on lending is the interest rate charged by banks on loans to private sector customers minus the \"risk free\" treasury bill interest rate at which short-term government securities are issued or traded in the market. In some countries this spread may be negative, indicating that the market considers its best corporate clients to be lower risk than the government. The terms and conditions attached to lending rates differ by country, however, limiting their comparability. This indicator is expressed as a percentage (a÷b)*100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Interest rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FS.AST.CGOV.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Claims on central government, etc. (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Claims on central government include loans to central government institutions net of deposits. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FS.AST.DOMO.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Claims on other sectors of the domestic economy (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Claims on other sectors of the domestic economy include gross credit from the financial system to households, nonprofit institutions serving households, nonfinancial corporations, state and local governments, and social security funds. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2001-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FS.AST.DOMS.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Domestic credit provided by financial sector (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In a few countries governments may hold international reserves as deposits in the banking system rather than in the central bank. Since claims on the central government are a net item (claims on the central government minus central government deposits), the figure may be negative, resulting in a negative figure for domestic credit provided by the banking sector." }, { "id": "Longdefinition", "value": "Domestic credit provided by the financial sector includes all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net. The financial sector includes monetary authorities and deposit money banks, as well as other financial corporations where data are available (including corporations that do not accept transferable deposits but do incur such liabilities as time and savings deposits). Examples of other financial corporations are finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FS.AST.PRVT.CN", "metatype": [ { "id": "IndicatorName", "value": "Banking survey: claims on private sector (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Financial Sector: Assets" } ], "source_id": "57" }, { "id": "FS.AST.PRVT.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development." }, { "id": "IndicatorName", "value": "Domestic credit to private sector (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Credit to the private sector may sometimes include credit to state-owned or partially state-owned enterprises." }, { "id": "Longdefinition", "value": "Domestic credit to private sector refers to financial resources provided to the private sector by financial corporations, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises. The financial corporations include monetary authorities and deposit money banks, as well as other financial corporations where data are available (including corporations that do not accept transferable deposits but do incur such liabilities as time and savings deposits). Examples of other financial corporations are finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability." }, { "id": "Topic", "value": "Financial Sector: Assets" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "FS.LBL.LIQU.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Liquid liabilities (M3) as % of GDP" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Liquid liabilities are also known as M3. They are the sum of currency and deposits in the central bank (M0), plus transferable deposits and electronic currency (M1), plus time and savings deposits, foreign currency transferable deposits, certificates of deposit, and securities repurchase agreements (M2), plus travelers checks, foreign currency time deposits, commercial paper, and shares of mutual funds or market funds held by residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" } ], "source_id": "57" }, { "id": "FS.LBL.QLIQ.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Quasi-liquid liabilities (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Quasi-liquid liabilities are the sum of currency and deposits in the central bank (M0), plus time and savings deposits, foreign currency transferable deposits, certificates of deposit, and securities repurchase agreements, plus travelers checks, foreign currency time deposits, commercial paper, and shares of mutual funds or market funds held by residents. They equal the M3 money supply less transferable deposits and electronic currency (M1)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates." }, { "id": "Topic", "value": "Financial Sector: Monetary holdings (liabilities)" } ], "source_id": "57" }, { "id": "FX.OWN.TOTL.40.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families. financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). https://www.sciencedirect.com/science/article/pii/S2110701724000027" }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, poorest 40% (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (poorest 40%, share of population ages 15+)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (poorest 40%, share of population ages 15+).\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (poorest 40%, share of population ages 15+)." }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "Sum" } ], "source_id": "57" }, { "id": "FX.OWN.TOTL.60.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda(GPFI, 2011).\n\n Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families (see, for example, Agarwal, 2010; Hannig and Stefan, 2010; Sarma and Pais, 2011; Kumar, 2013; Ghosh and Dixit, 2014; Talledo, 2015; Aparicio et al., 2016; Schmied and Marr, 2016; among others). \n\nFinancial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination. https://www.sciencedirect.com/science/article/pii/S2110701724000027" }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, richest 60% (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (richest 60%, share of population ages 15+)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (richest 60%, share of population ages 15+).\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (richest 60%, share of population ages 15+)." }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "Weighted average" } ], "source_id": "57" }, { "id": "FX.OWN.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families.\n\nIn addition, financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination. https://www.sciencedirect.com/science/article/pii/S2110701724000027" }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (female, % age 15+)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (female, % age 15+). Assessment of this occurs triennial\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (female, % age 15+). Assessment of this occurs triennial" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "FX.OWN.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination.\n\nFinancial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families.\nhttps://www.sciencedirect.com/science/article/pii/S2110701724000027" }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, male (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (male, % age 15+)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (male, % age 15+). Assessment of this occurs triennial.\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (male, % age 15+). Assessment of this occurs triennial." }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "Weighted average" } ], "source_id": "57" }, { "id": "FX.OWN.TOTL.OL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families \n\nfinancial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination. https://www.sciencedirect.com/science/article/pii/S2110701724000027" }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, older adults (% of population ages 25+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (older adults, % of population ages 25+)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (older adults, % of population ages 25+). This assessment occurs triennially\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (older adults, % of population ages 25+). This assessment occurs triennially" }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "FX.OWN.TOTL.PL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families (see, for example, Agarwal, 2010; Hannig and Stefan, 2010; Sarma and Pais, 2011; Kumar, 2013; Ghosh and Dixit, 2014; Talledo, 2015; Aparicio et al., 2016; Schmied and Marr, 2016; among others). In addition, nowadays, financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that it offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination." }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, primary education or less (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (primary education or less, % of population ages 15+)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (primary education or less, % of population ages 15+). This assessment is conducted triennially \nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (primary education or less, % of population ages 15+). This assessment is conducted triennially" }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "Weighted average" } ], "source_id": "57" }, { "id": "FX.OWN.TOTL.SO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families (see, for example, Agarwal, 2010; Hannig and Stefan, 2010; Sarma and Pais, 2011; Kumar, 2013; Ghosh and Dixit, 2014; Talledo, 2015; Aparicio et al., 2016; Schmied and Marr, 2016; among others). In addition, nowadays, financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination. https://www.sciencedirect.com/science/article/pii/S2110701724000027" }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, secondary education or more (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (secondary education or more, % of population ages 15+)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (secondary education or more, % of population ages 15+). This assessment is conducted triennially\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (secondary education or more, % of population ages 15+). This assessment is conducted triennially" }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "Weighted average" } ], "source_id": "57" }, { "id": "FX.OWN.TOTL.YG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families (see, for example, Agarwal, 2010; Hannig and Stefan, 2010; Sarma and Pais, 2011; Kumar, 2013; Ghosh and Dixit, 2014; Talledo, 2015; Aparicio et al., 2016; Schmied and Marr, 2016; among others). In addition, nowadays, financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination (Levine et al., 2000; Honohan, 2004; DFID, 2004; Levine, 2004; Andrianova and Demetriades, 2008). https://www.sciencedirect.com/science/article/pii/S2110701724000027" }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider, young adults (% of population ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (young adults, % of population ages 15-24)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (young adults, % of population ages 15-24). This assessment is conducted triennially.\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (young adults, % of population ages 15-24). This assessment is conducted triennially." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "FX.OWN.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families (see, for example, Agarwal, 2010; Hannig and Stefan, 2010; Sarma and Pais, 2011; Kumar, 2013; Ghosh and Dixit, 2014; Talledo, 2015; Aparicio et al., 2016; Schmied and Marr, 2016; among others). In addition, nowadays, financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination (Levine et al., 2000; Honohan, 2004; DFID, 2004; Levine, 2004; Andrianova and Demetriades, 2008). https://www.sciencedirect.com/science/article/pii/S2110701724000027" }, { "id": "IndicatorName", "value": "Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (% age 15+)." }, { "id": "Othernotes", "value": "Each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Referenceperiod", "value": "2011-2024" }, { "id": "Source", "value": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (% age 15+). Assessment conducted triennially\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (% age 15+). Assessment conducted triennially" }, { "id": "Topic", "value": "Financial Sector: Access" }, { "id": "Unitofmeasure", "value": "Weighted average" } ], "source_id": "57" }, { "id": "GB.XPD.RSDV.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Science, technology, and innovation constitute pivotal elements for achieving sustainable growth. Sustainable Development Goal (SDG) target 9.5 is dedicated to the enhancement of scientific research and the advancement of technological capabilities within industrial sectors, with a particular focus on low- and middle-income countries. Furthermore, this target encompasses the objective of augmenting the cadre of research and development personnel, as well as escalating expenditures in research." }, { "id": "IndicatorName", "value": "Research and development expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers. \n\n\n\n\n\n\n\nR&D totals from SNA accounts may differ from these estimates, due in part to the different treatments of software R&D in the totals." }, { "id": "Longdefinition", "value": "Gross domestic expenditures on research and development (R&D), expressed as a percent of GDP. They include both capital and current expenditures in the four main sectors: Business enterprise, Government, Higher education and Private non-profit. R&D covers basic research, applied research, and experimental development." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2024" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources/bulk, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-03-26, date published: 2025-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by taking the number of researchers in a specified year, dividing it by the total population—referencing the mid-year population figure—and then multiplying the result by one million.\n\n\n\n\n\n\n\n\n\nThe calculation of this indicator is performed by dividing the total domestic intramural expenditure on research and development (R&D) for a specified year by the gross domestic product (GDP)—which is the aggregate of gross value added by all resident producers in the economy, inclusive of distributive trades and transport, along with product taxes and less any subsidies not included in product values—and then multiplying the quotient by 100. \n\n\n\n\n\n\n\n\n\nData are collected through national research and experimental development (R&D) surveys, either by the national statistical office or a line ministry (such as the Ministry for Science and Technology). The data compilers are the UNESCO Institute for Statistics (UIS), Organisation for Economic Co-operation and Development (OECD), Eurostat (Statistical Office of the European Union) and the Network on Science and Technology Indicators – Ibero-American and Inter-American (RICYT), African Science, Technology and Innovation (STI) Indicators Initiative (ASTII) of the African Union Development Agency-NEPAD (AUDA-NEPAD).\nStatistical concept(s): The gross domestic expenditure on R&D indicator consists of the total expenditure (current and capital) on R&D by all resident companies, research institutes, university and government laboratories, etc. It excludes R&D expenditures financed by domestic firms but performed abroad. \n\n\n\n\n\n\n\n\n\nThe OECD's Frascati Manual defines research and experimental development as \"creative work undertaken on a systemic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications.\" R&D covers basic research, applied research, and experimental development.\n\n\n\n\n\n\n\n\n\n(1) Basic research - Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view\n\n\n\n\n\n\n\n\n\n(2) Applied research - Applied research is also original investigation undertaken in order to acquire new knowledge; it is, however, directed primarily towards a specific practical aim or objective.\n\n\n\n\n\n\n\n\n\n(3) Experimental development - Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed.\n\n\n\n\n\n\n\n\n\nThe fields of science and technology used to classify R&D according to the Revised Fields of Science and Technology Classification are:\n\n\n\n\n1. Natural sciences;\n\n\n\n\n2. Engineering and technology;\n\n\n\n\n3. Medical and health sciences;\n\n\n\n\n4. Agricultural sciences;\n\n\n\n\n5. Social sciences;\n\n\n\n\n6. Humanities and the arts.\n\n\n\n\n\n\n\n\n\nThe data are obtained through statistical surveys which are regularly conducted at national level covering R&D performing entities in the private and public sectors." }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "57" }, { "id": "GC.AST.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the transactions of the general government in financial assets. Data on transactions of the general government in financial assets provide information on how the government manages its investments and cash flow. These data help in assessing the government's fiscal strength, its ability to manage public debt, and the overall health of the economy. They are also used by policymakers to make informed decisions about monetary policy, budgeting, and economic planning. Additionally, these statistics are crucial for maintaining transparency and accountability in government operations, as they allow the public and investors to see how public funds are being utilized and managed. This can influence investor confidence and a country's credit rating, which in turn affects borrowing costs and investment levels. Overall, these data are essential for a comprehensive understanding of the government's financial position and for ensuring responsible financial governance." }, { "id": "IndicatorName", "value": "Net acquisition of financial assets (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net acquisition of government financial assets includes domestic and foreign financial claims, SDRs, and gold bullion held by monetary authorities as a reserve asset. The net acquisition of financial assets should be offset by the net incurrence of liabilities. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.AST.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the transactions of the general government in financial assets. Data on transactions of the general government in financial assets provide information on how the government manages its investments and cash flow. These data help in assessing the government's fiscal strength, its ability to manage public debt, and the overall health of the economy. They are also used by policymakers to make informed decisions about monetary policy, budgeting, and economic planning. Additionally, these statistics are crucial for maintaining transparency and accountability in government operations, as they allow the public and investors to see how public funds are being utilized and managed. This can influence investor confidence and a country's credit rating, which in turn affects borrowing costs and investment levels. Overall, these data are essential for a comprehensive understanding of the government's financial position and for ensuring responsible financial governance." }, { "id": "IndicatorName", "value": "Net acquisition of financial assets (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net acquisition of government financial assets includes domestic and foreign financial claims, SDRs, and gold bullion held by monetary authorities as a reserve asset. The net acquisition of financial assets should be offset by the net incurrence of liabilities. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.BAL.CASH.CN", "metatype": [ { "id": "IndicatorName", "value": "Cash surplus/deficit (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Cash surplus or deficit is revenue (including grants) minus expense, minus net acquisition of nonfinancial assets. In the 1986 GFS manual nonfinancial assets were included under revenue and expenditure in gross terms. This cash surplus or deficit is closest to the earlier overall budget balance (still missing is lending minus repayments, which are now a financing item under net acquisition of financial assets)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Government Finance Statistics Yearbook and data files." }, { "id": "Statisticalconceptandmethodology", "value": "The IMF's Government Finance Statistics Manual 2014, harmonized with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, revenues, and expenses, not just those represented by cash transactions. It accounts for all changes in stocks, so stock data at the end of an accounting period equal stock data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks.\n\nGovernment finance statistics are reported in local currency. Many countries report government finance data by fiscal year; see country metadata for information on fiscal year end by country." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" } ], "source_id": "57" }, { "id": "GC.BAL.CASH.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Cash surplus/deficit (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Cash surplus or deficit is revenue (including grants) minus expense, minus net acquisition of nonfinancial assets. In the 1986 GFS manual nonfinancial assets were included under revenue and expenditure in gross terms. This cash surplus or deficit is closest to the earlier overall budget balance (still missing is lending minus repayments, which are now a financing item under net acquisition of financial assets)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Government Finance Statistics Yearbook and data files, and World Bank and OECD GDP estimates." }, { "id": "Statisticalconceptandmethodology", "value": "The IMF's Government Finance Statistics Manual 2014, harmonized with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, revenues, and expenses, not just those represented by cash transactions. It accounts for all changes in stocks, so stock data at the end of an accounting period equal stock data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks.\n\nGovernment finance statistics are reported in local currency. Many countries report government finance data by fiscal year; see country metadata for information on fiscal year end by country." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" } ], "source_id": "57" }, { "id": "GC.DOD.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the government debt. Statistics on government debt provide essential information for economic planning, as they can influence a country's fiscal policies and spending. Government debt levels are also a key indicator for investors, who use this information to assess the risk of investing in a country's bonds. High debt levels can lead to lower investor confidence and higher interest rates. Furthermore, debt statistics are crucial for evaluating the sustainability of a government's fiscal policy. They help determine whether adjustments are needed to avoid potential default. These statistics also enable international comparisons, allowing for benchmarking against other countries and identifying potential issues. Accurate debt statistics are vital for the formulation of monetary and fiscal policies, including decisions on taxation and government spending. They also promote public awareness by providing transparency and accountability in how public funds are managed. Lastly, credit rating agencies use government debt statistics to assign credit ratings, which affect a country's borrowing costs and its ability to attract investment. Overall, government debt statistics are a key component of a country's economic analysis and are essential for informed decision-making by policymakers, investors, and the public." }, { "id": "IndicatorName", "value": "Central government debt, total (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Debt is the entire stock of direct government fixed-term contractual obligations to others outstanding on a particular date. It includes domestic and foreign liabilities such as currency and money deposits, securities other than shares, and loans. It is the gross amount of government liabilities reduced by the amount of equity and financial derivatives held by the government. Because debt is a stock rather than a flow, it is measured as of a given date, usually the last day of the fiscal year. Central government is the part of general government that includes all administrative departments of the national executive, legislative, and judicial functions, other central agencies and those non-market producers controlled by the central government, whose competence extends normally over the whole economic territory. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.DOD.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the government debt. Statistics on government debt provide essential information for economic planning, as they can influence a country's fiscal policies and spending. Government debt levels are also a key indicator for investors, who use this information to assess the risk of investing in a country's bonds. High debt levels can lead to lower investor confidence and higher interest rates. Furthermore, debt statistics are crucial for evaluating the sustainability of a government's fiscal policy. They help determine whether adjustments are needed to avoid potential default. These statistics also enable international comparisons, allowing for benchmarking against other countries and identifying potential issues. Accurate debt statistics are vital for the formulation of monetary and fiscal policies, including decisions on taxation and government spending. They also promote public awareness by providing transparency and accountability in how public funds are managed. Lastly, credit rating agencies use government debt statistics to assign credit ratings, which affect a country's borrowing costs and its ability to attract investment. Overall, government debt statistics are a key component of a country's economic analysis and are essential for informed decision-making by policymakers, investors, and the public." }, { "id": "IndicatorName", "value": "Central government debt, total (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Debt is the entire stock of direct government fixed-term contractual obligations to others outstanding on a particular date. It includes domestic and foreign liabilities such as currency and money deposits, securities other than shares, and loans. It is the gross amount of government liabilities reduced by the amount of equity and financial derivatives held by the government. Because debt is a stock rather than a flow, it is measured as of a given date, usually the last day of the fiscal year. Central government is the part of general government that includes all administrative departments of the national executive, legislative, and judicial functions, other central agencies and those non-market producers controlled by the central government, whose competence extends normally over the whole economic territory. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.FIN.DOMS.CN", "metatype": [ { "id": "IndicatorName", "value": "Net incurrence of liabilities, domestic (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net incurrence of government liabilities includes foreign financing (obtained from nonresidents) and domestic financing (obtained from residents), or the means by which a government provides financial resources to cover a budget deficit or allocates financial resources arising from a budget surplus. The net incurrence of liabilities should be offset by the net acquisition of financial assets (a third financing item). The difference between the cash surplus or deficit and the three financing items is the net change in the stock of cash." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Government Finance Statistics Yearbook and data files." }, { "id": "Statisticalconceptandmethodology", "value": "The IMF's Government Finance Statistics Manual 2014, harmonized with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, revenues, and expenses, not just those represented by cash transactions. It accounts for all changes in stocks, so stock data at the end of an accounting period equal stock data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks.\n\nGovernment finance statistics are reported in local currency. Many countries report government finance data by fiscal year; see country metadata for information on fiscal year end by country." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" } ], "source_id": "57" }, { "id": "GC.FIN.DOMS.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Net incurrence of liabilities, domestic (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net incurrence of government liabilities includes foreign financing (obtained from nonresidents) and domestic financing (obtained from residents), or the means by which a government provides financial resources to cover a budget deficit or allocates financial resources arising from a budget surplus. The net incurrence of liabilities should be offset by the net acquisition of financial assets (a third financing item). The difference between the cash surplus or deficit and the three financing items is the net change in the stock of cash." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Government Finance Statistics Yearbook and data files, and World Bank and OECD GDP estimates." }, { "id": "Statisticalconceptandmethodology", "value": "The IMF's Government Finance Statistics Manual 2014, harmonized with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, revenues, and expenses, not just those represented by cash transactions. It accounts for all changes in stocks, so stock data at the end of an accounting period equal stock data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks.\n\nGovernment finance statistics are reported in local currency. Many countries report government finance data by fiscal year; see country metadata for information on fiscal year end by country." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" } ], "source_id": "57" }, { "id": "GC.FIN.FRGN.CN", "metatype": [ { "id": "IndicatorName", "value": "Net incurrence of liabilities, foreign (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net incurrence of government liabilities includes foreign financing (obtained from nonresidents) and domestic financing (obtained from residents), or the means by which a government provides financial resources to cover a budget deficit or allocates financial resources arising from a budget surplus. The net incurrence of liabilities should be offset by the net acquisition of financial assets (a third financing item). The difference between the cash surplus or deficit and the three financing items is the net change in the stock of cash." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Government Finance Statistics Yearbook and data files." }, { "id": "Statisticalconceptandmethodology", "value": "The IMF's Government Finance Statistics Manual 2014, harmonized with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, revenues, and expenses, not just those represented by cash transactions. It accounts for all changes in stocks, so stock data at the end of an accounting period equal stock data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks.\n\nGovernment finance statistics are reported in local currency. Many countries report government finance data by fiscal year; see country metadata for information on fiscal year end by country." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" } ], "source_id": "57" }, { "id": "GC.FIN.FRGN.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Net incurrence of liabilities, foreign (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net incurrence of government liabilities includes foreign financing (obtained from nonresidents) and domestic financing (obtained from residents), or the means by which a government provides financial resources to cover a budget deficit or allocates financial resources arising from a budget surplus. The net incurrence of liabilities should be offset by the net acquisition of financial assets (a third financing item). The difference between the cash surplus or deficit and the three financing items is the net change in the stock of cash." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Government Finance Statistics Yearbook and data files, and World Bank and OECD GDP estimates." }, { "id": "Statisticalconceptandmethodology", "value": "The IMF's Government Finance Statistics Manual 2014, harmonized with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, revenues, and expenses, not just those represented by cash transactions. It accounts for all changes in stocks, so stock data at the end of an accounting period equal stock data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks.\n\nGovernment finance statistics are reported in local currency. Many countries report government finance data by fiscal year; see country metadata for information on fiscal year end by country." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" } ], "source_id": "57" }, { "id": "GC.LBL.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the transactions of the general government in financial assets. Data on transactions of the general government in financial assets provide information on how the government manages its investments and cash flow. These data help in assessing the government's fiscal strength, its ability to manage public debt, and the overall health of the economy. They are also used by policymakers to make informed decisions about monetary policy, budgeting, and economic planning. Additionally, these statistics are crucial for maintaining transparency and accountability in government operations, as they allow the public and investors to see how public funds are being utilized and managed. This can influence investor confidence and a country's credit rating, which in turn affects borrowing costs and investment levels. Overall, these data are essential for a comprehensive understanding of the government's financial position and for ensuring responsible financial governance." }, { "id": "IndicatorName", "value": "Net incurrence of liabilities, total (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net incurrence of government liabilities includes foreign financing (obtained from nonresidents) and domestic financing (obtained from residents), or the means by which a government provides financial resources to cover a budget deficit or allocates financial resources arising from a budget surplus. The net incurrence of liabilities should be offset by the net acquisition of financial assets. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.LBL.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the transactions of the general government in financial assets. Data on transactions of the general government in financial assets provide information on how the government manages its investments and cash flow. These data help in assessing the government's fiscal strength, its ability to manage public debt, and the overall health of the economy. They are also used by policymakers to make informed decisions about monetary policy, budgeting, and economic planning. Additionally, these statistics are crucial for maintaining transparency and accountability in government operations, as they allow the public and investors to see how public funds are being utilized and managed. This can influence investor confidence and a country's credit rating, which in turn affects borrowing costs and investment levels. Overall, these data are essential for a comprehensive understanding of the government's financial position and for ensuring responsible financial governance." }, { "id": "IndicatorName", "value": "Net incurrence of liabilities, total (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net incurrence of government liabilities includes foreign financing (obtained from nonresidents) and domestic financing (obtained from residents), or the means by which a government provides financial resources to cover a budget deficit or allocates financial resources arising from a budget surplus. The net incurrence of liabilities should be offset by the net acquisition of financial assets. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.NFN.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance." }, { "id": "IndicatorName", "value": "Net investment in nonfinancial assets (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net investment in government nonfinancial assets includes fixed assets, inventories, valuables, and nonproduced assets. Nonfinancial assets are stores of value and provide benefits either through their use in the production of goods and services or in the form of property income and holding gains. Net investment in nonfinancial assets also includes consumption of fixed capital. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.NFN.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance." }, { "id": "IndicatorName", "value": "Net investment in nonfinancial assets (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net investment in government nonfinancial assets includes fixed assets, inventories, valuables, and nonproduced assets. Nonfinancial assets are stores of value and provide benefits either through their use in the production of goods and services or in the form of property income and holding gains. Net investment in nonfinancial assets also includes consumption of fixed capital. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.NLD.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is the balance of the government statistics. Balance items represent the difference between the total revenue and total expenditure of a government. If this balance is positive, it indicates net lending, meaning the government has a surplus and can potentially lend or invest the excess funds. Conversely, if the balance is negative, it indicates net borrowing, meaning the government has a deficit and may need to borrow funds to cover the shortfall. This figure is important because it provides a clear indicator of a government's fiscal health and its ability to finance its operations without resorting to additional borrowing. It is also a key indicator used by policymakers to make informed decisions about fiscal policy, taxation, and public spending. Furthermore, it is an essential metric for international organizations, investors, and credit rating agencies to assess a country's economic stability and creditworthiness." }, { "id": "IndicatorName", "value": "Net lending (+) / net borrowing (-) (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net lending (+) / net borrowing (–) equals government revenue minus expense, minus net investment in nonfinancial assets. It is also equal to the net result of transactions in financial assets and liabilities. Net lending/net borrowing is a summary measure indicating the extent to which government is either putting financial resources at the disposal of other sectors in the economy or abroad, or utilizing the financial resources generated by other sectors in the economy or from abroad. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.NLD.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is the balance of the government statistics. Balance items represent the difference between the total revenue and total expenditure of a government. If this balance is positive, it indicates net lending, meaning the government has a surplus and can potentially lend or invest the excess funds. Conversely, if the balance is negative, it indicates net borrowing, meaning the government has a deficit and may need to borrow funds to cover the shortfall. This figure is important because it provides a clear indicator of a government's fiscal health and its ability to finance its operations without resorting to additional borrowing. It is also a key indicator used by policymakers to make informed decisions about fiscal policy, taxation, and public spending. Furthermore, it is an essential metric for international organizations, investors, and credit rating agencies to assess a country's economic stability and creditworthiness." }, { "id": "IndicatorName", "value": "Net lending (+) / net borrowing (-) (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Net lending (+) / net borrowing (–) equals government revenue minus expense, minus net investment in nonfinancial assets. It is also equal to the net result of transactions in financial assets and liabilities. Net lending/net borrowing is a summary measure indicating the extent to which government is either putting financial resources at the disposal of other sectors in the economy or abroad, or utilizing the financial resources generated by other sectors in the economy or from abroad. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Deficit & financing" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.REV.GOTR.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Grants and other revenue (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Grants are transfers receivable by government units, from other resident or nonresident government units or international organizations, that do not meet the defi nition of a tax, subsidy, or social contribution. Other revenue is all revenue receivable excluding taxes, social contributions, and grants. This category of revenue includes property income, sales of goods and services, and miscellaneous other types of revenue. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.REV.GOTR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Grants and other revenue (% of revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Grants are transfers receivable by government units, from other resident or nonresident government units or international organizations, that do not meet the defi nition of a tax, subsidy, or social contribution. Other revenue is all revenue receivable excluding taxes, social contributions, and grants. This category of revenue includes property income, sales of goods and services, and miscellaneous other types of revenue. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.REV.SOCL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Social contributions (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Social contributions are actual or imputed contributions payable to social insurance schemes to\n\n\nmake provisions for social benefits to be paid. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.REV.SOCL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Social contributions (% of revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Social contributions are actual or imputed contributions payable to social insurance schemes to\n\n\nmake provisions for social benefits to be paid. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.REV.XGRT.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Revenue, excluding grants (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Revenue is an increase in net worth resulting from a transaction. Grants are excluded from this figure. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.REV.XGRT.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Revenue, excluding grants (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Revenue is an increase in net worth resulting from a transaction. Grants are excluded from this figure. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.TAX.EXPT.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on exports (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Export taxes are taxes on goods or services that become payable to government when the goods leave the economic territory or when the services are delivered to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.TAX.EXPT.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on exports (% of tax revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Export taxes are taxes on goods or services that become payable to government when the goods leave the economic territory or when the services are delivered to non-residents. This indicator is expressed as a percentage of tax revenue which includes compulsory, unrequited payments, in cash or in kind, made by institutional units to government units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.TAX.GSRV.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on goods and services (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "General taxes on goods and services are taxes levied on the production, leasing, delivery, sale, purchase or other change of ownership of a wide range of goods and the provision of a wide range of services. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.TAX.GSRV.RV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on goods and services (% of revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "General taxes on goods and services are taxes levied on the production, leasing, delivery, sale, purchase or other change of ownership of a wide range of goods and the provision of a wide range of services. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.TAX.GSRV.VA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on goods and services (% of industry and services value added)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "General taxes on goods and services are taxes levied on the production, leasing, delivery, sale, purchase or other change of ownership of a wide range of goods and the provision of a wide range of services. This indicator is expressed as a percentage of value added in industry and services which is the contribution to the economy by industries in ISIC (Rev. 3) divisions 05-43 and 50-99." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.TAX.IMPT.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Customs and other import duties (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes and duties on imports are taxes on goods and services that become payable at the moment when goods enter the economic territory or when services are delivered by non-resident producers to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.TAX.IMPT.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Customs and other import duties (% of tax revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes and duties on imports are taxes on goods and services that become payable at the moment when goods enter the economic territory or when services are delivered by non-resident producers to residents. This indicator is expressed as a percentage of tax revenue which includes compulsory, unrequited payments, in cash or in kind, made by institutional units to government units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.TAX.INTT.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on international trade (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes on international trade are taxes that become payable when goods cross the national or customs frontiers of the economic territory or when transactions in services exchange between residents and non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.TAX.INTT.RV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on international trade (% of revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes on international trade are taxes that become payable when goods cross the national or customs frontiers of the economic territory or when transactions in services exchange between residents and non-residents. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.TAX.OTHR.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Other taxes (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Other taxes include employer payroll or labor taxes, taxes on property, and taxes not allocable to other categories, such as penalties for late payment or nonpayment of taxes. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.TAX.OTHR.RV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Other taxes (% of revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Other taxes include employer payroll or labor taxes, taxes on property, and taxes not allocable to other categories, such as penalties for late payment or nonpayment of taxes. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.TAX.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Tax revenue (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes are compulsory, unrequited payments, in cash or in kind, made by institutional\n\n\nunits to government units. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.TAX.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Tax revenue (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes are compulsory, unrequited payments, in cash or in kind, made by institutional\n\n\nunits to government units. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.TAX.YPKG.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on income, profits and capital gains (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes on income, profits, and capital gains are taxes payable on the actual or presumed incomes, profits and capital gains. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.TAX.YPKG.RV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on income, profits and capital gains (% of revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes on income, profits, and capital gains are taxes payable on the actual or presumed incomes, profits and capital gains. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.TAX.YPKG.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market." }, { "id": "IndicatorName", "value": "Taxes on income, profits and capital gains (% of total taxes)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Taxes on income, profits, and capital gains are taxes payable on the actual or presumed incomes, profits and capital gains. This indicator is expressed as a percentage of total taxes which includes all compulsory, unrequited payments, in cash or in kind, made by institutional units to government units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Revenue" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.XPN.COMP.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Compensation of employees (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Compensation of employees is defined as the total remuneration, in cash or in kind, payable by an enterprise to an employee in return for work done by the latter during the accounting period. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.XPN.COMP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Compensation of employees (% of expense)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Compensation of employees is defined as the total remuneration, in cash or in kind, payable by an enterprise to an employee in return for work done by the latter during the accounting period. This indicator is expressed as percentage of total expenses which is any decrease in net worth resulting from a transaction." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.XPN.GSRV.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Goods and services expense (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Goods and services include all government payments in exchange for goods and services used for the production of market and nonmarket goods and services. Use of goods and services for account capital formation is excluded. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.XPN.GSRV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Goods and services expense (% of expense)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Goods and services include all government payments in exchange for goods and services used for the production of market and nonmarket goods and services. Use of goods and services for own account capital formation is excluded. This indicator is expressed as percentage of total expenses which is any decrease in net worth resulting from a transaction." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.XPN.INTP.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Interest payments (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Interest payments include interest payments on government debt (including long-term bonds, long-term loans, and other debt instruments) to domestic and foreign residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.XPN.INTP.RV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Interest payments (% of revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Interest payments include interest payments on government debt (including long-term bonds, long-term loans, and other debt instruments) to domestic and foreign residents. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.XPN.INTP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Interest payments (% of expense)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Interest payments include interest payments on government debt (including long-term bonds, long-term loans, and other debt instruments) to domestic and foreign residents. This indicator is expressed as percentage of total expenses which is any decrease in net worth resulting from a transaction." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.XPN.OTHR.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Other expense (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Other expense is spending on dividends, rent, and other miscellaneous expenses, including provision for consumption of fixed capital. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.XPN.OTHR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Other expense (% of expense)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Other expense is spending on dividends, rent, and other miscellaneous expenses, including provision for consumption of fixed capital. This indicator is expressed as percentage of total expenses which is any decrease in net worth resulting from a transaction." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.XPN.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Expense (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Expense is a decrease in net worth resulting from a transaction. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.XPN.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Expense (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Expense is a decrease in net worth resulting from a transaction. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF);\nWorld Development Indicators, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GC.XPN.TRFT.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Subsidies and other transfers (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Subsidies are current unrequited payments that government units, including nonresident government units, make to enterprises on the basis of the levels of their production activities or the quantities or values of the goods or services that they produce, sell, export or import. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "GC.XPN.TRFT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt." }, { "id": "IndicatorName", "value": "Subsidies and other transfers (% of expense)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Subsidies are current unrequited payments that government units, including nonresident government units, make to enterprises on the basis of the levels of their production activities or the quantities or values of the goods or services that they produce, sell, export or import. This indicator is expressed as percentage of total expenses which is any decrease in net worth resulting from a transaction." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1972-2024" }, { "id": "Source", "value": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy." }, { "id": "Topic", "value": "Public Sector: Government finance: Expense" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "GE.EST", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "Percentile" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Government Effectiveness: Estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them. The WGI measures six dimensions of governance: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. \n\nGovernment Effectiveness captures perceptions and views of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies.\n\nEstimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5." }, { "id": "Othernotes", "value": "The UCM assigns greater weight to data sources that tend to be more strongly correlated with each other. While this weighting improves the statistical precision of the aggregate indicators, it typically does not affect very much the ranking of countries on the aggregate indicators. The composite measures of governance generated by the UCM are in units of a standard normal distribution, with mean zero, standard deviation of one, and running from approximately -2.5 to 2.5, with higher values corresponding to better governance. The data is also reported in percentile rank terms, ranging from 0 (lowest rank) to 100 (highest rank).\n\nStatistical concept(s): The six aggregate indicators are reported in two ways: (1) in their standard normal units, ranging from approximately -2.5 to 2.5, and (2) in percentile rank terms from 0 to 100, with higher values corresponding to better outcomes.\n\nA key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). A key feature of the methodology is that it generates margins of error for each governance estimate. These margins of error need to be taken into account when making comparisons across countries and over time. \n\nEach of the six aggregate WGI measures are constructed by averaging together data from the underlying sources that correspond to the concept of governance being measured. This is done in the three steps:\n\nSTEP 1: Assigning data from individual sources to the six aggregate indicators. Individual questions from the underlying data sources are assigned to each of the six aggregate indicators. For example, a firm survey question on the regulatory environment would be assigned to Regulatory Quality, or a measure of press freedom would be assigned to Voice and Accountability. The individual variables used in the WGI and how they are assigned to the six aggregate indicators, can be found on the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators]. Note that not all of the data sources cover all countries, and so the aggregate governance scores are based on different sets of underlying data for different countries.\n\nSTEP 2: Rescaling of the individual source data to run from 0 to 1. The questions from the individual data sources are first rescaled to range from 0 to 1, with higher values corresponding to better outcomes. If, for example, a survey question asks for responses on a scale from a minimum of 1 to a maximum of 4, we rescale a score of 2 as (2-min)/(max-min)=(2-1)/3=0.33. When an individual data source provides more than one question relating to a particular dimension of governance, the rescaled scores are averaged together.\nThe 0-1 rescaled data from the individual sources are available interactively through the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators/interactive-data-access] and in the data files for each individual source [https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2]. Although nominally in the same 0-1 units, this rescaled data is not necessarily comparable across sources. For example, one data source might use a 0-10 scale but in practice most scores are clustered between 6 and 10, while another data source might also use a 0-10 scale but have responses spread out over the entire range. While the max-min rescaling above does not correct for this source of non-comparability, the procedure used to construct the aggregate indicators does (see below).\n\nSTEP 3: Using an Unobserved Components Model (UCM) to construct a weighted average of the individual indicators for each source. A statistical tool known as an Unobserved Components Model (UCM) is used to make the 0-1 rescaled data comparable across sources, and then to construct a weighted average of the data from each source for each country. The UCM assumes that the observed data from each source are a linear function of the unobserved level of governance, plus an error term. This linear function is different for different data sources, and so corrects for the remaining non-comparability of units of the rescaled data noted above. The resulting estimates of governance are a weighted average of the data from each source, with weights reflecting the pattern of correlation among data sources. The weights applied to the component indicators." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Units of a standard normal distribution (between -2.5 and 2.5, approximately)" } ], "source_id": "57" }, { "id": "GE.NO.SRC", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Government Effectiveness: Number of Sources" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data sources based on expert assessments have advantages and disadvantages relative to surveys. One advantage is that they lend themselves well to cross-country comparisons, as their methodologies are explicitly designed for this purpose. Expert assessments can also provide more granular technical assessments, for example on the quality of specific types of public institutions, that would be more difficult for a typical household or firm survey respondent to provide and informed view on. Expert assessments also are less likely to be affected by respondent reticence, a concern in household and firm surveys where respondents may be unwilling to give candid responses to sensitive questions about corruption or other dimensions of governance, particularly in countries where governance is weak. \n\nOn the other hand, a shortcoming of expert assessments is that they reflect the views of a narrower set of respondents than household or firm surveys. It also is possible that the ratings provided by one expert assessment to some extent reflect the views of other expert assessments, so that each assessment does not bring completely independent information on the underlying governance concept of interest. To guard against this, the WGI do not use expert assessments that are explicitly based on other existing data sources.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nNumber of sources indicates the number of underlying data sources on which the aggregate estimate is based.\n\nThe WGI are based on a large number of different data sources, capturing the views and experiences of survey respondents and experts in the public and private sectors, as well as various NGOs. These data sources include: (a) surveys of households and firms (e.g. Afrobarometer surveys, Gallup World Poll, and Global Competitiveness Report survey), (b) NGOs (e.g. Global Integrity, Freedom House, Reporters Without Borders), (c) commercial business information providers (e.g. Economist Intelligence Unit, S&P Global, Political Risk Services), and (d) public sector organizations (e.g. CPIA assessments of World Bank and regional development banks). \n\nGovernment Effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI compile and summarize information from over 30 existing data sources that report the views and experiences of citizens, entrepreneurs, and experts in the public, private and NGO sectors from around the world, on the quality of various aspects of governance.\n\n•\tThe data sources must provide subjective perceptions of relevant dimensions of governance, as the WGI are based exclusively on this type of data.\n•\tThe sources must provide original primary data produced using a well-defined methodology.\n•\tThe data sources must cover multiple countries, so that cross-country comparisons are possible.\n•\tThe data sources must be updated regularly, ideally every year, although some WGI data sources are updated once every two or three years.\n\nThe WGI draw on four different types of source data:\n\n•\tSurveys of households and firms, including the Afrobarometer surveys, Gallup World Poll, and Global Competitiveness Report survey,\n•\tCommercial business information providers, including the Economist Intelligence Unit, S&P Global, and Political Risk Services,\n•\tNon-governmental organizations, including Global Integrity, Freedom House, Reporters Without Borders, and\n•\tPublic sector organizations, including the Country Policy and Institutional Assessments (CPIA) assessments of World Bank and regional development banks.\n\nFor the detailed list of sources, please refer to: https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2 \nStatistical concept(s): Number of sources indicates the number of underlying data sources on which the aggregate estimate is based.\n\nVariables from the data sources are assigned to each of these six governance dimensions. For example, an assessment of the quality of the bureaucracy would be assigned to Government Effectiveness, a question about confidence in the police or the courts would be assigned to Rule of Law, and a question about the perceived likelihood of having to pay a bribe would be assigned to Control of Corruption. In some cases, a single data source will have multiple questions that can be assigned to the same dimension. In this case, the WGI use the average across all relevant questions from that data source. In addition each question from each data source is assigned to only one of the six governance dimensions, selecting the dimension that best matches the content of the question." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "GE.PER.RNK", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Government Effectiveness: Percentile Rank" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nGovernment Effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI." }, { "id": "Othernotes", "value": "The UCM assigns greater weight to data sources that tend to be more strongly correlated with each other. While this weighting improves the statistical precision of the aggregate indicators, it typically does not affect very much the ranking of countries on the aggregate indicators. The composite measures of governance generated by the UCM are in units of a standard normal distribution, with mean zero, standard deviation of one, and running from approximately -2.5 to 2.5, with higher values corresponding to better governance. The data is also reported in percentile rank terms, ranging from 0 (lowest rank) to 100 (highest rank).\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). A key feature of the methodology is that it generates margins of error for each governance estimate. These margins of error need to be taken into account when making comparisons across countries and over time. \n\nEach of the six aggregate WGI measures are constructed by averaging together data from the underlying sources that correspond to the concept of governance being measured. This is done in the three steps:\n\nSTEP 1: Assigning data from individual sources to the six aggregate indicators. Individual questions from the underlying data sources are assigned to each of the six aggregate indicators. For example, a firm survey question on the regulatory environment would be assigned to Regulatory Quality, or a measure of press freedom would be assigned to Voice and Accountability. The individual variables used in the WGI and how they are assigned to the six aggregate indicators, can be found on the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators]. Note that not all of the data sources cover all countries, and so the aggregate governance scores are based on different sets of underlying data for different countries.\n\nSTEP 2: Rescaling of the individual source data to run from 0 to 1. The questions from the individual data sources are first rescaled to range from 0 to 1, with higher values corresponding to better outcomes. If, for example, a survey question asks for responses on a scale from a minimum of 1 to a maximum of 4, we rescale a score of 2 as (2-min)/(max-min)=(2-1)/3=0.33. When an individual data source provides more than one question relating to a particular dimension of governance, the rescaled scores are averaged together.\nThe 0-1 rescaled data from the individual sources are available interactively through the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators/interactive-data-access] and in the data files for each individual source [https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2]. Although nominally in the same 0-1 units, this rescaled data is not necessarily comparable across sources. For example, one data source might use a 0-10 scale but in practice most scores are clustered between 6 and 10, while another data source might also use a 0-10 scale but have responses spread out over the entire range. While the max-min rescaling above does not correct for this source of non-comparability, the procedure used to construct the aggregate indicators does (see below).\n\nSTEP 3: Using an Unobserved Components Model (UCM) to construct a weighted average of the individual indicators for each source. A statistical tool known as an Unobserved Components Model (UCM) is used to make the 0-1 rescaled data comparable across sources, and then to construct a weighted average of the data from each source for each country. The UCM assumes that the observed data from each source are a linear function of the unobserved level of governance, plus an error term. This linear function is different for different data sources, and so corrects for the remaining non-comparability of units of the rescaled data noted above. The resulting estimates of governance are a weighted average of the data from each source, with weights reflecting the pattern of correlation among data sources. The weights applied to the component indicators." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile" } ], "source_id": "57" }, { "id": "GE.PER.RNK.LOWER", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Government Effectiveness: Percentile Rank, Lower Bound of 90% Confidence Interval" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nGovernment Effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Lower refers to lower bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether.\n\nThe standard deviations are essential to the interpretation of the WGI. It often is more appropriate to think of the WGI methodology as identifying a statistically likely range of values for the unobserved “true” level of governance in a country. For example, the assumption of normality tells us that there is a 90 percent probability that the true unobserved level of governance conditional on the available data for a country is in a range given by plus or minus 1.64 standard deviations around the estimate of governance. These confidence intervals are informally referred to as the “margin of error” around the estimates of governance.\n\nThese 90 percent confidence interval are also reported in percentile rank terms (the percentile rank among all country estimates of governance, of the upper and lower bounds of the 90 percent confidence interval for each country).\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Lower refers to lower bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile" } ], "source_id": "57" }, { "id": "GE.PER.RNK.UPPER", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide" }, { "id": "IndicatorName", "value": "Government Effectiveness: Percentile Rank, Upper Bound of 90% Confidence Interval" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide\n\nGovernment Effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Upper refers to upper bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether.\n\nThe standard deviations are essential to the interpretation of the WGI. It often is more appropriate to think of the WGI methodology as identifying a statistically likely range of values for the unobserved “true” level of governance in a country. For example, the assumption of normality tells us that there is a 90 percent probability that the true unobserved level of governance conditional on the available data for a country is in a range given by plus or minus 1.64 standard deviations around the estimate of governance. These confidence intervals are informally referred to as the “margin of error” around the estimates of governance.\n\nThese 90 percent confidence interval are also reported in percentile rank terms (the percentile rank among all country estimates of governance, of the upper and lower bounds of the 90 percent confidence interval for each country).\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Upper refers to upper bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile Rank" } ], "source_id": "57" }, { "id": "GE.STD.ERR", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Government Effectiveness: Standard Error" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data sources based on expert assessments have advantages and disadvantages relative to surveys. One advantage is that they lend themselves well to cross-country comparisons, as their methodologies are explicitly designed for this purpose. Expert assessments can also provide more granular technical assessments, for example on the quality of specific types of public institutions, that would be more difficult for a typical household or firm survey respondent to provide and informed view on. Expert assessments also are less likely to be affected by respondent reticence, a concern in household and firm surveys where respondents may be unwilling to give candid responses to sensitive questions about corruption or other dimensions of governance, particularly in countries where governance is weak. \n\nOn the other hand, a shortcoming of expert assessments is that they reflect the views of a narrower set of respondents than household or firm surveys. It also is possible that the ratings provided by one expert assessment to some extent reflect the views of other expert assessments, so that each assessment does not bring completely independent information on the underlying governance concept of interest. To guard against this, the WGI do not use expert assessments that are explicitly based on other existing data sources.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nStandard error indicates the precision of the estimate of governance. Larger values of the standard error indicate less precise estimates. \n\nA 90 percent confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error.\n\nGovernment Effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). The six aggregate indicators are reported in two ways : (1) in their standard normal units, ranging from approximately -2.5 to 2.5, and (2) in percentile rank terms from 0 to 100, with higher values corresponding to better outcomes.\n\nA key feature of the WGI is that all country scores are accompanied by standard errors. These margins of error need to be taken into account when making comparisons across countries and over time. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether. \n\nPlease see more information at: https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#1\nStatistical concept(s): Standard error indicates the precision of the estimate of governance. Larger values of the standard error indicate less precise estimates. A 90 percent confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Standard error" } ], "source_id": "57" }, { "id": "GF.XPD.BUDG.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The indicator attempts to capture the reliability of government budgets: do governments spend what they intend to and do they collect what they set out to collect. The ability to implement the enacted budget is an important factor in government’s ability to deliver public services and achieve development objectives. The deviation between approved and actual spending is measured over a 12-month period (the budget year) and may have important implications for macroeconomic stability, public service delivery, and social welfare. A credibly implemented budget has only small deviations from the approved one. If expenditure is under-executed, beneficiaries may not receive crucial services. Over-executed budgets may result in budget deficits and increased public debt levels and can influence the macroeconomic stability. In both cases, lack of budget credibility undermines the usefulness of the budget process for policy making and implementation and erodes public trust in government." }, { "id": "IndicatorName", "value": "Primary government expenditures as a proportion of original approved budget (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although not all countries have used the PEFA methodology on an annual basis for the PEFA PI-1 indicator, the methodology relies on standard data sets for approved and final budget outturns which are commonly produced at least annually in every country. The countries that have not used the methodology to date are primarily highly developed countries which would have less difficulty in providing the necessary data than those in the lower and middle income categories that have been primary users of Public Expenditure and Financial Accountability (PEFA) to date. (As of: 2024-07-29) \n\nOne limitation of the indicator is that it is an aggregate indicator of budget reliability. While it can be disaggregated across regions, it is not disaggregated across various budget subcomponents. Different indicators are used for assessing changes in expenditure composition in the PEFA framework. Also, while this indicator is intended to measure budget reliability it should be understood that actual expenditure outturns can deviate from the originally approved budget for reasons unrelated to the accuracy of forecasts—for example, as a result of a major macroeconomic shock. However, the calibration of this indicator accommodates one unusual or “outlier” year and focuses on deviations from the forecast which occur in two of the three years covered by the assessment. Therefore, single year shocks are discounted allowing a more balanced assessment. \n\nThe broader context in which the indicator was developed is as follows. PEFA is a tool for assessing the status of public financial management and reporting on the strengths and weaknesses of Public Financial Management (PFM). A PEFA assessment provides a thorough, consistent and evidence-based analysis of PFM performance at a specific point in time and can be reapplied in successive assessments to track changes over time. The PEFA framework provides the foundation for evidence-based measurement of countries’ PFM systems using 31 performance indicators that are further disaggregated into 94 dimensions. A PEFA assessment measures the extent to which PFM systems, processes and institutions contribute to the achievement of desirable budget outcomes: aggregate fiscal discipline, strategic allocation of resources, and efficient service delivery." }, { "id": "Longdefinition", "value": "Primary government expenditures as a proportion of original approved budget measures the extent to which aggregate budget expenditure outturn reflects the amount originally approved, as defined in government budget documentation and fiscal reports. The coverage is budgetary central government (BCG) and the time period covered is the last three completed fiscal years." }, { "id": "Othernotes", "value": "Aggregate expenditure includes actual expenditures incorporating those incurred as a result of unplanned or exceptional events—for example, armed conflicts or natural disasters. Expenditures financed by windfall revenues, including privatization, should be included and noted in the supporting fiscal tables and narrative. Expenditures financed externally by loans or grants should be included, if covered by the budget along with contingency vote(s) and interest on debt. \n\nExpenditure assigned to suspense accounts is not included in the aggregate. However, if amounts are held in suspense accounts at the end of any year that could affect the scores if included in the calculations, they can be included. In such cases the reason(s) for inclusion must be clearly stated. \n\nActual expenditure outturns can deviate from the originally approved budget for reasons unrelated to the accuracy of forecasts—for example, as a result of a major macroeconomic shock. The calibration of this indicator accommodates one unusual or “outlier” year and focuses on deviations from the forecast which occur in two of the three years covered by the assessment. \nDetailed resources are available at www.pefa.org" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2024" }, { "id": "Source", "value": "Public Expenditure and Financial Accountability (PEFA), World Bank (WB), uri: https://www.pefa.org/node/5239, note: The raw data collected in order to calculate this indicator are the initially Approved and Executed Budgets. Budget Laws of countries is the usual source of the approved budget of countries. The end-of year fiscal reports (budget execution reports) are the sources of the actual spending. This data is typically obtained from websites of the Ministry of Finance (MoF) or the national Parliament, or data are collected through communication with the MoF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The PEFA PI-1 Indicator is used as a basis for the SDG 16.6.1 Indicator, following the measurement guidance and coverage. In order to make the computation and the analysis of data over time easy and applicable for all countries, it was decided that SDG 16.6.1 indicator will be based on the annual data collection on approved and executed budgets for all countries and will be calculated annually. \n\nThe simple calculation for every year for every country is for the: Aggregate expenditure outturn = Executed Budget/Approved Budget*100 \n\nIn the countries and regional groupings, analysis of the deviations are done according regions/years/countries, using the requirements of PEFA PI-1 indicator.\n\nAlthough the computation and scoring used for PI-1 indicator are not applied for the SDG 16.6.1 indicator, the categorization described below is applied and is the basis for the SDG 16.6.1 indicator. PEFA Methodology The methodology for calculating the PEFA PI-1 indicator is provided in a spreadsheet (titled “En PI-1 and PI-2 Exp Calculation-Feb 1 2016 (xls)”) and is based on the PEFA Public Expenditure and Financial Accountability (PEFA) Framework. \n\nScoring is at the heart of the indicator. A country is scored separately on a four-point ordinal scale: A, B, C, or D, according to precise criteria: (A) Aggregate expenditure outturn was between 95% and 105% of the approved aggregate budgeted expenditure in at least two of the last three years. (B) Aggregate expenditure outturn was between 90% and 110% of the approved aggregate budgeted expenditure in at least two of the last three years. (C Aggregate expenditure outturn was between 85% and 115% of the approved aggregate budgeted expenditure in at least two of the last three years. (D) Performance is less than required for a C score. In order to justify a particular score, every aspect specified in the scoring requirements must be fulfilled. \n\nIf the requirements are only partly met, the criteria are not satisfied and a lower score should be given that coincides with achievement of all requirements for the lower performance rating. A score of C reflects the basic level of performance for each indicator and dimension, consistent with good international practices. A score of D means that the feature being measured is present at less than the basic level of performance or is absent altogether, or that there is insufficient information to score the dimension. \n\nThe D score indicates performance that falls below the basic level. ‘D’ is applied if the performance observed is less than required for any higher score. For this reason, a D score is warranted when sufficient information is not available to establish the actual level of performance. A score of D due to insufficient information is distinguished from D scores for low-level performance by the use of an asterisk—that is, D* at the dimension level. The asterisk is not included at the indicator level. \n\nThe coverage is budgetary central government (BCG) and requires data for three consecutive years as a basis for assessment. The data would cover the most recent completed fiscal year for which data is available and the two immediately preceding years. \nStatistical concept(s): This indicator measures the extent to which aggregate budget expenditure outturn reflects the amount originally approved, as defined in government budget documentation and fiscal reports. The coverage is budgetary central government and the time period covers every fiscal year for the countries.\n\nRefer to Other notes for more details." }, { "id": "Topic", "value": "Public Sector: Government finance" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "HD.HCI.OVRL", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI) (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "57" }, { "id": "HD.HCI.OVRL.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI), female (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "57" }, { "id": "HD.HCI.OVRL.LB", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI), lower bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI lower bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "57" }, { "id": "HD.HCI.OVRL.LB.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI), female, lower bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI lower bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "57" }, { "id": "HD.HCI.OVRL.LB.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI), male, lower bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI lower bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "57" }, { "id": "HD.HCI.OVRL.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI), male (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "57" }, { "id": "HD.HCI.OVRL.UB", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI), upper bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI upper bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "57" }, { "id": "HD.HCI.OVRL.UB.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI), female, upper bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI upper bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "57" }, { "id": "HD.HCI.OVRL.UB.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Human capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs and skills helps develop human capital, and this is key to ending extreme poverty and creating more inclusive societies.\n\n\n\nAs noted in the World Development Report (WDR) 2019: The Changing Nature of Work, the frontier for skills is moving rapidly, bringing both opportunities and risks. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete e?ectively in the global economy. The cost of inaction on human capital development is going up.\n\n\n\nFinance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development." }, { "id": "IndicatorName", "value": "Human capital index (HCI), male, upper bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The HCI upper bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Referenceperiod", "value": "2010-2020" }, { "id": "Source", "value": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://openknowledge.worldbank.org/handle/10986/30498" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The index is a summary measure of the amount of human capital that a child born today can expect to acquire by age 18, given the risks of poor health and poor education that prevail in the country where she lives. A full accounting of the HCI methodology is available on the World Bank’s Open Knowledge Repository.\n\n\n\nA signi?cant innovation is that the index measures the contribution of health and education to the productivity of individuals and countries, anchored in rigorous micro-econometric studies.\n\n\n\nRanging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve full health (de?ned as no stunting and survival up to at least age 60) and achieve her formal education potential (de?ned as 14 years of high-quality school by age 18).\n\n\n\nA country’s score is its distance to the “frontier” of complete education and full health. If it scores 0.70 in the Human Capital Index, this indicates that the future earnings potential of children born today will be 70% of what they could have been with complete education and full health.\n\n\n\nThe index can directly be linked to scenarios for the future income of countries as well as individuals. If a country has a score of 0.50, then future GDP per worker could be twice as high if the country reached the benchmark of complete education and full health.\n\n\n\nThe index is presented as a country average and includes a breakdown by gender for countries where data is available. \n\n\nStatistical concept(s): Composite Health & Education Measure" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-1)" } ], "source_id": "57" }, { "id": "IC.BRE.BE.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Registered companies benefit from a variety of advantages, including the legal and financial services provided by courts and banks. Their employees enjoy social security protection. Additionally, the economy takes advantage of positive spillovers: where formal entrepreneurship is high, job creation and economic growth also tend to be high. As more businesses formalize, the tax base also expands, enabling the government to spend on productivity-enhancing areas and pursue other social and economic policy goals. However, entrepreneurs often encounter barriers to entry into the formal economy.\n\nThere is evidence that higher costs for business start-ups are associated with lower business entry and lower levels of employment and productivity. Cumbersome regulations for business start-ups are associated with high levels of corruption and informality. A simple business start-up process is a positive factor for fostering formal entrepreneurship. Moreover, digital technology and transparency of information can encourage businesses to register and promote private sector growth." }, { "id": "IndicatorName", "value": "B-READY: Business Entry: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Entry topic measures the process of registration and start of operations of new limited liability companies (LLCs) across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.BE.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Registered companies benefit from a variety of advantages, including the legal and financial services provided by courts and banks. Their employees enjoy social security protection. Additionally, the economy takes advantage of positive spillovers: where formal entrepreneurship is high, job creation and economic growth also tend to be high. As more businesses formalize, the tax base also expands, enabling the government to spend on productivity-enhancing areas and pursue other social and economic policy goals. However, entrepreneurs often encounter barriers to entry into the formal economy.\n\nThere is evidence that higher costs for business start-ups are associated with lower business entry and lower levels of employment and productivity. Cumbersome regulations for business start-ups are associated with high levels of corruption and informality. A simple business start-up process is a positive factor for fostering formal entrepreneurship. Moreover, digital technology and transparency of information can encourage businesses to register and promote private sector growth." }, { "id": "IndicatorName", "value": "B-READY: Business Entry Pillar 1: Quality of Regulations for Business Entry" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Entry topic measures the process of registration and start of operations of new limited liability companies (LLCs) across three different dimensions, or pillars. The first pillar assesses the quality of regulations for business entry, covering de jure features of a regulatory framework that are necessary for the adoption of good practices for business start-ups." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.BE.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Registered companies benefit from a variety of advantages, including the legal and financial services provided by courts and banks. Their employees enjoy social security protection. Additionally, the economy takes advantage of positive spillovers: where formal entrepreneurship is high, job creation and economic growth also tend to be high. As more businesses formalize, the tax base also expands, enabling the government to spend on productivity-enhancing areas and pursue other social and economic policy goals. However, entrepreneurs often encounter barriers to entry into the formal economy.\n\nThere is evidence that higher costs for business start-ups are associated with lower business entry and lower levels of employment and productivity. Cumbersome regulations for business start-ups are associated with high levels of corruption and informality. A simple business start-up process is a positive factor for fostering formal entrepreneurship. Moreover, digital technology and transparency of information can encourage businesses to register and promote private sector growth." }, { "id": "IndicatorName", "value": "B-READY: Business Entry Pillar 2: Digital Public Services and Transparency of Information for Business Entry" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Entry topic measures the process of registration and start of operations of new limited liability companies (LLCs) across three different dimensions, or pillars. The second pillar measures the availability of digital public services and transparency of information for business entry." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.BE.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Registered companies benefit from a variety of advantages, including the legal and financial services provided by courts and banks. Their employees enjoy social security protection. Additionally, the economy takes advantage of positive spillovers: where formal entrepreneurship is high, job creation and economic growth also tend to be high. As more businesses formalize, the tax base also expands, enabling the government to spend on productivity-enhancing areas and pursue other social and economic policy goals. However, entrepreneurs often encounter barriers to entry into the formal economy.\n\nThere is evidence that higher costs for business start-ups are associated with lower business entry and lower levels of employment and productivity. Cumbersome regulations for business start-ups are associated with high levels of corruption and informality. A simple business start-up process is a positive factor for fostering formal entrepreneurship. Moreover, digital technology and transparency of information can encourage businesses to register and promote private sector growth." }, { "id": "IndicatorName", "value": "B-READY: Business Entry Pillar 3: Operational Efficiency of Business Entry" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Entry topic measures the process of registration and start of operations of new limited liability companies (LLCs) across three different dimensions, or pillars. The third pillar measures the time and cost required to register new domestic and foreign firms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.BI.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The efficient and rapid exit of nonviable firms plays an important cyclical role in renewing the economy by removing firms that are not productive and making way for more productive ones. The purpose of an efficient insolvency framework is to ensure that nonviable firms are swiftly liquidated, and viable firms are effectively restructured in a sustainable way. When insolvency regimes do not have the adequate tools to handle the restructuring and liquidation of companies in a timely and effective manner these companies’ economic distress is amplified, jeopardizing the stability of the financial system. In economies where creditor recovery rates are high and resolution times are quicker, restructuring within the formal bankruptcy process fulfills its cyclical role during economic downturns by keeping companies afloat." }, { "id": "IndicatorName", "value": "B-READY: Business Insolvency: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Insolvency topic measures key features of insolvency systems on a regulatory level. It also assesses the institutional and operational infrastructure associated with insolvency proceedings (judicial services), as well as the operational efficiency of insolvency proceedings across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.BI.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The efficient and rapid exit of nonviable firms plays an important cyclical role in renewing the economy by removing firms that are not productive and making way for more productive ones. The purpose of an efficient insolvency framework is to ensure that nonviable firms are swiftly liquidated, and viable firms are effectively restructured in a sustainable way. When insolvency regimes do not have the adequate tools to handle the restructuring and liquidation of companies in a timely and effective manner these companies’ economic distress is amplified, jeopardizing the stability of the financial system. In economies where creditor recovery rates are high and resolution times are quicker, restructuring within the formal bankruptcy process fulfills its cyclical role during economic downturns by keeping companies afloat." }, { "id": "IndicatorName", "value": "B-READY: Business Insolvency Pillar 1: Quality of Regulations for Judicial Insolvency Proceedings" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Insolvency topic measures key features of insolvency systems on a regulatory level. It also assesses the institutional and operational infrastructure associated with insolvency proceedings (judicial services), as well as the operational efficiency of insolvency proceedings across three different dimensions, or pillars. The first pillar assesses the quality of regulation pertaining to judicial insolvency proceedings—liquidation and reorganization, covering de jure features of a regulatory framework that are necessary for structured debt resolution processes and effective creditor and debtor regimes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.BI.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The efficient and rapid exit of nonviable firms plays an important cyclical role in renewing the economy by removing firms that are not productive and making way for more productive ones. The purpose of an efficient insolvency framework is to ensure that nonviable firms are swiftly liquidated, and viable firms are effectively restructured in a sustainable way. When insolvency regimes do not have the adequate tools to handle the restructuring and liquidation of companies in a timely and effective manner these companies’ economic distress is amplified, jeopardizing the stability of the financial system. In economies where creditor recovery rates are high and resolution times are quicker, restructuring within the formal bankruptcy process fulfills its cyclical role during economic downturns by keeping companies afloat." }, { "id": "IndicatorName", "value": "B-READY: Business Insolvency Pillar 2: Quality of Institutional and Operational Infrastructure for Judicial Insolvency Proceedings" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Insolvency topic measures key features of insolvency systems on a regulatory level. It also assesses the institutional and operational infrastructure associated with insolvency proceedings (judicial services), as well as the operational efficiency of insolvency proceedings across three different dimensions, or pillars. The second pillar measures the quality of institutional and operational infrastructure for judicial insolvency proceedings, thus assessing the de facto aspects of insolvency resolution mechanisms and the infrastructure required to implement the legal framework on insolvency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.BI.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The efficient and rapid exit of nonviable firms plays an important cyclical role in renewing the economy by removing firms that are not productive and making way for more productive ones. The purpose of an efficient insolvency framework is to ensure that nonviable firms are swiftly liquidated, and viable firms are effectively restructured in a sustainable way. When insolvency regimes do not have the adequate tools to handle the restructuring and liquidation of companies in a timely and effective manner these companies’ economic distress is amplified, jeopardizing the stability of the financial system. In economies where creditor recovery rates are high and resolution times are quicker, restructuring within the formal bankruptcy process fulfills its cyclical role during economic downturns by keeping companies afloat." }, { "id": "IndicatorName", "value": "B-READY: Business Insolvency Pillar 3: Operational Efficiency of Resolving Judicial Insolvency Proceedings" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Insolvency topic measures key features of insolvency systems on a regulatory level. It also assesses the institutional and operational infrastructure associated with insolvency proceedings (judicial services), as well as the operational efficiency of insolvency proceedings across three different dimensions, or pillars. The third pillar measures the time and cost required to resolve in-court liquidation and reorganization proceedings." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.BL.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acquiring the physical space where a business will operate is a crucial ingredient of success for many firms, even in the digital age. Getting the right location can influence business access to customers, transportation, labor, and materials, as well as determine taxes, regulations, and environmental commitments they must comply with.\n\nWhether an entrepreneur is leasing or purchasing a commercial property, the regulatory framework and the public services related to acquiring a location can have an impact on how conducive the business environment is for individual firms and the private sector development of an economy.\n\nFirms are more likely to invest in economies with strong property rights in which they are confident that their immovable property investments will be safe. A reliable land administration system, which provides clear information on property ownership, facilitates the development of real estate markets, and supports tenure security. These factors not only provide confidence to the private sector but also indicate the economy’s prospects for economic growth. Transparency in land administration also reduces information asymmetries, which increases market efficiency, further supporting economic development. \n\nWhen investors and entrepreneurs acquire a new location for their business, the process often involves licensing requirements for altering a property or changing tenancy. Building-related permits are essential for public safety, strengthening property rights, and contributing to capital formation. Last but not least, transparent and accessible environmental regulations related to building control reduce the regulatory burden on firms by offering clarity on rules and regulations." }, { "id": "IndicatorName", "value": "B-READY: Business Location: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Location topic measures three different options—purchase, lease, or build—that are available to entrepreneurs to choose the adequate location to set up their company, across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.BL.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acquiring the physical space where a business will operate is a crucial ingredient of success for many firms, even in the digital age. Getting the right location can influence business access to customers, transportation, labor, and materials, as well as determine taxes, regulations, and environmental commitments they must comply with.\n\nWhether an entrepreneur is leasing or purchasing a commercial property, the regulatory framework and the public services related to acquiring a location can have an impact on how conducive the business environment is for individual firms and the private sector development of an economy.\n\nFirms are more likely to invest in economies with strong property rights in which they are confident that their immovable property investments will be safe. A reliable land administration system, which provides clear information on property ownership, facilitates the development of real estate markets, and supports tenure security. These factors not only provide confidence to the private sector but also indicate the economy’s prospects for economic growth. Transparency in land administration also reduces information asymmetries, which increases market efficiency, further supporting economic development. \n\nWhen investors and entrepreneurs acquire a new location for their business, the process often involves licensing requirements for altering a property or changing tenancy. Building-related permits are essential for public safety, strengthening property rights, and contributing to capital formation. Last but not least, transparent and accessible environmental regulations related to building control reduce the regulatory burden on firms by offering clarity on rules and regulations." }, { "id": "IndicatorName", "value": "B-READY: Business Location Pillar 1: Quality of Regulations for Business Location" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Location topic measures three different options—purchase, lease, or build—that are available to entrepreneurs to choose the adequate location to set up their company, across three different dimensions, or pillars. The first pillar assesses the quality of regulations pertaining to property transfer, building, and environmental permitting, covering de jure features of a regulatory framework that are necessary for immovable property lease, property ownership, urban planning, and environmental licenses." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.BL.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acquiring the physical space where a business will operate is a crucial ingredient of success for many firms, even in the digital age. Getting the right location can influence business access to customers, transportation, labor, and materials, as well as determine taxes, regulations, and environmental commitments they must comply with.\n\nWhether an entrepreneur is leasing or purchasing a commercial property, the regulatory framework and the public services related to acquiring a location can have an impact on how conducive the business environment is for individual firms and the private sector development of an economy.\n\nFirms are more likely to invest in economies with strong property rights in which they are confident that their immovable property investments will be safe. A reliable land administration system, which provides clear information on property ownership, facilitates the development of real estate markets, and supports tenure security. These factors not only provide confidence to the private sector but also indicate the economy’s prospects for economic growth. Transparency in land administration also reduces information asymmetries, which increases market efficiency, further supporting economic development. \n\nWhen investors and entrepreneurs acquire a new location for their business, the process often involves licensing requirements for altering a property or changing tenancy. Building-related permits are essential for public safety, strengthening property rights, and contributing to capital formation. Last but not least, transparent and accessible environmental regulations related to building control reduce the regulatory burden on firms by offering clarity on rules and regulations." }, { "id": "IndicatorName", "value": "B-READY: Business Location Pillar 2: Quality of Public Services and Transparency of Information for Business Location" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Location topic measures three different options—purchase, lease, or build—that are available to entrepreneurs to choose the adequate location to set up their company, across three different dimensions, or pillars. The second pillar assesses the quality of public services and transparency of information in the provision of property transfer, building, and environmental permitting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.BL.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acquiring the physical space where a business will operate is a crucial ingredient of success for many firms, even in the digital age. Getting the right location can influence business access to customers, transportation, labor, and materials, as well as determine taxes, regulations, and environmental commitments they must comply with.\n\nWhether an entrepreneur is leasing or purchasing a commercial property, the regulatory framework and the public services related to acquiring a location can have an impact on how conducive the business environment is for individual firms and the private sector development of an economy.\n\nFirms are more likely to invest in economies with strong property rights in which they are confident that their immovable property investments will be safe. A reliable land administration system, which provides clear information on property ownership, facilitates the development of real estate markets, and supports tenure security. These factors not only provide confidence to the private sector but also indicate the economy’s prospects for economic growth. Transparency in land administration also reduces information asymmetries, which increases market efficiency, further supporting economic development. \n\nWhen investors and entrepreneurs acquire a new location for their business, the process often involves licensing requirements for altering a property or changing tenancy. Building-related permits are essential for public safety, strengthening property rights, and contributing to capital formation. Last but not least, transparent and accessible environmental regulations related to building control reduce the regulatory burden on firms by offering clarity on rules and regulations." }, { "id": "IndicatorName", "value": "B-READY: Business Location Pillar 3: Operational Efficiency of Establishing a Business Location" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Location topic measures three different options—purchase, lease, or build—that are available to entrepreneurs to choose the adequate location to set up their company, across three different dimensions, or pillars. The third pillar measures the operational efficiency of establishing a business location in practice." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.DR.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Inability to resolve commercial disputes promptly and fairly can lead to adverse economic outcomes in the private sector, ranging from reduced entrepreneurship and lower investment to macroeconomic volatility. This makes efficient and quality dispute resolution essential for a healthy business environment. Having time- and cost-effective mechanisms for resolving disputes is critical because excessively long and expensive proceedings may defeat the very purpose of bringing a case to formal institutions, making them unattractive and unaffordable. In fact, correlations have been established between judicial efficiency and facilitated entrepreneurial activity. Evidence also suggests that under a more effective court system businesses are likely to have greater access to finance and borrow more. The quality of the dispute resolution process also matters. Claims should be considered with due care by credible institutions capable of issuing sound judgments. It was found that in economies with low confidence in court systems, firms are less willing to expand their businesses and look for alternative trade partners. To attract more investors, economies therefore should ensure not only judiciaries’ effectiveness but also their strength and reliability." }, { "id": "IndicatorName", "value": "B-READY: Dispute Resolution: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Dispute Resolution topic measures efficiency and quality of the resolution of commercial disputes—those arising in the business context between firms—across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.DR.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Inability to resolve commercial disputes promptly and fairly can lead to adverse economic outcomes in the private sector, ranging from reduced entrepreneurship and lower investment to macroeconomic volatility. This makes efficient and quality dispute resolution essential for a healthy business environment. Having time- and cost-effective mechanisms for resolving disputes is critical because excessively long and expensive proceedings may defeat the very purpose of bringing a case to formal institutions, making them unattractive and unaffordable. In fact, correlations have been established between judicial efficiency and facilitated entrepreneurial activity. Evidence also suggests that under a more effective court system businesses are likely to have greater access to finance and borrow more. The quality of the dispute resolution process also matters. Claims should be considered with due care by credible institutions capable of issuing sound judgments. It was found that in economies with low confidence in court systems, firms are less willing to expand their businesses and look for alternative trade partners. To attract more investors, economies therefore should ensure not only judiciaries’ effectiveness but also their strength and reliability." }, { "id": "IndicatorName", "value": "B-READY: Dispute Resolution Pillar 1: Quality of Regulations for Dispute Resolution" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Dispute Resolution topic measures efficiency and quality of the resolution of commercial disputes—those arising in the business context between firms—across three different dimensions, or pillars. The first pillar assesses the adequacy of legislation pertaining to both court processes and alternative dispute resolution (ADR), covering de jure features that are necessary for the efficient processing of cases, facilitated resolution of cross-border claims, creating alternative venues for settling disputes, and ensuring trust in relevant institutions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.DR.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Inability to resolve commercial disputes promptly and fairly can lead to adverse economic outcomes in the private sector, ranging from reduced entrepreneurship and lower investment to macroeconomic volatility. This makes efficient and quality dispute resolution essential for a healthy business environment. Having time- and cost-effective mechanisms for resolving disputes is critical because excessively long and expensive proceedings may defeat the very purpose of bringing a case to formal institutions, making them unattractive and unaffordable. In fact, correlations have been established between judicial efficiency and facilitated entrepreneurial activity. Evidence also suggests that under a more effective court system businesses are likely to have greater access to finance and borrow more. The quality of the dispute resolution process also matters. Claims should be considered with due care by credible institutions capable of issuing sound judgments. It was found that in economies with low confidence in court systems, firms are less willing to expand their businesses and look for alternative trade partners. To attract more investors, economies therefore should ensure not only judiciaries’ effectiveness but also their strength and reliability." }, { "id": "IndicatorName", "value": "B-READY: Dispute Resolution Pillar 2: Public Services for Dispute Resolution" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Dispute Resolution topic measures efficiency and quality of the resolution of commercial disputes—those arising in the business context between firms—across three different dimensions, or pillars. The second pillar focuses on judicial organizational structure, courts’ digitization and transparency, as well as ADR-related services, thus capturing the de facto provision of public services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.DR.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Inability to resolve commercial disputes promptly and fairly can lead to adverse economic outcomes in the private sector, ranging from reduced entrepreneurship and lower investment to macroeconomic volatility. This makes efficient and quality dispute resolution essential for a healthy business environment. Having time- and cost-effective mechanisms for resolving disputes is critical because excessively long and expensive proceedings may defeat the very purpose of bringing a case to formal institutions, making them unattractive and unaffordable. In fact, correlations have been established between judicial efficiency and facilitated entrepreneurial activity. Evidence also suggests that under a more effective court system businesses are likely to have greater access to finance and borrow more. The quality of the dispute resolution process also matters. Claims should be considered with due care by credible institutions capable of issuing sound judgments. It was found that in economies with low confidence in court systems, firms are less willing to expand their businesses and look for alternative trade partners. To attract more investors, economies therefore should ensure not only judiciaries’ effectiveness but also their strength and reliability." }, { "id": "IndicatorName", "value": "B-READY: Dispute Resolution Pillar 3: Ease of Resolving a Commercial Dispute" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Dispute Resolution topic measures efficiency and quality of the resolution of commercial disputes—those arising in the business context between firms—across three different dimensions, or pillars. The third pillar measures the reliability of dispute resolution, the time and cost required to resolve a dispute, as well as the time and cost associated with the recognition and enforcement of decisions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.FS.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to finance remains a major constraint for firms worldwide, despite being essential for their operations and expansion and positively associated with innovation. Additionally, access to finance affects firms’ ability to manage a volatile cash flow and directly contributes to their resilience, which was underscored during the global pandemic. Research has also shown that private sector financing in developing economies has positive macroeconomic effects as firm-level employment often benefits from improved access to finance.\n\nAccess to finance also plays an important role in maintaining a company’s financial stability. Removing bottlenecks associated with making and receiving payments further strengthens firms’ financial security. In recent years, cashless transactions (including e-payments) have continued growing. However, economies’ ever-increasing digitalization requires modern regulations that enable electronic solutions to reap the benefits of technological progress. This would unlock the extensive use of electronic payments (e-payments), which is associated with reduced tax evasion and lower informality in the private sector." }, { "id": "IndicatorName", "value": "B-READY: Financial Services: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Financial Services topic measures four areas—Commercial Lending; Secured Transactions; e-Payments; and Credit Information—across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.FS.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to finance remains a major constraint for firms worldwide, despite being essential for their operations and expansion and positively associated with innovation. Additionally, access to finance affects firms’ ability to manage a volatile cash flow and directly contributes to their resilience, which was underscored during the global pandemic. Research has also shown that private sector financing in developing economies has positive macroeconomic effects as firm-level employment often benefits from improved access to finance.\n\nAccess to finance also plays an important role in maintaining a company’s financial stability. Removing bottlenecks associated with making and receiving payments further strengthens firms’ financial security. In recent years, cashless transactions (including e-payments) have continued growing. However, economies’ ever-increasing digitalization requires modern regulations that enable electronic solutions to reap the benefits of technological progress. This would unlock the extensive use of electronic payments (e-payments), which is associated with reduced tax evasion and lower informality in the private sector." }, { "id": "IndicatorName", "value": "B-READY: Financial Services Pillar 1: Quality of Regulations for Financial Services" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Financial Services topic measures four areas—Commercial Lending; Secured Transactions; e-Payments; and Credit Information—across three different dimensions, or pillars. The first pillar assesses the effectiveness of regulation pertaining to commercial lending, secured transactions, and e-payments, covering the de jure features of regulatory frameworks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.FS.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to finance remains a major constraint for firms worldwide, despite being essential for their operations and expansion and positively associated with innovation. Additionally, access to finance affects firms’ ability to manage a volatile cash flow and directly contributes to their resilience, which was underscored during the global pandemic. Research has also shown that private sector financing in developing economies has positive macroeconomic effects as firm-level employment often benefits from improved access to finance.\n\nAccess to finance also plays an important role in maintaining a company’s financial stability. Removing bottlenecks associated with making and receiving payments further strengthens firms’ financial security. In recent years, cashless transactions (including e-payments) have continued growing. However, economies’ ever-increasing digitalization requires modern regulations that enable electronic solutions to reap the benefits of technological progress. This would unlock the extensive use of electronic payments (e-payments), which is associated with reduced tax evasion and lower informality in the private sector." }, { "id": "IndicatorName", "value": "B-READY: Financial Services Pillar 2: Accessibility of Information in Credit Infrastructure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Financial Services topic measures four areas—Commercial Lending; Secured Transactions; e-Payments; and Credit Information—across three different dimensions, or pillars. The second pillar measures the accessibility of information in credit infrastructure by evaluating the operation of credit bureaus and registries and the operation of collateral registries. Thus, the second pillar assesses de facto and some de jure aspects of financial services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.FS.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to finance remains a major constraint for firms worldwide, despite being essential for their operations and expansion and positively associated with innovation. Additionally, access to finance affects firms’ ability to manage a volatile cash flow and directly contributes to their resilience, which was underscored during the global pandemic. Research has also shown that private sector financing in developing economies has positive macroeconomic effects as firm-level employment often benefits from improved access to finance.\n\nAccess to finance also plays an important role in maintaining a company’s financial stability. Removing bottlenecks associated with making and receiving payments further strengthens firms’ financial security. In recent years, cashless transactions (including e-payments) have continued growing. However, economies’ ever-increasing digitalization requires modern regulations that enable electronic solutions to reap the benefits of technological progress. This would unlock the extensive use of electronic payments (e-payments), which is associated with reduced tax evasion and lower informality in the private sector." }, { "id": "IndicatorName", "value": "B-READY: Financial Services Pillar 3: Operational Efficiency of Receiving Financial Services" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Financial Services topic measures four areas—Commercial Lending; Secured Transactions; e-Payments; and Credit Information—across three different dimensions, or pillars. The third pillar measures the operational efficiency of (i) obtaining a loan; (ii) registering a security interest as well as the timeliness of credit information sharing; and (iii) e-payments usage and their efficiency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.IT.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "International trade is a key driver of economic growth and private sector development. Through competition among domestic and foreign firms, it promotes specialization and resource reallocation to the most productive firms. While there are winners and losers among firms, workers, and consumers, international trade can generate overall benefits for the private sector and society. To remain competitive, firms must continuously adapt, innovate, and improve their efficiency, resulting in aggregate productivity growth and welfare. Trade openness may generate further productivity gains by creating economies of scale and providing access to cheaper intermediate inputs of higher quality and variety, as well as facilitating knowledge and technology transfers. Increased access to foreign inputs may enhance productivity and export performance, and it may provide opportunities to diversify the economy and reduce its dependence on a single product or market. This shows the complementarities between exports and imports and emphasizes the importance of trade openness to reap the benefits of international trade.\n\nTo fully realize the benefits of international trade, it is necessary to have a conducive business environment that reduces trade barriers and lowers compliance and transaction costs for the private sector. A regulatory framework that establishes a nondiscriminatory, transparent, predictable, and safe trading environment generates incentives to engage in international trade and provides a level playing field. Furthermore, it is crucial to have regulations that strike a balance between public policy objectives, including protecting public health and the environment, and the requirements they impose, which can create market distortions that impede trade. Finally, policies that improve the quality of physical and digital infrastructure, as well as border management, reduce the time and cost borne by the private sector, which represents a substantial barrier to trade, and increase participation in international trade for small, medium, and large firms." }, { "id": "IndicatorName", "value": "B-READY: International Trade: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The International Trade topic measures different aspects of international trade—trade in goods, trade in services, and digital trade—across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.IT.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "International trade is a key driver of economic growth and private sector development. Through competition among domestic and foreign firms, it promotes specialization and resource reallocation to the most productive firms. While there are winners and losers among firms, workers, and consumers, international trade can generate overall benefits for the private sector and society. To remain competitive, firms must continuously adapt, innovate, and improve their efficiency, resulting in aggregate productivity growth and welfare. Trade openness may generate further productivity gains by creating economies of scale and providing access to cheaper intermediate inputs of higher quality and variety, as well as facilitating knowledge and technology transfers. Increased access to foreign inputs may enhance productivity and export performance, and it may provide opportunities to diversify the economy and reduce its dependence on a single product or market. This shows the complementarities between exports and imports and emphasizes the importance of trade openness to reap the benefits of international trade.\n\nTo fully realize the benefits of international trade, it is necessary to have a conducive business environment that reduces trade barriers and lowers compliance and transaction costs for the private sector. A regulatory framework that establishes a nondiscriminatory, transparent, predictable, and safe trading environment generates incentives to engage in international trade and provides a level playing field. Furthermore, it is crucial to have regulations that strike a balance between public policy objectives, including protecting public health and the environment, and the requirements they impose, which can create market distortions that impede trade. Finally, policies that improve the quality of physical and digital infrastructure, as well as border management, reduce the time and cost borne by the private sector, which represents a substantial barrier to trade, and increase participation in international trade for small, medium, and large firms." }, { "id": "IndicatorName", "value": "B-READY: International Trade Pillar 1: Quality of Regulations for International Trade" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The International Trade topic measures different aspects of international trade—trade in goods, trade in services, and digital trade—across three different dimensions, or pillars. The first pillar assesses the quality of regulations pertaining to international trade, covering de jure features of a regulatory framework that are necessary to establish a nondiscriminatory, transparent, predictable, and safe environment to harness the potential of international trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.IT.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "International trade is a key driver of economic growth and private sector development. Through competition among domestic and foreign firms, it promotes specialization and resource reallocation to the most productive firms. While there are winners and losers among firms, workers, and consumers, international trade can generate overall benefits for the private sector and society. To remain competitive, firms must continuously adapt, innovate, and improve their efficiency, resulting in aggregate productivity growth and welfare. Trade openness may generate further productivity gains by creating economies of scale and providing access to cheaper intermediate inputs of higher quality and variety, as well as facilitating knowledge and technology transfers. Increased access to foreign inputs may enhance productivity and export performance, and it may provide opportunities to diversify the economy and reduce its dependence on a single product or market. This shows the complementarities between exports and imports and emphasizes the importance of trade openness to reap the benefits of international trade.\n\nTo fully realize the benefits of international trade, it is necessary to have a conducive business environment that reduces trade barriers and lowers compliance and transaction costs for the private sector. A regulatory framework that establishes a nondiscriminatory, transparent, predictable, and safe trading environment generates incentives to engage in international trade and provides a level playing field. Furthermore, it is crucial to have regulations that strike a balance between public policy objectives, including protecting public health and the environment, and the requirements they impose, which can create market distortions that impede trade. Finally, policies that improve the quality of physical and digital infrastructure, as well as border management, reduce the time and cost borne by the private sector, which represents a substantial barrier to trade, and increase participation in international trade for small, medium, and large firms." }, { "id": "IndicatorName", "value": "B-READY: International Trade Pillar 2: Quality of Public Services for the Facilitation of International Trade" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The International Trade topic measures different aspects of international trade—trade in goods, trade in services, and digital trade—across three different dimensions, or pillars. The second pillar assesses digital and physical infrastructure concerning international trade and the quality of border management, thus assessing de facto provision of public services for international trade facilitation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.IT.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "International trade is a key driver of economic growth and private sector development. Through competition among domestic and foreign firms, it promotes specialization and resource reallocation to the most productive firms. While there are winners and losers among firms, workers, and consumers, international trade can generate overall benefits for the private sector and society. To remain competitive, firms must continuously adapt, innovate, and improve their efficiency, resulting in aggregate productivity growth and welfare. Trade openness may generate further productivity gains by creating economies of scale and providing access to cheaper intermediate inputs of higher quality and variety, as well as facilitating knowledge and technology transfers. Increased access to foreign inputs may enhance productivity and export performance, and it may provide opportunities to diversify the economy and reduce its dependence on a single product or market. This shows the complementarities between exports and imports and emphasizes the importance of trade openness to reap the benefits of international trade.\n\nTo fully realize the benefits of international trade, it is necessary to have a conducive business environment that reduces trade barriers and lowers compliance and transaction costs for the private sector. A regulatory framework that establishes a nondiscriminatory, transparent, predictable, and safe trading environment generates incentives to engage in international trade and provides a level playing field. Furthermore, it is crucial to have regulations that strike a balance between public policy objectives, including protecting public health and the environment, and the requirements they impose, which can create market distortions that impede trade. Finally, policies that improve the quality of physical and digital infrastructure, as well as border management, reduce the time and cost borne by the private sector, which represents a substantial barrier to trade, and increase participation in international trade for small, medium, and large firms." }, { "id": "IndicatorName", "value": "B-READY: International Trade Pillar 3: Efficiency of Importing Goods, Exporting Goods, and Engaging in Digital Trade" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The International Trade topic measures different aspects of international trade—trade in goods, trade in services, and digital trade—across three different dimensions, or pillars. The third pillar measures the time and cost to comply with export and import requirements, participation in cross-border digital trade, as well as the perceived major obstacles for international trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.LB.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Labor is arguably the most important factor of production in most businesses and the primary source of income for most people. Regulations and public services related to labor are fundamental drivers of private sector development from the perspective of both enterprises and workers. These regulations and public services affect firms’ decisions on whether to expand by hiring labor, and whether to do so formally or informally. In addition, they impact the well-being of potential workers by providing them with good jobs and opportunities for growth.\n\nFor formally employed workers, labor regulations matter—they protect their rights, reduce the risk of job loss, and promote equity and welfare. For workers employed in the informal sector, labor regulations can affect their ability to enter the formal workforce. If labor regulations make hiring costs too high or the rules too cumbersome, firms may opt to use more capital than labor or to hire informally. Sound and balanced labor regulations are essential for ensuring that both firms and workers benefit from a dynamic and innovative labor market without compromising income security or basic workers' rights. In addition, public services may address market imperfections and have important implications for the functioning of the labor market and firm choices." }, { "id": "IndicatorName", "value": "B-READY: Labor: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Labor topic measures good practices in employment regulations and public services from the perspective of both enterprises and employees across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.LB.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Labor is arguably the most important factor of production in most businesses and the primary source of income for most people. Regulations and public services related to labor are fundamental drivers of private sector development from the perspective of both enterprises and workers. These regulations and public services affect firms’ decisions on whether to expand by hiring labor, and whether to do so formally or informally. In addition, they impact the well-being of potential workers by providing them with good jobs and opportunities for growth.\n\nFor formally employed workers, labor regulations matter—they protect their rights, reduce the risk of job loss, and promote equity and welfare. For workers employed in the informal sector, labor regulations can affect their ability to enter the formal workforce. If labor regulations make hiring costs too high or the rules too cumbersome, firms may opt to use more capital than labor or to hire informally. Sound and balanced labor regulations are essential for ensuring that both firms and workers benefit from a dynamic and innovative labor market without compromising income security or basic workers' rights. In addition, public services may address market imperfections and have important implications for the functioning of the labor market and firm choices." }, { "id": "IndicatorName", "value": "B-READY: Labor Pillar 1: Quality of Labor Regulations" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Labor topic measures good practices in employment regulations and public services from the perspective of both enterprises and employees across three different dimensions, or pillars. The first pillar assesses the quality of labor regulations pertaining to workers' conditions and employment restrictions and costs, covering de jure features of the regulatory framework that are necessary for the functioning of the labor market and to provide employers and employees with their obligations and relevant safeguards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.LB.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Labor is arguably the most important factor of production in most businesses and the primary source of income for most people. Regulations and public services related to labor are fundamental drivers of private sector development from the perspective of both enterprises and workers. These regulations and public services affect firms’ decisions on whether to expand by hiring labor, and whether to do so formally or informally. In addition, they impact the well-being of potential workers by providing them with good jobs and opportunities for growth.\n\nFor formally employed workers, labor regulations matter—they protect their rights, reduce the risk of job loss, and promote equity and welfare. For workers employed in the informal sector, labor regulations can affect their ability to enter the formal workforce. If labor regulations make hiring costs too high or the rules too cumbersome, firms may opt to use more capital than labor or to hire informally. Sound and balanced labor regulations are essential for ensuring that both firms and workers benefit from a dynamic and innovative labor market without compromising income security or basic workers' rights. In addition, public services may address market imperfections and have important implications for the functioning of the labor market and firm choices." }, { "id": "IndicatorName", "value": "B-READY: Labor Pillar 2: Adequacy of Public Services for Labor" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Labor topic measures good practices in employment regulations and public services from the perspective of both enterprises and employees across three different dimensions, or pillars. The second pillar measures the adequacy of public services for labor, assessing the de facto provision of social protection and the institutional framework on which the labor market and the enforcement of labor regulations depend." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.LB.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Labor is arguably the most important factor of production in most businesses and the primary source of income for most people. Regulations and public services related to labor are fundamental drivers of private sector development from the perspective of both enterprises and workers. These regulations and public services affect firms’ decisions on whether to expand by hiring labor, and whether to do so formally or informally. In addition, they impact the well-being of potential workers by providing them with good jobs and opportunities for growth.\n\nFor formally employed workers, labor regulations matter—they protect their rights, reduce the risk of job loss, and promote equity and welfare. For workers employed in the informal sector, labor regulations can affect their ability to enter the formal workforce. If labor regulations make hiring costs too high or the rules too cumbersome, firms may opt to use more capital than labor or to hire informally. Sound and balanced labor regulations are essential for ensuring that both firms and workers benefit from a dynamic and innovative labor market without compromising income security or basic workers' rights. In addition, public services may address market imperfections and have important implications for the functioning of the labor market and firm choices." }, { "id": "IndicatorName", "value": "B-READY: Labor Pillar 3: Operational Efficiency of Labor Regulations and Public Services in Practice" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Labor topic measures good practices in employment regulations and public services from the perspective of both enterprises and employees across three different dimensions, or pillars. The third pillar measures the operational efficiency of labor regulations and public services in practice, assessing employment restrictions and cost, as well as public services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.MC.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "There is substantial economic evidence that a fair level of market competition spurs economic growth by increasing industry and firm innovation and productivity, leading to better products, more and better jobs, and higher incomes. By affecting market entry and exit, competition stimulates product innovation and service quality, protects consumers, and forces market operators to provide their products and services at cost. But competition is rarely perfect. Markets fail either due to firms’ behaviors or government interventions. Market power—a firm’s ability to raise prices well above cost, offer a low-quality good or service, and drive out competition—must be kept in check." }, { "id": "IndicatorName", "value": "B-READY: Market Competition: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Market Competition topic measures good practices related to the enforcement of competition policy, intellectual property rights and innovation policy, and regulations that focus on improving competition and innovation in markets where the government is a purchaser of services or goods, across the three different pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.MC.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "There is substantial economic evidence that a fair level of market competition spurs economic growth by increasing industry and firm innovation and productivity, leading to better products, more and better jobs, and higher incomes. By affecting market entry and exit, competition stimulates product innovation and service quality, protects consumers, and forces market operators to provide their products and services at cost. But competition is rarely perfect. Markets fail either due to firms’ behaviors or government interventions. Market power—a firm’s ability to raise prices well above cost, offer a low-quality good or service, and drive out competition—must be kept in check." }, { "id": "IndicatorName", "value": "B-READY: Market Competition Pillar 1: Quality of Regulations that Promote Market Competition" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Market Competition topic measures good practices related to the enforcement of competition policy, intellectual property rights and innovation policy, and regulations that focus on improving competition and innovation in markets where the government is a purchaser of services or goods, across the three different pillars. The first pillar assesses the quality of regulations that promote market competition, covering de jure features of a regulatory framework that enable firms to participate in fair market conditions and innovate, and where firms can participate in open and competitive government markets." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.MC.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "There is substantial economic evidence that a fair level of market competition spurs economic growth by increasing industry and firm innovation and productivity, leading to better products, more and better jobs, and higher incomes. By affecting market entry and exit, competition stimulates product innovation and service quality, protects consumers, and forces market operators to provide their products and services at cost. But competition is rarely perfect. Markets fail either due to firms’ behaviors or government interventions. Market power—a firm’s ability to raise prices well above cost, offer a low-quality good or service, and drive out competition—must be kept in check." }, { "id": "IndicatorName", "value": "B-READY: Market Competition Pillar 2: Public Services that Promote Market Competition" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Market Competition topic measures good practices related to the enforcement of competition policy, intellectual property rights and innovation policy, and regulations that focus on improving competition and innovation in markets where the government is a purchaser of services or goods, across the three different pillars. The second pillar measures the adequacy of public services that promote market competition, thus assessing the de facto provision of services that create an equal level of playing field in markets, and that foster and promote innovation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.MC.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "There is substantial economic evidence that a fair level of market competition spurs economic growth by increasing industry and firm innovation and productivity, leading to better products, more and better jobs, and higher incomes. By affecting market entry and exit, competition stimulates product innovation and service quality, protects consumers, and forces market operators to provide their products and services at cost. But competition is rarely perfect. Markets fail either due to firms’ behaviors or government interventions. Market power—a firm’s ability to raise prices well above cost, offer a low-quality good or service, and drive out competition—must be kept in check." }, { "id": "IndicatorName", "value": "B-READY: Market Competition Pillar 3: Implementation of Key Services Promoting Market Competition" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Market Competition topic measures good practices related to the enforcement of competition policy, intellectual property rights and innovation policy, and regulations that focus on improving competition and innovation in markets where the government is a purchaser of services or goods, across the three different pillars. The third pillar measures the operational efficiency in the implementation of key services promoting market competition (reflecting both the ease of compliance with the regulatory framework and the effective provision of public services directly relevant to firms that contribute in practice to the promotion of market competition)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.P1.RF", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The business environment can be defined as the set of conditions outside a firm’s control that have a significant influence on how businesses behave and perform throughout their life cycle. This set of conditions can be very large, from macroeconomic policy to microeconomic rules. B-READY concentrates on the regulatory framework and public service provision at the microeconomic level: that is, as enacted and implemented to directly affect firms’ behavior and performance. B-READY assesses an economy’s business environment by focusing on the regulatory framework and the provision of related public services directed at firms and markets, as well as the efficiency with which regulatory framework and public services are combined in practice. B-READY seeks a balanced approach when assessing the business environment: between ease of conducting a business and broader private sector benefits, between regulatory framework and public services, between de jure laws and regulations and de facto practical implementation, and between data representativeness and data comparability. For nearly all indicators, the regulatory framework pillar captures de jure information, and the public services and efficiency pillars capture de facto information." }, { "id": "IndicatorName", "value": "B-READY: Pillar 1: Regulatory Framework" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY assesses the economy’s business environment by focusing on the regulatory framework and the provision of related public services for firms and markets, as well as the operational efficiency with which they are combined in practice. B-READY’s three pillars—the Regulatory Framework, Public Services, and Operational Efficiency. The Regulatory Framework comprises the rules and regulations that firms must follow as they open, operate, and close a business." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.P2.PS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The business environment can be defined as the set of conditions outside a firm’s control that have a significant influence on how businesses behave and perform throughout their life cycle. This set of conditions can be very large, from macroeconomic policy to microeconomic rules. B-READY concentrates on the regulatory framework and public service provision at the microeconomic level: that is, as enacted and implemented to directly affect firms’ behavior and performance. B-READY assesses an economy’s business environment by focusing on the regulatory framework and the provision of related public services directed at firms and markets, as well as the efficiency with which regulatory framework and public services are combined in practice. B-READY seeks a balanced approach when assessing the business environment: between ease of conducting a business and broader private sector benefits, between regulatory framework and public services, between de jure laws and regulations and de facto practical implementation, and between data representativeness and data comparability. For nearly all indicators, the regulatory framework pillar captures de jure information, and the public services and efficiency pillars capture de facto information." }, { "id": "IndicatorName", "value": "B-READY: Pillar 2: Public Services" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY assesses the economy’s business environment by focusing on the regulatory framework and the provision of related public services for firms and markets, as well as the operational efficiency with which they are combined in practice. B-READY’s three pillars—the Regulatory Framework, Public Services, and Operational Efficiency. Public Services refers to both the facilities that governments provide directly or through private firms to support compliance with regulations and the critical institutions and infrastructure that enable business activities. Public services considered by B-READY are limited to the scope of the business environment areas related to the life cycle of the firm." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.P3.OE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The business environment can be defined as the set of conditions outside a firm’s control that have a significant influence on how businesses behave and perform throughout their life cycle. This set of conditions can be very large, from macroeconomic policy to microeconomic rules. B-READY concentrates on the regulatory framework and public service provision at the microeconomic level: that is, as enacted and implemented to directly affect firms’ behavior and performance. B-READY assesses an economy’s business environment by focusing on the regulatory framework and the provision of related public services directed at firms and markets, as well as the efficiency with which regulatory framework and public services are combined in practice. B-READY seeks a balanced approach when assessing the business environment: between ease of conducting a business and broader private sector benefits, between regulatory framework and public services, between de jure laws and regulations and de facto practical implementation, and between data representativeness and data comparability. For nearly all indicators, the regulatory framework pillar captures de jure information, and the public services and efficiency pillars capture de facto information." }, { "id": "IndicatorName", "value": "B-READY: Pillar 3: Operational Efficiency" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY assesses the economy’s business environment by focusing on the regulatory framework and the provision of related public services for firms and markets, as well as the operational efficiency with which they are combined in practice. B-READY’s three pillars—the Regulatory Framework, Public Services, and Operational Efficiency. Operational Efficiency comprises both the ease of compliance with the regulatory framework and the effective use of public services directly relevant to firms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.TX.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Taxation is a powerful policy tool that governments use to generate revenue to finance their operations and provide public goods and services. Taxation affects the private sector development through a variety of interrelated channels. On the one hand, it creates enabling conditions for the growth and development of the private sector by financing critical infrastructure, investing in human capital, ensuring law enforcement, and supporting other vital services. On the other hand, excessive taxation can distort markets, impair investment decisions, and foster tax evasion. Likewise, cumbersome regulations, complex tax reporting requirements, and inefficient and inconsistent tax procedures increase compliance costs on firms, thereby discouraging formalization. Identifying the key challenges faced by taxpayers is essential for guiding reforms that support private sector development while pursuing domestic resource mobilization objectives." }, { "id": "IndicatorName", "value": "B-READY: Taxation: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Taxation topic measures the quality of regulation, administration, and practical implementation of tax systems across the three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.TX.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Taxation is a powerful policy tool that governments use to generate revenue to finance their operations and provide public goods and services. Taxation affects the private sector development through a variety of interrelated channels. On the one hand, it creates enabling conditions for the growth and development of the private sector by financing critical infrastructure, investing in human capital, ensuring law enforcement, and supporting other vital services. On the other hand, excessive taxation can distort markets, impair investment decisions, and foster tax evasion. Likewise, cumbersome regulations, complex tax reporting requirements, and inefficient and inconsistent tax procedures increase compliance costs on firms, thereby discouraging formalization. Identifying the key challenges faced by taxpayers is essential for guiding reforms that support private sector development while pursuing domestic resource mobilization objectives." }, { "id": "IndicatorName", "value": "B-READY: Taxation Pillar 1: Quality of Regulations on Taxation" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Taxation topic measures the quality of regulation, administration, and practical implementation of tax systems across the three different dimensions, or pillars. The first pillar assesses the quality of regulation related to taxation, encompassing both the legal framework (de jure) and the implementation (de facto) of the legal requirements." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.TX.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Taxation is a powerful policy tool that governments use to generate revenue to finance their operations and provide public goods and services. Taxation affects the private sector development through a variety of interrelated channels. On the one hand, it creates enabling conditions for the growth and development of the private sector by financing critical infrastructure, investing in human capital, ensuring law enforcement, and supporting other vital services. On the other hand, excessive taxation can distort markets, impair investment decisions, and foster tax evasion. Likewise, cumbersome regulations, complex tax reporting requirements, and inefficient and inconsistent tax procedures increase compliance costs on firms, thereby discouraging formalization. Identifying the key challenges faced by taxpayers is essential for guiding reforms that support private sector development while pursuing domestic resource mobilization objectives." }, { "id": "IndicatorName", "value": "B-READY: Taxation Pillar 2: Public Services Provided by the Tax Administration" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Taxation topic measures the quality of regulation, administration, and practical implementation of tax systems across the three different dimensions, or pillars. The second pillar measures the quality of tax administration by assessing the public services related to tax matters." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.TX.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Taxation is a powerful policy tool that governments use to generate revenue to finance their operations and provide public goods and services. Taxation affects the private sector development through a variety of interrelated channels. On the one hand, it creates enabling conditions for the growth and development of the private sector by financing critical infrastructure, investing in human capital, ensuring law enforcement, and supporting other vital services. On the other hand, excessive taxation can distort markets, impair investment decisions, and foster tax evasion. Likewise, cumbersome regulations, complex tax reporting requirements, and inefficient and inconsistent tax procedures increase compliance costs on firms, thereby discouraging formalization. Identifying the key challenges faced by taxpayers is essential for guiding reforms that support private sector development while pursuing domestic resource mobilization objectives." }, { "id": "IndicatorName", "value": "B-READY: Taxation Pillar 3: Operational Efficiency of Tax Systems in Practice" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Taxation topic measures the quality of regulation, administration, and practical implementation of tax systems across the three different dimensions, or pillars. The third pillar evaluates the practical effectiveness of the implemented tax regulations and public services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.US.OS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By providing essential services—electricity, water, and digital connectivity—utilities play an important role in supporting economic and social development. Without these services, businesses cannot function, and households cannot lead quality lives. Yet, more than 30 percent of businesses globally identify electricity supply as a major constraint to their operations, according to the World Bank Enterprise Surveys. Disruptions in electricity supply impair firm productivity, revenues, and economic growth. Similarly, inadequate water supply can lead to decreased firm productivity, deterioration of machinery, and reduced profits. Access to reliable internet is another critical element in today’s digitalized world, where the use of digital technologies improve productivity. However, as of 2021, just over 15 percent of people globally had fixed broadband subscriptions, and only 1.4 percent in the least developed countries. The provision of basic utility services should be effective and reliable. Facilitating timely access to such services in an environmentally sustainable manner is instrumental for economic growth.\n\nThe effectiveness of regulatory frameworks, good governance, transparency, and efficiency of utility services are pivotal elements of a good business environment. An effective regulatory framework, for example, is a fundamental steppingstone for the provision of high-quality utility services. Furthermore, the reliability and sustainability of utility services should be maintained through monitoring the quality of service supply and connection safety, fostering public accountability and safety. Interoperability through agency coordination and digitalization of utilities can also help improve the quality of public services and the customer experience." }, { "id": "IndicatorName", "value": "B-READY: Utility Services: Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Utility Services topic measures the effectiveness of regulatory frameworks, and the quality of governance and transparency of service delivery mechanisms, as well as the operational efficiency of providing electricity, water, and internet services. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.US.P1", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By providing essential services—electricity, water, and digital connectivity—utilities play an important role in supporting economic and social development. Without these services, businesses cannot function, and households cannot lead quality lives. Yet, more than 30 percent of businesses globally identify electricity supply as a major constraint to their operations, according to the World Bank Enterprise Surveys. Disruptions in electricity supply impair firm productivity, revenues, and economic growth. Similarly, inadequate water supply can lead to decreased firm productivity, deterioration of machinery, and reduced profits. Access to reliable internet is another critical element in today’s digitalized world, where the use of digital technologies improve productivity. However, as of 2021, just over 15 percent of people globally had fixed broadband subscriptions, and only 1.4 percent in the least developed countries. The provision of basic utility services should be effective and reliable. Facilitating timely access to such services in an environmentally sustainable manner is instrumental for economic growth.\n\nThe effectiveness of regulatory frameworks, good governance, transparency, and efficiency of utility services are pivotal elements of a good business environment. An effective regulatory framework, for example, is a fundamental steppingstone for the provision of high-quality utility services. Furthermore, the reliability and sustainability of utility services should be maintained through monitoring the quality of service supply and connection safety, fostering public accountability and safety. Interoperability through agency coordination and digitalization of utilities can also help improve the quality of public services and the customer experience." }, { "id": "IndicatorName", "value": "B-READY: Utility Services Pillar 1: Quality of Regulations on Utility Services" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Utility Services topic measures the effectiveness of regulatory frameworks, and the quality of governance and transparency of service delivery mechanisms, as well as the operational efficiency of providing electricity, water, and internet services. Under the first pillar the Utility Services topic assesses the effectiveness of regulation pertaining to electricity, water, and internet services, covering de jure features of a regulatory framework that are necessary for the efficient deployment of connections, reliable service, safety, and environmental sustainability of provision and use of utility services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.US.P2", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By providing essential services—electricity, water, and digital connectivity—utilities play an important role in supporting economic and social development. Without these services, businesses cannot function, and households cannot lead quality lives. Yet, more than 30 percent of businesses globally identify electricity supply as a major constraint to their operations, according to the World Bank Enterprise Surveys. Disruptions in electricity supply impair firm productivity, revenues, and economic growth. Similarly, inadequate water supply can lead to decreased firm productivity, deterioration of machinery, and reduced profits. Access to reliable internet is another critical element in today’s digitalized world, where the use of digital technologies improve productivity. However, as of 2021, just over 15 percent of people globally had fixed broadband subscriptions, and only 1.4 percent in the least developed countries. The provision of basic utility services should be effective and reliable. Facilitating timely access to such services in an environmentally sustainable manner is instrumental for economic growth.\n\nThe effectiveness of regulatory frameworks, good governance, transparency, and efficiency of utility services are pivotal elements of a good business environment. An effective regulatory framework, for example, is a fundamental steppingstone for the provision of high-quality utility services. Furthermore, the reliability and sustainability of utility services should be maintained through monitoring the quality of service supply and connection safety, fostering public accountability and safety. Interoperability through agency coordination and digitalization of utilities can also help improve the quality of public services and the customer experience." }, { "id": "IndicatorName", "value": "B-READY: Utility Services Pillar 2: Quality of Governance and Transparency of Utility Services" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Utility Services topic measures the effectiveness of regulatory frameworks, and the quality of governance and transparency of service delivery mechanisms, as well as the operational efficiency of providing electricity, water, and internet services. The second pillar of the topic measures the quality of governance and transparency in the provision of utility services, thus assessing the de facto provision of utility services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BRE.US.P3", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By providing essential services—electricity, water, and digital connectivity—utilities play an important role in supporting economic and social development. Without these services, businesses cannot function, and households cannot lead quality lives. Yet, more than 30 percent of businesses globally identify electricity supply as a major constraint to their operations, according to the World Bank Enterprise Surveys. Disruptions in electricity supply impair firm productivity, revenues, and economic growth. Similarly, inadequate water supply can lead to decreased firm productivity, deterioration of machinery, and reduced profits. Access to reliable internet is another critical element in today’s digitalized world, where the use of digital technologies improve productivity. However, as of 2021, just over 15 percent of people globally had fixed broadband subscriptions, and only 1.4 percent in the least developed countries. The provision of basic utility services should be effective and reliable. Facilitating timely access to such services in an environmentally sustainable manner is instrumental for economic growth.\n\nThe effectiveness of regulatory frameworks, good governance, transparency, and efficiency of utility services are pivotal elements of a good business environment. An effective regulatory framework, for example, is a fundamental steppingstone for the provision of high-quality utility services. Furthermore, the reliability and sustainability of utility services should be maintained through monitoring the quality of service supply and connection safety, fostering public accountability and safety. Interoperability through agency coordination and digitalization of utilities can also help improve the quality of public services and the customer experience." }, { "id": "IndicatorName", "value": "B-READY: Utility Services Pillar 3: Operational Efficiency of Utility Service Provision" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Utility Services topic measures the effectiveness of regulatory frameworks, and the quality of governance and transparency of service delivery mechanisms, as well as the operational efficiency of providing electricity, water, and internet services. The third pillar measures the time required to obtain electricity, water, and internet connections, as well as the reliability of utility service supply." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2024-2024" }, { "id": "Source", "value": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores. \nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles. \nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics. \nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "0-100 scale" } ], "source_id": "57" }, { "id": "IC.BUS.DFRN.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "When compared across years, the ease of doing business score shows how much the regulatory environment for local entrepreneurs in an economy has changed over time in absolute terms, whereas the ease of doing business ranking shows only how much the regulatory environment has changed relative to that in other economies." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Ease of doing business score (0 = lowest performance to 100 = best performance)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ease of doing business scores benchmark economies with respect to regulatory best practice, showing the proximity to the best regulatory performance on each Doing Business indicator. An economy’s score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate ease of doing business score for each economy is the simple average of their scores on each of the 10 topics included in the ranking: starting a business, dealing with construction permits, getting electricity, registering property, getting credit, protecting minority investors, paying taxes, trading across borders, enforcing contracts and resolving insolvency. All topics are weighted equally. Please refer to the Doing Business website for a detailed description of the methodology: https://www.doingbusiness.org/en/methodology" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.BUS.DISC.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Business extent of disclosure index (0=less disclosure to 10=more disclosure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Disclosure index measures the extent to which investors are protected through disclosure of ownership and financial information. The index ranges from 0 to 10, with higher values indicating more disclosure." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nCorporations are instruments of entrepreneurship and growth. They can also be abused for personal gain. The indicator measures the strength of minority shareholder protections against directors' misuse of corporate assets for personal gain.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.BUS.EASE.XQ", "metatype": [ { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. \nThe Doing Business project provides objective measures of business regulations and their enforcement across 190 economies and selected cities at the subnational and regional level.\nThe Doing Business project, launched in 2002, looks at domestic small and medium-size companies and measures the regulations applying to them through their life cycle.\nBy gathering and analyzing comprehensive quantitative data to compare business regulation environments across economies and over time, Doing Business encourages economies to compete towards more efficient regulation; offers measurable benchmarks for reform; and serves as a resource for academics, journalists, private sector researchers and others interested in the business climate of each economy." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year. The country ranking is only available for the latest year." }, { "id": "IndicatorName", "value": "Ease of doing business rank (1=most business-friendly regulations)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Ease of doing business ranks economies from 1 to 190, with first place being the best. The ranking of economies is determined by sorting the aggregate ease of doing business scores. A high ranking (a low numerical rank) means that the regulatory environment is conducive to business operation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "The ranking of economies is determined by sorting the aggregate ease of doing business scores. The aggregate ease of doing business score for each economy is the simple average of their scores on each of the 10 topics included in the ranking: starting a business, dealing with construction permits, getting electricity, registering property, getting credit, protecting minority investors, paying taxes, trading across borders, enforcing contracts and resolving insolvency. All topics are weighted equally. \n\nData are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected. Please also refer to the Doing Business website for additional information: https://www.doingbusiness.org/en/methodology" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.BUS.NDNS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Entrepreneurial activity is a pillar of economic growth. The Entrepreneurship Database is a critical source of data that facilitates the measurement of entrepreneurial activity across countries and over time. The data also allow for a deeper understanding of the relationship between new firm registration, the regulatory environment, and economic growth. Previous research using the Entrepreneurship Database has shown a significant relationship between the cost of compliance required to start a business and new firm registration." }, { "id": "IndicatorName", "value": "New business density (new registrations per 1,000 people ages 15-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. Data collected from economies categorized as offshore financial centers by Eurostat are excluded from the analysis, because registered entities in these countries may not fit the objective of the Entrepreneurship Database aiming at measuring formally registered companies with actual economic activities. The information provided by these economies likely reflects a number of shell companies, defined as companies that are registered for tax purposes but are not economically active. The information on offshore centers is collected and published by the Entrepreneurship Database, but it is not used for trend analysis purposes." }, { "id": "Longdefinition", "value": "The number of newly registered firms with limited liability per 1,000 working-age people (ages 15-64) per calendar year." }, { "id": "Othernotes", "value": "For cross-country comparability, only limited liability corporations that operate in the formal sector are included." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2022" }, { "id": "Source", "value": "Entrepreneurship Database, World Bank (WB), uri: https://www.worldbank.org/en/programs/entrepreneurship" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Entrepreneurship Database has developed a data collection methodology to systematically measure entrepreneurial activity. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data – most often due to an absence of digitized registration systems – the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and publicly available data. \nStatistical concept(s): In order to measure entrepreneurship in a way that is universally comparable, the Entrepreneurship Database employs a methodology that can be applied across heterogeneous legal regimes and economic systems. The concept of entrepreneurship can cover a wide range of activities. For the purposes of this project, entrepreneurship is defined as the activities of an individual or a group of individuals aimed at initiating economic enterprise in the formal sector under a legal form of business.\n\nThe legal form of business refers to private companies with limited liability. Limited liability companies are those in which the financial liability of the firm’s members is limited to the value of their investment in the company. A limited liability company is a separate legal entity that has its own privileges and liabilities. Although the laws on business registration vary greatly across countries, the approach to legal entities is largely uniform: any business with a unique legal entity (or “corporate personhood”) separate from its owners must be registered." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Share (proportion) [SHARE]" } ], "source_id": "57" }, { "id": "IC.BUS.NREG", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Entrepreneurial activity is a pillar of economic growth. The Entrepreneurship Database is a critical source of data that facilitates the measurement of entrepreneurial activity across countries and over time. The data also allow for a deeper understanding of the relationship between new firm registration, the regulatory environment, and economic growth. Research using the Entrepreneurship Database has shown a significant relationship between the cost of compliance required to start a business and new firm registration." }, { "id": "IndicatorName", "value": "New businesses registered (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. Data collected from economies categorized as offshore financial centers by Eurostat are excluded from the analysis, because registered entities in these countries may not fit the objective of the Entrepreneurship Database aiming at measuring formally registered companies with actual economic activities. The information provided by these economies likely reflects a number of shell companies, defined as companies that are registered for tax purposes but are not economically active. The information on offshore centers is collected and published by the Entrepreneurship Database, but it is not used for trend analysis purposes." }, { "id": "Longdefinition", "value": "New businesses registered are the number of new limited liability corporations (or its equivalent) registered in the calendar year." }, { "id": "Othernotes", "value": "For cross-country comparability, only limited liability corporations that operate in the formal sector are included." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2022" }, { "id": "Source", "value": "Entrepreneurship Database, World Bank (WB), uri: https://www.worldbank.org/en/programs/entrepreneurship" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Entrepreneurship Database has developed a data collection methodology to systematically measure entrepreneurial activity. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data – most often due to an absence of digitized registration systems – the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and publicly available data. \nStatistical concept(s): In order to measure entrepreneurship in a way that is universally comparable, the Entrepreneurship Database employs a methodology that can be applied across heterogeneous legal regimes and economic systems. The concept of entrepreneurship can cover a wide range of activities. For the purposes of this project, entrepreneurship is defined as the activities of an individual or a group of individuals aimed at initiating economic enterprise in the formal sector under a legal form of business.\n\nThe legal form of business refers to private companies with limited liability. Limited liability companies are those in which the financial liability of the firm’s members is limited to the value of their investment in the company. A limited liability company is a separate legal entity that has its own privileges and liabilities. Although the laws on business registration vary greatly across countries, the approach to legal entities is largely uniform: any business with a unique legal entity (or “corporate personhood”) separate from its owners must be registered." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "IC.CRD.INFO.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "Access to finance can expand opportunities for all with higher levels of access and use of banking services associated with lower financing obstacles for people and businesses. A stable financial system that promotes efficient savings and investment is also crucial for a thriving democracy and market economy.\n\nThere are several aspects of access to financial services: availability, cost, and quality of services. The development and growth of credit markets depend on access to timely, reliable, and accurate data on borrowers' credit experiences. Access to credit can be improved by making it easy to create and enforce collateral agreements and by increasing information about potential borrowers' creditworthiness. Lenders look at a borrower's credit history and collateral. Where credit registries and effective collateral laws are absent - as in many developing countries - banks make fewer loans. Indicators that cover getting credit include the strength of legal rights index and the depth of credit information index.\n\nThe economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Depth of credit information index (0=low to 8=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Depth of credit information index measures rules affecting the scope, accessibility, and quality of credit information available through public or private credit registries. The index ranges from 0 to 8, with higher values indicating the availability of more credit information, from either a public registry or a private bureau, to facilitate lending decisions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "For Doing Business 2015, the credit information index has expanded with two new measurements, namely whether (1) banks and financial institutions access credit bureaus and credit registries' databases through an online platform or system-to-system connection; and (2) bureau or registry credit scores are offered as a value added service to help banks and financial institutions to assess the creditworthiness of borrowers. Furthermore, if the credit bureau or registry is not operational or covers less than 5% of the adult population, the score on the depth of credit information index is 0. (Previously, the coverage threshold to score on the credit information index was 0.1%. In Doing Business 2015 this threshold has been increased to 5%.)\n\nData are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected. For more information on methodology, see http://www.doingbusiness.org/Methodology/getting-credit#legalRights." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.CRD.PRVT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "World Bank Group?research informs the actions of policymakers,?helps countries?make better-informed decisions,?and?allows stakeholders to measure economic and social?improvements more accurately.?Such?research has also been a?valuable tool?for?the private sector,?civil society, academia,?journalists, and others, broadening understanding of global issues. https://www.worldbank.org/en/news/statement/2021/09/16/world-bank-group-to-discontinue-doing-business-report.\n\ndata irregularities on Doing Business 2018 and 2020 reported?internally in June 2020, World Bank management?paused?the next Doing Business report and?initiated?a series of?reviews?and?audits?of the report and its methodology. World Bank Group?management?on findings took a decision to?discontinue the?Doing Business report. The World Bank Group however remains firmly committed to advancing the role of the private sector in development and providing support to governments to design the regulatory environment that supports this. Going forward, the Bank will be working on a new approach to assessing the business and investment climate." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Private credit bureau coverage (% of adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Longdefinition", "value": "Private credit bureau coverage reports the number of individuals or firms listed by a private credit bureau with current information on repayment history, unpaid debts, or credit outstanding. The number is expressed as a percentage of the adult population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Doing Business project, World Bank (WB), uri: http://www.doingbusiness.org/, note: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data irregularities on Doing Business 2018 and 2020 reported internally in June 2020, World Bank management paused the next Doing Business report and initiated a series of reviews and audits of the report and its methodology. World Bank Group management on findings took a decision to discontinue the Doing Business report. \nThe World Bank Group however remains firmly committed to advancing the role of the private sector in development and providing support to governments to design the regulatory environment that supports this. Going forward, the Bank will be working on a new approach to assessing the business and investment climate." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentile" } ], "source_id": "57" }, { "id": "IC.CRD.PUBL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year. Data starting in 2013 reflect the DB15-17 methodology change." }, { "id": "IndicatorName", "value": "Public credit registry coverage (% of adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Public credit registry coverage reports the number of individuals and firms listed in a public credit registry with current information on repayment history, unpaid debts, or credit outstanding. The number is expressed as a percentage of the adult population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Please see: https://www.doingbusiness.org/en/methodology/getting-credit" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.CUS.DURS.EX", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nOpen markets allow firms to expand, raise standards for efficiency on exporters, and enable firms to import low cost supplies. However, trading also forces firms to deal with customs services and trade regulations, obtain export and import licenses, and in some cases, firms also face additional costs due to losses during transport. The Enterprise Surveys collect information on the operational constraints faced by exporters and importers and quantifies the trade activity of firms. Indicators provide a measure of the intensity of foreign trade in the private sector." }, { "id": "IndicatorName", "value": "Average time to clear exports through customs (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average number of days to clear direct exports through customs." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Ad hoc [adhoc]" }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data, note: All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Number of days" } ], "source_id": "57" }, { "id": "IC.CUS.DURS.IM", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Average time to clear imports through customs (days)" }, { "id": "Longdefinition", "value": "Average number of days to clear imports from customs in the manufacturing sector." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data , note: All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.ELC.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nA strong infrastructure enhances the competitiveness of an economy and generates a business environment conducive to firm growth and development. Good infrastructure efficiently connects firms to their customers and suppliers, and enables the use of modern production technologies. Conversely, deficiencies in infrastructure create barriers to productive opportunities and increase costs for all firms, from micro enterprises to large multinational corporations. \n\nThe Enterprise Surveys capture the dual challenge of providing a strong infrastructure for electricity, water supply, internet connections, etc., and the development of institutions that effectively provide and maintain public services. These indicators show the extent to which firms are faced with failures in the provision of electricity and the effect of these failures on sales. Inadequate electricity supply can increase costs, disrupt production, and reduce profitability. Additionally, these indicators measure the efficiency of the water supply for the manufacturing sector. Many manufacturing sectors depend on reliable and efficient sources of water for their operations. The indicators can also be used to evaluate the efficiency of infrastructure services by quantifying the delays in obtaining electricity, water, and telephone connections. Service delays impose additional costs on firms and may act as barriers to entry and investment." }, { "id": "IndicatorName", "value": "Time to obtain an electrical connection (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average wait, in days, experienced to obtain electrical connection from the day this establishment applied for it to the day it received the service." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Number of days" } ], "source_id": "57" }, { "id": "IC.ELC.OUTG", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Firms evaluating investment options, governments interested in improving business conditions, and economists seeking to explain economic performance have all grappled with defining and measuring the business environment. The firm-level data from Enterprise Surveys provide a useful tool for benchmarking economies across a large number of indicators measured at the firm level.\n\nInternational trade can be beneficial for firms in terms of less expensive inputs for manufacturing and new markets for exporting finished products and services. Time spent waiting for imports and exports to clear customs can be costly for firms and deter them from engaging in trade or making them uncompetitive globally." }, { "id": "IndicatorName", "value": "Power outages in firms in a typical month (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Longdefinition", "value": "Power outages are the average number of power outages that establishments experience in a typical month." }, { "id": "Notesfromoriginalsource", "value": "All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: http://www.enterprisesurveys.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Firm-level surveys have been conducted since the 1990's by different units within the World Bank. Since 2005-06, most data collection efforts have been centralized within the Enterprise Analysis Unit. Surveys implemented by the Enterprise Analysis Unit follow the Global Methodology.\n\nPrivate contractors conduct the Enterprise Surveys on behalf of the World Bank. Due to sensitive survey questions addressing business-government relations and bribery-related topics, private contractors, rather than any government agency or an organization/institution associated with government, are hired by the World Bank to collect the data.\n\nConfidentiality of the survey respondents and the sensitive information they provide is necessary to ensure the greatest degree of survey participation, integrity and confidence in the quality of the data. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but confidentiality is never compromised.\n\nThe Enterprise Survey is answered by business owners and top managers. Sometimes the survey respondent calls company accountants and human resource managers into the interview to answer questions in the sales and labor sections of the survey. Typically 1200-1800 interviews are conducted in larger economies, 360 interviews are conducted in medium-sized economies, and for smaller economies, 150 interviews take place.\n\nThe manufacturing and services sectors are the primary business sectors of interest. This corresponds to firms classified with ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies with 5 or more employees are targeted for interview. Services firms include construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government/state ownership are not eligible to participate in an Enterprise Survey. Occasionally, for a few surveyed countries, other sectors are included in the companies surveyed such as education or health-related businesses. In each country, businesses in the cities/regions of major economic activity are interviewed.\n\nIn some countries, other surveys, which depart from the usual Enterprise Survey methodology, are conducted. Examples include 1) Informal Surveys- surveys of informal (unregistered) enterprises, 2) Micro Surveys- surveys fielded to registered firms with less than five employees, and 3) Financial Crisis Assessment Surveys- short surveys administered by telephone to assess the effects of the global financial crisis of 2008-09.\n\nThe Enterprise Surveys Unit uses two instruments: the Manufacturing Questionnaire and the Services Questionnaire. Although many questions overlap, some are only applicable to one type of business. For example, retail firms are not asked about production and nonproduction workers.\n\nThe standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.\n\nOver 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Number of Months" } ], "source_id": "57" }, { "id": "IC.ELC.OUTG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nA strong infrastructure enhances the competitiveness of an economy and generates a business environment conducive to firm growth and development. Good infrastructure efficiently connects firms to their customers and suppliers, and enables the use of modern production technologies. Conversely, deficiencies in infrastructure create barriers to productive opportunities and increase costs for all firms, from micro enterprises to large multinational corporations. \n\nThe Enterprise Surveys capture the dual challenge of providing a strong infrastructure for electricity, water supply, internet connections, etc., and the development of institutions that effectively provide and maintain public services. These indicators show the extent to which firms are faced with failures in the provision of electricity and the effect of these failures on sales. Inadequate electricity supply can increase costs, disrupt production, and reduce profitability. Additionally, these indicators measure the efficiency of the water supply for the manufacturing sector. Many manufacturing sectors depend on reliable and efficient sources of water for their operations. The indicators can also be used to evaluate the efficiency of infrastructure services by quantifying the delays in obtaining electricity, water, and telephone connections. Service delays impose additional costs on firms and may act as barriers to entry and investment." }, { "id": "IndicatorName", "value": "Firms experiencing electrical outages (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms that experienced power outages over the last complete fiscal year." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.ELC.TIME", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time required to get electricity (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Time required to get electricity is the number of days to obtain a permanent electricity connection. The measure captures the median duration that the electricity utility and experts indicate is necessary in practice, rather than required by law, to complete a procedure." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nData records all procedures required for a business to obtain a permanent electricity connection. These procedures include applications and contracts with electricity utilities, all necessary inspections and clearances from the utility and other agencies and the external and final connection works.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected. Data starting in 2014 reflect the DB15-17 methodology change. Please also see: https://www.doingbusiness.org/en/methodology/getting-electricity" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.EMP.FIRE.WK", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Firing cost (weeks of wages)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Firing cost is the cost of advanced notice requirements, severance payments, and penalties due when terminating a redundant worker, expressed in weekly wages. One month is recorded as 4 1/3 weeks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.EXP.COST.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The Doing Business report was first published in 2003 with five indicator sets measuring business regulation in 133 economies. The report has grown into an annual publication covering 11 indicator sets and 189 economies. In these 10 years Doing Business has recorded nearly 2,000 business regulation reforms in the areas covered by the indicators and researchers have produced well over 1,000 articles in peer-reviewed journals using the data published by Doing Business - work that helps explore many of the key development questions of our time.\n\nThe Doing Business indicators points to important trends in regulatory reform and identifies the regions and economies making the biggest improvements for local entrepreneurs. It highlights both the areas of business regulation that have received the most attention and those where more progress remains to be made. The report also reviews research on which regulatory reforms have worked and how. Among the highlights are smarter business regulation supports economic growth, simpler business registration promotes greater entrepreneurship and firm productivity, while lower-cost registration improves formal employment opportunities, an effective regulatory environment boosts trade performance, and sound financial market infrastructure - courts, creditor and insolvency laws, and credit and collateral registries - improves access to credit." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Cost to export (US$ per container)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the costs to export or import are from the World Bank's Doing Business surveys, which compile procedural requirements for exporting and importing a standardized cargo of goods by ocean transport from local freight forwarders, shipping lines, customs brokers, port officials, and banks.\n\nThe procedural requirements for exporting and importing goods involved filling out several required documents. These documents are associated with every official procedure - from the contractual agreement between the 2 parties to the delivery of goods, and needs to be filled and submitted in a timely manner.\n\nSeveral assumptions about the business and traded goods are used. For the business, it is assumed that it has at least 60 employees, located in the economy's largest business city, is a private limited-liability company, is 100% domestically owned, and exports more than 10% of its sales. For the traded goods, it is assumed that the product travels in a dry-cargo, 20-foot, full container load, weighs 10 tons, and valued at $20,000. The assumptions about the product being traded are that it is not hazardous or include any military items, does not require refrigeration or any other special environment, requires only the internationally accepted safety standards, and is one of the economy's leading export or import products.\n\nTrade facilitation encompasses customs efficiency and other physical and regulatory environments where trade takes place, harmonization of standards and conformance to international regulations, and the logistics of moving goods and associated documentation through countries and ports. Though collection of trade facilitation data has improved over the last decade, data that allow meaningful evaluation, especially for developing economies, are lacking." }, { "id": "Longdefinition", "value": "Cost measures the fees levied on a 20-foot container in U.S. dollars. All the fees associated with completing the procedures to export or import the goods are included. These include costs for documents, administrative fees for customs clearance and technical control, customs broker fees, terminal handling charges and inland transport. The cost measure does not include tariffs or trade taxes. Only official costs are recorded. Several assumptions are made for the business surveyed: Has 60 or more employees; Is located in the country's most populous city; Is a private, limited liability company. It does not operate within an export processing zone or an industrial estate with special export or import privileges; Is domestically owned with no foreign ownership; Exports more than 10% of its sales. Assumptions about the traded goods: The traded product travels in a dry-cargo, 20-foot, full container load. The product: Is not hazardous nor does it include military items; Does not require refrigeration or any other special environment; Does not require any special phytosanitary or environmental safety standards other than accepted international standards." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Cost measures the fees levied on a 20-foot container in U.S. dollars. All the fees associated with completing the procedures to export or import the goods are included. These include costs for documents, administrative fees for customs clearance and technical control, customs broker fees, terminal handling charges and inland transport. The cost measure does not include tariffs or trade taxes. Only official costs are recorded." }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "The World Bank's Doing Business measures the time and cost (excluding tariffs) associated with exporting and importing a standardized cargo of goods by sea transport. The time and cost necessary to complete every official procedure for exporting and importing the goods are recorded; however, the time and cost for sea transport are not included. All documents needed by the trader to export or import the goods across the border are also recorded. For exporting goods, procedures range from packing the goods into the container at the warehouse to their departure from the port of exit. For importing goods, procedures range from the vessel's arrival at the port of entry to the cargo's delivery at the warehouse. For landlocked economies, these include procedures at the inland border post, since the port is located in the transit economy. Payment is made by letter of credit, and the time, cost and documents required for the issuance or advising of a letter of credit are taken into account. The ranking on the ease of trading across borders is the simple average of the percentile rankings on its component indicators.\n\nLocal freight forwarders, shipping lines, customs brokers, port officials and banks provide information on required documents and cost as well as the time to complete each procedure. To make the data comparable across economies, several assumptions about the business and the traded goods are used.\n\nCost measures the fees levied on a 20-foot container in U.S. dollars. All the fees associated with completing the procedures to export or import the goods are taken into account. These include costs for documents, administrative fees for customs clearance and inspections, customs broker fees, port-related charges and inland transport costs. The cost does not include customs tariffs and duties or costs related to sea transport. Only official costs are recorded." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.EXP.CSBC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Insurance cost and informal payments for which no receipt is issued are excluded from the costs recorded. Costs are reported in U.S. dollars. Contributors are asked to convert local currency into U.S. dollars based on the exchange rate prevailing on the day they answer the questionnaire. Contributors are private sector experts in international trade logistics and are informed about exchange rates and their movements.\n\nData are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Cost to export, border compliance (US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "If inspections by agencies other than customs are conducted in 20% or fewer cases, the border compliance time and cost measures take into account only clearance and inspections by customs (the standard case). If inspections by other agencies take place in more than 20% of cases, the time and cost measures account for clearance and inspections by all agencies. Different types of inspections may take place with different probabilities—for example, scanning may take place in 100% of cases while physical inspection occurs in 5% of cases. In situations like this, Doing Business would count the time only for scanning because it happens in more than 20% of cases while physical inspection does not. The border compliance time and cost for an economy do not include the time and cost for compliance with the regulations of any other economy." }, { "id": "Longdefinition", "value": "Border compliance captures the time and cost associated with compliance with the economy’s customs regulations and with regulations relating to other inspections that are mandatory in order for the shipment to cross the economy’s border, as well as the time and cost for handling that takes place at its port or border. The time and cost for this segment include time and cost for customs clearance and inspection procedures conducted by other government agencies." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "The computation of border compliance time and cost depends on where the border compliance procedures take place, who requires and conducts the procedures and what is the probability that inspections will be conducted. If all customs clearance and other inspections take place at the port or border, the time estimate for border compliance takes this simultaneity into account. It is entirely possible that the border compliance time and cost could be negligible or zero, as in the case of trade between members of the European Union or other customs unions.\n\nIf some or all customs or other inspections take place at other locations, the time and cost for these procedures are added to the time and cost for those that take place at the port or border. In Kazakhstan, for example, all customs clearance and inspections take place at a customs post in Almaty that is not at the land border between Kazakhstan and China. In this case border compliance time is the sum of the time spent at the terminal in Almaty and the handling time at the border.\n\nDoing Business asks contributors to estimate the time and cost for clearance and inspections by customs agencies— defined as documentary and physical inspections for the purpose of calculating duties by verifying product classification, confirming quantity, determining origin and checking the veracity of other information on the customs declaration. (This category includes all inspections aimed at preventing smuggling.) These are clearance and inspection procedures that take place in the majority of cases and thus are considered the \"standard\" case. The time and cost estimates capture the efficiency of the customs agency of the economy.\n\nDoing Business also asks contributors to estimate the total time and cost for clearance and inspections by customs and all other government agencies for the specified product. These estimates account for inspections related to health, safety, phytosanitary standards, conformity and the like, and thus capture the efficiency of agencies that require and conduct these additional inspections." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "IC.EXP.CSDC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Insurance cost and informal payments for which no receipt is issued are excluded from the costs recorded. Costs are reported in U.S. dollars. Contributors are asked to convert local currency into U.S. dollars based on the exchange rate prevailing on the day they answer the questionnaire. Contributors are private sector experts in international trade logistics and are informed about exchange rates and their movements.\n\nData are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Cost to export, documentary compliance (US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Documentary compliance captures the time and cost associated with compliance with the documentary requirements of all government agencies of the origin economy, the destination economy and any transit economies. The aim is to measure the total burden of preparing the bundle of documents that will enable completion of the international trade for the product and partner pair assumed in the case study." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "The time and cost for documentary compliance include the time and cost for obtaining documents (such as time spent to get the document issued and stamped); preparing documents (such as time spent gathering information to complete the customs declaration or certificate of origin); processing documents (such as time spent waiting for the relevant authority to issue a phytosanitary certificate); presenting documents (such as time spent showing a port terminal receipt to port authorities); and submitting documents (such as time spent submitting a customs declaration to the customs agency in person or electronically).\n\nAll electronic or paper submissions of information requested by any government agency in connection with the shipment are considered to be documents obtained, prepared and submitted during the export or import process. All documents prepared by the freight forwarder or customs broker for the product and partner pair assumed in the case study are included regardless of whether they are required by law or in practice. Any documents prepared and submitted so as to get access to preferential treatment— for example, a certificate of origin—are included in the calculation of the time and cost for documentary compliance. Any documents prepared and submitted because of a perception that they ease the passage of the shipment are also included (for example, freight forwarders may prepare a packing list because in their experience this reduces the probability of physical or other intrusive inspections).\n\nIn addition, any documents that are mandatory for exporting or importing are included in the calculation of time and cost. Documents that need to be obtained only once are not counted, however. And Doing Business does not include documents needed to produce and sell in the domestic market—such as certificates of third-party safety standards testing that may be required to sell toys domestically—unless a government agency needs to see these documents during the export process." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "IC.EXP.DOCS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The Doing Business report was first published in 2003 with five indicator sets measuring business regulation in 133 economies. The report has grown into an annual publication covering 11 indicator sets and 189 economies. In these 10 years Doing Business has recorded nearly 2,000 business regulation reforms in the areas covered by the indicators and researchers have produced well over 1,000 articles in peer-reviewed journals using the data published by Doing Business - work that helps explore many of the key development questions of our time.\n\nThe Doing Business indicators points to important trends in regulatory reform and identifies the regions and economies making the biggest improvements for local entrepreneurs. It highlights both the areas of business regulation that have received the most attention and those where more progress remains to be made. The report also reviews research on which regulatory reforms have worked and how. Among the highlights are smarter business regulation supports economic growth, simpler business registration promotes greater entrepreneurship and firm productivity, while lower-cost registration improves formal employment opportunities, an effective regulatory environment boosts trade performance, and sound financial market infrastructure - courts, creditor and insolvency laws, and credit and collateral registries - improves access to credit." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Documents to export (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the number of documents needed to export or import are from the World Bank's Doing Business surveys, which compile procedural requirements for exporting and importing a standardized cargo of goods by ocean transport from local freight forwarders, shipping lines, customs brokers, port officials, and banks.\n\nThe procedural requirements for exporting and importing goods involved filling out several required documents. These documents are associated with every official procedure - from the contractual agreement between the 2 parties to the delivery of goods, and needs to be filled and submitted in a timely manner.\n\nSeveral assumptions about the business and traded goods are used. For the business, it is assumed that it has at least 60 employees, located in the economy's largest business city, is a private limited-liability company, is 100% domestically owned, and exports more than 10% of its sales. For the traded goods, it is assumed that the product travels in a dry-cargo, 20-foot, full container load, weighs 10 tons, and valued at $20,000. The assumptions about the product being traded are that it is not hazardous or include any military items, does not require refrigeration or any other special environment, requires only the internationally accepted safety standards, and is one of the economy's leading export or import products.\n\nTrade facilitation encompasses customs efficiency and other physical and regulatory environments where trade takes place, harmonization of standards and conformance to international regulations, and the logistics of moving goods and associated documentation through countries and ports. Though collection of trade facilitation data has improved over the last decade, data that allow meaningful evaluation, especially for developing economies, are lacking." }, { "id": "Longdefinition", "value": "All documents required per shipment to export goods are recorded. It is assumed that the contract has already been agreed upon and signed by both parties. Documents required for clearance by government ministries, customs authorities, port and container terminal authorities, health and technical control agencies and banks are taken into account. Since payment is by letter of credit, all documents required by banks for the issuance or securing of a letter of credit are also taken into account. Documents that are renewed annually and that do not require renewal per shipment (for example, an annual tax clearance certificate) are not included." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Documents to export are all documents required per shipment by government ministries, customs authorities, port and container terminals, health and technical control agencies, and banks to export goods. Documents renewed annually and not requiring renewal per shipment are excluded." }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "The World Bank's Doing Business measures the time and cost (excluding tariffs) associated with exporting and importing a standardized cargo of goods by sea transport. The time and cost necessary to complete every official procedure for exporting and importing the goods are recorded; however, the time and cost for sea transport are not included. All documents needed by the trader to export or import the goods across the border are also recorded. For exporting goods, procedures range from packing the goods into the container at the warehouse to their departure from the port of exit. For importing goods, procedures range from the vessel's arrival at the port of entry to the cargo's delivery at the warehouse. For landlocked economies, these include procedures at the inland border post, since the port is located in the transit economy. Payment is made by letter of credit, and the time, cost and documents required for the issuance or advising of a letter of credit are taken into account. The ranking on the ease of trading across borders is the simple average of the percentile rankings on its component indicators. \n\nLocal freight forwarders, shipping lines, customs brokers, port officials and banks provide information on required documents and cost as well as the time to complete each procedure. To make the data comparable across economies, several assumptions about the business and the traded goods are used.\n\nAll documents required per shipment to export and import the goods are recorded. It is assumed that a new contract is drafted per shipment and that the contract has already been agreed upon and executed by both parties. Documents required for clearance by relevant agencies - including government ministries, customs, port authorities and other control agencies - are taken into account. Since payment is by letter of credit, all documents required by banks for the issuance or securing of a letter of credit are also taken into account. Documents that are requested at the time of clearance but that are valid for a year or longer and do not require renewal per shipment (for example, an annual tax clearance certificate) are not included.\n\nDocuments to export include bank documents, customs clearance documents, port and terminal handling documents, and transport documents." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "IC.EXP.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The Doing Business report was first published in 2003 with five indicator sets measuring business regulation in 133 economies. The report has grown into an annual publication covering 11 indicator sets and 189 economies. In these 10 years Doing Business has recorded nearly 2,000 business regulation reforms in the areas covered by the indicators and researchers have produced well over 1,000 articles in peer-reviewed journals using the data published by Doing Business - work that helps explore many of the key development questions of our time.\n\nThe Doing Business indicators points to important trends in regulatory reform and identifies the regions and economies making the biggest improvements for local entrepreneurs. It highlights both the areas of business regulation that have received the most attention and those where more progress remains to be made. The report also reviews research on which regulatory reforms have worked and how. Among the highlights are smarter business regulation supports economic growth, simpler business registration promotes greater entrepreneurship and firm productivity, while lower-cost registration improves formal employment opportunities, an effective regulatory environment boosts trade performance, and sound financial market infrastructure - courts, creditor and insolvency laws, and credit and collateral registries - improves access to credit." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time to export (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the number of days needed to export or import are from the World Bank's Doing Business surveys, which compile procedural requirements for exporting and importing a standardized cargo of goods by ocean transport from local freight forwarders, shipping lines, customs brokers, port officials, and banks.\n\nThe procedural requirements for exporting and importing goods involved filling out several required documents. These documents are associated with every official procedure - from the contractual agreement between the 2 parties to the delivery of goods, and needs to be filled and submitted in a timely manner.\n\nSeveral assumptions about the business and traded goods are used. For the business, it is assumed that it has at least 60 employees, located in the economy's largest business city, is a private limited-liability company, is 100% domestically owned, and exports more than 10% of its sales. For the traded goods, it is assumed that the product travels in a dry-cargo, 20-foot, full container load, weighs 10 tons, and valued at $20,000. The assumptions about the product being traded are that it is not hazardous or include any military items, does not require refrigeration or any other special environment, requires only the internationally accepted safety standards, and is one of the economy's leading export or import products.\n\nTrade facilitation encompasses customs efficiency and other physical and regulatory environments where trade takes place, harmonization of standards and conformance to international regulations, and the logistics of moving goods and associated documentation through countries and ports. Though collection of trade facilitation data has improved over the last decade, data that allow meaningful evaluation, especially for developing economies, are lacking." }, { "id": "Longdefinition", "value": "Time to export is the time necessary to comply with all procedures required to export goods. Time is recorded in calendar days. The time calculation for a procedure starts from the moment it is initiated and runs until it is completed. If a procedure can be accelerated for an additional cost, the fastest legal procedure is chosen. It is assumed that neither the exporter nor the importer wastes time and that each commits to completing each remaining procedure without delay. Procedures that can be completed in parallel are measured as simultaneous. The waiting time between procedures--for example, during unloading of the cargo--is included in the measure." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "The World Bank's Doing Business measures the time and cost (excluding tariffs) associated with exporting and importing a standardized cargo of goods by sea transport. The time and cost necessary to complete every official procedure for exporting and importing the goods are recorded; however, the time and cost for sea transport are not included. All documents needed by the trader to export or import the goods across the border are also recorded. For exporting goods, procedures range from packing the goods into the container at the warehouse to their departure from the port of exit. For importing goods, procedures range from the vessel's arrival at the port of entry to the cargo's delivery at the warehouse. For landlocked economies, these include procedures at the inland border post, since the port is located in the transit economy. Payment is made by letter of credit, and the time, cost and documents required for the issuance or advising of a letter of credit are taken into account. The ranking on the ease of trading across borders is the simple average of the percentile rankings on its component indicators.\n\nLocal freight forwarders, shipping lines, customs brokers, port officials and banks provide information on required documents and cost as well as the time to complete each procedure. To make the data comparable across economies, several assumptions about the business and the traded goods are used.\n\nThe time for exporting and importing is recorded in calendar days. The time calculation for a procedure starts from the moment it is initiated and runs until it is completed. If a procedure can be accelerated for an additional cost and is available to all trading companies, the fastest legal procedure is chosen. Fast-track procedures applying only to firms located in an export processing zone, or only to certain accredited firms under authorized economic operator programs, are not taken into account because they are not available to all trading companies.\n\nIt is assumed that neither the exporter nor the importer wastes time and that each commits to completing each remaining procedure without delay. Procedures that can be completed in parallel are measured as simultaneous. But it is assumed that document preparation, inland transport, customs and other clearance, and port and terminal handling require a minimum time of 1 day each and cannot take place simultaneously. The waiting time between procedures - for example, during unloading of the cargo - is included in the measure." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "IC.EXP.TMBC", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Time is measured in hours, and 1 day is 24 hours (for example, 22 days are recorded as 22 × 24 = 528 hours). If customs clearance takes 7.5 hours, the data are recorded as is. Alternatively, suppose that documents are submitted to a customs agency at 8:00 a.m., are processed overnight and can be picked up at 8:00 a.m. the next day. In this case the time for customs clearance would be recorded as 24 hours because the actual procedure took 24 hours.\n\nData are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time to export, border compliance (hours)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "If inspections by agencies other than customs are conducted in 20% or fewer cases, the border compliance time and cost measures take into account only clearance and inspections by customs (the standard case). If inspections by other agencies take place in more than 20% of cases, the time and cost measures account for clearance and inspections by all agencies. Different types of inspections may take place with different probabilities—for example, scanning may take place in 100% of cases while physical inspection occurs in 5% of cases. In situations like this, Doing Business would count the time only for scanning because it happens in more than 20% of cases while physical inspection does not. The border compliance time and cost for an economy do not include the time and cost for compliance with the regulations of any other economy." }, { "id": "Longdefinition", "value": "Border compliance captures the time and cost associated with compliance with the economy’s customs regulations and with regulations relating to other inspections that are mandatory in order for the shipment to cross the economy’s border, as well as the time and cost for handling that takes place at its port or border. The time and cost for this segment include time and cost for customs clearance and inspection procedures conducted by other government agencies." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "The computation of border compliance time and cost depends on where the border compliance procedures take place, who requires and conducts the procedures and what is the probability that inspections will be conducted. If all customs clearance and other inspections take place at the port or border, the time estimate for border compliance takes this simultaneity into account. It is entirely possible that the border compliance time and cost could be negligible or zero, as in the case of trade between members of the European Union or other customs unions.\n\nIf some or all customs or other inspections take place at other locations, the time and cost for these procedures are added to the time and cost for those that take place at the port or border. In Kazakhstan, for example, all customs clearance and inspections take place at a customs post in Almaty that is not at the land border between Kazakhstan and China. In this case border compliance time is the sum of the time spent at the terminal in Almaty and the handling time at the border.\n\nDoing Business asks contributors to estimate the time and cost for clearance and inspections by customs agencies— defined as documentary and physical inspections for the purpose of calculating duties by verifying product classification, confirming quantity, determining origin and checking the veracity of other information on the customs declaration. (This category includes all inspections aimed at preventing smuggling.) These are clearance and inspection procedures that take place in the majority of cases and thus are considered the \"standard\" case. The time and cost estimates capture the efficiency of the customs agency of the economy.\n\nDoing Business also asks contributors to estimate the total time and cost for clearance and inspections by customs and all other government agencies for the specified product. These estimates account for inspections related to health, safety, phytosanitary standards, conformity and the like, and thus capture the efficiency of agencies that require and conduct these additional inspections." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "IC.EXP.TMDC", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Time is measured in hours, and 1 day is 24 hours (for example, 22 days are recorded as 22 × 24 = 528 hours). If customs clearance takes 7.5 hours, the data are recorded as is. Alternatively, suppose that documents are submitted to a customs agency at 8:00 a.m., are processed overnight and can be picked up at 8:00 a.m. the next day. In this case the time for customs clearance would be recorded as 24 hours because the actual procedure took 24 hours.\n\nData are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time to export, documentary compliance (hours)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Documentary compliance captures the time and cost associated with compliance with the documentary requirements of all government agencies of the origin economy, the destination economy and any transit economies. The aim is to measure the total burden of preparing the bundle of documents that will enable completion of the international trade for the product and partner pair assumed in the case study." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "The time and cost for documentary compliance include the time and cost for obtaining documents (such as time spent to get the document issued and stamped); preparing documents (such as time spent gathering information to complete the customs declaration or certificate of origin); processing documents (such as time spent waiting for the relevant authority to issue a phytosanitary certificate); presenting documents (such as time spent showing a port terminal receipt to port authorities); and submitting documents (such as time spent submitting a customs declaration to the customs agency in person or electronically).\n\nAll electronic or paper submissions of information requested by any government agency in connection with the shipment are considered to be documents obtained, prepared and submitted during the export or import process. All documents prepared by the freight forwarder or customs broker for the product and partner pair assumed in the case study are included regardless of whether they are required by law or in practice. Any documents prepared and submitted so as to get access to preferential treatment— for example, a certificate of origin—are included in the calculation of the time and cost for documentary compliance. Any documents prepared and submitted because of a perception that they ease the passage of the shipment are also included (for example, freight forwarders may prepare a packing list because in their experience this reduces the probability of physical or other intrusive inspections).\n\nIn addition, any documents that are mandatory for exporting or importing are included in the calculation of time and cost. Documents that need to be obtained only once are not counted, however. And Doing Business does not include documents needed to produce and sell in the domestic market—such as certificates of third-party safety standards testing that may be required to sell toys domestically—unless a government agency needs to see these documents during the export process." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "IC.FRM.BKWC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators of how firms finance their operations and of the characteristics of their financial transactions. For example, Enterprise Surveys provide indicators that compare the relative use of various sources to finance investment. Excessive reliance on internal funds is a sign of potentially inefficient financial intermediation. Another set of indicators measures the use of financial markets by individual firms. It presents the percentage of working capital that is financed by external sources to the firm, and a measure of the burden imposed by loan requirements measured by collateral levels relative to the value of the loans. Additional indicators focus on the use of financial services by private firms both on the credit side, by measuring the percentage of firms with bank loans or lines or credit, and on the deposit mobilization side, by measuring the percentage of firms with checking or savings accounts." }, { "id": "IndicatorName", "value": "Firms using banks to finance working capital (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms using bank loans to finance working capital." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\n\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.BNKL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms with a bank loan/line of credit (% of firms)" }, { "id": "Longdefinition", "value": "Percentage of firms that have bank loans or line of credit." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.BNKS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators of how firms finance their operations and of the characteristics of their financial transactions. For example, Enterprise Surveys provide indicators that compare the relative use of various sources to finance investment. Excessive reliance on internal funds is a sign of potentially inefficient financial intermediation. Another set of indicators measures the use of financial markets by individual firms. It presents the percentage of working capital that is financed by external sources to the firm, and a measure of the burden imposed by loan requirements measured by collateral levels relative to the value of the loans. Additional indicators focus on the use of financial services by private firms both on the credit side, by measuring the percentage of firms with bank loans or lines or credit, and on the deposit mobilization side, by measuring the percentage of firms with checking or savings accounts." }, { "id": "IndicatorName", "value": "Firms using banks to finance investment (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms using banks to finance purchases of fixed assets." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\n\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.BRIB.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nCorruption by public officials may present a major administrative and financial burden on firms. Corruption creates an unfavorable business environment by undermining the operational efficiency of firms and raising the costs and risks associated with doing business.\n\nInefficient regulations constrain firm efficiency as they present opportunities for soliciting bribes where firms are required to make “unofficial” payments to public officials to get things done. In many countries bribes are common and quite high and they add to the bureaucratic costs in obtaining required permits and licenses. They can be a serious impediment for firms’ growth and development." }, { "id": "IndicatorName", "value": "Bribery incidence (% of firms experiencing at least one bribe payment request)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percent of firms experiencing at least one bribe payment request across 6 public transactions dealing with utilities access, permits, licenses, and taxes." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.CDP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms that use a third party to resolve commercial disputes (% of firms)" }, { "id": "Longdefinition", "value": "Percentage of firms that use courts, arbitration, mediation, or conciliation to resolve or attempt to resolve its commercial disputes." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2021-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.CMPU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nA large informal sector has serious consequences for the formal private sector. The informal sector may pose unfair competition for formal firms and also deprive governments of potential tax revenue and diminish a government's capacity for regulatory oversight. The Enterprise Surveys capture key dimensions the degree of informality in an economy. For example, the set of indicators (unregistered start-ups) shows the percentage of firms that started operation without being formally registered. It approximates the prevalence of informality in the private economy." }, { "id": "IndicatorName", "value": "Firms competing against unregistered firms (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms competing against unregistered or informal firms." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.CO2.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms monitoring own CO2 emissions (% of firms)" }, { "id": "Longdefinition", "value": "Percentage of firms tracking their own CO2 emissions over the past three years" }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2021-2025" }, { "id": "Source", "value": "Enterprise Surveys , World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.CORR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nCorruption by public officials may present a major administrative and financial burden on firms. Corruption creates an unfavorable business environment by undermining the operational efficiency of firms and raising the costs and risks associated with doing business.\n\nInefficient regulations constrain firm efficiency as they present opportunities for soliciting bribes where firms are required to make “unofficial” payments to public officials to get things done. In many countries bribes are common and quite high and they add to the bureaucratic costs in obtaining required permits and licenses. They can be a serious impediment for firms’ growth and development." }, { "id": "IndicatorName", "value": "Informal payments to public officials (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of establishments that consider that firms with characteristics similar to theirs are making informal payments or giving gifts to public officials to \"get things done” with regard to customs, taxes, licenses, regulations, services etc." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\n\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.CRDC.FL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms that are fully credit constrained (% of firms)" }, { "id": "Longdefinition", "value": "Firms are categorized as fully credit constrained if they do not have access to external finance, and any of the following two conditions are met: (1) the firm did not apply for a loan for any reason other than the lack of need for it; or (2) the firm applied for a loan but the application was rejected, even when it has access to equity financing. \n\nThis indicator is based on Islam and Rodriguez Meza (2023, Islam, Asif Mohammed and Jorge Luis Rodriguez Meza. “How Prevalent Are Credit-Constrained Firms in the Formal Private Sector? Evidence Using Global Surveys”. World Bank Policy Research Working Paper; no. WPS 10502)." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2013-2025" }, { "id": "Source", "value": "Enterprise Surveys , World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.CRDC.PT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms that are partially credit constrained (% of firms)" }, { "id": "Longdefinition", "value": "Firms are categorized as partially credit constrained if any of the following conditions are met: (1) the firm applied for a loan and the application was partially approved; (2) the firm applied for a loan and the application was rejected, but the firm has access to external sources of finance excluding any equity finance; or (3) the firm has external finance but did not apply for a loan due to any reason other than no need for it. \n\nThis indicator is based on Islam and Rodriguez Meza (2023, Islam, Asif Mohammed and Jorge Luis Rodriguez Meza. “How Prevalent Are Credit-Constrained Firms in the Formal Private Sector? Evidence Using Global Surveys”. World Bank Policy Research Working Paper; no. WPS 10502)." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2013-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.CRIM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Firms evaluating investment options, governments interested in improving business conditions, and economists seeking to explain economic performance have all grappled with defining and measuring the business environment. The firm-level data from Enterprise Surveys provide a useful tool for benchmarking economies across a large number of indicators measured at the firm level.\n\nCrime imposes costs on firms when they are forced to divert resources from productive uses to cover security costs. Both foreign and domestic investors perceive crime as an indication of social instability, and crime drives up the cost of doing business. Also, commercial disputes between firms and their clients occur regularly in the course of doing business. Resolving these disputes can be challenging when legal institutions are weak or nonexistent.\n\nCrime, theft, and disorder may impose additional costs to businesses and society, and consume considerable resources." }, { "id": "IndicatorName", "value": "Losses due to theft and vandalism (% of annual sales of affected firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Longdefinition", "value": "Average losses as a result of theft, robbery, vandalism or arson that occurred on the establishment’s premises calculated as a percentage of annual sales. The value represents the average losses for all firms which reported losses (please see indicator IC.FRM.THEV.ZS)." }, { "id": "Notesfromoriginalsource", "value": "All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: http://www.enterprisesurveys.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Firm-level surveys have been conducted since the 1990's by different units within the World Bank. Since 2005-06, most data collection efforts have been centralized within the Enterprise Analysis Unit. Surveys implemented by the Enterprise Analysis Unit follow the Global Methodology.\n\nPrivate contractors conduct the Enterprise Surveys on behalf of the World Bank. Due to sensitive survey questions addressing business-government relations and bribery-related topics, private contractors, rather than any government agency or an organization/institution associated with government, are hired by the World Bank to collect the data.\n\nConfidentiality of the survey respondents and the sensitive information they provide is necessary to ensure the greatest degree of survey participation, integrity and confidence in the quality of the data. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but confidentiality is never compromised.\n\nThe Enterprise Survey is answered by business owners and top managers. Sometimes the survey respondent calls company accountants and human resource managers into the interview to answer questions in the sales and labor sections of the survey. Typically 1200-1800 interviews are conducted in larger economies, 360 interviews are conducted in medium-sized economies, and for smaller economies, 150 interviews take place.\n\nThe manufacturing and services sectors are the primary business sectors of interest. This corresponds to firms classified with ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies with 5 or more employees are targeted for interview. Services firms include construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government/state ownership are not eligible to participate in an Enterprise Survey. Occasionally, for a few surveyed countries, other sectors are included in the companies surveyed such as education or health-related businesses. In each country, businesses in the cities/regions of major economic activity are interviewed.\n\nIn some countries, other surveys, which depart from the usual Enterprise Survey methodology, are conducted. Examples include 1) Informal Surveys- surveys of informal (unregistered) enterprises, 2) Micro Surveys- surveys fielded to registered firms with less than five employees, and 3) Financial Crisis Assessment Surveys- short surveys administered by telephone to assess the effects of the global financial crisis of 2008-09.\n\nThe Enterprise Surveys Unit uses two instruments: the Manufacturing Questionnaire and the Services Questionnaire. Although many questions overlap, some are only applicable to one type of business. For example, retail firms are not asked about production and nonproduction workers.\n\nThe standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.\n\nOver 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews.\n\nPlease also refer to the methodology information on the Enterprise Surveys site: https://www.enterprisesurveys.org/en/methodology" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.FRM.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nGood economic governance in areas of regulations, permits and licenses are among the fundamental pillars for the creation of a favorable business environment. \n\nThe Enterprise Surveys provide qualitative and quantitative measures of regulations. For example, the Enterprise Surveys approximates the “time tax” imposed by regulations: it measures the time spent by senior management in meetings with public officials. Another indicator, the average number of visits or required meetings with tax officials, measures the average number of tax inspections or meetings with tax inspectors in each year. \n\nEffective regulations address market failures that inhibit productive investment and reconcile private and public interests. The number of permits and approvals that businesses need to obtain, and the time it takes to obtain them, are expensive and time consuming. The existing legislation of a country also determines the mix of legal forms private firms take and determines the level of protection for investors thus affecting the incentives to invest. Those indicators focus on the efficiency of business licensing and permit services. The indicators evaluate the delays faced when demanding these services." }, { "id": "IndicatorName", "value": "Time required to obtain an operating license (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The average wait, in days, to obtain an operating license, from the day of the application to the day it was granted." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2003-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "IC.FRM.ENGM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms adopting energy management measures to reduce emissions (% of firms)" }, { "id": "Longdefinition", "value": "Share of companies adopting energy-saving practices to reduce emissions over the past three years." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2021-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.EXS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms exporting directly at least 10% of sales (% of firms)" }, { "id": "Longdefinition", "value": "Percentage of firms that export directly at least 10% of their total annual sales." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.FEMM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators that describe several dimensions of gender composition in the workforce. It also collects information on the characteristics of the workforce employed in the non-agricultural private economy. The set of indicators presents the composition of the firm's workforce by type of contract and gender. Labor regulations have a direct effect on the type of employment favored by firms and they may have a different impact by gender. Other indicators present the composition of the workforce classified into temporary and permanent workers and reflect the participation of women in regular full time employment, along with the firms’ inclusion of women in formal trainings." }, { "id": "IndicatorName", "value": "Firms with female top manager (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms with females as the top manager." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity. \n\nRelevance to gender indicator: Women are vastly underrepresented in decision making positions at the top level in the private sector and this indicator monitors progress that has been made." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2007-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.FEMO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators that describe several dimensions of gender composition in the workforce. It also collects information on the characteristics of the workforce employed in the non-agricultural private economy. The set of indicators presents the composition of the firm's workforce by type of contract and gender. Labor regulations have a direct effect on the type of employment favored by firms and they may have a different impact by gender. Other indicators present the composition of the workforce classified into temporary and permanent workers and reflect the participation of women in regular full time employment, along with the firms’ inclusion of women in formal trainings." }, { "id": "IndicatorName", "value": "Firms with female participation in ownership (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms with females among the owners." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.FO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms with at least 10% foreign ownership" }, { "id": "Longdefinition", "value": "Percentage of firms that have at least 10% owned by private foreign individuals, companies or organizations." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.FREG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nA large informal sector has serious consequences for the formal private sector. The informal sector may pose unfair competition for formal firms and also deprive governments of potential tax revenue and diminish a government's capacity for regulatory oversight. The Enterprise Surveys capture key dimensions the degree of informality in an economy. For example, the set of indicators (unregistered start-ups) shows the percentage of firms that started operation without being formally registered. It approximates the prevalence of informality in the private economy." }, { "id": "IndicatorName", "value": "Firms formally registered when operations started (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms formally registered when they started operations in the country." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.INFM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Firms that do not report all sales for tax purposes (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Longdefinition", "value": "Firms that do not report all sales for tax purposes are the percentage of firms that expressed that a typical firm reports less than 100 percent of sales for tax purposes; such firms are termed \"informal firms.\"" }, { "id": "Othernotes", "value": "Statistical concept(s): The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure). The strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). \n\nSince in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity. Ideally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. \n\nIn surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets. Obtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2010" }, { "id": "Shortdefinition", "value": "Firms that do not report all sales for tax purposes are the percentage of firms that expressed that a typical firm reports less than 100 percent of sales for tax purposes; such firms are termed \"informal firms.\"" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: http://www.enterprisesurveys.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Firm-level surveys have been conducted since the 1990's by different units within the World Bank. Since 2005-06, most data collection efforts have been centralized within the Enterprise Analysis Unit. Surveys implemented by the Enterprise Analysis Unit follow the Global Methodology.\n\nPrivate contractors conduct the Enterprise Surveys on behalf of the World Bank. Due to sensitive survey questions addressing business-government relations and bribery-related topics, private contractors, rather than any government agency or an organization/institution associated with government, are hired by the World Bank to collect the data.\n\nConfidentiality of the survey respondents and the sensitive information they provide is necessary to ensure the greatest degree of survey participation, integrity and confidence in the quality of the data. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but confidentiality is never compromised.\n\nThe Enterprise Survey is answered by business owners and top managers. Sometimes the survey respondent calls company accountants and human resource managers into the interview to answer questions in the sales and labor sections of the survey. Typically 1200-1800 interviews are conducted in larger economies, 360 interviews are conducted in medium-sized economies, and for smaller economies, 150 interviews take place.\n\nThe manufacturing and services sectors are the primary business sectors of interest. This corresponds to firms classified with ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies with 5 or more employees are targeted for interview. Services firms include construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government/state ownership are not eligible to participate in an Enterprise Survey. Occasionally, for a few surveyed countries, other sectors are included in the companies surveyed such as education or health-related businesses. In each country, businesses in the cities/regions of major economic activity are interviewed.\n\nIn some countries, other surveys, which depart from the usual Enterprise Survey methodology, are conducted. Examples include 1) Informal Surveys- surveys of informal (unregistered) enterprises, 2) Micro Surveys- surveys fielded to registered firms with less than five employees, and 3) Financial Crisis Assessment Surveys- short surveys administered by telephone to assess the effects of the global financial crisis of 2008-09.\n\nThe Enterprise Surveys Unit uses two instruments: the Manufacturing Questionnaire and the Services Questionnaire. Although many questions overlap, some are only applicable to one type of business. For example, retail firms are not asked about production and nonproduction workers.\n\nThe standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.\n\nOver 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentile" } ], "source_id": "57" }, { "id": "IC.FRM.LOTM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms where largest owner is also the top manager (% of firms)" }, { "id": "Longdefinition", "value": "Percent of firms where the largest owner is also the top manager." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2009-2025" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.METG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms visited or required meetings with tax officials (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms that were visited or inspected by tax officials or were required to meet with them over the last year." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Ad hoc [adhoc]" }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.NPRD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms that introduced a new product/service and process, and spent on R&D" }, { "id": "Longdefinition", "value": "Percentage of medium and large firms that introduced a new product/service and process over last 3 years, and spent on R&D over last fiscal year (excluding small firms)." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.OUTG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nA strong infrastructure enhances the competitiveness of an economy and generates a business environment conducive to firm growth and development. Good infrastructure efficiently connects firms to their customers and suppliers, and enables the use of modern production technologies. Conversely, deficiencies in infrastructure create barriers to productive opportunities and increase costs for all firms, from micro enterprises to large multinational corporations. \n\nThe Enterprise Surveys capture the dual challenge of providing a strong infrastructure for electricity, water supply, internet connections, etc., and the development of institutions that effectively provide and maintain public services. These indicators show the extent to which firms are faced with failures in the provision of electricity and the effect of these failures on sales. Inadequate electricity supply can increase costs, disrupt production, and reduce profitability. Additionally, these indicators measure the efficiency of the water supply for the manufacturing sector. Many manufacturing sectors depend on reliable and efficient sources of water for their operations. The indicators can also be used to evaluate the efficiency of infrastructure services by quantifying the delays in obtaining electricity, water, and telephone connections. Service delays impose additional costs on firms and may act as barriers to entry and investment." }, { "id": "IndicatorName", "value": "Value lost due to electrical outages (% of sales for affected firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Losses due to electrical outages, as percentage of total annual sales. The value represents average losses for all firms which reported outages (please see indicator IC.ELC.OUTG.ZS)." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.RSDV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Firms evaluating investment options, governments interested in improving business conditions, and economists seeking to explain economic performance have all grappled with defining and measuring the business environment. The firm-level data from Enterprise Surveys provide a useful tool for benchmarking economies across a large number of indicators measured at the firm level.\n\nInformality is associated with business operations without registration. The informal sector in an economy may be a source of unfair competition to formal firms and also deprive governments of potential tax revenue and diminish a government's capacity for regulatory oversight.\n\nInformality can be defined along different dimensions such as operating without registration, income tax evasion, labor tax evasion, or operating outside the legal framework of an economy. Firms may show different degrees of informality along these dimensions which may also overlap.\n\nA large informal sector has serious consequences for the formal private sector, and may pose unfair competition for formal firms. It is an approximation to the prevalence of informality in the private economy." }, { "id": "IndicatorName", "value": "Firms that spend on R&D (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Longdefinition", "value": "Percent of firms that spend on research and development." }, { "id": "Notesfromoriginalsource", "value": "All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: http://www.enterprisesurveys.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Firm-level surveys have been conducted since the 1990's by different units within the World Bank. Since 2005-06, most data collection efforts have been centralized within the Enterprise Analysis Unit. Surveys implemented by the Enterprise Analysis Unit follow the Global Methodology.\n\nPrivate contractors conduct the Enterprise Surveys on behalf of the World Bank. Due to sensitive survey questions addressing business-government relations and bribery-related topics, private contractors, rather than any government agency or an organization/institution associated with government, are hired by the World Bank to collect the data.\n\nConfidentiality of the survey respondents and the sensitive information they provide is necessary to ensure the greatest degree of survey participation, integrity and confidence in the quality of the data. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but confidentiality is never compromised.\n\nThe Enterprise Survey is answered by business owners and top managers. Sometimes the survey respondent calls company accountants and human resource managers into the interview to answer questions in the sales and labor sections of the survey. Typically 1200-1800 interviews are conducted in larger economies, 360 interviews are conducted in medium-sized economies, and for smaller economies, 150 interviews take place.\n\nThe manufacturing and services sectors are the primary business sectors of interest. This corresponds to firms classified with ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies with 5 or more employees are targeted for interview. Services firms include construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government/state ownership are not eligible to participate in an Enterprise Survey. Occasionally, for a few surveyed countries, other sectors are included in the companies surveyed such as education or health-related businesses. In each country, businesses in the cities/regions of major economic activity are interviewed.\n\nIn some countries, other surveys, which depart from the usual Enterprise Survey methodology, are conducted. Examples include 1) Informal Surveys- surveys of informal (unregistered) enterprises, 2) Micro Surveys- surveys fielded to registered firms with less than five employees, and 3) Financial Crisis Assessment Surveys- short surveys administered by telephone to assess the effects of the global financial crisis of 2008-09.\n\nThe Enterprise Surveys Unit uses two instruments: the Manufacturing Questionnaire and the Services Questionnaire. Although many questions overlap, some are only applicable to one type of business. For example, retail firms are not asked about production and nonproduction workers.\n\nThe standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.\n\nOver 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.FRM.TAXE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Firms filling taxes electronically (% of firms)" }, { "id": "Longdefinition", "value": "Percentage of firms filing taxes electronically either fully or partially." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2021-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.FRM.THEV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Firms evaluating investment options, governments interested in improving business conditions, and economists seeking to explain economic performance have all grappled with defining and measuring the business environment. The firm-level data from Enterprise Surveys provide a useful tool for benchmarking economies across a large number of indicators measured at the firm level.\n\nInformality is associated with business operations without registration. The informal sector in an economy may be a source of unfair competition to formal firms and also deprive governments of potential tax revenue and diminish a government's capacity for regulatory oversight.\n\nInformality can be defined along different dimensions such as operating without registration, income tax evasion, labor tax evasion, or operating outside the legal framework of an economy. Firms may show different degrees of informality along these dimensions which may also overlap.\n\nA large informal sector has serious consequences for the formal private sector, and may pose unfair competition for formal firms. It is an approximation to the prevalence of informality in the private economy." }, { "id": "IndicatorName", "value": "Firms experiencing losses due to theft and vandalism (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Longdefinition", "value": "Percent of firms experiencing losses due to theft, robbery, vandalism or arson that occurred on the establishment's premises." }, { "id": "Notesfromoriginalsource", "value": "All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: http://www.enterprisesurveys.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Firm-level surveys have been conducted since the 1990's by different units within the World Bank. Since 2005-06, most data collection efforts have been centralized within the Enterprise Analysis Unit. Surveys implemented by the Enterprise Analysis Unit follow the Global Methodology.\n\nPrivate contractors conduct the Enterprise Surveys on behalf of the World Bank. Due to sensitive survey questions addressing business-government relations and bribery-related topics, private contractors, rather than any government agency or an organization/institution associated with government, are hired by the World Bank to collect the data.\n\nConfidentiality of the survey respondents and the sensitive information they provide is necessary to ensure the greatest degree of survey participation, integrity and confidence in the quality of the data. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but confidentiality is never compromised.\n\nThe Enterprise Survey is answered by business owners and top managers. Sometimes the survey respondent calls company accountants and human resource managers into the interview to answer questions in the sales and labor sections of the survey. Typically 1200-1800 interviews are conducted in larger economies, 360 interviews are conducted in medium-sized economies, and for smaller economies, 150 interviews take place.\n\nThe manufacturing and services sectors are the primary business sectors of interest. This corresponds to firms classified with ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies with 5 or more employees are targeted for interview. Services firms include construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government/state ownership are not eligible to participate in an Enterprise Survey. Occasionally, for a few surveyed countries, other sectors are included in the companies surveyed such as education or health-related businesses. In each country, businesses in the cities/regions of major economic activity are interviewed.\n\nIn some countries, other surveys, which depart from the usual Enterprise Survey methodology, are conducted. Examples include 1) Informal Surveys- surveys of informal (unregistered) enterprises, 2) Micro Surveys- surveys fielded to registered firms with less than five employees, and 3) Financial Crisis Assessment Surveys- short surveys administered by telephone to assess the effects of the global financial crisis of 2008-09.\n\nThe Enterprise Surveys Unit uses two instruments: the Manufacturing Questionnaire and the Services Questionnaire. Although many questions overlap, some are only applicable to one type of business. For example, retail firms are not asked about production and nonproduction workers.\n\nThe standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.\n\nOver 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.FRM.TRNG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators that describe information on the characteristics of the workforce employed in the non-agricultural private economy. The set of indicators presents the composition of the firm's workforce by type of contract and gender, the composition of the workforce classified into temporary and permanent workers, and reflects the participation of women in regular full-time employment. Labor regulations have a direct effect on the type of employment favored by firms and they may have a different impact by gender." }, { "id": "IndicatorName", "value": "Firms offering formal training (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms offering formal training programs for its permanent, full-time employees." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Ad hoc [adhoc]" }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.GOV.DURS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nGood economic governance in areas of regulations, permits and licenses are among the fundamental pillars for the creation of a favorable business environment. \n\nThe Enterprise Surveys provide qualitative and quantitative measures of regulations. For example, the Enterprise Surveys approximates the “time tax” imposed by regulations: it measures the time spent by senior management in meetings with public officials. Another indicator, the average number of visits or required meetings with tax officials, measures the average number of tax inspections or meetings with tax inspectors in each year. \n\nEffective regulations address market failures that inhibit productive investment and reconcile private and public interests. The number of permits and approvals that businesses need to obtain, and the time it takes to obtain them, are expensive and time consuming. The existing legislation of a country also determines the mix of legal forms private firms take and determines the level of protection for investors thus affecting the incentives to invest. Those indicators focus on the efficiency of business licensing and permit services. The indicators evaluate the delays faced when demanding these services." }, { "id": "IndicatorName", "value": "Time spent dealing with the requirements of government regulations (% of senior management time)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average percentage of senior management’s time that is spent in a typical week dealing with requirements imposed by government regulations (eg. Taxes, customs, labor regulations, licensing and registration), including dealings with officials, completing forms, et cetera." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.IMP.COST.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The Doing Business report was first published in 2003 with five indicator sets measuring business regulation in 133 economies. The report has grown into an annual publication covering 11 indicator sets and 189 economies. In these 10 years Doing Business has recorded nearly 2,000 business regulation reforms in the areas covered by the indicators and researchers have produced well over 1,000 articles in peer-reviewed journals using the data published by Doing Business - work that helps explore many of the key development questions of our time.\n\nThe Doing Business indicators points to important trends in regulatory reform and identifies the regions and economies making the biggest improvements for local entrepreneurs. It highlights both the areas of business regulation that have received the most attention and those where more progress remains to be made. The report also reviews research on which regulatory reforms have worked and how. Among the highlights are smarter business regulation supports economic growth, simpler business registration promotes greater entrepreneurship and firm productivity, while lower-cost registration improves formal employment opportunities, an effective regulatory environment boosts trade performance, and sound financial market infrastructure - courts, creditor and insolvency laws, and credit and collateral registries - improves access to credit." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Cost to import (US$ per container)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the costs to export or import are from the World Bank's Doing Business surveys, which compile procedural requirements for exporting and importing a standardized cargo of goods by ocean transport from local freight forwarders, shipping lines, customs brokers, port officials, and banks.\n\nThe procedural requirements for exporting and importing goods involved filling out several required documents. These documents are associated with every official procedure - from the contractual agreement between the 2 parties to the delivery of goods, and needs to be filled and submitted in a timely manner.\n\nSeveral assumptions about the business and traded goods are used. For the business, it is assumed that it has at least 60 employees, located in the economy's largest business city, is a private limited-liability company, is 100% domestically owned, and exports more than 10% of its sales. For the traded goods, it is assumed that the product travels in a dry-cargo, 20-foot, full container load, weighs 10 tons, and valued at $20,000. The assumptions about the product being traded are that it is not hazardous or include any military items, does not require refrigeration or any other special environment, requires only the internationally accepted safety standards, and is one of the economy's leading export or import products.\n\nTrade facilitation encompasses customs efficiency and other physical and regulatory environments where trade takes place, harmonization of standards and conformance to international regulations, and the logistics of moving goods and associated documentation through countries and ports. Though collection of trade facilitation data has improved over the last decade, data that allow meaningful evaluation, especially for developing economies, are lacking." }, { "id": "Longdefinition", "value": "Cost measures the fees levied on a 20-foot container in U.S. dollars. All the fees associated with completing the procedures to export or import the goods are included. These include costs for documents, administrative fees for customs clearance and technical control, customs broker fees, terminal handling charges and inland transport. The cost measure does not include tariffs or trade taxes. Only official costs are recorded." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "The World Bank's Doing Business measures the time and cost (excluding tariffs) associated with exporting and importing a standardized cargo of goods by sea transport. The time and cost necessary to complete every official procedure for exporting and importing the goods are recorded; however, the time and cost for sea transport are not included. All documents needed by the trader to export or import the goods across the border are also recorded. For exporting goods, procedures range from packing the goods into the container at the warehouse to their departure from the port of exit. For importing goods, procedures range from the vessel's arrival at the port of entry to the cargo's delivery at the warehouse. For landlocked economies, these include procedures at the inland border post, since the port is located in the transit economy. Payment is made by letter of credit, and the time, cost and documents required for the issuance or advising of a letter of credit are taken into account. The ranking on the ease of trading across borders is the simple average of the percentile rankings on its component indicators.\n\nLocal freight forwarders, shipping lines, customs brokers, port officials and banks provide information on required documents and cost as well as the time to complete each procedure. To make the data comparable across economies, several assumptions about the business and the traded goods are used.\n\nCost measures the fees levied on a 20-foot container in U.S. dollars. All the fees associated with completing the procedures to export or import the goods are taken into account. These include costs for documents, administrative fees for customs clearance and inspections, customs broker fees, port-related charges and inland transport costs. The cost does not include customs tariffs and duties or costs related to sea transport. Only official costs are recorded." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.IMP.CSBC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Insurance cost and informal payments for which no receipt is issued are excluded from the costs recorded. Costs are reported in U.S. dollars. Contributors are asked to convert local currency into U.S. dollars based on the exchange rate prevailing on the day they answer the questionnaire. Contributors are private sector experts in international trade logistics and are informed about exchange rates and their movements.\n\nData are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Cost to import, border compliance (US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "If inspections by agencies other than customs are conducted in 20% or fewer cases, the border compliance time and cost measures take into account only clearance and inspections by customs (the standard case). If inspections by other agencies take place in more than 20% of cases, the time and cost measures account for clearance and inspections by all agencies. Different types of inspections may take place with different probabilities—for example, scanning may take place in 100% of cases while physical inspection occurs in 5% of cases. In situations like this, Doing Business would count the time only for scanning because it happens in more than 20% of cases while physical inspection does not. The border compliance time and cost for an economy do not include the time and cost for compliance with the regulations of any other economy." }, { "id": "Longdefinition", "value": "Border compliance captures the time and cost associated with compliance with the economy’s customs regulations and with regulations relating to other inspections that are mandatory in order for the shipment to cross the economy’s border, as well as the time and cost for handling that takes place at its port or border. The time and cost for this segment include time and cost for customs clearance and inspection procedures conducted by other government agencies." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "The computation of border compliance time and cost depends on where the border compliance procedures take place, who requires and conducts the procedures and what is the probability that inspections will be conducted. If all customs clearance and other inspections take place at the port or border, the time estimate for border compliance takes this simultaneity into account. It is entirely possible that the border compliance time and cost could be negligible or zero, as in the case of trade between members of the European Union or other customs unions.\n\nIf some or all customs or other inspections take place at other locations, the time and cost for these procedures are added to the time and cost for those that take place at the port or border. In Kazakhstan, for example, all customs clearance and inspections take place at a customs post in Almaty that is not at the land border between Kazakhstan and China. In this case border compliance time is the sum of the time spent at the terminal in Almaty and the handling time at the border.\n\nDoing Business asks contributors to estimate the time and cost for clearance and inspections by customs agencies— defined as documentary and physical inspections for the purpose of calculating duties by verifying product classification, confirming quantity, determining origin and checking the veracity of other information on the customs declaration. (This category includes all inspections aimed at preventing smuggling.) These are clearance and inspection procedures that take place in the majority of cases and thus are considered the \"standard\" case. The time and cost estimates capture the efficiency of the customs agency of the economy.\n\nDoing Business also asks contributors to estimate the total time and cost for clearance and inspections by customs and all other government agencies for the specified product. These estimates account for inspections related to health, safety, phytosanitary standards, conformity and the like, and thus capture the efficiency of agencies that require and conduct these additional inspections." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "IC.IMP.CSDC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Insurance cost and informal payments for which no receipt is issued are excluded from the costs recorded. Costs are reported in U.S. dollars. Contributors are asked to convert local currency into U.S. dollars based on the exchange rate prevailing on the day they answer the questionnaire. Contributors are private sector experts in international trade logistics and are informed about exchange rates and their movements.\n\nData are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Cost to import, documentary compliance (US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Documentary compliance captures the time and cost associated with compliance with the documentary requirements of all government agencies of the origin economy, the destination economy and any transit economies. The aim is to measure the total burden of preparing the bundle of documents that will enable completion of the international trade for the product and partner pair assumed in the case study." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "The time and cost for documentary compliance include the time and cost for obtaining documents (such as time spent to get the document issued and stamped); preparing documents (such as time spent gathering information to complete the customs declaration or certificate of origin); processing documents (such as time spent waiting for the relevant authority to issue a phytosanitary certificate); presenting documents (such as time spent showing a port terminal receipt to port authorities); and submitting documents (such as time spent submitting a customs declaration to the customs agency in person or electronically).\n\nAll electronic or paper submissions of information requested by any government agency in connection with the shipment are considered to be documents obtained, prepared and submitted during the export or import process. All documents prepared by the freight forwarder or customs broker for the product and partner pair assumed in the case study are included regardless of whether they are required by law or in practice. Any documents prepared and submitted so as to get access to preferential treatment— for example, a certificate of origin—are included in the calculation of the time and cost for documentary compliance. Any documents prepared and submitted because of a perception that they ease the passage of the shipment are also included (for example, freight forwarders may prepare a packing list because in their experience this reduces the probability of physical or other intrusive inspections).\n\nIn addition, any documents that are mandatory for exporting or importing are included in the calculation of time and cost. Documents that need to be obtained only once are not counted, however. And Doing Business does not include documents needed to produce and sell in the domestic market—such as certificates of third-party safety standards testing that may be required to sell toys domestically—unless a government agency needs to see these documents during the export process." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "IC.IMP.DOCS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The Doing Business report was first published in 2003 with five indicator sets measuring business regulation in 133 economies. The report has grown into an annual publication covering 11 indicator sets and 189 economies. In these 10 years Doing Business has recorded nearly 2,000 business regulation reforms in the areas covered by the indicators and researchers have produced well over 1,000 articles in peer-reviewed journals using the data published by Doing Business - work that helps explore many of the key development questions of our time.\n\nThe Doing Business indicators points to important trends in regulatory reform and identifies the regions and economies making the biggest improvements for local entrepreneurs. It highlights both the areas of business regulation that have received the most attention and those where more progress remains to be made. The report also reviews research on which regulatory reforms have worked and how. Among the highlights are smarter business regulation supports economic growth, simpler business registration promotes greater entrepreneurship and firm productivity, while lower-cost registration improves formal employment opportunities, an effective regulatory environment boosts trade performance, and sound financial market infrastructure - courts, creditor and insolvency laws, and credit and collateral registries - improves access to credit." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Documents to import (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the number of documents needed to export or import are from the World Bank's Doing Business surveys, which compile procedural requirements for exporting and importing a standardized cargo of goods by ocean transport from local freight forwarders, shipping lines, customs brokers, port officials, and banks.\n\nThe procedural requirements for exporting and importing goods involved filling out several required documents. These documents are associated with every official procedure - from the contractual agreement between the 2 parties to the delivery of goods, and needs to be filled and submitted in a timely manner.\n\nSeveral assumptions about the business and traded goods are used. For the business, it is assumed that it has at least 60 employees, located in the economy's largest business city, is a private limited-liability company, is 100% domestically owned, and exports more than 10% of its sales. For the traded goods, it is assumed that the product travels in a dry-cargo, 20-foot, full container load, weighs 10 tons, and valued at $20,000. The assumptions about the product being traded are that it is not hazardous or include any military items, does not require refrigeration or any other special environment, requires only the internationally accepted safety standards, and is one of the economy's leading export or import products.\n\nTrade facilitation encompasses customs efficiency and other physical and regulatory environments where trade takes place, harmonization of standards and conformance to international regulations, and the logistics of moving goods and associated documentation through countries and ports. Though collection of trade facilitation data has improved over the last decade, data that allow meaningful evaluation, especially for developing economies, are lacking." }, { "id": "Longdefinition", "value": "All documents required per shipment to import goods are recorded. It is assumed that the contract has already been agreed upon and signed by both parties. Documents required for clearance by government ministries, customs authorities, port and container terminal authorities, health and technical control agencies and banks are taken into account. Since payment is by letter of credit, all documents required by banks for the issuance or securing of a letter of credit are also taken into account. Documents that are renewed annually and that do not require renewal per shipment (for example, an annual tax clearance certificate) are not included." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Documents to import are all documents required per shipment by government ministries, customs authorities, port and container terminals, health and technical control agencies, and banks to import goods. Documents renewed annually and not requiring renewal per shipment are excluded." }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "The World Bank's Doing Business measures the time and cost (excluding tariffs) associated with exporting and importing a standardized cargo of goods by sea transport. The time and cost necessary to complete every official procedure for exporting and importing the goods are recorded; however, the time and cost for sea transport are not included. All documents needed by the trader to export or import the goods across the border are also recorded. For exporting goods, procedures range from packing the goods into the container at the warehouse to their departure from the port of exit. For importing goods, procedures range from the vessel's arrival at the port of entry to the cargo's delivery at the warehouse. For landlocked economies, these include procedures at the inland border post, since the port is located in the transit economy. Payment is made by letter of credit, and the time, cost and documents required for the issuance or advising of a letter of credit are taken into account. The ranking on the ease of trading across borders is the simple average of the percentile rankings on its component indicators.\n\nLocal freight forwarders, shipping lines, customs brokers, port officials and banks provide information on required documents and cost as well as the time to complete each procedure. To make the data comparable across economies, several assumptions about the business and the traded goods are used.\n\nAll documents required per shipment to export and import the goods are recorded. It is assumed that a new contract is drafted per shipment and that the contract has already been agreed upon and executed by both parties. Documents required for clearance by relevant agencies - including government ministries, customs, port authorities and other control agencies - are taken into account. Since payment is by letter of credit, all documents required by banks for the issuance or securing of a letter of credit are also taken into account. Documents that are requested at the time of clearance but that are valid for a year or longer and do not require renewal per shipment (for example, an annual tax clearance certificate) are not included.\n\nDocuments to import include bank documents, customs clearance documents, port and terminal handling documents, and transport documents." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "IC.IMP.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The Doing Business report was first published in 2003 with five indicator sets measuring business regulation in 133 economies. The report has grown into an annual publication covering 11 indicator sets and 189 economies. In these 10 years Doing Business has recorded nearly 2,000 business regulation reforms in the areas covered by the indicators and researchers have produced well over 1,000 articles in peer-reviewed journals using the data published by Doing Business - work that helps explore many of the key development questions of our time.\n\nThe Doing Business indicators points to important trends in regulatory reform and identifies the regions and economies making the biggest improvements for local entrepreneurs. It highlights both the areas of business regulation that have received the most attention and those where more progress remains to be made. The report also reviews research on which regulatory reforms have worked and how. Among the highlights are smarter business regulation supports economic growth, simpler business registration promotes greater entrepreneurship and firm productivity, while lower-cost registration improves formal employment opportunities, an effective regulatory environment boosts trade performance, and sound financial market infrastructure - courts, creditor and insolvency laws, and credit and collateral registries - improves access to credit." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time to import (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the number of days needed to export or import are from the World Bank's Doing Business surveys, which compile procedural requirements for exporting and importing a standardized cargo of goods by ocean transport from local freight forwarders, shipping lines, customs brokers, port officials, and banks.\n\nThe procedural requirements for exporting and importing goods involved filling out several required documents. These documents are associated with every official procedure - from the contractual agreement between the 2 parties to the delivery of goods, and needs to be filled and submitted in a timely manner.\n\nSeveral assumptions about the business and traded goods are used. For the business, it is assumed that it has at least 60 employees, located in the economy's largest business city, is a private limited-liability company, is 100% domestically owned, and exports more than 10% of its sales. For the traded goods, it is assumed that the product travels in a dry-cargo, 20-foot, full container load, weighs 10 tons, and valued at $20,000. The assumptions about the product being traded are that it is not hazardous or include any military items, does not require refrigeration or any other special environment, requires only the internationally accepted safety standards, and is one of the economy's leading export or import products.\n\nTrade facilitation encompasses customs efficiency and other physical and regulatory environments where trade takes place, harmonization of standards and conformance to international regulations, and the logistics of moving goods and associated documentation through countries and ports. Though collection of trade facilitation data has improved over the last decade, data that allow meaningful evaluation, especially for developing economies, are lacking." }, { "id": "Longdefinition", "value": "Time to import is the time necessary to comply with all procedures required to import goods. Time is recorded in calendar days. The time calculation for a procedure starts from the moment it is initiated and runs until it is completed. If a procedure can be accelerated for an additional cost, the fastest legal procedure is chosen. It is assumed that neither the exporter nor the importer wastes time and that each commits to completing each remaining procedure without delay. Procedures that can be completed in parallel are measured as simultaneous. The waiting time between procedures--for example, during unloading of the cargo--is included in the measure." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "The World Bank's Doing Business measures the time and cost (excluding tariffs) associated with exporting and importing a standardized cargo of goods by sea transport. The time and cost necessary to complete every official procedure for exporting and importing the goods are recorded; however, the time and cost for sea transport are not included. All documents needed by the trader to export or import the goods across the border are also recorded. For exporting goods, procedures range from packing the goods into the container at the warehouse to their departure from the port of exit. For importing goods, procedures range from the vessel's arrival at the port of entry to the cargo's delivery at the warehouse. For landlocked economies, these include procedures at the inland border post, since the port is located in the transit economy. Payment is made by letter of credit, and the time, cost and documents required for the issuance or advising of a letter of credit are taken into account. The ranking on the ease of trading across borders is the simple average of the percentile rankings on its component indicators.\n\nLocal freight forwarders, shipping lines, customs brokers, port officials and banks provide information on required documents and cost as well as the time to complete each procedure. To make the data comparable across economies, several assumptions about the business and the traded goods are used.\n\nThe time for exporting and importing is recorded in calendar days. The time calculation for a procedure starts from the moment it is initiated and runs until it is completed. If a procedure can be accelerated for an additional cost and is available to all trading companies, the fastest legal procedure is chosen. Fast-track procedures applying only to firms located in an export processing zone, or only to certain accredited firms under authorized economic operator programs, are not taken into account because they are not available to all trading companies.\n\nIt is assumed that neither the exporter nor the importer wastes time and that each commits to completing each remaining procedure without delay. Procedures that can be completed in parallel are measured as simultaneous. But it is assumed that document preparation, inland transport, customs and other clearance, and port and terminal handling require a minimum time of 1 day each and cannot take place simultaneously. The waiting time between procedures - for example, during unloading of the cargo - is included in the measure." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "IC.IMP.TMBC", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Time is measured in hours, and 1 day is 24 hours (for example, 22 days are recorded as 22 × 24 = 528 hours). If customs clearance takes 7.5 hours, the data are recorded as is. Alternatively, suppose that documents are submitted to a customs agency at 8:00 a.m., are processed overnight and can be picked up at 8:00 a.m. the next day. In this case the time for customs clearance would be recorded as 24 hours because the actual procedure took 24 hours.\n\nData are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time to import, border compliance (hours)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "If inspections by agencies other than customs are conducted in 20% or fewer cases, the border compliance time and cost measures take into account only clearance and inspections by customs (the standard case). If inspections by other agencies take place in more than 20% of cases, the time and cost measures account for clearance and inspections by all agencies. Different types of inspections may take place with different probabilities—for example, scanning may take place in 100% of cases while physical inspection occurs in 5% of cases. In situations like this, Doing Business would count the time only for scanning because it happens in more than 20% of cases while physical inspection does not. The border compliance time and cost for an economy do not include the time and cost for compliance with the regulations of any other economy." }, { "id": "Longdefinition", "value": "Border compliance captures the time and cost associated with compliance with the economy’s customs regulations and with regulations relating to other inspections that are mandatory in order for the shipment to cross the economy’s border, as well as the time and cost for handling that takes place at its port or border. The time and cost for this segment include time and cost for customs clearance and inspection procedures conducted by other government agencies." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "The computation of border compliance time and cost depends on where the border compliance procedures take place, who requires and conducts the procedures and what is the probability that inspections will be conducted. If all customs clearance and other inspections take place at the port or border, the time estimate for border compliance takes this simultaneity into account. It is entirely possible that the border compliance time and cost could be negligible or zero, as in the case of trade between members of the European Union or other customs unions.\n\nIf some or all customs or other inspections take place at other locations, the time and cost for these procedures are added to the time and cost for those that take place at the port or border. In Kazakhstan, for example, all customs clearance and inspections take place at a customs post in Almaty that is not at the land border between Kazakhstan and China. In this case border compliance time is the sum of the time spent at the terminal in Almaty and the handling time at the border.\n\nDoing Business asks contributors to estimate the time and cost for clearance and inspections by customs agencies— defined as documentary and physical inspections for the purpose of calculating duties by verifying product classification, confirming quantity, determining origin and checking the veracity of other information on the customs declaration. (This category includes all inspections aimed at preventing smuggling.) These are clearance and inspection procedures that take place in the majority of cases and thus are considered the \"standard\" case. The time and cost estimates capture the efficiency of the customs agency of the economy.\n\nDoing Business also asks contributors to estimate the total time and cost for clearance and inspections by customs and all other government agencies for the specified product. These estimates account for inspections related to health, safety, phytosanitary standards, conformity and the like, and thus capture the efficiency of agencies that require and conduct these additional inspections." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "IC.IMP.TMDC", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Generalcomments", "value": "Time is measured in hours, and 1 day is 24 hours (for example, 22 days are recorded as 22 × 24 = 528 hours). If customs clearance takes 7.5 hours, the data are recorded as is. Alternatively, suppose that documents are submitted to a customs agency at 8:00 a.m., are processed overnight and can be picked up at 8:00 a.m. the next day. In this case the time for customs clearance would be recorded as 24 hours because the actual procedure took 24 hours.\n\nData are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time to import, documentary compliance (hours)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Documentary compliance captures the time and cost associated with compliance with the documentary requirements of all government agencies of the origin economy, the destination economy and any transit economies. The aim is to measure the total burden of preparing the bundle of documents that will enable completion of the international trade for the product and partner pair assumed in the case study." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "The time and cost for documentary compliance include the time and cost for obtaining documents (such as time spent to get the document issued and stamped); preparing documents (such as time spent gathering information to complete the customs declaration or certificate of origin); processing documents (such as time spent waiting for the relevant authority to issue a phytosanitary certificate); presenting documents (such as time spent showing a port terminal receipt to port authorities); and submitting documents (such as time spent submitting a customs declaration to the customs agency in person or electronically).\n\nAll electronic or paper submissions of information requested by any government agency in connection with the shipment are considered to be documents obtained, prepared and submitted during the export or import process. All documents prepared by the freight forwarder or customs broker for the product and partner pair assumed in the case study are included regardless of whether they are required by law or in practice. Any documents prepared and submitted so as to get access to preferential treatment— for example, a certificate of origin—are included in the calculation of the time and cost for documentary compliance. Any documents prepared and submitted because of a perception that they ease the passage of the shipment are also included (for example, freight forwarders may prepare a packing list because in their experience this reduces the probability of physical or other intrusive inspections).\n\nIn addition, any documents that are mandatory for exporting or importing are included in the calculation of time and cost. Documents that need to be obtained only once are not counted, however. And Doing Business does not include documents needed to produce and sell in the domestic market—such as certificates of third-party safety standards testing that may be required to sell toys domestically—unless a government agency needs to see these documents during the export process." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "IC.ISV.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time to resolve insolvency (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Time to resolve insolvency is the number of years from the filing for insolvency in court until the resolution of distressed assets." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nThe indicator measures the time, cost, and outcome of insolvency proceedings involving domestic entities. The time required for creditors to recover their credit is recorded in calendar years. The cost of the proceedings is recorded as a percentage of the value of the debtor's estate.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.LGL.CRED.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "Access to finance can expand opportunities for all with higher levels of access and use of banking services associated with lower financing obstacles for people and businesses. A stable financial system that promotes efficient savings and investment is also crucial for a thriving democracy and market economy.\n\nThere are several aspects of access to financial services: availability, cost, and quality of services. The development and growth of credit markets depend on access to timely, reliable, and accurate data on borrowers' credit experiences. Access to credit can be improved by making it easy to create and enforce collateral agreements and by increasing information about potential borrowers' creditworthiness. Lenders look at a borrower's credit history and collateral. Where credit registries and effective collateral laws are absent - as in many developing countries - banks make fewer loans. Indicators that cover getting credit include the strength of legal rights index and the depth of credit information index.\n\nThe economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Strength of legal rights index (0=weak to 12=strong)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Strength of legal rights index measures the degree to which collateral and bankruptcy laws protect the rights of borrowers and lenders and thus facilitate lending. The index ranges from 0 to 12, with higher scores indicating that these laws are better designed to expand access to credit." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected. Data starting in 2013 reflect the DB15-17 methodology change. For more information on methodology, see http://www.doingbusiness.org/Methodology/getting-credit#legalRights." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.LGL.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time required to enforce a contract (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Time required to enforce a contract is the number of calendar days from the filing of the lawsuit in court until the final determination and, in appropriate cases, payment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nA judicial system that provides effective commercial dispute resolution is crucial to a healthy economy. Without one, firms risk finding themselves operating in an environment where compliance with contractual obligations is not the norm.\n\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.LGL.PROC", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Procedures to enforce a contract (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Number of procedures to enforce a contract are the number of independent actions, mandated by law or courts, that demand interaction between the parties of a contract or between them and the judge or court officer." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nA judicial system that provides effective commercial dispute resolution is crucial to a healthy economy. Without one, firms risk finding themselves operating in an environment where compliance with contractual obligations is not the norm.\n\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.MNG.IND", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Management practices index" }, { "id": "Longdefinition", "value": "Average management practices index for medium and large firms: composite index that combines information from eight management practices indicators included in the Enterprise Surveys.\n\nThe Enterprise Surveys provide indicators that describe several dimensions of management practices. These indicators measure the extent to which firms implement better practices such as taking long-term actions to fix and avoid problems in production or service-delivery; number, time-horizon, and other features of production of service-provision targets; use of bonuses or promotion to reward better performance, and demotion to limit under-performance." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Referenceperiod", "value": "2018-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Index 0-100" } ], "source_id": "57" }, { "id": "IC.PRP.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time required to register property (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Time required to register property is the number of calendar days needed for businesses to secure rights to property." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nThe indicator records the procedures necessary for a business to purchase a property from another business and to formally transfer the property title to the buyer's name. The process starts with obtaining the necessary documents, such as a copy of the seller's title, and ends when the buyer is registered as the new owner of the property. Every procedure required by law or necessary in practice is included, whether it is the responsibility of the seller or the buyer and even if it must be completed by a third party on their behalf.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.PRP.PROC", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Procedures to register property (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Number of procedures to register property is the number of procedures required for a businesses to secure rights to property." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nThe indicator records the procedures necessary for a business to purchase a property from another business and to formally transfer the property title to the buyer's name. The process starts with obtaining the necessary documents, such as a copy of the seller's title, and ends when the buyer is registered as the new owner of the property. Every procedure required by law or necessary in practice is included, whether it is the responsibility of the seller or the buyer and even if it must be completed by a third party on their behalf.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected. Data starting in 2015 reflect the DB15-17 methodology change. Please also see: https://www.doingbusiness.org/en/methodology/registering-property" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.REG.COST.PC.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Cost of business start-up procedures, female (% of GNI per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Cost to register a business is normalized by presenting it as a percentage of gross national income (GNI) per capita." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nEntrepreneurs around the world face a range of challenges. One of them is inefficient regulation. This indicator measures the number of procedures, time, cost and paid-in minimum capital requirement for a small- to medium-size limited liability company to start up and formally operate in each economy’s largest business city. To make the data comparable across 190 economies, Doing Business uses a standardized business that is 100% domestically owned, has a start-up capital equivalent to 10 times the income per capita, engages in general industrial or commercial activities and employs between 10 and 50 people one month after the commencement of operations, all of whom are domestic nationals. The starting a business indicators consider two cases of local limited liability companies that are identical in all aspects, except that one company is owned by five married women and the other by five married men. \n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.REG.COST.PC.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Cost of business start-up procedures, male (% of GNI per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Cost to register a business is normalized by presenting it as a percentage of gross national income (GNI) per capita." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nEntrepreneurs around the world face a range of challenges. One of them is inefficient regulation. This indicator measures the paid-in minimum capital requirement for a small- to medium-size limited liability company to start up and formally operate in each economy’s largest business city. To make the data comparable across 190 economies, Doing Business uses a standardized business that is 100% domestically owned, has a start-up capital equivalent to 10 times the income per capita, engages in general industrial or commercial activities and employs between 10 and 50 people one month after the commencement of operations, all of whom are domestic nationals. The starting a business indicators consider two cases of local limited liability companies that are identical in all aspects, except that one company is owned by five married women and the other by five married men. \n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.REG.COST.PC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Cost of business start-up procedures (% of GNI per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Cost to register a business is normalized by presenting it as a percentage of gross national income (GNI) per capita." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nEntrepreneurs around the world face a range of challenges. One of them is inefficient regulation. The indicator measures the procedures, time, cost and paid-in minimum capital required for a small or medium-size limited liability company to start up and formally operate.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.REG.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time required to start a business (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Time required to start a business is the number of calendar days needed to complete the procedures to legally operate a business. If a procedure can be speeded up at additional cost, the fastest procedure, independent of cost, is chosen." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nEntrepreneurs around the world face a range of challenges. One of them is inefficient regulation. The indicator measures the procedures, time, cost and paid-in minimum capital required for a small or medium-size limited liability company to start up and formally operate.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.REG.DURS.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time required to start a business, female (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Time required to start a business is the number of calendar days needed to complete the procedures to legally operate a business. If a procedure can be speeded up at additional cost, the fastest procedure, independent of cost, is chosen." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nEntrepreneurs around the world face a range of challenges. One of them is inefficient regulation. This indicator measures the time for a small- to medium-size limited liability company to start up and formally operate in each economy’s largest business city. To make the data comparable across 190 economies, Doing Business uses a standardized business that is 100% domestically owned, has a start-up capital equivalent to 10 times the income per capita, engages in general industrial or commercial activities and employs between 10 and 50 people one month after the commencement of operations, all of whom are domestic nationals. The starting a business indicators consider two cases of local limited liability companies that are identical in all aspects, except that one company is owned by five married women and the other by five married men. \n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.REG.DURS.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The Doing Business data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time required to start a business, male (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Time required to start a business is the number of calendar days needed to complete the procedures to legally operate a business. If a procedure can be speeded up at additional cost, the fastest procedure, independent of cost, is chosen." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nEntrepreneurs around the world face a range of challenges. One of them is inefficient regulation. This indicator measures the time for a small- to medium-size limited liability company to start up and formally operate in each economy’s largest business city. To make the data comparable across 190 economies, Doing Business uses a standardized business that is 100% domestically owned, has a start-up capital equivalent to 10 times the income per capita, engages in general industrial or commercial activities and employs between 10 and 50 people one month after the commencement of operations, all of whom are domestic nationals. The starting a business indicators consider two cases of local limited liability companies that are identical in all aspects, except that one company is owned by five married women and the other by five married men. \n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.REG.PROC", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Start-up procedures to register a business (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Start-up procedures are those required to start a business, including interactions to obtain necessary permits and licenses and to complete all inscriptions, verifications, and notifications to start operations. Data are for businesses with specific characteristics of ownership, size, and type of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nEntrepreneurs around the world face a range of challenges. One of them is inefficient regulation. The indicator measures the procedures, time, cost and paid-in minimum capital required for a small or medium-size limited liability company to start up and formally operate.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.REG.PROC.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Start-up procedures to register a business, female (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Start-up procedures are those required to start a business, including interactions to obtain necessary permits and licenses and to complete all inscriptions, verifications, and notifications to start operations. Data are for businesses with specific characteristics of ownership, size, and type of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nEntrepreneurs around the world face a range of challenges. One of them is inefficient regulation. This indicator measures the number of procedure for a small- to medium-size limited liability company to start up and formally operate in each economy’s largest business city. To make the data comparable across 190 economies, Doing Business uses a standardized business that is 100% domestically owned, has a start-up capital equivalent to 10 times the income per capita, engages in general industrial or commercial activities and employs between 10 and 50 people one month after the commencement of operations, all of whom are domestic nationals. The starting a business indicators consider two cases of local limited liability companies that are identical in all aspects, except that one company is owned by five married women and the other by five married men. \n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.REG.PROC.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Start-up procedures to register a business, male (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Start-up procedures are those required to start a business, including interactions to obtain necessary permits and licenses and to complete all inscriptions, verifications, and notifications to start operations. Data are for businesses with specific characteristics of ownership, size, and type of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "This indicator measures the number of procedures for a small- to medium-size limited liability company to start up and formally operate in each economy’s largest business city. To make the data comparable across 190 economies, Doing Business uses a standardized business that is 100% domestically owned, has a start-up capital equivalent to 10 times the income per capita, engages in general industrial or commercial activities and employs between 10 and 50 people one month after the commencement of operations, all of whom are domestic nationals. The starting a business indicators consider two cases of local limited liability companies that are identical in all aspects, except that one company is owned by five married women and the other by five married men." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.SME.TOTL", "metatype": [ { "id": "IndicatorName", "value": "Micro, small and medium enterprises (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Micro, small, and medium-size enterprises are business that may be defined by the number of employees. There is no international standard definition of firm size; however, many institutions that collect information use the following size categories: micro enterprises have 0-9 employees, small enterprises have 10-49 employees, and medium-size enterprises have 50-249 employees." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Finance Corporation's micro, small, and medium-size enterprises database (http://www.ifc.org/ifcext/sme.nsf/Content/Resources)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.SME.TOTL.P3", "metatype": [ { "id": "IndicatorName", "value": "Micro, small and medium enterprises (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Micro, small, and medium-size enterprises are business that may be defined by the number of employees. There is no international standard definition of firm size; however, many institutions that collect information use the following size categories: micro enterprises have 0-9 employees, small enterprises have 10-49 employees, and medium-size enterprises have 50-249 employees." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Finance Corporation's micro, small, and medium-size enterprises database (http://www.ifc.org/ifcext/sme.nsf/Content/Resources)." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.TAX.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The total tax rate payable by businesses provides a comprehensive measure of the cost of all the taxes a business bears. It differs from the statutory tax rate, which is the factor applied to the tax base. In computing business tax rates, actual tax payable is divided by commercial profit.\n\nTaxes are the main source of revenue for most governments. The sources of tax revenue and their relative contributions are determined by government policy choices about where and how to impose taxes and by changes in the structure of the economy. Tax policy may reflect concerns about distributional effects, economic efficiency (including corrections for externalities), and the practical problems of administering a tax system. There is no ideal level of taxation. But taxes influence incentives and thus the behavior of economic actors and the economy's competitiveness." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time to prepare and pay taxes (hours)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "To make the data comparable across countries, several assumptions are made about businesses. The main assumptions are that they are limited liability companies, they operate in the country's most populous city, they are domestically owned, they perform general industrial or commercial activities, and they have certain levels of start-up capital, employees, and turnover.\n\nThe Doing Business methodology on business taxes is consistent with the Total Tax Contribution framework developed by PricewaterhouseCoopers (now PwC), which measures the taxes that are borne by companies and that affect their income statements. However, PwC bases its calculation on data from the largest companies in the economy, while Doing Business focuses on a standardized medium-size company." }, { "id": "Longdefinition", "value": "Time to prepare and pay taxes is the time, in hours per year, it takes to prepare, file, and pay (or withhold) three major types of taxes: the corporate income tax, the value added or sales tax, and labor taxes, including payroll taxes and social security contributions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "The data covering taxes payable by businesses, measure all taxes and contributions that are government mandated (at any level - federal, state, or local), apply to standardized businesses, and have an impact in their income statements. The taxes covered go beyond the definition of a tax for government national accounts (compulsory, unrequited payments to general government) and also measure any imposts that affect business accounts. The main differences are in labor contributions and value added taxes.\n\nThe data account for government-mandated contributions paid by the employer to a requited private pension fund or workers insurance fund but exclude value added taxes because they do not affect the accounting profits of the business - that is, they are not reflected in the income statement." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.TAX.GIFT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nCorruption by public officials may present a major administrative and financial burden on firms. Corruption creates an unfavorable business environment by undermining the operational efficiency of firms and raising the costs and risks associated with doing business.\n\nInefficient regulations constrain firm efficiency as they present opportunities for soliciting bribes where firms are required to make “unofficial” payments to public officials to get things done. In many countries bribes are common and quite high and they add to the bureaucratic costs in obtaining required permits and licenses. They can be a serious impediment for firms’ growth and development." }, { "id": "IndicatorName", "value": "Firms expected to give gifts in meetings with tax officials (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of firms expected to give gifts or informal payments during meetings with tax officials." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "IC.TAX.LABR.CP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The total tax rate payable by businesses provides a comprehensive measure of the cost of all the taxes a business bears. It differs from the statutory tax rate, which is the factor applied to the tax base. In computing business tax rates, actual tax payable is divided by commercial profit.\n\nTaxes are the main source of revenue for most governments. The sources of tax revenue and their relative contributions are determined by government policy choices about where and how to impose taxes and by changes in the structure of the economy. Tax policy may reflect concerns about distributional effects, economic efficiency (including corrections for externalities), and the practical problems of administering a tax system. There is no ideal level of taxation. But taxes influence incentives and thus the behavior of economic actors and the economy's competitiveness." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Labor tax and contributions (% of commercial profits)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "To make the data comparable across countries, several assumptions are made about businesses. The main assumptions are that they are limited liability companies, they operate in the country's most populous city, they are domestically owned, they perform general industrial or commercial activities, and they have certain levels of start-up capital, employees, and turnover.\n\nThe Doing Business methodology on business taxes is consistent with the Total Tax Contribution framework developed by PricewaterhouseCoopers (now PwC), which measures the taxes that are borne by companies and that affect their income statements. However, PwC bases its calculation on data from the largest companies in the economy, while Doing Business focuses on a standardized medium-size company." }, { "id": "Longdefinition", "value": "Labor tax and contributions is the amount of taxes and mandatory contributions on labor paid by the business." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "The data covering taxes payable by businesses, measure all taxes and contributions that are government mandated (at any level - federal, state, or local), apply to standardized businesses, and have an impact in their income statements. The taxes covered go beyond the definition of a tax for government national accounts (compulsory, unrequited payments to general government) and also measure any imposts that affect business accounts. The main differences are in labor contributions and value added taxes.\n\nThe data account for government-mandated contributions paid by the employer to a requited private pension fund or workers insurance fund but exclude value added taxes because they do not affect the accounting profits of the business - that is, they are not reflected in the income statement." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.TAX.METG", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity—key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country’s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector. \n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms’ perception about the taxes and their administration in their respective economy." }, { "id": "IndicatorName", "value": "Number of visits or required meetings with tax officials (average for affected firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average number of visits or required meetings with tax officials." }, { "id": "Othernotes", "value": "The WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth. \n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded. \n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population. \n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity." }, { "id": "Periodicity", "value": "Ad hoc [adhoc]" }, { "id": "Referenceperiod", "value": "2006-2025" }, { "id": "Source", "value": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n•\tFirms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n•\tFormal (registered) companies with five or more workers.\n•\tFirms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents’ opinions on obstacles to their firm’s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n•\tfirm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n•\tbusiness sector\n•\tgeographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning an economy’s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.TAX.OTHR.CP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "Low ratios of tax revenue to GDP may reflect weak administration and large-scale tax avoidance or evasion. Low ratios may also reflect a sizable parallel economy with unrecorded and undisclosed incomes. Tax revenue ratios tend to rise with income, with higher income countries relying on taxes to finance a much broader range of social services and social security than lower income countries are able to. The total tax rate payable by businesses provides a comprehensive measure of the cost of all the taxes a business bears. It differs from the statutory tax rate, which is the factor applied to the tax base. In computing business tax rates, actual tax payable is divided by commercial profit.\n\nTaxes are the main source of revenue for most governments. The sources of tax revenue and their relative contributions are determined by government policy choices about where and how to impose taxes and by changes in the structure of the economy. Tax policy may reflect concerns about distributional effects, economic efficiency (including corrections for externalities), and the practical problems of administering a tax system. There is no ideal level of taxation. But taxes influence incentives and thus the behavior of economic actors and the economy's competitiveness." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Other taxes payable by businesses (% of commercial profits)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "To make the data comparable across countries, several assumptions are made about businesses. The main assumptions are that they are limited liability companies, they operate in the country's most populous city, they are domestically owned, they perform general industrial or commercial activities, and they have certain levels of start-up capital, employees, and turnover.\n\nThe Doing Business methodology on business taxes is consistent with the Total Tax Contribution framework developed by PricewaterhouseCoopers (now PwC), which measures the taxes that are borne by companies and that affect their income statements. However, PwC bases its calculation on data from the largest companies in the economy, while Doing Business focuses on a standardized medium-size company." }, { "id": "Longdefinition", "value": "Other taxes payable by businesses include the amounts paid for property taxes, turnover taxes, and other small taxes such as municipal fees and vehicle and fuel taxes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "The data covering taxes payable by businesses, measure all taxes and contributions that are government mandated (at any level - federal, state, or local), apply to standardized businesses, and have an impact in their income statements. The taxes covered go beyond the definition of a tax for government national accounts (compulsory, unrequited payments to general government) and also measure any imposts that affect business accounts. The main differences are in labor contributions and value added taxes.\n\nThe data account for government-mandated contributions paid by the employer to a requited private pension fund or workers insurance fund but exclude value added taxes because they do not affect the accounting profits of the business - that is, they are not reflected in the income statement." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.TAX.PAYM", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The total tax rate payable by businesses provides a comprehensive measure of the cost of all the taxes a business bears. It differs from the statutory tax rate, which is the factor applied to the tax base. In computing business tax rates, actual tax payable is divided by commercial profit.\n\nTaxes are the main source of revenue for most governments. The sources of tax revenue and their relative contributions are determined by government policy choices about where and how to impose taxes and by changes in the structure of the economy. Tax policy may reflect concerns about distributional effects, economic efficiency (including corrections for externalities), and the practical problems of administering a tax system. There is no ideal level of taxation. But taxes influence incentives and thus the behavior of economic actors and the economy's competitiveness." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Tax payments (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "To make the data comparable across countries, several assumptions are made about businesses. The main assumptions are that they are limited liability companies, they operate in the country's most populous city, they are domestically owned, they perform general industrial or commercial activities, and they have certain levels of start-up capital, employees, and turnover. \n\nThe Doing Business methodology on business taxes is consistent with the Total Tax Contribution framework developed by PricewaterhouseCoopers (now PwC), which measures the taxes that are borne by companies and that affect their income statements. However, PwC bases its calculation on data from the largest companies in the economy, while Doing Business focuses on a standardized medium-size company." }, { "id": "Longdefinition", "value": "Tax payments by businesses are the total number of taxes paid by businesses, including electronic filing. The tax is counted as paid once a year even if payments are more frequent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "The data covering taxes payable by businesses, measure all taxes and contributions that are government mandated (at any level - federal, state, or local), apply to standardized businesses, and have an impact in their income statements. The taxes covered go beyond the definition of a tax for government national accounts (compulsory, unrequited payments to general government) and also measure any imposts that affect business accounts. The main differences are in labor contributions and value added taxes.\n\nThe data account for government-mandated contributions paid by the employer to a requited private pension fund or workers insurance fund but exclude value added taxes because they do not affect the accounting profits of the business - that is, they are not reflected in the income statement." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.TAX.PRFT.CP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The total tax rate payable by businesses provides a comprehensive measure of the cost of all the taxes a business bears. It differs from the statutory tax rate, which is the factor applied to the tax base. In computing business tax rates, actual tax payable is divided by commercial profit.\n\nTaxes are the main source of revenue for most governments. The sources of tax revenue and their relative contributions are determined by government policy choices about where and how to impose taxes and by changes in the structure of the economy. Tax policy may reflect concerns about distributional effects, economic efficiency (including corrections for externalities), and the practical problems of administering a tax system. There is no ideal level of taxation. But taxes influence incentives and thus the behavior of economic actors and the economy's competitiveness." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Profit tax (% of commercial profits)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "To make the data comparable across countries, several assumptions are made about businesses. The main assumptions are that they are limited liability companies, they operate in the country's most populous city, they are domestically owned, they perform general industrial or commercial activities, and they have certain levels of start-up capital, employees, and turnover. \n\nThe Doing Business methodology on business taxes is consistent with the Total Tax Contribution framework developed by PricewaterhouseCoopers (now PwC), which measures the taxes that are borne by companies and that affect their income statements. However, PwC bases its calculation on data from the largest companies in the economy, while Doing Business focuses on a standardized medium-size company." }, { "id": "Longdefinition", "value": "Profit tax is the amount of taxes on profits paid by the business." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "The data covering taxes payable by businesses, measure all taxes and contributions that are government mandated (at any level - federal, state, or local), apply to standardized businesses, and have an impact in their income statements. The taxes covered go beyond the definition of a tax for government national accounts (compulsory, unrequited payments to general government) and also measure any imposts that affect business accounts. The main differences are in labor contributions and value added taxes.\n\nThe data account for government-mandated contributions paid by the employer to a requited private pension fund or workers insurance fund but exclude value added taxes because they do not affect the accounting profits of the business - that is, they are not reflected in the income statement." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.TAX.TOTL.CP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The total tax rate payable by businesses provides a comprehensive measure of the cost of all the taxes a business bears. It differs from the statutory tax rate, which is the factor applied to the tax base. In computing business tax rates, actual tax payable is divided by commercial profit.\n\nTaxes are the main source of revenue for most governments. The sources of tax revenue and their relative contributions are determined by government policy choices about where and how to impose taxes and by changes in the structure of the economy. Tax policy may reflect concerns about distributional effects, economic efficiency (including corrections for externalities), and the practical problems of administering a tax system. There is no ideal level of taxation. But taxes influence incentives and thus the behavior of economic actors and the economy's competitiveness." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Total tax and contribution rate (% of profit)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "To make the data comparable across countries, several assumptions are made about businesses. The main assumptions are that they are limited liability companies, they operate in the country's most populous city, they are domestically owned, they perform general industrial or commercial activities, and they have certain levels of start-up capital, employees, and turnover. \n\nThe Doing Business methodology on business taxes is consistent with the Total Tax Contribution framework developed by PricewaterhouseCoopers (now PwC), which measures the taxes that are borne by companies and that affect their income statements. However, PwC bases its calculation on data from the largest companies in the economy, while Doing Business focuses on a standardized medium-size company." }, { "id": "Longdefinition", "value": "Total tax rate measures the amount of taxes and mandatory contributions payable by businesses after accounting for allowable deductions and exemptions as a share of commercial profits. Taxes withheld (such as personal income tax) or collected and remitted to tax authorities (such as value added taxes, sales taxes or goods and service taxes) are excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "The data covering taxes payable by businesses, measure all taxes and contributions that are government mandated (at any level - federal, state, or local), apply to standardized businesses, and have an impact in their income statements. The taxes covered go beyond the definition of a tax for government national accounts (compulsory, unrequited payments to general government) and also measure any imposts that affect business accounts. The main differences are in labor contributions and value added taxes.\n\nThe data account for government-mandated contributions paid by the employer to a requited private pension fund or workers insurance fund but exclude value added taxes because they do not affect the accounting profits of the business - that is, they are not reflected in the income statement." }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.WRH.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Time required to build a warehouse (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Time required to build a warehouse is the number of calendar days needed to complete the required procedures for building a warehouse. If a procedure can be speeded up at additional cost, the fastest procedure, independent of cost, is chosen." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nTo build a simple commercial warehouse and connect it to water, sewerage and a fixed telephone line, many construction regulations are required. Construction regulation matters for public safety. If procedures are too complicated or costly, builders tend to proceed without a permit. By some estimates 60-80 percent of building projects in developing economies are undertaken without the proper permits and approvals. Good regulations help ensure the safety standards that protect the public while making the permitting process efficient, transparent and affordable. Time is recorded in calendar days. The measure captures the median duration that local experts indicate is necessary to complete a procedure in practice. It is assumed that the minimum time required for each procedure is one day, except for procedures that can be fully completed online, for which the time required is recorded as half a day. Although procedures may take place simultaneously, they cannot start on the same day (that is, simultaneous procedures start on consecutive days), again with the exception of procedures that can be fully completed online. If a procedure can be accelerated legally for an additional cost, the fastest procedure is chosen if that option is more bene?cial to the economy’s score. It is assumed that BuildCo does not waste time and commits to completing each remaining procedure without delay. The time that BuildCo spends on gathering information is not taken into account. It is assumed that BuildCo follows all building requirements and their sequence as required. Data starting in 2014 reflect the DB15-17 methodology change.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected. Please also see: https://www.doingbusiness.org/en/methodology/dealing-with-construction-permits" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IC.WRH.PROC", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Procedures to build a warehouse (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures. Please also see: https://www.doingbusiness.org/en/about-us/faq" }, { "id": "Longdefinition", "value": "Number of procedures to build a warehouse is the number of interactions of a company's employees or managers with external parties, including government agency staff, public inspectors, notaries, land registry and cadastre staff, and technical experts apart from architects and engineers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nTo build a simple commercial warehouse and connect it to water, sewerage and a fixed telephone line, many construction regulations are required. Construction regulation matters for public safety. If procedures are too complicated or costly, builders tend to proceed without a permit. By some estimates 60-80 percent of building projects in developing economies are undertaken without the proper permits and approvals. Good regulations help ensure the safety standards that protect the public while making the permitting process efficient, transparent and affordable. A procedure is any interaction of the building company’s employees, managers, or any party acting on behalf of the company, with external parties, including government agencies, notaries, the land registry, the cadastre, utility companies and public inspectors—and the hiring of external private inspectors and technical experts where needed. Interactions between company employees, such as development of the warehouse plans and inspections by the in-house engineer, are not counted as procedures. However, interactions with external parties that are required for the architect to prepare the plans and drawings (such as obtaining topographic or geological surveys), or to have such documents approved or stamped by external parties, are counted as procedures. Procedures that the company undergoes to connect the warehouse to water and sewerage are included. All procedures that are legally required and that are done in practice by the majority of companies to build a warehouse are counted, even if they may be avoided in exceptional cases. For example, obtaining technical conditions for electricity or a clearance of the electrical plans are counted as separate procedures if they are required for obtaining a building permit. Data starting in 2014 reflect the DB15-17 methodology change.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected. Please also see: https://www.doingbusiness.org/en/methodology/dealing-with-construction-permits" }, { "id": "Topic", "value": "Private Sector & Trade: Business environment" } ], "source_id": "57" }, { "id": "IE.PPI.ENGY.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investment in infrastructure projects with private participation has made important contributions to easing fiscal constraints, improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, pioneering better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure." }, { "id": "IndicatorName", "value": "Investment in energy with private participation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nInvestment commitments are the sum of investments in physical assets and payments to the government. Investments in physical assets are resources the project company commits to invest during the contract period in new facilities or in expansion and modernization of existing facilities. Payments to the government are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "The Private Participation in Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Investment in energy projects with private participation refers to commitments to infrastructure projects in energy (electricity and natural gas: generation, transmission and distribution) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2023" }, { "id": "Source", "value": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A team of researchers gather data for each of the regions using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit’s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial’s Project Finance International, Euromoney’s Project Finance, Media Analytics’ Global Water Intelligence, Pisent Masons’ Water Yearbooks, and Platt’s Power in Asia, etc.), specialist portal (such as Privatization, IPAnet, and Privatization Barometer), Internet resources (such as web sites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\n\nStatistical concept(s): PPPs is defined as “any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.”\nThe term infrastructure refers to:\n• Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n• Information and communications technology (ICT): ICT backbone infrastructure\n• Transport: Airports, railways, ports, and roads.\n• Water: potable water treatment and distribution, and sewerage collection and treatment." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" }, { "id": "Unitofmeasure", "value": "USD current [USD_CUR]" } ], "source_id": "57" }, { "id": "IE.PPI.ICTI.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investment in infrastructure projects with private participation has made important contributions to improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, looking for better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure." }, { "id": "IndicatorName", "value": "Investment in ICT with private participation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment commitments (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported. Data are available 2015 onwards only for ICT." }, { "id": "Longdefinition", "value": "The Private Participation in Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Investment in ICT projects with private participation refers to commitments to infrastructure projects in ICT (including land based and submarine cables except purely private telecoms. Instead, it will track ICT backbone infrastructure (fiber optic cables etc) that has an active government component) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: http://ppi.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A team of researchers gather data for each of the regions using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit’s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial’s Project Finance International, Euromoney’s Project Finance, Media Analytics’ Global Water Intelligence, Pisent Masons’ Water Yearbooks, and Platt’s Power in Asia, etc.), specialist portal (such as Privatization, IPAnet, and Privatization Barometer), Internet resources (such as web sites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\n\nStatistical concept(s): PPPs is defined as “any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.”\nThe term infrastructure refers to:\n• Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n• Information and communications technology (ICT): ICT backbone infrastructure\n• Transport: Airports, railways, ports, and roads.\n• Water: potable water treatment and distribution, and sewerage collection and treatment." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" }, { "id": "Unitofmeasure", "value": "USD current [USD_CUR]" } ], "source_id": "57" }, { "id": "IE.PPI.TRAN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investment in infrastructure projects with private participation has made important contributions to easing fiscal constraints, improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, pioneering better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure." }, { "id": "IndicatorName", "value": "Investment in transport with private participation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "The Private Participation In Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Investment in transport projects with private participation refers to commitments to infrastructure projects in transport [(a) airport runways and terminals, (b) railways (including fixed assets, freight, intercity passenger, and local passenger); (c) toll roads, bridges, highways, and tunnels, (d) port infrastructure, superstructures, terminals, and channels)] that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2023" }, { "id": "Source", "value": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A team of researchers gather data for each of the regions using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit’s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial’s Project Finance International, Euromoney’s Project Finance, Media Analytics’ Global Water Intelligence, Pisent Masons’ Water Yearbooks, and Platt’s Power in Asia, etc.), specialist portal (such as Privatization, IPAnet, and Privatization Barometer), Internet resources (such as web sites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their Web sites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\n\nStatistical concept(s): PPPs is defined as “any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.”\nThe term infrastructure refers to:\n• Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n• Information and communications technology (ICT): ICT backbone infrastructure\n• Transport: Airports, railways, ports, and roads.\n• Water: potable water treatment and distribution, and sewerage collection and treatment." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" }, { "id": "Unitofmeasure", "value": "USD current [USD_CUR]" } ], "source_id": "57" }, { "id": "IE.PPI.WATR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investment in infrastructure projects with private participation has made important contributions to easing fiscal constraints, improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, pioneering better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure." }, { "id": "IndicatorName", "value": "Investment in water and sanitation with private participation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "The Private Participation in Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Investment in water projects with private participation refers to commitments to infrastructure projects in potable water (treatment and distribution, and sewerage collection and treatment that has an active government component) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1987-2023" }, { "id": "Source", "value": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A team of researchers gather data for each of the regions using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit’s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial’s Project Finance International, Euromoney’s Project Finance, Media Analytics’ Global Water Intelligence, Pisent Masons’ Water Yearbooks, and Platt’s Power in Asia, etc.), specialist portal (such as Privatization, IPAnet, and Privatization Barometer), Internet resources (such as web sites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their Websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template to make sure data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then for the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\nStatistical concept(s): PPPs is defined as “any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.”\nThe term infrastructure refers to:\n• Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n• Information and communications technology (ICT): ICT backbone infrastructure\n• Transport: Airports, railways, ports, and roads.\n• Water: potable water treatment and distribution, and sewerage collection and treatment." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" }, { "id": "Unitofmeasure", "value": "USD current [USD_CUR]" } ], "source_id": "57" }, { "id": "IE.PPN.ENGY.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure. Investment in infrastructure projects with private participation has made important contributions to improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, looking for better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth." }, { "id": "IndicatorName", "value": "Public private partnerships investment in energy (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment commitments (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "The Private Participation In Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Public private partnerships in energy refers to commitments to infrastructure projects in energy (electricity and natural gas: generation, transmission, and distribution) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, and greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility). Divestitures and merchant projects are excluded. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) Payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2023" }, { "id": "Source", "value": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A team of researchers gather data using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit’s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial’s Project Finance International, Euromoney’s Project Finance, Media Analytics’ Global Water Intelligence, Pisent Masons’ Water Yearbooks, and Platt’s Power in Asia, etc.), specialist portals (such as Privatization, IPAnet, and Privatization Barometer), online resources (such as websites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\nStatistical concept(s): PPPs is defined as “any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.”\nThe term infrastructure refers to:\n• Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n• Information and communications technology (ICT): ICT backbone infrastructure\n• Transport: Airports, railways, ports, and roads.\n• Water: potable water treatment and distribution, and sewerage collection and treatment." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" }, { "id": "Unitofmeasure", "value": "USD current [USD_CUR]" } ], "source_id": "57" }, { "id": "IE.PPN.ICTI.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure. Investment in infrastructure projects with private participation has made important contributions to improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, looking for better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth." }, { "id": "IndicatorName", "value": "Public private partnerships investment in ICT (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment commitments (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "The Private Participation in Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Public private partnerships in ICT refers to commitments to projects in ICT backbone infrastructure (including land based and submarine cables) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, and greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility). It excludes divestitures and merchant projects. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) Payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars and available 2015 onwards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A team of researchers gather data using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit’s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial’s Project Finance International, Euromoney’s Project Finance, Media Analytics’ Global Water Intelligence, Pisent Masons’ Water Yearbooks, and Platt’s Power in Asia, etc.), specialist portals (such as Privatization, IPAnet, and Privatization Barometer), online resources (such as websites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\nStatistical concept(s): PPPs is defined as “any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.”\nThe term infrastructure refers to:\n• Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n• Information and communications technology (ICT): ICT backbone infrastructure\n• Transport: Airports, railways, ports, and roads.\n• Water: potable water treatment and distribution, and sewerage collection and treatment." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" }, { "id": "Unitofmeasure", "value": "USD current" } ], "source_id": "57" }, { "id": "IE.PPN.TRAN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure. Investment in infrastructure projects with private participation has made important contributions to improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, looking for better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth." }, { "id": "IndicatorName", "value": "Public private partnerships investment in transport (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment commitments (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "The Private Participation In Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Public private partnerships in transport refers to commitments to infrastructure projects in transport [(a) airport runways and terminals, (b) railways (including fixed assets, freight, intercity passenger, and local passenger); (c) toll roads, bridges, highways, and tunnels, (d) port infrastructure, superstructures, terminals, and channels)] that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, and greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility). Divestitures and merchant projects are excluded. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) Payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2023" }, { "id": "Source", "value": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A team of researchers gather data using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit’s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial’s Project Finance International, Euromoney’s Project Finance, Media Analytics’ Global Water Intelligence, Pisent Masons’ Water Yearbooks, and Platt’s Power in Asia, etc.), specialist portals (such as Privatization, IPAnet, and Privatization Barometer), online resources (such as websites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\nStatistical concept(s): PPPs is defined as “any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.”\nThe term infrastructure refers to:\n• Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n• Information and communications technology (ICT): ICT backbone infrastructure\n• Transport: Airports, railways, ports, and roads.\n• Water: potable water treatment and distribution, and sewerage collection and treatment." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" }, { "id": "Unitofmeasure", "value": "USD current [USD_CUR]" } ], "source_id": "57" }, { "id": "IE.PPN.WATR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Private sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure. Investment in infrastructure projects with private participation has made important contributions to improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, looking for better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth." }, { "id": "IndicatorName", "value": "Public private partnerships investment in water and sanitation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on investment in infrastructure projects with private participation refer to all investment commitments (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported." }, { "id": "Longdefinition", "value": "The Private Participation in Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Public private partnerships in water refers to commitments to infrastructure projects in potable water (treatment and distribution, and sewerage collection and treatment that has an active government component) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, and greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility). It excludes divestitures and merchant projects. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) Payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1987-2023" }, { "id": "Source", "value": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A team of researchers gather data using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit’s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial’s Project Finance International, Euromoney’s Project Finance, Media Analytics’ Global Water Intelligence, Pisent Masons’ Water Yearbooks, and Platt’s Power in Asia, etc.), specialist portals (such as Privatization, IPAnet, and Privatization Barometer), online resources (such as websites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\nStatistical concept(s): PPPs is defined as “any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.”\nThe term infrastructure refers to:\n• Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n• Information and communications technology (ICT): ICT backbone infrastructure\n• Transport: Airports, railways, ports, and roads.\n• Water: potable water treatment and distribution, and sewerage collection and treatment." }, { "id": "Topic", "value": "Private Sector & Trade: Private infrastructure investment" }, { "id": "Unitofmeasure", "value": "USD current [USD_CUR]" } ], "source_id": "57" }, { "id": "IP.IDS.NRCT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The progress and well-being of humanity depend on our capacity to come up with new ideas and creations. Technological progress requires the development and application of new inventions, while a vibrant culture will constantly seek new ways to express itself. \n\nIntellectual property rights are also vital. Inventors, artists, scientists and businesses put a lot of time, money, energy and thought into developing their innovations and creations. To encourage them to do that, they need the chance to make a fair return on their investment. That means giving them rights to protect their intellectual property. Essentially, intellectual property rights such as copyright, patents and trademarks can be viewed like any other property right. They allow the creators or owners of IP to benefit from their work or from their investment in a creation by giving them control over how their property is used. \n\nIP rights have long been recognized within various legal systems. For example, patents to protect inventions were granted in Venice as far back as the fifteenth century. Modern initiatives to protect IP through international law started with the Paris Convention for the Protection of Industrial Property (1883) and the Berne Convention for the Protection of Literary and Artistic Works (1886). These days, there are more than 25 international treaties on IP administered by WIPO. IP rights are also safeguarded by Article 27 of the Universal Declaration of Human Rights.\n\nCreativity and inventiveness are vital. They spur economic growth, create new jobs and industries, and enhance the quality and enjoyment of life. The intellectual property system needs to balance the rights and interests of different groups: of creators and consumers; of businesses and their competitors; of high- and low-income countries. An efficient and fair IP system benefits everyone – including ordinary users and consumers.\n\nSome examples: (a) The multibillion-dollar film, recording, publishing and software industries – which bring pleasure to millions of people worldwide – would not thrive without copyright protection.\n(b) The patent system rewards researchers and inventors while also ensuring that they share their knowledge by making patent applications publicly available, which helps stimulate more innovation. (c) Trademark protection discourages counterfeiting, so businesses can compete on a level playing field and users can be confident they are buying the genuine article.\n\n(source: https://doi.org/10.34667/tind.42176)" }, { "id": "IndicatorName", "value": "Industrial design applications, nonresident, by count" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are based on information supplied to World Intellectual Property Organization (WIPO) by IP offices in annual surveys, supplemented by data in national IP office reports. Data may be missing for some offices or periods." }, { "id": "Longdefinition", "value": "Industrial design applications are applications to register an industrial design with a national or regional Intellectual Property (IP) offices and designations received by relevant offices through the Hague System. Non-resident application refers to an application filed with the IP office of or acting on behalf of a state or jurisdiction in which the first-named applicant in the application is not domiciled. Design count is used to render application data for industrial applications across offices comparable, as some offices follow a single-class/single-design filing system while other have a multiple class/design filing system." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2021" }, { "id": "Source", "value": "Statistics Database, World Intellectual Property Organization (WIPO), uri: www.wipo.int/ipstats/, note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Information about the World Intellectual Property Organization (WIPO) data collection can be accessed on the WIPO website: https://www.wipo.int/en/web/ip-statistics/about\n\nStatistical concept(s): Industrial designs are applied to a wide variety of industrial products and handicrafts. They refer to the ornamental or aesthetic aspects of a useful article, including compositions of lines or colors or any three-dimensional forms that give a special appearance to a product or handicraft. The holder of a registered industrial design has exclusive rights against unauthorized copying or imitation of the design by third parties. Industrial design registrations are valid for a limited period. The term of protection is usually 15 years in most jurisdictions. However, differences in legislation exist, notably in China (which provides for a 10-year term from the application date).\n\nNon-resident application refers to an application filed with the IP office of or acting on behalf of a state or jurisdiction in which the first-named applicant in the application is not domiciled. \n\nDesign count: The number of designs contained in an industrial design application or registration. Under the Hague System for the International Registration of Industrial Designs, it is possible for an applicant to obtain protection for up to 100 industrial designs for products belonging to one and the same class by filing a single application. Some national or regional IP offices allow applications to contain more than one design for the same product or within the same class, while others allow only one design per application. In order to capture the differences in application and registration numbers across offices, it is useful to compare their respective application and registration design counts." }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "IP.IDS.RSCT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The progress and well-being of humanity depend on our capacity to come up with new ideas and creations. Technological progress requires the development and application of new inventions, while a vibrant culture will constantly seek new ways to express itself. \n\nIntellectual property rights are also vital. Inventors, artists, scientists and businesses put a lot of time, money, energy and thought into developing their innovations and creations. To encourage them to do that, they need the chance to make a fair return on their investment. That means giving them rights to protect their intellectual property. Essentially, intellectual property rights such as copyright, patents and trademarks can be viewed like any other property right. They allow the creators or owners of IP to benefit from their work or from their investment in a creation by giving them control over how their property is used. \n\nIP rights have long been recognized within various legal systems. For example, patents to protect inventions were granted in Venice as far back as the fifteenth century. Modern initiatives to protect IP through international law started with the Paris Convention for the Protection of Industrial Property (1883) and the Berne Convention for the Protection of Literary and Artistic Works (1886). These days, there are more than 25 international treaties on IP administered by WIPO. IP rights are also safeguarded by Article 27 of the Universal Declaration of Human Rights.\n\nCreativity and inventiveness are vital. They spur economic growth, create new jobs and industries, and enhance the quality and enjoyment of life. The intellectual property system needs to balance the rights and interests of different groups: of creators and consumers; of businesses and their competitors; of high- and low-income countries. An efficient and fair IP system benefits everyone – including ordinary users and consumers.\n\nSome examples: (a) The multibillion-dollar film, recording, publishing and software industries – which bring pleasure to millions of people worldwide – would not thrive without copyright protection.\n(b) The patent system rewards researchers and inventors while also ensuring that they share their knowledge by making patent applications publicly available, which helps stimulate more innovation. (c) Trademark protection discourages counterfeiting, so businesses can compete on a level playing field and users can be confident they are buying the genuine article.\n\n(source: https://doi.org/10.34667/tind.42176)" }, { "id": "IndicatorName", "value": "Industrial design applications, resident, by count" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are based on information supplied to World Intellectual Property Organization (WIPO) by IP offices in annual surveys, supplemented by data in national IP office reports. Data may be missing for some offices or periods." }, { "id": "Longdefinition", "value": "Industrial design applications are applications to register an industrial design with a national or regional Intellectual Property (IP) offices and designations received by relevant offices through the Hague System. A resident application refers to an application filed with the IP office of, or acting for, the state or jurisdiction in which the first named applicant in the application is resident. Design count is used to render application data for industrial applications across offices comparable, as some offices follow a single-class/single-design filing system while other have a multiple class/design filing system." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2021" }, { "id": "Source", "value": "Statistics Database, World Intellectual Property Organization (WIPO), uri: www.wipo.int/ipstats/, note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Information about the World Intellectual Property Organization (WIPO) data collection can be accessed on the WIPO website: https://www.wipo.int/en/web/ip-statistics/about\nStatistical concept(s): Industrial designs are applied to a wide variety of industrial products and handicrafts. They refer to the ornamental or aesthetic aspects of a useful article, including compositions of lines or colors or any three-dimensional forms that give a special appearance to a product or handicraft. The holder of a registered industrial design has exclusive rights against unauthorized copying or imitation of the design by third parties. Industrial design registrations are valid for a limited period. The term of protection is usually 15 years in most jurisdictions. However, differences in legislation exist, notably in China (which provides for a 10-year term from the application date).\n\nFor statistical purposes, a resident application refers to an application filed with the IP office of, or acting for, the state or jurisdiction in which the first named applicant in the application is resident. For example, an application filed with the Japan Patent Office (JPO) by a resident of Japan is considered a resident application from the perspective of the JPO. Resident applications are sometimes referred to as “domestic applications.” A resident grant/registration is an IP right issued on the basis of a resident application.\n\nDesign count: The number of designs contained in an industrial design application or registration. Under the Hague System for the International Registration of Industrial Designs, it is possible for an applicant to obtain protection for up to 100 industrial designs for products belonging to one and the same class by filing a single application. Some national or regional IP offices allow applications to contain more than one design for the same product or within the same class, while others allow only one design per application. In order to capture the differences in application and registration numbers across offices, it is useful to compare their respective application and registration design counts." }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "IP.JRN.ARTC.SC", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A scientific journal is a periodical publication intended to further the progress of science, usually by reporting new research. Most journals are highly specialized, although some of the oldest journals such as Nature publish articles and scientific papers across a wide range of scientific fields. Scientific journals contain articles that have been peer reviewed. When a scientific journal describes experiments or calculations, they must supply enough details that an independent researcher could repeat the experiment or calculation to verify the results. Each such journal article becomes part of the permanent scientific record.\n\nSome journals, such as Nature, Science, Proceedings of the National Academy of Sciences of the United States of America (PNAS), and Physical Review Letters, have a reputation of publishing articles that mark a fundamental breakthrough in their respective fields." }, { "id": "IndicatorName", "value": "Scientific and technical journal articles" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the bibliometric database is constantly updated, the National Center for Science and Engineering Statistics (NCSES) does not recommend comparing bibliometric data across different editions of the Science and Engineering Indicators publication. For each edition of Indicators, NCSES uses a fixed snapshot of the database. This means that although trends are comparable, the exact number of articles, citations, and other data will vary across editions. For more information about comparing fixed versus dynamic journal data sets, see Schneider et al. (2019). Data before 2003 is sourced from earlier editions of the Science and Engineering Indicators report and may not be strictly comparable with 2003-2022 data.\n\nThe Scopus database is constructed from articles and conference proceedings with an English-language title and abstract; therefore, the database contains an unmeasurable bias because not all science and engineering (S&E) articles and conference proceedings meet the English language requirement (Elsevier 2020). (Source: https://ncses.nsf.gov/pubs/nsb202333/technical-appendix)" }, { "id": "Longdefinition", "value": "Article counts refer to publications from a selection of conference proceedings and peer-reviewed journals from Scopus in science and engineering fields, according to the National Center for Science and Engineering Statistics Taxonomy of Disciplines." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2022" }, { "id": "Source", "value": "Science and Engineering Indicators, National Science Foundation (NSF), uri: https://ncses.nsf.gov/indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Science and Engineering Indicators 2024 report “Publications Output: U.S. Trends and International Comparisons” uses a large database of publication records as a source of bibliometric data. Bibliometric data include each article’s title, author(s), authors’ institution(s), references, journal title, unique article-identifying information (journal volume, issue, and page numbers or digital object identifier), and year or date of publication. The PBS report uses Scopus, a bibliometric database owned by Elsevier and containing scientific literature with English titles and abstracts, to examine national and global scientific publication–related activity.? \n\nArticle counts refer to publications from a selection of conference proceedings and peer-reviewed journals in science and engineering fields from Scopus, according to the National Center for Science and Engineering Statistics Taxonomy of Disciplines: agricultural sciences, astronomy and astrophysics, biological and biomedical sciences, chemistry, computer and information sciences; engineering; geosciences, atmospheric sciences, and ocean sciences; health sciences; material sciences; mathematics and statistics; natural resources and conservation; physics; psychology; social sciences.\n\n\n\nStatistical concept(s): The number of journal articles is presented using fractional counting: a method of counting science and engineering publications in which credit for coauthored publications is divided among the collaborating institutions or regions, countries, or economies based on the proportion of their participating authors. Fractional counting allocates the publication count based on the proportion of the coauthors named on the article with institutional addresses from each region, country, or economy. Fractional counting enables the counts to sum up to the number of total articles. (Source: https://ncses.nsf.gov/pubs/nsb202333/glossary)" }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "Fractional count" } ], "source_id": "57" }, { "id": "IP.PAT.NRES", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Patent Cooperation Treaty (www.wipo.int/pct) provides a two phase system for filing patent. International applications under the treaty provide for a national patent grant only - there is no international patent. The national filing represents the applicant's seeking of patent protection for a given territory, whereas international filings, while representing a legal right, do not accurately reflect where patent protection is sought. Resident filings are those from residents of the country concerned. Nonresident filings are from applicants abroad. For regional offices applications from residents of any member state of the regional patent convention are considered nonresident filings. Some offices (notably the U.S. Patent and Trademark Office) use the residence of the inventor rather than the applicant to classify filings.\n\nPatent data are a great resource for the study of technical change in a country or region. Patent data provide a uniquely detailed source of information on inventive activity and the multiple dimensions of the inventive process (e.g. geographical location, technical and institutional origin, individuals and networks). Furthermore, patent data form a consistent basis for comparisons across time and across countries.\n\nPatent data can be used in the analysis of a wide array of topics related to technical change and patenting activity including industry-science linkages, patenting strategies by companies, internationalization of research, and indicators on the value of patents. Patent-based statistics reflect the inventive performance of countries, regions and firms, as well as other aspects of the dynamics of the innovation process such as co-operation in innovation or technology paths." }, { "id": "IndicatorName", "value": "Patent applications, nonresidents" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A patent is an exclusive right granted for a specified period (generally 20 years) for a new way of doing something or a new technical solution to a problem - an invention. The invention must be of practical use and display a characteristic unknown in the existing body of knowledge in its field. Most countries have systems to protect patentable inventions.\n\nUnless otherwise stated, statistics on the number of resident and non-resident patent applications include those filed via the PCT system as PCT national/regional phase entries." }, { "id": "Longdefinition", "value": "Patent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention--a product or process that provides a new way of doing something or offers a new technical solution to a problem. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2021" }, { "id": "Source", "value": "WIPO Patent Report: Statistics on Worldwide Patent Activity, World Intellectual Property Organization (WIPO), note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Non-resident patent applications are from applicants outside the relevant State or region. Patent data cover applications and grants classified by field of technology. International applications series distinguish four subcategories: a) patents taken out by residents of a country in that country; b) patents taken out in a country by non-residents of that country; c) total patents registered in the country or naming it; d) patents taken out outside a country by its residents. Data on patents granted only distinguish between patents awarded to residents and to non-residents. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years.\n\nPatent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention - a product or process that provides a new way of doing something or offers a new technical solution to a problem." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "57" }, { "id": "IP.PAT.RESD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Patent Cooperation Treaty (www.wipo.int/pct) provides a two phase system for filing patent. International applications under the treaty provide for a national patent grant only - there is no international patent. The national filing represents the applicant's seeking of patent protection for a given territory, whereas international filings, while representing a legal right, do not accurately reflect where patent protection is sought. Resident filings are those from residents of the country concerned. Nonresident filings are from applicants abroad. For regional offices applications from residents of any member state of the regional patent convention are considered nonresident filings. Some offices (notably the U.S. Patent and Trademark Office) use the residence of the inventor rather than the applicant to classify filings.\n\nPatent data are a great resource for the study of technical change in a country or region. Patent data provide a uniquely detailed source of information on inventive activity and the multiple dimensions of the inventive process (e.g. geographical location, technical and institutional origin, individuals and networks). Furthermore, patent data form a consistent basis for comparisons across time and across countries.\n\nPatent data can be used in the analysis of a wide array of topics related to technical change and patenting activity including industry-science linkages, patenting strategies by companies, internationalization of research, and indicators on the value of patents. Patent-based statistics reflect the inventive performance of countries, regions and firms, as well as other aspects of the dynamics of the innovation process such as co-operation in innovation or technology paths." }, { "id": "IndicatorName", "value": "Patent applications, residents" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A patent is an exclusive right granted for a specified period (generally 20 years) for a new way of doing something or a new technical solution to a problem - an invention. The invention must be of practical use and display a characteristic unknown in the existing body of knowledge in its field. Most countries have systems to protect patentable inventions." }, { "id": "Longdefinition", "value": "Patent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention--a product or process that provides a new way of doing something or offers a new technical solution to a problem. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2021" }, { "id": "Source", "value": "WIPO Patent Report: Statistics on Worldwide Patent Activity, World Intellectual Property Organization (WIPO), note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Resident patent applications are those for which the first-named applicant or assignee is a resident of the State or region concerned. In the case of regional offices such as the European Patent Office, a resident is an applicant from any of the member States of the regional patent convention.\n\nPatent data cover applications and grants classified by field of technology. International applications series distinguish four subcategories: a) patents taken out by residents of a country in that country; b) patents taken out in a country by non-residents of that country; c) total patents registered in the country or naming it; d) patents taken out outside a country by its residents. Data on patents granted only distinguish between patents awarded to residents and to non-residents. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years.\n\nPatent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention - a product or process that provides a new way of doing something or offers a new technical solution to a problem." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "57" }, { "id": "IP.TMK.AGGD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Trademark applications, aggregate direct" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Trademark applications filed are applications to register a trademark with a national or regional Intellectual Property (IP) office. A trademark is a distinctive sign which identifies certain goods or services as those produced or provided by a specific person or enterprise. A trademark provides protection to the owner of the mark by ensuring the exclusive right to use it to identify goods or services, or to authorize another to use it in return for payment. The period of protection varies, but a trademark can be renewed indefinitely beyond the time limit on payment of additional fees. Aggregate direct trademark applications are those filed by applicants without regard to the residency of the applicant. This figure is used when the national office does not provide a breakdown by direct resident and direct nonresident applications." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Trademark applications filed are applications to register a trademark with a national or regional Intellectual Property (IP) office. Aggregate direct trademark applications are those filed by applicants without regard to the residency of the applicant. This figure is used when the national office does not provide a breakdown by direct resident and direct nonresident applications." }, { "id": "Source", "value": "World Intellectual Property Organization (WIPO), WIPO Patent Report: Statistics on Worldwide Patent Activity. The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "57" }, { "id": "IP.TMK.MDRD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Trademark applications, Madrid" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Trademark applications filed are applications to register a trademark with a national or regional Intellectual Property (IP) office. A trademark is a distinctive sign which identifies certain goods or services as those produced or provided by a specific person or enterprise. A trademark provides protection to the owner of the mark by ensuring the exclusive right to use it to identify goods or services, or to authorize another to use it in return for payment. The period of protection varies, but a trademark can be renewed indefinitely beyond the time limit on payment of additional fees. Madrid trademark applications are those received by the national or regional IP office as a result of an international application filed via the WIPO-administered Madrid System." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Trademark applications filed are applications to register a trademark with a national or regional Intellectual Property (IP) office. Madrid trademark applications are those received by the national or regional IP office as a result of an international application filed via the WIPO-administered Madrid System." }, { "id": "Source", "value": "World Intellectual Property Organization (WIPO), WIPO Patent Report: Statistics on Worldwide Patent Activity. The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "57" }, { "id": "IP.TMK.NRCT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The progress and well-being of humanity depend on our capacity to come up with new ideas and creations. Technological progress requires the development and application of new inventions, while a vibrant culture will constantly seek new ways to express itself. \n\nIntellectual property rights are also vital. Inventors, artists, scientists and businesses put a lot of time, money, energy and thought into developing their innovations and creations. To encourage them to do that, they need the chance to make a fair return on their investment. That means giving them rights to protect their intellectual property. Essentially, intellectual property rights such as copyright, patents and trademarks can be viewed like any other property right. They allow the creators or owners of IP to benefit from their work or from their investment in a creation by giving them control over how their property is used. \n\nIP rights have long been recognized within various legal systems. For example, patents to protect inventions were granted in Venice as far back as the fifteenth century. Modern initiatives to protect IP through international law started with the Paris Convention for the Protection of Industrial Property (1883) and the Berne Convention for the Protection of Literary and Artistic Works (1886). These days, there are more than 25 international treaties on IP administered by WIPO. IP rights are also safeguarded by Article 27 of the Universal Declaration of Human Rights.\n\nCreativity and inventiveness are vital. They spur economic growth, create new jobs and industries, and enhance the quality and enjoyment of life. The intellectual property system needs to balance the rights and interests of different groups: of creators and consumers; of businesses and their competitors; of high- and low-income countries. An efficient and fair IP system benefits everyone – including ordinary users and consumers.\n\nSome examples: (a) The multibillion-dollar film, recording, publishing and software industries – which bring pleasure to millions of people worldwide – would not thrive without copyright protection.\n(b) The patent system rewards researchers and inventors while also ensuring that they share their knowledge by making patent applications publicly available, which helps stimulate more innovation. (c) Trademark protection discourages counterfeiting, so businesses can compete on a level playing field and users can be confident they are buying the genuine article.\n\n(source: https://doi.org/10.34667/tind.42176)" }, { "id": "IndicatorName", "value": "Trademark applications, nonresident, by count" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are based on information supplied to World Intellectual Property Organization (WIPO) by IP offices in annual surveys, supplemented by data in national IP office reports. Data may be missing for some offices or periods." }, { "id": "Longdefinition", "value": "A trademark is a sign capable of distinguishing the goods or services of one enterprise from those of other enterprises. Trademarks are protected by intellectual property rights. Non-resident application refers to an application filed with the IP office of or acting on behalf of a state or jurisdiction in which the first-named applicant in the application is not domiciled. Class count is used to render application data for trademark applications across offices comparable, as some offices follow a single-class/single-design filing system while other have a multiple class/design filing system." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2021" }, { "id": "Source", "value": "Statistics Database, World Intellectual Property Organization (WIPO), uri: www.wipo.int/ipstats/, note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Information about the World Intellectual Property Organization (WIPO) data collection can be accessed on the WIPO website: https://www.wipo.int/en/web/ip-statistics/about\nStatistical concept(s): Trademark: A sign used to distinguish the goods or services of one undertaking from those of another. A trademark may consist of words and combinations of words (for instance, names or slogans), logos, figures and images, letters, numbers, sounds, or, in rare instances, smells or moving images, or a combination thereof. The procedures for registering trademarks are governed by the legislation and procedures of national and regional IP offices and WIPO. Trademark rights are limited to the jurisdiction of the IP office that registers the trademark. Trademarks can be registered by filing an application at the relevant national or regional office(s), or by filing an international application through the Madrid System.\n\nNon-resident application refers to an application filed with the IP office of or acting on behalf of a state or jurisdiction in which the first-named applicant in the application is not domiciled.\n\nClass count: The number of classes specified in a trademark application or registration. In the international trademark system, and at certain national and regional offices, an applicant can file a trademark application specifying one or more of the 45 goods and services classes of the Nice Classification. Offices use either a multi-class or a single filing system. For example, the offices of Japan, the Republic of Korea and the United States of America (US), as well as many European IP offices, have multi-class filing systems. On the other hand, the offices of Brazil, Mexico, and South Africa follow a single-class filing system, requiring a separate application for each class in which an applicant seeks trademark protection. To capture the differences in application and registration numbers across offices, it is useful to compare their respective application and registration class counts." }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "IP.TMK.NRES", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Trademark applications, direct nonresident" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Trademark applications filed are applications to register a trademark with a national or regional Intellectual Property (IP) office. A trademark is a distinctive sign which identifies certain goods or services as those produced or provided by a specific person or enterprise. A trademark provides protection to the owner of the mark by ensuring the exclusive right to use it to identify goods or services, or to authorize another to use it in return for payment. The period of protection varies, but a trademark can be renewed indefinitely beyond the time limit on payment of additional fees. Direct nonresident trademark applications are those filed by applicants from abroad directly at a given national IP office." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Trademark applications filed are applications to register a trademark with a national or regional Intellectual Property (IP) office. Direct nonresident trademark applications are those filed by applicants from abroad directly at a given national IP office." }, { "id": "Source", "value": "World Intellectual Property Organization (WIPO), WIPO Patent Report: Statistics on Worldwide Patent Activity. The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. Data last retrieved January 2021." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "57" }, { "id": "IP.TMK.RESD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Trademark applications, direct resident" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Trademark applications filed are applications to register a trademark with a national or regional Intellectual Property (IP) office. A trademark is a distinctive sign which identifies certain goods or services as those produced or provided by a specific person or enterprise. A trademark provides protection to the owner of the mark by ensuring the exclusive right to use it to identify goods or services, or to authorize another to use it in return for payment. The period of protection varies, but a trademark can be renewed indefinitely beyond the time limit on payment of additional fees. Direct resident trademark applications are those filed by domestic applicants directly at a given national IP office." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Trademark applications filed are applications to register a trademark with a national or regional Intellectual Property (IP) office. Direct resident trademark applications are those filed by domestic applicants directly at a given national IP office." }, { "id": "Source", "value": "World Intellectual Property Organization (WIPO), WIPO Patent Report: Statistics on Worldwide Patent Activity. The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. Data last retrieved January 2021." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "57" }, { "id": "IP.TMK.RSCT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The progress and well-being of humanity depend on our capacity to come up with new ideas and creations. Technological progress requires the development and application of new inventions, while a vibrant culture will constantly seek new ways to express itself. \n\nIntellectual property rights are also vital. Inventors, artists, scientists and businesses put a lot of time, money, energy and thought into developing their innovations and creations. To encourage them to do that, they need the chance to make a fair return on their investment. That means giving them rights to protect their intellectual property. Essentially, intellectual property rights such as copyright, patents and trademarks can be viewed like any other property right. They allow the creators or owners of IP to benefit from their work or from their investment in a creation by giving them control over how their property is used. \n\nIP rights have long been recognized within various legal systems. For example, patents to protect inventions were granted in Venice as far back as the fifteenth century. Modern initiatives to protect IP through international law started with the Paris Convention for the Protection of Industrial Property (1883) and the Berne Convention for the Protection of Literary and Artistic Works (1886). These days, there are more than 25 international treaties on IP administered by WIPO. IP rights are also safeguarded by Article 27 of the Universal Declaration of Human Rights.\n\nCreativity and inventiveness are vital. They spur economic growth, create new jobs and industries, and enhance the quality and enjoyment of life. The intellectual property system needs to balance the rights and interests of different groups: of creators and consumers; of businesses and their competitors; of high- and low-income countries. An efficient and fair IP system benefits everyone – including ordinary users and consumers.\n\nSome examples: (a) The multibillion-dollar film, recording, publishing and software industries – which bring pleasure to millions of people worldwide – would not thrive without copyright protection.\n(b) The patent system rewards researchers and inventors while also ensuring that they share their knowledge by making patent applications publicly available, which helps stimulate more innovation. (c) Trademark protection discourages counterfeiting, so businesses can compete on a level playing field and users can be confident they are buying the genuine article.\n\n(source: https://doi.org/10.34667/tind.42176)" }, { "id": "IndicatorName", "value": "Trademark applications, resident, by count" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are based on information supplied to World Intellectual Property Organization (WIPO) by IP offices in annual surveys, supplemented by data in national IP office reports. Data may be missing for some offices or periods." }, { "id": "Longdefinition", "value": "A trademark is a sign capable of distinguishing the goods or services of one enterprise from those of other enterprises. Trademarks are protected by intellectual property rights. A resident application refers to an application filed with the IP office of, or acting for, the state or jurisdiction in which the first named applicant in the application is resident. Class count is used to render application data for trademark applications across offices comparable, as some offices follow a single-class/single-design filing system while other have a multiple class/design filing system." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2021" }, { "id": "Source", "value": "Statistics Database, World Intellectual Property Organization (WIPO), uri: www.wipo.int/ipstats/, note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Information about the World Intellectual Property Organization (WIPO) data collection can be accessed on the WIPO website: https://www.wipo.int/en/web/ip-statistics/about\nStatistical concept(s): Trademark: A sign used to distinguish the goods or services of one undertaking from those of another. A trademark may consist of words and combinations of words (for instance, names or slogans), logos, figures and images, letters, numbers, sounds, or, in rare instances, smells or moving images, or a combination thereof. The procedures for registering trademarks are governed by the legislation and procedures of national and regional IP offices and WIPO. Trademark rights are limited to the jurisdiction of the IP office that registers the trademark. Trademarks can be registered by filing an application at the relevant national or regional office(s), or by filing an international application through the Madrid System.\n\nFor statistical purposes, a resident application refers to an application filed with the IP office of, or acting for, the state or jurisdiction in which the first named applicant in the application is resident. For example, an application filed with the Japan Patent Office (JPO) by a resident of Japan is considered a resident application from the perspective of the JPO. Resident applications are sometimes referred to as “domestic applications.” A resident grant/registration is an IP right issued on the basis of a resident application.\n\nClass count: The number of classes specified in a trademark application or registration. In the international trademark system, and at certain national and regional offices, an applicant can file a trademark application specifying one or more of the 45 goods and services classes of the Nice Classification. Offices use either a multi-class or a single filing system. For example, the offices of Japan, the Republic of Korea and the United States of America (US), as well as many European IP offices, have multi-class filing systems. On the other hand, the offices of Brazil, Mexico, and South Africa follow a single-class filing system, requiring a separate application for each class in which an applicant seeks trademark protection. To capture the differences in application and registration numbers across offices, it is useful to compare their respective application and registration class counts." }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "IP.TMK.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "A trademark is a distinctive sign that identifies certain goods or services as those produced or provided by a specific person or enterprise. The holder of a registered trademark has the legal right to exclusive use of the mark in relation to the products or services for which it is registered. The owner can prevent unauthorized use of the trademark, or a confusingly similar mark, so as to prevent consumers and the public in general from being misled. Unlike patents, trademarks can be maintained indefinitely by paying renewal fees.\n\n The procedures for registering trademarks are governed by the rules and regulations of national and regional IP offices. Trademark rights are limited to the jurisdiction of the authority that registers the trademark. Trademarks can be registered by filing an application at the relevant national or regional office(s), or by filing an international application through the Madrid system.\n\nMany offices in middle- and low-income economies have considerably high numbers of trademark applications compared to other forms of IP, showing the emphasis placed on trademark rights in these markets." }, { "id": "IndicatorName", "value": "Trademark applications, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Detailed components of trademark filings are available at the World Bank at http://data.worldbank.org. Data includes applications filed by direct residents (domestic applicants filing directly at a given national or regional intellectual property [IP] office); direct nonresident (foreign applicants filing directly at a given national or regional IP office); aggregate direct (applicants not identified as direct resident or direct nonresident by the national or regional office); and Madrid (designations received by the national or regional IP office based on international applications filed via the World Intellectual Property Organization-administered Madrid System).\n\nData are based on information supplied to World Intellectual Property Organization (WIPO) by IP offices in annual surveys, supplemented by data in national IP office reports. Data may be missing for some offices or periods.\n\nTrademark registrations are exclusive rights, issued to an applicant by an IP office. For example, registrations are issued to applicants to make use of and exploit their trademark or industrial design for a limited period of time and can, in some cases, particularly in the case of trademarks, be renewed indefinitely." }, { "id": "Longdefinition", "value": "Trademark applications filed are applications to register a trademark with a national or regional Intellectual Property (IP) office. A trademark is a distinctive sign which identifies certain goods or services as those produced or provided by a specific person or enterprise. A trademark provides protection to the owner of the mark by ensuring the exclusive right to use it to identify goods or services, or to authorize another to use it in return for payment. The period of protection varies, but a trademark can be renewed indefinitely beyond the time limit on payment of additional fees." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Trademark applications filed are applications to register a trademark with a national or regional Intellectual Property (IP) office." }, { "id": "Source", "value": "World Intellectual Property Organization (WIPO), World Intellectual Property Indicators and www.wipo.int/econ_stat. The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. Data last retrieved January 2021." }, { "id": "Statisticalconceptandmethodology", "value": "A trademark is a distinctive sign identifying goods or services as produced or provided by a specific person or enterprise. A trademark protects the owner of the mark by ensuring exclusive right to use it to identify goods or services or to authorize another to use it. The period of protection varies, but a trademark can be renewed indefinitely for an additional fee." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "57" }, { "id": "IQ.CPA.BREG.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA business regulatory environment rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThis Business Regulatory Environment criterion assesses the extent to which the legal, regulatory, and policy environment helps or hinders private business in investing, creating jobs, and becoming more productive. The\nemphasis is on direct regulations of business activity and regulation of goods and factor markets. Three sub-components are measured: (a) regulations affecting entry, exit, and competition; (b) regulations of ongoing business operations; and (c) regulations of factor markets (labor and land)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.CPA.DEBT.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA debt policy rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Debt Policy and Management criterion assesses whether the country’s debt management strategy is conducive to ensure medium-term debt sustainability and minimize budgetary risks. The criterion covers: (a) the extent to which external and domestic debt is contracted with a view to achieving/maintaining debt sustainability; and (b) the effectiveness of debt management functions (including the degree of coordination between debt management and other macroeconomic policies, the effectiveness of the debt management unit, and the existence of a debt management strategy and of a legal framework for borrowing)." }, { "id": "Othernotes", "value": "Statistical concept(s): The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector). \n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector). \n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nEach of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\nRefer to Other notes for the Statistical Concept(s)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.CPA.ECON.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA economic management cluster average (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Economic Management cluster includes monetary and exchange rate policies, fiscal policy, and debt policy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.CPA.ENVR.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA policy and institutions for environmental sustainability rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThis criterion assesses the extent to which environmental policies and institutions foster the protection and sustainable use of natural resources and the management of pollution." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.CPA.FINQ.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA quality of budgetary and financial management rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe quality of budgetary and financial management criterion assesses the extent to which there is: (a) a comprehensive and credible budget, linked to policy priorities; (b) effective financial management systems to ensure that the budget is implemented as intended in a controlled and predictable way; and (c) timely and accurate accounting and fiscal reporting, including timely audit of public accounts and effective arrangements for follow up." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "57" }, { "id": "IQ.CPA.FINS.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA financial sector rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe financial sector criterion assesses the policies and regulations that affect financial sector development. Three dimensions are covered: (a) financial stability; (b) the sector’s efficiency, depth, and resource mobilization strength; and (c) access to financial services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: http://www.worldbank.org/ida" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.CPA.FISP.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA fiscal policy rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThis CPIA fiscal policy criterion assesses the quality of the fiscal policy in its stabilization and allocation functions. The stabilization function deals with achieving macroeconomic policy objectives in conjunction with coherent monetary and exchange rate policies—smoothing business cycle fluctuations, accommodating shocks. The allocation function is concerned with the appropriate provision of public goods. The criterion pays attention to public expenditure composition, including, for example, the provision of public infrastructure and agriculture related public goods and services that support medium-term growth." }, { "id": "Othernotes", "value": "Statistical concept(s): The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector). \n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector). \n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nEach of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\nRefer to Other notes for the Statistical Concept(s)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.CPA.GNDR.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA gender equality rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe CPIA gender equality rating criterion assesses the extent to which the country has enacted and put in place institutions and programs to enforce laws and policies that: (a) promote equal access for men and women to human capital development; (b) promote equal access for men and women to productive and economic resources; and (c) give men and women equal status and protection under the law. For the human capital development dimension, the focus is on primary completion and access to secondary education, access to health care during delivery and to family planning, and adolescent fertility rate. For access to economic and productive resources, the focus is on labor force participation, land tenure and property and inheritance rights. For Agency for change and equalization of status and protection under the law the focus is on individual and family rights and personal security (violence against women, trafficking, or sexual harassment) and political participation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.CPA.HRES.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA building human resources rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe CPIA building human resources criterion assesses the national policies and public and private sector service delivery that affect access to and quality of health and education-related services. The criterion has two components: (a) health, including population and reproductive health, and nutrition as well as the prevention and treatment of communicable diseases such as HIV/AIDS, tuberculosis, and malaria; and (b) education, training and literacy programs, and early child development (ECD) programs, including both formal and non-formal programs (which may combine education, health, and nutrition interventions) aimed at children aged 0-6." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.CPA.IRAI.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "IDA resource allocation index (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score (the IDA resource allocation index) and scores for sixteen criteria that compose the CPIA. \n\nThese criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.CPA.MACR.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA macroeconomic management rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe CPIA macroeconomic management cluster assesses the monetary, exchange rate, and fiscal policy, as well as debt policy and management." }, { "id": "Othernotes", "value": "Statistical concept(s): The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector). \n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector). \n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nEach of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\nRefer to Other notes for the Statistical Concept(s)." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.CPA.PADM.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA quality of public administration rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe CPIA Quality of Public Administration criterion covers the core administration defined as the civilian central government (and subnational governments, to the extent that their size or policy responsibilities are significant) excluding health and education personnel, and police. The criterion assesses the functioning of the core administration in three areas: (a) managing its own operations; (b) ensuring quality in policy implementation and regulatory management; and (c) coordinating the larger public sector Human Resources Management regime outside the core administration (de-concentrated and arms-length bodies and subsidiary governments)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.CPA.PRES.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA equity of public resource use rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Equity of public resource use assesses the extent to which the pattern of public expenditures and revenue collection affects the poor and is consistent with national poverty reduction priorities." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: All criteria within each cluster receive equal weight, and each cluster has a 25 percent weight in the overall score, which is obtained by averaging the average scores of the four clusters. For each of the 16 criteria countries are rated on a scale of 1 (low) to 6 (high). The scores depend on the level of performance in a given year assessed against the criteria, rather than on changes in performance compared with the previous year. All 16 CPIA criteria contain a detailed description of each rating level. In assessing country performance, World Bank staff evaluate the country's performance on each of the criteria and assign a rating. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge and on relevant publicly available indicators. In interpreting the assessment scores, it should be noted that the criteria are designed in a developmentally neutral manner. Accordingly, higher scores can be attained by a country that, given its stage of development, has a policy and institutional framework that more strongly fosters growth and poverty reduction.\n\nThe country teams that prepare the ratings are very familiar with the country, and their assessments are based on country diagnostic studies prepared by the World Bank or other development organizations and on their own professional judgment. An early consultation is conducted with country authorities to make sure that the assessments are informed by up-to-date information. To ensure that scores are consistent across countries, the process involves two key phases. In the benchmarking phase a small representative sample of countries drawn from all regions is rated. Country teams prepare proposals that are reviewed first at the regional level and then in a Bankwide review process. A similar process is followed to assess the performance of the remaining countries, using the benchmark countries' scores as guideposts. The final ratings are determined following a Bankwide review. The overall numerical IRAI score and the separate criteria scores were first publicly disclosed in June 2006." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "57" }, { "id": "IQ.CPA.PROP.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA property rights and rule-based governance rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Property Rights and Rule-Based Governance criterion assesses the extent to which economic activity is facilitated by an effective legal system and rule-based governance structure in which property and contract rights are reliably respected and enforced. It encompasses three dimensions: (a) legal framework for secure property and contract rights, including predictability and impartiality of laws and regulations; (b) quality of the legal and judicial system, as measured by independence, accessibility, legitimacy, efficiency, transparency, and integrity of the courts and other relevant dispute resolution mechanisms; and (c) crime and violence as an impediment to economic\nactivity and citizen security." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.CPA.PROT.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA social protection rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Social Protection criterion assess government policies in social protection and labor market regulations that reduce the risk of becoming poor, assist those who are poor to better manage further risks, and ensure a minimal level of welfare to all people. Specifically it evaluates social protection (SP) and labor policies, namely those engaged in risk prevention by supporting savings and risk pooling through social insurance, protection against destitution through redistributive safety net programs and promotion of human capital development and income generation, including labor market programs. It also assesses the functioning of an SP system, including its effectiveness in a crisis and in providing arrangements and incentives to help beneficiaries to move from protection to promotion and prevention, including through interactions with private, informal means of SP. The criterion covers: (a) the overall SP system; (b) social safety net programs; (c) labor markets programs and policies, namely those aiming to promote employment creation and productivity growth while protecting core labor standards and ensuring adequate working conditions; (d) local service delivery and civil society participation in community development programs; and (e) pension and old age savings programs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.CPA.PUBS.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA public sector management and institutions cluster average (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Public Sector Management and Institutions cluster includes property rights and rule-based governance, quality of budgetary and financial management, efficiency of revenue mobilization, quality of public administration, and transparency, accountability, and corruption in the public sector." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.CPA.REVN.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA efficiency of revenue mobilization rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThis Efficiency of Revenue Mobilization criterion assesses the overall pattern of revenue mobilization, not only the tax structure as it exists on paper, but revenue from all sources as they are collected. Separate sub-ratings\nare provided for (a) tax policy and (b) tax administration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "57" }, { "id": "IQ.CPA.SOCI.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA policies for social inclusion/equity cluster average (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Policies for Social Inclusion and Equity cluster includes gender equality, equity of public resource use, building human resources, social protection and labor, and policies and institutions for environmental sustainability." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.CPA.STRC.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA structural policies cluster average (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Structural Policies cluster includes trade, financial sector, and business regulatory environment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.CPA.TRAD.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The International Development Association (IDA) is the part of the World Bank Group that helps the poorest countries reduce poverty by providing concessional loans and grants for programs aimed at boosting economic growth and improving living conditions. IDA funding helps these countries deal with the complex challenges they face in meeting the Millennium Development Goals.\n\nThe World Bank's IDA Resource Allocation Index (IRAI) is based on the results of the annual Country Policy and Institutional Assessment (CPIA) exercise, which covers the IDA-eligible countries. Country assessments have been carried out annually since the mid-1970s by World Bank staff. Over time the criteria have been revised from a largely macroeconomic focus to include governance aspects and a broader coverage of social and structural dimensions. Country performance is assessed against a set of 16 criteria grouped into four clusters: economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions. IDA resources are allocated to a country on per capita terms based on its IDA country performance rating and, to a limited extent, based on its per capita gross national income. This ensures that good performers receive a higher IDA allocation in per capita terms. The IRAI is a key element in the country performance rating." }, { "id": "IndicatorName", "value": "CPIA trade rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance." }, { "id": "Longdefinition", "value": "Trade assesses how the policy framework fosters trade in goods." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: http://www.worldbank.org/ida" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: All criteria within each cluster receive equal weight, and each cluster has a 25 percent weight in the overall score, which is obtained by averaging the average scores of the four clusters. For each of the 16 criteria countries are rated on a scale of 1 (low) to 6 (high). The scores depend on the level of performance in a given year assessed against the criteria, rather than on changes in performance compared with the previous year. All 16 CPIA criteria contain a detailed description of each rating level. In assessing country performance, World Bank staff evaluate the country's performance on each of the criteria and assign a rating. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge and on relevant publicly available indicators. In interpreting the assessment scores, it should be noted that the criteria are designed in a developmentally neutral manner. Accordingly, higher scores can be attained by a country that, given its stage of development, has a policy and institutional framework that more strongly fosters growth and poverty reduction.\n\nThe country teams that prepare the ratings are very familiar with the country, and their assessments are based on country diagnostic studies prepared by the World Bank or other development organizations and on their own professional judgment. An early consultation is conducted with country authorities to make sure that the assessments are informed by up-to-date information. To ensure that scores are consistent across countries, the process involves two key phases. In the benchmarking phase a small representative sample of countries drawn from all regions is rated. Country teams prepare proposals that are reviewed first at the regional level and then in a Bankwide review process. A similar process is followed to assess the performance of the remaining countries, using the benchmark countries' scores as guideposts. The final ratings are determined following a Bankwide review. The overall numerical IRAI score and the separate criteria scores were first publicly disclosed in June 2006." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "57" }, { "id": "IQ.CPA.TRAN.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank’s concessional lending (lending by the World Bank Group’s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country’s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation — allocations are performance based. A country’s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI)." }, { "id": "IndicatorName", "value": "CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Longdefinition", "value": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Transparency, Accountability, and Corruption in the Public Sector criterion assesses the extent to which the executive, legislators, and other high-level officials can be held accountable for their use of funds, administrative decisions, and results obtained. Accountability is generally enhanced by transparency in decision-making, access to relevant and timely information, public and media scrutiny, and by institutional checks (e.g., inspector general, ombudsman, or independent audit) on the authority of the chief executive. The criterion covers four dimensions: (a) the accountability of the executive and other top officials to effective oversight institutions; (b) access of civil society to timely and reliable information on public affairs and public policies, including fiscal information (on public expenditures, revenues, and large contract awards); (c) state capture by narrow vested interests; and (d) integrity in the management of public resources, including aid and natural resource revenues." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country’s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country’s actual performance on each of the criteria and assign a rating. These scores are averaged—first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given. \n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Score [SCORE]" } ], "source_id": "57" }, { "id": "IQ.SCI.MTHD", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "Methodology assessment of statistical capacity (scale 0 - 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The methodology indicator measures a country’s ability to adhere to internationally recommended standards and methods. The methodology score is calculated as the weighted average of 10 underlying indicator scores. The final methodology score contributes 1/3 of the overall Statistical Capacity Indicator score." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Bulletin Board on Statistical Capacity (http://bbsc.worldbank.org)." }, { "id": "Statisticalconceptandmethodology", "value": "The Practice score is calculated as weighted average of all 10 Practice indicator scores." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "57" }, { "id": "IQ.SCI.OVRL", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "Statistical Capacity is a nation’s ability to collect, analyze, and disseminate high-quality data about its population and economy. Quality statistics are essential for all stages of evidence-based decision-making, including: Monitoring social and economic indicators, Allocating political representation and government resources, Guiding private sector investment, as well as Informing the international donor community for program design and policy formulation." }, { "id": "IndicatorName", "value": "Statistical Capacity Score (Overall Average) (scale 0 - 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The Statistical Capacity Indicator is a composite score assessing the capacity of a country’s statistical system. It is based on a diagnostic framework assessing the following areas: methodology; data sources; and periodicity and timeliness. Countries are scored against 25 criteria in these areas, using publicly available information and/or country input. The overall Statistical Capacity score is then calculated as a simple average of all three area scores on a scale of 0-100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Bulletin Board on Statistical Capacity (http://bbsc.worldbank.org)." }, { "id": "Statisticalconceptandmethodology", "value": "The Statistical Capacity Indicator score is calculated as the average of the scores of the 3 dimensions, i.e. Availability, Collection, Practice." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "57" }, { "id": "IQ.SCI.PRDC", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "Periodicity and timeliness assessment of statistical capacity (scale 0 - 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The periodicity and timeliness indicator assesses the availability and periodicity of key socioeconomic indicators. It measures the extent to which data are made accessible to users through transformation of source data into timely statistical outputs. The periodicity score is calculated as the weighted average of 10 underlying indicator scores. The final periodicity score contributes 1/3 of the overall Statistical Capacity Indicator score." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Bulletin Board on Statistical Capacity (http://bbsc.worldbank.org)." }, { "id": "Statisticalconceptandmethodology", "value": "The Availability score is calculated as weighted average of all 10 Availability indicator scores." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "57" }, { "id": "IQ.SCI.SRCE", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "IndicatorName", "value": "Source data assessment of statistical capacity (scale 0 - 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The source data indicator reflects whether a country conducts data collection activities in line with internationally recommended periodicity, and whether data from administrative systems are available. The source data score is calculated as the weighted average of 5 underlying indicator scores. The final source data score contributes 1/3 of the overall Statistical Capacity Indicator score." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Bulletin Board on Statistical Capacity (http://bbsc.worldbank.org)." }, { "id": "Statisticalconceptandmethodology", "value": "The Collection score is calculated as weighted average of all 5 Collection indicator scores." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" } ], "source_id": "57" }, { "id": "IQ.SPI.OVRL", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The new Statistical Performance Indicators (SPI) will replace the Statistical Capacity Index (SCI), which the World Bank has regularly published since 2004. Although the goals are the same, to offer a better tool to measure the statistical systems of countries, the new SPI framework has expanded into new areas including in the areas of data use, administrative data, geospatial data, data services, and data infrastructure. The SPI provides a framework that can help countries measure where they stand in several dimensions and offers an ambitious measurement agenda for the international community." }, { "id": "IndicatorName", "value": "Statistical performance indicators (SPI): Overall score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Like all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries." }, { "id": "Longdefinition", "value": "The SPI overall score is a composite score measuring country performance across five pillars: data use, data services, data products, data sources, and data infrastructure. The new Statistical Performance Indicators (SPI) will replace the Statistical Capacity Index (SCI), which the World Bank has regularly published since 2004. Although the goals are the same, to offer a better tool to measure the statistical systems of countries, the new SPI framework has expanded into new areas including in the areas of data use, administrative data, geospatial data, data services, and data infrastructure. The SPI provides a framework that can help countries measure where they stand in several dimensions and offers an ambitious measurement agenda for the international community." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2016-2024" }, { "id": "Source", "value": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Weighted average of all statistical performance indicators. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-100)" } ], "source_id": "57" }, { "id": "IQ.SPI.PIL1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The data use (outcome) pillar is segmented by five types of users: (i) the legislature, (ii) the executive branch, (iii) civil society (including sub-national actors), (iv) academia and (v) international bodies. Each dimension would have associated indicators to measure performance. A mature system would score well across all dimensions whereas a less mature one would have weaker scores along certain dimensions. The gaps would give insights into prioritization among user groups and help answer questions as to why the existing services are not resulting in higher use of national statistics in a particular segment." }, { "id": "IndicatorName", "value": "Statistical performance indicators (SPI): Pillar 1 data use score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Currently, the dashboard only features indicators for one of the five dimensions of data use, which is data use by international organizations. Indicators on whether statistical systems are providing useful data to their national governments (legislature and executive branches), to civil society, and to academia are absent. Thus the dashboard does not yet assess if national statistical systems are meeting the data needs of a large swathe of users." }, { "id": "Longdefinition", "value": "The data use overall score is a composite score measuring the demand side of the statistical system. The data use pillar is segmented by five types of users: (i) the legislature, (ii) the executive branch, (iii) civil society (including sub-national actors), (iv) academia and (v) international bodies. Each dimension would have associated indicators to measure performance. A mature system would score well across all dimensions whereas a less mature one would have weaker scores along certain dimensions. The gaps would give insights into prioritization among user groups and help answer questions as to why the existing services are not resulting in higher use of national statistics in a particular segment. Currently, the SPI only features indicators for one of the five dimensions of data use, which is data use by international organizations. Indicators on whether statistical systems are providing useful data to their national governments (legislature and executive branches), to civil society, and to academia are absent. Thus the dashboard does not yet assess if national statistical systems are meeting the data needs of a large swathe of users." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2024" }, { "id": "Source", "value": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Weighted average of statistical performance indicators related to data use. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-100)" } ], "source_id": "57" }, { "id": "IQ.SPI.PIL2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The data services (output) pillar is segmented by four service types: (i) the quality of data releases, (ii) the richness and openness of online access, (iii) the effectiveness of advisory and analytical services related to statistics, and (iv) the availability and use of data access services such as secure microdata access. Advisory and analytical services might incorporate elements related to data stewardship services including input to national data strategies, advice on data ethics and calling out misuse of data in accordance with the Fundamental Principles of Official Statistics." }, { "id": "IndicatorName", "value": "Statistical performance indicators (SPI): Pillar 2 data services score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Under the pillar of data services an area that needs improvement is the measurement of advisory and analytical services provided by NSOs, such as data stewardship services. By measuring this type of work done by NSOs that goes beyond producing data, the international community and the NSOs themselves can better assess whether this type of support is in place." }, { "id": "Longdefinition", "value": "The data services pillar overall score is a composite indicator based on four dimensions of data services: (i) the quality of data releases, (ii) the richness and openness of online access, (iii) the effectiveness of advisory and analytical services related to statistics, and (iv) the availability and use of data access services such as secure microdata access. Advisory and analytical services might incorporate elements related to data stewardship services including input to national data strategies, advice on data ethics and calling out misuse of data in accordance with the Fundamental Principles of Official Statistics." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2016-2024" }, { "id": "Source", "value": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Weighted average of statistical performance indicators related to data services. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-100)" } ], "source_id": "57" }, { "id": "IQ.SPI.PIL3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system’s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country." }, { "id": "IndicatorName", "value": "Statistical performance indicators (SPI): Pillar 3 data products score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The data products overall score is a composite score measureing whether the country is able to produce relevant indicators, primarily related to SDGs. The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system’s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2024" }, { "id": "Source", "value": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Weighted average of statistical performance indicators related to data products. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-100)" } ], "source_id": "57" }, { "id": "IQ.SPI.PIL4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country’s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met." }, { "id": "IndicatorName", "value": "Statistical performance indicators (SPI): Pillar 4 data sources score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In the data sources pillar, more information is needed in the areas of administrative data, geospatial data, and private and citizen generated data. On administrative data, the picture is incomplete with no measures of whether countries have administrative data systems in place to measure health, education, labor, and social protection program statistics. For the geospatial indicator, there is a proxy measure of whether the country is able to produce indicators at the sub-national level, but as yet, no understanding of how countries are using geospatial information in other ways, for instance using satellite data. And while the world is increasingly awash with private and citizen generated data (e.g., on mobility, job search, or social networking), on a global scale there is no reliable source to measure how national statistical systems are incorporating this information." }, { "id": "Longdefinition", "value": "The data sources overall score is a composity measure of whether countries have data available from the following sources: Censuses and surveys, administrative data, geospatial data, and private sector/citizen generated data. The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country’s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2015-2024" }, { "id": "Source", "value": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Weighted average of statistical performance indicators related to data sources. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-100)" } ], "source_id": "57" }, { "id": "IQ.SPI.PIL5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The data infrastructure (capability) pillar includes hard and soft infrastructure segments, itemizing essential cross cutting requirements for an effective statistical system. The segments are: (i) legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system; (ii) standards and methods addressing compliance with recognized frameworks and concepts; (iii) skills including level of skills within the statistical system and among users (statistical literacy); (iv) partnerships reflecting the need for the statistical system to be inclusive and coherent; and (v) finance mobilized both domestically and from donors." }, { "id": "IndicatorName", "value": "Statistical performance indicators (SPI): Pillar 5 data infrastructure score (scale 0-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Several of the ‘soft’ components of the data infrastructure pillar lack adequate data. This includes the areas of skills and of partnerships between entities in the national statistical system. The dashboard makes use of the PARIS21 led SDG indicator on whether the statistical legislations in countries met the standards of the UN Fundamental Principles of Statistics, but this was not incorporated into the overall SPI score, because of inadequate country coverage. This is also true of the PARIS21 led SDG indicator on whether the national statistical system is fully funded. Countries would need to be encouraged to report on this information." }, { "id": "Longdefinition", "value": "The data infrastructure pillar overall score measures the hard and soft infrastructure segments, itemizing essential cross cutting requirements for an effective statistical system. The segments are: (i) legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system; (ii) standards and methods addressing compliance with recognized frameworks and concepts; (iii) skills including level of skills within the statistical system and among users (statistical literacy); (iv) partnerships reflecting the need for the statistical system to be inclusive and coherent; and (v) finance mobilized both domestically and from donors." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2016-2024" }, { "id": "Source", "value": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Weighted average of statistical performance indicators related to data infrastructure. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index" }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Index (0-100)" } ], "source_id": "57" }, { "id": "IQ.WEF.CUST.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The World Economic Forum's annual Global Competitiveness Reports have studied and benchmarked the many factors underpinning national compeititiveness. The goal has been to provide insight and stimulate the discussion among all stakeholders on the best strategies and policies to help countries overcome the obstacles to improving competitiveness. It serves as a critical reminder of the importance of structural economic fundamentals for sustained growth.\n\nBurden of customs procedure falls under WEF's sixth pillar: Goods market efficiency. Countries with efficient goods markets are well positioned to produce the right mix of products and services given their particular supply-and-demand conditions, as well as to ensure that these goods can be most effectively traded in the economy. Healthy market competition, both domestic and foreign, is important in driving market efficiency and thus business productivity by ensuring that the most efficient firms, producing goods demanded by the market, are those that thrive. The best possible environment for the exchange of goods requires a minimum of impediments to business activity through government intervention. Protectionist measures are counterproductive as they reduce aggregate economic activity. Market efficiency also depends on demand conditions such as customer orientation and buyer sophistication. For cultural or historical reasons, customers may be more demanding in some countries than in others. This can create an important competitive advantage, as it forces companies to be more innovative and customer-oriented and thus imposes the discipline necessary for efficiency to be achieved in the market." }, { "id": "IndicatorName", "value": "Burden of customs procedure, WEF (1=extremely inefficient to 7=extremely efficient)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although data were collected for almost 150 economies in 2012, after the editing process only data for 140 economies were used. Company size is defined as the number of employees of the firm in the country of the Survey respondent. Adjustments were made to the data based on searches in company directories and data gathered through the administration of the Survey in past years. In order to reach the required number of surveys in each country (80 for most economies and 300 for the BRIC countries and the United States), a Partner Institute will use the response rate from previous years. In cases where the information about the company's sector of activity is missing, the average response values across the surveys are apportioned to the other sectors according to the sample sizes in those other sectors. This has the effect of including these surveys on a one-for-one basis as they occur in the sample with no adjustment for sector. If the weight of an individual response exceeds 10 percent of the country sample, the sector-weighted average is abandoned for the benefit of a simple average." }, { "id": "Longdefinition", "value": "Burden of Customs Procedure measures business executives' perceptions of their country's efficiency of customs procedures. The rating ranges from 1 to 7, with a higher score indicating greater efficiency. Data are from the World Economic Forum's Executive Opinion Survey, conducted for 30 years in collaboration with 150 partner institutes. The 2009 round included more than 13,000 respondents from 133 countries. Sampling follows a dual stratification based on company size and the sector of activity. Data are collected online or through in-person interviews. Responses are aggregated using sector-weighted averaging. The data for the latest year are combined with the data for the previous year to create a two-year moving average. Respondents evaluated the efficiency of customs procedures in their country. The lowest score (1) rates the customs procedure as extremely inefficient, and the highest score (7) as extremely efficient." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Burden of Customs Procedure measures business executives' perceptions of their country's efficiency of customs procedures. The rating ranges from 1 to 7, with a higher score indicating greater efficiency." }, { "id": "Source", "value": "World Economic Forum, Global Competiveness Report and data files." }, { "id": "Statisticalconceptandmethodology", "value": "Data on the burden of customs procedures are from the World Economic Forum's (WEF) Executive Opinion Survey. The latest round included over 14,000 respondents from 144 economies. Data are collected online, through in-person interviews with business executives, and through mail and telephone interviews, with an online survey as an alternative. Sampling follows a dual stratification based on company size and the sector of activity. Responses are aggregated using sector-weighted averaging. The data for the latest year are combined with the data for the previous year to create a two-year moving average. Respondents evaluated the efficiency of customs procedures (related to the entry and exit of merchandise) in their country. The lowest value (1) rates the customs procedure as extremely inefficient, and the highest score (7) as extremely efficient.\n\nThe yearly administration of the Survey is carried out with a strong network of over 160 Partner Institutes worldwide. The Partner Institutes are typically recognized research institutes, universities, business organizations, and in some cases survey consultancies. The Partner Institutes are tasked to follow detailed sampling guidelines in view of capturing a strong and representative sample. The Partner Institutes must: prepare a \"sample frame,\" or large list of potential respondents, which includes firms representing the main sectors of the economy (agriculture, manufacturing industry, non-manufacturing industry, and services); separate the frame into two lists: one that includes only large firms, and a second list that includes all other firms; and based on these lists, choose a random selection of these firms to receive the Survey.\n\nSurveys with less than 50% completion rate are excluded. In addition, WEF uses Mahalanobis distance technique and a univariate outlier test to remove outliers. To weight the data by sector, individual answers are aggregated at country level and weighted by the estimated contributions of each of the four main economic sectors (agriculture, manufacturing industry, non-manufacturing industry, and services) to a country's gross domestic product. The weights of the other sectors are then adjusted proportionally to their weight in the country's GDP.\n\nAs a final step, the sector-weighted country averages for 2012 are combined with the 2011 averages to produce the country scores that are used for the computation of the GCI 2012-2013 and for other projects. This moving average technique consists of taking a weighted average of the most recent year's Survey results together with a discounted average of the previous year." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "57" }, { "id": "IQ.WEF.PORT.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "The World Economic Forum's annual Global Competitiveness Reports have studied and benchmarked the many factors underpinning national compeititiveness. The goal has been to provide insight and stimulate the discussion among all stakeholders on the best strategies and policies to help countries overcome the obstacles to improving competitiveness. It serves as a critical reminder of the importance of structural economic fundamentals for sustained growth.\n\nThe quality of port infrastructure falls under WEF's second pillar: Infrastructure. Extensive and efficient infrastructure is critical for ensuring the effective functioning of the economy, as it is an important factor in determining the location of economic activity and the kinds of activities or sectors that can develop in a particular instance. Well-developed infrastructure reduces the effect of distance between regions, integrating the national market and connecting it at low cost to markets in other countries and regions. In addition, the quality and extensiveness of infrastructure networks significantly impact economic growth and reduce income inequalities and poverty in a variety of ways. A well-developed transport and communications infrastructure network is a prerequisite for the access of less-developed communities to core economic activities and services. Effective modes of transport - including quality roads, railroads, ports, and air transport - enable entrepreneurs to get their goods and services to market in a secure and timely manner and facilitate the movement of workers to the most suitable jobs." }, { "id": "IndicatorName", "value": "Quality of port infrastructure, WEF (1=extremely underdeveloped to 7=well developed and efficient by international standards)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although data were collected for almost 150 economies in 2012, after the editing process only data for 140 economies were used. Company size is defined as the number of employees of the firm in the country of the Survey respondent. Adjustments were made to the data based on searches in company directories and data gathered through the administration of the Survey in past years. In order to reach the required number of surveys in each country (80 for most economies and 300 for the BRIC countries and the United States), a Partner Institute will use the response rate from previous years. In cases where the information about the company's sector of activity is missing, the average response values across the surveys are apportioned to the other sectors according to the sample sizes in those other sectors. This has the effect of including these surveys on a one-for-one basis as they occur in the sample with no adjustment for sector. If the weight of an individual response exceeds 10 percent of the country sample, the sector-weighted average is abandoned for the benefit of a simple average." }, { "id": "Longdefinition", "value": "The Quality of Port Infrastructure measures business executives' perception of their country's port facilities. Data are from the World Economic Forum's Executive Opinion Survey, conducted for 30 years in collaboration with 150 partner institutes. The 2009 round included more than 13,000 respondents from 133 countries. Sampling follows a dual stratification based on company size and the sector of activity. Data are collected online or through in-person interviews. Responses are aggregated using sector-weighted averaging. The data for the latest year are combined with the data for the previous year to create a two-year moving average. Scores range from 1 (port infrastructure considered extremely underdeveloped) to 7 (port infrastructure considered efficient by international standards). Respondents in landlocked countries were asked how accessible are port facilities (1 = extremely inaccessible; 7 = extremely accessible)." }, { "id": "Othernotes", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2025 (July 1, 2024-June 30, 2025)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Quality of Port Infrastructure measures business executives' perceptions of their country's port facilities. The rating ranges from 1 to 7, with a higher score indicating better development of port infrastructure." }, { "id": "Source", "value": "World Economic Forum, Global Competiveness Report." }, { "id": "Statisticalconceptandmethodology", "value": "Data on the quality of port Infrastructure index are from the World Economic Forum's (WEF) Executive Opinion Survey. The latest round included respondents from 144 economies. Data are collected online, through in-person interviews with business executives, and through mail and telephone interviews, with an online survey as an alternative. Sampling follows a dual stratification based on company size and the sector of activity. Responses are aggregated using sector-weighted averaging. The data for the latest year are combined with the data for the previous year to create a two-year moving average. Respondents were asked to assess the port facilities in their country. The lowest value (1) rates the port facilities as extremely underdeveloped, and the highest value (7) rates them well developed and efficient by international standards. For landlocked countries, respondents were asked how accessible are port facilities, where the lowest value (1) was extremely inaccessible and the highest value (7) extremely accessible.\n \nThe yearly administration of the Survey is carried out with a strong network of over 160 Partner Institutes worldwide. The Partner Institutes are typically recognized research institutes, universities, business organizations, and in some cases survey consultancies. The Partner Institutes are tasked to follow detailed sampling guidelines in view of capturing a strong and representative sample. The Partner Institutes must: prepare a \"sample frame,\" or large list of potential respondents, which includes firms representing the main sectors of the economy (agriculture, manufacturing industry, non-manufacturing industry, and services); separate the frame into two lists: one that includes only large firms, and a second list that includes all other firms; and based on these lists, choose a random selection of these firms to receive the Survey.\n\nSurveys with less than 50% completion rate are excluded. In addition, WEF uses Mahalanobis distance technique and a univariate outlier test to remove outliers. To weight the data by sector, individual answers are aggregated at country level and weighted by the estimated contributions of each of the four main economic sectors (agriculture, manufacturing industry, non-manufacturing industry, and services) to a country's gross domestic product. The weights of the other sectors are then adjusted proportionally to their weight in the country's GDP.\n\nAs a final step, the sector-weighted country averages for 2012 are combined with the 2011 averages to produce the country scores that are used for the computation of the GCI 2012-2013 and for other projects. This moving average technique consists of taking a weighted average of the most recent year's Survey results together with a discounted average of the previous year." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "57" }, { "id": "IS.AIR.DPRT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Aviation traffic data are essential for understanding the role of air transport in economic development, global connectivity, and social progress. These statistics, covering passenger volumes, freight volumes, and aircraft departures, provide a standardized measure of air transport activity across countries and regions and are closely linked to economic growth, trade integration, tourism development, and labor mobility. \n\nAviation traffic indicators also help assess the resilience and efficiency of transport systems, track the recovery from economic shocks or crises, and identify capacity constraints or infrastructure investment needs.\n\nFrom a development perspective, aviation statistics support evidence-based policy making in areas such as transport planning, regional integration, climate and emissions management, and inclusive access to services, making them a key input for monitoring progress toward sustainable and connected economies." }, { "id": "IndicatorName", "value": "Air transport, registered carrier departures worldwide" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While ICAO aviation data provide a globally standardized and comprehensive view of air transport activity, several limitations affect their accuracy and comparability. Data quality depends on the completeness and consistency of country reporting, which can vary due to differences in national statistical capacity, regulatory environments, and adherence to ICAO definitions. Some countries may not report all relevant data, leading ICAO to estimate missing values using historical submissions and published schedules, which can introduce uncertainty. Changes in airline registration, mergers, or operational practices may also affect the attribution of traffic statistics. Additionally, the aggregation of scheduled and non-scheduled services, as well as the treatment of transit and transfer passengers, may differ across countries, impacting cross-country comparability." }, { "id": "Longdefinition", "value": "Registered carrier departures worldwide are domestic takeoffs and takeoffs abroad of air carriers registered in the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Civil Aviation Statistics of the World, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers;\nICAO Staff estimates, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data is obtained from airports, airport operators, airport websites and/or civil aviation authorities. Data on passengers are reported annually by each State (for all its commercial air carriers, including scheduled and non-scheduled flights) via standardized forms sent to ICAO. \n\nEach passenger is counted once per flight number and not repeatedly on each individual stage of that flight, with a single exception that a passenger flying on both the international and domestic stages of the same flight should be counted as both a domestic and an international passenger.\n\nWhere some carriers do not report, ICAO may use historical reports or published flight-schedules to estimate their traffic.\nStatistical concept(s): Aircraft departures represent the number of take-offs of aircraft. For statistical purposes, departures are equal to the number of landings made or flight stages flown. \n\nA flight stage is the operation of an aircraft from take-off to its next landing. A flight stage is classified as either international or domestic. International flight stage is one or both terminals in the territory of a State, other than the State in which the air carrier has its principal place of business." }, { "id": "Topic", "value": "Infrastructure: Transportation" }, { "id": "Unitofmeasure", "value": "Count" } ], "source_id": "57" }, { "id": "IS.AIR.GOOD.MT.K1", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Aviation traffic data are essential for understanding the role of air transport in economic development, global connectivity, and social progress. These statistics, covering passenger volumes, freight volumes, and aircraft departures, provide a standardized measure of air transport activity across countries and regions and are closely linked to economic growth, trade integration, tourism development, and labor mobility. \n\nAviation traffic indicators also help assess the resilience and efficiency of transport systems, track the recovery from economic shocks or crises, and identify capacity constraints or infrastructure investment needs.\n\nFrom a development perspective, aviation statistics support evidence-based policy making in areas such as transport planning, regional integration, climate and emissions management, and inclusive access to services, making them a key input for monitoring progress toward sustainable and connected economies." }, { "id": "IndicatorName", "value": "Air transport, freight (million ton-km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While ICAO aviation data provide a globally standardized and comprehensive view of air transport activity, several limitations affect their accuracy and comparability. Data quality depends on the completeness and consistency of country reporting, which can vary due to differences in national statistical capacity, regulatory environments, and adherence to ICAO definitions. Some countries may not report all relevant data, leading ICAO to estimate missing values using historical submissions and published schedules, which can introduce uncertainty. Changes in airline registration, mergers, or operational practices may also affect the attribution of traffic statistics. Additionally, the aggregation of scheduled and non-scheduled services, as well as the treatment of transit and transfer passengers, may differ across countries, impacting cross-country comparability." }, { "id": "Longdefinition", "value": "Air freight is the volume of freight, express, and diplomatic bags carried on each flight stage (operation of an aircraft from takeoff to its next landing), measured in metric tons times kilometers traveled." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Civil Aviation Statistics of the World, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers;\nICAO Staff estimates, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data is obtained from airports, airport operators, airport websites and/or civil aviation authorities. Data on passengers are reported annually by each State (for all its commercial air carriers, including scheduled and non-scheduled flights) via standardized forms sent to ICAO. \n\nEach passenger is counted once per flight number and not repeatedly on each individual stage of that flight, with a single exception that a passenger flying on both the international and domestic stages of the same flight should be counted as both a domestic and an international passenger.\n\nWhere some carriers do not report, ICAO may use historical reports or published flight-schedules to estimate their traffic.\nStatistical concept(s): A metric tonne of freight or mail carried one kilometre. Freight tonne-kilometres equal the sum of the products obtained by multiplying the number of tonnes of freight, express, diplomatic bags carried on each flight stage by the stage distance. For ICAO statistical purposes freight includes express and diplomatic bags but not passenger baggage. Mail tonne-kilometres are computed in the same way as freight tonne-kilometres." }, { "id": "Topic", "value": "Infrastructure: Transportation" }, { "id": "Unitofmeasure", "value": "tonnes-km" } ], "source_id": "57" }, { "id": "IS.AIR.PSGR", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Aviation traffic data are essential for understanding the role of air transport in economic development, global connectivity, and social progress. These statistics, covering passenger volumes, freight volumes, and aircraft departures, provide a standardized measure of air transport activity across countries and regions and are closely linked to economic growth, trade integration, tourism development, and labor mobility. \n\nAviation traffic indicators also help assess the resilience and efficiency of transport systems, track the recovery from economic shocks or crises, and identify capacity constraints or infrastructure investment needs.\n\nFrom a development perspective, aviation statistics support evidence-based policy making in areas such as transport planning, regional integration, climate and emissions management, and inclusive access to services, making them a key input for monitoring progress toward sustainable and connected economies." }, { "id": "IndicatorName", "value": "Air transport, passengers carried" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While ICAO aviation data provide a globally standardized and comprehensive view of air transport activity, several limitations affect their accuracy and comparability. Data quality depends on the completeness and consistency of country reporting, which can vary due to differences in national statistical capacity, regulatory environments, and adherence to ICAO definitions. Some countries may not report all relevant data, leading ICAO to estimate missing values using historical submissions and published schedules, which can introduce uncertainty. Changes in airline registration, mergers, or operational practices may also affect the attribution of traffic statistics. Additionally, the aggregation of scheduled and non-scheduled services, as well as the treatment of transit and transfer passengers, may differ across countries, impacting cross-country comparability." }, { "id": "Longdefinition", "value": "Air carrier data per country refers to passengers carried by airlines registered in that country regardless of the origin or destination of the passengers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Civil Aviation Statistics of the World, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers;\nICAO Staff estimates, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data is obtained from airports, airport operators, airport websites and/or civil aviation authorities. Data on passengers are reported annually by each State (for all its commercial air carriers, including scheduled and non-scheduled flights) via standardized forms sent to ICAO. \n\nEach passenger is counted once per flight number and not repeatedly on each individual stage of that flight, with a single exception that a passenger flying on both the international and domestic stages of the same flight should be counted as both a domestic and an international passenger.\n\nWhere some carriers do not report, ICAO may use historical reports or published flight-schedules to estimate their traffic.\nStatistical concept(s): The number of passengers carried is obtained by counting each passenger on a particular flight (with one flight number) once only and not repeatedly on each individual stage of that flight, with a single exception that a passenger flying on both the international and domestic stages of the same flight should be counted as both a domestic and an international passenger." }, { "id": "Topic", "value": "Infrastructure: Transportation" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "IS.ROD.DESL.KT", "metatype": [ { "id": "Developmentrelevance", "value": "Road vehicles dominate global oil consumption, consuming as much as 80 percent of transport energy and are one of the fastest growing energy end-uses. As a result, transport sector's share of oil consumption has been increasing steadily at around 0.5 percent per year. In the mid-2000s, some 60 percent of oil was consumed in this sector worldwide. There has been a slow progress in consumer behavioral changes in purchasing more fuel efficient vehicles such as diesel engines. Diesel engines have inherently lower losses and are generally one-third more efficient than their gasoline counterparts. Recent advances in diesel technologies and fuels are making diesels more attractive.\n\nAccording to the US Department of Energy diesel engines are more powerful and fuel-efficient than similar-sized gasoline engines (about 30-35% more fuel efficient). Although emissions of particulates and smog-forming nitrogen oxides (NOx) are still relatively high, new \"clean\" diesel fuels, such as ultra-low sulfur diesel and biodiesel, and advances in emission control technologies is expected to reduce these pollutants also. New engine designs, along with noise- and vibration-damping technologies, have made diesel vehicles quieter and smoother. There has been a slow and steady progress in consumer behavioral towards purchasing more fuel efficient vehicles with diesel engines.\n\nTraffic congestion in urban areas constrains economic productivity, damages people's health, and degrades the quality of life. In recent years ownership of passenger cars has increased, and the expansion of economic activity has led to more goods and services being transported by road over greater distances. These developments have increased demand for roads and vehicles, adding to urban congestion, air pollution, health hazards, and traffic accidents and injuries.\n\nTransport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe road transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. Economic growth, technological change, market liberalization, and oil prices affect road transport throughout the world.\n\nThe US Congress recently passed legislation to decrease United States' dependence on oil by increasing corporate average fuel economy (CAFE) standards on new cars and trucks to 35 mpg by model year 2020; this could potentially reduce petroleum use by 25 billion gallons by 2030." }, { "id": "Generalcomments", "value": "Restricted use: Reproduction is strictly prohibited. Extracts must be quoted, after agreement with IRF Geneva, providing the source as IRF World Road Statistics. Please contact info@irfnet.ch and stats@irfnet.ch. [Note: Data have been removed from external publication pending a review of their licensing agreement.]" }, { "id": "IndicatorName", "value": "Road sector diesel fuel consumption (kt of oil equivalent)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IRF terms and conditions." }, { "id": "License_URL", "value": "https://www.irf.global/terms/" }, { "id": "Limitationsandexceptions", "value": "National road associations are the primary source of International Road Federation (IRF) data. In countries where a national road association is lacking or does not respond, other agencies are contacted, such as road directorates, ministries of transport or public works, or central statistical offices. As a result, definitions and data collection methods and quality differ, and the compiled data are of uneven quality. Moreover, the quality of transport service (reliability, transit time, and condition of goods delivered) is rarely measured, though it may be as important as quantity in assessing an economy's transport system.\n\nData for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\"" }, { "id": "Longdefinition", "value": "Diesel is heavy oils used as a fuel for internal combustion in diesel engines." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Road Federation, World Road Statistics and electronic files, except where noted, and International Energy Agency (IEA Statistics © OECD/IEA, http://www.iea.org/stats/index.asp)." }, { "id": "Statisticalconceptandmethodology", "value": "Road sector energy consumption includes energy from petroleum products, natural gas, renewable and combustible waste, and electricity. Biodiesel and biogasoline, forms of renewable energy, are biodegradable and emit less sulfur and carbon monoxide than petroleum-derived ones. They can be produced from vegetable oils, such as soybean, corn, palm, peanut, or sunflower oil, and can be used directly only in a modified internal combustion engine. The unit of measurement is kilotonnes (kt) of oil equivalent." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "57" }, { "id": "IS.ROD.DESL.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Road vehicles dominate global oil consumption, consuming as much as 80 percent of transport energy and are one of the fastest growing energy end-uses. As a result, transport sector's share of oil consumption has been increasing steadily at around 0.5 percent per year. In the mid-2000s, some 60 percent of oil was consumed in this sector worldwide. There has been a slow progress in consumer behavioral changes in purchasing more fuel efficient vehicles such as diesel engines. Diesel engines have inherently lower losses and are generally one-third more efficient than their gasoline counterparts. Recent advances in diesel technologies and fuels are making diesels more attractive.\n\nAccording to the US Department of Energy diesel engines are more powerful and fuel-efficient than similar-sized gasoline engines (about 30-35% more fuel efficient). Although emissions of particulates and smog-forming nitrogen oxides (NOx) are still relatively high, new \"clean\" diesel fuels, such as ultra-low sulfur diesel and biodiesel, and advances in emission control technologies is expected to reduce these pollutants also. New engine designs, along with noise- and vibration-damping technologies, have made diesel vehicles quieter and smoother. There has been a slow and steady progress in consumer behavioral towards purchasing more fuel efficient vehicles with diesel engines.\n\nTraffic congestion in urban areas constrains economic productivity, damages people's health, and degrades the quality of life. In recent years ownership of passenger cars has increased, and the expansion of economic activity has led to more goods and services being transported by road over greater distances. These developments have increased demand for roads and vehicles, adding to urban congestion, air pollution, health hazards, and traffic accidents and injuries.\n\nTransport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe road transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. Economic growth, technological change, market liberalization, and oil prices affect road transport throughout the world.\n\nThe US Congress recently passed legislation to decrease United States' dependence on oil by increasing corporate average fuel economy (CAFE) standards on new cars and trucks to 35 mpg by model year 2020; this could potentially reduce petroleum use by 25 billion gallons by 2030." }, { "id": "Generalcomments", "value": "Restricted use: Reproduction is strictly prohibited. Extracts must be quoted, after agreement with IRF Geneva, providing the source as IRF World Road Statistics. Please contact info@irfnet.ch and stats@irfnet.ch. [Note: Data have been removed from external publication pending a review of their licensing agreement.]" }, { "id": "IndicatorName", "value": "Road sector diesel fuel consumption per capita (kg of oil equivalent)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IRF terms and conditions." }, { "id": "License_URL", "value": "https://www.irf.global/terms/" }, { "id": "Limitationsandexceptions", "value": "National road associations are the primary source of International Road Federation (IRF) data. In countries where a national road association is lacking or does not respond, other agencies are contacted, such as road directorates, ministries of transport or public works, or central statistical offices. As a result, definitions and data collection methods and quality differ, and the compiled data are of uneven quality. Moreover, the quality of transport service (reliability, transit time, and condition of goods delivered) is rarely measured, though it may be as important as quantity in assessing an economy's transport system.\n\nData for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\"" }, { "id": "Longdefinition", "value": "Diesel is heavy oils used as a fuel for internal combustion in diesel engines." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Road Federation, World Road Statistics and electronic files, except where noted, and International Energy Agency (IEA Statistics © OECD/IEA, http://www.iea.org/stats/index.asp)." }, { "id": "Statisticalconceptandmethodology", "value": "Road sector energy consumption includes energy from petroleum products, natural gas, renewable and combustible waste, and electricity. Biodiesel and biogasoline, forms of renewable energy, are biodegradable and emit less sulfur and carbon monoxide than petroleum-derived ones. They can be produced from vegetable oils, such as soybean, corn, palm, peanut, or sunflower oil, and can be used directly only in a modified internal combustion engine.The unit of measurement is kilotonnes (kt) of oil equivalent. World Bank staff estimated population data are used to derive per capita estimates." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "57" }, { "id": "IS.ROD.ENGY.KT", "metatype": [ { "id": "Developmentrelevance", "value": "Road vehicles dominate global oil consumption, consuming as much as 80 percent of transport energy and are one of the fastest growing energy end-uses. As a result, transport sector's share of oil consumption has been increasing steadily at around 0.5 percent per year. In the mid-2000s, some 60 percent of oil was consumed in this sector worldwide. There has been a slow progress in consumer behavioral changes in purchasing more fuel efficient vehicles such as diesel engines.\n\nIn gasoline-powered vehicles, only about 14-26 percent of fuel energy gets used to move a car, depending on the drive cycle. The rest of the energy is lost to engine and driveline inefficiencies or used to power accessories; almost 70 percent of the energy from fuel goes to engine losses such as friction, pumping, combustion, exhaust heat, and radiator. Advanced technologies such as variable valve timing and lift (VVT&L), turbocharging, direct fuel injection, and cylinder deactivation are increasingly been used to reduce these losses. The potential to improve fuel efficiency with advanced technologies is enormous.\n\nTraffic congestion in urban areas constrains economic productivity, damages people's health, and degrades the quality of life. In recent years ownership of passenger cars has increased, and the expansion of economic activity has led to more goods and services being transported by road over greater distances. These developments have increased demand for roads and vehicles, adding to urban congestion, air pollution, health hazards, and traffic accidents and injuries.\n\nTransport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe road transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. Economic growth, technological change, market liberalization, and oil prices affect road transport throughout the world.\n\nThe US Congress recently passed legislation to decrease United States' dependence on oil by increasing corporate average fuel economy (CAFE) standards on new cars and trucks to 35 mpg by model year 2020; this could potentially reduce petroleum use by 25 billion gallons by 2030." }, { "id": "Generalcomments", "value": "Restricted use: Reproduction is strictly prohibited. Extracts must be quoted, after agreement with IRF Geneva, providing the source as IRF World Road Statistics. Please contact info@irfnet.ch and stats@irfnet.ch. [Note: Data have been removed from external publication pending a review of their licensing agreement.]" }, { "id": "IndicatorName", "value": "Road sector energy consumption (kt of oil equivalent)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IRF terms and conditions." }, { "id": "License_URL", "value": "https://www.irf.global/terms/" }, { "id": "Limitationsandexceptions", "value": "National road associations are the primary source of International Road Federation (IRF) data. In countries where a national road association is lacking or does not respond, other agencies are contacted, such as road directorates, ministries of transport or public works, or central statistical offices. As a result, definitions and data collection methods and quality differ, and the compiled data are of uneven quality. Moreover, the quality of transport service (reliability, transit time, and condition of goods delivered) is rarely measured, though it may be as important as quantity in assessing an economy's transport system.\n\nData for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\"" }, { "id": "Longdefinition", "value": "Road sector energy consumption is the total energy used in the road sector including petroleum products, natural gas, electricity, and combustible renewable and waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Road Federation, World Road Statistics and electronic files, except where noted, and International Energy Agency (IEA Statistics © OECD/IEA, http://www.iea.org/stats/index.asp)." }, { "id": "Statisticalconceptandmethodology", "value": "Road sector energy consumption includes energy from petroleum products, natural gas, renewable and combustible waste, and electricity. Biodiesel and biogasoline, forms of renewable energy, are biodegradable and emit less sulfur and carbon monoxide than petroleum-derived ones. They can be produced from vegetable oils, such as soybean, corn, palm, peanut, or sunflower oil, and can be used directly only in a modified internal combustion engine. The unit of measurement is kilotonnes (kt) of oil equivalent." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "57" }, { "id": "IS.ROD.ENGY.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Road vehicles dominate global oil consumption, consuming as much as 80 percent of transport energy and are one of the fastest growing energy end-uses. As a result, transport sector's share of oil consumption has been increasing steadily at around 0.5 percent per year. In the mid-2000s, some 60 percent of oil was consumed in this sector worldwide. There has been a slow progress in consumer behavioral changes in purchasing more fuel efficient vehicles such as diesel engines.\n\nIn gasoline-powered vehicles, only about 14-26 percent of fuel energy gets used to move a car, depending on the drive cycle. The rest of the energy is lost to engine and driveline inefficiencies or used to power accessories; almost 70 percent of the energy from fuel goes to engine losses such as friction, pumping, combustion, exhaust heat, and radiator. Advanced technologies such as variable valve timing and lift (VVT&L), turbocharging, direct fuel injection, and cylinder deactivation are increasingly been used to reduce these losses. The potential to improve fuel efficiency with advanced technologies is enormous.\n\nTraffic congestion in urban areas constrains economic productivity, damages people's health, and degrades the quality of life. In recent years ownership of passenger cars has increased, and the expansion of economic activity has led to more goods and services being transported by road over greater distances. These developments have increased demand for roads and vehicles, adding to urban congestion, air pollution, health hazards, and traffic accidents and injuries.\n\nTransport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe road transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. Economic growth, technological change, market liberalization, and oil prices affect road transport throughout the world.\n\nThe US Congress recently passed legislation to decrease United States' dependence on oil by increasing corporate average fuel economy (CAFE) standards on new cars and trucks to 35 mpg by model year 2020; this could potentially reduce petroleum use by 25 billion gallons by 2030." }, { "id": "Generalcomments", "value": "Restricted use: Reproduction is strictly prohibited. Extracts must be quoted, after agreement with IRF Geneva, providing the source as IRF World Road Statistics. Please contact info@irfnet.ch and stats@irfnet.ch. [Note: Data have been removed from external publication pending a review of their licensing agreement.]" }, { "id": "IndicatorName", "value": "Road sector energy consumption per capita (kg of oil equivalent)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IRF terms and conditions." }, { "id": "License_URL", "value": "https://www.irf.global/terms/" }, { "id": "Limitationsandexceptions", "value": "National road associations are the primary source of International Road Federation (IRF) data. In countries where a national road association is lacking or does not respond, other agencies are contacted, such as road directorates, ministries of transport or public works, or central statistical offices. As a result, definitions and data collection methods and quality differ, and the compiled data are of uneven quality. Moreover, the quality of transport service (reliability, transit time, and condition of goods delivered) is rarely measured, though it may be as important as quantity in assessing an economy's transport system.\n\nData for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\"" }, { "id": "Longdefinition", "value": "Road sector energy consumption is the total energy used in the road sector including petroleum products, natural gas, electricity, and combustible renewable and waste." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Road Federation, World Road Statistics and electronic files, except where noted, and International Energy Agency (IEA Statistics © OECD/IEA, http://www.iea.org/stats/index.asp)." }, { "id": "Statisticalconceptandmethodology", "value": "Road sector energy consumption includes energy from petroleum products, natural gas, renewable and combustible waste, and electricity. Biodiesel and biogasoline, forms of renewable energy, are biodegradable and emit less sulfur and carbon monoxide than petroleum-derived ones. They can be produced from vegetable oils, such as soybean, corn, palm, peanut, or sunflower oil, and can be used directly only in a modified internal combustion engine. The unit of measurement is kilotonnes (kt) of oil equivalent." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "57" }, { "id": "IS.ROD.ENGY.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Road vehicles dominate global oil consumption, consuming as much as 80 percent of transport energy and are one of the fastest growing energy end-uses. As a result, transport sector's share of oil consumption has been increasing steadily at around 0.5 percent per year. In the mid-2000s, some 60 percent of oil was consumed in this sector worldwide. There has been a slow progress in consumer behavioral changes in purchasing more fuel efficient vehicles such as diesel engines.\n\nIn gasoline-powered vehicles, only about 14-26 percent of fuel energy gets used to move a car, depending on the drive cycle. The rest of the energy is lost to engine and driveline inefficiencies or used to power accessories; almost 70 percent of the energy from fuel goes to engine losses such as friction, pumping, combustion, exhaust heat, and radiator. Advanced technologies such as variable valve timing and lift (VVT&L), turbocharging, direct fuel injection, and cylinder deactivation are increasingly been used to reduce these losses. The potential to improve fuel efficiency with advanced technologies is enormous.\n\nTraffic congestion in urban areas constrains economic productivity, damages people's health, and degrades the quality of life. In recent years ownership of passenger cars has increased, and the expansion of economic activity has led to more goods and services being transported by road over greater distances. These developments have increased demand for roads and vehicles, adding to urban congestion, air pollution, health hazards, and traffic accidents and injuries.\n\nTransport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe road transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. Economic growth, technological change, market liberalization, and oil prices affect road transport throughout the world.\n\nThe US Congress recently passed legislation to decrease United States' dependence on oil by increasing corporate average fuel economy (CAFE) standards on new cars and trucks to 35 mpg by model year 2020; this could potentially reduce petroleum use by 25 billion gallons by 2030." }, { "id": "Generalcomments", "value": "Restricted use: Reproduction is strictly prohibited. Extracts must be quoted, after agreement with IRF Geneva, providing the source as IRF World Road Statistics. Please contact info@irfnet.ch and stats@irfnet.ch. [Note: Data have been removed from external publication pending a review of their licensing agreement.]" }, { "id": "IndicatorName", "value": "Road sector energy consumption (% of total energy consumption)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IRF terms and conditions." }, { "id": "License_URL", "value": "https://www.irf.global/terms/" }, { "id": "Limitationsandexceptions", "value": "National road associations are the primary source of International Road Federation (IRF) data. In countries where a national road association is lacking or does not respond, other agencies are contacted, such as road directorates, ministries of transport or public works, or central statistical offices. As a result, definitions and data collection methods and quality differ, and the compiled data are of uneven quality. Moreover, the quality of transport service (reliability, transit time, and condition of goods delivered) is rarely measured, though it may be as important as quantity in assessing an economy's transport system.\n\nData for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\"" }, { "id": "Longdefinition", "value": "Road sector energy consumption is the total energy used in the road sector including petroleum products, natural gas, electricity, and combustible renewable and waste. Total energy consumption is the total country energy consumption." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Road Federation, World Road Statistics and electronic files, except where noted, and International Energy Agency (IEA Statistics © OECD/IEA, http://www.iea.org/stats/index.asp)." }, { "id": "Statisticalconceptandmethodology", "value": "Total energy consumption is the country's total energy consumption from all sources as reported by International Energy Agency and is used to derive the percentage values. Road sector energy consumption includes energy from petroleum products, natural gas, renewable and combustible waste, and electricity. Biodiesel and biogasoline, forms of renewable energy, are biodegradable and emit less sulfur and carbon monoxide than petroleum-derived ones. They can be produced from vegetable oils, such as soybean, corn, palm, peanut, or sunflower oil, and can be used directly only in a modified internal combustion engine." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "57" }, { "id": "IS.ROD.SGAS.KT", "metatype": [ { "id": "Developmentrelevance", "value": "Road vehicles dominate global oil consumption, consuming as much as 80 percent of transport energy and are one of the fastest growing energy end-uses. As a result, transport sector's share of oil consumption has been increasing steadily at around 0.5 percent per year. In the mid-2000s, some 60 percent of oil was consumed in this sector worldwide. There has been a slow progress in consumer behavioral changes in purchasing more fuel efficient vehicles such as diesel engines. \n\nIn gasoline-powered vehicles, only about 14-26 percent of fuel energy gets used to move a car, depending on the drive cycle. The rest of the energy is lost to engine and driveline inefficiencies or used to power accessories; almost 70 percent of the energy from fuel goes to engine losses such as friction, pumping, combustion, exhaust heat, and radiator. Advanced technologies such as variable valve timing and lift (VVT&L), turbocharging, direct fuel injection, and cylinder deactivation are increasingly been used to reduce these losses. The potential to improve fuel efficiency with advanced technologies is enormous.\n\nTraffic congestion in urban areas constrains economic productivity, damages people's health, and degrades the quality of life. In recent years ownership of passenger cars has increased, and the expansion of economic activity has led to more goods and services being transported by road over greater distances. These developments have increased demand for roads and vehicles, adding to urban congestion, air pollution, health hazards, and traffic accidents and injuries.\n\nTransport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe road transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. Economic growth, technological change, market liberalization, and oil prices affect road transport throughout the world.\n\nThe US Congress recently passed legislation to decrease United States' dependence on oil by increasing corporate average fuel economy (CAFE) standards on new cars and trucks to 35 mpg by model year 2020; this could potentially reduce petroleum use by 25 billion gallons by 2030." }, { "id": "Generalcomments", "value": "Restricted use: Reproduction is strictly prohibited. Extracts must be quoted, after agreement with IRF Geneva, providing the source as IRF World Road Statistics. Please contact info@irfnet.ch and stats@irfnet.ch. [Note: Data have been removed from external publication pending a review of their licensing agreement.]" }, { "id": "IndicatorName", "value": "Road sector gasoline fuel consumption (kt of oil equivalent)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IRF terms and conditions." }, { "id": "License_URL", "value": "https://www.irf.global/terms/" }, { "id": "Limitationsandexceptions", "value": "National road associations are the primary source of International Road Federation (IRF) data. In countries where a national road association is lacking or does not respond, other agencies are contacted, such as road directorates, ministries of transport or public works, or central statistical offices. As a result, definitions and data collection methods and quality differ, and the compiled data are of uneven quality. Moreover, the quality of transport service (reliability, transit time, and condition of goods delivered) is rarely measured, though it may be as important as quantity in assessing an economy's transport system.\n\nData for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\"" }, { "id": "Longdefinition", "value": "Gasoline is light hydrocarbon oil use in internal combustion engine such as motor vehicles, excluding aircraft." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Road Federation, World Road Statistics and electronic files, except where noted, and International Energy Agency (IEA Statistics © OECD/IEA, http://www.iea.org/stats/index.asp)." }, { "id": "Statisticalconceptandmethodology", "value": "Road sector energy consumption includes energy from petroleum products, natural gas, renewable and combustible waste, and electricity. Biodiesel and biogasoline, forms of renewable energy, are biodegradable and emit less sulfur and carbon monoxide than petroleum-derived ones. They can be produced from vegetable oils, such as soybean, corn, palm, peanut, or sunflower oil, and can be used directly only in a modified internal combustion engine. The unit of measurement is kilotonnes (kt) of oil equivalent." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "57" }, { "id": "IS.ROD.SGAS.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Road vehicles dominate global oil consumption, consuming as much as 80 percent of transport energy and are one of the fastest growing energy end-uses. As a result, transport sector's share of oil consumption has been increasing steadily at around 0.5 percent per year. In the mid-2000s, some 60 percent of oil was consumed in this sector worldwide. There has been a slow progress in consumer behavioral changes in purchasing more fuel efficient vehicles such as diesel engines.\n\nIn gasoline-powered vehicles, only about 14-26 percent of fuel energy gets used to move a car, depending on the drive cycle. The rest of the energy is lost to engine and driveline inefficiencies or used to power accessories; almost 70 percent of the energy from fuel goes to engine losses such as friction, pumping, combustion, exhaust heat, and radiator. Advanced technologies such as variable valve timing and lift (VVT&L), turbocharging, direct fuel injection, and cylinder deactivation are increasingly been used to reduce these losses. The potential to improve fuel efficiency with advanced technologies is enormous.\n\nTraffic congestion in urban areas constrains economic productivity, damages people's health, and degrades the quality of life. In recent years ownership of passenger cars has increased, and the expansion of economic activity has led to more goods and services being transported by road over greater distances. These developments have increased demand for roads and vehicles, adding to urban congestion, air pollution, health hazards, and traffic accidents and injuries.\n\nTransport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe road transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. Economic growth, technological change, market liberalization, and oil prices affect road transport throughout the world.\n\nThe US Congress recently passed legislation to decrease United States' dependence on oil by increasing corporate average fuel economy (CAFE) standards on new cars and trucks to 35 mpg by model year 2020; this could potentially reduce petroleum use by 25 billion gallons by 2030." }, { "id": "Generalcomments", "value": "Restricted use: Reproduction is strictly prohibited. Extracts must be quoted, after agreement with IRF Geneva, providing the source as IRF World Road Statistics. Please contact info@irfnet.ch and stats@irfnet.ch. [Note: Data have been removed from external publication pending a review of their licensing agreement.]" }, { "id": "IndicatorName", "value": "Road sector gasoline fuel consumption per capita (kg of oil equivalent)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to IRF terms and conditions." }, { "id": "License_URL", "value": "https://www.irf.global/terms/" }, { "id": "Limitationsandexceptions", "value": "National road associations are the primary source of International Road Federation (IRF) data. In countries where a national road association is lacking or does not respond, other agencies are contacted, such as road directorates, ministries of transport or public works, or central statistical offices. As a result, definitions and data collection methods and quality differ, and the compiled data are of uneven quality. Moreover, the quality of transport service (reliability, transit time, and condition of goods delivered) is rarely measured, though it may be as important as quantity in assessing an economy's transport system.\n\nData for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\"" }, { "id": "Longdefinition", "value": "Gasoline is light hydrocarbon oil use in internal combustion engine such as motor vehicles, excluding aircraft." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Road Federation, World Road Statistics and electronic files, except where noted, and International Energy Agency (IEA Statistics © OECD/IEA, http://www.iea.org/stats/index.asp)." }, { "id": "Statisticalconceptandmethodology", "value": "Road sector energy consumption includes energy from petroleum products, natural gas, renewable and combustible waste, and electricity. Biodiesel and biogasoline, forms of renewable energy, are biodegradable and emit less sulfur and carbon monoxide than petroleum-derived ones. They can be produced from vegetable oils, such as soybean, corn, palm, peanut, or sunflower oil, and can be used directly only in a modified internal combustion engine. The unit of measurement is kilograms of oil equivalent. World Bank estimates of population data are used for calculation of per capita data." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "57" }, { "id": "IS.RRS.GOOD.MT.K6", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Transport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe railway transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. Economic growth, technological change, and market liberalization affect road transport throughout the world.\n\nRailways have helped in the industrialization process of a country by easy transportation of coal and raw-materials at a cheaper rate. As railways require huge capital outlay, they may give rise to monopolies and work against public interest at large. Even if controlled and managed by the government, lack of competition sometimes results in inefficiency and high costs. Also, many times it is not economical to operate railways in sparsely settled rural areas. Thus, in many developing countries large rural areas have no railway even today.\n\nRail transport is a major form of passenger and freight transport in many countries. It is ubiquitous in Europe, with an integrated network covering virtually the whole continent. In India, China, South Korea and Japan, many millions use trains as regular transport. In the North America, freight rail transport is widespread and heavily used in for transporting gods. The western Europe region has the highest railway density in the world and has many individual trains which operate through several countries despite technical and organizational differences in each national network. Australia has a generally sparse network, mostly along its densely populated urban centers.\n\nBulk freight handling is a key advantage for rail transport. Low or even zero transshipment costs combined with energy efficiency and low inventory costs allow trains to handle bulk much cheaper than by road. Typical bulk cargo includes coal, ore, grains and liquids. Bulk goods can be transported in open-topped cars, hopper cars and tank cars. Container trains have become the dominant type in the US for non-bulk haulage." }, { "id": "IndicatorName", "value": "Railways, goods transported (million ton-km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\" The data from UIC is based on voluntary reporting by railway companies, and can show drastic increases or decreases for some of the years due to lack of reporting by some of the companies in that country." }, { "id": "Longdefinition", "value": "Goods transported by railway are the volume of goods transported by railway, measured in metric tons times kilometers traveled." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2021" }, { "id": "Source", "value": "Railisa Database (UIC), International Union of Railways (UIC), uri: https://uic-stats.uic.org/select/;\nOECD Statistics, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Freight traffic on any mode is typically measured in tons and ton-kilometers. A ton-kilometer equals cargo weight transported times distance transported. For railways, an important measure of work performed is gross ton-kilometers, this measure includes rail wagons' empty weight for both empty and loaded movements. This measure of gross ton-kilometers is also called ‘trailing tons' or the total tons being hauled. Sometimes gross ton-kilometer measures include the weight of locomotives used to haul freight trains.\n\nThe indicator measures the tonne.kilometers of freight on the national territory of the railway.\n\nThe weight taken into account is the actual weight or chargeable weight of the goods carried. Weight means the quantity of goods in thousands of tonnes. The weight to be taken into consideration includes, in addition to the weight of the goods transported, the weight of packaging and the tare weight of containers, swap bodies, pallets as well as road vehicles transported by rail in the course of combined transport operations. If the goods are transported using the services of more than one railway undertaking (e.g. within the group etc.), when possible the weight of goods should not be counted more than once.\n\nThe number of tonne-kilometres in millions represents the weight of freight traffic (in millions of tonnes) over the charging distance (in kilometres). \n\nStatistical concept(s): Tonne-kilometre (tkm) is a unit of measurement of goods transport which represents the transport of one tonne of goods over a distance of one kilometre.\n\nThe distance to be covered is the distance actually travelled on the considered network. To avoid double counting each country should count only the tkm performed on its territory. If it is not available, then the distance charged or estimated should be taken into account." }, { "id": "Topic", "value": "Infrastructure: Transportation" }, { "id": "Unitofmeasure", "value": "Ton-km" } ], "source_id": "57" }, { "id": "IS.RRS.PASG.KM", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Transport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe railway transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. Economic growth, technological change, and market liberalization affect road transport throughout the world.\n\nRailways have helped in the industrialization process of a country by easy transportation of coal and raw-materials at a cheaper rate. As railways require huge capital outlay, they may give rise to monopolies and work against public interest at large. Even if controlled and managed by the government, lack of competition sometimes results in inefficiency and high costs. Also, many times it is not economical to operate railways in sparsely settled rural areas. Thus, in many developing countries large rural areas have no railway even today.\n\nRail transport is a major form of passenger and freight transport in many countries. Passenger trains can involve a variety of functions including long distance travel, daily commuter trips, or local urban transit services. Railways are very popular mode of transportation in Europe, with an integrated network covering virtually the whole continent. In India, China, South Korea and Japan, many millions use trains as regular transport. In North America, freight rail transport is widespread and heavily used in for transporting goods. The western Europe region has the highest railway density in the world and has many individual trains which operate through several countries despite technical and organizational differences in each national network. Australia has a generally sparse network, mostly along its densely populated urban centers." }, { "id": "IndicatorName", "value": "Railways, passengers carried (million passenger-km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\" The data from UIC is based on voluntary reporting by railway companies, and can show drastic increases or decreases for some of the years due to lack of reporting by some of the companies in that country." }, { "id": "Longdefinition", "value": "Passengers carried by railway are the number of passengers transported by rail multiplied by kilometers traveled." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2021" }, { "id": "Source", "value": "Railisa Database (UIC), International Union of Railways (UIC), uri: https://uic-stats.uic.org/select/;\nOECD Statistics, Organisation for Economic Co-operation and Development (OECD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Passenger-kilometers are usually measured on the basis of the rail travel distance between origin and destination multiplied by the number of passengers traveling between each origin and destination. This variable relates to passengers, irrespective of the fare paid and also including free travelling passengers, but excluding members of the train crew. \n\nThe number of passengers should be calculated as number of passenger journeys. A journey is the act of moving from one place (origin) to another (destination) using a given transport mode (e.g. railway). A journey may consist of one or several stages depending on whether one has to change transport means (e.g. using more than one train) in order to get from the origin to the destination. In other words, a journey consists either of a single stage or a sequence of stages using the same transport mode (e.g. railway), following each other in such a way that the destination of one stage coincides with the origin of the next.\n\nJourneys should be considered finished when an overnight stay occurs. For practical purposes, journeys can be considered finished when a change in transport mode or transport company occurs. \n\nPassenger-kilometers is the total distance travelled by all the passengers. For instance, one person travelling for 20km contributes for 20 passenger-kilometres; four people, travelling for 20km each, contribute for 80 passenger- kilometers\n\nStatistical concept(s): A passenger kilometer is performed when a passenger is carried for one kilometer. Rail passenger-kilometer (pkm) is a unit of measurement representing the transport of one rail passenger by rail over a distance of one kilometer.\n\nThe distance to be taken into consideration should be the distance actually travelled by the passenger on the network. To avoid double counting each country should count only the pkm performed on its territory. If this is not available, then the distance charged or estimated should be used." }, { "id": "Topic", "value": "Infrastructure: Transportation" }, { "id": "Unitofmeasure", "value": "Passenger-kilometers" } ], "source_id": "57" }, { "id": "IS.RRS.TOTL.KM", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Transport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, governments, and the private sector. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nRailway transport is a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. \n\nRailways have helped in the industrialization process by easy transportation of raw-materials at a cheaper rate. As railways require huge capital outlay, they may give rise to monopolies and work against public interest at large. Lack of competition sometimes results in inefficiency and high costs. Also, many times it is not economical to operate railways in sparsely settled rural areas. Thus, in many developing countries large rural areas have no railway even today.\n\nRail transport is a major form of passenger and freight transport in many countries. It is ubiquitous in Europe, with an integrated network covering virtually the whole continent. In India, China, South Korea and Japan, many millions use trains as regular transport. In the North America, freight rail transport is widespread and heavily used in for transporting gods. The western Europe region has the highest railway density in the world and has many individual trains which operate through several countries despite technical and organizational differences in each national network. Australia has a generally sparse network, mostly along its densely populated urban centers." }, { "id": "IndicatorName", "value": "Rail lines (total route-km)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized\". The data from UIC is based on voluntary reporting by railway companies, and can show drastic increases or decreases for some of the years due to lack of reporting by some of the companies in that country." }, { "id": "Longdefinition", "value": "The total length of rail line in the country operated for passenger transport, goods transport, or both." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2021" }, { "id": "Source", "value": "Railisa Database, International Union of Railways (UIC), uri: https://uic-stats.uic.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Rail lines are the length of railway route available for train service, irrespective of the number of parallel tracks. It includes railway routes that are open for public passenger and freight services and excludes dedicated private resource railways.\n\nGauge:\tN (standard gauge: 1,435 m); L (broad gauge: exact rail gauge inserted); E (narrow gauge: exact rail gauge inserted). The length of railway lines worked is obtained by taking these sections including main-line track listed in the Capital Expenditure Account.\n\nSections not worked are deducted only in cases where they are permanently out of use, that is, if they are no longer maintained in working order. Lines temporarily out of use continue to form part of the length of lines worked.\n\nThe length of a section is measured in the middle of the section, from center to the center of the passenger buildings, or of the corresponding service buildings, of stations which are shown as independent points of departure or arrival for the conveyance of passengers or freight. If the boundary of the rail network falls in open track, the length of the section is measured up to that point.\n\nThe section situated between a station approach and the join to the main line of two lines or more which is used by all trains in either direction over these lines, is only counted once. However, if for one or more of these lines, tracks are normally allocated, the length of these lines is counted separately.\n\nIf between two stations there are one or more parallel tracks (siding-lines) to the main line, only the length of the latter is counted.\n\nIn the case of regular lines worked exclusively during part of the year (seasonal lines), their length is included in the end-of-year statement (Var 1112: Total length of lines worked at the end of the year in the Railisa database).\nStatistical concept(s): Railway line: one or more adjacent running tracks forming a route between two points. Where a section of network comprises two or more lines running alongside one another, there are as many lines as routes to which tracks are allotted exclusively." }, { "id": "Topic", "value": "Infrastructure: Transportation" }, { "id": "Unitofmeasure", "value": "Kilometers" } ], "source_id": "57" }, { "id": "IS.SHP.GCNW.XQ", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The liner shipping connectivity index (LSCI) aims at capturing a country's integration level into global liner shipping networks. A country's access to world markets depends largely on their transport connectivity, especially in regard to regular shipping services for the import and export of manufactured goods.\n\nTrade facilitation encompasses customs efficiency and other physical and regulatory environments where trade takes place, harmonization of standards and conformance to international regulations, and the logistics of moving goods and associated documentation through countries and ports. Though collection of trade facilitation data has improved over the last decade, data that allow meaningful evaluation, especially for developing economies, are lacking. The quality and accessibility of ports and roads affect logistics performance.\n\nAccess to global shipping and air freight networks and the quality and accessibility of ports and roads affect logistics performance. Maritime transport is the backbone of international trade and a key engine driving globalization. Around 80 per cent of global trade by volume and over 70 per cent by value is carried by sea and is handled by ports worldwide; these shares are even higher in the case of most developing countries.\n\nA total of 60 per cent of world seaborne trade by volume is loaded, and 57 per cent unloaded, in developing-country ports. That is a remarkable shift away from previous patterns, in which developing economies served mainly as loading areas for raw materials and natural resources." }, { "id": "IndicatorName", "value": "Liner shipping connectivity index (maximum value in 2004 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on trade facilitation are drawn from research by private and international agencies. Most data are perception-based evaluations by business executives and professionals. Because of different backgrounds, values, and personalities, those surveyed may evaluate the same situation differently. Caution should thus be used when interpreting perception- based indicators. Nevertheless, they convey much needed information on trade facilitation." }, { "id": "Longdefinition", "value": "The Liner Shipping Connectivity Index captures how well countries are connected to global shipping networks. It is computed by the United Nations Conference on Trade and Development (UNCTAD) based on five components of the maritime transport sector: number of ships, their container-carrying capacity, maximum vessel size, number of services, and number of companies that deploy container ships in a country's ports. For each component a country's value is divided by the maximum value of each component in 2004, the five components are averaged for each country, and the average is divided by the maximum average for 2004 and multiplied by 100. The index generates a value of 100 for the country with the highest average index in 2004. . The underlying data come from Containerisation International Online." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2021" }, { "id": "Source", "value": "Review of Maritime Transport 2010., UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Liner Shipping Connectivity Index captures how well countries are connected to global shipping networks. Starting in 2020 the index was improved and is published as a quarterly series with the index set at 100 for the country with the highest average in the first quarter of 2006. It is computed by the United Nations Conference on Trade and Development (UNCTAD) based on six components of the maritime transport sector: number of ships, their container-carrying capacity, maximum vessel size, number of services, the number of country-pairs with a direct connection, and number of companies that deploy container ships in a country's ports. The data are derived from Containerisation International Online (www.ci-online.co.uk). For each of the six components, a country's value is divided by the maximum value of that component in Q1 2006, and for each country, the average of the six components is calculated. This average is then divided by the maximum average for Q1 2006 and multiplied by 100. In this way, the index generates the value 100 for the country with the highest average index of the six components in Q1 2006. Annual values of the index equal the values of the first quarter of the same corresponding year." }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "57" }, { "id": "IS.SHP.GOOD.TU", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Transport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe sea transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses, and stimulating foreign investment and international trade. Economic growth, technological change, market liberalization, and oil prices affect sea transport throughout the world." }, { "id": "IndicatorName", "value": "Container port traffic (TEU: 20 foot equivalent units)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Measures of port container traffic, much of it commodities of medium to high value added, give some indication of economic growth in a country. But when traffic is merely transshipment, much of the economic benefit goes to the terminal operator and ancillary services for ships and containers rather than to the country more broadly. In transshipment centers empty containers may account for as much as 40 percent of traffic.\n\nData cover coastal shipping as well as international journeys. Transshipment traffic is counted as two lifts at the intermediate port (once to off-load and again as an outbound lift) and includes empty units. Data for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\"" }, { "id": "Longdefinition", "value": "Port container traffic measures the flow of containers from land to sea transport modes, and vice versa, in twenty-foot equivalent units (TEUs), a standard-size container. Data refer to coastal shipping as well as international journeys. Transshipment traffic is counted as two lifts at the intermediate port (once to off-load and again as an outbound lift) and includes empty units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "UN Conference on Trade and Development (UNCTAD), uri: http://unctad.org/en/Pages/statistics.aspx" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: TEU is the standard unit, referring to 20-foot equivalent units or 20-foot-long cargo container. The size of cargo containers range from 20 feet long to more than 50 feet long. The international measure is the smallest box, the 20-footer or 20-foot-equivalent unit (TEU). Two twenty-foot containers (TEUs) equal one FEU. Container vessel capacity and port throughput capacity are frequently referred to in TEUs.\n\nFor any questions related to the country data and methodology, please contact the Review of Maritime Transport team at: unctad-rmt@un.org" }, { "id": "Topic", "value": "Infrastructure: Transportation" } ], "source_id": "57" }, { "id": "IT.BBD.USEC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Developmentrelevance", "value": "Access to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Fixed broadband sub-basket (US$ per month)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Where several offers are available, preference is given to the cheapest available connection that offers a speed of at least 256 kbit/s and 1 GB of data volume. If providers set a limit of less than 1 GB on the amount of data that can be transferred within a month, then the price per additional byte is added to the monthly price so as to calculate the cost of 1 GB of data per month. Preference should be given to the most widely used fixed (wired)-broadband technology (DSL, cable, etc.). The sub-basket does not include installation charges, modem prices or telephone-line rentals that are often required for a DSL service. The price represents the broadband entry plan in terms of the minimum speed of 256 kbit/s, but does not take into account special offers that are limited in time or to specific geographic areas. The plan does not necessarily represent the fastest or most cost-effective connection, since often the price for a higher-speed plan is cheaper in relative terms (i.e. in terms of the price per Mbit/s)." }, { "id": "Longdefinition", "value": "Fixed broadband sub-basket is the price of the monthly subscription to an entry-level fixed broadband plan. For comparability reason, the fixed broadband sub-basket is based on a monthly usage of (a minimum of) 1 Gigabyte (GB). For plans that limit the monthly amount of data transferred by including caps below 1 Gigabyte, the cost for additional bytes is added to the sub-basket. The minimum speed of a broadband connection is 256 kbit/s." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Statisticalconceptandmethodology", "value": "Fixed-broadband sub-basket refers to the price of the monthly subscription to an entry-level fixed broadband plan. For comparability reason, the fixed broadband sub-basket is based on a monthly usage of (a minimum of) 1 Gigabyte (GB). For plans that limit the monthly amount of data transferred by including caps below 1 Gigabyte, the cost for additional bytes is added to the sub-basket. The minimum speed of a broadband connection is 256 kbit/s.\n\nPrices are reported and collected in national currency and then converted to USD. Prices include taxes." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.CEL.COVR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Access to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Population covered by mobile cellular network (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Note that this is not the same as the mobile subscription density or penetration. When there are multiple operators offering the service, the maximum amount of population covered should be reported." }, { "id": "Longdefinition", "value": "Population covered by a mobile-cellular network is the percentage of people within range of a mobile-cellular signal, irrespective of whether they are subscribers or users or not. This is calculated by dividing the number of people within range of a mobile-cellular signal by the total population and multiplying by 100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Statisticalconceptandmethodology", "value": "Percentage of population covered by a mobile cellular telephone network refers to the percentage of a country's inhabitants that live within areas served by a mobile cellular signal, irrespective of whether or not they choose to use it. \nThis is calculated by dividing the number of inhabitants within range of a mobile cellular signal by the total population." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.CEL.SETS", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. The International Telecommunication Union (ITU) estimates that there were about 6 billion mobile subscriptions globally in the early 2010s. No technology has ever spread faster around the world. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nMobile cellular telephone subscriptions are subscriptions to a public mobile telephone service using cellular technology, which provide access to the public switched telephone network (PSTN) using cellular technology. It includes postpaid and prepaid subscriptions and includes analogue and digital cellular systems.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "IndicatorName", "value": "Mobile cellular subscriptions" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU.\n\nMobile subscriptions include both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services are also excluded, but all mobile cellular subscriptions that offer voice communications are included. Both postpaid and prepaid subscriptions are included." }, { "id": "Longdefinition", "value": "Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Refers to the subscriptions to a public mobile telephone service and provides access to Public Switched Telephone Network (PSTN) using cellular technology, including number of pre-paid SIM cards active during the past three months. This includes both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services should also be excluded. This should include all mobile cellular subscriptions that offer voice communications.\n\nData on mobile cellular subscribers are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators.\n\nData for this indicator are readily available for approximately 90 percent of countries, either through ITU's World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator's website. For the rest, information can be aggregated through operators' data (mainly through annual reports) and complemented by market research reports. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): Data can be collected from all licensed mobile-cellular operators in the country, and then aggregated at the country level. If retail mobile-cellular services are also provided by nonfacilities-based operators (i.e., mobile virtual network operators), care should be taken to avoid double counting. One difficulty that may arise is that operators may have different definitions of ‘active’ and therefore may not be able to provide the data according to the recommended definition (i.e., used in the last three months)." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "number of subscriptions" } ], "source_id": "57" }, { "id": "IT.CEL.SETS.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. The International Telecommunication Union (ITU) estimates that there were about 6 billion mobile subscriptions globally in the early 2010s. No technology has ever spread faster around the world. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nMobile cellular telephone subscriptions are subscriptions to a public mobile telephone service using cellular technology, which provide access to the public switched telephone network (PSTN) using cellular technology. It includes postpaid and prepaid subscriptions and includes analogue and digital cellular systems.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "IndicatorName", "value": "Mobile cellular subscriptions (per 100 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU.\n\nMobile subscriptions include both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services are also excluded, but all mobile cellular subscriptions that offer voice communications are included. Both postpaid and prepaid subscriptions are included." }, { "id": "Longdefinition", "value": "Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Refers to the subscriptions to a public mobile telephone service and provides access to Public Switched Telephone Network (PSTN) using cellular technology, including number of pre-paid SIM cards active during the past three months. This includes both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services should also be excluded. This should include all mobile cellular subscriptions that offer voice communications.\n\nData on mobile cellular subscribers are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators.\n\nData for this indicator are readily available for approximately 90 percent of countries, either through ITU's World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator's website. For the rest, information can be aggregated through operators' data (mainly through annual reports) and complemented by market research reports.\n\nMobile cellular subscriptions (per 100 people) indicator is derived by all mobile subscriptions divided by the country's population and multiplied by 100. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): Data can be collected from all licensed mobile-cellular operators in the country, and then aggregated at the country level. If retail mobile-cellular services are also provided by nonfacilities-based operators (i.e., mobile virtual network operators), care should be taken to avoid double counting. One difficulty that may arise is that operators may have different definitions of ‘active’ and therefore may not be able to provide the data according to the recommended definition (i.e., used in the last three months). This indicator can be divided by the population and multiplied by 100 to obtain mobile cellular subscriptions per 100 people." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "number of subscriptions*100/population" } ], "source_id": "57" }, { "id": "IT.CEL.USEC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Price basket for mobile (US$ per month)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Price basket for mobile is calculated as the pre-paid price for 25 calls per month spread over the same mobile network, other mobile networks, and mobile to fixed calls and during peak, off-peak, and weekend times. It also includes 30 text messages per month." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.CMP.PCMP", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Personal computers" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Personal computers are self-contained computers designed to be used by a single individual." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.CMP.PCMP.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Personal computers (per 100 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Personal computers are self-contained computers designed to be used by a single individual." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.INT.TTRF.MN", "metatype": [ { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "International voice traffic, total fixed and mobile (out and in, minutes)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "International voice traffic is the sum of international incoming and outgoing telephone traffic (in minutes)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.INT.TTRF.MN.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "International voice traffic, total fixed and mobile (minutes per person)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "International voice traffic is the sum of international incoming and outgoing telephone traffic (in minutes)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.MBL.USEC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Developmentrelevance", "value": "Access to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. The International Telecommunication Union (ITU) estimates that there were about 6 billion mobile subscriptions globally in the early 2010s. No technology has ever spread faster around the world. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Mobile cellular sub-basket (US$ per month)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Unlike the 2009 OECD methodology, which is based on the prices of the two largest mobile operators, the ITU mobile sub-basket uses only the largest mobile operator's prices. Additionally, the ITU mobile-cellular sub-basket does not take into account calls to voicemail (which in the OECD basket represent 4 per cent of all calls), nor non-recurring charges, such as the one-time charge for a SIM card. The basket gives the price of a standard basket of mobile monthly usage in USD determined by the OECD for 30 outgoing calls per month in predetermined ratios plus 100 sms messages. The cost of national sms is the charge to the consumer for sending a single sms text message. Both on-net and off-net sms prices are taken into account. The basket considers on-net and off-net calls as well as calls to a fixed telephone and, since the price of calls often depends on the time of day or week it is made, peak, off-peak and weekend periods are also taken into consideration." }, { "id": "Longdefinition", "value": "Mobile cellular sub-basket refers to the price of a standard basket of mobile monthly usage for 30 outgoing calls per month (on-net, off-net, to a fixed line and for peak and off-peak times) in predetermined ratios, plus 100 SMS messages. The mobile cellular sub-basket is based on prepaid tariffs although postpaid tariffs may be used for countries where prepaid subscriptions make up less than three per cent of all mobile cellular subscriptions. The mobile cellular prepaid sub-basket is largely based, but does not entirely follow, the 2009 methodology of the OECD low-user basket." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Statisticalconceptandmethodology", "value": "Mobile-cellular sub-basket refers to the price of a standard basket of mobile monthly usage for 30 outgoing calls per month (on-net, off-net, to a fixed line and for peak and off-peak times) in predetermined ratios, plus 100 SMS messages. The mobile-cellular sub-basket is based on prepaid tariffs although post-paid tariffs may be used for countries where prepaid subscriptions make up less than three per cent of all mobile-cellular subscriptions. The mobile-cellular prepaid sub-basket is largely based, but does not entirely follow, the 2009 methodology of the OECD low-user basket.\n\nPrepaid prices were chosen because they are often the only payment method available to low-income users, who might not have a regular income and will thus not qualify for a postpaid subscription. Rather than reflecting the cheapest option available, the mobile-cellular sub-basket therefore corresponds to a basic, representative (low-usage) package available to all customers. In countries where no prepaid offers are available, the monthly fixed cost (minus the free minutes of calls included, if applicable) of a postpaid subscription is added to the basket. To make prices comparable, a number of rules are applied.\n\nPrices are reported and collected in national currency and then converted to USD. Prices include taxes." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.MLT.3MIN.CD.US", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Telephone average cost of call to US (US$ per three minutes)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Cost of international call to U.S. is the cost of a three-minute, peak rate, fixed line call from the country to the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.MLT.FALT.M2", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Telephone faults (per 100 mainlines)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Telephone mainline faults is the number of reported telephone faults for the year per 100 telephone mainlines." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.MLT.MAIN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout.\n\nFixed telephone lines are those that connect a subscriber's terminal equipment to the public switched telephone network and that have a port on a telephone exchange. This term is synonymous with the term main station or Direct Exchange Line (DEL) that is commonly used in telecommunication documents. Integrated services digital network channels and fixed wireless subscribers are included. A fixed line also refers to a phone which uses a solid medium telephone line such as a metal wire or fiber optic cable for transmission as distinguished from a mobile cellular line which uses radio waves for transmission.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "IndicatorName", "value": "Fixed telephone subscriptions" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU." }, { "id": "Longdefinition", "value": "Fixed telephone subscriptions refers to the sum of active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A fixed telephone line (previously called main telephone line in operation) is an active line connecting the subscriber's terminal equipment to the public switched telephone network (PSTN) and which has a dedicated port in the telephone exchange equipment. This term is synonymous with the terms main station or Direct Exchange Line (DEL) that are commonly used in telecommunication documents. It may not be the same as an access line or a subscriber. This should include the active number of analog fixed telephone lines, ISDN channels, fixed wireless, public payphones and VoIP subscriptions. Active lines are those that have registered an activity in the past three months.\n\nData on fixed telephone lines are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators. Data are considered to be very reliable, timely, and complete.\n\nData for this indicator are readily available for approximately 90 percent of countries, either through ITU's World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator's website. For the rest, information can be aggregated through operators' data (mainly through annual reports) and complemented by market research reports. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): Data can be collected and aggregated at the country level by asking all licensed fixed-telephone line operators how many fixed-telephone subscriptions they have." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "number of subscriptions" } ], "source_id": "57" }, { "id": "IT.MLT.MAIN.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout.\n\nFixed telephone lines are those that connect a subscriber's terminal equipment to the public switched telephone network and that have a port on a telephone exchange. This term is synonymous with the term main station or Direct Exchange Line (DEL) that is commonly used in telecommunication documents. Integrated services digital network channels and fixed wireless subscribers are included. A fixed line also refers to a phone which uses a solid medium telephone line such as a metal wire or fiber optic cable for transmission as distinguished from a mobile cellular line which uses radio waves for transmission.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "IndicatorName", "value": "Fixed telephone subscriptions (per 100 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU." }, { "id": "Longdefinition", "value": "Fixed telephone subscriptions refers to the sum of active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A fixed telephone line (previously called main telephone line in operation) is an active line connecting the subscriber's terminal equipment to the public switched telephone network (PSTN) and which has a dedicated port in the telephone exchange equipment. This term is synonymous with the terms main station or Direct Exchange Line (DEL) that are commonly used in telecommunication documents. It may not be the same as an access line or a subscriber. This should include the active number of analog fixed telephone lines, ISDN channels, fixed wireless, public payphones and VoIP subscriptions. Active lines are those that have registered an activity in the past three months.\n\nData on fixed telephone lines are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators. Data are considered to be very reliable, timely, and complete.\n\nData for this indicator are readily available for approximately 90 percent of countries, either through ITU's World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator's website. For the rest, information can be aggregated through operators' data (mainly through annual reports) and complemented by market research reports.\n\nTelephone lines (per 100 people) indicator is derived by all telephone lines divided by the country's population and multiplied by 100. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): Data can be collected and aggregated at the country level by asking all licensed fixed-telephone line operators how many fixed-telephone subscriptions they have. This indicator can be divided by the population and multiplied by 100 to obtain fixed telephone subscriptions per 100 people." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "number of subscriptions*100/population" } ], "source_id": "57" }, { "id": "IT.MLT.USEC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Price basket for residential fixed line (US$ per month)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Price basket for residential fixed line is calculated as one-fifth of the installation charge, the monthly subscription charge, and the cost of local calls (15 peak and 15 off-peak calls of three minutes each)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Calculated by the World Bank based on International Telecommunication Union data." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.NET.BBND", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Fixed broadband subscriptions" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Fixed broadband subscriptions refers to fixed subscriptions to high-speed access to the public Internet (a TCP/IP connection), at downstream speeds equal to, or greater than, 256 kbit/s. This includes cable modem, DSL, fiber-to-the-home/building, other fixed (wired)-broadband subscriptions, satellite broadband and terrestrial fixed wireless broadband. This total is measured irrespective of the method of payment. It excludes subscriptions that have access to data communications (including the Internet) via mobile-cellular networks. It should include fixed WiMAX and any other fixed wireless technologies. It includes both residential subscriptions and subscriptions for organizations." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data can be collected by asking all ISPs in the country to provide the number of their fixed -broadband subscriptions (by type – cable, DSL, optical fiber, other, satellite, and terrestrial fixed wireless broadband).\nStatistical concept(s): The data can be collected by asking all ISPs in the country to provide the number of their fixed -broadband subscriptions (by type – cable, DSL, optical fiber, other, satellite, and terrestrial fixed wireless broadband)." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "number of subscriptions" } ], "source_id": "57" }, { "id": "IT.NET.BBND.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains.\n\nThere are several economic gains associated with broadband. For example, with DSL, users can use a single standard phone line for both voice and data services. This enables them to surf the Internet and call a friend at the same time - all using the same phone line. Broadband also enhances many Internet applications such as new e-government services like electronic tax filing, online health care services, e-learning and increased levels of electronic commerce.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "IndicatorName", "value": "Fixed broadband subscriptions (per 100 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are collected by national statistics offices through household surveys. Because survey questions and definitions differ, the estimates may not be strictly comparable across countries.\n\nFixed broadband Internet includes cable modem, DSL, fibre and other fixed broadband technology (such as satellite broadband Internet, Ethernet LANs, fixed-wireless access, Wireless Local Area Network, WiMAX etc.). Subscribers with access to data communications (including the Internet) via mobile cellular networks are excluded.\n\nAdvertised and real speeds can differ substantially. In some countries, regulatory authorities monitor the speed and quality of broadband services and oblige operators to provide accurate quality-of-service information to end users. Regional and global totals are calculated as unweighted sums of the country values. Regional and global penetration rates (per 100 inhabitants) are weighted averages of the country values weighted by the population of the countries/regions.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "Fixed broadband subscriptions refers to fixed subscriptions to high-speed access to the public Internet (a TCP/IP connection), at downstream speeds equal to, or greater than, 256 kbit/s. This includes cable modem, DSL, fiber-to-the-home/building, other fixed (wired)-broadband subscriptions, satellite broadband and terrestrial fixed wireless broadband. This total is measured irrespective of the method of payment. It excludes subscriptions that have access to data communications (including the Internet) via mobile-cellular networks. It should include fixed WiMAX and any other fixed wireless technologies. It includes both residential subscriptions and subscriptions for organizations." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data refer to subscriptions to high-speed access to the public Internet (a TCP/IP connection), at downstream speeds equal to, or greater than, 256 kbit/s. This includes cable modem, DSL, fibre-to-the-home/building and other fixed (wired)-broadband subscriptions. This total is measured irrespective of the method of payment. It excludes subscriptions that have access to data communications (including the Internet) via mobile-cellular networks. It excludes technologies listed under the wireless-broadband category.\n\nFixed broadband Internet subscribers per 100 people is obtained by dividing the number of fixed broadband Internet subscribers by the population and then multiplying by 100. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): The data can be collected by asking all ISPs in the country to provide the number of their fixed -broadband subscriptions (by type – cable, DSL, optical fiber, other, satellite, and terrestrial fixed wireless broadband). This indicator can be divided by the population and multiplied by 100 to obtain fixed-broadband subscriptions per 100 people." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "number of subscriptions*100/population" } ], "source_id": "57" }, { "id": "IT.NET.BNDW", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "International Internet bandwidth (Mbps)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "International Internet bandwidth refers to the total used capacity of international Internet bandwidth. Used international Internet bandwidth refers to the average traffic load of international fiber-optic cables and radio links for carrying Internet traffic. The average should be calculated over the 12-month period of the reference year, and should take into consideration the traffic of all international Internet links. If the traffic is asymmetric (i.e. more incoming (downlink) than outgoing (uplink) traffic), then the average incoming (downlink) traffic load should be provided. The combined average traffic load of different international Internet links can be reported as the addition of the average traffic load of each link." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database, and TeleGeography." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.NET.BNDW.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe. Today's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "International Internet bandwidth (bits per second per Internet user)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are collected by national statistics offices through household surveys. Because survey questions and definitions differ, the estimates may not be strictly comparable across countries.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "International Internet bandwidth refers to the total used capacity of international Internet bandwidth, in bits per second per Internet user. Used international Internet bandwidth refers to the average traffic load (expressed in Mbit/s) of international fiber-optic cables and radio links for carrying Internet traffic. The average should be calculated over the 12-month period of the reference year, and should take into consideration the traffic of all international Internet links. If the traffic is asymmetric (i.e. more incoming (downlink) than outgoing (uplink) traffic), then the average incoming (downlink) traffic load should be provided. The combined average traffic load of different international Internet links can be reported as the addition of the average traffic load of each link." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Statisticalconceptandmethodology", "value": "International Internet bandwidth refers to the total used capacity of international Internet bandwidth, in megabits per second (Mbit/s). It is measured as the sum of used capacity of all Internet exchanges (locations where Internet traffic is exchanged) offering international bandwidth. If capacity is asymmetric (i.e. more incoming (downlink) than outgoing (uplink) capacity), then the incoming (downlink) capacity should be provided.\n\nInternational Internet bandwidth per Internet user is obtained by dividing the amount of bandwidth (in bits/second) by the total number of Internet users." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.NET.SECR", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Comparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. \n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met. Over the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "IndicatorName", "value": "Secure Internet servers" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Netcraft only visits sites on the standard HTTPS port, 443. Whilst it is possible to run an HTTPS server on a different port (using a URL like https://example.com:7000) this behaviour is quite rare on public websites. For example, usual ports are often used for administrative interfaces and other services not intended for the general public.\n\nNetcraft tries to visit every public secure website. Note that the survey does not include secure mail servers (SMTP) or intranet sites. So the final number of certificates for each certificate authority will be lower than the total number of server certificates sold by that authority." }, { "id": "Longdefinition", "value": "The number of distinct, publicly-trusted TLS/SSL certificates found in the Netcraft Secure Server Survey (by hosting country)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2010-2024" }, { "id": "Source", "value": "Secure Server Survey, Netcraft, uri: http://www.netcraft.com/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Netcraft's survey counts (unique) valid certificates issued by widely-trusted third-party certification authorities. A certificate must be valid, that is it must be within its validity period (certificates are usually valid for up to 39 months), and the digital signatures on the certificate must check successfully. It must be issued by third party certificate issuer that is recognised by Netcraft. \nNetcraft gathers a list of possible SSL web sites to investigate from a range of different sources. The data reflects the December survey in that year.\n\nSSL (Secure Socket Layer) is a protocol developed by Netscape for encrypted transmission over TCP/IP networks. It sets up a secure end-to-end link over which HTTP or any other application protocol can operate. The most common application of SSL is HTTPS for SSL-encrypted HTTP. It has now been replaced with a IETF-standardised version, TLS.\n\nSurveying: Netcraft only visits sites on the standard HTTPS port, 443. Whilst it is possible to run an HTTPS server on a different port (using a URL like https://example.com:7000) this behavior is quite rare on public websites. For example, usual ports are often used for administrative interfaces and other services not intended for the general public.\n\nNetcraft tries to visit every public secure website. Note that the survey does not include secure mail servers (SMTP) or intranet sites. So the final number of certificates for each certificate authority will be lower than the total number of server certificates sold by that authority. The survey makes multiple HTTPS request types to identify both web server capabilities, and the certificates in use. Netcraft makes retry visits to any non-responding IP addresses once, several hours after the failed visits. The information made available by an HTTPS server is more substantial than with http servers. The most interesting piece of information available from HTTP servers is the server signature; this can be analyzed to give straightforward empirical evidence about the relative popularity of server software on web sites across the Internet. The same information is also available from HTTPS servers. Additionally, the content of the site's X.509 certificate is available, providing details about both the company or organization owning the site, and the certificate issuer. Furthermore, in most cases the TCP/IP characteristics of the network connection allows to determine the operating system used.\n\nStatistical concept(s): The survey examines the use of encrypted transactions through extensive automated exploration, tallying the number of web sites using HTTPS. This analysis relates to those sites found in the survey where the certificate is valid for the hostname, and the certificate has been issued from a publicly-trusted root. The indicator refers to valid, third-party certificates. Included are sites found in the survey where the common name in the certificate matched the hostname, and the certificate's digital signature was not detected as being self-signed. The location is derived from the hosting location of the sites using the certificates (rather than the countries indicated on the certificates themselves.) This analysis relates to those sites found in the survey where the common name in the certificate matched the hostname, and the certificate's digital signature was not detected as being self-signed." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "Count" } ], "source_id": "57" }, { "id": "IT.NET.SECR.P6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Comparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. \n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met. Over the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "IndicatorName", "value": "Secure Internet servers (per 1 million people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Netcraft only visits sites on the standard HTTPS port, 443. Whilst it is possible to run an HTTPS server on a different port (using a URL like https://example.com:7000) this behaviour is quite rare on public websites. For example, usual ports are often used for administrative interfaces and other services not intended for the general public.\n\nNetcraft tries to visit every public secure website. Note that the survey does not include secure mail servers (SMTP) or intranet sites. So the final number of certificates for each certificate authority will be lower than the total number of server certificates sold by that authority." }, { "id": "Longdefinition", "value": "The number of distinct, publicly-trusted TLS/SSL certificates found in the Netcraft Secure Server Survey (by hosting country), per 1 million people." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2010-2024" }, { "id": "Source", "value": "Secure Server Survey, Netcraft, uri: http://www.netcraft.com/;\nWorld Bank population estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Netcraft's survey counts (unique) valid certificates issued by widely-trusted third-party certification authorities. A certificate must be valid, that is it must be within its validity period (certificates are usually valid for up to 39 months), and the digital signatures on the certificate must check successfully. It must be issued by third party certificate issuer that is recognised by Netcraft. \nNetcraft gathers a list of possible SSL web sites to investigate from a range of different sources. The data reflects the December survey in that year.\n\nSSL (Secure Socket Layer) is a protocol developed by Netscape for encrypted transmission over TCP/IP networks. It sets up a secure end-to-end link over which HTTP or any other application protocol can operate. The most common application of SSL is HTTPS for SSL-encrypted HTTP. It has now been replaced with a IETF-standardised version, TLS.\n\nSurveying: Netcraft only visits sites on the standard HTTPS port, 443. Whilst it is possible to run an HTTPS server on a different port (using a URL like https://example.com:7000) this behavior is quite rare on public websites. For example, usual ports are often used for administrative interfaces and other services not intended for the general public.\n\nNetcraft tries to visit every public secure website. Note that the survey does not include secure mail servers (SMTP) or intranet sites. So the final number of certificates for each certificate authority will be lower than the total number of server certificates sold by that authority. The survey makes multiple HTTPS request types to identify both web server capabilities, and the certificates in use. Netcraft makes retry visits to any non-responding IP addresses once, several hours after the failed visits. The information made available by an HTTPS server is more substantial than with http servers. The most interesting piece of information available from HTTP servers is the server signature; this can be analyzed to give straightforward empirical evidence about the relative popularity of server software on web sites across the Internet. The same information is also available from HTTPS servers. Additionally, the content of the site's X.509 certificate is available, providing details about both the company or organization owning the site, and the certificate issuer. Furthermore, in most cases the TCP/IP characteristics of the network connection allows to determine the operating system used.\nStatistical concept(s): The survey examines the use of encrypted transactions through extensive automated exploration, tallying the number of web sites using HTTPS. This analysis relates to those sites found in the survey where the certificate is valid for the hostname, and the certificate has been issued from a publicly-trusted root. The indicator refers to valid, third-party certificates. Included are sites found in the survey where the common name in the certificate matched the hostname, and the certificate's digital signature was not detected as being self-signed. The location is derived from the hosting location of the sites using the certificates (rather than the countries indicated on the certificates themselves.) This analysis relates to those sites found in the survey where the common name in the certificate matched the hostname, and the certificate's digital signature was not detected as being self-signed." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "Count" } ], "source_id": "57" }, { "id": "IT.NET.USEC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Price basket for Internet (US$ per month)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Price basket for Internet is calculated based on the cheapest available tariff for accessing the Internet 20 hours a month (10 hours peak and 10 hours off-peak). The basket does not include the telephone line rental but does include telephone usage charges if applicable. Data are compiled in the national currency and converted to U.S. dollars using the annual average exchange rate." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.NET.USER", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Internet users" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.NET.USER.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nToday's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains." }, { "id": "IndicatorName", "value": "Individuals using the Internet, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "This indicator refers to female individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx" }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "IT.NET.USER.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nToday's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains." }, { "id": "IndicatorName", "value": "Individuals using the Internet, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "This indicator refers to male individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx" }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "IT.NET.USER.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nToday's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains." }, { "id": "Generalcomments", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "IndicatorName", "value": "Internet users (per 100 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Statisticalconceptandmethodology", "value": "The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.NET.USER.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nToday's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains." }, { "id": "IndicatorName", "value": "Individuals using the Internet (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc." }, { "id": "Othernotes", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU), uri: https://datahub.itu.int/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): The number of in-scope individuals using the Internet is calculated by aggregating the weighted responses. The proportion of individuals using the Internet is expressed as a percentage and is calculated by dividing the total number of in-scope individuals using the Internet by the total number of in-scope individuals, and then multiplying the result by 100." }, { "id": "Topic", "value": "Infrastructure: Communications" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "57" }, { "id": "IT.PRT.NEWS.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Daily newspapers (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Daily newspapers refer to those published at least four times a week and calculated as average circulation (or copies printed) per 1,000 people." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.RES.USEC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Developmentrelevance", "value": "Access to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Fixed telephone sub-basket (US$ per month)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The fixed-telephone sub-basket does not take into consideration the one-time connection charge. This choice has been made in order to improve comparability with the other sub-baskets, which include only recurring monthly charges. If the monthly subscription includes free calls/minutes, then these are taken into consideration and deducted from the total cost of the fixed-telephone sub-basket." }, { "id": "Longdefinition", "value": "Fixed telephone sub-basket is the monthly price charged for subscribing to the public switched telephone network (PTSN), plus the cost of 30 local calls to the same (fixed) network (15 peak and 15 off-peak) of three minutes each. The service refers to the traditional fixed telephone line and does not refer to, for example, prices for managed VoIP." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Statisticalconceptandmethodology", "value": "Fixed-telephone sub-basket refers to the monthly price charged for subscribing to the Public Switched Telephone Network (PSTN), plus the cost of 30 local calls to the same (fixed) network (15 peak and 15 off-peak calls) of three minutes each. The service refers to the traditional fixed telephone line and does not refer to, for example, prices for managed voice over Internet protocol (VoIP).\n\nThe cost of a three-minute local call refers to the cost of a three-minute call within the same exchange area (local call) using the subscriber's equipment (i.e. not from a public telephone). It thus refers to the amount the subscriber must pay for a three-minute call, and not the average price for each three-minute interval. For example, some operators charge a one-time connection fee for every call, or a different price for the first minute of a call. In such cases, the actual amount for the first three minutes of a call is calculated.\n\nMany operators indicate whether advertised prices include taxes or not. If they are not included, taxes are added to the sub-basket, so as to improve the comparability of prices between countries. The sub-basket does not take into consideration the price of a telephone set.\n\nFixed-telephone access remains an important access technology in its own right in a large number of countries. Additionally, the conventional fixed-telephone line is used not only for dial-up Internet access, but also as a basis for upgrading to DSL broadband technology. While more and more countries are moving away from narrowband/dialup Internet access to broadband, dial-up Internet access still remains the only Internet access available to some people in developing countries.\n\nPrices are reported and collected in national currency and then converted to USD." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.TEL.EMPL.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Telephone employees, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Telephone employees refer to the total full-time telecommunications staff." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.TEL.INVS.RV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Telecommunications investment (% of revenue)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Telecommunications investment refers to the investment during the financial year in telecommunication services (including fixed, mobile and Internet services) for acquiring or upgrading property and networks. Property includes tangible assets such as plant, intellectual and non-tangible assets such as computer software. The indicator is a measure of investment in telecommunication infrastructure in the country, and includes expenditure on initial installations and additions to existing installations where the usage is expected to be over an extended period of time. It excludes expenditure on research and development (R&D), annual fees for operating licences and the use of radio spectrum." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.TEL.REVN.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Access to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Telecommunications revenue (% GDP)" }, { "id": "License_Type", "value": "Use and distribution of these data are subject to ITU terms and conditions." }, { "id": "License_URL", "value": "http://www.itu.int" }, { "id": "Limitationsandexceptions", "value": "Revenues from non-telecommunications services should be excluded." }, { "id": "Longdefinition", "value": "Telecommunications revenue is the revenue from the provision of telecommunications services such as fixed-line, mobile, and data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database, and World Bank estimates." }, { "id": "Statisticalconceptandmethodology", "value": "Total (gross) telecommunication revenue is the revenue earned from all (fixed, mobile and data including Internet) operators (both network and virtual operators) offering services within the country excluding revenues from non-telecommunications services. Revenue (turnover) consists of telecommunication service earnings during the financial year under review. This should refer to actual revenues earned by retailers and not from wholesale. Revenue should not include monies received in respect of revenue earned during previous financial years, neither does it include monies received by way of loans from governments, or external investors, nor monies received from repayable subscribers' contributions or deposits. Revenues should be net of royalties. It should exclude revenues generated from traditional broadcasting." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.TEL.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Access to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. No technology has ever spread faster around the world. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Fixed line and mobile cellular subscriptions" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU." }, { "id": "Longdefinition", "value": "Fixed line and mobile cellular subscriptions are total telephone subscriptions (fixed line plus mobile). Fixed telephone subscriptions refers to the sum of active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones. Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Fixed line and mobile cellular subscriptions are total telephone subscriptions (fixed line plus mobile)." }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Statisticalconceptandmethodology", "value": "Total telephone subscriptions are mobile cellular plus fixed-telephone subscriptions.\n\nMobile-cellular telephone subscriptions refers to the number of subscriptions to a public mobile-telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile-cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services.\n\nFixed-telephone subscriptions refers to the sum of active number of analogue fixed-telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.TEL.TOTL.EM", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Developmentrelevance", "value": "Access to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. No technology has ever spread faster around the world. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Fixed line and mobile cellular subscriptions per employee" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU." }, { "id": "Longdefinition", "value": "Fixed line and mobile cellular subscriptions per employee are telephone subscriptions (fixed line plus mobile) divided by the total number of telecommunications employees." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Fixed line and mobile cellular subscriptions per employee are telephone subscriptions (fixed line plus mobile) divided by the total number of telecommunications employees." }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Statisticalconceptandmethodology", "value": "Mobile cellular and fixed-telephone subscriptions per employee are telephone subscriptions (fixed telephone plus mobile) divided by the total number of telecommunications employees.\n\nMobile-cellular telephone subscriptions refers to the number of subscriptions to a public mobile-telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile-cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services.\n\nFixed-telephone subscriptions refers to the sum of active number of analogue fixed-telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones.\n\nTelecommunications efficiency is measured by total telecommunications revenue divided by GDP and by mobile cellular and fixed-telephone subscriptions per employee." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.TEL.TOTL.P2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Access to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. No technology has ever spread faster around the world. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency." }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Fixed line and mobile cellular subscriptions (per 100 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU." }, { "id": "Longdefinition", "value": "Fixed line and mobile cellular subscriptions are total telephone subscriptions (fixed line plus mobile). Fixed telephone subscriptions refers to the sum of active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones. Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Fixed line and mobile cellular subscriptions are total telephone subscriptions (fixed line plus mobile)." }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Statisticalconceptandmethodology", "value": "Total telephone subscriptions are mobile cellular plus fixed-telephone subscriptions. Data are divided by the total population and multiplied by 100.\n\nMobile-cellular telephone subscriptions refers to the number of subscriptions to a public mobile-telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile-cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services.\n\nFixed-telephone subscriptions refers to the sum of active number of analogue fixed-telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "IT.TVS.HOUS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains." }, { "id": "Generalcomments", "value": "Restricted use: Please cite the International Telecommunication Union for third-party use of these data. This indicator is not available in the World Development Indicators time series database." }, { "id": "IndicatorName", "value": "Households with television (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of households with television are derived from household surveys. Some countries report only the number of households with a color television set, and so the true number may be higher than reported. Discrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. Discrepancy may also arise in cases where the end of a fiscal year varies from that used by ITU, which is end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "Households with television are the share of households with a television set. Some countries report only the number of households with a color television set, and therefore the true number may be higher than reported." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Telecommunication Union, World Telecommunication/ICT Development Report and database." }, { "id": "Statisticalconceptandmethodology", "value": "The proportion of households with a TV is calculated by dividing the number of in-scope households with a TV by the total number of in-scope households. A TV (television) is a stand-alone device capable of receiving broadcast television signals, using popular access means such as over-the-air, cable and satellite. It excludes TV functionality integrated with another device, such as a computer or a mobile phone." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "LO.PISA.MAT.0", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by mathematics proficiency level (%). Below Level 1" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students below the lowest proficiency level (scoring 358 or below) on the PISA mathematics scale. Students below Level 1 may be able to perform very direct and straightforward mathematical tasks, such as reading a single value from a well-labeled chart or table where the labels on the chart match the words in the stimulus and question, so that the selection criteria are clear and the relationship between the chart and the aspects of the context depicted are evident, and performing arithmetic calculations with whole numbers by following clear and well-defined instructions. Data reflects country performance in the stated year according to PISA reports, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "57" }, { "id": "LO.PISA.REA.0", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by reading proficiency level (%). Below Level 1B" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring students below the lowest proficiency level (1B) on the PISA reading scale. Students with scores below Level 1b (less or equal to 262 points) usually do not succeed at the most basic reading tasks that PISA measures. This does not necessarily mean that they are illiterate, but that there is insufficient information on which to base a description of their reading proficiency. 2000, 2003, and 2006 PISA assessments used a different reading proficiency scale (Below Level 1 to Level 5) than later assessments, which use a reading scale from Below Level 1B to Level 6. Because an equivalent proficiency level to \"Below Level 1B\" and Level 1B were not calculated for the 2000, 2003, and 2006 PISA Reports, the data are not currently available for those years. Use caution in comparing proficiency scores across years. For more information on comparability of results, consult the PISA website: http://www.oecd.org/pisa/." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "OECD Programme for International Student Assessment (PISA) (http://www.oecd.org/pisa/)" }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "57" }, { "id": "LO.PISA.SCI.0", "metatype": [ { "id": "IndicatorName", "value": "PISA: 15-year-olds by science proficiency level (%). Below Level 1B" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of 15-year-old students scoring below the lowest proficiency level (less than or equal to 261) on the PISA science scale. No item in the PISA assessment can indicate what students who perform below Level 1b can do. Students below Level 1b may have acquired some elements of science knowledge and skills, but based on the tasks included in the PISA test, their ability can only be described in terms of what they cannot do – and they are unlikely to be able to solve, other than by guessing, any of the PISA tasks. Data reflect country performance in the stated year, but may not be comparable across years or countries. Consult the PISA website for more detailed information: http://www.oecd.org/pisa/" }, { "id": "Topic", "value": "Learning Outcomes" } ], "source_id": "57" }, { "id": "LP.EXP.DURS.MD", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce.\n\nA useful outcome measure of logistics performance is the time taken to complete trade transactions. The median import lead time for port and airport supply chains, as measured for the LPI, is more than 3.5 times longer in low performing countries than in high-performing countries. The difference is around three times for land supply chains. The association suggests that geographical hurdles, and perhaps internal transport markets, still pose substantial difficulties in many countries. Besides geography and speed en route, another factor in import lead times is the border process. Time can be reduced at all stages of this process, but especially in the clearance of goods on arrival. Countries with low logistics performance need to reform their border management so that they can reduce red tape, excessive and opaque procedural requirements, and physical inspections. Although the time to clear goods through customs is a fairly small fraction of total import time for all LPI quintiles, it rises sharply if goods are physically inspected. Core customs procedures are similar across quintiles. But low-performing countries have a far higher prevalence of physical inspection, even subjecting the same shipment to repeated inspections by multiple agencies.\n\nMany low-income countries have long export lead times, reducing their export competitiveness and ability to participate in international trade." }, { "id": "IndicatorName", "value": "Lead time to export, median case (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Feedback from operators is supplemented with quantitative data on the performance of key components of the logistics chain in the country of work. Thus, the LPI consists of both qualitative and quantitative measures.\n\nIn addition, despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Lead time to export is the median time (the value for 50 percent of shipments) from shipment point to port of loading. Data are from the Logistics Performance Index survey. Respondents provided separate values for the best case (10 percent of shipments) and the median case (50 percent of shipments). The data are exponentiated averages of the logarithm of single value responses and of midpoint values of range responses for the median case." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2007-2018" }, { "id": "Source", "value": "Logistic Performance Index Surveys, World Bank (WB), uri: https://lpi.worldbank.org/, note: Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.;\nTurku School of Economics, uri: https://lpi.worldbank.org/, note: Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on lead time to export are from the Logistics Performance Index (LPI) survey. Respondents provided separate values for the best case (10 percent of shipments) and the median case (50 percent of shipments) of shipments from the point of origin (the seller's factory, typically located either in the capital city or in the largest commercial center) to the port of loading or equivalent (port/airport), and excluding international shipping.\n\nThe Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and at the main express carriers. The 2012 LPI data are based on the 2011 survey, which was administered to nearly 1,000 respondents at international logistics companies in 143 countries (domestic performance indicators). The international LPI covers 155 countries. The LPI assesses both large companies and small and medium enterprises. Most of the responses are from small and medium enterprises, with large companies (those with 250 employees or more) accounting for roughly 18 percent of responses. The respondents include groups of professionals who are directly involved in day-today operations, from company headquarters and from country offices such as senior executives, area or country managers, and department managers. Many of the respondents are at corporate or regional headquarters or at country branch offices. The rest are at local branch offices or independent firms. The majority of respondents are involved in providing most logistics services as their main line of work such as warehousing and distribution, customer-tailored logistics solutions, courier services, bulk or break bulk cargo transport, and less-than-full container, full-container, or full-trailer load transport. \n\nFor the lead time to export, respondents were asked for quantitative information on their countries' international supply chains by picking choices from a dropdown menu. When a response indicates a single value, the answer is coded as the logarithm of that value. When a response indicates a range, the answer is coded as the logarithm of the midpoint of that range. Country scores are produced by exponentiating the average of responses in logarithms across all respondents for a given country. This method is equivalent to taking a geometric average in levels. Scores for regions, income groups, and LPI quintiles are simple averages of the relevant country scores." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "LP.IMP.DURS.MD", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce.\n\nA useful outcome measure of logistics performance is the time taken to complete trade transactions. The median import lead time for port and airport supply chains, as measured for the LPI, is more than 3.5 times longer in low performing countries than in high-performing countries. The difference is around three times for land supply chains. The association suggests that geographical hurdles, and perhaps internal transport markets, still pose substantial difficulties in many countries. Besides geography and speed en route, another factor in import lead times is the border process. Time can be reduced at all stages of this process, but especially in the clearance of goods on arrival. Countries with low logistics performance need to reform their border management so that they can reduce red tape, excessive and opaque procedural requirements, and physical inspections. Although the time to clear goods through customs is a fairly small fraction of total import time for all LPI quintiles, it rises sharply if goods are physically inspected. Core customs procedures are similar across quintiles. But low-performing countries have a far higher prevalence of physical inspection, even subjecting the same shipment to repeated inspections by multiple agencies.\n\nMany low-income countries have long export lead times, reducing their export competitiveness and ability to participate in international trade." }, { "id": "IndicatorName", "value": "Lead time to import, median case (days)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Feedback from operators is supplemented with quantitative data on the performance of key components of the logistics chain in the country of work. Thus, the LPI consists of both qualitative and quantitative measures.\n\nIn addition, despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Lead time to import is the median time (the value for 50 percent of shipments) from port of discharge to arrival at the consignee. Data are from the Logistics Performance Index survey. Respondents provided separate values for the best case (10 percent of shipments) and the median case (50 percent of shipments). The data are exponentiated averages of the logarithm of single value responses and of midpoint values of range responses for the median case." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2007-2018" }, { "id": "Source", "value": "Logistic Performance Index Surveys, World Bank (WB), uri: https://lpi.worldbank.org/, note: Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.;\nTurku School of Economics, uri: https://lpi.worldbank.org/, note: Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on lead time to import are from the Logistics Performance Index (LPI) survey. Respondents provided separate values for the best case (10 percent of shipments) and the median case (50 percent of shipments) of shipments from the port of discharge or equivalent to the buyer's warehouse.\n\nThe Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and at the main express carriers. The 2012 LPI data are based on the 2011 survey, which was administered to nearly 1,000 respondents at international logistics companies in 143 countries (domestic performance indicators). The international LPI covers 155 countries. The LPI assesses both large companies and small and medium enterprises. Most of the responses are from small and medium enterprises, with large companies (those with 250 employees or more) accounting for roughly 18 percent of responses. The respondents include groups of professionals who are directly involved in day-today operations, from company headquarters and from country offices such as senior executives, area or country managers, and department managers. Many of the respondents are at corporate or regional headquarters or at country branch offices. The rest are at local branch offices or independent firms. The majority of respondents are involved in providing most logistics services as their main line of work such as warehousing and distribution, customer-tailored logistics solutions, courier services, bulk or break bulk cargo transport, and less-than-full container, full-container, or full-trailer load transport.\n\nFor the lead time to import, respondents were asked for quantitative information on their countries' international supply chains by picking choices from a dropdown menu. When a response indicates a single value, the answer is coded as the logarithm of that value. When a response indicates a range, the answer is coded as the logarithm of the midpoint of that range. Country scores are produced by exponentiating the average of responses in logarithms across all respondents for a given country. This method is equivalent to taking a geometric average in levels. Scores for regions, income groups, and LPI quintiles are simple averages of the relevant country scores." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "LP.LPI.CUST.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI." }, { "id": "IndicatorName", "value": "Logistics performance index: Efficiency of customs clearance process (1=low to 5=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents evaluated efficiency of customs clearance processes (i.e. speed, simplicity and predictability of formalities), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents." }, { "id": "Othernotes", "value": "This indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/." }, { "id": "Referenceperiod", "value": "2007-2022" }, { "id": "Source", "value": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "LP.LPI.INFR.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI." }, { "id": "IndicatorName", "value": "Logistics performance index: Quality of trade and transport-related infrastructure (1=low to 5=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents evaluated the quality of trade and transport related infrastructure (e.g. ports, railroads, roads, information technology), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents." }, { "id": "Othernotes", "value": "This indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/." }, { "id": "Referenceperiod", "value": "2007-2022" }, { "id": "Source", "value": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "LP.LPI.ITRN.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI." }, { "id": "IndicatorName", "value": "Logistics performance index: Ease of arranging competitively priced shipments (1=low to 5=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents assessed the ease of arranging competitively priced shipments to markets, on a rating ranging from 1 (very difficult) to 5 (very easy). Scores are averaged across all respondents." }, { "id": "Othernotes", "value": "This indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/." }, { "id": "Referenceperiod", "value": "2007-2022" }, { "id": "Source", "value": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "LP.LPI.LOGS.XQ", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI." }, { "id": "IndicatorName", "value": "Logistics performance index: Competence and quality of logistics services (1=low to 5=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents evaluated the overall level of competence and quality of logistics services (e.g. transport operators, customs brokers), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents." }, { "id": "Othernotes", "value": "This indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/." }, { "id": "Referenceperiod", "value": "2007-2022" }, { "id": "Source", "value": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "LP.LPI.OVRL.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI." }, { "id": "IndicatorName", "value": "Logistics performance index: Overall (1=low to 5=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "The Logistics Performance Index overall score reflects perceptions of a country's logistics based on the efficiency of customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The index ranges from 1 to 5, with a higher score representing better performance. \n\nData are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. \n\nScores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. \n\nDetails of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report." }, { "id": "Othernotes", "value": "This indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/." }, { "id": "Referenceperiod", "value": "2007-2022" }, { "id": "Source", "value": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "LP.LPI.TIME.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI." }, { "id": "IndicatorName", "value": "Logistics performance index: Frequency with which shipments reach consignee within scheduled or expected time (1=low to 5=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents assessed how often the shipments to assessed markets reach the consignee within the scheduled or expected delivery time, on a rating ranging from 1 (hardly ever) to 5 (nearly always). Scores are averaged across all respondents." }, { "id": "Othernotes", "value": "This indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/." }, { "id": "Referenceperiod", "value": "2007-2022" }, { "id": "Source", "value": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "LP.LPI.TRAC.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI." }, { "id": "IndicatorName", "value": "Logistics performance index: Ability to track and trace consignments (1=low to 5=high)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms." }, { "id": "Longdefinition", "value": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents evaluated the ability to track and trace consignments when shipping to the market, on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents." }, { "id": "Othernotes", "value": "This indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/." }, { "id": "Referenceperiod", "value": "2007-2022" }, { "id": "Source", "value": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals." }, { "id": "Topic", "value": "Private Sector & Trade: Trade facilitation" } ], "source_id": "57" }, { "id": "MS.MIL.MPRT.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic." }, { "id": "IndicatorName", "value": "Arms imports (SIPRI trend indicator values)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "SIPRI calculates the volume of transfers to, from and between all parties using the TIV and the number of weapon systems or subsystems delivered in a given year. This data is intended to provide a common unit to allow the measurement if trends in the flow of arms to particular countries and regions over time. Therefore, the main priority is to ensure that the TIV system remains consistent over time, and that any changes introduced are backdated.\n\nSIPRI TIV figures do not represent sales prices for arms transfers. They should therefore not be directly compared with gross domestic product (GDP), military expenditure, sales values or the financial value of export licences in an attempt to measure the economic burden of arms imports or the economic benefits of exports. They are best used as the raw data for calculating trends in international arms transfers over periods of time, global percentages for suppliers and recipients, and percentages for the volume of transfers to or from particular states.\n\nExcluded are transfers of other military equipment such as small arms and light weapons, trucks, small artillery, ammunition, support equipment, technology transfers, and other services." }, { "id": "Longdefinition", "value": "Arms transfers (imports) cover the volume of transfers of major arms through sales and gifts, and those made through manufacturing licenses. Data cover major conventional weapons such as aircraft, armored vehicles, artillery, radar systems, missiles, and ships. Figures are SIPRI Trend Indicator Values (TIVs). A '0' indicates that the volume of deliveries is between 0 and 0.5 million SIPRI TIV." }, { "id": "Othernotes", "value": "Data for some countries are based on partial or uncertain data or rough estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Arms Transfers Programme, Stockholm International Peace Research Institute (SIPRI), uri: https://armstransfers.sipri.org/ArmsTransfer/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Stockholm International Peace Research Institute (SIPRI)'s Arms Transfers Program collects data on arms transfers from open sources. Since publicly available information is inadequate for tracking all weapons and other military equipment, SIPRI covers only what it terms major conventional weapons. Data cover the supply of weapons through sales, aid, gifts, and manufacturing licenses; therefore the term arms transfers rather than arms trade is used. SIPRI data also cover weapons supplied to or from rebel forces in an armed conflict as well as arms deliveries for which neither the supplier nor the recipient can be identified with acceptable certainty; these data are available in SIPRI's database.\n\nData cover major conventional weapons such as aircraft, armored vehicles, artillery, radar systems and other sensors, missiles, and ships designed for military use as well as some major components such as turrets for armored vehicles and engines. Excluded are other military equipment such as most small arms and light weapons, trucks, small artillery, ammunition, support equipment, technology transfers, and other services.\n\nWorld total includes arms transfers values for paramilitary groups.\n\nFor the method used for the SIPRI TIV see ." }, { "id": "Topic", "value": "Public Sector: Defense & arms trade" }, { "id": "Unitofmeasure", "value": "SIPRI trend-indicator values (TIVs)" } ], "source_id": "57" }, { "id": "MS.MIL.TOTL.P1", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic." }, { "id": "IndicatorName", "value": "Armed forces personnel, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data excludes personnel not on active duty, therefore it underestimates the share of the labor force working for the defense establishment. The cooperation of governments of all countries listed in “The Military Balance” has been sought by IISS and, in many cases, received. However, some data in “The Military Balance” is estimated." }, { "id": "Longdefinition", "value": "Armed forces personnel are active duty military personnel, including paramilitary forces if the training, organization, equipment, and control suggest they may be used to support or replace regular military forces." }, { "id": "Othernotes", "value": "Data for some countries are based on partial or uncertain data or rough estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2020" }, { "id": "Source", "value": "The Military Balance, International Institute for Strategic Studies" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Military data on manpower represent quantitative assessment of the personnel strengths of the world's armed forces. The IISS collects the data from a wide variety of sources. The numbers are based on the most accurate data available to, or on the best estimate that can be made by the International Institute for Strategic Studies (IISS) at the time of its annual publication. The current WDI indicator includes active armed forces and active paramilitary (but not reservists). Armed forces personnel comprise all servicemen and women on full-time duty, including conscripts and long-term assignments from the Reserves (“Reserve” describes formations and units not fully manned or operational in peacetime, but which can be mobilized by recalling reservists in an emergency). The indicator includes paramilitary forces. The source of the data (IISS) reports armed forces and paramilitary forces separately, however these figures are added for the purpose of computing this series. Home Guard units are counted as paramilitary." }, { "id": "Topic", "value": "Public Sector: Defense & arms trade" } ], "source_id": "57" }, { "id": "MS.MIL.TOTL.TF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic." }, { "id": "IndicatorName", "value": "Armed forces personnel (% of total labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data exclude personnel not on active duty, therefore they underestimate the share of the labor force working for the defense establishment. Governments rarely report the size of their armed forces, so such data typically come from intelligence sources. Unless otherwise indicated, reserves includes all reservists committed to rejoining the armed forces in an emergency, except when national reserve service obligations following conscription last almost a lifetime." }, { "id": "Longdefinition", "value": "Armed forces personnel are active duty military personnel, including paramilitary forces if the training, organization, equipment, and control suggest they may be used to support or replace regular military forces. Labor force comprises all people who meet the International Labour Organization's definition of the economically active population." }, { "id": "Othernotes", "value": "Data for some countries are based on partial or uncertain data or rough estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2020" }, { "id": "Source", "value": "The Military Balance, International Institute for Strategic Studies" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Military data on manpower represent quantitative assessment of the personnel strengths of the world's armed forces. The numbers are based on the most accurate data available to, or, on the best estimate that can be made by the International Institute for Strategic Studies (IISS) at the time of its annual publication. The IISS collects the data from national governments.\n\nArmed forces personnel comprise all servicemen and women on full-time duty (including conscripts and long-term assignments from the Reserves). Reserve describes formations and units not fully manned or operational in peacetime, but which can be mobilized by recalling reservists in an emergency. IISS estimates of effective reservist strengths on the numbers available within five years of completing full-time service, unless there is good evidence that obligations are enforced for longer. Although paramilitary forces whose training, organization, equipment and control suggest they may be used to support or replace regular military forces, they are not included in the armed forces personnel. Home Guard units are counted as paramilitary.\n\nData are shown as percentage of total labor force. According to International Labour Organization (ILO armed forces occupations include all jobs held by members of the armed forces. Members of the armed forces are those personnel who are currently serving in the armed forces, including auxiliary services, whether on a voluntary or compulsory basis, and who are not free to accept civilian employment and are subject to military discipline. Included are regular members of the army, navy, air force and other military services, as well as conscripts enrolled for military training or other service for a specified period. Excluded are persons in civilian employment of government establishments concerned with defense issues; police (other than military police); customs inspectors and members of border or other armed civilian services; persons who have been temporarily withdrawn from civilian life for a short period of military training or retraining, according to national requirements, and members of military reserves not currently on active service." }, { "id": "Topic", "value": "Public Sector: Defense & arms trade" } ], "source_id": "57" }, { "id": "MS.MIL.XPND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures as a share of gross domestic product (GDP) are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nAs an \"input\" measure military expenditures are not directly related to the \"output\" of military activities, capabilities, or security. Comparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic." }, { "id": "IndicatorName", "value": "Military expenditure (current USD)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "SIPRI strives to compile reliable, consistent military expenditure data by assessing multiple sources, but accuracy depends on the quality and transparency of those sources. Challenges arise from two key issues: whether reported figures reflect actual spending and how closely they match SIPRI’s definition. While data is generally accurate in developed and many developing countries, weak governance, corruption, and secret transfers in others can lead to major discrepancies. SIPRI sometimes makes estimates, when sources conflict or lack coverage, introducing uncertainty, especially for countries like China or the UAE. Definitions also vary: official defense budgets may omit pensions, paramilitary forces, or extra- and off-budget spending such as resource funds or military commercial activities, which can be substantial but often untraceable, particularly in Africa, the Middle East, and parts of Asia. SIPRI notes these gaps in footnotes, but where figures cannot be obtained, estimates remain incomplete, limiting comparability across countries." }, { "id": "Longdefinition", "value": "Military spending USD, in current prices (converted at the exchange rate for the given year)" }, { "id": "Othernotes", "value": "Statistical concept(s): Although the lack of sufficiently detailed data makes it difficult to apply a common definition of military expenditure on a worldwide basis, SIPRI has adopted a definition as a guideline. Where possible, SIPRI military expenditure data include all current and capital expenditure on: (a) the armed forces, including peacekeeping forces; (b) defense ministries and other government agencies engaged in defense projects; (c) paramilitary forces, when judged to be trained and equipped for military operations; and (d) military space activities.\n\nThis should include expenditure on: (i) personnel, including: salaries of military and civil personnel; b. retirement pensions of military personnel, and; social services for personnel; (ii) operations and maintenance; (iii) procurement; (iv) military research and development; (v) military infrastructure spending, including military bases; and (vi) military aid (in the military expenditure of the donor country). \n\nSIPRI’s estimate of military aid includes financial contributions, training and operational costs, replacement costs of the military equipment stocks donated to recipients and payments to procure additional military equipment for the recipient. However, it does not include the estimated value of military equipment stocks donated.\n\nCivil defense and spending related to past military activities—like veterans’ benefits or demobilization—are excluded. Because many countries do not publish data detailed enough to perfectly match SIPRI’s definition, SIPRI often relies on national figures and prioritizes internal consistency over time rather than strict cross-country uniformity. As a result, SIPRI data are most reliable for analyzing trends rather than precise comparisons between countries, and users should consult footnotes for known deviations from the definition.\n\nData for some countries are based on partial or uncertain data or rough estimates. For additional details please refer to the military expenditure database on the SIPRI website: https://sipri.org/databases/milex" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "SIPRI Military Expenditure Database, Stockholm International Peace Research Institute (SIPRI), uri: https://www.sipri.org/databases" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Military expenditure data is collected from primary and secondary sources. Primary sources include official government publications such as national budgets, defense white papers, financial statistics, and responses to questionnaires from SIPRI, the UN, or the OSCE, as well as expert analyses of government budgets. Secondary sources draw on these primary materials and include international datasets produced by organizations like NATO and the IMF, as well as reference works such as the German Statistisches Jahrbuch, the Europa Yearbook, and Economist Intelligence Unit country reports. Other secondary sources are journals and newspapers. Historically, especially before 1988, secondary sources (notably IMF and UN statistics) were used more heavily due to limited availability of official national data. In recent years, the availability of primary government data has increased significantly.\n\nSIPRI uses government-reported military expenditure data as the baseline and only produces its own estimates when official data are incomplete or inconsistent across years. Estimates are created through detailed budget analysis or by merging overlapping data sources, giving priority to those that best fit SIPRI’s definition, are up-to-date, and provide continuous time series. Older pre-1988 data often required combining secondary sources like IMF GFS and UNSY, which differ in definitions (e.g., excluding military pensions). SIPRI avoids making assumptions and does not estimate spending for countries lacking any official data. In SIPRI’s database, estimated values appear in blue, while figures considered uncertain, because of weak sources or volatile conditions, appear in red. For recent years, budget projections and deflator-based adjustments are common but flagged only when uncertainty is unusually high.\n\nSIPRI presents military expenditure data on a calendar-year basis (except for the U.S., which uses financial years) and converts figures to constant prices using national consumer price indices to reflect opportunity costs. Local-currency data are converted to US dollars using average market exchange rates. \n\nMilitary spending as a share of GDP (“military burden”) is calculated using nominal local-currency values for both military expenditure and GDP. SIPRI also provides military spending as a share of total government expenditure, where IMF data permit. \n\nFor additional information, please refer to the SIPRI website: https://www.sipri.org/databases/milex \n\nRefer to Other notes for the Statistical Concept(s)." }, { "id": "Topic", "value": "Public Sector: Defense & arms trade" }, { "id": "Unitofmeasure", "value": "USD current" } ], "source_id": "57" }, { "id": "MS.MIL.XPND.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures are a rough indicator of the portion of national resources used for military activities and of the burden on the economy. Comparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic." }, { "id": "IndicatorName", "value": "Military expenditure (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "SIPRI strives to compile reliable, consistent military expenditure data by assessing multiple sources, but accuracy depends on the quality and transparency of those sources. Challenges arise from two key issues: whether reported figures reflect actual spending and how closely they match SIPRI’s definition. While data is generally accurate in developed and many developing countries, weak governance, corruption, and secret transfers in others can lead to major discrepancies. SIPRI sometimes makes estimates, when sources conflict or lack coverage, introducing uncertainty, especially for countries like China or the UAE. Definitions also vary: official defense budgets may omit pensions, paramilitary forces, or extra- and off-budget spending such as resource funds or military commercial activities, which can be substantial but often untraceable, particularly in Africa, the Middle East, and parts of Asia. SIPRI notes these gaps in footnotes, but where figures cannot be obtained, estimates remain incomplete, limiting comparability across countries." }, { "id": "Longdefinition", "value": "Military expenditure in local currency at current prices (by calendar year)" }, { "id": "Othernotes", "value": "Statistical concept(s): Although the lack of sufficiently detailed data makes it difficult to apply a common definition of military expenditure on a worldwide basis, SIPRI has adopted a definition as a guideline. Where possible, SIPRI military expenditure data include all current and capital expenditure on: (a) the armed forces, including peacekeeping forces; (b) defense ministries and other government agencies engaged in defense projects; (c) paramilitary forces, when judged to be trained and equipped for military operations; and (d) military space activities.\n\nThis should include expenditure on: (i) personnel, including: salaries of military and civil personnel; b. retirement pensions of military personnel, and; social services for personnel; (ii) operations and maintenance; (iii) procurement; (iv) military research and development; (v) military infrastructure spending, including military bases; and (vi) military aid (in the military expenditure of the donor country). \n\nSIPRI’s estimate of military aid includes financial contributions, training and operational costs, replacement costs of the military equipment stocks donated to recipients and payments to procure additional military equipment for the recipient. However, it does not include the estimated value of military equipment stocks donated.\n\nCivil defense and spending related to past military activities—like veterans’ benefits or demobilization—are excluded. Because many countries do not publish data detailed enough to perfectly match SIPRI’s definition, SIPRI often relies on national figures and prioritizes internal consistency over time rather than strict cross-country uniformity. As a result, SIPRI data are most reliable for analyzing trends rather than precise comparisons between countries, and users should consult footnotes for known deviations from the definition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "SIPRI Military Expenditure Database, Stockholm International Peace Research Institute (SIPRI), uri: https://www.sipri.org/databases" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Military expenditure data is collected from primary and secondary sources. Primary sources include official government publications such as national budgets, defense white papers, financial statistics, and responses to questionnaires from SIPRI, the UN, or the OSCE, as well as expert analyses of government budgets. Secondary sources draw on these primary materials and include international datasets produced by organizations like NATO and the IMF, as well as reference works such as the German Statistisches Jahrbuch, the Europa Yearbook, and Economist Intelligence Unit country reports. Other secondary sources are journals and newspapers. Historically, especially before 1988, secondary sources (notably IMF and UN statistics) were used more heavily due to limited availability of official national data. In recent years, the availability of primary government data has increased significantly.\n\nSIPRI uses government-reported military expenditure data as the baseline and only produces its own estimates when official data are incomplete or inconsistent across years. Estimates are created through detailed budget analysis or by merging overlapping data sources, giving priority to those that best fit SIPRI’s definition, are up-to-date, and provide continuous time series. Older pre-1988 data often required combining secondary sources like IMF GFS and UNSY, which differ in definitions (e.g., excluding military pensions). SIPRI avoids making assumptions and does not estimate spending for countries lacking any official data. In SIPRI’s database, estimated values appear in blue, while figures considered uncertain, because of weak sources or volatile conditions, appear in red. For recent years, budget projections and deflator-based adjustments are common but flagged only when uncertainty is unusually high.\n\nSIPRI presents military expenditure data on a calendar-year basis (except for the U.S., which uses financial years) and converts figures to constant prices using national consumer price indices to reflect opportunity costs. Local-currency data are converted to US dollars using average market exchange rates. \n\nMilitary spending as a share of GDP (“military burden”) is calculated using nominal local-currency values for both military expenditure and GDP. SIPRI also provides military spending as a share of total government expenditure, where IMF data permit. \n\nFor additional information, please refer to the SIPRI website: https://www.sipri.org/databases/milex \n\nRefer to Other notes for the Statistical Concept(s)." }, { "id": "Topic", "value": "Public Sector: Defense & arms trade" }, { "id": "Unitofmeasure", "value": "Domestic currency" } ], "source_id": "57" }, { "id": "MS.MIL.XPND.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures as a share of gross domestic product (GDP) are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nAs an \"input\" measure military expenditures are not directly related to the \"output\" of military activities, capabilities, or security. Comparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic." }, { "id": "IndicatorName", "value": "Military expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "SIPRI strives to compile reliable, consistent military expenditure data by assessing multiple sources, but accuracy depends on the quality and transparency of those sources. Challenges arise from two key issues: whether reported figures reflect actual spending and how closely they match SIPRI’s definition. While data is generally accurate in developed and many developing countries, weak governance, corruption, and secret transfers in others can lead to major discrepancies. SIPRI sometimes makes estimates, when sources conflict or lack coverage, introducing uncertainty, especially for countries like China or the UAE. Definitions also vary: official defense budgets may omit pensions, paramilitary forces, or extra- and off-budget spending such as resource funds or military commercial activities, which can be substantial but often untraceable, particularly in Africa, the Middle East, and parts of Asia. SIPRI notes these gaps in footnotes, but where figures cannot be obtained, estimates remain incomplete, limiting comparability across countries." }, { "id": "Longdefinition", "value": "Military expenditure by country as percentage of gross domestic product" }, { "id": "Othernotes", "value": "Statistical concept(s): Although the lack of sufficiently detailed data makes it difficult to apply a common definition of military expenditure on a worldwide basis, SIPRI has adopted a definition as a guideline. Where possible, SIPRI military expenditure data include all current and capital expenditure on: (a) the armed forces, including peacekeeping forces; (b) defense ministries and other government agencies engaged in defense projects; (c) paramilitary forces, when judged to be trained and equipped for military operations; and (d) military space activities.\n\nThis should include expenditure on: (i) personnel, including: salaries of military and civil personnel; b. retirement pensions of military personnel, and; social services for personnel; (ii) operations and maintenance; (iii) procurement; (iv) military research and development; (v) military infrastructure spending, including military bases; and (vi) military aid (in the military expenditure of the donor country). \n\nSIPRI’s estimate of military aid includes financial contributions, training and operational costs, replacement costs of the military equipment stocks donated to recipients and payments to procure additional military equipment for the recipient. However, it does not include the estimated value of military equipment stocks donated.\n\nCivil defense and spending related to past military activities—like veterans’ benefits or demobilization—are excluded. Because many countries do not publish data detailed enough to perfectly match SIPRI’s definition, SIPRI often relies on national figures and prioritizes internal consistency over time rather than strict cross-country uniformity. As a result, SIPRI data are most reliable for analyzing trends rather than precise comparisons between countries, and users should consult footnotes for known deviations from the definition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "SIPRI Military Expenditure Database, Stockholm International Peace Research Institute (SIPRI), uri: https://www.sipri.org/databases" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Military expenditure data is collected from primary and secondary sources. Primary sources include official government publications such as national budgets, defense white papers, financial statistics, and responses to questionnaires from SIPRI, the UN, or the OSCE, as well as expert analyses of government budgets. Secondary sources draw on these primary materials and include international datasets produced by organizations like NATO and the IMF, as well as reference works such as the German Statistisches Jahrbuch, the Europa Yearbook, and Economist Intelligence Unit country reports. Other secondary sources are journals and newspapers. Historically, especially before 1988, secondary sources (notably IMF and UN statistics) were used more heavily due to limited availability of official national data. In recent years, the availability of primary government data has increased significantly.\n\nSIPRI uses government-reported military expenditure data as the baseline and only produces its own estimates when official data are incomplete or inconsistent across years. Estimates are created through detailed budget analysis or by merging overlapping data sources, giving priority to those that best fit SIPRI’s definition, are up-to-date, and provide continuous time series. Older pre-1988 data often required combining secondary sources like IMF GFS and UNSY, which differ in definitions (e.g., excluding military pensions). SIPRI avoids making assumptions and does not estimate spending for countries lacking any official data. In SIPRI’s database, estimated values appear in blue, while figures considered uncertain, because of weak sources or volatile conditions, appear in red. For recent years, budget projections and deflator-based adjustments are common but flagged only when uncertainty is unusually high.\n\nSIPRI presents military expenditure data on a calendar-year basis (except for the U.S., which uses financial years) and converts figures to constant prices using national consumer price indices to reflect opportunity costs. Local-currency data are converted to US dollars using average market exchange rates. \n\nMilitary spending as a share of GDP (“military burden”) is calculated using nominal local-currency values for both military expenditure and GDP. SIPRI also provides military spending as a share of total government expenditure, where IMF data permit. \n\nFor additional information, please refer to the SIPRI website: https://www.sipri.org/databases/milex \nRefer to Other notes for the Statistical Concept(s)." }, { "id": "Topic", "value": "Public Sector: Defense & arms trade" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "57" }, { "id": "MS.MIL.XPND.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures as a share of gross domestic product (GDP) are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nAs an \"input\" measure military expenditures are not directly related to the \"output\" of military activities, capabilities, or security. Comparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic." }, { "id": "IndicatorName", "value": "Military expenditure (% of general government expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "SIPRI strives to compile reliable, consistent military expenditure data by assessing multiple sources, but accuracy depends on the quality and transparency of those sources. Challenges arise from two key issues: whether reported figures reflect actual spending and how closely they match SIPRI’s definition. While data is generally accurate in developed and many developing countries, weak governance, corruption, and secret transfers in others can lead to major discrepancies. SIPRI sometimes makes estimates, when sources conflict or lack coverage, introducing uncertainty, especially for countries like China or the UAE. Definitions also vary: official defense budgets may omit pensions, paramilitary forces, or extra- and off-budget spending such as resource funds or military commercial activities, which can be substantial but often untraceable, particularly in Africa, the Middle East, and parts of Asia. SIPRI notes these gaps in footnotes, but where figures cannot be obtained, estimates remain incomplete, limiting comparability across countries." }, { "id": "Longdefinition", "value": "Military expenditure expressed as a percentage of general government expenditure" }, { "id": "Othernotes", "value": "Statistical concept(s): Although the lack of sufficiently detailed data makes it difficult to apply a common definition of military expenditure on a worldwide basis, SIPRI has adopted a definition as a guideline. Where possible, SIPRI military expenditure data include all current and capital expenditure on: (a) the armed forces, including peacekeeping forces; (b) defense ministries and other government agencies engaged in defense projects; (c) paramilitary forces, when judged to be trained and equipped for military operations; and (d) military space activities.\n\nThis should include expenditure on: (i) personnel, including: salaries of military and civil personnel; b. retirement pensions of military personnel, and; social services for personnel; (ii) operations and maintenance; (iii) procurement; (iv) military research and development; (v) military infrastructure spending, including military bases; and (vi) military aid (in the military expenditure of the donor country). \n\nSIPRI’s estimate of military aid includes financial contributions, training and operational costs, replacement costs of the military equipment stocks donated to recipients and payments to procure additional military equipment for the recipient. However, it does not include the estimated value of military equipment stocks donated.\n\nCivil defense and spending related to past military activities—like veterans’ benefits or demobilization—are excluded. Because many countries do not publish data detailed enough to perfectly match SIPRI’s definition, SIPRI often relies on national figures and prioritizes internal consistency over time rather than strict cross-country uniformity. As a result, SIPRI data are most reliable for analyzing trends rather than precise comparisons between countries, and users should consult footnotes for known deviations from the definition.\n\nData for some countries are based on partial or uncertain data or rough estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2024" }, { "id": "Source", "value": "SIPRI Military Expenditure Database, Stockholm International Peace Research Institute (SIPRI), uri: https://www.sipri.org/databases" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Military expenditure data is collected from primary and secondary sources. Primary sources include official government publications such as national budgets, defense white papers, financial statistics, and responses to questionnaires from SIPRI, the UN, or the OSCE, as well as expert analyses of government budgets. Secondary sources draw on these primary materials and include international datasets produced by organizations like NATO and the IMF, as well as reference works such as the German Statistisches Jahrbuch, the Europa Yearbook, and Economist Intelligence Unit country reports. Other secondary sources are journals and newspapers. Historically, especially before 1988, secondary sources (notably IMF and UN statistics) were used more heavily due to limited availability of official national data. In recent years, the availability of primary government data has increased significantly.\n\nSIPRI uses government-reported military expenditure data as the baseline and only produces its own estimates when official data are incomplete or inconsistent across years. Estimates are created through detailed budget analysis or by merging overlapping data sources, giving priority to those that best fit SIPRI’s definition, are up-to-date, and provide continuous time series. Older pre-1988 data often required combining secondary sources like IMF GFS and UNSY, which differ in definitions (e.g., excluding military pensions). SIPRI avoids making assumptions and does not estimate spending for countries lacking any official data. In SIPRI’s database, estimated values appear in blue, while figures considered uncertain, because of weak sources or volatile conditions, appear in red. For recent years, budget projections and deflator-based adjustments are common but flagged only when uncertainty is unusually high.\n\nSIPRI presents military expenditure data on a calendar-year basis (except for the U.S., which uses financial years) and converts figures to constant prices using national consumer price indices to reflect opportunity costs. Local-currency data are converted to US dollars using average market exchange rates. \n\nMilitary spending as a share of GDP (“military burden”) is calculated using nominal local-currency values for both military expenditure and GDP. SIPRI also provides military spending as a share of total government expenditure, where IMF data permit. \n\nFor additional information, please refer to the SIPRI website: https://www.sipri.org/databases/milex \n\nRefer to Other notes for the Statistical Concept(s)." }, { "id": "Topic", "value": "Public Sector: Defense & arms trade" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "MS.MIL.XPRT.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic." }, { "id": "IndicatorName", "value": "Arms exports (SIPRI trend indicator values)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "SIPRI calculates the volume of transfers to, from and between all parties using the TIV and the number of weapon systems or subsystems delivered in a given year. This data is intended to provide a common unit to allow the measurement if trends in the flow of arms to particular countries and regions over time. Therefore, the main priority is to ensure that the TIV system remains consistent over time, and that any changes introduced are backdated.\n\nSIPRI TIV figures do not represent sales prices for arms transfers. They should therefore not be directly compared with gross domestic product (GDP), military expenditure, sales values or the financial value of export licences in an attempt to measure the economic burden of arms imports or the economic benefits of exports. They are best used as the raw data for calculating trends in international arms transfers over periods of time, global percentages for suppliers and recipients, and percentages for the volume of transfers to or from particular states.\n\nExcluded are transfers of other military equipment such as small arms and light weapons, trucks, small artillery, ammunition, support equipment, technology transfers, and other services." }, { "id": "Longdefinition", "value": "Arms transfers (exports) cover the volume of transfers of major arms through sales and gifts, and those made through manufacturing licenses. Data cover major conventional weapons such as aircraft, armored vehicles, artillery, radar systems, missiles, and ships. Figures are SIPRI Trend Indicator Values (TIVs). A '0' indicates that the volume of deliveries is between 0 and 0.5 million SIPRI TIV." }, { "id": "Othernotes", "value": "Data for some countries are based on partial or uncertain data or rough estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Arms Transfers Programme, Stockholm International Peace Research Institute (SIPRI), uri: https://armstransfers.sipri.org/ArmsTransfer/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Stockholm International Peace Research Institute (SIPRI)'s Arms Transfers Program collects data on arms transfers from open sources. Since publicly available information is inadequate for tracking all weapons and other military equipment, SIPRI covers only what it terms major conventional weapons. Data cover the supply of weapons through sales, aid, gifts, and manufacturing licenses; therefore the term arms transfers rather than arms trade is used. SIPRI data also cover weapons supplied to or from rebel forces in an armed conflict as well as arms deliveries for which neither the supplier nor the recipient can be identified with acceptable certainty; these data are available in SIPRI's database.\n\nData cover major conventional weapons such as aircraft, armored vehicles, artillery, radar systems and other sensors, missiles, and ships designed for military use as well as some major components such as turrets for armored vehicles and engines. Excluded are other military equipment such as most small arms and light weapons, trucks, small artillery, ammunition, support equipment, technology transfers, and other services.\n\nWorld total includes arms transfers values for paramilitary groups.\n\nFor the method used for the SIPRI TIV see ." }, { "id": "Topic", "value": "Public Sector: Defense & arms trade" }, { "id": "Unitofmeasure", "value": "SIPRI trend-indicator values (TIVs)" } ], "source_id": "57" }, { "id": "NE.CON.GOVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "General government final consumption expenditure (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total." }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NE.CON.GOVT.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "General government final consumption expenditure (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NE.CON.GOVT.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "General government final consumption expenditure (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nMeasures of growth in consumption and capital formation are subject to two kinds of inaccuracy. The first stems from the difficulty of measuring expenditures at current price levels. The second arises in deflating current price data to measure volume growth, where results depend on the relevance and reliability of the price indexes and weights used. Measuring price changes is more difficult for investment goods than for consumption goods because of the one-time nature of many investments and because the rate of technological progress in capital goods makes capturing change in quality difficult. (An example is computers - prices have fallen as quality has improved.)\n\n\n\n\n\nTo obtain government consumption in constant prices, countries may deflate current values by applying a wage (price) index or extrapolate from the change in government employment. Neither technique captures improvements in productivity or changes in the quality of government services." }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NE.CON.GOVT.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "General government final consumption expenditure (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.CON.GOVT.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "General government final consumption expenditure (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NE.CON.GOVT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "General government final consumption expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total." }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.CON.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households and NPISHs final consumption expenditure (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NE.CON.PRVT.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households and NPISHs final consumption expenditure (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NE.CON.PRVT.CN.AD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households and NPISHs final consumption expenditure, linked series (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NE.CON.PRVT.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households and NPISHs final consumption expenditure (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total. Household final consumption expenditure is often estimated as a residual, by subtracting all other known expenditures from GDP. The resulting aggregate may incorporate fairly large discrepancies. When household consumption is calculated separately, many of the estimates are based on household surveys, which tend to be one-year studies with limited coverage. Thus the estimates quickly become outdated and must be supplemented by estimates using price- and quantity-based statistical procedures. Complicating the issue, in many developing countries the distinction between cash outlays for personal business and those for household use may be blurred.\n\n\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\n\n\n\n\nMeasures of growth in consumption and capital formation are subject to two kinds of inaccuracy. The first stems from the difficulty of measuring expenditures at current price levels. The second arises in deflating current price data to measure volume growth, where results depend on the relevance and reliability of the price indexes and weights used. Measuring price changes is more difficult for investment goods than for consumption goods because of the one-time nature of many investments and because the rate of technological progress in capital goods makes capturing change in quality difficult. (An example is computers - prices have fallen as quality has improved.)" }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NE.CON.PRVT.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households and NPISHs final consumption expenditure (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.CON.PRVT.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households and NPISHs final consumption expenditure (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NE.CON.PRVT.PC.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households and NPISHs final consumption expenditure per capita (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total. Household final consumption expenditure is often estimated as a residual, by subtracting all other known expenditures from GDP. The resulting aggregate may incorporate fairly large discrepancies. When household consumption is calculated separately, many of the estimates are based on household surveys, which tend to be one-year studies with limited coverage. Thus the estimates quickly become outdated and must be supplemented by estimates using price- and quantity-based statistical procedures. Complicating the issue, in many developing countries the distinction between cash outlays for personal business and those for household use may be blurred.\n\n\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\n\n\n\n\nMeasures of growth in consumption and capital formation are subject to two kinds of inaccuracy. The first stems from the difficulty of measuring expenditures at current price levels. The second arises in deflating current price data to measure volume growth, where results depend on the relevance and reliability of the price indexes and weights used. Measuring price changes is more difficult for investment goods than for consumption goods because of the one-time nature of many investments and because the rate of technological progress in capital goods makes capturing change in quality difficult. (An example is computers - prices have fallen as quality has improved.)" }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NE.CON.PRVT.PC.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households final consumption expenditure per capita (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.CON.PRVT.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Households and NPISHs Final consumption expenditure, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for final consumption expenditure expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nHouseholds and NPISHs final consumption expenditure includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Current international $" } ], "source_id": "57" }, { "id": "NE.CON.PRVT.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Households and NPISHs Final consumption expenditure, PPP (constant 2021 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for final consumption expenditure expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nHouseholds and NPISHs final consumption expenditure includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Constant 2021 international $" } ], "source_id": "57" }, { "id": "NE.CON.PRVT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Households and NPISHs final consumption expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total. Household final consumption expenditure is often estimated as a residual, by subtracting all other known expenditures from GDP. The resulting aggregate may incorporate fairly large discrepancies. When household consumption is calculated separately, many of the estimates are based on household surveys, which tend to be one-year studies with limited coverage. Thus the estimates quickly become outdated and must be supplemented by estimates using price- and quantity-based statistical procedures. Complicating the issue, in many developing countries the distinction between cash outlays for personal business and those for household use may be blurred.\n\n\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians." }, { "id": "Longdefinition", "value": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.CON.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Final consumption expenditure (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NE.CON.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Final consumption expenditure (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NE.CON.TOTL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Final consumption expenditure (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NE.CON.TOTL.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Final consumption expenditure (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.CON.TOTL.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Final consumption expenditure (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NE.CON.TOTL.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Final consumption expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.DAB.DEFL.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross national expenditure deflator (base year varies by country)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. A deflator is the ratio of an indicator in current prices over the same series in constant prices. The base year varies by country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "index" } ], "source_id": "57" }, { "id": "NE.DAB.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross national expenditure (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NE.DAB.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross national expenditure (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NE.DAB.TOTL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross national expenditure (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NE.DAB.TOTL.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross national expenditure (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NE.DAB.TOTL.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross national expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.EXP.GNFS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Exports of goods and services (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics." }, { "id": "Longdefinition", "value": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NE.EXP.GNFS.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Exports of goods and services (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NE.EXP.GNFS.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Exports of goods and services (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics." }, { "id": "Longdefinition", "value": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NE.EXP.GNFS.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Exports of goods and services (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.EXP.GNFS.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Exports of goods and services (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NE.EXP.GNFS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Exports of goods and services (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics." }, { "id": "Longdefinition", "value": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.GDI.FPRV.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross fixed capital formation, private sector (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Private investment covers outlays by the private sector (including private nonprofit agencies) on additions to its fixed domestic assets. Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NE.GDI.FPRV.GI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Private fixed investment (% of GDFI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" } ], "source_id": "57" }, { "id": "NE.GDI.FPRV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross fixed capital formation, private sector (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Private investment covers outlays by the private sector (including private nonprofit agencies) on additions to its fixed domestic assets. Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.GDI.FTOT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross fixed capital formation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NE.GDI.FTOT.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross fixed capital formation (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NE.GDI.FTOT.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross fixed capital formation (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NE.GDI.FTOT.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross fixed capital formation (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.GDI.FTOT.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross fixed capital formation (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NE.GDI.FTOT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross fixed capital formation (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.GDI.STKB.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Changes in inventories (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Changes in inventories is the value of entries into inventories less the value of withdrawals and less the value of any recurrent losses of goods held in inventories during the accounting period.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NE.GDI.STKB.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Changes in inventories (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Changes in inventories is the value of entries into inventories less the value of withdrawals and less the value of any recurrent losses of goods held in inventories during the accounting period.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NE.GDI.STKB.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Changes in inventories (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Changes in inventories is the value of entries into inventories less the value of withdrawals and less the value of any recurrent losses of goods held in inventories during the accounting period.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NE.GDI.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross capital formation (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on capital formation may be estimated from direct surveys of enterprises and administrative records or based on the commodity flow method using data from production, trade, and construction activities. The quality of data on government fixed capital formation depends on the quality of government accounting systems (which tend to be weak in developing countries). Measures of fixed capital formation by households and corporations - particularly capital outlays by small, unincorporated enterprises - are usually unreliable.\n\n\n\n\n\nEstimates of changes in inventories are rarely complete but usually include the most important activities or commodities. In some countries these estimates are derived as a composite residual along with household final consumption expenditure. According to national accounts conventions, adjustments should be made for appreciation of the value of inventory holdings due to price changes, but this is not always done. In highly inflationary economies this element can be substantial." }, { "id": "Longdefinition", "value": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NE.GDI.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross capital formation (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NE.GDI.TOTL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross capital formation (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on capital formation may be estimated from direct surveys of enterprises and administrative records or based on the commodity flow method using data from production, trade, and construction activities. The quality of data on government fixed capital formation depends on the quality of government accounting systems (which tend to be weak in developing countries). Measures of fixed capital formation by households and corporations - particularly capital outlays by small, unincorporated enterprises - are usually unreliable.\n\n\n\n\n\nEstimates of changes in inventories are rarely complete but usually include the most important activities or commodities. In some countries these estimates are derived as a composite residual along with household final consumption expenditure. According to national accounts conventions, adjustments should be made for appreciation of the value of inventory holdings due to price changes, but this is not always done. In highly inflationary economies this element can be substantial.\n\n\n\n\n\nMeasures of growth in consumption and capital formation are subject to two kinds of inaccuracy. The first stems from the difficulty of measuring expenditures at current price levels. The second arises in deflating current price data to measure volume growth, where results depend on the relevance and reliability of the price indexes and weights used. Measuring price changes is more difficult for investment goods than for consumption goods because of the one-time nature of many investments and because the rate of technological progress in capital goods makes capturing change in quality difficult. (An example is computers - prices have fallen as quality has improved.) Several countries estimate capital formation from the supply side, identifying capital goods entering an economy directly from detailed production and international trade statistics. This means that the price indexes used in deflating production and international trade, reflecting delivered or offered prices, will determine the deflator for capital formation expenditures on the demand side." }, { "id": "Longdefinition", "value": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NE.GDI.TOTL.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross capital formation (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.GDI.TOTL.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross capital formation (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NE.GDI.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Gross capital formation (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on capital formation may be estimated from direct surveys of enterprises and administrative records or based on the commodity flow method using data from production, trade, and construction activities. The quality of data on government fixed capital formation depends on the quality of government accounting systems (which tend to be weak in developing countries). Measures of fixed capital formation by households and corporations - particularly capital outlays by small, unincorporated enterprises - are usually unreliable.\n\n\n\n\n\nEstimates of changes in inventories are rarely complete but usually include the most important activities or commodities. In some countries these estimates are derived as a composite residual along with household final consumption expenditure. According to national accounts conventions, adjustments should be made for appreciation of the value of inventory holdings due to price changes, but this is not always done. In highly inflationary economies this element can be substantial." }, { "id": "Longdefinition", "value": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.IMP.GNFS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Imports of goods and services (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics." }, { "id": "Longdefinition", "value": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NE.IMP.GNFS.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Imports of goods and services (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NE.IMP.GNFS.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Imports of goods and services (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics." }, { "id": "Longdefinition", "value": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NE.IMP.GNFS.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Imports of goods and services (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.IMP.GNFS.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Imports of goods and services (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NE.IMP.GNFS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Imports of goods and services (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics." }, { "id": "Longdefinition", "value": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.RSB.GNFS.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "External balance on goods and services (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The balance of international trade in goods and services is the difference between the exports and imports of goods and services. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NE.RSB.GNFS.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "External balance on goods and services (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The balance of international trade in goods and services is the difference between the exports and imports of goods and services. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NE.RSB.GNFS.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "External balance on goods and services (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The balance of international trade in goods and services is the difference between the exports and imports of goods and services. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NE.RSB.GNFS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "External balance on goods and services (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The balance of international trade in goods and services is the difference between the exports and imports of goods and services. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NE.TRD.GNFS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending." }, { "id": "IndicatorName", "value": "Trade (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Trade is the sum of exports and imports of goods and services. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NV.AGR.EMPL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Agriculture, forestry, and fishing, value added per worker (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output is not easily available, especially in low- and middle-income countries, because the definition, coverage, and methodology are not always consistent across countries. For more details, see Agriculture, forestry, and fishing, value added (constant 2015 US$) [NV.AGR.TOTL.KD], Industry (including construction), value added (constant 2015 US$) [NV.IND.TOTL.KD], and Services, value added (constant 2015 US$) [NV.SRV.TOTL.KD]." }, { "id": "Longdefinition", "value": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "World Development Indicators database, World Bank (WB);\nILOSTAT database, International Labour Organization (ILO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Value added per worker is calculated by dividing value added of a sector by the number employed in the sector.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NV.AGR.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Agriculture, forestry, and fishing, value added (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently \"netted out\" using equally crude and ad hoc approximations." }, { "id": "Longdefinition", "value": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Value added" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NV.AGR.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Agriculture, forestry, and fishing, value added (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Value added" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NV.AGR.TOTL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Agriculture, forestry, and fishing, value added (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently \"netted out\" using equally crude and ad hoc approximations." }, { "id": "Longdefinition", "value": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NV.AGR.TOTL.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Agriculture, forestry, and fishing, value added (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently \"netted out\" using equally crude and ad hoc approximations." }, { "id": "Longdefinition", "value": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NV.AGR.TOTL.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Agriculture, forestry, and fishing, value added (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Value added" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NV.AGR.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Agriculture, forestry, and fishing, value added (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently \"netted out\" using equally crude and ad hoc approximations." }, { "id": "Longdefinition", "value": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period. Note: For VAB countries, gross value added at factor cost is used as the denominator." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NV.FSM.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Financial Intermediary Services Indirectly Measured (FISIM) (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Financial intermediation services which are implicitly charged in the form of either the difference between a reference rate and the interest rate actually paid to depositors, or the difference between the interest rate charged to borrowers and a reference rate. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Value added" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NV.FSM.TOTL.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Financial Intermediary Services Indirectly Measured (FISIM) (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Financial intermediation services which are implicitly charged in the form of either the difference between a reference rate and the interest rate actually paid to depositors, or the difference between the interest rate charged to borrowers and a reference rate. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1965-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Value added" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NV.IND.EMPL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Industry, including construction, value added per worker (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output is not easily available, especially in low- and middle-income countries, because the definition, coverage, and methodology are not always consistent across countries. For more details, see Agriculture, forestry, and fishing, value added (constant 2015 US$) [NV.AGR.TOTL.KD], Industry (including construction), value added (constant 2015 US$) [NV.IND.TOTL.KD], and Services, value added (constant 2015 US$) [NV.SRV.TOTL.KD]." }, { "id": "Longdefinition", "value": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. The core indicator has been divided by the number of workers in the economy to derive a measure of labor productivity. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "World Development Indicators database, World Bank (WB);\nILOSTAT database, International Labour Organization (ILO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Value added per worker is calculated by dividing value added of a sector by the number employed in the sector.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NV.IND.MANF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Manufacturing, value added (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"" }, { "id": "Longdefinition", "value": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical tranformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Value added" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NV.IND.MANF.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Manufacturing, value added (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical tranformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Value added" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NV.IND.MANF.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Manufacturing, value added (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical tranformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NV.IND.MANF.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Manufacturing, value added (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical tranformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NV.IND.MANF.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Manufacturing, value added (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical tranformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Value added" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NV.IND.MANF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Manufacturing, value added (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical tranformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NV.IND.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Industry, including construction, value added (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Value added" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NV.IND.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Industry, including construction, value added (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Value added" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NV.IND.TOTL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Industry, including construction, value added (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NV.IND.TOTL.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Industry, including construction, value added (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NV.IND.TOTL.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Industry, including construction, value added (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Value added" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NV.IND.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Industry, including construction, value added (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms." }, { "id": "Longdefinition", "value": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NV.MNF.CHEM.ZS.UN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Chemicals (% of value added in manufacturing)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"" }, { "id": "Longdefinition", "value": "Manufacturing of chemicals and chemical prodcuts includes industries classified in ISIC (Rev. 3) division 24. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of value added in manufacturing which is the contribution to the economy by the manufacturing sector (ISIC Rev. 3 major division D)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2023" }, { "id": "Source", "value": "International Yearbook of Industrial Statistics, UN Industrial Development Organization (UNIDO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NV.MNF.FBTO.ZS.UN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Food, beverages and tobacco (% of value added in manufacturing)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"" }, { "id": "Longdefinition", "value": "Manufacturing of food, beverages, and tobacco includes industries classified in ISIC (Rev. 3) divisions 15 and 16. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of value added in manufacturing which is the contribution to the economy by the manufacturing sector (ISIC Rev. 3 major division D)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2023" }, { "id": "Source", "value": "International Yearbook of Industrial Statistics, UN Industrial Development Organization (UNIDO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NV.MNF.MTRN.ZS.UN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Machinery and transport equipment (% of value added in manufacturing)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"" }, { "id": "Longdefinition", "value": "Machinery and transport equipment manufacturing includes industries classified in ISIC (Rev. 3) divisions 29-35. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of value added in manufacturing which is the contribution to the economy by the manufacturing sector (ISIC Rev. 3 major division D)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2023" }, { "id": "Source", "value": "International Yearbook of Industrial Statistics, UN Industrial Development Organization (UNIDO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NV.MNF.OTHR.ZS.UN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Other manufacturing (% of value added in manufacturing)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"" }, { "id": "Longdefinition", "value": "Other manufacturing, a residual, covers wood and related products (ISIC Rev. 3 division 20), paper and related products (ISIC Rev. 3 divisions 21 and 22), petroleum and related products (ISIC Rev. 3 division 23), basic metals and mineral products (ISIC Rev. 3 division27), fabricated metal products and professional goods (ISIC Rev. 3 division 28), and other industries (ISIC Rev. 3 divisions 25, 26, 31, 33, 36, and 37). Includes unallocated data. When data for textiles, machinery, or chemicals are shown as not available, they are included in other manufacturing. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of value added in manufacturing which is the contribution to the economy by the manufacturing sector (ISIC Rev. 3 major division D)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2023" }, { "id": "Source", "value": "International Yearbook of Industrial Statistics, UN Industrial Development Organization (UNIDO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NV.MNF.TECH.ZS.UN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Industrial development generally entails a structural transition from resource-based and low technology activities to medium and high-tech industry (MHT) activities. A modern, highly complex production structure offers better opportunities for skills development and technological innovation. MHT activities are also the high value addition industries of manufacturing with higher technological intensity and labour productivity. Increasing the share of MHT sectors also reflects the impact of innovation" }, { "id": "IndicatorName", "value": "Medium and high-tech manufacturing value added (% manufacturing value added)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Value added by economic activity should be reported at least at 3-digit ISIC for compiling MHT values. Missing values at country level are imputed based on the methodology from Competitive Industrial Performance Report (UNIDO, 2017. Conversion to USD or difference in ISIC combinations may cause discrepancy between national and international figures. For additional information please see UNIDO (2017): http://stat.unido.org/content/publications/volume-i%252c-competitive-industrial-performance-report-2016" }, { "id": "Longdefinition", "value": "The proportion of medium and high-tech industry value added in total value added of manufacturing" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2022" }, { "id": "Source", "value": "Competitive Industrial Performance (CIP) database, UN Industrial Development Organization (UNIDO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated as the share of the sum of the value added from medium and high-tech industry economic activities to manufacturing value added. The medium and high-tech industry is defined using OECD classification as the following by International Standard Industrial Classification of All Economic Activities (ISIC) Revision 3 and Revision 4 Division respectively: ISIC Rev. 3 (24, 29, 30, 31, 32, 33, 34, 35 excluding 351). Manufacturing value added is the value added of manufacturing industry, which is Section C of ISIC Rev.4, and Section D of ISIC Rev.3. Data can be found in UNIDO INDSTAT4 Database by ISIC Revision 3 and ISIC Revision 4 respectively. Data are collected using General Industrial Statistics Questionnaire which is filled by NSOs and submitted to UNIDO annually. Data for OECD countries are obtained directly from OECD. Country data are also collected from official publications and official web-sites. For additional information please see Table B.2.2 in Appendix B of UNIDO (2017): http://stat.unido.org/content/publications/volume-i%252c-competitive-industrial-performance-report-2016" }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" } ], "source_id": "57" }, { "id": "NV.MNF.TXTL.ZS.UN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Textiles and clothing (% of value added in manufacturing)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"" }, { "id": "Longdefinition", "value": "Textiles and clothing refers to industries in ISIC (rev. 3) divisions 17-19 and includes manufacturing of textiles, apparel, dying of fur, and tanning of leather. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of value added in manufacturing which is the contribution to the economy by the manufacturing sector (ISIC Rev. 3 major division D)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2023" }, { "id": "Source", "value": "International Yearbook of Industrial Statistics, UN Industrial Development Organization (UNIDO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NV.SRV.EMPL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Services, value added per worker (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output is not easily available, especially in low- and middle-income countries, because the definition, coverage, and methodology are not always consistent across countries. For more details, see Agriculture, forestry, and fishing, value added (constant 2015 US$) [NV.AGR.TOTL.KD], Industry (including construction), value added (constant 2015 US$) [NV.IND.TOTL.KD], and Services, value added (constant 2015 US$) [NV.SRV.TOTL.KD]." }, { "id": "Longdefinition", "value": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and retaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. The core indicator has been divided by the number of workers in the economy to derive a measure of labor productivity. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "World Development Indicators database, World Bank (WB);\nILOSTAT database, International Labour Organization (ILO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Value added per worker is calculated by dividing value added of a sector by the number employed in the sector.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NV.SRV.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Services, value added (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and retaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Value added" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NV.SRV.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Services, value added (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and retaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Value added" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NV.SRV.TOTL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Services, value added (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult." }, { "id": "Longdefinition", "value": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and retaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Value added" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NV.SRV.TOTL.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Services, value added (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult." }, { "id": "Longdefinition", "value": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and retaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NV.SRV.TOTL.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Services, value added (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and retaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Othernotes", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Value added" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NV.SRV.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "IndicatorName", "value": "Services, value added (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In the services industry the many self-employed workers and one-person businesses are sometimes difficult to locate, and they have little incentive to respond to surveys, let alone to report their full earnings. Compounding these problems are the many forms of economic activity that go unrecorded, including the work that women and children do for little or no pay." }, { "id": "Longdefinition", "value": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and retaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.ADJ.AEDU.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, education expenditure (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Education expenditure refers to the current operating expenditures in education, including wages and salaries and excluding capital investments in buildings and equipment. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nStatistical Yearbook, United Nations (UN), publisher: UN Statistics Division;\nOnline database, UN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.ADJ.AEDU.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, education expenditure (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Public education expenditures are considered an addition to savings. However, because of the wide variability in the effectiveness of public education expenditures, these figures cannot be construed as the value of investments in human capital. A current expenditure of $1 on education does not necessarily yield $1 of human capital. The calculation should also consider private education expenditure, but data are not available for a large number of countries." }, { "id": "Longdefinition", "value": "Education expenditure refers to the current operating expenditures in education, including wages and salaries and excluding capital investments in buildings and equipment. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nStatistical Yearbook, United Nations (UN), publisher: UN Statistics Division;\nOnline database, UN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.ADJ.DCO2.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, carbon dioxide damage (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Cost of damage due to carbon dioxide emissions from fossil fuel use and the manufacture of cement, estimated to be US$40 per ton of CO2 (the unit damage in 2017 US dollars for CO2 emitted in 2020) times the number of tons of CO2 emitted. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.ADJ.DCO2.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, carbon dioxide damage (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Cost of damage due to carbon dioxide emissions from fossil fuel use and the manufacture of cement, estimated to be US$40 per ton of CO2 (the unit damage in 2017 US dollars for CO2 emitted in 2020) times the number of tons of CO2 emitted. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.ADJ.DFOR.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, net forest depletion (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net forest depletion is calculated as the product of unit resource rents and the excess of roundwood harvest over natural growth. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.ADJ.DFOR.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, net forest depletion (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A positive net depletion figure for forest resources implies that the harvest rate exceeds the rate of natural growth; this is not the same as deforestation, which represents a change in land use. In principle, there should be an addition to savings in countries where growth exceeds harvest, but empirical estimates suggest that most of this net growth is in forested areas that cannot currently be exploited economically. Because the depletion estimates reflect only timber values, they ignore all the external and nontimber benefits associated with standing forests." }, { "id": "Longdefinition", "value": "Net forest depletion is calculated as the product of unit resource rents and the excess of roundwood harvest over natural growth. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.ADJ.DKAP.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, consumption of fixed capital (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Consumption of fixed capital represents the replacement value of capital used up in the process of production. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.ADJ.DKAP.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, consumption of fixed capital (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Consumption of fixed capital represents the replacement value of capital used up in the process of production. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nNational Accounts Statistics, United Nations (UN), publisher: UN Statistics Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.ADJ.DMIN.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, mineral depletion (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mineral depletion is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime (capped at 25 years). It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.ADJ.DMIN.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, mineral depletion (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mineral depletion is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime (capped at 25 years). It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.ADJ.DNGY.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, energy depletion (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Energy depletion is the ratio of the value of the stock of energy resources to the remaining reserve lifetime (capped at 25 years). It covers coal, crude oil, and natural gas. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.ADJ.DNGY.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, energy depletion (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Energy depletion is the ratio of the value of the stock of energy resources to the remaining reserve lifetime (capped at 25 years). It covers coal, crude oil, and natural gas. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.ADJ.DPEM.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, particulate emission damage (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Particulate emissions damage is the damage due to exposure of a country's population to ambient concentrations of particulates measuring less than 2.5 microns in diameter (PM2.5), ambient ozone pollution, and indoor concentrations of PM2.5 in households cooking with solid fuels. Damages are calculated as foregone labor income due to premature death. Estimates of health impacts from the Global Burden of Disease Study 2013 are for 1990, 1995, 2000, 2005, 2010, and 2013. Data for other years have been extrapolated from trends in mortality rates. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Global Burden of Disease 2013 study, Institute for Health Metrics and Evaluation (IHME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.ADJ.DPEM.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, particulate emission damage (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Labor productivity losses, as calculated within the framework of adjusted net savings, represent only part of the economic costs of air pollution and should be interpreted as a lower-end estimate." }, { "id": "Longdefinition", "value": "Particulate emissions damage is the damage due to exposure of a country's population to ambient concentrations of particulates measuring less than 2.5 microns in diameter (PM2.5), ambient ozone pollution, and indoor concentrations of PM2.5 in households cooking with solid fuels. Damages are calculated as foregone labor income due to premature death. Estimates of health impacts from the Global Burden of Disease Study 2013 are for 1990, 1995, 2000, 2005, 2010, and 2013. Data for other years have been extrapolated from trends in mortality rates. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Global Burden of Disease 2013 study, Institute for Health Metrics and Evaluation (IHME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.ADJ.DRES.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, natural resources depletion (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Net forest depletion is not the monetary value of deforestation. Roundwood and fuelwood production are different from deforestation, which represents a permanent change in land use and, thus, is not comparable. Areas logged out but intended for regeneration are not included in deforestation figures; rather, they are counted as producing timber depletion. Net forest depletion includes only timber values and does not include the loss of nontimber forest benefits and nonuse benefits.\n\n\n\n\n\nFor both energy and mineral depletion, unit resource rent is calculated as (unit world price - average cost) / unit world price. Marginal cost should be used instead of average cost in order to calculate the true opportunity cost of extraction; however, marginal cost is difficult to compute and data are not readily available. Unit prices refer to international rather than local prices to reflect the social cost of natural resources depletion. This differs from methodologies of national accounts, which may use local prices to measure energy or mineral GDP. This difference explains eventual discrepancies in the values for energy or mineral depletion, verses energy or mineral GDP." }, { "id": "Longdefinition", "value": "Natural resource depletion is the sum of net forest depletion, energy depletion, and mineral depletion. Net forest depletion is unit resource rents times the excess of roundwood harvest over natural growth. Energy depletion is the ratio of the value of the stock of energy resources to the remaining reserve lifetime (capped at 25 years). It covers coal, crude oil, and natural gas. Mineral depletion is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime (capped at 25 years). It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.ADJ.ICTR.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, gross savings (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because gross savings is calculated as a residual it includes errors, which may not be offsetting, in its components." }, { "id": "Longdefinition", "value": "Gross savings are the difference between gross national income and public and private consumption, plus net current transfers. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.ADJ.NNAT.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, net national savings (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net national savings are equal to gross national savings less the value of consumption of fixed capital. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.ADJ.NNAT.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, net national savings (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net national savings are equal to gross national savings less the value of consumption of fixed capital. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.ADJ.NNTY.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Adjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators." }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.ADJ.NNTY.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Adjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators." }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NY.ADJ.NNTY.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Adjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators." }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1971-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.ADJ.NNTY.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.ADJ.NNTY.PC.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income per capita (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Adjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators." }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NY.ADJ.NNTY.PC.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net national income per capita (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Adjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators." }, { "id": "Longdefinition", "value": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1971-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.ADJ.SVNG.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net savings, including particulate emission damage (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide and particulate emissions damage. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.ADJ.SVNG.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net savings, including particulate emission damage (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The exercise treats public education expenditures as an addition to savings. However, because of the wide variability in the effectiveness of public education expenditures, these figures cannot be construed as the value of investments in human capital. A current expenditure of $1 on education does not necessarily yield $1 of human capital. The calculation should also consider private education expenditure, but data are not available for a large number of countries.\n\n\n\n\n\nWhile extensive, the accounting of natural resource depletion and pollution costs still has some gaps. Key estimates missing on the resource side include the value of fossil water extracted from aquifers, net depletion of fish stocks, and depletion and degradation of soils. Important pollutants affecting human health and economic assets are excluded because no internationally comparable data are widely available on damage from ground-level ozone or sulfur oxides." }, { "id": "Longdefinition", "value": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide and particulate emissions damage. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.ADJ.SVNX.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net savings, excluding particulate emission damage (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide. This series excludes particulate emissions damage. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.ADJ.SVNX.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted net savings, excluding particulate emission damage (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide. This series excludes particulate emissions damage. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Adjusted savings & income" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.EXP.CAPM.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Exports as a capacity to import (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Exports as a capacity to import equals the current price value of exports of goods and services deflated by the import price index. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Other items" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NY.GDP.COAL.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Coal rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Coal rents are the difference between the value of both hard and soft coal production at world prices and their total costs of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "57" }, { "id": "NY.GDP.DEFL.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Inflation, GDP deflator (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.GDP.DEFL.KD.ZG.AD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Inflation, GDP deflator, linked series (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.GDP.DEFL.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP deflator (base year varies by country)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. The base year varies by country. This indicator is expressed as a ratio (a÷b)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "index" } ], "source_id": "57" }, { "id": "NY.GDP.DEFL.ZS.AD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP deflator, linked series (base year varies by country)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. The base year varies by country. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed as a ratio (a÷b)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "ratio" } ], "source_id": "57" }, { "id": "NY.GDP.DISC.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Discrepancy in expenditure estimate of GDP (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Although the SNA ensures there is perfect consistency between the three measures of GDP, this is a conceptual consistency that in general does not emerge naturally from data compilations. This is because of the wide disparity of data sources that must be called on and the fact that any error in any source will lead to a difference between at least two of the GDP measures. In practice it is inevitable that many such data errors will exist and will become apparent in exercises such as the balancing of supply and use tables. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NY.GDP.DISC.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Discrepancy in expenditure estimate of GDP (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Although the SNA ensures there is perfect consistency between the three measures of GDP, this is a conceptual consistency that in general does not emerge naturally from data compilations. This is because of the wide disparity of data sources that must be called on and the fact that any error in any source will lead to a difference between at least two of the GDP measures. In practice it is inevitable that many such data errors will exist and will become apparent in exercises such as the balancing of supply and use tables. This indicator is expressed in constant prices, meaning the underlying series have been adjusted to account for price changes over time. The reference year for this adjustment varies by country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NY.GDP.FCST.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross Value Added (GVA) at basic prices (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross value added at basic prices reflects the price of products receivable by the producer exclusive of taxes payable on products and inclusive of subsidies receivable on products, less intermediate consumption valued at purchasers' prices. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.GDP.FCST.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross Value Added (GVA) at basic prices (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross value added at basic prices reflects the price of products receivable by the producer exclusive of taxes payable on products and inclusive of subsidies receivable on products, less intermediate consumption valued at purchasers' prices. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NY.GDP.FCST.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross Value Added (GVA) at basic prices (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross value added at basic prices reflects the price of products receivable by the producer exclusive of taxes payable on products and inclusive of subsidies receivable on products, less intermediate consumption valued at purchasers' prices. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NY.GDP.FCST.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross Value Added (GVA) at basic prices (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross value added at basic prices reflects the price of products receivable by the producer exclusive of taxes payable on products and inclusive of subsidies receivable on products, less intermediate consumption valued at purchasers' prices. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NY.GDP.FRST.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Forest rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Forest rents are roundwood harvest times the product of regional prices and a regional rental rate." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "57" }, { "id": "NY.GDP.MINR.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Mineral rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mineral rents are the difference between the value of production for a stock of minerals at world prices and their total costs of production. Minerals included in the calculation are tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "57" }, { "id": "NY.GDP.MKTP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Gross domestic product (GDP), though widely tracked, may not always be the most relevant summary of aggregated economic performance for all economies, especially when production occurs at the expense of consuming capital stock.\n\n\n\n\n\nWhile GDP estimates based on the production approach are generally more reliable than estimates compiled from the income or expenditure side, different countries use different definitions, methods, and reporting standards. World Bank staff review the quality of national accounts data and sometimes make adjustments to improve consistency with international guidelines. Nevertheless, significant discrepancies remain between international standards and actual practice. Many statistical offices, especially those in developing countries, face severe limitations in the resources, time, training, and budgets required to produce reliable and comprehensive series of national accounts statistics.\n\n\n\n\n\nAmong the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money." }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.GDP.MKTP.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NY.GDP.MKTP.CN.AD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP, linked series (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NY.GDP.MKTP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Each industry's contribution to growth in the economy's output is measured by growth in the industry's value added. In principle, value added in constant prices can be estimated by measuring the quantity of goods and services produced in a period, valuing them at an agreed set of base year prices, and subtracting the cost of intermediate inputs, also in constant prices. This double-deflation method requires detailed information on the structure of prices of inputs and outputs.\n\n\n\n\n\nIn many industries, however, value added is extrapolated from the base year using single volume indexes of outputs or, less commonly, inputs. Particularly in the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult.\n\n\n\n\n\nMoreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in quality lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of inflation.\n\n\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\n\n\n\n\nRebasing of national accounts can alter the measured growth rate of an economy and lead to breaks in series that affect the consistency of data over time. When countries rebase their national accounts, they update the weights assigned to various components to better reflect current patterns of production or uses of output. The new base year should represent normal operation of the economy - it should be a year without major shocks or distortions. Some developing countries have not rebased their national accounts for many years. Using an old base year can be misleading because implicit price and volume weights become progressively less relevant and useful.\n\n\n\n\n\nTo obtain comparable series of constant price data for computing aggregates, the World Bank rescales GDP and value added by industrial origin to a common reference year. Because rescaling changes the implicit weights used in forming regional and income group aggregates, aggregate growth rates are not comparable with those from earlier editions with different base years. Rescaling may result in a discrepancy between the rescaled GDP and the sum of the rescaled components. To avoid distortions in the growth rates, the discrepancy is left unallocated. As a result, the weighted average of the growth rates of the components generally does not equal the GDP growth rate." }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NY.GDP.MKTP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Each industry's contribution to growth in the economy's output is measured by growth in the industry's value added. In principle, value added in constant prices can be estimated by measuring the quantity of goods and services produced in a period, valuing them at an agreed set of base year prices, and subtracting the cost of intermediate inputs, also in constant prices. This double-deflation method requires detailed information on the structure of prices of inputs and outputs.\n\n\n\n\n\nIn many industries, however, value added is extrapolated from the base year using single volume indexes of outputs or, less commonly, inputs. Particularly in the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult.\n\n\n\n\n\nMoreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in quality lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of inflation.\n\n\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\n\n\n\n\nRebasing of national accounts can alter the measured growth rate of an economy and lead to breaks in series that affect the consistency of data over time. When countries rebase their national accounts, they update the weights assigned to various components to better reflect current patterns of production or uses of output. The new base year should represent normal operation of the economy - it should be a year without major shocks or distortions. Some developing countries have not rebased their national accounts for many years. Using an old base year can be misleading because implicit price and volume weights become progressively less relevant and useful.\n\n\n\n\n\nTo obtain comparable series of constant price data for computing aggregates, the World Bank rescales GDP and value added by industrial origin to a common reference year. Because rescaling changes the implicit weights used in forming regional and income group aggregates, aggregate growth rates are not comparable with those from earlier editions with different base years. Rescaling may result in a discrepancy between the rescaled GDP and the sum of the rescaled components. To avoid distortions in the growth rates, the discrepancy is left unallocated. As a result, the weighted average of the growth rates of the components generally does not equal the GDP growth rate." }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.GDP.MKTP.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NY.GDP.MKTP.PP.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress. \n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross domestic product (GDP) expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat, date accessed: Periodical update;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-https://data-explorer.oecd.org/, publisher: OECD, date accessed: Periodical update;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases. \n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Current international $" } ], "source_id": "57" }, { "id": "NY.GDP.MKTP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP, PPP (constant 2021 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross domestic product (GDP) expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Constant 2021 international $" } ], "source_id": "57" }, { "id": "NY.GDP.NGAS.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Natural gas rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Natural gas rents are the difference between the value of natural gas production at regional prices and total costs of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "57" }, { "id": "NY.GDP.PCAP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.GDP.PCAP.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP per capita (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NY.GDP.PCAP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP per capita (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NY.GDP.PCAP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP per capita (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.GDP.PCAP.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP per capita (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NY.GDP.PCAP.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross domestic product (GDP) per person expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Current international $" } ], "source_id": "57" }, { "id": "NY.GDP.PCAP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP per capita, PPP (constant 2021 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross domestic product (GDP) per person expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Constant 2021 international $" } ], "source_id": "57" }, { "id": "NY.GDP.PETR.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Oil rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Oil rents are the difference between the value of crude oil production at regional prices and total costs of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "57" }, { "id": "NY.GDP.TOTL.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Total natural resources rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total natural resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2021" }, { "id": "Source", "value": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Environment: Natural resources contribution to GDP" }, { "id": "Unitofmeasure", "value": "% (share) of GDP" } ], "source_id": "57" }, { "id": "NY.GDS.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross domestic savings (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant US$" } ], "source_id": "57" }, { "id": "NY.GDS.TOTL.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross domestic savings (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NY.GDS.TOTL.KN", "metatype": [ { "id": "BasePeriod", "value": "varies by country" }, { "id": "IndicatorName", "value": "Gross domestic savings (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption). Data are in constant local currency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" } ], "source_id": "57" }, { "id": "NY.GDS.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross domestic savings (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption). This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.GDY.TOTL.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "BasePeriod", "value": "2015" }, { "id": "IndicatorName", "value": "Gross domestic income (constant 2015 prices, US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross domestic income is derived as the sum of GDP and the terms of trade adjustment. Data are in constant 2015 prices, expressed in U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Aggregate indicators" } ], "source_id": "57" }, { "id": "NY.GDY.TOTL.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross domestic income (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Real gross domestic income (real GDI) measures the purchasing power of the total incomes generated by domestic production. It is a concept that exists in real terms only. When the terms of trade change there may be a significant divergence between the movements of GDP in volume terms and real GDI. The difference between the change in GDP in volume terms and real GDI is generally described as the “trading gain” (or loss) or, to turn this round, the trading gain or loss from changes in the terms of trade is the difference between real GDI and GDP in volume terms. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Other items" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NY.GNP.ATLS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI, Atlas method (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This figure is converted to U.S. dollars using the World Bank Atlas method. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Atlas GNI & GNI per capita" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.GNP.MKTP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.GNP.MKTP.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NY.GNP.MKTP.CN.AD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI, linked series (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Expenditure on GDP" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NY.GNP.MKTP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NY.GNP.MKTP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.GNP.MKTP.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NY.GNP.MKTP.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross national income (GNI) expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Current international $" } ], "source_id": "57" }, { "id": "NY.GNP.MKTP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI, PPP (constant 2021 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross national income (GNI) expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Constant 2021 international $" } ], "source_id": "57" }, { "id": "NY.GNP.PCAP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita, Atlas method (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This figure is converted to U.S. dollars using the World Bank Atlas method, and divided by the midyear population. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank's official estimates of the size of economies and country classifications by income level are based on Gross National Income (GNI) per capita. For cross-national comparisons, estimates are converted from local currency units (LCU) to current U.S. dollars using the Atlas method, referring to a former World Bank publication called the Atlas of Global Development. The Atlas method smooths exchange rate fluctuations using a three-year moving average, price-adjusted conversion factor. The USD estimate of GNI per capita is derived by applying the Atlas conversion factor to estimates measured in LCU.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Atlas GNI & GNI per capita" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.GNP.PCAP.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NY.GNP.PCAP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita (constant 2015 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at constant 2015 prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant 2015 US$" } ], "source_id": "57" }, { "id": "NY.GNP.PCAP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Growth rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.GNP.PCAP.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NY.GNP.PCAP.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross national income (GNI) per person expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Current international $" } ], "source_id": "57" }, { "id": "NY.GNP.PCAP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GNI per capita, PPP (constant 2021 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides values for gross national income (GNI) per person expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP’s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. \n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. \n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Constant 2021 international $" } ], "source_id": "57" }, { "id": "NY.GNS.ICTR.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross savings (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Savings is an amount that represent the part of disposable income (adjusted for the\n\n\nchange in pension entitlements) that is not spent on final consumption. Gross savings are calculated as gross national income less total consumption, plus net transfers. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.GNS.ICTR.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross savings (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Savings is an amount that represent the part of disposable income (adjusted for the\n\n\nchange in pension entitlements) that is not spent on final consumption. Gross savings are calculated as gross national income less total consumption, plus net transfers. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NY.GNS.ICTR.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross savings (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Savings is an amount that represent the part of disposable income (adjusted for the\n\n\nchange in pension entitlements) that is not spent on final consumption. Gross savings are calculated as gross national income less total consumption, plus net transfers. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.GNS.ICTR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Gross savings (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Savings is an amount that represent the part of disposable income (adjusted for the\n\n\nchange in pension entitlements) that is not spent on final consumption. Gross savings are calculated as gross national income less total consumption, plus net transfers. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Shares of GDP & other" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "NY.GNY.TOTL.KN", "metatype": [ { "id": "BasePeriod", "value": "varies by country" }, { "id": "IndicatorName", "value": "Gross national income (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gross national income is derived as the sum of GNP and the terms of trade adjustment. Data are in constant local currency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Other items" } ], "source_id": "57" }, { "id": "NY.GSR.NFCY.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Net primary income (net income from abroad) (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net primary income includes the net labor income and net property and entrepreneurial income components of the SNA. Labor income covers compensation of employees paid to nonresident workers. Property and entrepreneurial income covers investment income from the ownership of foreign financial claims (interest, dividends, rent, etc.) and nonfinancial property income (patents, copyrights, etc.). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.GSR.NFCY.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Net primary income (net income from abroad) (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net primary income includes the net labor income and net property and entrepreneurial income components of the SNA. Labor income covers compensation of employees paid to nonresident workers. Property and entrepreneurial income covers investment income from the ownership of foreign financial claims (interest, dividends, rent, etc.) and nonfinancial property income (patents, copyrights, etc.). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NY.GSR.NFCY.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning." }, { "id": "IndicatorName", "value": "Net primary income (net income from abroad) (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net primary income includes the net labor income and net property and entrepreneurial income components of the SNA. Labor income covers compensation of employees paid to nonresident workers. Property and entrepreneurial income covers investment income from the ownership of foreign financial claims (interest, dividends, rent, etc.) and nonfinancial property income (patents, copyrights, etc.). This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NY.TAX.NIND.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Taxes less subsidies on products (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Taxes less subsidies on production includes taxes payable less subsidies receivable on goods or services produced as outputs including other taxes or subsidies on production such as those payable on the labour, machinery, buildings or other assets used in production. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.TAX.NIND.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Taxes less subsidies on products (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Taxes less subsidies on production includes taxes payable less subsidies receivable on goods or services produced as outputs including other taxes or subsidies on production such as those payable on the labour, machinery, buildings or other assets used in production. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NY.TAX.NIND.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Taxes less subsidies on products (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Taxes less subsidies on production includes taxes payable less subsidies receivable on goods or services produced as outputs including other taxes or subsidies on production such as those payable on the labour, machinery, buildings or other assets used in production. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NY.TRF.NCTR.CD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies." }, { "id": "IndicatorName", "value": "Net secondary income (net current transfers from abroad) (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net secondary income (from abroad) comprises transfers of income between residents of the reporting country and the rest of the world that carry no provisions for repayment. Net secondary income is equal to the unrequited transfers of income from nonresidents to residents minus the unrequited transfers from residents to nonresidents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "NY.TRF.NCTR.CN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies." }, { "id": "IndicatorName", "value": "Net secondary income (net current transfers from abroad) (current LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net secondary income (from abroad) comprises transfers of income between residents of the reporting country and the rest of the world that carry no provisions for repayment. Net secondary income is equal to the unrequited transfers of income from nonresidents to residents minus the unrequited transfers from residents to nonresidents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at current prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "current LCU" } ], "source_id": "57" }, { "id": "NY.TRF.NCTR.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies." }, { "id": "IndicatorName", "value": "Net secondary income (net current transfers from abroad) (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net secondary income (from abroad) comprises transfers of income between residents of the reporting country and the rest of the world that carry no provisions for repayment. Net secondary income is equal to the unrequited transfers of income from nonresidents to residents minus the unrequited transfers from residents to nonresidents. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2013" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Aggregate indicators" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "NY.TTF.GNFS.KN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Terms of trade adjustment (constant LCU)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The terms of trade adjustment is equal to the capacity to import (current price value of exports of goods and services deflated by the import price index) less exports of goods and services in constant prices. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: Local currency at constant prices: Other items" }, { "id": "Unitofmeasure", "value": "constant LCU" } ], "source_id": "57" }, { "id": "PA.NUS.ATLS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In a market-based economy, household, producer, and government choices about resource allocation are influenced by relative prices, including the real exchange rate, real wages, real interest rates, and other prices in the economy. Relative prices also largely reflect these agents' choices. Thus relative prices convey vital information about the interaction of economic agents in an economy and with the rest of the world." }, { "id": "IndicatorName", "value": "DEC alternative conversion factor (LCU per US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The DEC alternative conversion factor is the underlying annual exchange rate (the price of one country’s currency in relation to another country's currency) used for the World Bank Atlas method. As a rule, it is the official exchange rate reported in the IMF's International Financial Statistics. Exceptions arise where further refinements are made by World Bank staff. It is expressed in local currency units per U.S. dollar." }, { "id": "Othernotes", "value": "In the WDI database, the DEC alternative conversion factor is used to convert data in local currency units (LCU) into U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. In certain countries, multiple or dual exchange rate activity exists and must be accounted for appropriately in underlying statistics. Doing so better reflects economic reality and leads to more accurate cross-country comparisons and country classifications by income level. Consequently, an alternative conversion factor is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate effectively applied to domestic transactions of foreign currencies and traded products. This applies to only a small number of countries, as shown in the country-level metadata. An alternative conversion factor is also used when the period covered by national accounts differs from the calendar year and the alternative conversion factor will then cover the same period. Alternative conversion factors are used in the Atlas methodology and elsewhere in World Development Indicators as single-year conversion factors.\nStatistical concept(s): The World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. In certain countries, multiple or dual exchange rate activity exists and must be accounted for appropriately in underlying statistics. Doing so better reflects economic reality and leads to more accurate cross-country comparisons and country classifications by income level. Consequently, an alternative conversion factor is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate effectively applied to domestic transactions of foreign currencies and traded products. This applies to only a small number of countries, as shown in the country-level metadata. An alternative conversion factor is also used when the period covered by national accounts differs from the calendar year and the alternative conversion factor will then cover the same period. Alternative conversion factors are used in the Atlas methodology and elsewhere in World Development Indicators as single-year conversion factors." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "LCU per US$" } ], "source_id": "57" }, { "id": "PA.NUS.FCRF", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In a market-based economy, household, producer, and government choices about resource allocation are influenced by relative prices, including the real exchange rate, real wages, real interest rates, and other prices in the economy. Relative prices also largely reflect these agents' choices. Thus relative prices convey vital information about the interaction of economic agents in an economy and with the rest of the world." }, { "id": "IndicatorName", "value": "Official exchange rate (LCU per US$, period average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Official or market exchange rates are often used to convert economic statistics in local currencies to a common currency in order to make comparisons across countries. Since market rates reflect at best the relative prices of tradable goods, the volume of goods and services that a U.S. dollar buys in the United States may not correspond to what a U.S. dollar converted to another country's currency at the official exchange rate would buy in that country, particularly when nontradable goods and services account for a significant share of a country's output. An alternative exchange rate - the purchasing power parity (PPP) conversion factor - is preferred because it reflects differences in price levels for both tradable and nontradable goods and services and therefore provides a more meaningful comparison of real output." }, { "id": "Longdefinition", "value": "Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. This indicator represents the ratio of Local Currency Units relative to United States dollars.This indicator is derived as an average over the reference period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The exchange rate is the price of one currency divided by another. Official exchange rates and exchange rate arrangements are established by governments. Other exchange rates recognized by governments include market rates, which are determined largely by legal market forces, and for countries with multiple exchange arrangements, principal rates, secondary rates, and tertiary rates. Annual average exchange rates are derived as the simple average of daily exchange rates for a specified calendar year\nStatistical concept(s): The exchange rate is the price of one currency in terms of another. Official exchange rates and exchange rate arrangements are established by governments. Other exchange rates recognized by governments include market rates, which are determined largely by legal market forces, and for countries with multiple exchange arrangements, principal rates, secondary rates, and tertiary rates." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "LCU per US$" } ], "source_id": "57" }, { "id": "PA.NUS.PPP", "metatype": [ { "id": "Aggregationmethod", "value": "No aggregation provided for this indicator." }, { "id": "DataQuality", "value": "The International Comparison Program (ICP) conducts multiple rounds of validation at global, regional, and national levels in the process of producing benchmark PPP estimates. Please refer to its guidelines (“Operational Guidelines and Procedures for Measuring the Real Size of the World Economy”) for details of validation. https://www.worldbank.org/en/programs/icp/brief/2011-operational-guidelines" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nPPPs, PLIs, and the PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress. \n- Recommended uses of PPPs include: to make spatial comparisons of GDP and its expenditure components; to make spatial comparisons of price levels; and to group countries by their per capita volume indexes and price level indexes.\n- Recommended uses of PPPs with limitations include: to analyze changes over time in relative GDP per capita and relative prices; to analyze price convergence; to make spatial comparisons of the cost of living; and to use PPPs calculated for GDP and its expenditure components as deflators for other values." }, { "id": "IndicatorName", "value": "PPP conversion factor, GDP (LCU per international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global PPP estimates provided by ICP are produced by the ICP Global Office and regional implementing agencies, based on data supplied by the national implementing agencies in the participating economies, and in accordance with the methodology recommended by the ICP Technical Advisory Group and approved by the ICP Governing Board. As such, these results are not produced by participating economies as part of their national official statistics.\n\nPPPs are not recommended to be used as: a precise measure to establish strict rankings of countries; a means of constructing national growth rates; a measure to generate output and productivity comparisons by industry; an indicator of the undervaluation or overvaluation of currencies; and as an equilibrium exchange rate." }, { "id": "Longdefinition", "value": "The purchasing power parity (PPP) conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of gross domestic product (GDP) and its expenditure components. This conversion factor is for the level of GDP and the base currency is the US dollar." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, publisher: International Comparison Program, type: International statistical program, date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat, type: International statistical program;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-https://data-explorer.oecd.org/, publisher: OECD" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model.\n\nICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. Description of WDI extrapolation approach is available here: https://datahelpdesk.worldbank.org/knowledgebase/articles/665452-how-do-you-extrapolate-the-ppp-conversion-factors\n\nFor the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. For Eurostat-OECD PPP Programme, please refer to the following websites.\n(http://www.oecd.org/sdd/prices-ppp/)\n(https://ec.europa.eu/eurostat/web/purchasing-power-parities/overview)\n\nFor more information on the ICP and PPPs, please refer to the ICP website at https://www.worldbank.org/en/programs/icp.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. \n\nPPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. See https://www.worldbank.org/en/programs/icp/methodology." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Local currency unit per international dollar" } ], "source_id": "57" }, { "id": "PA.NUS.PPPC.RF", "metatype": [ { "id": "Aggregationmethod", "value": "No aggregation provided for this indicator." }, { "id": "DataQuality", "value": "The International Comparison Program (ICP) conducts multiple rounds of validation at global, regional, and national levels in the process of producing benchmark PPP estimates which are used in the calculation of price level ratios. Please refer to its guidelines (“Operational Guidelines and Procedures for Measuring the Real Size of the World Economy”) for details of validation. https://www.worldbank.org/en/programs/icp/brief/2011-operational-guidelines" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "( 'PA.NUS.PPP' / 'PA.NUS.ATLS' )" }, { "id": "Developmentrelevance", "value": "The price level ratio provides a comparison of price levels across countries. If a country’s price level ratio is lower than that of another country, then its items or expenditure aggregates are less expensive than those in the other country. Conversely, if a country’s PLI is higher than that of another country, then its items or expenditure aggregates are more expensive than those in the other country.\n\nPurchasing power parities (PPPs), price level ratios, and PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress. \n\n- Recommended uses of price level ratios include: to make spatial comparisons of price levels.\n- Recommended uses of price level ratios with limitations include: to analyze changes over time in relative prices; to analyze price convergence; and to make spatial comparisons of the cost of living." }, { "id": "IndicatorName", "value": "Price level ratio of PPP conversion factor (GDP) to market exchange rate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global PPP estimates underlying this indicator are produced by the ICP Global Office and regional implementing agencies, based on data supplied by the national implementing agencies in the participating economies, and in accordance with the methodology recommended by the ICP Technical Advisory Group and approved by the ICP Governing Board. As such, these results are not produced by participating economies as part of their national official statistics.\n\nPrice level ratios are not recommended to use as a precise measure to establish strict rankings of countries." }, { "id": "Longdefinition", "value": "The price level ratio, or price level index, is the ratio of a purchasing power parity (PPP) conversion factor to the corresponding market exchange rate between two countries. For this series the base country is the United States. It provides a measure of the differences in price level between the country and the United States by indicating the number of units of the common currency (US dollars) needed to buy the same volume of the aggregation level in each country. At the level of GDP, the price level ratio provides a measure of the differences in the general price levels of countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "World Development Indicators, World Bank (WB), uri: https://databank.worldbank.org/source/world-development-indicators, publisher: World Development Indicators, type: International database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: For more information on market exchange rate, please refer to the metadata for \"DEC alternative conversion factor (LCU per US$)\" [PA.NUS.ATLS].\nFor the concept and methodology of PPP, please refer to the International Comparison Program (ICP)’s website (https://www.worldbank.org/en/programs/icp).\nStatistical concept(s): 1. The ratio of the GDPs of two economies when both are valued at national price levels and\nexpressed in local currency units has three component ratios:\nGDP ratio = price level ratio × volume ratio × currency ratio. (B3.1.1)\n2. When converting the GDP ratio in equation (B3.1.1) to a common currency using the\nmarket exchange rate, the resulting GDPXR ratio has two component ratios:\nGDPXR ratio = price level ratio × volume ratio. (B3.1.2)\n The GDP ratio in equation (B3.1.2) is expressed in a common currency, but it reflects both\nthe price level differences and the volume differences between the two economies.\n3. A PPP is defined as a spatial price deflator and currency converter. It is composed of two\ncomponent ratios:\nPPP = price level ratio × currency ratio. (B3.1.3)\n4. When a PPP is used, the GDP ratio in equation (B3.1.1) is divided by equation (B3.1.3),\nand the resulting GDPPPP ratio has only one component ratio:\nGDPPPP ratio = volume ratio. (B3.1.4)\n The GDP ratio in equation (B3.1.4) is expressed in a common currency, is valued at a common price level, and reflects only differences in volume between the two economies." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Indexed to United States = 1.00" } ], "source_id": "57" }, { "id": "PA.NUS.PRVT.PP", "metatype": [ { "id": "Aggregationmethod", "value": "No aggregation provided for this indicator." }, { "id": "DataQuality", "value": "The International Comparison Program (ICP) conducts multiple rounds of validation at global, regional, and national levels in the process of producing benchmark PPP estimates. Please refer to its guidelines (“Operational Guidelines and Procedures for Measuring the Real Size of the World Economy”) for details of validation. https://www.worldbank.org/en/programs/icp/brief/2011-operational-guidelines" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "PPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country’s PPP to its market exchange rate, to directly compare price levels across countries.\nPPPs, PLIs, and the PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress. \n- Recommended uses of PPPs include: to make spatial comparisons of GDP and its expenditure components; to make spatial comparisons of price levels; and to group countries by their per capita volume indexes and price level indexes.\n- Recommended uses of PPPs with limitations include: to analyze changes over time in relative GDP per capita and relative prices; to analyze price convergence; to make spatial comparisons of the cost of living; and to use PPPs calculated for GDP and its expenditure components as deflators for other values." }, { "id": "IndicatorName", "value": "PPP conversion factor, households and NPISHs Final consumption expenditure (LCU per international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Global PPP estimates provided by ICP are produced by the ICP Global Office and regional implementing agencies, based on data supplied by the national implementing agencies in the participating economies, and in accordance with the methodology recommended by the ICP Technical Advisory Group and approved by the ICP Governing Board. As such, these results are not produced by participating economies as part of their national official statistics.\n\nPPPs are not recommended to be used as: a precise measure to establish strict rankings of countries; a means of constructing national growth rates; a measure to generate output and productivity comparisons by industry; an indicator of the undervaluation or overvaluation of currencies; and as an equilibrium exchange rate." }, { "id": "Longdefinition", "value": "The purchasing power parity (PPP) conversion factor is a currency conversion factor and a spatial price deflator. They convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of gross domestic product (GDP) and its expenditure components. This conversion factor is for households and NPISHs Final consumption expenditure and the base currency is the US dollar." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, publisher: International Comparison Program, type: International statistical program, date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat, type: International statistical program;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-https://data-explorer.oecd.org/, publisher: OECD" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The International Comparison Program (ICP) estimates PPPs for the world’s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model.\n\nICP-estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. Description of WDI extrapolation approach is available here: https://datahelpdesk.worldbank.org/knowledgebase/articles/665452-how-do-you-extrapolate-the-ppp-conversion-factors\n\nFor the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations’ databases. For Eurostat-OECD PPP Programme, please refer to the following websites.\n(http://www.oecd.org/sdd/prices-ppp/)\n(https://ec.europa.eu/eurostat/web/purchasing-power-parities/overview)\n\nFor more information on the ICP and PPPs, please refer to the ICP website at https://www.worldbank.org/en/programs/icp.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. \n\nPPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. See https://www.worldbank.org/en/programs/icp/methodology." }, { "id": "Topic", "value": "Economic Policy & Debt: Purchasing power parity" }, { "id": "Unitofmeasure", "value": "Local currency unit per international dollar" } ], "source_id": "57" }, { "id": "PE.NUS.FCAE", "metatype": [ { "id": "IndicatorName", "value": "Official exchange rate (LCU per US$, end period)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. This series shows the end period value of local currency units relative to the U.S. dollar." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" } ], "source_id": "57" }, { "id": "PE.USG.LNDN", "metatype": [ { "id": "IndicatorName", "value": "London gold price (US$ per ounce)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics and data files." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" } ], "source_id": "57" }, { "id": "per_allsp.adq_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Adequacy of social protection and labor programs (% of total welfare of beneficiary households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Adequacy of social protection and labor programs (SPL) is measured by the total transfer amount received by the population participating in social insurance, social safety net, and unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_allsp.ben_q1_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Benefit incidence of social protection and labor programs to poorest quintile (% of total SPL benefits)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Benefit incidence of social protection and labor programs (SPL) to poorest quintile shows the percentage of total social protection and labor programs benefits received by the poorest 20% of the population. Social protection and labor programs include social insurance, social safety nets, and unemployment benefits and active labor market programs. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_allsp.cov_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social protection and labor programs (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social protection and labor programs (SPL) shows the percentage of population participating in social insurance, social safety net, and unemployment benefits and active labor market programs. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_lm_alllm.adq_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Adequacy of unemployment benefits and ALMP (% of total welfare of beneficiary households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Adequacy of unemployment benefits and active labor market programs (ALMP) is measured by the total transfer amount received by the population participating in unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_lm_alllm.ben_q1_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Benefit incidence of unemployment benefits and ALMP to poorest quintile (% of total U/ALMP benefits)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Benefit incidence of unemployment benefits and active labor market programs (ALMP) to poorest quintile shows the percentage of total unemployment and active labor market programs benefits received by the poorest 20% of the population. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_lm_alllm.cov_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_lm_alllm.cov_q1_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP in poorest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_lm_alllm.cov_q2_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP in 2nd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_lm_alllm.cov_q3_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP in 3rd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_lm_alllm.cov_q4_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP in 4th quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_lm_alllm.cov_q5_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of unemployment benefits and ALMP in richest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_sa_allsa.adq_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Adequacy of social safety net programs (% of total welfare of beneficiary households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Adequacy of social safety net programs is measured by the total transfer amount received by the population participating in social safety net programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Social safety net programs include cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_sa_allsa.ben_q1_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Benefit incidence of social safety net programs to poorest quintile (% of total safety net benefits)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Benefit incidence of social safety net programs to poorest quintile shows the percentage of total social safety net benefits received by the poorest 20% of the population. Social safety net programs include cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_sa_allsa.cov_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social safety net programs (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_sa_allsa.cov_q1_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social safety net programs in poorest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_sa_allsa.cov_q2_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social safety net programs in 2nd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_sa_allsa.cov_q3_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social safety net programs in 3rd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_sa_allsa.cov_q4_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social safety net programs in 4th quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_sa_allsa.cov_q5_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social safety net programs in richest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_si_allsi.adq_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Adequacy of social insurance programs (% of total welfare of beneficiary households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Adequacy of social insurance programs is measured by the total transfer amount received by the population participating in social insurance programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Social insurance programs include old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_si_allsi.ben_q1_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Benefit incidence of social insurance programs to poorest quintile (% of total social insurance benefits)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Benefit incidence of social insurance programs to poorest quintile shows the percentage of total social insurance benefits received by the poorest 20% of the population. Social insurance programs include old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_si_allsi.cov_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social insurance programs (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_si_allsi.cov_q1_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social insurance programs in poorest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_si_allsi.cov_q2_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social insurance programs in 2nd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_si_allsi.cov_q3_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social insurance programs in 3rd quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_si_allsi.cov_q4_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social insurance programs in 4th quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "per_si_allsi.cov_q5_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "By harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries’ Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries." }, { "id": "IndicatorName", "value": "Coverage of social insurance programs in richest quintile (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate" }, { "id": "Topic", "value": "Social Protection & Labor: Performance" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "PV.EST", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Political Stability and Absence of Violence/Terrorism: Estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them. The WGI measures six dimensions of governance: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. \n\nPolitical Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism. \n\nEstimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5." }, { "id": "Othernotes", "value": "The UCM assigns greater weight to data sources that tend to be more strongly correlated with each other. While this weighting improves the statistical precision of the aggregate indicators, it typically does not affect very much the ranking of countries on the aggregate indicators. The composite measures of governance generated by the UCM are in units of a standard normal distribution, with mean zero, standard deviation of one, and running from approximately -2.5 to 2.5, with higher values corresponding to better governance. The data is also reported in percentile rank terms, ranging from 0 (lowest rank) to 100 (highest rank).\nStatistical concept(s): The six aggregate indicators are reported in two ways: (1) in their standard normal units, ranging from approximately -2.5 to 2.5, and (2) in percentile rank terms from 0 to 100, with higher values corresponding to better outcomes.\n\nA key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). A key feature of the methodology is that it generates margins of error for each governance estimate. These margins of error need to be taken into account when making comparisons across countries and over time. \n\nEach of the six aggregate WGI measures are constructed by averaging together data from the underlying sources that correspond to the concept of governance being measured. This is done in the three steps:\n\nSTEP 1: Assigning data from individual sources to the six aggregate indicators. Individual questions from the underlying data sources are assigned to each of the six aggregate indicators. For example, a firm survey question on the regulatory environment would be assigned to Regulatory Quality, or a measure of press freedom would be assigned to Voice and Accountability. The individual variables used in the WGI and how they are assigned to the six aggregate indicators, can be found on the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators]. Note that not all of the data sources cover all countries, and so the aggregate governance scores are based on different sets of underlying data for different countries.\n\nSTEP 2: Rescaling of the individual source data to run from 0 to 1. The questions from the individual data sources are first rescaled to range from 0 to 1, with higher values corresponding to better outcomes. If, for example, a survey question asks for responses on a scale from a minimum of 1 to a maximum of 4, we rescale a score of 2 as (2-min)/(max-min)=(2-1)/3=0.33. When an individual data source provides more than one question relating to a particular dimension of governance, the rescaled scores are averaged together.\nThe 0-1 rescaled data from the individual sources are available interactively through the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators/interactive-data-access] and in the data files for each individual source [https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2]. Although nominally in the same 0-1 units, this rescaled data is not necessarily comparable across sources. For example, one data source might use a 0-10 scale but in practice most scores are clustered between 6 and 10, while another data source might also use a 0-10 scale but have responses spread out over the entire range. While the max-min rescaling above does not correct for this source of non-comparability, the procedure used to construct the aggregate indicators does (see below).\n\nSTEP 3: Using an Unobserved Components Model (UCM) to construct a weighted average of the individual indicators for each source. A statistical tool known as an Unobserved Components Model (UCM) is used to make the 0-1 rescaled data comparable across sources, and then to construct a weighted average of the data from each source for each country. The UCM assumes that the observed data from each source are a linear function of the unobserved level of governance, plus an error term. This linear function is different for different data sources, and so corrects for the remaining non-comparability of units of the rescaled data noted above. The resulting estimates of governance are a weighted average of the data from each source, with weights reflecting the pattern of correlation among data sources. The weights applied to the component indicators." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Units of a standard normal distribution (between -2.5 and 2.5, approximately)" } ], "source_id": "57" }, { "id": "PV.NO.SRC", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Political Stability and Absence of Violence/Terrorism: Number of Sources" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data sources based on expert assessments have advantages and disadvantages relative to surveys. One advantage is that they lend themselves well to cross-country comparisons, as their methodologies are explicitly designed for this purpose. Expert assessments can also provide more granular technical assessments, for example on the quality of specific types of public institutions, that would be more difficult for a typical household or firm survey respondent to provide and informed view on. Expert assessments also are less likely to be affected by respondent reticence, a concern in household and firm surveys where respondents may be unwilling to give candid responses to sensitive questions about corruption or other dimensions of governance, particularly in countries where governance is weak. \n\nOn the other hand, a shortcoming of expert assessments is that they reflect the views of a narrower set of respondents than household or firm surveys. It also is possible that the ratings provided by one expert assessment to some extent reflect the views of other expert assessments, so that each assessment does not bring completely independent information on the underlying governance concept of interest. To guard against this, the WGI do not use expert assessments that are explicitly based on other existing data sources.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nNumber of sources indicates the number of underlying data sources on which the aggregate estimate is based.\n\nThe WGI are based on a large number of different data sources, capturing the views and experiences of survey respondents and experts in the public and private sectors, as well as various NGOs. These data sources include: (a) surveys of households and firms (e.g. Afrobarometer surveys, Gallup World Poll, and Global Competitiveness Report survey), (b) NGOs (e.g. Global Integrity, Freedom House, Reporters Without Borders), (c) commercial business information providers (e.g. Economist Intelligence Unit, S&P Global, Political Risk Services), and (d) public sector organizations (e.g. CPIA assessments of World Bank and regional development banks). \n\nPolitical Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI compile and summarize information from over 30 existing data sources that report the views and experiences of citizens, entrepreneurs, and experts in the public, private and NGO sectors from around the world, on the quality of various aspects of governance.\n\n•\tThe data sources must provide subjective perceptions of relevant dimensions of governance, as the WGI are based exclusively on this type of data.\n•\tThe sources must provide original primary data produced using a well-defined methodology.\n•\tThe data sources must cover multiple countries, so that cross-country comparisons are possible.\n•\tThe data sources must be updated regularly, ideally every year, although some WGI data sources are updated once every two or three years.\n\nThe WGI draw on four different types of source data:\n\n•\tSurveys of households and firms, including the Afrobarometer surveys, Gallup World Poll, and Global Competitiveness Report survey,\n•\tCommercial business information providers, including the Economist Intelligence Unit, S&P Global, and Political Risk Services,\n•\tNon-governmental organizations, including Global Integrity, Freedom House, Reporters Without Borders, and\n•\tPublic sector organizations, including the Country Policy and Institutional Assessments (CPIA) assessments of World Bank and regional development banks.\n\nFor the detailed list of sources, please refer to: https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2 \nStatistical concept(s): Number of sources indicates the number of underlying data sources on which the aggregate estimate is based.\n\nVariables from the data sources are assigned to each of these six governance dimensions. For example, an assessment of the quality of the bureaucracy would be assigned to Government Effectiveness, a question about confidence in the police or the courts would be assigned to Rule of Law, and a question about the perceived likelihood of having to pay a bribe would be assigned to Control of Corruption. In some cases, a single data source will have multiple questions that can be assigned to the same dimension. In this case, the WGI use the average across all relevant questions from that data source. In addition each question from each data source is assigned to only one of the six governance dimensions, selecting the dimension that best matches the content of the question." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "PV.PER.RNK", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Political Stability and Absence of Violence/Terrorism: Percentile Rank" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nPolitical Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI." }, { "id": "Othernotes", "value": "The UCM assigns greater weight to data sources that tend to be more strongly correlated with each other. While this weighting improves the statistical precision of the aggregate indicators, it typically does not affect very much the ranking of countries on the aggregate indicators. The composite measures of governance generated by the UCM are in units of a standard normal distribution, with mean zero, standard deviation of one, and running from approximately -2.5 to 2.5, with higher values corresponding to better governance. \n\nThe data is also reported in percentile rank terms, ranging from 0 (lowest rank) to 100 (highest rank).\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). A key feature of the methodology is that it generates margins of error for each governance estimate. These margins of error need to be taken into account when making comparisons across countries and over time. \n\nEach of the six aggregate WGI measures are constructed by averaging together data from the underlying sources that correspond to the concept of governance being measured. This is done in the three steps:\n\nSTEP 1: Assigning data from individual sources to the six aggregate indicators. Individual questions from the underlying data sources are assigned to each of the six aggregate indicators. For example, a firm survey question on the regulatory environment would be assigned to Regulatory Quality, or a measure of press freedom would be assigned to Voice and Accountability. The individual variables used in the WGI and how they are assigned to the six aggregate indicators, can be found on the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators]. Note that not all of the data sources cover all countries, and so the aggregate governance scores are based on different sets of underlying data for different countries.\n\nSTEP 2: Rescaling of the individual source data to run from 0 to 1. The questions from the individual data sources are first rescaled to range from 0 to 1, with higher values corresponding to better outcomes. If, for example, a survey question asks for responses on a scale from a minimum of 1 to a maximum of 4, we rescale a score of 2 as (2-min)/(max-min)=(2-1)/3=0.33. When an individual data source provides more than one question relating to a particular dimension of governance, the rescaled scores are averaged together.\nThe 0-1 rescaled data from the individual sources are available interactively through the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators/interactive-data-access] and in the data files for each individual source [https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2]. Although nominally in the same 0-1 units, this rescaled data is not necessarily comparable across sources. For example, one data source might use a 0-10 scale but in practice most scores are clustered between 6 and 10, while another data source might also use a 0-10 scale but have responses spread out over the entire range. While the max-min rescaling above does not correct for this source of non-comparability, the procedure used to construct the aggregate indicators does (see below).\n\nSTEP 3: Using an Unobserved Components Model (UCM) to construct a weighted average of the individual indicators for each source. A statistical tool known as an Unobserved Components Model (UCM) is used to make the 0-1 rescaled data comparable across sources, and then to construct a weighted average of the data from each source for each country. The UCM assumes that the observed data from each source are a linear function of the unobserved level of governance, plus an error term. This linear function is different for different data sources, and so corrects for the remaining non-comparability of units of the rescaled data noted above. The resulting estimates of governance are a weighted average of the data from each source, with weights reflecting the pattern of correlation among data sources. The weights applied to the component indicators." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile" } ], "source_id": "57" }, { "id": "PV.PER.RNK.LOWER", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Political Stability and Absence of Violence/Terrorism: Percentile Rank, Lower Bound of 90% Confidence Interval" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nPolitical Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Lower refers to lower bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether.\n\nThe standard deviations are essential to the interpretation of the WGI. It often is more appropriate to think of the WGI methodology as identifying a statistically likely range of values for the unobserved “true” level of governance in a country. For example, the assumption of normality tells us that there is a 90 percent probability that the true unobserved level of governance conditional on the available data for a country is in a range given by plus or minus 1.64 standard deviations around the estimate of governance. These confidence intervals are informally referred to as the “margin of error” around the estimates of governance.\n\nThese 90 percent confidence interval are also reported in percentile rank terms (the percentile rank among all country estimates of governance, of the upper and lower bounds of the 90 percent confidence interval for each country).\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Lower refers to lower bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile" } ], "source_id": "57" }, { "id": "PV.PER.RNK.UPPER", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide" }, { "id": "IndicatorName", "value": "Political Stability and Absence of Violence/Terrorism: Percentile Rank, Upper Bound of 90% Confidence Interval" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide\n\nPolitical Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Upper refers to upper bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether.\n\nThe standard deviations are essential to the interpretation of the WGI. It often is more appropriate to think of the WGI methodology as identifying a statistically likely range of values for the unobserved “true” level of governance in a country. For example, the assumption of normality tells us that there is a 90 percent probability that the true unobserved level of governance conditional on the available data for a country is in a range given by plus or minus 1.64 standard deviations around the estimate of governance. These confidence intervals are informally referred to as the “margin of error” around the estimates of governance.\n\nThese 90 percent confidence interval are also reported in percentile rank terms (the percentile rank among all country estimates of governance, of the upper and lower bounds of the 90 percent confidence interval for each country).\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Upper refers to upper bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile Rank" } ], "source_id": "57" }, { "id": "PV.STD.ERR", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Political Stability and Absence of Violence/Terrorism: Standard Error" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data sources based on expert assessments have advantages and disadvantages relative to surveys. One advantage is that they lend themselves well to cross-country comparisons, as their methodologies are explicitly designed for this purpose. Expert assessments can also provide more granular technical assessments, for example on the quality of specific types of public institutions, that would be more difficult for a typical household or firm survey respondent to provide and informed view on. Expert assessments also are less likely to be affected by respondent reticence, a concern in household and firm surveys where respondents may be unwilling to give candid responses to sensitive questions about corruption or other dimensions of governance, particularly in countries where governance is weak. \n\nOn the other hand, a shortcoming of expert assessments is that they reflect the views of a narrower set of respondents than household or firm surveys. It also is possible that the ratings provided by one expert assessment to some extent reflect the views of other expert assessments, so that each assessment does not bring completely independent information on the underlying governance concept of interest. To guard against this, the WGI do not use expert assessments that are explicitly based on other existing data sources.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nStandard error indicates the precision of the estimate of governance. Larger values of the standard error indicate less precise estimates. \n\nA 90 percent confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error.\n\nPolitical Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). The six aggregate indicators are reported in two ways : (1) in their standard normal units, ranging from approximately -2.5 to 2.5, and (2) in percentile rank terms from 0 to 100, with higher values corresponding to better outcomes.\n\nA key feature of the WGI is that all country scores are accompanied by standard errors. These margins of error need to be taken into account when making comparisons across countries and over time. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether. \n\nPlease see more information at: https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#1\nStatistical concept(s): Standard error indicates the precision of the estimate of governance. Larger values of the standard error indicate less precise estimates. A 90 percent confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Standard error" } ], "source_id": "57" }, { "id": "PX.REC.REER", "metatype": [ { "id": "BasePeriod", "value": "2010" }, { "id": "IndicatorName", "value": "Real effective exchange rate index (line rec, 2010 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Real effective exchange rate is the nominal effective exchange rate (a measure of the value of a currency against a weighted average of several foreign currencies) divided by a price deflator or index of costs. This indicator corresponds to the IFS's line rec, and is based on a nominal rate adjusted for relative changes in consumer prices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" } ], "source_id": "57" }, { "id": "PX.REX.REER", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In a market-based economy, household, producer, and government choices about resource allocation are influenced by relative prices, including the real exchange rate, real wages, real interest rates, and other prices in the economy. Relative prices also largely reflect these agents' choices. Thus relative prices convey vital information about the interaction of economic agents in an economy and with the rest of the world." }, { "id": "IndicatorName", "value": "Real effective exchange rate index (2010 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because of conceptual and data limitations, changes in real effective exchange rates should be interpreted with caution." }, { "id": "Longdefinition", "value": "Real effective exchange rate is the nominal effective exchange rate (a measure of the value of a currency against a weighted average of several foreign currencies) divided by a price deflator or index of costs. This indicator is an index series where 2010=100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1979-2024" }, { "id": "Source", "value": "International Financial Statistics database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The real effective exchange rate is a nominal effective exchange rate index adjusted for relative movements in national price or cost indicators of the home country, selected countries, and the euro area. A nominal effective exchange rate index is the ratio (expressed on the base 2010 = 100) of an index of a currency's period-average exchange rate to a weighted geometric average of exchange rates for currencies of selected countries and the euro area. For most high-income countries weights are derived from industrial country trade in manufactured goods. Data are compiled from the nominal effective exchange rate index and a cost indicator of relative normalized unit labor costs in manufacturing. For selected other countries the nominal effective exchange rate index is based on manufactured goods and primary products trade with partner or competitor countries. For these countries the real effective exchange rate index is the nominal index adjusted for relative changes in consumer prices; an increase represents an appreciation of the local currency.\nStatistical concept(s): The real effective exchange rate is a nominal effective exchange rate index adjusted for relative movements in national price or cost indicators of the home country, selected countries, and the euro area. A nominal effective exchange rate index is the ratio (expressed on the base 2010 = 100) of an index of a currency's period-average exchange rate to a weighted geometric average of exchange rates for currencies of selected countries and the euro area. For most high-income countries weights are derived from industrial country trade in manufactured goods. Data are compiled from the nominal effective exchange rate index and a cost indicator of relative normalized unit labor costs in manufacturing. For selected other countries the nominal effective exchange rate index is based on manufactured goods and primary products trade with partner or competitor countries. For these countries the real effective exchange rate index is the nominal index adjusted for relative changes in consumer prices; an increase represents an appreciation of the local currency." }, { "id": "Topic", "value": "Financial Sector: Exchange rates & prices" }, { "id": "Unitofmeasure", "value": "index (2010 = 100)" } ], "source_id": "57" }, { "id": "RL.EST", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Rule of Law: Estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them. The WGI measures six dimensions of governance: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. \n\nRule of Law captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. \n\nEstimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5.\n\nFor each dimension of governance the following information is available in the database: estimate, percentile rank, lower bound of 90% confidence interval, upper bound of 90% confidence interval, standard error, number of sources." }, { "id": "Othernotes", "value": "The UCM assigns greater weight to data sources that tend to be more strongly correlated with each other. While this weighting improves the statistical precision of the aggregate indicators, it typically does not affect very much the ranking of countries on the aggregate indicators. The composite measures of governance generated by the UCM are in units of a standard normal distribution, with mean zero, standard deviation of one, and running from approximately -2.5 to 2.5, with higher values corresponding to better governance. The data is also reported in percentile rank terms, ranging from 0 (lowest rank) to 100 (highest rank).\nStatistical concept(s): The six aggregate indicators are reported in two ways: (1) in their standard normal units, ranging from approximately -2.5 to 2.5, and (2) in percentile rank terms from 0 to 100, with higher values corresponding to better outcomes.\n\nA key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). A key feature of the methodology is that it generates margins of error for each governance estimate. These margins of error need to be taken into account when making comparisons across countries and over time. \n\nEach of the six aggregate WGI measures are constructed by averaging together data from the underlying sources that correspond to the concept of governance being measured. This is done in the three steps:\n\nSTEP 1: Assigning data from individual sources to the six aggregate indicators. Individual questions from the underlying data sources are assigned to each of the six aggregate indicators. For example, a firm survey question on the regulatory environment would be assigned to Regulatory Quality, or a measure of press freedom would be assigned to Voice and Accountability. The individual variables used in the WGI and how they are assigned to the six aggregate indicators, can be found on the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators]. Note that not all of the data sources cover all countries, and so the aggregate governance scores are based on different sets of underlying data for different countries.\n\nSTEP 2: Rescaling of the individual source data to run from 0 to 1. The questions from the individual data sources are first rescaled to range from 0 to 1, with higher values corresponding to better outcomes. If, for example, a survey question asks for responses on a scale from a minimum of 1 to a maximum of 4, we rescale a score of 2 as (2-min)/(max-min)=(2-1)/3=0.33. When an individual data source provides more than one question relating to a particular dimension of governance, the rescaled scores are averaged together.\nThe 0-1 rescaled data from the individual sources are available interactively through the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators/interactive-data-access] and in the data files for each individual source [https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2]. Although nominally in the same 0-1 units, this rescaled data is not necessarily comparable across sources. For example, one data source might use a 0-10 scale but in practice most scores are clustered between 6 and 10, while another data source might also use a 0-10 scale but have responses spread out over the entire range. While the max-min rescaling above does not correct for this source of non-comparability, the procedure used to construct the aggregate indicators does (see below).\n\nSTEP 3: Using an Unobserved Components Model (UCM) to construct a weighted average of the individual indicators for each source. A statistical tool known as an Unobserved Components Model (UCM) is used to make the 0-1 rescaled data comparable across sources, and then to construct a weighted average of the data from each source for each country. The UCM assumes that the observed data from each source are a linear function of the unobserved level of governance, plus an error term. This linear function is different for different data sources, and so corrects for the remaining non-comparability of units of the rescaled data noted above. The resulting estimates of governance are a weighted average of the data from each source, with weights reflecting the pattern of correlation among data sources. The weights applied to the component indicators." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Units of a standard normal distribution (between -2.5 and 2.5, approximately)" } ], "source_id": "57" }, { "id": "RL.NO.SRC", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Rule of Law: Number of Sources" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data sources based on expert assessments have advantages and disadvantages relative to surveys. One advantage is that they lend themselves well to cross-country comparisons, as their methodologies are explicitly designed for this purpose. Expert assessments can also provide more granular technical assessments, for example on the quality of specific types of public institutions, that would be more difficult for a typical household or firm survey respondent to provide and informed view on. Expert assessments also are less likely to be affected by respondent reticence, a concern in household and firm surveys where respondents may be unwilling to give candid responses to sensitive questions about corruption or other dimensions of governance, particularly in countries where governance is weak. \n\nOn the other hand, a shortcoming of expert assessments is that they reflect the views of a narrower set of respondents than household or firm surveys. It also is possible that the ratings provided by one expert assessment to some extent reflect the views of other expert assessments, so that each assessment does not bring completely independent information on the underlying governance concept of interest. To guard against this, the WGI do not use expert assessments that are explicitly based on other existing data sources.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nNumber of sources indicates the number of underlying data sources on which the aggregate estimate is based.\n\nThe WGI are based on a large number of different data sources, capturing the views and experiences of survey respondents and experts in the public and private sectors, as well as various NGOs. These data sources include: (a) surveys of households and firms (e.g. Afrobarometer surveys, Gallup World Poll, and Global Competitiveness Report survey), (b) NGOs (e.g. Global Integrity, Freedom House, Reporters Without Borders), (c) commercial business information providers (e.g. Economist Intelligence Unit, S&P Global, Political Risk Services), and (d) public sector organizations (e.g. CPIA assessments of World Bank and regional development banks). \n\nRule of Law captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. \n\nThe WGI compile and summarize information from over 30 existing data sources that report the views and experiences of citizens, entrepreneurs, and experts in the public, private and NGO sectors from around the world, on the quality of various aspects of governance.\n\n•\tThe data sources must provide subjective perceptions of relevant dimensions of governance, as the WGI are based exclusively on this type of data.\n•\tThe sources must provide original primary data produced using a well-defined methodology.\n•\tThe data sources must cover multiple countries, so that cross-country comparisons are possible.\n•\tThe data sources must be updated regularly, ideally every year, although some WGI data sources are updated once every two or three years.\n\nThe WGI draw on four different types of source data:\n\n•\tSurveys of households and firms, including the Afrobarometer surveys, Gallup World Poll, and Global Competitiveness Report survey,\n•\tCommercial business information providers, including the Economist Intelligence Unit, S&P Global, and Political Risk Services,\n•\tNon-governmental organizations, including Global Integrity, Freedom House, Reporters Without Borders, and\n•\tPublic sector organizations, including the Country Policy and Institutional Assessments (CPIA) assessments of World Bank and regional development banks.\n\nFor the detailed list of sources, please refer to: https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2 \nStatistical concept(s): Number of sources indicates the number of underlying data sources on which the aggregate estimate is based.\n\nVariables from the data sources are assigned to each of these six governance dimensions. For example, an assessment of the quality of the bureaucracy would be assigned to Government Effectiveness, a question about confidence in the police or the courts would be assigned to Rule of Law, and a question about the perceived likelihood of having to pay a bribe would be assigned to Control of Corruption. In some cases, a single data source will have multiple questions that can be assigned to the same dimension. In this case, the WGI use the average across all relevant questions from that data source. In addition each question from each data source is assigned to only one of the six governance dimensions, selecting the dimension that best matches the content of the question." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "RL.PER.RNK", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Rule of Law: Percentile Rank" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nRule of Law captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI." }, { "id": "Othernotes", "value": "The UCM assigns greater weight to data sources that tend to be more strongly correlated with each other. While this weighting improves the statistical precision of the aggregate indicators, it typically does not affect very much the ranking of countries on the aggregate indicators. The composite measures of governance generated by the UCM are in units of a standard normal distribution, with mean zero, standard deviation of one, and running from approximately -2.5 to 2.5, with higher values corresponding to better governance. \n\nThe data is also reported in percentile rank terms, ranging from 0 (lowest rank) to 100 (highest rank).\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). A key feature of the methodology is that it generates margins of error for each governance estimate. These margins of error need to be taken into account when making comparisons across countries and over time. \n\nEach of the six aggregate WGI measures are constructed by averaging together data from the underlying sources that correspond to the concept of governance being measured. This is done in the three steps:\n\nSTEP 1: Assigning data from individual sources to the six aggregate indicators. Individual questions from the underlying data sources are assigned to each of the six aggregate indicators. For example, a firm survey question on the regulatory environment would be assigned to Regulatory Quality, or a measure of press freedom would be assigned to Voice and Accountability. The individual variables used in the WGI and how they are assigned to the six aggregate indicators, can be found on the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators]. Note that not all of the data sources cover all countries, and so the aggregate governance scores are based on different sets of underlying data for different countries.\n\nSTEP 2: Rescaling of the individual source data to run from 0 to 1. The questions from the individual data sources are first rescaled to range from 0 to 1, with higher values corresponding to better outcomes. If, for example, a survey question asks for responses on a scale from a minimum of 1 to a maximum of 4, we rescale a score of 2 as (2-min)/(max-min)=(2-1)/3=0.33. When an individual data source provides more than one question relating to a particular dimension of governance, the rescaled scores are averaged together.\nThe 0-1 rescaled data from the individual sources are available interactively through the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators/interactive-data-access] and in the data files for each individual source [https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2]. Although nominally in the same 0-1 units, this rescaled data is not necessarily comparable across sources. For example, one data source might use a 0-10 scale but in practice most scores are clustered between 6 and 10, while another data source might also use a 0-10 scale but have responses spread out over the entire range. While the max-min rescaling above does not correct for this source of non-comparability, the procedure used to construct the aggregate indicators does (see below).\n\nSTEP 3: Using an Unobserved Components Model (UCM) to construct a weighted average of the individual indicators for each source. A statistical tool known as an Unobserved Components Model (UCM) is used to make the 0-1 rescaled data comparable across sources, and then to construct a weighted average of the data from each source for each country. The UCM assumes that the observed data from each source are a linear function of the unobserved level of governance, plus an error term. This linear function is different for different data sources, and so corrects for the remaining non-comparability of units of the rescaled data noted above. The resulting estimates of governance are a weighted average of the data from each source, with weights reflecting the pattern of correlation among data sources. The weights applied to the component indicators." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile" } ], "source_id": "57" }, { "id": "RL.PER.RNK.LOWER", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Rule of Law: Percentile Rank, Lower Bound of 90% Confidence Interval" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nRule of Law captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Lower refers to lower bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether.\n\nThe standard deviations are essential to the interpretation of the WGI. It often is more appropriate to think of the WGI methodology as identifying a statistically likely range of values for the unobserved “true” level of governance in a country. For example, the assumption of normality tells us that there is a 90 percent probability that the true unobserved level of governance conditional on the available data for a country is in a range given by plus or minus 1.64 standard deviations around the estimate of governance. These confidence intervals are informally referred to as the “margin of error” around the estimates of governance.\n\nThese 90 percent confidence interval are also reported in percentile rank terms (the percentile rank among all country estimates of governance, of the upper and lower bounds of the 90 percent confidence interval for each country).\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Lower refers to lower bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile" } ], "source_id": "57" }, { "id": "RL.PER.RNK.UPPER", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide" }, { "id": "IndicatorName", "value": "Rule of Law: Percentile Rank, Upper Bound of 90% Confidence Interval" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide\n\nRule of Law captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Upper refers to upper bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether.\n\nThe standard deviations are essential to the interpretation of the WGI. It often is more appropriate to think of the WGI methodology as identifying a statistically likely range of values for the unobserved “true” level of governance in a country. For example, the assumption of normality tells us that there is a 90 percent probability that the true unobserved level of governance conditional on the available data for a country is in a range given by plus or minus 1.64 standard deviations around the estimate of governance. These confidence intervals are informally referred to as the “margin of error” around the estimates of governance.\n\nThese 90 percent confidence interval are also reported in percentile rank terms (the percentile rank among all country estimates of governance, of the upper and lower bounds of the 90 percent confidence interval for each country).\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Upper refers to upper bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile Rank" } ], "source_id": "57" }, { "id": "RL.STD.ERR", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide" }, { "id": "IndicatorName", "value": "Rule of Law: Standard Error" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data sources based on expert assessments have advantages and disadvantages relative to surveys. One advantage is that they lend themselves well to cross-country comparisons, as their methodologies are explicitly designed for this purpose. Expert assessments can also provide more granular technical assessments, for example on the quality of specific types of public institutions, that would be more difficult for a typical household or firm survey respondent to provide and informed view on. Expert assessments also are less likely to be affected by respondent reticence, a concern in household and firm surveys where respondents may be unwilling to give candid responses to sensitive questions about corruption or other dimensions of governance, particularly in countries where governance is weak. \n\nOn the other hand, a shortcoming of expert assessments is that they reflect the views of a narrower set of respondents than household or firm surveys. It also is possible that the ratings provided by one expert assessment to some extent reflect the views of other expert assessments, so that each assessment does not bring completely independent information on the underlying governance concept of interest. To guard against this, the WGI do not use expert assessments that are explicitly based on other existing data sources.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nStandard error indicates the precision of the estimate of governance. Larger values of the standard error indicate less precise estimates. \n\nA 90 percent confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error.\n\nRule of Law captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). The six aggregate indicators are reported in two ways : (1) in their standard normal units, ranging from approximately -2.5 to 2.5, and (2) in percentile rank terms from 0 to 100, with higher values corresponding to better outcomes.\n\nA key feature of the WGI is that all country scores are accompanied by standard errors. These margins of error need to be taken into account when making comparisons across countries and over time. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether. \n\nPlease see more information at: https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#1\nStatistical concept(s): Standard error indicates the precision of the estimate of governance. Larger values of the standard error indicate less precise estimates. A 90 percent confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Standard error" } ], "source_id": "57" }, { "id": "RQ.EST", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Regulatory Quality: Estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them. The WGI measures six dimensions of governance: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. \n\nRegulatory Quality captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. \n\nEstimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5.\n\nFor each dimension of governance the following information is available in the database: estimate, percentile rank, lower bound of 90% confidence interval, upper bound of 90% confidence interval, standard error, number of sources." }, { "id": "Othernotes", "value": "The UCM assigns greater weight to data sources that tend to be more strongly correlated with each other. While this weighting improves the statistical precision of the aggregate indicators, it typically does not affect very much the ranking of countries on the aggregate indicators. The composite measures of governance generated by the UCM are in units of a standard normal distribution, with mean zero, standard deviation of one, and running from approximately -2.5 to 2.5, with higher values corresponding to better governance. The data is also reported in percentile rank terms, ranging from 0 (lowest rank) to 100 (highest rank).\nStatistical concept(s): The six aggregate indicators are reported in two ways: (1) in their standard normal units, ranging from approximately -2.5 to 2.5, and (2) in percentile rank terms from 0 to 100, with higher values corresponding to better outcomes.\n\nA key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). A key feature of the methodology is that it generates margins of error for each governance estimate. These margins of error need to be taken into account when making comparisons across countries and over time. \n\nEach of the six aggregate WGI measures are constructed by averaging together data from the underlying sources that correspond to the concept of governance being measured. This is done in the three steps:\n\nSTEP 1: Assigning data from individual sources to the six aggregate indicators. Individual questions from the underlying data sources are assigned to each of the six aggregate indicators. For example, a firm survey question on the regulatory environment would be assigned to Regulatory Quality, or a measure of press freedom would be assigned to Voice and Accountability. The individual variables used in the WGI and how they are assigned to the six aggregate indicators, can be found on the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators]. Note that not all of the data sources cover all countries, and so the aggregate governance scores are based on different sets of underlying data for different countries.\n\nSTEP 2: Rescaling of the individual source data to run from 0 to 1. The questions from the individual data sources are first rescaled to range from 0 to 1, with higher values corresponding to better outcomes. If, for example, a survey question asks for responses on a scale from a minimum of 1 to a maximum of 4, we rescale a score of 2 as (2-min)/(max-min)=(2-1)/3=0.33. When an individual data source provides more than one question relating to a particular dimension of governance, the rescaled scores are averaged together.\nThe 0-1 rescaled data from the individual sources are available interactively through the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators/interactive-data-access] and in the data files for each individual source [https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2]. Although nominally in the same 0-1 units, this rescaled data is not necessarily comparable across sources. For example, one data source might use a 0-10 scale but in practice most scores are clustered between 6 and 10, while another data source might also use a 0-10 scale but have responses spread out over the entire range. While the max-min rescaling above does not correct for this source of non-comparability, the procedure used to construct the aggregate indicators does (see below).\n\nSTEP 3: Using an Unobserved Components Model (UCM) to construct a weighted average of the individual indicators for each source. A statistical tool known as an Unobserved Components Model (UCM) is used to make the 0-1 rescaled data comparable across sources, and then to construct a weighted average of the data from each source for each country. The UCM assumes that the observed data from each source are a linear function of the unobserved level of governance, plus an error term. This linear function is different for different data sources, and so corrects for the remaining non-comparability of units of the rescaled data noted above. The resulting estimates of governance are a weighted average of the data from each source, with weights reflecting the pattern of correlation among data sources. The weights applied to the component indicators." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Units of a standard normal distribution (between -2.5 and 2.5, approximately)" } ], "source_id": "57" }, { "id": "RQ.NO.SRC", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Regulatory Quality: Number of Sources" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data sources based on expert assessments have advantages and disadvantages relative to surveys. One advantage is that they lend themselves well to cross-country comparisons, as their methodologies are explicitly designed for this purpose. Expert assessments can also provide more granular technical assessments, for example on the quality of specific types of public institutions, that would be more difficult for a typical household or firm survey respondent to provide and informed view on. Expert assessments also are less likely to be affected by respondent reticence, a concern in household and firm surveys where respondents may be unwilling to give candid responses to sensitive questions about corruption or other dimensions of governance, particularly in countries where governance is weak. \n\nOn the other hand, a shortcoming of expert assessments is that they reflect the views of a narrower set of respondents than household or firm surveys. It also is possible that the ratings provided by one expert assessment to some extent reflect the views of other expert assessments, so that each assessment does not bring completely independent information on the underlying governance concept of interest. To guard against this, the WGI do not use expert assessments that are explicitly based on other existing data sources.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nNumber of sources indicates the number of underlying data sources on which the aggregate estimate is based.\n\nThe WGI are based on a large number of different data sources, capturing the views and experiences of survey respondents and experts in the public and private sectors, as well as various NGOs. These data sources include: (a) surveys of households and firms (e.g. Afrobarometer surveys, Gallup World Poll, and Global Competitiveness Report survey), (b) NGOs (e.g. Global Integrity, Freedom House, Reporters Without Borders), (c) commercial business information providers (e.g. Economist Intelligence Unit, S&P Global, Political Risk Services), and (d) public sector organizations (e.g. CPIA assessments of World Bank and regional development banks). \n\nRegulatory Quality captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI compile and summarize information from over 30 existing data sources that report the views and experiences of citizens, entrepreneurs, and experts in the public, private and NGO sectors from around the world, on the quality of various aspects of governance.\n\n•\tThe data sources must provide subjective perceptions of relevant dimensions of governance, as the WGI are based exclusively on this type of data.\n•\tThe sources must provide original primary data produced using a well-defined methodology.\n•\tThe data sources must cover multiple countries, so that cross-country comparisons are possible.\n•\tThe data sources must be updated regularly, ideally every year, although some WGI data sources are updated once every two or three years.\n\nThe WGI draw on four different types of source data:\n\n•\tSurveys of households and firms, including the Afrobarometer surveys, Gallup World Poll, and Global Competitiveness Report survey,\n•\tCommercial business information providers, including the Economist Intelligence Unit, S&P Global, and Political Risk Services,\n•\tNon-governmental organizations, including Global Integrity, Freedom House, Reporters Without Borders, and\n•\tPublic sector organizations, including the Country Policy and Institutional Assessments (CPIA) assessments of World Bank and regional development banks.\n\nFor the detailed list of sources, please refer to: https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2 \nStatistical concept(s): Number of sources indicates the number of underlying data sources on which the aggregate estimate is based.\n\nVariables from the data sources are assigned to each of these six governance dimensions. For example, an assessment of the quality of the bureaucracy would be assigned to Government Effectiveness, a question about confidence in the police or the courts would be assigned to Rule of Law, and a question about the perceived likelihood of having to pay a bribe would be assigned to Control of Corruption. In some cases, a single data source will have multiple questions that can be assigned to the same dimension. In this case, the WGI use the average across all relevant questions from that data source. In addition each question from each data source is assigned to only one of the six governance dimensions, selecting the dimension that best matches the content of the question." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "RQ.PER.RNK", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Regulatory Quality: Percentile Rank" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nRegulatory Quality captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI." }, { "id": "Othernotes", "value": "The UCM assigns greater weight to data sources that tend to be more strongly correlated with each other. While this weighting improves the statistical precision of the aggregate indicators, it typically does not affect very much the ranking of countries on the aggregate indicators. The composite measures of governance generated by the UCM are in units of a standard normal distribution, with mean zero, standard deviation of one, and running from approximately -2.5 to 2.5, with higher values corresponding to better governance. \n\nThe data is also reported in percentile rank terms, ranging from 0 (lowest rank) to 100 (highest rank).\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). A key feature of the methodology is that it generates margins of error for each governance estimate. These margins of error need to be taken into account when making comparisons across countries and over time. \n\nEach of the six aggregate WGI measures are constructed by averaging together data from the underlying sources that correspond to the concept of governance being measured. This is done in the three steps:\n\nSTEP 1: Assigning data from individual sources to the six aggregate indicators. Individual questions from the underlying data sources are assigned to each of the six aggregate indicators. For example, a firm survey question on the regulatory environment would be assigned to Regulatory Quality, or a measure of press freedom would be assigned to Voice and Accountability. The individual variables used in the WGI and how they are assigned to the six aggregate indicators, can be found on the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators]. Note that not all of the data sources cover all countries, and so the aggregate governance scores are based on different sets of underlying data for different countries.\n\nSTEP 2: Rescaling of the individual source data to run from 0 to 1. The questions from the individual data sources are first rescaled to range from 0 to 1, with higher values corresponding to better outcomes. If, for example, a survey question asks for responses on a scale from a minimum of 1 to a maximum of 4, we rescale a score of 2 as (2-min)/(max-min)=(2-1)/3=0.33. When an individual data source provides more than one question relating to a particular dimension of governance, the rescaled scores are averaged together.\nThe 0-1 rescaled data from the individual sources are available interactively through the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators/interactive-data-access] and in the data files for each individual source [https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2]. Although nominally in the same 0-1 units, this rescaled data is not necessarily comparable across sources. For example, one data source might use a 0-10 scale but in practice most scores are clustered between 6 and 10, while another data source might also use a 0-10 scale but have responses spread out over the entire range. While the max-min rescaling above does not correct for this source of non-comparability, the procedure used to construct the aggregate indicators does (see below).\n\nSTEP 3: Using an Unobserved Components Model (UCM) to construct a weighted average of the individual indicators for each source. A statistical tool known as an Unobserved Components Model (UCM) is used to make the 0-1 rescaled data comparable across sources, and then to construct a weighted average of the data from each source for each country. The UCM assumes that the observed data from each source are a linear function of the unobserved level of governance, plus an error term. This linear function is different for different data sources, and so corrects for the remaining non-comparability of units of the rescaled data noted above. The resulting estimates of governance are a weighted average of the data from each source, with weights reflecting the pattern of correlation among data sources. The weights applied to the component indicators." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile" } ], "source_id": "57" }, { "id": "RQ.PER.RNK.LOWER", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Regulatory Quality: Percentile Rank, Lower Bound of 90% Confidence Interval" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nRegulatory Quality captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Lower refers to lower bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether.\n\nThe standard deviations are essential to the interpretation of the WGI. It often is more appropriate to think of the WGI methodology as identifying a statistically likely range of values for the unobserved “true” level of governance in a country. For example, the assumption of normality tells us that there is a 90 percent probability that the true unobserved level of governance conditional on the available data for a country is in a range given by plus or minus 1.64 standard deviations around the estimate of governance. These confidence intervals are informally referred to as the “margin of error” around the estimates of governance.\n\nThese 90 percent confidence interval are also reported in percentile rank terms (the percentile rank among all country estimates of governance, of the upper and lower bounds of the 90 percent confidence interval for each country).\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Lower refers to lower bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile" } ], "source_id": "57" }, { "id": "RQ.PER.RNK.UPPER", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide" }, { "id": "IndicatorName", "value": "Regulatory Quality: Percentile Rank, Upper Bound of 90% Confidence Interval" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide\n\nRegulatory Quality captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Upper refers to upper bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether.\n\nThe standard deviations are essential to the interpretation of the WGI. It often is more appropriate to think of the WGI methodology as identifying a statistically likely range of values for the unobserved “true” level of governance in a country. For example, the assumption of normality tells us that there is a 90 percent probability that the true unobserved level of governance conditional on the available data for a country is in a range given by plus or minus 1.64 standard deviations around the estimate of governance. These confidence intervals are informally referred to as the “margin of error” around the estimates of governance.\n\nThese 90 percent confidence interval are also reported in percentile rank terms (the percentile rank among all country estimates of governance, of the upper and lower bounds of the 90 percent confidence interval for each country).\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Upper refers to upper bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile Rank" } ], "source_id": "57" }, { "id": "RQ.STD.ERR", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Regulatory Quality: Standard Error" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data sources based on expert assessments have advantages and disadvantages relative to surveys. One advantage is that they lend themselves well to cross-country comparisons, as their methodologies are explicitly designed for this purpose. Expert assessments can also provide more granular technical assessments, for example on the quality of specific types of public institutions, that would be more difficult for a typical household or firm survey respondent to provide and informed view on. Expert assessments also are less likely to be affected by respondent reticence, a concern in household and firm surveys where respondents may be unwilling to give candid responses to sensitive questions about corruption or other dimensions of governance, particularly in countries where governance is weak. \n\nOn the other hand, a shortcoming of expert assessments is that they reflect the views of a narrower set of respondents than household or firm surveys. It also is possible that the ratings provided by one expert assessment to some extent reflect the views of other expert assessments, so that each assessment does not bring completely independent information on the underlying governance concept of interest. To guard against this, the WGI do not use expert assessments that are explicitly based on other existing data sources.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nStandard error indicates the precision of the estimate of governance. Larger values of the standard error indicate less precise estimates. \n\nA 90 percent confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error.\n\nRegulatory Quality captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). The six aggregate indicators are reported in two ways : (1) in their standard normal units, ranging from approximately -2.5 to 2.5, and (2) in percentile rank terms from 0 to 100, with higher values corresponding to better outcomes.\n\nA key feature of the WGI is that all country scores are accompanied by standard errors. These margins of error need to be taken into account when making comparisons across countries and over time. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether. \n\nPlease see more information at: https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#1\nStatistical concept(s): Standard error indicates the precision of the estimate of governance. Larger values of the standard error indicate less precise estimates. A 90 percent confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Standard error" } ], "source_id": "57" }, { "id": "SE.ADT.1524.LT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth female (% of females ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate youths divided by the total number of youths, excluding youths with unknown literacy status. \n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of females ages 15-24" } ], "source_id": "57" }, { "id": "SE.ADT.1524.LT.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEliminating gender disparities in education would help increase the status and capabilities of women. Literate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth (ages 15-24), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Gender parity index for youth literacy rate is the ratio of females to males ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female youth literacy rate by male youth literacy rate. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiteracy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "ratio" } ], "source_id": "57" }, { "id": "SE.ADT.1524.LT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth male (% of males ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate youths divided by the total number of youths, excluding youths with unknown literacy status. \n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of males ages 15-24" } ], "source_id": "57" }, { "id": "SE.ADT.1524.LT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, youth total (% of people ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data is calculated by dividing the number of literate persons by the total number of persons in the same age group, excluding persons with unknown literacy status.\n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "Percent of people ages 15-24" } ], "source_id": "57" }, { "id": "SE.ADT.LITR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, adult female (% of females ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status. \n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of females ages 15 and above" } ], "source_id": "57" }, { "id": "SE.ADT.LITR.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, adult male (% of males ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status. \n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of males ages 15 and above" } ], "source_id": "57" }, { "id": "SE.ADT.LITR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, adult total (% of people ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status. \n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of people ages 15 and above" } ], "source_id": "57" }, { "id": "SE.COM.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Compulsory education is recognized as a fundamental human right. The Universal Declaration of Human Rights (https://www.un.org/en/about-us/universal-declaration-of-human-rights/) advocates for free and mandatory primary education. Additionally, the Convention on the Rights of the Child (https://www.ohchr.org/en/instruments-mechanisms/instruments/convention-rights-child ) expands on this by mandating that states should strive to make secondary education available and accessible to everyone." }, { "id": "IndicatorName", "value": "Compulsory education, duration (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The presence of national legislation does not guarantee that countries will implement it effectively, nor does it ensure that parents will take advantage of the provisions available for their children." }, { "id": "Longdefinition", "value": "Duration of compulsory education is the number of years that children are legally obliged to attend school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1975-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Compulsory education is defined as educational programs that children and young people are legally obliged to attend, usually defined in terms of a number of grades or an age range, or both (UNESCO, https://unesdoc.unesco.org/ark:/48223/pf0000141639)." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "57" }, { "id": "SE.ENR.PRIM.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "IndicatorName", "value": "School enrollment, primary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in primary education is the ratio of girls to boys enrolled at primary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in primary education by male gross enrollment ratio in primary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.ENR.PRSC.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "IndicatorName", "value": "School enrollment, primary and secondary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in primary and secondary education is the ratio of girls to boys enrolled at primary and secondary levels in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in primary and secondary education by male gross enrollment ratio in primary and secondary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.ENR.SECO.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "IndicatorName", "value": "School enrollment, secondary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in secondary education is the ratio of girls to boys enrolled at secondary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in secondary education by male gross enrollment ratio in secondary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.ENR.TERT.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Education is a basic human entitlement, and it is crucial that both girls and boys are afforded equal chances to learn. The Sustainable Development Goal (SDG) Target 4.5 focuses on eliminating gender disparities in education and ensuring equal access to all levels of education for both girls and boys. This target is part of a broader commitment to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all, as outlined in SDG 4. The pursuit of gender equality in education is not only a matter of fairness and equity but also has significant implications for economic development, empowerment, and the well-being of communities and nations." }, { "id": "IndicatorName", "value": "School enrollment, tertiary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The gross enrolment ratio is a general measure of participation in tertiary education. However, it does not account for variations in the duration of programs between countries or across different levels of education and fields of study. While it is somewhat standardized by measuring it relative to a 5-year age group for all countries, it may still underestimate participation, particularly in countries with underdeveloped tertiary education systems or where offerings are limited to initial tertiary programs, which are typically shorter than 5 years in duration." }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in tertiary education is the ratio of women to men enrolled at tertiary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in tertiary education by male gross enrollment ratio in tertiary education. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "ratio" } ], "source_id": "57" }, { "id": "SE.LPV.PRIM", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Learning poverty: Share of Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The construct of “all children reading by age 10” is an ideal that embodies normative statements about both learning and access. To achieve it, not only should all children be reading proficiently after 3 full years in primary education, but they should also have entered school at age 6 or 7. \n\nBy contrast, the actual indicators used to measure learning poverty are based on grade rather than age. Since the assessments are of 4th- through 6th-graders, the children tested will have had at least 3 to 5 years in school to reach what, according to the ideal, should 10 be an age-10 minimum proficiency, or even the entire primary-school-age segment for the out-of-school indicator.\n\nDue to different assessment availability within and between countries, data comparability, both within countries over time and across countries still poses a significant challenge. The additional out of school component further limits comparability.\n\nThe learning poverty indicator is based on data covering four-fifths of children at the end of primary school. In other words, a little more than 80 percent of children in low- and middle-income countries live in a country with at least one learning assessment at the end of primary, carried out in the past 9 years. For regional and global aggregates, weighted imputations affect regions with less data coverage. The major gaps are concentrated in countries where the learning crisis is most acute. Less than half of children in Sub-Saharan Africa live in a country with a National Large-Scale Learning Assessment (NLSA) or a international of regional large-scale learning assessment (ILSA or RLSA) of adequate quality to be used for this purpose.\n\nThis extensive coverage became possible only in recent years, with the progress in measuring learning in countries and the GAML’s efforts to establish comparability, which has made possible the construction of a global indicator based on harmonized proficiency levels. Future efforts by coalition organizations are also ensuring more flexible assessment options are available for expanding data availability for countries, such as the Assessment of Minimum Proficiency Levels (AMPL) and policy linking exercises led by UIS." }, { "id": "Longdefinition", "value": "The share of 10-year-olds who cannot read and understand a short passage of age-appropriate material—in other words, those who are below the “minimum proficiency” threshold for reading. This measure is defined as the union of two deprivations: 1) schooling deprivation and 2) learning deprivation. A child is considered schooling-deprived (SD) if he or she is of primary school age and out-of-school. The dimension of learning deprivation (LD) applies only for children in school, and identifies those pupils who are below the minimum proficiency level (MPL) for reading, as defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. This “union approach” to measurement reflects the choice that, as presented in the SDGs, all age 10 children must be both in school and learning. The final learning poverty measure combines the two dimensions in a single indicator using the following formula: LP = SD + [(1-SD) x LD]" }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The learning poverty indicator brings together schooling and learning indicators. It starts with the share of children in school who haven’t achieved minimum reading proficiency (Learning Deprived) and adjusts it by the proportion of children who are out of school (Schooling Deprived). \n\n\nFormally, Learning Poverty is calculated as: [LD* (1-SD)] + [1 * SD]\n\n\nwhere LP = Learning poverty; LD = Learning deprivation or the share of children at the end of primary who read at below the minimum proficiency level, as defined by the Global Alliance to Monitor Learning (GAML) in the context of the SDG 4.1.1 monitoring; SD = Schooling deprivation or the share of primary-school-age children who are out-of-school (OOS) and in which all OOS are regarded as being below the minimum proficiency level.\n\n\nBecause out-of-school children are treated as non-proficient in reading, learning poverty will always be higher than the share of children in school who haven't achieved minimum reading proficiency. For countries with a very low schooling deprivation, the learning deprivation value will be very close to Learning Poverty. \n\n\nEstimating the current level of global and regional learning poverty requires deciding how to define “current.” We include results of assessments within four years before or after a set anchor year. This decision is driven by data availability. International and regional large-scale learning assessments used for SDG 4.1.1b reporting are carried out only every 3 to 4 years. And even where assessments have been carried out recently, there is a lag of a couple of years before the data are available. This band is intended as a moving window. In the original 2019 release, the anchor year used was 2015 (Assessments between 2011 and 2019 are included in the learning poverty estimate). In the 2022 Global Update, the anchor year was moved to 2019 (assessments between 2015 and 2023 are included).\n\n\nAggregations for each region comprise the average learning poverty of countries with available data, weighted by their population ages 10–14 years old. To obtain a global estimate, we weight the regional aggregations by the 10–14-year-old population regardless of data availability. This is equivalent to imputing missing country data using regional values.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "SE.LPV.PRIM.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Learning poverty: Share of Female Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The construct of “all children reading by age 10” is an ideal that embodies normative statements about both learning and access. To achieve it, not only should all children be reading proficiently after 3 full years in primary education, but they should also have entered school at age 6 or 7. \n\nBy contrast, the actual indicators used to measure learning poverty are based on grade rather than age. Since the assessments are of 4th- through 6th-graders, the children tested will have had at least 3 to 5 years in school to reach what, according to the ideal, should 10 be an age-10 minimum proficiency, or even the entire primary-school-age segment for the out-of-school indicator.\n\nDue to different assessment availability within and between countries, data comparability, both within countries over time and across countries still poses a significant challenge. The additional out of school component further limits comparability.\n\nThe learning poverty indicator is based on data covering four-fifths of children at the end of primary school. In other words, a little more than 80 percent of children in low- and middle-income countries live in a country with at least one learning assessment at the end of primary, carried out in the past 9 years. For regional and global aggregates, weighted imputations affect regions with less data coverage. The major gaps are concentrated in countries where the learning crisis is most acute. Less than half of children in Sub-Saharan Africa live in a country with a National Large-Scale Learning Assessment (NLSA) or a international of regional large-scale learning assessment (ILSA or RLSA) of adequate quality to be used for this purpose.\n\nThis extensive coverage became possible only in recent years, with the progress in measuring learning in countries and the GAML’s efforts to establish comparability, which has made possible the construction of a global indicator based on harmonized proficiency levels. Future efforts by coalition organizations are also ensuring more flexible assessment options are available for expanding data availability for countries, such as the Assessment of Minimum Proficiency Levels (AMPL) and policy linking exercises led by UIS." }, { "id": "Longdefinition", "value": "The share of female 10-year-olds who cannot read and understand a short passage of age-appropriate material—in other words, those who are below the “minimum proficiency” threshold for reading. This measure is defined as the union of two deprivations: 1) schooling deprivation and 2) learning deprivation. A child is considered schooling-deprived (SD) if he or she is of primary school age and out-of-school. The dimension of learning deprivation (LD) applies only for children in school, and identifies those pupils who are below the minimum proficiency level (MPL) for reading, as defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. This “union approach” to measurement reflects the choice that, as presented in the SDGs, all age 10 children must be both in school and learning. The final learning poverty measure combines the two dimensions in a single indicator using the following formula: LP = SD + [(1-SD) x LD]" }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The learning poverty indicator brings together schooling and learning indicators. It starts with the share of children in school who haven’t achieved minimum reading proficiency (Learning Deprived) and adjusts it by the proportion of children who are out of school (Schooling Deprived). \n\n\nFormally, Learning Poverty is calculated as: [LD* (1-SD)] + [1 * SD]\n\n\nwhere LP = Learning poverty; LD = Learning deprivation or the share of children at the end of primary who read at below the minimum proficiency level, as defined by the Global Alliance to Monitor Learning (GAML) in the context of the SDG 4.1.1 monitoring; SD = Schooling deprivation or the share of primary-school-age children who are out-of-school (OOS) and in which all OOS are regarded as being below the minimum proficiency level.\n\n\nBecause out-of-school children are treated as non-proficient in reading, learning poverty will always be higher than the share of children in school who haven't achieved minimum reading proficiency. For countries with a very low schooling deprivation, the learning deprivation value will be very close to Learning Poverty. \n\n\nEstimating the current level of global and regional learning poverty requires deciding how to define “current.” We include results of assessments within four years before or after a set anchor year. This decision is driven by data availability. International and regional large-scale learning assessments used for SDG 4.1.1b reporting are carried out only every 3 to 4 years. And even where assessments have been carried out recently, there is a lag of a couple of years before the data are available. This band is intended as a moving window. In the original 2019 release, the anchor year used was 2015 (Assessments between 2011 and 2019 are included in the learning poverty estimate). In the 2022 Global Update, the anchor year was moved to 2019 (assessments between 2015 and 2023 are included).\n\n\nAggregations for each region comprise the average learning poverty of countries with available data, weighted by their population ages 10–14 years old. To obtain a global estimate, we weight the regional aggregations by the 10–14-year-old population regardless of data availability. This is equivalent to imputing missing country data using regional values.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "SE.LPV.PRIM.LD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Pupils below minimum reading proficiency at end of primary (%). Low GAML threshold" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The process of equating proficiency levels on different assessments to the GAML definition is not straightforward. Even the long-running regional assessment initiatives like PASEC (West and Central Africa) and LLECE (Latin America and the Caribbean) use different definitions and a different number of levels than other assessments like PIRLS, and those might not even be the same over time. Their test development methodologies and test administration procedures also vary. Moreover, because not all countries participate in global or regional assessments, for some major countries we rely on their interim reporting using their national assessments; equating these assessments is even more challenging. UIS and the World Bank have mapped how proficiency levels between assessment can equate to one another, but they are not strictly comparable. \n\nAmong the differences across assessments, one important point concerns the age at which children are tested. The reference age for our exercise is age 10. However, all learning assessments used in this analysis are sampled based on specific grades rather than age. PIRLS and TIMSS are administered in Grade 4, meaning that the average student assessed is indeed 10 years old, but this is not the case for the regional assessments. PASEC and LLECE are administered in Grade 6, so the average age in those assessments is 12.8 and 12.4, respectively. National assessments are administered at different grades, so to incorporate those assessments, we chose for each country the grade between 4 and 6 (inclusive) for which relevant and reliable data were available. This is consistent with the SDG monitoring by UIS and GAML, which lists “End of Primary (or Grades 4 to 6)” as the relevant age category for the end-of-primary students (SDG 4.1.1b).\n\nIn some cases, National Learning Assessments that have not been policy linked are used for learning poverty, if country teams and experts determine that an assessment is of sufficiently quality or has undertaken steps to align their assessments with the Global Proficiency Framework. They will often be reported as interim learning poverty indicators, as they are not fully aligned with SDG 4.1.1b." }, { "id": "Longdefinition", "value": "The share of pupils at the end of primary schooling who are below the minimum proficiency level (MPL) for reading or learning deprived. The MPL in reading at the end of primary is defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. It is “Students independently and fluently read simple, short narrative and expository texts. They locate explicitly-stated information. They interpret and give some explanations about the key ideas in these texts. They provide simple, personal opinions or judgements about the information, events and characters in a text.” (UIS and GAML 2019). In other words, a child “attaining” minimum proficiency has the ability to read and understand a short passage of age-appropriate material, whether a simple story or non-fiction narrative of a few paragraphs. In addition to this nutshell statement, the GAML has also proposed a common terminology to describe classifications in the context of the MPL. This is a critical first step toward linking cross-national and national learning assessments with a common benchmark." }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Learning assessments used to calculate Learning Poverty have a minimum proficiency level (MPL) benchmarked by Global Alliance to Monitor Learning (GAML) under the leadership of the UNESCO Institute of Statistics (UIS), which occurred within the reporting window. To operationalize this concept, the current SDG monitoring process is followed by defining “proficiency” as reaching at least the Low International Benchmark on the international PIRLS literacy assessment. \n\n\nPIRLS is the major global primary-age assessment focused on reading, and if all countries participated in it, the task of constructing global estimates of minimum proficiency would be trivial, as it would require aggregating results from a single cross-national assessment. However, most countries participating in PIRLS are high-income, and only a small minority of low- and middle-income countries participate in the assessment. One of the main contributions of the GAML process is that it has overcome this data gap by benchmarking several major cross-national assessments—and increasingly national learning assessments as well—against the standard. \n\n\nThe MPL for each learning assessment is used to calculate the reading proficiency rate for that country, which is the share of students scoring at or above the minimum proficiency level, and conversely to calculate the learning deprivation.\n\n\nThe Proficiency and Grade Levels used for each assessment is as follows: PIRLS (grade 4) - Level 2 (Low international benchmark, 400 points); TIMSS (grade 4) - Level 2 (Low international benchmark, 400 points); LLECE (SERCE, grade 6) - Level 3 (513.66 points); PASEC (grades 5 and 6) - Level 4 (595.1 points); SEA-PLM (grade 5) - Level 6 and above; National Learning Assessment (grade 4, 5 and 6) - Varies by country. \n\n\nWhen a given country had administered multiple types of learning assessments, a hierarchy is applied in the order listed below to ensure best comparability across countries: International or Regional Learning Assessment for Reading (PIRLS, LLECE, PASEC, SEA-PLM) > TIMSS Science > Statistical or Pairwise Linking Exercises > AMPL-bs, Policy Linked National Learning Assessments or Policy Linked Service Delivery Indicators (SDIs) > Non-Policy Linked NLAs (Interim Reporting).\n\n\nNote that as the GAML and joint coalitions continue their efforts to improve learning data coverage, the hierarchy may be revised.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "SE.LPV.PRIM.LD.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Female pupils below minimum reading proficiency at end of primary (%). Low GAML threshold" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The process of equating proficiency levels on different assessments to the GAML definition is not straightforward. Even the long-running regional assessment initiatives like PASEC (West and Central Africa) and LLECE (Latin America and the Caribbean) use different definitions and a different number of levels than other assessments like PIRLS, and those might not even be the same over time. Their test development methodologies and test administration procedures also vary. Moreover, because not all countries participate in global or regional assessments, for some major countries we rely on their interim reporting using their national assessments; equating these assessments is even more challenging. UIS and the World Bank have mapped how proficiency levels between assessment can equate to one another, but they are not strictly comparable. \n\nAmong the differences across assessments, one important point concerns the age at which children are tested. The reference age for our exercise is age 10. However, all learning assessments used in this analysis are sampled based on specific grades rather than age. PIRLS and TIMSS are administered in Grade 4, meaning that the average student assessed is indeed 10 years old, but this is not the case for the regional assessments. PASEC and LLECE are administered in Grade 6, so the average age in those assessments is 12.8 and 12.4, respectively. National assessments are administered at different grades, so to incorporate those assessments, we chose for each country the grade between 4 and 6 (inclusive) for which relevant and reliable data were available. This is consistent with the SDG monitoring by UIS and GAML, which lists “End of Primary (or Grades 4 to 6)” as the relevant age category for the end-of-primary students (SDG 4.1.1b).\n\nIn some cases, National Learning Assessments that have not been policy linked are used for learning poverty, if country teams and experts determine that an assessment is of sufficiently quality or has undertaken steps to align their assessments with the Global Proficiency Framework. They will often be reported as interim learning poverty indicators, as they are not fully aligned with SDG 4.1.1b." }, { "id": "Longdefinition", "value": "The share of female pupils at the end of primary schooling who are below the minimum proficiency level (MPL) for reading or learning deprived. The MPL in reading at the end of primary is defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. It is “Students independently and fluently read simple, short narrative and expository texts. They locate explicitly-stated information. They interpret and give some explanations about the key ideas in these texts. They provide simple, personal opinions or judgements about the information, events and characters in a text.” (UIS and GAML 2019). In other words, a child “attaining” minimum proficiency has the ability to read and understand a short passage of age-appropriate material, whether a simple story or non-fiction narrative of a few paragraphs. In addition to this nutshell statement, the GAML has also proposed a common terminology to describe classifications in the context of the MPL. This is a critical first step toward linking cross-national and national learning assessments with a common benchmark." }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Learning assessments used to calculate Learning Poverty have a minimum proficiency level (MPL) benchmarked by Global Alliance to Monitor Learning (GAML) under the leadership of the UNESCO Institute of Statistics (UIS), which occurred within the reporting window. To operationalize this concept, the current SDG monitoring process is followed by defining “proficiency” as reaching at least the Low International Benchmark on the international PIRLS literacy assessment. \n\n\nPIRLS is the major global primary-age assessment focused on reading, and if all countries participated in it, the task of constructing global estimates of minimum proficiency would be trivial, as it would require aggregating results from a single cross-national assessment. However, most countries participating in PIRLS are high-income, and only a small minority of low- and middle-income countries participate in the assessment. One of the main contributions of the GAML process is that it has overcome this data gap by benchmarking several major cross-national assessments—and increasingly national learning assessments as well—against the standard. \n\n\nThe MPL for each learning assessment is used to calculate the reading proficiency rate for that country, which is the share of students scoring at or above the minimum proficiency level, and conversely to calculate the learning deprivation.\n\n\nThe Proficiency and Grade Levels used for each assessment is as follows: PIRLS (grade 4) - Level 2 (Low international benchmark, 400 points); TIMSS (grade 4) - Level 2 (Low international benchmark, 400 points); LLECE (SERCE, grade 6) - Level 3 (513.66 points); PASEC (grades 5 and 6) - Level 4 (595.1 points); SEA-PLM (grade 5) - Level 6 and above; National Learning Assessment (grade 4, 5 and 6) - Varies by country. \n\n\nWhen a given country had administered multiple types of learning assessments, a hierarchy is applied in the order listed below to ensure best comparability across countries: International or Regional Learning Assessment for Reading (PIRLS, LLECE, PASEC, SEA-PLM) > TIMSS Science > Statistical or Pairwise Linking Exercises > AMPL-bs, Policy Linked National Learning Assessments or Policy Linked Service Delivery Indicators (SDIs) > Non-Policy Linked NLAs (Interim Reporting).\n\n\nNote that as the GAML and joint coalitions continue their efforts to improve learning data coverage, the hierarchy may be revised.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "SE.LPV.PRIM.LD.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Male pupils below minimum reading proficiency at end of primary (%). Low GAML threshold" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The process of equating proficiency levels on different assessments to the GAML definition is not straightforward. Even the long-running regional assessment initiatives like PASEC (West and Central Africa) and LLECE (Latin America and the Caribbean) use different definitions and a different number of levels than other assessments like PIRLS, and those might not even be the same over time. Their test development methodologies and test administration procedures also vary. Moreover, because not all countries participate in global or regional assessments, for some major countries we rely on their interim reporting using their national assessments; equating these assessments is even more challenging. UIS and the World Bank have mapped how proficiency levels between assessment can equate to one another, but they are not strictly comparable. \n\nAmong the differences across assessments, one important point concerns the age at which children are tested. The reference age for our exercise is age 10. However, all learning assessments used in this analysis are sampled based on specific grades rather than age. PIRLS and TIMSS are administered in Grade 4, meaning that the average student assessed is indeed 10 years old, but this is not the case for the regional assessments. PASEC and LLECE are administered in Grade 6, so the average age in those assessments is 12.8 and 12.4, respectively. National assessments are administered at different grades, so to incorporate those assessments, we chose for each country the grade between 4 and 6 (inclusive) for which relevant and reliable data were available. This is consistent with the SDG monitoring by UIS and GAML, which lists “End of Primary (or Grades 4 to 6)” as the relevant age category for the end-of-primary students (SDG 4.1.1b).\n\nIn some cases, National Learning Assessments that have not been policy linked are used for learning poverty, if country teams and experts determine that an assessment is of sufficiently quality or has undertaken steps to align their assessments with the Global Proficiency Framework. They will often be reported as interim learning poverty indicators, as they are not fully aligned with SDG 4.1.1b." }, { "id": "Longdefinition", "value": "The share of male pupils at the end of primary schooling who are below the minimum proficiency level (MPL) for reading or learning deprived. The MPL in reading at the end of primary is defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. It is “Students independently and fluently read simple, short narrative and expository texts. They locate explicitly-stated information. They interpret and give some explanations about the key ideas in these texts. They provide simple, personal opinions or judgements about the information, events and characters in a text.” (UIS and GAML 2019). In other words, a child “attaining” minimum proficiency has the ability to read and understand a short passage of age-appropriate material, whether a simple story or non-fiction narrative of a few paragraphs. In addition to this nutshell statement, the GAML has also proposed a common terminology to describe classifications in the context of the MPL. This is a critical first step toward linking cross-national and national learning assessments with a common benchmark." }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Learning assessments used to calculate Learning Poverty have a minimum proficiency level (MPL) benchmarked by Global Alliance to Monitor Learning (GAML) under the leadership of the UNESCO Institute of Statistics (UIS), which occurred within the reporting window. To operationalize this concept, the current SDG monitoring process is followed by defining “proficiency” as reaching at least the Low International Benchmark on the international PIRLS literacy assessment. \n\n\nPIRLS is the major global primary-age assessment focused on reading, and if all countries participated in it, the task of constructing global estimates of minimum proficiency would be trivial, as it would require aggregating results from a single cross-national assessment. However, most countries participating in PIRLS are high-income, and only a small minority of low- and middle-income countries participate in the assessment. One of the main contributions of the GAML process is that it has overcome this data gap by benchmarking several major cross-national assessments—and increasingly national learning assessments as well—against the standard. \n\n\nThe MPL for each learning assessment is used to calculate the reading proficiency rate for that country, which is the share of students scoring at or above the minimum proficiency level, and conversely to calculate the learning deprivation.\n\n\nThe Proficiency and Grade Levels used for each assessment is as follows: PIRLS (grade 4) - Level 2 (Low international benchmark, 400 points); TIMSS (grade 4) - Level 2 (Low international benchmark, 400 points); LLECE (SERCE, grade 6) - Level 3 (513.66 points); PASEC (grades 5 and 6) - Level 4 (595.1 points); SEA-PLM (grade 5) - Level 6 and above; National Learning Assessment (grade 4, 5 and 6) - Varies by country. \n\n\nWhen a given country had administered multiple types of learning assessments, a hierarchy is applied in the order listed below to ensure best comparability across countries: International or Regional Learning Assessment for Reading (PIRLS, LLECE, PASEC, SEA-PLM) > TIMSS Science > Statistical or Pairwise Linking Exercises > AMPL-bs, Policy Linked National Learning Assessments or Policy Linked Service Delivery Indicators (SDIs) > Non-Policy Linked NLAs (Interim Reporting).\n\n\nNote that as the GAML and joint coalitions continue their efforts to improve learning data coverage, the hierarchy may be revised.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "SE.LPV.PRIM.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Learning poverty: Share of Male Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The construct of “all children reading by age 10” is an ideal that embodies normative statements about both learning and access. To achieve it, not only should all children be reading proficiently after 3 full years in primary education, but they should also have entered school at age 6 or 7. \n\nBy contrast, the actual indicators used to measure learning poverty are based on grade rather than age. Since the assessments are of 4th- through 6th-graders, the children tested will have had at least 3 to 5 years in school to reach what, according to the ideal, should 10 be an age-10 minimum proficiency, or even the entire primary-school-age segment for the out-of-school indicator.\n\nDue to different assessment availability within and between countries, data comparability, both within countries over time and across countries still poses a significant challenge. The additional out of school component further limits comparability.\n\nThe learning poverty indicator is based on data covering four-fifths of children at the end of primary school. In other words, a little more than 80 percent of children in low- and middle-income countries live in a country with at least one learning assessment at the end of primary, carried out in the past 9 years. For regional and global aggregates, weighted imputations affect regions with less data coverage. The major gaps are concentrated in countries where the learning crisis is most acute. Less than half of children in Sub-Saharan Africa live in a country with a National Large-Scale Learning Assessment (NLSA) or a international of regional large-scale learning assessment (ILSA or RLSA) of adequate quality to be used for this purpose.\n\nThis extensive coverage became possible only in recent years, with the progress in measuring learning in countries and the GAML’s efforts to establish comparability, which has made possible the construction of a global indicator based on harmonized proficiency levels. Future efforts by coalition organizations are also ensuring more flexible assessment options are available for expanding data availability for countries, such as the Assessment of Minimum Proficiency Levels (AMPL) and policy linking exercises led by UIS." }, { "id": "Longdefinition", "value": "The share of male 10-year-olds who cannot read and understand a short passage of age-appropriate material—in other words, those who are below the “minimum proficiency” threshold for reading. This measure is defined as the union of two deprivations: 1) schooling deprivation and 2) learning deprivation. A child is considered schooling-deprived (SD) if he or she is of primary school age and out-of-school. The dimension of learning deprivation (LD) applies only for children in school, and identifies those pupils who are below the minimum proficiency level (MPL) for reading, as defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. This “union approach” to measurement reflects the choice that, as presented in the SDGs, all age 10 children must be both in school and learning. The final learning poverty measure combines the two dimensions in a single indicator using the following formula: LP = SD + [(1-SD) x LD]" }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The learning poverty indicator brings together schooling and learning indicators. It starts with the share of children in school who haven’t achieved minimum reading proficiency (Learning Deprived) and adjusts it by the proportion of children who are out of school (Schooling Deprived). \n\n\nFormally, Learning Poverty is calculated as: [LD* (1-SD)] + [1 * SD]\n\n\nwhere LP = Learning poverty; LD = Learning deprivation or the share of children at the end of primary who read at below the minimum proficiency level, as defined by the Global Alliance to Monitor Learning (GAML) in the context of the SDG 4.1.1 monitoring; SD = Schooling deprivation or the share of primary-school-age children who are out-of-school (OOS) and in which all OOS are regarded as being below the minimum proficiency level.\n\n\nBecause out-of-school children are treated as non-proficient in reading, learning poverty will always be higher than the share of children in school who haven't achieved minimum reading proficiency. For countries with a very low schooling deprivation, the learning deprivation value will be very close to Learning Poverty. \n\n\nEstimating the current level of global and regional learning poverty requires deciding how to define “current.” We include results of assessments within four years before or after a set anchor year. This decision is driven by data availability. International and regional large-scale learning assessments used for SDG 4.1.1b reporting are carried out only every 3 to 4 years. And even where assessments have been carried out recently, there is a lag of a couple of years before the data are available. This band is intended as a moving window. In the original 2019 release, the anchor year used was 2015 (Assessments between 2011 and 2019 are included in the learning poverty estimate). In the 2022 Global Update, the anchor year was moved to 2019 (assessments between 2015 and 2023 are included).\n\n\nAggregations for each region comprise the average learning poverty of countries with available data, weighted by their population ages 10–14 years old. To obtain a global estimate, we weight the regional aggregations by the 10–14-year-old population regardless of data availability. This is equivalent to imputing missing country data using regional values.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "SE.LPV.PRIM.SD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Primary school age children out-of-school (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The enrollment for a given learning poverty release are not strictly comparable between countries, due to the differences in enrollment years and definitions, which are determined by best-match with the assessment year and data availability of enrollment indicators. \n\nThe measure will also differ from out of school estimates using household survey data, which UIS reports for SDG 4.1.4. Households survey estimates are not used for Schooling Deprivation because data is typically reported for countries in various years and with time gaps. School surveys are more feasibly collected annually, while household data collection occurs every few years and can also depend on country demand. School surveys also allow more global consistency as the same survey and source data are used across countries. However, the source used to compute the total school-age population differ in some cases where a country provides their national estimates over the default UNDP population data. However, there are potential trade-offs in precision from school surveys as responses come from school representatives rather than using microdata.\n\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, enrolment rate (any definition) is affected by different age-reference points for enrollment. The length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced. The population data further affects the reference age.\n\nDue to the limitations described, in some cases, country specialists will provide a data point that better reflects enrollment in the country." }, { "id": "Longdefinition", "value": "The share of children of primary-school age who are out of school or schooling deprived. This dimension is linked to the indicator 4.1.4 from the SDG 4 thematic framework. This element reflects the belief that all primary-age children should be learning in schools of some type, a belief that every country has enshrined in law and that is enshrined in the SDGs. In addition to fulfilling a universal right and serving as a necessary condition for sustained learning, schooling offers many benefits beyond learning. It contributes to children’s health and well-being such as promoting safety, nutrition, and socialization, and facilitating parents' labor market participation and, at the macro level, schooling can help build social cohesion, democracy, and peace. All those complementary functions mean that schooling has value over and above the measured cognitive learning that it leads to, and they justify including schooling deprivation in the concept of learning poverty." }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Schooling Deprivation is derived using enrollment rates computed by UIS using administrative records and follows the SDG 4.1.4 indicator. To ensure country coverage of data, we consider other enrollment definitions to use for learning poverty, if the first-best option is not available. \n\n\nWe construct an enrollment dataset from 1990 to the year of the current release, relying on multiple enrollment definitions. Our dataset is constructed from UIS (UNESCO Institute of Statistics), and other sources suggested by World Bank regional or country education specialists. Data sources are typically from administrative records (school registers or school censuses) for data on enrolment by age, and UNPD population estimates for school-age population (UIS). Enrolment by single years of age in all levels of education and the total primary-school-age population are used to compute enrollment rates.\n\n\nWe follow this hierarchy of enrollment definitions: Country specialist validated data > Adjusted Net Enrollment Rate (ANER) > Total Net Enrollment Rate (TNER) > Net enrollment rate (NER) > Gross Enrollment Rate (GER; if the gross enrollment rate is higher than 100%, it is adjusted to be 100%).\n\n\nOur preferred measure of school participation is ANER, because it accounts for some primary school aged children who might enter primary school early and advance to secondary school before they reach the official upper age limit of primary education. Adjusted net enrollment is a measure of both “stock” and “flow” and accounts for both age- and grade-based distortions, as it is the percent of primary school age children enrolled either in primary or secondary education, as opposed to gross enrollment which is the share of children of any age that are enrolled in primary school, or net enrollment which is the share of primary school age children that are enrolled in primary school. The next-best indicator is used if ANER is unavailable. In some cases, country specialists will provide a data point that reflects enrollment in the country better than UIS statistics which is used. In future Learning Poverty releases, the enrollment hierarchy may be adjusted as availability of indicator definitions change. \n\n\nThe enrollment year used is the one that best pairs with the assessment year used to compute Learning Deprivation. The year of the preferred assessment is the base. If the same enrollment year is not available, we use a step function to fill the data in with the value of the closest year. If there is data available for two years equally close to the year to fill, the older value is used. This procedure to extrapolate enrollment for missing values is required for us to pair the proficiency measure with enrollment measures from the same year, or its best proxy when enrollment is not available for the same year of the assessment.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "SE.LPV.PRIM.SD.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Female primary school age children out-of-school (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The enrollment for a given learning poverty release are not strictly comparable between countries, due to the differences in enrollment years and definitions, which are determined by best-match with the assessment year and data availability of enrollment indicators. \n\nThe measure will also differ from out of school estimates using household survey data, which UIS reports for SDG 4.1.4. Households survey estimates are not used for Schooling Deprivation because data is typically reported for countries in various years and with time gaps. School surveys are more feasibly collected annually, while household data collection occurs every few years and can also depend on country demand. School surveys also allow more global consistency as the same survey and source data are used across countries. However, the source used to compute the total school-age population differ in some cases where a country provides their national estimates over the default UNDP population data. However, there are potential trade-offs in precision from school surveys as responses come from school representatives rather than using microdata.\n\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, enrolment rate (any definition) is affected by different age-reference points for enrollment. The length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced. The population data further affects the reference age.\n\nDue to the limitations described, in some cases, country specialists will provide a data point that better reflects enrollment in the country." }, { "id": "Longdefinition", "value": "The share of female children of primary-school age who are out of school or schooling deprived. This dimension is linked to the indicator 4.1.4 from the SDG 4 thematic framework. This element reflects the belief that all primary-age children should be learning in schools of some type, a belief that every country has enshrined in law and that is enshrined in the SDGs. In addition to fulfilling a universal right and serving as a necessary condition for sustained learning, schooling offers many benefits beyond learning. It contributes to children’s health and well-being such as promoting safety, nutrition, and socialization, and facilitating parents' labor market participation and, at the macro level, schooling can help build social cohesion, democracy, and peace. All those complementary functions mean that schooling has value over and above the measured cognitive learning that it leads to, and they justify including schooling deprivation in the concept of learning poverty." }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Schooling Deprivation is derived using enrollment rates computed by UIS using administrative records and follows the SDG 4.1.4 indicator. To ensure country coverage of data, we consider other enrollment definitions to use for learning poverty, if the first-best option is not available. \n\n\nWe construct an enrollment dataset from 1990 to the year of the current release, relying on multiple enrollment definitions. Our dataset is constructed from UIS (UNESCO Institute of Statistics), and other sources suggested by World Bank regional or country education specialists. Data sources are typically from administrative records (school registers or school censuses) for data on enrolment by age, and UNPD population estimates for school-age population (UIS). Enrolment by single years of age in all levels of education and the total primary-school-age population are used to compute enrollment rates.\n\n\nWe follow this hierarchy of enrollment definitions: Country specialist validated data > Adjusted Net Enrollment Rate (ANER) > Total Net Enrollment Rate (TNER) > Net enrollment rate (NER) > Gross Enrollment Rate (GER; if the gross enrollment rate is higher than 100%, it is adjusted to be 100%).\n\n\nOur preferred measure of school participation is ANER, because it accounts for some primary school aged children who might enter primary school early and advance to secondary school before they reach the official upper age limit of primary education. Adjusted net enrollment is a measure of both “stock” and “flow” and accounts for both age- and grade-based distortions, as it is the percent of primary school age children enrolled either in primary or secondary education, as opposed to gross enrollment which is the share of children of any age that are enrolled in primary school, or net enrollment which is the share of primary school age children that are enrolled in primary school. The next-best indicator is used if ANER is unavailable. In some cases, country specialists will provide a data point that reflects enrollment in the country better than UIS statistics which is used. In future Learning Poverty releases, the enrollment hierarchy may be adjusted as availability of indicator definitions change. \n\n\nThe enrollment year used is the one that best pairs with the assessment year used to compute Learning Deprivation. The year of the preferred assessment is the base. If the same enrollment year is not available, we use a step function to fill the data in with the value of the closest year. If there is data available for two years equally close to the year to fill, the older value is used. This procedure to extrapolate enrollment for missing values is required for us to pair the proficiency measure with enrollment measures from the same year, or its best proxy when enrollment is not available for the same year of the assessment.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "SE.LPV.PRIM.SD.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. \n\n\nThe LP indicator illustrates progress toward SDG 4’s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank’s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries’ Human Capital Index scores." }, { "id": "IndicatorName", "value": "Male primary school age children out-of-school (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The enrollment for a given learning poverty release are not strictly comparable between countries, due to the differences in enrollment years and definitions, which are determined by best-match with the assessment year and data availability of enrollment indicators. \n\nThe measure will also differ from out of school estimates using household survey data, which UIS reports for SDG 4.1.4. Households survey estimates are not used for Schooling Deprivation because data is typically reported for countries in various years and with time gaps. School surveys are more feasibly collected annually, while household data collection occurs every few years and can also depend on country demand. School surveys also allow more global consistency as the same survey and source data are used across countries. However, the source used to compute the total school-age population differ in some cases where a country provides their national estimates over the default UNDP population data. However, there are potential trade-offs in precision from school surveys as responses come from school representatives rather than using microdata.\n\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, enrolment rate (any definition) is affected by different age-reference points for enrollment. The length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced. The population data further affects the reference age.\n\nDue to the limitations described, in some cases, country specialists will provide a data point that better reflects enrollment in the country." }, { "id": "Longdefinition", "value": "The share of male children of primary-school age who are out of school or schooling deprived. This dimension is linked to the indicator 4.1.4 from the SDG 4 thematic framework. This element reflects the belief that all primary-age children should be learning in schools of some type, a belief that every country has enshrined in law and that is enshrined in the SDGs. In addition to fulfilling a universal right and serving as a necessary condition for sustained learning, schooling offers many benefits beyond learning. It contributes to children’s health and well-being such as promoting safety, nutrition, and socialization, and facilitating parents' labor market participation and, at the macro level, schooling can help build social cohesion, democracy, and peace. All those complementary functions mean that schooling has value over and above the measured cognitive learning that it leads to, and they justify including schooling deprivation in the concept of learning poverty." }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Schooling Deprivation is derived using enrollment rates computed by UIS using administrative records and follows the SDG 4.1.4 indicator. To ensure country coverage of data, we consider other enrollment definitions to use for learning poverty, if the first-best option is not available. \n\n\nWe construct an enrollment dataset from 1990 to the year of the current release, relying on multiple enrollment definitions. Our dataset is constructed from UIS (UNESCO Institute of Statistics), and other sources suggested by World Bank regional or country education specialists. Data sources are typically from administrative records (school registers or school censuses) for data on enrolment by age, and UNPD population estimates for school-age population (UIS). Enrolment by single years of age in all levels of education and the total primary-school-age population are used to compute enrollment rates.\n\n\nWe follow this hierarchy of enrollment definitions: Country specialist validated data > Adjusted Net Enrollment Rate (ANER) > Total Net Enrollment Rate (TNER) > Net enrollment rate (NER) > Gross Enrollment Rate (GER; if the gross enrollment rate is higher than 100%, it is adjusted to be 100%).\n\n\nOur preferred measure of school participation is ANER, because it accounts for some primary school aged children who might enter primary school early and advance to secondary school before they reach the official upper age limit of primary education. Adjusted net enrollment is a measure of both “stock” and “flow” and accounts for both age- and grade-based distortions, as it is the percent of primary school age children enrolled either in primary or secondary education, as opposed to gross enrollment which is the share of children of any age that are enrolled in primary school, or net enrollment which is the share of primary school age children that are enrolled in primary school. The next-best indicator is used if ANER is unavailable. In some cases, country specialists will provide a data point that reflects enrollment in the country better than UIS statistics which is used. In future Learning Poverty releases, the enrollment hierarchy may be adjusted as availability of indicator definitions change. \n\n\nThe enrollment year used is the one that best pairs with the assessment year used to compute Learning Deprivation. The year of the preferred assessment is the base. If the same enrollment year is not available, we use a step function to fill the data in with the value of the closest year. If there is data available for two years equally close to the year to fill, the older value is used. This procedure to extrapolate enrollment for missing values is required for us to pair the proficiency measure with enrollment measures from the same year, or its best proxy when enrollment is not available for the same year of the assessment.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading" }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of Total" } ], "source_id": "57" }, { "id": "SE.PRE.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The significance of pre-primary education lies in its role in laying a strong groundwork for children's social, emotional, and general well-being. Sustainable Development Goal (SDG) target 4.2 is dedicated to ensuring that all children can participate in high-quality early childhood development, care, and pre-primary education programs. This particular indicator is instrumental in determining the number of children eligible for pre-primary education and is a critical component for the computation of additional indicators, as well as for assessing a nation's ability to fulfill the educational needs at this fundamental level of learning." }, { "id": "IndicatorName", "value": "Preprimary education, duration (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The presence of national legislation does not guarantee that countries will implement it effectively, nor does it ensure that parents will take advantage of the provisions available for their children." }, { "id": "Longdefinition", "value": "Preprimary duration refers to the number of grades (years) in preprimary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the number of years that a country's laws or regulations specify for the preprimary stage of education. The definition of pre-primary education as programs that introduce children, typically starting at the age of 3, to a structured school environment and serve as a transition from home to school aligns with UNESCO's description(https://learningportal.iiep.unesco.org/en/glossary/pre-primary-education)." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "57" }, { "id": "SE.PRE.ENRL.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, preprimary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Preprimary school pupil-teacher ratio is the average number of pupils per teacher in preprimary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.PRE.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, preprimary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for pre-primary school is calculated by dividing the number of students enrolled in pre-primary education regardless of age by the population of the age group which officially corresponds to pre-primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.PRE.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, preprimary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for pre-primary school is calculated by dividing the number of students enrolled in pre-primary education regardless of age by the population of the age group which officially corresponds to pre-primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.PRE.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, preprimary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2022" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for pre-primary school is calculated by dividing the number of students enrolled in pre-primary education regardless of age by the population of the age group which officially corresponds to pre-primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.PRE.TCAQ.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The percentage of trained educators serves as a barometer for a country's commitment to improving its teaching workforce, and increasing this percentage aligns with the aims of Sustainable Development Goal target 4.c. Female educators, in particular, are vital as they provide inspiration and encouragement for young girls to continue their education. These professionals are instrumental in engaging and maintaining girls' attendance in schools, confronting entrenched gender biases in communities, raising parental aspirations for their daughters, and aiding in the reduction of the educational attainment disparity between male and female students.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in preprimary education, female (% of female teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in preprimary education are the percentage of preprimary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of female teachers in preprimary education" } ], "source_id": "57" }, { "id": "SE.PRE.TCAQ.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in preprimary education, male (% of male teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in preprimary education are the percentage of preprimary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of male teachers in preprimary education" } ], "source_id": "57" }, { "id": "SE.PRE.TCAQ.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in preprimary education (% of total teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in preprimary education are the percentage of preprimary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total teachers in preprimary education" } ], "source_id": "57" }, { "id": "SE.PRM.AGES", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Education is recognized as an essential human right that must be afforded to all individuals. Primary education serves as the cornerstone for the development of fundamental literacy and numeracy skills, which are crucial for a solid foundation in the learning process. It also plays a significant role in promoting personal and social development. The importance of primary education is underscored by Sustainable Development Goal (SDG) target 4.1, which advocates for inclusive and equitable quality education at the primary level for all children." }, { "id": "IndicatorName", "value": "Primary school starting age (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The theoretical entrance age to a given programme or level is typically, but not always, the most common entrance age." }, { "id": "Longdefinition", "value": "Primary school starting age is the age at which students would enter primary education, assuming they had started at the official entrance age for the lowest level of education, had studied full-time throughout and had progressed through the system without repeating or skipping a grade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator provides insights into the demand for educational services across various educational levels. Additionally, it serves as a crucial data point necessary for the generation of numerous educational indicators." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "57" }, { "id": "SE.PRM.CMPR.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Primary completion rate, female (% of relevant age group, DHS/MICS)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Longdefinition", "value": "Primary completion rate. Female is the total number of female students of any age in the last grade of primary school, minus the number of female repeaters in that grade, divided by the number of female children of official graduation age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "57" }, { "id": "SE.PRM.CMPR.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Primary completion rate, male (% of relevant age group, DHS/MICS)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Longdefinition", "value": "Primary completion rate. Male is the total number of male students of any age in the last grade of primary school, minus the number of male repeaters in that grade, divided by the number of male children of official graduation age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "57" }, { "id": "SE.PRM.CMPR.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Primary completion rate, poorest quintile (% of relevant age group, DHS/MICS)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Longdefinition", "value": "Primary completion rate. Quintile 1 (lowest) is the total number of quintile 1 students of any age in the last grade of primary school, minus the number of quintile 1 repeaters in that grade, divided by the number of quintile 1 children of official graduation age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "57" }, { "id": "SE.PRM.CMPR.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Primary completion rate, richest quintile (% of relevant age group, DHS/MICS)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Longdefinition", "value": "Primary completion rate. Quintile 5 (highest) is the total number of quintile 5 students of any age in the last grade of primary school, minus the number of quintile 5 repeaters in that grade, divided by the number of quintile 5 children of official graduation age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "57" }, { "id": "SE.PRM.CMPR.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Primary completion rate, rural (% of relevant age group, DHS/MICS)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Longdefinition", "value": "Primary completion rate. Rural is the total number of rural students of any age in the last grade of primary school, minus the number of rural repeaters in that grade, divided by the number of rural children of official graduation age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "57" }, { "id": "SE.PRM.CMPR.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Primary completion rate, urban (% of relevant age group, DHS/MICS)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The completion rate can exceed 100 percent if there are many overage students in the last grade of primary school." }, { "id": "Longdefinition", "value": "Primary completion rate. Urban is the total number of urban students of any age in the last grade of primary school, minus the number of urban repeaters in that grade, divided by the number of urban children of official graduation age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "57" }, { "id": "SE.PRM.CMPT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education." }, { "id": "IndicatorName", "value": "Primary completion rate, female (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "57" }, { "id": "SE.PRM.CMPT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education." }, { "id": "IndicatorName", "value": "Primary completion rate, male (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "57" }, { "id": "SE.PRM.CMPT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education." }, { "id": "IndicatorName", "value": "Primary completion rate, total (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "57" }, { "id": "SE.PRM.CUAT.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital?" }, { "id": "IndicatorName", "value": "Educational attainment, at least completed primary, population 25+ years, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed primary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "57" }, { "id": "SE.PRM.CUAT.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed primary, population 25+ years, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed primary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "57" }, { "id": "SE.PRM.CUAT.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed primary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "57" }, { "id": "SE.PRM.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education serves as the cornerstone for the development of fundamental literacy and numeracy skills, which are crucial for a solid foundation in the learning process. It also plays a significant role in promoting personal and social development. The importance of primary education is underscored by Sustainable Development Goal (SDG) target 4.1, which advocates completion of inclusive and equitable quality education at the primary level for all children." }, { "id": "IndicatorName", "value": "Primary education, duration (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The presence of national legislation does not guarantee that countries will implement it effectively, nor does it ensure that parents will take advantage of the provisions available for their children." }, { "id": "Longdefinition", "value": "Primary duration refers to the number of grades (years) in primary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the number of years that a country's laws or regulations specify for primary education. It aids in identifying the population of school-aged children at different educational levels. Additionally, this metric serves as crucial input data necessary for generating various indicators and evaluating a country's capacity to meet educational demand." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "57" }, { "id": "SE.PRM.ENRL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education is a fundamental human right. It serves as the cornerstone for subsequent achievement and creates opportunities for further progress. Its extensive influence extends to individuals and the collective society, playing a critical role in the alleviation of extreme poverty, the promotion of health, and the advancement of gender equality. The SDG target 4.1 advocates for the completion of free, equitable, and high-quality primary and secondary education for all children, aiming to achieve meaningful and impactful learning outcomes by the year 2030." }, { "id": "IndicatorName", "value": "Primary education, pupils" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Primary education pupils is the total number of pupils enrolled at primary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Enrollment includes Individuals officially registered in a given educational programme, or stage or module thereof, regardless of age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Educational programs at the International Standard Classification of Education (ISCED) level 1, commonly referred to as primary education, are fundamentally structured to equip students with essential literacy and numeracy skills. These programs aim to lay a robust groundwork for learning, fostering an understanding of key knowledge domains, and promoting personal and social growth, thereby readying students for the subsequent stage of lower secondary education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEntry into this level is predominantly based on age, with the typical or legally mandated age of enrollment ranging from 5 to 7 years old. The duration of primary education is generally six years, though it can vary from four to seven years, concluding when students are between 10 to 12 years old (refer to Paragraphs 132 to 134 for further details). Upon successful completion of primary education, students are eligible to progress to ISCED level 2, which corresponds to lower secondary education." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SE.PRM.ENRL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of girls allows an assessment on gender composition in school enrollment. A value greater than 50% indicates participation of more girls at a specific level or programme of education." }, { "id": "IndicatorName", "value": "Primary education, pupils (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The percentage of female enrollment is limited in assessing gender parity, because it's affected by the gender composition of population. Ratio of female to male in enrollment rate provides a population adjusted measure of gender parity." }, { "id": "Longdefinition", "value": "Female pupils as a percentage of total pupils at primary level include enrollments in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentage of female enrollment is calculated by dividing the total number of female students at a given level of education by the total enrollment at the same level, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.PRM.ENRL.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, primary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Primary school pupil-teacher ratio is the average number of pupils per teacher in primary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.PRM.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education is fundamental to future educational success and opens pathways for continued advancement. This indicator measures the overall rate of participation in primary education, signifying the education system's ability to enroll students within a specific age cohort." }, { "id": "IndicatorName", "value": "School enrollment, primary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for primary school is calculated by dividing the number of students enrolled in primary education regardless of age by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of population in the 5-year age group immediately following preprimary education" } ], "source_id": "57" }, { "id": "SE.PRM.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education is fundamental to future educational success and opens pathways for continued advancement. This indicator measures the overall rate of participation in primary education, signifying the education system's ability to enroll students within a specific age cohort." }, { "id": "IndicatorName", "value": "School enrollment, primary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for primary school is calculated by dividing the number of students enrolled in primary education regardless of age by the population of the age group which officially corresponds to primary education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of female population in the 5-year age group immediately following preprimary education" } ], "source_id": "57" }, { "id": "SE.PRM.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Primary education is fundamental to future educational success and opens pathways for continued advancement. This indicator measures the overall rate of participation in primary education, signifying the education system's ability to enroll students within a specific age cohort." }, { "id": "IndicatorName", "value": "School enrollment, primary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for primary school is calculated by dividing the number of students enrolled in primary education regardless of age by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of male population in the 5-year age group immediately following preprimary education" } ], "source_id": "57" }, { "id": "SE.PRM.GINT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The gross intake ratio in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A low gross intake ratio in the first grade of primary education reflects the fact that many children do not enter primary education even though school attendance, at least through the primary level, is mandatory in most countries. Because the gross intake ratio includes all new entrants regardless of age, it can exceed 100 percent in some situations, such as immediately after fees have been abolished or when the number of reenrolled children is large." }, { "id": "IndicatorName", "value": "Gross intake ratio in first grade of primary education, female (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Gross intake ratio in first grade of primary education is the number of new entrants in the first grade of primary education regardless of age, expressed as a percentage of the population of the official primary entrance age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross intake ratio in the first grade of primary education is calculated by dividing the number of new entrants (enrollments minus repeaters) in the first grade of primary education, regardless of age, by the population of the official primary entrance age and multiplying the result by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.PRM.GINT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The gross intake ratio in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A low gross intake ratio in the first grade of primary education reflects the fact that many children do not enter primary education even though school attendance, at least through the primary level, is mandatory in most countries. Because the gross intake ratio includes all new entrants regardless of age, it can exceed 100 percent in some situations, such as immediately after fees have been abolished or when the number of reenrolled children is large." }, { "id": "IndicatorName", "value": "Gross intake ratio in first grade of primary education, male (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Gross intake ratio in first grade of primary education is the number of new entrants in the first grade of primary education regardless of age, expressed as a percentage of the population of the official primary entrance age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross intake ratio in the first grade of primary education is calculated by dividing the number of new entrants (enrollments minus repeaters) in the first grade of primary education, regardless of age, by the population of the official primary entrance age and multiplying the result by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.PRM.GINT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The gross intake ratio in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A low gross intake ratio in the first grade of primary education reflects the fact that many children do not enter primary education even though school attendance, at least through the primary level, is mandatory in most countries. Because the gross intake ratio includes all new entrants regardless of age, it can exceed 100 percent in some situations, such as immediately after fees have been abolished or when the number of reenrolled children is large." }, { "id": "IndicatorName", "value": "Gross intake ratio in first grade of primary education, total (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Gross intake ratio in first grade of primary education is the number of new entrants in the first grade of primary education regardless of age, expressed as a percentage of the population of the official primary entrance age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross intake ratio in the first grade of primary education is calculated by dividing the number of new entrants (enrollments minus repeaters) in the first grade of primary education, regardless of age, by the population of the official primary entrance age and multiplying the result by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.PRM.NENR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, primary (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.PRM.NENR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, primary, female (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "UNESCO Institute for Statistics (UIS), UN Educational, Scientific and Cultural Organization (UNESCO), uri: http://uis.unesco.org/, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.PRM.NENR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, primary, male (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.PRM.NINT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The net intake rate in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A high net intake rate indicates a high degree of access to primary education for the official primary school entrance age children." }, { "id": "IndicatorName", "value": "Net intake rate in grade 1, female (% of official school-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Net intake rate in grade 1 is the number of new entrants in the first grade of primary education who are of official primary school entrance age, expressed as a percentage of the population of the corresponding age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net intake rate in the first grade of primary education is calculated by dividing the number of children of official primary school entrance age who enter grade 1 of primary education for the first time by the population of the same age, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.PRM.NINT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The net intake rate in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A high net intake rate indicates a high degree of access to primary education for the official primary school entrance age children." }, { "id": "IndicatorName", "value": "Net intake rate in grade 1, male (% of official school-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Net intake rate in grade 1 is the number of new entrants in the first grade of primary education who are of official primary school entrance age, expressed as a percentage of the population of the corresponding age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net intake rate in the first grade of primary education is calculated by dividing the number of children of official primary school entrance age who enter grade 1 of primary education for the first time by the population of the same age, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.PRM.NINT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The net intake rate in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A high net intake rate indicates a high degree of access to primary education for the official primary school entrance age children." }, { "id": "IndicatorName", "value": "Net intake rate in grade 1 (% of official school-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors." }, { "id": "Longdefinition", "value": "Net intake rate in grade 1 is the number of new entrants in the first grade of primary education who are of official primary school entrance age, expressed as a percentage of the population of the corresponding age." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net intake rate in the first grade of primary education is calculated by dividing the number of children of official primary school entrance age who enter grade 1 of primary education for the first time by the population of the same age, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.PRM.OENR.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Over-age students, primary, female (% of female enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Over-age students are the percentage of those enrolled who are older than the official school-age range for primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The percentage of over-age students is calculated by dividing the number of students who are older than the official school-age range for primary education by primary school enrollment, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.PRM.OENR.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Over-age students, primary, male (% of male enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Over-age students are the percentage of those enrolled who are older than the official school-age range for primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The percentage of over-age students is calculated by dividing the number of students who are older than the official school-age range for primary education by primary school enrollment, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.PRM.OENR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Over-age students, primary (% of enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Over-age students are the percentage of those enrolled who are older than the official school-age range for primary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The percentage of over-age students is calculated by dividing the number of students who are older than the official school-age range for primary education by primary school enrollment, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SE.PRM.PRIV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The indicator reflects the proportion of students attending private educational institutions. Globally, private schools educate approximately 350 million children, and there has been a notable rise in the prevalence of private institutions (https://world-education-blog.org/2021/12/10/new-2021-2-gem-report-out-today-who-chooses-who-loses/). UNESCO emphasizes the importance of comprehensively understanding the contexts and frameworks within which both public and private schools function in each nation. This understanding is vital to guarantee that children's right to education is upheld and that their specific educational requirements are addressed." }, { "id": "IndicatorName", "value": "School enrollment, primary, private (% of total primary)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Religious or private schools, which are not registered with the government or don't follow the common national curriculum, may not be captured." }, { "id": "Longdefinition", "value": "Private enrollment refers to pupils or students enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body such as a nongovernmental organization, religious body, special interest group, foundation or business enterprise." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of students in private primary school is calculated by dividing the number of students enrolled in private educational institutions at primary level by total enrollment (public and private) at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of enrollment in private institutions indicates the scale and capacity of private education within a country. A high percentage suggests strong involvement of the non-governmental sector (including religious bodies, other organizations, associations, communities, private enterprises or persons) in providing organized educational programmes. However, in countries where private institutions are substantially subsidized or aided by the government, the distinction between private and public educational institutions may be less clear-cut especially when certain students are directly financed through government scholarships." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of total enrollment in primary school (both public and private)" } ], "source_id": "57" }, { "id": "SE.PRM.PRS5.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to grade 5, female (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to grade 5 (percentage of cohort reaching grade 5) is the share of children enrolled in the first grade of primary school who eventually reach grade 5. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "57" }, { "id": "SE.PRM.PRS5.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to grade 5, male (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to grade 5 (percentage of cohort reaching grade 5) is the share of children enrolled in the first grade of primary school who eventually reach grade 5. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "57" }, { "id": "SE.PRM.PRS5.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to grade 5, total (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to grade 5 (percentage of cohort reaching grade 5) is the share of children enrolled in the first grade of primary school who eventually reach grade 5. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "57" }, { "id": "SE.PRM.PRSL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to last grade of primary, female (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to last grade of primary is the percentage of children enrolled in the first grade of primary school who eventually reach the last grade of primary education. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels. Survival rate to the last grade of primary education is of particular interest for monitoring universal primary education." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "57" }, { "id": "SE.PRM.PRSL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to last grade of primary, male (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to last grade of primary is the percentage of children enrolled in the first grade of primary school who eventually reach the last grade of primary education. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels. Survival rate to the last grade of primary education is of particular interest for monitoring universal primary education." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "57" }, { "id": "SE.PRM.PRSL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level." }, { "id": "IndicatorName", "value": "Persistence to last grade of primary, total (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured." }, { "id": "Longdefinition", "value": "Persistence to last grade of primary is the percentage of children enrolled in the first grade of primary school who eventually reach the last grade of primary education. The estimate is based on the reconstructed cohort method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2023" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. \n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels. Survival rate to the last grade of primary education is of particular interest for monitoring universal primary education." }, { "id": "Topic", "value": "Education: Efficiency" }, { "id": "Unitofmeasure", "value": "% of cohort" } ], "source_id": "57" }, { "id": "SE.PRM.REPT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on repeaters are often used to indicate an education system's internal efficiency. Repeaters not only increase the cost of education for the family and the school system, but also use limited school resources." }, { "id": "IndicatorName", "value": "Repeaters, primary, female (% of female enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Country policies on repetition and promotion differ. In some cases the number of repeaters is controlled because of limited capacity. In other cases the number of repeaters is almost 0 because of automatic promotion – suggesting a system that is highly efficient but that may not be endowing students with enough cognitive skills." }, { "id": "Longdefinition", "value": "Repeaters in primary school are the number of students enrolled in the same grade as in the previous year, as a percentage of all students enrolled in primary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of repeaters in primary school is calculated by dividing the sum of repeaters in all grades of primary school by the total number of students enrolled in primary school, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.PRM.REPT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on repeaters are often used to indicate an education system's internal efficiency. Repeaters not only increase the cost of education for the family and the school system, but also use limited school resources." }, { "id": "IndicatorName", "value": "Repeaters, primary, male (% of male enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Country policies on repetition and promotion differ. In some cases the number of repeaters is controlled because of limited capacity. In other cases the number of repeaters is almost 0 because of automatic promotion – suggesting a system that is highly efficient but that may not be endowing students with enough cognitive skills." }, { "id": "Longdefinition", "value": "Repeaters in primary school are the number of students enrolled in the same grade as in the previous year, as a percentage of all students enrolled in primary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of repeaters in primary school is calculated by dividing the sum of repeaters in all grades of primary school by the total number of students enrolled in primary school, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.PRM.REPT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on repeaters are often used to indicate an education system's internal efficiency. Repeaters not only increase the cost of education for the family and the school system, but also use limited school resources." }, { "id": "IndicatorName", "value": "Repeaters, primary, total (% of total enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Country policies on repetition and promotion differ. In some cases the number of repeaters is controlled because of limited capacity. In other cases the number of repeaters is almost 0 because of automatic promotion – suggesting a system that is highly efficient but that may not be endowing students with enough cognitive skills." }, { "id": "Longdefinition", "value": "Repeaters in primary school are the number of students enrolled in the same grade as in the previous year, as a percentage of all students enrolled in primary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of repeaters in primary school is calculated by dividing the sum of repeaters in all grades of primary school by the total number of students enrolled in primary school, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.PRM.TCAQ.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The percentage of trained educators serves as a barometer for a country's commitment to improving its teaching workforce, and increasing this percentage aligns with the aims of Sustainable Development Goal target 4.c. Female educators, in particular, are vital as they provide inspiration and encouragement for young girls to continue their education. These professionals are instrumental in engaging and maintaining girls' attendance in schools, confronting entrenched gender biases in communities, raising parental aspirations for their daughters, and aiding in the reduction of the educational attainment disparity between male and female students.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in primary education, female (% of female teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in primary education are the percentage of primary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of female teachers in primary education" } ], "source_id": "57" }, { "id": "SE.PRM.TCAQ.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in primary education, male (% of male teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in primary education are the percentage of primary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of male teachers in primary education" } ], "source_id": "57" }, { "id": "SE.PRM.TCAQ.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in primary education (% of total teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in primary education are the percentage of primary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total teachers in primary education" } ], "source_id": "57" }, { "id": "SE.PRM.TCHR", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Teachers are pivotal in molding the future and unleashing the potential of each student. The United Nations Educational, Scientific and Cultural Organization (UNESCO) highlights a global teacher shortage as a significant obstacle in realizing Sustainable Development Goal 4, which aims for inclusive and equitable quality education." }, { "id": "IndicatorName", "value": "Primary education, teachers" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The precision of this indicator can be influenced by the enumeration method used, such as headcount or 'full-time equivalent' count of teachers." }, { "id": "Longdefinition", "value": "Primary education, teachers refers to the total number of teachers at primary level, including full-time and part-time teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Teachers refer to persons employed full-time or part-time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) or who work occasionally or in a voluntary capacity in educational institutions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The metric of personnel count primarily engaged in teaching and/or research reflects the scale, diversity, and distribution of educational staff within a nation's academic institutions. An increased count is anticipated to enrich the educational setting by means of dedicated teaching, practical research, academic pursuits, and contributions to the service of national educational policies." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SE.PRM.TCHR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although there have been advancements, girls in low-income countries continue to face significant barriers to accessing secondary education. The presence of female teachers is crucial in this context, as they act as role models, inspiring and motivating girls to pursue their education. These educators play a pivotal role in attracting and retaining girls in schools, challenging deep-seated gender stereotypes within communities, elevating parental expectations for their daughters, and contributing to the narrowing of the educational achievement gap between boys and girls." }, { "id": "IndicatorName", "value": "Primary education, teachers (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator measures the level of gender representation in the teaching profession, rather than the effectiveness and quality of teaching." }, { "id": "Longdefinition", "value": "Female teachers as a percentage of total primary education teachers includes full-time and part-time teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of female teachers in primary education is calculated by dividing the total number of female teachers at primary level of education by the total number of teachers at the same level, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of female teachers shows the level of gender representation in the teaching force. A value of greater than 50% indicates more opportunities or preference for women to participate in teaching activities." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total primary education teachers" } ], "source_id": "57" }, { "id": "SE.PRM.TENR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Relevance to gender indicator: Women teachers are important as they serve as role models to girls and help to attract and retain girls in school." }, { "id": "IndicatorName", "value": "Adjusted net enrollment rate, primary (% of primary school age children)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Adjusted net enrollment rate in primary education is calculated by dividing the number of children in the official primary school age who are enrolled in primary or secondary education by the population of the same age group and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.PRM.TENR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Relevance to gender indicator: Women teachers are important as they serve as role models to girls and help to attract and retain girls in school." }, { "id": "IndicatorName", "value": "Adjusted net enrollment rate, primary, female (% of primary school age children)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Adjusted net enrollment rate in primary education is calculated by dividing the number of children in the official primary school age who are enrolled in primary or secondary education by the population of the same age group and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.PRM.TENR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments. The adjusted net enrollment rate in primary education captures primary school-age children who have progressed to secondary education faster than their peers have and who are not counted in the traditional net enrollment rate." }, { "id": "IndicatorName", "value": "Adjusted net enrollment rate, primary, male (% of primary school age children)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Adjusted net enrollment rate in primary education is calculated by dividing the number of children in the official primary school age who are enrolled in primary or secondary education by the population of the same age group and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.PRM.UNER", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Children out of school, primary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to different data sources for enrollment and population data, the number may not capture the actual number of children not attending in primary school." }, { "id": "Longdefinition", "value": "Children out of school are the number of primary-school-age children not enrolled in primary or secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of out-of-school children is calculated by subtracting the number of primary school-age children enrolled in primary or secondary school from the total population of the official primary school-age children. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SE.PRM.UNER.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Children out of school, primary, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to different data sources for enrollment and population data, the number may not capture the actual number of children not attending in primary school." }, { "id": "Longdefinition", "value": "Children out of school are the number of primary-school-age children not enrolled in primary or secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of out-of-school children is calculated by subtracting the number of primary school-age children enrolled in primary or secondary school from the total population of the official primary school-age children. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SE.PRM.UNER.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Children out of school, female (% of female primary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The rate of out-of-school children allows to compare across countries with different population sizes. It shows the share of official primary-school-age children who never attended school or dropped out to the population of official primary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of female children in primary school age" } ], "source_id": "57" }, { "id": "SE.PRM.UNER.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Children out of school, primary, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to different data sources for enrollment and population data, the number may not capture the actual number of children not attending in primary school." }, { "id": "Longdefinition", "value": "Children out of school are the number of primary-school-age children not enrolled in primary or secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of out-of-school children is calculated by subtracting the number of primary school-age children enrolled in primary or secondary school from the total population of the official primary school-age children. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SE.PRM.UNER.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Children out of school, male (% of male primary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The rate of out-of-school children allows to compare across countries with different population sizes. It shows the share of official primary-school-age children who never attended school or dropped out to the population of official primary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of male children in primary school age" } ], "source_id": "57" }, { "id": "SE.PRM.UNER.Q1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Children out of school, primary, poorest quintile (% of relevant age group, DHS/MICS)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Quintile 1 (lowest) is the number of quintile 1 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 1 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.PRM.UNER.Q5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Children out of school, primary, richest quintile (% of relevant age group, DHS/MICS)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The sum of the net primary attendance rate and the proportion out of school do not add to 100 percent because the net primary attendance rate does not count students who are in secondary." }, { "id": "Longdefinition", "value": "Proportion of out-of-school. Primary. Quintile 5 (highest) is the number of quintile 5 children in the official primary school age range who are not attending primary or secondary education, expressed as a percentage of quintile 5 children of the official primary school age range. By definition, children in the official primary-age range, who are attending pre-primary education, are considered out-of-school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.PRM.UNER.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Children out of school (% of primary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The rate of out-of-school children allows to compare across countries with different population sizes. It shows the share of official primary-school-age children who never attended school or dropped out to the population of official primary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of children in primary school age" } ], "source_id": "57" }, { "id": "SE.SCH.LIFE.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "School life expectancy indicates the average number of years of schooling that the education system can offer. A high value indicates a probability for children to spend more years in education. Note that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition." }, { "id": "IndicatorName", "value": "Expected years of schooling, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The expected number of years of schooling may not be comparable across countries due to different lengths of the school year and policies on repetition and promotion. It is also affected by the magnitude of children who never go to school." }, { "id": "Longdefinition", "value": "Expected years of schooling is the number of years a child of school entrance age is expected to spend at school, or university, including years spent on repetition. It is the sum of the age-specific enrolment ratios for primary, secondary, post-secondary non-tertiary and tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds." }, { "id": "Statisticalconceptandmethodology", "value": "The school life expectancy is calculated as the sum of the age specific enrollment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.SCH.LIFE.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "School life expectancy indicates the average number of years of schooling that the education system can offer. A high value indicates a probability for children to spend more years in education. Note that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition." }, { "id": "IndicatorName", "value": "Expected years of schooling, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The expected number of years of schooling may not be comparable across countries due to different lengths of the school year and policies on repetition and promotion. It is also affected by the magnitude of children who never go to school." }, { "id": "Longdefinition", "value": "Expected years of schooling is the number of years a child of school entrance age is expected to spend at school, or university, including years spent on repetition. It is the sum of the age-specific enrolment ratios for primary, secondary, post-secondary non-tertiary and tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds." }, { "id": "Statisticalconceptandmethodology", "value": "The school life expectancy is calculated as the sum of the age specific enrollment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.SEC.AGES", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Education is recognized as an essential human right that must be afforded to all individuals. Secondary education imparts critical skills that enable individuals to engage fully in societal activities. Sustainable Development Goal (SDG) target 4.1 highlights the significance of secondary education by promoting inclusive and equitable quality education at this level for all youth." }, { "id": "IndicatorName", "value": "Lower secondary school starting age (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The theoretical entrance age to a given programme or level is typically, but not always, the most common entrance age." }, { "id": "Longdefinition", "value": "Lower secondary school starting age is the age at which students would enter lower secondary education, assuming they had started at the official entrance age for the lowest level of education, had studied full-time throughout and had progressed through the system without repeating or skipping a grade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator provides insights into the demand for educational services across various educational levels. Additionally, it serves as a crucial data point necessary for the generation of numerous educational indicators." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "57" }, { "id": "SE.SEC.CMPT.LO.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Lower secondary education serves as a critical platform for lifelong learning and human development, providing a foundation for further educational pursuits. In certain systems, it includes vocational education programs that equip individuals with skills pertinent to the workforce. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator is particularly important for policymakers who are committed to improving children's access to and participation in education. It measures the ability of the education system to enroll and retain students from the designated starting age through to the completion of all levels of lower secondary education." }, { "id": "IndicatorName", "value": "Lower secondary completion rate, female (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "57" }, { "id": "SE.SEC.CMPT.LO.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Lower secondary education serves as a critical platform for lifelong learning and human development, providing a foundation for further educational pursuits. In certain systems, it includes vocational education programs that equip individuals with skills pertinent to the workforce. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator is particularly important for policymakers who are committed to improving children's access to and participation in education. It measures the ability of the education system to enroll and retain students from the designated starting age through to the completion of all levels of lower secondary education." }, { "id": "IndicatorName", "value": "Lower secondary completion rate, male (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "57" }, { "id": "SE.SEC.CMPT.LO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Lower secondary education serves as a critical platform for lifelong learning and human development, providing a foundation for further educational pursuits. In certain systems, it includes vocational education programs that equip individuals with skills pertinent to the workforce. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator is particularly important for policymakers who are committed to improving children's access to and participation in education. It measures the ability of the education system to enroll and retain students from the designated starting age through to the completion of all levels of lower secondary education." }, { "id": "IndicatorName", "value": "Lower secondary completion rate, total (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education." }, { "id": "Longdefinition", "value": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of relevant age group" } ], "source_id": "57" }, { "id": "SE.SEC.CUAT.LO.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed lower secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "57" }, { "id": "SE.SEC.CUAT.LO.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed lower secondary, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed lower secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "57" }, { "id": "SE.SEC.CUAT.LO.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed lower secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed lower secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "57" }, { "id": "SE.SEC.CUAT.PO.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed post-secondary, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed post-secondary non-tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "57" }, { "id": "SE.SEC.CUAT.PO.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed post-secondary, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed post-secondary non-tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "57" }, { "id": "SE.SEC.CUAT.PO.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed post-secondary non-tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "57" }, { "id": "SE.SEC.CUAT.UP.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed upper secondary, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed upper secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed upper secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "57" }, { "id": "SE.SEC.CUAT.UP.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed upper secondary, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed upper secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed upper secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "57" }, { "id": "SE.SEC.CUAT.UP.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed upper secondary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed upper secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "57" }, { "id": "SE.SEC.DURS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Secondary education equips individuals with vital skills that are essential for active participation in society. Sustainable Development Goal (SDG) target 4.1 promotes completion of secondary education for all young people by endorsing free, equitable, and high-quality education at this level." }, { "id": "IndicatorName", "value": "Secondary education, duration (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The presence of national legislation does not guarantee that countries will implement it effectively, nor does it ensure that parents will take advantage of the provisions available for their children." }, { "id": "Longdefinition", "value": "Secondary duration refers to the number of grades (years) in secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the number of years that a country's laws or regulations specify for secondary education. It aids in identifying the population of school-aged children at different educational levels. Additionally, this metric serves as crucial input data necessary for generating various indicators and evaluating a country's capacity to meet educational demand." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "57" }, { "id": "SE.SEC.ENRL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Secondary education provides children with essential skills that enable their full participation in society. Sustainable Development Goal target 4.1 calls for all children to complete free, equitable, and high-quality primary and secondary education, with the goal of attaining significant and effective learning outcomes by 2030." }, { "id": "IndicatorName", "value": "Secondary education, pupils" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Secondary education pupils is the total number of pupils enrolled at secondary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Enrollment includes Individuals officially registered in a given educational programme, or stage or module thereof, regardless of age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The International Standard Classification of Education (ISCED) differentiates between lower secondary education and upper secondary education within its educational programs. ISCED level 2, or lower secondary education, are designed to build upon the foundational literacy and numeracy skills acquired at ISCED level 1. The objective at this stage is to establish a base for lifelong learning and human development, which can be further enhanced by subsequent educational opportunities. Certain education systems may introduce vocational education programs at this level to equip individuals with skills that are directly applicable to the workforce.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAt ISCED level 3, or upper secondary education, programs are structured to finalize the secondary education phase, preparing students for higher education or to enter the job market with relevant skills, or in some cases, to achieve both objectives." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SE.SEC.ENRL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of girls allows an assessment on gender composition in school enrollment. A value greater than 50% indicates participation of more girls at a specific level or programme of education." }, { "id": "IndicatorName", "value": "Secondary education, pupils (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The percentage of female enrollment is limited in assessing gender parity, because it's affected by the gender composition of population. Ratio of female to male in enrollment rate provides a population adjusted measure of gender parity." }, { "id": "Longdefinition", "value": "Female pupils as a percentage of total pupils at secondary level includes enrollments in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentage of female enrollment is calculated by dividing the total number of female students at a given level of education by the total enrollment at the same level, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.SEC.ENRL.GC", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Secondary education, general pupils" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Secondary general pupils are the number of secondary students enrolled in general education programs, including teacher training." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Enrollment includes Individuals officially registered in a given educational programme, or stage or module thereof, regardless of age.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.SEC.ENRL.GC.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of girls allows an assessment on gender composition in school enrollment. A value greater than 50% indicates participation of more girls at a specific level or programme of education." }, { "id": "IndicatorName", "value": "Secondary education, general pupils (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The percentage of female enrollment is limited in assessing gender parity, because it's affected by the gender composition of population. Ratio of female to male in enrollment rate provides a population adjusted measure of gender parity." }, { "id": "Longdefinition", "value": "Secondary general pupils are the number of secondary students enrolled in general education programs, including teacher training." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentage of female enrollment is calculated by dividing the total number of female students at a given level of education by the total enrollment at the same level, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.SEC.ENRL.LO.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, lower secondary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Lower secondary school pupil-teacher ratio is the average number of pupils per teacher in lower secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1981-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.SEC.ENRL.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, secondary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Secondary school pupil-teacher ratio is the average number of pupils per teacher in secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.SEC.ENRL.UP.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, upper secondary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Upper secondary school pupil-teacher ratio is the average number of pupils per teacher in upper secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1981-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.SEC.ENRL.VO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Secondary education, vocational pupils" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Secondary vocational pupils are the number of secondary students enrolled in technical and vocational education programs, including teacher training." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Enrollment includes Individuals officially registered in a given educational programme, or stage or module thereof, regardless of age.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.SEC.ENRL.VO.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of girls allows an assessment on gender composition in school enrollment. A value greater than 50% indicates participation of more girls at a specific level or programme of education." }, { "id": "IndicatorName", "value": "Secondary education, vocational pupils (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The percentage of female enrollment is limited in assessing gender parity, because it's affected by the gender composition of population. Ratio of female to male in enrollment rate provides a population adjusted measure of gender parity." }, { "id": "Longdefinition", "value": "Secondary vocational pupils are the number of secondary students enrolled in technical and vocational education programs, including teacher training." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentage of female enrollment is calculated by dividing the total number of female students at a given level of education by the total enrollment at the same level, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.SEC.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Secondary education acts as a critical intermediary that not only builds upon the foundational knowledge acquired in primary education but also equips students for various pathways, including immediate entry into the workforce, further education in postsecondary non-tertiary institutions, or advancement to higher education. This indicator assesses the aggregate participation rate in secondary education, reflecting the education system's capacity to enroll students within a designated age group." }, { "id": "IndicatorName", "value": "School enrollment, secondary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of population in the 5-year age group immediately following primary education" } ], "source_id": "57" }, { "id": "SE.SEC.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Secondary education acts as a critical intermediary that not only builds upon the foundational knowledge acquired in primary education but also equips students for various pathways, including immediate entry into the workforce, further education in postsecondary non-tertiary institutions, or advancement to higher education. This indicator assesses the aggregate participation rate in secondary education, reflecting the education system's capacity to enroll students within a designated age group." }, { "id": "IndicatorName", "value": "School enrollment, secondary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of female population in the 5-year age group immediately following primary education" } ], "source_id": "57" }, { "id": "SE.SEC.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Secondary education acts as a critical intermediary that not only builds upon the foundational knowledge acquired in primary education but also equips students for various pathways, including immediate entry into the workforce, further education in postsecondary non-tertiary institutions, or advancement to higher education. This indicator assesses the aggregate participation rate in secondary education, reflecting the education system's capacity to enroll students within a designated age group." }, { "id": "IndicatorName", "value": "School enrollment, secondary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of male population in the 5-year age group immediately following primary education" } ], "source_id": "57" }, { "id": "SE.SEC.NENR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, secondary (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for secondary school is calculated by dividing the number of students of official school age enrolled in secondary education by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.SEC.NENR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, secondary, female (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for secondary school is calculated by dividing the number of students of official school age enrolled in secondary education by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.SEC.NENR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments." }, { "id": "IndicatorName", "value": "School enrollment, secondary, male (% net)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Net enrollment rate for secondary school is calculated by dividing the number of students of official school age enrolled in secondary education by the population of the age group which officially corresponds to secondary education, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.SEC.PRIV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The indicator reflects the proportion of students attending private educational institutions. Globally, private schools educate approximately 350 million children, and there has been a notable rise in the prevalence of private institutions (https://world-education-blog.org/2021/12/10/new-2021-2-gem-report-out-today-who-chooses-who-loses/). UNESCO emphasizes the importance of comprehensively understanding the contexts and frameworks within which both public and private schools function in each nation. This understanding is vital to guarantee that children's right to education is upheld and that their specific educational requirements are addressed." }, { "id": "IndicatorName", "value": "School enrollment, secondary, private (% of total secondary)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Religious or private schools, which are not registered with the government or don't follow the common national curriculum, may not be captured." }, { "id": "Longdefinition", "value": "Private enrollment refers to pupils or students enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body such as a nongovernmental organization, religious body, special interest group, foundation or business enterprise." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of students in private secondary school is calculated by dividing the number of students enrolled in private educational institutions at secondary level by total enrollment (public and private) at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of enrollment in private institutions indicates the scale and capacity of private education within a country. A high percentage suggests strong involvement of the non-governmental sector (including religious bodies, other organizations, associations, communities, private enterprises or persons) in providing organized educational programmes. However, in countries where private institutions are substantially subsidized or aided by the government, the distinction between private and public educational institutions may be less clear-cut especially when certain students are directly financed through government scholarships." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of total enrollment in secondary school (both public and private)" } ], "source_id": "57" }, { "id": "SE.SEC.PROG.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The effective transition rate from primary to secondary education conveys the degree of access or transition between the two levels. As completing primary education is a prerequisite for participating in lower secondary education, growing numbers of primary completers will inevitably create pressure for more available places at the secondary level. A low effective transition rate can signal such problems as an inadequate examination and promotion system or insufficient secondary education capacity." }, { "id": "IndicatorName", "value": "Progression to secondary school, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data on the transition rate is affected when new entrants and repeaters are not correctly distinguished. Students who interrupt their studies after completing primary education could also affect data quality." }, { "id": "Longdefinition", "value": "Progression to secondary school refers to the number of new entrants to the first grade of secondary school in a given year as a percentage of the number of students enrolled in the final grade of primary school in the previous year (minus the number of repeaters from the last grade of primary education in the given year)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Effective transition rate is calculated by dividing the number of new entrants in the first grade of secondary education in a given year (t) by the number of students who enrolled in the final grade of primary education in the previous school year (t-1) minus the number of repeaters from the last grade of primary education in the given year (t), and multiplying by 100. \nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.SEC.PROG.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The effective transition rate from primary to secondary education conveys the degree of access or transition between the two levels. As completing primary education is a prerequisite for participating in lower secondary education, growing numbers of primary completers will inevitably create pressure for more available places at the secondary level. A low effective transition rate can signal such problems as an inadequate examination and promotion system or insufficient secondary education capacity." }, { "id": "IndicatorName", "value": "Progression to secondary school, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data on the transition rate is affected when new entrants and repeaters are not correctly distinguished. Students who interrupt their studies after completing primary education could also affect data quality." }, { "id": "Longdefinition", "value": "Progression to secondary school refers to the number of new entrants to the first grade of secondary school in a given year as a percentage of the number of students enrolled in the final grade of primary school in the previous year (minus the number of repeaters from the last grade of primary education in the given year)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Effective transition rate is calculated by dividing the number of new entrants in the first grade of secondary education in a given year (t) by the number of students who enrolled in the final grade of primary education in the previous school year (t-1) minus the number of repeaters from the last grade of primary education in the given year (t), and multiplying by 100. \nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.SEC.PROG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The effective transition rate from primary to secondary education conveys the degree of access or transition between the two levels. As completing primary education is a prerequisite for participating in lower secondary education, growing numbers of primary completers will inevitably create pressure for more available places at the secondary level. A low effective transition rate can signal such problems as an inadequate examination and promotion system or insufficient secondary education capacity." }, { "id": "IndicatorName", "value": "Progression to secondary school (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The quality of data on the transition rate is affected when new entrants and repeaters are not correctly distinguished. Students who interrupt their studies after completing primary education could also affect data quality." }, { "id": "Longdefinition", "value": "Progression to secondary school refers to the number of new entrants to the first grade of secondary school in a given year as a percentage of the number of students enrolled in the final grade of primary school in the previous year (minus the number of repeaters from the last grade of primary education in the given year)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Effective transition rate is calculated by dividing the number of new entrants in the first grade of secondary education in a given year (t) by the number of students who enrolled in the final grade of primary education in the previous school year (t-1) minus the number of repeaters from the last grade of primary education in the given year (t), and multiplying by 100. \nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.SEC.REPT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Data on repeaters are often used to indicate an education system's internal efficiency. Repeaters not only increase the cost of education for the family and the school system, but also use limited school resources." }, { "id": "IndicatorName", "value": "Repeaters, secondary, female (% of female enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Country policies on repetition and promotion differ. In some cases the number of repeaters is controlled because of limited capacity. In other cases the number of repeaters is almost 0 because of automatic promotion – suggesting a system that is highly efficient but that may not be endowing students with enough cognitive skills." }, { "id": "Longdefinition", "value": "Repeaters in secondary school are the number of students enrolled in the same grade as in the previous year, as a percentage of all students enrolled in secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics." }, { "id": "Statisticalconceptandmethodology", "value": "Share of repeaters in secondary school is calculated by dividing the sum of repeaters in all grades of secondary school by the total number of students enrolled in secondary school, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.SEC.REPT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Data on repeaters are often used to indicate an education system's internal efficiency. Repeaters not only increase the cost of education for the family and the school system, but also use limited school resources." }, { "id": "IndicatorName", "value": "Repeaters, secondary, male (% of male enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Country policies on repetition and promotion differ. In some cases the number of repeaters is controlled because of limited capacity. In other cases the number of repeaters is almost 0 because of automatic promotion – suggesting a system that is highly efficient but that may not be endowing students with enough cognitive skills." }, { "id": "Longdefinition", "value": "Repeaters in secondary school are the number of students enrolled in the same grade as in the previous year, as a percentage of all students enrolled in secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics." }, { "id": "Statisticalconceptandmethodology", "value": "Share of repeaters in secondary school is calculated by dividing the sum of repeaters in all grades of secondary school by the total number of students enrolled in secondary school, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.SEC.REPT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Data on repeaters are often used to indicate an education system's internal efficiency. Repeaters not only increase the cost of education for the family and the school system, but also use limited school resources." }, { "id": "IndicatorName", "value": "Repeaters, secondary, total (% of total enrollment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Country policies on repetition and promotion differ. In some cases the number of repeaters is controlled because of limited capacity. In other cases the number of repeaters is almost 0 because of automatic promotion – suggesting a system that is highly efficient but that may not be endowing students with enough cognitive skills." }, { "id": "Longdefinition", "value": "Repeaters in secondary school are the number of students enrolled in the same grade as in the previous year, as a percentage of all students enrolled in secondary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics." }, { "id": "Statisticalconceptandmethodology", "value": "Share of repeaters in secondary school is calculated by dividing the sum of repeaters in all grades of secondary school by the total number of students enrolled in secondary school, and multiplying by 100. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Efficiency" } ], "source_id": "57" }, { "id": "SE.SEC.TCAQ.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The percentage of trained educators serves as a barometer for a country's commitment to improving its teaching workforce, and increasing this percentage aligns with the aims of Sustainable Development Goal target 4.c. Female educators, in particular, are vital as they provide inspiration and encouragement for young girls to continue their education. These professionals are instrumental in engaging and maintaining girls' attendance in schools, confronting entrenched gender biases in communities, raising parental aspirations for their daughters, and aiding in the reduction of the educational attainment disparity between male and female students.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in secondary education, female (% of female teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in secondary education are the percentage of secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of female teachers in secondary education" } ], "source_id": "57" }, { "id": "SE.SEC.TCAQ.LO.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The percentage of trained educators serves as a barometer for a country's commitment to improving its teaching workforce, and increasing this percentage aligns with the aims of Sustainable Development Goal target 4.c. Female educators, in particular, are vital as they provide inspiration and encouragement for young girls to continue their education. These professionals are instrumental in engaging and maintaining girls' attendance in schools, confronting entrenched gender biases in communities, raising parental aspirations for their daughters, and aiding in the reduction of the educational attainment disparity between male and female students.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in lower secondary education, female (% of female teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in lower secondary education are the percentage of lower secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of female teachers in lower secondary education" } ], "source_id": "57" }, { "id": "SE.SEC.TCAQ.LO.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in lower secondary education, male (% of male teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in lower secondary education are the percentage of lower secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of male teachers in lower secondary education" } ], "source_id": "57" }, { "id": "SE.SEC.TCAQ.LO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in lower secondary education (% of total teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in lower secondary education are the percentage of lower secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total teachers in lower secondary education" } ], "source_id": "57" }, { "id": "SE.SEC.TCAQ.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in secondary education, male (% of male teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in secondary education are the percentage of secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of male teachers in secondary education" } ], "source_id": "57" }, { "id": "SE.SEC.TCAQ.UP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The percentage of trained educators serves as a barometer for a country's commitment to improving its teaching workforce, and increasing this percentage aligns with the aims of Sustainable Development Goal target 4.c. Female educators, in particular, are vital as they provide inspiration and encouragement for young girls to continue their education. These professionals are instrumental in engaging and maintaining girls' attendance in schools, confronting entrenched gender biases in communities, raising parental aspirations for their daughters, and aiding in the reduction of the educational attainment disparity between male and female students.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in upper secondary education, female (% of female teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in upper secondary education are the percentage of upper secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of female teachers in upper secondary education" } ], "source_id": "57" }, { "id": "SE.SEC.TCAQ.UP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in upper secondary education, male (% of male teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in upper secondary education are the percentage of upper secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of male teachers in upper secondary education" } ], "source_id": "57" }, { "id": "SE.SEC.TCAQ.UP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in upper secondary education (% of total teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in upper secondary education are the percentage of upper secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total teachers in upper secondary education" } ], "source_id": "57" }, { "id": "SE.SEC.TCAQ.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions." }, { "id": "IndicatorName", "value": "Trained teachers in secondary education (% of total teachers)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "This indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary." }, { "id": "Longdefinition", "value": "Trained teachers in secondary education are the percentage of secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total teachers in secondary education" } ], "source_id": "57" }, { "id": "SE.SEC.TCHR", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Teachers are pivotal in molding the future and unleashing the potential of each student. The United Nations Educational, Scientific and Cultural Organization (UNESCO) highlights a global teacher shortage as a significant obstacle in realizing Sustainable Development Goal 4, which aims for inclusive and equitable quality education." }, { "id": "IndicatorName", "value": "Secondary education, teachers" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The precision of this indicator can be influenced by the enumeration method used, such as headcount or 'full-time equivalent' count of teachers." }, { "id": "Longdefinition", "value": "Secondary education, teachers refers to the total number of teachers at secondary level, including full-time and part-time teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Teachers refer to persons employed full-time or part-time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) or who work occasionally or in a voluntary capacity in educational institutions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The metric of personnel count primarily engaged in teaching and/or research reflects the scale, diversity, and distribution of educational staff within a nation's academic institutions. An increased count is anticipated to enrich the educational setting by means of dedicated teaching, practical research, academic pursuits, and contributions to the service of national educational policies." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SE.SEC.TCHR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although there have been advancements, girls in low-income countries continue to face significant barriers to accessing secondary education. The presence of female teachers is crucial in this context, as they act as role models, inspiring and motivating girls to pursue their education. These educators play a pivotal role in attracting and retaining girls in schools, challenging deep-seated gender stereotypes within communities, elevating parental expectations for their daughters, and contributing to the narrowing of the educational achievement gap between boys and girls." }, { "id": "IndicatorName", "value": "Secondary education, teachers, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator measures the level of gender representation in the teaching profession, rather than the effectiveness and quality of teaching." }, { "id": "Longdefinition", "value": "Secondary education, teachers, female, refers to the total number of female teachers at secondary level, including full-time and part-time teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Teachers refer to persons employed full-time or part-time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) or who work occasionally or in a voluntary capacity in educational institutions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The metric of personnel count primarily engaged in teaching and/or research reflects the scale, diversity, and distribution of educational staff within a nation's academic institutions. An increased count is anticipated to enrich the educational setting by means of dedicated teaching, practical research, academic pursuits, and contributions to the service of national educational policies." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SE.SEC.TCHR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although there have been advancements, girls in low-income countries continue to face significant barriers to accessing secondary education. The presence of female teachers is crucial in this context, as they act as role models, inspiring and motivating girls to pursue their education. These educators play a pivotal role in attracting and retaining girls in schools, challenging deep-seated gender stereotypes within communities, elevating parental expectations for their daughters, and contributing to the narrowing of the educational achievement gap between boys and girls." }, { "id": "IndicatorName", "value": "Secondary education, teachers (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator measures the level of gender representation in the teaching profession, rather than the effectiveness and quality of teaching." }, { "id": "Longdefinition", "value": "Female teachers as a percentage of total secondary education teachers includes full-time and part-time teachers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of female teachers in secondary education is calculated by dividing the total number of female teachers at secondary level of education by the total number of teachers at the same level, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of female teachers shows the level of gender representation in the teaching force. A value of greater than 50% indicates more opportunities or preference for women to participate in teaching activities." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total secondary education teachers" } ], "source_id": "57" }, { "id": "SE.SEC.UNER", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data are based on World Bank estimates." }, { "id": "IndicatorName", "value": "Children out of school, secondary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.SEC.UNER.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data are based on World Bank estimates." }, { "id": "IndicatorName", "value": "Children out of school, secondary, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.SEC.UNER.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Aggregate data are based on World Bank estimates." }, { "id": "IndicatorName", "value": "Children out of school, secondary, female (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.SEC.UNER.LO.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Adolescents out of school, female (% of female lower secondary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Adolescents out of school are the percentage of lower secondary school age adolescents who are not enrolled in school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The rate of out-of-school adolescents allows to compare across countries with different population sizes. It shows the share of official lower secondary age adolescents who never attended school or dropped out to the population of official lower secondary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of female adolescents in lower secondary school age" } ], "source_id": "57" }, { "id": "SE.SEC.UNER.LO.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Adolescents out of school, male (% of male lower secondary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Adolescents out of school are the percentage of lower secondary school age adolescents who are not enrolled in school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The rate of out-of-school adolescents allows to compare across countries with different population sizes. It shows the share of official lower secondary age adolescents who never attended school or dropped out to the population of official lower secondary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of male adolescents in lower secondary school age" } ], "source_id": "57" }, { "id": "SE.SEC.UNER.LO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Ensuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility." }, { "id": "IndicatorName", "value": "Adolescents out of school (% of lower secondary school age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries." }, { "id": "Longdefinition", "value": "Adolescents out of school are the percentage of lower secondary school age adolescents who are not enrolled in school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The rate of out-of-school adolescents allows to compare across countries with different population sizes. It shows the share of official lower secondary age adolescents who never attended school or dropped out to the population of official lower secondary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of adolescents in lower secondary school age" } ], "source_id": "57" }, { "id": "SE.SEC.UNER.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data are based on World Bank estimates." }, { "id": "IndicatorName", "value": "Children out of school, secondary, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.SEC.UNER.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Aggregate data are based on World Bank estimates." }, { "id": "IndicatorName", "value": "Children out of school, secondary, male (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.SEC.UNER.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Aggregate data are based on World Bank estimates." }, { "id": "IndicatorName", "value": "Children out of school, secondary (% of relevant age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.TER.CUAT.BA.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Bachelor's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "57" }, { "id": "SE.TER.CUAT.BA.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Bachelor's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "57" }, { "id": "SE.TER.CUAT.BA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Bachelor's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "57" }, { "id": "SE.TER.CUAT.DO.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, Doctoral or equivalent, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Doctoral or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Doctoral or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "57" }, { "id": "SE.TER.CUAT.DO.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, Doctoral or equivalent, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Doctoral or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Doctoral or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero..\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "57" }, { "id": "SE.TER.CUAT.DO.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Doctoral or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Doctoral or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "57" }, { "id": "SE.TER.CUAT.MS.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Master's or equivalent, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Master's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Master's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "57" }, { "id": "SE.TER.CUAT.MS.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Master's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Master's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "57" }, { "id": "SE.TER.CUAT.MS.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed Master's or equivalent." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Master's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "57" }, { "id": "SE.TER.CUAT.ST.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed short-cycle tertiary, population 25+, female (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed short-cycle tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed short-cycle tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of female population ages 25+" } ], "source_id": "57" }, { "id": "SE.TER.CUAT.ST.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed short-cycle tertiary, population 25+, male (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed short-cycle tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed short-cycle tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of male population ages 25+" } ], "source_id": "57" }, { "id": "SE.TER.CUAT.ST.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital." }, { "id": "IndicatorName", "value": "Educational attainment, at least completed short-cycle tertiary, population 25+, total (%) (cumulative)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts)." }, { "id": "Longdefinition", "value": "The percentage of population ages 25 and over that attained or completed short-cycle tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of population ages 25 and older who attained or completed short-cycle tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities." }, { "id": "Topic", "value": "Education: Outcomes" }, { "id": "Unitofmeasure", "value": "% of total population ages 25+" } ], "source_id": "57" }, { "id": "SE.TER.ENRL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Tertiary education, pupils" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Tertiary education pupils is the total number of pupils enrolled at tertiary level in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.TER.ENRL.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Tertiary education, pupils (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female pupils as a percentage of total pupils at tertiary level includes enrollments in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Female pupils as a percentage of total pupils at tertiary level includes enrollments in public and private schools." }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.TER.ENRL.TC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education." }, { "id": "IndicatorName", "value": "Pupil-teacher ratio, tertiary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors." }, { "id": "Longdefinition", "value": "Tertiary school pupil-teacher ratio is the average number of pupils per teacher in tertiary school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.TER.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.3 is committed to providing equitable access to affordable and high-quality technical, vocational, and tertiary education, including university, for both women and men. This particular indicator reflects the overall capacity of the educational infrastructure to support enrolment within a specified age demographic at the tertiary level." }, { "id": "IndicatorName", "value": "School enrollment, tertiary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of population in the 5-year age group immediately following upper secondary education" } ], "source_id": "57" }, { "id": "SE.TER.ENRR.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.3 is committed to providing equitable access to affordable and high-quality technical, vocational, and tertiary education, including university, for both women and men. This particular indicator reflects the overall capacity of the educational infrastructure to support enrolment within a specified age demographic at the tertiary level." }, { "id": "IndicatorName", "value": "School enrollment, tertiary, female (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of female population in the 5-year age group immediately following upper secondary education" } ], "source_id": "57" }, { "id": "SE.TER.ENRR.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sustainable Development Goal (SDG) target 4.3 is committed to providing equitable access to affordable and high-quality technical, vocational, and tertiary education, including university, for both women and men. This particular indicator reflects the overall capacity of the educational infrastructure to support enrolment within a specified age demographic at the tertiary level." }, { "id": "IndicatorName", "value": "School enrollment, tertiary, male (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Education: Participation" }, { "id": "Unitofmeasure", "value": "% of male population in the 5-year age group immediately following upper secondary education" } ], "source_id": "57" }, { "id": "SE.TER.GRAD.FE.SI.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female share of graduates from Science, Technology, Engineering and Mathematics (STEM) programmes, tertiary (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Female share of graduates in the given field of education, tertiary is the number of female graduates expressed as a percentage of the total number of graduates in the given field of education from tertiary education." }, { "id": "Statisticalconceptandmethodology", "value": "Percentage of female graduates by field of study in tertiary education is calculated by dividing the number of female graduates in a given field of education from tertiary education by the total number of graduates in the same field, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Outcomes" } ], "source_id": "57" }, { "id": "SE.TER.TCHR", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Tertiary education, academic staff" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Tertiary education, academic staff is the number of academic staff in tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.TER.TCHR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator reflects the gender distribution within the teaching profession. It serves as a tool for evaluating the necessity of creating opportunities and incentives to promote female participation in educational instruction at various levels. According to UNESCO, there is a global trend of women being disproportionately represented in the teaching workforce. Nonetheless, this representation declines at the tertiary education level, where men are more prevalent, and women are less likely to attain senior and leadership roles within higher education institutions." }, { "id": "IndicatorName", "value": "Tertiary education, academic staff (% female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator measures the level of gender representation in the teaching profession, rather than the effectiveness and quality of teaching." }, { "id": "Longdefinition", "value": "Tertiary education, academic staff (% female) is the share of female academic staff in tertiary education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of female academic staffs in tertiary education is calculated by dividing the total number of female academic staffs at tertiary level of education by the total number of academic staffs at the same level, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of female teachers shows the level of gender representation in the teaching force. A value of greater than 50% indicates more opportunities or preference for women to participate in teaching activities." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of academic staff in tertiary education" } ], "source_id": "57" }, { "id": "SE.XPD.CPRM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investing in education is a catalyst for economic expansion, productivity improvement, and the advancement of both individual and societal well-being. It also serves as a mechanism to mitigate social disparities. The value of this indicator highlights the focus of governmental policies, as evidenced by the distribution of spending by expenditure type and the nature of expenditures throughout various educational stages." }, { "id": "IndicatorName", "value": "Current education expenditure, primary (% of total expenditure in primary public institutions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator depends on comprehensive and accurate data regarding government expenditure by type and nature of spending. In some cases, data on total government expenditure on education only includes the Ministry of Education, excluding other ministries that may also allocate part of their budget to educational services." }, { "id": "Longdefinition", "value": "Current expenditure is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Current expenditure, primary is calculated by dividing all current expenditure in public institutions of primary education by total expenditure (current and capital) in public institutions of primary education, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on education spending is obtained from national governments through their responses to the annual UIS survey on formal education or the UNESCO-OECD-Eurostat (UOE) data collection initiative. The figures reported in the education expenditure questionnaire are usually derived from the annual financial reports of the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office.\nStatistical concept(s): Educational expenditure in public institutions includes all staff compensation, teaching staff/non teaching staff compensation, current expenditure other than staff compensation, expenditure on school books and teaching material." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total expenditure in primary public institutions" } ], "source_id": "57" }, { "id": "SE.XPD.CSEC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investing in education is a catalyst for economic expansion, productivity improvement, and the advancement of both individual and societal well-being. It also serves as a mechanism to mitigate social disparities. The value of this indicator highlights the focus of governmental policies, as evidenced by the distribution of spending by expenditure type and the nature of expenditures throughout various educational stages." }, { "id": "IndicatorName", "value": "Current education expenditure, secondary (% of total expenditure in secondary public institutions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator depends on comprehensive and accurate data regarding government expenditure by type and nature of spending. In some cases, data on total government expenditure on education only includes the Ministry of Education, excluding other ministries that may also allocate part of their budget to educational services." }, { "id": "Longdefinition", "value": "Current expenditure is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Current expenditure, secondary is calculated by dividing all current expenditure in public institutions of secondary education by total expenditure (current and capital) in public institutions of secondary education, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on education spending is obtained from national governments through their responses to the annual UIS survey on formal education or the UNESCO-OECD-Eurostat (UOE) data collection initiative. The figures reported in the education expenditure questionnaire are usually derived from the annual financial reports of the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office.\nStatistical concept(s): Educational expenditure in public institutions includes all staff compensation, teaching staff/non teaching staff compensation, current expenditure other than staff compensation, expenditure on school books and teaching material." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total expenditure in secondary public institutions" } ], "source_id": "57" }, { "id": "SE.XPD.CTER.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investing in education is a catalyst for economic expansion, productivity improvement, and the advancement of both individual and societal well-being. It also serves as a mechanism to mitigate social disparities. The value of this indicator highlights the focus of governmental policies, as evidenced by the distribution of spending by expenditure type and the nature of expenditures throughout various educational stages." }, { "id": "IndicatorName", "value": "Current education expenditure, tertiary (% of total expenditure in tertiary public institutions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator depends on comprehensive and accurate data regarding government expenditure by type and nature of spending. In some cases, data on total government expenditure on education only includes the Ministry of Education, excluding other ministries that may also allocate part of their budget to educational services." }, { "id": "Longdefinition", "value": "Current expenditure is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Current expenditure, tertiary is calculated by dividing all current expenditure in public institutions of tertiary education by total expenditure (current and capital) in public institutions of tertiary education, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on education spending is obtained from national governments through their responses to the annual UIS survey on formal education or the UNESCO-OECD-Eurostat (UOE) data collection initiative. The figures reported in the education expenditure questionnaire are usually derived from the annual financial reports of the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office.\nStatistical concept(s): Educational expenditure in public institutions includes all staff compensation, teaching staff/non teaching staff compensation, current expenditure other than staff compensation, expenditure on school books and teaching material." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total expenditure in tertiary public institutions" } ], "source_id": "57" }, { "id": "SE.XPD.CTOT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investing in education is a catalyst for economic expansion, productivity improvement, and the advancement of both individual and societal well-being. It also serves as a mechanism to mitigate social disparities. The value of this indicator highlights the focus of governmental policies, as evidenced by the distribution of spending by expenditure type and the nature of expenditures throughout various educational stages." }, { "id": "IndicatorName", "value": "Current education expenditure, total (% of total expenditure in public institutions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator depends on comprehensive and accurate data regarding government expenditure by type and nature of spending. In some cases, data on total government expenditure on education only includes the Ministry of Education, excluding other ministries that may also allocate part of their budget to educational services." }, { "id": "Longdefinition", "value": "Current expenditure is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2024" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Current expenditure, total is calculated by dividing all current expenditure in public institutions of all levels of education by total expenditure (current and capital) in public institutions of all levels of education, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on education spending is obtained from national governments through their responses to the annual UIS survey on formal education or the UNESCO-OECD-Eurostat (UOE) data collection initiative. The figures reported in the education expenditure questionnaire are usually derived from the annual financial reports of the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office.\nStatistical concept(s): Educational expenditure in public institutions includes all staff compensation, teaching staff/non teaching staff compensation, current expenditure other than staff compensation, expenditure on school books and teaching material." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of total expenditure in public institutions" } ], "source_id": "57" }, { "id": "SE.XPD.MPRM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "All education staff compensation, primary (% of total expenditure in primary public institutions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "All staff (teacher and non-teachers) compensation is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programs, and other allowances and benefits." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds." }, { "id": "Statisticalconceptandmethodology", "value": "All staff compensation, primary is calculated by dividing all staff compensation in public institutions of primary education by total expenditure (current and capital) in public institutions of primary education, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.XPD.MSEC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "All education staff compensation, secondary (% of total expenditure in secondary public institutions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "All staff (teacher and non-teachers) compensation is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programs, and other allowances and benefits." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds." }, { "id": "Statisticalconceptandmethodology", "value": "All staff compensation, secondary is calculated by dividing all staff compensation in public institutions of secondary education by total expenditure (current and capital) in public institutions of secondary education, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.XPD.MTER.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "All education staff compensation, tertiary (% of total expenditure in tertiary public institutions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "All staff (teacher and non-teachers) compensation is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programs, and other allowances and benefits." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds." }, { "id": "Statisticalconceptandmethodology", "value": "All staff compensation, tertiary is calculated by dividing all staff compensation in public institutions of tertiary education by total expenditure (current and capital) in public institutions of tertiary education, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.XPD.MTOT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "All education staff compensation, total (% of total expenditure in public institutions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "All staff (teacher and non-teachers) compensation is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programs, and other allowances and benefits." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds." }, { "id": "Statisticalconceptandmethodology", "value": "All staff compensation, total is calculated by dividing all staff compensation in public institutions of all levels of education by total expenditure (current and capital) in public institutions of all levels of education, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example)." }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.XPD.PRIM.PC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Government expenditure per student, primary (% of GDP per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: General government expenditure per student in primary education is calculated by dividing total government expenditure on primary education by the number of students at primary level, expressed as a percentage of GDP per capita. Aggregate data are World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Data on GDP per capita come from the World Bank. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.XPD.PRIM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of government expenditure for a specific education level allows an assessment of the priority a government assigns to a level of education relative to other levels. Enrolment and the relative costs per student between different levels of education should be also taken into account." }, { "id": "IndicatorName", "value": "Expenditure on primary education (% of government expenditure on education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data disaggregated by level of education are estimates in some instances. It is often difficult to separate lower from upper secondary education expenditure, or pre-primary from primary." }, { "id": "Longdefinition", "value": "Expenditure on primary education is expressed as a percentage of total general government expenditure on education. General government usually refers to local, regional and central governments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of expenditure on primary education to total government expenditure on education is calculated by dividing government expenditure on primary education by total government expenditure on education (all levels combined), and multiplying by 100. Aggregate data are based on World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.XPD.PTCH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Spending on teaching materials, primary (% of primary expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Spending on teaching materials, primary is the percentage of public education expenditure for primary education that is spent on teaching materials. Current educational expenditure includes direct current expenditure on public educational institutions from all sources (public, private and international). Public expenditure includes government spending on educational institutions, education administration as well as subsidies for private entities (students/households and other private entities)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.XPD.SECO.PC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Government expenditure per student, secondary (% of GDP per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: General government expenditure per student in secondary education is calculated by dividing total government expenditure on secondary education by the number of students at secondary level, expressed as a percentage of GDP per capita. Aggregate data are World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Data on GDP per capita come from the World Bank. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.XPD.SECO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of government expenditure for a specific education level allows an assessment of the priority a government assigns to a level of education relative to other levels. Enrolment and the relative costs per student between different levels of education should be also taken into account." }, { "id": "IndicatorName", "value": "Expenditure on secondary education (% of government expenditure on education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data disaggregated by level of education are estimates in some instances. It is often difficult to separate lower from upper secondary education expenditure, or pre-primary from primary." }, { "id": "Longdefinition", "value": "Expenditure on secondary education is expressed as a percentage of total general government expenditure on education. General government usually refers to local, regional and central governments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of expenditure on secondary education to total government expenditure on education is calculated by dividing government expenditure on secondary education by total government expenditure on education (all levels combined), and multiplying by 100. Aggregate data are based on World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.XPD.STCH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Spending on teaching materials, secondary (% of secondary expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Spending on teaching materials, secondary is the percentage of public education expenditure for secondary education that is spent on teaching materials. Current educational expenditure includes direct current expenditure on public educational institutions from all sources (public, private and international). Public expenditure includes government spending on educational institutions, education administration as well as subsidies for private entities (students/households and other private entities)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.XPD.TCHR.XC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Teachers' salaries (% of current education expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Teachers' salaries is the percentage of current education expenditure for teachers' salaries. Current educational expenditure includes direct current expenditure on public educational institutions from all sources (public, private and international). public expenditure includes government spending on educational institutions, education administration as well as subsidies for private entities (students/households and other private entities)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.XPD.TERT.PC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Government expenditure per student, tertiary (% of GDP per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2018" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: General government expenditure per student in tertiary education is calculated by dividing total government expenditure on tertiary education by the number of students at tertiary level, expressed as a percentage of GDP per capita. Aggregate data are World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Data on GDP per capita come from the World Bank. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.XPD.TERT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The share of government expenditure for a specific education level allows an assessment of the priority a government assigns to a level of education relative to other levels. Enrolment and the relative costs per student between different levels of education should be also taken into account." }, { "id": "IndicatorName", "value": "Expenditure on tertiary education (% of government expenditure on education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data disaggregated by level of education are estimates in some instances. It is often difficult to separate lower from upper secondary education expenditure, or pre-primary from primary." }, { "id": "Longdefinition", "value": "Expenditure on tertiary education is expressed as a percentage of total general government expenditure on education. General government usually refers to local, regional and central governments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2019" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of expenditure on tertiary education to total government expenditure on education is calculated by dividing government expenditure on tertiary education by total government expenditure on education (all levels combined), and multiplying by 100. Aggregate data are based on World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.XPD.TOTL.GB.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investment in education serves as a driving force for economic growth, enhancement of productivity, and the promotion of individual and collective prosperity. This indicator is instrumental in evaluating the extent to which a government prioritizes education, whether over time or in relation to other nations. Furthermore, it reflects the government's dedication to the investment in human capital development." }, { "id": "IndicatorName", "value": "Government expenditure on education, total (% of government expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on government expenditure on education may refer to spending by the ministry of education only (excluding spending on educational activities by other ministries). In addition, definitions and methods of data on total general government expenditure may differ across countries." }, { "id": "Longdefinition", "value": "General government expenditure on education (current, capital, and transfers) is expressed as a percentage of total general government expenditure on all sectors (including health, education, social services, etc.). It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Expenditure on education, total (% of government expenditure) is calculated by dividing total government expenditure on education by the total government expenditure on all sectors and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nInformation regarding educational expenditures is obtained from national governments through their responses to the annual survey on formal education conducted by the UNESCO Institute for Statistics (UIS). The data provided for this questionnaire often originates from the annual financial reports of the Ministry of Finance or the Ministry of Education, or from the national accounts compiled by the National Statistical Office. Additionally, comprehensive data on general government expenditure across all sectors are sourced from the International Monetary Fund's (IMF) World Economic Outlook database, which undergoes an annual update.\nStatistical concept(s): A greater allocation of government funds towards education reflects a significant emphasis on educational priorities in comparison to other public sector investments. It is important to consider, however, that the capacity of governments to spend varies, resulting in differing budget sizes. Additionally, demographic factors such as the age distribution within a country can influence the proportion of spending on education versus other areas like health or social security." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of government expenditure" } ], "source_id": "57" }, { "id": "SE.XPD.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Investment in education acts as a driving force for economic growth, enhances productivity, and promotes the betterment of individual and collective welfare. This indicator evaluates the extent to which a government prioritizes education in relation to its overall economic prosperity." }, { "id": "IndicatorName", "value": "Government expenditure on education, total (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data may refer to spending by the ministry of education only (excluding spending on educational activities by other ministries)." }, { "id": "Longdefinition", "value": "General government expenditure on education (current, capital, and transfers) is expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Government expenditure on education, total (% of GDP) is calculated by dividing total government expenditure for all levels of education by the GDP, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nInformation pertaining to educational expenditures is sourced from national governments, which provide the data in response to the annual survey conducted by the UNESCO Institute for Statistics (UIS) or through the joint UNESCO-OECD-Eurostat (UOE) data collection initiative. The responses to the questionnaire regarding educational spending are typically derived from the annual financial statements issued by either the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office. Additionally, data concerning GDP and overall government expenditure are accessible via the IMF’s World Economic Outlook database, which is updated annually.\nStatistical concept(s): Generally, elevated levels of the indicator suggest that a government places a high priority on educational policy. Values ranging from 4% to 6% are indicative of a country achieving the benchmark set forth by the Education 2030 Framework for Action (https://unesdoc.unesco.org/ark:/48223/pf0000245656).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEducational expenditure encompasses spending on fundamental educational goods and services, including teaching personnel, school infrastructure, textbooks, and instructional materials, as well as on ancillary educational goods and services such as support services, general administration, and other related activities.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFunding for education may originate from public sources, encompassing all government ministries and agencies that finance or support educational programs within the country, as well as from international and private sources, such as household contributions." }, { "id": "Topic", "value": "Education: Inputs" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "57" }, { "id": "SE.XPD.TOTL.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Public spending on education, total (% of GNI, UNESCO)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public expenditure on education consists of current and capital government spending on educational institutions (both public and private), education administration as well as subsidies for private entities (students/households and other privates entities)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education: Inputs" } ], "source_id": "57" }, { "id": "SE.YRS.SCHL.1519.Q1", "metatype": [ { "id": "IndicatorName", "value": "Average years of schooling, poorest quintile (ages 15-19, DHS/MICS)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 1 (lowest) is the number of years of formal schooling received, on average, by the quintile 1 population of the given age group." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SE.YRS.SCHL.1519.Q5", "metatype": [ { "id": "IndicatorName", "value": "Average years of schooling, richest quintile (ages 15-19, DHS/MICS)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average years of schooling by age group. Age 15-19. Quintile 5 (highest) is the number of years of formal schooling received, on average, by the quintile 5 population of the given age group." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Education: Participation" } ], "source_id": "57" }, { "id": "SG.DMK.ALLD.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Women participating in the three decisions (own health care, major household purchases, and visiting family) (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Women participating in the three decisions (own health care, major household purchases, and visiting family) is the percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in all of the three decisions (own health care, large purchases and visits to family, relatives, and friends)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_DMAK_W_3DC; \tIndicator name from the original source: Final say in all of the decisions [Women], publisher: The DHS program (ICF), type: API, date accessed: 2023-08-18" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Women participating in the three decisions (own health care, major household purchases, and visiting family) is the number of currently married women aged 15-49 who say they alone or jointly have the final say in the three decisions, expressed as percentage of currently married women age 15-49 who have been interviewed and It’s derived by dividing the number of currently married women aged 15-49 who responded they alone or jointly have the final say in the three decisions by total number of currently married women age 15-49 who have been interviewed.\nStatistical concept(s): This indicator assesses the level of women's participation in household decision-making. It emphasizes the importance of decisions regarding their own health care, which are deemed essential to women's self-interest. The indicator also evaluates women's involvement in making substantial economic decisions, such as those related to significant purchases, to gauge their economic decision-making power within the household. Additionally, it measures women's autonomy in deciding on visits to family or friends, which can indicate their freedom of movement and the ability to engage with their birth family." }, { "id": "Topic", "value": "Gender: Public life & decision making" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "57" }, { "id": "SG.DMK.SRCR.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development." }, { "id": "IndicatorName", "value": "Women making their own informed decisions regarding sexual relations, contraceptive use and reproductive health care (% of women age 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Current estimates of the indicator are based on currently married or in union women of reproductive age (15-49 years old) who are using any type of contraception. In the current Demographic and Health Surveys (DHS), the question on decision-making on use of contraception is only asked to women who are currently using contraception. Because the questions on decision- making on sexual relations and health care are restricted to women (15-49) currently married or in union, the denominator for Indicator 5.6.1 is women 15-49, who are currently married or in union and currently using contraception. However, agreement has been reached with Macro/ICF for upcoming DHS surveys to ask the question on decision on use of contraception to all married/ in union women aged 15-49 years, whether they are currently using any contraception or not. The DHS model questionnaire for Phase 7 already includes the question on decision-making for women who are not currently using any contraception." }, { "id": "Longdefinition", "value": "Proportion of women ages 15-49 years (married or in union) who make their own decision on all three selected areas i.e. can say no to sexual intercourse with their husband or partner if they do not want; decide on use of contraception; and decide on their own health care. Only women who provide a “yes” answer to all three components are considered as women who “make her own decisions regarding sexual and reproductive”." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.6.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2006-2022" }, { "id": "Source", "value": "Demographic and Health Surveys compiled by United Nations Population Fund, United Nations (UN), uri: https://unstats.un.org/sdgs/UNSDGAPIV5/swagger/index.html, note: Indicator code from the original source: SH_FPL_INFM; \tIndicator name from the original source: Proportion of women aged 15–49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care, publisher: UN Statistics Division, type: API" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Numerator of the indicator is number of married or in union women ages 15-49 who have been interviewed and satisfy all three empowerment criteria: 1)who can say 'no' to sex; and 2)for whom the decision on contraception is not mainly made by the husband/partner; and 3) for whom decision on health care for themselves ins not usually made by the husband/partner or someone else. Denominator of the indicator is the total number of women ages 15-49 who are married or in union and who have been interviewed. \n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data for this indicator are primarily sourced from nationally representative Demographic and Health Surveys (DHS).\nStatistical concept(s): A woman is deemed to possess autonomy in reproductive health decision-making and to be empowered to assert her reproductive rights when she has the ability to: (1) make decisions regarding her own health care, independently or in conjunction with her husband or partner, (2) determine the use or non-use of contraception, on her own or together with her husband or partner, and (3) refuse sexual relations with her husband or partner if she chooses." }, { "id": "Topic", "value": "Gender: Public life & decision making" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "57" }, { "id": "SG.GEN.LSOM.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Changes in the occupational distribution of an economy can be used to identify and analyse stages of development. In the textbook case of economic development, when labour flows from agriculture to the industrial and services sectors, these flows will be visible in the occupational distribution as well. The share of skilled agricultural and fishery workers will decrease, while rising educational attainment levels are likely to be reflected in a decreasing share of elementary occupations and rising shares of high-skilled occupational groups such as professionals and technicians.\n\nIn developed economies, which already have relatively well-educated labour forces, increases in the shares of high-skilled occupational groups are associated with the advance of the knowledge economy and additional changes in the structure of economies. Furthermore, shifts within occupational groups may be equally important. For example, the growing importance of information and communication technology (ICT) has resulted in a proliferation of ICT-related jobs. Similarly, aging of populations in many advanced economies has led to an expansion of the number of health professionals and health associate professionals.\n\nThe breakdown of the indicator by sex allows for an analysis of gender segregation of employment. Division of labour markets on the basis of sex is one of the most pervasive characteristics of labour markets around the world, which is reflected in differentials in occupational distributions between men and women. Such differentials can be analysed at detailed levels of the occupational classification,1but even at the most aggregated level, large differences by sex are evident.\n\nDespite much progress in recent decades, gender inequalities remain pervasive in many dimensions of life - worldwide. But while disparities exist throughout the world, they are most prevalent in developing countries. Gender inequalities in the allocation of such resources as education, health care, nutrition, and political voice matter because of the strong association with well-being, productivity, and economic growth. These patterns of inequality begin at an early age, with boys routinely receiving a larger share of education and health spending than do girls, for example.\n\nThe female share of high-skilled occupations such as legislators, senior officials, and managers indicates gender segregation of employment. Women are vastly underrepresented in decision-making positions , although there is some evidence of recent improvement." }, { "id": "IndicatorName", "value": "Female legislators, senior officials and managers (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Most of the information derives from labour force surveys. In a limited number of countries the information is derived from other household surveys, population censuses, and official estimates. There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries." }, { "id": "Longdefinition", "value": "Female legislators, senior officials and managers (% of total) refers to the share of legislators, senior officials and managers who are female." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ILO Key Indicators of the Labour Market (KILM)." }, { "id": "Statisticalconceptandmethodology", "value": "The indicator for employment by occupation classifies jobs into major groups, with the groups defined by the classification that is used. Most internationally comparable data currently available are classified according to the International Standard Classification of Occupations, 1988 (ISCO-88), with the following major groups (1) Legislators, senior officials and managers; (2) Professionals; (3) Technicians and associate professionals; (4) Clerks; (5) Service workers and shop and market sales workers; (6) Skilled agricultural and fishery workers; (7) Craft and related trades workers; (8) Plant and machine operators and assemblers; (9) Elementary occupations; and (10) Armed forces.\n\nA job is defined as a set of tasks and duties performed, or meant to be performed, by one person, including for an employer or in self-employment. An occupation is defined as a set of jobs whose main tasks and duties are characterised by a high degree of similarity.2Occupational classifications categorize all jobs into groups, which are hierarchically structured in a number of levels.\n\nThe ten major groups in ISCO-88 and the most recent revision, ISCO-08, are associated with four broad skill levels. These levels are defined in relation to the levels of education specified in the International Standard Classification of Education (ISCED). The use of ISCED categories to assist in defining the four skill levels does not imply that the skills necessary to perform the tasks and duties of a given job can be acquired only through formal education. The skills may be, and often are, acquired through (informal) training and experience. In addition, it should be emphasized that the focus in both ISCO-88 and ISCO-08 is on the skills required to carry out the tasks and duties of an occupation, and not on whether a worker employed in a particular occupation is more or less skilled than another worker in the same occupation.\n\nEmployment is defined as persons above a specified age who performed any work at all, in the reference period, for pay or profit (or pay in kind), or were temporarily absent from a job for such reasons as illness, maternity or parental leave, holiday, training or industrial dispute. Unpaid family workers who work for at least one hour should be included in the count of employment, although many countries use a higher hour limit in their definition.\nData on employment are drawn from a variety of sources including labor force surveys, household surveys, official estimates, and censuses. In a very few cases and only where other types of sources are not available, information is derived from insurance records and establishment surveys Employment data include both full-time and part-time workers." }, { "id": "Topic", "value": "Gender: Public life & decision making" } ], "source_id": "57" }, { "id": "SG.GEN.PARL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite much progress in recent decades, gender inequalities remain pervasive in many dimensions of life - worldwide. But while disparities exist throughout the world, they are most prevalent in developing countries. Gender inequalities in the allocation of such resources as education, health care, nutrition, and political voice matter because of the strong association with well-being, productivity, and economic growth. These patterns of inequality begin at an early age, with boys routinely receiving a larger share of education and health spending than do girls, for example.\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen are vastly underrepresented in decision-making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.\n\n\n\n\n\n\n\n\n\n\n\n\n\nA strong and vibrant democracy is possible only when parliament is fully inclusive of the population it represents. Parliaments cannot consider themselves inclusive, however, until they can boast the full participation of women. This is not just about women's right to equality and their contribution to the conduct of public affairs, but also about using women's resources and potential to determine political and development priorities that benefit societies and the global community." }, { "id": "IndicatorName", "value": "Proportion of seats held by women in national parliaments (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The number of countries covered varies with suspensions or dissolutions of parliaments. There can be difficulties in obtaining information on by-election results and replacements due to death or resignation. These changes are ad hoc events which are more difficult to keep track of. By-elections, for instance, are often not announced internationally as general elections are. Parliaments vary considerably in their internal workings and procedures, however, generally legislate, oversee government and represent the electorate. In terms of measuring women's contribution to political decision making, this indicator may not be sufficient because some women may face obstacles in fully and efficiently carrying out their parliamentary mandate.\n\n\n\n\n\n\n\n\n\n\n\nThe data is compiled by the Inter-Parliamentary Union on the basis of information provided by National Parliaments. The percentages do not take into account the case of parliaments for which no data was available at that date. Information is available in all countries where a national legislature exists and therefore does not include parliaments that have been dissolved or suspended for an indefinite period." }, { "id": "Longdefinition", "value": "Women in parliaments are the percentage of parliamentary seats in a single or lower chamber held by women." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Women are vastly underrepresented in decision making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.\n\nThis is the Sustainable Development Goal indicator 5.5.1 (a). [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1997-2025" }, { "id": "Source", "value": "Monthly ranking of women in national parliaments, Inter-Parliamentary Union (IPU), uri: https://data.ipu.org/women-ranking/, note: For the year of 1998, the data is as of August 10, 1998., type: Excel, date accessed: 2026-03-29" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of seats held by women in national parliaments is the number of seats held by women members in single or lower chambers of national parliaments, expressed as a percentage of all occupied seats; it is derived by dividing the total number of seats occupied by women by the total number of seats in parliament.\nStatistical concept(s): This indicator assesses the extent to which women are provided with equal opportunities to participate in parliamentary decision-making processes. It applies to the sole chamber of unicameral national parliaments and the lower chamber in the case of bicameral systems. The upper chamber in bicameral parliaments is not included in this measure. Parliamentary seats are typically occupied by individuals who are victorious in general elections, though they can also be acquired through nomination, appointment, indirect election, member rotation, or by-elections. The term 'seats' refers to the total count of parliamentary mandates or the total number of parliament members." }, { "id": "Topic", "value": "Gender: Public life & decision making" }, { "id": "Unitofmeasure", "value": "% of single or lower chamber seats" } ], "source_id": "57" }, { "id": "SG.JOB.NOPN.EQ", "metatype": [ { "id": "IndicatorName", "value": "Nonpregnant and nonnursing women can do the same jobs as men (1=yes; 0=no)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Non-pregnant and non-nursing women can do the same jobs as men indicates whether there are specific jobs that women explicitly or implicitly cannot perform except in limited circumstances. Both partial and full restrictions on women’s work are counted as restrictions. For example, if women are only allowed to work in certain jobs within the mining industry, e.g., as health care professionals within mines but not as miners, this is a restriction." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank: Women, Business and the Law." }, { "id": "Topic", "value": "Gender: Participation & access" } ], "source_id": "57" }, { "id": "SG.LAW.CHMR", "metatype": [ { "id": "IndicatorName", "value": "Law prohibits or invalidates child or early marriage (1=yes; 0=no)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Law prohibits or invalidates child or early marriage is whether there are provisions that prevent the marriage of girls, boys, or both before they reach the legal age of marriage or the age of marriage with consent, including, for example, a prohibition on registering the marriage or provisions stating that such a marriage is null and void." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank: Women, Business and the Law." }, { "id": "Topic", "value": "Gender: Public life & decision making" } ], "source_id": "57" }, { "id": "SG.LAW.EQRM.WK", "metatype": [ { "id": "Developmentrelevance", "value": "The knowledge and analysis provided by Women, Business and the Law make a strong economic case for laws that empower women. Better performance in the areas measured by the Women, Business and the Law index is associated with more women in the labor force and with higher income and improved development outcomes. Equality before the law and of economic opportunity are not only wise social policy but also good economic policy. The equal participation of women and men will give every economy a chance to achieve its potential. Given the economic significance of women's empowerment, the ultimate goal of Women, Business and the Law is to encourage governments to reform laws that hold women back from working and doing business." }, { "id": "Generalcomments", "value": "For the reference period, WDI and Gender Databases take the data coverage years instead of reporting years used in WBL (https://wbl.worldbank.org/). For example, the data for YR2020 in WBL (report year) corresponds to data for YR2019 in WDI and Gender Databases." }, { "id": "IndicatorName", "value": "Law mandates equal remuneration for females and males for work of equal value (1=yes; 0=no)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The indicator measures whether there is a law that obligates employers to pay equal remuneration to male and female employees who do work of equal value. “Remuneration” refers to the ordinary, basic or minimum wage or salary and any additional emoluments payable directly or indirectly, whether in cash or in kind, by the employer to the worker and arising out of the worker’s employment. “Work of equal value” refers not only to the same or similar jobs but also to different jobs of the same value." }, { "id": "Notesfromoriginalsource", "value": "This is one of the 35 scored indicators." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress toward legal equality between men and women in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 2,000 respondents with expertise in family, labor, and criminal law, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and regulations. The Women, Business and the Law team collects the texts of these codified sources of national law - constitutions, codes, laws, statutes, rules, regulations, and procedures - and checks questionnaire responses for accuracy. Thirty-five data points are scored across eight indicators of four or five binary questions, with each indicator representing a different phase of a woman’s career. Indicator-level scores are obtained by calculating the unweighted average of the questions within that indicator and scaling the result to 100. Overall scores are then calculated by taking the average of each indicator, with 100 representing the highest possible score." }, { "id": "Topic", "value": "Gender: Public life & decision making" } ], "source_id": "57" }, { "id": "SG.LAW.INDX", "metatype": [ { "id": "Aggregationmethod", "value": "Simple average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The knowledge and analysis provided by Women, Business and the Law make a strong economic case for laws that empower women. Better performance in the areas measured by the Women, Business and the Law index is associated with more women in the labor force and with higher income and improved development outcomes. Equality before the law and of economic opportunity are not only wise social policy but also good economic policy. The equal participation of women and men will give every economy a chance to achieve its potential. Given the economic significance of women's empowerment, the ultimate goal of Women, Business and the Law is to encourage governments to reform laws that hold women back from working and doing business." }, { "id": "IndicatorName", "value": "Women Business and the Law Index Score (scale 1-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Women, Business and the Law methodology has limitations that should be considered when interpreting the data. All eight indicators are based on standardized assumptions to ensure comparability across economies. Comparability is one of the strengths of the data, but the assumptions can also be limitations as they may not capture all restrictions or represent all particularities in a country. It is assumed that the woman resides in the economy's main business city. In federal economies, laws affecting women can vary by state or province. Even in nonfederal economies, women in rural areas and small towns could face more restrictive local legislation. Such restrictions are not captured by Women, Business and the Law unless they are also found in the main business city. The woman has reached the legal age of majority and is capable of making decisions as an adult, is in good health and has no criminal record. She is a lawful citizen of the economy being examined, and she works as a cashier in the food retail sector in a supermarket or grocery store that has 60 employees. She is a cisgender, heterosexual woman in a monogamous first marriage registered with the appropriate authorities (de facto marriages and customary unions are not measured), she is of the same religion as her husband, and is in a marriage under the rules of the default marital property regime, or the most common regime for that jurisdiction, which will not change during the course of the marriage. She is not a member of a union, unless membership is mandatory. Membership is considered mandatory when collective bargaining agreements cover more than 50 percent of the workforce in the food retail sector and when they apply to individuals who were not party to the original collective bargaining agreement. Where personal law prescribes different rights and obligations for different groups of women, the data focus on the most populous group, which may mean that restrictions that apply only to minority populations are missed. Women, Business and the Law focuses solely on the ways in which the formal legal and regulatory environment determines whether women can work or open their own businesses. The data set is constructed using laws and regulations that are codified (de jure) and currently in force, therefore implementation of laws (de facto) is not measured. The data looks only at laws that apply to the private sector. These assumptions can limit the representativeness of the data for the entire population in each country. Finally, Women, Business and the Law recognizes that the laws it measures do not apply to all women in the same way. Women face intersectional forms of discrimination based on gender, sex, sexuality, race, gender identity, religion, family status, ethnicity, nationality, disability, and a myriad of other grounds. Women, Business and the Law therefore encourages readers to interpret the data in conjunction with other available research." }, { "id": "Longdefinition", "value": "The index measures how laws and regulations affect women’s economic opportunity. Overall scores are calculated by taking the average score of each index (Mobility, Workplace, Pay, Marriage, Parenthood, Entrepreneurship, Assets and Pension), with 100 representing the highest possible score." }, { "id": "Othernotes", "value": "1. For the reference period, WDI and Gender Databases take the data coverage years instead of reporting years used in WBL (https://wbl.worldbank.org/). For example, the data for YR2020 in WBL (report year) corresponds to data for YR2019 in WDI and Gender Databases. 2. The 2024 Women, Business and the Law (WBL) report has introduced two distinct datasets, labeled as 1.0 and 2.0. The WBL data in the Gender database is based on the dataset 1.0. This dataset maintains consistency with the indicators used in previous WBL reports from 2020 to 2023. In contrast, the WBL 2.0 dataset includes new areas of childcare and safety. For those interested in exploring the WBL 2.0 dataset, it is available on the WBL website at https://wbl.worldbank.org." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "NA" }, { "id": "Source", "value": "Women, Business and the Law, World Bank (WB), uri: https://wbl.worldbank.org/en/wbl-data, type: Excel, date accessed: 2024-03-04, date published: 2024-03-04" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Thirty-five data points are scored across eight indicators (Mobility, Workplace, Pay, Marriage, Parenthood, Entrepreneurship, Assets and Pension) of four or five binary questions, with each indicator representing a different phase of a woman’s career. Indicator-level scores are obtained by calculating the unweighted average of the questions within that indicator and scaling the result to 100. Overall scores are then calculated by taking the average of each indicator, with 100 representing the highest possible score.\nStatistical concept(s): Women, Business and the Law tracks progress toward legal equality between men and women in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 2,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and regulations. The Women, Business and the Law team collects the texts of these codified sources of national law - constitutions, codes, laws, statutes, rules, regulations, and procedures - and checks questionnaire responses for accuracy." }, { "id": "Topic", "value": "Employment and Time Use" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "57" }, { "id": "SG.LAW.LEVE.PU", "metatype": [ { "id": "IndicatorName", "value": "Law mandates paid or unpaid maternity leave (1=yes; 0=no)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Law mandates paid or unpaid maternity leave is whether there is a law mandating paid or unpaid maternity leave available only to the mother. Provisions for circumstantial leave by which an employee is entitled to a certain number of days of paid leave (usually fewer than five days) upon the birth of a child are considered paternity leave; even if the law is gender-neutral, such leave is not considered maternity leave if the law covers maternity leave elsewhere." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank: Women, Business and the Law." }, { "id": "Topic", "value": "Gender: Public life & decision making" } ], "source_id": "57" }, { "id": "SG.LAW.NODC.HR", "metatype": [ { "id": "Developmentrelevance", "value": "The knowledge and analysis provided by Women, Business and the Law make a strong economic case for laws that empower women. Better performance in the areas measured by the Women, Business and the Law index is associated with more women in the labor force and with higher income and improved development outcomes. Equality before the law and of economic opportunity are not only wise social policy but also good economic policy. The equal participation of women and men will give every economy a chance to achieve its potential. Given the economic significance of women's empowerment, the ultimate goal of Women, Business and the Law is to encourage governments to reform laws that hold women back from working and doing business." }, { "id": "Generalcomments", "value": "For the reference period, WDI and Gender Databases take the data coverage years instead of reporting years used in WBL (https://wbl.worldbank.org/). For example, the data for YR2020 in WBL (report year) corresponds to data for YR2019 in WDI and Gender Databases." }, { "id": "IndicatorName", "value": "Law prohibits discrimination in employment based on gender (1=yes; 0=no)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The indicator measures whether the law generally prevents or penalizes gender-based discrimination in employment. Laws that mandate equal treatment or equality between women and men in employment are also counted for this question. It is not considered whether the laws only prohibit discrimination in one aspect of employment, such as pay or dismissal." }, { "id": "Notesfromoriginalsource", "value": "This is one of the 35 scored indicators." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress toward legal equality between men and women in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 2,000 respondents with expertise in family, labor, and criminal law, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and regulations. The Women, Business and the Law team collects the texts of these codified sources of national law - constitutions, codes, laws, statutes, rules, regulations, and procedures - and checks questionnaire responses for accuracy. Thirty-five data points are scored across eight indicators of four or five binary questions, with each indicator representing a different phase of a woman’s career. Indicator-level scores are obtained by calculating the unweighted average of the questions within that indicator and scaling the result to 100. Overall scores are then calculated by taking the average of each indicator, with 100 representing the highest possible score." }, { "id": "Topic", "value": "Gender: Public life & decision making" } ], "source_id": "57" }, { "id": "SG.LEG.DVAW", "metatype": [ { "id": "Developmentrelevance", "value": "The knowledge and analysis provided by Women, Business and the Law make a strong economic case for laws that empower women. Better performance in the areas measured by the Women, Business and the Law index is associated with more women in the labor force and with higher income and improved development outcomes. Equality before the law and of economic opportunity are not only wise social policy but also good economic policy. The equal participation of women and men will give every economy a chance to achieve its potential. Given the economic significance of women's empowerment, the ultimate goal of Women, Business and the Law is to encourage governments to reform laws that hold women back from working and doing business." }, { "id": "Generalcomments", "value": "For the reference period, WDI and Gender Databases take the data coverage years instead of reporting years used in WBL (https://wbl.worldbank.org/). For example, the data for YR2020 in WBL (report year) corresponds to data for YR2019 in WDI and Gender Databases." }, { "id": "IndicatorName", "value": "There is legislation specifically addressing domestic violence (1=yes; 0=no)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The indicator measures whether there is legislation addressing domestic violence that includes criminal sanctions or provides for protection orders for domestic violence, or the legislation addresses \"harassment\" that clearly leads to physical or mental harm in the context of domestic violence." }, { "id": "Notesfromoriginalsource", "value": "This is one of the 35 scored indicators." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank: Women, Business and the Law. https://wbl.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Women, Business and the Law tracks progress toward legal equality between men and women in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 2,000 respondents with expertise in family, labor, and criminal law, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and regulations. The Women, Business and the Law team collects the texts of these codified sources of national law - constitutions, codes, laws, statutes, rules, regulations, and procedures - and checks questionnaire responses for accuracy. Thirty-five data points are scored across eight indicators of four or five binary questions, with each indicator representing a different phase of a woman’s career. Indicator-level scores are obtained by calculating the unweighted average of the questions within that indicator and scaling the result to 100. Overall scores are then calculated by taking the average of each indicator, with 100 representing the highest possible score." }, { "id": "Topic", "value": "Gender: Public life & decision making" } ], "source_id": "57" }, { "id": "SG.MMR.LEVE.EP", "metatype": [ { "id": "IndicatorName", "value": "Mothers are guaranteed an equivalent position after maternity leave (1=yes; 0=no)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mothers are guaranteed an equivalent position after maternity leave is whether employers of women returning from maternity leave are legally obligated to provide them with an equivalent position after maternity leave. It takes into account paid and unpaid maternity leave and captures whether the employer has a legal obligation to reinstate the returning employee in an equivalent or better position and salary than the employee had pre-leave. Where the maternity leave regime explicitly states that the employee may not be indefinitely replaced, the answer is assumed to be “Yes.” Where the maternity leave regime explicitly establishes a suspension of the employee’s contract, the answer is assumed to be “Yes.” In economies that also have parental leave and the law guarantees return after the leave to the same or an equivalent position paid at the same rate but is silent on guaranteeing the same position after maternity leave, the answer is “Yes.” The answer is “N/A” if no paid or unpaid maternity leave is available." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank: Women, Business and the Law." }, { "id": "Topic", "value": "Gender: Public life & decision making" } ], "source_id": "57" }, { "id": "SG.NOD.CONS", "metatype": [ { "id": "IndicatorName", "value": "Nondiscrimination clause mentions gender in the constitution (1=yes; 0=no)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Nondiscrimination clause mentions gender in the constitution is whether there is a nondiscrimination clause in the constitution which mentions gender. For the answer to be “Yes,” the constitution must use either the word discrimination or the word nondiscrimination or even when there is a “clawback” provision granting exceptions to the nondiscrimination clause for certain areas of the law, such as inheritance, family and customary law. The answer is “No” if there is no nondiscrimination provision, or the nondiscrimination language is present in the preamble but not in an article of the constitution, or there is a provision that merely stipulates that the sexes are equal, or the sexes have equal rights and obligations. The answer is \"N/A\" if there is no nondiscrimination provision." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank: Women, Business and the Law." }, { "id": "Topic", "value": "Gender: Public life & decision making" } ], "source_id": "57" }, { "id": "SG.TIM.UWRK.FE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Women often spend disproportionately more time on unpaid domestic and care work than men. This unequal division of responsibilities is correlated with gender differences in economic opportunities, includign low female labor force participation, occupational sex segregation, and earnings diffrentials. The need for a gender balance in the distribution of unpaid domestic and care work has been increasingly recognized and the Sustainable Development Goals address the issue in the target 5.4." }, { "id": "IndicatorName", "value": "Proportion of time spent on unpaid domestic and care work, female (% of 24 hour day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data may not be strictly comparable across countries as the methods and sampling involved for data collection may differ." }, { "id": "Longdefinition", "value": "The average time women spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.4.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "National statistical offices or national database and publications, United Nations (UN), uri: https://unstats.un.org/sdgs/dataportal/database, note: Indicator code from the original source: SH_FPL_INFM; \tIndicator name from the original source: Proportion of time spent on unpaid domestic chores and care work, by sex, age and location (%), publisher: UN Statistics Division, type: Excel" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of time allocated to unpaid domestic and caregiving tasks is determined by dividing the daily average time spent on such activities by the total number of hours in a day (24 hours). The data for this indicator are presented as a daily time proportion. For instance, if women ages 15+ dedicate 10% of their day to unpaid domestic and caregiving work, while men of the same age bracket allocate 1%, this translates to women spending an average of 2.4 hours (or 2 hours and 24 minutes) and men spending 14.4 minutes per day on these tasks.\n\n\n\n\n\n\nTo ascertain the daily average, weekly data are averaged across all seven days.\nStatistical concept(s): This indicator measures the average amount of on unpaid domestic and care work as a proportion in a day. The objective of this indicator is to quantify the time allocation of both women and men to unpaid tasks, thereby recognizing the value of all forms of work, irrespective of monetary compensation. Furthermore, it serves as a gauge for gender equality by revealing the disparity in time spent by women and men on unpaid activities, such as household chores, caregiving, and childcare.\n\n\n\n\n\n\n\n\n\n\n\nThe daily average is derived from a mean calculated over the data collection reference period, which does not imply that individuals allocate the specified amounts of time to these activities every day." }, { "id": "Topic", "value": "Gender: Participation & access" }, { "id": "Unitofmeasure", "value": "% of a 24 hour day" } ], "source_id": "57" }, { "id": "SG.TIM.UWRK.MA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Women often spend disproportionately more time on unpaid domestic and care work than men. This unequal division of responsibilities is correlated with gender differences in economic opportunities, includign low female labor force participation, occupational sex segregation, and earnings diffrentials. The need for a gender balance in the distribution of unpaid domestic and care work has been increasingly recognized and the Sustainable Development Goals address the issue in the target 5.4." }, { "id": "IndicatorName", "value": "Proportion of time spent on unpaid domestic and care work, male (% of 24 hour day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data may not be strictly comparable across countries as the methods and sampling involved for data collection may differ." }, { "id": "Longdefinition", "value": "The average time men spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.4.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "National statistical offices or national database and publications compiled by United Nations Statistics Division., United Nations (UN), uri: https://unstats.un.org/sdgs/dataportal/database, note: Indicator code from the original source: SH_FPL_INFM; \tIndicator name from the original source: Proportion of time spent on unpaid domestic chores and care work, by sex, age and location (%), publisher: UN Statistics Division, type: Excel" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of time allocated to unpaid domestic and caregiving tasks is determined by dividing the daily average time spent on such activities by the total number of hours in a day (24 hours). The data for this indicator are presented as a daily time proportion. For instance, if women ages 15+ dedicate 10% of their day to unpaid domestic and caregiving work, while men of the same age bracket allocate 1%, this translates to women spending an average of 2.4 hours (or 2 hours and 24 minutes) and men spending 14.4 minutes per day on these tasks.\n\n\n\n\n\n\nTo ascertain the daily average, weekly data are averaged across all seven days.\nStatistical concept(s): This indicator measures the average amount of on unpaid domestic and care work as a proportion in a day. The objective of this indicator is to quantify the time allocation of both women and men to unpaid tasks, thereby recognizing the value of all forms of work, irrespective of monetary compensation. Furthermore, it serves as a gauge for gender equality by revealing the disparity in time spent by women and men on unpaid activities, such as household chores, caregiving, and childcare.\n\n\n\n\n\n\n\n\n\n\n\nThe daily average is derived from a mean calculated over the data collection reference period, which does not imply that individuals allocate the specified amounts of time to these activities every day." }, { "id": "Topic", "value": "Gender: Participation & access" }, { "id": "Unitofmeasure", "value": "% of a 24 hour day" } ], "source_id": "57" }, { "id": "SG.VAW.1549.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children." }, { "id": "IndicatorName", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (% of ever-partnered women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting." }, { "id": "Longdefinition", "value": "Proportion of women subjected to physical and/or sexual violence in the last 12 months is the percentage of ever partnered women age 15-49 who are subjected to physical violence, sexual violence or both by a current or former intimate partner in the last 12 months." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.2.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2017" }, { "id": "Source", "value": "United Nations (UN), uri: https://unstats.un.org/sdgs/dataportal/database, note: Indicator code from the original source: VC_VAW_MARR; \tIndicator name from the original source: Proportion of ever-partnered women and girls subjected to physical and/or sexual violence by a current or former intimate partner in the previous 12 months, by age (%)\n\n\n\n\n\n\n\n\n, publisher: UN Statistics Division, type: API;\nGlobal Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/proportion-of-ever-partnered-women-and-girls-aged-15-49-years-subjected-to-physical-and-or-sexual-violence-by-a-current-or-former-intimate-partner-in-the-previous-12-months, publisher: WHO" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of ever-partnered women (ages 15 and above) subjected to any act of physical violece, sexual violence or both divided by a current or former intimate partner in the previous 12 months divided by the number of ever-partnered women (aged 15 years and above) in the population multiplied by 100. The main source of data are from the Demographic and Health Surveys (DHS), specialized surveys on violence against women and crime victimasation surveys.\nStatistical concept(s): Physical violence is defined as acts that can physically hurt the victim, including, but not limited to: being slapped or having something thrown at you that could hurt you; being pushed or shoved; being hit with a fist or something else that could hurt; being kicked, dragged or beaten up; being choked or burnt on purpose; and/or being threatened with or actually having a gun, knife or other weapon used on you. Sexual violence is operationalized as: being physically forced to have sexual intercourse when you do not want to; having sexual intercourse out of fear for what your partner might do or through coercion; and/or being forced to do something sexual that you consider humiliating or degrading (Reference: WHO, Violence Against Women Prevalence Estimates, 2018. https://www.who.int/publications/i/item/9789240022256). \n\n\n\n\n\n\n\n\n\n\n\nCurrent intimate partner includes current or most recent husbands of ever-married women (and men they live with as if married) and the current intimate partner of never-married women. Former husband/intimate partner is a husband (or partner she is living with as if married) other than the current husband (or man she is living with as if married) for currently married women, any intimate partner for never-married women who do not currently have an intimate partner, and a husband/partner other than the most recent husband/partner for divorced, separated, or widowed women." }, { "id": "Topic", "value": "Gender: Health" }, { "id": "Unitofmeasure", "value": "% of ever-partnered women and girls ages 15 years and older" } ], "source_id": "57" }, { "id": "SG.VAW.ARGU.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she argues with him (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she argues with him." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_ARG; \tIndicator name from the original source: Wife beating justified if she argues with him [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Gender: Health" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "57" }, { "id": "SG.VAW.BURN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she burns the food (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she burns the food." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_BFD; \tIndicator name from the original source: Wife beating justified if she burns the food [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Gender: Health" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "57" }, { "id": "SG.VAW.GOES.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she goes out without telling him (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she goes out without telling him." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_OUT; \tIndicator name from the original source: Wife beating justified if she goes out without telling him [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Gender: Health" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "57" }, { "id": "SG.VAW.MARR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Spousal physical or sexual violence in last 12 months (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Spousal physical or sexual violence is the percentage of ever-married women aged 15-49 who have experienced physical or sexual violence in the last 12 months committed by their husband or partner." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)." }, { "id": "Topic", "value": "Gender: Health" } ], "source_id": "57" }, { "id": "SG.VAW.NEGL.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she neglects the children (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she neglects the children." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_NEG; \tIndicator name from the original source: Wife beating justified if she neglects the children [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Gender: Health" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "57" }, { "id": "SG.VAW.REAS.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife (any of five reasons) (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner for any of the following five reasons: argues with him; refuses to have sex; burns the food; goes out without telling him; or when she neglects the children." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_AGR; \tIndicator name from the original source: Wife beating justified for at least one specific reason [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Gender: Health" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "57" }, { "id": "SG.VAW.REFU.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children." }, { "id": "IndicatorName", "value": "Women who believe a husband is justified in beating his wife when she refuses sex with him (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she refuses sex with him." }, { "id": "Othernotes", "value": "Supportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2023" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS), DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_REF; \tIndicator name from the original source: Wife beating justified if she refuses to have sex with him [Women], publisher: The DHS Program (ICF), type: API, date accessed: 2023-02-10" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women." }, { "id": "Topic", "value": "Gender: Health" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "57" }, { "id": "SH.ACS.PROB.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Problems in accessing health care (any of the specified problems) (% of women): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.ACS.PROB.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Problems in accessing health care (any of the specified problems) (% of women): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.ADM.INPT", "metatype": [ { "id": "IndicatorName", "value": "Inpatient admission rate (% of population )" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Inpatient admission rate is the percentage of the population admitted to hospitals during a year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO, OECD and supplemented by country data." }, { "id": "Topic", "value": "Health: Health systems" } ], "source_id": "57" }, { "id": "SH.ALC.PCAP.FE.LI", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions." }, { "id": "IndicatorName", "value": "Total alcohol consumption per capita, female (liters of pure alcohol, projected estimates, female 15+ years of age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.5.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2020" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.ALC.PCAP.LI", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions." }, { "id": "IndicatorName", "value": "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.5.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2020" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.ALC.PCAP.MA.LI", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Acoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions." }, { "id": "IndicatorName", "value": "Total alcohol consumption per capita, male (liters of pure alcohol, projected estimates, male 15+ years of age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.5.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2020" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.ANM.ALLW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of anemia among women of reproductive age (% of women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of anemia among women of reproductive age refers to the combined prevalence of both non-pregnant with haemoglobin levels below 12 g/dL and pregnant women with haemoglobin levels below 11 g/dL." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on the prevalence of anaemia and/or mean haemoglobin levels in women of reproductive age, collected between 1995 and 2019, were obtained from 408 population-representative data sources across 124 countries worldwide. A Bayesian hierarchical mixture model was employed to estimate haemoglobin distributions, systematically addressing missing data, non-linear time trends, and the representativeness of data sources. Full details on data sources are available on the GHO Anaemia page. Detailed information on the statistical methods can be found in the following reference: Finucane MM, Paciorek CJ, Stevens GA EM. Semiparametric Bayesian density estimation with disparate data sources: a meta-analysis of global childhood undernutrition. J Am Stat Assoc. 2015;110(511):889–901." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percent of women ages 15-49" } ], "source_id": "57" }, { "id": "SH.ANM.CHLD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of anemia among children (% of children ages 6-59 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data for blood haemoglobin concentrations are still limited, compared to other nutritional indicators such as hild anthropometry. As a result, the estimates may not capture the full variation across countries and regions." }, { "id": "Longdefinition", "value": "Prevalence of anemia, children ages 6-59 months, is the percentage of children ages 6-59 months whose hemoglobin level is less than 110 grams per liter, adjusted for altitude." }, { "id": "Othernotes", "value": "Anemia is defined as a low blood haemoglobin concentration. Anaemia may result from a number of causes, with the most significant contributor being iron deficiency. Anaemia resulting from iron deficiency adversely affects cognitive and motor development and causes fatigue and low productivity. Children under age 5 and pregnant women have the highest risk for anemia." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2019" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on anemia are compiled by the WHO, and a statistical model was used to estimate trends. WHO’s hemoglobin threshold concentration in blood was used." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "57" }, { "id": "SH.ANM.NPRG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of anemia among non-pregnant women (% of women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of anemia, non-pregnant women, is the percentage of non-pregnant women whose hemoglobin level is less than 120 grams per liter at sea level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on the prevalence of anaemia and/or mean haemoglobin levels in women of reproductive age, collected between 1995 and 2019, were obtained from 408 population-representative data sources across 124 countries worldwide. A Bayesian hierarchical mixture model was employed to estimate haemoglobin distributions, systematically addressing missing data, non-linear time trends, and the representativeness of data sources. Full details on data sources are available on the GHO Anaemia page. Detailed information on the statistical methods can be found in the following reference: Finucane MM, Paciorek CJ, Stevens GA EM. Semiparametric Bayesian density estimation with disparate data sources: a meta-analysis of global childhood undernutrition. J Am Stat Assoc. 2015;110(511):889–901." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percent of women ages 15-49" } ], "source_id": "57" }, { "id": "SH.CON.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "According to UNAIDS estimates, HIV incidence has fallen in many of the most severely affected countries because adolescents and young people are adopting safer sexual practices and more young people living with HIV are accessing treatment to lower their viral load. When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs)." }, { "id": "IndicatorName", "value": "Condom use, population ages 15-24, female (% of females ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Condom use, female is the percentage of the female population ages 15-24 who used a condom at last intercourse in the last 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2015" }, { "id": "Source", "value": "Demographic and Health Surveys;\nUNAIDS, Joint United Nations Programme on HIV/AIDS (UNAIDS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Household Surveys\nStatistical concept(s): When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.CON.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "According to UNAIDS estimates, HIV incidence has fallen in many of the most severely affected countries because adolescents and young people are adopting safer sexual practices and more young people living with HIV are accessing treatment to lower their viral load. When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs)." }, { "id": "IndicatorName", "value": "Condom use, population ages 15-24, male (% of males ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Condom use, male is the percentage of the male population ages 15-24 who used a condom at last intercourse in the last 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2014" }, { "id": "Source", "value": "Demographic and Health Surveys;\nUNAIDS, Joint United Nations Programme on HIV/AIDS (UNAIDS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Household Surveys\nStatistical concept(s): When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.DTH.0509", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of deaths ages 5-9 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of children ages 5-9 years" }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SH.DTH.0514", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Number of deaths ages 5-14 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of deaths of children ages 5-14 years" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "57" }, { "id": "SH.DTH.1014", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of deaths ages 10-14 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of adolescents ages 10-14 years" }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SH.DTH.1519", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of deaths ages 15-19 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of adolescents ages 15-19 years" }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SH.DTH.2024", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of deaths ages 20-24 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of deaths of youths ages 20-24 years" }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SH.DTH.COMM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Understanding the distribution of causes of death is critical for health planning, policy formulation, and the allocation of resources. Information on the cause of death— communicable diseases and maternal, prenatal and nutrition conditions, non-communicable diseases (NCDs), injury, and, in conjunction other COVID-19 pandemic-related outcomes—provide a foundation for assessing epidemiological transitions, identifying emerging health threats, and monitoring progress toward national and global health goals, including the Sustainable Development Goals (SDGs); in particular, SDG Target 3.4 aims to reduce premature mortality from NCDs, while communicable diseases remain a key focus under Targets 3.3 and 3.8, which address disease-specific burdens and universal health coverage.\n\nInformation on the cause of death are especially important for low- and middle-income countries, where health systems must simultaneously address both infectious disease burdens and the rising prevalence of NCDs and injury-related mortality. Disaggregated cause-of-death data enable governments and development partners to prioritize interventions, assess health system performance, and guide investments in prevention and care. Moreover, monitoring mortality by cause helps identify disparities across population groups and geographies, ensuring that public health responses are equitable and data-driven." }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Limited data availability on health status remains a major constraint in assessing health conditions in low-and middle-income countries. Surveillance systems are often weak or absent for major public health issues, and available estimates of disease prevalence and incidence may be incomplete or unreliable. National capacities and commitments to data collection and reporting vary significantly. To address these gaps and enhance reliability and comparability, the World Health Organization (WHO) produces estimates using epidemiological models.\n\nThe COVID-19 pandemic introduced exceptional challenges. Even in countries with relatively complete vital registration systems, excess mortality may have been misclassified between COVID-19 and other causes of death. In countries lacking comprehensive registration systems, greater reliance on modelled estimates—including categories such as “other pandemic-related mortality”—may have resulted in underestimation or redistribution of deaths from specific causes, particularly cardiovascular diseases and other non-communicable diseases." }, { "id": "Longdefinition", "value": "Cause of death refers to the share of all deaths for all ages by underlying causes. Communicable diseases and maternal, prenatal and nutrition conditions include infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates Deaths by Cause, Age, Sex, by Country and by Region, 2000-2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on cause of death are compiled by the World Health Organization (WHO), using information from civil registration and vital statistics systems (CRVS), local health and demographic studies and other sources, supplemented by vital registration and verbal autopsy in communities as well as regular household health surveys. To address incomplete or inconsistent reporting—particularly in countries with limited vital registration coverage—WHO applies statistical models that account for underreporting, demographic differentials, and expected cause-of-death distribution. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, refer to the WHO Global Health Estimates methodological documentation.\nStatistical concept(s): Causes of death in the Global Health Estimates (GHE) are classified according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) or ICD-9. Reported data from countries are mapped to a standardized cause list structured by WHO in a three-level hierarchy. Each death is assigned to a single underlying cause based on ICD rules. To ensure internal consistency, all cause-specific mortality estimates are constrained to fit within an all-cause mortality envelope derived from demographic estimates prepared by the UN Population Division and WHO. The cause list is designed to be mutually exclusive and collectively exhaustive, allowing full decomposition of total mortality by age, sex, country, and year.\n\nFor countries with incomplete, poor-quality, or no usable cause-of-death data, WHO uses a compositional cause modeling strategy. This approach estimates the distribution of causes within broad cause groups based on epidemiological and demographic covariates. Redistribution algorithms are applied to correct for misclassification or ill-defined causes. All modeling approaches are aligned with WHO’s goal of maximizing comparability, transparency, and usability of mortality data across settings with diverse data availability and health system capacities." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.DTH.IMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of infant deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of infants dying before reaching one year of age." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SH.DTH.INJR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Understanding the distribution of causes of death is critical for health planning, policy formulation, and the allocation of resources. Information on the cause of death— communicable diseases and maternal, prenatal and nutrition conditions, non-communicable diseases (NCDs), injury, and, in conjunction other COVID-19 pandemic-related outcomes—provide a foundation for assessing epidemiological transitions, identifying emerging health threats, and monitoring progress toward national and global health goals, including the Sustainable Development Goals (SDGs); in particular, SDG Target 3.4 aims to reduce premature mortality from NCDs, while communicable diseases remain a key focus under Targets 3.3 and 3.8, which address disease-specific burdens and universal health coverage.\n\nInformation on the cause of death are especially important for low- and middle-income countries, where health systems must simultaneously address both infectious disease burdens and the rising prevalence of NCDs and injury-related mortality. Disaggregated cause-of-death data enable governments and development partners to prioritize interventions, assess health system performance, and guide investments in prevention and care. Moreover, monitoring mortality by cause helps identify disparities across population groups and geographies, ensuring that public health responses are equitable and data-driven." }, { "id": "IndicatorName", "value": "Cause of death, by injury (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Limited data availability on health status remains a major constraint in assessing health conditions in low-and middle-income countries. Surveillance systems are often weak or absent for major public health issues, and available estimates of disease prevalence and incidence may be incomplete or unreliable. National capacities and commitments to data collection and reporting vary significantly. To address these gaps and enhance reliability and comparability, the World Health Organization (WHO) produces estimates using epidemiological models.\n\nThe COVID-19 pandemic introduced exceptional challenges. Even in countries with relatively complete vital registration systems, excess mortality may have been misclassified between COVID-19 and other causes of death. In countries lacking comprehensive registration systems, greater reliance on modelled estimates—including categories such as “other pandemic-related mortality”—may have resulted in underestimation or redistribution of deaths from specific causes, particularly cardiovascular diseases and other non-communicable diseases." }, { "id": "Longdefinition", "value": "Cause of death refers to the share of all deaths for all ages by underlying causes. Injuries include unintentional and intentional injuries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates Deaths by Cause, Age, Sex, by Country and by Region, 2000-2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on cause of death are compiled by the World Health Organization (WHO), using information from civil registration and vital statistics systems (CRVS), local health and demographic studies and other sources, supplemented by vital registration and verbal autopsy in communities as well as regular household health surveys. To address incomplete or inconsistent reporting—particularly in countries with limited vital registration coverage—WHO applies statistical models that account for underreporting, demographic differentials, and expected cause-of-death distribution. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, refer to the WHO Global Health Estimates methodological documentation.\nStatistical concept(s): Causes of death in the Global Health Estimates (GHE) are classified according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) or ICD-9. Reported data from countries are mapped to a standardized cause list structured by WHO in a three-level hierarchy. Each death is assigned to a single underlying cause based on ICD rules. To ensure internal consistency, all cause-specific mortality estimates are constrained to fit within an all-cause mortality envelope derived from demographic estimates prepared by the UN Population Division and WHO. The cause list is designed to be mutually exclusive and collectively exhaustive, allowing full decomposition of total mortality by age, sex, country, and year.\n\nFor countries with incomplete, poor-quality, or no usable cause-of-death data, WHO uses a compositional cause modeling strategy. This approach estimates the distribution of causes within broad cause groups based on epidemiological and demographic covariates. Redistribution algorithms are applied to correct for misclassification or ill-defined causes. All modeling approaches are aligned with WHO’s goal of maximizing comparability, transparency, and usability of mortality data across settings with diverse data availability and health system capacities." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.DTH.MORT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of under-five deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of children dying before reaching age five." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SH.DTH.NCOM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Understanding the distribution of causes of death is critical for health planning, policy formulation, and the allocation of resources. Information on the cause of death— communicable diseases and maternal, prenatal and nutrition conditions, non-communicable diseases (NCDs), injury, and, in conjunction other COVID-19 pandemic-related outcomes—provide a foundation for assessing epidemiological transitions, identifying emerging health threats, and monitoring progress toward national and global health goals, including the Sustainable Development Goals (SDGs); in particular, SDG Target 3.4 aims to reduce premature mortality from NCDs, while communicable diseases remain a key focus under Targets 3.3 and 3.8, which address disease-specific burdens and universal health coverage.\n\nInformation on the cause of death are especially important for low- and middle-income countries, where health systems must simultaneously address both infectious disease burdens and the rising prevalence of NCDs and injury-related mortality. Disaggregated cause-of-death data enable governments and development partners to prioritize interventions, assess health system performance, and guide investments in prevention and care. Moreover, monitoring mortality by cause helps identify disparities across population groups and geographies, ensuring that public health responses are equitable and data-driven." }, { "id": "IndicatorName", "value": "Cause of death, by non-communicable diseases (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Limited data availability on health status remains a major constraint in assessing health conditions in low-and middle-income countries. Surveillance systems are often weak or absent for major public health issues, and available estimates of disease prevalence and incidence may be incomplete or unreliable. National capacities and commitments to data collection and reporting vary significantly. To address these gaps and enhance reliability and comparability, the World Health Organization (WHO) produces estimates using epidemiological models.\n\nThe COVID-19 pandemic introduced exceptional challenges. Even in countries with relatively complete vital registration systems, excess mortality may have been misclassified between COVID-19 and other causes of death. In countries lacking comprehensive registration systems, greater reliance on modelled estimates—including categories such as “other pandemic-related mortality”—may have resulted in underestimation or redistribution of deaths from specific causes, particularly cardiovascular diseases and other non-communicable diseases." }, { "id": "Longdefinition", "value": "Cause of death refers to the share of all deaths for all ages by underlying causes. Non-communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates Deaths by Cause, Age, Sex, by Country and by Region, 2000-2021" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on cause of death are compiled by the World Health Organization (WHO), using information from civil registration and vital statistics systems (CRVS), local health and demographic studies and other sources, supplemented by vital registration and verbal autopsy in communities as well as regular household health surveys. To address incomplete or inconsistent reporting—particularly in countries with limited vital registration coverage—WHO applies statistical models that account for underreporting, demographic differentials, and expected cause-of-death distribution. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, refer to the WHO Global Health Estimates methodological documentation.\nStatistical concept(s): Causes of death in the Global Health Estimates (GHE) are classified according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) or ICD-9. Reported data from countries are mapped to a standardized cause list structured by WHO in a three-level hierarchy. Each death is assigned to a single underlying cause based on ICD rules. To ensure internal consistency, all cause-specific mortality estimates are constrained to fit within an all-cause mortality envelope derived from demographic estimates prepared by the UN Population Division and WHO. The cause list is designed to be mutually exclusive and collectively exhaustive, allowing full decomposition of total mortality by age, sex, country, and year.\n\nFor countries with incomplete, poor-quality, or no usable cause-of-death data, WHO uses a compositional cause modeling strategy. This approach estimates the distribution of causes within broad cause groups based on epidemiological and demographic covariates. Redistribution algorithms are applied to correct for misclassification or ill-defined causes. All modeling approaches are aligned with WHO’s goal of maximizing comparability, transparency, and usability of mortality data across settings with diverse data availability and health system capacities." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.DTH.NMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Number of neonatal deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Number of neonates dying before reaching 28 days of age." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis indicator is related to Sustainable Development Goal 3.2.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SH.DYN.0509", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Probability of dying among children ages 5-9 years (per 1,000)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 5-9 years of age expressed per 1,000 children aged 5, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000" } ], "source_id": "57" }, { "id": "SH.DYN.0514", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "IndicatorName", "value": "Probability of dying at age 5-14 years (per 1,000 children age 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 5-14 years of age expressed per 1,000 children aged 5, if subject to age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org." }, { "id": "Statisticalconceptandmethodology", "value": "The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\nEstimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "57" }, { "id": "SH.DYN.1014", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Probability of dying among adolescents ages 10-14 years (per 1,000)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 10-14 years of age expressed per 1,000 adolescents age 10, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000" } ], "source_id": "57" }, { "id": "SH.DYN.1519", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Probability of dying among adolescents ages 15-19 years (per 1,000)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 15-19 years of age expressed per 1,000 adolescents age 15, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000" } ], "source_id": "57" }, { "id": "SH.DYN.2024", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Probability of dying among youth ages 20-24 years (per 1,000)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Probability of dying between age 20-24 years of age expressed per 1,000 youths age 20, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000" } ], "source_id": "57" }, { "id": "SH.DYN.AIDS.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Women's share of population ages 15+ living with HIV (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV is the percentage of people who are infected with HIV. Female rate is as a percentage of the total population ages 15+ who are living with HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated as the number of women aged 15 and older living with HIV divided by the total number of people aged 15 and older living with HIV. Estimates of people living with HIV are produced by UNAIDS using a common modelling framework (Spectrum), which integrates country-reported HIV surveillance data, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators.\n\n\n\nReference: Annex 1. Methods for deriving UNAIDS HIV estimates, 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform:\n\nhttps://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.DYN.AIDS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS." }, { "id": "IndicatorName", "value": "Prevalence of HIV, total (% of population ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\nStatistical concept(s): HIV prevalence rates reflect the rate of HIV infection in each country's population. Low national prevalence rates can be misleading, however. They often disguise epidemics that are initially concentrated in certain localities or population groups and threaten to spill over into the wider population. In many developing countries most new infections occur in young adults, with young women especially vulnerable." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.DYN.CHLD.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Relevance to gender indicator: Child mortality captures the effect of gender discrimination better than infant mortality, as malnutrition and medical interventions have significant impacts on this age group. Where female child mortality is higher, girls are likely to have less access to resources than boys." }, { "id": "IndicatorName", "value": "Mortality rate, female child (per 1,000 female children age one)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Child mortality rate is the probability of dying between the exact ages of one and five, if subject to age-specific mortality rates of the specified year. The probability is expressed as a rate per 1,000." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys by ICF International, Multiple Indicators Cluster Surveys by UNICEF, Reproductive Health Surveys by U.S. Center for Disease Control, and Family Health Surveys by Pan Arab Project for Family Health. See footnotes for a source." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "57" }, { "id": "SH.DYN.CHLD.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "Relevance to gender indicator: Child mortality captures the effect of gender discrimination better than infant mortality, as malnutrition and medical interventions have significant impacts on this age group. Where female child mortality is higher, girls are likely to have less access to resources than boys." }, { "id": "IndicatorName", "value": "Mortality rate, male child (per 1,000 male children age one)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Child mortality rate is the probability of dying between the exact ages of one and five, if subject to age-specific mortality rates of the specified year. The probability is expressed as a rate per 1,000." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demographic and Health Surveys by ICF International, Multiple Indicators Cluster Surveys by UNICEF, Reproductive Health Surveys by U.S. Center for Disease Control, and Family Health Surveys by Pan Arab Project for Family Health. See footnotes for a source." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "57" }, { "id": "SH.DYN.MORT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, under-5 (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is the Sustainable Development Goal indicator 3.2.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org, publisher: UNICEF, WHO, World Bank, United Nations Population Division;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "57" }, { "id": "SH.DYN.MORT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, under-5, female (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "57" }, { "id": "SH.DYN.MORT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, under-5, male (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male age-specific mortality rates of the specified year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "57" }, { "id": "SH.DYN.MORT.Q1", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Under-5 mortality rate (per 1,000 live births): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Under-5 mortality rate: Number of deaths to children under age five years per 1000 live births, based on experience during the reference period before the survey. The reference period is ten years preceding the survey for DHS surveys, and the reference period varies for MICS surveys (often three to five years preceding the survey)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.DYN.MORT.Q5", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Under-5 mortality rate (per 1,000 live births): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Under-5 mortality rate: Number of deaths to children under age five years per 1000 live births, based on experience during the reference period before the survey. The reference period is ten years preceding the survey for DHS surveys, and the reference period varies for MICS surveys (often three to five years preceding the survey)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.DYN.NCOM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates.\n\n\nThe current estimates for 2020 and 2021 are likely underestimated in countries where high-quality vital registration data was lacking at the time of GHE2021 production. This is because the modeled estimates cannot fully account for deaths from the four major NCDs indirectly attributed to the COVID-19 pandemic. Therefore, the data for 2020 and 2021 should be interpreted with caution." }, { "id": "Longdefinition", "value": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of death between the exact ages of 30 and 70 is calculated using cause-specific mortality rates for each 5-year age group, applying standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE). These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Percent of females ages 30 years old" } ], "source_id": "57" }, { "id": "SH.DYN.NCOM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of death between the exact ages of 30 and 70 is calculated using cause-specific mortality rates for each 5-year age group, applying standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE). These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Percent of males ages 30 years old" } ], "source_id": "57" }, { "id": "SH.DYN.NCOM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS)." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.4.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of death between the exact ages of 30 and 70 is calculated using cause-specific mortality rates for each 5-year age group, applying standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE). These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Percent of people ages 30 years old" } ], "source_id": "57" }, { "id": "SH.DYN.NMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, neonatal (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Neonatal mortality rate is the number of neonates dying before reaching 28 days of age, per 1,000 live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\n\nThis is the Sustainable Development Goal indicator 3.2.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "57" }, { "id": "SH.FPL.KNOW.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Knowledge of contraception (any method) (% of married women): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Knowledge of contraception: Percentage of currently married women who know at least one contraceptive method and at least one modern contraceptive method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.FPL.KNOW.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Knowledge of contraception (any method) (% of married women): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Knowledge of contraception: Percentage of currently married women who know at least one contraceptive method and at least one modern contraceptive method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.FPL.SATM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Demand for family planning satisfied by modern methods (% of married women with demand for family planning)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Demand for family planning satisfied by modern methods refers to the percentage of married women ages 15-49 years whose need for family planning is satisfied with modern methods." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.7.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "Demographic and Health Surveys (DHS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated from nationally-representative household survey data. Relevant data for this indicator are collected through various multi-country survey programs, including Contraceptive Prevalence Surveys (CPS), Demographic and Health Surveys (DHS), Fertility and Family Surveys (FFS), Reproductive Health Surveys (RHS), Multiple Indicator Cluster Surveys (MICS), Performance Monitoring and Accountability 2020 surveys (PMA), World Fertility Surveys (WFS), other international survey programs, and national surveys.\n\n\n\n\n\nData compilation involves systematic searches of websites of international survey programs, survey databases (e.g., the Integrated Household Survey Network (IHSN) database), websites of national statistical offices, SDG national reporting platforms, and ad hoc queries. Additionally, country-specific information from UNFPA country offices is utilized." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SH.H2O.BASW.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using at least basic drinking water services, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water). This indicator encompasses both people using basic water services as well as those using safely managed water services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "57" }, { "id": "SH.H2O.BASW.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using at least basic drinking water services, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water). This indicator encompasses both people using basic water services as well as those using safely managed water services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "57" }, { "id": "SH.H2O.BASW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using at least basic drinking water services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water). This indicator encompasses both people using basic water services as well as those using safely managed water services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "57" }, { "id": "SH.H2O.SAFE.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities.\n\nLack of access to adequate water contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and include diarrhea, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improvement of access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity.\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families.\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of improved drinking water include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "Improved water source, rural (% of rural population with access)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Please note that the data for this indicator have not been updated since 2015. The WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation has introduced updated water and sanitation indicators. For the most recent data on water access in rural areas, please see the following indicators: People using safely managed drinking water services, rural (% of rural population) (SH.H2O.SMDW.RU.ZS) and People using basic drinking water services, rural (% of rural population) (SH.H2O.BASW.RU.ZS).\n\n\nThe data on access to an improved water source measure the percentage of the population with ready access to water for domestic purposes.\n\nAccess to drinking water from an improved source does not ensure that the water is safe or adequate, as these characteristics are not tested at the time of survey. But improved drinking water technologies are more likely than those characterized as unimproved to provide safe drinking water and to prevent contact with human excreta. While information on access to an improved water source is widely used, it is extremely subjective, and such terms as safe, improved, adequate, and reasonable may have different meanings in different countries despite official WHO definitions (see Definitions). Even in high-income countries treated water may not always be safe to drink. Access to an improved water source is equated with connection to a supply system; it does not take into account variations in the quality and cost (broadly defined) of the service." }, { "id": "Longdefinition", "value": "Access to an improved water source, rural, refers to the percentage of the rural population using an improved drinking water source. The improved drinking water source includes piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), and other improved drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "The data are derived by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on national censuses and nationally representative household surveys. The coverage rates for water and sanitation are based on information from service users on the facilities their households actually use rather than on information from service providers, which may include nonfunctioning systems.\n\nWHO/UNICEF define an improved drinking-water source as one that, by nature of its construction or through active intervention, is protected from outside contamination, in particular from contamination with fecal matter. Improved water sources include piped water into dwelling, plot or yard; piped water into neighbor's plot; public tap/standpipe; tube well/borehole; protected dug well; protected spring; and rainwater." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "57" }, { "id": "SH.H2O.SAFE.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities.\n\nLack of access to adequate water contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and include diarrhea, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improvement of access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity.\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families.\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of improved drinking water include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "Improved water source, urban (% of urban population with access)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Please note that the data for this indicator have not been updated since 2015. The WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation has introduced updated water and sanitation indicators. For the most recent data on water access in urban areas, please see the following indicators: People using safely managed drinking water services, urban (% of urban population) (SH.H2O.SMDW.UR.ZS) and \nPeople using basic drinking water services, urban (% of urban population) (SH.H2O.BASW.UR.ZS).\n\nThe data on access to an improved water source measure the percentage of the population with ready access to water for domestic purposes.\n\nAccess to drinking water from an improved source does not ensure that the water is safe or adequate, as these characteristics are not tested at the time of survey. But improved drinking water technologies are more likely than those characterized as unimproved to provide safe drinking water and to prevent contact with human excreta. While information on access to an improved water source is widely used, it is extremely subjective, and such terms as safe, improved, adequate, and reasonable may have different meanings in different countries despite official WHO definitions (see Definitions). Even in high-income countries treated water may not always be safe to drink. Access to an improved water source is equated with connection to a supply system; it does not take into account variations in the quality and cost (broadly defined) of the service." }, { "id": "Longdefinition", "value": "Access to an improved water source, urban, refers to the percentage of the urban population using an improved drinking water source. The improved drinking water source includes piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), and other improved drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "The data are derived by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on national censuses and nationally representative household surveys. The coverage rates for water and sanitation are based on information from service users on the facilities their households actually use rather than on information from service providers, which may include nonfunctioning systems.\n\nWHO/UNICEF define an improved drinking-water source as one that, by nature of its construction or through active intervention, is protected from outside contamination, in particular from contamination with fecal matter. Improved water sources include piped water into dwelling, plot or yard; piped water into neighbor's plot; public tap/standpipe; tube well/borehole; protected dug well; protected spring; and rainwater." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "57" }, { "id": "SH.H2O.SAFE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities.\n\nLack of access to adequate water contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and include diarrhea, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improvement of access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity.\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families.\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of improved drinking water include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "Improved water source (% of population with access)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Please note that the data for this indicator have not been updated since 2015. The WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation has introduced updated water and sanitation indicators. For the most recent data on water access, please see the following indicators: People using safely managed drinking water services (% of population) (SH.H2O.SMDW.ZS) and People using basic drinking water services (% of population) (SH.H2O.BASW.ZS).\n\nThe data on access to an improved water source measure the percentage of the population with ready access to water for domestic purposes.\n\nAccess to drinking water from an improved source does not ensure that the water is safe or adequate, as these characteristics are not tested at the time of survey. But improved drinking water technologies are more likely than those characterized as unimproved to provide safe drinking water and to prevent contact with human excreta. While information on access to an improved water source is widely used, it is extremely subjective, and such terms as safe, improved, adequate, and reasonable may have different meanings in different countries despite official WHO definitions (see Definitions). Even in high-income countries treated water may not always be safe to drink. Access to an improved water source is equated with connection to a supply system; it does not take into account variations in the quality and cost (broadly defined) of the service." }, { "id": "Longdefinition", "value": "Access to an improved water source refers to the percentage of the population using an improved drinking water source. The improved drinking water source includes piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), and other improved drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "The data are derived by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on national censuses and nationally representative household surveys. The coverage rates for water and sanitation are based on information from service users on the facilities their households actually use rather than on information from service providers, which may include nonfunctioning systems.\n\nWHO/UNICEF define an improved drinking-water source as one that, by nature of its construction or through active intervention, is protected from outside contamination, in particular from contamination with fecal matter. Improved water sources include piped water into dwelling, plot or yard; piped water into neighbor's plot; public tap/standpipe; tube well/borehole; protected dug well; protected spring; and rainwater." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "57" }, { "id": "SH.H2O.SMDW.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using safely managed drinking water services, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality). Many countries lack data on one or more elements of safely managed drinking water. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability). Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.1.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed drinking water services are defined as the water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "57" }, { "id": "SH.H2O.SMDW.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using safely managed drinking water services, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality). Many countries lack data on one or more elements of safely managed drinking water. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability). Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.1.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed drinking water services are defined as the water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "57" }, { "id": "SH.H2O.SMDW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using safely managed drinking water services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality). Many countries lack data on one or more elements of safely managed drinking water. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability). Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis indicator (Total) is calculated as a population-weighted average of the RURAL and URBAN aggregates when sufficient data are available for both domains. When coverage at either the RURAL or URBAN level is insufficient, but country-level TOTAL data meet the minimum population coverage threshold (30%), TOTAL aggregates are calculated directly from country-level TOTAL estimates. Because these two aggregation approaches may be applied in different years as data availability improves, methodological switches can occur and may result in discontinuities in the time series.\n\n\n\nThis indicator corresponds to Sustainable Development Goal indicator 6.1.1 (see UN SDG metadata: https://unstats.un.org/sdgs/metadata/)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed drinking water services are defined as the water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "57" }, { "id": "SH.HIV.0014", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Children (0-14) living with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Children living with HIV refers to the number of children ages 0-14 who are infected with HIV." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SH.HIV.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Prevalence of HIV, female (% ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV, female is the percentage of females who are infected with HIV. Youth rates are as a percentage of the relevant age group." }, { "id": "Othernotes", "value": "In many developing countries most new infections occur in young adults, with young women especially vulnerable." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.HIV.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies." }, { "id": "IndicatorName", "value": "Prevalence of HIV, male (% ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Prevalence of HIV, male is the percentage of males who are infected with HIV. Youth rates are as a percentage of the relevant age group." }, { "id": "Othernotes", "value": "In many developing countries most new infections occur in young adults, with young women being especially vulnerable." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.HIV.ARTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Antiretroviral therapy (ART) is central to the global HIV response and has significantly improved both survival and quality of life for people living with HIV. Effective ART suppresses viral load to undetectable levels, preventing progression to AIDS. When viral load remains undetectable, HIV is not sexually transmitted to HIV-negative partners. \n\nDespite this progress, gaps in treatment persist. Nearly 10 million people living with HIV are not receiving ART, and according to UNAIDS, about half of them reside in Africa. Expanding access to ART remains essential for reducing HIV-related morbidity and mortality and for achieving global targets for ending AIDS as a public health threat. (Reference: https://www.unaids.org/sites/default/files/2025-07/2025-global-aids-update-JC3153_en.pdf)" }, { "id": "IndicatorName", "value": "Antiretroviral therapy coverage (% of people living with HIV)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Antiretroviral therapy coverage indicates the percentage of all people living with HIV who are receiving antiretroviral therapy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.HIV.INCD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Adults (ages 15-49) newly infected with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of adults (ages 15-49) newly infected with HIV." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SH.HIV.INCD.14", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Children (ages 0-14) newly infected with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of children (ages 0-14) newly infected with HIV." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SH.HIV.INCD.TL", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Adults (ages 15+) and children (ages 0-14) newly infected with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of adults (ages 15+) and children (ages 0-14) newly infected with HIV." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SH.HIV.INCD.TL.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Incidence of HIV, all (per 1,000 uninfected population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected populations expressed per 1,000 uninfected population in the year before the period." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.HIV.INCD.YG", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Young people (ages 15-24) newly infected with HIV" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of young people (ages 15-24) newly infected with HIV." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SH.HIV.INCD.YG.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected populations ages 15-24 expressed per 1,000 uninfected population ages 15-24 in the year before the period." }, { "id": "Othernotes", "value": "This is an age-disaggregated indicator for Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.HIV.INCD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission." }, { "id": "IndicatorName", "value": "Incidence of HIV, ages 15-49 (per 1,000 uninfected population ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of new HIV infections among uninfected populations ages 15-49 expressed per 1,000 uninfected population in the year before the period." }, { "id": "Othernotes", "value": "This is an age-disaggregated indicator for Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates — 2025 Global AIDS Update — AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf" }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.HIV.PMTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "HIV can be transmitted through sexual contact, blood transfusions, and the sharing of contaminated needles, and it can also be transmitted from mother to child during pregnancy, childbirth, or breastfeeding. Although the number of children acquiring HIV has decreased over time, mother-to-child transmission remains a significant public health concern.\n\nPrevention of mother-to-child transmission (PMTCT) is critical for reducing new pediatric HIV infections. However, many pregnant and breastfeeding women still do not begin ART or discontinue treatment during this period, contributing to continued transmission risks. Strengthening PMTCT services and ensuring continuity of care are essential for achieving global HIV prevention goals. (Reference: https://www.unaids.org/sites/default/files/2025-07/2025-global-aids-update-JC3153_en.pdf)" }, { "id": "IndicatorName", "value": "Antiretroviral therapy coverage for PMTCT (% of pregnant women living with HIV)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of pregnant women with HIV who receive antiretroviral medicine for prevention of mother-to-child transmission (PMTCT)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The coverage of antiretrovirals for PMTCT is calculated by dividing the number of pregnant women living with HIV who received antiretrovirals for PMTCT by the estimated number of pregnant women living with HIV who need antiretrovirals for PMTCT in the country. \n\n\n\n\nEstimating the Numerator: The number of pregnant women living with HIV receiving antiretrovirals for PMTCT is derived from national program data aggregated from facilities or other service delivery sites and reported by the country.\n\n\n\n\nEstimating the Denominator: The number of pregnant women living with HIV who need antiretroviral medicine for PMTCT is estimated using standardized statistical modeling based on UNAIDS/WHO methods. These methods consider various epidemic and demographic parameters, such as HIV prevalence among women of reproductive age, the effect of HIV on fertility, and national program coverage of antiretroviral therapy. These statistical modeling procedures provide a comprehensive population-based estimate of the number of pregnant women living with HIV who need antiretrovirals for PMTCT in the country." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Percent of pregnant women living with HIV" } ], "source_id": "57" }, { "id": "SH.IMM.ALLV.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Vaccinations (all vaccinations) (% of children ages 12-23 months): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.IMM.ALLV.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Vaccinations (all vaccinations) (% of children ages 12-23 months): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.IMM.HEPB", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, HepB3 (% of one-year-old children)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization rate, hepatitis B is the percentage of children ages 12-23 months who received hepatitis B vaccinations before 12 months or at any time before the survey. A child is considered adequately immunized after three doses." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2024" }, { "id": "Source", "value": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year. Notes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.IMM.IDPT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, DPT (% of children ages 12-23 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization, DPT, measures the percentage of children ages 12-23 months who received DPT vaccinations before 12 months or at any time before the survey. A child is considered adequately immunized against diphtheria, pertussis (or whooping cough), and tetanus (DPT) after receiving three doses of vaccine." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.b.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2024" }, { "id": "Source", "value": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year. Notes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.IMM.MEAS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Immunization, measles (% of children ages 12-23 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics." }, { "id": "Longdefinition", "value": "Child immunization, measles, measures the percentage of children ages 12-23 months who received the measles vaccination before 12 months or at any time before the survey. A child is considered adequately immunized against measles after receiving one dose of vaccine." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2024" }, { "id": "Source", "value": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF), uri: https://data.unicef.org/topic/child-health/immunization/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year. Notes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.MED.BEDS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Hospital beds are used to indicate the availability of inpatient services." }, { "id": "IndicatorName", "value": "Hospital beds (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Depending on the source and means of monitoring, data may not be exactly comparable across countries. For more information, see the original source." }, { "id": "Longdefinition", "value": "Hospital beds include inpatient beds available in public, private, general, and specialized hospitals and rehabilitation centers. In most cases beds for both acute and chronic care are included." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "WHO data, supplemented by country data, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data were compiled from the WHO Regional offices and country sources other (e.g. ministry of health, national statistical office) and modified to standardize the unit of measure of per 10 000 population by WHO.\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\n\n\nAvailability and use of health services, such as hospital beds per 1,000 people, reflect both demand- and supply-side factors. In the absence of a consistent definition this is a crude indicator of the extent of physical, financial, and other barriers to health care." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "57" }, { "id": "SH.MED.CMHW.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The WHO estimates that at least 2.5 medical staff (physicians, nurses and midwives) per 1,000 people are needed to provide adequate coverage with primary care interventions (WHO, World Health Report 2006)." }, { "id": "IndicatorName", "value": "Community health workers (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The WHO compiles data from household and labor force surveys, censuses, and administrative records. Data comparability is limited by differences in definitions and training of medical personnel varies. In addition, human resources tend to be concentrated in urban areas, so that average densities do not provide a full picture of health personnel available to the entire population." }, { "id": "Longdefinition", "value": "Community health workers include various types of community health aides, many with country-specific occupational titles such as community health officers, community health-education workers, family health workers, lady health visitors and health extension package workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2016" }, { "id": "Source", "value": "Global Health Workforce Statistics, World Health Organization (WHO);\nOrganisation for Economic Co-operation and Development (OECD);\nCountry data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The method of estimation for number of community health workers (including community health officers, community health-education workers, community health aides, family health workers and associated occupations) depends on the nature of the original data source. Enumeration based on population census data is a count of the number of people reporting 'community health worker' as their current occupation (as classified according to the tasks and duties of their job). A similar method is used for estimates based on labour force survey data, with the additional application of a sampling weight to calibrate for national representation. Data from health facility assessments and administrative reporting systems may be based on head counts of employees, staffing records, payroll records, training records, or tallies from other types of routine administrative records on human resources.\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\n\n\nData on health worker (physicians, nurses and midwives, and community health workers) density show the availability of medical personnel." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "57" }, { "id": "SH.MED.NUMW.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The WHO estimates that at least 2.5 medical staff (physicians, nurses and midwives) per 1,000 people are needed to provide adequate coverage with primary care interventions (WHO, World Health Report 2006)." }, { "id": "IndicatorName", "value": "Nurses and midwives (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The WHO compiles data from household and labor force surveys, censuses, and administrative records. Data comparability is limited by differences in definitions and training of medical personnel varies. In addition, human resources tend to be concentrated in urban areas, so that average densities do not provide a full picture of health personnel available to the entire population." }, { "id": "Longdefinition", "value": "Nurses and midwives include professional nurses, professional midwives, auxiliary nurses, auxiliary midwives, enrolled nurses, enrolled midwives and other associated personnel, such as dental nurses and primary care nurses." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.c.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "Global Health Workforce Statistics, World Health Organization (WHO);\nOrganisation for Economic Co-operation and Development (OECD);\nCountry data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National focal points share the data with WHO through the online NHWA data platform. The platform hosted in WHO, is built to facilitate data reporting on the indicators listed in the NHWA Handbook and data sharing across all the 3 levels of WHO.\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\n\n\nData on health worker (physicians, nurses and midwives, and community health workers) density show the availability of medical personnel." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "57" }, { "id": "SH.MED.PHYS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The WHO estimates that at least 2.5 medical staff (physicians, nurses and midwives) per 1,000 people are needed to provide adequate coverage with primary care interventions (WHO, World Health Report 2006)." }, { "id": "IndicatorName", "value": "Physicians (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The WHO compiles data from household and labor force surveys, censuses, and administrative records. Data comparability is limited by differences in definitions and training of medical personnel varies. In addition, human resources tend to be concentrated in urban areas, so that average densities do not provide a full picture of health personnel available to the entire population." }, { "id": "Longdefinition", "value": "Physicians include generalist and specialist medical practitioners." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.c.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Global Health Workforce Statistics, World Health Organization (WHO);\nOrganisation for Economic Co-operation and Development (OECD);\nCountry data" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National focal points share the data with WHO through the online NHWA data platform. The platform hosted in WHO, is built to facilitate data reporting on the indicators listed in the NHWA Handbook and data sharing across all the 3 levels of WHO.\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\n\n\nData on health worker (physicians, nurses and midwives, and community health workers) density show the availability of medical personnel." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "57" }, { "id": "SH.MED.SAOP.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Billions people lack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked." }, { "id": "IndicatorName", "value": "Specialist surgical workforce (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Prior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that “We can’t manage what we don’t measure”, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups." }, { "id": "Longdefinition", "value": "Specialist surgical workforce is the number of specialist surgical, anaesthetic, and obstetric (SAO) providers who are working in each country per 100,000 population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2008-2018" }, { "id": "Source", "value": "Lancet Commission on Global Surgery, uri: www.lancetglobalsurgery.org, note: Data collected by the Lancet Commission on Global Surgery;\nWHO Collaborating Centre for Surgery and Public Health, note: data collected by WHO Collaborating Centre for Surgery and Public Health at Lund University from various sources including Ministries of Health or equivalent national regulatory bodies, national official entities such as medical councils, Eurostat, OECD, WHO Euro Health For All Database, WHO EURO Technical resources for health Database;\nBMJ Glob Health" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of specialist surgical, anaesthetic, and obstetric (SAO) providers who are working in each country per 100 000 population.\nStatistical concept(s): The Lancet Commission on Global Surgery, assembled in 2013 to assess surgical care around the world. Commissioners engaged in an iterative global consultative process with partners in over 110 countries to develop six core indicators of the strength of a surgical system. Two indicators assess a country’s preparedness to deliver safe surgery and anesthesia, two assess the current delivery of safe care, and two assess the state of financial risk protection for those seeking surgery." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "57" }, { "id": "SH.MLR.INCD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Notified cases of malaria (per 100,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Malaria incidence is expressed as the number of new cases of malaria per 100,000 people each year. The number of cases reported is adjusted to take into account incompleteness in reporting systems, patients seeking treatment in the private sector, self-medicating or not seeking treatment at all, and potential over-diagnosis through the lack of laboratory confirmation of cases." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.MLR.INCD.P3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes. It is preventable and curable. There are 5 parasite species that cause malaria in humans, and 2 of these species – Plasmodium falciparum and Plasmodium vivax – pose the greatest threat." }, { "id": "IndicatorName", "value": "Incidence of malaria (per 1,000 population at risk)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Incidence of malaria is the number of new cases of malaria in a year per 1,000 population at risk." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.3.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO), uri: http://apps.who.int/ghodata/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Confirmed malaria cases for countries and areas outside Africa, and for low-transmission countries and areas in Africa are adjusted for extent of health service use (treatment seeking), underreporting and lack of case confirmation (the likelihood that cases are parasite positive). In high transmission areas in which the quality of surveillance data does not permit a robust estimate from the number of reported cases, but good data on parasite prevalence is available, the number of cases can be estimated from parasite prevalence. The denominator is estimated, using official UN population and population at risk estimates for countries with sub-national endemicity.\nStatistical concept(s): Complete data on malaria cases reported through surveillance systems are the best source of data but are rarely available for large populations at high quality and accuracy. Reported data on malaria cases generally need to be adjusted for extent of health service use (treatment seeking), underreporting and lack of case confirmation (the likelihood that cases are parasite positive). WHO compiles data on reported confirmed cases of malaria and suspected cases tested with microscopy or RDT, submitted by national malaria control programmes. Underreporting is reported or estimated by countries. The extent of health service use (treatment seeking) data were obtained from nationally representative household surveys on health service use." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Per 1000 population at risk" } ], "source_id": "57" }, { "id": "SH.MLR.NETS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Use of insecticide-treated bed nets (% of under-5 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Use of insecticide-treated bed nets refers to the percentage of children under age five who slept under an insecticide-treated bednet to prevent malaria." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Malaria is endemic to the poorest countries in the world, mainly in tropical and subtropical regions of Africa, Asia, and the Americas. Insecticide-treated nets, properly used and maintained, are one of the most important malaria-preventive strategies to limit human-mosquito contact." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "57" }, { "id": "SH.MLR.TRET.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Children with fever receiving antimalarial drugs (% of children under age 5 with fever)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Malaria treatment refers to the percentage of children under age five who were ill with fever in the last two weeks and received any appropriate (locally defined) anti-malarial drugs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Malaria is endemic to the poorest countries in the world, mainly in tropical and subtropical regions of Africa, Asia, and the Americas. Prompt and effective treatment of malaria is a critical element of malaria control. It is vital that sufferers, especially children under age 5, start treatment within 24 hours of the onset of symptoms, to prevent progression - often rapid - to severe malaria and death. Data on malaria are from national-level surveys, including Multiple Indicator Cluster Surveys, Demographic and Health Surveys, and Malaria Indicator Surveys." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "57" }, { "id": "SH.MMR.DTHS", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Number of maternal deaths" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The figures cannot be assumed to provide exact estimates." }, { "id": "Longdefinition", "value": "A maternal death refers to the death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management but not from accidental or incidental causes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO);\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SH.MMR.RISK", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Lifetime risk of maternal death (1 in: rate varies by country)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The probability cannot be assumed to provide an exact estimate of risk of maternal death." }, { "id": "Longdefinition", "value": "Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO);\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Number of 15-year old women for which 1 maternal death occurs assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future" } ], "source_id": "57" }, { "id": "SH.MMR.RISK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Lifetime risk of maternal death (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The probability cannot be assumed to provide an exact estimate of risk of maternal death." }, { "id": "Longdefinition", "value": "Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO);\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.PRG.ANEM", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of anemia among pregnant women (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data should be used with caution because surveys differ in quality, coverage, age group interviewed, and treatment of missing values across countries and over time.\n\n\n\nData on anemia are compiled by the WHO based mainly on nationally representative surveys, which measure hemoglobin in the blood. WHO's hemoglobin thresholds are then used to determine anemia status based on age, sex, and physiological status." }, { "id": "Longdefinition", "value": "Prevalence of anemia, pregnant women, is the percentage of pregnant women whose hemoglobin level is less than 110 grams per liter at sea level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Anemia is a condition in which the number of red blood cells or their oxygen-carrying capacity is insufficient to meet physiologic needs, which vary by age, sex, altitude, smoking status, and pregnancy status. In its severe form it is associated with fatigue, weakness, dizziness, and drowsiness. Children under age 5 and pregnant women have the highest risk for anemia." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "57" }, { "id": "SH.PRV.SMOK", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of current tobacco use (% of adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution." }, { "id": "Longdefinition", "value": "The percentage of the population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, total (ages 15+)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes. The indicator name and definition were updated in December, 2020." }, { "id": "Periodicity", "value": "Biennial" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nA statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. \n\n\n\nThe model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. \n\nDepending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015)." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.PRV.SMOK.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of current tobacco use, females (% of female adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution." }, { "id": "Longdefinition", "value": "The percentage of the female population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, females (% of adults)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes it. The indicator name and definition were updated in December, 2020." }, { "id": "Periodicity", "value": "Biennial" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nA statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. \n\n\n\nThe model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. \n\nDepending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015)." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.PRV.SMOK.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Prevalence of current tobacco use, males (% of male adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution." }, { "id": "Longdefinition", "value": "The percentage of the male population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, males (% of adults)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes it. The indicator name and definition were updated in December, 2020." }, { "id": "Periodicity", "value": "Biennial" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nSmoking is the most common form of tobacco use and the prevalence of smoking is therefore a good measure of the tobacco epidemic. (Corrao MA, Guindon GE, Sharma N, Shokoohi DF (eds). Tobacco Control Country Profiles, 2000, American Cancer Society, Atlanta.) Tobacco use causes heart and other vascular diseases and cancers of the lung and other organs. Given the long delay between starting to smoke and the onset of disease, the health impact of smoking will increase rapidly only in the next few decades. The data presented are age-standardized rates for adults ages 15 and older from the WHO." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.SGR.CRSK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Billions people lack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked." }, { "id": "IndicatorName", "value": "Risk of catastrophic expenditure for surgical care (% of people at risk)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Prior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that “We can’t manage what we don’t measure”, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups." }, { "id": "Longdefinition", "value": "The proportion of population at risk of catastrophic expenditure when surgical care is required. Catastrophic expenditure is defined as direct out of pocket payments for surgical and anaesthesia care exceeding 10% of total income." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2003-2022" }, { "id": "Source", "value": "Program in Global Surgery and Social Change (PGSSC), Harvard Medical School, uri: https://www.pgssc.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of experiencing impoverishment when surgical care is required and the probability of experiencing catastrophic expenditure (10 percent of total income) when surgical care is required.\nStatistical concept(s): The Lancet Commission on Global Surgery, assembled in 2013 to assess surgical care around the world. Commissioners engaged in an iterative global consultative process with partners in over 110 countries to develop six core indicators of the strength of a surgical system. Two indicators assess a country’s preparedness to deliver safe surgery and anesthesia, two assess the current delivery of safe care, and two assess the state of financial risk protection for those seeking surgery." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.SGR.IRSK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Billions people lack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked." }, { "id": "IndicatorName", "value": "Risk of impoverishing expenditure for surgical care (% of people at risk)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Prior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that “We can’t manage what we don’t measure”, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups." }, { "id": "Longdefinition", "value": "The proportion of population at risk of impoverishing expenditure when surgical care is required. Impoverishing expenditure is defined as direct out of pocket payments for surgical and anaesthesia care which drive people below a poverty threshold (using a threshold of $2.15 PPP/day)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2003-2022" }, { "id": "Source", "value": "Program in Global Surgery and Social Change (PGSSC), Harvard Medical School, uri: https://www.pgssc.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The probability of experiencing impoverishment when surgical care is required and the probability of experiencing catastrophic expenditure (10 percent of total income) when surgical care is required.\nStatistical concept(s): The Lancet Commission on Global Surgery, assembled in 2013 to assess surgical care around the world. Commissioners engaged in an iterative global consultative process with partners in over 110 countries to develop six core indicators of the strength of a surgical system. Two indicators assess a country’s preparedness to deliver safe surgery and anesthesia, two assess the current delivery of safe care, and two assess the state of financial risk protection for those seeking surgery." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.SGR.PROC.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Billions people lack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked." }, { "id": "IndicatorName", "value": "Number of surgical procedures (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Prior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that “We can’t manage what we don’t measure”, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups." }, { "id": "Longdefinition", "value": "The number of procedures undertaken in an operating theatre per 100,000 population per year in each country. A procedure is defined as the incision, excision, or manipulation of tissue that needs regional or general anaesthesia, or profound sedation to control pain." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2002-2023" }, { "id": "Source", "value": "Lancet Commission on Global Surgery, uri: www.lancetglobalsurgery.org, note: Data from various sources compiled by the Lancet Commission on Global Surgery and the Center for Health Equity in Surgery and Anesthesia at UCSF Medical Center;\nCenter for Health Equity in Surgery and Anesthesia, note: Data from various sources compiled by the Lancet Commission on Global Surgery and the Center for Health Equity in Surgery and Anesthesia at UCSF Medical Center" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of procedures undertaken in an operating theatre per 100 000 population per year in each country. A procedure is defined as the incision, excision, or manipulation of tissue that needs regional or general anaesthesia, or profound sedation to control pain.\nStatistical concept(s): The Lancet Commission on Global Surgery, assembled in 2013 to assess surgical care around the world. Commissioners engaged in an iterative global consultative process with partners in over 110 countries to develop six core indicators of the strength of a surgical system. Two indicators assess a country’s preparedness to deliver safe surgery and anesthesia, two assess the current delivery of safe care, and two assess the state of financial risk protection for those seeking surgery." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "57" }, { "id": "SH.STA.ACSN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child.\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Improved sanitation can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria.\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements.\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation are rarely aware of either the origin of their ills, or the true costs of their deficit. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world.\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffers as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "Improved sanitation facilities (% of population with access)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Please note that the data for this indicator have not been updated since 2015. The WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation has introduced updated water and sanitation indicators. For the most recent data on access to sanitation facilities, please see the following indicators: People using safely managed sanitation services (% of population) (SH.STA.SMSS.ZS) and People using basic sanitation services (% of population) (SH.STA.BASS.ZS).\n\nThe data are derived by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on national censuses and nationally representative household surveys. The coverage rates for sanitation are based on information from service users on the facilities their households actually use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Access to improved sanitation facilities refers to the percentage of the population using improved sanitation facilities. Improved sanitation facilities are likely to ensure hygienic separation of human excreta from human contact. They include flush/pour flush (to piped sewer system, septic tank, pit latrine), ventilated improved pit (VIP) latrine, pit latrine with slab, and composting toilet." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on access to sanitation are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on national censuses and nationally representative household surveys. The coverage rates for water and sanitation are based on information from service users on the facilities their households actually use rather than on information from service providers, which may include nonfunctioning systems.\n\nAn improved sanitation facility is defined as one that hygienically separates human excreta from human contact. Improved sanitation facilities range from simple but protected pit latrines to flush toilets with a sewerage connection. To be effective, facilities must be correctly constructed and properly maintained." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "57" }, { "id": "SH.STA.ACSN.RU", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child.\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Improved sanitation can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria.\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements.\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation are rarely aware of either the origin of their ills, or the true costs of their deficit. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world.\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffers as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "Improved sanitation facilities, rural (% of rural population with access)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Please note that the data for this indicator have not been updated since 2015. The WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation has introduced updated water and sanitation indicators. For the most recent data on access to sanitation facilities in rural areas, please see the following indicators: People using safely managed sanitation services, rural (% of rural population) (SH.STA.SMSS.RU.ZS) and People using basic sanitation services, rural (% of rural population) (SH.STA.BASS.RU.ZS).\n\nThe data are derived by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on national censuses and nationally representative household surveys. The coverage rates for sanitation are based on information from service users on the facilities their households actually use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Access to improved sanitation facilities, rural, refers to the percentage of the rural population using improved sanitation facilities. Improved sanitation facilities are likely to ensure hygienic separation of human excreta from human contact. They include flush/pour flush (to piped sewer system, septic tank, pit latrine), ventilated improved pit (VIP) latrine, pit latrine with slab, and composting toilet." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on access to sanitation are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on national censuses and nationally representative household surveys. The coverage rates for water and sanitation are based on information from service users on the facilities their households actually use rather than on information from service providers, which may include nonfunctioning systems.\n\nAn improved sanitation facility is defined as one that hygienically separates human excreta from human contact. Improved sanitation facilities range from simple but protected pit latrines to flush toilets with a sewerage connection. To be effective, facilities must be correctly constructed and properly maintained." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "57" }, { "id": "SH.STA.ACSN.UR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child.\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Improved sanitation can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria.\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements.\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation are rarely aware of either the origin of their ills, or the true costs of their deficit. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world.\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffers as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "Improved sanitation facilities, urban (% of urban population with access)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Please note that the data for this indicator have not been updated since 2015. The WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation has introduced updated water and sanitation indicators. For the most recent data on access to sanitation facilities in urban areas, please see the following indicators: People using safely managed sanitation services, urban (% of urban population)( SH.STA.SMSS.UR.ZS) and People using basic sanitation services, urban (% of urban population) (SH.STA.BASS.UR.ZS).\n\nThe data are derived by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on national censuses and nationally representative household surveys. The coverage rates for sanitation are based on information from service users on the facilities their households actually use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Access to improved sanitation facilities, urban, refers to the percentage of the urban population using improved sanitation facilities. Improved sanitation facilities are likely to ensure hygienic separation of human excreta from human contact. They include flush/pour flush (to piped sewer system, septic tank, pit latrine), ventilated improved pit (VIP) latrine, pit latrine with slab, and composting toilet." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on access to sanitation are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on national censuses and nationally representative household surveys. The coverage rates for water and sanitation are based on information from service users on the facilities their households actually use rather than on information from service providers, which may include nonfunctioning systems.\n\nAn improved sanitation facility is defined as one that hygienically separates human excreta from human contact. Improved sanitation facilities range from simple but protected pit latrines to flush toilets with a sewerage connection. To be effective, facilities must be correctly constructed and properly maintained." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "57" }, { "id": "SH.STA.AIRP.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries." }, { "id": "IndicatorName", "value": "Mortality rate attributed to household and ambient air pollution, age-standardized, female (per 100,000 female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions" }, { "id": "Longdefinition", "value": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2019" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "57" }, { "id": "SH.STA.AIRP.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries." }, { "id": "IndicatorName", "value": "Mortality rate attributed to household and ambient air pollution, age-standardized, male (per 100,000 male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions" }, { "id": "Longdefinition", "value": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2019" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "57" }, { "id": "SH.STA.AIRP.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Air pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries." }, { "id": "IndicatorName", "value": "Mortality rate attributed to household and ambient air pollution, age-standardized (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions" }, { "id": "Longdefinition", "value": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years)." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2019" }, { "id": "Source", "value": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003)." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "57" }, { "id": "SH.STA.ANVC.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Antenatal care (any skilled personnel) (% of women with a birth): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Antenatal care: Percentage of women with one or more live births in the three (one, two) years preceding the survey who have received at least one antenatal care during pregnancy before the most recent birth from any skilled personnel and from a doctor. If the respondent mentioned more than one provider, only the most qualified provider is considered. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.STA.ANVC.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Antenatal care (any skilled personnel) (% of women with a birth): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Antenatal care: Percentage of women with one or more live births in the three (one, two) years preceding the survey who have received at least one antenatal care during pregnancy before the most recent birth from any skilled personnel and from a doctor. If the respondent mentioned more than one provider, only the most qualified provider is considered. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.STA.ANVC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Pregnant women receiving prenatal care (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources." }, { "id": "Longdefinition", "value": "Pregnant women receiving prenatal care are the percentage of women attended at least once during pregnancy by skilled health personnel for reasons related to pregnancy." }, { "id": "Othernotes", "value": "Good prenatal and postnatal care improve maternal health and reduce maternal and infant mortality." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nGood prenatal and postnatal care improves maternal health and reduces maternal and infant mortality. However, indicators on use of antenatal care services provide no information on the content or quality of the services. Data on antenatal care are obtained mostly from household surveys, which ask women who have had a live birth whether and from whom they received antenatal care." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "57" }, { "id": "SH.STA.ARIC.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Treatment of acute respiratory infection (ARI) (% of children under 5 taken to a health provider): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Treatment of acute respiratory infection (ARI): Percentage of children under age five years with acute respiratory infection (ARI) in the two weeks preceding the survey who were taken to a health facility." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.STA.ARIC.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Treatment of acute respiratory infection (ARI) (% of children under 5 taken to a health provider): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Treatment of acute respiratory infection (ARI): Percentage of children under age five years with acute respiratory infection (ARI) in the two weeks preceding the survey who were taken to a health facility." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.STA.ARIC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "ARI treatment (% of children under 5 taken to a health provider)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Children with acute respiratory infection (ARI) who are taken to a health provider refers to the percentage of children under age five with ARI in the last two weeks who were taken to an appropriate health provider, including hospital, health center, dispensary, village health worker, clinic, and private physician." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Acute respiratory infection continues to be a leading cause of death among young children. Data are drawn mostly from household health surveys in which mothers report on number of episodes and treatment for acute respiratory infection." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "57" }, { "id": "SH.STA.ARIF.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Prevalence of acute respiratory infection (ARI) (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of acute respiratory infection (ARI): Percentage of children under age five years who were ill with a cough accompanied with rapid breathing in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.STA.ARIF.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Prevalence of acute respiratory infection (ARI) (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of acute respiratory infection (ARI): Percentage of children under age five years who were ill with a cough accompanied with rapid breathing in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.STA.ARIF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "ARI prevalence (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Children with acute respiratory infection (ARI) refers to the percentage of children under age five with ARI in the last two weeks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.STA.BASS.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using at least basic sanitation services, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation). This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "57" }, { "id": "SH.STA.BASS.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using at least basic sanitation services, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation). This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "57" }, { "id": "SH.STA.BASS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using at least basic sanitation services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation). This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "57" }, { "id": "SH.STA.BFED.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "For optimal infant and young child feeding, mothers initiate breastfeeding within one hour of birth, breastfeed exclusively for the first six months, and continue to breastfeed for two years or more while providing nutritionally adequate, safe, and age-appropriate solid, semisolid, and soft foods. Breast milk alone contains all the nutrients, antibodies, hormones, and antioxidants an infant needs to thrive. It protects babies from diarrhea and acute respiratory infections, stimulates their immune systems and response to vaccination, and may confer cognitive benefits." }, { "id": "IndicatorName", "value": "Exclusive breastfeeding (% of children under 6 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Most of the data on breastfeeding are derived from household surveys. For the data that are from household surveys, the year refers to the survey year." }, { "id": "Longdefinition", "value": "Exclusive breastfeeding refers to the percentage of children less than six months old who are fed breast milk alone (no other liquids) in the past 24 hours." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2020" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing the number of infants aged 0–5 months who received only breast milk during the previous day by the total number of infants aged 0–5 months, then multiplying the result by 100.\n\n\nData collection involves Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), which include questions about liquids and foods given the previous day, as well as the number of milk feeds the previous day, to determine if the child is being exclusively breastfed. WHO and UNICEF jointly collect data on infant and young child feeding, pooling information from national surveys. Additionally, the WHO Programme of Nutrition, Physical Activity, and Obesity at the Regional Office for Europe independently compiles country-specific information on exclusive breastfeeding." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percent of children under 6 months" } ], "source_id": "57" }, { "id": "SH.STA.BRTC.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Assistance during delivery (any skilled personnel) (% of births): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Assistance during delivery (Assisted births): Percentage of live births in the three (one, two) years preceding the survey attended by any skilled personnel and by a doctor. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.STA.BRTC.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Assistance during delivery (any skilled personnel) (% of births): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Assistance during delivery (Assisted births): Percentage of live births in the three (one, two) years preceding the survey attended by any skilled personnel and by a doctor. The DHS surveys refer births in the three years preceding the survey, the MICS2 surveys refer births in the one year preceding the survey, and the MICS3 surveys refer births in the two years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.STA.BRTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\n\n\nThe share of births attended by skilled health staff is an indicator of a health system's ability to provide adequate care for pregnant women." }, { "id": "IndicatorName", "value": "Births attended by skilled health staff (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources." }, { "id": "Longdefinition", "value": "Births attended by skilled health staff are the percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliveries on their own; and to care for newborns." }, { "id": "Othernotes", "value": "Assistance by trained professionals during birth reduces the incidence of maternal deaths during childbirth. The share of births attended by skilled health staff is an indicator of a health system’s ability to provide adequate care for pregnant women.\n\nThis is the Sustainable Development Goal indicator 3.1.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2022" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National-level household surveys are the primary sources for collecting data on skilled health personnel providing childbirth care. These surveys include Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), Reproductive Health Surveys (RHS), and other national surveys based on similar methodologies. Respondents in these surveys are asked about their last live birth and who assisted during delivery, covering a period of up to five years before the interview.\n\n\nAs part of the data harmonization process and interaction with countries, UNICEF conducts an annual country consultation. During this consultation, SDG country focal points are contacted to update and verify values included in the database and to obtain new data sources. These new data sources are reviewed and assessed jointly with WHO. Additionally, the national categories or occupational titles of skilled health personnel are verified. The reported data for some countries may include additional categories of trained personnel beyond doctors, nurses, and midwives." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Percent of live births" } ], "source_id": "57" }, { "id": "SH.STA.BRTW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Low birth-weight, which is associated with maternal malnutrition, raises the risk of infant mortality and stunts growth in infancy and childhood. There is also emerging evidence that low-birth-weight babies are more prone to non-communicable diseases such as diabetes and cardiovascular diseases. Low birth-weight can arise as a result of a baby being born too soon or too small for gestational age. Babies born prematurely, who are also small for their gestational age, have the worst prognosis.\n\n\n\nIn low- and middle-income countries low birth-weight stems primarily from poor maternal health and nutrition. Three factors have the most impact: poor maternal nutritional status before conception, mother's short stature (due mostly to under-nutrition and infections during childhood), and poor nutrition during pregnancy (UNICEF Data, https://data.unicef.org/)." }, { "id": "IndicatorName", "value": "Low-birthweight babies (% of births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Low-birthweight babies are newborns weighing less than 2,500 grams, with the measurement taken within the first hour of life, before significant postnatal weight loss has occurred." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2020" }, { "id": "Source", "value": "UNICEF-WHO Low birthweight estimates, UN Children's Fund (UNICEF), uri: data.unicef.org;\nWorld Health Organization (WHO), uri: data.unicef.org, note: UNICEF-WHO Low birthweight estimates" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Household Surveys including DHS, MICS and other national surveys\nStatistical concept(s): Low birthweight babies are more likely to die during their first month of life and those who survived face lifelong consequences including a higher risk of stunted growth, lower IQ ,and adult-onset chronic conditions such as obesity and diabetes." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.DIAB.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Diabetes, an important cause of ill health and a risk factor for other diseases in developed countries, is spreading rapidly in developing countries. Highest among the elderly, prevalence rates are rising among younger and productive populations in developing countries. Economic development has led to the spread of Western lifestyles and diet to developing countries, resulting in a substantial increase in diabetes. Without effective prevention and control programs, diabetes will likely continue to increase." }, { "id": "IndicatorName", "value": "Diabetes prevalence (% of population ages 20 to 79)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information." }, { "id": "Longdefinition", "value": "Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes. It is calculated by adjusting to a standard population age-structure." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Diabetes Atlas, International Diabetes Federation" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data used to estimate diabetes prevalence were gathered from various sources. Most of the data were extracted from peer-reviewed publications and national health surveys, including selected WHO STEPwise approach to surveillance (WHO STEPS) studies. Additionally, data from other official sources, such as registries and reports from health regulatory bodies, were utilized, provided there was sufficient information to assess their quality. Data sources with adequate methodological information on key areas of interest, such as the method of diagnosis and sample representativeness, were included. Given the significance of age as a major determinant for diabetes prevalence, only studies with at least three age-specific estimates were considered. After selecting the data sources, the reported age- and sex-specific data in each source were smoothed using a logistic regression model." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Percent of population ages 20 to 79" } ], "source_id": "57" }, { "id": "SH.STA.DIRH.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Prevalence of diarrhea (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of diarrhea: Percentage of children under age five years who had diarrhea in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.STA.DIRH.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Prevalence of diarrhea (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of diarrhea: Percentage of children under age five years who had diarrhea in the two weeks preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.STA.DIRH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Diarrhea prevalence (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Diarrhea prevalence is the percentage of children under age five who had diarrhea in the two weeks prior to the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Children's Fund, Demographic and Health Surveys, and Multiple Indicator Cluster Surveys." }, { "id": "Topic", "value": "Health: Risk factors" } ], "source_id": "57" }, { "id": "SH.STA.FGMS.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "FGM is a harmful practice involving the cutting or removal of the external female genitalia. It does not have any health benefits but rather causes serious risks to women’s physical and psychological health, including chronic infections, pain, menstrual problems, and complications during childbirth. FGM has been practiced mainly in the western, eastern, and north-eastern regions of Africa and some countries in the Middle East and Asia. It is reported that FGM is also found in western countries such as United Kingdom, United States, and Canada.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFGM is a violation of girls’ and women’s human rights, as well as a violation of women’s rights to health, security, and physical integrity. However, its eradication is now becoming a global concern and has even been set as one the SDGs, specifically as SDG target 5.3." }, { "id": "IndicatorName", "value": "Female genital mutilation prevalence (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data on FGM should be interpreted with caution for several reasons. Women may be reluctant to disclose undergoing FGM due to its sensitivity or illegal status, and some may be unaware of the procedure, especially if performed at an early age. The data is retrospective, not reflecting recent changes, with reports from girls aged 15 to 19 years referring to events 14 to 18 years earlier. Surveys like MICS and DHS only include FGM questions in countries where the practice is prevalent, meaning FGM may still exist in countries without data, including high-income countries with migrant populations and certain low- and middle-income countries. National-level estimates may be misleading as FGM is often practiced by specific ethnic groups in certain locations, thus not accurately representing the prevalence. Reference: A Generation to Protect: Monitoring violence exploitation and abuse of children within the SDG framework (UNICEF 2020). https://data.unicef.org/wp-content/uploads/2020/06/A-Generation-to-Protect-publication-English_2020.pdf" }, { "id": "Longdefinition", "value": "Percentage of women aged 15–49 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.3.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UNICEF DATA, UN Children's Fund (UNICEF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_15-49_FGM; \tIndicator name from the original source: Percentage of girls and women (aged 15-49 years) who have undergone female genital mutilation (FGM), type: API, date accessed: 2023-12-07;\nDemographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and other surverys, DHS Program (ICF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_15-49_FGM; \tIndicator name from the original source: Percentage of girls and women (aged 15-49 years) who have undergone female genital mutilation (FGM), publisher: The DHS Program (ICF), type: API, date accessed: 2023-12-07;\nDHS API, DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: PT_F_15-49_FGM; \tIndicator name from the original source: Percentage of girls and women (aged 15-49 years) who have undergone female genital mutilation (FGM), date accessed: 2023-12-07" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of women ages 15-49 who have undergone FGM divided by the total number of women ages 15-49 in the population multiplied by 100. The primary sources for this indicator are the Multiple Indicator Cluster Surveys (MICS) and the Demographic and Health Surveys (DHS). The majority of the data are compiled by UNICEF, which coordinates with countries to gather the information.\nStatistical concept(s): Female genital mutilation (FGM) encompasses all practices that involve the partial or total removal of the external female genitalia, or other injury to the female genital organs, for non-medical reasons. Typically, this procedure is carried out on minors." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "% of women ages 15-49" } ], "source_id": "57" }, { "id": "SH.STA.HYGN.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Generally, data on handwashing facilities are limited in high-income countries due to the infrequent collection of such information. In the early 2000s, even low- and middle-income countries often lacked this data. However, the recent standardization of hygiene-related questions in international surveys has led to an improvement in the availability of data." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Othernotes", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Utilizing national-level data derived from household surveys, mainly the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), the JMP calculates the proportion of the population with access to basic handwashing facilities at home with soap and water for each country by using a simple linear regression.\nStatistical concept(s): This indicator is measured using a straightforward indicator that examines the presence of handwashing facilities with soap within homes mainly through national household surveys. \n\n\n\n\n\n\n\n\n\n\nCollecting accurate information on handwashing practices presents challenges. Self-reported handwashing is an unreliable measure due to the potential for inaccurate reporting. Direct observation of handwashing can lead to observer bias, as individuals may alter their behavior when they know they are being watched, and implementing such observations on a large scale is resource-intensive. A more effective method involves survey enumerators observing the designated handwashing areas in homes and verifying the availability of water and soap, or a local substitute. This approach provides a more dependable and practical measure of handwashing behavior than relying on self-reported data." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "57" }, { "id": "SH.STA.HYGN.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Generally, data on handwashing facilities are limited in high-income countries due to the infrequent collection of such information. In the early 2000s, even low- and middle-income countries often lacked this data. However, the recent standardization of hygiene-related questions in international surveys has led to an improvement in the availability of data." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Othernotes", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Utilizing national-level data derived from household surveys, mainly the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), the JMP calculates the proportion of the population with access to basic handwashing facilities at home with soap and water for each country by using a simple linear regression.\nStatistical concept(s): This indicator is measured using a straightforward indicator that examines the presence of handwashing facilities with soap within homes mainly through national household surveys. \n\n\n\n\n\n\n\n\n\n\nCollecting accurate information on handwashing practices presents challenges. Self-reported handwashing is an unreliable measure due to the potential for inaccurate reporting. Direct observation of handwashing can lead to observer bias, as individuals may alter their behavior when they know they are being watched, and implementing such observations on a large scale is resource-intensive. A more effective method involves survey enumerators observing the designated handwashing areas in homes and verifying the availability of water and soap, or a local substitute. This approach provides a more dependable and practical measure of handwashing behavior than relying on self-reported data." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "57" }, { "id": "SH.STA.HYGN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Generally, data on handwashing facilities are limited in high-income countries due to the infrequent collection of such information. In the early 2000s, even low- and middle-income countries often lacked this data. However, the recent standardization of hygiene-related questions in international surveys has led to an improvement in the availability of data." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Generally, data on handwashing facilities are limited in high-income countries due to the infrequent collection of such information. In the early 2000s, even low- and middle-income countries often lacked this data. However, the recent standardization of hygiene-related questions in international surveys has led to an improvement in the availability of data." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Utilizing national-level data derived from household surveys, mainly the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), the JMP calculates the proportion of the population with access to basic handwashing facilities at home with soap and water for each country by using a simple linear regression.\nStatistical concept(s): This indicator is measured using a straightforward indicator that examines the presence of handwashing facilities with soap within homes mainly through national household surveys. \n\n\n\n\n\n\n\n\n\n\nCollecting accurate information on handwashing practices presents challenges. Self-reported handwashing is an unreliable measure due to the potential for inaccurate reporting. Direct observation of handwashing can lead to observer bias, as individuals may alter their behavior when they know they are being watched, and implementing such observations on a large scale is resource-intensive. A more effective method involves survey enumerators observing the designated handwashing areas in homes and verifying the availability of water and soap, or a local substitute. This approach provides a more dependable and practical measure of handwashing behavior than relying on self-reported data." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "57" }, { "id": "SH.STA.MALN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of underweight, weight for age, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of underweight, female, is the percentage of girls under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child underweight belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.MALN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of underweight, weight for age, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of underweight, male, is the percentage of boys under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child underweight belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.MALN.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Malnourished children (underweight, -2SD) (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.STA.MALN.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Malnourished children (underweight, -2SD) (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Malnourished children: Percentage of children under age five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). Each index is expressed in terms of the number of standard deviation (SD) units from the median of the WHO Child Growth Standards. Children are classified as malnourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the WHO Child Growth Standards. The percentage below -2 SD includes children who are below -3 SD." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.STA.MALN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of underweight, weight for age (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of underweight children is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child underweight belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.MMRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Maternal mortality ratio (modeled estimate, per 100,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality." }, { "id": "Longdefinition", "value": "Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs)." }, { "id": "Othernotes", "value": "This indicator represents the risk associated with each pregnancy and is also a Sustainable Development Goal Indicator (3.1.1) for monitoring maternal health." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Trends in Maternal Mortality, World Health Organization (WHO), uri: https://www.who.int/news/item/23-02-2023-a-woman-dies-every-two-minutes-due-to-pregnancy-or-childbirth--un-agencies;\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Per 100,000 live births" } ], "source_id": "57" }, { "id": "SH.STA.MMRT.NE", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants." }, { "id": "IndicatorName", "value": "Maternal mortality ratio (national estimate, per 100,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality.\n\nMaternal mortality ratios collected directly from Demographic and Health Surveys are presented in the survey year, but reference time of these maternal mortality ratios is for the seven years preceding the survey." }, { "id": "Longdefinition", "value": "Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Maternal Mortality Estimation Inter-Agency Group (MMEIG), World Health Organization (WHO), note: The country data compiled, adjusted and used in the estimation model by the Maternal Mortality Estimation Inter-Agency Group (MMEIG). The country data were compiled from the following sources: civil registration and vital statistics; specialized studies on maternal mortality; population based surveys and censuses; other available data sources including data from surveillance sites.;\nUN Children's Fund (UNICEF), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nUN Population Fund (UNFPA), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nWorld Bank Group (WBG), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nUnited Nations (UN), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nPAHO, note: Core Indicators Portal;\nICF, note: The DHS Program, Demographic and Health Surveys" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The national estimates of maternal mortality ratios are based on national surveys, vital registration records, and surveillance data or are derived from community and hospital records.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "57" }, { "id": "SH.STA.ODFC.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to sanitation is a fundamental human right. Open defecation, which is the practice of relieving oneself outside without proper facilities, is a violation of human dignity and poses a significant threat to public health and nutrition. Poor sanitation is a leading cause of infectious diseases globally, and enhancing sanitation services has been proven to have a substantial positive effect on health outcomes. The provision of basic and safely managed sanitation can decrease the incidence of diarrheal diseases and mitigate the health consequences of other serious illnesses that cause widespread morbidity and mortality among children. Diarrheal conditions and parasitic infections debilitate children, increasing their vulnerability to malnutrition and secondary infections such as pneumonia, measles, and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe absence of adequate sanitation is especially harmful to women who are forced to defecate in the open, as it compromises their privacy and exposes them to a greater risk of assault and violence. The elimination of open defecation is a specific target within the Sustainable Development Goals (SDG target 6.2), underscoring the international commitment to addressing this critical issue." }, { "id": "IndicatorName", "value": "People practicing open defecation, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Othernotes", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "57" }, { "id": "SH.STA.ODFC.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to sanitation is a fundamental human right. Open defecation, which is the practice of relieving oneself outside without proper facilities, is a violation of human dignity and poses a significant threat to public health and nutrition. Poor sanitation is a leading cause of infectious diseases globally, and enhancing sanitation services has been proven to have a substantial positive effect on health outcomes. The provision of basic and safely managed sanitation can decrease the incidence of diarrheal diseases and mitigate the health consequences of other serious illnesses that cause widespread morbidity and mortality among children. Diarrheal conditions and parasitic infections debilitate children, increasing their vulnerability to malnutrition and secondary infections such as pneumonia, measles, and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe absence of adequate sanitation is especially harmful to women who are forced to defecate in the open, as it compromises their privacy and exposes them to a greater risk of assault and violence. The elimination of open defecation is a specific target within the Sustainable Development Goals (SDG target 6.2), underscoring the international commitment to addressing this critical issue." }, { "id": "IndicatorName", "value": "People practicing open defecation, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Othernotes", "value": "This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "57" }, { "id": "SH.STA.ODFC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Access to sanitation is a fundamental human right. Open defecation, which is the practice of relieving oneself outside without proper facilities, is a violation of human dignity and poses a significant threat to public health and nutrition. Poor sanitation is a leading cause of infectious diseases globally, and enhancing sanitation services has been proven to have a substantial positive effect on health outcomes. The provision of basic and safely managed sanitation can decrease the incidence of diarrheal diseases and mitigate the health consequences of other serious illnesses that cause widespread morbidity and mortality among children. Diarrheal conditions and parasitic infections debilitate children, increasing their vulnerability to malnutrition and secondary infections such as pneumonia, measles, and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe absence of adequate sanitation is especially harmful to women who are forced to defecate in the open, as it compromises their privacy and exposes them to a greater risk of assault and violence. The elimination of open defecation is a specific target within the Sustainable Development Goals (SDG target 6.2), underscoring the international commitment to addressing this critical issue." }, { "id": "IndicatorName", "value": "People practicing open defecation (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "57" }, { "id": "SH.STA.ORCF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Most diarrhea-related deaths are due to dehydration, and many of these deaths can be prevented with the use of oral rehydration salts at home." }, { "id": "IndicatorName", "value": "Diarrhea treatment (% of children under 5 receiving oral rehydration and continued feeding)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Recommendations for the use of oral rehydration therapy have changed over time based on scientific progress, so it is difficult to accurately compare use rates across countries. Until the current recommended method for home management of diarrhea is adopted and applied in all countries, the data should be used with caution. Also, the prevalence of diarrhea may vary by season. Since country surveys are administered at different times, data comparability is further affected." }, { "id": "Longdefinition", "value": "Children with diarrhea who received oral rehydration and continued feeding refer to the percentage of children under age five with diarrhea in the two weeks prior to the survey who received either oral rehydration therapy or increased fluids, with continued feeding." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Mothers or caregivers of children under five years old are asked whether the child experienced diarrhea at any point in the past two weeks. If the child did have diarrhea, they are further asked whether either oral rehydration therapy or increased fluids, with continued feeding was administered. The term \"diarrhea,\" as defined by the DHS, should include all forms of diarrhea, such as bloody stools (indicative of dysentery), watery stools, and other variations. This definition encompasses both the mother's understanding and locally-used terms." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SH.STA.ORHF.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Treatment of diarrhea (ORS, RHS or increased fluids) (% of children under 5): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Treatment of diarrhea (ORS, RHS or increased fluids): Percentage of children under age five years with diarrhea in the two weeks preceding the survey who received oral rehydration solution (ORS), recommended home solution (RHS) or increased fluids." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.STA.ORHF.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Treatment of diarrhea (ORS, RHS or increased fluids) (% of children under 5): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Treatment of diarrhea (ORS, RHS or increased fluids): Percentage of children under age five years with diarrhea in the two weeks preceding the survey who received oral rehydration solution (ORS), recommended home solution (RHS) or increased fluids." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Health" } ], "source_id": "57" }, { "id": "SH.STA.ORTH", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Diarrhea treatment (% of children under 5 who received ORS packet)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator does not assess the severity of the diarrhea." }, { "id": "Longdefinition", "value": "Percentage of children under age 5 with diarrhea in the two weeks preceding the survey who received oral rehydration salts (ORS packets or pre-packaged ORS fluids)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Source", "value": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, DHS Program (ICF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Mothers or caregivers of children under five years old are asked whether the child experienced diarrhea at any point in the past two weeks. If the child did have diarrhea, they are further asked whether Oral Rehydration Solution (ORS) was administered. The term \"diarrhea,\" as defined by the DHS, should include all forms of diarrhea, such as bloody stools (indicative of dysentery), watery stools, and other variations. This definition encompasses both the mother's understanding and locally-used terms." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SH.STA.OWGH.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight, weight for height, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.OWGH.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight, weight for height, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of overweight, male, is the percentage of boys under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.OWGH.ME.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight, female (modeled estimate, % of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time." }, { "id": "Longdefinition", "value": "Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\n\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates." }, { "id": "Periodicity", "value": "Every two years" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME);\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.OWGH.ME.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight, male (modeled estimate, % of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time." }, { "id": "Longdefinition", "value": "Prevalence of overweight, male, is the percentage of boys under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\n\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates." }, { "id": "Periodicity", "value": "Every two years" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME);\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.OWGH.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight (modeled estimate, % of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time." }, { "id": "Longdefinition", "value": "Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates." }, { "id": "Periodicity", "value": "Every two years" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME);\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.OWGH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "See SH.STA.OWGH.ME.ZS for aggregation" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of overweight, weight for height (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.POIS.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unintentional poisoning (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unintentional poisonings is the number of deaths from unintentional poisonings in a year per 100,000 population. Unintentional poisoning can\n\n\nbe caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.9.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for unintentional poisoning mortality are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database are used, with necessary adjustments for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. For countries lacking high-quality death registration data, cause of death estimates are calculated using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates. The complete methodology can be found at the following: https://www.who.int/docs/defaultsource/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf" }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "57" }, { "id": "SH.STA.POIS.P5.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unintentional poisoning, female (per 100,000 female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unintentional poisonings is the number of female deaths from unintentional poisonings in a year per 100,000 female population. Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for unintentional poisoning mortality are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database are used, with necessary adjustments for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. For countries lacking high-quality death registration data, cause of death estimates are calculated using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates. The complete methodology can be found at the following: https://www.who.int/docs/defaultsource/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf" }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 female population" } ], "source_id": "57" }, { "id": "SH.STA.POIS.P5.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unintentional poisoning, male (per 100,000 male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unintentional poisonings is the number of male deaths from unintentional poisonings in a year per 100,000 male population. Unintentional poisoning can\n\n\nbe caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.3[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The estimates for unintentional poisoning mortality are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database are used, with necessary adjustments for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. For countries lacking high-quality death registration data, cause of death estimates are calculated using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates. The complete methodology can be found at the following: https://www.who.int/docs/defaultsource/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf" }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 male population" } ], "source_id": "57" }, { "id": "SH.STA.SMSS.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using safely managed sanitation services, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporality and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site). Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system. If no information is available, it is assumed that 50 percent is safely managed. Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed sanitation facilities are defined as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of rural population" } ], "source_id": "57" }, { "id": "SH.STA.SMSS.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using safely managed sanitation services, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporality and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site). Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system. If no information is available, it is assumed that 50 percent is safely managed. Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed sanitation facilities are defined as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "57" }, { "id": "SH.STA.SMSS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using safely managed sanitation services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporality and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site). Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system. If no information is available, it is assumed that 50 percent is safely managed. Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\nThis indicator (Total) is calculated as a population-weighted average of the RURAL and URBAN aggregates when sufficient data are available for both domains. When coverage at either the RURAL or URBAN level is insufficient, but country-level TOTAL data meet the minimum population coverage threshold (30%), TOTAL aggregates are calculated directly from country-level TOTAL estimates. Because these two aggregation approaches may be applied in different years as data availability improves, methodological switches can occur and may result in discontinuities in the time series.\n\n\n\nThis indicator corresponds to Sustainable Development Goal indicator 6.2.1 (see UN SDG metadata: https://unstats.un.org/sdgs/metadata/)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed sanitation facilities are defined as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "57" }, { "id": "SH.STA.STNT.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of stunting, female, is the percentage of girls under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.STNT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.STNT.ME.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, female (modeled estimate, % of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time." }, { "id": "Longdefinition", "value": "Prevalence of stunting, female, is the percentage of girls under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\n\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates." }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME);\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.STNT.ME.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age, male (modeled estimate, % of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time." }, { "id": "Longdefinition", "value": "Prevalence of stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\n\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates." }, { "id": "Periodicity", "value": "Every two years" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME);\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.STNT.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age (modeled estimate, % of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time." }, { "id": "Longdefinition", "value": "Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates." }, { "id": "Periodicity", "value": "Every two years" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, publisher: JME;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME), publisher: JME;\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME), publisher: JME" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.STNT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "See SH.STA.STNT.ME.ZS for aggregation" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of stunting, height for age (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels." }, { "id": "Longdefinition", "value": "Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.SUIC.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Suicide mortality rate, female (per 100,000 female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of suicide deaths in a year by the mid-year population for the same calendar year, then multiplying by 100,000. The estimates are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the vital registration data submitted by member states to the WHO Mortality Database are used, with adjustments made where necessary (e.g., for under-reporting of deaths, unknown age and sex, and ill-defined causes of death). For countries without high-quality death registration data, cause of death estimates are calculated using other sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not be identical to official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 female population" } ], "source_id": "57" }, { "id": "SH.STA.SUIC.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Suicide mortality rate, male (per 100,000 male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted)." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of suicide deaths in a year by the mid-year population for the same calendar year, then multiplying by 100,000. The estimates are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the vital registration data submitted by member states to the WHO Mortality Database are used, with adjustments made where necessary (e.g., for under-reporting of deaths, unknown age and sex, and ill-defined causes of death). For countries without high-quality death registration data, cause of death estimates are calculated using other sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not be identical to official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 male population" } ], "source_id": "57" }, { "id": "SH.STA.SUIC.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Suicide mortality rate (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Due to changes in input data and methods, the revisions of the data are not comparable to previously published estimates." }, { "id": "Longdefinition", "value": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted)." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.4.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: It is calculated by dividing the number of suicide deaths in a year by the mid-year population for the same calendar year, then multiplying by 100,000. The estimates are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the vital registration data submitted by member states to the WHO Mortality Database are used, with adjustments made where necessary (e.g., for under-reporting of deaths, unknown age and sex, and ill-defined causes of death). For countries without high-quality death registration data, cause of death estimates are calculated using other sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not be identical to official national estimates." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "57" }, { "id": "SH.STA.TRAF.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Road traffic injuries and deaths is a major global public health problem. Road traffic crashes are currently the leading cause of death for children and young adults in the world. There is a strong association between the risk of road traffic death and the income level of countries. The burden of road traffic deaths is disproportionately high among low- and middle-income countries." }, { "id": "IndicatorName", "value": "Mortality caused by road traffic injury (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mortality caused by road traffic injury is estimated road traffic fatal injury deaths per 100,000 population." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.6.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2019" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The methods used for analyzing causes of death vary based on the type of data available from different countries. For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database is utilized, with necessary adjustments made for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. In contrast, for countries lacking high-quality death registration data, cause of death estimates are derived using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "57" }, { "id": "SH.STA.WASH.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unsafe drinking water, unsafe sanitation and lack of hygiene are important causes of death. Most diarrheal deaths in the world are caused by unsafe water, sanitation or hygiene. According to the World Health Organization, in addition to diarrea, the following diseases could be prevented if adequate WASH services are provided: malnutrition, intestinal nematode infections, lymphatic filariasis, trachoma, schistosomiasis and malaria." }, { "id": "IndicatorName", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (per 100,000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Some countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries." }, { "id": "Longdefinition", "value": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.9.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2019-2019" }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: To estimate the portion of deaths from diarrhea and acute respiratory infections attributable to unsafe water, sanitation, and hygiene (WASH), a comparative risk assessment approach is used. This involves calculating the attributable disease deaths by combining information on the increased (or relative) risk of a disease resulting from exposure with the prevalence of that exposure in the population. This calculation yields the 'population attributable fraction' (PAF), which represents the fraction of disease in a population that can be attributed to the exposure, in this case, unsafe WASH.\n\n\nBy applying the PAF to the total deaths from diarrhea or acute respiratory infections, the number of deaths resulting from inadequate WASH can be determined. Additionally, deaths from protein-energy malnutrition attributable to inadequate WASH are estimated by evaluating the impacts of repeated infectious diarrhea episodes on nutritional status, particularly stunting. All deaths from intestinal nematode infections are attributed to inadequate WASH due to their transmission pathway." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 100,000 population" } ], "source_id": "57" }, { "id": "SH.STA.WAST.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of wasting, weight for height, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of wasting, female, is the proportion of girls under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.WAST.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of wasting, weight for height, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of wasting, male, is the proportion of boys under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.STA.WAST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of wasting, weight for height (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of wasting is the proportion of children under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.SVR.WAST.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of severe wasting, weight for height, female (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of severe wasting, female, is the proportion of girls under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.SVR.WAST.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of severe wasting, weight for height, male (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of severe wasting, male, is the proportion of boys under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1986-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.SVR.WAST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Linear mixed-effect model estimates" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Child growth is an internationally accepted outcome reflecting child nutritional status." }, { "id": "IndicatorName", "value": "Prevalence of severe wasting, weight for height (% of children under 5)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided." }, { "id": "Longdefinition", "value": "Prevalence of severe wasting is the proportion of children under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months." }, { "id": "Othernotes", "value": "Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1983-2024" }, { "id": "Source", "value": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.TBS.CURE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tuberculosis (TB) is a preventable and usually curable disease. Yet TB is one of the world’s leading causes of death from a single infectious agent. Millions of people continue to fall ill with TB every year." }, { "id": "IndicatorName", "value": "Tuberculosis treatment success rate (% of new cases)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Tuberculosis treatment success rate is the percentage of all new tuberculosis cases (or new and relapse cases for some countries) registered under a national tuberculosis control programme in a given year that successfully completed treatment, with or without bacteriological evidence of success (\"cured\" and \"treatment completed\" respectively)." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "Global Tuberculosis Report, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of cases registered in a given year (excluding cases placed on a second-line drug regimen) that successfully completed treatment without bacteriological evidence of failure. All registered cases fall into one of the following five mutually exclusive categories: treatment success, failure, death, lost to follow-up, not evaluated (missing data on the outcome of treatment).\nStatistical concept(s): Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. Data on the success rate of tuberculosis treatment are provided for countries that have submitted data to the WHO. The treatment success rate for tuberculosis provides a useful indicator of the quality of health services. A low rate suggests that infectious patients may not be receiving adequate treatment. An important complement to the tuberculosis treatment success rate is the case detection rate, which indicates whether there is adequate coverage by the recommended case detection and treatment strategy." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.TBS.DOTS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Tuberculosis cases detected under DOTS (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "DOTS detection rate is the percentage of estimated new infectious tuberculosis cases detected under the directly observed treatment, short course case detection and treatment strategy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization, Global Tuberculosis Control Report." }, { "id": "Topic", "value": "Health: Disease prevention" } ], "source_id": "57" }, { "id": "SH.TBS.DTEC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tuberculosis (TB) is a preventable and usually curable disease. Yet TB is one of the world’s leading causes of death from a single infectious agent. Millions of people continue to fall ill with TB every year." }, { "id": "IndicatorName", "value": "Tuberculosis case detection rate (%, all forms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Tuberculosis case detection rate (all forms) is the number of new and relapse tuberculosis cases notified to WHO in a given year, divided by WHO's estimate of the number of incident tuberculosis cases for the same year, expressed as a percentage. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Tuberculosis Report, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The number of new and relapse TB cases diagnosed and treated in national TB control programmes and notified to WHO, divided by WHO's estimate of the number of incident TB cases for the same year, expressed as a percentage.\nStatistical concept(s): Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. This indicator shows the tuberculosis detection rate for all detection methods. Editions before 2010 included the tuberculosis detection rates by DOTS, the internationally recommended strategy for tuberculosis control. Thus data on the case detection rate from 2010 onward cannot be compared with data in previous editions." }, { "id": "Topic", "value": "Health: Disease prevention" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.TBS.INCD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tuberculosis (TB) is a preventable and usually curable disease. Yet TB is one of the world’s leading causes of death from a single infectious agent. Millions of people continue to fall ill with TB every year." }, { "id": "IndicatorName", "value": "Incidence of tuberculosis (per 100,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nUncertainty bounds for the incidence are available at http://data.worldbank.org" }, { "id": "Longdefinition", "value": "Incidence of tuberculosis is the estimated number of new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population. All forms of TB are included, including cases in people living with HIV. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously." }, { "id": "Othernotes", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization.\n\nThis is the Sustainable Development Goal indicator 3.3.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Tuberculosis Report, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of TB incidence are produced through a consultative and analytical process led by WHO and are published annually. These estimates are based on annual case notifications, assessments of the quality and coverage of TB notification data, national surveys of the prevalence of TB disease and on information from death (vital) registration systems.\nStatistical concept(s): Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. In developed countries tuberculosis has reemerged largely as a result of cases among immigrants. Since tuberculosis incidence cannot be directly measured, estimates are obtained by eliciting expert opinion or are derived from measurements of prevalence or mortality." }, { "id": "Topic", "value": "Health: Risk factors" }, { "id": "Unitofmeasure", "value": "Per 100,000 people" } ], "source_id": "57" }, { "id": "SH.UHC.CONS.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Number of people pushed below the 50% median consumption poverty line by out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of people pushed below the 50% median consumption poverty line by out-of-pocket health care expenditure" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Impoverishment at the Relative Poverty Line" }, { "id": "Source", "value": "Wagstaff et al. Progress on Impoverishing Health Spending: Results for 122 Countries. A Retrospective Observational Study, Lancet Global Health 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are impoverishing at the relative PL for a household when consumption gross of out-of-pocket payments is higher than the relative PL, but consumption net of out-of-pocket payments is lower than the relative PL. The relative poverty line used is define as 50% of the median consumption/income." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "57" }, { "id": "SH.UHC.CONS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Proportion of population pushed below the 50% median consumption poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of population pushed below the 50% median consumption poverty line by out-of-pocket health care expenditure, expressed as a percentage of a total population of a country" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Impoverishment at the Relative Poverty Line (%)" }, { "id": "Source", "value": "Wagstaff et al. Progress on Impoverishing Health Spending: Results for 122 Countries. A Retrospective Observational Study, Lancet Global Health 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are impoverishing at the relative PL for a household when consumption gross of out-of-pocket payments is higher than the relative PL, but consumption net of out-of-pocket payments is lower than the relative PL. The relative poverty line used is define as 50% of the median consumption/income." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "57" }, { "id": "SH.UHC.FBP1.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed further below the $2.15 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the $2.15 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the $2.15 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.UHC.FBP2.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed further below the $3.65 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the $3.65 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the $3.65 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.UHC.FBPR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed further below the 60% median consumption poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the 60% median consumption poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. \n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the 60% median consumption poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. \n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.UHC.NOP1.CG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (USD)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure, expressed in US dollars (2011 PPP). The poverty gap increase due to out-of-pocket health spending is one way to measure how much out-of-pocket health spending pushes people below or further below the poverty line (the difference in the poverty gap due to out-of-pocket health spending being included or excluded from the measure of household welfare). This difference corresponds to the total out-of-pocket health spending for households that are already below the poverty line, to the amount that exceeds the shortfall between the poverty line and total consumption for households that are impoverished by out-of-pocket health spending and to zero for households whose consumption is above the poverty line after accounting for out-of-pocket health spending." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Increase in poverty gap at $1.90 (USD)" }, { "id": "Source", "value": "World Health Organization and World Bank. 2019. Global Monitoring Report on Financial Protection in Health 2019. NOTE: This indicator has been discontinued as of December 2021. Please see the following indicators: SH.UHC.FBP1.ZS, SH.UHC.FBP2.ZS, SH.UHC.FBP1.TO and SH.UHC.FBP2.TO." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. This series measures the poverty gap increase attributable to OOP health expenditures. This amount can be interpreted as the per capita amount by which on average OOP spending pushes or further pushes the household below the PL. It is defined as the difference between the poverty gap based on a measure of consumption net of OOP health expenditures and a measure of consumption gross of OOP health expenditures. The difference is expressed in 2011 PPP international dollar." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "57" }, { "id": "SH.UHC.NOP1.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people pushed below the $2.15 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator shows the number of people living in households experiencing impoverishing out-of-pocket health expenditures, defined as expenditures without which the household they live in would have been above the $2.15 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n2. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Impoverishing health spending, 2.15$" }, { "id": "Source", "value": "Global Health Observatory. Geneva: World Health Organization. (https://www.who.int/data/gho/data/themes/topics/financial-protection)" }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "57" }, { "id": "SH.UHC.NOP1.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (% of poverty line)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure, as a percentage of the $1.90 poverty line. The poverty gap increase due to out-of-pocket health spending is one way to measure how much out-of-pocket health spending pushes people below or further below the poverty line (the difference in the poverty gap due to out-of-pocket health spending being included or excluded from the measure of household welfare). This difference corresponds to the total out-of-pocket health spending for households that are already below the poverty line, to the amount that exceeds the shortfall between the poverty line and total consumption for households that are impoverished by out-of-pocket health spending and to zero for households whose consumption is above the poverty line after accounting for out-of-pocket health spending." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Increase in poverty gap at $1.90 (% of poverty line)" }, { "id": "Source", "value": "World Health Organization and World Bank. 2019. Global Monitoring Report on Financial Protection in Health 2019. NOTE: This indicator has been discontinued as of December 2021. Please see the following indicators: SH.UHC.FBP1.ZS, SH.UHC.FBP2.ZS, SH.UHC.FBP1.TO and SH.UHC.FBP2.TO." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. This series measures the poverty gap increase attributable to OOP health expenditures. This amount can be interpreted as the per capita amount by which on average OOP spending pushes or further pushes the household below the PL. It is defined as the difference between the poverty gap based on a measure of consumption net of OOP health expenditures and a measure of consumption gross of OOP health expenditures. The difference is expressed as a percentage of the PL." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "57" }, { "id": "SH.UHC.NOP1.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed below the $2.15 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the $ 2.15 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the $ 2.15 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.UHC.NOP2.CG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Increase in poverty gap at $3.20 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (USD)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Increase in poverty gap at $3.20 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure, expressed in US dollars (2011 PPP). The poverty gap increase due to out-of-pocket health spending is one way to measure how much out-of-pocket health spending pushes people below or further below the poverty line (the difference in the poverty gap due to out-of-pocket health spending being included or excluded from the measure of household welfare). This difference corresponds to the total out-of-pocket health spending for households that are already below the poverty line, to the amount that exceeds the shortfall between the poverty line and total consumption for households that are impoverished by out-of-pocket health spending and to zero for households whose consumption is above the poverty line after accounting for out-of-pocket health spending." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Increase in poverty gap at $3.20 (USD)" }, { "id": "Source", "value": "World Health Organization and World Bank. 2019. Global Monitoring Report on Financial Protection in Health 2019. NOTE: This indicator has been discontinued as of December 2021. Please see the following indicators: SH.UHC.FBP1.ZS, SH.UHC.FBP2.ZS, SH.UHC.FBP1.TO and SH.UHC.FBP2.TO." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. This series measures the poverty gap increase attributable to OOP health expenditures. This amount can be interpreted as the per capita amount by which on average OOP spending pushes or further pushes the household below the PL. It is defined as the difference between the poverty gap based on a measure of consumption net of OOP health expenditures and a measure of consumption gross of OOP health expenditures. The difference is expressed in 2011 PPP international dollar." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "57" }, { "id": "SH.UHC.NOP2.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people pushed below the $3.65 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator shows the number of households experiencing impoverishing out-of-pocket health expenditures, defined as expenditures without which the household would have been above the $3.65 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n2. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Impoverishing health spending, 3.65$" }, { "id": "Source", "value": "Global Health Observatory. Geneva: World Health Organization. (https://www.who.int/data/gho/data/themes/topics/financial-protection)" }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "57" }, { "id": "SH.UHC.NOP2.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Increase in poverty gap at $3.20 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (% of poverty line)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Increase in poverty gap at $3.20 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure, as a percentage of the $3.20 poverty line. The poverty gap increase due to out-of-pocket health spending is one way to measure how much out-of-pocket health spending pushes people below or further below the poverty line (the difference in the poverty gap due to out-of-pocket health spending being included or excluded from the measure of household welfare). This difference corresponds to the total out-of-pocket health spending for households that are already below the poverty line, to the amount that exceeds the shortfall between the poverty line and total consumption for households that are impoverished by out-of-pocket health spending and to zero for households whose consumption is above the poverty line after accounting for out-of-pocket health spending." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Increase in poverty gap at $3.20 (% of poverty line)" }, { "id": "Source", "value": "World Health Organization and World Bank. 2019. Global Monitoring Report on Financial Protection in Health 2019. NOTE: This indicator has been discontinued as of December 2021. Please see the following indicators: SH.UHC.FBP1.ZS, SH.UHC.FBP2.ZS, SH.UHC.FBP1.TO and SH.UHC.FBP2.TO." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. This series measures the poverty gap increase attributable to OOP health expenditures. This amount can be interpreted as the per capita amount by which on average OOP spending pushes or further pushes the household below the PL. It is defined as the difference between the poverty gap based on a measure of consumption net of OOP health expenditures and a measure of consumption gross of OOP health expenditures. The difference is expressed as a percentage of the PL." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "57" }, { "id": "SH.UHC.NOP2.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed below the $3.65 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the $3.65 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the $3.65 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.UHC.NOPR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed below the 60% median consumption poverty line by out-of-pocket health expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the 60% median consumption but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the 60% median consumption but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.UHC.OOPC.10.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people spending more than 10% of household consumption or income on out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of people spending more than 10% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n2. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Catastrophic Health Expenditure, 10% of total expenditure/income" }, { "id": "Source", "value": "Global Health Observatory. Geneva: World Health Organization. (https://www.who.int/data/gho/data/themes/topics/financial-protection)" }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "57" }, { "id": "SH.UHC.OOPC.10.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.8.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.UHC.OOPC.25.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "Generalcomments", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Number of people spending more than 25% of household consumption or income on out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of people spending more than 25% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Notesfromoriginalsource", "value": "1. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used. For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.\n\n2. For details of the definition for out-of-pocket health spending, please see the following publication: World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Catastrophic Health Expenditure, 25% of total expenditure/income (thousands)" }, { "id": "Source", "value": "Global Health Observatory. Geneva: World Health Organization. (https://www.who.int/data/gho/data/themes/topics/financial-protection)" }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "57" }, { "id": "SH.UHC.OOPC.25.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.8.2[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.UHC.SRVS.CV.XD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need without facing financial hardship. It is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "UHC service coverage index" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Coverage index for essential health services (based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, noncommunicable diseases and service capacity and access). It is presented on a scale of 0 to 100." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.8.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Shortdefinition", "value": "Coverage index for essential health services (based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, noncommunicable diseases and service capacity and access). It is presented on a scale of 0 to 100." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/service-coverage" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: For each country, the most recent value for each tracer indicators is taken from WHO or other international agencies. The index is computed using geometric means of the tracer indicators.\nStatistical concept(s): The Service Coverage Index used to track SDG 3.8.1 includes four indicator categories, namely (1) reproductive, manternal and newborn and child health, (2) infectious diseases, (3) non-communicable diseases and (4) service capacity and access. Each category contains several tracers. The index is constructed from geometric means of the tracer indicators; first, within each of the four categories, and then across the four category-specific means to obtain the final summary index. See Source for details about methodology." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "index" } ], "source_id": "57" }, { "id": "SH.UHC.TOT1.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed or further pushed below the $2.15 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country's population who is either (1) living in households whose non-health expenditures are already below the $2.15 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the $2.15 poverty line but fall below the $2.15 poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country's population who is either (1) living in households whose non-health expenditures are already below the $2.15 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the $2.15 poverty line but fall below the $2.15 poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.UHC.TOT2.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed or further pushed below the $3.65 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country's population who is either (1) living in households whose non-health expenditures are already below the $3.65 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the $3.65 poverty line but fall below the $3.65 poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country's population who is either (1) living in households whose non-health expenditures are already below the $3.65 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the $3.65 poverty line but fall below the $3.65 poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.UHC.TOTR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development." }, { "id": "IndicatorName", "value": "Proportion of population pushed or further pushed below the 60% median consumption poverty line by out-of-pocket health expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator shows the fraction of a country's population who is either (1) living in households whose non-health expenditures are already below the relative poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the relative poverty line but fall below the relative poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Othernotes", "value": "This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2021" }, { "id": "Shortdefinition", "value": "This indicator shows the fraction of a country's population who is either (1) living in households whose non-health expenditures are already below the relative poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the relative poverty line but fall below the relative poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home)." }, { "id": "Source", "value": "Global Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/financial-protection" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of the population facing catastrophic expenditures is measured as the population-weighted average of the number of households with “large household expenditures on health” as a share of total household expenditure or income (household’s budget).\nStatistical concept(s): Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption." }, { "id": "Topic", "value": "Health: Universal Health Coverage" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.VAC.TTNS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Immunization is one of the most cost-effective public health interventions, and ??is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts." }, { "id": "IndicatorName", "value": "Newborns protected against tetanus (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Newborns protected against tetanus are the percentage of births by women of child-bearing age who are immunized against tetanus." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2024" }, { "id": "Source", "value": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.VST.OUTP", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Outpatient visits per capita" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Outpatient visits per capita are the number of visits to health care facilities per capita, including repeat visits." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO, OECD and supplemented by country data." }, { "id": "Topic", "value": "Health: Health systems" } ], "source_id": "57" }, { "id": "SH.XPD.CHEX.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Current health expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Level of current health expenditure expressed as a percentage of GDP. Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, IT and stocks of vaccines for emergency or outbreaks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.XPD.CHEX.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Current health expenditure per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current expenditures on health per capita in current US dollars. Estimates of current health expenditures include healthcare goods and services consumed during each year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "SH.XPD.CHEX.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Current health expenditure per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current expenditures on health per capita expressed in international dollars at purchasing power parity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current iternational $" } ], "source_id": "57" }, { "id": "SH.XPD.EHEX.CH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "External health expenditure (% of current health expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Share of current health expenditures funded from external sources. External sources compose of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country. External sources either flow through the government scheme or are channeled through non-governmental organizations or other schemes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.XPD.EHEX.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "External health expenditure per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current external expenditures on health per capita expressed in current US dollars. External sources are composed of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "SH.XPD.EHEX.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "External health expenditure per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current external expenditures on health per capita expressed in international dollars at purchasing power parity. External sources are composed of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current iternational $" } ], "source_id": "57" }, { "id": "SH.XPD.EXTR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources." }, { "id": "IndicatorName", "value": "External resources for health (% of total expenditure on health)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Country data may differ in terms of definitions, data collection methods, population coverage and estimation methods used.\n\nIn countries where the fiscal year spans two calendar years, expenditure data have been allocated to the later year (for example, 2010 data cover fiscal year 2009/10).\n\nExternal resources for health are disbursements to recipient countries as reported by donors, lagged one year to account for the delay between disbursement and expenditure. Except where a reliable full national health account study has been done, most data are from the Organisation for Economic Co-operation and Development's Development Assistance Committee's Creditor Reporting System database, which compiles data from government expenditure accounts, government records on external assistance, routine surveys of external financing assistance, and special services. Because of the variety of sources, caution should be used in interpreting the data." }, { "id": "Longdefinition", "value": "External resources for health are funds or services in kind that are provided by entities not part of the country in question. The resources may come from international organizations, other countries through bilateral arrangements, or foreign nongovernmental organizations. These resources are part of total health expenditure." }, { "id": "Notesfromoriginalsource", "value": "In some cases, the sum of public and private expenditures on health may not add up to 100% because of rounding. All the health expenditure indicators refer to expenditures by financing agent except external resources which is a financing source. When the number is smaller than 0.05%, the percentage may appear as zero. In countries where the fiscal year begins in July, expenditure data have been allocated to the later calendar year (for example, 2010 data will cover the fiscal year 2009–10), unless otherwise stated for the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates)." }, { "id": "Statisticalconceptandmethodology", "value": "Health expenditure data are broken down into public and private expenditures. In general, low-income economies have a higher share of private health expenditure than do middle- and high-income countries, and out-of-pocket expenditure (direct payments by households to providers) makes up the largest proportion of private expenditures. High out-of-pocket expenditures may discourage people from accessing preventive or curative care and can impoverish households that cannot afford necessary care. Health financing data are collected through national health accounts, which systematically, comprehensively, and consistently monitor health system resource flows. To establish a national health account, countries must define the boundaries of the health system and classify health expenditure information along several dimensions, including sources of financing, providers of health services, functional use of health expenditures, and beneficiaries of expenditures. The accounting system can then provide an accurate picture of resource envelopes and financial flows and allow analysis of the equity and efficiency of financing to inform policy." }, { "id": "Topic", "value": "Health: Health systems" } ], "source_id": "57" }, { "id": "SH.XPD.GHED.CH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic general government health expenditure (% of current health expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Share of current health expenditures funded from domestic public sources for health. Domestic public sources include domestic revenue as internal transfers and grants, transfers, subsidies to voluntary health insurance beneficiaries, non-profit institutions serving households (NPISH) or enterprise financing schemes as well as compulsory prepayment and social health insurance contributions. They do not include external resources spent by governments on health." }, { "id": "Periodicity", "value": "Annual" } ], "source_id": "57" }, { "id": "SH.XPD.GHED.CH.ZS", "metatype": [ { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.XPD.GHED.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic general government health expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public expenditure on health from domestic sources as a share of the economy as measured by GDP." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.XPD.GHED.GE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic general government health expenditure (% of general government expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public expenditure on health from domestic sources as a share of total public expenditure. It indicates the priority of the government to spend on health from own domestic public resources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.XPD.GHED.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic general government health expenditure per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public expenditure on health from domestic sources per capita expressed in current US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "SH.XPD.GHED.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic general government health expenditure per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public expenditure on health from domestic sources per capita expressed in international dollars at purchasing power parity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current iternational $" } ], "source_id": "57" }, { "id": "SH.XPD.OOPC.CH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Out-of-pocket expenditure (% of current health expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Share of out-of-pocket payments of total current health expenditures. Out-of-pocket payments are spending on health directly out-of-pocket by households." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. Aggregations are weighted by total current health expenditure (not by population).\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.XPD.OOPC.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Out-of-pocket expenditure per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Health expenditure through out-of-pocket payments per capita in USD. Out of pocket payments are spending on health directly out of pocket by households in each country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "SH.XPD.OOPC.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Out-of-pocket expenditure per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Health expenditure through out-of-pocket payments per capita in international dollars at purchasing power parity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current iternational $" } ], "source_id": "57" }, { "id": "SH.XPD.OOPC.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources." }, { "id": "IndicatorName", "value": "Out-of-pocket health expenditure (% of total expenditure on health)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Country data may differ in terms of definitions, data collection methods, population coverage and estimation methods used.\n\nIn countries where the fiscal year spans two calendar years, expenditure data have been allocated to the later year (for example, 2010 data cover fiscal year 2009/10)." }, { "id": "Longdefinition", "value": "Out of pocket expenditure is any direct outlay by households, including gratuities and in-kind payments, to health practitioners and suppliers of pharmaceuticals, therapeutic appliances, and other goods and services whose primary intent is to contribute to the restoration or enhancement of the health status of individuals or population groups. It is a part of private health expenditure." }, { "id": "Notesfromoriginalsource", "value": "All the health expenditure indicators refer to expenditures by financing agent except external resources which is a financing source. When the number is smaller than 0.05%, the percentage may appear as zero. In countries where the fiscal year begins in July, expenditure data have been allocated to the later calendar year (for example, 2010 data will cover the fiscal year 2009–10), unless otherwise stated for the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates)." }, { "id": "Statisticalconceptandmethodology", "value": "Health expenditure data are broken down into public and private expenditures. In general, low-income economies have a higher share of private health expenditure than do middle- and high-income countries, and out-of-pocket expenditure (direct payments by households to providers) makes up the largest proportion of private expenditures. High out-of-pocket expenditures may discourage people from accessing preventive or curative care and can impoverish households that cannot afford necessary care. Health financing data are collected through national health accounts, which systematically, comprehensively, and consistently monitor health system resource flows. To establish a national health account, countries must define the boundaries of the health system and classify health expenditure information along several dimensions, including sources of financing, providers of health services, functional use of health expenditures, and beneficiaries of expenditures. The accounting system can then provide an accurate picture of resource envelopes and financial flows and allow analysis of the equity and efficiency of financing to inform policy." }, { "id": "Topic", "value": "Health: Health systems" } ], "source_id": "57" }, { "id": "SH.XPD.OOPC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Out-of-pocket health expenditure (% of private expenditure on health)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Out of pocket expenditure is any direct outlay by households, including gratuities and in-kind payments, to health practitioners and suppliers of pharmaceuticals, therapeutic appliances, and other goods and services whose primary intent is to contribute to the restoration or enhancement of the health status of individuals or population groups. It is a part of private health expenditure." }, { "id": "Notesfromoriginalsource", "value": "All the health expenditure indicators refer to expenditures by financing agent except external resources which is a financing source. When the number is smaller than 0.05%, the percentage may appear as zero. In countries where the fiscal year begins in July, expenditure data have been allocated to the later calendar year (for example, 2010 data will cover the fiscal year 2009–10), unless otherwise stated for the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates)." }, { "id": "Topic", "value": "Health: Health systems" } ], "source_id": "57" }, { "id": "SH.XPD.PCAP", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources." }, { "id": "IndicatorName", "value": "Health expenditure per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Country data may differ in terms of definitions, data collection methods, population coverage and estimation methods used.\n\nIn countries where the fiscal year spans two calendar years, expenditure data have been allocated to the later year (for example, 2010 data cover fiscal year 2009/10)." }, { "id": "Longdefinition", "value": "Total health expenditure is the sum of public and private health expenditures as a ratio of total population. It covers the provision of health services (preventive and curative), family planning activities, nutrition activities, and emergency aid designated for health but does not include provision of water and sanitation. Data are in current U.S. dollars." }, { "id": "Notesfromoriginalsource", "value": "All the health expenditure indicators refer to expenditures by financing agent except external resources which is a financing source. In countries where the fiscal year begins in July, expenditure data have been allocated to the later calendar year (for example, 2010 data will cover the fiscal year 2009–10), unless otherwise stated for the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates)." }, { "id": "Statisticalconceptandmethodology", "value": "Health expenditure data are broken down into public and private expenditures. In general, low-income economies have a higher share of private health expenditure than do middle- and high-income countries, and out-of-pocket expenditure (direct payments by households to providers) makes up the largest proportion of private expenditures. High out-of-pocket expenditures may discourage people from accessing preventive or curative care and can impoverish households that cannot afford necessary care. Health financing data are collected through national health accounts, which systematically, comprehensively, and consistently monitor health system resource flows. To establish a national health account, countries must define the boundaries of the health system and classify health expenditure information along several dimensions, including sources of financing, providers of health services, functional use of health expenditures, and beneficiaries of expenditures. The accounting system can then provide an accurate picture of resource envelopes and financial flows and allow analysis of the equity and efficiency of financing to inform policy." }, { "id": "Topic", "value": "Health: Health systems" } ], "source_id": "57" }, { "id": "SH.XPD.PCAP.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2011" }, { "id": "Developmentrelevance", "value": "Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources." }, { "id": "IndicatorName", "value": "Health expenditure per capita, PPP (constant 2011 international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Country data may differ in terms of definitions, data collection methods, population coverage and estimation methods used.\n\nIn countries where the fiscal year spans two calendar years, expenditure data have been allocated to the later year (for example, 2010 data cover fiscal year 2009/10)." }, { "id": "Longdefinition", "value": "Total health expenditure is the sum of public and private health expenditures as a ratio of total population. It covers the provision of health services (preventive and curative), family planning activities, nutrition activities, and emergency aid designated for health but does not include provision of water and sanitation. Data are in international dollars converted using 2011 purchasing power parity (PPP) rates." }, { "id": "Notesfromoriginalsource", "value": "PPP series derived from the International Comparison Program (ICP) and estimated by the World Bank have been used. For countries where these are not available, PPPs are estimated by WHO. All the health expenditure indicators refer to expenditures by financing agent except external resources which is a financing source. When the number is smaller than 0.05%, the percentage may appear as zero. In countries where the fiscal year begins in July, expenditure data have been allocated to the later calendar year (for example, 2010 data will cover the fiscal year 2009–10), unless otherwise stated for the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates)." }, { "id": "Statisticalconceptandmethodology", "value": "Health expenditure data are broken down into public and private expenditures. In general, low-income economies have a higher share of private health expenditure than do middle- and high-income countries, and out-of-pocket expenditure (direct payments by households to providers) makes up the largest proportion of private expenditures. High out-of-pocket expenditures may discourage people from accessing preventive or curative care and can impoverish households that cannot afford necessary care. Health financing data are collected through national health accounts, which systematically, comprehensively, and consistently monitor health system resource flows. To establish a national health account, countries must define the boundaries of the health system and classify health expenditure information along several dimensions, including sources of financing, providers of health services, functional use of health expenditures, and beneficiaries of expenditures. The accounting system can then provide an accurate picture of resource envelopes and financial flows and allow analysis of the equity and efficiency of financing to inform policy." }, { "id": "Topic", "value": "Health: Health systems" } ], "source_id": "57" }, { "id": "SH.XPD.PRIV.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Health expenditure, private (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Private health expenditure includes direct household (out-of-pocket) spending, private insurance, charitable donations, and direct service payments by private corporations." }, { "id": "Notesfromoriginalsource", "value": "All the health expenditure indicators refer to expenditures by financing agent except external resources which is a financing source. When the number is smaller than 0.05%, the percentage may appear as zero. In countries where the fiscal year begins in July, expenditure data have been allocated to the later calendar year (for example, 2010 data will cover the fiscal year 2009–10), unless otherwise stated for the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates)." }, { "id": "Topic", "value": "Health: Health systems" } ], "source_id": "57" }, { "id": "SH.XPD.PUBL", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources." }, { "id": "IndicatorName", "value": "Health expenditure, public (% of total health expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Country data may differ in terms of definitions, data collection methods, population coverage and estimation methods used.\n\nIn countries where the fiscal year spans two calendar years, expenditure data have been allocated to the later year (for example, 2010 data cover fiscal year 2009/10)." }, { "id": "Longdefinition", "value": "Public health expenditure consists of recurrent and capital spending from government (central and local) budgets, external borrowings and grants (including donations from international agencies and nongovernmental organizations), and social (or compulsory) health insurance funds. Total health expenditure is the sum of public and private health expenditure. It covers the provision of health services (preventive and curative), family planning activities, nutrition activities, and emergency aid designated for health but does not include provision of water and sanitation." }, { "id": "Notesfromoriginalsource", "value": "In some cases, the sum of public and private expenditures on health may not add up to 100% because of rounding. All the health expenditure indicators refer to expenditures by financing agent except external resources which is a financing source. When the number is smaller than 0.05%, the percentage may appear as zero. In countries where the fiscal year begins in July, expenditure data have been allocated to the later calendar year (for example, 2010 data will cover the fiscal year 2009–10), unless otherwise stated for the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public health expenditure consists of recurrent and capital spending from government (central and local) budgets, external borrowings and grants (including donations from international agencies and nongovernmental organizations), and social (or compulsory) health insurance funds." }, { "id": "Source", "value": "World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates)." }, { "id": "Statisticalconceptandmethodology", "value": "Health expenditure data are broken down into public and private expenditures. In general, low-income economies have a higher share of private health expenditure than do middle- and high-income countries, and out-of-pocket expenditure (direct payments by households to providers) makes up the largest proportion of private expenditures. High out-of-pocket expenditures may discourage people from accessing preventive or curative care and can impoverish households that cannot afford necessary care. Health financing data are collected through national health accounts, which systematically, comprehensively, and consistently monitor health system resource flows. To establish a national health account, countries must define the boundaries of the health system and classify health expenditure information along several dimensions, including sources of financing, providers of health services, functional use of health expenditures, and beneficiaries of expenditures. The accounting system can then provide an accurate picture of resource envelopes and financial flows and allow analysis of the equity and efficiency of financing to inform policy." }, { "id": "Topic", "value": "Health: Health systems" } ], "source_id": "57" }, { "id": "SH.XPD.PUBL.GX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Health expenditure, public (% of government expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public health expenditure consists of recurrent and capital spending from government (central and local) budgets, external borrowings and grants (including donations from international agencies and nongovernmental organizations), and social (or compulsory) health insurance funds." }, { "id": "Notesfromoriginalsource", "value": "All the health expenditure indicators refer to expenditures by financing agent except external resources which is a financing source. When the number is smaller than 0.05%, the percentage may appear as zero. In countries where the fiscal year begins in July, expenditure data have been allocated to the later calendar year (for example, 2010 data will cover the fiscal year 2009–10), unless otherwise stated for the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates)." }, { "id": "Topic", "value": "Health: Health systems" } ], "source_id": "57" }, { "id": "SH.XPD.PUBL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Health expenditure, public (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Public health expenditure consists of recurrent and capital spending from government (central and local) budgets, external borrowings and grants (including donations from international agencies and nongovernmental organizations), and social (or compulsory) health insurance funds." }, { "id": "Notesfromoriginalsource", "value": "All the health expenditure indicators refer to expenditures by financing agent except external resources which is a financing source. When the number is smaller than 0.05%, the percentage may appear as zero. In countries where the fiscal year begins in July, expenditure data have been allocated to the later calendar year (for example, 2010 data will cover the fiscal year 2009–10), unless otherwise stated for the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates)." }, { "id": "Topic", "value": "Health: Health systems" } ], "source_id": "57" }, { "id": "SH.XPD.PVTD.CH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic private health expenditure (% of current health expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Share of current health expenditures funded from domestic private sources. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SH.XPD.PVTD.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic private health expenditure per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current private expenditures on health per capita expressed in current US dollars. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "SH.XPD.PVTD.PP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC." }, { "id": "IndicatorName", "value": "Domestic private health expenditure per capita, PPP (current international $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current private expenditures on health per capita expressed in international dollars at purchasing power parity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011)." }, { "id": "Topic", "value": "Health: Health systems" }, { "id": "Unitofmeasure", "value": "Current iternational $" } ], "source_id": "57" }, { "id": "SH.XPD.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources." }, { "id": "IndicatorName", "value": "Health expenditure, total (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Country data may differ in terms of definitions, data collection methods, population coverage and estimation methods used.\n\nIn countries where the fiscal year spans two calendar years, expenditure data have been allocated to the later year (for example, 2010 data cover fiscal year 2009/10)." }, { "id": "Longdefinition", "value": "Total health expenditure is the sum of public and private health expenditure. It covers the provision of health services (preventive and curative), family planning activities, nutrition activities, and emergency aid designated for health but does not include provision of water and sanitation." }, { "id": "Notesfromoriginalsource", "value": "All the health expenditure indicators refer to expenditures by financing agent except external resources which is a financing source. When the number is smaller than 0.05%, the percentage may appear as zero. In countries where the fiscal year begins in July, expenditure data have been allocated to the later calendar year (for example, 2010 data will cover the fiscal year 2009–10), unless otherwise stated for the country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates)." }, { "id": "Statisticalconceptandmethodology", "value": "Health expenditure data are broken down into public and private expenditures. In general, low-income economies have a higher share of private health expenditure than do middle- and high-income countries, and out-of-pocket expenditure (direct payments by households to providers) makes up the largest proportion of private expenditures. High out-of-pocket expenditures may discourage people from accessing preventive or curative care and can impoverish households that cannot afford necessary care. Health financing data are collected through national health accounts, which systematically, comprehensively, and consistently monitor health system resource flows. To establish a national health account, countries must define the boundaries of the health system and classify health expenditure information along several dimensions, including sources of financing, providers of health services, functional use of health expenditures, and beneficiaries of expenditures. The accounting system can then provide an accurate picture of resource envelopes and financial flows and allow analysis of the equity and efficiency of financing to inform policy." }, { "id": "Topic", "value": "Health: Health systems" } ], "source_id": "57" }, { "id": "SI.DST.02ND.20", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Income share held by second 20%" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the second quintile, meaning the second 20% of people when ranked from lowest to highest income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.DST.03RD.20", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Income share held by third 20%" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the third quintile, meaning the third 20% of people when ranked from lowest to highest income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.DST.04TH.20", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Income share held by fourth 20%" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the fourth quintile, meaning the fourth 20% of people when ranked from lowest to highest income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.DST.05TH.20", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Income share held by highest 20%" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the fifth or top quintile, meaning the top 20% of people when ranked from lowest to highest income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.DST.10TH.10", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Income share held by highest 10%" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\nStatistical concept(s): The percentage of total income in a population that is held by the tenth or top decile, meaning the top 10% of people when ranked from lowest to highest income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.DST.50MD", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Proportion of people living below 50 percent of median income (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2021 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the tenth or top decile, meaning the top 10% of people when ranked from lowest to highest income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.DST.FRST.10", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Income share held by lowest 10%" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. No adjustment has been made for spatial differences in cost of living within countries, because the data needed for such calculations are generally unavailable. For further details on the estimation method for low- and middle-income economies, see Ravallion and Chen (1996).\n\n\n\n\n\n\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected.\nStatistical concept(s): The percentage of a population whose income or consumption falls below half of the median in their country, essentially indicating the level of \"relative poverty\" within a society. It reflects the share of the population whose income or consumption is less than half of the typical standard in their society, highlighting income inequality at the lower end of the distribution." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.DST.FRST.20", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Income share held by lowest 20%" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the bottom quintile, meaning the bottom 20% of people when ranked from lowest to highest income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.2DAY", "metatype": [ { "id": "Developmentrelevance", "value": "The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The World Bank produced its first global poverty estimates for developing countries for World Development Report 1990: Poverty (World Bank 1990) using household survey data for 22 countries (Ravallion, Datt, and van de Walle 1991). Since then there has been considerable expansion in the number of countries that field household income and expenditure surveys. The World Bank's Development Research Group maintains a database that is updated annually as new survey data become available (and thus may contain more recent data or revisions) and conducts a major reassessment of progress against poverty every year. PovcalNet is an interactive computational tool that allows users to replicate these internationally comparable $1.90 and $3.10 a day global, regional and country-level poverty estimates and to compute poverty measures for custom country groupings and for different poverty lines. The Poverty and Equity Data portal provides access to the database and user-friendly dashboards with graphs and interactive maps that visualize trends in key poverty and inequality indicators for different regions and countries. The country dashboards display trends in poverty measures based on the national poverty lines alongside the internationally comparable estimates, produced from and consistent with PovcalNet." }, { "id": "Generalcomments", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at $3.10 a day (2011 PPP) (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty headcount ratio at $3.10 a day is the percentage of the population living on less than $3.10 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Poverty headcount ratio at $3.10 a day is the percentage of the population living on less than $3.10 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions." }, { "id": "Source", "value": "World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm)." }, { "id": "Statisticalconceptandmethodology", "value": "International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in October 2015, when we adopted $1.90 as the international poverty line using the 2011 PPP. Prior to that, the 2008 update set the international poverty line at $1.25 using the 2005 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time.\n\nCorresponding to the $2 a day (2005 PPP) poverty line, the equivalent poverty line based on 2011 PPP is $3.10 a day. \n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2011 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $1.90 a day in 2011 PPP terms, which represents the mean of the poverty lines found in the poorest 15 countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys. Analysis of some 20 countries for which income and consumption expenditure data were both available from the same surveys found income to yield a higher mean than consumption but also higher inequality. When poverty measures based on consumption and income were compared, the two effects roughly cancelled each other out: there was no significant statistical difference." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.DDAY", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. The World Bank defines extreme poverty as living on less than $3.00 a day (adjusted for purchasing power differences across countries). The value of $3.00 is the typical poverty line of low-income countries, which is the minimum amount of money people in low-income countries need to cover their daily basic needs, including food, clothing, and shelter. The share of population living on less than $3.00 a day is the first indicator the World Bank tracks in its Bank’s expanded vision indicators to create a world free of poverty in a livable planet. It is also the indicator the UN tracks for SDG 1.1. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at $3.00 a day (2021 PPP) (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty headcount ratio at $3.00 a day is the percentage of the population living on less than $3.00 a day at 2021 purchasing power adjusted prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\n\n\n\n\n\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.\n\n\n\n\n\n\n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\n\n\n\n\n\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): Poverty headcount ratio at $3.00 a day refers to the percentage of a population whose consumption or income per day falls short of the international poverty line of $3.00 a day (adjusted for purchasing power parity differences across countries), the poverty line typical of low-income countries." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.DDAY.CV", "metatype": [ { "id": "Developmentrelevance", "value": "The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The World Bank produced its first global poverty estimates for developing countries for World Development Report 1990: Poverty (World Bank 1990) using household survey data for 22 countries (Ravallion, Datt, and van de Walle 1991). Since then there has been considerable expansion in the number of countries that field household income and expenditure surveys." }, { "id": "Generalcomments", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 172 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "IndicatorName", "value": "Survey coverage for poverty headcount ratio (at $3.00 a day, 2021 PPP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Share of population covered by a survey within three years or reference year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "World Bank, Poverty and Inequality Platform: https://pip.worldbank.org/" }, { "id": "Shortdefinition", "value": "Share of population covered by a survey within three years or reference year." }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org." }, { "id": "Statisticalconceptandmethodology", "value": "International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in June 2025, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.\n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, 2011 and 2017 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the median of the poverty lines found in 23 low-income countries in circa 2021. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.DDAY.SH", "metatype": [ { "id": "Developmentrelevance", "value": "The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The World Bank produced its first global poverty estimates for developing countries for World Development Report 1990: Poverty (World Bank 1990) using household survey data for 22 countries (Ravallion, Datt, and van de Walle 1991). Since then there has been considerable expansion in the number of countries that field household income and expenditure surveys." }, { "id": "Generalcomments", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 172 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "IndicatorName", "value": "Share of total poor population (at $3.00 a day, 2021 PPP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The share of total global poor population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "World Bank, Poverty and Inequality Platform: https://pip.worldbank.org/" }, { "id": "Shortdefinition", "value": "The share of total global poor population." }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org." }, { "id": "Statisticalconceptandmethodology", "value": "International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2017 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.\n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, 2011 and 2017 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the median of the poverty lines found in 23 low-income countries in circa 2021. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.GAP2", "metatype": [ { "id": "Developmentrelevance", "value": "The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The World Bank produced its first global poverty estimates for developing countries for World Development Report 1990: Poverty (World Bank 1990) using household survey data for 22 countries (Ravallion, Datt, and van de Walle 1991). Since then there has been considerable expansion in the number of countries that field household income and expenditure surveys. The World Bank's Development Research Group maintains a database that is updated annually as new survey data become available (and thus may contain more recent data or revisions) and conducts a major reassessment of progress against poverty every year. PovcalNet is an interactive computational tool that allows users to replicate these internationally comparable $1.90 and $3.10 a day global, regional and country-level poverty estimates and to compute poverty measures for custom country groupings and for different poverty lines. The Poverty and Equity Data portal provides access to the database and user-friendly dashboards with graphs and interactive maps that visualize trends in key poverty and inequality indicators for different regions and countries. The country dashboards display trends in poverty measures based on the national poverty lines alongside the internationally comparable estimates, produced from and consistent with PovcalNet." }, { "id": "Generalcomments", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries." }, { "id": "IndicatorName", "value": "Poverty gap at $3.10 a day (2011 PPP) (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty gap at $3.10 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $3.10 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Poverty gap at $3.10 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $3.10 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions." }, { "id": "Source", "value": "World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm)." }, { "id": "Statisticalconceptandmethodology", "value": "International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in October 2015, when we adopted $1.90 as the international poverty line using the 2011 PPP. Prior to that, the 2008 update set the international poverty line at $1.25 using the 2005 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time.\n\nCorresponding to the $2 a day (2005 PPP) poverty line, the equivalent poverty line based on 2011 PPP is $3.10 a day. \n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2011 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $1.90 a day in 2011 PPP terms, which represents the mean of the poverty lines found in the poorest 15 countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys. Analysis of some 20 countries for which income and consumption expenditure data were both available from the same surveys found income to yield a higher mean than consumption but also higher inequality. When poverty measures based on consumption and income were compared, the two effects roughly cancelled each other out: there was no significant statistical difference." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.GAPS", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The poverty gap measures the depth of poverty—that is, how far below the poverty line extreme poor are living. The poverty gap measure is used to estimate the total value of monetary transfers that could lift the poor out of poverty, assuming poverty is transitory and there are no administrative costs of transfers." }, { "id": "IndicatorName", "value": "Poverty gap at $3.00 a day (2021 PPP) (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty gap at $3.00 a day (2021 PPP) is the mean shortfall in income or consumption from the poverty line $3.00 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.Since World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in June 2025, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.Early editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, 2011, 2021, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the median of the poverty lines found in 23 low-income countries. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.The statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): The poverty gap measures the average shortfall in income or consumption of individuals living below the international poverty line of $3.00 per day, adjusted to 2021 purchasing power parity (PPP), expressed as a percentage of that poverty line. It essentially measures the depth of poverty—that is, how far the extreme poor are living below the poverty line." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.GINI", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group's vision of promoting shared prosperity includes a measure that tracks the number of economies with high inequality, defined as those with a Gini index greater than 0.4" }, { "id": "IndicatorName", "value": "Gini index" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Gini coefficients are not unique. It is possible for two different Lorenz curves to give rise to the same Gini coefficient. Furthermore it is possible for the Gini coefficient of a developing country to rise (due to increasing inequality of income) while the number of people in absolute poverty decreases. This is because the Gini coefficient measures relative, not absolute, wealth.\n\n\n\n\n\n\n\nAnother limitation of the Gini coefficient is that it is not additive across groups, i.e. the total Gini of a society is not equal to the sum of the Gini's for its sub-groups. Thus, country-level Gini coefficients cannot be aggregated into regional or global Gini's, although a Gini coefficient can be computed for the aggregate.\n\n\n\n\n\n\n\nBecause the underlying household surveys differ in methods and types of welfare measures collected, data are not strictly comparable across countries or even across years within a country. Two sources of non-comparability should be noted for distributions of income in particular. First, the surveys can differ in many respects, including whether they use income or consumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much better welfare indicator, particularly in developing countries. Second, households differ in size (number of members) and in the extent of income sharing among members. And individuals differ in age and consumption needs. Differences among countries in these respects may bias comparisons of distribution. \n\n\n\n\n\n\n\nWorld Bank staff have made an effort to ensure that the data are as comparable as possible. Wherever possible, consumption has been used rather than income. Income distribution and Gini indexes for high-income economies are calculated directly from the Luxembourg Income Study database, using an estimation method consistent with that applied for developing countries." }, { "id": "Longdefinition", "value": "Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.\n\n\n\n\n\n\n\nThe Gini index provides a convenient summary measure of the degree of inequality. Data on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\nStatistical concept(s): The Gini index is the average of all pairwise absolute differences between individual consumption or income, normalized by twice the mean. More intuitively, the Gini index is the average share of mean consumption or income that needs to be transferred between two randomly selected individuals to achieve equality. A Gini index of 1 represents perfect inequality, in which total consumption or income goes to one individual. A Gini index of 0 indicates represents perfect equality, in which all individuals have the same level of consumption or income." }, { "id": "Topic", "value": "Poverty: Income distribution" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.LMIC", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank defines the poverty line of $4.20 (adjusted for purchasing power differences across countries) as a more relevant poverty line to monitor poverty in lower-middle-income countries. The value of $4.20 is the typical poverty line of lower-middle-income countries, which is the estimated minimum amount of money people in lower-middle-income countries need to cover their daily basic needs, including food, clothing, shelter, heath care, education, and so on. Policy dialogue can be facilitated with this poverty line, especially in middle-income countries." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at $4.20 a day (2021 PPP) (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty headcount ratio at $4.20 a day is the percentage of the population living on less than $4.20 a day at 2021 international prices." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\n\n\n\n\n\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.\n\n\n\n\n\n\n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\n\n\n\n\n\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): Poverty headcount ratio at $4.20 a day refers to the percentage of a population whose consumption or income per day falls short of $4.20 a day (adjusted for purchasing power parity differences across countries). $4.20 is the typical poverty line of lower-middle-income countries." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.LMIC.GP", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The poverty gap measures the depth of poverty—that is, how far below the lower-middle-income poverty line the poor are living. The poverty gap measure is used to estimate the total value of monetary transfers that could lift the poor out of poverty, assuming poverty is transitory and there are no administrative costs of transfers." }, { "id": "IndicatorName", "value": "Poverty gap at $4.20 a day (2021 PPP) (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty gap at $4.20 a day (2021 PPP) is the mean shortfall in income or consumption from the poverty line $4.20 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\n\n\n\n\n\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.\n\n\n\n\n\n\n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\n\n\n\n\n\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): The poverty gap at $4.20 measures the average shortfall in income or consumption of individuals living below the poverty line of $4.20 per day, adjusted to 2021 purchasing power parity (PPP), expressed as a percentage of that poverty line. It essentially measures the depth of poverty—that is, how far the poor are living below the lower-middle-income poverty line." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.MDIM", "metatype": [ { "id": "Derivationmethod", "value": "The design of a measure of multidimensional poverty is different in each country, but regardless of the exact methodology selected, it still follows a similar process to define the features of the measure, which include: i) the purpose of the measure; ii) the unit of identification (most frequently either the household or the individuals); iii) the dimensions and respective indicators that delimit which deprivations should be measured; iv) the methodology for developing the measure (including deprivation cut-offs, weights, and poverty cut-offs).\n\nThe most commonly used method is the Alkire Foster (AF) methodology which identifies dimensions, typically health, education and living standards and several indicators in each dimension. The unit of analysis could be either the individual or the household. The individuals or households are considered as multidimensionally poor if they are deprived in multiple dimensions, exceeding certain thresholds. \n\nEU Member States, Island, Norway, Albania, Kosovo, North Macedonia, Montenegro and Turkey have a different approach to measure the multidimensional poverty using the concept of \"people at risk of poverty or social exclusion\" (AROPE) calculated by EUROSTAT using the data from EU statistics on income and living conditions (EU-SILC). AROPE consists of three components, and individuals are considered as \"at risk of poverty or social exclusion\" if they are \"at risk of poverty\" or \"severely materially and socially deprived\" or \"living in a household with a very low work intensity\"." }, { "id": "IndicatorName", "value": "Multidimensional poverty headcount ratio (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The compiled data of multidimensional poverty is not intended to be comparable across countries due to national definitions. For instance, key parameters to calculate the measure such as the number of indicators, the weight allocated to each indicator etc, are tailored to the country specific context." }, { "id": "Shortdefinition", "value": "The percentage of people who are multidimensionally poor" }, { "id": "Source", "value": "Government statistical agencies. Data for EU countires are from the EUROSTAT" }, { "id": "Topic", "value": "Poverty:Multidimensional poverty" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.MDIM.17", "metatype": [ { "id": "Derivationmethod", "value": "The design of a measure of multidimensional poverty is different in each country, but regardless of the exact methodology selected, it still follows a similar process to define the features of the measure, which include: i) the purpose of the measure; ii) the unit of identification (most frequently either the household or the individuals); iii) the dimensions and respective indicators that delimit which deprivations should be measured; iv) the methodology for developing the measure (including deprivation cut-offs, weights, and poverty cut-offs).\n\nThe most commonly used method is the Alkire Foster (AF) methodology which identifies dimensions, typically health, education and living standards and several indicators in each dimension. The unit of analysis could be either the individual or the household. The individuals or households are considered as multidimensionally poor if they are deprived in multiple dimensions, exceeding certain thresholds. \n\nEU Member States, Island, Norway, Albania, Kosovo, North Macedonia, Montenegro and Turkey have a different approach to measure the multidimensional poverty using the concept of \"people at risk of poverty or social exclusion\" (AROPE) calculated by EUROSTAT using the data from EU statistics on income and living conditions (EU-SILC). AROPE consists of three components, and individuals are considered as \"at risk of poverty or social exclusion\" if they are \"at risk of poverty\" or \"severely materially and socially deprived\" or \"living in a household with a very low work intensity\"." }, { "id": "IndicatorName", "value": "Multidimensional poverty headcount ratio, children (% of child population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The compiled data of multidimensional poverty is not intended to be comparable across countries due to national definitions. For instance, key parameters to calculate the measure such as the number of indicators, the weight allocated to each indicator etc, are tailored to the country specific context." }, { "id": "Shortdefinition", "value": "The percentage of children who are multidimensionally poor" }, { "id": "Source", "value": "Government statistical agencies. Data for EU countires are from the EUROSTAT" }, { "id": "Topic", "value": "Poverty:Multidimensional poverty" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.MDIM.17.XQ", "metatype": [ { "id": "Derivationmethod", "value": "The Multidimensional poverty index for children is calculated by multiplying multidimensional poverty headcount and average number of deprivations (intensity) of children." }, { "id": "IndicatorName", "value": "Multidimensional poverty index, children (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The MPI here is a national MPI reported by each country and differs from the global MPI collected by the UNDP. Unlike the global MPI which uses the same dimensional and indicators, the national MPI is calculated using different dimensions and indicators by each country, therefore, it is not comparable across countries." }, { "id": "Shortdefinition", "value": "Proportion of the child population that is multidimensionally poor adjusted by the intensity of the deprivations" }, { "id": "Source", "value": "Government statistical agencies. Data for EU countires are from the EUROSTAT" }, { "id": "Topic", "value": "Poverty:Multidimensional poverty" } ], "source_id": "57" }, { "id": "SI.POV.MDIM.FE", "metatype": [ { "id": "Derivationmethod", "value": "The design of a measure of multidimensional poverty is different in each country, but regardless of the exact methodology selected, it still follows a similar process to define the features of the measure, which include: i) the purpose of the measure; ii) the unit of identification (most frequently either the household or the individuals); iii) the dimensions and respective indicators that delimit which deprivations should be measured; iv) the methodology for developing the measure (including deprivation cut-offs, weights, and poverty cut-offs).\n\nThe most commonly used method is the Alkire Foster (AF) methodology which identifies dimensions, typically health, education and living standards and several indicators in each dimension. The unit of analysis could be either the individual or the household. The individuals or households are considered as multidimensionally poor if they are deprived in multiple dimensions, exceeding certain thresholds. \n\nEU Member States, Island, Norway, Albania, Kosovo, North Macedonia, Montenegro and Turkey have a different approach to measure the multidimensional poverty using the concept of \"people at risk of poverty or social exclusion\" (AROPE) calculated by EUROSTAT using the data from EU statistics on income and living conditions (EU-SILC). AROPE consists of three components, and individuals are considered as \"at risk of poverty or social exclusion\" if they are \"at risk of poverty\" or \"severely materially and socially deprived\" or \"living in a household with a very low work intensity\"." }, { "id": "IndicatorName", "value": "Multidimensional poverty headcount ratio, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The compiled data of multidimensional poverty is not intended to be comparable across countries due to national definitions. For instance, key parameters to calculate the measure such as the number of indicators, the weight allocated to each indicator etc, are tailored to the country specific context." }, { "id": "Shortdefinition", "value": "The percentage of female population who are multidimensionally poor" }, { "id": "Source", "value": "Government statistical agencies. Data for EU countires are from the EUROSTAT" }, { "id": "Topic", "value": "Poverty:Multidimensional poverty" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.MDIM.HH", "metatype": [ { "id": "Derivationmethod", "value": "The design of a measure of multidimensional poverty is different in each country, but regardless of the exact methodology selected, it still follows a similar process to define the features of the measure, which include: i) the purpose of the measure; ii) the unit of identification (most frequently either the household or the individuals); iii) the dimensions and respective indicators that delimit which deprivations should be measured; iv) the methodology for developing the measure (including deprivation cut-offs, weights, and poverty cut-offs).\n\nThe most commonly used method is the Alkire Foster (AF) methodology which identifies dimensions, typically health, education and living standards and several indicators in each dimension. The unit of analysis could be either the individual or the household. The individuals or households are considered as multidimensionally poor if they are deprived in multiple dimensions, exceeding certain thresholds. \n\nEU Member States, Island, Norway, Albania, Kosovo, North Macedonia, Montenegro and Turkey have a different approach to measure the multidimensional poverty using the concept of \"people at risk of poverty or social exclusion\" (AROPE) calculated by EUROSTAT using the data from EU statistics on income and living conditions (EU-SILC). AROPE consists of three components, and individuals are considered as \"at risk of poverty or social exclusion\" if they are \"at risk of poverty\" or \"severely materially and socially deprived\" or \"living in a household with a very low work intensity\"." }, { "id": "IndicatorName", "value": "Multidimensional poverty headcount ratio, household (% of total households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The compiled data of multidimensional poverty is not intended to be comparable across countries due to national definitions. For instance, key parameters to calculate the measure such as the number of indicators, the weight allocated to each indicator etc, are tailored to the country specific context." }, { "id": "Shortdefinition", "value": "The percentage of households who are multidimensionally poor" }, { "id": "Source", "value": "Government statistical agencies. Data for EU countires are from the EUROSTAT" }, { "id": "Topic", "value": "Poverty:Multidimensional poverty" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.MDIM.IT", "metatype": [ { "id": "Derivationmethod", "value": "The multidimensional headcount is a useful measure, but it does not increase if poor people become more deprived. Because of that, we need a different set of measures, which is the average number of deprivations, also known as intensity. Intensity is calculated by adding up the proportion of total deprivations each person suffers and dividing by the total number of poor persons." }, { "id": "IndicatorName", "value": "Multidimensional poverty intensity" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "It should be noted that the intensity is not comparable across diffferent countries as it depends on the parameters used in the methodology such as what kind of dimensions and indicators are used, and how the weight is constructed, which varies significantly across countries." }, { "id": "Shortdefinition", "value": "The average percentage of dimensions in which poor people are deprived" }, { "id": "Source", "value": "Government statistical agencies. Data for EU countires are from the EUROSTAT" }, { "id": "Topic", "value": "Poverty:Multidimensional poverty" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.MDIM.MA", "metatype": [ { "id": "Derivationmethod", "value": "The design of a measure of multidimensional poverty is different in each country, but regardless of the exact methodology selected, it still follows a similar process to define the features of the measure, which include: i) the purpose of the measure; ii) the unit of identification (most frequently either the household or the individuals); iii) the dimensions and respective indicators that delimit which deprivations should be measured; iv) the methodology for developing the measure (including deprivation cut-offs, weights, and poverty cut-offs).\n\nThe most commonly used method is the Alkire Foster (AF) methodology which identifies dimensions, typically health, education and living standards and several indicators in each dimension. The unit of analysis could be either the individual or the household. The individuals or households are considered as multidimensionally poor if they are deprived in multiple dimensions, exceeding certain thresholds. \n\nEU Member States, Island, Norway, Albania, Kosovo, North Macedonia, Montenegro and Turkey have a different approach to measure the multidimensional poverty using the concept of \"people at risk of poverty or social exclusion\" (AROPE) calculated by EUROSTAT using the data from EU statistics on income and living conditions (EU-SILC). AROPE consists of three components, and individuals are considered as \"at risk of poverty or social exclusion\" if they are \"at risk of poverty\" or \"severely materially and socially deprived\" or \"living in a household with a very low work intensity\"." }, { "id": "IndicatorName", "value": "Multidimensional poverty headcount ratio, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The compiled data of multidimensional poverty is not intended to be comparable across countries due to national definitions. For instance, key parameters to calculate the measure such as the number of indicators, the weight allocated to each indicator etc, are tailored to the country specific context." }, { "id": "Shortdefinition", "value": "The percentage of male population who are multidimensionally poor" }, { "id": "Source", "value": "Government statistical agencies. Data for EU countires are from the EUROSTAT" }, { "id": "Topic", "value": "Poverty:Multidimensional poverty" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.MDIM.XQ", "metatype": [ { "id": "Derivationmethod", "value": "The Multidimensional poverty index is calculated by multiplying multidimensional poverty headcount and average number of deprivations (intensity)." }, { "id": "IndicatorName", "value": "Multidimensional poverty index (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The MPI here is a national MPI reported by each country and differs from the global MPI collected by the UNDP. Unlike the global MPI which uses the same dimensions and indicators, the national MPI is calculated using different dimensions and indicators by each country, therefore, it is not comparable across countries." }, { "id": "Shortdefinition", "value": "Proportion of the population that is multidimensionally poor adjusted by the intensity of the deprivations" }, { "id": "Source", "value": "Government statistical agencies. Data for EU countires are from the EUROSTAT" }, { "id": "Topic", "value": "Poverty:Multidimensional poverty" } ], "source_id": "57" }, { "id": "SI.POV.MPUN", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The global MPI was developed by Sabina Alkire and Maria Emma Santos (2014), in collaboration with the Human Development Report Office (HDRO) at UNDP as an internationally comparable measure of acute poverty. Technically, the global MPI relies on the Alkire-Foster method (2011). In 2018, five out of the ten indicators have been revised to better align with the SDGs (see Alkire, Kanagaratnam, Nogales and Suppa 2022; Alkire and Kanagaratnam 2020; Alkire and Jahan 2018)." }, { "id": "IndicatorName", "value": "Multidimensional poverty headcount ratio (UNDP) (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Besides the frequency and timeliness of survey data, other limitations include if the household survey data being used is missing any of the 10 indicators, that indicator is dropped from the calculation. The weights are then adjusted so that each dimension continues to be given a weight of one-third. MPI poverty estimates are only calculated if at least one indicator in health and education dimensions is available, and if at least four indicators in the living standards dimension are available." }, { "id": "Longdefinition", "value": "The multidimensional poverty headcount ratio (UNDP) is the percentage of a population living in poverty according to UNDPs multidimensional poverty index. The index includes three dimensions -- health, education, and living standards." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2013-2023" }, { "id": "Source", "value": "Alkire, S., Mishra, R., Selden, L. and Suppa, N. (2025). ‘The global Multidimensional Poverty Index (MPI) 2025: Country results and methodological note’, OPHI MPI Methodological Note 61, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford.url: https://ophi.org.uk/publications/MN-61, uri: https://ophi.org.uk/publications/MN-61, publisher: Oxford Poverty and Human Development Initiative (OPHI)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The global MPI, published annually since 2010, captures acute multidimensional poverty in developing regions of the world (Alkire and Santos, 2014). This measure is based on the dual-cutoff counting methodology developed by Alkire and Foster (2011). The global MPI is composed of three dimensions (health, education, and living standards) and 10 corresponding indicators (nutrition, child mortality, school attendance, years of schooling, electricity, drinking water, sanitation, cooking fuel, housing, and assets). The global MPI begins by constructing a deprivation profile for each household and person in it that tracks deprivations in each of the 10 indicators. For example, a household and all people living in it are deprived if any child is stunted or any child or adult for whom data are available is underweight; if any child died in the past five years; if any school-aged child is not attending school up to the age at which he or she would complete class 8 or no household member has completed six years of schooling; or if the household lacks access to electricity, an improved source of drinking water within a 30 minute walk round trip, an improved sanitation facility that is not shared, nonsolid cooking fuel, durable housing materials, and basic assets such as a radio, animal cart, phone, television, computer, refrigerator, bicycle or motorcycle. All indicators are equally weighted within each dimension, so the health and education indicators are weighted 1/6 each, and the standard of living indicators are weighted 1/18 each. A person’s deprivation score is the sum of the weighted deprivations she or he experiences. The global MPI identifies people as multidimensionally poor if their deprivation score is 1/3 or higher. MPI values are the product of the incidence (H, or the proportion of population who live in multidimensional poverty) and intensity of poverty (A, or the average deprivation score among multidimensionally poor people). MPI = H × A. The MPI ranges from 0 to 1, and higher values imply higher poverty. MPI values decline when fewer people are poor or when poor people have fewer deprivations.\nStatistical concept(s): The UNDP's multidimensional poverty index (MPI) summarizes the incidence of multiple dimensions of poverty in a country. The MPI assesses deprivation in three dimensions of well-being (health, education, and living standards) and 10 corresponding indicators (nutrition, child mortality, school attendance, years of schooling, and access to electricity, safe drinking water, improved sanitation, good cooking fuel, good housing infrastructure)." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.MPWB", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The first Sustainable Development Goal calls for ending poverty in all forms by 2030. Poverty is multidimensional, containing both monetary and non-monetary dimensions. The World Bank's multidimensional poverty measure captures both elements, allowing for a more complete tracking of poverty in all forms" }, { "id": "IndicatorName", "value": "Multidimensional poverty headcount ratio (World Bank) (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "The multidimensional poverty headcount ratio (World Bank) is the percentage of a population living in poverty according to the World Bank's Multidimensional Poverty Measure. The Multidimensional Poverty Measure includes three dimensions – monetary poverty, education, and basic infrastructure services – to capture a more complete picture of poverty." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2008-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank's Multidimensional Poverty Measure (MPM) seeks to understand poverty beyond monetary deprivations by including access to education and basic infrastructure along with the monetary headcount ratio at the $3.00 international poverty line.\n\n\n\nThe measure takes inspiration and guidance from other prominent global multidimensional measures, particularly the Multidimensional Poverty Index (MPI) developed by the United Nations Development Programme (UNDP) and Oxford University but differs from them in one important aspect: it includes monetary poverty less than $3.00 per day, the new International Poverty Line at 2021 PPP (Purchasing Power Parity), as one of the dimensions. \n\n\n\nThe MPM is composed of six indicators: consumption or income, educational attainment, educational enrollment, drinking water, sanitation, and electricity. These are mapped into three dimensions of well-being: monetary, education, and basic infrastructure services.\n\n\n\nThe three MPM dimensions are weighted equally, and within each dimension each indicator is also weighted equally. Individuals are considered multidimensionally deprived if they fall short of the threshold in at least one dimension or in a combination of indicators equivalent in weight to a full dimension. In other words, households will be considered poor if they are deprived in indicators whose weight adds up to 1/3 or more. Because the monetary dimension is measured using only one indicator, anyone who is income poor is automatically also poor under the multidimensional poverty measure. \n\n\n\nSummarizing the information on the different deprivations into a single index proves useful in making comparisons across populations and across time. However, any aggregation of indicators into a single index always involves a decision on how each indicator is to be weighted.\nStatistical concept(s): The World Bank’s multi-dimensional poverty measure (MPM) summarizes the incidence of multiple dimensions of poverty in a country. It captures the idea that poverty is multi-faceted, including both monetary and non-monetary aspects. It assesses deprivation in three dimensions of well-being (monetary poverty, education, and basic infrastructure) and 6 corresponding indicators (monetary poverty, educational attainment, educational enrollment, access to electricity, access to improved sanitation, access to safe drinking water)." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.NAGP", "metatype": [ { "id": "Generalcomments", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "IndicatorName", "value": "Poverty gap at national poverty lines (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Poverty gap at national poverty lines is the mean shortfall from the poverty lines (counting the nonpoor as having zero shortfall) as a percentage of the poverty lines. This measure reflects the depth of poverty as well as its incidence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Poverty headcount ratio among the population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income.\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies.\n\nAlmost all national poverty lines are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. The data is based on the two most recent years for which survey data are available.\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected." }, { "id": "Topic", "value": "Poverty: Poverty rates" } ], "source_id": "57" }, { "id": "SI.POV.NAHC", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The poverty rate as defined by national poverty lines reflects the share of the population that fails to meet the standard a country thinks is necessary to cover basic needs (typically in low- and middle-income countries) or afford a decent lifestyle (typically in high-income countries). SDG 1.2 aims to reduce by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions, by 2030." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at national poverty lines (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "National poverty headcount ratio is the percentage of the population living below the national poverty line(s). National estimates are based on population-weighted subgroup estimates from household surveys. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year." }, { "id": "Othernotes", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines., World Bank (WB), note: Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Poverty headcount ratio among the population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\n\n\n\n\n\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income. \n\n\n\n\n\n\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies. \n\n\n\n\n\n\n\nAlmost all national poverty lines in developing economies are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. \n\n\n\n\n\n\n\nThis series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year. For all other economies, the year reported is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which data collection started.\nStatistical concept(s): National poverty headcount ratio refers to the percentage of a population whose consumption or income per day falls short of the national poverty line. National poverty lines vary by country and over time. In low- and middle-income countries, national poverty lines tend to be absolute poverty lines, thus reflecting the estimated minimum amount of money needed to cover basic needs. In high-income countries, national poverty lines tend to be relative poverty lines, thus reflecting the typical amount of money needed for an individual to afford the typical standard of living and without any restraints to participating fully in the societies in which they live. National poverty lines tend to grow with economic growth, especially in high-income or upper-middle-income countries." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.NOP1", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The World Bank produced its first global poverty estimates for developing countries for World Development Report 1990: Poverty (World Bank 1990) using household survey data for 22 countries (Ravallion, Datt, and van de Walle 1991). Since then there has been considerable expansion in the number of countries that field household income and expenditure surveys." }, { "id": "Generalcomments", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 172 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "IndicatorName", "value": "Number of poor at $3.00 a day (2021 PPP) (millions)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Number of people, in millions, living on less than $3.00 a day at 2021 PPP is calculated by multiplying the poverty rate and the population. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "World Bank, Poverty and Inequality Platform: https://pip.worldbank.org/" }, { "id": "Shortdefinition", "value": "Number of people, in millions, living on less than $3.00 a day at 2021 PPP is calculated by multiplying the poverty rate and the population. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions." }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org." }, { "id": "Statisticalconceptandmethodology", "value": "International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2017 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.\n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, 2011 and 2017 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms,which represents the median of the poverty lines found in 23 low-income countries in circa 2021. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys." }, { "id": "Topic", "value": "Poverty: Poverty rates" } ], "source_id": "57" }, { "id": "SI.POV.RUGP", "metatype": [ { "id": "Generalcomments", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "IndicatorName", "value": "Rural poverty gap at national poverty lines (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Rural poverty gap at national poverty lines is the rural population's mean shortfall from the poverty lines (counting the nonpoor as having zero shortfall) as a percentage of the poverty lines. This measure reflects the depth of poverty as well as its incidence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Poverty headcount ratio among the rural population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income.\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies.\n\nAlmost all national poverty lines are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. The data is based on the two most recent years for which survey data are available.\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected." }, { "id": "Topic", "value": "Poverty: Poverty rates" } ], "source_id": "57" }, { "id": "SI.POV.RUHC", "metatype": [ { "id": "Generalcomments", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "IndicatorName", "value": "Rural poverty headcount ratio at national poverty lines (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Rural poverty headcount ratio is the percentage of the rural population living below the national poverty lines." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Poverty headcount ratio among the rural population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income.\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies.\n\nAlmost all national poverty lines are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. The data is based on the two most recent years for which survey data are available.\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected." }, { "id": "Topic", "value": "Poverty: Poverty rates" } ], "source_id": "57" }, { "id": "SI.POV.SOPO", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The first Sustainable Development Goal calls for ending poverty in all forms by 2030. Typically, countries' poverty lines increase in real value as their economies get richer. Essentially, this is because in a richer country it is costlier to participate in society (i.e., be considered non-poor). Yet as relative poverty lines can take on very low values for poor countries, one may want to ensure a lower bound which provides a fixed, absolute element to the SPL, which the study interprets as the cost of consuming some minimum bundle of goods. The Societal Poverty Line tracks poverty rates consistent with these considerations." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at societal poverty line (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2021 U.S. dollars and defined as max($3.00, $1.30 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $3.00. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Societal Poverty Line (SPL) adopted by the World Bank is calculated in 2021 PPP U.S. dollars as follows: SPL = max (US$3.00, US$1.30 + 0.5 median consumption). By this definition, societal poverty represents a combination of extreme poverty, which is fixed in value for everyone, and a relative dimension of well-being that differs in every country depending on the median level of consumption in that country. In countries with low median consumption (less than US$2.00 per person per day), a rise in median consumption does not change the SPL. Indeed, the SPL has the same value as the IPL in all countries with median consumption up to $3.40. However, as countries with median consumption at more than US$3.40 become richer, and the median consumption increases, the value of the SPL also rises. The slope of one-half, the rate at which the SPL is rising as countries become richer, comes from the empirical association observed between national poverty lines and different measures of overall consumption in society. It indicates that, on average, the national poverty lines are increasing at a rate equal to half the median consumption in the countries. The slope of one-half and the intercept of US$1.30 are the values that most closely fit the data provided by the national poverty lines and overall consumption in each country. The SPL and the International Poverty Line (IPL) share the same empirical underpinning. Both are anchored in the distribution of national poverty lines, which represent countries’ own judgements of what poverty means for them. Whereas the IPL focuses narrowly—and deliberately—on the choices of some of the poorest countries, the SPL is built on information from across the whole range of levels of development. In addition to fitting the data well, the slope coefficient of half the median is widely used by many countries and organizations as a measure of relative poverty and inclusion.\nStatistical concept(s): Poverty headcount ratio at societal poverty line measures the share of the population living below the societal poverty line. The societal poverty line combines elements of both absolute and relative poverty. A minimum amount of money is required for subsistence (absolute poverty), while an additional amount of money is required to afford the typical lifestyle expected in the society in which people live (relative poverty). The societal poverty line is expressed as: max (US$3.00, US$1.30 + 0.5 median consumption) in 2021 PPP U.S. dollars. $1.30 represents the standard for absolute poverty, while 0.5 median consumption represents the standard for relative poverty. In very poor countries, typically with median consumption up to $3.40, it makes more sense to consider that poverty is entirely absolute, so that the effective societal poverty line becomes the international poverty line of $3.00." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.UMIC", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank defines a higher poverty line of $8.30, in addition to the international poverty line of $3.00, to account for higher living standards in a changing world (these values adjust for purchasing power differences across countries). The value of $8.30 is the typical poverty line of upper-middle-income countries, which is the estimated minimum amount of money people in upper-middle-income countries need to cover their daily basic needs, including food, clothing, shelter, heath care, education, and so on. The share of population living on less than $8.30 a day is the second indicator the World Bank tracks in its expanded vision indicators to create a world free of poverty in a livable planet. A growing majority of the world’s population live in middle-income countries (for example, about three-quarters in 2024 compared to one-quarter in 1990), so a higher poverty line would be more representative of the word’s current demographic structure. Further, a higher standard of poverty would be necessary to build resilience in a world more susceptible to climate-related risks." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at $8.30 a day (2021 PPP) (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty headcount ratio at $8.30 a day is the percentage of the population living on less than $8.30 a day at 2021 international prices." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.Since World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.Early editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.The statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): Poverty headcount ratio at $8.30 a day refers to the percentage of a population whose consumption or income per day falls short of $8.30 a day (adjusted for purchasing power parity differences across countries). $8.30 is the typical poverty line of upper-middle-income countries." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.UMIC.GP", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The poverty gap measures the depth of poverty—that is, how far below the upper-middle-income poverty line the poor are living. The poverty gap measure is used to estimate the total value of monetary transfers that could lift the poor out of poverty, assuming poverty is transitory and there are no administrative costs of transfers." }, { "id": "IndicatorName", "value": "Poverty gap at $8.30 a day (2021 PPP) (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty gap at $8.30 a day (2021 PPP) is the mean shortfall in income or consumption from the poverty line $8.30 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence." }, { "id": "Othernotes", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.Since World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.Early editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.The statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): The poverty gap at $8.30 measures the average shortfall in income or consumption of individuals living below the poverty line of $8.30 per day, adjusted to 2021 purchasing power parity (PPP), expressed as a percentage of that poverty line. It essentially measures the depth of poverty—that is, how far the poor are living below the upper-middle-income poverty line." }, { "id": "Topic", "value": "Poverty: Poverty rates" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.POV.URGP", "metatype": [ { "id": "Generalcomments", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "IndicatorName", "value": "Urban poverty gap at national poverty lines (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Urban poverty gap at national poverty lines is the urban population's mean shortfall from the poverty lines (counting the nonpoor as having zero shortfall) as a percentage of the poverty lines. This measure reflects the depth of poverty as well as its incidence." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Poverty headcount ratio among the urban population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income.\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies.\n\nAlmost all national poverty lines are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. The data is based on the two most recent years for which survey data are available.\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected." }, { "id": "Topic", "value": "Poverty: Poverty rates" } ], "source_id": "57" }, { "id": "SI.POV.URHC", "metatype": [ { "id": "Generalcomments", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "IndicatorName", "value": "Urban poverty headcount ratio at national poverty lines (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Urban poverty headcount ratio is the percentage of the urban population living below the national poverty lines." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Poverty headcount ratio among the urban population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income.\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies.\n\nAlmost all national poverty lines are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. The data is based on the two most recent years for which survey data are available.\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected." }, { "id": "Topic", "value": "Poverty: Poverty rates" } ], "source_id": "57" }, { "id": "SI.RMT.COST.IB.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Reducing the cost of remittance transactions has a direct impact on development by freeing additional resources that, instead of being paid as transaction cost, will remain with the senders and receivers of the flows. Remittance cost is highlighted in Sustainable Development Goal 10. Target 10.c calls for reducing to less than 3 percent the transaction costs of migrant remittances and ensure that in no corridor remittance senders are required to pay more than 5 percent by 2030." }, { "id": "IndicatorName", "value": "Average transaction cost of sending remittances to a specific country (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Remittance service providers (RSPs) are excluded when they do not disclose the exchange rate applied to the transaction" }, { "id": "Longdefinition", "value": "Average transaction cost of sending remittance to a specific country is the average of the total transaction cost in percentage of the amount sent for sending USD 200 charged by each single remittance service provider (RSP) included in the Remittance Prices Worldwide (RPW) database to a specific country." }, { "id": "Periodicity", "value": "Quarterly (represented as Annual)" }, { "id": "Referenceperiod", "value": "2016-2023" }, { "id": "Source", "value": "Remittance Prices Worldwide, World Bank (WB), uri: http://remittanceprices.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank calculates and tracks the global average cost for sending remittances following each iteration of Remittance Prices Worldwide (RPW). This is intended to provide a tool to track the trend of remittance prices by various policy makers, including measuring progress towards the commitment by the G8 member countries to reduce the cost of remittances by five percentage points over five years (the “5x5 Objective”), as well as the commitment by the G20 member countries to also reduce the global average to 5 percent. The Global Average Total Cost is calculated as the average total cost for sending USD 200 with all remittance service providers (RSPs) worldwide. In other terms, the global average total cost is the simple average of the total cost for sending USD 200 charged by each single RSP included in the RPW database, expressed as the percentage of the amount sent. The regional and national average total costs are calculated using the same methodology used to calculate the Global Average Total Cost. These represent the simple average total cost for sending USD 200 with every single RSP to a specific region of the world (regional), or to a specific country (national). The reference years reflect third quarter data here; for example, data for 2016 refers to data in the third quarter of the year. For all quarterly data, visit http://remittanceprices.worldbank.org." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "SI.RMT.COST.OB.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Reducing the cost of remittance transactions has a direct impact on development by freeing additional resources that, instead of being paid as transaction cost, will remain with the senders and receivers of the flows. Remittance cost is highlighted in Sustainable Development Goal 10. Target 10.c calls for reducing to less than 3 percent the transaction costs of migrant remittances and ensure that in no corridor remittance senders are required to pay more than 5 percent by 2030." }, { "id": "IndicatorName", "value": "Average transaction cost of sending remittances from a specific country (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Remittance service providers (RSPs) are excluded when they do not disclose the exchange rate applied to the transaction." }, { "id": "Longdefinition", "value": "Average transaction cost of sending remittance from a specific country is the average of the total transaction cost in percentage of the amount sent for sending USD 200 charged by each single remittance service provider (RSP) included in the Remittance Prices Worldwide (RPW) database from a specific country." }, { "id": "Periodicity", "value": "Quarterly (represented as Annual)" }, { "id": "Referenceperiod", "value": "2016-2023" }, { "id": "Source", "value": "Remittance Prices Worldwide, World Bank (WB), uri: http://remittanceprices.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank calculates and tracks the global average cost for sending remittances following each iteration of Remittance Prices Worldwide (RPW). This is intended to provide a tool to track the trend of remittance prices by various policy makers, including measuring progress towards the commitment by the G8 member countries to reduce the cost of remittances by five percentage points over five years (the “5x5 Objective”), as well as the commitment by the G20 member countries to also reduce the global average to 5 percent. The Global Average Total Cost is calculated as the average total cost for sending USD 200 with all remittance service providers (RSPs) worldwide. In other terms, the global average total cost is the simple average of the total cost for sending USD 200 charged by each single RSP included in the RPW database, expressed as the percentage of the amount sent. The regional and national average total costs are calculated using the same methodology used to calculate the Global Average Total Cost. These represent the simple average total cost for sending USD 200 with every single RSP from a specific region of the world (regional), or from a specific country (national). The same applies to other averages such as the G8 average, which calculates the average cost of sending USD 200 from the G8 member countries, or the bank average, which represent the average cost of sending USD 200 with a bank worldwide. The reference years reflect third quarter data here; for example, data for 2016 refers to data in the third quarter of the year. For all quarterly data, visit http://remittanceprices.worldbank.org." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "SI.SPR.PC40", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures improvements in the well-being of the poor, thus monitoring inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Survey mean consumption or income per capita, bottom 40% of population (2021 PPP $ per day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Longdefinition", "value": "Mean consumption or income per capita (2021 PPP $ per day) of the bottom 40%, used in calculating the growth rate in the welfare aggregate of the bottom 40% of the population in the income distribution in a country." }, { "id": "Othernotes", "value": "The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Survey mean consumption or income per capita, bottom 40% of population measures the amount of consumption or income per person per day in the bottom 40% of the population. It is estimated from survey data and expressed in 2021 PPP dollars.\nStatistical concept(s): Survey mean consumption or income per capita, bottom 40% of population measures the amount of consumption or income per person per day in the bottom 40% of the population. It is estimated from survey data and expressed in 2021 PPP dollars." }, { "id": "Topic", "value": "Poverty: Shared prosperity" }, { "id": "Unitofmeasure", "value": "2021 PPP $" } ], "source_id": "57" }, { "id": "SI.SPR.PC40.05", "metatype": [ { "id": "Generalcomments", "value": "The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in PovcalNet. The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "IndicatorName", "value": "Survey mean consumption or income per capita, bottom 40% of population (2005 PPP $ per day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Longdefinition", "value": "Mean consumption or income per capita (2005 PPP $ per day) used in calculating the growth rate in the welfare aggregate of the bottom 40% of the population in the income distribution in a country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "World Bank, PovcalNet: an online poverty analysis tool, http://iresearch.worldbank.org/PovcalNet/index.htm" }, { "id": "Source", "value": "World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity)." }, { "id": "Topic", "value": "Poverty: Shared prosperity" }, { "id": "Unitofmeasure", "value": "2005 PPP $" } ], "source_id": "57" }, { "id": "SI.SPR.PC40.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "It measures improvements in the well-being of the poor, thus monitoring inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "IndicatorName", "value": "Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Longdefinition", "value": "The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2021 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered." }, { "id": "Othernotes", "value": "The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The annualized growth rate in per capita real survey mean consumption of the bottom 40% is computed in the following steps. First, obtain the mean consumption or income levels of the bottom 40% of the survey distribution in two different periods. The two survey data sets should be comparable - that is, they use a similar method of sampling, collecting data, and constructing the welfare aggregate. Second, the rate of change in the survey mean values of the bottom 40% is annualized.\nStatistical concept(s): The annualized growth rate in per capita real survey mean consumption or income of the bottom 40% measures the rate of change in per capita consumption or income of the bottom 40% in a year." }, { "id": "Topic", "value": "Poverty: Shared prosperity" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.SPR.PCAP", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group has a goal of promoting shared prosperity within and across countries. Growth is good for the poor and growth in poor countries reflects in improvements in the World Bank’s new shared prosperity measure, the Global Prosperity Gap." }, { "id": "IndicatorName", "value": "Survey mean consumption or income per capita, total population (2021 PPP $ per day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Longdefinition", "value": "Mean consumption or income per capita (2021 PPP $ per day) used in calculating the growth rate in the welfare aggregate of total population." }, { "id": "Othernotes", "value": "The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2004-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. \nStatistical concept(s): Survey mean consumption or income per capita, total population measures the amount of consumption or income per person per day in the population. It is estimated from survey data and expressed in 2021 PPP dollars." }, { "id": "Topic", "value": "Poverty: Shared prosperity" }, { "id": "Unitofmeasure", "value": "2021 PPP $" } ], "source_id": "57" }, { "id": "SI.SPR.PCAP.05", "metatype": [ { "id": "Generalcomments", "value": "The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in PovcalNet. The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "IndicatorName", "value": "Survey mean consumption or income per capita, total population (2005 PPP $ per day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Longdefinition", "value": "Mean consumption or income per capita (2005 PPP $ per day) used in calculating the growth rate in the welfare aggregate of total population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "World Bank, PovcalNet: an online poverty analysis tool, http://iresearch.worldbank.org/PovcalNet/index.htm" }, { "id": "Source", "value": "World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity)." }, { "id": "Topic", "value": "Poverty: Shared prosperity" }, { "id": "Unitofmeasure", "value": "2005 PPP $" } ], "source_id": "57" }, { "id": "SI.SPR.PCAP.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "NA" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group has a goal of promoting shared prosperity within and across countries. Growth is good for the poor and growth in poor countries reflects in improvements in the World Bank’s new shared prosperity measure, the Global Prosperity Gap." }, { "id": "IndicatorName", "value": "Annualized average growth rate in per capita real survey mean consumption or income, total population (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Longdefinition", "value": "The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2021 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered." }, { "id": "Othernotes", "value": "The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2010-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The annualized growth rate in per capita real survey mean consumption of the total population is computed in the following steps. First, obtain the mean consumption or income levels of the total population of the survey distribution in two different periods. The two survey data sets should be comparable - that is, they use a similar method of sampling, collecting data, and constructing the welfare aggregate. Second, the rate of change in the survey mean values of the total population is annualized.\nStatistical concept(s): The annualized growth rate in per capita real survey mean consumption or income measures the rate of change in per capita consumption or income changes in a year." }, { "id": "Topic", "value": "Poverty: Shared prosperity" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SI.SPR.PGAP", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group's mission is to end extreme poverty and boost shared prosperity on a livable planet. Boosting shared prosperity is key to ensure that development gains are shared across vulnerable groups. The goal of boosting shared prosperity is defined using the prosperity gap." }, { "id": "IndicatorName", "value": "Prosperity gap (average shortfall from a prosperity standard of $28/day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The average shortfall from a prosperity standard of $28 per day (adjusted for differences in purchasing power parity across countries). It is measured as the average factor by which incomes fall short of $28." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1963-2025" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Prosperity Gap is a measure of shared prosperity. As a distribution-sensitive measure, the gap narrows when incomes increase anywhere and falls fastest when incomes of the very poorest increase: growth in income of a person earning $3.00 per day gets ten times more weight than growth in income of a person earning $28/day. Improvements (i.e., reductions) in the Prosperity Gap reflect increases in average income, reductions in inequality within countries, and (for global/regional aggregates) reductions in inequality between countries. It is estimated from nationally representative household surveys. When survey data are missing, in order to create global and regional aggregates, data are interpolated and extrapolated following the same methods that are used for global poverty estimates: https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html\n\n\nStatistical concept(s): The Prosperity Gap measures the average factor by which everyone’s incomes in a society needs to vary to reach a prosperity standard of $28 per day (expressed in 2021 PPP dollars). Consider the following example of five individuals earning $2, $7, $14, $28, and $56. Their incomes will have to vary by a factor of 14, 4, 2, 1, and 0.5, respectively, to achieve a prosperity standard of $28. If the world consisted of only these five individuals, the Global Prosperity Gap would be the average of these factors (4.3). As shown in this example, poorer individuals contribute more to the Global Prosperity Gap. $28 is roughly the per capita household income at which countries transition from upper-middle-income to high-income status." }, { "id": "Topic", "value": "Poverty: Shared prosperity" }, { "id": "Unitofmeasure", "value": "NA" } ], "source_id": "57" }, { "id": "SL.AGR.0714.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in agriculture, female (% of female economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Agriculture corresponds to division 1 (ISIC revision 2), categories A and B (ISIC revision 3), or category A (ISIC revision 4) and includes hunting, forestry, and fishing. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.AGR.0714.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in agriculture, male (% of male economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Agriculture corresponds to division 1 (ISIC revision 2), categories A and B (ISIC revision 3), or category A (ISIC revision 4) and includes hunting, forestry, and fishing. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.AGR.0714.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in agriculture (% of economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Agriculture corresponds to division 1 (ISIC revision 2), categories A and B (ISIC revision 3), or category A (ISIC revision 4) and includes hunting, forestry, and fishing. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.AGR.EMPL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in agriculture, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "57" }, { "id": "SL.AGR.EMPL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in agriculture, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "57" }, { "id": "SL.AGR.EMPL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in agriculture (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "57" }, { "id": "SL.EMP.1524.SP.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, female (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female population ages 15-24" } ], "source_id": "57" }, { "id": "SL.EMP.1524.SP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, female (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female population ages 15-24" } ], "source_id": "57" }, { "id": "SL.EMP.1524.SP.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, male (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male population ages 15-24" } ], "source_id": "57" }, { "id": "SL.EMP.1524.SP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, male (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male population ages 15-24" } ], "source_id": "57" }, { "id": "SL.EMP.1524.SP.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, total (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-24" } ], "source_id": "57" }, { "id": "SL.EMP.1524.SP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, ages 15-24, total (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-24" } ], "source_id": "57" }, { "id": "SL.EMP.INSV.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Data on women in wage employment in the nonagricultural sector show the extent to which women have access to paid employment - which affects their integration into the monetary economy - and indicate the degree to which labor markets are open to women in industry and services - which affects not only equal employment opportunity for women, but also economic efficiency through flexibility of the labor market and the economy's capacity to adapt to changes over time.\n\nIn many developing countries nonagricultural wage employment accounts for only a small portion of total employment. As a result, the contribution of women to the national economy is underestimated and therefore misrepresented. The indicator is difficult to interpret without additional information on the share of women in total employment, which allows an assessment to be made of whether women are under- or overrepresented in nonagricultural wage employment. The indicator does not reveal differences in the quality of nonagricultural wage employment in terms of earnings, work conditions, or legal and social protection. The indicator also does not reflect whether women reap the economic benefits of such employment. Finally, female employment and the employment share of the agricultural sector for both men and women tend to be underreported.\n\nWomen's wage work is important for economic growth and the well-being of families. But women often face such obstacles as restricted access to credit markets, capital, land, and training and education; time constraints due to traditional family responsibilities; and labor market bias and discrimination. These obstacles force women to limit their full participation in paid economic activities, to be less productive, and to receive lower wages." }, { "id": "Generalcomments", "value": "Relevance to gender indicator: Women’s share in paid employment in the nonagricultural sector has risen marginally in some regions but remains less than 20 percent in South Asia and Sub-Saharan Africa. Women are also clearly segregated in sectors that are generally known to be lower paid. And in the sectors where women dominate, such as health care, women rarely hold upper-level management jobs." }, { "id": "IndicatorName", "value": "Share of women in wage employment in the nonagricultural sector (% of total nonagricultural employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working." }, { "id": "Longdefinition", "value": "Share of women in wage employment in the nonagricultural sector is the share of female workers in wage employment in the nonagricultural sector (industry and services), expressed as a percentage of total employment in the nonagricultural sector. Industry includes mining and quarrying (including oil production), manufacturing, construction, electricity, gas, and water, corresponding to divisions 2-5 (ISIC revision 2) or tabulation categories C-F (ISIC revision 3). Services include wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services-corresponding to divisions 6-9 (ISIC revision 2) or tabulation categories G-Q (ISIC revision 3)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization." }, { "id": "Statisticalconceptandmethodology", "value": "Employment is defined as persons above a specified age who performed any work at all, in the reference period, for pay or profit (or pay in kind), or were temporarily absent from a job for such reasons as illness, maternity or parental leave, holiday, training or industrial dispute. Unpaid family workers who work for at least one hour should be included in the count of employment, although many countries use a higher hour limit in their definition.\n\nLabor force statistics by gender is important to monitor gender disparities in employment patterns. Estimates of women in the labor force and employment are generally lower than those of men and are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "57" }, { "id": "SL.EMP.MPYR.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Employers, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a \"self-employment jobs\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of employers to the total employed is calculated as follows: Employers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "57" }, { "id": "SL.EMP.MPYR.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Employers, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a \"self-employment jobs\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of employers to the total employed is calculated as follows: Employers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "57" }, { "id": "SL.EMP.MPYR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Employers, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a \"self-employment jobs\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of employers to the total employed is calculated as follows: Employers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "57" }, { "id": "SL.EMP.SELF.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Self-employed, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include sub-categories of employers, own-account workers and members of producers' cooperatives and contributing family workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of self-employed workers to the total employed is calculated as follows: Self-employed workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "57" }, { "id": "SL.EMP.SELF.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Self-employed, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include sub-categories of employers, own-account workers and members of producers' cooperatives and contributing family workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of self-employed workers to the total employed is calculated as follows: Self-employed workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "57" }, { "id": "SL.EMP.SELF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Self-employed, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include sub-categories of employers, own-account workers and members of producers' cooperatives and contributing family workers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of self-employed workers to the total employed is calculated as follows: Self-employed workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "57" }, { "id": "SL.EMP.SMGT.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator provides a meaningful measure of the percentage of females who are employed in decision-making and management roles in government, large enterprises and institutions, thus providing some insight into women’s power in decision-making and in the economy, relative to men’s power." }, { "id": "IndicatorName", "value": "Female share of employment in senior and middle management (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The main limitation of this indicator is that it fails to capture the differences in the levels of responsibility of women in their respective managerial position, or the importance of the enterprises and organizations in which they are employed. Its quality is also significantly impacted by the reliability of the employment statistics by occupation at the two-digit level of the ISCO. Whenever data at the two-digit level of the ISCO are not available, data at the one-digit level could be used as a proxy, referring only to major group 1 of ISCO-08 or ISCO-88, rather than to also refer to major group 1 minus category 14 of ISCO-08 or major group 1 minus category 13 of ISCO-88. This implies referring to the female share in total management, rather than also to the female share in senior and middle management exclusively. This proxy should be used only in case of lack of availability of data at the two-digit level of the ISCO, as total management includes junior management, and women tend to be more represented in junior management positions than in senior or middle management positions, and thus, by referring only to total management one may over-estimate women’s impact in high-level decision-making roles." }, { "id": "Longdefinition", "value": "The female share of employment in senior and middle management conveys the number of women in management as a percentage of employment in management. Employment in management is defined based on the International Standard Classification of Occupations. This series refers to senior and middle management only, thus excluding junior management (category 1 in both ISCO-08 and ISCO-88 minus category 14 in ISCO-08 and minus category 13 in ISCO-88)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2025" }, { "id": "Source", "value": "Labour Market-related SDG Indicators database (ILOSDG), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data for this indicator is collected through labor force surveys or any other household survey which collects such data through a module on employment. Establishment/firm surveys or administrative records can also provide useful data on female-occupied management positions by ISCO groups. Surveys are conducted by national statistical offices or ministries of labor in countries.\nStatistical concept(s): Employment comprises all persons of working age who, during a short reference period (one week), were engaged in any activity to produce goods or provide services for pay or profit. For further clarification, see: Resolution concerning statistics of work, employment and labor underutilization (2013).\n\n\n\n\n\n\n\nEmployment in management is determined according to the categories of the latest version of the International Standard Classification of Occupations (ISCO-08), which organizes jobs into a clearly defined set of groups based on the tasks and duties undertaken in the job. For the purposes of this indicator, it is preferable to refer separately to senior and middle management only on one hand, and to total management (including junior management) on the other. Senior and middle management correspond to sub-major groups 11, 12 and 13 in ISCO-08 and sub-major groups 11 and 12 in ISCO-88. If statistics are not available disaggregated at the sub-major group level (two-digit level of ISCO), then major group 1 of ISCO-88 and ISCO-08 can be used as a proxy and the indicator would then refer only to total management (including junior management)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment in senior and middle management" } ], "source_id": "57" }, { "id": "SL.EMP.TOTL.SP.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, female (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female population ages 15+" } ], "source_id": "57" }, { "id": "SL.EMP.TOTL.SP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, female (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female population ages 15+" } ], "source_id": "57" }, { "id": "SL.EMP.TOTL.SP.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, male (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male population ages 15+" } ], "source_id": "57" }, { "id": "SL.EMP.TOTL.SP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, male (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male population ages 15+" } ], "source_id": "57" }, { "id": "SL.EMP.TOTL.SP.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "With the aim of promoting international comparability, statistics presented on ILOSTAT are based on standard international definitions wherever feasible and may differ from official national figures. This series is based on the 13th ICLS definitions. For time series comparability, it includes countries that have implemented the 19th ICLS standards." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\n\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, total (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Comparability of employment-to-population ratios across countries is affected most significantly by variations in the definitions used for the employment and population figures. Differences result from age coverage, such as the lower and upper bounds for labor force activity. Estimates of both employment and population are also likely to vary according to whether members of the armed forces are included. Another area with scope for measurement differences has to do with the national treatment of particular groups of workers. The international definition of employment calls for inclusion of all persons who worked for at least one hour during the reference period. Workers could be in paid employment or in self-employment, including in less obvious forms of work, some of which are dealt with in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeship or non-market production. The majority of exceptions to coverage of all persons employed in a labor force survey have to do with national variations to the international recommendation applicable to the alternate employment statuses. For example, some countries measure persons employed in paid employment only and some countries measure “all persons engaged”, meaning paid employees plus working proprietors who receive some remuneration based on corporate shares. Other possible variations to the norms pertaining to measurement of total employment include hours limits (beyond one hour) placed on contributing family members for inclusion in employment. Comparisons can also be problematic when the frequency of data collection varies. The range of information collection can run from one month to 12 months in a year. Given the fact that seasonality of various kinds is undoubtedly present in all countries, employment-to-population ratios can vary for this reason alone. Countries with employment-to-population ratios based on less than full-year survey periods can be expected to have ratios that are not directly comparable with those from full-year, month-by-month collections. For example, an annual average based on 12 months of observations, all other things being equal, is likely to be different from an annual average based on four (quarterly) observations." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\n\n\nEPR (%) = 100 x Persons employed / Working-age population\n\n\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\n\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total population ages 15+" } ], "source_id": "57" }, { "id": "SL.EMP.TOTL.SP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The notion that employment – specifically, access to decent work – is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world’s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on “achieving full and productive employment and decent work for all, including women and young people”. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of “promoting inclusive and sustainable economic growth, employment and decent work for all”.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive – for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies." }, { "id": "IndicatorName", "value": "Employment to population ratio, 15+, total (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country. \n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms." }, { "id": "Longdefinition", "value": "Employment to population ratio is the proportion of a country's population that is employed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total population ages 15+" } ], "source_id": "57" }, { "id": "SL.EMP.VULN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks. The vulnerable employment rate, which is the share of vulnerable employment in total employment, was an indicator of the (now finished) Millennium Development Goals, under the employment, target on decent work." }, { "id": "IndicatorName", "value": "Vulnerable employment, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of vulnerable employment to the total employed is calculated as follows: (Contributing family workers + own-account workers)/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "57" }, { "id": "SL.EMP.VULN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks. The vulnerable employment rate, which is the share of vulnerable employment in total employment, was an indicator of the (now finished) Millennium Development Goals, under the employment, target on decent work." }, { "id": "IndicatorName", "value": "Vulnerable employment, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of vulnerable employment to the total employed is calculated as follows: (Contributing family workers + own-account workers)/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "57" }, { "id": "SL.EMP.VULN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks. The vulnerable employment rate, which is the share of vulnerable employment in total employment, was an indicator of the (now finished) Millennium Development Goals, under the employment, target on decent work." }, { "id": "IndicatorName", "value": "Vulnerable employment, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of vulnerable employment to the total employed is calculated as follows: (Contributing family workers + own-account workers)/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "57" }, { "id": "SL.EMP.WORK.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Data disaggregated by status in employment are provided according to the latest version of the International Standard Classification of Status in Employment (ICSE-93). Data may have been regrouped from the national classifications, which may not be strictly compatible with ICSE." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\n\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Wage and salaried workers, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of wage and salaried workers to the total employed is calculated as follows: Wage and salaried workers /Total employment x 100. \n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "57" }, { "id": "SL.EMP.WORK.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Wage and salaried workers, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of wage and salaried workers to the total employed is calculated as follows: Wage and salaried workers /Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "57" }, { "id": "SL.EMP.WORK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Wage and salaried workers, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of wage and salaried workers to the total employed is calculated as follows: Wage and salaried workers /Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "57" }, { "id": "SL.FAM.0714.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, unpaid family workers, female (% of female children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Unpaid family workers are people who work without pay in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.FAM.0714.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, unpaid family workers, male (% of male children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Unpaid family workers are people who work without pay in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.FAM.0714.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, unpaid family workers (% of children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Unpaid family workers are people who work without pay in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.FAM.WORK.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Contributing family workers, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of contributing family workers to the total employed is calculated as follows: Contributing family workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "57" }, { "id": "SL.FAM.WORK.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Contributing family workers, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of contributing family workers to the total employed is calculated as follows: Contributing family workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "57" }, { "id": "SL.FAM.WORK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks." }, { "id": "IndicatorName", "value": "Contributing family workers, total (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic." }, { "id": "Longdefinition", "value": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of contributing family workers to the total employed is calculated as follows: Contributing family workers/Total employment x 100. \n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "57" }, { "id": "SL.GDP.PCAP.EM.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Labor productivity is used to assess a country's economic ability to create and sustain decent employment opportunities with fair and equitable remuneration. Productivity increases obtained through investment, trade, technological progress, or changes in work organization can increase social protection and reduce poverty, which in turn reduce vulnerable employment and working poverty. Productivity increases do not guarantee these improvements, but without them - and the economic growth they bring - improvements are highly unlikely.\n\n\n\nGDP per person employed is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.2.1]" }, { "id": "IndicatorName", "value": "GDP per person employed (constant 2021 PPP $)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output in both national currencies and purchasing power parity dollars is not easily available, especially in developing countries, because the definition, coverage, and methodology are not always consistent across countries. For example, countries employ different methodologies for estimating the missing values for the nonmarket service sectors and use different definitions of the informal sector." }, { "id": "Longdefinition", "value": "GDP per person employed is gross domestic product (GDP) divided by total employment in the economy. Purchasing power parity (PPP) GDP is GDP converted to 2021 constant international dollars using PPP rates. An international dollar has the same purchasing power over GDP that a U.S. dollar has in the United States." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB), note: Estimates are based on employment, population, GDP, and PPP data obtained from International Labour Organization, United Nations Population Division, Eurostat, OECD, and World Bank., type: estimates based on external database;\nInternational Labour Organization (ILO);\nUnited Nations (UN), publisher: UN Population Division;\nEurostat (ESTAT);\nOrganisation for Economic Co-operation and Development (OECD);\nWorld Development Indicators database, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are based on employment, population, GDP, and PPP data obtained from International Labour Organization, United Nations Population Division, Eurostat, OECD, and World Bank. The employment rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): GDP per person employed represents labor productivity—output per unit of labor input. To compare labor productivity levels across countries, GDP is converted to international dollars using purchasing power parity rates which take account of differences in relative prices between countries." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "constant 2017 PPP $" } ], "source_id": "57" }, { "id": "SL.GDP.PCAP.EM.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "GDP per person employed (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Annual growth rate for GDP per person employed. GDP per person employed is gross domestic product (GDP) divided by total employment. GDP is converted to 2017 international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States." }, { "id": "Notesfromoriginalsource", "value": "Given the exceptional situation, including the scarcity of relevant data, the ILO modeled estimates and projections from 2020 onwards are subject to substantial uncertainty." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, World Development Indicators database. Estimates are based on employment, population, GDP, and PPP data obtained from International Labour Organization, United Nations Population Division, Eurostat, OECD, and World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "GDP per person employed represents labor productivity—output per unit of labor input. To compare labor productivity levels across countries, GDP is converted to international dollars using purchasing power parity rates which take account of differences in relative prices between countries.\n\nEstimates are based on employment, population, GDP, and PPP data obtained from International Labour Organization, United Nations Population Division, Eurostat, OECD, and World Bank. The employment rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/" }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "57" }, { "id": "SL.GDP.PCAP.EM.XD", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "BasePeriod", "value": "2000" }, { "id": "IndicatorName", "value": "GDP per person employed, index (2000 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GDP per person employed is presented as an index with base year 2000 = 100. GDP per person employed is gross domestic product (GDP) divided by total employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Derived using data from International Labour Organization, ILOSTAT database. The data retrieved in June 21, 2020." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "57" }, { "id": "SL.IND.EMPL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in industry, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "57" }, { "id": "SL.IND.EMPL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in industry, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "57" }, { "id": "SL.IND.EMPL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in industry (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "57" }, { "id": "SL.ISV.IFRM.FE.ZS", "metatype": [ { "id": "Generalcomments", "value": "Harmonized series" }, { "id": "IndicatorName", "value": "Informal employment, female (% of total non-agricultural employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are limitations for comparing data across countries and over time even within a country, due to differences in definitions and methodology of data collection. For example, informal sector enterprises refer to non-registered enterprises in some countries but registration requirements can vary from country to country. Others apply the employment size criterion only (which may vary from country to country). For detailed information on definitions and coverage, see footnotes." }, { "id": "Longdefinition", "value": "Employment in the informal economy as a percentage of total non-agricultural employment. It basically includes all jobs in unregistered and/or small-scale private unincorporated enterprises that produce goods or services meant for sale or barter. Self-employed street vendors, taxi drivers and home-base workers, regardless of size, are all considered enterprises. However, agricultural and related activities, households producing goods exclusively for their own use (e.g. subsistence farming, domestic housework, care work, and employment of paid domestic workers), and volunteer services rendered to the community are excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of September 2020." }, { "id": "Statisticalconceptandmethodology", "value": "There are wide variations in definitions and methodology of data collection. In addition to employment in the informal economy, informal employment within the formal sector should be also taken into account. Casual, short term, and seasonal workers, for example, could be informally employed — lacking social protection, health benefits, legal status, rights and freedom of association. Some countries now provide data according to the guidelines, adopted by the 17th International Conference of Labour Statisticians (2003); Informal employment as the total number of informal jobs, whether carried out in formal sector enterprises, informal sector enterprises, or households, during a given reference period." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "57" }, { "id": "SL.ISV.IFRM.MA.ZS", "metatype": [ { "id": "Generalcomments", "value": "Harmonized series" }, { "id": "IndicatorName", "value": "Informal employment, male (% of total non-agricultural employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are limitations for comparing data across countries and over time even within a country, due to differences in definitions and methodology of data collection. For example, informal sector enterprises refer to non-registered enterprises in some countries but registration requirements can vary from country to country. Others apply the employment size criterion only (which may vary from country to country). For detailed information on definitions and coverage, see footnotes." }, { "id": "Longdefinition", "value": "Employment in the informal economy as a percentage of total non-agricultural employment. It basically includes all jobs in unregistered and/or small-scale private unincorporated enterprises that produce goods or services meant for sale or barter. Self-employed street vendors, taxi drivers and home-base workers, regardless of size, are all considered enterprises. However, agricultural and related activities, households producing goods exclusively for their own use (e.g. subsistence farming, domestic housework, care work, and employment of paid domestic workers), and volunteer services rendered to the community are excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of September 2020." }, { "id": "Statisticalconceptandmethodology", "value": "There are wide variations in definitions and methodology of data collection. In addition to employment in the informal economy, informal employment within the formal sector should be also taken into account. Casual, short term, and seasonal workers, for example, could be informally employed — lacking social protection, health benefits, legal status, rights and freedom of association. Some countries now provide data according to the guidelines, adopted by the 17th International Conference of Labour Statisticians (2003); Informal employment as the total number of informal jobs, whether carried out in formal sector enterprises, informal sector enterprises, or households, during a given reference period." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "57" }, { "id": "SL.ISV.IFRM.ZS", "metatype": [ { "id": "Generalcomments", "value": "Harmonized series" }, { "id": "IndicatorName", "value": "Informal employment (% of total non-agricultural employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are limitations for comparing data across countries and over time even within a country, due to differences in definitions and methodology of data collection. For example, informal sector enterprises refer to non-registered enterprises in some countries but registration requirements can vary from country to country. Others apply the employment size criterion only (which may vary from country to country). For detailed information on definitions and coverage, see footnotes." }, { "id": "Longdefinition", "value": "Employment in the informal economy as a percentage of total non-agricultural employment. It basically includes all jobs in unregistered and/or small-scale private unincorporated enterprises that produce goods or services meant for sale or barter. Self-employed street vendors, taxi drivers and home-base workers, regardless of size, are all considered enterprises. However, agricultural and related activities, households producing goods exclusively for their own use (e.g. subsistence farming, domestic housework, care work, and employment of paid domestic workers), and volunteer services rendered to the community are excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of September 2020." }, { "id": "Statisticalconceptandmethodology", "value": "There are wide variations in definitions and methodology of data collection. In addition to employment in the informal economy, informal employment within the formal sector should be also taken into account. Casual, short term, and seasonal workers, for example, could be informally employed — lacking social protection, health benefits, legal status, rights and freedom of association. Some countries now provide data according to the guidelines, adopted by the 17th International Conference of Labour Statisticians (2003); Informal employment as the total number of informal jobs, whether carried out in formal sector enterprises, informal sector enterprises, or households, during a given reference period." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "57" }, { "id": "SL.MNF.0714.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in manufacturing, female (% of female economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Manufacturing corresponds to division 3 (ISIC revision 2), category D (ISIC revision 3), or category C (ISIC revision 4). Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.MNF.0714.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in manufacturing, male (% of male economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Manufacturing corresponds to division 3 (ISIC revision 2), category D (ISIC revision 3), or category C (ISIC revision 4). Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.MNF.0714.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in manufacturing (% of economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Manufacturing corresponds to division 3 (ISIC revision 2), category D (ISIC revision 3), or category C (ISIC revision 4). Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.MNF.WAGE.FM", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "IndicatorName", "value": "Ratio of female to male wages in manufacturing (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Ratio of female to male wages in manufacturing refers to female to male wages and salaries in manufacturing." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "57" }, { "id": "SL.SLF.0714.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, self-employed, female (% of female children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Self-employed workers are people whose remuneration depends directly on the profits derived from the goods and services they produce, with or without other employees, and include employers, own-account workers, and members of producers cooperatives." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.SLF.0714.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, self-employed, male (% of male children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Self-employed workers are people whose remuneration depends directly on the profits derived from the goods and services they produce, with or without other employees, and include employers, own-account workers, and members of producers cooperatives." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.SLF.0714.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, self-employed (% of children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Self-employed workers are people whose remuneration depends directly on the profits derived from the goods and services they produce, with or without other employees, and include employers, own-account workers, and members of producers cooperatives." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.SRV.0714.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in services, female (% of female economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Services correspond to divisions 6-9 (ISIC revision 2), categories G-P (ISIC revision 3), or categories G-U (ISIC revision 4). Services include wholesale and retail trade, hotels and restaurants, transport, financial intermediation, real estate, public administration, education, health and social work, other community services, and private household activity. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.SRV.0714.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in services, male (% of male economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Services correspond to divisions 6-9 (ISIC revision 2), categories G-P (ISIC revision 3), or categories G-U (ISIC revision 4). Services include wholesale and retail trade, hotels and restaurants, transport, financial intermediation, real estate, public administration, education, health and social work, other community services, and private household activity. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.SRV.0714.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Child employment in services (% of economically active children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Services correspond to divisions 6-9 (ISIC revision 2), categories G-P (ISIC revision 3), or categories G-U (ISIC revision 4). Services include wholesale and retail trade, hotels and restaurants, transport, financial intermediation, real estate, public administration, education, health and social work, other community services, and private household activity. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1998-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.SRV.EMPL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in services, female (% of female employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of female employment" } ], "source_id": "57" }, { "id": "SL.SRV.EMPL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in services, male (% of male employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of male employment" } ], "source_id": "57" }, { "id": "SL.SRV.EMPL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries." }, { "id": "IndicatorName", "value": "Employment in services (% of total employment) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data." }, { "id": "Longdefinition", "value": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "57" }, { "id": "SL.TLF.0714.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, female (% of female children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.TLF.0714.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, male (% of male children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.TLF.0714.SW.FE.TM", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Average working hours of children, study and work, female, ages 7-14 (hours per week)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Average working hours of children studying and working refer to the average weekly working hours of those children who are attending school in combination with economic activity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "57" }, { "id": "SL.TLF.0714.SW.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, study and work, female (% of female children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Study and work refer to children attending school in combination with economic activity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.TLF.0714.SW.MA.TM", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Average working hours of children, study and work, male, ages 7-14 (hours per week)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Average working hours of children studying and working refer to the average weekly working hours of those children who are attending school in combination with economic activity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "57" }, { "id": "SL.TLF.0714.SW.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, study and work, male (% of male children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Study and work refer to children attending school in combination with economic activity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.TLF.0714.SW.TM", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Average working hours of children, study and work, ages 7-14 (hours per week)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Average working hours of children studying and working refer to the average weekly working hours of those children who are attending school in combination with economic activity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "57" }, { "id": "SL.TLF.0714.SW.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, study and work (% of children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Study and work refer to children attending school in combination with economic activity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.TLF.0714.WK.FE.TM", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Average working hours of children, working only, female, ages 7-14 (hours per week)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Average working hours of children working only refers to the average weekly working hours of those children who are involved in economic activity and not attending school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "57" }, { "id": "SL.TLF.0714.WK.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, work only, female (% of female children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. 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Work only refers to children involved in economic activity and not attending school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.TLF.0714.WK.MA.TM", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Average working hours of children, working only, male, ages 7-14 (hours per week)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Average working hours of children working only refers to the average weekly working hours of those children who are involved in economic activity and not attending school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "57" }, { "id": "SL.TLF.0714.WK.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, work only, male (% of male children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Work only refers to children involved in economic activity and not attending school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.TLF.0714.WK.TM", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Average working hours of children, working only, ages 7-14 (hours per week)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Average working hours of children working only refers to the average weekly working hours of those children who are involved in economic activity and not attending school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "57" }, { "id": "SL.TLF.0714.WK.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, work only (% of children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Work only refers to children involved in economic activity and not attending school." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.TLF.0714.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, total (% of children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.TLF.ACTI.1524.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, female (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of female population ages 15-24" } ], "source_id": "57" }, { "id": "SL.TLF.ACTI.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of female population ages 15-24" } ], "source_id": "57" }, { "id": "SL.TLF.ACTI.1524.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, male (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of male population ages 15-24" } ], "source_id": "57" }, { "id": "SL.TLF.ACTI.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, male (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of male population ages 15-24" } ], "source_id": "57" }, { "id": "SL.TLF.ACTI.1524.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, total (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-24" } ], "source_id": "57" }, { "id": "SL.TLF.ACTI.1524.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate for ages 15-24, total (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-24" } ], "source_id": "57" }, { "id": "SL.TLF.ACTI.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, female (% of female population ages 15-64) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of female population ages 15-64" } ], "source_id": "57" }, { "id": "SL.TLF.ACTI.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, male (% of male population ages 15-64) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of male population ages 15-64" } ], "source_id": "57" }, { "id": "SL.TLF.ACTI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, total (% of total population ages 15-64) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-64" } ], "source_id": "57" }, { "id": "SL.TLF.ADVN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with advanced education, female (% of female working-age population with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with advanced education to the working-age population with advanced education. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of female working-age population with advanced education" } ], "source_id": "57" }, { "id": "SL.TLF.ADVN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with advanced education, male (% of male working-age population with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with advanced education to the working-age population with advanced education. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of male working-age population with advanced education" } ], "source_id": "57" }, { "id": "SL.TLF.ADVN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with advanced education (% of total working-age population with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with advanced education to the working-age population with advanced education. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total working-age population with advanced education" } ], "source_id": "57" }, { "id": "SL.TLF.BASC.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with basic education, female (% of female working-age population with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with basic education to the working-age population with basic education. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of female working-age population with basic education" } ], "source_id": "57" }, { "id": "SL.TLF.BASC.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with basic education, male (% of male working-age population with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with basic education to the working-age population with basic education. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of male working-age population with basic education" } ], "source_id": "57" }, { "id": "SL.TLF.BASC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with basic education (% of total working-age population with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with basic education to the working-age population with basic education. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total working-age population with basic education" } ], "source_id": "57" }, { "id": "SL.TLF.CACT.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, female (% of female population ages 15+) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of female population ages 15+" } ], "source_id": "57" }, { "id": "SL.TLF.CACT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, female (% of female population ages 15+) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\n\n\n\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\n\n\n\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "The labor force participation rate is the labor force as a percent of the population ages 15 and older. The labor force is the sum of all persons of working age who are employed and those who are unemployed." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate (LFPR) is calculated as follows: \n\n\nLFPR (%) = 100 x Labor force / population of a given age group, where the labor force is equal to employment plus unemployment.\n\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Population censuses are another major source of data on the labor force and its components.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of female population ages 15+" } ], "source_id": "57" }, { "id": "SL.TLF.CACT.FM.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Estimates of women in the labor force and employment are generally lower than those of men and are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic. In many low-income countries women often work on farms or in other family enterprises without pay, and others work in or near their homes, mixing work and family activities during the day. In many high-income economies, women have been increasingly acquiring higher education that has led to better-compensated, longer-term careers rather than lower-skilled, shorter-term jobs. However, access to good- paying occupations for women remains unequal in many occupations and countries around the world. Labor force statistics by gender is important to monitor gender disparities in employment and unemployment patterns." }, { "id": "IndicatorName", "value": "Ratio of female to male labor force participation rate (%) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\n\n\n\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\n\n\n\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Ratio of female to male labor force participation rate is the proportion of female labor force participation relative to male labor force participation. The labor force participation rate is the labor force as a percent of the population ages 15 and older. The labor force is the sum of all persons of working age who are employed and those who are unemployed." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100. The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\n\n\n\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\n\n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SL.TLF.CACT.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Estimates of women in the labor force and employment are generally lower than those of men and are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic. In many low-income countries women often work on farms or in other family enterprises without pay, and others work in or near their homes, mixing work and family activities during the day. In many high-income economies, women have been increasingly acquiring higher education that has led to better-compensated, longer-term careers rather than lower-skilled, shorter-term jobs. However, access to good- paying occupations for women remains unequal in many occupations and countries around the world. Labor force statistics by gender is important to monitor gender disparities in employment and unemployment patterns." }, { "id": "IndicatorName", "value": "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100. The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "SL.TLF.CACT.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, male (% of male population ages 15+) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of male population ages 15+" } ], "source_id": "57" }, { "id": "SL.TLF.CACT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, male (% of male population ages 15+) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of male population ages 15+" } ], "source_id": "57" }, { "id": "SL.TLF.CACT.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, total (% of total population ages 15+) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total population ages 15+" } ], "source_id": "57" }, { "id": "SL.TLF.CACT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force participation rate, total (% of total population ages 15+) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total population ages 15+" } ], "source_id": "57" }, { "id": "SL.TLF.CHLD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Labor force, children 10-14 (% of age group)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "57" }, { "id": "SL.TLF.INTM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with intermediate education, female (% of female working-age population with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with intermediate education to the working-age population with intermediate education. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of female working-age population with intermediate education" } ], "source_id": "57" }, { "id": "SL.TLF.INTM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with intermediate education, male (% of male working-age population with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with intermediate education to the working-age population with intermediate education. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of male working-age population with intermediate education" } ], "source_id": "57" }, { "id": "SL.TLF.INTM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Statistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential." }, { "id": "IndicatorName", "value": "Labor force with intermediate education (% of total working-age population with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The ratio of the labor force with intermediate education to the working-age population with intermediate education. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total working-age population with intermediate education" } ], "source_id": "57" }, { "id": "SL.TLF.PART.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Part-time employment has been seen as an instrument to increase labor supply. Indeed, as part-time work may offer the chance of a better balance between working life and family responsibilities, and suits workers who prefer shorter working hours and more time for their private life, it may allow more working-age persons to actually join the labor force. Also, policy-makers have promoted part-time work in an attempt to redistribute working time in countries of high unemployment, thus lowering politically sensitive unemployment rates without requiring an increase in the total number of hours worked.\n\n\n\nPart-time employment, however, is not always a choice. While flexibility may be one advantage of part-time work, disadvantages may exist in comparison with colleagues who work full time. Since the early 1990s, most OECD countries have introduced measures to improve the quality of part-time work, for example with respect to social benefits for part-time workers in line with those of full-time workers. Nevertheless, occupational segregation between part-time and full-time work remains an issue in most countries as it limits the occupational choices of part-time workers.\n\n\n\nLooking at part-time employment by sex is useful to see the extent to which the female labor force is more likely to work part time than the male labor force." }, { "id": "IndicatorName", "value": "Part time employment, female (% of total female employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Part-time employment rate represents the percentage of employment that is part time. Part time employment in this series is based on a common definition of less than 35 actual weekly hours worked." }, { "id": "Othernotes", "value": "Relevance to gender indicator: More and more women are working part-time and one of the concern is that part time work does not provide the stability that full time work does." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1976-2025" }, { "id": "Source", "value": "Wages and Working Time Statistics database (COND), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are typically the preferred source of information on hours of work. Such surveys can be designed to cover virtually the entire non-institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders.\n\nOther types of household surveys could also be used as sources of data on hours of work, if they have an appropriate module on the topic.\n\nIn the absence of a labor force survey or other types of household surveys with a module on working time, an establishment survey can be used as a source of statistics on hours of work. However, the statistics derived from establishments surveys would typically not refer to the whole employed population but only to employees (and often only to formal sector employees or non-agricultural formal sector employees).\nStatistical concept(s): Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total female employment" } ], "source_id": "57" }, { "id": "SL.TLF.PART.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Part-time employment has been seen as an instrument to increase labor supply. Indeed, as part-time work may offer the chance of a better balance between working life and family responsibilities, and suits workers who prefer shorter working hours and more time for their private life, it may allow more working-age persons to actually join the labor force. Also, policy-makers have promoted part-time work in an attempt to redistribute working time in countries of high unemployment, thus lowering politically sensitive unemployment rates without requiring an increase in the total number of hours worked.\n\n\n\nPart-time employment, however, is not always a choice. While flexibility may be one advantage of part-time work, disadvantages may exist in comparison with colleagues who work full time. Since the early 1990s, most OECD countries have introduced measures to improve the quality of part-time work, for example with respect to social benefits for part-time workers in line with those of full-time workers. Nevertheless, occupational segregation between part-time and full-time work remains an issue in most countries as it limits the occupational choices of part-time workers.\n\n\n\nLooking at part-time employment by sex is useful to see the extent to which the female labor force is more likely to work part time than the male labor force." }, { "id": "IndicatorName", "value": "Part time employment, male (% of total male employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Part-time employment rate represents the percentage of employment that is part time. Part time employment in this series is based on a common definition of less than 35 actual weekly hours worked." }, { "id": "Othernotes", "value": "Relevance to gender indicator: More and more women are working part-time and one of the concern is that part time work does not provide the stability that full time work does." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1976-2025" }, { "id": "Source", "value": "Wages and Working Time Statistics database (COND), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are typically the preferred source of information on hours of work. Such surveys can be designed to cover virtually the entire non-institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders.\n\nOther types of household surveys could also be used as sources of data on hours of work, if they have an appropriate module on the topic.\n\nIn the absence of a labor force survey or other types of household surveys with a module on working time, an establishment survey can be used as a source of statistics on hours of work. However, the statistics derived from establishments surveys would typically not refer to the whole employed population but only to employees (and often only to formal sector employees or non-agricultural formal sector employees).\nStatistical concept(s): Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total male employment" } ], "source_id": "57" }, { "id": "SL.TLF.PART.TL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Women’s wage work is important for economic growth and the well-being of families. But women often face such obstacles as restricted access to credit markets, capital, land, and training and education; time constraints due to traditional family responsibilities; and labor market bias and discrimination. These obstacles force women to limit their full participation in paid economic activities, to be less productive, and to receive lower wages. More women than men are in unpaid family employment and part-time employment. And men and women have different occupational distributions." }, { "id": "IndicatorName", "value": "Part time employment, female (% of total part time employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There is no official ILO definition of full-time work, so the definition of part-time workers differs across countries, and thus comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Part time employment refers to regular employment in which working time is substantially less than normal. Definitions of part time employment differ by country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2020." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" } ], "source_id": "57" }, { "id": "SL.TLF.PART.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Part-time employment has been seen as an instrument to increase labor supply. Indeed, as part-time work may offer the chance of a better balance between working life and family responsibilities, and suits workers who prefer shorter working hours and more time for their private life, it may allow more working-age persons to actually join the labor force. Also, policy-makers have promoted part-time work in an attempt to redistribute working time in countries of high unemployment, thus lowering politically sensitive unemployment rates without requiring an increase in the total number of hours worked.\n\n\n\nPart-time employment, however, is not always a choice. While flexibility may be one advantage of part-time work, disadvantages may exist in comparison with colleagues who work full time. Since the early 1990s, most OECD countries have introduced measures to improve the quality of part-time work, for example with respect to social benefits for part-time workers in line with those of full-time workers. Nevertheless, occupational segregation between part-time and full-time work remains an issue in most countries as it limits the occupational choices of part-time workers.\n\n\n\nLooking at part-time employment by sex is useful to see the extent to which the female labor force is more likely to work part time than the male labor force." }, { "id": "IndicatorName", "value": "Part time employment, total (% of total employment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Part-time employment rate represents the percentage of employment that is part time. Part time employment in this series is based on a common definition of less than 35 actual weekly hours worked." }, { "id": "Othernotes", "value": "Relevance to gender indicator: More and more women are working part-time and one of the concern is that part time work does not provide the stability that full time work does." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1976-2025" }, { "id": "Source", "value": "Wages and Working Time Statistics database (COND), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are typically the preferred source of information on hours of work. Such surveys can be designed to cover virtually the entire non-institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders.\n\nOther types of household surveys could also be used as sources of data on hours of work, if they have an appropriate module on the topic.\n\nIn the absence of a labor force survey or other types of household surveys with a module on working time, an establishment survey can be used as a source of statistics on hours of work. However, the statistics derived from establishments surveys would typically not refer to the whole employed population but only to employees (and often only to formal sector employees or non-agricultural formal sector employees).\nStatistical concept(s): Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work)." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "% of total employment" } ], "source_id": "57" }, { "id": "SL.TLF.PRIM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Labor force by education attainment provides insights into skill levels of labor force (employed and unemployed) and may be used to draw inferences about changes in employment demand and education policy." }, { "id": "IndicatorName", "value": "Labor force with primary education, female (% of female labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the International Labour Organization (ILO) from labor force surveys, censuses, establishment censuses and surveys, and administrative records such as employment exchange registers and unemployment insurance schemes. For some countries a combination of these sources is used. \n\nLabor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector. \n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. \n\nBesides the limitations to comparability raised for measuring labor force, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Female labor force with primary education is the share of the female labor force that attained or completed primary education as the highest level of education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Labor force with primary education is calculated by dividing the number of labor force who attained or completed primary education as the highest level by the total number of labor force, and multiplying by 100. \n\nThe labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave. Usually active population is measured in relation to a long reference period such as a year, and the currently active population (labor force) is measured in relation to a short reference period such as one day or one week.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "57" }, { "id": "SL.TLF.PRIM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Labor force by education attainment provides insights into skill levels of labor force (employed and unemployed) and may be used to draw inferences about changes in employment demand and education policy." }, { "id": "IndicatorName", "value": "Labor force with primary education, male (% of male labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the International Labour Organization (ILO) from labor force surveys, censuses, establishment censuses and surveys, and administrative records such as employment exchange registers and unemployment insurance schemes. For some countries a combination of these sources is used. \n\nLabor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector. \n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. \n\nBesides the limitations to comparability raised for measuring labor force, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Male labor force with primary education is the share of the male labor force that attained or completed primary education as the highest level of education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Labor force with primary education is calculated by dividing the number of labor force who attained or completed primary education as the highest level by the total number of labor force, and multiplying by 100. \n\nThe labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave. Usually active population is measured in relation to a long reference period such as a year, and the currently active population (labor force) is measured in relation to a short reference period such as one day or one week.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "57" }, { "id": "SL.TLF.PRIM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Labor force by education attainment provides insights into skill levels of labor force (employed and unemployed) and may be used to draw inferences about changes in employment demand and education policy." }, { "id": "IndicatorName", "value": "Labor force with primary education (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the International Labour Organization (ILO) from labor force surveys, censuses, establishment censuses and surveys, and administrative records such as employment exchange registers and unemployment insurance schemes. For some countries a combination of these sources is used. \n\nLabor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector. \n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. \n\nBesides the limitations to comparability raised for measuring labor force, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Labor force with primary education is the share of the total labor force that attained or completed primary education as the highest level of education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Labor force with primary education is calculated by dividing the number of labor force who attained or completed primary education as the highest level by the total number of labor force, and multiplying by 100. \n\nThe labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave. Usually active population is measured in relation to a long reference period such as a year, and the currently active population (labor force) is measured in relation to a short reference period such as one day or one week.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "57" }, { "id": "SL.TLF.SECO.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Labor force by education attainment provides insights into skill levels of labor force (employed and unemployed) and may be used to draw inferences about changes in employment demand and education policy." }, { "id": "IndicatorName", "value": "Labor force with secondary education, female (% of female labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the International Labour Organization (ILO) from labor force surveys, censuses, establishment censuses and surveys, and administrative records such as employment exchange registers and unemployment insurance schemes. For some countries a combination of these sources is used. \n\nLabor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector. \n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. \n\nBesides the limitations to comparability raised for measuring labor force, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Female labor force with secondary education is the share of the female labor force that attained or completed secondary education as the highest level of education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Labor force with secondary education is calculated by dividing the number of labor force who attained or completed secondary education as the highest level by the total number of labor force, and multiplying by 100. \n\nThe labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave. Usually active population is measured in relation to a long reference period such as a year, and the currently active population (labor force) is measured in relation to a short reference period such as one day or one week.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "57" }, { "id": "SL.TLF.SECO.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Labor force by education attainment provides insights into skill levels of labor force (employed and unemployed) and may be used to draw inferences about changes in employment demand and education policy." }, { "id": "IndicatorName", "value": "Labor force with secondary education, male (% of male labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the International Labour Organization (ILO) from labor force surveys, censuses, establishment censuses and surveys, and administrative records such as employment exchange registers and unemployment insurance schemes. For some countries a combination of these sources is used. \n\nLabor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector. \n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. \n\nBesides the limitations to comparability raised for measuring labor force, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Male labor force with secondary education is the share of the male labor force that attained or completed secondary education as the highest level of education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Labor force with secondary education is calculated by dividing the number of labor force who attained or completed secondary education as the highest level by the total number of labor force, and multiplying by 100. \n\nThe labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave. Usually active population is measured in relation to a long reference period such as a year, and the currently active population (labor force) is measured in relation to a short reference period such as one day or one week.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "57" }, { "id": "SL.TLF.SECO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Labor force by education attainment provides insights into skill levels of labor force (employed and unemployed) and may be used to draw inferences about changes in employment demand and education policy." }, { "id": "IndicatorName", "value": "Labor force with secondary education (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the International Labour Organization (ILO) from labor force surveys, censuses, establishment censuses and surveys, and administrative records such as employment exchange registers and unemployment insurance schemes. For some countries a combination of these sources is used. \n\nLabor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector. \n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. \n\nBesides the limitations to comparability raised for measuring labor force, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Labor force with secondary education is the share of the total labor force that attained or completed secondary education as the highest level of education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Labor force with secondary education is calculated by dividing the number of labor force who attained or completed secondary education as the highest level by the total number of labor force, and multiplying by 100. \n\nThe labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave. Usually active population is measured in relation to a long reference period such as a year, and the currently active population (labor force) is measured in relation to a short reference period such as one day or one week.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "57" }, { "id": "SL.TLF.TERT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Labor force by education attainment provides insights into skill levels of labor force (employed and unemployed) and may be used to draw inferences about changes in employment demand and education policy." }, { "id": "IndicatorName", "value": "Labor force with tertiary education, female (% of female labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the International Labour Organization (ILO) from labor force surveys, censuses, establishment censuses and surveys, and administrative records such as employment exchange registers and unemployment insurance schemes. For some countries a combination of these sources is used. \n\nLabor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector. \n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. \n\nBesides the limitations to comparability raised for measuring labor force, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Female labor force with tertiary education is the share of the female labor force that attained or completed tertiary education as the highest level of education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Labor force with tertiary education is calculated by dividing the number of labor force who attained or completed tertiary education as the highest level by the total number of labor force, and multiplying by 100. \n\nThe labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave. Usually active population is measured in relation to a long reference period such as a year, and the currently active population (labor force) is measured in relation to a short reference period such as one day or one week.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "57" }, { "id": "SL.TLF.TERT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Labor force by education attainment provides insights into skill levels of labor force (employed and unemployed) and may be used to draw inferences about changes in employment demand and education policy." }, { "id": "IndicatorName", "value": "Labor force with tertiary education, male (% of male labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the International Labour Organization (ILO) from labor force surveys, censuses, establishment censuses and surveys, and administrative records such as employment exchange registers and unemployment insurance schemes. For some countries a combination of these sources is used. \n\nLabor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector. \n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. \n\nBesides the limitations to comparability raised for measuring labor force, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Male labor force with tertiary education is the share of the male labor force that attained or completed tertiary education as the highest level of education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Labor force with tertiary education is calculated by dividing the number of labor force who attained or completed tertiary education as the highest level by the total number of labor force, and multiplying by 100. \n\nThe labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave. Usually active population is measured in relation to a long reference period such as a year, and the currently active population (labor force) is measured in relation to a short reference period such as one day or one week.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "57" }, { "id": "SL.TLF.TERT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Labor force by education attainment provides insights into skill levels of labor force (employed and unemployed) and may be used to draw inferences about changes in employment demand and education policy." }, { "id": "IndicatorName", "value": "Labor force with tertiary education (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the International Labour Organization (ILO) from labor force surveys, censuses, establishment censuses and surveys, and administrative records such as employment exchange registers and unemployment insurance schemes. For some countries a combination of these sources is used. \n\nLabor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector. \n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. \n\nBesides the limitations to comparability raised for measuring labor force, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Labor force with tertiary education is the share of the total labor force that attained or completed tertiary education as the highest level of education." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Labor force with tertiary education is calculated by dividing the number of labor force who attained or completed tertiary education as the highest level by the total number of labor force, and multiplying by 100. \n\nThe labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave. Usually active population is measured in relation to a long reference period such as a year, and the currently active population (labor force) is measured in relation to a short reference period such as one day or one week.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" } ], "source_id": "57" }, { "id": "SL.TLF.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force, female (% of total labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "International Labour Organization (ILO), type: estimates based on external database;\nUnited Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are based on labor force participation rates and population data from International Labour Organization and United Nations Population Division. The labor force participation rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or voluntarily left work. In addition, persons who did not look for work but have an arrangement for a future job are also counted as unemployed. Still, some unemployment is unavoidable—at any time, some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. The labor force or the economically active portion of the population serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "% of total labor force" } ], "source_id": "57" }, { "id": "SL.TLF.TOTL.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "IndicatorName", "value": "Labor force, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2025" }, { "id": "Source", "value": "International Labour Organization (ILO), type: estimates based on external database;\nUnited Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are based on labor force participation rates and population data from International Labour Organization and United Nations Population Division. The labor force participation rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or voluntarily left work. In addition, persons who did not look for work but have an arrangement for a future job are also counted as unemployed. Still, some unemployment is unavoidable—at any time, some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. The labor force or the economically active portion of the population serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Social Protection & Labor: Labor force structure" }, { "id": "Unitofmeasure", "value": "Persons" } ], "source_id": "57" }, { "id": "SL.UEM.1524.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth female (% of female labor force ages 15-24) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\n\n\n\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\n\n\n\n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female labor force ages 15-24" } ], "source_id": "57" }, { "id": "SL.UEM.1524.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\n\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\n\n\n\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth female (% of female labor force ages 15-24) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\n\n\n\n\n\n\n\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\n\n\n\n\n\n\n\n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female labor force ages 15-24" } ], "source_id": "57" }, { "id": "SL.UEM.1524.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth male (% of male labor force ages 15-24) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male labor force ages 15-24" } ], "source_id": "57" }, { "id": "SL.UEM.1524.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth male (% of male labor force ages 15-24) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male labor force ages 15-24" } ], "source_id": "57" }, { "id": "SL.UEM.1524.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth total (% of total labor force ages 15-24) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of total labor force ages 15-24" } ], "source_id": "57" }, { "id": "SL.UEM.1524.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects." }, { "id": "IndicatorName", "value": "Unemployment, youth total (% of total labor force ages 15-24) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of total labor force ages 15-24" } ], "source_id": "57" }, { "id": "SL.UEM.ADVN.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with advanced education, female (% of female labor force with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female labor force with advanced education" } ], "source_id": "57" }, { "id": "SL.UEM.ADVN.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with advanced education, male (% of male labor force with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male labor force with advanced education" } ], "source_id": "57" }, { "id": "SL.UEM.ADVN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with advanced education (% of total labor force with advanced education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of total labor force with advanced education" } ], "source_id": "57" }, { "id": "SL.UEM.BASC.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with basic education, female (% of female labor force with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with a basic level of education who are unemployed. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female labor force with basic education" } ], "source_id": "57" }, { "id": "SL.UEM.BASC.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with basic education, male (% of male labor force with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with a basic level of education who are unemployed. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male labor force with basic education" } ], "source_id": "57" }, { "id": "SL.UEM.BASC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with basic education (% of total labor force with basic education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with a basic level of education who are unemployed. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of total labor force with basic education" } ], "source_id": "57" }, { "id": "SL.UEM.INTM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with intermediate education, female (% of female labor force with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female labor force with intermediate education" } ], "source_id": "57" }, { "id": "SL.UEM.INTM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with intermediate education, male (% of male labor force with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male labor force with intermediate education" } ], "source_id": "57" }, { "id": "SL.UEM.INTM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Focusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the “brain drain”, whereby educated professionals migrate in order to find employment in other areas of the world." }, { "id": "IndicatorName", "value": "Unemployment with intermediate education (% of total labor force with intermediate education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of total labor force with intermediate education" } ], "source_id": "57" }, { "id": "SL.UEM.LTRM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "While short periods of joblessness are of less concern, especially when unemployed persons are covered by unemployment insurance schemes or similar forms of support, prolonged periods of unemployment bring with them many undesirable effects, particularly loss of income and diminishing employability of the jobseeker. Moreover, short-term unemployment may even be viewed as desirable when it allows time for jobless persons to find optimal employment in line with the jobseeker's skills set and capabilities; also, in employment systems where workers can be temporarily laid off and then called back, short spells of unemployment allow employers to weather temporary declines in business activity.\nReducing the length of unemployment spells is a key element in many strategies to reduce overall unemployment. Long-duration unemployment is undesirable, especially in circumstances where unemployment results from difficulties in matching supply and demand because of demand deficiency. The longer a person is unemployed, the lower his or her chance of finding a job. Drawing income support for the period of unemployment certainly diminishes economic hardship, but financial support does not last indefinitely. In any case, unemployment insurance coverage is often insufficient and not available to every unemployed person; the most likely non-recipients are persons entering or re-entering the labour market. Eligibility criteria and the extent of coverage, as well as the very existence of insurance, vary widely across countries.\n\nUnemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nIn many developing countries women work on farms or in other family enterprises without pay and others work in or near their homes, mixing work and family activities during the day. Labor force statistics by gender is important to monitor gender disparities in unemployment patterns. In many developed economies, women have been increasingly acquiring higher education that has led to better-compensated, longer-term careers rather than lower-skilled, shorter-term jobs. However, access to good- paying occupations for women remains unequal in many occupations and countries around the world." }, { "id": "Generalcomments", "value": "Relevance to gender indicator: Even though, in most countries, long-term unemployment rate is higher for men than women, the repercussion of long-term unemployment is likely to be more pronounced for women." }, { "id": "IndicatorName", "value": "Long-term unemployment, female (% of female unemployment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on long-term unemployment are often collected in household labour force surveys. Some countries obtain the data from administrative records, such as those of employment exchanges or unemployment insurance schemes. In this case, data are less likely to be available by sex; moreover, since many insurance schemes are limited in their coverage, administrative data are likely to yield different distributions of unemployment duration. In addition, the use of administrative data reduces the likelihood that ratios can be calculated using a statistically consistent labour force base. Therefore, all the data for this indicator come from labour force surveys, alternative sources having been eliminated as likely to cause inconsistency across the countries for which data are provided.\n\nLabor force surveys generally yield the most comprehensive data because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These surveys generally use a definition of unemployment that follows the international recommendations more closely than that used by other sources and therefore generate statistics that are more comparable internationally. But the age group, geographic coverage, and collection methods could differ by country or change over time within a country. For detailed information, consult the original source.\n\nWhile data from household labour force surveys make international comparisons easier, they are not perfect. Questionnaire design, survey timing, differences in the age groups covered and other issues affecting comparability, mean that care is required in interpreting cross-country differences in levels of unemployment. Also, users will want to know something about the nature of unemployment insurance coverage in countries of interest to them, as substantial differences in such coverage - especially the lack of it altogether - can have a profound effect on differences in long-term unemployment.\n\nIt should also be acknowledged that the length of time that a person has been unemployed is, in general, more difficult to measure than many other statistics, particularly when the data are derived from labour force surveys. When unemployed persons are interviewed, their ability to recall with any degree of precision the length of time that they have been jobless diminishes significantly as the period of joblessness extends. Thus, as it nears a full year, it is quite easy to say \"one year\", when in reality the respondent may have been unemployed between 10 and 14 months. If the household respondent is a proxy for the unemployed person, the specific knowledge and the ability to recall are reduced even further. Moreover, as the jobless period lengthens, not only is the likelihood of accurate recall reduced, the jobless period is also more likely to have been interrupted by limited periods of work or spells of discouragement, but either this is forgotten over time or the unemployed person may not consider that work period as relevant to his or her \"real\"unemployment problem.\n\nThe ILO definition of unemployment notwithstanding, reference periods, the criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time vary across countries. In many developing countries it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey, for example, can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked." }, { "id": "Longdefinition", "value": "Long-term unemployment refers to the number of people with continuous periods of unemployment extending for a year or longer, expressed as a percentage of the total unemployed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of long-term unemployment covers all unemployed persons with continuous periods of unemployment extending for a year or longer (52 weeks and over), expressed as percentage of total unemployment. The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work. Persons who did not look for work but have an arrangements for a future job are counted as unemployed. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "57" }, { "id": "SL.UEM.LTRM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "While short periods of joblessness are of less concern, especially when unemployed persons are covered by unemployment insurance schemes or similar forms of support, prolonged periods of unemployment bring with them many undesirable effects, particularly loss of income and diminishing employability of the jobseeker. Moreover, short-term unemployment may even be viewed as desirable when it allows time for jobless persons to find optimal employment in line with the jobseeker's skills set and capabilities; also, in employment systems where workers can be temporarily laid off and then called back, short spells of unemployment allow employers to weather temporary declines in business activity.\nReducing the length of unemployment spells is a key element in many strategies to reduce overall unemployment. Long-duration unemployment is undesirable, especially in circumstances where unemployment results from difficulties in matching supply and demand because of demand deficiency. The longer a person is unemployed, the lower his or her chance of finding a job. Drawing income support for the period of unemployment certainly diminishes economic hardship, but financial support does not last indefinitely. In any case, unemployment insurance coverage is often insufficient and not available to every unemployed person; the most likely non-recipients are persons entering or re-entering the labour market. Eligibility criteria and the extent of coverage, as well as the very existence of insurance, vary widely across countries.\n\nUnemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nIn many developing countries women work on farms or in other family enterprises without pay and others work in or near their homes, mixing work and family activities during the day. Labor force statistics by gender is important to monitor gender disparities in unemployment patterns. In many developed economies, women have been increasingly acquiring higher education that has led to better-compensated, longer-term careers rather than lower-skilled, shorter-term jobs. However, access to good- paying occupations for women remains unequal in many occupations and countries around the world." }, { "id": "Generalcomments", "value": "Relevance to gender indicator: Even though, in most countries, long-term unemployment rate is higher for men than women, the repercussion of long-term unemployment is likely to be more pronounced for women." }, { "id": "IndicatorName", "value": "Long-term unemployment, male (% of male unemployment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on long-term unemployment are often collected in household labour force surveys. Some countries obtain the data from administrative records, such as those of employment exchanges or unemployment insurance schemes. In this case, data are less likely to be available by sex; moreover, since many insurance schemes are limited in their coverage, administrative data are likely to yield different distributions of unemployment duration. In addition, the use of administrative data reduces the likelihood that ratios can be calculated using a statistically consistent labour force base. Therefore, all the data for this indicator come from labour force surveys, alternative sources having been eliminated as likely to cause inconsistency across the countries for which data are provided.\n\nLabor force surveys generally yield the most comprehensive data because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These surveys generally use a definition of unemployment that follows the international recommendations more closely than that used by other sources and therefore generate statistics that are more comparable internationally. But the age group, geographic coverage, and collection methods could differ by country or change over time within a country. For detailed information, consult the original source.\n\nWhile data from household labour force surveys make international comparisons easier, they are not perfect. Questionnaire design, survey timing, differences in the age groups covered and other issues affecting comparability, mean that care is required in interpreting cross-country differences in levels of unemployment. Also, users will want to know something about the nature of unemployment insurance coverage in countries of interest to them, as substantial differences in such coverage - especially the lack of it altogether - can have a profound effect on differences in long-term unemployment.\n\nIt should also be acknowledged that the length of time that a person has been unemployed is, in general, more difficult to measure than many other statistics, particularly when the data are derived from labour force surveys. When unemployed persons are interviewed, their ability to recall with any degree of precision the length of time that they have been jobless diminishes significantly as the period of joblessness extends. Thus, as it nears a full year, it is quite easy to say \"one year\", when in reality the respondent may have been unemployed between 10 and 14 months. If the household respondent is a proxy for the unemployed person, the specific knowledge and the ability to recall are reduced even further. Moreover, as the jobless period lengthens, not only is the likelihood of accurate recall reduced, the jobless period is also more likely to have been interrupted by limited periods of work or spells of discouragement, but either this is forgotten over time or the unemployed person may not consider that work period as relevant to his or her \"real\"unemployment problem.\n\nThe ILO definition of unemployment notwithstanding, reference periods, the criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time vary across countries. In many developing countries it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey, for example, can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked." }, { "id": "Longdefinition", "value": "Long-term unemployment refers to the number of people with continuous periods of unemployment extending for a year or longer, expressed as a percentage of the total unemployed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of long-term unemployment covers all unemployed persons with continuous periods of unemployment extending for a year or longer (52 weeks and over), expressed as percentage of total unemployment. The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work. Persons who did not look for work but have an arrangements for a future job are counted as unemployed. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "57" }, { "id": "SL.UEM.LTRM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "While short periods of joblessness are of less concern, especially when unemployed persons are covered by unemployment insurance schemes or similar forms of support, prolonged periods of unemployment bring with them many undesirable effects, particularly loss of income and diminishing employability of the jobseeker. Moreover, short-term unemployment may even be viewed as desirable when it allows time for jobless persons to find optimal employment in line with the jobseeker's skills set and capabilities; also, in employment systems where workers can be temporarily laid off and then called back, short spells of unemployment allow employers to weather temporary declines in business activity.\nReducing the length of unemployment spells is a key element in many strategies to reduce overall unemployment. Long-duration unemployment is undesirable, especially in circumstances where unemployment results from difficulties in matching supply and demand because of demand deficiency. The longer a person is unemployed, the lower his or her chance of finding a job. Drawing income support for the period of unemployment certainly diminishes economic hardship, but financial support does not last indefinitely. In any case, unemployment insurance coverage is often insufficient and not available to every unemployed person; the most likely non-recipients are persons entering or re-entering the labour market. Eligibility criteria and the extent of coverage, as well as the very existence of insurance, vary widely across countries.\n\nUnemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nIn many developing countries women work on farms or in other family enterprises without pay and others work in or near their homes, mixing work and family activities during the day. Labor force statistics by gender is important to monitor gender disparities in unemployment patterns. In many developed economies, women have been increasingly acquiring higher education that has led to better-compensated, longer-term careers rather than lower-skilled, shorter-term jobs. However, access to good- paying occupations for women remains unequal in many occupations and countries around the world." }, { "id": "Generalcomments", "value": "Relevance to gender indicator: Even though, in most countries, long-term unemployment rate is higher for men than women, the repercussion of long-term unemployment is likely to be more pronounced for women." }, { "id": "IndicatorName", "value": "Long-term unemployment (% of total unemployment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on long-term unemployment are often collected in household labour force surveys. Some countries obtain the data from administrative records, such as those of employment exchanges or unemployment insurance schemes. In this case, data are less likely to be available by sex; moreover, since many insurance schemes are limited in their coverage, administrative data are likely to yield different distributions of unemployment duration. In addition, the use of administrative data reduces the likelihood that ratios can be calculated using a statistically consistent labour force base. Therefore, all the data for this indicator come from labour force surveys, alternative sources having been eliminated as likely to cause inconsistency across the countries for which data are provided.\n\nLabor force surveys generally yield the most comprehensive data because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These surveys generally use a definition of unemployment that follows the international recommendations more closely than that used by other sources and therefore generate statistics that are more comparable internationally. But the age group, geographic coverage, and collection methods could differ by country or change over time within a country. For detailed information, consult the original source.\n\nWhile data from household labour force surveys make international comparisons easier, they are not perfect. Questionnaire design, survey timing, differences in the age groups covered and other issues affecting comparability, mean that care is required in interpreting cross-country differences in levels of unemployment. Also, users will want to know something about the nature of unemployment insurance coverage in countries of interest to them, as substantial differences in such coverage - especially the lack of it altogether - can have a profound effect on differences in long-term unemployment.\n\nIt should also be acknowledged that the length of time that a person has been unemployed is, in general, more difficult to measure than many other statistics, particularly when the data are derived from labour force surveys. When unemployed persons are interviewed, their ability to recall with any degree of precision the length of time that they have been jobless diminishes significantly as the period of joblessness extends. Thus, as it nears a full year, it is quite easy to say \"one year\", when in reality the respondent may have been unemployed between 10 and 14 months. If the household respondent is a proxy for the unemployed person, the specific knowledge and the ability to recall are reduced even further. Moreover, as the jobless period lengthens, not only is the likelihood of accurate recall reduced, the jobless period is also more likely to have been interrupted by limited periods of work or spells of discouragement, but either this is forgotten over time or the unemployed person may not consider that work period as relevant to his or her \"real\"unemployment problem.\n\nThe ILO definition of unemployment notwithstanding, reference periods, the criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time vary across countries. In many developing countries it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey, for example, can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked." }, { "id": "Longdefinition", "value": "Long-term unemployment refers to the number of people with continuous periods of unemployment extending for a year or longer, expressed as a percentage of the total unemployed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of long-term unemployment covers all unemployed persons with continuous periods of unemployment extending for a year or longer (52 weeks and over), expressed as percentage of total unemployment. The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work. Persons who did not look for work but have an arrangements for a future job are counted as unemployed. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "57" }, { "id": "SL.UEM.NEET.FE.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, female (% of female youth population) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of youth population" } ], "source_id": "57" }, { "id": "SL.UEM.NEET.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, female (% of female youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female youth population" } ], "source_id": "57" }, { "id": "SL.UEM.NEET.MA.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, male (% of male youth population) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male youth population" } ], "source_id": "57" }, { "id": "SL.UEM.NEET.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, male (% of male youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male youth population" } ], "source_id": "57" }, { "id": "SL.UEM.NEET.ME.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, total (% of youth population) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2005-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female youth population" } ], "source_id": "57" }, { "id": "SL.UEM.NEET.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Unemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets." }, { "id": "IndicatorName", "value": "Share of youth not in education, employment or training, total (% of youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24." }, { "id": "Longdefinition", "value": "The share of youth not in education, employment or training (also known as “the NEET rate”) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1970-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The youth NEET rate is calculated as follows: NEET rate = (Youth – Youth in employment – Youth not in employment but in education or training) / Youth x 100. \n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) – (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100. \n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). \n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of youth population" } ], "source_id": "57" }, { "id": "SL.UEM.PRIM.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unemployment by level of educational attainment provides insights into the relation between the educational attainment of workers and unemployment and may be used to draw inferences about changes in employment demand. A high share of the unemployed with low education levels may suggest their education level be increased or more low-skill occupations be created. Similarly, a high share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs." }, { "id": "IndicatorName", "value": "Unemployment with primary education, female (% of female unemployment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on unemployment are drawn from labor force sample surveys and general household sample surveys, censuses, and official estimates, which are generally based on information from different sources and can be combined in many ways. Labor force surveys generally yield the most comprehensive data, because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These surveys generally use a definition of unemployment that follows the international recommendations more closely than that used by other sources and therefore generate statistics that are more comparable internationally. But the age group, geographic coverage, and collection methods could differ by country or change over time within a country. \n\nBesides the limitations to comparability raised for measuring unemployment, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Female unemployment with primary education is the share of the female unemployed who attained or completed primary education as the highest level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Unemployment with primary education is calculated by dividing the number of unemployed who attained or completed primary education as the highest level by the total number of unemployed, and multiplying by 100. \n\nThe unemployed are defined as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed – between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "57" }, { "id": "SL.UEM.PRIM.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unemployment by level of educational attainment provides insights into the relation between the educational attainment of workers and unemployment and may be used to draw inferences about changes in employment demand. A high share of the unemployed with low education levels may suggest their education level be increased or more low-skill occupations be created. Similarly, a high share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs." }, { "id": "IndicatorName", "value": "Unemployment with primary education, male (% of male unemployment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on unemployment are drawn from labor force sample surveys and general household sample surveys, censuses, and official estimates, which are generally based on information from different sources and can be combined in many ways. Labor force surveys generally yield the most comprehensive data, because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These surveys generally use a definition of unemployment that follows the international recommendations more closely than that used by other sources and therefore generate statistics that are more comparable internationally. But the age group, geographic coverage, and collection methods could differ by country or change over time within a country. \n\nBesides the limitations to comparability raised for measuring unemployment, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Male unemployment with primary education is the share of the male unemployed who attained or completed primary education as the highest level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Unemployment with primary education is calculated by dividing the number of unemployed who attained or completed primary education as the highest level by the total number of unemployed, and multiplying by 100. \n\nThe unemployed are defined as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed – between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "57" }, { "id": "SL.UEM.PRIM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unemployment by level of educational attainment provides insights into the relation between the educational attainment of workers and unemployment and may be used to draw inferences about changes in employment demand. A high share of the unemployed with low education levels may suggest their education level be increased or more low-skill occupations be created. Similarly, a high share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs." }, { "id": "IndicatorName", "value": "Unemployment with primary education (% of total unemployment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on unemployment are drawn from labor force sample surveys and general household sample surveys, censuses, and official estimates, which are generally based on information from different sources and can be combined in many ways. Labor force surveys generally yield the most comprehensive data, because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These surveys generally use a definition of unemployment that follows the international recommendations more closely than that used by other sources and therefore generate statistics that are more comparable internationally. But the age group, geographic coverage, and collection methods could differ by country or change over time within a country. \n\nBesides the limitations to comparability raised for measuring unemployment, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Unemployment with primary education is the share of the total unemployed who attained or completed primary education as the highest level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Unemployment with primary education is calculated by dividing the number of unemployed who attained or completed primary education as the highest level by the total number of unemployed, and multiplying by 100. \n\nThe unemployed are defined as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed – between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "57" }, { "id": "SL.UEM.SECO.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unemployment by level of educational attainment provides insights into the relation between the educational attainment of workers and unemployment and may be used to draw inferences about changes in employment demand. A high share of the unemployed with low education levels may suggest their education level be increased or more low-skill occupations be created. Similarly, a high share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs." }, { "id": "IndicatorName", "value": "Unemployment with secondary education, female (% of female unemployment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on unemployment are drawn from labor force sample surveys and general household sample surveys, censuses, and official estimates, which are generally based on information from different sources and can be combined in many ways. Labor force surveys generally yield the most comprehensive data, because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These surveys generally use a definition of unemployment that follows the international recommendations more closely than that used by other sources and therefore generate statistics that are more comparable internationally. But the age group, geographic coverage, and collection methods could differ by country or change over time within a country. \n\nBesides the limitations to comparability raised for measuring unemployment, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Female unemployment with secondary education is the share of the female unemployed who attained or completed secondary education as the highest level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Unemployment with secondary education is calculated by dividing the number of unemployed who attained or completed secondary education as the highest level by the total number of unemployed, and multiplying by 100. \n\nThe unemployed are defined as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed – between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "57" }, { "id": "SL.UEM.SECO.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unemployment by level of educational attainment provides insights into the relation between the educational attainment of workers and unemployment and may be used to draw inferences about changes in employment demand. A high share of the unemployed with low education levels may suggest their education level be increased or more low-skill occupations be created. Similarly, a high share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs." }, { "id": "IndicatorName", "value": "Unemployment with secondary education, male (% of male unemployment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on unemployment are drawn from labor force sample surveys and general household sample surveys, censuses, and official estimates, which are generally based on information from different sources and can be combined in many ways. Labor force surveys generally yield the most comprehensive data, because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These surveys generally use a definition of unemployment that follows the international recommendations more closely than that used by other sources and therefore generate statistics that are more comparable internationally. But the age group, geographic coverage, and collection methods could differ by country or change over time within a country. \n\nBesides the limitations to comparability raised for measuring unemployment, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Male unemployment with secondary education is the share of the male unemployed who attained or completed secondary education as the highest level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Unemployment with secondary education is calculated by dividing the number of unemployed who attained or completed secondary education as the highest level by the total number of unemployed, and multiplying by 100. \n\nThe unemployed are defined as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed – between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "57" }, { "id": "SL.UEM.SECO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unemployment by level of educational attainment provides insights into the relation between the educational attainment of workers and unemployment and may be used to draw inferences about changes in employment demand. A high share of the unemployed with low education levels may suggest their education level be increased or more low-skill occupations be created. Similarly, a high share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs." }, { "id": "IndicatorName", "value": "Unemployment with secondary education (% of total unemployment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on unemployment are drawn from labor force sample surveys and general household sample surveys, censuses, and official estimates, which are generally based on information from different sources and can be combined in many ways. Labor force surveys generally yield the most comprehensive data, because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These surveys generally use a definition of unemployment that follows the international recommendations more closely than that used by other sources and therefore generate statistics that are more comparable internationally. But the age group, geographic coverage, and collection methods could differ by country or change over time within a country. \n\nBesides the limitations to comparability raised for measuring unemployment, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Unemployment with secondary education is the share of the total unemployed who attained or completed secondary education as the highest level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Unemployment with secondary education is calculated by dividing the number of unemployed who attained or completed secondary education as the highest level by the total number of unemployed, and multiplying by 100. \n\nThe unemployed are defined as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed – between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "57" }, { "id": "SL.UEM.TERT.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unemployment by level of educational attainment provides insights into the relation between the educational attainment of workers and unemployment and may be used to draw inferences about changes in employment demand. A high share of the unemployed with low education levels may suggest their education level be increased or more low-skill occupations be created. Similarly, a high share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs." }, { "id": "IndicatorName", "value": "Unemployment with tertiary education, female (% of female unemployment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on unemployment are drawn from labor force sample surveys and general household sample surveys, censuses, and official estimates, which are generally based on information from different sources and can be combined in many ways. Labor force surveys generally yield the most comprehensive data, because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These surveys generally use a definition of unemployment that follows the international recommendations more closely than that used by other sources and therefore generate statistics that are more comparable internationally. But the age group, geographic coverage, and collection methods could differ by country or change over time within a country. \n\nBesides the limitations to comparability raised for measuring unemployment, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Female unemployment with tertiary education is the share of the female unemployed who attained or completed tertiary education as the highest level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Unemployment with tertiary education is calculated by dividing the number of unemployed who attained or completed tertiary education as the highest level by the total number of unemployed, and multiplying by 100. \n\nThe unemployed are defined as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed – between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "57" }, { "id": "SL.UEM.TERT.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unemployment by level of educational attainment provides insights into the relation between the educational attainment of workers and unemployment and may be used to draw inferences about changes in employment demand. A high share of the unemployed with low education levels may suggest their education level be increased or more low-skill occupations be created. Similarly, a high share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs." }, { "id": "IndicatorName", "value": "Unemployment with tertiary education, male (% of male unemployment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on unemployment are drawn from labor force sample surveys and general household sample surveys, censuses, and official estimates, which are generally based on information from different sources and can be combined in many ways. Labor force surveys generally yield the most comprehensive data, because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These surveys generally use a definition of unemployment that follows the international recommendations more closely than that used by other sources and therefore generate statistics that are more comparable internationally. But the age group, geographic coverage, and collection methods could differ by country or change over time within a country. \n\nBesides the limitations to comparability raised for measuring unemployment, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Male unemployment with tertiary education is the share of the male unemployed who attained or completed tertiary education as the highest level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Unemployment with tertiary education is calculated by dividing the number of unemployed who attained or completed tertiary education as the highest level by the total number of unemployed, and multiplying by 100. \n\nThe unemployed are defined as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed – between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "57" }, { "id": "SL.UEM.TERT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Unemployment by level of educational attainment provides insights into the relation between the educational attainment of workers and unemployment and may be used to draw inferences about changes in employment demand. A high share of the unemployed with low education levels may suggest their education level be increased or more low-skill occupations be created. Similarly, a high share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs." }, { "id": "IndicatorName", "value": "Unemployment with tertiary education (% of total unemployment)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on unemployment are drawn from labor force sample surveys and general household sample surveys, censuses, and official estimates, which are generally based on information from different sources and can be combined in many ways. Labor force surveys generally yield the most comprehensive data, because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These surveys generally use a definition of unemployment that follows the international recommendations more closely than that used by other sources and therefore generate statistics that are more comparable internationally. But the age group, geographic coverage, and collection methods could differ by country or change over time within a country. \n\nBesides the limitations to comparability raised for measuring unemployment, the different ways of classifying the education level may also cause inconsistency across countries. Still, information on educational attainment is the best available indicator of skill levels of the labor force to date." }, { "id": "Longdefinition", "value": "Unemployment with tertiary education is the share of the total unemployed who attained or completed tertiary education as the highest level." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, Key Indicators of the Labour Market database." }, { "id": "Statisticalconceptandmethodology", "value": "Unemployment with tertiary education is calculated by dividing the number of unemployed who attained or completed tertiary education as the highest level by the total number of unemployed, and multiplying by 100. \n\nThe unemployed are defined as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed – between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\nThe levels of educational attainment is based on the International Standard Classification of Education (ISCED), which was designed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to ensure the comparability of education programs at the international level. Note that primary education refers to ISCED 1 (primary) and 2 (lower secondary), and secondary education consists of ISCED 3 (upper secondary) and 4 (post-secondary) here." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" } ], "source_id": "57" }, { "id": "SL.UEM.TOTL.FE.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, female (% of female labor force) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female labor force" } ], "source_id": "57" }, { "id": "SL.UEM.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, female (% of female labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of female labor force" } ], "source_id": "57" }, { "id": "SL.UEM.TOTL.MA.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "With the aim of promoting international comparability, statistics presented on ILOSTAT are based on standard international definitions wherever feasible and may differ from official national figures. This series is based on the 13th ICLS definitions. For time series comparability, it includes countries that have implemented the 19th ICLS standards, for which data are also available in the Work Statistics -- 19th ICLS (WORK) database." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, male (% of male labor force) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While the unemployment rate may be considered the most informative labour market indicator, reflecting the general performance of the labour market and the economy as a whole, it should not be interpreted as a measure of economic hardship or of well-being. When based on the internationally-recommended standards, the unemployment rate simply reflects the proportion of the labour force that does not have a job but is available and actively looking for work. It says nothing about the economic resources of unemployed workers or their family members. Its use should, therefore, be limited to serving as a measurement of the utilization of labour and an indication of the failure to find work. Other measures, including income-related indicators, would be needed to evaluate economic hardship. An additional criticism of the aggregate unemployment measure is that it masks information on the composition of the jobless population and therefore misses out on the particularities of the education level, ethnic origin, socio-economic background, work experience, etc. of the unemployed. Moreover, the unemployment rate says nothing about the type of unemployment – whether it is cyclical and short-term or structural and long-term – which is a critical issue for policy makers in the development of their policy responses, especially given that structural unemployment cannot be addressed by boosting market demand only." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\n\n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male labor force" } ], "source_id": "57" }, { "id": "SL.UEM.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, male (% of male labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of male labor force" } ], "source_id": "57" }, { "id": "SL.UEM.TOTL.NE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, total (% of total labor force) (national estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country." }, { "id": "Othernotes", "value": "The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to “registered unemployed” only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of total labor force" } ], "source_id": "57" }, { "id": "SL.UEM.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings." }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "IndicatorName", "value": "Unemployment, total (% of total labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While the unemployment rate may be considered the most informative labour market indicator, reflecting the general performance of the labour market and the economy as a whole, it should not be interpreted as a measure of economic hardship or of well-being. When based on the internationally-recommended standards, the unemployment rate simply reflects the proportion of the labour force that does not have a job but is available and actively looking for work. It says nothing about the economic resources of unemployed workers or their family members. Its use should, therefore, be limited to serving as a measurement of the utilization of labour and an indication of the failure to find work. Other measures, including income-related indicators, would be needed to evaluate economic hardship. An additional criticism of the aggregate unemployment measure is that it masks information on the composition of the jobless population and therefore misses out on the particularities of the education level, ethnic origin, socio-economic background, work experience, etc. of the unemployed. Moreover, the unemployment rate says nothing about the type of unemployment – whether it is cyclical and short-term or structural and long-term – which is a critical issue for policy makers in the development of their policy responses, especially given that structural unemployment cannot be addressed by boosting market demand only." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Othernotes", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1991-2025" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\n\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social Protection & Labor: Unemployment" }, { "id": "Unitofmeasure", "value": "% of total labor force" } ], "source_id": "57" }, { "id": "SL.WAG.0714.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, wage workers, female (% of female children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Wage workers (also known as employees) are people who hold explicit (written or oral) or implicit employment contracts that provide basic remuneration that does not depend directly on the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.WAG.0714.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, wage workers, male (% of male children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Wage workers (also known as employees) are people who hold explicit (written or oral) or implicit employment contracts that provide basic remuneration that does not depend directly on the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SL.WAG.0714.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, wage workers (% of children in employment, ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included." }, { "id": "Longdefinition", "value": "Wage workers (also known as employees) are people who hold explicit (written or oral) or implicit employment contracts that provide basic remuneration that does not depend directly on the revenue of the unit for which they work." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1999-2016" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social Protection & Labor: Economic activity" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "57" }, { "id": "SM.POP.ASYS.EA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on asylum-seekers is vital for understanding the demand for international protection and the capacity of states to process asylum claims, aligning with the goals of the Global Compact on Refugees. This data underpins ongoing efforts by the World Bank and the Office of the United Nations High Commissioner for Refugees (UNHCR) to strengthen asylum systems through projects like the Asylum Capacity Support Program, which assists countries in enhancing their legal frameworks and operational capacities. Moreover, the data is used in international dialogues, such as the UN High-Level Meeting on Refugees and Migrants, to advocate for fair and efficient asylum procedures and ensure that human rights obligations are upheld." }, { "id": "IndicatorName", "value": "Asylum-seekers by country or territory of asylum" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Variability in national asylum procedures and processing times can lead to inconsistencies in data reporting, affecting the accuracy of cross-country comparisons. Unregistered asylum claims or informal border crossings may result in incomplete data, underestimating the true scope of the need for protection. Additionally, the changing status of individuals, such as those asylum-seekers that are recognized as refugees, complicates the tracking and categorization of data." }, { "id": "Longdefinition", "value": "Asylum-seekers are individuals who have sought international protection and whose claims for refugee status have not yet been determined. This includes those who are in various stages of the asylum process, such as initial application, appeal, or awaiting final decision. In specific contexts, asylum-seekers may also include those who are seeking protection under complementary forms of protection, and those enjoying temporary protection, but whose claims are still under consideration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on asylum-seekers is primarily handled through administrative records maintained by national authorities responsible for processing asylum claims. These records capture various stages of the asylum process, including the number of new applications, repeat applications, and cases that are pending resolution.\n\n\n\nThe methodology for collecting asylum-seeker data is guided by the International Recommendations on Refugee Statistics (IRRS), which provide standardized guidelines for data collection and reporting. These guidelines aim to ensure that data on asylum-seekers is comparable across different countries and regions, facilitating a coherent global understanding of asylum trends. The IRRS recommends that data be disaggregated by key variables such as the stage of the asylum process, the demographic characteristics of applicants, and the country of origin." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SM.POP.ASYS.EO", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on asylum-seekers is vital for understanding the demand for international protection and the capacity of states to process asylum claims, aligning with the goals of the Global Compact on Refugees. This data underpins ongoing efforts by the World Bank and the Office of the United Nations High Commissioner for Refugees (UNHCR) to strengthen asylum systems through projects like the Asylum Capacity Support Program, which assists countries in enhancing their legal frameworks and operational capacities. Moreover, the data is used in international dialogues, such as the UN High-Level Meeting on Refugees and Migrants, to advocate for fair and efficient asylum procedures and ensure that human rights obligations are upheld." }, { "id": "IndicatorName", "value": "Asylum-seekers by country or territory of origin" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Variability in national asylum procedures and processing times can lead to inconsistencies in data reporting, affecting the accuracy of cross-country comparisons. Unregistered asylum claims or informal border crossings may result in incomplete data, underestimating the true scope of the need for protection. Additionally, the changing status of individuals, such as those asylum-seekers that are recognized as refugees, complicates the tracking and categorization of data." }, { "id": "Longdefinition", "value": "Asylum-seekers are individuals who have sought international protection and whose claims for refugee status have not yet been determined. This includes those who are in various stages of the asylum process, such as initial application, appeal, or awaiting final decision. In specific contexts, asylum-seekers may also include those who are seeking protection under complementary forms of protection, and those enjoying temporary protection, but whose claims are still under consideration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on asylum-seekers is primarily handled through administrative records maintained by national authorities responsible for processing asylum claims. These records capture various stages of the asylum process, including the number of new applications, repeat applications, and cases that are pending resolution.\n\n\n\nThe methodology for collecting asylum-seeker data is guided by the International Recommendations on Refugee Statistics (IRRS), which provide standardized guidelines for data collection and reporting. These guidelines aim to ensure that data on asylum-seekers is comparable across different countries and regions, facilitating a coherent global understanding of asylum trends. The IRRS recommends that data be disaggregated by key variables such as the stage of the asylum process, the demographic characteristics of applicants, and the country of origin." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SM.POP.FDIP", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on forcibly displaced people provide a comprehensive measure of global displacement, encompassing refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) and the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), asylum-seekers, other people in need of international protection, and internally displaced people (IDPs). This aggregate data is essential for designing holistic responses to forced displacement, supporting initiatives like the Global Compact on Refugees. It enables governments, humanitarian organizations, and development partners to better understand the scale of displacement and mobilize resources to address the multifaceted needs of displaced populations." }, { "id": "IndicatorName", "value": "Forcibly displaced people" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregating data across diverse categories introduces challenges in maintaining comparability and consistency. Variations in data collection methods and definitions—such as differing criteria for refugee or IDP status—can complicate efforts to standardize figures across countries and regions. Additionally, dynamic changes in displacement, including secondary movements or status transitions (e.g., from asylum-seeker to refugee), may lead to double counting or data gaps. The diverse sources of data, ranging from administrative records to field surveys, further underscore the importance of contextualizing and cautiously interpreting the aggregate figures." }, { "id": "Longdefinition", "value": "Forcibly displaced people are represented by the sum of (1) refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR), (2) refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), (3) asylum-seekers, (4) other people in need of international protection, and (5) internally displaced people (IDPs). Situations in which people are reported in more than one of these categories are accounted for, to the extent possible." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2010-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on forcibly displaced people combines information on five key population groups: (1) refugees (and people in a refugee-like situation) under the mandate of the United Nations High Commissioner for Refugees (UNHCR), (2) refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), (3) asylum-seekers, (4) other people in need of international protection, and (5) internally displaced people (IDPs). The primary data sources include administrative records, population censuses, and household surveys, as well as operational data from humanitarian organizations such as UNHCR, UNRWA, and the Internal Displacement Monitoring Centre (IDMC). Data is reported as stock data at specific points in time, typically the end of the calendar year, providing a snapshot of forcibly displaced populations globally. Each category follows established international methodologies to ensure consistency and comparability.\n\nAt the end of 2023 (2024), the UNRWA estimates that 70 per cent of the 1.7 (2) million IDPs in the Gaza Strip at end-2023 (-2024) were Palestine refugees under its mandate. These internally displaced refugees under the mandate of UNRWA are only counted once in the forcibly displaced total (i.e. deducted once from the total amount to avoid double counting). The forcibly displaced population category provides a comprehensive and standardized picture of displacement dynamics across different contexts and regions. Statistics for the indicator are included from 2010 onwards, as at this time data on internal displacement situations became more available and more consistent." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SM.POP.IDPC", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on internally displaced people (IDPs) is essential for guiding humanitarian assistance, informing national development plans, and shaping international responses to internal displacement, particularly in fragile and conflict-affected states. This data is crucial for initiatives like the World Bank’s Fragility, Conflict, and Violence (FCV) Strategy, which aims to address" }, { "id": "IndicatorName", "value": "Internally displaced persons (IDPs) by country or territory of asylum / origin" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Collecting accurate data on internally displaced people (IDPs) is challenging in conflict zones and areas with limited access, leading to potential underreporting or incomplete coverage. Repeated displacements and the complex dynamics of internal conflicts can result in double counting or misclassification of IDPs. Additionally, differing definitions of IDPs across countries and regions can hinder the comparability and reliability of the data, complicating international efforts to address internal displacement." }, { "id": "Longdefinition", "value": "Internally displaced people (IDPs) are persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of, or in order to avoid the effects of armed conflict, situations of generalized violence, and violations of human rights, and who have not crossed an internationally recognized State border." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2009-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download;\nGlobal Internal Displacement Database, Internal Displacement Monitoring Centre (IDMC), uri: https://www.internal-displacement.org/database/displacement-data/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on internally displaced people (IDPs) is based on a combination of population censuses, household surveys, administrative records, and operational data from humanitarian organizations. Population censuses provide a baseline count of IDPs, while household surveys are often used to collect more detailed and current data, particularly in situations where displacement is ongoing or rapidly changing.\n\n\n\nAdministrative records from local governments and humanitarian agencies provide additional data on the movements, living conditions, and access to services for IDP populations. \n\n\n\nThe Internal Displacement Monitoring Centre (IDMC) compiles and publishes IDP statistics from multiple sources. These include operational data produced by the Joint IDP Profiling Service (JIPS) and the International Organization for Migration (IOM), particularly in conflict-affected regions where official statistics may be less reliable or incomplete.\n\n\n\nThe methodology for collecting and reporting IDP data adheres to the International Recommendations on IDP Statistics (IRIS), developed by the Expert Group on Refugee, IDP and Statelessness Statistics (EGRISS). The IRIS framework provides a detailed structure for defining and measuring IDP populations, ensuring that data on IDPs is consistent, reliable, and comparable across different contexts and countries. The framework includes recommendations for both stock data (the number of IDPs at a given time) and flow data (movements into and out of the IDP population), providing a comprehensive understanding of internal displacement dynamics. This methodology has been applied in various contexts, such as in the Democratic Republic of the Congo and Colombia, where detailed IDP profiling has been essential for understanding the needs and vulnerabilities of displaced populations." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SM.POP.NETM", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\n\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. In most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers.\n\n\n\nBut refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom." }, { "id": "IndicatorName", "value": "Net migration" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "International migration is the component of population change most difficult to measure and estimate reliably. Thus, the quality and quantity of the data used in the estimation and projection of net migration varies considerably by country. Furthermore, the movement of people across international boundaries, which is very often a response to changing socio-economic, political and environmental forces, is subject to a great deal of volatility. Refugee movements, for instance, may involve large numbers of people moving across boundaries in a short time. For these reasons, projections of future international migration levels are the least robust part of current population projections and reflect mainly a continuation of recent levels and trends in net migration." }, { "id": "Longdefinition", "value": "Net migration is the net total of migrants during the period, that is, the number of immigrants minus the number of emigrants, including both citizens and noncitizens." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: When there is insufficient data, net migration is derived through the difference between the overall population growth rate and the rate of natural increase (the difference between the birth rate and the death rate) during the same period. Such calculations are usually made for intercensal periods. The estimates are also derived from the data on foreign-born population - people who have residence in one country but were born in another country. When data on the foreign-born population are not available, data on foreign population - that is, people who are citizens of a country other than the country in which they reside - are used as estimates.\nStatistical concept(s): The United Nations Population Division provides data on net migration and migrant stock. Because data on migrant stock is difficult for countries to collect, the United Nations Population Division takes into account the past migration history of a country or area, the migration policy of a country, and the influx of refugees in recent periods when deriving estimates of net migration. The data to calculate these estimates come from a variety of sources, including border statistics, administrative records, surveys, and censuses." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SM.POP.OPIP.EA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on other people in need of international protection is essential for identifying and responding to the needs of individuals who fall outside traditional refugee definitions but still require protection and assistance. This data is crucial for initiatives like the Office of the United Nations High Commissioner for Refugees’ (UNHCR) broader protection strategies and supports the World Bank’s work on mixed migration flows, where displaced populations and migrants are addressed through integrated approaches. The data also plays a key role in informing policy discussions at international forums, such as the UN General Assembly, where member states explore comprehensive protection frameworks for vulnerable groups." }, { "id": "IndicatorName", "value": "Other people in need of international protection by country or territory of asylum" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The definitions of people included in this category can be inconsistent across countries in the data that is collected and reported, affecting its comparability across contexts." }, { "id": "Longdefinition", "value": "Other people in need of international protection refer to people who are outside their country or territory of origin, typically because they have been forcibly displaced across international borders, who have not been reported under other categories (including asylum-seekers and refugees) but who likely need international protection, including protection against forced return, as well as access to basic services on a temporary or longer-term basis." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2018-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on other people in need of international protection involves methodologies similar to those used for refugees and asylum-seekers. \n\n\n\nData is primarily collected through administrative records, which track the number of individuals identified as needing international protection but who do not fall under the traditional definitions of refugees or asylum-seekers. These records are often supplemented by data from humanitarian operations and targeted surveys conducted in areas experiencing large-scale displacement.\n\n\n\nSince 2018, the Office of the United Nations High Commissioner for Refugees (UNHCR) has expanded its statistical frameworks to include this category, reflecting the growing recognition of the diverse forms of displacement and protection needs that exist beyond the traditional categories. The methodology for collecting and reporting data on these populations ensures that they are accurately represented in global statistics, and are comprehensive and comparable across different regions and timeframes, supporting more targeted and effective protection responses." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SM.POP.OPIP.EO", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on other people in need of international protection is essential for identifying and responding to the needs of individuals who fall outside traditional refugee definitions but still require protection and assistance. This data is crucial for initiatives like the Office of the United Nations High Commissioner for Refugees’ (UNHCR) broader protection strategies and supports the World Bank’s work on mixed migration flows, where displaced populations and migrants are addressed through integrated approaches. The data also plays a key role in informing policy discussions at international forums, such as the UN General Assembly, where member states explore comprehensive protection frameworks for vulnerable groups." }, { "id": "IndicatorName", "value": "Other people in need of international protection by country or territory of origin" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The definitions of people included in this category can be inconsistent across countries in the data that is collected and reported, affecting its comparability across contexts." }, { "id": "Longdefinition", "value": "Other people in need of international protection refer to people who are outside their country or territory of origin, typically because they have been forcibly displaced across international borders, who have not been reported under other categories (including asylum-seekers and refugees) but who likely need international protection, including protection against forced return, as well as access to basic services on a temporary or longer-term basis." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2018-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on other people in need of international protection involves methodologies similar to those used for refugees and asylum-seekers. \n\n\n\nData is primarily collected through administrative records, which track the number of individuals identified as needing international protection but who do not fall under the traditional definitions of refugees or asylum-seekers. These records are often supplemented by data from humanitarian operations and targeted surveys conducted in areas experiencing large-scale displacement.\n\n\n\nSince 2018, the Office of the United Nations High Commissioner for Refugees (UNHCR) has expanded its statistical frameworks to include this category, reflecting the growing recognition of the diverse forms of displacement and protection needs that exist beyond the traditional categories. The methodology for collecting and reporting data on these populations ensures that they are accurately represented in global statistics, and are comprehensive and comparable across different regions and timeframes, supporting more targeted and effective protection responses." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SM.POP.REFG", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\nIn most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers. Globally, the number of refugees at end 2010 was 10.55 million, including 597,300 people considered by UNHCR to be in a refugee-like situation; developing countries hosted 8.5 million refugees, or 80 percent of the global refugee population.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights." }, { "id": "IndicatorName", "value": "Refugee population by country or territory of asylum" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are difficulties in collecting accurate statistics on refugees. Many refugees may not be aware of the need to register or may choose not to do so, and administrative records tend to overestimate the number of refugees because it is easier to register than to de-register. In addition, most industrialized countries lack a refugee register and are thus not in a position to provide accurate information on the number of refugees residing in their country. Many countries have registries that are only maintained at the local level, so the data is not centralized.\n\nAsylum-seekers are persons who have applied for asylum or refugee status, but who have not yet received a final decision on their application. A distinction should be made between the number of asylum-seekers who have submitted an individual request during a certain period (\"asylum applications submitted\") and the number of asylum-seekers whose individual asylum request has not yet been decided at a certain date (\"backlog of undecided or pending cases\"). Caution should therefore be exercised when interpreting data on asylum-seekers.\n\nThe United Nations High Commissioner for Refugees (UNHCR) collects and maintains data on refugees, except for Palestinian refugees residing in areas under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA). Registration is voluntary, and estimates by the UNRWA are not an accurate count of the Palestinian refugee population. The data shows estimates of refugees collected by the UNHCR, complemented by estimates of Palestinian refugees under the UNRWA mandate. Thus, the aggregates differ from those published by the UNHCR.\n\nStatistics concerning the former USSR have been reported under the Russian Federation, those concerning the former Czechoslovakia have been reported under the Czech Republic and those concerning the former Yugoslavia and 'Serbia and Montenegro' have been reported under Serbia. Since 2006, separate statistics are available for Serbia and for Montenegro. Prior to 2006, no separate statistics are available and both countries have been reported under Serbia." }, { "id": "Longdefinition", "value": "Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees or its 1967 Protocol, the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted refugee-like humanitarian status, and people provided temporary protection. Asylum seekers--people who have applied for asylum or refugee status and who have not yet received a decision or who are registered as asylum seekers--are excluded. Palestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948 and who lost their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict. Country of asylum is the country where an asylum claim was filed and granted." }, { "id": "Notesfromoriginalsource", "value": "The refugee population category from 2007 onwards also includes people in a refugee-like situation, most of who were previously included in the Others of concern group. This sub-category is descriptive in nature and includes groups of persons who are outside their country or territory of origin and who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained.\n\nStatistics concerning the former USSR have been reported under the Russian Federation, those concerning the former Czechoslovakia have been reported under the Czech Republic and those concerning the former Yugoslavia and 'Serbia and Montenegro' have been reported under Serbia. Since 2006, separate statistics are available for Serbia and for Montenegro. Prior to 2006, no separate statistics are available and both countries have been reported under Serbia." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees or its 1967 Protocol, the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted refugee-like humanitarian status, and people provided temporary protection. Asylum seekers -- people who have applied for asylum or refugee status and who have not yet received a decision or who are registered as asylum seekers--are excluded. Palestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948 and who lost their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict. Country of asylum is the country where an asylum claim was filed and granted." }, { "id": "Source", "value": "United Nations High Commissioner for Refugees (UNHCR) and UNRWA through UNHCR's Refugee Data Finder at https://www.unhcr.org/refugee-statistics/." }, { "id": "Statisticalconceptandmethodology", "value": "The United Nations High Commissioner for Refugees (UNHCR) collects and maintains data on refugees in their Statistical Online Population Database. The refugee data does not include Palestinian refugees residing in areas under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA). However, the Palestinian refugees living outside the UNRWA areas of operation do fall under the responsibility of UNHCR and are thus included in the Statistical Online Population Database.\n\nRefugees are an important part of migrant stock. The refugee data refer to people who have crossed an international border to find sanctuary and have been granted refugee or refugee-like status or temporary protection. There are three main providers of refugee data: governmental agencies, UNHCR field offices and NGOs. Registrations, together with other sources - including estimates and surveys - are the main sources of refugee data. In the absence of Government estimates, UNHCR has estimated the refugee population in most industrialized countries, based on recognition of asylum-seekers. Prior to 2007, resettled refugees were included in these estimates.\n\nUp to and including 2006, to ensure that the refugee population in countries that lack a refugee registry is reflected in the global statistics, the number of refugees was estimated by UNHCR based on the arrival of refugees through resettlement programmes and the individual recognition of refugees over a 10-year (Europe and, since 2006, the United States) or 5-year (the United States before 2006, Canada and Oceania) period. Starting with the 2007 data, the cut-off period has been harmonized and now covers a 10-year period for Europe and non-European countries. Resettled refugees, however, are excluded from the refugee estimates in all countries.\n\nThe 2007-2011 refugee population category includes people in a refugee-like situation, most of who were previously included in the Others of concern group. This sub-category is descriptive in nature and includes groups of persons who are outside their country or territory of origin and who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained.\n\nAsylum seekers - people who have applied for asylum or refugee status and who have not yet received a decision or who are registered as asylum seekers - and internally displaced people - who are often confused with refugees - are not included in the data. Unlike refugees, internally displaced people remain under the protection of their own government, even if their reason for fleeing was similar to that of refugees.\n\nPalestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948 and who lost their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" } ], "source_id": "57" }, { "id": "SM.POP.REFG.OR", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\nIn most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers. Globally, the number of refugees at end 2010 was 10.55 million, including 597,300 people considered by UNHCR to be in a refugee-like situation; developing countries hosted 8.5 million refugees, or 80 percent of the global refugee population.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights." }, { "id": "IndicatorName", "value": "Refugee population by country or territory of origin" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "There are difficulties in collecting accurate statistics on refugees. Many refugees may not be aware of the need to register or may choose not to do so, and administrative records tend to overestimate the number of refugees because it is easier to register than to de-register. In addition, most industrialized countries lack a refugee register and are thus not in a position to provide accurate information on the number of refugees residing in their country. Many countries have registries that are only maintained at the local level, so the data is not centralized.\n\nThe United Nations High Commissioner for Refugees (UNHCR) collects and maintains data on refugees, except for Palestinian refugees residing in areas under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA). Registration is voluntary, and estimates by the UNRWA are not an accurate count of the Palestinian refugee population. The data shows estimates of refugees collected by the UNHCR, complemented by estimates of Palestinian refugees under the UNRWA mandate. Thus, the aggregates differ from those published by the UNHCR.\n\nStatistics concerning the former USSR have been reported under the Russian Federation, those concerning the former Czechoslovakia have been reported under the Czech Republic and those concerning the former Yugoslavia and 'Serbia and Montenegro' have been reported under Serbia. Since 2006, separate statistics are available for Serbia and for Montenegro. Prior to 2006, no separate statistics are available and both countries have been reported under Serbia." }, { "id": "Longdefinition", "value": "Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees or its 1967 Protocol, the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted refugee-like humanitarian status, and people provided temporary protection. Asylum seekers--people who have applied for asylum or refugee status and who have not yet received a decision or who are registered as asylum seekers--are excluded. Palestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948 and who lost their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict. Country of origin generally refers to the nationality or country of citizenship of a claimant." }, { "id": "Notesfromoriginalsource", "value": "The refugee population category from 2007 onwards also includes people in a refugee-like situation, most of who were previously included in the Others of concern group. This sub-category is descriptive in nature and includes groups of persons who are outside their country or territory of origin and who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained.\n\nStatistics concerning the former USSR have been reported under the Russian Federation, those concerning the former Czechoslovakia have been reported under the Czech Republic and those concerning the former Yugoslavia and 'Serbia and Montenegro' have been reported under Serbia. Since 2006, separate statistics are available for Serbia and for Montenegro. Prior to 2006, no separate statistics are available and both countries have been reported under Serbia." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations High Commissioner for Refugees (UNHCR), Refugee Data Finder at https://www.unhcr.org/refugee-statistics/." }, { "id": "Statisticalconceptandmethodology", "value": "The United Nations High Commissioner for Refugees (UNHCR) collects and maintains data on refugees in their Statistical Online Population Database. The refugee data does not include Palestinian refugees residing in areas under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA). However, the Palestinian refugees living outside the UNRWA areas of operation do fall under the responsibility of UNHCR and are thus included in the Statistical Online Population Database.\n\nRefugees are an important part of migrant stock. The refugee data refer to people who have crossed an international border to find sanctuary and have been granted refugee or refugee-like status or temporary protection. There are three main providers of refugee data: governmental agencies, UNHCR field offices and NGOs. Registrations, together with other sources - including estimates and surveys - are the main sources of refugee data. In the absence of Government estimates, UNHCR has estimated the refugee population in most industrialized countries, based on recognition of asylum-seekers. Prior to 2007, resettled refugees were included in these estimates.\n\nUp to and including 2006, to ensure that the refugee population in countries that lack a refugee registry is reflected in the global statistics, the number of refugees was estimated by UNHCR based on the arrival of refugees through resettlement programmes and the individual recognition of refugees over a 10-year (Europe and, since 2006, the United States) or 5-year (the United States before 2006, Canada and Oceania) period. Starting with the 2007 data, the cut-off period has been harmonized and now covers a 10-year period for Europe and non-European countries. Resettled refugees, however, are excluded from the refugee estimates in all countries.\n\nThe 2007-2011 refugee population category includes people in a refugee-like situation, most of who were previously included in the Others of concern group. This sub-category is descriptive in nature and includes groups of persons who are outside their country or territory of origin and who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained.\n\nAsylum seekers - people who have applied for asylum or refugee status and who have not yet received a decision or who are registered as asylum seekers - and internally displaced people - who are often confused with refugees - are not included in the data. Unlike refugees, internally displaced people remain under the protection of their own government, even if their reason for fleeing was similar to that of refugees.\n\nPalestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948 and who lost their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" } ], "source_id": "57" }, { "id": "SM.POP.RHCR.EA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) is crucial for informing global and national policies that address the immediate and long-term needs of displaced populations. This data supports ongoing initiatives such as the World Bank’s Global Concessional Financing Facility, which provides financial resources to middle-income countries hosting large numbers of refugees, thereby fostering resilience and stability. Additionally, the data is instrumental for discussions in international forums like the Global Refugee Forum and the UNHCR Executive Committee, where stakeholders assess the effectiveness of refugee response strategies and mobilize support for burden-sharing among nations." }, { "id": "IndicatorName", "value": "Refugees under the mandate of the UNHCR by country or territory of asylum" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Inconsistencies in data collection standards across countries can complicate the comparability of refugee data globally, making it challenging to create uniform policies. The fluidity of refugee movements and states’ processing of status changes, such as shifts from asylum-seeker to refugee, can impact the timeliness of the data. Furthermore, political pressures may influence how governments report refugee figures, potentially leading to underreporting or misrepresentation of the situation." }, { "id": "Longdefinition", "value": "Refugees under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) include individuals recognized under the 1951 Convention relating to the Status of Refugees, its 1967 Protocol, the 1969 Organization of African Unity (OAU) Convention Governing the Specific Aspects of Refugee Problems in Africa, the refugee definition contained in the 1984 Cartagena Declaration on Refugees as incorporated into national laws, those recognized in accordance with the UNHCR Statute, individuals granted complementary forms of protection, and those enjoying temporary protection. The refugee population also includes people in a refugee-like situation, which is a category that is descriptive in nature and includes groups of people who are outside their country or territory of origin and who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained. Refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), Palestine Refugees, are not typically included in the statistics on refugees (and people in a refugee-like situation) under the mandate of the UNHCR." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) is based on a coordinated effort between national governments and UNHCR.\n\n\n\nThe primary sources of data include administrative records and registration systems maintained by immigration or asylum agencies, including UNHCR itself, which provide detailed demographic information, such as age, sex, and nationality. These records are generally reported as stock data, reflecting the number of refugees at a specific point in time, typically at the end of the calendar year. \n\n\n\nThe collection process follows the guidelines outlined in the International Recommendations on Refugee Statistics (IRRS), developed by the Expert Group on Refugee, IDP and Statelessness Statistics (EGRISS). The IRRS helps to standardize data collection methods across different countries. The IRRS emphasizes the importance of disaggregating data by key demographic variables and geographical locations, facilitating a more granular analysis of refugee populations." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SM.POP.RHCR.EO", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) is crucial for informing global and national policies that address the immediate and long-term needs of displaced populations. This data supports ongoing initiatives such as the World Bank’s Global Concessional Financing Facility, which provides financial resources to middle-income countries hosting large numbers of refugees, thereby fostering resilience and stability. Additionally, the data is instrumental for discussions in international forums like the Global Refugee Forum and the UNHCR Executive Committee, where stakeholders assess the effectiveness of refugee response strategies and mobilize support for burden-sharing among nations." }, { "id": "IndicatorName", "value": "Refugees under the mandate of the UNHCR by country or territory of origin" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Inconsistencies in data collection standards across countries can complicate the comparability of refugee data globally, making it challenging to create uniform policies. The fluidity of refugee movements and states’ processing of status changes, such as shifts from asylum-seeker to refugee, can impact the timeliness of the data. Furthermore, political pressures may influence how governments report refugee figures, potentially leading to underreporting or misrepresentation of the situation." }, { "id": "Longdefinition", "value": "Refugees under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) include individuals recognized under the 1951 Convention relating to the Status of Refugees, its 1967 Protocol, the 1969 Organization of African Unity (OAU) Convention Governing the Specific Aspects of Refugee Problems in Africa, the refugee definition contained in the 1984 Cartagena Declaration on Refugees as incorporated into national laws, those recognized in accordance with the UNHCR Statute, individuals granted complementary forms of protection, and those enjoying temporary protection. The refugee population also includes people in a refugee-like situation, which is a category that is descriptive in nature and includes groups of people who are outside their country or territory of origin and who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained. Refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), Palestine Refugees, are not typically included in the statistics on refugees (and people in a refugee-like situation) under the mandate of the UNHCR." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) is based on a coordinated effort between national governments and UNHCR.\n\n\n\nThe primary sources of data include administrative records and registration systems maintained by immigration or asylum agencies, including UNHCR itself, which provide detailed demographic information, such as age, sex, and nationality. These records are generally reported as stock data, reflecting the number of refugees at a specific point in time, typically at the end of the calendar year. \n\n\n\nThe collection process follows the guidelines outlined in the International Recommendations on Refugee Statistics (IRRS), developed by the Expert Group on Refugee, IDP and Statelessness Statistics (EGRISS). The IRRS helps to standardize data collection methods across different countries. The IRRS emphasizes the importance of disaggregating data by key demographic variables and geographical locations, facilitating a more granular analysis of refugee populations." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SM.POP.RRWA.EA", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) is critical for addressing the unique and ongoing displacement situation of Palestinian refugees. This data informs humanitarian and development strategies in associated key regions, including the Gaza Strip, and West Bank. It supports international initiatives, particularly in areas related to health, education, and poverty reduction. Additionally, the data plays a significant role in global policy discussions to ensure adequate resources for sustaining vital services for Palestinian refugees." }, { "id": "IndicatorName", "value": "Refugees under the mandate of the UNRWA by country or territory of asylum" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The long-standing nature of Palestinian refugee status creates challenges in distinguishing generational shifts and new displacements. Data collection is further hindered by the varying legal and socio-political conditions in host countries, impacting the accuracy and comparability of reported figures. Additionally, the absence of a durable solution for Palestinian refugees exacerbates data fluidity, with changes in status, location, and service eligibility complicating longitudinal tracking. These factors underline the need for cautious interpretation and contextual understanding of data on Palestinian refugees." }, { "id": "Longdefinition", "value": "Refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), Palestine Refugees, are persons whose normal place of residence was Palestine during the period 1 June 1946 to 15 May 1948 and who lost both home and means of livelihood as a result of the 1948 conflict. Palestine Refugees, and descendants of Palestine refugee males, including legally adopted children, are eligible to register for UNRWA services. UNRWA accepts new applications from persons who wish to be registered as Palestine Refugees. Once they are registered with UNRWA, persons in this category are referred to as Registered Refugees. Refugees under the mandate of the UNRWA are not typically included in the statistics on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download;\nStatistics Bulletin, UN Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), uri: https://www.unrwa.org/what-we-do/unrwa-statistics-bulletin" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) is primarily based on administrative records maintained by the UNRWA in line with UNRWA’s internal guidelines and operational frameworks.\n\n\n\nUNRWA's registration system is a key source, capturing detailed information on individuals registered as Palestine refugees, including their demographic characteristics as well as the country or territory of asylum, to facilitate a nuanced understanding of the refugee population's distribution and characteristics.\n\n\n\nIn addition to administrative records, UNRWA also collaborates with host countries and other international organizations to enrich its data, particularly in contexts where operational challenges might limit direct data collection." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SM.POP.RRWA.EO", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Data on refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) is critical for addressing the unique and ongoing displacement situation of Palestinian refugees. This data informs humanitarian and development strategies in associated key regions, including the Gaza Strip, and West Bank. It supports international initiatives, particularly in areas related to health, education, and poverty reduction. Additionally, the data plays a significant role in global policy discussions to ensure adequate resources for sustaining vital services for Palestinian refugees." }, { "id": "IndicatorName", "value": "Refugees under the mandate of the UNRWA by country or territory of origin" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The long-standing nature of Palestinian refugee status creates challenges in distinguishing generational shifts and new displacements. Data collection is further hindered by the varying legal and socio-political conditions in host countries, impacting the accuracy and comparability of reported figures. Additionally, the absence of a durable solution for Palestinian refugees exacerbates data fluidity, with changes in status, location, and service eligibility complicating longitudinal tracking. These factors underline the need for cautious interpretation and contextual understanding of data on Palestinian refugees." }, { "id": "Longdefinition", "value": "Refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), Palestine Refugees, are persons whose normal place of residence was Palestine during the period 1 June 1946 to 15 May 1948 and who lost both home and means of livelihood as a result of the 1948 conflict. Palestine Refugees, and descendants of Palestine refugee males, including legally adopted children, are eligible to register for UNRWA services. UNRWA accepts new applications from persons who wish to be registered as Palestine Refugees. Once they are registered with UNRWA, persons in this category are referred to as Registered Refugees. Refugees under the mandate of the UNRWA are not typically included in the statistics on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download;\nStatistics Bulletin, UN Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), uri: https://www.unrwa.org/what-we-do/unrwa-statistics-bulletin" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data collection on refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) is primarily based on administrative records maintained by the UNRWA in line with UNRWA’s internal guidelines and operational frameworks.\n\n\n\nUNRWA's registration system is a key source, capturing detailed information on individuals registered as Palestine refugees, including their demographic characteristics as well as the country or territory of asylum, to facilitate a nuanced understanding of the refugee population's distribution and characteristics.\n\n\n\nIn addition to administrative records, UNRWA also collaborates with host countries and other international organizations to enrich its data, particularly in contexts where operational challenges might limit direct data collection." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "SM.POP.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. In most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom." }, { "id": "IndicatorName", "value": "International migrant stock, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In deriving the estimates, an international migrant was equated to a person living in a country other than that in which he or she was born. That is, the number of international migrants, also called the international migrant stock, would represent the number of foreign-born persons enumerated in the countries or areas constituting the world. However, because several countries lack data on the foreign-born, data on the number of foreigners, if available, were used instead as the basis of estimation. Consequently, the overall number of migrants in world regions or at the global level do not quite represent the overall number of foreign-born persons.\n\nThe disintegration and reunification of countries causes discontinuities in the change of the international migrant stock. Because an international migrant is equated with a person who was born outside the country in which he or she resides, when a country disintegrates, persons who had been internal migrants because they had moved from one part of the country to another may become, overnight, international migrants without having moved at that time. Such changes introduce artificial but unavoidable discontinuities in the trend of the international migrant stock. The reunification of States also introduces discontinuities, but in the opposite direction.\n\nWorld aggregates are computed by the World Bank and include economies covered by the World Development Indicators. Therefore, the world total figures or world averages may differ from those published by the United Nations Population Division (UNPD).\n\nThe proportion of international migrant stock (SM.POP.TOTL.ZS) is calculated by the UNPD using UNPD’s total population data, which may differ from the total population data in the World Development Indicators (WDI). Consequently, the number of international migrant stock (SM.POP.TOTL) may not match the result obtained by multiplying the total population in the WDI by the proportion of international migrant stock (SM.POP.TOTL.ZS)." }, { "id": "Longdefinition", "value": "International migrant stock, total is the number of people at mid-year born in a country other than that in which they live. It also includes refugees." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Migrant Stock, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The basic data to estimate the international migrant stock were obtained mostly from population censuses held during the decennial rounds of censuses. Some of the data used were obtained from population registers and nationally representative surveys.\n\nIn the majority of cases, the sources available had gathered information on the place of birth of the enumerated population, thus allowing for the identification of the foreign-born population. In estimating the international migrant stock, international migrants have been equated with the foreign-born whenever possible. In most countries lacking data on place of birth, information on the country of citizenship of those enumerated was available and was used as the basis for the identification of international migrants, thus effectively equating international migrants with foreign citizens.\n\nFor countries or areas for which no information was available on the international migrant stock, the estimates were imputed." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" } ], "source_id": "57" }, { "id": "SM.POP.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. In most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom." }, { "id": "IndicatorName", "value": "International migrant stock (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In deriving the estimates, an international migrant was equated to a person living in a country other than that in which he or she was born. That is, the number of international migrants, also called the international migrant stock, would represent the number of foreign-born persons enumerated in the countries or areas constituting the world. However, because several countries lack data on the foreign-born, data on the number of foreigners, if available, were used instead as the basis of estimation. Consequently, the overall number of migrants in world regions or at the global level do not quite represent the overall number of foreign-born persons.\n\nThe disintegration and reunification of countries causes discontinuities in the change of the international migrant stock. Because an international migrant is equated with a person who was born outside the country in which he or she resides, when a country disintegrates, persons who had been internal migrants because they had moved from one part of the country to another may become, overnight, international migrants without having moved at that time. Such changes introduce artificial but unavoidable discontinuities in the trend of the international migrant stock. The reunification of States also introduces discontinuities, but in the opposite direction.\n\nWorld aggregates are computed by the World Bank and include economies covered by the World Development Indicators. Therefore, the world total figures or world averages may differ from those published by the United Nations Population Division (UNPD).\n\nThe proportion of international migrant stock (SM.POP.TOTL.ZS) is calculated by the UNPD using UNPD’s total population data, which may differ from the total population data in the World Development Indicators (WDI). Consequently, the number of international migrant stock (SM.POP.TOTL) may not match the result obtained by multiplying the total population in the WDI by the proportion of international migrant stock (SM.POP.TOTL.ZS)." }, { "id": "Longdefinition", "value": "International migrant stock (% of population) is the proportion of people at mid-year born in a country other than that in which they live. It also includes refugees." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2024" }, { "id": "Source", "value": "International Migrant Stock, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The basic data to estimate the international migrant stock were obtained mostly from population censuses held during the decennial rounds of censuses. Some of the data used were obtained from population registers and nationally representative surveys.\n\nIn the majority of cases, the sources available had gathered information on the place of birth of the enumerated population, thus allowing for the identification of the foreign-born population. In estimating the international migrant stock, international migrants have been equated with the foreign-born whenever possible. In most countries lacking data on place of birth, information on the country of citizenship of those enumerated was available and was used as the basis for the identification of international migrants, thus effectively equating international migrants with foreign citizens.\n\nFor countries or areas for which no information was available on the international migrant stock, the estimates were imputed." }, { "id": "Topic", "value": "Social Protection & Labor: Migration" } ], "source_id": "57" }, { "id": "SN.ITK.DEFC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good nutrition is the cornerstone for survival, health and development. Well-nourished children perform better in school, grow into healthy adults and in turn give their children a better start in life. Well-nourished women face fewer risks during pregnancy and childbirth, and their children set off on firmer developmental paths, both physically and mentally (UNICEF www.childinfo.org)." }, { "id": "IndicatorName", "value": "Prevalence of undernourishment (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "From a policy and program standpoint, this measure has its limits. First, food insecurity exists even where food availability is not a problem because of inadequate access of poor households to food. Second, food insecurity is an individual or household phenomenon, and the average food available to each person, even corrected for possible effects of low income, is not a good predictor of food insecurity among the population. And third, nutrition security is determined not only by food security but also by the quality of care of mothers and children and the quality of the household's health environment (Smith and Haddad 2000)." }, { "id": "Longdefinition", "value": "Prevalence of undernourishments is the percentage of the population whose habitual food consumption is insufficient to provide the dietary energy levels that are required to maintain a normal active and healthy life. Data showing as 2.5 may signify a prevalence of undernourishment below 2.5%." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 2.1.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2001-2023" }, { "id": "Source", "value": "Food and Agriculture Organization of the United Nations (FAO), uri: http://www.fao.org/faostat/en/#home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on undernourishment are from the Food and Agriculture Organization (FAO) of the United Nations and measure food deprivation based on average food available for human consumption per person, the level of inequality in access to food, and the minimum calories required for an average person.\nStatistical concept(s): Data on undernourishment are from the Food and Agriculture Organization (FAO) of the United Nations and measure food deprivation based on average food available for human consumption per person, the level of inequality in access to food, and the minimum calories required for an average person." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SN.ITK.DFCT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "The prevalence of undernourishment indicator provides only a partial picture of the food security situation. Recognizing this, FAO has compiled a preliminary set of food security indicators, available for most countries and years, to contribute to a more comprehensive assessment of the multiple dimensions and manifestations of food insecurity and to effective policies for more effective interventions and responses." }, { "id": "IndicatorName", "value": "Depth of the food deficit (kilocalories per person per day) - THIS INDICATOR IS DISCONTINUED" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Please note that this indicator has been discontinued. The depth of the food deficit indicates how many calories would be needed to lift the undernourished from their status, everything else being constant. The average intensity of food deprivation of the undernourished, estimated as the difference between the average dietary energy requirement and the average dietary energy consumption of the undernourished population (food-deprived), is multiplied by the number of undernourished to provide an estimate of the total food deficit in the country, which is then normalized by the total population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, Food Security Statistics." }, { "id": "Statisticalconceptandmethodology", "value": "The indicator is calculated as an average over 3 years." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "57" }, { "id": "SN.ITK.DPTH", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Depth of hunger (kilocalories per person per day) - THIS INDICATOR IS DISCONTINUED" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Please note that this indicator has been discontinued. Depth of hunger or the intensity of food deprivation, indicates how much food-deprived people fall short of minimum food needs in terms of dietary energy. The food deficit, in kilocalories per person per day, is measured by comparing the average amount of dietary energy that undernourished people get from the foods they eat with the minimum amount of dietary energy they need to maintain body weight and undertake light activity. The depth of hunger is low when it is less than 200 kilocalories per person per day, and high when it is higher than 300 kilocalories per person per day." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, Food Security Statistics (http://www.fao.org/economic/ess/food-security-statistics/en/)." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "57" }, { "id": "SN.ITK.MSFI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Food insecurity at moderate levels of severity is typically associated with the inability to regularly eat healthy, balanced diets. As such, high prevalence of food insecurity at moderate levels can be considered a predictor of various forms of diet-related health conditions in the population, associated with micronutrient deficiency and unbalanced diets. Severe levels of food insecurity, on the other hand, imply a high probability of reduced food intake and therefore can lead to more severe forms of undernutrition, including hunger. FAO has identified the FIES as the tool with the greatest potential for becoming a global standard capable of providing comparable information on food insecurity experience across countries and population groups to track progress on reducing food insecurity and\n\nhunger" }, { "id": "IndicatorName", "value": "Prevalence of moderate or severe food insecurity in the population (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of people in the population who live in households classified as moderately or severely food insecure. A household is classified as moderately or severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to low quality diets and might have been forced to also reduce the quantity of food they would normally eat because of a lack of money or other resources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2015-2023" }, { "id": "Source", "value": "Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The assessment is conducted using data collected with the Food Insecurity Experience Scale or a compatible experience-based food security measurement questionnaire (such as the HFSSM). The probability to be food insecure is estimated using the one-parameter logistic Item Response Theory model (the Rasch model) and thresholds for classification are made cross country comparable by calibrating the metrics obtained in each country against the FIES global reference scale, maintained by FAO. The threshold to classify \"moderate or severe\" food insecurity corresponds to the severity associated with the item \"having to eat less\" on the global FIES scale. It is an indicator of lack of food access.The indicator is calculated as an average over 3 years (eg. data for 2015 is the average of 2014-2016 data)." }, { "id": "Topic", "value": "Health" } ], "source_id": "57" }, { "id": "SN.ITK.SALT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Iodine deficiency can lead to a variety of health and developmental consequences known as iodine deficiency disorders (IDDs). Iodine deficiency is a major cause of preventable mental retardation. It is especially damaging during pregnancy and in early childhood. In their most severe forms, IDDs can lead to cretinism, stillbirth and miscarriage; even mild deficiency can cause a significant loss of learning ability. Thus, it is crucially important that pregnant women and young children in particular get adequate levels of iodine.\n\nIDD can easily be prevented at low cost, however, with small quantities of iodine. One of the best and least expensive methods of preventing iodine deficiency disorder is by simply iodizing table salt, which is currently done in many countries. It represents one of the easiest and most cost-effective interventions for social and economic development." }, { "id": "IndicatorName", "value": "Consumption of iodized salt (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage of households which have salt they used for cooking that tested positive (>0ppm) for presence of iodine." }, { "id": "Othernotes", "value": "Iodine deficiency is the single most important cause of preventable mental retardation, contributes significantly to the risk of stillbirth and miscarriage, and increases the incidence of infant mortality. A diet low in iodine is the main cause of iodine deficiency. It usually occurs among populations living in areas where the soil has been depleted of iodine. If soil is deficient in iodine, then so are the plants grown in it, including the grains and vegetables that people and animals consume. There are almost no countries in the world where iodine deficiency has not been a public health problem. Many newborns in low- and middle-income countries remain unprotected from the lifelong consequences of brain damage associated with iodine deficiency disorders, which affect a child's ability to learn and to earn a living as an adult, and in turn prevents children, communities, and countries from fulfilling their potential (UNICEF, www.childinfo.org). Widely used and inexpensive, iodized salt is the best source of iodine, and a global campaign to iodize edible salt is significantly reducing the risks associated with iodine deficiency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1994-2020" }, { "id": "Source", "value": "UNICEF Global Databases on Iodized salt, UN Children's Fund (UNICEF), publisher: Division of Data, Analysis, Planning and Monitoring" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Most of the data on consumption of iodized salt are derived from household surveys. For the data that are from household surveys, the year refers to the survey year." }, { "id": "Topic", "value": "Health: Nutrition" } ], "source_id": "57" }, { "id": "SN.ITK.SVFI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Food insecurity at moderate levels of severity is typically associated with the inability to regularly eat healthy, balanced diets. As such, high prevalence of food insecurity at moderate levels can be considered a predictor of various forms of diet-related health conditions in the population, associated with micronutrient deficiency and unbalanced diets. Severe levels of food insecurity, on the other hand, imply a high probability of reduced food intake and therefore can lead to more severe forms of undernutrition, including hunger. FAO has identified the FIES as the tool with the greatest potential for becoming a global standard capable of providing comparable information on food insecurity experience across countries and population groups to track progress on reducing food insecurity and\n\nhunger" }, { "id": "IndicatorName", "value": "Prevalence of severe food insecurity in the population (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The percentage of people in the population who live in households classified as severely food insecure. A household is classified as severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to several of the most severe experiences described in the FIES questions, such as to have been forced to reduce the quantity of the food, to have skipped meals, having gone hungry, or having to go for a whole day without eating because of a lack of money or other resources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2015-2023" }, { "id": "Source", "value": "Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The assessment is conducted using data collected with the Food Insecurity Experience Scale or a compatible experience-based food security measurement questionnaire (such as the HFSSM). The probability to be food insecure is estimated using the one-parameter logistic Item Response Theory model (the Rasch model) and thresholds for classification are made cross country comparable by calibrating the metrics obtained in each country against the FIES global reference scale, maintained by FAO. The threshold to classify \"severe\" food insecurity corresponds to the severity associated with the item \"having not eaten for an entire day\" on the global FIES scale. It is an indicator of lack of food access.The indicator is calculated as an average over 3 years (eg. data for 2015 is the average of 2014-2016 data)." }, { "id": "Topic", "value": "Health" } ], "source_id": "57" }, { "id": "SN.ITK.VITA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Vitamin A deficiency is the leading cause of preventable childhood blindness and increases the risk of death from common childhood illnesses such as diarrhoea. Periodic, high-dose vitamin A supplementation is a proven, low-cost intervention which has been shown to reduce all-cause mortality." }, { "id": "IndicatorName", "value": "Vitamin A supplementation coverage rate (% of children ages 6-59 months)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Vitamin A supplementation coverage rate refers to the percentage of children ages 6-59 months old receiving two high-dose vitamin A supplements in a calendar year." }, { "id": "Othernotes", "value": "Vitamin A is essential for optimal functioning of the immune system. Vitamin A deficiency, a leading cause of blindness, also causes a greater risk of dying from a range of childhood ailments such as measles, malaria, and diarrhea. In low- and middle-income countries, where vitamin A is consumed largely in fruits and vegetables, daily per capita intake is often insufficient to meet dietary requirements. Providing young children with two high-dose vitamin A capsules a year is a safe, cost-effective, efficient strategy for eliminating vitamin A deficiency and improving child survival. Giving vitamin A to new breastfeeding mothers helps protect their children during the first few months of life. Food fortification with vitamin A is being introduced in many developing countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2023" }, { "id": "Source", "value": "UNICEF Global Databases, UN Children's Fund (UNICEF), uri: https://data.unicef.org/topic/nutrition/vitamin-a-deficiency/, note: based on administrative reports from countries" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Household Surveys including DHS, MICS and other national surveys\nStatistical concept(s): The World Health Organization has classified vitamin A deficiency as a public health problem affecting many children ages 6-59 months, with the highest rates in sub-Saharan Africa and South Asia." }, { "id": "Topic", "value": "Health: Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.ADO.TFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Adolescent childbearing is associated with a wide range of risks for young mothers. Women who become pregnant and give birth very early in their lives as well as their newborns are subject to elevated health risks." }, { "id": "IndicatorName", "value": "Adolescent fertility rate (births per 1,000 women ages 15-19)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adolescent fertility rate is the number of births per 1,000 women ages 15-19." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 3.7.2 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Adolescent fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without vital registration systems fertility rates are generally based on censuses or surveys.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Per 1000 women" } ], "source_id": "57" }, { "id": "SP.DTH.INFR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Completeness of infant death reporting (% of reported infant deaths to estimated infant deaths)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Completeness of infant death reporting is the number of infant deaths reported by national statistics authorities to the United Nations Statistics Division's Demography Yearbook divided by the number of infant deaths estimated by the United Nations Population Division." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Statistics Division's Population and Vital Statistics Report and the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "57" }, { "id": "SP.DTH.REPT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Completeness of total death reporting (% of reported total deaths to estimated total deaths)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Completeness of total death reporting is the number of total deaths reported by national statistics authorities to the United Nations Statistics Division's Demography Yearbook divided by the number of total deaths estimated by the United Nations Population Division." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The United Nations Statistics Division's Population and Vital Statistics Report and the United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Mortality" } ], "source_id": "57" }, { "id": "SP.DYN.AMRT.FE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "If available, derived from life tables of Human Mortality Database (HMD) by Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France)." }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, adult, female (per 1,000 female adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data from United Nations Population Division's World Populaton Prospects are originally 5-year period data and the presented are linearly interpolated by the World Bank for annual series. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Adult mortality rate, female, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old female dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nHuman Mortality Database, Max Planck Institute for Demographic Research, uri: www.mortality.org;\nUniversity of California, Berkeley, uri: www.mortality.org, note: Human Mortality Database;\nFrench Institute for Demographic Studies, uri: www.mortality.org, note: Human Mortality Database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors, l(x), at exact age x in a female period life table. The formula is: (l(60)-l(15))/(l(15))*1000.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data. Where reliable age-specific mortality data are available, life tables can be constructed from age-specific mortality data, and adult mortality rates can be calculated from life tables." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 female adults" } ], "source_id": "57" }, { "id": "SP.DYN.AMRT.MA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "If available, derived from life tables of Human Mortality Database (HMD) by Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France)." }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, adult, male (per 1,000 male adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data from United Nations Population Division's World Populaton Prospects are originally 5-year period data and the presented are linearly interpolated by the World Bank for annual series. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Adult mortality rate, male, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old male dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nHuman Mortality Database, Max Planck Institute for Demographic Research, uri: www.mortality.org;\nUniversity of California, Berkeley, uri: www.mortality.org, note: Human Mortality Database;\nFrench Institute for Demographic Studies, uri: www.mortality.org, note: Human Mortality Database" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors, l(x), at exact age x in a male period life table. The formula is: (l(60)-l(15))/(l(15))*1000.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data. Where reliable age-specific mortality data are available, life tables can be constructed from age-specific mortality data, and adult mortality rates can be calculated from life tables." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 male adults" } ], "source_id": "57" }, { "id": "SP.DYN.CBRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The crude birth rate is not appropriate for comparison of different populations or areas with large differences in age-distributions. When the crude death rate is subtracted from the crude birth rate, the result is the rate of natural increase, which is the rate of population change in the absence of migration." }, { "id": "IndicatorName", "value": "Birth rate, crude (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Vital registers are the preferred source for these data, but in many developing countries systems for registering births and deaths are absent or incomplete because of deficiencies in the coverage of events or geographic areas. Many developing countries carry out special household surveys that ask respondents about recent births and deaths. Estimates derived in this way are subject to sampling errors and recall errors." }, { "id": "Longdefinition", "value": "Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT);\nPopulation and Vital Statistics Report (various years), United Nations (UN), publisher: UN Statistical Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The crude birth rate is calculated as the number of births in a given period divided by the average population in that period. For human populations the period is usually one year and, if the population changes in size over the year, the divisor is taken as the population at the mid-year. The rate is usually expressed in terms of 1,000 people: for example, a crude birth rate of 9.5 (per 1000 people) in a population of 1 million would imply 9500 births per year in the entire population.\nStatistical concept(s): Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration. Vital rates are based on data from birth and death registration systems, censuses, and sample surveys by national statistical offices and other organizations, or on demographic analysis. Data for the most recent year for some high-income countries are provisional estimates based on vital registers. The estimates for many other countries are from the United Nations Population Division." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "57" }, { "id": "SP.DYN.CDRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The crude death rate is a good indicator of the general health status of a geographic area or population. The crude death rate is not appropriate for comparison of different populations or areas with large differences in age-distributions. Higher crude death rates can be found in some developed countries, despite high life expectancy, because typically these countries have a much higher proportion of older people, due to lower recent birth rates and lower age-specific mortality rates." }, { "id": "IndicatorName", "value": "Death rate, crude (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Vital registers are the preferred source for these data, but in many developing countries systems for registering births and deaths are absent or incomplete because of deficiencies in the coverage of events or geographic areas. Many developing countries carry out special household surveys that ask respondents about recent births and deaths. Estimates derived in this way are subject to sampling errors and recall errors." }, { "id": "Longdefinition", "value": "Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT);\nPopulation and Vital Statistics Report (various years), United Nations (UN), publisher: UN Statistical Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The crude death rate is calculated as the number of deaths in a given period divided by the population exposed to risk of death in that period. For human populations the period is usually one year and, if the population changes in size over the year, the divisor is taken as the population at the mid-year. The rate is usually expressed in terms of 1,000 people: for example, a crude death rate of 9.5 (per 1000 people) in a population of 1 million would imply 9500 deaths per year in the entire population.\nStatistical concept(s): Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration. Vital rates are based on data from birth and death registration systems, censuses, and sample surveys by national statistical offices and other organizations, or on demographic analysis. Data for the most recent year for some high-income countries are provisional estimates based on vital registers. The estimates for many other countries are from the United Nations Population Division." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "57" }, { "id": "SP.DYN.CONM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Contraceptive prevalence among women of reproductive age is related to maternal and child health, as well as gender equality and HIV/AIDS. Contraceptives enable women and men to make informed decisions on family planning – whether, when, and how many children they would have. \n\n\n\nPreventing unwanted pregnancies is essential to reducing maternal deaths, especially in low- and middle- income countries where maternal mortality rate is high. With effective contraception, life-threatening pregnancy complications can be reduced, and thus maternal deaths can be averted. \n\n\n\nUsing condoms (one of the modern contraceptive methods) can prevent pregnancy as well as sexually transmitted diseases, including HIV." }, { "id": "IndicatorName", "value": "Contraceptive prevalence, any modern method (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While the data availability on contraceptive use has increased, in many countries the contraceptive use data are available only for married women. \n\n\n\nThe time frame used to assess contraceptive prevalence may vary. In many surveys, it is left to the respondent to determine what is meant by “currently using” a method of contraception." }, { "id": "Longdefinition", "value": "Contraceptive prevalence, any modern method is the percentage of married women ages 15-49 who are practicing, or whose sexual partners are practicing, at least one modern method of contraception. Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Contraceptive prevalence rates are obtained mainly from nationally representative household surveys, including: Demographic and Health Surveys; Multiple Indicator Cluster Surveys; Contraceptive Prevalence Surveys; Gender and Generations Survey; Reproductive Health Surveys; and World Fertility Surveys. Additional information was provided by other international survey programs and national surveys. \n\n\n\nMarried women refer to women who are married (defined in relation to the marriage laws or customs of a country) and to women in a union, which refers to women living with their partner in the same household (also referred to as cohabiting unions, consensual unions, unmarried unions, or “living together”)." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "57" }, { "id": "SP.DYN.CONU.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Current use of contraception (any method) (% of married women): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current use of contraception: Percentage of currently married women who are using or whose partners are using any method of contraception and modern method of contraception. Modern method includes female sterilization, male sterilization, pill, IUD, injections, implants, male condom, female condom, diaphragm, foam, and jelly. Traditional method includes periodic abstinence, withdrawal, long term abstinence, folk method, and others." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "57" }, { "id": "SP.DYN.CONU.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Current use of contraception (any method) (% of married women): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Current use of contraception: Percentage of currently married women who are using or whose partners are using any method of contraception and modern method of contraception. Modern method includes female sterilization, male sterilization, pill, IUD, injections, implants, male condom, female condom, diaphragm, foam, and jelly. Traditional method includes periodic abstinence, withdrawal, long term abstinence, folk method, and others." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "57" }, { "id": "SP.DYN.CONU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Contraceptive prevalence among women of reproductive age is related to maternal and child health, as well as gender equality and HIV/AIDS. Contraceptives enable women and men to make informed decisions on family planning – whether, when, and how many children they would have. \n\n\n\nPreventing unwanted pregnancies is essential to reducing maternal deaths, especially in low- and middle- income countries where maternal mortality rate is high. With effective contraception, life-threatening pregnancy complications can be reduced, and thus maternal deaths can be averted. \n\n\n\nUsing condoms (one of the modern contraceptive methods) can prevent pregnancy as well as sexually transmitted diseases, including HIV." }, { "id": "IndicatorName", "value": "Contraceptive prevalence, any method (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While the data availability on contraceptive use has increased, in many countries the contraceptive use data are available only for married women. \n\n\n\nThe time frame used to assess contraceptive prevalence may vary. In many surveys, it is left to the respondent to determine what is meant by “currently using” a method of contraception." }, { "id": "Longdefinition", "value": "Contraceptive prevalence, any method is the percentage of married women ages 15-49 who are practicing, or whose sexual partners are practicing, any method of contraception (modern or traditional). Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception. Traditional methods of contraception include rhythm (e.g., fertility awareness based methods, periodic abstinence), withdrawal and other traditional methods." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2024" }, { "id": "Source", "value": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Contraceptive prevalence rates are obtained mainly from nationally representative household surveys, including: Demographic and Health Surveys; Multiple Indicator Cluster Surveys; Contraceptive Prevalence Surveys; Gender and Generations Survey; Reproductive Health Surveys; and World Fertility Surveys. Additional information was provided by other international survey programs and national surveys. \n\n\n\nMarried women refer to women who are married (defined in relation to the marriage laws or customs of a country) and to women in a union, which refers to women living with their partner in the same household (also referred to as cohabiting unions, consensual unions, unmarried unions, or “living together”)." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "57" }, { "id": "SP.DYN.IMRT.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, infant, female (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Infant mortality rate, female is the number of female infants dying before reaching one year of age, per 1,000 female live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "57" }, { "id": "SP.DYN.IMRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, infant (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "57" }, { "id": "SP.DYN.IMRT.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Mortality rate, infant, male (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Infant mortality rate, male is the number of male infants dying before reaching one year of age, per 1,000 male live births in a given year." }, { "id": "Othernotes", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "57" }, { "id": "SP.DYN.IMRT.Q1", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Infant mortality rate (per 1,000 live births): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Infant mortality rate: Number of deaths to children under age twelve months per 1000 live births, based on experience during the reference period before the survey. The reference period is ten years preceding the survey for DHS surveys, and the reference period varies for MICS surveys (often three to five years preceding the survey)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "57" }, { "id": "SP.DYN.IMRT.Q5", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Infant mortality rate (per 1,000 live births): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Infant mortality rate: Number of deaths to children under age twelve months per 1000 live births, based on experience during the reference period before the survey. The reference period is ten years preceding the survey for DHS surveys, and the reference period varies for MICS surveys (often three to five years preceding the survey)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "57" }, { "id": "SP.DYN.LE00.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, female (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "57" }, { "id": "SP.DYN.LE00.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, total (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), uri: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices, note: Derived from male and female life expectancy at birth from sources such as statistical databases and publications from national statistical offices.;\nDemographic Statistics, Eurostat (ESTAT), note: Derived from male and female life expectancy at birth from sources such as Eurostat: Demographic Statistics." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates, or derived from male and female life expectancy at birth.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "57" }, { "id": "SP.DYN.LE00.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, male (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "57" }, { "id": "SP.DYN.TFRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries." }, { "id": "IndicatorName", "value": "Fertility rate, total (births per woman)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year." }, { "id": "Othernotes", "value": "Relevance to gender indicator: it can indicate the status of women within households and a woman’s decision about the number and spacing of children." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Total fertility rate is the sum of the age-specific fertility rates (multiplied by five, if the age-specific fertility rates are for 5-year age groups).\nStatistical concept(s): Total fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without reliable vital registration systems fertility rates are generally based on extrapolations from trends observed in censuses or surveys from earlier years." }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Births per woman" } ], "source_id": "57" }, { "id": "SP.DYN.TFRT.Q1", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Total fertility rate (TFR) (births per woman): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total fertility rate (TFR): The number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates currently observed. The reference period is three years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "57" }, { "id": "SP.DYN.TFRT.Q5", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Total fertility rate (TFR) (births per woman): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total fertility rate (TFR): The number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates currently observed. The reference period is three years preceding the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "57" }, { "id": "SP.DYN.TO65.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. The lower the age specific mortality rates before age 65, the higher the proportion of people survive to age 65." }, { "id": "IndicatorName", "value": "Survival to age 65, female (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Survival to age 65 refers to the percentage of a cohort of newborn infants that would survive to age 65, if subject to age specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors, l(x), at exact age x in a female period life table. The formula is: (l(65))/(l(0))*100.\nStatistical concept(s): Survival to age 65 is calculated in a period life table. It provides a population's mortality level up to age 65 at a given time. It therefore does not reflect the mortality level that a person actually experiences during his/her life, which can be calculated in a cohort life table." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.DYN.TO65.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. The lower the age specific mortality rates before age 65, the higher the proportion of people survive to age 65." }, { "id": "IndicatorName", "value": "Survival to age 65, male (% of cohort)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Survival to age 65 refers to the percentage of a cohort of newborn infants that would survive to age 65, if subject to age specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using number of survivors l(x) at exact age x, in a male period life table. The formula is: (l(65))/(l(0))*100.\nStatistical concept(s): Survival to age 65 is calculated in a period life table. It provides a population's mortality level up to age 65 at a given time. It therefore does not reflect the mortality level that a person actually experiences during his/her life, which can be calculated in a cohort life table." }, { "id": "Topic", "value": "Health: Mortality" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.DYN.WFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Wanted fertility rate (births per woman)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Wanted fertility rate is an estimate of what the total fertility rate would be if all unwanted births were avoided." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Demographic and Health Surveys" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data is calculated by summing the seven age-specific wanted fertility rates, multiplying the result by five, and dividing by 1000. A birth is considered wanted if the number of living children at the time of conception is less than the ideal number of children as reported by the respondent. Special responses such as \"don't know,\" \"up to God,\" or other non-numeric responses for the ideal number of children are assumed to indicate a high ideal number of children. For more details, please refer to the DHS website: https://dhsprogram.com/data/Guide-to-DHS-Statistics/Wanted_Fertility.htm" }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Births per woman" } ], "source_id": "57" }, { "id": "SP.HOU.FEMA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The composition of households plays a pivotal role in determining the well-being of families and individuals. Research from multiple developed countries indicates that female-headed households, especially those with single mothers, face a higher risk of poverty than those with two parents (United Nations, \"Patterns and trends in household size and composition: Evidence from a United Nations dataset,\" 2019). Understanding the diversity in household structures across various populations is essential for achieving Sustainable Development Goal 1, which is dedicated to eradicating poverty in all its forms." }, { "id": "IndicatorName", "value": "Female headed households (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The definition of female-headed household differs greatly across countries, making cross-country comparison difficult. In some cases it is assumed that a woman cannot be the head of any household with an adult male, because of sex-biased stereotype. Caution should be used in interpreting the data." }, { "id": "Longdefinition", "value": "Female headed households refers to the percentage of households that are headed by females." }, { "id": "Othernotes", "value": "The composition of a household plays a role in the determining other characteristics of a household, such as how many children are sent to school and the distribution of family income." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "DHS API, DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: HC_HHHD_H_FEM; \tIndicator name from the original source: Female-headed households, publisher: DHS Program (ICF), type: API, date accessed: 2024-06-14" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of households headed by women divided by the total number of households. \n\n\n\n\n\n\n\n\n\n\n\nThe definition of a household is a person or group of related or unrelated persons who live together in the same dwelling unit(s), who acknowledge one adult male or female as the head of the household, who share the same housekeeping arrangements and who are considered a single unit.\nStatistical concept(s): The information on the characteristics of household head (e.g., sex, age) is collected the household questionnaire in the Demographic and Health Surveys (DHS). Typically, this data is obtained by detailing the connection of each member of the household to a designated central figure, who is considered the primary reference for the household." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "% of households" } ], "source_id": "57" }, { "id": "SP.M15.2024.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although the legal age of marriage is defined as 18 years in most countries, the practice of child marriage remains widespread. A women’s access to education and later her employment opportunities as well as the nature and terms of her work are often compromised by this practice. Young married girls whose schooling is cut short often lack the knowledge and skills for formal work and are limited to occupations with lower incomes and inferior working conditions. Sustainable Development Goal 5 commits to eliminate the practice of child marriage." }, { "id": "IndicatorName", "value": "Women who were first married by age 15 (% of women ages 20-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The measure of child marriage is designed to be retrospective, focusing on the age at first marriage among adult women who have already passed the risk period. Although it is feasible to assess the current marital status of girls under 15, this approach could underestimate the true extent of child marriage. This is because girls who are not married at the time of survey may still marry before reaching 15." }, { "id": "Longdefinition", "value": "Women who were first married by age 15 refers to the percentage of women ages 20-24 who were first married by age 15." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.3.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "UNICEF Data, UN Children's Fund (UNICEF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_20-24_MRD_U15; \tIndicator name from the original source: Percentage of women (aged 20-24 years) married or in union before age 15, type: API;\nDHS API, DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: MA_MBAG_W_B15; \tIndicator name from the original source: Women first married by exact age 15, type: API" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Number of women aged 20-24 who were first married or in union before age 15 divided by the total number of women aged 20-24 in the population multiplied by 100. The primary sources for this indicator are the Multiple Indicator Cluster Surveys (MICS) and the Demographic and Health Surveys (DHS). Additionally, other national household surveys and censuses contribute to the data. These figures are compiled by UNICEF, which coordinates with countries to gather the information.\nStatistical concept(s): This indicator includes both formal marriages and informal cohabiting relationships. Informal relationships are usually defined as those where a couple lives together with the intention of a long-term relationship but without a formal civil or religious ceremony. The incidence of child marriage is assessed retrospectively among women who are past the risk of marrying as children. The age range of 20 to 24 years is conventionally used to reflect the current prevalence of child marriage." }, { "id": "Topic", "value": "Gender: Agency" }, { "id": "Unitofmeasure", "value": "% of women ages 20-24" } ], "source_id": "57" }, { "id": "SP.M18.2024.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although the legal age of marriage is defined as 18 years in most countries, the practice of child marriage remains widespread. A women’s access to education and later her employment opportunities as well as the nature and terms of her work are often compromised by this practice. Young married girls whose schooling is cut short often lack the knowledge and skills for formal work and are limited to occupations with lower incomes and inferior working conditions. Sustainable Development Goal 5 commits to eliminate the practice of child marriage." }, { "id": "IndicatorName", "value": "Women who were first married by age 18 (% of women ages 20-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The measure of child marriage is designed to be retrospective, focusing on the age at first marriage among adult women who have already passed the risk period. Although it is feasible to assess the current marital status of girls under 18, this approach could underestimate the true extent of child marriage. This is because girls who are not married at the time of survey may still marry before reaching 18." }, { "id": "Longdefinition", "value": "Women who were first married by age 18 refers to the percentage of women ages 20-24 who were first married by age 18." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 5.3.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "UNICEF Data, UN Children's Fund (UNICEF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_20-24_MRD_U18; \tIndicator name from the original source: Percentage of women (aged 20-24 years) married or in union before age 18, type: API;\nDHS API, DHS Program (ICF), uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: MA_MBAG_W_B18; \tIndicator name from the original source: Women first married by exact age 18, type: API" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Number of women aged 20-24 who were first married or in union before age 18 divided by the total number of women aged 20-24 in the population multiplied by 100. The primary sources for this indicator are the Multiple Indicator Cluster Surveys (MICS) and the Demographic and Health Surveys (DHS). Additionally, other national household surveys and censuses contribute to the data. These figures are compiled by UNICEF, which coordinates with countries to gather the information.\nStatistical concept(s): This indicator includes both formal marriages and informal cohabiting relationships. Informal relationships are usually defined as those where a couple lives together with the intention of a long-term relationship but without a formal civil or religious ceremony. The incidence of child marriage is assessed retrospectively among women who are past the risk of marrying as children. The age range of 20 to 24 years is conventionally used to reflect the current prevalence of child marriage." }, { "id": "Topic", "value": "Gender: Agency" }, { "id": "Unitofmeasure", "value": "% of women ages 20-24" } ], "source_id": "57" }, { "id": "SP.MTR.1519.Q1.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Teenage pregnancy and motherhood (% of women ages 15-19 who have had children or are currently pregnant): Q1 (lowest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Teenage pregnancy and motherhood: Percentage of women aged 15-19 years who are mothers or pregnant with their first child." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "57" }, { "id": "SP.MTR.1519.Q5.ZS", "metatype": [ { "id": "Generalcomments", "value": "Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data." }, { "id": "IndicatorName", "value": "Teenage pregnancy and motherhood (% of women ages 15-19 who have had children or are currently pregnant): Q5 (highest)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Teenage pregnancy and motherhood: Percentage of women aged 15-19 years who are mothers or pregnant with their first child." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household Surveys (DHS, MICS)" }, { "id": "Topic", "value": "Quintile: Population" } ], "source_id": "57" }, { "id": "SP.MTR.1519.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Having a child during the teenage years limits girls' opportunities for better education, jobs, and income. Pregnancy is more likely to be unintended during the teenage years, and births are more likely to be premature and are associated with greater risks of complications during delivery and of death. In many countries maternal mortality is a leading cause of death among women of reproductive age, although most of those deaths are preventable. Infants of adolescent mothers are also more likely to have low birth weight, which can have a long-term impact on their health and development. Complications from pregnancy and childbirth are the leading cause of death among girls aged 15-19 years in many low- and middle-income countries." }, { "id": "IndicatorName", "value": "Teenage mothers (% of women ages 15-19 who have had children or are currently pregnant)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Teenage mothers are the percentage of women ages 15-19 who already have children or are currently pregnant." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1985-2023" }, { "id": "Source", "value": "Demographic and Health Surveys" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data represents the combined percentage of women aged 15-19 who are mothers and those who are pregnant with their first child. This information is gathered through individual interviews with women of reproductive age during household surveys, including Demographic and Health Surveys. For more details, please refer to the DHS website: https://dhsprogram.com/data/Guide-to-DHS-Statistics/Teenage_Pregnancy_and_Motherhood.htm" }, { "id": "Topic", "value": "Health: Reproductive health" }, { "id": "Unitofmeasure", "value": "Percent of women ages 15-19 who have had children or are currently pregnant" } ], "source_id": "57" }, { "id": "SP.POP.0004.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 00-04, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 4 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.0004.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 00-04, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 4 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.0014.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SP.POP.0014.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 0 to 14 as a percentage of the total female population. Population is based on the de facto definition of population." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.0014.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SP.POP.0014.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 0 to 14 as a percentage of the total male population. Population is based on the de facto definition of population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.0014.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "Staff estimates, World Bank (WB), note: Staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects., publisher: World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data.;\nWorld Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SP.POP.0014.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 0-14 (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Population between the ages 0 to 14 as a percentage of the total population. Population is based on the de facto definition of population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "United Nations Population Division. World Population Prospects., United Nations Population Division, uri: https://population.un.org/wpp/, publisher: United Nations Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.0509.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 05-09, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 5 to 9 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.0509.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 05-09, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 5 to 9 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.1014.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 10-14, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 10 to 14 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.1014.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 10-14, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 10 to 14 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.1519.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 15-19, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 15 to 19 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.1519.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 15-19, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 15 to 19 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.1564.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SP.POP.1564.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 15 to 64 as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.1564.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SP.POP.1564.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 15 to 64 as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.1564.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SP.POP.1564.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 15-64 (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Total population between the ages 15 to 64 as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.2024.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 20-24, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 20 to 24 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.2024.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 20-24, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 20 to 24 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.2529.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 25-29, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 25 to 29 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.2529.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 25-29, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 25 to 29 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.3034.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 30-34, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 30 to 34 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.3034.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 30-34, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 30 to 34 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.3539.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 35-39, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 35 to 39 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.3539.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 35-39, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 35 to 39 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.4044.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 40-44, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 40 to 44 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.4044.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 40-44, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 40 to 44 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.4549.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 45-49, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 45 to 49 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.4549.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 45-49, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 45 to 49 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.5054.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 50-54, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 50 to 54 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.5054.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 50-54, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 50 to 54 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.5559.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 55-59, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 55 to 59 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.5559.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 55-59, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 55 to 59 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.6064.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 60-64, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 60 to 64 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.6064.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 60-64, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 60 to 64 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.6569.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 65-69, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 65 to 69 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.6569.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 65-69, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 65 to 69 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.65UP.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SP.POP.65UP.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population 65 years of age or older as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Othernotes", "value": "Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.65UP.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SP.POP.65UP.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population 65 years of age or older as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.65UP.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SP.POP.65UP.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Population ages 65 and above as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.7074.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 70-74, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 70 to 74 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.7074.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 70-74, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 70 to 74 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.7579.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 75-79, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 75 to 79 as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.7579.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 75-79, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 75 to 79 as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.80UP.FE.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 80 and above, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population between the ages 80 and above as a percentage of the total female population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.80UP.MA.5Y", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Population ages 80 and above, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population between the ages 80 and above as a percentage of the total male population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.BRTH.MF", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In the absence of interference, it is expected that the sex ratio at birth is fairly stable within the range of 1.03 to 1.07 boys born per 1.00 girls. However, in some populations, the observed sex ratio at birth is well above this range because of sex-selection driven by the preference for sons over daughters." }, { "id": "IndicatorName", "value": "Sex ratio at birth (male births per female births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Sex ratio at birth refers to male births per female births." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Sex ratio at birth is calculated as number of male births divided by number of female births.\nStatistical concept(s): If the sex ratio at birth is greater than 1, it indicates more boys born that year than girls." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Male births per female births" } ], "source_id": "57" }, { "id": "SP.POP.DPND", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Age dependency ratio (% of working-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Othernotes", "value": "Relevance to gender indicator: this indicator implies the dependency burden that the working-age population bears in relation to children and the elderly. Many times single or widowed women who are the sole caregiver of a household have a high dependency ratio." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Age dependency ratio is calculated as 100 x (Population (0-14) + Population (65+)) / Population (15-64). Data are shown as the proportion of dependents per 100 working-age population.\nStatistical concept(s): Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But dependency ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.\n\n\n\nAge structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.DPND.OL", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Age dependency ratio, old (% of working-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Age dependency ratio, old is calculated as 100 x (Population (65+)) / Population (15-64). Data are shown as the proportion of old dependents per 100 working-age population.\nStatistical concept(s): Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But dependency ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.\n\n\n\nAge structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.DPND.YG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development." }, { "id": "IndicatorName", "value": "Age dependency ratio, young (% of working-age population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Age dependency ratio, young, is the ratio of younger dependents--people younger than 15--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Age dependency ratio, young is calculated as 100 x (Population (0-14)) / Population (15-64). Data are shown as the proportion of young dependents per 100 working-age population.\nStatistical concept(s): Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But dependency ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.\n\n\n\nAge structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.GROW", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "Derived from total population. Population source: United Nations Population Division, National Statistical Offices, Eurostat, United Nations Statistics Division." }, { "id": "Developmentrelevance", "value": "Increases in human population, whether as a result of immigration or more births than deaths, can impact natural resources and social infrastructure. This can place pressure on a country's sustainability. A significant growth in population will negatively impact the availability of land for agricultural production, and will aggravate demand for food, energy, water, social services, and infrastructure. On the other hand, decreasing population size - a result of fewer births than deaths, and people moving out of a country - can impact a government's commitment to maintain services and infrastructure." }, { "id": "IndicatorName", "value": "Population growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), note: Derived from total population, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices, note: Derived from total population;\nDemographic Statistics, Eurostat (ESTAT), note: Derived from total population;\nPopulation and Vital Statistics Report (various years), United Nations (UN), note: Derived from total population, publisher: UN Statistical Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The growth rate is computed using the exponential growth formula:\n\n\n\nr = ln(pn/p0)/n, \n\n\n\nwhere r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods.\n\n\n\nFor information on total population from which the growth rates are calculated, see total population (SP.POP.TOTL).\nStatistical concept(s): Total population growth rates are calculated on the assumption that rate of growth is constant between two points in time." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.SCIE.RD.P6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Science, technology, and innovation constitute pivotal elements for achieving sustainable growth. Sustainable Development Goal (SDG) target 9.5 is dedicated to the enhancement of scientific research and the advancement of technological capabilities within industrial sectors, with a particular focus on low- and middle-income countries. Furthermore, this target encompasses the objective of augmenting the cadre of research and development personnel, as well as escalating expenditures in research." }, { "id": "IndicatorName", "value": "Researchers in R&D (per million people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers." }, { "id": "Longdefinition", "value": "The number of researchers engaged in Research &Development (R&D), expressed as per million. Researchers are professionals who conduct research and improve or develop concepts, theories, models techniques instrumentation, software of operational methods. R&D covers basic research, applied research, and experimental development." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2024" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources/bulk, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-03-26, date published: 2025-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by taking the number of researchers in a specified year, dividing it by the total population—referencing the mid-year population figure—and then multiplying the result by one million.\n\n\n\n\n\n\n\n\n\nData are collected through national research and experimental development (R&D) surveys, either by the national statistical office or a line ministry (such as the Ministry for Science and Technology). The data compilers are the UNESCO Institute for Statistics (UIS), Organisation for Economic Co-operation and Development (OECD), Eurostat (Statistical Office of the European Union) and the Network on Science and Technology Indicators – Ibero-American and Inter-American (RICYT), African Science, Technology and Innovation (STI) Indicators Initiative (ASTII) of the African Union Development Agency-NEPAD (AUDA-NEPAD).\nStatistical concept(s): Researchers are professionals engaged in the conception or creation of new knowledge, products, processes, methods and systems, as well as in the management of these projects. Students studying at the master’s or doctoral level (ISCED2011 level 7 or 8) engaged in R&D are included. \n\n\n\n\n\n\n\n\n\nThe OECD's Frascati Manual defines research and experimental development as \"creative work undertaken on a systemic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications.\" R&D covers basic research, applied research, and experimental development (Reference: https://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en). \n\n\n\n\n\n\n\n\n\n(1) Basic research - Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view.\n\n\n\n\n\n\n\n\n\n(2) Applied research - Applied research is also original investigation undertaken in order to acquire new knowledge; it is, however, directed primarily towards a specific practical aim or objective.\n\n\n\n\n\n\n\n\n\n(3) Experimental development - Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed.\n\n\n\n\n\n\n\n\n\nThe fields of science and technology used to classify R&D according to the Revised Fields of Science and Technology Classification are:\n\n\n\n\n1. Natural sciences;\n\n\n\n\n2. Engineering and technology;\n\n\n\n\n3. Medical and health sciences;\n\n\n\n\n4. Agricultural sciences;\n\n\n\n\n5. Social sciences;\n\n\n\n\n6. Humanities and the arts.\n\n\n\n\n\n\n\n\n\nData are for full-time equivalent (FTE); the FTE of R&D personnel is defined as the ratio of working hours actually spent on R&D during a specific reference period (usually a calendar year) divided by the total number of hours conventionally worked in the same period by an individual or by a group. \n\n\n\n\n\n\n\n\n\nThe data are obtained through statistical surveys which are regularly conducted at national level covering R&D performing entities in the private and public sectors." }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "Per million people" } ], "source_id": "57" }, { "id": "SP.POP.TECH.RD.P6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Technicians in R&D (per million people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Estimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers." }, { "id": "Longdefinition", "value": "The number of technicians participated in Research & Development (R&D), expressed as per million. Technicians and equivalent staff are people who perform scientific and technical tasks involving the application of concepts and operational methods, normally under the supervision of researchers. R&D covers basic research, applied research, and experimental development." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2018" }, { "id": "Source", "value": "UNESCO Institute of Statistics (UIS), UN Educational, Scientific and Cultural Organization (UNESCO), uri: http://uis.unesco.org, note: Data as of March 2021, publisher: UNESCO Institute of Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Technicians in research and development (R&D) are persons whose main tasks require technical knowledge and experience in one or more fields of engineering, physical and life sciences, or social sciences and humanities. \n\nThe OECD's Frascati Manual defines research and experimental development as \"creative work undertaken on a systemic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications.\" R&D covers basic research, applied research, and experimental development.\n\n(1) Basic research - Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view.\n\n(2) Applied research - Applied research is also original investigation undertaken in order to acquire new knowledge; it is, however, directed primarily towards a specific practical aim or objective.\n\n(3) Experimental development - Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed.\n\nThe fields of science and technology used to classify R&D according to the Revised Fields of Science and Technology Classification are:\n1. Natural sciences;\n2. Engineering and technology;\n3. Medical and health sciences;\n4. Agricultural sciences;\n5. Social sciences;\n6. Humanities and the arts.\n\nData are for full-time equivalent (FTE); the FTE of R&D personnel is defined as the ratio of working hours actually spent on R&D during a specific reference period (usually a calendar year) divided by the total number of hours conventionally worked in the same period by an individual or by a group. \n\nThe data are obtained through statistical surveys which are regularly conducted at national level covering R&D performing entities in the private and public sectors." }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "57" }, { "id": "SP.POP.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Increases in human population, whether as a result of immigration or more births than deaths, can impact natural resources and social infrastructure. This can place pressure on a country's sustainability. A significant growth in population will negatively impact the availability of land for agricultural production, and will aggravate demand for food, energy, water, social services, and infrastructure. On the other hand, decreasing population size - a result of fewer births than deaths, and people moving out of a country - can impact a government's commitment to maintain services and infrastructure." }, { "id": "IndicatorName", "value": "Population, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Current population estimates for developing countries that lack (i) reliable recent census data, and (ii) pre- and post-census estimates for countries with census data, are provided by the United Nations Population Division and other agencies. \n\n\n\nThe cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in both the model and the data.\n\n\n\nBecause future trends cannot be known with certainty, population projections have a wide range of uncertainty." }, { "id": "Longdefinition", "value": "Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates." }, { "id": "Othernotes", "value": "Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices, uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population estimates are usually based on national population censuses, and estimates of fertility, mortality and migration.\n\n\n\nErrors and undercounting in census occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census.\n\n\n\nThe quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data.\n\n\n\nThe currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census.\n\n\n\nThe United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems.\n\n\n\nInternational migration is the only other factor besides birth and death rates that directly determines a country's population change. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect.\n\n\n\nOne of the major data sources of this indicator is UN Population Division's World Population Prospects, which use the cohort component method to produce population estimates and projections.\n\n\n\nPopulation projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant.\nStatistical concept(s): Estimates of total population describe the size of total population. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SP.POP.TOTL.FE.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Females comprise almost one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population is based on the de facto definition of population, which counts all female residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SP.POP.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age/sex distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Females comprise almost one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, female (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female population is the percentage of the population that is female. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2022 Revision" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.POP.TOTL.MA.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Males comprise about one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population is based on the de facto definition of population, which counts all male residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SP.POP.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates based on age/sex distributions of United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Males comprise about one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population." }, { "id": "IndicatorName", "value": "Population, male (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Male population is the percentage of the population that is male. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2022 Revision" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Health: Population: Structure" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.REG.BRTH.FE.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 16.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2003-2021" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "57" }, { "id": "SP.REG.BRTH.MA.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Othernotes", "value": "This is a sex-disaggregated indicator for Sustainable Development Goal 16.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2003-2021" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "57" }, { "id": "SP.REG.BRTH.RU.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration, rural (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "57" }, { "id": "SP.REG.BRTH.UR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration, urban (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Othernotes", "value": "This is a disaggregated indicator (residence) for Sustainable Development Goal 16.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2021" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "57" }, { "id": "SP.REG.BRTH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Completeness of birth registration (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered." }, { "id": "Othernotes", "value": "This is the Sustainable Development Goal indicator 16.9.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Health: Population: Dynamics" } ], "source_id": "57" }, { "id": "SP.REG.DTHS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Completeness of death registration with cause-of-death information (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Completeness of death registration is the estimated percentage of deaths that are registered with their cause of death information in the vital registration system of a country." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2017" }, { "id": "Source", "value": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO), uri: http://apps.who.int/gho/data/node.main.1?lang=en" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The completeness of death registration is calculated by dividing the total number of deaths registered with cause-of-death information in the vital registration system for a given country-year by the total estimated deaths for that year for the national population. The national level of completeness is provided by the National Statistical Offices of all countries and areas to the United Nations Statistics Division as part of the annual data collection for the United Nations Demographic Yearbook. Currently, the threshold used for compiling the data for this indicator is 75 percent for death registration." }, { "id": "Topic", "value": "Health: Population: Dynamics" }, { "id": "Unitofmeasure", "value": "Percent of completeness of death registration" } ], "source_id": "57" }, { "id": "SP.RUR.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and urban/rural distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "The rural population is calculated using the urban share reported by the United Nations Population Division.\n\nThe two distinct images - isolated farm, thriving metropolis - represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income.\n\nA 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population density and remoteness together define a set of rural areas that face special development challenges.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\"\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nRural population methodology is defined by various national statistical offices. In the United States, for example, the US Census Bureau's urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. \"Rural\" encompasses all population, housing, and territory not included within an urban area." }, { "id": "IndicatorName", "value": "Rural population" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population. Aggregation of urban and rural population may not add up to total population because of different country coverages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using World Bank's total population estimates and rural ratios derived from the United Nations World Urbanization Prospects.\nStatistical concept(s): Rural population is calculated as the difference between the total population and the urban population. Rural population is approximated as the midyear nonurban population. While a practical means of identifying the rural population, it is not a precise measure." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SP.RUR.TOTL.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and urban/rural distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "The rural population is calculated using the urban share reported by the United Nations Population Division.\n\nThe two distinct images - isolated farm, thriving metropolis - represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income.\n\nA 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population density and remoteness together define a set of rural areas that face special development challenges.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\"\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nRural population methodology is defined by various national statistical offices. In the United States, for example, the US Census Bureau's urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. \"Rural\" encompasses all population, housing, and territory not included within an urban area." }, { "id": "IndicatorName", "value": "Rural population growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. \n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Annual rural population growth rate for year t is the exponential rate of growth of midyear rural population from year t-1 to t, expressed as a percentage. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2025" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated from rural population estimates. The rural population estimates are calulcated using World Bank's total population estimates and rural ratios derived from the United Nations World Urbanization Prospects.\n\n\n\n\n\n\n\n\n\n\n\nThe growth rate is computed using the exponential growth formula:\n\n\n\n\n\n\n\n\n\n\n\nr = ln(pn/p0)/n, \n\n\n\n\n\n\n\n\n\n\n\nwhere r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods.\nStatistical concept(s): Rural population is calculated as the difference between the total population and the urban population. Rural population is approximated as the midyear nonurban population. While a practical means of identifying the rural population, it is not a precise measure." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.RUR.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The rural population is calculated using the urban share reported by the United Nations Population Division.\n\nThe two distinct images - isolated farm, thriving metropolis - represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income.\n\nA 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population density and remoteness together define a set of rural areas that face special development challenges.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\"\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nRural population methodology is defined by various national statistical offices. In the United States, for example, the US Census Bureau's urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. \"Rural\" encompasses all population, housing, and territory not included within an urban area." }, { "id": "IndicatorName", "value": "Rural population (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population." }, { "id": "Longdefinition", "value": "Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentages rural are calculated as the difference between 100 and the proportion of urban population in percentage.\nStatistical concept(s): Rural population is calculated as the difference between the total population and the urban population. Rural population is approximated as the midyear nonurban population. While a practical means of identifying the rural population, it is not a precise measure." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.URB.GROW", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and urban/rural distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Most countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Annual urban population growth rate for year t is the exponential rate of growth of midyear urban population from year t-1 to t, expressed as a percentage. Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank total population estimates and urban ratios from the United Nations World Urbanization Prospects." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1961-2025" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated from urban population estimates. The urban population estimates are calulcated using World Bank's total population estimates and urban ratios from the United Nations World Urbanization Prospects.\n\n\n\n\n\n\n\n\n\n\n\nThe growth rate is computed using the exponential growth formula:\n\n\n\n\n\n\n\n\n\n\n\nr = ln(pn/p0)/n, \n\n\n\n\n\n\n\n\n\n\n\nwhere r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods.\nStatistical concept(s): Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.URB.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Derivationmethod", "value": "World Bank staff estimates using the World Bank's total population and urban/rural distributions of the United Nations Population Division's data." }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. \n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. Aggregation of urban and rural population may not add up to total population because of different country coverages." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated using World Bank's total population estimates and urban ratios from the United Nations World Urbanization Prospects.\nStatistical concept(s): Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "57" }, { "id": "SP.URB.TOTL.IN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage.\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2025" }, { "id": "Source", "value": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Percentages urban are the numbers of persons residing in an area defined as ''urban'' per 100 total population.\nStatistical concept(s): Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Environment: Density & urbanization" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "57" }, { "id": "SP.UWT.TFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Unmet need for contraception (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Unmet need for contraception is the percentage of fertile, married women of reproductive age who do not want to become pregnant and are not using contraception." }, { "id": "Othernotes", "value": "Unmet need for contraception measures the capacity women have in achieving their desired family size and birth spacing. Many couples in developing countries want to limit or postpone childbearing but are not using effective contraception. These couples have an unmet need for contraception. Common reasons are lack of knowledge about contraceptive methods and concerns about possible side effects." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1984-2024" }, { "id": "Source", "value": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMany couples in developing countries want to limit or postpone childbearing but are not using effective contraception. These couples have an unmet need for contraception. Common reasons are lack of knowledge about contraceptive methods and concerns about possible side effects. This indicator excludes women not exposed to the risk of unintended pregnancy because of menopause, infertility, or postpartum anovulation." }, { "id": "Topic", "value": "Health: Reproductive health" } ], "source_id": "57" }, { "id": "ST.INT.ARVL", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities." }, { "id": "IndicatorName", "value": "International tourism, number of arrivals" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Tourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival. The data on inbound tourism show the arrivals of nonresident tourists (overnight visitors) at national borders. When data on international tourists are unavailable or incomplete, the data show the arrivals of international visitors, which include tourists, same-day visitors, cruise passengers, and crew members.\n\nSources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries." }, { "id": "Longdefinition", "value": "International inbound tourists (overnight visitors) are the number of tourists who travel to a country other than that in which they have their usual residence, but outside their usual environment, for a period not exceeding 12 months and whose main purpose in visiting is other than an activity remunerated from within the country visited. When data on number of tourists are not available, the number of visitors, which includes tourists, same-day visitors, cruise passengers, and crew members, is shown instead. Sources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries. The data on inbound tourists refer to the number of arrivals, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Statistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nArrivals data measure the flows of international visitors to the country of reference: each arrival corresponds to one in inbound tourism trip. If a person visits several countries during the course of a single trip, his/her arrival in each country is recorded separately. In an accounting period, arrivals are not necessarily equal to the number of persons travelling (when a person visits the same country several times a year, each trip by the same person is counted as a separate arrival).\n\nArrivals data should correspond to inbound visitors by including both tourists and same-day non-resident visitors. All other types of travelers (such as border, seasonal and other short-term workers, long-term students and others) should be excluded as they do not qualify as visitors.\n\nData are obtained from different sources: administrative records (immigration, traffic counts, and other possible types of controls), border surveys or a mix of them. If data are obtained from accommodation surveys, the number of guests is used as estimate of arrival figures; consequently, in this case, breakdowns by regions, main purpose of the trip, modes of transport used or forms of organization of the trip are based on complementary visitor surveys." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "57" }, { "id": "ST.INT.DPRT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities." }, { "id": "IndicatorName", "value": "International tourism, number of departures" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Tourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling." }, { "id": "Longdefinition", "value": "International outbound tourists are the number of departures that people make from their country of usual residence to any other country for any purpose other than a remunerated activity in the country visited. The data on outbound tourists refer to the number of departures, not to the number of people traveling. Thus a person who makes several trips from a country during a given period is counted each time as a new departure." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Statistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nDepartures data measure the flows of resident visitors leaving the country of reference. Departures are not necessarily equal to the number of arrivals reported by international destinations for the country of reference.\n\nIn many countries, the characteristics of trips and visitors are established through questions on the entry/departure cards, in surveys at the borders, at destination (accommodation surveys) or as part of household surveys (for domestic and outbound tourism). The entry/departure cards, or records of entry and departure, captured and reconciled by the immigration authorities are often the basic source for establishing the flows of inbound and outbound visitors. These cards usually collect information on a census basis on name, sex, age, nationality, current address, date of arrival (or departure in the departure card), purpose of trip, main destination visited and length of stay (expected on arrival and actual on departure for inbound visitors; expected on departure and actual on arrival for outbound visitors).\n\nData is collected using one of three methods, or a combination of these to determine the flows of outbound visitors: using an entry/departure card; a specific survey at the border, or observing them from household surveys because they belong to resident households. In the latter case, the information on outbound trips is usually collected at the same time as that on domestic trips." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "57" }, { "id": "ST.INT.RCPT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities." }, { "id": "IndicatorName", "value": "International tourism, receipts (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Tourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival. The data on inbound tourism show the arrivals of nonresident tourists (overnight visitors) at national borders. When data on international tourists are unavailable or incomplete, the data show the arrivals of international visitors, which include tourists, same-day visitors, cruise passengers, and crew members.\n\nSources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries.\n\nExpenditure associated with the activity of international visitors has been traditionally identified with the travel item of the Balance of Payments (BOP): in the case of inbound tourism, those expenditures associated with inbound visitors are registered as \"credits\" in the BOP and refers to \"travel receipts\".\n\nThe 2008 International Recommendations for Tourism Statistics consider that \"tourism industries and products\" includes transport of passengers. Consequently, a better estimate of tourism-related expenditure by inbound and outbound visitors in an international scenario would be, in terms of the BOP, the value of the travel item plus that of the passenger transport item.\n\nNevertheless, users should be aware that BOP estimates include, in addition to expenditures associated to visitors, those related to other types of travelers (these might be substantial in some countries; for instance, long-term students or patients, border and seasonal workers, etc.). Also data on expenditure by main purpose of the trip are BOP data." }, { "id": "Longdefinition", "value": "International tourism receipts are expenditures by international inbound visitors, including payments to national carriers for international transport. These receipts include any other prepayment made for goods or services received in the destination country. They also may include receipts from same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include receipts for passenger transport items. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inbound tourism expenditures may include receipts from same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include receipts for passenger transport items. Their share in exports is calculated as a ratio to exports of goods and services (all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services).\n\nStatistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization (WTO) reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nThe World Tourism Organization is improving its coverage of tourism expenditure data, using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. These data include travel and passenger transport items as defined in the IMF's Balance of Payments. When the IMF does not report data on passenger transport items, expenditure data for travel items are shown.\n\nThe aggregates are calculated using the World Bank's weighted aggregation methodology and differ from the World Tourism Organization's aggregates." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "57" }, { "id": "ST.INT.RCPT.XP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities.\n\nThis measure reflects the importance of tourism as an internationally traded service relative to other categories of exports. Such a measure reveals the degree of tourism specialization in a country's export structure and the relative capability of tourism in generating foreign revenues." }, { "id": "IndicatorName", "value": "International tourism, receipts (% of total exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Tourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival. The data on inbound tourism show the arrivals of nonresident tourists (overnight visitors) at national borders. When data on international tourists are unavailable or incomplete, the data show the arrivals of international visitors, which include tourists, same-day visitors, cruise passengers, and crew members.\n\nSources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries.\n\nExpenditure associated with the activity of international visitors has been traditionally identified with the travel item of the Balance of Payments (BOP): in the case of inbound tourism, those expenditures associated with inbound visitors are registered as \"credits\" in the BOP and refers to \"travel receipts\".\n\nThe 2008 International Recommendations for Tourism Statistics consider that \"tourism industries and products\" includes transport of passengers. Consequently, a better estimate of tourism-related expenditure by inbound and outbound visitors in an international scenario would be, in terms of the BOP, the value of the travel item plus that of the passenger transport item.\n\nNevertheless, users should be aware that BOP estimates include, in addition to expenditures associated to visitors, those related to other types of travelers (these might be substantial in some countries; for instance, long-term students or patients, border and seasonal workers, etc.). Also data on expenditure by main purpose of the trip are BOP data." }, { "id": "Longdefinition", "value": "International tourism receipts are expenditures by international inbound visitors, including payments to national carriers for international transport. These receipts include any other prepayment made for goods or services received in the destination country. They also may include receipts from same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include receipts for passenger transport items. Their share in exports is calculated as a ratio to exports of goods and services, which comprise all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism;\nIMF exports estimates, International Monetary Fund (IMF);\nWorld Bank exports estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inbound tourism expenditures may include receipts from same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include receipts for passenger transport items. Their share in exports is calculated as a ratio to exports of goods and services (all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services).\n\nInternational tourism expenditures' share in exports is calculated as a ratio to exports of goods and services, which comprise all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services.\n\nStatistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization (WTO) reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nThe World Tourism Organization is improving its coverage of tourism expenditure data, using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. These data include travel and passenger transport items as defined in the IMF's Balance of Payments. When the IMF does not report data on passenger transport items, expenditure data for travel items are shown.\n\nThe aggregates are calculated using the World Bank's weighted aggregation methodology and differ from the World Tourism Organization's aggregates." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "57" }, { "id": "ST.INT.TRNR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "International tourism, receipts for passenger transport items (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "International tourism receipts for passenger transport items are expenditures by international inbound visitors for all services provided in the international transportation by resident carriers. Also included are passenger services performed within an economy by nonresident carriers. Excluded are passenger services provided to nonresidents by resident carriers within the resident economies; these are included in travel items. In addition to the services covered by passenger fares--including fares that are a part of package tours but excluding cruise fares, which are included in travel--passenger services include such items as charges for excess baggage, vehicles, or other personal accompanying effects and expenditures for food, drink, or other items for which passengers make expenditures while on board carriers. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Tourism data is compiled based on the International Recommendations for Tourism Statistics (IRTS 2008). The World Tourism Organization enhances its coverage of tourism expenditure data by using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. When data on passenger transport items is unavailable, expenditure data for travel items are shown." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "57" }, { "id": "ST.INT.TRNX.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "International tourism, expenditures for passenger transport items (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "International tourism expenditures for passenger transport items are expenditures of international outbound visitors in other countries for all services provided during international transportation by nonresident carriers. Also included are passenger services performed within an economy by nonresident carriers. Excluded are passenger services provided to nonresidents by resident carriers within the resident economies; these are included in travel items. In addition to the services covered by passenger fares--including fares that are a part of package tours but excluding cruise fares, which are included in travel--passenger services include such items as charges for excess baggage, vehicles, or other personal accompanying effects and expenditures for food, drink, or other items for which passengers make expenditures while on board carriers. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Tourism data is compiled based on the International Recommendations for Tourism Statistics (IRTS 2008). The World Tourism Organization enhances its coverage of tourism expenditure data by using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. When data on passenger transport items is unavailable, expenditure data for travel items are shown." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "57" }, { "id": "ST.INT.TVLR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "International tourism, receipts for travel items (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "International tourism receipts for travel items are expenditures by international inbound visitors in the reporting economy. The goods and services are purchased by, or on behalf of, the traveler or provided, without a quid pro quo, for the traveler to use or give away. These receipts should include any other prepayment made for goods or services received in the destination country. They also may include receipts from same-day visitors, except in cases where these are so important as to justify a separate classification. Excluded is the international carriage of travelers, which is covered in passenger travel items. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Tourism data is compiled based on the International Recommendations for Tourism Statistics (IRTS 2008). The World Tourism Organization enhances its coverage of tourism expenditure data by using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. When data on passenger transport items is unavailable, expenditure data for travel items are shown." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "57" }, { "id": "ST.INT.TVLX.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "International tourism, expenditures for travel items (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "International tourism expenditures are expenditures of international outbound visitors in other countries. The goods and services are purchased by, or on behalf of, the traveler or provided, without a quid pro quo, for the traveler to use or give away. These may include expenditures by residents traveling abroad as same-day visitors, except in cases where these are so important as to justify a separate classification. Excluded is the international carriage of travelers, which is covered in passenger travel items. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Tourism data is compiled based on the International Recommendations for Tourism Statistics (IRTS 2008). The World Tourism Organization enhances its coverage of tourism expenditure data by using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. When data on passenger transport items is unavailable, expenditure data for travel items are shown." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "57" }, { "id": "ST.INT.XPND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities." }, { "id": "IndicatorName", "value": "International tourism, expenditures (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Tourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling.\n\nExpenditure associated with the activity of international visitors has been traditionally identified with the travel item of the Balance of Payments (BOP).\n\nThe 2008 International Recommendations for Tourism Statistics consider that \"tourism industries and products\" includes transport of passengers. Consequently, a better estimate of tourism-related expenditure by inbound and outbound visitors in an international scenario would be, in terms of the BOP, the value of the travel item plus that of the passenger transport item.\n\nNevertheless, users should be aware that BOP estimates include, in addition to expenditures associated to visitors, those related to other types of travelers (these might be substantial in some countries; for instance, long-term students or patients, border and seasonal workers, etc.). Also data on expenditure by main purpose of the trip are BOP data." }, { "id": "Longdefinition", "value": "International tourism expenditures are expenditures of international outbound visitors in other countries, including payments to foreign carriers for international transport. These expenditures may include those by residents traveling abroad as same-day visitors, except in cases where these are important enough to justify separate classification. For some countries they do not include expenditures for passenger transport items. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Outbound tourism expenditures may include those by residents traveling abroad as same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include expenditures for passenger transport items. Their share in imports is calculated as a ratio to imports of goods and services (all transactions between residents of a country and the rest of the world involving a change of ownership from nonresidents to residents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services).\n\nStatistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nThe World Tourism Organization is improving its coverage of tourism expenditure data, using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. These data include travel and passenger transport items as defined in the IMF's Balance of Payments. When the IMF does not report data on passenger transport items, expenditure data for travel items are shown.\n\nThe aggregates are calculated using the World Bank's weighted aggregation methodology and differ from the World Tourism Organization's aggregates." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "57" }, { "id": "ST.INT.XPND.MP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Tourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities.\n\nThis measure reflects the importance of tourism as an internationally traded service relative to other categories of imports. Such a measure reveals the predilection for tourism in a country's import structure and the relative degree of an economy's domestic revenue outflows due to international tourism." }, { "id": "IndicatorName", "value": "International tourism, expenditures (% of total imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Tourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling.\n\nExpenditure associated with the activity of international visitors has been traditionally identified with the travel item of the Balance of Payments (BOP).\n\nThe 2008 International Recommendations for Tourism Statistics consider that \"tourism industries and products\" includes transport of passengers. Consequently, a better estimate of tourism-related expenditure by inbound and outbound visitors in an international scenario would be, in terms of the BOP, the value of the travel item plus that of the passenger transport item.\n\nNevertheless, users should be aware that BOP estimates include, in addition to expenditures associated to visitors, those related to other types of travelers (these might be substantial in some countries; for instance, long-term students or patients, border and seasonal workers, etc.). Also data on expenditure by main purpose of the trip are BOP data." }, { "id": "Longdefinition", "value": "International tourism expenditures are expenditures of international outbound visitors in other countries, including payments to foreign carriers for international transport. These expenditures may include those by residents traveling abroad as same-day visitors, except in cases where these are important enough to justify separate classification. For some countries they do not include expenditures for passenger transport items. Their share in imports is calculated as a ratio to imports of goods and services, which comprise all transactions between residents of a country and the rest of the world involving a change of ownership from nonresidents to residents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism;\nIMF imports estimates, International Monetary Fund (IMF);\nWorld Bank imports estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Outbound tourism expenditures may include those by residents traveling abroad as same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include expenditures for passenger transport items. Their share in imports is calculated as a ratio to imports of goods and services (all transactions between residents of a country and the rest of the world involving a change of ownership from nonresidents to residents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services).\n\nInternational tourism expenditures' share in imports is calculated as a ratio to imports of goods and services, which comprise all transactions between residents of a country and the rest of the world involving a change of ownership from nonresidents to residents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services.\n\nStatistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nThe World Tourism Organization is improving its coverage of tourism expenditure data, using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. These data include travel and passenger transport items as defined in the IMF's Balance of Payments. When the IMF does not report data on passenger transport items, expenditure data for travel items are shown.\n\nThe aggregates are calculated using the World Bank's weighted aggregation methodology and differ from the World Tourism Organization's aggregates." }, { "id": "Topic", "value": "Private Sector & Trade: Travel & tourism" } ], "source_id": "57" }, { "id": "TG.VAL.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Merchandise trade (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "General merchandise trade includes goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e. It is the total of merchandise exports plus merchandise imports. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Trade Organization (WTO);\nWorld Bank GDP estimates, World Bank (WB);\nWorld Development Indicators, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Total merchandise trade" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TM.QTY.MRCH.XD.WD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Import volume index (2015 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Import volume indexes are derived from UNCTAD's volume index series and are the ratio of the import value indexes to the corresponding unit value indexes. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year's trade values at the Standard International Trade Classification three-digit level as weights." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2023" }, { "id": "Source", "value": "UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is constructed as the ratio of the import value index to the corresponding unit value index, both compiled by the United Nations Conference on Trade and Development (UNCTAD). The import value index reflects the nominal value of imports over time, while the unit value index serves as a proxy for import prices by capturing changes in average prices per unit. Unit value indexes are based primarily on data reported by national statistical authorities. To ensure data reliability, only values from countries that demonstrate internal consistency and meet quality thresholds defined by UNCTAD are used. Where data are incomplete or fail to meet these standards, UNCTAD supplements them with its own estimates.\n\nThe estimation procedure for unit value indexes involves applying trade values from the previous year as weights, using the Standard International Trade Classification (SITC) at the three-digit level of aggregation. This method helps to reduce distortions from shifts in trade structure or classification inconsistencies. The resulting indexes are chain-linked to ensure temporal comparability and are rebased to the year 2015 (2015 = 100). The final import volume index allows for the analysis of changes in the physical volume of imports over time, net of price effects, and is particularly useful for assessing trends in trade flows in real terms." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.MANF.BC.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Binding coverage, manufactured products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2022" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated as the proportion of tariff lines for manufactured products that are bound under World Trade Organization (WTO) commitments. A tariff line is considered bound if a maximum rate is legally committed in the WTO schedule of concessions. The binding coverage is computed by dividing the number of bound tariff lines by the total number of tariff lines in the relevant product category and multiplying the result by 100 to express it as a percentage. Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Manufactured products are commodities classified in Standard International Trade Classification (SITC) revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator is compiled using only officially reported bound statuses without imputation. It is methodologically consistent across countries, with tariff line concordance procedures applied to standardize classification and maintain comparability across HS revisions." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.MANF.BR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Bound rate, simple mean, manufactured products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2022" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the unweighted average of final bound tariff rates across all manufactured products tariff lines. The calculation involves summing the bound rates applied to each individual tariff line and dividing by the total number of lines within the product category. Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Manufactured products are commodities classified in Standard International Trade Classification (SITC) revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator reflects commitments under WTO agreements and is not adjusted for trade volume. It includes only those lines for which binding rates have been reported. The methodology ensures consistency across countries by converting national tariff submissions to a harmonized HS version." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.MANF.IP.ZS", "metatype": [ { "id": "Dataset", "value": "WDI" }, { "id": "IndicatorName", "value": "Share of tariff lines with international peaks, manufactured products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Longdefinition", "value": "Share of tariff lines with international peaks is the share of lines in the tariff schedule with tariff rates that exceed 15 percent. It provides an indication of how selectively tariffs are applied. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of tariff lines for manufactured products where the applied most-favored-nation (most-favored-nation (MFN)) rate exceeds 15%, which qualifies as an international tariff peak. It is calculated by dividing the number of tariff lines exceeding the 15% threshold by the total number of lines in the product group and multiplying the result by 100. Share of tariff lines with international peaks is the share of lines in the tariff schedule with tariff rates that exceed 15 percent. It provides an indication of how selectively tariffs are applied. Manufactured products are commodities classified in Standard International Trade Classification (SITC) revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The use of a fixed threshold across countries and years ensures cross-national comparability. No imputation is applied. Classification alignment across HS versions is performed to maintain methodological consistency." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.MANF.SM.AR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, applied, simple mean, manufactured products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the simple (unweighted) average of applied most-favored-nation (MFN) tariff rates across all tariff lines within the manufactured products category. Each line is given equal weight regardless of the trade volume associated with it. The mean is calculated by summing the applied rates and dividing by the number of lines. Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator excludes missing or unreported lines. Harmonization across HS revisions is conducted to ensure international comparability, and no adjustments are made beyond validation of submitted data." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.MANF.SM.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, most favored nation, simple mean, manufactured products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator calculates the simple (unweighted) mean of statutory most-favored-nation (MFN) tariff rates across all tariff lines for manufactured products. It reflects tariff rates that apply to all World Trade Organization (WTO) members on a non-discriminatory basis unless preferential agreements are in force. Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The calculation is performed by summing the MFN rates and dividing by the total number of tariff lines. No trade-based weighting is applied. Only reported rates are included, and harmonized classifications ensure comparability across countries." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.MANF.SR.ZS", "metatype": [ { "id": "Dataset", "value": "WDI" }, { "id": "IndicatorName", "value": "Share of tariff lines with specific rates, manufactured products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Longdefinition", "value": "Share of tariff lines with specific rates is the share of lines in the tariff schedule that are set on a per unit basis or that combine ad valorem and per unit rates. It shows the extent to which countries use tariffs based on physical quantities or other, non-ad valorem measures. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the share of tariff lines for manufactured products that are expressed in specific rather than ad valorem terms. Specific rates are defined as those based on quantity, weight, volume, or other non-percentage measures. The indicator is computed by dividing the number of specific-rate lines by the total number of lines and expressing the result as a percentage. Share of tariff lines with specific rates is the share of lines in the tariff schedule that are set on a per unit basis or that combine ad valorem and per unit rates. It shows the extent to which countries use tariffs based on physical quantities or other, non-ad valorem measures. Manufactured products are commodities classified in Standard International Trade Classification (SITC) revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Only duty types explicitly identified as specific in national tariff schedules are counted. The methodology applies standardized classification across countries to enable cross-national comparability. No imputation is used." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.MANF.WM.AR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, applied, weighted mean, manufactured products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator computes the average of applied most-favored-nation (MFN) tariff rates for manufactured products, using import values as weights. Tariff lines with higher trade volumes have a proportionally greater influence on the final average. The weighted mean is derived by multiplying each tariff rate by its corresponding import value, summing these products, and dividing by the total import value. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Trade data used for weighting are obtained from official national or international sources. Only lines with both valid tariff rates and trade values are included. Harmonized classifications are used to align trade and tariff data consistently." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.MANF.WM.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, most favored nation, weighted mean, manufactured products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the trade-weighted average of most-favored-nation (MFN) tariff rates across all manufactured products tariff lines. Import values are used to weight each rate, reflecting the relative economic significance of different products in international trade. The weighted average is calculated by taking the sum of the products of MFN rates and import values, divided by the total import value. Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Only tariff lines with both reported MFN rates and corresponding trade values are included. The WTO applies standardized concordance procedures to match trade and tariff data using harmonized product classifications." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.MRCH.BC.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Binding coverage, all products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2022" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated as the proportion of tariff lines for all products that are bound under World Trade Organization (WTO) commitments. A tariff line is considered bound if a maximum rate is legally committed in the WTO schedule of concessions. The binding coverage is computed by dividing the number of bound tariff lines by the total number of tariff lines in the relevant product category and multiplying the result by 100 to express it as a percentage. Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator is compiled using only officially reported bound statuses without imputation. It is methodologically consistent across countries, with tariff line concordance procedures applied to standardize classification and maintain comparability across HS revisions." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.MRCH.BR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Bound rate, simple mean, all products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2022" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the unweighted average of final bound tariff rates across all all products tariff lines. The calculation involves summing the bound rates applied to each individual tariff line and dividing by the total number of lines within the product category. Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator reflects commitments under WTO agreements and is not adjusted for trade volume. It includes only those lines for which binding rates have been reported. The methodology ensures consistency across countries by converting national tariff submissions to a harmonized HS version." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.MRCH.IP.ZS", "metatype": [ { "id": "Dataset", "value": "WDI" }, { "id": "IndicatorName", "value": "Share of tariff lines with international peaks, all products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Longdefinition", "value": "Share of tariff lines with international peaks is the share of lines in the tariff schedule with tariff rates that exceed 15 percent. It provides an indication of how selectively tariffs are applied." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of tariff lines for all products where the applied most-favored-nation (most-favored-nation (MFN)) rate exceeds 15%, which qualifies as an international tariff peak. It is calculated by dividing the number of tariff lines exceeding the 15% threshold by the total number of lines in the product group and multiplying the result by 100. Share of tariff lines with international peaks is the share of lines in the tariff schedule with tariff rates that exceed 15 percent. It provides an indication of how selectively tariffs are applied. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The use of a fixed threshold across countries and years ensures cross-national comparability. No imputation is applied. Classification alignment across HS versions is performed to maintain methodological consistency." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.MRCH.SM.AR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Poor people in developing countries work primarily in agriculture and labor-intensive manufactures, sectors that confront the greatest trade barriers. Removing barriers to merchandise trade could increase growth in these countries - even more if trade in services were also liberalized.\n\nIn general, tariffs in high-income countries on imports from developing countries, though low, are twice those collected from other high-income countries. But protection is also an issue for developing countries, which maintain high tariffs on agricultural commodities, labor-intensive manufactures, and other products and services.\n\nCountries use a combination of tariff and nontariff measures to regulate imports. The most common form of tariff is an ad valorem duty, based on the value of the import, but tariffs may also be levied on a specific, or per unit, basis or may combine ad valorem and specific rates. Tariffs may be used to raise fiscal revenues or to protect domestic industries from foreign competition - or both. Nontariff barriers, which limit the quantity of imports of a particular good, include quotas, prohibitions, licensing schemes, export restraint arrangements, and health and quarantine measures. Because of the difficulty of combining nontariff barriers into an aggregate indicator, they are not included in the data.\n\nSome countries set fairly uniform tariff rates across all imports. Others are selective, setting high tariffs to protect favored domestic industries. The effective rate of protection - the degree to which the value added in an industry is protected - may exceed the nominal rate if the tariff system systematically differentiates among imports of raw materials, intermediate products, and finished goods." }, { "id": "IndicatorName", "value": "Tariff rate, applied, simple mean, all products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the simple (unweighted) average of applied most-favored-nation (MFN) tariff rates across all tariff lines within the all products category. Each line is given equal weight regardless of the trade volume associated with it. The mean is calculated by summing the applied rates and dividing by the number of lines. Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator excludes missing or unreported lines. Harmonization across HS revisions is conducted to ensure international comparability, and no adjustments are made beyond validation of submitted data." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.MRCH.SM.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, most favored nation, simple mean, all products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator calculates the simple (unweighted) mean of statutory most-favored-nation (MFN) tariff rates across all tariff lines for all products. It reflects tariff rates that apply to all World Trade Organization (WTO) members on a non-discriminatory basis unless preferential agreements are in force. Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The calculation is performed by summing the MFN rates and dividing by the total number of tariff lines. No trade-based weighting is applied. Only reported rates are included, and harmonized classifications ensure comparability across countries." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.MRCH.SR.ZS", "metatype": [ { "id": "Dataset", "value": "WDI" }, { "id": "IndicatorName", "value": "Share of tariff lines with specific rates, all products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Longdefinition", "value": "Share of tariff lines with specific rates is the share of lines in the tariff schedule that are set on a per unit basis or that combine ad valorem and per unit rates. It shows the extent to which countries use tariffs based on physical quantities or other, non-ad valorem measures." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the share of tariff lines for all products that are expressed in specific rather than ad valorem terms. Specific rates are defined as those based on quantity, weight, volume, or other non-percentage measures. The indicator is computed by dividing the number of specific-rate lines by the total number of lines and expressing the result as a percentage. Share of tariff lines with specific rates is the share of lines in the tariff schedule that are set on a per unit basis or that combine ad valorem and per unit rates. It shows the extent to which countries use tariffs based on physical quantities or other, non-ad valorem measures. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Only duty types explicitly identified as specific in national tariff schedules are counted. The methodology applies standardized classification across countries to enable cross-national comparability. No imputation is used." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.MRCH.WM.AR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, applied, weighted mean, all products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator computes the average of applied most-favored-nation (MFN) tariff rates for all products, using import values as weights. Tariff lines with higher trade volumes have a proportionally greater influence on the final average. The weighted mean is derived by multiplying each tariff rate by its corresponding import value, summing these products, and dividing by the total import value. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Trade data used for weighting are obtained from official national or international sources. Only lines with both valid tariff rates and trade values are included. Harmonized classifications are used to align trade and tariff data consistently." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.MRCH.WM.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, most favored nation, weighted mean, all products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the trade-weighted average of most-favored-nation (MFN) tariff rates across all all products tariff lines. Import values are used to weight each rate, reflecting the relative economic significance of different products in international trade. The weighted average is calculated by taking the sum of the products of MFN rates and import values, divided by the total import value. Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Only tariff lines with both reported MFN rates and corresponding trade values are included. The WTO applies standardized concordance procedures to match trade and tariff data using harmonized product classifications." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.TCOM.BC.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Binding coverage, primary products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2022" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated as the proportion of tariff lines for primary products that are bound under World Trade Organization (WTO) commitments. A tariff line is considered bound if a maximum rate is legally committed in the WTO schedule of concessions. The binding coverage is computed by dividing the number of bound tariff lines by the total number of tariff lines in the relevant product category and multiplying the result by 100 to express it as a percentage. Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Primary products are commodities classified in Standard International Trade Classification (SITC) revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator is compiled using only officially reported bound statuses without imputation. It is methodologically consistent across countries, with tariff line concordance procedures applied to standardize classification and maintain comparability across HS revisions." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.TCOM.BR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Bound rate, simple mean, primary products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2022" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the unweighted average of final bound tariff rates across all primary products tariff lines. The calculation involves summing the bound rates applied to each individual tariff line and dividing by the total number of lines within the product category. Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Primary products are commodities classified in Standard International Trade Classification (SITC) revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator reflects commitments under WTO agreements and is not adjusted for trade volume. It includes only those lines for which binding rates have been reported. The methodology ensures consistency across countries by converting national tariff submissions to a harmonized HS version." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.TCOM.IP.ZS", "metatype": [ { "id": "Dataset", "value": "WDI" }, { "id": "IndicatorName", "value": "Share of tariff lines with international peaks, primary products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Longdefinition", "value": "Share of tariff lines with international peaks is the share of lines in the tariff schedule with tariff rates that exceed 15 percent. It provides an indication of how selectively tariffs are applied. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of tariff lines for primary products where the applied most-favored-nation (most-favored-nation (MFN)) rate exceeds 15%, which qualifies as an international tariff peak. It is calculated by dividing the number of tariff lines exceeding the 15% threshold by the total number of lines in the product group and multiplying the result by 100. Share of tariff lines with international peaks is the share of lines in the tariff schedule with tariff rates that exceed 15 percent. It provides an indication of how selectively tariffs are applied. Primary products are commodities classified in Standard International Trade Classification (SITC) revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The use of a fixed threshold across countries and years ensures cross-national comparability. No imputation is applied. Classification alignment across HS versions is performed to maintain methodological consistency." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.TCOM.SM.AR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, applied, simple mean, primary products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the simple (unweighted) average of applied most-favored-nation (MFN) tariff rates across all tariff lines within the primary products category. Each line is given equal weight regardless of the trade volume associated with it. The mean is calculated by summing the applied rates and dividing by the number of lines. Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator excludes missing or unreported lines. Harmonization across HS revisions is conducted to ensure international comparability, and no adjustments are made beyond validation of submitted data." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.TCOM.SM.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, most favored nation, simple mean, primary products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator calculates the simple (unweighted) mean of statutory most-favored-nation (MFN) tariff rates across all tariff lines for primary products. It reflects tariff rates that apply to all World Trade Organization (WTO) members on a non-discriminatory basis unless preferential agreements are in force. Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The calculation is performed by summing the MFN rates and dividing by the total number of tariff lines. No trade-based weighting is applied. Only reported rates are included, and harmonized classifications ensure comparability across countries." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.TCOM.SR.ZS", "metatype": [ { "id": "Dataset", "value": "WDI" }, { "id": "IndicatorName", "value": "Share of tariff lines with specific rates, primary products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Longdefinition", "value": "Share of tariff lines with specific rates is the share of lines in the tariff schedule that are set on a per unit basis or that combine ad valorem and per unit rates. It shows the extent to which countries use tariffs based on physical quantities or other, non-ad valorem measures. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the share of tariff lines for primary products that are expressed in specific rather than ad valorem terms. Specific rates are defined as those based on quantity, weight, volume, or other non-percentage measures. The indicator is computed by dividing the number of specific-rate lines by the total number of lines and expressing the result as a percentage. Share of tariff lines with specific rates is the share of lines in the tariff schedule that are set on a per unit basis or that combine ad valorem and per unit rates. It shows the extent to which countries use tariffs based on physical quantities or other, non-ad valorem measures. Primary products are commodities classified in Standard International Trade Classification (SITC) revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Only duty types explicitly identified as specific in national tariff schedules are counted. The methodology applies standardized classification across countries to enable cross-national comparability. No imputation is used." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.TCOM.WM.AR.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, applied, weighted mean, primary products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator computes the average of applied most-favored-nation (MFN) tariff rates for primary products, using import values as weights. Tariff lines with higher trade volumes have a proportionally greater influence on the final average. The weighted mean is derived by multiplying each tariff rate by its corresponding import value, summing these products, and dividing by the total import value. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Trade data used for weighting are obtained from official national or international sources. Only lines with both valid tariff rates and trade values are included. Harmonized classifications are used to align trade and tariff data consistently." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.TAX.TCOM.WM.FN.ZS", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Tariff rate, most favored nation, weighted mean, primary products (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1988-2022" }, { "id": "Source", "value": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the trade-weighted average of most-favored-nation (MFN) tariff rates across all primary products tariff lines. Import values are used to weight each rate, reflecting the relative economic significance of different products in international trade. The weighted average is calculated by taking the sum of the products of MFN rates and import values, divided by the total import value. Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Only tariff lines with both reported MFN rates and corresponding trade values are included. The WTO applies standardized concordance procedures to match trade and tariff data using harmonized product classifications." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.UVI.MRCH.XD.WD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Import unit value index (2015 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Import unit value indices come from UNCTAD's trade database. Unit value indices are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD’s estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates. This indicator is an index series where 2015=100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2024" }, { "id": "Source", "value": "Handbook of Statistics and data files., UN Conference on Trade and Development (UNCTAD), uri: http://unctadstat.unctad.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Trade price indices" }, { "id": "Unitofmeasure", "value": "index (2015 = 100)" } ], "source_id": "57" }, { "id": "TM.VAL.AGRI.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Agricultural raw materials imports (% of merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Agricultural raw materials comprise section 2 of SITC Rev. 3 (crude materials, inedible, except fuels) excluding divisions 22 (oil-seeds and oleaginous fruits), 27 (crude fertilizers and minerals excluding coal, petroleum, and precious stones), and 28 (metalliferous ores and scrap). This indicator is expressed as a percentage of merchandise imports which is comprised of goods whose economic ownership is changed between a non-resident and a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise import shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TM.VAL.FOOD.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Food imports (% of merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Food comprises the commodities in SITC (Rev. 3) sections 0 (food and live animals), 1 (beverages and tobacco), and 4 (animal and vegetable oils and fats) and division 22 (oil seeds, oil nuts, and oil kernels). This indicator is expressed as a percentage of merchandise imports which is comprised of goods whose economic ownership is changed between a non-resident and a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise import shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TM.VAL.FUEL.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Fuel imports (% of merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Fuels comprise the commodities in SITC (Rev. 3) section 3 (mineral fuels, lubricants and related materials). This indicator is expressed as a percentage of merchandise imports which is comprised of goods whose economic ownership is changed between a non-resident and a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise import shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TM.VAL.ICTG.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. For more information see www.itu.int/ITU-D/ict/partnership/.\n\nThe work of the Partnership is directed towards achieving internationally comparable and reliable ICT statistics. In order to achieve this, its members are involved in developing and maintaining a core list of ICT indicators. Other activities include the compilation and dissemination of ICT data, and the provision of technical assistance enabling statistical agencies to collect data that underlie the core list of ICT indicators." }, { "id": "IndicatorName", "value": "ICT goods imports (% total goods imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Detailed trade data are widely available from country trade statistics. These are collected by the UNSD and published in their UN COMTRADE database. The ICT goods trade indicators are usually compiled by interested international and national agencies using COMTRADE data. Concepts are therefore consistent with those applying to the COMTRADE database.\n\nThe main statistical issue associated with this indicator appears to be the different treatment of re-exports and re-imports by countries, depending on whether the Special or General Trade System is used.2 Re-imports are separately reported for some countries and the value of ICT re-imports (which is included in the value of ICT imports for those countries) is generally small." }, { "id": "Longdefinition", "value": "Information and communication technology goods imports include computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components, and other information and technology goods (miscellaneous)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "UNCTADstat database, UN Conference on Trade and Development (UNCTAD), uri: http://unctadstat.unctad.org/ReportFolders/reportFolders.aspx" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Information and communication technology goods imports include computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components, and other information and technology goods (miscellaneous). Software is generally excluded, as there is a preference to record it under services (not an ICT good but an ICT product) to the extent possible. However it is hard to completely exclude embedded software from certain types of ICT goods, such as video game consoles (see for example the discussion on page 30 of the OECD guide cited below). ICT goods imports as a percentage of total imports is calculated for each country by dividing the value of its ICT goods imports by the total value of its goods imports. The result is then multiplied by 100 to be expressed as a percentage.\n\nICT goods are defined according to the OECD’s Guide on Measuring the Information Society 2011 for Harmonized System (HS) 2007 and adapted to HS12 by UNCTAD in collaboration with UNSD (United Nations Statistics Division). This new list consists of 93 goods defined at the 6 digit level of the 2012 version of the HS. The technical note is available online at: http://unctad.org/en/PublicationsLibrary/tn_unctad_ict4d02_en.pdf\nData were downloaded from COMTRADE according to the reported classification (HS92, 96, 02, 07, 12) and aggregated into ICT groups by UNCTAD." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "TM.VAL.INSF.ZS.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Insurance and financial services (% of commercial service imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Financial services covers services related to financial intermediation, financial risk management, liquidity transformation or auxiliary financial activities. It also includes insurance and pension scheme services which are services related to providing life insurance and annuities, non-life insurance, reinsurance, pensions, standardised guarantees and auxiliary services to insurance, pension schemes, and standardised guarantee schemes. This indicator is expressed as a percentage of service imports which are commercial services provided by non-residents to residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TM.VAL.MANF.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Manufactures imports (% of merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Manufactures comprise commodities in SITC (Rev. 3) sections 5 (chemicals), 6 (basic manufactures), 7 (machinery and transport equipment), and 8 (miscellaneous manufactured goods), excluding division 68 (non-ferrous metals). This indicator is expressed as a percentage of merchandise imports which is comprised of goods whose economic ownership is changed between a non-resident and a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise import shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TM.VAL.MMTL.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Ores and metals imports (% of merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Ores and metals comprise the commodities in SITC (Rev. 3) sections 27 (crude fertilizer, minerals nes); 28 (metalliferous ores, scrap); and 68 (non-ferrous metals). Imports of services are services provided by non-residents to residents. This indicator is expressed as a percentage of merchandise imports which is comprised of goods whose economic ownership is changed between a non-resident and a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise import shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TM.VAL.MRCH.AL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports from economies in the Arab World (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports from economies in the Arab World are the sum of merchandise imports by the reporting economy from economies in the Arab World. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from economies in the Arab World. The purpose is to assess the relative importance of trade with economies in the Arab World economies within the broader context of global imports. The numerator includes the total value of goods imported from economies in the Arab World, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of economies in the Arab World follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "57" }, { "id": "TM.VAL.MRCH.CD.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "IndicatorName", "value": "Merchandise imports (UN, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports show the c.i.f. value of goods received from the rest of the world valued in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates through the WITS platform from the Comtrade database maintained by the United Nations Statistics Division." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "57" }, { "id": "TM.VAL.MRCH.CD.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Merchandise imports (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The value of imports is generally recorded as the cost of the goods when purchased by the importer plus the cost of transport and insurance to the frontier of the importing country - the cost, insurance, and freight (c.i.f.) value, corresponding to the landed cost at the point of entry of foreign goods into the country. A few countries collect import data on a free on board (f.o.b.) basis and adjust them for freight and insurance costs.\n\n\n\n\n\nCountries may report trade according to the general or special system of trade. Under the general system imports include goods imported for domestic consumption and imports into bonded warehouses and free trade zones. Under the special system imports comprise goods imported for domestic consumption (including transformation and repair) and withdrawals for domestic consumption from bonded warehouses and free trade zones. Goods transported through a country en route to another are excluded.\n\n\n\n\n\nData on imports of goods are derived from the same sources as data on exports. In principle, world exports and imports should be identical. Similarly, exports from an economy should equal the sum of imports by the rest of the world from that economy. But differences in timing and definitions result in discrepancies in reported values at all levels." }, { "id": "Longdefinition", "value": "Merchandise imports includes goods whose economic ownership is changed from a non-resident to a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "TM.VAL.MRCH.HI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Low- and middle-income economies are an increasingly important part of the global trading system. Trade between high-income economies and low- and middle-income economies has grown faster than trade between high-income economies. This increased trade benefits both producers and consumers in developing and high-income economies." }, { "id": "IndicatorName", "value": "Merchandise imports from high-income economies (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated." }, { "id": "Longdefinition", "value": "Merchandise imports from high-income economies are the sum of merchandise imports by the reporting economy from high-income economies according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from high-income economies. The purpose is to assess the relative importance of trade with high-income economies economies within the broader context of global imports. The numerator includes the total value of goods imported from high-income economies, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of high-income economies follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "57" }, { "id": "TM.VAL.MRCH.OR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although global integration has increased, low- and middle-income economies still face trade barriers when accessing other markets." }, { "id": "IndicatorName", "value": "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated." }, { "id": "Longdefinition", "value": "Merchandise imports from low- and middle-income economies outside region are the sum of merchandise imports by the reporting economy from other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies outside region. The purpose is to assess the relative importance of trade with low- and middle-income economies outside region economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies outside region, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies outside region follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "57" }, { "id": "TM.VAL.MRCH.R1.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports from low- and middle-income economies in East Asia and Pacific are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the East Asia and Pacific region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in East Asia & Pacific. The purpose is to assess the relative importance of trade with low- and middle-income economies in East Asia & Pacific economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in East Asia & Pacific, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in East Asia & Pacific follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "57" }, { "id": "TM.VAL.MRCH.R2.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports from low- and middle-income economies in Europe & Central Asia (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports from low- and middle-income economies in Europe and Central Asia are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Europe and Central Asia region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in Europe & Central Asia. The purpose is to assess the relative importance of trade with low- and middle-income economies in Europe & Central Asia economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in Europe & Central Asia, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in Europe & Central Asia follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "57" }, { "id": "TM.VAL.MRCH.R3.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports from low- and middle-income economies in Latin America & the Caribbean (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports from low- and middle-income economies in Latin America and the Caribbean are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Latin America and the Caribbean region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in Latin America & the Caribbean. The purpose is to assess the relative importance of trade with low- and middle-income economies in Latin America & the Caribbean economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in Latin America & the Caribbean, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in Latin America & the Caribbean follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "57" }, { "id": "TM.VAL.MRCH.R4.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports from low- and middle-income economies in Middle East & North Africa (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports from low- and middle-income economies in Middle East and North Africa are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Middle East and North Africa region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in Middle East & North Africa. The purpose is to assess the relative importance of trade with low- and middle-income economies in Middle East & North Africa economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in Middle East & North Africa, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in Middle East & North Africa follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "57" }, { "id": "TM.VAL.MRCH.R5.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports from low- and middle-income economies in South Asia are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the South Asia region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in South Asia. The purpose is to assess the relative importance of trade with low- and middle-income economies in South Asia economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in South Asia, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in South Asia follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "57" }, { "id": "TM.VAL.MRCH.R6.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports from low- and middle-income economies in Sub-Saharan Africa (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports from low- and middle-income economies in Sub-Saharan Africa are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Sub-Saharan Africa region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in Sub-Saharan Africa. The purpose is to assess the relative importance of trade with low- and middle-income economies in Sub-Saharan Africa economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in Sub-Saharan Africa, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in Sub-Saharan Africa follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "57" }, { "id": "TM.VAL.MRCH.RS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports by the reporting economy, residual (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports by the reporting economy residuals are the total merchandise imports by the reporting economy from the rest of the world as reported in the IMF's Direction of trade database, less the sum of imports by the reporting economy from high-, low-, and middle-income economies according to the World Bank classification of economies. Includes trade with unspecified partners or with economies not covered by World Bank classification. Data are as a percentage of total merchandise imports by the economy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the total value of merchandise imports reported by a country over a specified period, typically a calendar year. The value includes all goods that physically enter the country’s economic territory and are intended for consumption, processing, or re-export. Data are expressed in nominal terms using current U.S. dollars. The data source is primarily customs administrative records submitted by importers, compiled by national statistical or trade authorities.\n\nThe reported value of imports generally includes the cost of goods plus insurance and freight (CIF), which reflects the full landed value at the port of entry. The general trade system is used in most cases, meaning that imports into bonded warehouses and free zones are also included. The data exclude services and focus solely on physical goods crossing borders. Corrections may be applied for coverage limitations, underreporting, valuation discrepancies, or misclassifications to improve comparability.\n\nSome versions of the indicator may exclude re-imports, which are goods previously exported and returned to the country without significant transformation. This distinction ensures that the import figures do not overstate economic activity by counting the same goods more than once. Users of the indicator include government agencies monitoring trade flows, businesses analyzing sourcing strategies, and researchers studying trade balances. It also provides input for calculating the balance of trade and understanding integration into global markets." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "57" }, { "id": "TM.VAL.MRCH.WL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise imports by the reporting economy (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise imports by the reporting economy are the total merchandise imports by the reporting economy from the rest of the world, as reported in the IMF's Direction of trade database. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator represents the total value of merchandise imports reported by a country over a specified period, typically a calendar year. The value includes all goods that physically enter the country’s economic territory and are intended for consumption, processing, or re-export. Data are expressed in nominal terms using current U.S. dollars. The data source is primarily customs administrative records submitted by importers, compiled by national statistical or trade authorities.\n\nThe reported value of imports generally includes the cost of goods plus insurance and freight (CIF), which reflects the full landed value at the port of entry. The general trade system is used in most cases, meaning that imports into bonded warehouses and free zones are also included. The data exclude services and focus solely on physical goods crossing borders. Corrections may be applied for coverage limitations, underreporting, valuation discrepancies, or misclassifications to improve comparability.\n\nSome versions of the indicator may exclude re-imports, which are goods previously exported and returned to the country without significant transformation. This distinction ensures that the import figures do not overstate economic activity by counting the same goods more than once. Users of the indicator include government agencies monitoring trade flows, businesses analyzing sourcing strategies, and researchers studying trade balances. It also provides input for calculating the balance of trade and understanding integration into global markets." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "57" }, { "id": "TM.VAL.MRCH.WR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The relative importance of intraregional trade is higher for both landlocked countries and small countries with close trade links to the largest regional economy. For most low- and middle-income economies - especially smaller ones - there is a \"geographic bias\" favoring intraregional trade. Despite the broad trend toward globalization and the reduction of trade barriers, the relative share of intraregional trade increased for most economies between 1999 and 2010. This is due partly to trade-related advantages, such as proximity, lower transport costs, increased knowledge from repeated interaction, and cultural and historical affinity. The direction of trade is also influenced by preferential trade agreements that a country has made with other economies. Though formal agreements on trade liberalization do not automatically increase trade, they nevertheless affect the direction of trade between the participating economies." }, { "id": "IndicatorName", "value": "Merchandise imports from low- and middle-income economies within region (% of total merchandise imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated." }, { "id": "Longdefinition", "value": "Merchandise imports from low- and middle-income economies within region are the sum of merchandise imports by the reporting economy from other low- and middle-income economies in the same World Bank region according to the World Bank classification of economies. Data are as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data. No figures are shown for high-income economies, because they are a separate category in the World Bank classification of economies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies within region. The purpose is to assess the relative importance of trade with low- and middle-income economies within region economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies within region, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies within region follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" } ], "source_id": "57" }, { "id": "TM.VAL.MRCH.XD.WD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Import value index (2015 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Import value indexes are the current value of imports (c.i.f.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2015). UNCTAD's import value indexes are reported for most economies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2023" }, { "id": "Source", "value": "UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Import Value Index (base year 2015 = 100) captures the movement in the total value of merchandise imports over time, using 2015 as the reference period. This index reflects changes in both the price and quantity of imported goods. An index value above 100 indicates that the total value of imports has increased compared to the base year, while a value below 100 suggests a decline. The indicator is constructed using current-dollar import values, typically sourced from customs data or trade statistics reports. \n\nImport values are aggregated across all products and trading partners. Because the index combines both price and volume effects, it does not isolate whether changes are due to rising prices, increased quantities, or both. For this reason, it is often interpreted alongside other indicators such as unit value or volume indexes.\n\nIndex values are calculated by comparing the nominal value of imports in each year to that of the base year, then scaling the result so that the base year equals 100. This normalization allows for straightforward interpretation and cross-country comparisons. The index is useful for identifying structural shifts in import demand, the impact of currency fluctuations, and the effects of trade policy. It can also be used in macroeconomic modeling to estimate import deflators or as an input for terms-of-trade calculations." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TM.VAL.OTHR.ZS.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Computer, communications and other services (% of commercial service imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Computer, communications and other services include such activities as international telecommunications, and postal and courier services; computer data; news-related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; and personal, cultural, and recreational services. This indicator is expressed as a percentage of service imports which are commercial services provided by non-residents to residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TM.VAL.SERV.CD.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Commercial service imports (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Commercial service imports are total service imports minus imports of government services not included elsewhere. Imports of services are services provided by non-residents to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "TM.VAL.TRAN.ZS.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Transport services (% of commercial service imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Transport is the process of carriage of people and objects from one location to another as well as related supporting and auxiliary services. Also included are postal and courier services. This indicator is expressed as a percentage of service imports which are commercial services provided by non-residents to residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TM.VAL.TRVL.ZS.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Travel services (% of commercial service imports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Travel services cover goods and services for own use or to give away acquired from an economy by nonresidents during visits to that economy, or acquired from other economies by residents during visits to these other economies. This indicator is expressed as a percentage of service imports which are commercial services provided by non-residents to residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Imports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TT.PRI.MRCH.XD.WD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Net barter terms of trade index (2015 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Net barter terms of trade index is calculated as the percentage ratio of the export unit value indexes to the import unit value indexes, measured relative to the base year 2015." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2023" }, { "id": "Source", "value": "UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Net Barter Terms of Trade Index (base year 2015 = 100) is calculated as the ratio of an export price index to an import price index, multiplied by 100. This index measures the rate at which a country can exchange its exports for imports. A value greater than 100 means the terms of trade have improved relative to the base year, allowing the country to obtain more imports for a given level of exports. Conversely, a value below 100 indicates that the terms have worsened.\n\nExport and import price indexes are derived from unit value data or sampled transaction-level data, depending on country-specific reporting practices. The price indexes may be computed using fixed or chain-weighted methods. To ensure temporal comparability, the export and import series are indexed to a common base year (2015 = 100).\n\nThe terms of trade index is sensitive to fluctuations in global commodity prices, exchange rate movements, and changes in trade composition. It is used to assess trade gains or losses, especially for countries reliant on exports of primary commodities. Although it does not capture changes in volumes traded, it provides insight into the relative value received for exports. Policymakers and researchers may use this indicator to monitor competitiveness and evaluate exposure to external shocks." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TX.MNF.TECH.ZS.UN", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Industrial development generally entails a structural transition from resource-based and low technology activities to medium and high-tech industry (MHT) activities. A modern, highly complex production structure offers better opportunities for skills development and technological innovation. MHT activities are also the high value addition industries of manufacturing with higher technological intensity and labor productivity. Increasing the share of MHT sectors also reflects the impact of innovation." }, { "id": "IndicatorName", "value": "Medium and high-tech exports (% manufactured exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Missing values at country level are imputed based on the methodology from Competitive Industrial Performance Report (UNIDO, 2017)." }, { "id": "Longdefinition", "value": "Share of medium and high-tech manufactured exports in total manufactured exports." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2022" }, { "id": "Source", "value": "Competitive Industrial Performance (CIP) database, UN Industrial Development Organization (UNIDO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data from UN COMTRADE is downloaded in SITC Revision 3, 3-digit, by reporting country, year, partner code, commodity and flow (export and re-export). SITC medium technology: 266, 267, 512, 513, 533, 553, 554, 562, 571, 572, 573, 574, 575, 579, 581, 582, 583, 591, 593, 597, 598, 653, 671, 672, 678, 711, 712,713, 714, 721, 722, 723, 724, 725, 726, 727, 728, 731, 733, 735, 737, 741, 742, 743, 744, 745, 746, 747, 748, 749, 761, 762, 763, 772, 773, 775, 778, 781, 782, 783, 784, 785, 786, 791, 793, 811, 812, 813, 872, 873, 882, 884, 885; SITC high technology: 525, 541, 542, 716, 718, 751, 752, 759, 764, 771, 774, 776, 792, 871, 874, 881, 891. Net-exports are calculated as exports minus re-exports. Manufactured exports, is the sum of the four categories resource-based exports, low-tech exports, medium tech exports and high-tech exports; and medium-high technology exports, is the sum of medium tech exports and high-tech exports. The world value of manufacturing exports is the sum of all manufacturing net exports. For additional information please see Table B.2.1 in Appendix B of UNIDO (2017): http://stat.unido.org/content/publications/volume-i%252c-competitive-industrial-performance-report-2016" }, { "id": "Topic", "value": "Infrastructure: Technology" } ], "source_id": "57" }, { "id": "TX.QTY.MRCH.XD.WD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Export volume index (2015 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Export volume indexes are derived from UNCTAD's volume index series and are the ratio of the export value indexes to the corresponding unit value indexes. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year's trade values at the Standard International Trade Classification three-digit level as weights." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2023" }, { "id": "Source", "value": "UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Export Volume Index (base year 2015 = 100) measures changes in the physical volume of merchandise exports over time, adjusting for price effects. By The Export Volume Index (base year 2015 = 100) measures changes in the physical volume of merchandise exports over time, adjusting for price effects. By removing the influence of price changes, this index isolates real growth or contraction in exported quantities. A value above 100 indicates that the volume of exports has increased relative to the base year, while a value below 100 signals a reduction.\n\nTo calculate the index, nominal export values are deflated using export price indices or unit value series, depending on the availability of data. These deflated values are then compared to the base year to produce an index that is normalized to 100 in 2015. The index covers all merchandise exports, and values are aggregated at the national level across products and partner economies.\n\nThe Export Volume Index helps distinguish between growth due to increased trade activity and growth driven solely by price inflation. It is particularly relevant for monitoring export performance in real terms, analyzing supply chain trends, and identifying structural shifts in trade composition. This index can inform decisions on industrial policy, export diversification, and investment in production capacity. It may also support economic modeling exercises requiring real trade variables." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TX.UVI.MRCH.XD.WD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Export unit value index (2015 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Export unit value indices come from UNCTAD's trade database. Unit value indices are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD’s estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, interna¬tional and national sources, and UNCTAD secretariat estimates. This indicator is an index series where 2015=100." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2024" }, { "id": "Source", "value": "Handbook of Statistics and data files., UN Conference on Trade and Development (UNCTAD), uri: http://unctadstat.unctad.org" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Trade price indices" }, { "id": "Unitofmeasure", "value": "index (2015 = 100)" } ], "source_id": "57" }, { "id": "TX.VAL.AGRI.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Agricultural raw materials exports (% of merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Agricultural raw materials comprise section 2 of SITC Rev. 3 (crude materials, inedible, except fuels) excluding divisions 22 (oil-seeds and oleaginous fruits), 27 (crude fertilizers and minerals excluding coal, petroleum, and precious stones), and 28 (metalliferous ores and scrap). This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise export shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TX.VAL.FOOD.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Food exports (% of merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Food comprises the commodities in SITC (Rev. 3) sections 0 (food and live animals), 1 (beverages and tobacco), and 4 (animal and vegetable oils and fats) and division 22 (oil seeds, oil nuts, and oil kernels). This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise export shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TX.VAL.FUEL.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Fuel exports (% of merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Fuels comprise the commodities in SITC (Rev. 3) section 3 (mineral fuels, lubricants and related materials). This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise export shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nComtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS), World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TX.VAL.ICTG.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. For more information see www.itu.int/ITU-D/ict/partnership/.\n\nThe work of the Partnership is directed towards achieving internationally comparable and reliable ICT statistics. In order to achieve this, its members are involved in developing and maintaining a core list of ICT indicators. Other activities include the compilation and dissemination of ICT data, and the provision of technical assistance enabling statistical agencies to collect data that underlie the core list of ICT indicators." }, { "id": "IndicatorName", "value": "ICT goods exports (% of total goods exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Detailed trade data are widely available from country trade statistics. These are collected by the UNSD and published in their UN COMTRADE database. The ICT goods trade indicators are usually compiled by interested international and national agencies using COMTRADE data. Concepts are therefore consistent with those applying to the COMTRADE database.\n\nThe main statistical issue associated with this indicator appears to be the different treatment of re-exports and re-imports by countries, depending on whether the Special or General Trade System is used.2 Re-imports are separately reported for some countries and the value of ICT re-imports (which is included in the value of ICT imports for those countries) is generally small." }, { "id": "Longdefinition", "value": "Information and communication technology goods exports include computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components, and other information and technology goods (miscellaneous)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2000-2022" }, { "id": "Source", "value": "UNCTADstat database, UN Conference on Trade and Development (UNCTAD), uri: http://unctadstat.unctad.org/ReportFolders/reportFolders.aspx" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Information and communication technology goods exports include computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components, and other information and technology goods (miscellaneous). Software is generally excluded, as there is a preference to record it under services (not an ICT good but an ICT product) to the extent possible. However it is hard to completely exclude embedded software from certain types of ICT goods, such as video game consoles (see for example the discussion on page 30 of the OECD guide cited below). ICT goods exports as a percentage of total goods exports is calculated for each country by dividing the value of its ICT goods exports by the total value of its goods exports. The result is then multiplied by 100 to be expressed as a percentage.\n\nICT goods are defined according to the OECD’s Guide on Measuring the Information Society 2011 for Harmonized System (HS) 2007 and adapted to HS12 by UNCTAD in collaboration with UNSD (United Nations Statistics Division). This new list consists of 93 goods defined at the 6 digit level of the 2012 version of the HS. The technical note is available online at: http://unctad.org/en/PublicationsLibrary/tn_unctad_ict4d02_en.pdf\nData were downloaded from COMTRADE according to the reported classification (HS92, 96, 02, 07, 12) and aggregated into ICT groups by UNCTAD." }, { "id": "Topic", "value": "Infrastructure: Communications" } ], "source_id": "57" }, { "id": "TX.VAL.INSF.ZS.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Insurance and financial services (% of commercial service exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Financial services covers services related to financial intermediation, financial risk management, liquidity transformation or auxiliary financial activities. It also includes insurance and pension scheme services which are services related to providing life insurance and annuities, non-life insurance, reinsurance, pensions, standardised guarantees and auxiliary services to insurance, pension schemes, and standardised guarantee schemes. This indicator is expressed as a percentage of service exports which are commercial services provided by residents to non-residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TX.VAL.MANF.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Manufactures exports (% of merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Manufactures comprise commodities in SITC (Rev. 3) sections 5 (chemicals), 6 (basic manufactures), 7 (machinery and transport equipment), and 8 (miscellaneous manufactured goods), excluding division 68 (non-ferrous metals). This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise export shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TX.VAL.MMTL.ZS.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Ores and metals exports (% of merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another." }, { "id": "Longdefinition", "value": "Ores and metals comprise the commodities in SITC (Rev. 3) sections 27 (crude fertilizer, minerals nes); 28 (metalliferous ores, scrap); and 68 (non-ferrous metals). Exports of services are services provided by residents to non-residents. This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e." }, { "id": "Othernotes", "value": "Merchandise export shares may not sum to 100 percent because of unclassified trade." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1962-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.AL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports to economies in the Arab World (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to economies in the Arab World are the sum of merchandise exports by the reporting economy to economies in the Arab World. Data are expressed as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.CD.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "IndicatorName", "value": "Merchandise exports (UN, current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports show the f.o.b. value of goods provided to the rest of the world valued in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates through the WITS platform from the Comtrade database maintained by the United Nations Statistics Division." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.CD.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Merchandise exports (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Exports are recorded as the cost of the goods delivered to the frontier of the exporting country for shipment - the free on board (f.o.b.) value.\n\n\n\n\n\nCountries may report trade according to the general or special system of trade. Under the general system exports comprise outward-moving goods that are (a) goods wholly or partly produced in the country; (b) foreign goods, neither transformed nor declared for domestic consumption in the country, that move outward from customs storage; and (c) goods previously included as imports for domestic consumption but subsequently exported without transformation. Under the special system exports comprise categories a and c. In some compilations categories b and c are classified as re-exports. Because of differences in reporting practices, data on exports may not be fully comparable across economies.\n\n\n\n\n\nData on exports of goods are derived from the same sources as data on imports. In principle, world exports and imports should be identical. Similarly, exports from an economy should equal the sum of imports by the rest of the world from that economy. But differences in timing and definitions result in discrepancies in reported values at all levels." }, { "id": "Longdefinition", "value": "Merchandise exports includes goods whose economic ownership is changed from a resident to a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "World Trade Organization (WTO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.HI.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Merchandise exports to high-income economies (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to high-income economies are the sum of merchandise exports from the reporting economy to high-income economies according to the World Bank classification of economies. Data are in current U.S. dollars. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Merchandise exports to high-income economies are the sum of merchandise exports from the reporting economy to high-income economies according to the World Bank classification of economies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank staff estimates based data from International Monetary Fund's Direction of Trade database." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.HI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Low- and middle-income economies are an increasingly important part of the global trading system. Trade between high-income economies and low- and middle-income economies has grown faster than trade between high-income economies. This increased trade benefits both producers and consumers in developing and high-income economies.\n\nAt the regional level most exports from low- and middle-income economies are to high-income economies, but the share of intraregional trade is increasing. Geographic patterns of trade vary widely by country and commodity. Larger shares of exports from oil- and resource-rich economies are to high-income economies." }, { "id": "IndicatorName", "value": "Merchandise exports to high-income economies (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated." }, { "id": "Longdefinition", "value": "Merchandise exports to high-income economies are the sum of merchandise exports from the reporting economy to high-income economies according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.OR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies outside region (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies outside region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are in current U.S. dollars. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Merchandise exports to low- and middle-income economies outside region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank staff estimates based data from International Monetary Fund's Direction of Trade database." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.OR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although global integration has increased, low- and middle-income economies still face trade barriers when accessing other markets." }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated." }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies outside region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.R1.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in East Asia & Pacific (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in East Asia and Pacific are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the East Asia and Pacific region according to World Bank classification of economies. Data are in current U.S. dollars. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Merchandise exports to low- and middle-income economies in East Asia and Pacific are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the East Asia and Pacific region according to World Bank classification of economies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank staff estimates based data from International Monetary Fund's Direction of Trade database." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.R1.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in East Asia and Pacific are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the East Asia and Pacific region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.R2.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in Europe & Central Asia (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in Europe and Central Asia are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Europe and Central Asia region according to World Bank classification of economies. Data are in current U.S. dollars. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Merchandise exports to low- and middle-income economies in Europe and Central Asia are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Europe and Central Asia region according to World Bank classification of economies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank staff estimates based data from International Monetary Fund's Direction of Trade database." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.R2.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in Europe & Central Asia (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in Europe and Central Asia are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Europe and Central Asia region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.R3.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in Latin America & the Caribbean (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in Latin America and the Caribbean are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Latin America and the Caribbean region according to World Bank classification of economies. Data are in current U.S. dollars. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Merchandise exports to low- and middle-income economies in Latin America and the Caribbean are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Latin America and the Caribbean region according to World Bank classification of economies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank staff estimates based data from International Monetary Fund's Direction of Trade database." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.R3.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in Latin America & the Caribbean (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in Latin America and the Caribbean are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Latin America and the Caribbean region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.R4.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in Middle East & North Africa (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in Middle East and North Africa are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Middle East and North Africa region according to World Bank classification of economies. Data are in current U.S. dollars. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Merchandise exports to low- and middle-income economies in Middle East and North Africa are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Middle East and North Africa region according to World Bank classification of economies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank staff estimates based data from International Monetary Fund's Direction of Trade database." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.R4.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in Middle East & North Africa (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in Middle East and North Africa are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Middle East and North Africa region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.R5.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in South Asia (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in South Asia are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the South Asia region according to World Bank classification of economies. Data are in current U.S. dollars. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Merchandise exports to low- and middle-income economies in South Asia are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the South Asia region according to World Bank classification of economies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank staff estimates based data from International Monetary Fund's Direction of Trade database." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.R5.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in South Asia (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in South Asia are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the South Asia region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.R6.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in Sub-Saharan Africa (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in Sub-Saharan Africa are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Sub-Saharan Africa region according to World Bank classification of economies. Data are in current U.S. dollars. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Merchandise exports to low- and middle-income economies in Sub-Saharan Africa are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Sub-Saharan Africa region according to World Bank classification of economies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank staff estimates based data from International Monetary Fund's Direction of Trade database." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.R6.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies in Sub-Saharan Africa (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies in Sub-Saharan Africa are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Sub-Saharan Africa region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.RS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports by the reporting economy, residual (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports by the reporting economy residuals are the total merchandise exports by the reporting economy to the rest of the world as reported in the IMF's Direction of trade database, less the sum of exports by the reporting economy to high-, low-, and middle-income economies according to the World Bank classification of economies. Includes trade with unspecified partners or with economies not covered by World Bank classification. Data are as a percentage of total merchandise exports by the economy." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: \"This indicator presents the total value of all merchandise exports made by the reporting country over a given period, typically a calendar year. Exports are This This indicator presents the total value of all merchandise exports made by the reporting country over a given period, typically a calendar year. Exports are measured in nominal terms using current U.S. dollars and represent the total value of goods crossing the economic border of the country for sale, exchange, or further processing abroad. Data are reported by national customs authorities and compiled by statistical offices.\n\nExport values are recorded on a free-on-board (FOB) basis, which includes the cost of goods up to the point of shipment, excluding transportation and insurance costs beyond the exporting country. All merchandise goods are included, regardless of their final use in the destination country. Re-exports may be included or excluded depending on national reporting practices; when excluded, the values represent domestic exports only. Goods exported for processing, assembly, or resale are included if ownership is transferred to a foreign entity.\n\nThis indicator provides a comprehensive view of the country’s trade performance, export capacity, and integration into the global market. Changes in total exports can reflect economic cycles, shifts in production and competitiveness, changes in external demand, or exchange rate dynamics. Policymakers, economists, and trade analysts often rely on this measure to assess external sector performance, inform macroeconomic modeling, or design export promotion strategies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.WL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Merchandise exports by the reporting economy (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports by the reporting economy are the total merchandise exports by the reporting economy to the rest of the world, as reported in the IMF's Direction of trade database. Data are in current US$." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: \"This indicator presents the total value of all merchandise exports made by the reporting country over a given period, typically a calendar year. Exports are This This indicator presents the total value of all merchandise exports made by the reporting country over a given period, typically a calendar year. Exports are measured in nominal terms using current U.S. dollars and represent the total value of goods crossing the economic border of the country for sale, exchange, or further processing abroad. Data are reported by national customs authorities and compiled by statistical offices.\n\nExport values are recorded on a free-on-board (FOB) basis, which includes the cost of goods up to the point of shipment, excluding transportation and insurance costs beyond the exporting country. All merchandise goods are included, regardless of their final use in the destination country. Re-exports may be included or excluded depending on national reporting practices; when excluded, the values represent domestic exports only. Goods exported for processing, assembly, or resale are included if ownership is transferred to a foreign entity.\n\nThis indicator provides a comprehensive view of the country’s trade performance, export capacity, and integration into the global market. Changes in total exports can reflect economic cycles, shifts in production and competitiveness, changes in external demand, or exchange rate dynamics. Policymakers, economists, and trade analysts often rely on this measure to assess external sector performance, inform macroeconomic modeling, or design export promotion strategies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.WR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies within region (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies within region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in the same World Bank region. Data are in current U.S. dollars. Data are computed only if at least half of the economies in the partner country group had non-missing data. No figures are shown for high-income economies, because they are a separate category in the World Bank classification of economies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Merchandise exports to low- and middle-income economies within region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in the same World Bank region. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank staff estimates based data from International Monetary Fund's Direction of Trade database." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.WR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "The relative importance of intraregional trade is higher for both landlocked countries and small countries with close trade links to the largest regional economy. For most low- and middle-income economies - especially smaller ones - there is a \"geographic bias\" favoring intraregional trade. Despite the broad trend toward globalization and the reduction of trade barriers, the relative share of intraregional trade increased for most economies between 1999 and 2010. This is due partly to trade-related advantages, such as proximity, lower transport costs, increased knowledge from repeated interaction, and cultural and historical affinity. The direction of trade is also influenced by preferential trade agreements that a country has made with other economies. Though formal agreements on trade liberalization do not automatically increase trade, they nevertheless affect the direction of trade between the participating economies." }, { "id": "IndicatorName", "value": "Merchandise exports to low- and middle-income economies within region (% of total merchandise exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated." }, { "id": "Longdefinition", "value": "Merchandise exports to low- and middle-income economies within region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in the same World Bank region as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data. No figures are shown for high-income economies, because they are a separate category in the World Bank classification of economies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2023" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator expresses the percentage of a country’s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" } ], "source_id": "57" }, { "id": "TX.VAL.MRCH.XD.WD", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "IndicatorName", "value": "Export value index (2015 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Export values are the current value of exports (f.o.b.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2015). UNCTAD's export value indexes are reported for most economies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1980-2023" }, { "id": "Source", "value": "UN Conference on Trade and Development (UNCTAD)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The value indices show the current value of exports (f.o.b.) for an economy, after conversion to United States dollars and with the reference year =100. Thus it is simply derived from monetary values from the total trade series or from the trade matrix and exchange rates." }, { "id": "Topic", "value": "Trade" } ], "source_id": "57" }, { "id": "TX.VAL.OTHR.ZS.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Computer, communications and other services (% of commercial service exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Computer, communications and other services include such activities as international telecommunications, and postal and courier services; computer data; news-related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; and personal, cultural, and recreational services. This indicator is expressed as a percentage of service exports which are commercial services provided by residents to non-residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TX.VAL.SERV.CD.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Commercial service exports (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Commercial service exports are total service exports minus exports of government services not included elsewhere. Exports of services are services provided by residents to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "57" }, { "id": "TX.VAL.TECH.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "High-technology exports represent the value of products with high research and development (R&D) intensity, such as aerospace, computers, pharmaceuticals, scientific instruments, and advanced machinery, exported by a country in a given year. High-tech exports are defined according to a standard classification (based on SITC Rev.4, using a product-level approach originally developed by OECD in collaboration with Eurostat), which aggregates product codes associated with high R&D intensity.\n\nFrom a development perspective, tracking high-technology exports reveals structural shifts in an economy: a rising volume of such exports may signal successful technology transfer, increasing domestic capacity for innovation, and greater economic sophistication. It provides empirical evidence of movement toward higher value-added production, which is often associated with stronger productivity growth, better jobs, improved trade balances, and long-term competitiveness. \n\nMoreover, by benchmarking across countries, the indicator helps policymakers and researchers assess how well an economy is diversifying away from resource- or low-technology-based exports and embedding itself in segments of global trade characterized by rapid innovation and technological change. Tracking high-technology exports also helps policymakers identify strengths and gaps in national innovation systems, benchmark progress against peers, and design targeted interventions to foster knowledge-intensive industries and diversify export portfolios." }, { "id": "IndicatorName", "value": "High-technology exports (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator is based on data reported by countries to COMTRADE. The export values presented in the World Development Indicators represent Gross Exports less Re-Exports. The values may be impacted in cases of reporting errors or missing data, for example if countries do not report Re-Exports for one or more periods." }, { "id": "Longdefinition", "value": "High-technology exports are products with high R&D intensity, such as aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2007-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), uri: comtrade.un.org, publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS), World Bank (WB), uri: https://wits.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data for high-technology exports are sourced from the United Nations Comtrade database and accessed through the World Integrated Trade Solution (WITS) platform. Export values are calculated as gross exports minus re-exports, using country-reported trade statistics. The methodology aggregates the value of exports for all products classified as high-technology according to SITC Rev.4 codes, as defined by Eurostat and the OECD. Periodic updates to product codes and conversion tables (e.g., from HS to SITC) are incorporated to maintain consistency and address data gaps. The indicator is published annually, and methodological adjustments—such as the inclusion of additional product codes to correct for conversion issues—are documented in the metadata to ensure transparency and comparability over time.\nStatistical concept(s): High technology products are defined according to SITC Rev.4 as the sum of the following products: Aerospace, Computers-office machines, Electronics-telecommunications, Pharmacy, Scientific instruments, Electrical machinery, Chemistry, Non-electrical machinery, Armament. The following product codes are used: Aerospace: (714 – 71489 -71499)+7921+7922+7924+7925+79291+79293+87411; Computers-office machines: 75194+75195+752+75997; Electronics-communication: 76331+7638+(764-76493-76499)+7722+77261+77318+77625+77627+7763+7764+7768+89844+89846; Pharmacy: 5413+5415+5416+5421+5422; Scientific instruments: 774+871+87211+(874-87411-8742)+88111+88121+88411+88419+(8996-89965-89969); Electrical machinery: (7786-77861-777866-77869)+7787+77884; Chemistry: 52222+52223+52229+52269+525+531+57433+591; Non-electrical machinery: 71489+71499+7187+72847+7311+73131+73135+73142+73144+73151+73153+73161+73163+73165+73312+73314+73316+7359+73733+73735; Armament: 891\nThe list can also be accessed on the Eurostat website. This list, based on the OECD definition, contains technical products of which the manufacturing involved a high intensity of R&D. The original high-tech products classification is based on SITC Rev. 3 and is taken from Table 4 of Annex 2 of the 1997 working paper of Thomas Hatzichronouglou, OECD. In September 2019 the definition in the World Development Indicators database was updated to SITC Rev.4 from SITC Rev. 3. The data are in current U.S. dollars and are sourced from the UN's Comtrade database.\n\nNote: The definition of high technology exports in WDI was modified as of October 2024. Specifically the list of SITC Rev.4 high technology product codes now includes product code 776 in its entirety and that all data in its sub-categories is incorporated. This means that products codes 776.11, 776.12, 776.21, 776.23, 776.29 have been added to the high-tech list of products, which does not comply with OECD’s 2008 definition of high-tech.\n\nThis change was implemented to address the problem of missing data which occurs during the data conversion process from HS2022 to SITC4. This happens if a country submits export data in the HS2022 coding system and is no longer in HS2017. Specifically, the HS2022-to-SITC4 conversion table (downloaded from COMTRADE) does not break down product code 776.4+ into sub-categories, and the export data of these sub-categories has been subsumed into 776. Since there is no break down for 776.4+, extracting data from the SITC4 dataset in the COMTRADE database will yield zero value for SITC4 codes 776.4, 776.42, 776.44, 776.46, and 776.49 as defined in the OECD high-tech definition. Hence, when calculating high-tech export values using the forward method of summing up data from these five SITC4 codes will result in missing data caused by the conversion process. This can result in a significant year-on-year decline in high-tech export values for affected countries." }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "USD current" } ], "source_id": "57" }, { "id": "TX.VAL.TECH.MF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "High-technology exports represent the value of products with high research and development (R&D) intensity, such as aerospace, computers, pharmaceuticals, scientific instruments, and advanced machinery, exported by a country in a given year. High-tech exports are defined according to a standard classification (based on SITC Rev.4, using a product-level approach originally developed by OECD in collaboration with Eurostat), which aggregates product codes associated with high R&D intensity.\n\nFrom a development perspective, tracking high-technology exports reveals structural shifts in an economy: a rising volume of such exports may signal successful technology transfer, increasing domestic capacity for innovation, and greater economic sophistication. It provides empirical evidence of movement toward higher value-added production, which is often associated with stronger productivity growth, better jobs, improved trade balances, and long-term competitiveness. \n\nMoreover, by benchmarking across countries, the indicator helps policymakers and researchers assess how well an economy is diversifying away from resource- or low-technology-based exports and embedding itself in segments of global trade characterized by rapid innovation and technological change. Tracking high-technology exports also helps policymakers identify strengths and gaps in national innovation systems, benchmark progress against peers, and design targeted interventions to foster knowledge-intensive industries and diversify export portfolios." }, { "id": "IndicatorName", "value": "High-technology exports (% of manufactured exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The indicator is based on data reported by countries to COMTRADE. The export values presented in the World Development Indicators represent Gross Exports less Re-Exports. The values may be impacted in cases of reporting errors or missing data, for example if countries do not report Re-Exports for one or more periods." }, { "id": "Longdefinition", "value": "High-technology exports are products with high R&D intensity, such as in aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2007-2024" }, { "id": "Source", "value": "Comtrade database, United Nations (UN), uri: comtrade.un.org, publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS), World Bank (WB), uri: https://wits.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data for high-technology exports are sourced from the United Nations Comtrade database and accessed through the World Integrated Trade Solution (WITS) platform. Export values are calculated as gross exports minus re-exports, using country-reported trade statistics. The methodology aggregates the value of exports for all products classified as high-technology according to SITC Rev.4 codes, as defined by Eurostat and the OECD. Periodic updates to product codes and conversion tables (e.g., from HS to SITC) are incorporated to maintain consistency and address data gaps. The indicator is published annually, and methodological adjustments—such as the inclusion of additional product codes to correct for conversion issues—are documented in the metadata to ensure transparency and comparability over time.\nStatistical concept(s): High technology products are defined according to SITC Rev.4 as the sum of the following products: Aerospace, Computers-office machines, Electronics-telecommunications, Pharmacy, Scientific instruments, Electrical machinery, Chemistry, Non-electrical machinery, Armament. The following product codes are used: Aerospace: (714 – 71489 -71499)+7921+7922+7924+7925+79291+79293+87411; Computers-office machines: 75194+75195+752+75997; Electronics-communication: 76331+7638+(764-76493-76499)+7722+77261+77318+77625+77627+7763+7764+7768+89844+89846; Pharmacy: 5413+5415+5416+5421+5422; Scientific instruments: 774+871+87211+(874-87411-8742)+88111+88121+88411+88419+(8996-89965-89969); Electrical machinery: (7786-77861-777866-77869)+7787+77884; Chemistry: 52222+52223+52229+52269+525+531+57433+591; Non-electrical machinery: 71489+71499+7187+72847+7311+73131+73135+73142+73144+73151+73153+73161+73163+73165+73312+73314+73316+7359+73733+73735; Armament: 891\nThe list can also be accessed on the Eurostat website. This list, based on the OECD definition, contains technical products of which the manufacturing involved a high intensity of R&D. The original high-tech products classification is based on SITC Rev. 3 and is taken from Table 4 of Annex 2 of the 1997 working paper of Thomas Hatzichronouglou, OECD. In September 2019 the definition in the World Development Indicators database was updated to SITC Rev.4 from SITC Rev. 3. The data are in current U.S. dollars and are sourced from the UN's Comtrade database.\n\nNote: The definition of high technology exports in WDI was modified as of October 2024. Specifically the list of SITC Rev.4 high technology product codes now includes product code 776 in its entirety and that all data in its sub-categories is incorporated. This means that products codes 776.11, 776.12, 776.21, 776.23, 776.29 have been added to the high-tech list of products, which does not comply with OECD’s 2008 definition of high-tech.\n\nThis change was implemented to address the problem of missing data which occurs during the data conversion process from HS2022 to SITC4. This happens if a country submits export data in the HS2022 coding system and is no longer in HS2017. Specifically, the HS2022-to-SITC4 conversion table (downloaded from COMTRADE) does not break down product code 776.4+ into sub-categories, and the export data of these sub-categories has been subsumed into 776. Since there is no break down for 776.4+, extracting data from the SITC4 dataset in the COMTRADE database will yield zero value for SITC4 codes 776.4, 776.42, 776.44, 776.46, and 776.49 as defined in the OECD high-tech definition. Hence, when calculating high-tech export values using the forward method of summing up data from these five SITC4 codes will result in missing data caused by the conversion process. This can result in a significant year-on-year decline in high-tech export values for affected countries." }, { "id": "Topic", "value": "Infrastructure: Technology" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "57" }, { "id": "TX.VAL.TRAN.ZS.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Transport services (% of commercial service exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Transport is the process of carriage of people and objects from one location to another as well as related supporting and auxiliary services. Also included are postal and courier services. This indicator is expressed as a percentage of service exports which are commercial services provided by residents to non-residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "TX.VAL.TRVL.ZS.WT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions." }, { "id": "IndicatorName", "value": "Travel services (% of commercial service exports)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Balance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments." }, { "id": "Longdefinition", "value": "Travel services cover goods and services for own use or to give away acquired from an economy by nonresidents during visits to that economy, or acquired from other economies by residents during visits to these other economies. This indicator is expressed as a percentage of service exports which are commercial services provided by residents to non-residents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1960-2024" }, { "id": "Source", "value": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service." }, { "id": "Topic", "value": "Private Sector & Trade: Exports" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "57" }, { "id": "VA.EST", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Voice and Accountability: Estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them. The WGI measures six dimensions of governance: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. \n\nVoice and Accountability captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. \n\nEstimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. \n\nFor each dimension of governance the following information is available in the database: estimate, percentile rank, lower bound of 90% confidence interval, upper bound of 90% confidence interval, standard error, number of sources." }, { "id": "Othernotes", "value": "The UCM assigns greater weight to data sources that tend to be more strongly correlated with each other. While this weighting improves the statistical precision of the aggregate indicators, it typically does not affect very much the ranking of countries on the aggregate indicators. The composite measures of governance generated by the UCM are in units of a standard normal distribution, with mean zero, standard deviation of one, and running from approximately -2.5 to 2.5, with higher values corresponding to better governance. The data is also reported in percentile rank terms, ranging from 0 (lowest rank) to 100 (highest rank).\nStatistical concept(s): The six aggregate indicators are reported in two ways: (1) in their standard normal units, ranging from approximately -2.5 to 2.5, and (2) in percentile rank terms from 0 to 100, with higher values corresponding to better outcomes.\n\nA key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). A key feature of the methodology is that it generates margins of error for each governance estimate. These margins of error need to be taken into account when making comparisons across countries and over time. \n\nEach of the six aggregate WGI measures are constructed by averaging together data from the underlying sources that correspond to the concept of governance being measured. This is done in the three steps:\n\nSTEP 1: Assigning data from individual sources to the six aggregate indicators. Individual questions from the underlying data sources are assigned to each of the six aggregate indicators. For example, a firm survey question on the regulatory environment would be assigned to Regulatory Quality, or a measure of press freedom would be assigned to Voice and Accountability. The individual variables used in the WGI and how they are assigned to the six aggregate indicators, can be found on the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators]. Note that not all of the data sources cover all countries, and so the aggregate governance scores are based on different sets of underlying data for different countries.\n\nSTEP 2: Rescaling of the individual source data to run from 0 to 1. The questions from the individual data sources are first rescaled to range from 0 to 1, with higher values corresponding to better outcomes. If, for example, a survey question asks for responses on a scale from a minimum of 1 to a maximum of 4, we rescale a score of 2 as (2-min)/(max-min)=(2-1)/3=0.33. When an individual data source provides more than one question relating to a particular dimension of governance, the rescaled scores are averaged together.\nThe 0-1 rescaled data from the individual sources are available interactively through the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators/interactive-data-access] and in the data files for each individual source [https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2]. Although nominally in the same 0-1 units, this rescaled data is not necessarily comparable across sources. For example, one data source might use a 0-10 scale but in practice most scores are clustered between 6 and 10, while another data source might also use a 0-10 scale but have responses spread out over the entire range. While the max-min rescaling above does not correct for this source of non-comparability, the procedure used to construct the aggregate indicators does (see below).\n\nSTEP 3: Using an Unobserved Components Model (UCM) to construct a weighted average of the individual indicators for each source. A statistical tool known as an Unobserved Components Model (UCM) is used to make the 0-1 rescaled data comparable across sources, and then to construct a weighted average of the data from each source for each country. The UCM assumes that the observed data from each source are a linear function of the unobserved level of governance, plus an error term. This linear function is different for different data sources, and so corrects for the remaining non-comparability of units of the rescaled data noted above. The resulting estimates of governance are a weighted average of the data from each source, with weights reflecting the pattern of correlation among data sources. The weights applied to the component indicators." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Units of a standard normal distribution (between -2.5 and 2.5, approximately)" } ], "source_id": "57" }, { "id": "VA.NO.SRC", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Voice and Accountability: Number of Sources" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data sources based on expert assessments have advantages and disadvantages relative to surveys. One advantage is that they lend themselves well to cross-country comparisons, as their methodologies are explicitly designed for this purpose. Expert assessments can also provide more granular technical assessments, for example on the quality of specific types of public institutions, that would be more difficult for a typical household or firm survey respondent to provide and informed view on. Expert assessments also are less likely to be affected by respondent reticence, a concern in household and firm surveys where respondents may be unwilling to give candid responses to sensitive questions about corruption or other dimensions of governance, particularly in countries where governance is weak. \n\nOn the other hand, a shortcoming of expert assessments is that they reflect the views of a narrower set of respondents than household or firm surveys. It also is possible that the ratings provided by one expert assessment to some extent reflect the views of other expert assessments, so that each assessment does not bring completely independent information on the underlying governance concept of interest. To guard against this, the WGI do not use expert assessments that are explicitly based on other existing data sources.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nNumber of sources indicates the number of underlying data sources on which the aggregate estimate is based.\n\nThe WGI are based on a large number of different data sources, capturing the views and experiences of survey respondents and experts in the public and private sectors, as well as various NGOs. These data sources include: (a) surveys of households and firms (e.g. Afrobarometer surveys, Gallup World Poll, and Global Competitiveness Report survey), (b) NGOs (e.g. Global Integrity, Freedom House, Reporters Without Borders), (c) commercial business information providers (e.g. Economist Intelligence Unit, S&P Global, Political Risk Services), and (d) public sector organizations (e.g. CPIA assessments of World Bank and regional development banks). \n\nVoice and Accountability captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. \n\nThe WGI compile and summarize information from over 30 existing data sources that report the views and experiences of citizens, entrepreneurs, and experts in the public, private and NGO sectors from around the world, on the quality of various aspects of governance.\n\n•\tThe data sources must provide subjective perceptions of relevant dimensions of governance, as the WGI are based exclusively on this type of data.\n•\tThe sources must provide original primary data produced using a well-defined methodology.\n•\tThe data sources must cover multiple countries, so that cross-country comparisons are possible.\n•\tThe data sources must be updated regularly, ideally every year, although some WGI data sources are updated once every two or three years.\n\nThe WGI draw on four different types of source data:\n\n•\tSurveys of households and firms, including the Afrobarometer surveys, Gallup World Poll, and Global Competitiveness Report survey,\n•\tCommercial business information providers, including the Economist Intelligence Unit, S&P Global, and Political Risk Services,\n•\tNon-governmental organizations, including Global Integrity, Freedom House, Reporters Without Borders, and\n•\tPublic sector organizations, including the Country Policy and Institutional Assessments (CPIA) assessments of World Bank and regional development banks.\n\nFor the detailed list of sources, please refer to: https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2 \n\nStatistical concept(s): Number of sources indicates the number of underlying data sources on which the aggregate estimate is based.\n\nVariables from the data sources are assigned to each of these six governance dimensions. For example, an assessment of the quality of the bureaucracy would be assigned to Government Effectiveness, a question about confidence in the police or the courts would be assigned to Rule of Law, and a question about the perceived likelihood of having to pay a bribe would be assigned to Control of Corruption. In some cases, a single data source will have multiple questions that can be assigned to the same dimension. In this case, the WGI use the average across all relevant questions from that data source. In addition each question from each data source is assigned to only one of the six governance dimensions, selecting the dimension that best matches the content of the question." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "57" }, { "id": "VA.PER.RNK", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Voice and Accountability: Percentile Rank" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nVoice and Accountability captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI." }, { "id": "Othernotes", "value": "The UCM assigns greater weight to data sources that tend to be more strongly correlated with each other. While this weighting improves the statistical precision of the aggregate indicators, it typically does not affect very much the ranking of countries on the aggregate indicators. The composite measures of governance generated by the UCM are in units of a standard normal distribution, with mean zero, standard deviation of one, and running from approximately -2.5 to 2.5, with higher values corresponding to better governance. \n\nThe data is also reported in percentile rank terms, ranging from 0 (lowest rank) to 100 (highest rank).\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). A key feature of the methodology is that it generates margins of error for each governance estimate. These margins of error need to be taken into account when making comparisons across countries and over time. \n\nEach of the six aggregate WGI measures are constructed by averaging together data from the underlying sources that correspond to the concept of governance being measured. This is done in the three steps:\n\nSTEP 1: Assigning data from individual sources to the six aggregate indicators. Individual questions from the underlying data sources are assigned to each of the six aggregate indicators. For example, a firm survey question on the regulatory environment would be assigned to Regulatory Quality, or a measure of press freedom would be assigned to Voice and Accountability. The individual variables used in the WGI and how they are assigned to the six aggregate indicators, can be found on the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators]. Note that not all of the data sources cover all countries, and so the aggregate governance scores are based on different sets of underlying data for different countries.\n\nSTEP 2: Rescaling of the individual source data to run from 0 to 1. The questions from the individual data sources are first rescaled to range from 0 to 1, with higher values corresponding to better outcomes. If, for example, a survey question asks for responses on a scale from a minimum of 1 to a maximum of 4, we rescale a score of 2 as (2-min)/(max-min)=(2-1)/3=0.33. When an individual data source provides more than one question relating to a particular dimension of governance, the rescaled scores are averaged together.\nThe 0-1 rescaled data from the individual sources are available interactively through the WGI website [https://www.worldbank.org/en/publication/worldwide-governance-indicators/interactive-data-access] and in the data files for each individual source [https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#2]. Although nominally in the same 0-1 units, this rescaled data is not necessarily comparable across sources. For example, one data source might use a 0-10 scale but in practice most scores are clustered between 6 and 10, while another data source might also use a 0-10 scale but have responses spread out over the entire range. While the max-min rescaling above does not correct for this source of non-comparability, the procedure used to construct the aggregate indicators does (see below).\n\nSTEP 3: Using an Unobserved Components Model (UCM) to construct a weighted average of the individual indicators for each source. A statistical tool known as an Unobserved Components Model (UCM) is used to make the 0-1 rescaled data comparable across sources, and then to construct a weighted average of the data from each source for each country. The UCM assumes that the observed data from each source are a linear function of the unobserved level of governance, plus an error term. This linear function is different for different data sources, and so corrects for the remaining non-comparability of units of the rescaled data noted above. The resulting estimates of governance are a weighted average of the data from each source, with weights reflecting the pattern of correlation among data sources. The weights applied to the component indicators." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile" } ], "source_id": "57" }, { "id": "VA.PER.RNK.LOWER", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Voice and Accountability: Percentile Rank, Lower Bound of 90% Confidence Interval" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nVoice and Accountability captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Lower refers to lower bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether.\n\nThe standard deviations are essential to the interpretation of the WGI. It often is more appropriate to think of the WGI methodology as identifying a statistically likely range of values for the unobserved “true” level of governance in a country. For example, the assumption of normality tells us that there is a 90 percent probability that the true unobserved level of governance conditional on the available data for a country is in a range given by plus or minus 1.64 standard deviations around the estimate of governance. These confidence intervals are informally referred to as the “margin of error” around the estimates of governance.\n\nThese 90 percent confidence interval are also reported in percentile rank terms (the percentile rank among all country estimates of governance, of the upper and lower bounds of the 90 percent confidence interval for each country).\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Lower refers to lower bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile" } ], "source_id": "57" }, { "id": "VA.PER.RNK.UPPER", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide" }, { "id": "IndicatorName", "value": "Voice and Accountability: Percentile Rank, Upper Bound of 90% Confidence Interval" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time. However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.\n\nThe WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nVoice and Accountability captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. \n\nPercentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Upper refers to upper bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A key feature of the WGI is that all country scores are accompanied by standard errors. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether.\n\nThe standard deviations are essential to the interpretation of the WGI. It often is more appropriate to think of the WGI methodology as identifying a statistically likely range of values for the unobserved “true” level of governance in a country. For example, the assumption of normality tells us that there is a 90 percent probability that the true unobserved level of governance conditional on the available data for a country is in a range given by plus or minus 1.64 standard deviations around the estimate of governance. These confidence intervals are informally referred to as the “margin of error” around the estimates of governance.\n\nThese 90 percent confidence interval are also reported in percentile rank terms (the percentile rank among all country estimates of governance, of the upper and lower bounds of the 90 percent confidence interval for each country).\nStatistical concept(s): Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. \n\nPercentile Rank Upper refers to upper bound of 90 percent confidence interval for governance, expressed in percentile rank terms." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Percentile Rank" } ], "source_id": "57" }, { "id": "VA.STD.ERR", "metatype": [ { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Good governance is essential for development. It helps countries improve economic growth, build human capital, and strengthen social cohesion. Empirical evidence shows a strong causal relationship between better governance and better development outcomes. The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide." }, { "id": "IndicatorName", "value": "Voice and Accountability: Standard Error" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data sources based on expert assessments have advantages and disadvantages relative to surveys. One advantage is that they lend themselves well to cross-country comparisons, as their methodologies are explicitly designed for this purpose. Expert assessments can also provide more granular technical assessments, for example on the quality of specific types of public institutions, that would be more difficult for a typical household or firm survey respondent to provide and informed view on. Expert assessments also are less likely to be affected by respondent reticence, a concern in household and firm surveys where respondents may be unwilling to give candid responses to sensitive questions about corruption or other dimensions of governance, particularly in countries where governance is weak. \n\nOn the other hand, a shortcoming of expert assessments is that they reflect the views of a narrower set of respondents than household or firm surveys. It also is possible that the ratings provided by one expert assessment to some extent reflect the views of other expert assessments, so that each assessment does not bring completely independent information on the underlying governance concept of interest. To guard against this, the WGI do not use expert assessments that are explicitly based on other existing data sources.\n\nThe WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources." }, { "id": "Longdefinition", "value": "The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.\n\nStandard error indicates the precision of the estimate of governance. Larger values of the standard error indicate less precise estimates. \n\nA 90 percent confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error.\n\nVoice and Accountability captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media." }, { "id": "Referenceperiod", "value": "1996-2023" }, { "id": "Source", "value": "Worldwide Governance Indicators, World Bank (WB), uri: www.govindicators.org, note: The Worldwide Governance Indicators (WGI) are a product of the staff of the World Bank with external contributions." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). The six aggregate indicators are reported in two ways : (1) in their standard normal units, ranging from approximately -2.5 to 2.5, and (2) in percentile rank terms from 0 to 100, with higher values corresponding to better outcomes.\n\nA key feature of the WGI is that all country scores are accompanied by standard errors. These margins of error need to be taken into account when making comparisons across countries and over time. These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors). These standard errors reflect the reality that governance is difficult to measure using any kind of data. In most measures of governance or the investment climate they are however left implicit or ignored altogether. \n\nPlease see more information at: https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#1\nStatistical concept(s): Standard error indicates the precision of the estimate of governance. Larger values of the standard error indicate less precise estimates. A 90 percent confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error." }, { "id": "Topic", "value": "Public Sector: Policy & institutions" }, { "id": "Unitofmeasure", "value": "Standard error" } ], "source_id": "57" }, { "id": "VC.BTL.DETH", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Monitoring battle-related deaths is critical for understanding the human cost of conflict and its implications for development. High levels of conflict-related mortality often coincide with displacement, destruction of infrastructure, and disruption of economic activity, which can reverse progress on poverty reduction and human development. Reliable data on conflict intensity inform policy responses aimed at peacebuilding, humanitarian assistance, and post-conflict recovery. They also support global efforts to track progress toward peace and security goals, by providing evidence for interventions that reduce violence and promote stability." }, { "id": "IndicatorName", "value": "Battle-related deaths (number of people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "While the best estimate for battle-related deaths is considered a conservative minimum, the high estimate may still undercount actual fatalities due to unreported events, and users are cautioned that the apparent precision of the numbers does not eliminate underlying uncertainty." }, { "id": "Longdefinition", "value": "Battle-related deaths are deaths in battle-related conflicts between warring parties in the conflict dyad (two conflict units that are parties to a conflict). Battle-related deaths refer to those deaths caused by the warring parties that can be directly related to combat. This includes battlefield fighting, guerrilla activities (e.g. hit and-run attacks/ambushes) and all kinds of bombardments of military bases, cities and villages etc. The target for the attacks is either the military forces or representatives for the parties, though there is often substantial collateral damage in the form of civilians being killed in the crossfire, indiscriminate bombings, etc. All fatalities, military as well as civilian, incurred in such situations are counted as battle-related deaths." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1989-2024" }, { "id": "Source", "value": "UCDP Battle-related Deaths Dataset , Uppsala Conflict Data Program (UCDP), uri: https://ucdp.uu.se/downloads/, publisher: Uppsala University" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The dataset is constructed by automatically filtering and aggregating the UCDP Georeferenced Event Dataset from the incident level to the conflict/dyad-year level, and then joining this with additional conflict-year data from related UCDP datasets. Source material is gathered in a two-pass system: first, global newswire reporting is reviewed, followed by targeted searches in local and specialized sources such as local media, NGO and IGO reports, field reports, and social media. This approach ensures comprehensive coverage and cross-verification of events. Detailed procedures, including search strategies, are further described in the UCDP GED Codebook.\n\n\nStatistical concept(s): The UCDP provides three estimates for battle-related deaths (best, low, and high) to account for the uncertainty inherent in conflict reporting. The \"best estimate\" (which is the value available in the World Development Indicators database) aggregates the most reliable numbers for all incidents each year, favoring the lower figure when sources are equally credible. The \"low estimate\" sums the lowest plausible figures, while the \"high estimate\" aggregates the highest plausible numbers, including incidents with uncertain party involvement. All estimates are based on publicly accessible sources and are subject to revision as new information emerges." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" }, { "id": "Unitofmeasure", "value": "Count" } ], "source_id": "57" }, { "id": "VC.IDP.NWCV", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although all persons affected by conflict and/or human rights violations suffer, displacement from one's place of residence may make the internally displaced particularly vulnerable. Following are some of the factors that are likely to increase the need for protection:\n \n1) Internally displaced persons may be in transit from one place to another, may be in hiding, may be forced toward unhealthy or inhospitable environments, or face other circumstances that make them especially vulnerable.\n \n2) The social organization of displaced communities may have been destroyed or damaged by the act of physical displacement; family groups may be separated or disrupted; women may be forced to assume non-traditional roles or face particular vulnerabilities. Internally displaced populations, and especially groups like children, the elderly, or pregnant women, may experience profound psychosocial distress related to displacement.\n \n3) Removal from sources of income and livelihood may add to physical and psychosocial vulnerability for displaced people.\n \n4) Schooling for children and adolescents may be disrupted.\n \n5) Internal displacement to areas where local inhabitants are of different groups or inhospitable may increase risk to internally displaced communities; internally displaced persons may face language barriers during displacement.\n \n6) The condition of internal displacement may raise the suspicions of or lead to abuse by armed combatants, or other parties to conflict.\n \n7) Internally displaced persons may lack identity documents essential to receiving benefits or legal recognition; in some cases, fearing persecution, displaced persons have sometimes got rid of such documents.\n \n8) According to the Internal Displacement Monitoring Centre (IDMC) tens of millions people around the world are displaced every year within their countries by conflict, human rights violations, natural disasters and climate change. Unlike refugees who cross national borders and benefit from an established system of international protection and assistance, those forcibly uprooted within their own countries, by armed conflict, large-scale development projects, systematic violations of human rights, or natural disasters, lack predictable structures of support. Internal displacement has become one of the more pressing humanitarian, human rights and security problems confronting affected countries and the international community at large.\n \nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights." }, { "id": "IndicatorName", "value": "Internally displaced persons, new displacement associated with conflict and violence (number of cases)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Please note that most of the figures are estimates. The definition highlights two issues:\n\n1) The coercive or otherwise involuntary character of movement. The definition mentions some of the most common causes of involuntary movements, such as armed conflict, violence, human rights violations and disasters. These causes have in common that they give no choice to people but to leave their homes and deprive them of the most essential protection mechanisms, such as community networks, access to services, livelihoods. Displacement severely affects the physical, socio-economic and legal safety of people and should be systematically regarded as an indicator of potential vulnerability.\n \n2) The fact that such movement takes place within national borders. Unlike refugees, who have been deprived of the protection of their state of origin, IDPs remain legally under the protection of national authorities of their country of habitual residence. IDPs should therefore enjoy the same rights as the rest of the population. The Guiding Principles on Internal Displacement remind national authorities and other relevant actors of their responsibility to ensure that IDPs' rights are respected and fulfilled, despite the vulnerability generated by their displacement." }, { "id": "Longdefinition", "value": "Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. \"New Displacement\" refers to the number of new cases or incidents of displacement recorded over the specified year, rather than the number of people displaced. This is done because people may have been displaced more than once." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2009-2023" }, { "id": "Source", "value": "Internal Displacement Monitoring Centre (IDMC), uri: http://www.internal-displacement.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Internally displaced persons are \"persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized state border.\" Internally displaced people are often confused with refugees. Unlike refugees, internally displaced people remain under the protection of their own government, even if their reason for fleeing was similar to that of refugees. Refugees are people who have crossed an international border to find sanctuary and have been granted refugee or refugee-like status or temporary protection. For more information on methodology, please refer to the information published by IDMC: http://www.internal-displacement.org/database/" }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "57" }, { "id": "VC.IDP.NWDS", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "Although all persons affected by conflict and/or human rights violations suffer, displacement from one's place of residence may make the internally displaced particularly vulnerable. Following are some of the factors that are likely to increase the need for protection:\n \n1) Internally displaced persons may be in transit from one place to another, may be in hiding, may be forced toward unhealthy or inhospitable environments, or face other circumstances that make them especially vulnerable.\n \n2) The social organization of displaced communities may have been destroyed or damaged by the act of physical displacement; family groups may be separated or disrupted; women may be forced to assume non-traditional roles or face particular vulnerabilities. Internally displaced populations, and especially groups like children, the elderly, or pregnant women, may experience profound psychosocial distress related to displacement.\n \n3) Removal from sources of income and livelihood may add to physical and psychosocial vulnerability for displaced people.\n \n4) Schooling for children and adolescents may be disrupted.\n \n5) Internal displacement to areas where local inhabitants are of different groups or inhospitable may increase risk to internally displaced communities; internally displaced persons may face language barriers during displacement.\n \n6) The condition of internal displacement may raise the suspicions of or lead to abuse by armed combatants, or other parties to conflict.\n \n7) Internally displaced persons may lack identity documents essential to receiving benefits or legal recognition; in some cases, fearing persecution, displaced persons have sometimes got rid of such documents.\n \n8) According to the Internal Displacement Monitoring Centre (IDMC) tens of millions people around the world are displaced every year within their countries by conflict, human rights violations, natural disasters and climate change. Unlike refugees who cross national borders and benefit from an established system of international protection and assistance, those forcibly uprooted within their own countries, by armed conflict, large-scale development projects, systematic violations of human rights, or natural disasters, lack predictable structures of support. Internal displacement has become one of the more pressing humanitarian, human rights and security problems confronting affected countries and the international community at large.\n \nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights." }, { "id": "IndicatorName", "value": "Internally displaced persons, new displacement associated with disasters (number of cases)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Please note that most of the figures are estimates. The definition highlights two issues:\n\n1) The coercive or otherwise involuntary character of movement. The definition mentions some of the most common causes of involuntary movements, such as armed conflict, violence, human rights violations and disasters. These causes have in common that they give no choice to people but to leave their homes and deprive them of the most essential protection mechanisms, such as community networks, access to services, livelihoods. Displacement severely affects the physical, socio-economic and legal safety of people and should be systematically regarded as an indicator of potential vulnerability.\n \n2) The fact that such movement takes place within national borders. Unlike refugees, who have been deprived of the protection of their state of origin, IDPs remain legally under the protection of national authorities of their country of habitual residence. IDPs should therefore enjoy the same rights as the rest of the population. The Guiding Principles on Internal Displacement remind national authorities and other relevant actors of their responsibility to ensure that IDPs' rights are respected and fulfilled, despite the vulnerability generated by their displacement." }, { "id": "Longdefinition", "value": "Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. \"New Displacement\" refers to the number of new cases or incidents of displacement recorded over the specified year, rather than the number of people displaced. This is done because people may have been displaced more than once." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2008-2023" }, { "id": "Source", "value": "Internal Displacement Monitoring Centre (IDMC), uri: http://www.internal-displacement.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Internally displaced persons are \"persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized state border.\" Internally displaced people are often confused with refugees. Unlike refugees, internally displaced people remain under the protection of their own government, even if their reason for fleeing was similar to that of refugees. Refugees are people who have crossed an international border to find sanctuary and have been granted refugee or refugee-like status or temporary protection." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "57" }, { "id": "VC.IDP.TOCV", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Although all persons affected by conflict and/or human rights violations suffer, displacement from one's place of residence may make the internally displaced particularly vulnerable. Following are some of the factors that are likely to increase the need for protection:\n \n1) Internally displaced persons may be in transit from one place to another, may be in hiding, may be forced toward unhealthy or inhospitable environments, or face other circumstances that make them especially vulnerable.\n \n2) The social organization of displaced communities may have been destroyed or damaged by the act of physical displacement; family groups may be separated or disrupted; women may be forced to assume non-traditional roles or face particular vulnerabilities. Internally displaced populations, and especially groups like children, the elderly, or pregnant women, may experience profound psychosocial distress related to displacement.\n \n3) Removal from sources of income and livelihood may add to physical and psychosocial vulnerability for displaced people.\n \n4) Schooling for children and adolescents may be disrupted.\n \n5) Internal displacement to areas where local inhabitants are of different groups or inhospitable may increase risk to internally displaced communities; internally displaced persons may face language barriers during displacement.\n \n6) The condition of internal displacement may raise the suspicions of or lead to abuse by armed combatants, or other parties to conflict.\n \n7) Internally displaced persons may lack identity documents essential to receiving benefits or legal recognition; in some cases, fearing persecution, displaced persons have sometimes got rid of such documents.\n \n8) According to the Internal Displacement Monitoring Centre (IDMC) tens of millions people around the world are displaced every year within their countries by conflict, human rights violations, natural disasters and climate change. Unlike refugees who cross national borders and benefit from an established system of international protection and assistance, those forcibly uprooted within their own countries, by armed conflict, large-scale development projects, systematic violations of human rights, or natural disasters, lack predictable structures of support. Internal displacement has become one of the more pressing humanitarian, human rights and security problems confronting affected countries and the international community at large.\n \nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights." }, { "id": "IndicatorName", "value": "Internally displaced persons, total displaced by conflict and violence (number of people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Please note that most of the figures are estimates. The definition highlights two issues:\n\n1) The coercive or otherwise involuntary character of movement. The definition mentions some of the most common causes of involuntary movements, such as armed conflict, violence, human rights violations and disasters. These causes have in common that they give no choice to people but to leave their homes and deprive them of the most essential protection mechanisms, such as community networks, access to services, livelihoods. Displacement severely affects the physical, socio-economic and legal safety of people and should be systematically regarded as an indicator of potential vulnerability.\n \n2) The fact that such movement takes place within national borders. Unlike refugees, who have been deprived of the protection of their state of origin, IDPs remain legally under the protection of national authorities of their country of habitual residence. IDPs should therefore enjoy the same rights as the rest of the population. The Guiding Principles on Internal Displacement remind national authorities and other relevant actors of their responsibility to ensure that IDPs' rights are respected and fulfilled, despite the vulnerability generated by their displacement." }, { "id": "Longdefinition", "value": "Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. “People displaced” refers to the number of people living in displacement as of the end of each year, and reflects the stock of people displaced at the end of the previous year, plus inflows of new cases arriving over the year as well as births over the year to those displaced, minus outflows which may include returnees, those who settled elsewhere, those who integrated locally, those who travelled over borders, and deaths." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The Internal Displacement Monitoring Centre (http://www.internal-displacement.org/)" }, { "id": "Statisticalconceptandmethodology", "value": "Internally displaced persons are \"persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized state border.\" Internally displaced people are often confused with refugees. Unlike refugees, internally displaced people remain under the protection of their own government, even if their reason for fleeing was similar to that of refugees. Refugees are people who have crossed an international border to find sanctuary and have been granted refugee or refugee-like status or temporary protection. “People displaced” refers to the number of people living in displacement as of the end of each year, and reflects the stock of people displaced at the end of the previous year, plus inflows of new cases arriving over the year as well as births over the year to those displaced, minus outflows which may include returnees, those who settled elsewhere, those who integrated locally, those who travelled over borders, and deaths." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "57" }, { "id": "VC.IDP.TOTL.HE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Although all persons affected by conflict and/or human rights violations suffer, displacement from one's place of residence may make the internally displaced particularly vulnerable. Following are some of the factors that are likely to increase the need for protection:\n\n1 Internally displaced persons may be in transit from one place to another, may be in hiding, may be forced toward unhealthy or inhospitable environments, or face other circumstances that make them especially vulnerable.\n\n2 The social organization of displaced communities may have been destroyed or damaged by the act of physical displacement; family groups may be separated or disrupted; women may be forced to assume non-traditional roles or face particular vulnerabilities. Internally displaced populations, and especially groups like children, the elderly, or pregnant women, may experience profound psychosocial distress related to displacement.\n\n3 Removal from sources of income and livelihood may add to physical and psychosocial vulnerability for displaced people.\n\n4 Schooling for children and adolescents may be disrupted.\n\n5 Internal displacement to areas where local inhabitants are of different groups or inhospitable may increase risk to internally displaced communities; internally displaced persons may face language barriers during displacement.\n\n6 The condition of internal displacement may raise the suspicions of or lead to abuse by armed combatants, or other parties to conflict.\n\n7 Internally displaced persons may lack identity documents essential to receiving benefits or legal recognition; in some cases, fearing persecution, displaced persons have sometimes got rid of such documents.\n\n8 According to the Brookings-LSE Project on Internal Displacement, more than 27 million people around the world are currently displaced within their countries by conflict, human rights violations, natural disasters and climate change. Unlike refugees who cross national borders and benefit from an established system of international protection and assistance, those forcibly uprooted within their own countries, by armed conflict, large-scale development projects, systematic violations of human rights, or natural disasters, lack predictable structures of support. Internal displacement has become one of the more pressing humanitarian, human rights and security problems confronting affected countries and the international community at large.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights." }, { "id": "IndicatorName", "value": "Internally displaced persons (number, high estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Please note that most of the figures are estimates. The definition highlights two issues:\n\n1) The coercive or otherwise involuntary character of movement. The definition mentions some of the most common causes of involuntary movements, such as armed conflict, violence, human rights violations and disasters. These causes have in common that they give no choice to people but to leave their homes and deprive them of the most essential protection mechanisms, such as community networks, access to services, livelihoods. Displacement severely affects the physical, socio-economic and legal safety of people and should be systematically regarded as an indicator of potential vulnerability.\n \n2) The fact that such movement takes place within national borders. Unlike refugees, who have been deprived of the protection of their state of origin, IDPs remain legally under the protection of national authorities of their country of habitual residence. IDPs should therefore enjoy the same rights as the rest of the population. The Guiding Principles on Internal Displacement remind national authorities and other relevant actors of their responsibility to ensure that IDPs' rights are respected and fulfilled, despite the vulnerability generated by their displacement." }, { "id": "Longdefinition", "value": "Internally displaced persons are people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Internal Displacement Monitoring Centre." }, { "id": "Statisticalconceptandmethodology", "value": "Internally displaced persons are \"persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized state border.\"\n\nInternally displaced people are often confused with refugees. Unlike refugees, internally displaced people remain under the protection of their own government, even if their reason for fleeing was similar to that of refugees. Refugees are people who have crossed an international border to find sanctuary and have been granted refugee or refugee-like status or temporary protection." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "57" }, { "id": "VC.IDP.TOTL.LE", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Although all persons affected by conflict and/or human rights violations suffer, displacement from one's place of residence may make the internally displaced particularly vulnerable. Following are some of the factors that are likely to increase the need for protection:\n\n1 Internally displaced persons may be in transit from one place to another, may be in hiding, may be forced toward unhealthy or inhospitable environments, or face other circumstances that make them especially vulnerable.\n\n2 The social organization of displaced communities may have been destroyed or damaged by the act of physical displacement; family groups may be separated or disrupted; women may be forced to assume non-traditional roles or face particular vulnerabilities. Internally displaced populations, and especially groups like children, the elderly, or pregnant women, may experience profound psychosocial distress related to displacement.\n\n3 Removal from sources of income and livelihood may add to physical and psychosocial vulnerability for displaced people.\n\n4 Schooling for children and adolescents may be disrupted.\n\n5 Internal displacement to areas where local inhabitants are of different groups or inhospitable may increase risk to internally displaced communities; internally displaced persons may face language barriers during displacement.\n\n6 The condition of internal displacement may raise the suspicions of or lead to abuse by armed combatants, or other parties to conflict.\n\n7 Internally displaced persons may lack identity documents essential to receiving benefits or legal recognition; in some cases, fearing persecution, displaced persons have sometimes got rid of such documents.\n\n8 According to the Brookings-LSE Project on Internal Displacement, more than 27 million people around the world are currently displaced within their countries by conflict, human rights violations, natural disasters and climate change. Unlike refugees who cross national borders and benefit from an established system of international protection and assistance, those forcibly uprooted within their own countries, by armed conflict, large-scale development projects, systematic violations of human rights, or natural disasters, lack predictable structures of support. Internal displacement has become one of the more pressing humanitarian, human rights and security problems confronting affected countries and the international community at large.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights." }, { "id": "IndicatorName", "value": "Internally displaced persons (number, low estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Please note that most of the figures are estimates. The definition highlights two issues:\n\n1) The coercive or otherwise involuntary character of movement. The definition mentions some of the most common causes of involuntary movements, such as armed conflict, violence, human rights violations and disasters. These causes have in common that they give no choice to people but to leave their homes and deprive them of the most essential protection mechanisms, such as community networks, access to services, livelihoods. Displacement severely affects the physical, socio-economic and legal safety of people and should be systematically regarded as an indicator of potential vulnerability.\n \n2) The fact that such movement takes place within national borders. Unlike refugees, who have been deprived of the protection of their state of origin, IDPs remain legally under the protection of national authorities of their country of habitual residence. IDPs should therefore enjoy the same rights as the rest of the population. The Guiding Principles on Internal Displacement remind national authorities and other relevant actors of their responsibility to ensure that IDPs' rights are respected and fulfilled, despite the vulnerability generated by their displacement." }, { "id": "Longdefinition", "value": "Internally displaced persons are people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Internal Displacement Monitoring Centre." }, { "id": "Statisticalconceptandmethodology", "value": "Internally displaced persons are \"persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized state border.\"\n\nInternally displaced people are often confused with refugees. Unlike refugees, internally displaced people remain under the protection of their own government, even if their reason for fleeing was similar to that of refugees. Refugees are people who have crossed an international border to find sanctuary and have been granted refugee or refugee-like status or temporary protection." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "57" }, { "id": "VC.IHR.ICTS.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Intentional homicides, UN Crime Trends Survey (CTS) source (per 100,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UN Office on Drugs and Crime's International Homicide Statistics database." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "57" }, { "id": "VC.IHR.IPBH.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Intentional homicides, international public health sources (per 100,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UN Office on Drugs and Crime's International Homicide Statistics database." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "57" }, { "id": "VC.IHR.IPOL.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Intentional homicides, international police sources (per 100,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UN Office on Drugs and Crime's International Homicide Statistics database." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "57" }, { "id": "VC.IHR.NPOL.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Intentional homicides, government police sources (per 100,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UN Office on Drugs and Crime's International Homicide Statistics database." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "57" }, { "id": "VC.IHR.PSRC.FE.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Aggregate values are computed by UNODC. For additional information, please see the UNODC website: https://dataunodc.un.org/sites/dataunodc.un.org/files/metadata_intentional_homicide.pdf" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In some regions, organized crime, drug trafficking and the violent cultures of youth gangs are predominantly responsible for the high levels of homicide. There has been a sharp increase in homicides in some countries, particularly in Central America, are making the activities of organized crime and drug trafficking more visible. Greater use of firearms is often associated with the illicit activities of organized criminal groups, which are often linked to drug trafficking.\n\nKnowledge of the patterns and causes of violent crime are crucial to forming preventive strategies. Young males are the group most affected by violent crime in all regions, particularly in the Americas. Yet women of all ages are the victims of intimate partner and family-related violence in all regions and countries. Indeed, in many of them, it is within the home where a woman is most likely to be killed.\n\nData on intentional homicides are from the United Nations Office on Drugs and Crime (UNODC), which uses a variety of national and international sources on homicides - primarily criminal justice sources as well as public health data from the World Health Organization (WHO) and the Pan American Health Organization - and the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems to present accurate and comparable statistics. The UNODC defines homicide as \"unlawful death purposefully inflicted on a person by another person.\" This definition excludes deaths arising from armed conflict." }, { "id": "IndicatorName", "value": "Intentional homicides, female (per 100,000 female)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Statistics reported to the United Nations in the context of its various surveys on crime levels and criminal justice trends are incidents of victimization that have been reported to the authorities in any given country. That means that this data is subject to the problems of accuracy of all official crime data. The survey results provide an overview of trends and interrelationships between various parts of the criminal justice system to promote informed decision-making in administration, nationally and internationally.\n\nThe degree to which different societies apportion the level of culpability to acts resulting in death is also subject to variation. Consequently, the comparison between countries and regions of \"intentional homicide\", or unlawful death purposefully inflicted on a person by another person, is also a comparison of the extent to which different countries deem that a killing be classified as such, as well as the capacity of their legal systems to record it. Caution should therefore be applied when evaluating and comparing homicide data." }, { "id": "Longdefinition", "value": "An intentional homicide is defined as an unlawful death inflicted upon a person with the intent to cause death or serious injury." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UNODC Research - Data Portal – Intentional Homicide, UN Office on Drugs and Crime (UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data are sourced by UNODC from either criminal justice or public health systems. In the former, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event, whereas in the latter, data are produced by health authorities certifying the cause of death of an individual. \n\nThese data are collected from national authorities with the annual United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS). National focal points working in national agencies responsible for statistics on crime and the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the UN-CTS to UNODC. Following the submission, UNODC checks for consistency and coherence with other data sources. Member States which are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union are sending their response to the UN-CTS to Eurostat for validation. \n\nData submitted by Member States through other means or taken from other sources are added to the dataset after review by Member States. \n\nThe population data is sourced from the World Population Prospect, Population Division, United Nations Department of Economic and Social Affairs. \nStatistical concept(s): The International Classification of Crime for Statistical Purposes (ICCS) is the source of the definition of intentional homicide. The definitions of the disaggregation of victims of intentional homicide included in these tables (by situational context, by relationship to perpetrator and by mechanisms) are also from the ICCS. \n\nThe ICCS includes more information on what is included and excluded in these offences. Intentional homicide (ICCS 0101): Unlawful death inflicted upon a person with the intent to cause death or serious injury. \n\nThe statistical definition contains three elements that characterize the killing of a person as “intentional homicide”: \n1. The killing of a person by another person (objective element) \n2. The intent of the perpetrator to kill or seriously injure the victim (subjective element) \n3. The unlawfulness of the killing (legal element) \n\nFor recording purposes, all killings that meet the criteria listed above are to be considered intentional homicides, irrespective of definitions provided by national legislations or practices. Killings as a result of terrorist activities are also to be classified as a form of intentional homicide." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" }, { "id": "Unitofmeasure", "value": "Rate per 100,000 population" } ], "source_id": "57" }, { "id": "VC.IHR.PSRC.MA.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Aggregate values are computed by UNODC. For additional information, please see the UNODC website: https://dataunodc.un.org/sites/dataunodc.un.org/files/metadata_intentional_homicide.pdf" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In some regions, organized crime, drug trafficking and the violent cultures of youth gangs are predominantly responsible for the high levels of homicide. There has been a sharp increase in homicides in some countries, particularly in Central America, are making the activities of organized crime and drug trafficking more visible. Greater use of firearms is often associated with the illicit activities of organized criminal groups, which are often linked to drug trafficking.\n\nKnowledge of the patterns and causes of violent crime are crucial to forming preventive strategies. Young males are the group most affected by violent crime in all regions, particularly in the Americas. Yet women of all ages are the victims of intimate partner and family-related violence in all regions and countries. Indeed, in many of them, it is within the home where a woman is most likely to be killed.\n\nData on intentional homicides are from the United Nations Office on Drugs and Crime (UNODC), which uses a variety of national and international sources on homicides - primarily criminal justice sources as well as public health data from the World Health Organization (WHO) and the Pan American Health Organization - and the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems to present accurate and comparable statistics. The UNODC defines homicide as \"unlawful death purposefully inflicted on a person by another person.\" This definition excludes deaths arising from armed conflict." }, { "id": "IndicatorName", "value": "Intentional homicides, male (per 100,000 male)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Statistics reported to the United Nations in the context of its various surveys on crime levels and criminal justice trends are incidents of victimization that have been reported to the authorities in any given country. That means that this data is subject to the problems of accuracy of all official crime data. The survey results provide an overview of trends and interrelationships between various parts of the criminal justice system to promote informed decision-making in administration, nationally and internationally.\n\nThe degree to which different societies apportion the level of culpability to acts resulting in death is also subject to variation. Consequently, the comparison between countries and regions of \"intentional homicide\", or unlawful death purposefully inflicted on a person by another person, is also a comparison of the extent to which different countries deem that a killing be classified as such, as well as the capacity of their legal systems to record it. Caution should therefore be applied when evaluating and comparing homicide data." }, { "id": "Longdefinition", "value": "An intentional homicide is defined as an unlawful death inflicted upon a person with the intent to cause death or serious injury." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UNODC Research - Data Portal – Intentional Homicide, UN Office on Drugs and Crime (UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data are sourced by UNODC from either criminal justice or public health systems. In the former, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event, whereas in the latter, data are produced by health authorities certifying the cause of death of an individual. \n\nThese data are collected from national authorities with the annual United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS). National focal points working in national agencies responsible for statistics on crime and the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the UN-CTS to UNODC. Following the submission, UNODC checks for consistency and coherence with other data sources. Member States which are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union are sending their response to the UN-CTS to Eurostat for validation. \n\nData submitted by Member States through other means or taken from other sources are added to the dataset after review by Member States. \n\nThe population data is sourced from the World Population Prospect, Population Division, United Nations Department of Economic and Social Affairs. \nStatistical concept(s): The International Classification of Crime for Statistical Purposes (ICCS) is the source of the definition of intentional homicide. The definitions of the disaggregation of victims of intentional homicide included in these tables (by situational context, by relationship to perpetrator and by mechanisms) are also from the ICCS. \n\nThe ICCS includes more information on what is included and excluded in these offences. Intentional homicide (ICCS 0101): Unlawful death inflicted upon a person with the intent to cause death or serious injury. \n\nThe statistical definition contains three elements that characterize the killing of a person as “intentional homicide”: \n1. The killing of a person by another person (objective element) \n2. The intent of the perpetrator to kill or seriously injure the victim (subjective element) \n3. The unlawfulness of the killing (legal element) \n\nFor recording purposes, all killings that meet the criteria listed above are to be considered intentional homicides, irrespective of definitions provided by national legislations or practices. Killings as a result of terrorist activities are also to be classified as a form of intentional homicide." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" }, { "id": "Unitofmeasure", "value": "Rate per 100,000 population" } ], "source_id": "57" }, { "id": "VC.IHR.PSRC.P5", "metatype": [ { "id": "Aggregationmethod", "value": "Aggregate values are computed by UNODC. For additional information, please see the UNODC website: https://dataunodc.un.org/sites/dataunodc.un.org/files/metadata_intentional_homicide.pdf" }, { "id": "Dataset", "value": "WB_WDI" }, { "id": "Developmentrelevance", "value": "In some regions, organized crime, drug trafficking and the violent cultures of youth gangs are predominantly responsible for the high levels of homicide. There has been a sharp increase in homicides in some countries, particularly in Central America, are making the activities of organized crime and drug trafficking more visible. Greater use of firearms is often associated with the illicit activities of organized criminal groups, which are often linked to drug trafficking.\n\nKnowledge of the patterns and causes of violent crime are crucial to forming preventive strategies. Young males are the group most affected by violent crime in all regions, particularly in the Americas. Yet women of all ages are the victims of intimate partner and family-related violence in all regions and countries. Indeed, in many of them, it is within the home where a woman is most likely to be killed.\n\nData on intentional homicides are from the United Nations Office on Drugs and Crime (UNODC), which uses a variety of national and international sources on homicides - primarily criminal justice sources as well as public health data from the World Health Organization (WHO) and the Pan American Health Organization - and the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems to present accurate and comparable statistics. The UNODC defines homicide as \"unlawful death purposefully inflicted on a person by another person.\" This definition excludes deaths arising from armed conflict." }, { "id": "IndicatorName", "value": "Intentional homicides (per 100,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Statistics reported to the United Nations in the context of its various surveys on crime levels and criminal justice trends are incidents of victimization that have been reported to the authorities in any given country. That means that this data is subject to the problems of accuracy of all official crime data. The survey results provide an overview of trends and interrelationships between various parts of the criminal justice system to promote informed decision-making in administration, nationally and internationally.\n\nThe degree to which different societies apportion the level of culpability to acts resulting in death is also subject to variation. Consequently, the comparison between countries and regions of \"intentional homicide\", or unlawful death purposefully inflicted on a person by another person, is also a comparison of the extent to which different countries deem that a killing be classified as such, as well as the capacity of their legal systems to record it. Caution should therefore be applied when evaluating and comparing homicide data." }, { "id": "Longdefinition", "value": "An intentional homicide is defined as an unlawful death inflicted upon a person with the intent to cause death or serious injury." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1990-2023" }, { "id": "Source", "value": "UNODC Research - Data Portal – Intentional Homicide, UN Office on Drugs and Crime (UNODC)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data are sourced by UNODC from either criminal justice or public health systems. In the former, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event, whereas in the latter, data are produced by health authorities certifying the cause of death of an individual. \n\nThese data are collected from national authorities with the annual United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS). National focal points working in national agencies responsible for statistics on crime and the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the UN-CTS to UNODC. Following the submission, UNODC checks for consistency and coherence with other data sources. Member States which are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union are sending their response to the UN-CTS to Eurostat for validation. \n\nData submitted by Member States through other means or taken from other sources are added to the dataset after review by Member States. \n\nThe population data is sourced from the World Population Prospect, Population Division, United Nations Department of Economic and Social Affairs. \nStatistical concept(s): The International Classification of Crime for Statistical Purposes (ICCS) is the source of the definition of intentional homicide. The definitions of the disaggregation of victims of intentional homicide included in these tables (by situational context, by relationship to perpetrator and by mechanisms) are also from the ICCS. \n\nThe ICCS includes more information on what is included and excluded in these offences. Intentional homicide (ICCS 0101): Unlawful death inflicted upon a person with the intent to cause death or serious injury. \n\nThe statistical definition contains three elements that characterize the killing of a person as “intentional homicide”: \n1. The killing of a person by another person (objective element) \n2. The intent of the perpetrator to kill or seriously injure the victim (subjective element) \n3. The unlawfulness of the killing (legal element) \n\nFor recording purposes, all killings that meet the criteria listed above are to be considered intentional homicides, irrespective of definitions provided by national legislations or practices. Killings as a result of terrorist activities are also to be classified as a form of intentional homicide." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" }, { "id": "Unitofmeasure", "value": "Rate per 100,000 population" } ], "source_id": "57" }, { "id": "VC.PKP.TOTL.UN", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "According to the Geneva Declaration on Armed Violence and Development, more than 526,000 people die each year because of the violence associated with armed conflict and large- and small-scale criminality. Recovery and rebuilding can take years, and the challenges are numerous: infrastructure to be rebuilt, persistently high crime, widespread health problems, education systems in disrepair, and unexploded ordnance to be cleared.\n\nPeacebuilding reduces the risk of lapsing or relapsing into conflict by strengthening national capacities at all levels of for conflict management, and to lay the foundation for sustainable peace and development. Peacekeepers provide essential security to preserve the peace, however fragile, where fighting has been halted, and to assist in implementing agreements achieved by the peacemakers. Peacekeepers deploy to war-torn regions where no one else is willing or able to go and prevent conflict from returning or escalating.\n\nMost countries emerging from conflict lack the capacity to rebuild the economy. Thus, capacity building is one of the first tasks for restoring growth and is linked to building peace and creating the conditions that lead to sustained poverty reduction. UN Peacekeepers serve in some of the most difficult and dangerous situations around the globe. United Nations Peacekeeping force, comprised of civilian, police and military personnel, helps countries torn by conflict create the conditions for lasting peace. In addition to maintaining peace and security, peacekeepers are increasingly charged with assisting in political processes; reforming judicial systems; training law enforcement and police forces; disarming and reintegrating former combatants; supporting the return of internally displaced persons and refugees.\n\nThe World Bank and other international development agencies can help, but countries with fragile situations have to build their own institutions tailored to their own needs. Peacekeeping operations in post-conflict situations have been effective in reducing the risks of reversion to conflict." }, { "id": "IndicatorName", "value": "Presence of peace keepers (number of troops, police, and military observers in mandate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Peacekeepers include police, troops, and military observers. UN peacekeeping operations are authorized by the UN Secretary-General and planned, managed, directed, and supported by the United Nations Department of Peacekeeping Operations and the Department of Field Support. The UN Charter gives the Security Council primary responsibility for maintaining international peace and security, including the establishment of a UN peacekeeping operation." }, { "id": "Longdefinition", "value": "Presence of peacebuilders and peacekeepers are active in peacebuilding and peacekeeping. Peacebuilding reduces the risk of lapsing or relapsing into conflict by strengthening national capacities at all levels of for conflict management, and to lay the foundation for sustainable peace and development. Peacekeepers provide essential security to preserve the peace, however fragile, where fighting has been halted, and to assist in implementing agreements achieved by the peacemakers. Peacekeepers deploy to war-torn regions where no one else is willing or able to go and prevent conflict from returning or escalating. Peacekeepers include police, troops, and military observers." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Presence of peacebuilders and peacekeepers are active in peacebuilding and peacekeeping. Peacebuilding reduces the risk of lapsing or relapsing into conflict by strengthening national capacities at all levels of for conflict management, and to lay the foundation for sustainable peace and development. Peacekeepers include police, troops, and military observers." }, { "id": "Source", "value": "UN Department of Peacekeeping Operations, http://www.un.org/en/peacekeeping/." }, { "id": "Statisticalconceptandmethodology", "value": "Peacekeepers include police, troops, and military observers. UN peacekeeping operations are authorized by the UN Secretary-General and planned, managed, directed, and supported by the United Nations Department of Peacekeeping Operations and the Department of Field Support. The UN Charter gives the Security Council primary responsibility for maintaining international peace and security, including the establishment of a UN peacekeeping operation. Data are presented as of December of a given year.\n\nPeacebuilding and peacekeeping refer to operations that engage in peace building (reducing the risk of lapsing or relapsing into conflict by strengthening national capacities for conflict management and laying the foundation for sustainable peace and development) or peacekeeping (providing essential security to preserve the peace where fighting has been halted and to assist in implementing agreements achieved by the peacemakers)." }, { "id": "Topic", "value": "Public Sector: Conflict & fragility" } ], "source_id": "57" }, { "id": "WP_time_01.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account at a financial institution (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account at a financial institution denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP_time_01.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account at a financial institution, male (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account at a financial institution denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP_time_01.3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account at a financial institution, female (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account at a financial institution denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP_time_01.8", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account at a financial institution, income, poorest 40% (% ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account at a financial institution denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP_time_01.9", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account at a financial institution, income, richest 60% (% ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Account at a financial institution denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP_time_10.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (% age 15+). [ts: data are available for multiple waves]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP_time_10.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account, male (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (male, % age 15+). [ts: data are available for multiple waves]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP_time_10.3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account, female (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (female, % age 15+). [ts: data are available for multiple waves]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP_time_10.4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account, young adults (% ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (% ages 15-24). [ts: data are available for multiple waves]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP_time_10.5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account, older adults (% ages 25+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (% age 25+). [ts: data are available for multiple waves]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP_time_10.6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account, primary education or less (% ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (primary education or less, % age 15+). [ts: data are available for multiple waves]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP_time_10.7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account, secondary education or more (% ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (secondary education or more, % age 15+). [ts: data are available for multiple waves]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP_time_10.8", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account, income, poorest 40% (% ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (income, poorest 40%, % age 15+). [ts: data are available for multiple waves]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP_time_10.9", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Account, income, richest 60% (% ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (income, richest 60%, % age 15+). [ts: data are available for multiple waves]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP15163_4.1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Mobile account (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mobile account denotes the percentage of respondents who report personally using a mobile phone to pay bills or to send or receive money through a GSM Association (GSMA) Mobile Money for the Unbanked (MMU) service in the past 12 months; or receiving wages, government transfers, or payments for agricultural products through a mobile phone in the past 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2014" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP15163_4.2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Mobile account, male (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mobile account denotes the percentage of respondents who report personally using a mobile phone to pay bills or to send or receive money through a GSM Association (GSMA) Mobile Money for the Unbanked (MMU) service in the past 12 months; or receiving wages, government transfers, or payments for agricultural products through a mobile phone in the past 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2014" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP15163_4.3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Mobile account, female (% age 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mobile account denotes the percentage of respondents who report personally using a mobile phone to pay bills or to send or receive money through a GSM Association (GSMA) Mobile Money for the Unbanked (MMU) service in the past 12 months; or receiving wages, government transfers, or payments for agricultural products through a mobile phone in the past 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2014" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP15163_4.8", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Mobile account, income, poorest 40% (% ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mobile account denotes the percentage of respondents who report personally using a mobile phone to pay bills or to send or receive money through a GSM Association (GSMA) Mobile Money for the Unbanked (MMU) service in the past 12 months; or receiving wages, government transfers, or payments for agricultural products through a mobile phone in the past 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2014" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "WP15163_4.9", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Mobile account, income, richest 60% (% ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mobile account denotes the percentage of respondents who report personally using a mobile phone to pay bills or to send or receive money through a GSM Association (GSMA) Mobile Money for the Unbanked (MMU) service in the past 12 months; or receiving wages, government transfers, or payments for agricultural products through a mobile phone in the past 12 months." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "2014" }, { "id": "Source", "value": "Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank." }, { "id": "Topic", "value": "Financial Sector: Access" } ], "source_id": "57" }, { "id": "SH.UHC.NOP1.CG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (USD)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure, expressed in US dollars (2011 PPP)" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Increase in poverty gap at $1.90 (USD)" }, { "id": "Source", "value": "Wagstaff et al. Progress on Impoverishing Health Spending: Results for 122 Countries. A Retrospective Observational Study, Lancet Global Health 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. This series measures the poverty gap increase attributable to OOP health expenditures. This amount can be interpreted as the per capita amount by which on average OOP spending pushes or further pushes the household below the PL. It is defined as the difference between the poverty gap based on a measure of consumption net of OOP health expenditures and a measure of consumption gross of OOP health expenditures. The difference is expressed in 2011 PPP international dollar." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "58" }, { "id": "SH.UHC.NOP1.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Number of people pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of people pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Impoverishment at the $1.90 Poverty Line" }, { "id": "Source", "value": "Wagstaff et al. Progress on Impoverishing Health Spending: Results for 122 Countries. A Retrospective Observational Study, Lancet Global Health 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are impoverishing at the $1.90 PL (PPP) for a household when consumption gross of out-of-pocket payments is higher than the $1.90 PL, but consumption net of out-of-pocket payments is lower than the 1.90 PL." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "58" }, { "id": "SH.UHC.NOP1.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (% of poverty line)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure, as a percentage of the $1.90 poverty line" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Increase in poverty gap at $1.90 (% of poverty line)" }, { "id": "Source", "value": "Wagstaff et al. Progress on Impoverishing Health Spending: Results for 122 Countries. A Retrospective Observational Study, Lancet Global Health 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. This series measures the poverty gap increase attributable to OOP health expenditures. This amount can be interpreted as the per capita amount by which on average OOP spending pushes or further pushes the household below the PL. It is defined as the difference between the poverty gap based on a measure of consumption net of OOP health expenditures and a measure of consumption gross of OOP health expenditures. The difference is expressed as a percentage of the PL." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "58" }, { "id": "SH.UHC.NOP1.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure, expressed as a percentage of a total population of a country" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Impoverishment at the $1.90 Poverty Line (%)" }, { "id": "Source", "value": "Wagstaff et al. Progress on Impoverishing Health Spending: Results for 122 Countries. A Retrospective Observational Study, Lancet Global Health 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are impoverishing at the $1.90 PL (PPP) for a household when consumption gross of out-of-pocket payments is higher than the $1.90 PL, but consumption net of out-of-pocket payments is lower than the 1.90 PL." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "58" }, { "id": "SH.UHC.NOP2.CG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Increase in poverty gap at $3.10 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (USD)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Increase in poverty gap at $3.10 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure, expressed in US dollars (2011 PPP)" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Increase in poverty gap at $3.10 (USD)" }, { "id": "Source", "value": "Wagstaff et al. Progress on Impoverishing Health Spending: Results for 122 Countries. A Retrospective Observational Study, Lancet Global Health 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. This series measures the poverty gap increase attributable to OOP health expenditures. This amount can be interpreted as the per capita amount by which on average OOP spending pushes or further pushes the household below the PL. It is defined as the difference between the poverty gap based on a measure of consumption net of OOP health expenditures and a measure of consumption gross of OOP health expenditures. The difference is expressed in 2011 PPP international dollar." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "58" }, { "id": "SH.UHC.NOP2.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Number of people pushed below the $3.10 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of people pushed below the $3.10 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Impoverishment at the $3.10 Poverty Line" }, { "id": "Source", "value": "Wagstaff et al. Progress on Impoverishing Health Spending: Results for 122 Countries. A Retrospective Observational Study, Lancet Global Health 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are impoverishing at the $3.10 PL (PPP) for a household when consumption gross of out-of-pocket payments is higher than the $3.10 PL, but consumption net of out-of-pocket payments is lower than the 3.10 PL." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "58" }, { "id": "SH.UHC.NOP2.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Increase in poverty gap at $3.10 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (% of poverty line)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Increase in poverty gap at $3.10 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure, as a percentage of the $1.90 poverty line" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Increase in poverty gap at $3.10 (% of poverty line)" }, { "id": "Source", "value": "Wagstaff et al. Progress on Impoverishing Health Spending: Results for 122 Countries. A Retrospective Observational Study, Lancet Global Health 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. This series measures the poverty gap increase attributable to OOP health expenditures. This amount can be interpreted as the per capita amount by which on average OOP spending pushes or further pushes the household below the PL. It is defined as the difference between the poverty gap based on a measure of consumption net of OOP health expenditures and a measure of consumption gross of OOP health expenditures. The difference is expressed as a percentage of the PL." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "58" }, { "id": "SH.UHC.NOP2.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Proportion of population pushed below the $3.10 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of population pushed below the $3.10 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure, expressed as a percentage of a total population of a country" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Impoverishment at the $3.10 Poverty Line (%)" }, { "id": "Source", "value": "Wagstaff et al. Progress on Impoverishing Health Spending: Results for 122 Countries. A Retrospective Observational Study, Lancet Global Health 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are impoverishing at the $3.10 PL (PPP) for a household when consumption gross of out-of-pocket payments is higher than the $3.10 PL, but consumption net of out-of-pocket payments is lower than the 3.10 PL." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "58" }, { "id": "SH.UHC.OOPC.10.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Number of people spending more than 10% of household consumption or income on out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of people spending more than 10% of household consumption or income on out-of-pocket health care expenditure" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Catastrophic Health Expenditure, 10% of total expenditure/income" }, { "id": "Source", "value": "Wagstaff et al. Progress on catastrophic health spending: results for 133 countries. A retrospective observational study, Lancet Global Health 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% threshold when they represent 10% or more of total consumption or income." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "58" }, { "id": "SH.UHC.OOPC.10.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure, expressed as a percentage of a total population of a country" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Catastrophic Health Expenditure, 10% of total expenditure/income (%)" }, { "id": "Source", "value": "Wagstaff et al. Progress on catastrophic health spending: results for 133 countries. A retrospective observational study, Lancet Global Health 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% threshold when they represent 10% or more of total consumption or income." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "58" }, { "id": "SH.UHC.OOPC.25.TO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Number of people spending more than 25% of household consumption or income on out-of-pocket health care expenditure" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Number of people spending more than 25% of household consumption or income on out-of-pocket health care expenditure" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Catastrophic Health Expenditure, 25% of total expenditure/income (thousands)" }, { "id": "Source", "value": "Wagstaff et al. Progress on catastrophic health spending: results for 133 countries. A retrospective observational study, Lancet Global Health 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 25% threshold when they represent 25% or more of total consumption or income." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "58" }, { "id": "SH.UHC.OOPC.25.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure, expressed as a percentage of a total population of a country" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Catastrophic Health Expenditure, 25% of total expenditure/income (%)" }, { "id": "Source", "value": "Wagstaff et al. Progress on catastrophic health spending: results for 133 countries. A retrospective observational study, Lancet Global Health 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 25% threshold when they represent 25% or more of total consumption or income." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "58" }, { "id": "SH.UHC.SRVS.CV.XD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need – without facing financial hardship – is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere)." }, { "id": "IndicatorName", "value": "UHC service coverage index" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Coverage index for essential health services (based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, noncommunicable diseases and service capacity and access). It is presented on a scale of 0 to 100. Values greater than or equal to 80 are presented as 80 as the index does not provide fine resolution at high values." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "UHC service coverage index" }, { "id": "Source", "value": "Hogan et al. An index of the coverage of essential health services for monitoring UHC within the SDGs, Lancet Global Health 2017." }, { "id": "Statisticalconceptandmethodology", "value": "Under SDG 3.8.1, four categories were defined RMNCH, infectious diseases, non-communicable diseases and service capacity and access. Each category contains several tracers. The index is constructed from geometric means of the tracer indicators; first, within each of the four categories, and then across the four category-specific means to obtain the final summary index. See Source for details about methodology." }, { "id": "Topic", "value": "Health: Universal Health Coverage" } ], "source_id": "58" }, { "id": "NW.DOW.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Domestic comprehensive wealth per capita index (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "The domestic comprehensive wealth index excludes net foreign financial assets and aggregates produced capital, nonrenewable and renewable natural capital, and human capital. The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index, and cannot be aggregated by summing the sub-wealth categories." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Total wealth" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.DOW.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Domestic comprehensive wealth per capita index (current US$)" }, { "id": "Longdefinition", "value": "The domestic comprehensive wealth index excludes net foreign financial assets and aggregates produced capital, nonrenewable and renewable natural capital, and human capital. The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index, and cannot be aggregated by summing the sub-wealth categories." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Total wealth" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.DOW.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Domestic comprehensive wealth index (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "The domestic comprehensive wealth index excludes net foreign financial assets and aggregates produced capital, nonrenewable and renewable natural capital, and human capital. The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index, and cannot be aggregated by summing the sub-wealth categories." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Total wealth" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.DOW.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Domestic comprehensive wealth index (current US$)" }, { "id": "Longdefinition", "value": "The domestic comprehensive wealth index excludes net foreign financial assets and aggregates produced capital, nonrenewable and renewable natural capital, and human capital. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Total wealth" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.HCA.FEMA.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Human capital per capita, female (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Human capital is calculated as the present value of future earnings for the working population, categorized by age, gender, and education. 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Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Human capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.HCA.FEMA.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Human capital per capita, female (current US$)" }, { "id": "Longdefinition", "value": "Nominal human capital is calculated as the present value of future earnings for the working population, categorized by age, gender, and education. 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While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Human capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.HCA.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Human capital (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Human capital is calculated as the present value of future earnings for the working population, categorized by age, gender, and education. 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The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.FSTW.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital, forest water ecosystem services (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber, and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.FSTW.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital, forest water ecosystem services (current US$)" }, { "id": "Longdefinition", "value": "Nominal renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. The values are reported in current US dollars, calculated using market exchange rates and without the adjustments applied for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.FTIM.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital per capita, timber (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber, and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of renewable natural capital, like timber, are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.FTIM.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital per capita, timber (current US$)" }, { "id": "Longdefinition", "value": "Nominal renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. 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The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.FTIM.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital, timber (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber, and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.FTIM.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital, timber (current US$)" }, { "id": "Longdefinition", "value": "Nominal renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. The values are reported in current US dollars, calculated using market exchange rates and without the adjustments applied for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.HYDR.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital per capita, hydropower energy (current US$)" }, { "id": "Longdefinition", "value": "Nominal renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. 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The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.HYDR.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital, hydropower energy (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber, and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.HYDR.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital, hydropower energy (current US$)" }, { "id": "Longdefinition", "value": "Nominal renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. 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The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MANG.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital per capita, mangroves (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber, and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MANG.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital, mangroves (current US$)" }, { "id": "Longdefinition", "value": "Nominal renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. The values are reported in current US dollars, calculated using market exchange rates and without the adjustments applied for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MANG.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital, mangroves (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber, and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of renewable natural capital, like mangroves, are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MANG.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital per capita, mangroves (current US$)" }, { "id": "Longdefinition", "value": "Nominal renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. The values are reported in current US dollars, calculated using market exchange rates and without the adjustments applied for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MBAU.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: bauxite (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of nonrenewable natural capital are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MBAU.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: bauxite (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MBAU.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: bauxite (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of nonrenewable natural capital are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MBAU.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: bauxite (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MCOB.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: cobalt (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of nonrenewable natural capital are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MCOB.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: cobalt (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MCOB.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: cobalt (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MCOB.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: cobalt (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MCOP.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: copper (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of nonrenewable natural capital are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MCOP.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: copper (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MCOP.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: copper (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of nonrenewable natural capital are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MCOP.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: copper (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MGOL.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: gold (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of nonrenewable natural capital are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MGOL.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: gold (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MGOL.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: gold (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of nonrenewable natural capital are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MGOL.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: gold (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MINR.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals, sub-index, total (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of nonrenewable natural capital are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MINR.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals, sub-index, total (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MINR.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals, sub-index, total (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of nonrenewable natural capital are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MINR.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals, sub-index, total (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MIRO.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: iron ore (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of nonrenewable natural capital are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MIRO.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: iron ore (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MIRO.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: iron ore (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of nonrenewable natural capital are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MIRO.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: iron ore (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MLEA.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: lead (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of nonrenewable natural capital are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MLEA.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: lead (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MLEA.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: lead (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of nonrenewable natural capital are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MLEA.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: lead (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MLIT.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: lithium (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of nonrenewable natural capital are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MLIT.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: lithium (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MLIT.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: lithium (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of nonrenewable natural capital are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MLIT.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: lithium (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MMOL.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: molybdenum (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. Individual sub-components of nonrenewable natural capital are not calculated using the Törnqvist index, as their trends follow changes in physical volumes expressed in real 2019 chained US dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MMOL.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: molybdenum (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MMOL.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: molybdenum (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MMOL.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: molybdenum (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MNIC.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: nickel (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MNIC.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: nickel (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MNIC.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: nickel (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MNIC.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: nickel (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MPHO.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: phosphate (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MPHO.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: phosphate (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MPHO.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: phosphate (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MPHO.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: phosphate (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MSIL.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: silver (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MSIL.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: silver (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MSIL.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: silver (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MTIN.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: tin (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MTIN.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: tin (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MTIN.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: tin (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MTIN.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: tin (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.MZIN.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: zinc (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MZIN.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, metals and minerals: zinc (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.MZIN.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, metals and minerals: zinc (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.PAST.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital per capita, agricultural land: pastureland (current US$)" }, { "id": "Longdefinition", "value": "Nominal renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. The values are reported in current US dollars, calculated using market exchange rates and without the adjustments applied for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.PAST.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital, agricultural land: pastureland (current US$)" }, { "id": "Longdefinition", "value": "Nominal renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. The values are reported in current US dollars, calculated using market exchange rates and without the adjustments applied for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.SACO.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, coal (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.SACO.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, coal (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in current US dollars, based on market exchange rates and without adjustments for inflation or volume indices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.SACO.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, coal (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.SACO.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, coal (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.SAGA.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, natural gas (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NCA.SAGA.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, natural gas (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). The values are reported in real chained 2019 US dollars, calculated using the Törnqvist volume index method. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.SAGA.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, natural gas (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.SAOI.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, oil (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.SSOI.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital per capita, total (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. 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These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Nonrenewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.SSOI.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Nonrenewable natural capital, total (current US$)" }, { "id": "Longdefinition", "value": "Nominal nonrenewable natural capital includes assets such as oil, natural gas, coal, and metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, cobalt, molybdenum, platinum, and lithium). 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Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.TOTL.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital per capita, total (current US$)" }, { "id": "Longdefinition", "value": "Nominal renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. 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The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Renewable natural capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NCA.TOTL.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Renewable natural capital, total (current US$)" }, { "id": "Longdefinition", "value": "Nominal renewable natural capital includes assets such as agricultural land (cropland and pastureland), forests (timber and three ecosystem services: water, recreation, and non-wood forest products), mangroves, marine fish stocks, and hydropower. 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These assets are reported in real chained 2019 US dollars, adjusted for inflation using the consumer price index (CPI)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Foreign assets" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NFA.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Foreign assets per capita (current US$)" }, { "id": "Longdefinition", "value": "Nominal foreign financial assets include portfolio equity, foreign direct investment (FDI), debt assets, financial derivatives, and foreign exchange reserves (excluding gold). 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While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Foreign assets" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NFA.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Foreign assets (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Foreign financial assets encompass portfolio equity, FDI, debt instruments, financial derivatives, and foreign exchange reserves (excluding gold). These assets are reported in real chained 2019 US dollars, adjusted for inflation using the consumer price index (CPI)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Foreign assets" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NFA.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Foreign assets (current US$)" }, { "id": "Longdefinition", "value": "Nominal foreign financial assets include portfolio equity, foreign direct investment (FDI), debt assets, financial derivatives, and foreign exchange reserves (excluding gold). The values are reported in current US dollars, obtained directly from the External Wealth of Nations (EWN) database." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Foreign assets" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NFL.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Foreign liabilities per capita (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Foreign financial liabilities include portfolio equity, FDI, debt liabilities, and financial derivatives. These liabilities are reported in real chained 2019 US dollars, adjusted for inflation using the consumer price index (CPI)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Foreign liabilities" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NFL.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Foreign liabilities per capita (current US$)" }, { "id": "Longdefinition", "value": "Nominal foreign liabilities include portfolio equity liabilities, foreign direct investment (FDI) liabilities, debt liabilities, and financial derivatives liabilities. The values are reported in current US dollars, obtained directly from the External Wealth of Nations (EWN) database." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Foreign liabilities" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.NFL.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Foreign liabilities (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Foreign financial liabilities include portfolio equity, FDI, debt liabilities, and financial derivatives. These liabilities are reported in real chained 2019 US dollars, adjusted for inflation using the consumer price index (CPI)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Foreign liabilities" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.NFL.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Foreign liabilities (current US$)" }, { "id": "Longdefinition", "value": "Nominal foreign liabilities include portfolio equity liabilities, foreign direct investment (FDI) liabilities, debt liabilities, and financial derivatives liabilities. The values are reported in current US dollars, obtained directly from the External Wealth of Nations (EWN) database." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Foreign liabilities" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.PCA.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Produced capital per capita (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Produced capital includes the value of machinery, buildings, equipment, and urban land. The values are reported in real chained 2019 US dollars, calculated using the Törnqvist index method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Produced capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.PCA.PC.EX.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Produced capital per capita, excluding urban land (current US$)" }, { "id": "Longdefinition", "value": "Nominal produced capital, without urban land includes the value of machinery, buildings, and equipment. The values are reported in current US dollars, calculated using market exchange rates, with country-specific GDP deflators applied to adjust for inflation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Produced capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.PCA.PC.IN.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Produced capital per capita, including urban land (current US$)" }, { "id": "Longdefinition", "value": "Nominal produced capital includes the value of machinery, buildings, equipment, and urban land. The values are reported in current US dollars, calculated using market exchange rates, with country-specific GDP deflators applied to adjust for inflation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Produced capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.PCA.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Produced capital (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "Produced capital includes the value of machinery, buildings, equipment, and urban land. The values are reported in real chained 2019 US dollars, calculated using the Törnqvist index method." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Produced capital" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.PCA.TO.EX.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Produced capital, excluding urban land (current US$)" }, { "id": "Longdefinition", "value": "Nominal produced capital, without urban land includes the value of machinery, buildings, and equipment. The values are reported in current US dollars, calculated using market exchange rates, with country-specific GDP deflators applied to adjust for inflation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Produced capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.PCA.TO.IN.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "Produced capital, including urban land (current US$)" }, { "id": "Longdefinition", "value": "Nominal produced capital includes the value of machinery, buildings, equipment, and urban land. The values are reported in current US dollars, calculated using market exchange rates, with country-specific GDP deflators applied to adjust for inflation." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Produced capital" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.TOW.PC", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "National comprehensive wealth per capita index (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "The national comprehensive wealth index aggregates produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets, using the Törnqvist volume index. The values are reported in real chained 2019 US dollars and cannot simply be aggregated by summing the sub-wealth categories." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Total wealth" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.TOW.PC.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "National comprehensive wealth per capita (current US$)" }, { "id": "Longdefinition", "value": "The national comprehensive wealth aggregates produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. The values are reported in current US dollars and can be directly aggregated by summing the sub-wealth categories." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Total wealth" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "NW.TOW.TO", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "National comprehensive wealth index (real chained 2019 US$)" }, { "id": "Longdefinition", "value": "The national comprehensive wealth index aggregates produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets, using the Törnqvist volume index. The values are reported in real chained 2019 US dollars and cannot simply be aggregated by summing the sub-wealth categories." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "The aggregate national comprehensive wealth index is calculated using the Törnqvist volume index, applied to wealth categories like produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. For each asset, except financial assets, an unchained index is first computed as a weighted geometric mean of the \"quantity relatives\" between periods, using nominal value shares as weights. These unchained indices are then chained to form a time series with 2019 as the base year. Since each category is calculated using this method, they cannot be simply added to estimate the total index. The final chained index is expressed in monetary terms by applying the chained Törnqvist index to the base year’s aggregate nominal value, providing a more accurate reflection of wealth changes and sustainability." }, { "id": "Topic", "value": "Total wealth" }, { "id": "Unitofmeasure", "value": "Real chained 2019 US$" } ], "source_id": "59" }, { "id": "NW.TOW.TO.CD", "metatype": [ { "id": "Generalcomments", "value": "License Type: CC BY-4.0 (https://datacatalog.worldbank.org/public-licenses#cc-by)" }, { "id": "IndicatorName", "value": "National comprehensive wealth (current US$)" }, { "id": "Longdefinition", "value": "The national comprehensive wealth aggregates produced capital, nonrenewable and renewable natural capital, human capital, and net foreign financial assets. The values are reported in current US dollars and can be directly aggregated by summing the sub-wealth categories." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Referenceperiod", "value": "1995-2020" }, { "id": "Source", "value": "World Bank. 2024. The Changing Wealth of Nations 2024: Revisiting the Measurement of Comprehensive Wealth. Washington, DC: World Bank." }, { "id": "Statisticalconceptandmethodology", "value": "Total wealth is calculated by summing up the nominal estimates of each component of wealth: produced capital, natural capital, human capital, and net foreign assets. The construction of these wealth accounts is guided by the concepts and methods of the System of National Accounts (SNA), a framework developed by the UN Statistical Commission used by virtually all countries. While produced capital and net foreign assets are derived from observed transactions, the values of natural capital and human capital are estimated using the discounted stream of expected net earnings (resource rents for natural capital and wages for human capital) over their lifetime. The values are expressed in current US dollars without adjustments for inflation or changes in asset volumes over time." }, { "id": "Topic", "value": "Total wealth" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "59" }, { "id": "EF.EFM.OVRL.XD", "metatype": [ { "id": "IndicatorName", "value": "Economic Fitness Metric" }, { "id": "Limitationsandexceptions", "value": "The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed." }, { "id": "Longdefinition", "value": "Economic Fitness (EF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis.  Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries.   Countries with low EF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the COMTRADE list of products exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth" }, { "id": "Statisticalconceptandmethodology", "value": "The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods.  The interaction among products traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.  A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data." }, { "id": "Topic", "value": "Economic Policy & Debt" } ], "source_id": "60" }, { "id": "EF.EFM.RANK.XD", "metatype": [ { "id": "IndicatorName", "value": "Economic Fitness Ranking (1 = high, 149 = low)" }, { "id": "Limitationsandexceptions", "value": "The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth" }, { "id": "Statisticalconceptandmethodology", "value": "The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods.  The interaction among products traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.  A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data." }, { "id": "Topic", "value": "Economic Policy & Debt" } ], "source_id": "60" }, { "id": "IQ.PPN.REGQ.S0", "metatype": [ { "id": "IndicatorName", "value": "Assessment of country’s adherence to the best regulatory practices at the preparation stage of PPP project (scale 1-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides an assessment of a country's adherence to the best regulatory practices (scored from 1 to 100 (best practice)) during the period and activities that inform the decision of whether to launch a PPP procurement process. It is determined by assessing whether the identification of a prospective PPP project happens within the broader context of public investments, consistent with other government priorities. It also examines the different types of assessments and the methodologies used to set the rules for these assessments. Furthermore, it considers other activities undertaken before publishing the public tender notice, such as preparing the draft contract and tender documents and obtaining land and permits, that lead to the procurement of the PPP project." }, { "id": "Source", "value": "Procuring Infrastructure PPPs 2018 (http://bpp.worldbank.org/)" } ], "source_id": "61" }, { "id": "IQ.PPN.REGQ.S1", "metatype": [ { "id": "IndicatorName", "value": "Assessment of country’s adherence to the best regulatory practices at the procurement stage of PPP project (scale 1-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "This indicator provides an assessment of a country's adherence to the best regulatory practices (scored from 1 to 100 (best practice)) during the selection of a private partner who takes on the responsibility of developing the PPP project. It focuses on fairness, neutrality, and transparency of the process, as well as provisions assuring competition between the potential private partners." }, { "id": "Source", "value": "Procuring Infrastructure PPPs 2018 (http://bpp.worldbank.org/)" } ], "source_id": "61" }, { "id": "IQ.PPN.REGQ.S2", "metatype": [ { "id": "IndicatorName", "value": "Assessment of country’s adherence to the best regulatory practices at the management stage of PPP project (scale 1-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The indicator provides assessment of whether the contract management framework is in place to facilitate the implementation of PPP projects, as well as the existing monitoring and evaluation systems. It also examines the regulatory provisions regarding PPP contract modification and renegotiation, dispute resolution, lender step-in rights, and termination of contracts." }, { "id": "Source", "value": "Procuring Infrastructure PPPs 2018 (http://bpp.worldbank.org/)" } ], "source_id": "61" }, { "id": "IQ.PPN.REGQ.S3", "metatype": [ { "id": "IndicatorName", "value": "Assessment of country’s adherence to the best regulatory practices, procurement of unsolicited proposals (scale 1-100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The indicator provides assessment of whether a specific process is in place to evaluate the feasibility of the unsolicited proposals (USPs). It evaluates USPs' alignment with other government priorities; whether specific compensation mechanisms are in place for USPs; and whether a competitive process is required to select the private partner for the PPP project. Only the countries where USPs are allowed and take place have received scores. No scores have been given to those countries where the regulatory framework specifically prohibits the submission of USPs or where the USPs do not happen in practice." }, { "id": "Source", "value": "Procuring Infrastructure PPPs 2018 (http://bpp.worldbank.org/)" } ], "source_id": "61" }, { "id": "HD.HCI.AMRT", "metatype": [ { "id": "IndicatorName", "value": "Survival Rate from Age 15-60" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adult survival rate is calculated by subtracting the mortality rate for 15-60 year-olds from 1. Most recent estimates are used. Year of most recent estimate shown in data notes." }, { "id": "Source", "value": "United Nations Population Division, World Population Prospects: 2019 Revision, supplemented with data provided by World Bank Staff." } ], "source_id": "63" }, { "id": "HD.HCI.AMRT.FE", "metatype": [ { "id": "IndicatorName", "value": "Survival Rate from Age 15-60, Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adult survival rate is calculated by subtracting the mortality rate for 15-60 year-olds from 1. Most recent estimates are used. Year of most recent estimate shown in data notes." }, { "id": "Source", "value": "United Nations Population Division, World Population Prospects: 2019 Revision, supplemented with data provided by World Bank Staff." } ], "source_id": "63" }, { "id": "HD.HCI.AMRT.MA", "metatype": [ { "id": "IndicatorName", "value": "Survival Rate from Age 15-60, Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Adult survival rate is calculated by subtracting the mortality rate for 15-60 year-olds from 1. Most recent estimates are used. Year of most recent estimate shown in data notes." }, { "id": "Source", "value": "United Nations Population Division, World Population Prospects: 2019 Revision, supplemented with data provided by World Bank Staff." } ], "source_id": "63" }, { "id": "HD.HCI.EYRS", "metatype": [ { "id": "IndicatorName", "value": "Expected Years of School" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Expected years of school is calculated as the sum of age-specific enrollment rates between ages 4 and 17. Age-specific enrollment rates are approximated using school enrollment rates at different levels: pre-primary enrollment rates approximate the age-specific enrolment rates for 4 and 5 year-olds; the primary rate approximates for 6-11 year-olds; the lower-secondary rate approximates for 12-14 year-olds; and the upper-secondary approximates for 15-17 year-olds. Most recent estimates are used. Year of most recent primary enrollment rate used is shown in data notes." }, { "id": "Source", "value": "World Bank staff estimates based on data from UNESCO Institute for Statistics, supplemented with data provided by World Bank staff." } ], "source_id": "63" }, { "id": "HD.HCI.EYRS.FE", "metatype": [ { "id": "IndicatorName", "value": "Expected Years of School, Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Expected years of school is calculated as the sum of age-specific enrollment rates between ages 4 and 17. Age-specific enrollment rates are approximated using school enrollment rates at different levels: pre-primary enrollment rates approximate the age-specific enrolment rates for 4 and 5 year-olds; the primary rate approximates for 6-11 year-olds; the lower-secondary rate approximates for 12-14 year-olds; and the upper-secondary approximates for 15-17 year-olds. Most recent estimates are used. Year of most recent primary enrollment rate used is shown in data notes." }, { "id": "Source", "value": "World Bank staff estimates based on data from UNESCO Institute for Statistics, supplemented with data provided by World Bank staff." } ], "source_id": "63" }, { "id": "HD.HCI.EYRS.MA", "metatype": [ { "id": "IndicatorName", "value": "Expected Years of School, Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Expected years of school is calculated as the sum of age-specific enrollment rates between ages 4 and 17. Age-specific enrollment rates are approximated using school enrollment rates at different levels: pre-primary enrollment rates approximate the age-specific enrolment rates for 4 and 5 year-olds; the primary rate approximates for 6-11 year-olds; the lower-secondary rate approximates for 12-14 year-olds; and the upper-secondary approximates for 15-17 year-olds. Most recent estimates are used. Year of most recent primary enrollment rate used is shown in data notes." }, { "id": "Source", "value": "World Bank staff estimates based on data from UNESCO Institute for Statistics, supplemented with data provided by World Bank staff." } ], "source_id": "63" }, { "id": "HD.HCI.HLOS", "metatype": [ { "id": "IndicatorName", "value": "Harmonized Test Scores" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Harmonized test scores from major international student achievement testing programs.They are measured in TIMSS-equivalent units, where 300 is minimal attainment and 625 is advanced attainment. Most recent estimates are used. Year of most recent estimate shown in data notes. \n\nTest scores from the following testing programs are included:\n• TIMSS/PIRLS: Refers to average of test scores from TIMSS (Trends in International Maths and Science Study) and PIRLS (Progress in International Reading Literacy Study), both carried out by the International Association for the Evaluation of Educational Achievement. Data from each PIRLS round is moved to the year of the nearest TIMSS round and averaged with the TIMSS data. \n• PISA: Refers to test scores from Programme for International Student Assessment\n• PISA+TIMSS/PIRLS: Refers to the average of these programs for countries and years where both are available\n• SACMEQ: Refers to test scores from Southern and Eastern Africa Consortium for Monitoring Educational Quality \n• PASEC: Refers to test scores from Program of Analysis of Education Systems\n• LLECE: Refers to test scores from Latin American Laboratory for Assessment of the Quality of Education\n• PILNA: Refers to test scores from Pacific Islands Literacy and Numeracy Assessment\n• EGRA: Refers to test scores from nationally-representative Early Grade Reading Assessments \n• EGRANR: Refers to test scores from non-nationally-representative Early Grade Reading Assessments" }, { "id": "Source", "value": "Patrinos and Angrist (2018). http://documents.worldbank.org/curated/en/390321538076747773/Global-Dataset-on-Education-Quality-A-Review-and-Update-2000-2017" } ], "source_id": "63" }, { "id": "HD.HCI.HLOS.FE", "metatype": [ { "id": "IndicatorName", "value": "Harmonized Test Scores, Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Harmonized test scores from major international student achievement testing programs.They are measured in TIMSS-equivalent units, where 300 is minimal attainment and 625 is advanced attainment. Most recent estimates are used. Year of most recent estimate shown in data notes. \n\nTest scores from the following testing programs are included:\n• TIMSS/PIRLS: Refers to average of test scores from TIMSS (Trends in International Maths and Science Study) and PIRLS (Progress in International Reading Literacy Study), both carried out by the International Association for the Evaluation of Educational Achievement. Data from each PIRLS round is moved to the year of the nearest TIMSS round and averaged with the TIMSS data. \n• PISA: Refers to test scores from Programme for International Student Assessment\n• PISA+TIMSS/PIRLS: Refers to the average of these programs for countries and years where both are available\n• SACMEQ: Refers to test scores from Southern and Eastern Africa Consortium for Monitoring Educational Quality \n• PASEC: Refers to test scores from Program of Analysis of Education Systems\n• LLECE: Refers to test scores from Latin American Laboratory for Assessment of the Quality of Education\n• PILNA: Refers to test scores from Pacific Islands Literacy and Numeracy Assessment\n• EGRA: Refers to test scores from nationally-representative Early Grade Reading Assessments \n• EGRANR: Refers to test scores from non-nationally-representative Early Grade Reading Assessments" }, { "id": "Source", "value": "Patrinos and Angrist (2018). http://documents.worldbank.org/curated/en/390321538076747773/Global-Dataset-on-Education-Quality-A-Review-and-Update-2000-2017" } ], "source_id": "63" }, { "id": "HD.HCI.HLOS.MA", "metatype": [ { "id": "IndicatorName", "value": "Harmonized Test Scores, Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Harmonized test scores from major international student achievement testing programs.They are measured in TIMSS-equivalent units, where 300 is minimal attainment and 625 is advanced attainment. Most recent estimates are used. Year of most recent estimate shown in data notes. \n\nTest scores from the following testing programs are included:\n• TIMSS/PIRLS: Refers to average of test scores from TIMSS (Trends in International Maths and Science Study) and PIRLS (Progress in International Reading Literacy Study), both carried out by the International Association for the Evaluation of Educational Achievement. Data from each PIRLS round is moved to the year of the nearest TIMSS round and averaged with the TIMSS data. \n• PISA: Refers to test scores from Programme for International Student Assessment\n• PISA+TIMSS/PIRLS: Refers to the average of these programs for countries and years where both are available\n• SACMEQ: Refers to test scores from Southern and Eastern Africa Consortium for Monitoring Educational Quality \n• PASEC: Refers to test scores from Program of Analysis of Education Systems\n• LLECE: Refers to test scores from Latin American Laboratory for Assessment of the Quality of Education\n• PILNA: Refers to test scores from Pacific Islands Literacy and Numeracy Assessment\n• EGRA: Refers to test scores from nationally-representative Early Grade Reading Assessments \n• EGRANR: Refers to test scores from non-nationally-representative Early Grade Reading Assessments" }, { "id": "Source", "value": "Patrinos and Angrist (2018). http://documents.worldbank.org/curated/en/390321538076747773/Global-Dataset-on-Education-Quality-A-Review-and-Update-2000-2017" } ], "source_id": "63" }, { "id": "HD.HCI.LAYS", "metatype": [ { "id": "IndicatorName", "value": "Learning-Adjusted Years of School" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Learning-adjusted years of school are calculated by multiplying the estimates of expected years of school by the ratio of most recent harmonized test scores to 625." }, { "id": "Source", "value": "World Bank staff calculation based on methodology in Filmer et al. (2018). http://documents.worldbank.org/curated/en/243261538075151093/Learning-Adjusted-Years-of-Schooling-LAYS-Defining-A-New-Macro-Measure-of-Education" } ], "source_id": "63" }, { "id": "HD.HCI.LAYS.FE", "metatype": [ { "id": "IndicatorName", "value": "Learning-Adjusted Years of School, Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Learning-adjusted years of school are calculated by multiplying the estimates of expected years of school by the ratio of most recent harmonized test scores to 625." }, { "id": "Source", "value": "World Bank staff calculation based on methodology in Filmer et al. (2018). http://documents.worldbank.org/curated/en/243261538075151093/Learning-Adjusted-Years-of-Schooling-LAYS-Defining-A-New-Macro-Measure-of-Education" } ], "source_id": "63" }, { "id": "HD.HCI.LAYS.MA", "metatype": [ { "id": "IndicatorName", "value": "Learning-Adjusted Years of School, Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Learning-adjusted years of school are calculated by multiplying the estimates of expected years of school by the ratio of most recent harmonized test scores to 625." }, { "id": "Source", "value": "World Bank staff calculation based on methodology in Filmer et al. (2018). http://documents.worldbank.org/curated/en/243261538075151093/Learning-Adjusted-Years-of-Schooling-LAYS-Defining-A-New-Macro-Measure-of-Education" } ], "source_id": "63" }, { "id": "HD.HCI.MORT", "metatype": [ { "id": "IndicatorName", "value": "Probability of Survival to Age 5" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Probability of survival to age 5 is calculated by subtracting the under-5 mortality rate from 1. Most recent estimates are used. Year of most recent estimate shown in data notes." }, { "id": "Source", "value": "United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), supplemented with data provided by World Bank staff." } ], "source_id": "63" }, { "id": "HD.HCI.MORT.FE", "metatype": [ { "id": "IndicatorName", "value": "Probability of Survival to Age 5, Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Probability of survival to age 5 is calculated by subtracting the under-5 mortality rate from 1. Most recent estimates are used. Year of most recent estimate shown in data notes." }, { "id": "Source", "value": "UNESCO Institute for Statistics." } ], "source_id": "63" }, { "id": "HD.HCI.MORT.MA", "metatype": [ { "id": "IndicatorName", "value": "Probability of Survival to Age 5, Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Probability of survival to age 5 is calculated by subtracting the under-5 mortality rate from 1. Most recent estimates are used. Year of most recent estimate shown in data notes." }, { "id": "Source", "value": "United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), supplemented with data provided by World Bank staff." } ], "source_id": "63" }, { "id": "HD.HCI.OVRL", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI) (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education." }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498." } ], "source_id": "63" }, { "id": "HD.HCI.OVRL.FE", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI), Female (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education." }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498." } ], "source_id": "63" }, { "id": "HD.HCI.OVRL.LB", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index, Lower Bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The HCI lower bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in Kraay (2018). http://documents.worldbank.org/curated/en/300071537907028892/Methodology-for-a-World-Bank-Human-Capital-Index" } ], "source_id": "63" }, { "id": "HD.HCI.OVRL.LB.FE", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index, Female,Lower Bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The HCI lower bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in Kraay (2018). http://documents.worldbank.org/curated/en/300071537907028892/Methodology-for-a-World-Bank-Human-Capital-Index" } ], "source_id": "63" }, { "id": "HD.HCI.OVRL.LB.MA", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index, Male, Lower Bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The HCI lower bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in Kraay (2018). http://documents.worldbank.org/curated/en/300071537907028892/Methodology-for-a-World-Bank-Human-Capital-Index" } ], "source_id": "63" }, { "id": "HD.HCI.OVRL.MA", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index (HCI), Male (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education." }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498." } ], "source_id": "63" }, { "id": "HD.HCI.OVRL.UB", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index, Upper Bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The HCI upper bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in Kraay (2018). http://documents.worldbank.org/curated/en/300071537907028892/Methodology-for-a-World-Bank-Human-Capital-Index" } ], "source_id": "63" }, { "id": "HD.HCI.OVRL.UB.FE", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index, Female, Upper Bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The HCI upper bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in Kraay (2018). http://documents.worldbank.org/curated/en/300071537907028892/Methodology-for-a-World-Bank-Human-Capital-Index" } ], "source_id": "63" }, { "id": "HD.HCI.OVRL.UB.MA", "metatype": [ { "id": "IndicatorName", "value": "Human Capital Index, Male, Upper Bound (scale 0-1)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The HCI upper bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful." }, { "id": "Source", "value": "World Bank staff calculations based on the methodology described in Kraay (2018). http://documents.worldbank.org/curated/en/300071537907028892/Methodology-for-a-World-Bank-Human-Capital-Index" } ], "source_id": "63" }, { "id": "HD.HCI.STNT", "metatype": [ { "id": "IndicatorName", "value": "Fraction of Children Under 5 Not Stunted" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage not stunted is calculated by subtracting stunting rates from 1. Most recent estimates are used. Year of most recent estimate shown in data notes." }, { "id": "Source", "value": "UNICEF-WHO-World Bank Joint Malnutrition Estimates, supplemented with data provided by World Bank staff." } ], "source_id": "63" }, { "id": "HD.HCI.STNT.FE", "metatype": [ { "id": "IndicatorName", "value": "Fraction of Children Under 5 Not Stunted, Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage not stunted is calculated by subtracting stunting rates from 1. Most recent estimates are used. Year of most recent estimate shown in data notes." }, { "id": "Source", "value": "UNICEF-WHO-World Bank Joint Malnutrition Estimates, supplemented with data provided by World Bank staff." } ], "source_id": "63" }, { "id": "HD.HCI.STNT.MA", "metatype": [ { "id": "IndicatorName", "value": "Fraction of Children Under 5 Not Stunted, Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Percentage not stunted is calculated by subtracting stunting rates from 1. Most recent estimates are used. Year of most recent estimate shown in data notes." }, { "id": "Source", "value": "UNICEF-WHO-World Bank Joint Malnutrition Estimates, supplemented with data provided by World Bank staff." } ], "source_id": "63" }, { "id": "HF.CON.AIDS.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Condom use in last intercourse (% of females at risk population)" }, { "id": "Longdefinition", "value": "Percentage of women age 18-49 who had more than one sexual partner in the last 12 months and used a condom during last intercourse" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.CON.AIDS.FE.ZS.Q1", "metatype": [ { "id": "IndicatorName", "value": "Condom use in last intercourse (% of females at risk population): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of women age 18-49 who had more than one sexual partner in the last 12 months and used a condom during last intercourse" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.CON.AIDS.FE.ZS.Q2", "metatype": [ { "id": "IndicatorName", "value": "Condom use in last intercourse (% of females at risk population): Q2" }, { "id": "Longdefinition", "value": "Percentage of women age 18-49 who had more than one sexual partner in the last 12 months and used a condom during last intercourse" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.CON.AIDS.FE.ZS.Q3", "metatype": [ { "id": "IndicatorName", "value": "Condom use in last intercourse (% of females at risk population): Q3" }, { "id": "Longdefinition", "value": "Percentage of women age 18-49 who had more than one sexual partner in the last 12 months and used a condom during last intercourse" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.CON.AIDS.FE.ZS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Condom use in last intercourse (% of females at risk population): Q4" }, { "id": "Longdefinition", "value": "Percentage of women age 18-49 who had more than one sexual partner in the last 12 months and used a condom during last intercourse" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.CON.AIDS.FE.ZS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Condom use in last intercourse (% of females at risk population): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of women age 18-49 who had more than one sexual partner in the last 12 months and used a condom during last intercourse" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.AIDS.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of HIV, total (% of population ages 15-49)" }, { "id": "Longdefinition", "value": "Percentage of population age 15-49 who had blood tests that are positive for HIV1 or HIV2" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.AIDS.ZS.Q1", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of HIV, total (% of population ages 15-49): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of population age 15-49 who had blood tests that are positive for HIV1 or HIV2" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.AIDS.ZS.Q2", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of HIV, total (% of population ages 15-49): Q2" }, { "id": "Longdefinition", "value": "Percentage of population age 15-49 who had blood tests that are positive for HIV1 or HIV2" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.AIDS.ZS.Q3", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of HIV, total (% of population ages 15-49): Q3" }, { "id": "Longdefinition", "value": "Percentage of population age 15-49 who had blood tests that are positive for HIV1 or HIV2" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.AIDS.ZS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of HIV, total (% of population ages 15-49): Q4" }, { "id": "Longdefinition", "value": "Percentage of population age 15-49 who had blood tests that are positive for HIV1 or HIV2" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.AIDS.ZS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of HIV, total (% of population ages 15-49): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of population age 15-49 who had blood tests that are positive for HIV1 or HIV2" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.CONM.ZS", "metatype": [ { "id": "IndicatorName", "value": "Contraceptive prevalence, modern methods (% of females ages 15-49)" }, { "id": "Longdefinition", "value": "Percentage of women age 15-49 who are married or live in union and currently use a modern method of contraception. Modern methods are defined as female sterilization, male sterilization, the contraceptive pill, intrauterine contraceptive device (IUD), injectables, implants, female condom, male condom, diaphragm, contraceptive foam and contraceptive jelly, lactational amenorrhea method (LAM), emergency contraception, country-specific modern methods and other modern contraceptive methods respondent mentioned." }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.CONM.ZS.Q1", "metatype": [ { "id": "IndicatorName", "value": "Contraceptive prevalence, modern methods (% of females ages 15-49): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of women age 15-49 who are married or live in union and currently use a modern method of contraception. Modern methods are defined as female sterilization, male sterilization, the contraceptive pill, intrauterine contraceptive device (IUD), injectables, implants, female condom, male condom, diaphragm, contraceptive foam and contraceptive jelly, lactational amenorrhea method (LAM), emergency contraception, country-specific modern methods and other modern contraceptive methods respondent mentioned." }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.CONM.ZS.Q2", "metatype": [ { "id": "IndicatorName", "value": "Contraceptive prevalence, modern methods (% of females ages 15-49): Q2" }, { "id": "Longdefinition", "value": "Percentage of women age 15-49 who are married or live in union and currently use a modern method of contraception. Modern methods are defined as female sterilization, male sterilization, the contraceptive pill, intrauterine contraceptive device (IUD), injectables, implants, female condom, male condom, diaphragm, contraceptive foam and contraceptive jelly, lactational amenorrhea method (LAM), emergency contraception, country-specific modern methods and other modern contraceptive methods respondent mentioned." }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.CONM.ZS.Q3", "metatype": [ { "id": "IndicatorName", "value": "Contraceptive prevalence, modern methods (% of females ages 15-49): Q3" }, { "id": "Longdefinition", "value": "Percentage of women age 15-49 who are married or live in union and currently use a modern method of contraception. Modern methods are defined as female sterilization, male sterilization, the contraceptive pill, intrauterine contraceptive device (IUD), injectables, implants, female condom, male condom, diaphragm, contraceptive foam and contraceptive jelly, lactational amenorrhea method (LAM), emergency contraception, country-specific modern methods and other modern contraceptive methods respondent mentioned." }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.CONM.ZS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Contraceptive prevalence, modern methods (% of females ages 15-49): Q4" }, { "id": "Longdefinition", "value": "Percentage of women age 15-49 who are married or live in union and currently use a modern method of contraception. Modern methods are defined as female sterilization, male sterilization, the contraceptive pill, intrauterine contraceptive device (IUD), injectables, implants, female condom, male condom, diaphragm, contraceptive foam and contraceptive jelly, lactational amenorrhea method (LAM), emergency contraception, country-specific modern methods and other modern contraceptive methods respondent mentioned." }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.CONM.ZS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Contraceptive prevalence, modern methods (% of females ages 15-49): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of women age 15-49 who are married or live in union and currently use a modern method of contraception. Modern methods are defined as female sterilization, male sterilization, the contraceptive pill, intrauterine contraceptive device (IUD), injectables, implants, female condom, male condom, diaphragm, contraceptive foam and contraceptive jelly, lactational amenorrhea method (LAM), emergency contraception, country-specific modern methods and other modern contraceptive methods respondent mentioned." }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.IMRT.IN", "metatype": [ { "id": "IndicatorName", "value": "Mortality rate, infant (per 1,000 live births)" }, { "id": "Longdefinition", "value": "Deaths of children before their 1st birthday per 1,000 live births. Sample: children born up to 5 years before the survey for full population mortality estimates, and up to 10 years before the survey for wealth quintile specific mortality estimates" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.IMRT.IN.Q1", "metatype": [ { "id": "IndicatorName", "value": "Mortality rate, infant (per 1,000 live births): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Deaths of children before their 1st birthday per 1,000 live births. Sample: children born up to 5 years before the survey for full population mortality estimates, and up to 10 years before the survey for wealth quintile specific mortality estimates" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.IMRT.IN.Q2", "metatype": [ { "id": "IndicatorName", "value": "Mortality rate, infant (per 1,000 live births): Q2" }, { "id": "Longdefinition", "value": "Deaths of children before their 1st birthday per 1,000 live births. Sample: children born up to 5 years before the survey for full population mortality estimates, and up to 10 years before the survey for wealth quintile specific mortality estimates" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.IMRT.IN.Q3", "metatype": [ { "id": "IndicatorName", "value": "Mortality rate, infant (per 1,000 live births): Q3" }, { "id": "Longdefinition", "value": "Deaths of children before their 1st birthday per 1,000 live births. Sample: children born up to 5 years before the survey for full population mortality estimates, and up to 10 years before the survey for wealth quintile specific mortality estimates" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.IMRT.IN.Q4", "metatype": [ { "id": "IndicatorName", "value": "Mortality rate, infant (per 1,000 live births): Q4" }, { "id": "Longdefinition", "value": "Deaths of children before their 1st birthday per 1,000 live births. Sample: children born up to 5 years before the survey for full population mortality estimates, and up to 10 years before the survey for wealth quintile specific mortality estimates" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.IMRT.IN.Q5", "metatype": [ { "id": "IndicatorName", "value": "Mortality rate, infant (per 1,000 live births): Q5 (highest)" }, { "id": "Longdefinition", "value": "Deaths of children before their 1st birthday per 1,000 live births. Sample: children born up to 5 years before the survey for full population mortality estimates, and up to 10 years before the survey for wealth quintile specific mortality estimates" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.MORT", "metatype": [ { "id": "IndicatorName", "value": "Mortality rate, under-5 (per 1,000)" }, { "id": "Longdefinition", "value": "Deaths of children before their 5th birthday per 1,000 live births. Sample: children born up to 5 years before the survey for full population mortality estimates, and up to 10 years before the survey for wealth quintile specific mortality estimates" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.MORT.Q1", "metatype": [ { "id": "IndicatorName", "value": "Mortality rate, under-5 (per 1,000): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Deaths of children before their 5th birthday per 1,000 live births. Sample: children born up to 5 years before the survey for full population mortality estimates, and up to 10 years before the survey for wealth quintile specific mortality estimates" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.MORT.Q2", "metatype": [ { "id": "IndicatorName", "value": "Mortality rate, under-5 (per 1,000): Q2" }, { "id": "Longdefinition", "value": "Deaths of children before their 5th birthday per 1,000 live births. Sample: children born up to 5 years before the survey for full population mortality estimates, and up to 10 years before the survey for wealth quintile specific mortality estimates" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.MORT.Q3", "metatype": [ { "id": "IndicatorName", "value": "Mortality rate, under-5 (per 1,000): Q3" }, { "id": "Longdefinition", "value": "Deaths of children before their 5th birthday per 1,000 live births. Sample: children born up to 5 years before the survey for full population mortality estimates, and up to 10 years before the survey for wealth quintile specific mortality estimates" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.MORT.Q4", "metatype": [ { "id": "IndicatorName", "value": "Mortality rate, under-5 (per 1,000): Q4" }, { "id": "Longdefinition", "value": "Deaths of children before their 5th birthday per 1,000 live births. Sample: children born up to 5 years before the survey for full population mortality estimates, and up to 10 years before the survey for wealth quintile specific mortality estimates" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.MORT.Q5", "metatype": [ { "id": "IndicatorName", "value": "Mortality rate, under-5 (per 1,000): Q5 (highest)" }, { "id": "Longdefinition", "value": "Deaths of children before their 5th birthday per 1,000 live births. Sample: children born up to 5 years before the survey for full population mortality estimates, and up to 10 years before the survey for wealth quintile specific mortality estimates" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.SMEA", "metatype": [ { "id": "IndicatorName", "value": "Pap smear in last 3 years (% of females 20-69)" }, { "id": "Longdefinition", "value": "Percentage of women who received a pap smear in the last 3 years (preferably age 20-69 but age groups may vary)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.SMEA.Q1", "metatype": [ { "id": "IndicatorName", "value": "Pap smear in last 3 years (% of females 20-69): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of women who received a pap smear in the last 3 years (preferably age 20-69 but age groups may vary)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.SMEA.Q2", "metatype": [ { "id": "IndicatorName", "value": "Pap smear in last 3 years (% of females 20-69): Q2" }, { "id": "Longdefinition", "value": "Percentage of women who received a pap smear in the last 3 years (preferably age 20-69 but age groups may vary)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.SMEA.Q3", "metatype": [ { "id": "IndicatorName", "value": "Pap smear in last 3 years (% of females 20-69): Q3" }, { "id": "Longdefinition", "value": "Percentage of women who received a pap smear in the last 3 years (preferably age 20-69 but age groups may vary)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.SMEA.Q4", "metatype": [ { "id": "IndicatorName", "value": "Pap smear in last 3 years (% of females 20-69): Q4" }, { "id": "Longdefinition", "value": "Percentage of women who received a pap smear in the last 3 years (preferably age 20-69 but age groups may vary)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.DYN.SMEA.Q5", "metatype": [ { "id": "IndicatorName", "value": "Pap smear in last 3 years (% of females 20-69): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of women who received a pap smear in the last 3 years (preferably age 20-69 but age groups may vary)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.IMM.FULL", "metatype": [ { "id": "IndicatorName", "value": "Immunization, full (% of children ages 15-23 months)" }, { "id": "Longdefinition", "value": "Percentage of children age 15-23 months who received Bacillus Calmette-Guerin (BCG), measles/Measles-Mumps-Rubella (MMR), 3 doses of polio (excluding polio given at birth) and 3 doses of diphtheria-pertussis-tetanus (DPT)/Pentavalent vaccinations, either verified by vaccination card or by recall of respondent" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.IMM.FULL.Q1", "metatype": [ { "id": "IndicatorName", "value": "Immunization, full (% of children ages 15-23 months): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of children age 15-23 months who received Bacillus Calmette-Guerin (BCG), measles/Measles-Mumps-Rubella (MMR), 3 doses of polio (excluding polio given at birth) and 3 doses of diphtheria-pertussis-tetanus (DPT)/Pentavalent vaccinations, either verified by vaccination card or by recall of respondent" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.IMM.FULL.Q2", "metatype": [ { "id": "IndicatorName", "value": "Immunization, full (% of children ages 15-23 months): Q2" }, { "id": "Longdefinition", "value": "Percentage of children age 15-23 months who received Bacillus Calmette-Guerin (BCG), measles/Measles-Mumps-Rubella (MMR), 3 doses of polio (excluding polio given at birth) and 3 doses of diphtheria-pertussis-tetanus (DPT)/Pentavalent vaccinations, either verified by vaccination card or by recall of respondent" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.IMM.FULL.Q3", "metatype": [ { "id": "IndicatorName", "value": "Immunization, full (% of children ages 15-23 months): Q3" }, { "id": "Longdefinition", "value": "Percentage of children age 15-23 months who received Bacillus Calmette-Guerin (BCG), measles/Measles-Mumps-Rubella (MMR), 3 doses of polio (excluding polio given at birth) and 3 doses of diphtheria-pertussis-tetanus (DPT)/Pentavalent vaccinations, either verified by vaccination card or by recall of respondent" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.IMM.FULL.Q4", "metatype": [ { "id": "IndicatorName", "value": "Immunization, full (% of children ages 15-23 months): Q4" }, { "id": "Longdefinition", "value": "Percentage of children age 15-23 months who received Bacillus Calmette-Guerin (BCG), measles/Measles-Mumps-Rubella (MMR), 3 doses of polio (excluding polio given at birth) and 3 doses of diphtheria-pertussis-tetanus (DPT)/Pentavalent vaccinations, either verified by vaccination card or by recall of respondent" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.IMM.FULL.Q5", "metatype": [ { "id": "IndicatorName", "value": "Immunization, full (% of children ages 15-23 months): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of children age 15-23 months who received Bacillus Calmette-Guerin (BCG), measles/Measles-Mumps-Rubella (MMR), 3 doses of polio (excluding polio given at birth) and 3 doses of diphtheria-pertussis-tetanus (DPT)/Pentavalent vaccinations, either verified by vaccination card or by recall of respondent" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.IMM.MEAS", "metatype": [ { "id": "IndicatorName", "value": "Immunization, measles (% of children ages 15-23 months)" }, { "id": "Longdefinition", "value": "Percentage of children age 15-23 months who received measles or MMR vaccination, either verified by vaccination card or by recall of respondent" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.IMM.MEAS.Q1", "metatype": [ { "id": "IndicatorName", "value": "Immunization, measles (% of children ages 15-23 months): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of children age 15-23 months who received measles or MMR vaccination, either verified by vaccination card or by recall of respondent" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.IMM.MEAS.Q2", "metatype": [ { "id": "IndicatorName", "value": "Immunization, measles (% of children ages 15-23 months): Q2" }, { "id": "Longdefinition", "value": "Percentage of children age 15-23 months who received measles or MMR vaccination, either verified by vaccination card or by recall of respondent" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.IMM.MEAS.Q3", "metatype": [ { "id": "IndicatorName", "value": "Immunization, measles (% of children ages 15-23 months): Q3" }, { "id": "Longdefinition", "value": "Percentage of children age 15-23 months who received measles or MMR vaccination, either verified by vaccination card or by recall of respondent" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.IMM.MEAS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Immunization, measles (% of children ages 15-23 months): Q4" }, { "id": "Longdefinition", "value": "Percentage of children age 15-23 months who received measles or MMR vaccination, either verified by vaccination card or by recall of respondent" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.IMM.MEAS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Immunization, measles (% of children ages 15-23 months): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of children age 15-23 months who received measles or MMR vaccination, either verified by vaccination card or by recall of respondent" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.MLR.NETS.ZS", "metatype": [ { "id": "IndicatorName", "value": "Use of insecticide-treated bed nets (% of under-5 population)" }, { "id": "Longdefinition", "value": "Percentage of children under 5 who slept under an insecticide treated bed net (ITN) the night before the survey. A bed net is considered treated if it a) is a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pre-treated net which was soaked in insecticides less than 12 months ago" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.MLR.NETS.ZS.Q1", "metatype": [ { "id": "IndicatorName", "value": "Use of insecticide-treated bed nets (% of under-5 population): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of children under 5 who slept under an insecticide treated bed net (ITN) the night before the survey. A bed net is considered treated if it a) is a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pre-treated net which was soaked in insecticides less than 12 months ago" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.MLR.NETS.ZS.Q2", "metatype": [ { "id": "IndicatorName", "value": "Use of insecticide-treated bed nets (% of under-5 population): Q2" }, { "id": "Longdefinition", "value": "Percentage of children under 5 who slept under an insecticide treated bed net (ITN) the night before the survey. A bed net is considered treated if it a) is a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pre-treated net which was soaked in insecticides less than 12 months ago" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.MLR.NETS.ZS.Q3", "metatype": [ { "id": "IndicatorName", "value": "Use of insecticide-treated bed nets (% of under-5 population): Q3" }, { "id": "Longdefinition", "value": "Percentage of children under 5 who slept under an insecticide treated bed net (ITN) the night before the survey. A bed net is considered treated if it a) is a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pre-treated net which was soaked in insecticides less than 12 months ago" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.MLR.NETS.ZS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Use of insecticide-treated bed nets (% of under-5 population): Q4" }, { "id": "Longdefinition", "value": "Percentage of children under 5 who slept under an insecticide treated bed net (ITN) the night before the survey. A bed net is considered treated if it a) is a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pre-treated net which was soaked in insecticides less than 12 months ago" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.MLR.NETS.ZS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Use of insecticide-treated bed nets (% of under-5 population): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of children under 5 who slept under an insecticide treated bed net (ITN) the night before the survey. A bed net is considered treated if it a) is a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pre-treated net which was soaked in insecticides less than 12 months ago" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.ANV4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Pregnant women receiving prenatal care of at least four visits (% of pregnant women)" }, { "id": "Longdefinition", "value": "Percentage of most recent births in last two years with at least 4 antenatal care visits (women age 15-49 at the time of the survey)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.ANV4.ZS.Q1", "metatype": [ { "id": "IndicatorName", "value": "Pregnant women receiving prenatal care of at least four visits (% of pregnant women): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of most recent births in last two years with at least 4 antenatal care visits (women age 15-49 at the time of the survey)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.ANV4.ZS.Q2", "metatype": [ { "id": "IndicatorName", "value": "Pregnant women receiving prenatal care of at least four visits (% of pregnant women): Q2" }, { "id": "Longdefinition", "value": "Percentage of most recent births in last two years with at least 4 antenatal care visits (women age 15-49 at the time of the survey)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.ANV4.ZS.Q3", "metatype": [ { "id": "IndicatorName", "value": "Pregnant women receiving prenatal care of at least four visits (% of pregnant women): Q3" }, { "id": "Longdefinition", "value": "Percentage of most recent births in last two years with at least 4 antenatal care visits (women age 15-49 at the time of the survey)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.ANV4.ZS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Pregnant women receiving prenatal care of at least four visits (% of pregnant women): Q4" }, { "id": "Longdefinition", "value": "Percentage of most recent births in last two years with at least 4 antenatal care visits (women age 15-49 at the time of the survey)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.ANV4.ZS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Pregnant women receiving prenatal care of at least four visits (% of pregnant women): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of most recent births in last two years with at least 4 antenatal care visits (women age 15-49 at the time of the survey)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.ARIC.ZS", "metatype": [ { "id": "IndicatorName", "value": "Acute respiratory infections treated (% of children under 5 with cough and rapid breathing)" }, { "id": "Longdefinition", "value": "Percentage of children under 5 with cough and rapid breathing in the two weeks preceding the survey (DHS, WHS) who had a consultation with a formal healthcare provider (excluding pharmacies and visits to “other” healthcare providers). MICS data points use sample of children under 5 with cough and rapid breathing in the 2 weeks preceding the survey which originated from the chest. The definition of formal healthcare providers varies by country and data source." }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.ARIC.ZS.Q1", "metatype": [ { "id": "IndicatorName", "value": "Acute respiratory infections treated (% of children under 5 with cough and rapid breathing): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of children under 5 with cough and rapid breathing in the two weeks preceding the survey (DHS, WHS) who had a consultation with a formal healthcare provider (excluding pharmacies and visits to “other” healthcare providers). MICS data points use sample of children under 5 with cough and rapid breathing in the 2 weeks preceding the survey which originated from the chest. The definition of formal healthcare providers varies by country and data source." }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.ARIC.ZS.Q2", "metatype": [ { "id": "IndicatorName", "value": "Acute respiratory infections treated (% of children under 5 with cough and rapid breathing): Q2" }, { "id": "Longdefinition", "value": "Percentage of children under 5 with cough and rapid breathing in the two weeks preceding the survey (DHS, WHS) who had a consultation with a formal healthcare provider (excluding pharmacies and visits to “other” healthcare providers). MICS data points use sample of children under 5 with cough and rapid breathing in the 2 weeks preceding the survey which originated from the chest. The definition of formal healthcare providers varies by country and data source." }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.ARIC.ZS.Q3", "metatype": [ { "id": "IndicatorName", "value": "Acute respiratory infections treated (% of children under 5 with cough and rapid breathing): Q3" }, { "id": "Longdefinition", "value": "Percentage of children under 5 with cough and rapid breathing in the two weeks preceding the survey (DHS, WHS) who had a consultation with a formal healthcare provider (excluding pharmacies and visits to “other” healthcare providers). MICS data points use sample of children under 5 with cough and rapid breathing in the 2 weeks preceding the survey which originated from the chest. The definition of formal healthcare providers varies by country and data source." }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.ARIC.ZS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Acute respiratory infections treated (% of children under 5 with cough and rapid breathing): Q4" }, { "id": "Longdefinition", "value": "Percentage of children under 5 with cough and rapid breathing in the two weeks preceding the survey (DHS, WHS) who had a consultation with a formal healthcare provider (excluding pharmacies and visits to “other” healthcare providers). MICS data points use sample of children under 5 with cough and rapid breathing in the 2 weeks preceding the survey which originated from the chest. The definition of formal healthcare providers varies by country and data source." }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.ARIC.ZS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Acute respiratory infections treated (% of children under 5 with cough and rapid breathing): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of children under 5 with cough and rapid breathing in the two weeks preceding the survey (DHS, WHS) who had a consultation with a formal healthcare provider (excluding pharmacies and visits to “other” healthcare providers). MICS data points use sample of children under 5 with cough and rapid breathing in the 2 weeks preceding the survey which originated from the chest. The definition of formal healthcare providers varies by country and data source." }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BLSG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Blood sugar measured in last 5 years (% of population at risk of diabetes)" }, { "id": "Longdefinition", "value": "Percentage of population aged 40-69 at increased risk of diabetes (overweight, obese) having their blood sugar measured in the last 5 years" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BLSG.ZS.Q1", "metatype": [ { "id": "IndicatorName", "value": "Blood sugar measured in last 5 years (% of population at risk of diabetes): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of population aged 40-69 at increased risk of diabetes (overweight, obese) having their blood sugar measured in the last 5 years" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BLSG.ZS.Q2", "metatype": [ { "id": "IndicatorName", "value": "Blood sugar measured in last 5 years (% of population at risk of diabetes): Q2" }, { "id": "Longdefinition", "value": "Percentage of population aged 40-69 at increased risk of diabetes (overweight, obese) having their blood sugar measured in the last 5 years" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BLSG.ZS.Q3", "metatype": [ { "id": "IndicatorName", "value": "Blood sugar measured in last 5 years (% of population at risk of diabetes): Q3" }, { "id": "Longdefinition", "value": "Percentage of population aged 40-69 at increased risk of diabetes (overweight, obese) having their blood sugar measured in the last 5 years" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BLSG.ZS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Blood sugar measured in last 5 years (% of population at risk of diabetes): Q4" }, { "id": "Longdefinition", "value": "Percentage of population aged 40-69 at increased risk of diabetes (overweight, obese) having their blood sugar measured in the last 5 years" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BLSG.ZS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Blood sugar measured in last 5 years (% of population at risk of diabetes): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of population aged 40-69 at increased risk of diabetes (overweight, obese) having their blood sugar measured in the last 5 years" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BM15.FE", "metatype": [ { "id": "IndicatorName", "value": "Mean BMI, female (ages 15-49)" }, { "id": "Longdefinition", "value": "Mean BMI of female population aged 15-49 (excludes currently pregnant women and women having given birth in the three months preceding the survey)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BM15.FE.Q1", "metatype": [ { "id": "IndicatorName", "value": "Mean BMI, female (ages 15-49): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Mean BMI of female population aged 15-49 (excludes currently pregnant women and women having given birth in the three months preceding the survey)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BM15.FE.Q2", "metatype": [ { "id": "IndicatorName", "value": "Mean BMI, female (ages 15-49): Q2" }, { "id": "Longdefinition", "value": "Mean BMI of female population aged 15-49 (excludes currently pregnant women and women having given birth in the three months preceding the survey)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BM15.FE.Q3", "metatype": [ { "id": "IndicatorName", "value": "Mean BMI, female (ages 15-49): Q3" }, { "id": "Longdefinition", "value": "Mean BMI of female population aged 15-49 (excludes currently pregnant women and women having given birth in the three months preceding the survey)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BM15.FE.Q4", "metatype": [ { "id": "IndicatorName", "value": "Mean BMI, female (ages 15-49): Q4" }, { "id": "Longdefinition", "value": "Mean BMI of female population aged 15-49 (excludes currently pregnant women and women having given birth in the three months preceding the survey)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BM15.FE.Q5", "metatype": [ { "id": "IndicatorName", "value": "Mean BMI, female (ages 15-49): Q5 (highest)" }, { "id": "Longdefinition", "value": "Mean BMI of female population aged 15-49 (excludes currently pregnant women and women having given birth in the three months preceding the survey)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BM18", "metatype": [ { "id": "IndicatorName", "value": "Mean BMI, adults (age 18+)" }, { "id": "Longdefinition", "value": "Mean BMI of population aged 18 or older" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BM18.FE", "metatype": [ { "id": "IndicatorName", "value": "Mean BMI, female (age 18+)" }, { "id": "Longdefinition", "value": "Mean BMI of female population aged 18 or older" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BM18.FE.Q1", "metatype": [ { "id": "IndicatorName", "value": "Mean BMI, female (age 18+): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Mean BMI of female population aged 18 or older" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BM18.FE.Q2", "metatype": [ { "id": "IndicatorName", "value": "Mean BMI, female (age 18+): Q2" }, { "id": "Longdefinition", "value": "Mean BMI of female population aged 18 or older" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BM18.FE.Q3", "metatype": [ { "id": "IndicatorName", "value": "Mean BMI, female (age 18+): Q3" }, { "id": "Longdefinition", "value": "Mean BMI of female population aged 18 or older" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BM18.FE.Q4", "metatype": [ { "id": "IndicatorName", "value": "Mean BMI, female (age 18+): Q4" }, { "id": "Longdefinition", "value": "Mean BMI of female population aged 18 or older" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.STA.BM18.FE.Q5", "metatype": [ { "id": "IndicatorName", "value": "Mean BMI, female (age 18+): Q5 (highest)" }, { "id": "Longdefinition", "value": "Mean BMI of female population aged 18 or older" }, { "id": "Source", "value": "Health Equity and Financial 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population pushed below the 60% median consumption poverty line by out-of-pocket health care expenditure (%): Q3" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 60% of median consumption by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.CONS.ZS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the 60% median consumption poverty line by out-of-pocket health care expenditure (%): Q4" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 60% of median consumption by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.CONS.ZS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the 60% median 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World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 1.90 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP1.ZS.Q1", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 1.90 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP1.ZS.Q2", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q2" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 1.90 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP1.ZS.Q3", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q3" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 1.90 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP1.ZS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q4" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 1.90 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP1.ZS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 1.90 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP2.CG", "metatype": [ { "id": "IndicatorName", "value": "Change in poverty gap due to out-of-pocket health spending ($ 2011 PPP), $3.20 poverty line" }, { "id": "Longdefinition", "value": "Change (in international $) in poverty gap due to out-of-pocket health spending using $3.20 poverty line, defined as 3.2 times the difference between the per capita poverty gap for consumption (or income) net of out-of-pocket expenditures and the per capita poverty gap for consumption (or income) gross of out-of-pocket expenditures" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 3.20 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP2.ZS.Q1", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 3.20 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP2.ZS.Q2", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q2" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 3.20 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP2.ZS.Q3", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q3" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 3.20 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP2.ZS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q4" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 3.20 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP2.ZS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 3.20 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP3.CG", "metatype": [ { "id": "IndicatorName", "value": "Change in poverty gap due to out-of-pocket health spending ($ 2011 PPP), $5.50 poverty line" }, { "id": "Longdefinition", "value": "Change (in international $) in poverty gap due to out-of-pocket health spending using $5.50 poverty line, defined as 5.5 times the difference between the per capita poverty gap for consumption (or income) net of out-of-pocket expenditures and the per capita poverty gap for consumption (or income) gross of out-of-pocket expenditures" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $5.50 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 5.50 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP3.ZS.Q1", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $5.50 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 5.50 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP3.ZS.Q2", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $5.50 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q2" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 5.50 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP3.ZS.Q3", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $5.50 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q3" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 5.50 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP3.ZS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $5.50 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q4" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 5.50 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP3.ZS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $5.50 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 5.50 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP4.CG", "metatype": [ { "id": "IndicatorName", "value": "Change in poverty gap due to out-of-pocket health spending ($ 2011 PPP), $21.70 poverty line" }, { "id": "Longdefinition", "value": "Change (in international $) in poverty gap due to out-of-pocket health spending using $21.70 poverty line, defined as 21.7 times the difference between the per capita poverty gap for consumption (or income) net of out-of-pocket expenditures and the per capita poverty gap for consumption (or income) gross of out-of-pocket expenditures" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $21.70 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 21.70 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP4.ZS.Q1", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $21.70 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 21.70 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP4.ZS.Q2", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $21.70 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q2" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 21.70 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP4.ZS.Q3", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $21.70 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q3" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 21.70 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP4.ZS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $21.70 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q4" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 21.70 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOP4.ZS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed below the $21.70 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of population pushed below 21.70 international $ per day consumption poverty line by out-of-pocket health spending" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOPX.ZS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line (%)" }, { "id": "Longdefinition", "value": "Percentage of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOPX.ZS.Q1", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line (%)               : Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOPX.ZS.Q2", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line (%)               : Q2" }, { "id": "Longdefinition", "value": "Percentage of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOPX.ZS.Q3", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line (%)               : Q3" }, { "id": "Longdefinition", "value": "Percentage of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOPX.ZS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line (%)               : Q4" }, { "id": "Longdefinition", "value": "Percentage of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.NOPX.ZS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line (%)               : Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOP.CG", "metatype": [ { "id": "IndicatorName", "value": "Mean household per capita out-of-pocket health spending ($ 2011 PPP)" }, { "id": "Longdefinition", "value": "Mean household per capita out-of-pocket health spending ($ 2011 PPP)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOP.ZS", "metatype": [ { "id": "IndicatorName", "value": "Mean share of household consumption or income used on out-of-pocket health spending (%)" }, { "id": "Longdefinition", "value": "Mean share of household consumption or income used on out-of-pocket health spending (%)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOP.ZS.Q1", "metatype": [ { "id": "IndicatorName", "value": "Mean share of household consumption or income used on out-of-pocket health spending (%): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Mean share of household consumption or income used on out-of-pocket health spending (%)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOP.ZS.Q2", "metatype": [ { "id": "IndicatorName", "value": "Mean share of household consumption or income used on out-of-pocket health spending (%): Q2" }, { "id": "Longdefinition", "value": "Mean share of household consumption or income used on out-of-pocket health spending (%)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOP.ZS.Q3", "metatype": [ { "id": "IndicatorName", "value": "Mean share of household consumption or income used on out-of-pocket health spending (%): Q3" }, { "id": "Longdefinition", "value": "Mean share of household consumption or income used on out-of-pocket health spending (%)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOP.ZS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Mean share of household consumption or income used on out-of-pocket health spending (%): Q4" }, { "id": "Longdefinition", "value": "Mean share of household consumption or income used on out-of-pocket health spending (%)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOP.ZS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Mean share of household consumption or income used on out-of-pocket health spending (%): Q5 (highest)" }, { "id": "Longdefinition", "value": "Mean share of household consumption or income used on out-of-pocket health spending (%)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOPC.10.ZS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)" }, { "id": "Longdefinition", "value": "Percentage of population with out-of-pocket health spending larger than 10% of total household expenditure" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOPC.10.ZS.Q1", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of population with out-of-pocket health spending larger than 10% of total household expenditure" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOPC.10.ZS.Q2", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%): Q2" }, { "id": "Longdefinition", "value": "Percentage of population with out-of-pocket health spending larger than 10% of total household expenditure" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOPC.10.ZS.Q3", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%): Q3" }, { "id": "Longdefinition", "value": "Percentage of population with out-of-pocket health spending larger than 10% of total household expenditure" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOPC.10.ZS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%): Q4" }, { "id": "Longdefinition", "value": "Percentage of population with out-of-pocket health spending larger than 10% of total household expenditure" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOPC.10.ZS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of population with out-of-pocket health spending larger than 10% of total household expenditure" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOPC.25.ZS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%)" }, { "id": "Longdefinition", "value": "Percentage of population with out-of-pocket health spending larger than 25% of total household expenditure" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOPC.25.ZS.Q1", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of population with out-of-pocket health spending larger than 25% of total household expenditure" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOPC.25.ZS.Q2", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%): Q2" }, { "id": "Longdefinition", "value": "Percentage of population with out-of-pocket health spending larger than 25% of total household expenditure" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOPC.25.ZS.Q3", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%): Q3" }, { "id": "Longdefinition", "value": "Percentage of population with out-of-pocket health spending larger than 25% of total household expenditure" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOPC.25.ZS.Q4", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%): Q4" }, { "id": "Longdefinition", "value": "Percentage of population with out-of-pocket health spending larger than 25% of total household expenditure" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UHC.OOPC.25.ZS.Q5", "metatype": [ { "id": "IndicatorName", "value": "Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of population with out-of-pocket health spending larger than 25% of total household expenditure" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UWT.TFRT", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for contraception (% of females ages 15-49)" }, { "id": "Longdefinition", "value": "Percentage of women age 15-49 who are married or live in union who do not want to become pregnant but are not using contraception (revised definition by Bradley et al. 2012)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UWT.TFRT.Q1", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for contraception (% of females ages 15-49): Q1 (lowest)" }, { "id": "Longdefinition", "value": "Percentage of women age 15-49 who are married or live in union who do not want to become pregnant but are not using contraception (revised definition by Bradley et al. 2012)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UWT.TFRT.Q2", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for contraception (% of females ages 15-49): Q2" }, { "id": "Longdefinition", "value": "Percentage of women age 15-49 who are married or live in union who do not want to become pregnant but are not using contraception (revised definition by Bradley et al. 2012)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UWT.TFRT.Q3", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for contraception (% of females ages 15-49): Q3" }, { "id": "Longdefinition", "value": "Percentage of women age 15-49 who are married or live in union who do not want to become pregnant but are not using contraception (revised definition by Bradley et al. 2012)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UWT.TFRT.Q4", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for contraception (% of females ages 15-49): Q4" }, { "id": "Longdefinition", "value": "Percentage of women age 15-49 who are married or live in union who do not want to become pregnant but are not using contraception (revised definition by Bradley et al. 2012)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "HF.UWT.TFRT.Q5", "metatype": [ { "id": "IndicatorName", "value": "Unmet need for contraception (% of females ages 15-49): Q5 (highest)" }, { "id": "Longdefinition", "value": "Percentage of women age 15-49 who are married or live in union who do not want to become pregnant but are not using contraception (revised definition by Bradley et al. 2012)" }, { "id": "Source", "value": "Health Equity and Financial Protection Indicators (HEFPI) database, World Bank" } ], "source_id": "65" }, { "id": "LP.LPI.CUST.RK", "metatype": [ { "id": "IndicatorName", "value": "Efficiency of the clearance process, rank (1=highest performer)" }, { "id": "Longdefinition", "value": "The Logistics Performance Index reflects perceptions of a country's logistics performance based on six components. The component \"Efficiency of the clearance process\" includes results from the survey question \"Rate the efficiency of the clearance process (i.e. speed, simplicity and predictability of formalities) by border control agencies in country [x].\" Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2018 round of surveys covered close to 6,000 country assessments by around 1,000 international freight forwarders. Respondents evaluate eight economies on six core dimensions on a scale from 1 (worst) to 5 (best). The economies are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are available in Arvis, Ojala, Wiederer, Shepherd, Raj, Dairabayeva, Kiiski: \"Connecting to Compete 2018: The Logistics Performance Index and Its Indicators\", see Appendix 5 for methodology." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.CUST.XQ", "metatype": [ { "id": "IndicatorName", "value": "Efficiency of the clearance process, score (1=low to 5=high)" }, { "id": "Longdefinition", "value": "The Logistics Performance Index reflects perceptions of a country's logistics performance based on six components. The component \"Efficiency of the clearance process\" includes results from the survey question \"Rate the efficiency of the clearance process (i.e. speed, simplicity and predictability of formalities) by border control agencies in country [x].\" Scores range from 1 to 5, with a higher score representing better performance. Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2018 round of surveys covered close to 6,000 country assessments by around 1,000 international freight forwarders. Respondents evaluate eight economies on six core dimensions on a scale from 1 (worst) to 5 (best). The economies are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are available in Arvis, Ojala, Wiederer, Shepherd, Raj, Dairabayeva, Kiiski: \"Connecting to Compete 2018: The Logistics Performance Index and Its Indicators\", see Appendix 5 for methodology." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.INFR.RK", "metatype": [ { "id": "IndicatorName", "value": "Quality of trade- and transport-related infrastructure, rank (1=highest performer)" }, { "id": "Longdefinition", "value": "The Logistics Performance Index reflects perceptions of a country's logistics performance based on six components. The component \"Quality of trade- and transport-related infrastructure\" includes results from the survey question \"Evaluate the quality of trade- and transport related infrastructure (e.g. ports, railroads, roads, information technology) in country [x].\" Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2018 round of surveys covered close to 6,000 country assessments by around 1,000 international freight forwarders. Respondents evaluate eight economies on six core dimensions on a scale from 1 (worst) to 5 (best). The economies are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are available in Arvis, Ojala, Wiederer, Shepherd, Raj, Dairabayeva, Kiiski: \"Connecting to Compete 2018: The Logistics Performance Index and Its Indicators\", see Appendix 5 for methodology." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.INFR.XQ", "metatype": [ { "id": "IndicatorName", "value": "Quality of trade- and transport-related infrastructure, score (1=low to 5=high)" }, { "id": "Longdefinition", "value": "The Logistics Performance Index reflects perceptions of a country's logistics performance based on six components. The component \"Quality of trade- and transport-related infrastructure\" includes results from the survey question \"Evaluate the quality of trade- and transport related infrastructure (e.g. ports, railroads, roads, information technology) in country [x].\" Scores range from 1 to 5, with a higher score representing better performance. Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2018 round of surveys covered close to 6,000 country assessments by around 1,000 international freight forwarders. Respondents evaluate eight economies on six core dimensions on a scale from 1 (worst) to 5 (best). The economies are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are available in Arvis, Ojala, Wiederer, Shepherd, Raj, Dairabayeva, Kiiski: \"Connecting to Compete 2018: The Logistics Performance Index and Its Indicators\", see Appendix 5 for methodology." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.ITRN.RK", "metatype": [ { "id": "IndicatorName", "value": "Ease of arranging competitively priced international shipments, rank (1=highest performer)" }, { "id": "Longdefinition", "value": "The Logistics Performance Index reflects perceptions of a country's logistics performance based on six components. The component \"Ease of arranging competitively priced international shipments\" includes results from the survey question \"Assess the ease of arranging competitively priced shipments to country [x].\" Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2018 round of surveys covered close to 6,000 country assessments by around 1,000 international freight forwarders. Respondents evaluate eight economies on six core dimensions on a scale from 1 (worst) to 5 (best). The economies are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are available in Arvis, Ojala, Wiederer, Shepherd, Raj, Dairabayeva, Kiiski: \"Connecting to Compete 2018: The Logistics Performance Index and Its Indicators\", see Appendix 5 for methodology." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.ITRN.XQ", "metatype": [ { "id": "IndicatorName", "value": "Ease of arranging competitively priced international shipments, score (1=low to 5=high)" }, { "id": "Longdefinition", "value": "The Logistics Performance Index reflects perceptions of a country's logistics performance based on six components. The component \"Ease of arranging competitively priced international shipments\" includes results from the survey question \"Assess the ease of arranging competitively priced shipments to country [x].\" Scores range from 1 to 5, with a higher score representing better performance. Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2018 round of surveys covered close to 6,000 country assessments by around 1,000 international freight forwarders. Respondents evaluate eight economies on six core dimensions on a scale from 1 (worst) to 5 (best). The economies are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are available in Arvis, Ojala, Wiederer, Shepherd, Raj, Dairabayeva, Kiiski: \"Connecting to Compete 2018: The Logistics Performance Index and Its Indicators\", see Appendix 5 for methodology." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.LOGS.RK", "metatype": [ { "id": "IndicatorName", "value": "Competence and quality of logistics services, rank (1=highest performer)" }, { "id": "Longdefinition", "value": "The Logistics Performance Index reflects perceptions of a country's logistics performance based on six components. The component \"Competence and quality of logistics services\" includes results from the survey question \"Evaluate the overall level of competence and quality of logistics services (e.g. transport operators, customs brokers) in country [x].\" Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2018 round of surveys covered close to 6,000 country assessments by around 1,000 international freight forwarders. Respondents evaluate eight economies on six core dimensions on a scale from 1 (worst) to 5 (best). The economies are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are available in Arvis, Ojala, Wiederer, Shepherd, Raj, Dairabayeva, Kiiski: \"Connecting to Compete 2018: The Logistics Performance Index and Its Indicators\", see Appendix 5 for methodology." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.LOGS.XQ", "metatype": [ { "id": "IndicatorName", "value": "Competence and quality of logistics services, score (1=low to 5=high)" }, { "id": "Longdefinition", "value": "The Logistics Performance Index reflects perceptions of a country's logistics performance based on six components. The component \"Competence and quality of logistics services\" includes results from the survey question \"Evaluate the overall level of competence and quality of logistics services (e.g. transport operators, customs brokers) in country [x].\" Scores range from 1 to 5, with a higher score representing better performance. Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2018 round of surveys covered close to 6,000 country assessments by around 1,000 international freight forwarders. Respondents evaluate eight economies on six core dimensions on a scale from 1 (worst) to 5 (best). The economies are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are available in Arvis, Ojala, Wiederer, Shepherd, Raj, Dairabayeva, Kiiski: \"Connecting to Compete 2018: The Logistics Performance Index and Its Indicators\", see Appendix 5 for methodology." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.OVRL.RK", "metatype": [ { "id": "IndicatorName", "value": "Logistics performance index: Overall rank (1=highest performance)" }, { "id": "Longdefinition", "value": "The Logistics Performance Index overall rank reflects perceptions of a country's logistics performance based on the efficiency of the customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced international shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The ranks range from 1 to 160 (in the 2018, 2016, and 2014 edition; 155 in 2012 and 2010, and 150 in 2007), with a lower rank representing better performance. Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2018 round of surveys covered close to 6,000 country assessments by around 1,000 international freight forwarders. Details of the survey methodology and index construction methodology are available in Arvis, Ojala, Wiederer, Shepherd, Raj, Dairabayeva, Kiiski: \"Connecting to Compete 2018: The Logistics Performance Index and Its Indicators\", see Appendix 5 for methodology." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.OVRL.RK.LB", "metatype": [ { "id": "IndicatorName", "value": "Logistics performance index: Overall rank (1=highest performance), lower bound" }, { "id": "Longdefinition", "value": "The Logistics Performance Index overall rank reflects perceptions of a country's logistics performance based on the efficiency of the customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced international shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The ranks range from 1 to 160 (in the 2018, 2016, and 2014 edition; 155 in 2012 and 2010, and 150 in 2007), with a lower rank representing better performance. Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. To account for the sampling error created by the LPI’s survey-based dataset, LPI ranks are presented with approximate 80 percent confidence intervals (see appendix 5 of the LPI report available at lpi.worldbank.org). These intervals yield upper and lower bounds for a country’s LPI rank. Upper bounds for LPI ranks are calculated by increasing a country’s LPI score to its upper bound while maintaining all other country scores constant and then recalculating LPI ranks. An analogous procedure is adopted for lower bounds." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.OVRL.RK.UB", "metatype": [ { "id": "IndicatorName", "value": "Logistics performance index: Overall rank (1=highest performance), upper bound" }, { "id": "Longdefinition", "value": "The Logistics Performance Index overall rank reflects perceptions of a country's logistics performance based on the efficiency of the customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced international shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The ranks range from 1 to 160 (in the 2018, 2016, and 2014 edition; 155 in 2012 and 2010, and 150 in 2007), with a lower rank representing better performance. Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. To account for the sampling error created by the LPI’s survey-based dataset, LPI ranks are presented with approximate 80 percent confidence intervals (see appendix 5 of the LPI report available at lpi.worldbank.org). These intervals yield upper and lower bounds for a country’s LPI rank. Upper bounds for LPI ranks are calculated by increasing a country’s LPI score to its upper bound while maintaining all other country scores constant and then recalculating LPI ranks. An analogous procedure is adopted for lower bounds." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.OVRL.RK.ZS", "metatype": [ { "id": "IndicatorName", "value": "Logistics performance index: Percent of highest performer)" }, { "id": "Longdefinition", "value": "The Logistics Performance Index reflects perceptions of a country's logistics performance based on the efficiency of the customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced international shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The index ranges from 1 to 5, with a higher score representing better performance. Percent of highest performer refers to the percentage of the score of a country in comparison to the score of the highest performing country that year. Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2018 round of surveys covered close to 6,000 country assessments by around 1,000 international freight forwarders. Respondents evaluate eight economies on six core dimensions on a scale from 1 (worst) to 5 (best). The economies are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are available in Arvis, Ojala, Wiederer, Shepherd, Raj, Dairabayeva, Kiiski: \"Connecting to Compete 2018: The Logistics Performance Index and Its Indicators\", see Appendix 5 for methodology." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.OVRL.XQ", "metatype": [ { "id": "IndicatorName", "value": "Logistics performance index: Overall score (1=low to 5=high)" }, { "id": "Longdefinition", "value": "The Logistics Performance Index overall score reflects perceptions of a country's logistics performance based on the efficiency of the customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced international shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The index ranges from 1 to 5, with a higher score representing better performance. Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2018 round of surveys covered close to 6,000 country assessments by around 1,000 international freight forwarders. Respondents evaluate eight economies on six core dimensions on a scale from 1 (worst) to 5 (best). The economies are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are available in Arvis, Ojala, Wiederer, Shepherd, Raj, Dairabayeva, Kiiski (2018): \"Connecting to Compete 2018: The Logistics Performance Index and Its Indicators\", available at lpi.worldbank.org, see Appendix 5 for methodology." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.OVRL.XQ.LB", "metatype": [ { "id": "IndicatorName", "value": "Logistics performance index: Overall score (1=low to 5=high), lower bound" }, { "id": "Longdefinition", "value": "The Logistics Performance Index overall score reflects perceptions of a country's logistics performance based on the efficiency of the customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced international shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The index ranges from 1 to 5, with a higher score representing better performance. To account for the sampling error created by the LPI’s survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals (see appendix 5 of the LPI report available at lpi.worldbank.org). These intervals yield upper and lower bounds for a country’s LPI score and rank. Upper bounds for LPI scores are calculated by increasing a country’s LPI score to its upper bound while maintaining all other country scores constant and then recalculating LPI ranks. An analogous procedure is adopted for lower bounds." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.OVRL.XQ.UB", "metatype": [ { "id": "IndicatorName", "value": "Logistics performance index: Overall score (1=low to 5=high), upper bound" }, { "id": "Longdefinition", "value": "The Logistics Performance Index overall score reflects perceptions of a country's logistics performance based on the efficiency of the customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced international shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The index ranges from 1 to 5, with a higher score representing better performance. To account for the sampling error created by the LPI’s survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals (see appendix 5 of the LPI report available at lpi.worldbank.org). These intervals yield upper and lower bounds for a country’s LPI score and rank. Upper bounds for LPI scores are calculated by increasing a country’s LPI score to its upper bound while maintaining all other country scores constant and then recalculating LPI ranks. An analogous procedure is adopted for lower bounds." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.TIME.RK", "metatype": [ { "id": "IndicatorName", "value": "Frequency with which shipments reach consignee within scheduled or expected time, rank (1=highest performer)" }, { "id": "Longdefinition", "value": "The Logistics Performance Index reflects perceptions of a country's logistics performance based on six components. The component \"Frequency with which shipments reach consignee within scheduled or expected time\" includes results from the survey question \"When arranging shipments to the countries listed below, how often do they reach the consignee within the scheduled or expected delivery time?\" Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2018 round of surveys covered close to 6,000 country assessments by around 1,000 international freight forwarders. Respondents evaluate eight economies on six core dimensions on a scale from 1 (worst) to 5 (best). The economies are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are available in Arvis, Ojala, Wiederer, Shepherd, Raj, Dairabayeva, Kiiski: \"Connecting to Compete 2018: The Logistics Performance Index and Its Indicators\", see Appendix 5 for methodology." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.TIME.XQ", "metatype": [ { "id": "IndicatorName", "value": "Frequency with which shipments reach consignee within scheduled or expected time, score (1=low to 5=high)" }, { "id": "Longdefinition", "value": "The Logistics Performance Index reflects perceptions of a country's logistics performance based on six components. The component \"Frequency with which shipments reach consignee within scheduled or expected time\" includes results from the survey question \"When arranging shipments to the countries listed below, how often do they reach the consignee within the scheduled or expected delivery time?\" Scores range from 1 to 5, with a higher score representing better performance. Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2018 round of surveys covered close to 6,000 country assessments by around 1,000 international freight forwarders. Respondents evaluate eight economies on six core dimensions on a scale from 1 (worst) to 5 (best). The economies are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are available in Arvis, Ojala, Wiederer, Shepherd, Raj, Dairabayeva, Kiiski: \"Connecting to Compete 2018: The Logistics Performance Index and Its Indicators\", see Appendix 5 for methodology." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.TRAC.RK", "metatype": [ { "id": "IndicatorName", "value": "Ability to track and trace consignments, rank (1=highest performer)" }, { "id": "Longdefinition", "value": "The Logistics Performance Index reflects perceptions of a country's logistics performance based on six components. The component \"Ability to track and trace consignments\" includes results from the survey question \"Rate the ability to track and trace your consignments when shipping to country [x].\" Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2018 round of surveys covered close to 6,000 country assessments by around 1,000 international freight forwarders. Respondents evaluate eight economies on six core dimensions on a scale from 1 (worst) to 5 (best). The economies are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are available in Arvis, Ojala, Wiederer, Shepherd, Raj, Dairabayeva, Kiiski: \"Connecting to Compete 2018: The Logistics Performance Index and Its Indicators\", see Appendix 5 for methodology." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "LP.LPI.TRAC.XQ", "metatype": [ { "id": "IndicatorName", "value": "Ability to track and trace consignments, score (1=low to 5=high)" }, { "id": "Longdefinition", "value": "The Logistics Performance Index reflects perceptions of a country's logistics performance based on six components. The component \"Ability to track and trace consignments\" includes results from the survey question \"Rate the ability to track and trace your consignments when shipping to country [x].\" Scores range from 1 to 5, with a higher score representing better performance. Data are from the Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2018 round of surveys covered close to 6,000 country assessments by around 1,000 international freight forwarders. Respondents evaluate eight economies on six core dimensions on a scale from 1 (worst) to 5 (best). The economies are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are available in Arvis, Ojala, Wiederer, Shepherd, Raj, Dairabayeva, Kiiski: \"Connecting to Compete 2018: The Logistics Performance Index and Its Indicators\", see Appendix 5 for methodology." }, { "id": "Source", "value": "World Bank Logistics Performance Index Surveys. Data and methodology are available at: http://www.worldbank.org/lpi" } ], "source_id": "66" }, { "id": "PI-01.1", "metatype": [ { "id": "IndicatorName", "value": "Aggregate expenditure out-turn" }, { "id": "Longdefinition", "value": "Aggregate expenditure out-turn" }, { "id": "Shortdefinition", "value": "Aggregate expenditure out-turn" } ], "source_id": "68" }, { "id": "FB.CAP.INST.ST.DM", "metatype": [ { "id": "Generalcomments", "value": "Does a DEDICATED, NATIONAL, MULTI-STAKEHOLDER STRUCTURE exist to promote and coordinate provision of FINANCIAL EDUCATION?" }, { "id": "IndicatorName", "value": "Does a dedicated, national, multi-stakeholder structure exist to promote and coordinate provision of financial education?" }, { "id": "Othernotes", "value": "VHQB_00" }, { "id": "Shortdefinition", "value": "Does a dedicated, national, multi-stakeholder structure exist to promote and coordinate the provision of financial education in your country? \n- Yes" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.INST.ST.MS.AL", "metatype": [ { "id": "Generalcomments", "value": "Does GOVERNMENT, INDUSTRY, AND NGOs participate in the multi-stakeholder structure to promote and coordinate financial education?" }, { "id": "IndicatorName", "value": "Does government, industry, and NGOs participate in the multi-stakeholder structure to promote and coordinate financial education?" }, { "id": "Shortdefinition", "value": "Who participates in the multi-stakeholder structure? \n- Government, industry, and NGOs" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.INST.ST.MS.GI", "metatype": [ { "id": "Generalcomments", "value": "Does GOVERNMENT AND INDUSTRY ONLY participate in the multi-stakeholder structure to promote and coordinate financial education?" }, { "id": "IndicatorName", "value": "Does government and industry only participate in the multi-stakeholder structure to promote and coordinate financial education?" }, { "id": "Shortdefinition", "value": "Who participates in the multi-stakeholder structure? \n- Government and industry only" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.INST.ST.MS.GP", "metatype": [ { "id": "Generalcomments", "value": "Does GOVERNMENT participate in the multi-stakeholder structure to promote and coordinate financial education?" }, { "id": "IndicatorName", "value": "Does government participate in the multi-stakeholder structure to promote and coordinate financial education?" }, { "id": "Shortdefinition", "value": "Who participates in the multi-stakeholder structure? \n- Government authorities" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.INST.ST.MS.IO", "metatype": [ { "id": "Generalcomments", "value": "Does INDUSTRY ONLY participate in the multi-stakeholder structure to promote and coordinate financial education?" }, { "id": "IndicatorName", "value": "Does industry only participate in the multi-stakeholder structure to promote and coordinate financial education?" }, { "id": "Shortdefinition", "value": "Who participates in the multi-stakeholder structure? \n- Industry (e.g. financial service providers)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.INST.ST.MU", "metatype": [ { "id": "Generalcomments", "value": "Are there MULTIPLE AGENCIES responsible for FINANCIAL EDUCATION POLICY AND PROGRAMS?" }, { "id": "IndicatorName", "value": "Are there multiple agencies responsible for financial education policy and programs?" }, { "id": "Othernotes", "value": "VHQA_01" }, { "id": "Shortdefinition", "value": "Is there any agency responsible for leading and/or coordinating financial education policy and programs? \n- Yes, multiple agencies" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.INST.ST.SG", "metatype": [ { "id": "Generalcomments", "value": "Is there a SINGLE AGENCY responsible for FINANCIAL EDUCATION POLICY AND PROGRAMS?" }, { "id": "IndicatorName", "value": "Is there a single agency responsible for financial education policy and programs?" }, { "id": "Othernotes", "value": "VHQA_00" }, { "id": "Shortdefinition", "value": "Is there any agency responsible for leading and/or coordinating financial education policy and programs? \n- Yes, a single agency." }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.LEGL.DF.FE", "metatype": [ { "id": "Generalcomments", "value": "Does this country have a formal definition for FINANCIAL EDUCATION or FINANCIAL LITERACY or FINANCIAL CAPABILITY ?" }, { "id": "IndicatorName", "value": "Does this country have a formal definition for financial education or financial literacy or financial capability?" }, { "id": "Othernotes", "value": "VHPA_00" }, { "id": "Shortdefinition", "value": "Does your country have an official definition of any of the following terms?\n- Financial Education, Financial Literacy, Financial Capability" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.FE.CTR.WS", "metatype": [ { "id": "Generalcomments", "value": "Does the GOVERNMENT itself or with partners maintain a WEBSITE WITH EDUCATIONAL CONTENT, TOOLS, AND RESOURCES FOR BROADER FINANCIAL EDUCATION?" }, { "id": "IndicatorName", "value": "Does the government itself or with partners maintain a website with educational content, tools, and resources for broader financial education?" }, { "id": "Othernotes", "value": "VHXA_01" }, { "id": "Shortdefinition", "value": "Does the government, either by itself or in coordination with another institution, maintain a website with the objective of improving the financial capability of the public? \n- Yes, with educational content, tools, and resources for broader financial education purposes" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.G2P.FE", "metatype": [ { "id": "Generalcomments", "value": "Is FINANCIAL EDUCATION integrated into any GOVERNMENT-PROVIDED SOCIAL ASSISTANCE PROGRAMS?" }, { "id": "IndicatorName", "value": "Is financial education integrated into any government-provided social assistance programs?" }, { "id": "Othernotes", "value": "VHVA_00" }, { "id": "Shortdefinition", "value": "Is financial education included as a component of any government-provided social assistance programs (e.g. as an element of cash transfer programs)?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.GL.AP", "metatype": [ { "id": "Generalcomments", "value": "Has the GOVERNMENT issued written guidelines DIRECTED TO ALL PROVIDERS OF FINANCIAL EDUCATION on content and/or methodology?" }, { "id": "IndicatorName", "value": "Has the government issued written guidelines directed to all providers of financial education on content and/or methodology?" }, { "id": "Shortdefinition", "value": "Has the government issued written guidelines to providers of financial education on content and/or approaches to the provision of financial education? \n- Yes, directed at all providers of financial education" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.GL.SP", "metatype": [ { "id": "Generalcomments", "value": "Has the GOVERNMENT issued written guidelines DIRECTED TO A LIMITED SET OF PROVIDERS OF FINANCIAL EDUCATION on content and/or methodology?" }, { "id": "IndicatorName", "value": "Has the government issued written guidelines directed to a limited set of providers of financial education on content and/or methodology?" }, { "id": "Shortdefinition", "value": "Has the government issued written guidelines to providers of financial education on content and/or approaches to the provision of financial education? \n- Yes, directed at a defined or limited set of providers of financial education (e.g. schools)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.NM.5Y", "metatype": [ { "id": "Generalcomments", "value": "Has the GOVERNMENT, either by itself or with partners, undertaken a NATIONAL MAPPING OF FINANCIAL EDUCATION ACTIVITIES in the PAST FIVE YEARS?" }, { "id": "IndicatorName", "value": "Has the government, either by itself or with partners, undertaken a national mapping of financial education activities in the past five years?" }, { "id": "Othernotes", "value": "VHRA_00" }, { "id": "Shortdefinition", "value": "Has the government, either by itself or in coordination with another institution, undertaken a national mapping of financial education activities in the past five years?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.NS.BF.5Y", "metatype": [ { "id": "Generalcomments", "value": "Has a nationally representative BROADER SURVEY WITH A COMPONENT ON FINANCIAL CAPABILITY OF INDIVIDUALS OR HOUSEHOLDS been conducted in the past five years?" }, { "id": "IndicatorName", "value": "Has a nationally representative broader survey with a component on financial capability of individuals or households been conducted in the past five years?" }, { "id": "Othernotes", "value": "VHTA_01" }, { "id": "Shortdefinition", "value": "Has a nationally representative individual- or household-level survey of financial capability been conducted in your country in the past five years? \n- Yes, as part of a broader survey (e.g. related financial inclusion or financial consumer protection)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.NS.FC.5Y", "metatype": [ { "id": "Generalcomments", "value": "Has a nationally representative DEDICATED SURVEY OF FINANCIAL CAPABILITY OF INDIVIDUALS OR HOUSEHOLDS been conducted in the past five years?" }, { "id": "IndicatorName", "value": "Has a nationally representative dedicated survey of financial capability of individuals or households been conducted in the past five years?" }, { "id": "Othernotes", "value": "VHTA_00" }, { "id": "Shortdefinition", "value": "Has a nationally representative individual- or household-level survey of financial capability been conducted in your country in the past five years? \n- Yes, a dedicated survey on financial capability" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.PSC.CD.2Y", "metatype": [ { "id": "Generalcomments", "value": "Is FINANCIAL EDUCATION at the stage of CURRICULUM DEVELOPMENT PLANNED WITHIN THE NEXT 1-2 YEARS within public school curriculums?" }, { "id": "IndicatorName", "value": "Is financial education at the stage of curriculum development planned within the next 1-2 years within public school curriculums?" }, { "id": "Othernotes", "value": "VHWA_03" }, { "id": "Shortdefinition", "value": "Is financial education included as a topic or subject in public school curriculums? \n- No, but planned development of curriculum within next 1-2 years" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.PSC.DT.FE", "metatype": [ { "id": "Generalcomments", "value": "Is FINANCIAL EDUCATION included in public school curriculums as a DISTINCT TOPIC OR SUBJECT?" }, { "id": "IndicatorName", "value": "Is financial education included in public school curriculums as a distinct topic or subject?" }, { "id": "Othernotes", "value": "VHWA_00" }, { "id": "Shortdefinition", "value": "Is financial education included as a topic or subject in public school curriculums? \n- Yes, as a distinct topic or subject" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.PSC.IP.2Y", "metatype": [ { "id": "Generalcomments", "value": "Is FINANCIAL EDUCATION at the stage of IMPLEMENTATION PLANNED WITHIN THE NEXT 1-2 YEARS within public school curriculums?" }, { "id": "IndicatorName", "value": "Is financial education at the stage of implementation planned within the next 1-2 years within public school curriculums?" }, { "id": "Othernotes", "value": "VHWA_02" }, { "id": "Shortdefinition", "value": "Is financial education included as a topic or subject in public school curriculums? \n- No, but planned implementation within next 1-2 years" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.PSC.JS.FE", "metatype": [ { "id": "Generalcomments", "value": "Is FINANCIAL EDUCATION currently or soon to be included as a topic in public school curriculums AT JUNIOR SECONDARY LEVEL?" }, { "id": "IndicatorName", "value": "Is financial education currently or soon to be included as a topic in public school curriculums at junior secondary level?" }, { "id": "Othernotes", "value": "VHWB_01" }, { "id": "Shortdefinition", "value": "For which education levels is financial education included as a topic in public school curriculums (current or planned)? \n- Junior secondary" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.PSC.NI", "metatype": [ { "id": "Generalcomments", "value": "Is FINANCIAL EDUCATION at the stage of NOT BEING INCLUDED in public school curriculums?" }, { "id": "IndicatorName", "value": "Is financial education at the stage of not being included in public school curriculums?" }, { "id": "Othernotes", "value": "VHWA_04" }, { "id": "Shortdefinition", "value": "Is financial education included as a topic or subject in public school curriculums? \n- No" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.PSC.PR.FE", "metatype": [ { "id": "Generalcomments", "value": "Is FINANCIAL EDUCATION currently or soon to be included as a topic in public school curriculums AT PRIMARY LEVEL?" }, { "id": "IndicatorName", "value": "Is financial education currently or soon to be included as a topic in public school curriculums at primary level?" }, { "id": "Othernotes", "value": "VHWB_00" }, { "id": "Shortdefinition", "value": "For which education levels is financial education included as a topic in public school curriculums (current or planned)? \n- Primary" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.PSC.SS.FE", "metatype": [ { "id": "Generalcomments", "value": "Is FINANCIAL EDUCATION currently or soon to be included as a topic in public school curriculums AT SENIOR SECONDARY LEVEL?" }, { "id": "IndicatorName", "value": "Is financial education currently or soon to be included as a topic in public school curriculums at senior secondary level?" }, { "id": "Othernotes", "value": "VHWB_02" }, { "id": "Shortdefinition", "value": "For which education levels is financial education included as a topic in public school curriculums (current or planned)? \n- Senior secondary" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.PSC.ST.FE", "metatype": [ { "id": "Generalcomments", "value": "Is FINANCIAL EDUCATION included in public school curriculums as a SUBTOPIC INTEGRATED INTO ONE OR MULTIPLE OTHER TOPICS OR SUBJECTS?" }, { "id": "IndicatorName", "value": "Is financial education included in public school curriculums as a subtopic integrated into one or multiple other topics or subjects?" }, { "id": "Othernotes", "value": "VHWA_01" }, { "id": "Shortdefinition", "value": "Is financial education included as a topic or subject in public school curriculums? \n- Yes, as a subtopic integrated into one or multiple other topics or subjects" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.PSC.UN.FE", "metatype": [ { "id": "Generalcomments", "value": "Is FINANCIAL EDUCATION currently or soon to be included as a topic in public school curriculums AT UNIVERSITY LEVEL?" }, { "id": "IndicatorName", "value": "Is financial education currently or soon to be included as a topic in public school curriculums at university level?" }, { "id": "Othernotes", "value": "VHWB_03" }, { "id": "Shortdefinition", "value": "For which education levels is financial education included as a topic in public school curriculums (current or planned)? \n- University level" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.PTC.INF.WS", "metatype": [ { "id": "Generalcomments", "value": "Does the GOVERNMENT itself or with partners maintain a WEBSITE TO DISCLOSE PRICING AND TERMS AND CONDITIONS INFORMATION?" }, { "id": "IndicatorName", "value": "Does the government itself or with partners maintain a website to disclose pricing and terms and conditions information?" }, { "id": "Othernotes", "value": "VHXA_00" }, { "id": "Shortdefinition", "value": "Does the government, either by itself or in coordination with another institution, maintain a website with the objective of improving the financial capability of the public? \n- Yes, to disclose information on the pricing and terms of financial products and services" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.RE.AP", "metatype": [ { "id": "Generalcomments", "value": "Does the government explicitly require ALL FINANCIAL SERVICE PROVIDERS to offer financial education?" }, { "id": "IndicatorName", "value": "Does the government explicitly require all financial service providers to offer financial education?" }, { "id": "Shortdefinition", "value": "Does the government explicitly require (i.e. via regulation, guidelines, or circular) financial institutions to provide financial education? \n- Yes, directed at all financial institutions" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.RE.SP", "metatype": [ { "id": "Generalcomments", "value": "Does the government explicitly require A LIMITED SET OF FINANCIAL SERVICE PROVIDERS to offer financial education?" }, { "id": "IndicatorName", "value": "Does the government explicitly require a limited set of financial service providers to offer financial education?" }, { "id": "Shortdefinition", "value": "Does the government explicitly require (i.e. via regulation, guidelines, or circular) financial institutions to provide financial education? \n- Yes, directed at a defined or limited set of financial institutions (e.g. cooperatives, or financial institutions in a specific region)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.RG.DC.AP", "metatype": [ { "id": "Generalcomments", "value": "Does the GOVERNMENT regularly collect data from ALL KNOWN PROVIDERS OF FINANCIAL EDUCATION on the reach of their programs?" }, { "id": "IndicatorName", "value": "Does the government regularly collect data from all known providers of financial education on the reach of their programs?" }, { "id": "Shortdefinition", "value": "Does the government regularly collect data directly from providers of financial education programs on the reach (e.g. number of beneficiaries) of their programs? \n- Yes, from all known providers of financial education" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.CAP.POLI.RG.DC.SP", "metatype": [ { "id": "Generalcomments", "value": "Does the GOVERNMENT regularly collect data from A LIMITED SET OF PROVIDERS OF FINANCIAL EDUCATION on the reach of their programs?" }, { "id": "IndicatorName", "value": "Does the government regularly collect data from a limited set of providers of financial education on the reach of their programs?" }, { "id": "Shortdefinition", "value": "Does the government regularly collect data directly from providers of financial education programs on the reach (e.g. number of beneficiaries) of their programs? \n- Yes, from a defined or limited set of providers of financial education" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.AL.CO.NP", "metatype": [ { "id": "Generalcomments", "value": "Do laws and regulations ALLOW CONSUMERS A COOLING-OFF PERIOD during which they can WITHDRAW FROM A PRODUCT OR SERVICE WITHOUT A PENALTY?" }, { "id": "IndicatorName", "value": "Do laws and regulations allow consumers a cooling-off period during which they can withdraw from a product or service without a penalty?" }, { "id": "Othernotes", "value": "VHHA_02" }, { "id": "Shortdefinition", "value": "Are there any of the following provisions in existing laws and/or regulations that prohibit or restrict terms and practices which limit customer mobility between financial institutions \n- Provisions which allow consumers a cooling-off period for certain products during which they can withdraw from the product or service without incurring penalties;" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.EB.AR", "metatype": [ { "id": "Generalcomments", "value": "Do laws and regulations require ASSESSMENT OF BORROWER'S ABILITY TO REPAY, WITHOUT SPECIFIC BORROWING LIMITS?" }, { "id": "IndicatorName", "value": "Do laws and regulations require assessment of borrower's ability to repay, without specific borrowing limits?" }, { "id": "Othernotes", "value": "VHFA_01" }, { "id": "Shortdefinition", "value": "Are there any provisions in the existing laws and regulations that restrict excessive borrowing by individuals (to ensure affordability/avoid over-indebtedness by individual borrowers) \n- Yes, regulations require lending institutions to assess borrower ability to repay the loan, but no specific limits are set" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Responsible Lending" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.EB.EL", "metatype": [ { "id": "Generalcomments", "value": "Do laws and regulations set EXPLICIT LIMITS ON EXCESSIVE BORROWING?" }, { "id": "IndicatorName", "value": "Do laws and regulations set explicit limits on excessive borrowing?" }, { "id": "Othernotes", "value": "VHFA_00" }, { "id": "Shortdefinition", "value": "Are there any provisions in the existing laws and regulations that restrict excessive borrowing by individuals (to ensure affordability/avoid over-indebtedness by individual borrowers) \n- Yes, there are explicit limits set by regulations (i.e. debt to income ratio)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Responsible Lending" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.EB.NP", "metatype": [ { "id": "Generalcomments", "value": "Do laws and regulations contain NO PROVISIONS TO RESTRICT EXCESSIVE BORROWING?" }, { "id": "IndicatorName", "value": "Do laws and regulations contain no provisions to restrict excessive borrowing?" }, { "id": "Othernotes", "value": "VHFA_03" }, { "id": "Shortdefinition", "value": "Are there any provisions in the existing laws and regulations that restrict excessive borrowing by individuals (to ensure affordability/avoid over-indebtedness by individual borrowers) \n- No provisions" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Responsible Lending" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.EB.OR", "metatype": [ { "id": "Generalcomments", "value": "Do laws and regulations SET OTHER RESTRICTIONS ON EXCESSIVE BORROWING?" }, { "id": "IndicatorName", "value": "Do laws and regulations set other restrictions on excessive borrowing?" }, { "id": "Othernotes", "value": "VHFA_02" }, { "id": "Shortdefinition", "value": "Are there any provisions in the existing laws and regulations that restrict excessive borrowing by individuals (to ensure affordability/avoid over-indebtedness by individual borrowers) \n- Yes, other (please describe briefly)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Responsible Lending" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.ML.PC.AG", "metatype": [ { "id": "Generalcomments", "value": "Are financial institutions required to have a MINIMUM LEVEL OF PROFESSIONAL COMPETENCE/TRAINING for AGENTS AND INTERMEDIARIES?" }, { "id": "IndicatorName", "value": "Are financial institutions required to have a minimum level of professional competence/training for agents and intermediaries?" }, { "id": "Othernotes", "value": "VHIB_04" }, { "id": "Shortdefinition", "value": "Please specify to which categories the law or regulation to have certain minimum level of professional competence/training applies: \n - Agents and intermediaries" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.ML.PC.AP", "metatype": [ { "id": "Generalcomments", "value": "Are financial institutions required to have a MINIMUM LEVEL OF PROFESSIONAL COMPETENCE/TRAINING for ALL RELEVANT PERSONNEL dealing with consumers?" }, { "id": "IndicatorName", "value": "Are financial institutions required to have a minimum level of professional competence/training for all relevant personnel dealing with consumers?" }, { "id": "Othernotes", "value": "VHIA_00" }, { "id": "Shortdefinition", "value": "Are financial institutions required by law or regulation to have certain minimum level of professional competence/training for relevant personnel dealing with consumers?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.ML.PC.CO", "metatype": [ { "id": "Generalcomments", "value": "Are financial institutions required to have a MINIMUM LEVEL OF PROFESSIONAL COMPETENCE/TRAINING for COMPLAINTS OFFICERS?" }, { "id": "IndicatorName", "value": "Are financial institutions required to have a minimum level of professional competence/training for complaints officers?" }, { "id": "Othernotes", "value": "VHIB_03" }, { "id": "Shortdefinition", "value": "Please specify to which categories the law or regulation to have certain minimum level of professional competence/training applies: \n - Complaints officers" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.ML.PC.CR", "metatype": [ { "id": "Generalcomments", "value": "Are financial institutions required to have a MINIMUM LEVEL OF PROFESSIONAL COMPETENCE/TRAINING for CREDIT OFFICERS?" }, { "id": "IndicatorName", "value": "Are financial institutions required to have a minimum level of professional competence/training for credit officers?" }, { "id": "Othernotes", "value": "VHIB_00" }, { "id": "Shortdefinition", "value": "Please specify to which categories the law or regulation to have certain minimum level of professional competence/training applies: \n - Credit officers" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.ML.PC.CS", "metatype": [ { "id": "Generalcomments", "value": "Are financial institutions required to have a MINIMUM LEVEL OF PROFESSIONAL COMPETENCE/TRAINING for CUSTOMER SERVICE REPRESENTATIVES?" }, { "id": "IndicatorName", "value": "Are financial institutions required to have a minimum level of professional competence/training for customer service representatives?" }, { "id": "Othernotes", "value": "VHIB_05" }, { "id": "Shortdefinition", "value": "Please specify to which categories the law or regulation to have certain minimum level of professional competence/training applies: \n - Customer service representatives" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.ML.PC.MS", "metatype": [ { "id": "Generalcomments", "value": "Are financial institutions required to have a MINIMUM LEVEL OF PROFESSIONAL COMPETENCE/TRAINING for MARKETING STAFF?" }, { "id": "IndicatorName", "value": "Are financial institutions required to have a minimum level of professional competence/training for marketing staff?" }, { "id": "Othernotes", "value": "VHIB_02" }, { "id": "Shortdefinition", "value": "Please specify to which categories the law or regulation to have certain minimum level of professional competence/training applies: \n - Marketing staff" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.ML.PC.RO", "metatype": [ { "id": "Generalcomments", "value": "Are financial institutions required to have a MINIMUM LEVEL OF PROFESSIONAL COMPETENCE/TRAINING for RECOVERY OFFICERS?" }, { "id": "IndicatorName", "value": "Are financial institutions required to have a minimum level of professional competence/training for recovery officers?" }, { "id": "Othernotes", "value": "VHIB_01" }, { "id": "Shortdefinition", "value": "Please specify to which categories the law or regulation to have certain minimum level of professional competence/training applies: \n - Recovery officers" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.MS.DC", "metatype": [ { "id": "Generalcomments", "value": "Do laws or regulations REQUIRE MINIMUM STANDARDS FOR DEBT COLLECTION PRACTICES?" }, { "id": "IndicatorName", "value": "Do laws or regulations require minimum standards for debt collection practices?" }, { "id": "Othernotes", "value": "VHJA_00" }, { "id": "Shortdefinition", "value": "Are there any provisions in the existing laws or regulations that require minimum standards for debt collection practices?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.PR.BU.ND", "metatype": [ { "id": "Generalcomments", "value": "Do laws and regulations PROHIBIT OR RESTRICT BUNDLING AND TYING SERVICES AND PRODUCTS in a manner that unduly restricts the choice of consumers?" }, { "id": "IndicatorName", "value": "Do laws and regulations prohibit or restrict bundling and tying services and products in a manner that unduly restricts the choice of consumers?" }, { "id": "Othernotes", "value": "VHGA_04" }, { "id": "Shortdefinition", "value": "Are there any provisions in the existing laws and regulations that prohibit or restrict financial institutions from carrying out any of the following practices: \n - Bundling and tying services and products in a manner that unduly restricts the choice of consumers." }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.PR.DI.SC", "metatype": [ { "id": "Generalcomments", "value": "Do laws and regulations PROHIBIT OR RESTRICT DISCRIMINATING CONSUMERS BASED ON GENDER, ETHNICITY, FAITH, POLITICAL AFFILIATION, OR APPEARANCE?" }, { "id": "IndicatorName", "value": "Do laws and regulations prohibit or restrict discriminating consumers based on gender, ethnicity, faith, political affiliation, or appearance?" }, { "id": "Othernotes", "value": "VHGA_03" }, { "id": "Shortdefinition", "value": "Are there any provisions in the existing laws and regulations that prohibit or restrict financial institutions from carrying out any of the following practices: \n - Discriminating certain segments, such as women, indigenous populations, or based on faith, political affiliation, the manner a consumer dresses;" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.PR.EF.AC", "metatype": [ { "id": "Generalcomments", "value": "Do laws and regulations LIMIT FEES AND CHARGES FOR ACCOUNT CLOSURE that impede customer mobility between financial institutions?" }, { "id": "IndicatorName", "value": "Do laws and regulations limit fees and charges for account closure that impede customer mobility between financial institutions?" }, { "id": "Othernotes", "value": "VHHA_00" }, { "id": "Shortdefinition", "value": "Are there any of the following provisions in existing laws and/or regulations that prohibit or restrict terms and practices which limit customer mobility between financial institutions \n- Provisions that limit fees and charges for account closure;" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.PR.EF.RP", "metatype": [ { "id": "Generalcomments", "value": "Do laws and regulations LIMIT EARLY REPAYMENT PENALTIES that impede customer mobility between financial institutions?" }, { "id": "IndicatorName", "value": "Do laws and regulations limit early repayment penalties that impede customer mobility between financial institutions?" }, { "id": "Othernotes", "value": "VHHA_03" }, { "id": "Shortdefinition", "value": "Are there any of the following provisions in existing laws and/or regulations that prohibit or restrict terms and practices which limit customer mobility between financial institutions \n- Provisions that limit early repayment penalties." }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.PR.EP.AC", "metatype": [ { "id": "Generalcomments", "value": "Do laws and regulations PROHIBIT EXTRA BURDENING PROCEDURES FOR ACCOUNT CLOSURE that limit customer mobility between financial institutions?" }, { "id": "IndicatorName", "value": "Do laws and regulations prohibit extra burdening procedures for account closure that limit customer mobility between financial institutions?" }, { "id": "Othernotes", "value": "VHHA_01" }, { "id": "Shortdefinition", "value": "Are there any of the following provisions in existing laws and/or regulations that prohibit or restrict terms and practices which limit customer mobility between financial institutions \n- Provisions that prohibit extra burdening procedures for account closure;" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.PR.TC.RC", "metatype": [ { "id": "Generalcomments", "value": "Do laws and regulations PROHIBIT OR RESTRICT the use in consumer agreements of ANY TERM OR CONDITION THAT EXCLUDES OR RESTRICTS THE RIGHT OF THE CONSUMER?" }, { "id": "IndicatorName", "value": "Do laws and regulations prohibit or restrict the use in consumer agreements of any term or condition that excludes or restricts the right of the consumer?" }, { "id": "Othernotes", "value": "VHGA_02" }, { "id": "Shortdefinition", "value": "Are there any provisions in the existing laws and regulations that prohibit or restrict financial institutions from carrying out any of the following practices: \n - Using in a consumer agreement, any term or condition that excludes or restricts the right of the consumer;" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.PR.TC.RL", "metatype": [ { "id": "Generalcomments", "value": "Do laws and regulations PROHIBIT OR RESTRICT the use in consumer agreements of ANY TERM OR CONDITION THAT EXCLUDES OR RESTRICTS THE LIABILITY OF THE FINANCIAL SERVICE PROVIDER?" }, { "id": "IndicatorName", "value": "Do laws and regulations prohibit or restrict the use in consumer agreements of any term or condition that excludes or restricts the liability of the financial service provider?" }, { "id": "Othernotes", "value": "VHGA_01" }, { "id": "Shortdefinition", "value": "Are there any provisions in the existing laws and regulations that prohibit or restrict financial institutions from carrying out any of the following practices: \n - Using in a consumer agreement, any term or condition that excludes or restricts the liability of the financial service provider;" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.BREG.PR.TC.UF", "metatype": [ { "id": "Generalcomments", "value": "Do laws and regulations PROHIBIT OR RESTRICT the use in consumer agreements of ANY TERM OR CONDITION THAT IS UNFAIR, EXCESSIVELY UNBALANCED OR ABUSIVE?" }, { "id": "IndicatorName", "value": "Do laws and regulations prohibit or restrict the use in consumer agreements of any term or condition that is unfair, excessively unbalanced or abusive?" }, { "id": "Othernotes", "value": "VHGA_00" }, { "id": "Shortdefinition", "value": "Are there any provisions in the existing laws and regulations that prohibit or restrict financial institutions from carrying out any of the following practices: \n - Using, in a consumer agreement, any term or condition that is unfair, excessively unbalanced or abusive;" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Business Conduct" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.AD", "metatype": [ { "id": "Generalcomments", "value": "Does the alternative dispute resolution (ADR) entity ANALYZE THE COMPLAINTS DATA TO IDENTIFY TRENDS?" }, { "id": "IndicatorName", "value": "Does the alternative dispute resolution (ADR) entity analyze the complaints data to identify trends?" }, { "id": "Othernotes", "value": "VHMF_01" }, { "id": "Shortdefinition", "value": "Does the ADR entity conduct any of the following activities?  \n- Analyze the complaints/complaints data to identify trends" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.AN", "metatype": [ { "id": "Generalcomments", "value": "Does the ALTERNATIVE DISPUTE RESOLUTION (ADR) ENTITY also cover NON-FINANCIAL SERVICES?" }, { "id": "IndicatorName", "value": "Does the alternative dispute resolution (ADR) entity also cover non-financial services?" }, { "id": "Othernotes", "value": "VHMB_01" }, { "id": "Shortdefinition", "value": "What is the sectoral mandate of the alternative dispute resolution (ADR) entity?\n- Financial and non-financial services" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.BM", "metatype": [ { "id": "Generalcomments", "value": "Is there an OUT-OF-COURT ALTERNATIVE DISPUTE RESOLUTION (ADR) SCHEME that provides BOTH BINDING DECISIONS AND MEDIATION SERVICES?" }, { "id": "IndicatorName", "value": "Is there an out-of-court alternative dispute resolution (ADR) scheme that provides both binding decisions and mediation services?" }, { "id": "Othernotes", "value": "VHLA_01" }, { "id": "Shortdefinition", "value": "Is there any out-of-court alternative dispute resolution (ADR) entity in place that allows a customer of a financial institution to seek affordable and efficient recourse with a third party in the event that the customer's complaint is not resolved to the customer's satisfaction under internal procedures of the relevant financial institution? \n - Yes, a scheme that provides mediation services" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.BO", "metatype": [ { "id": "Generalcomments", "value": "Is there an OUT-OF-COURT ALTERNATIVE DISPUTE RESOLUTION (ADR) SCHEME that provides BINDING DECISIONS ONLY?" }, { "id": "IndicatorName", "value": "Is there an out-of-court alternative dispute resolution (ADR) scheme that provides binding decisions only?" }, { "id": "Othernotes", "value": "VHLA_02" }, { "id": "Shortdefinition", "value": "Is there any out-of-court alternative dispute resolution (ADR) entity in place that allows a customer of a financial institution to seek affordable and efficient recourse with a third party in the event that the customer's complaint is not resolved to the customer's satisfaction under internal procedures of the relevant financial institution?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.CI", "metatype": [ { "id": "Generalcomments", "value": "Does the alternative dispute resolution (ADR) entity require that CONSUMERS FIRST SUBMIT THEIR COMPLAINTS TO THE RELEVANT FINANCIAL INSTITUTION?" }, { "id": "IndicatorName", "value": "Does the alternative dispute resolution (ADR) entity require that consumers first submit their complaints to the relevant financial institution?" }, { "id": "Othernotes", "value": "VHME_00" }, { "id": "Shortdefinition", "value": "Does the ADR entity require that consumers first submit their complaints to the relevant financial institution?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.CT", "metatype": [ { "id": "Generalcomments", "value": "Does the alternative dispute resolution (ADR) entity COMMUNICATE COMPLAINT TRENDS TO THE REGULATOR?" }, { "id": "IndicatorName", "value": "Does the alternative dispute resolution (ADR) entity communicate complaint trends to the regulator?" }, { "id": "Othernotes", "value": "VHMF_02" }, { "id": "Shortdefinition", "value": "Does the ADR entity conduct any of the following activities?  \n- Communicate these trends to the regulator" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.DC", "metatype": [ { "id": "Generalcomments", "value": "Does the alternative dispute resolution (ADR) entity MAINTAIN A DATABASE OF REGISTERED/RECORDED COMPLAINTS?" }, { "id": "IndicatorName", "value": "Does the alternative dispute resolution (ADR) entity maintain a database of registered/recorded complaints?" }, { "id": "Othernotes", "value": "VHMF_00" }, { "id": "Shortdefinition", "value": "Does the ADR entity conduct any of the following activities?  \n- Maintain a database of registered/recorded complaints" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.FA", "metatype": [ { "id": "Generalcomments", "value": "Is the ALTERNATIVE DISPUTE RESOLUTION (ADR) ENTITY funded from the BUDGET OF A SPECIFIC GOVERNMENT AGENCY?" }, { "id": "IndicatorName", "value": "Is the alternative dispute resolution (ADR) entity funded from the budget of a specific government agency?" }, { "id": "Othernotes", "value": "VHMD_01" }, { "id": "Shortdefinition", "value": "Regarding the ADR entity specified in above, how is it funded?  \n- From an annual budget allocated by a government authority (i.e. Ministry, Central Bank, Financial Regulator, Consumer Protection Agency, etc.)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.FC", "metatype": [ { "id": "Generalcomments", "value": "Is the ALTERNATIVE DISPUTE RESOLUTION (ADR) ENTITY funded BY A COMBINATION OF GOVERNMENT AND OTHER SOURCES?" }, { "id": "IndicatorName", "value": "Is the alternative dispute resolution (ADR) entity funded by a combination of government and other sources?" }, { "id": "Othernotes", "value": "VHMD_04" }, { "id": "Shortdefinition", "value": "Regarding the ADR entity specified in above, how is it funded?  \n- By a combination of government and other sources" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.FG", "metatype": [ { "id": "Generalcomments", "value": "Is the ALTERNATIVE DISPUTE RESOLUTION (ADR) ENTITY funded from a BUDGET ALLOCATED BY THE CENTRAL GOVERNMENT?" }, { "id": "IndicatorName", "value": "Is the alternative dispute resolution (ADR) entity funded from a budget allocated by the central government?" }, { "id": "Othernotes", "value": "VHMD_00" }, { "id": "Shortdefinition", "value": "Regarding the ADR entity specified above, how is it funded?  \n- From a budget allocated by the central government" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.FI", "metatype": [ { "id": "Generalcomments", "value": "Is the ALTERNATIVE DISPUTE RESOLUTION (ADR) ENTITY funded BY A FINANCIAL INDUSTRY ASSOCIATION?" }, { "id": "IndicatorName", "value": "Is the alternative dispute resolution (ADR) entity funded by a financial industry association?" }, { "id": "Othernotes", "value": "VHMD_02" }, { "id": "Shortdefinition", "value": "Regarding the ADR entity specified in above, how is it funded?  \n- By a financial industry association" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.FM", "metatype": [ { "id": "Generalcomments", "value": "Is the ALTERNATIVE DISPUTE RESOLUTION (ADR) ENTITY funded BY DIRECT CONTRIBUTIONS OF ITS MEMBERS?" }, { "id": "IndicatorName", "value": "Is the alternative dispute resolution (ADR) entity funded by direct contributions of its members?" }, { "id": "Othernotes", "value": "VHMD_03" }, { "id": "Shortdefinition", "value": "Regarding the ADR entity specified in above, how is it funded?  \n- By direct contribution of members to the ADR entity" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.FO", "metatype": [ { "id": "Generalcomments", "value": "Does the ALTERNATIVE DISPUTE RESOLUTION (ADR) ENTITY focus ONLY ON FINANCIAL SERVICES?" }, { "id": "IndicatorName", "value": "Does the alternative dispute resolution (ADR) entity focus only on financial services?" }, { "id": "Othernotes", "value": "VHMB_00" }, { "id": "Shortdefinition", "value": "What is the sectoral mandate of the alternative dispute resolution (ADR) entity?\n- Financial services only" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.AG", "metatype": [ { "id": "Generalcomments", "value": "Are AGENTS among the top-three ISSUES complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Are agents among the top-three issues complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHNA_06" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- Agents" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.AT", "metatype": [ { "id": "Generalcomments", "value": "Are ATM TRANSACTIONS among the top-three ISSUES complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Are ATM transactions among the top-three issues complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHNA_05" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- ATM transactions (withdrawal, deposit" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.BL", "metatype": [ { "id": "Generalcomments", "value": "Is BUNDLING OR TYING OF PRODUCTS among the top-three ISSUES complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Is bundling or tying of products among the top-three issues complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHNA_04" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- Bundling and tying of products" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.CA", "metatype": [ { "id": "Generalcomments", "value": "Are CURRENT ACCOUNTS among the top-three PRODUCTS complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Are current accounts among the top-three products complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHOA_06" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- Current account" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.CC", "metatype": [ { "id": "Generalcomments", "value": "Are CREDIT CARDS among the top-three PRODUCTS complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Are credit cards among the top-three products complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHOA_00" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- Credit card" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.CL", "metatype": [ { "id": "Generalcomments", "value": "Are CONSUMER LOANS among the top-three PRODUCTS complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Are consumer loans among the top-three products complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHOA_03" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- Consumer loan" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.DA", "metatype": [ { "id": "Generalcomments", "value": "Are DEPOSIT ACCOUNTS among the top-three PRODUCTS complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Are deposit accounts among the top-three products complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHOA_05" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- Deposit account" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.DC", "metatype": [ { "id": "Generalcomments", "value": "Are DEBIT CARDS among the top-three PRODUCTS complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Are debit cards among the top-three products complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHOA_01" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- Debit card" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.EC", "metatype": [ { "id": "Generalcomments", "value": "Are EXCESSIVE INTEREST OR FEES among the top-three ISSUES complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Are excessive interest or fees among the top-three issues complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHNA_01" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- Excessive interest or fees (any product type)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.EM", "metatype": [ { "id": "Generalcomments", "value": "Are E-MONEY PRODUCTS among the top-three PRODUCTS complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Are e-money products among the top-three products complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHOA_09" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- E-money product" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.FD", "metatype": [ { "id": "Generalcomments", "value": "Is FRAUD among the top-three ISSUES complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Is fraud among the top-three issues complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHNA_02" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- Fraud" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.HL", "metatype": [ { "id": "Generalcomments", "value": "Are MORTGAGE OR HOUSING LOANS among the top-three PRODUCTS complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Are mortgage or housing loans among the top-three products complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHOA_02" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- Mortgage/housing loan" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.LI", "metatype": [ { "id": "Generalcomments", "value": "Is LIFE INSURANCE among the top-three PRODUCTS complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Is life insurance among the top-three products complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHOA_07" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- Life insurance" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.ML", "metatype": [ { "id": "Generalcomments", "value": "Are MICRO LOANS among the top-three PRODUCTS complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Are micro loans among the top-three products complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHOA_04" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- Micro loan" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.NI", "metatype": [ { "id": "Generalcomments", "value": "Is NON-LIFE INSURANCE among the top-three PRODUCTS complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Is non-life insurance among the top-three products complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHOA_08" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- Non-life insurance products" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.OT", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER ISSUES among the top-three ISSUES complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Are other issues among the top-three issues complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHNA_09" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- Other" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.PC", "metatype": [ { "id": "Generalcomments", "value": "Is PRODUCT SWITCHING OR CLOSURE among the top-three ISSUES complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Is product switching or closure among the top-three issues complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHNA_08" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- Undue burden to switch providers or terminate product" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.UC", "metatype": [ { "id": "Generalcomments", "value": "Are UNCLEAR INTEREST OR FEES among the top three ISSUES complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Are unclear interest or fees among the top three issues complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHNA_00" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")?\n- Unclear interest or fees (any product type)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.UI", "metatype": [ { "id": "Generalcomments", "value": "Are UNPAID INSURANCE CLAIMS among the top-three ISSUES complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Are unpaid insurance claims among the top-three issues complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHNA_07" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")?\n- Insurance claim not paid" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IC.UT", "metatype": [ { "id": "Generalcomments", "value": "Are MISTAKEN OR UNAUTHORIZED TRANSACTIONS among the top-three ISSUES complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "IndicatorName", "value": "Are mistaken or unauthorized transactions among the top-three issues complained about to the alternative dispute resolution (ADR) entity?" }, { "id": "Othernotes", "value": "VHNA_03" }, { "id": "Shortdefinition", "value": "What are the most frequent reasons for complaints received by the ADR entity related to financial consumer protection (not including \"not being approved for a loan\")? \n- Mistaken/unauthorized transactions" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Frequent Complaints" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.IR", "metatype": [ { "id": "Generalcomments", "value": "Is the ALTERNATIVE DISPUTE RESOLUTION (ADR) ENTITY INDEPENDENT FROM THE FINANCIAL SECTOR REGULATOR?" }, { "id": "IndicatorName", "value": "Is the alternative dispute resolution (ADR) entity independent from the financial sector regulator?" }, { "id": "Othernotes", "value": "VHMC_01" }, { "id": "Shortdefinition", "value": "What is the institutional model for the alternative dispute resolution (ADR) entity?\n- Is independent from the financial sector regulator." }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.MI", "metatype": [ { "id": "Generalcomments", "value": "Is the ALTERNATIVE DISPUTE RESOLUTION (ADR) ENTITY a MANDATORY, INDUSTRY-BASED ENTITY?" }, { "id": "IndicatorName", "value": "Is the alternative dispute resolution (ADR) entity a mandatory, industry-based entity?" }, { "id": "Othernotes", "value": "VHMA_02" }, { "id": "Shortdefinition", "value": "What is the type of the alternative dispute resolution (ADR) entity in this country? \n- Mandatory, industry-based entity" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.MO", "metatype": [ { "id": "Generalcomments", "value": "Is there an OUT-OF-COURT ALTERNATIVE DISPUTE RESOLUTION (ADR) SCHEME that provides MEDIATION SERVICES ONLY?" }, { "id": "IndicatorName", "value": "Is there an out-of-court alternative dispute resolution (ADR) scheme that provides mediation services only?" }, { "id": "Othernotes", "value": "VHLA_03" }, { "id": "Shortdefinition", "value": "Is there any out-of-court alternative dispute resolution (ADR) entity in place that allows a customer of a financial institution to seek affordable and efficient recourse with a third party in the event that the customer's complaint is not resolved to the customer's satisfaction under internal procedures of the relevant financial institution?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.PS", "metatype": [ { "id": "Generalcomments", "value": "Does the alternative dispute resolution (ADR) entity REGULARLY PUBLISH COMPLAINT STATISTICS?" }, { "id": "IndicatorName", "value": "Does the alternative dispute resolution (ADR) entity regularly publish complaint statistics?" }, { "id": "Othernotes", "value": "VHMF_03" }, { "id": "Shortdefinition", "value": "Does the ADR entity conduct any of the following activities?  \n- Regularly publish complaints statistics" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.RS", "metatype": [ { "id": "Generalcomments", "value": "Does the alternative dispute resolution (ADR) entity REPORT COMPLAINT STATISTICS TO THE REGULATOR?" }, { "id": "IndicatorName", "value": "Does the alternative dispute resolution (ADR) entity report complaint statistics to the regulator?" }, { "id": "Othernotes", "value": "VHMF_04" }, { "id": "Shortdefinition", "value": "Does the ADR entity conduct any of the following activities?  \n- Report complaints statistics to the regulator" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.RT", "metatype": [ { "id": "Generalcomments", "value": "Is there an OUT-OF-COURT ALTERNATIVE DISPUTE RESOLUTION (ADR) ENTITY that provides consumers of financial services AFFORDABLE AND EFFICIENT RECOURSE WITH A THIRD PARTY?" }, { "id": "IndicatorName", "value": "Is there an out-of-court alternative dispute resolution (ADR) entity that provides consumers of financial services affordable and efficient recourse with a third party?" }, { "id": "Othernotes", "value": "VHLA_00" }, { "id": "Shortdefinition", "value": "Is there any out-of-court alternative dispute resolution (ADR) entity in place that allows a customer of a financial institution to seek affordable and efficient recourse with a third party in the event that the customer's complaint is not resolved to the customer's satisfaction under internal procedures of the relevant financial institution? \n - Yes, a scheme that provides binding decisions (e.g. ombudsman, adjudication)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.SE", "metatype": [ { "id": "Generalcomments", "value": "Is the ALTERNATIVE DISPUTE RESOLUTION (ADR) ENTITY a STATUTORY ENTITY?" }, { "id": "IndicatorName", "value": "Is the alternative dispute resolution (ADR) entity a statutory entity?" }, { "id": "Othernotes", "value": "VHMA_00" }, { "id": "Shortdefinition", "value": "What is the type of the alternative dispute resolution (ADR) entity in this country? \n- Statutory entity" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.VI", "metatype": [ { "id": "Generalcomments", "value": "Is the ALTERNATIVE DISPUTE RESOLUTION (ADR) ENTITY a VOLUNTARY, INDUSTRY-BASED ENTITY?" }, { "id": "IndicatorName", "value": "Is the alternative dispute resolution (ADR) entity a voluntary, industry-based entity?" }, { "id": "Othernotes", "value": "VHMA_01" }, { "id": "Shortdefinition", "value": "What is the type of the alternative dispute resolution (ADR) entity in this country? \n- Voluntary, industry-based entity" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.AS.WR", "metatype": [ { "id": "Generalcomments", "value": "Is the ALTERNATIVE DISPUTE RESOLUTION (ADR) ENTITY established WITHIN THE FINANCIAL SECTOR REGULATOR?" }, { "id": "IndicatorName", "value": "Is the alternative dispute resolution (ADR) entity established within the financial sector regulator?" }, { "id": "Othernotes", "value": "VHMC_00" }, { "id": "Shortdefinition", "value": "What is the institutional model for the alternative dispute resolution (ADR) entity?\n- Within the financial sector regulator" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Alternative Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.CH.AC", "metatype": [ { "id": "Generalcomments", "value": "Do laws or regulations SET STANDARDS FOR ACCESSIBILITY IN RESOLVING CUSTOMER COMPLAINTS?" }, { "id": "IndicatorName", "value": "Do laws or regulations set standards for accessibility in resolving customer complaints?" }, { "id": "Othernotes", "value": "VHKB_03" }, { "id": "Shortdefinition", "value": "Does any law or regulation set standards in any of the following areas for complaints resolution and handling by financial institutions? \n- Accessibility (i.e. consumer can file a complaint via multiple channels, including local branch, by phone, e-mail, etc.)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Internal Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.CH.DU", "metatype": [ { "id": "Generalcomments", "value": "Do laws or regulations REQUIRE FINANCIAL INSTITUTIONS TO HAVE A DESIGNATED OFFICE OR UNIT FOR RESOLVING CUSTOMER COMPLAINTS ?" }, { "id": "IndicatorName", "value": "Do laws or regulations require financial institutions to have a designated office or unit for resolving customer complaints?" }, { "id": "Othernotes", "value": "VHKB_01" }, { "id": "Shortdefinition", "value": "Does any law or regulation set standards in any of the following areas for complaints resolution and handling by financial institutions? \n - Requirement for financial institutions to have a designated, independent offer or unit in charge of handling customer complaints" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Internal Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.CH.EM", "metatype": [ { "id": "Generalcomments", "value": "Do laws or regulations SET STANDARDS FOR PROVIDING CONSUMERS THE DETAILS OF A RELEVANT EXTERNAL DISPUTE RESOLUTION MECHANISM (IF ANY)?" }, { "id": "IndicatorName", "value": "Do laws or regulations set standards for providing consumers the details of a relevant external dispute resolution mechanism (if any)?" }, { "id": "Othernotes", "value": "VHKB_06" }, { "id": "Shortdefinition", "value": "Does any law or regulation set standards in any of the following areas for complaints resolution and handling by financial institutions? \n - Providing consumers the details of a relevant external dispute resolution mechanism (if any)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Internal Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.CH.IP", "metatype": [ { "id": "Generalcomments", "value": "Do laws or regulations REQUIRE IMPLEMENTING PROCEDURES AND PROCESSES TO RESOLVE CUSTOMER COMPLAINTS ?" }, { "id": "IndicatorName", "value": "Do laws or regulations require implementing procedures and processes to resolve customer complaints?" }, { "id": "Othernotes", "value": "VHKB_00" }, { "id": "Shortdefinition", "value": "Does any law or regulation set standards in any of the following areas for complaints resolution and handling by financial institutions? \n - Requirements for financial institutions to implement procedures and processes for resolving customer complaints" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Internal Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.CH.RG", "metatype": [ { "id": "Generalcomments", "value": "Do laws or regulations SET STANDARDS FOR REPORTING COMPLAINTS DATA TO A GOVERNMENT AGENCY?" }, { "id": "IndicatorName", "value": "Do laws or regulations set standards for reporting complaints data to a government agency?" }, { "id": "Othernotes", "value": "VHKB_05" }, { "id": "Shortdefinition", "value": "Does any law or regulation set standards in any of the following areas for complaints resolution and handling by financial institutions? \n - Reporting complaints data to a government agency" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Internal Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.CH.RK", "metatype": [ { "id": "Generalcomments", "value": "Do laws or regulations SET STANDARDS FOR RECORD-KEEPING OF CUSTOMER COMPLAINTS?" }, { "id": "IndicatorName", "value": "Do laws or regulations set standards for record-keeping of customer complaints?" }, { "id": "Othernotes", "value": "VHKB_04" }, { "id": "Shortdefinition", "value": "Does any law or regulation set standards in any of the following areas for complaints resolution and handling by financial institutions? \n - Record-keeping of complaints" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Internal Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.CH.ST", "metatype": [ { "id": "Generalcomments", "value": "Do laws or regulations SET STANDARDS FOR COMPLAINTS RESOLUTION AND HANDLING by financial institutions?" }, { "id": "IndicatorName", "value": "Do laws or regulations set standards for complaints resolution and handling by financial institutions?" }, { "id": "Othernotes", "value": "VHKA_00" }, { "id": "Shortdefinition", "value": "Does any law or regulation set standards for complaints resolution and handling by financial institutions?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Internal Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.DISR.CH.TR", "metatype": [ { "id": "Generalcomments", "value": "Do laws or regulations SET STANDARDS FOR TIMELINESS OF RESPONSE TO CUSTOMER COMPLAINTS by financial institutions?" }, { "id": "IndicatorName", "value": "Do laws or regulations set standards for timeliness of response to customer complaints by financial institutions?" }, { "id": "Othernotes", "value": "VHKB_02" }, { "id": "Shortdefinition", "value": "Does any law or regulation set standards in any of the following areas for complaints resolution and handling by financial institutions? \n - Timeliness of response by financial institution" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Internal Dispute Resolution" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.DR.AF", "metatype": [ { "id": "Generalcomments", "value": "Do disclosure requirements for COMMERCIAL BANKS include ANY FORM REQUIREMENTS?" }, { "id": "IndicatorName", "value": "Do disclosure requirements for commercial banks include any form requirements?" }, { "id": "Othernotes", "value": "VGYA_02" }, { "id": "Shortdefinition", "value": "By law or regulation, at the shopping and/or pre-contractual stage, do the disclosure requirements cover: \n- Commercial Banks: Any form requirements - e.g. durable media, oral communication" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.DR.LL", "metatype": [ { "id": "Generalcomments", "value": "Do disclosure requirements for COMMERCIAL BANKS include a LOCAL LANGUAGE REQUIREMENT?" }, { "id": "IndicatorName", "value": "Do disclosure requirements for commercial banks include a local language requirement?" }, { "id": "Othernotes", "value": "VGYA_01" }, { "id": "Shortdefinition", "value": "By law or regulation, at the shopping and/or pre-contractual stage, do the disclosure requirements cover: \n- Commercial Banks: Local language requirement" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.DR.PL", "metatype": [ { "id": "Generalcomments", "value": "Do disclosure requirements for COMMERCIAL BANKS include a PLAIN LANGUAGE REQUIREMENT?" }, { "id": "IndicatorName", "value": "Do disclosure requirements for commercial banks include a plain language requirement?" }, { "id": "Othernotes", "value": "VGYA_00" }, { "id": "Shortdefinition", "value": "By law or regulation, at the shopping and/or pre-contractual stage, do the disclosure requirements cover: \n- Commercial Banks: Plain language requirement (clear and simple language that can be readily understood by any customer)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.DR.RR", "metatype": [ { "id": "Generalcomments", "value": "Do disclosure requirements for COMMERCIAL BANKS include INFORMATION ON RECOURSE RIGHTS AND PROCESSES?" }, { "id": "IndicatorName", "value": "Do disclosure requirements for commercial banks include information on recourse rights and processes?" }, { "id": "Othernotes", "value": "VGYA_03" }, { "id": "Shortdefinition", "value": "By law or regulation, at the shopping and/or pre-contractual stage, do the disclosure requirements cover: \n- Commercial Banks: Recourse rights and processes" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.DS.AS", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to provide customers with specific types of product information AT THE ADVERTISEMENT STAGE?" }, { "id": "IndicatorName", "value": "Are commercial banks required to provide customers with specific types of product information at the advertisement stage?" }, { "id": "Othernotes", "value": "VGWA_00" }, { "id": "Shortdefinition", "value": "Are there any requirements (law or regulation) for financial institutions to provide customers, in paper or electronic form, specific types of information (e.g. interest rate, fees and penalties, etc.) of the relevant financial product? \n- Commercial Banks: At the advertisement stage" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.DS.CS", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to provide customers with specific types of product information AT THE CONTRACTUAL STAGE?" }, { "id": "IndicatorName", "value": "Are commercial banks required to provide customers with specific types of product information at the contractual stage?" }, { "id": "Othernotes", "value": "VGWA_03" }, { "id": "Shortdefinition", "value": "Are there any requirements (law or regulation) for financial institutions to provide customers, in paper or electronic form, specific types of information (e.g. interest rate, fees and penalties, etc.) of the relevant financial product? \n- Commercial Banks: At the contractual stage" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.DS.PS", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to provide customers with specific types of product information AT THE PRECONTRACTUAL STAGE?" }, { "id": "IndicatorName", "value": "Are commercial banks required to provide customers with specific types of product information at the precontractual stage?" }, { "id": "Othernotes", "value": "VGWA_02" }, { "id": "Shortdefinition", "value": "Are there any requirements (law or regulation) for financial institutions to provide customers, in paper or electronic form, specific types of information (e.g. interest rate, fees and penalties, etc.) of the relevant financial product? \n- Commercial Banks: At the precontractual stage" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.DS.SS", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to provide customers with specific types of product information AT THE SHOPPING STAGE?" }, { "id": "IndicatorName", "value": "Are commercial banks required to provide customers with specific types of product information at the shopping stage?" }, { "id": "Othernotes", "value": "VGWA_01" }, { "id": "Shortdefinition", "value": "Are there any requirements (law or regulation) for financial institutions to provide customers, in paper or electronic form, specific types of information (e.g. interest rate, fees and penalties, etc.) of the relevant financial product? \n- Commercial Banks: At the shopping stage" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.DS.UR", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to provide customers with specific types of product information UPON REQUEST?" }, { "id": "IndicatorName", "value": "Are commercial banks required to provide customers with specific types of product information upon request?" }, { "id": "Othernotes", "value": "VGWA_04" }, { "id": "Shortdefinition", "value": "Are there any requirements (law or regulation) for financial institutions to provide customers, in paper or electronic form, specific types of information (e.g. interest rate, fees and penalties, etc.) of the relevant financial product? \n- Commercial Banks: Upon Request" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.PD.CF", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to disclose ACCOUNT CLOSURE FEES at the shopping and/or pre-contractual stage?" }, { "id": "IndicatorName", "value": "Are commercial banks required to disclose account closure fees at the shopping and/or pre-contractual stage?" }, { "id": "Othernotes", "value": "VGZA_03" }, { "id": "Shortdefinition", "value": "By law or regulation, at the shopping and/or pre-contractual stage, do the disclosure requirements cover: \n- Commercial Banks: Account closure fees" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.PD.CM", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to disclose COMPUTATION METHOD at the shopping and/or pre-contractual stage?" }, { "id": "IndicatorName", "value": "Are commercial banks required to disclose computation method at the shopping and/or pre-contractual stage?" }, { "id": "Othernotes", "value": "VHAA_02" }, { "id": "Shortdefinition", "value": "By law or regulation, at the shopping and/or pre-contractual stage, do the disclosure requirements cover: \n- Commercial Banks: Computation method (average balance, interest)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.PD.DI", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to disclose DEPOSIT INSURANCE COVERAGE AVAILABILITY at the shopping and/or pre-contractual stage?" }, { "id": "IndicatorName", "value": "Are commercial banks required to disclose deposit insurance coverage availability at the shopping and/or pre-contractual stage?" }, { "id": "Othernotes", "value": "VGZA_04" }, { "id": "Shortdefinition", "value": "By law or regulation, at the shopping and/or pre-contractual stage, do the disclosure requirements cover: \n- Commercial Banks: Deposit insurance coverage availability" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.PD.ER", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to disclose EFFECTIVE INTEREST RATE at the shopping and/or pre-contractual stage?" }, { "id": "IndicatorName", "value": "Are commercial banks required to disclose effective interest rate at the shopping and/or pre-contractual stage?" }, { "id": "Othernotes", "value": "VHAA_00" }, { "id": "Shortdefinition", "value": "By law or regulation, at the shopping and/or pre-contractual stage, do the disclosure requirements cover: \n- Commercial Banks: Effective interest rate calculated using a standard formula" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.PD.FP", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to disclose FEES AND PENALTIES at the shopping and/or pre-contractual stage?" }, { "id": "IndicatorName", "value": "Are commercial banks required to disclose fees and penalties at the shopping and/or pre-contractual stage?" }, { "id": "Othernotes", "value": "VHAA_01" }, { "id": "Shortdefinition", "value": "By law or regulation, at the shopping and/or pre-contractual stage, do the disclosure requirements cover: \n- Commercial Banks: Fees and penalties" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.PD.MB", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to disclose MINIMUM BALANCE REQUIREMENTS at the shopping and/or pre-contractual stage?" }, { "id": "IndicatorName", "value": "Are commercial banks required to disclose minimum balance requirements at the shopping and/or pre-contractual stage?" }, { "id": "Othernotes", "value": "VGZA_00" }, { "id": "Shortdefinition", "value": "By law or regulation, at the shopping and/or pre-contractual stage, do the disclosure requirements cover: \n- Commercial Banks: Minimum balance requirements" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.PD.MF", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to disclose ACCOUNT MAINTENANCE FEE at the shopping and/or pre-contractual stage?" }, { "id": "IndicatorName", "value": "Are commercial banks required to disclose account maintenance fee at the shopping and/or pre-contractual stage?" }, { "id": "Othernotes", "value": "VGZA_02" }, { "id": "Shortdefinition", "value": "By law or regulation, at the shopping and/or pre-contractual stage, do the disclosure requirements cover: \n- Commercial Banks: Account maintenance fee" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.PD.OF", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to disclose ACCOUNT OPENING FEE at the shopping and/or pre-contractual stage?" }, { "id": "IndicatorName", "value": "Are commercial banks required to disclose account opening fee at the shopping and/or pre-contractual stage?" }, { "id": "Othernotes", "value": "VGZA_01" }, { "id": "Shortdefinition", "value": "By law or regulation, at the shopping and/or pre-contractual stage, do the disclosure requirements cover: \n- Commercial Banks: Account opening fee" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.PD.RI", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to disclose REQUIRED INSURANCE at the shopping and/or pre-contractual stage?" }, { "id": "IndicatorName", "value": "Are commercial banks required to disclose required insurance at the shopping and/or pre-contractual stage?" }, { "id": "Othernotes", "value": "VHAA_03" }, { "id": "Shortdefinition", "value": "By law or regulation, at the shopping and/or pre-contractual stage, do the disclosure requirements cover: \n- Commercial Banks: Required insurance" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.PD.SW", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to disclose SPECIFIC WARNINGS at the shopping and/or pre-contractual stage?" }, { "id": "IndicatorName", "value": "Are commercial banks required to disclose specific warnings at the shopping and/or pre-contractual stage?" }, { "id": "Othernotes", "value": "VHAA_04" }, { "id": "Shortdefinition", "value": "By law or regulation, at the shopping and/or pre-contractual stage, do the disclosure requirements cover: \n- Commercial Banks: Specific warnings (e.g. related to overindebtedness or late repayment)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.CB.SF.AS", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to provide customers with specific types of product information IN A STANDARDIZED FORMAT AT ANY STAGE?" }, { "id": "IndicatorName", "value": "Are commercial banks required to provide customers with specific types of product information in a standardized format at any stage?" }, { "id": "Othernotes", "value": "VGXG_00" }, { "id": "Shortdefinition", "value": "With regard to the disclosure requirements referenced above, are there any requirements (law or regulation) for financial institutions to use a document with a standardized format in paper or electronic form (e.g. key facts statement, key disclosure document, synthesis document)? \n- Commercial Banks: In a standardized format, at any stage" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.EP.IF", "metatype": [ { "id": "Generalcomments", "value": "Does the financial consumer protection (FCP) agency have enforcement powers to IMPOSE FINES AND PENALTIES?" }, { "id": "IndicatorName", "value": "Does the financial consumer protection (FCP) agency have enforcement powers to impose fines and penalties?" }, { "id": "Othernotes", "value": "VGVB_03" }, { "id": "Shortdefinition", "value": "What actions can your agency take to enforce consumer protection laws and regulations? \n- Impose fines and penalties" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Enforcement Powers" } ], "source_id": "69" }, { "id": "FB.FCP.INST.EP.IN", "metatype": [ { "id": "Generalcomments", "value": "Does the financial consumer protection (FCP) agency have enforcement powers to ISSUE PUBLIC NOTICE OF VIOLATIONS?" }, { "id": "IndicatorName", "value": "Does the financial consumer protection (FCP) agency have enforcement powers to issue public notice of violations?" }, { "id": "Othernotes", "value": "VGVB_04" }, { "id": "Shortdefinition", "value": "What actions can your agency take to enforce consumer protection laws and regulations? \n- Issue public notice of violations" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Enforcement Powers" } ], "source_id": "69" }, { "id": "FB.FCP.INST.EP.IS", "metatype": [ { "id": "Generalcomments", "value": "Does the financial consumer protection (FCP) agency have enforcement powers to ISSUE ADMINISTRATIVE SANCTIONS TO SENIOR MANAGEMENT?" }, { "id": "IndicatorName", "value": "Does the financial consumer protection (FCP) agency have enforcement powers to issue administrative sanctions to senior management?" }, { "id": "Othernotes", "value": "VGVB_06" }, { "id": "Shortdefinition", "value": "What actions can your agency take to enforce consumer protection laws and regulations? \n- Issue administrative sanctions to senior management" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Enforcement Powers" } ], "source_id": "69" }, { "id": "FB.FCP.INST.EP.IW", "metatype": [ { "id": "Generalcomments", "value": "Does the financial consumer protection (FCP) agency have enforcement powers to ISSUE WARNINGS TO FINANCIAL INSTITUTIONS?" }, { "id": "IndicatorName", "value": "Does the financial consumer protection (FCP) agency have enforcement powers to issue warnings to financial institutions?" }, { "id": "Othernotes", "value": "VGVB_00" }, { "id": "Shortdefinition", "value": "What actions can your agency take to enforce consumer protection laws and regulations? \n- Issue warnings to financial institutions" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Enforcement Powers" } ], "source_id": "69" }, { "id": "FB.FCP.INST.EP.RF", "metatype": [ { "id": "Generalcomments", "value": "Does the financial consumer protection (FCP) agency have enforcement powers to REQUIRE PROVIDERS TO REFUND FEES AND CHARGES?" }, { "id": "IndicatorName", "value": "Does the financial consumer protection (FCP) agency have enforcement powers to require providers to refund fees and charges?" }, { "id": "Othernotes", "value": "VGVB_01" }, { "id": "Shortdefinition", "value": "What actions can your agency take to enforce consumer protection laws and regulations? \n- Require providers to refund fees and charges" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Enforcement Powers" } ], "source_id": "69" }, { "id": "FB.FCP.INST.EP.RL", "metatype": [ { "id": "Generalcomments", "value": "Does the financial consumer protection (FCP) agency have enforcement powers to REVOKE OR RECOMMEND TO REVOKE LICENSE?" }, { "id": "IndicatorName", "value": "Does the financial consumer protection (FCP) agency have enforcement powers to revoke or recommend to revoke license?" }, { "id": "Othernotes", "value": "VGVB_05" }, { "id": "Shortdefinition", "value": "What actions can your agency take to enforce consumer protection laws and regulations? \n- Revoke or recommend to revoke the offending provider's license to operate (or to operate a certain type of the business)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Enforcement Powers" } ], "source_id": "69" }, { "id": "FB.FCP.INST.EP.WA", "metatype": [ { "id": "Generalcomments", "value": "Does the financial consumer protection (FCP) agency have enforcement powers to REQUIRE PROVIDERS TO WITHDRAW MISLEADING ADVERTISEMENTS?" }, { "id": "IndicatorName", "value": "Does the financial consumer protection (FCP) agency have enforcement powers to require providers to withdraw misleading advertisements?" }, { "id": "Othernotes", "value": "VGVB_02" }, { "id": "Shortdefinition", "value": "What actions can your agency take to enforce consumer protection laws and regulations? \n- Require providers to withdraw misleading advertisements" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Enforcement Powers" } ], "source_id": "69" }, { "id": "FB.FCP.INST.ES.AF.10", "metatype": [ { "id": "Generalcomments", "value": "Was the financial consumer protection (FCP) unit in this country ESTABLISHED AFTER THE YEAR 2010 ?" }, { "id": "IndicatorName", "value": "Was the financial consumer protection (FCP) unit in this country established after the year 2010?" }, { "id": "Shortdefinition", "value": "When was the financial consumer protection (FCP) unit or team established? Please specify when the separate unit(s) or team(s) designated to implement, oversee and/or enforce aspects of FCP law or regulation in your agency was established. If more than one unit or team exists, please respond in reference to the unit or team responsible for FCP supervision of commercial banks: \n- Year the FCP unit or team established: after 2010" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Institutional Arrangements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.ES.BF.2K", "metatype": [ { "id": "Generalcomments", "value": "Was the financial consumer protection (FCP) unit in this country ESTABLISHED BEFORE THE YEAR 2000 ?" }, { "id": "IndicatorName", "value": "Was the financial consumer protection (FCP) unit in this country established before the year 2000?" }, { "id": "Shortdefinition", "value": "When was the financial consumer protection (FCP) unit or team established? Please specify when the separate unit(s) or team(s) designated to implement, oversee and/or enforce aspects of FCP law or regulation in your agency was established. If more than one unit or team exists, please respond in reference to the unit or team responsible for FCP supervision of commercial banks: \n- Year the FCP unit or team established: before 2000" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Institutional Arrangements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.ES.BW.01", "metatype": [ { "id": "Generalcomments", "value": "Was the financial consumer protection (FCP) unit in this country ESTABLISHED BETWEEN THE YEARS 2000 AND 2010 ?" }, { "id": "IndicatorName", "value": "Was the financial consumer protection (FCP) unit in this country established between the years 2000 and 2010?" }, { "id": "Shortdefinition", "value": "When was the financial consumer protection (FCP) unit or team established? Please specify when the separate unit(s) or team(s) designated to implement, oversee and/or enforce aspects of FCP law or regulation in your agency was established. If more than one unit or team exists, please respond in reference to the unit or team responsible for FCP supervision of commercial banks: \n- Year the FCP unit or team established: between 2000 and 2010" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Institutional Arrangements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.FC.SF.AS", "metatype": [ { "id": "Generalcomments", "value": "Are FINANCIAL COOPERATIVES required to provide customers with specific types of product information IN A STANDARDIZED FORMAT AT ANY STAGE?" }, { "id": "IndicatorName", "value": "Are financial cooperatives required to provide customers with specific types of product information in a standardized format at any stage?" }, { "id": "Othernotes", "value": "VGXG_02" }, { "id": "Shortdefinition", "value": "With regard to the disclosure requirements referenced above, are there any requirements (law or regulation) for financial institutions to use a document with a standardized format in paper or electronic form (e.g. key facts statement, key disclosure document, synthesis document)? \n- Financial Cooperatives: In a standardized format, at any stage" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.MC.SF.AS", "metatype": [ { "id": "Generalcomments", "value": "Are MICROCREDIT INSTITUTIONS (MCIs) required to provide customers with specific types of product information IN A STANDARDIZED FORMAT AT ANY STAGE?" }, { "id": "IndicatorName", "value": "Are microcredit institutions (MCIs) required to provide customers with specific types of product information in a standardized format at any stage?" }, { "id": "Othernotes", "value": "VGXG_04" }, { "id": "Shortdefinition", "value": "With regard to the disclosure requirements referenced above, are there any requirements (law or regulation) for financial institutions to use a document with a standardized format in paper or electronic form (e.g. key facts statement, key disclosure document, synthesis document)? \n- MCIs: In a standardized format, at any stage" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.NB.SF.AS", "metatype": [ { "id": "Generalcomments", "value": "Are NON-BANK E-MONEY ISSUERS (NBEIs) required to provide customers with specific types of product information IN A STANDARDIZED FORMAT AT ANY STAGE?" }, { "id": "IndicatorName", "value": "Are non-bank e-money issuers (NBEIs) required to provide customers with specific types of product information in a standardized format at any stage?" }, { "id": "Othernotes", "value": "VGXG_05" }, { "id": "Shortdefinition", "value": "With regard to the disclosure requirements referenced above, are there any requirements (law or regulation) for financial institutions to use a document with a standardized format in paper or electronic form (e.g. key facts statement, key disclosure document, synthesis document)? \n- NBEIs: In a standardized format, at any stage" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.NS.BW.LC", "metatype": [ { "id": "Generalcomments", "value": "Does the financial consumer protection (FCP) unit in this country HAVE THE STAFF OF BETWEEN 50 AND 99 PEOPLE ?" }, { "id": "IndicatorName", "value": "Does the financial consumer protection (FCP) unit in this country have the staff of between 50 and 99 people?" }, { "id": "Shortdefinition", "value": "How large is the financial consumer protection (FCP) unit or team in this country? Please specify the size of the separate unit(s) or team(s) designated to implement, oversee and/or enforce aspects of FCP law or regulation in your agency. If more than one unit or team exists, please respond in reference to the unit or team responsible for FCP supervision of commercial banks: \n- Size of the FCP unit or team: 50-99 people" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Institutional Arrangements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.NS.BW.XL", "metatype": [ { "id": "Generalcomments", "value": "Does the financial consumer protection (FCP) unit in this country HAVE THE STAFF OF BETWEEN 10 AND 49 PEOPLE ?" }, { "id": "IndicatorName", "value": "Does the financial consumer protection (FCP) unit in this country have the staff of between 10 and 49 people?" }, { "id": "Shortdefinition", "value": "How large is the financial consumer protection (FCP) unit or team in this country? Please specify the size of the separate unit(s) or team(s) designated to implement, oversee and/or enforce aspects of FCP law or regulation in your agency. If more than one unit or team exists, please respond in reference to the unit or team responsible for FCP supervision of commercial banks: \n- Size of the FCP unit or team: 10-49 people" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Institutional Arrangements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.NS.LS.XP", "metatype": [ { "id": "Generalcomments", "value": "Does the financial consumer protection (FCP) unit in this country HAVE THE STAFF OF LESS THAN 10 PEOPLE ?" }, { "id": "IndicatorName", "value": "Does the financial consumer protection (FCP) unit in this country have the staff of less than 10 people?" }, { "id": "Shortdefinition", "value": "How large is the financial consumer protection (FCP) unit or team in this country? Please specify the size of the separate unit(s) or team(s) designated to implement, oversee and/or enforce aspects of FCP law or regulation in your agency. If more than one unit or team exists, please respond in reference to the unit or team responsible for FCP supervision of commercial banks: \n- Size of the FCP unit or team: less than 10 people" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Institutional Arrangements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.NS.MT.CM", "metatype": [ { "id": "Generalcomments", "value": "Does the financial consumer protection (FCP) unit in this country HAVE THE STAFF OF 100 OR MORE PEOPLE ?" }, { "id": "IndicatorName", "value": "Does the financial consumer protection (FCP) unit in this country have the staff of 100 or more people?" }, { "id": "Shortdefinition", "value": "How large is the financial consumer protection (FCP) unit or team in this country? Please specify the size of the separate unit(s) or team(s) designated to implement, oversee and/or enforce aspects of FCP law or regulation in your agency. If more than one unit or team exists, please respond in reference to the unit or team responsible for FCP supervision of commercial banks: \n- Size of the FCP unit or team: more than 100 people" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Institutional Arrangements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.OB.SF.AS", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER BANKS required to provide customers with specific types of product information IN A STANDARDIZED FORMAT AT ANY STAGE?" }, { "id": "IndicatorName", "value": "Are other banks required to provide customers with specific types of product information in a standardized format at any stage?" }, { "id": "Othernotes", "value": "VGXG_01" }, { "id": "Shortdefinition", "value": "With regard to the disclosure requirements referenced above, are there any requirements (law or regulation) for financial institutions to use a document with a standardized format in paper or electronic form (e.g. key facts statement, key disclosure document, synthesis document)? \n- Other Banks: In a standardized format, at any stage" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.OD.SF.AS", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs) required to provide customers with specific types of product information IN A STANDARDIZED FORMAT AT ANY STAGE?" }, { "id": "IndicatorName", "value": "Are other deposit taking institutions (ODTIs) required to provide customers with specific types of product information in a standardized format at any stage?" }, { "id": "Othernotes", "value": "VGXG_03" }, { "id": "Shortdefinition", "value": "With regard to the disclosure requirements referenced above, are there any requirements (law or regulation) for financial institutions to use a document with a standardized format in paper or electronic form (e.g. key facts statement, key disclosure document, synthesis document)? \n- ODTIs: In a standardized format, at any stage" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.SA.MA.CH", "metatype": [ { "id": "Generalcomments", "value": "Is COMPLAINTS HANDLING one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "IndicatorName", "value": "Is complaints handling one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "Othernotes", "value": "VGVA_01" }, { "id": "Shortdefinition", "value": "What are the main activities of the separate unit(s) or team(s) with respect to financial consumer protection for commercial banks/other banks/financial cooperatives/ODTIs/MCIs/NBEIs? \n- Complaints handling" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Supervisory Activities" } ], "source_id": "69" }, { "id": "FB.FCP.INST.SA.MA.EI", "metatype": [ { "id": "Generalcomments", "value": "Are OFF-SITE INSPECTION OF FINANCIAL INSTITUTIONS one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "IndicatorName", "value": "Are off-site inspection of financial institutions one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "Othernotes", "value": "VGVA_08" }, { "id": "Shortdefinition", "value": "What are the main activities of the separate unit(s) or team(s) with respect to financial consumer protection for commercial banks/other banks/financial cooperatives/ODTIs/MCIs/NBEIs? \n- Off-site inspection of financial institutions" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Supervisory Activities" } ], "source_id": "69" }, { "id": "FB.FCP.INST.SA.MA.FE", "metatype": [ { "id": "Generalcomments", "value": "Is FINANCIAL EDUCATION one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "IndicatorName", "value": "Is financial education one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "Othernotes", "value": "VGVA_10" }, { "id": "Shortdefinition", "value": "What are the main activities of the separate unit(s) or team(s) with respect to financial consumer protection for commercial banks/other banks/financial cooperatives/ODTIs/MCIs/NBEIs? \n- Financial education" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Supervisory Activities" } ], "source_id": "69" }, { "id": "FB.FCP.INST.SA.MA.II", "metatype": [ { "id": "Generalcomments", "value": "Are ON-SITE INSPECTION AND INVESTIGATION OF FINANCIAL INSTITUTIONS one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "IndicatorName", "value": "Are on-site inspection and investigation of financial institutions one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "Othernotes", "value": "VGVA_07" }, { "id": "Shortdefinition", "value": "What are the main activities of the separate unit(s) or team(s) with respect to financial consumer protection for commercial banks/other banks/financial cooperatives/ODTIs/MCIs/NBEIs? \n- On-site inspection and investigation of financial institutions" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Supervisory Activities" } ], "source_id": "69" }, { "id": "FB.FCP.INST.SA.MA.IR", "metatype": [ { "id": "Generalcomments", "value": "Is DRAFTING OR PROVIDING INPUTS INTO REGULATION one of the main activities of the financial consumer protection (FCP) unit ?" }, { "id": "IndicatorName", "value": "Is drafting or providing inputs into regulation one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "Othernotes", "value": "VGVA_00" }, { "id": "Shortdefinition", "value": "What are the main activities of the separate unit(s) or team(s) with respect to financial consumer protection for commercial banks/other banks/financial cooperatives/ODTIs/MCIs/NBEIs? \n- Drafting or providing inputs into regulation" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Supervisory Activities" } ], "source_id": "69" }, { "id": "FB.FCP.INST.SA.MA.MM", "metatype": [ { "id": "Generalcomments", "value": "Is MARKET MONITORING one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "IndicatorName", "value": "Is market monitoring one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "Othernotes", "value": "VGVA_05" }, { "id": "Shortdefinition", "value": "What are the main activities of the separate unit(s) or team(s) with respect to financial consumer protection for commercial banks/other banks/financial cooperatives/ODTIs/MCIs/NBEIs? \n- Market monitoring, including providers' advertisements, sales materials, websites, etc." }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Supervisory Activities" } ], "source_id": "69" }, { "id": "FB.FCP.INST.SA.MA.MR", "metatype": [ { "id": "Generalcomments", "value": "Is MARKET RESEARCH BY STUDYING CONSUMER BEHAVIOR one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "IndicatorName", "value": "Is market research by studying consumer behavior one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "Othernotes", "value": "VGVA_06" }, { "id": "Shortdefinition", "value": "What are the main activities of the separate unit(s) or team(s) with respect to financial consumer protection for commercial banks/other banks/financial cooperatives/ODTIs/MCIs/NBEIs? \n- Market research by studying consumer behavior via interviews, focus groups and other research" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Supervisory Activities" } ], "source_id": "69" }, { "id": "FB.FCP.INST.SA.MA.MS", "metatype": [ { "id": "Generalcomments", "value": "Is MYSTERY/INCOGNITO SHOPPING one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "IndicatorName", "value": "Is mystery/incognito shopping one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "Othernotes", "value": "VGVA_04" }, { "id": "Shortdefinition", "value": "What are the main activities of the separate unit(s) or team(s) with respect to financial consumer protection for commercial banks/other banks/financial cooperatives/ODTIs/MCIs/NBEIs? \n- Mystery/incognito shopping" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Supervisory Activities" } ], "source_id": "69" }, { "id": "FB.FCP.INST.SA.MA.NC", "metatype": [ { "id": "Generalcomments", "value": "Is COLLECTING DATA FROM FINANCIAL INSTITUTIONS on the NUMBER OF COMPLAINTS RECEIVED one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "IndicatorName", "value": "Is collecting data from financial institutions on the number of complaints received one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "Othernotes", "value": "VGVA_02" }, { "id": "Shortdefinition", "value": "What are the main activities of the separate unit(s) or team(s) with respect to financial consumer protection for commercial banks/other banks/financial cooperatives/ODTIs/MCIs/NBEIs? \n- Collection of data from financial institutions on the number of complaints received" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Supervisory Activities" } ], "source_id": "69" }, { "id": "FB.FCP.INST.SA.MA.RF", "metatype": [ { "id": "Generalcomments", "value": "Is COLLECTING DATA FROM FINANCIAL INSTITUTIONS on the RATES AND FEES FOR FINANCIAL SERVICES one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "IndicatorName", "value": "Is collecting data from financial institutions on the rates and fees for financial services one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "Othernotes", "value": "VGVA_03" }, { "id": "Shortdefinition", "value": "What are the main activities of the separate unit(s) or team(s) with respect to financial consumer protection for commercial banks/other banks/financial cooperatives/ODTIs/MCIs/NBEIs? \n- Collection of data from financial institutions on rates and fees for financial services" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Supervisory Activities" } ], "source_id": "69" }, { "id": "FB.FCP.INST.SA.MA.TR", "metatype": [ { "id": "Generalcomments", "value": "Are THEMATIC REVIEWS one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "IndicatorName", "value": "Are thematic reviews one of the main activities of the financial consumer protection (FCP) unit?" }, { "id": "Othernotes", "value": "VGVA_09" }, { "id": "Shortdefinition", "value": "What are the main activities of the separate unit(s) or team(s) with respect to financial consumer protection for commercial banks/other banks/financial cooperatives/ODTIs/MCIs/NBEIs? \n- Thematic reviews" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Supervisory Activities" } ], "source_id": "69" }, { "id": "FB.FCP.INST.SI.SF.AS", "metatype": [ { "id": "Generalcomments", "value": "Are AT LEAST SOME FINANCIAL INSTITUTIONS required to provide customers with specific types of product information IN A STANDARDIZED FORMAT?" }, { "id": "IndicatorName", "value": "Are at least some financial institutions required to provide customers with specific types of product information in a standardized format?" }, { "id": "Othernotes", "value": "VGXH_00" }, { "id": "Shortdefinition", "value": "With regard to the disclosure requirements referenced above, are there any requirements (law or regulation) for financial institutions to use a document with a standardized format in paper or electronic form (e.g. key facts statement, key disclosure document, synthesis document)? \n- At least some financial institutions, in a standardized format" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Disclosure requirements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.ST.RS.DP", "metatype": [ { "id": "Generalcomments", "value": "In terms of INSTITUTIONAL STRUCTURE for financial consumer protection (FCP) regulation and supervision, does this country have a DEDICATED FINANCIAL CONSUMER PROTECTION AUTHORITY MODEL ?" }, { "id": "IndicatorName", "value": "In terms of institutional structure for financial consumer protection (FCP) regulation and supervision, does this country have a dedicated financial consumer protection authority model?" }, { "id": "Shortdefinition", "value": "Please indicate which statement best describes the structure for financial consumer protection regulation and supervision (and more broadly market conduct) in your country? \n- Dedicated Market Conduct Agency Model. Financial consumer protection supervision responsibilities fall under a single agency dedicated to financial consumer protection supervision. Please provide the name of the agency." }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Institutional Arrangements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.ST.RS.GP", "metatype": [ { "id": "Generalcomments", "value": "In terms of INSTITUTIONAL STRUCTURE for financial consumer protection (FCP) regulation and supervision, does this country have a GENERAL CONSUMER PROTECTION AUTHORITY MODEL ?" }, { "id": "IndicatorName", "value": "In terms of institutional structure for financial consumer protection (FCP) regulation and supervision, does this country have a general consumer protection authority model?" }, { "id": "Shortdefinition", "value": "Please indicate which statement best describes the structure for financial consumer protection regulation and supervision (and more broadly market conduct) in your country? \n- General Consumer Protection Agency Model which covers FCP - single institution. Financial consumer protection supervision responsibilities fall under an agency or agencies responsible for broader consumer protection supervision within the jurisdiction, including non-financial activities. Please provide the name of all relevant agencies." }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Institutional Arrangements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.ST.RS.IA", "metatype": [ { "id": "Generalcomments", "value": "In terms of INSTITUTIONAL STRUCTURE for financial consumer protection (FCP) regulation and supervision, does this country have an INTEGRATED SINGLE FINANCIAL SECTOR AUTHORITY MODEL ?" }, { "id": "IndicatorName", "value": "In terms of institutional structure for financial consumer protection (FCP) regulation and supervision, does this country have an integrated single financial sector authority model?" }, { "id": "Shortdefinition", "value": "Please indicate which statement best describes the structure for financial consumer protection regulation and supervision (and more broadly market conduct) in your country? \n- Integrated Single Agency Model. Financial consumer protection supervision responsibilities fall under a single agency that is responsible for all aspects of supervision (e.g., prudential and financial consumer protection) of all financial service providers operating within the jurisdiction. Please provide the name of the agency." }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Institutional Arrangements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.ST.RS.IS", "metatype": [ { "id": "Generalcomments", "value": "In terms of INSTITUTIONAL STRUCTURE for financial consumer protection (FCP) regulation and supervision, does this country have an INTEGRATED SECTORAL FINANCIAL SECTOR AUTHORITY MODEL ?" }, { "id": "IndicatorName", "value": "In terms of institutional structure for financial consumer protection (FCP) regulation and supervision, does this country have an integrated sectoral financial sector authority model?" }, { "id": "Shortdefinition", "value": "Please indicate which statement best describes the structure for financial consumer protection regulation and supervision (and more broadly market conduct) in your country? \n- Integrated Multiple Agency Model (Sectoral). Financial consumer protection supervision responsibilities fall under multiple agencies that hold responsibility for all aspects of supervision (e.g., prudential and financial consumer protection) of financial service providers operating within specific financial sectors (banking, insurance, securities, etc.). Please list the name of relevant agencies and the sectors they oversee." }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Institutional Arrangements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.ST.RS.SH", "metatype": [ { "id": "Generalcomments", "value": "In terms of INSTITUTIONAL STRUCTURE for financial consumer protection (FCP) regulation and supervision, does this country have a SHARED FINANCIAL AND GENERAL CONSUMER PROTECTION AUTHORITY MODEL ?" }, { "id": "IndicatorName", "value": "In terms of institutional structure for financial consumer protection (FCP) regulation and supervision, does this country have a shared financial and general consumer protection authority model?" }, { "id": "Shortdefinition", "value": "Please indicate which statement best describes the structure for financial consumer protection regulation and supervision (and more broadly market conduct) in your country? \n- Shared financial and general consumer protection authority model - two institutions" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Institutional Arrangements" } ], "source_id": "69" }, { "id": "FB.FCP.INST.ST.UA.DF", "metatype": [ { "id": "Generalcomments", "value": "Under the agency overseeing financial consumer protection (FCP) in this country, is there a DEDICATED FCP UNIT?" }, { "id": "IndicatorName", "value": "Under the agency overseeing financial consumer protection (FCP) in this country, is there a dedicated FCP unit?" }, { "id": "Othernotes", "value": "VGUD_00" }, { "id": "Shortdefinition", "value": "If your agency has the responsibility to implement, oversee and/or enforce any aspect of financial consumer protection law or regulation, is there a separate unit(s) or team(s) designated to work on consumer protection in your agency?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Institutional Arrangements" } ], "source_id": "69" }, { "id": "FB.FCP.LEGL.FL.SA.EX", "metatype": [ { "id": "Generalcomments", "value": "Is there a STAND-ALONE FINANCIAL CONSUMER PROTECTION LAW in place?" }, { "id": "IndicatorName", "value": "Is there a stand-alone financial consumer protection law in place?" }, { "id": "Othernotes", "value": "VGUA_02" }, { "id": "Shortdefinition", "value": "What type(s) of legal framework exists in your country pertaining to financial consumer protection? \n- Stand-alone financial consumer protection law" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Legal Framework" } ], "source_id": "69" }, { "id": "FB.FCP.LEGL.FR.CP.EX", "metatype": [ { "id": "Generalcomments", "value": "Are CONSUMER PROTECTION PROVISIONS in place within BROADER FINANCIAL SECTOR REGULATIONS?" }, { "id": "IndicatorName", "value": "Are consumer protection provisions in place within broader financial sector regulations?" }, { "id": "Othernotes", "value": "VGUB_01" }, { "id": "Shortdefinition", "value": "What type(s) regulations exist in your country pertaining to financial consumer protection? \n- Consumer protection provisions within the regulations pertaining to the financial sector (e.g. regulation of agents, electronic channels, certain products like credit cards)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Legal Framework" } ], "source_id": "69" }, { "id": "FB.FCP.LEGL.GL.EF.EX", "metatype": [ { "id": "Generalcomments", "value": "Is there a GENERAL CONSUMER PROTECTION LAW WITH EXPLICIT REFERENCE TO FINANCIAL SERVICES in place?" }, { "id": "IndicatorName", "value": "Is there a general consumer protection law with explicit reference to financial services in place?" }, { "id": "Othernotes", "value": "VGUA_01" }, { "id": "Shortdefinition", "value": "What type(s) of legal framework exists in your country pertaining to financial consumer protection? \n- General consumer protection law with explicit reference to financial services" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Legal Framework" } ], "source_id": "69" }, { "id": "FB.FCP.LEGL.GL.NF.EX", "metatype": [ { "id": "Generalcomments", "value": "Is there a GENERAL CONSUMER PROTECTION LAW WITHOUT EXPLICIT REFERENCE TO FINANCIAL SERVICES in place?" }, { "id": "IndicatorName", "value": "Is there a general consumer protection law without explicit reference to financial services in place?" }, { "id": "Othernotes", "value": "VGUA_00" }, { "id": "Shortdefinition", "value": "What type(s) of legal framework exists in your country pertaining to financial consumer protection? \n- General consumer protection law without explicit reference to financial services" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Legal Framework" } ], "source_id": "69" }, { "id": "FB.FCP.LEGL.NP.NF.NE", "metatype": [ { "id": "Generalcomments", "value": "Is there no legal framework for FINANCIAL CONSUMER PROTECTION in this country?" }, { "id": "IndicatorName", "value": "Is there no legal framework for financial consumer protection in this country?" }, { "id": "Othernotes", "value": "VGUA_04" }, { "id": "Shortdefinition", "value": "What type(s) of legal framework exists in your country pertaining to financial consumer protection? \n- No legal framework exists pertaining to financial consumer protection" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Legal Framework" } ], "source_id": "69" }, { "id": "FB.FCP.LEGL.PR.SA.EX", "metatype": [ { "id": "Generalcomments", "value": "Are STAND-ALONE FINANCIAL CONSUMER PROTECTION (FCP) regulations in place?" }, { "id": "IndicatorName", "value": "Are stand-alone financial consumer protection (FCP) regulations in place?" }, { "id": "Othernotes", "value": "VGUB_00" }, { "id": "Shortdefinition", "value": "What type(s) regulations exist in your country pertaining to financial consumer protection? \n- Stand-alone financial consumer protection regulations (e.g. a regulation on consumer disclosure)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Legal Framework" } ], "source_id": "69" }, { "id": "FB.FCP.LEGL.SL.PP.EX", "metatype": [ { "id": "Generalcomments", "value": "Are there CONSUMER PROTECTION PROVISIONS in place within broader financial sector laws?" }, { "id": "IndicatorName", "value": "Are there consumer protection provisions in place within broader financial sector laws?" }, { "id": "Othernotes", "value": "VGUA_03" }, { "id": "Shortdefinition", "value": "What type(s) of legal framework exists in your country pertaining to financial consumer protection? \n- Consumer protection provisions within the financial sector legal framework (e.g., banking law, insurance law)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Legal Framework" } ], "source_id": "69" }, { "id": "FB.INC.BNKG.AC.SC.MC", "metatype": [ { "id": "Generalcomments", "value": "Is the CEILING ON THE MINIMUM BALANCE FOR SAVINGS/CURRENT ACCOUNTS regulated in this country?" }, { "id": "IndicatorName", "value": "Is the ceiling on the minimum balance for savings/current accounts regulated in this country?" }, { "id": "Othernotes", "value": "VGTA_01" }, { "id": "Shortdefinition", "value": "Which of the following are determined by law or regulation in your country regarding cost of customer accounts? \n- A ceiling on the minimum balance that a provider can impose for savings or current accounts" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Account Costs" } ], "source_id": "69" }, { "id": "FB.INC.BNKG.AC.SC.MM", "metatype": [ { "id": "Generalcomments", "value": "Are the MAXIMUM MAINTENANCE FEES FOR SAVINGS/CURRENT ACCOUNTS regulated in this country?" }, { "id": "IndicatorName", "value": "Are the maximum maintenance fees for savings/current accounts regulated in this country?" }, { "id": "Othernotes", "value": "VGTA_02" }, { "id": "Shortdefinition", "value": "Which of the following are determined by law or regulation in your country regarding cost of customer accounts? \n- The maximum maintenance fees for savings or current accounts (e.g. monthly or yearly account ownership fee)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Account Costs" } ], "source_id": "69" }, { "id": "FB.INC.BNKG.AC.SC.MO", "metatype": [ { "id": "Generalcomments", "value": "Are the MAXIMUM OVERDRAFT PENALTY or BELOW MINIMUM BALANCE PENALTY regulated in this country?" }, { "id": "IndicatorName", "value": "Are the maximum overdraft penalty or below minimum balance penalty regulated in this country?" }, { "id": "Othernotes", "value": "VGTA_03" }, { "id": "Shortdefinition", "value": "Which of the following are determined by law or regulation in your country regarding cost of customer accounts? \n- The maximum overdraft penalty or below-minimum balance penalty that providers can charge" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Account Costs" } ], "source_id": "69" }, { "id": "FB.INC.BNKG.AC.SC.MX", "metatype": [ { "id": "Generalcomments", "value": "Is the MAXIMUM COST TO OPEN A SAVINGS/CURRENT ACCOUNT regulated in this country?" }, { "id": "IndicatorName", "value": "Is the maximum cost to open a savings/current account regulated in this country?" }, { "id": "Othernotes", "value": "VGTA_00" }, { "id": "Shortdefinition", "value": "Which of the following are determined by law or regulation in your country regarding cost of customer accounts? \n- The maximum cost for customers of opening a savings or current account" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Account Costs" } ], "source_id": "69" }, { "id": "FB.INC.BNKG.AC.SC.NC", "metatype": [ { "id": "Generalcomments", "value": "Is there NO COST FOR CUSTOMERS TO OPEN A SAVINGS/CURRENT ACCOUNT as per regulation in this country?" }, { "id": "IndicatorName", "value": "Is there no cost for customers to open a savings/current account as per regulation in this country?" }, { "id": "Othernotes", "value": "VGTA_06" }, { "id": "Shortdefinition", "value": "Which of the following are determined by law or regulation in your country regarding cost of customer accounts? \n- No cost for opening a savings or current account" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Account Costs" } ], "source_id": "69" }, { "id": "FB.INC.BNKG.AC.SC.NL", "metatype": [ { "id": "Generalcomments", "value": "Is there NO LAW OR REGULATION regarding the cost of customer accounts in this country?" }, { "id": "IndicatorName", "value": "Is there no law or regulation regarding the cost of customer accounts in this country?" }, { "id": "Othernotes", "value": "VGTA_05" }, { "id": "Shortdefinition", "value": "Which of the following are determined by law or regulation in your country regarding cost of customer accounts? \n- No law or regulation addresses the costs of customer accounts" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Account Costs" } ], "source_id": "69" }, { "id": "FB.INC.BNKG.AC.SC.OF", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER FACTORS in the cost of customer accounts regulated in this country?" }, { "id": "IndicatorName", "value": "Are other factors in the cost of customer accounts regulated in this country?" }, { "id": "Othernotes", "value": "VGTA_04" }, { "id": "Shortdefinition", "value": "Which of the following are determined by law or regulation in your country regarding cost of customer accounts? \n- Other" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Account Costs" } ], "source_id": "69" }, { "id": "FB.INC.EMNY.NB.IN.PM", "metatype": [ { "id": "Generalcomments", "value": "Are NON-BANK E-MONEY ISSUERS (NBEIs) permitted to PAY INTEREST on customers' e-money accounts?" }, { "id": "IndicatorName", "value": "Are non-bank e-money issuers (NBEIs) permitted to pay interest on customers' e-money accounts?" }, { "id": "Othernotes", "value": "VGOA_00" }, { "id": "Shortdefinition", "value": "Are non-bank e-money issuers permitted by law or regulation to pay interest on customers' e-money accounts or share profits with their e-money customers? \n- Pay interest" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Electronic Money" } ], "source_id": "69" }, { "id": "FB.INC.EMNY.NB.IP.NP", "metatype": [ { "id": "Generalcomments", "value": "Are NON-BANK E-MONEY ISSUERS (NBEIs) NOT PERMITTED to PAY INTEREST OR SHARE PROFITS with their e-money customers?" }, { "id": "IndicatorName", "value": "Are non-bank e-money issuers (NBEIs) not permitted to pay interest or share profits with their e-money customers?" }, { "id": "Othernotes", "value": "VGOA_02" }, { "id": "Shortdefinition", "value": "Are non-bank e-money issuers permitted by law or regulation to pay interest on customers' e-money accounts or share profits with their e-money customers? \n- Neither" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Electronic Money" } ], "source_id": "69" }, { "id": "FB.INC.EMNY.NB.PU.RR", "metatype": [ { "id": "Generalcomments", "value": "Are NON-BANK E-MONEY ISSUERS (NBEIs) prohibited from using customer funds for PURPOSES OTHER THAN redeeming e-money and executing fund transfers?" }, { "id": "IndicatorName", "value": "Are non-bank e-money issuers (NBEIs) prohibited from using customer funds for purposes other than redeeming e-money and executing fund transfers?" }, { "id": "Othernotes", "value": "VGNA_00" }, { "id": "Shortdefinition", "value": "Are non-bank e-money issuers prohibited by law or regulation from using customer funds for purposes other than redeeming e-money and executing fund transfers?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Electronic Money" } ], "source_id": "69" }, { "id": "FB.INC.EMNY.NB.SP.PM", "metatype": [ { "id": "Generalcomments", "value": "Are NON-BANK E-MONEY ISSUERS (NBEIs) permitted to SHARE PROFITS with their e-money customers?" }, { "id": "IndicatorName", "value": "Are non-bank e-money issuers (NBEIs) permitted to share profits with their e-money customers?" }, { "id": "Othernotes", "value": "VGOA_01" }, { "id": "Shortdefinition", "value": "Are non-bank e-money issuers permitted by law or regulation to pay interest on customers' e-money accounts or share profits with their e-money customers? \n- Share profits" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Electronic Money" } ], "source_id": "69" }, { "id": "FB.INC.EMNY.SF.TA.CB", "metatype": [ { "id": "Generalcomments", "value": "Is the use of ACCOUNTS AT THE CENTRAL BANK required to SAFEGUARD E-MONEY FUNDS?" }, { "id": "IndicatorName", "value": "Is the use of accounts at the central bank required to safeguard e-money funds?" }, { "id": "Othernotes", "value": "VGMA_03" }, { "id": "Shortdefinition", "value": "Is it specified in law or regulation which type of account must be used to safeguard e-money funds?\n- Account at the Central Bank" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Electronic Money" } ], "source_id": "69" }, { "id": "FB.INC.EMNY.SF.TA.EA", "metatype": [ { "id": "Generalcomments", "value": "Is the use of ESCROW ACCOUNTS required to SAFEGUARD E-MONEY FUNDS?" }, { "id": "IndicatorName", "value": "Is the use of escrow accounts required to safeguard e-money funds?" }, { "id": "Othernotes", "value": "VGMA_01" }, { "id": "Shortdefinition", "value": "Is it specified in law or regulation which type of account must be used to safeguard e-money funds? \n- Escrow account" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Electronic Money" } ], "source_id": "69" }, { "id": "FB.INC.EMNY.SF.TA.ND", "metatype": [ { "id": "Generalcomments", "value": "Is the TYPE OF ACCOUNTS to SAFEGUARD E-MONEY FUNDS NOT DEFINED?" }, { "id": "IndicatorName", "value": "Is the type of accounts to safeguard e-money funds not defined?" }, { "id": "Othernotes", "value": "VGMA_04" }, { "id": "Shortdefinition", "value": "Is it specified in law or regulation which type of account must be used to safeguard e-money funds? \n- Not defined" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Electronic Money" } ], "source_id": "69" }, { "id": "FB.INC.EMNY.SF.TA.OT", "metatype": [ { "id": "Generalcomments", "value": "Is SOME OTHER TYPE OF ACCOUNTS (not mentioned above) required to SAFEGUARD E-MONEY FUNDS?" }, { "id": "IndicatorName", "value": "Is some other type of accounts (not mentioned above) required to safeguard e-money funds?" }, { "id": "Othernotes", "value": "VGMA_05" }, { "id": "Shortdefinition", "value": "Is it specified in law or regulation which type of account must be used to safeguard e-money funds? \n- Some other type - not mentioned above" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Electronic Money" } ], "source_id": "69" }, { "id": "FB.INC.EMNY.SF.TA.RA", "metatype": [ { "id": "Generalcomments", "value": "Is the use of REGULAR ACCOUNTS required to SAFEGUARD E-MONEY FUNDS?" }, { "id": "IndicatorName", "value": "Is the use of regular accounts required to safeguard e-money funds?" }, { "id": "Othernotes", "value": "VGMA_02" }, { "id": "Shortdefinition", "value": "Is it specified in law or regulation which type of account must be used to safeguard e-money funds? \n- Regular account" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Electronic Money" } ], "source_id": "69" }, { "id": "FB.INC.EMNY.SF.TA.TA", "metatype": [ { "id": "Generalcomments", "value": "Is the use of TRUST ACCOUNTS required to SAFEGUARD E-MONEY FUNDS?" }, { "id": "IndicatorName", "value": "Is the use of trust accounts required to safeguard e-money funds?" }, { "id": "Othernotes", "value": "VGMA_00" }, { "id": "Shortdefinition", "value": "Is it specified in law or regulation which type of account must be used to safeguard e-money funds?\n- Trust account" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Electronic Money" } ], "source_id": "69" }, { "id": "FB.INC.EMNY.SP.IF.AM", "metatype": [ { "id": "Generalcomments", "value": "Are 100% of CUSTOMERS' E-MONEY FUNDS required to be SEPARATED FROM E-MONEY ISSUER'S FUNDS and kept AT MORE THAN ONE prudentially regulated financial institution, possibly the Central Bank and others?" }, { "id": "IndicatorName", "value": "Are 100% of customers' e-money funds required to be separated from e-money issuer's funds and kept at more than one prudentially regulated financial institution, possibly the central bank and others?" }, { "id": "Shortdefinition", "value": "What fraction of customers' e-money funds is required to be separated from e-money issuer's funds and kept at how many prudentially regulated financial institutions?\n- 100% of customers' e-money funds; kept at more than one prudentially regulated financial institution" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Electronic Money" } ], "source_id": "69" }, { "id": "FB.INC.EMNY.SP.IF.AO", "metatype": [ { "id": "Generalcomments", "value": "Are 100% of CUSTOMERS' E-MONEY FUNDS required to be SEPARATED FROM E-MONEY ISSUER'S FUNDS and kept AT A SINGLE prudentially regulated financial institution, possibly the Central Bank?" }, { "id": "IndicatorName", "value": "Are 100% of customers' e-money funds required to be separated from e-money issuer's funds and kept at a single prudentially regulated financial institution, possibly the central bank?" }, { "id": "Shortdefinition", "value": "What fraction of customers' e-money funds is required to be separated from e-money issuer's funds and kept at how many prudentially regulated financial institutions?\n- 100% of customers' e-money funds; kept at a single prudentially regulated financial institution" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Electronic Money" } ], "source_id": "69" }, { "id": "FB.INC.EMNY.SP.IF.NR", "metatype": [ { "id": "Generalcomments", "value": "Is it NOT REQUIRED that CUSTOMERS' E-MONEY FUNDS are SEPARATED FROM E-MONEY ISSUER'S FUNDS?" }, { "id": "IndicatorName", "value": "Is it not required that customers' e-money funds are separated from e-money issuer's funds?" }, { "id": "Shortdefinition", "value": "What fraction of customers' e-money funds is required to be separated from e-money issuer's funds and kept at how many prudentially regulated financial institutions?\n- No requirement to separate customers' e-money funds" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Electronic Money" } ], "source_id": "69" }, { "id": "FB.INC.EMNY.SP.IF.SM", "metatype": [ { "id": "Generalcomments", "value": "Is ONLY A FRACTION OF CUSTOMERS' E-MONEY FUNDS required to be SEPARATED FROM E-MONEY ISSUER'S FUNDS and kept AT ONE OR MORE prudentially regulated financial institutions, possibly the Central Bank and others?" }, { "id": "IndicatorName", "value": "Is only a fraction of customers' e-money funds required to be separated from e-money issuer's funds and kept at one or more prudentially regulated financial institutions, possibly the central bank and others?" }, { "id": "Shortdefinition", "value": "What fraction of customers' e-money funds is required to be separated from e-money issuer's funds and kept at how many prudentially regulated financial institutions?\n- Only a fraction of customers' e-money funds; kept at one or more prudentially regulated financial institutions" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Electronic Money" } ], "source_id": "69" }, { "id": "FB.INC.INST.CB.AU.AR", "metatype": [ { "id": "Generalcomments", "value": "For COMMERCIAL BANKS, is AUTHORIZATION of new or modified financial products ALWAYS EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For commercial banks, is authorization of new or modified financial products always explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- Commercial Banks" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.CB.AU.NR", "metatype": [ { "id": "Generalcomments", "value": "For COMMERCIAL BANKS, is AUTHORIZATION of new or modified financial products NEVER EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For commercial banks, is authorization of new or modified financial products never explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- Commercial Banks" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.CB.AU.SR", "metatype": [ { "id": "Generalcomments", "value": "For COMMERCIAL BANKS, is AUTHORIZATION of new or modified financial products ONLY IN SOME CASES EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For commercial banks, is authorization of new or modified financial products only in some cases explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- Commercial Banks" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.CB.CD.SE", "metatype": [ { "id": "Generalcomments", "value": "For COMMERCIAL BANKS, are there SIMPLIFICATIONS or EXCEPTIONS to the documentation requirements for some types of applicants or deposit account products?" }, { "id": "IndicatorName", "value": "For commercial banks, are there simplifications or exceptions to the documentation requirements for some types of applicants or deposit account products?" }, { "id": "Othernotes", "value": "VGRA_00" }, { "id": "Shortdefinition", "value": "Are there simplifications or exceptions to the documentation requirements for certain types of applicants (e.g. low income) or deposit account products (e.g. small-value, low-risk transactions or basic accounts)?\n- Commercial Banks" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.CB.CRB.CR", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS required to CHECK/REPORT TO CREDIT BUREAU for some/all loans?" }, { "id": "IndicatorName", "value": "Are commercial banks required to check/report to credit bureau for some/all loans?" }, { "id": "Othernotes", "value": "VGHA_00" }, { "id": "Shortdefinition", "value": "Do financial institutions have access, or report, to a credit bureau or credit registry?  \n- Commercial Banks" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Credit Infrastructure" } ], "source_id": "69" }, { "id": "FB.INC.INST.CB.LN.AL", "metatype": [ { "id": "Generalcomments", "value": "For COMMERCIAL BANKS, is all lending subject to INTEREST RATE CAPS OR PRICING LIMITS?" }, { "id": "IndicatorName", "value": "For commercial banks, is all lending subject to interest rate caps or pricing limits?" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- Commercial Banks: All lending is subject to interest rate caps or pricing limits" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "69" }, { "id": "FB.INC.INST.CB.LN.NL", "metatype": [ { "id": "Generalcomments", "value": "For COMMERCIAL BANKS, are there NO INTEREST RATE CAPS OR PRICING LIMITS?" }, { "id": "IndicatorName", "value": "For commercial banks, are there no interest rate caps or pricing limits?" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- Commercial Banks: No interest rate caps or pricing limits of any kind" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "69" }, { "id": "FB.INC.INST.CB.LN.SL", "metatype": [ { "id": "Generalcomments", "value": "For COMMERCIAL BANKS, are there SOME INTEREST RATE CAPS OR PRICING LIMITS that apply to certain products or segments?" }, { "id": "IndicatorName", "value": "For commercial banks, are there some interest rate caps or pricing limits that apply to certain products or segments?" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- Commercial Banks: Some interest rate caps or pricing limits apply to certain products or consumer segments" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "69" }, { "id": "FB.INC.INST.CB.LT.NF", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS allowed to implement NON-FACE-TO-FACE CUSTOMER DUE DILIGENCE (CDD) under the risk-based approach to AML/CFT regulation?" }, { "id": "IndicatorName", "value": "Are commercial banks allowed to implement non-face-to-face customer due diligence (CDD) under the risk-based approach to AML/CFT regulation?" }, { "id": "Othernotes", "value": "VGSA_01" }, { "id": "Shortdefinition", "value": "What elements of a risk-based approach to AML/CFT regulation have been implemented? \n- Commercial Banks: Non-face-to-face customer due diligence (by agents and/or via mobile phone or other mobile device)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.CB.LT.NI", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS allowed to accept NON-STANDARD ID DOCUMENTS under the risk-based approach to AML/CFT regulation?" }, { "id": "IndicatorName", "value": "Are commercial banks allowed to accept non-standard id documents under the risk-based approach to AML/CFT regulation?" }, { "id": "Othernotes", "value": "VGSA_00" }, { "id": "Shortdefinition", "value": "What elements of a risk-based approach to AML/CFT regulation have been implemented? \n- Commercial Banks: Acceptance of non-standard identification documents" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.CB.LT.OE", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS allowed to implement OTHER ELEMENTS under the risk-based approach to AML/CFT regulation?" }, { "id": "IndicatorName", "value": "Are commercial banks allowed to implement other elements under the risk-based approach to AML/CFT regulation?" }, { "id": "Othernotes", "value": "VGSA_03" }, { "id": "Shortdefinition", "value": "What elements of a risk-based approach to AML/CFT regulation have been implemented? \n- Commercial Banks: Other" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.CB.LT.SM", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS allowed to implement SIMPLIFIED TRANSACTION MONITORING under the risk-based approach to AML/CFT regulation?" }, { "id": "IndicatorName", "value": "Are commercial banks allowed to implement simplified transaction monitoring under the risk-based approach to AML/CFT regulation?" }, { "id": "Othernotes", "value": "VGSA_02" }, { "id": "Shortdefinition", "value": "What elements of a risk-based approach to AML/CFT regulation have been implemented? \n- Commercial Banks: Allowing simplified transaction monitoring based on lower assessed risk" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.CB.TP.LI", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS liable for any ACTIONS OR OMISSIONS OF THEIR AGENTS?" }, { "id": "IndicatorName", "value": "Are commercial banks liable for any actions or omissions of their agents?" }, { "id": "Othernotes", "value": "VGFA_00" }, { "id": "Shortdefinition", "value": "If financial institutions are allowed to have agents, please specify which type of rules apply regulating the relationships between the financial institution, the agent, and the consumer: \n- Commercial Banks: Are there specific rules which indicate that financial service providers are liable for any actions or omissions of the agent?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.FC.AU.AR", "metatype": [ { "id": "Generalcomments", "value": "For FINANCIAL COOPERATIVES, is AUTHORIZATION of new or modified financial products ALWAYS EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For financial cooperatives, is authorization of new or modified financial products always explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- Financial Cooperatives" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.FC.AU.NR", "metatype": [ { "id": "Generalcomments", "value": "For FINANCIAL COOPERATIVES, is AUTHORIZATION of new or modified financial products NEVER EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For financial cooperatives, is authorization of new or modified financial products never explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- Financial Cooperatives" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.FC.AU.SR", "metatype": [ { "id": "Generalcomments", "value": "For FINANCIAL COOPERATIVES, is AUTHORIZATION of new or modified financial products ONLY IN SOME CASES EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For financial cooperatives, is authorization of new or modified financial products only in some cases explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- Financial Cooperatives" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.FC.CD.SE", "metatype": [ { "id": "Generalcomments", "value": "For FINANCIAL COOPERATIVES, are there SIMPLIFICATIONS or EXCEPTIONS to the documentation requirements for some types of applicants or deposit account products?" }, { "id": "IndicatorName", "value": "For financial cooperatives, are there simplifications or exceptions to the documentation requirements for some types of applicants or deposit account products?" }, { "id": "Othernotes", "value": "VGRA_02" }, { "id": "Shortdefinition", "value": "Are there simplifications or exceptions to the documentation requirements for certain types of applicants (e.g. low income) or deposit account products (e.g. small-value, low-risk transactions or basic accounts)?\n- Financial Cooperatives" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.FC.CRB.CR", "metatype": [ { "id": "Generalcomments", "value": "Are FINANCIAL COOPERATIVES required to CHECK/REPORT TO CREDIT BUREAU for some/all loans?" }, { "id": "IndicatorName", "value": "Are financial cooperatives required to check/report to credit bureau for some/all loans?" }, { "id": "Othernotes", "value": "VGHA_02" }, { "id": "Shortdefinition", "value": "Do financial institutions have access, or report, to a credit bureau or credit registry?  \n- Financial Cooperatives" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Credit Infrastructure" } ], "source_id": "69" }, { "id": "FB.INC.INST.FC.LN.AL", "metatype": [ { "id": "Generalcomments", "value": "For FINANCIAL COOPERATIVES, is all lending subject to INTEREST RATE CAPS OR PRICING LIMITS?" }, { "id": "IndicatorName", "value": "For financial cooperatives, is all lending subject to interest rate caps or pricing limits?" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- Financial Cooperatives: All lending is subject to interest rate caps or pricing limits" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "69" }, { "id": "FB.INC.INST.FC.LN.NL", "metatype": [ { "id": "Generalcomments", "value": "For FINANCIAL COOPERATIVES, are there NO INTEREST RATE CAPS OR PRICING LIMITS?" }, { "id": "IndicatorName", "value": "For financial cooperatives, are there no interest rate caps or pricing limits?" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- Financial Cooperatives: No interest rate caps or pricing limits of any kind" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "69" }, { "id": "FB.INC.INST.FC.LN.SL", "metatype": [ { "id": "Generalcomments", "value": "For FINANCIAL COOPERATIVES, are there SOME INTEREST RATE CAPS OR PRICING LIMITS that apply to certain products or segments?" }, { "id": "IndicatorName", "value": "For financial cooperatives, are there some interest rate caps or pricing limits that apply to certain products or segments?" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- Financial Cooperatives: Some interest rate caps or pricing limits apply to certain products or consumer segments" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "69" }, { "id": "FB.INC.INST.FC.LT.NF", "metatype": [ { "id": "Generalcomments", "value": "Are FINANCIAL COOPERATIVES allowed to implement NON-FACE-TO-FACE CUSTOMER DUE DILIGENCE (CDD) under the risk-based approach to AML/CFT regulation?" }, { "id": "IndicatorName", "value": "Are financial cooperatives allowed to implement non-face-to-face customer due diligence (CDD) under the risk-based approach to AML/CFT regulation?" }, { "id": "Othernotes", "value": "VGSC_01" }, { "id": "Shortdefinition", "value": "What elements of a risk-based approach to AML/CFT regulation have been implemented?\n- Financial Cooperatives: Non-face-to-face customer due diligence (by agents and/or via mobile phone or other mobile device)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.FC.LT.NI", "metatype": [ { "id": "Generalcomments", "value": "Are FINANCIAL COOPERATIVES allowed to accept NON-STANDARD ID DOCUMENTS under the risk-based approach to AML/CFT regulation?" }, { "id": "IndicatorName", "value": "Are financial cooperatives allowed to accept non-standard id documents under the risk-based approach to AML/CFT regulation?" }, { "id": "Othernotes", "value": "VGSC_00" }, { "id": "Shortdefinition", "value": "What elements of a risk-based approach to AML/CFT regulation have been implemented? \n- Financial Cooperatives: Acceptance of non-standard identification documents" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.FC.LT.OE", "metatype": [ { "id": "Generalcomments", "value": "Are FINANCIAL COOPERATIVES allowed to implement OTHER ELEMENTS under the risk-based approach to AML/CFT regulation?" }, { "id": "IndicatorName", "value": "Are financial cooperatives allowed to implement other elements under the risk-based approach to AML/CFT regulation?" }, { "id": "Othernotes", "value": "VGSC_03" }, { "id": "Shortdefinition", "value": "What elements of a risk-based approach to AML/CFT regulation have been implemented? \n- Financial Cooperatives: Other" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.FC.LT.SM", "metatype": [ { "id": "Generalcomments", "value": "Are FINANCIAL COOPERATIVES allowed to implement SIMPLIFIED TRANSACTION MONITORING under the risk-based approach to AML/CFT regulation?" }, { "id": "IndicatorName", "value": "Are financial cooperatives allowed to implement simplified transaction monitoring under the risk-based approach to AML/CFT regulation?" }, { "id": "Othernotes", "value": "VGSC_02" }, { "id": "Shortdefinition", "value": "What elements of a risk-based approach to AML/CFT regulation have been implemented? \n- Financial Cooperatives: Allowing simplified transaction monitoring based on lower assessed risk" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.FC.TP.LI", "metatype": [ { "id": "Generalcomments", "value": "Are FINANCIAL COOPERATIVES liable for any ACTIONS OR OMISSIONS OF THEIR AGENTS?" }, { "id": "IndicatorName", "value": "Are financial cooperatives liable for any actions or omissions of their agents?" }, { "id": "Othernotes", "value": "VGFC_00" }, { "id": "Shortdefinition", "value": "If financial institutions are allowed to have agents, please specify which type of rules apply regulating the relationships between the financial institution, the agent, and the consumer: \n- Financial Cooperatives: Are there specific rules which indicate that financial service providers are liable for any actions or omissions of the agent?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.FP.AU.CR", "metatype": [ { "id": "Generalcomments", "value": "Are CONSUMER RISKS assessed during AUTHORIZATION PROCESS for modified or new financial products?" }, { "id": "IndicatorName", "value": "Are consumer risks assessed during authorization process for modified or new financial products?" }, { "id": "Othernotes", "value": "VGKA_02" }, { "id": "Shortdefinition", "value": "What types of risks are assessed during authorization process for modified or new financial products? \n- Consumer risks" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.FP.AU.LR", "metatype": [ { "id": "Generalcomments", "value": "Are AML/CFT RISKS assessed during AUTHORIZATION PROCESS for modified or new financial products?" }, { "id": "IndicatorName", "value": "Are AML/CFT risks assessed during authorization process for modified or new financial products?" }, { "id": "Othernotes", "value": "VGKA_01" }, { "id": "Shortdefinition", "value": "What types of risks are assessed during authorization process for modified or new financial products? \n- AML/CFT risks" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.FP.AU.OR", "metatype": [ { "id": "Generalcomments", "value": "Are OPERATIONAL RISKS assessed during AUTHORIZATION PROCESS for modified or new financial products?" }, { "id": "IndicatorName", "value": "Are operational risks assessed during authorization process for modified or new financial products?" }, { "id": "Othernotes", "value": "VGKA_00" }, { "id": "Shortdefinition", "value": "What types of risks are assessed during authorization process for modified or new financial products? \n- Operational risks" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.FW.CB", "metatype": [ { "id": "Generalcomments", "value": "Does a regulatory/supervisory framework exist for COMMERCIAL BANKS?" }, { "id": "IndicatorName", "value": "Does a regulatory/supervisory framework exist for commercial banks?" }, { "id": "Othernotes", "value": "VGAA_00" }, { "id": "Shortdefinition", "value": "Are commercial banks codified in the regulatory/supervisory framework?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Financial Institutions" } ], "source_id": "69" }, { "id": "FB.INC.INST.FW.FC", "metatype": [ { "id": "Generalcomments", "value": "Does a regulatory/supervisory framework exist for FINANCIAL COOPERATIVES?" }, { "id": "IndicatorName", "value": "Does a regulatory/supervisory framework exist for financial cooperatives?" }, { "id": "Othernotes", "value": "VGAA_02" }, { "id": "Shortdefinition", "value": "Are financial cooperatives codified in the regulatory/supervisory framework?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Financial Institutions" } ], "source_id": "69" }, { "id": "FB.INC.INST.FW.MC", "metatype": [ { "id": "Generalcomments", "value": "Does a regulatory/supervisory framework exist for MICROCREDIT INSTITUTIONS (MCIs)?" }, { "id": "IndicatorName", "value": "Does a regulatory/supervisory framework exist for microcredit institutions (MCIs)?" }, { "id": "Othernotes", "value": "VGAA_04" }, { "id": "Shortdefinition", "value": "Are microcredit institutions (MCIs) codified in the regulatory/supervisory framework?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Financial Institutions" } ], "source_id": "69" }, { "id": "FB.INC.INST.FW.NB", "metatype": [ { "id": "Generalcomments", "value": "Does a regulatory/supervisory framework exist for NON-BANK E-MONEY ISSUERS (NBEIS)?" }, { "id": "IndicatorName", "value": "Does a regulatory/supervisory framework exist for non-bank e-money issuers (NBEIs)?" }, { "id": "Othernotes", "value": "VGAA_05" }, { "id": "Shortdefinition", "value": "Are non-bank e-money issuers (NBEIS) codified in the regulatory/supervisory framework?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Financial Institutions" } ], "source_id": "69" }, { "id": "FB.INC.INST.FW.OB", "metatype": [ { "id": "Generalcomments", "value": "Does a regulatory/supervisory framework exist for OTHER BANKS?" }, { "id": "IndicatorName", "value": "Does a regulatory/supervisory framework exist for other banks?" }, { "id": "Othernotes", "value": "VGAA_01" }, { "id": "Shortdefinition", "value": "Are other banks codified in the regulatory/supervisory framework?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Financial Institutions" } ], "source_id": "69" }, { "id": "FB.INC.INST.FW.OD", "metatype": [ { "id": "Generalcomments", "value": "Does a regulatory/supervisory framework exist for OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs)?" }, { "id": "IndicatorName", "value": "Does a regulatory/supervisory framework exist for other deposit taking institutions (ODTIs)?" }, { "id": "Othernotes", "value": "VGAA_03" }, { "id": "Shortdefinition", "value": "Are other deposit taking institutions (ODTIs) codified in the regulatory/supervisory framework?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Financial Institutions" } ], "source_id": "69" }, { "id": "FB.INC.INST.MC.AU.AR", "metatype": [ { "id": "Generalcomments", "value": "For MICROCREDIT INSTITUTIONS (MCIs), is AUTHORIZATION of new or modified financial products ALWAYS EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For microcredit institutions (MCIs), is authorization of new or modified financial products always explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- MCIs" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.MC.AU.NR", "metatype": [ { "id": "Generalcomments", "value": "For MICROCREDIT INSTITUTIONS (MCIs), is AUTHORIZATION of new or modified financial products NEVER EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For microcredit institutions (MCIs), is authorization of new or modified financial products never explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- MCIs" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.MC.AU.SR", "metatype": [ { "id": "Generalcomments", "value": "For MICROCREDIT INSTITUTIONS (MCIs), is AUTHORIZATION of new or modified financial products ONLY IN SOME CASES EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For microcredit institutions (MCIs), is authorization of new or modified financial products only in some cases explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- MCIs" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.MC.CRB.CR", "metatype": [ { "id": "Generalcomments", "value": "Are MICROCREDIT INSTITUTIONS (MCIs) required to CHECK/REPORT TO CREDIT BUREAU for some/all loans?" }, { "id": "IndicatorName", "value": "Are microcredit institutions (MCIs) required to check/report to credit bureau for some/all loans?" }, { "id": "Othernotes", "value": "VGHA_04" }, { "id": "Shortdefinition", "value": "Do financial institutions have access, or report, to a credit bureau or credit registry?  \n- MCIs" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Credit Infrastructure" } ], "source_id": "69" }, { "id": "FB.INC.INST.MC.LN.AL", "metatype": [ { "id": "Generalcomments", "value": "For MICROCREDIT INSTITUTIONS (MCIs), is all lending subject to INTEREST RATE CAPS OR PRICING LIMITS?" }, { "id": "IndicatorName", "value": "For microcredit institutions (MCIs), is all lending subject to interest rate caps or pricing limits?" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- MCIs: All lending is subject to interest rate caps or pricing limits" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "69" }, { "id": "FB.INC.INST.MC.LN.NL", "metatype": [ { "id": "Generalcomments", "value": "For MICROCREDIT INSTITUTIONS (MCIs), are there NO INTEREST RATE CAPS OR PRICING LIMITS?" }, { "id": "IndicatorName", "value": "For microcredit institutions (MCIs), are there no interest rate caps or pricing limits?" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- MCIs: No interest rate caps or pricing limits of any kind" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "69" }, { "id": "FB.INC.INST.MC.LN.SL", "metatype": [ { "id": "Generalcomments", "value": "For MICROCREDIT INSTITUTIONS (MCIs), are there SOME INTEREST RATE CAPS OR PRICING LIMITS that apply to certain products or segments?" }, { "id": "IndicatorName", "value": "For microcredit institutions (MCIs), are there some interest rate caps or pricing limits that apply to certain products or segments?" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- MCIs: Some interest rate caps or pricing limits apply to certain products or consumer segments" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "69" }, { "id": "FB.INC.INST.MC.TP.LI", "metatype": [ { "id": "Generalcomments", "value": "Are MICROCREDIT INSTITUTIONS (MCIs) liable for any ACTIONS OR OMISSIONS OF THEIR AGENTS?" }, { "id": "IndicatorName", "value": "Are microcredit institutions (MCIs) liable for any actions or omissions of their agents?" }, { "id": "Othernotes", "value": "VGFE_00" }, { "id": "Shortdefinition", "value": "If financial institutions are allowed to have agents, please specify which type of rules apply regulating the relationships between the financial institution, the agent, and the consumer: \n- MCIs: Are there specific rules which indicate that financial service providers are liable for any actions or omissions of the agent?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.NB.AU.AR", "metatype": [ { "id": "Generalcomments", "value": "For NON-BANK E-MONEY ISSUERS (NBEIs), is AUTHORIZATION of new or modified financial products ALWAYS EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For non-bank e-money issuers (NBEIs), is authorization of new or modified financial products always explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- NBEIs" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.NB.AU.NR", "metatype": [ { "id": "Generalcomments", "value": "For NON-BANK E-MONEY ISSUERS (NBEIs), is AUTHORIZATION of new or modified financial products NEVER EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For non-bank e-money issuers (NBEIs), is authorization of new or modified financial products never explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- NBEIs" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.NB.AU.SR", "metatype": [ { "id": "Generalcomments", "value": "For NON-BANK E-MONEY ISSUERS (NBEIs), is AUTHORIZATION of new or modified financial products ONLY IN SOME CASES EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For non-bank e-money issuers (NBEIs), is authorization of new or modified financial products only in some cases explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- NBEIs" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.NB.CRB.CR", "metatype": [ { "id": "Generalcomments", "value": "Are NON-BANK E-MONEY ISSUERS (NBEIs) required to CHECK/REPORT TO CREDIT BUREAU for some/all loans?" }, { "id": "IndicatorName", "value": "Are non-bank e-money issuers (NBEIs) required to check/report to credit bureau for some/all loans?" }, { "id": "Othernotes", "value": "VGHA_05" }, { "id": "Shortdefinition", "value": "Do financial institutions have access, or report, to a credit bureau or credit registry?  \n- NBEIs" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Credit Infrastructure" } ], "source_id": "69" }, { "id": "FB.INC.INST.NB.TP.LI", "metatype": [ { "id": "Generalcomments", "value": "Are NON-BANK E-MONEY ISSUERS (NBEIs) liable for any ACTIONS OR OMISSIONS OF THEIR AGENTS?" }, { "id": "IndicatorName", "value": "Are non-bank e-money issuers (NBEIs) liable for any actions or omissions of their agents?" }, { "id": "Othernotes", "value": "VGFF_00" }, { "id": "Shortdefinition", "value": "If financial institutions are allowed to have agents, please specify which type of rules apply regulating the relationships between the financial institution, the agent, and the consumer: \n- NBEIs: Are there specific rules which indicate that financial service providers are liable for any actions or omissions of the agent?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.OB.AU.AR", "metatype": [ { "id": "Generalcomments", "value": "For OTHER BANKS, is AUTHORIZATION of new or modified financial products ALWAYS EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For other banks, is authorization of new or modified financial products always explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- Other Banks" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.OB.AU.NR", "metatype": [ { "id": "Generalcomments", "value": "For OTHER BANKS, is AUTHORIZATION of new or modified financial products NEVER EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For other banks, is authorization of new or modified financial products never explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- Other Banks" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.OB.AU.SR", "metatype": [ { "id": "Generalcomments", "value": "For OTHER BANKS, is AUTHORIZATION of new or modified financial products ONLY IN SOME CASES EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For other banks, is authorization of new or modified financial products only in some cases explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- Other Banks" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.OB.CD.SE", "metatype": [ { "id": "Generalcomments", "value": "For OTHER BANKS, are there SIMPLIFICATIONS or EXCEPTIONS to the documentation requirements for some types of applicants or deposit account products?" }, { "id": "IndicatorName", "value": "For other banks, are there simplifications or exceptions to the documentation requirements for some types of applicants or deposit account products?" }, { "id": "Othernotes", "value": "VGRA_01" }, { "id": "Shortdefinition", "value": "Are there simplifications or exceptions to the documentation requirements for certain types of applicants (e.g. low income) or deposit account products (e.g. small-value, low-risk transactions or basic accounts)?\n- Other Banks" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.OB.CRB.CR", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER BANKS required to CHECK/REPORT TO CREDIT BUREAU for some/all loans?" }, { "id": "IndicatorName", "value": "Are other banks required to check/report to credit bureau for some/all loans?" }, { "id": "Othernotes", "value": "VGHA_01" }, { "id": "Shortdefinition", "value": "Do financial institutions have access, or report, to a credit bureau or credit registry?  \n- Other Banks" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Credit Infrastructure" } ], "source_id": "69" }, { "id": "FB.INC.INST.OB.LN.AL", "metatype": [ { "id": "Generalcomments", "value": "For OTHER BANKS, is all lending subject to INTEREST RATE CAPS OR PRICING LIMITS?" }, { "id": "IndicatorName", "value": "For other banks, is all lending subject to interest rate caps or pricing limits?" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- Other Banks: All lending is subject to interest rate caps or pricing limits" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "69" }, { "id": "FB.INC.INST.OB.LN.NL", "metatype": [ { "id": "Generalcomments", "value": "For OTHER BANKS, are there NO INTEREST RATE CAPS OR PRICING LIMITS?" }, { "id": "IndicatorName", "value": "For other banks, are there no interest rate caps or pricing limits?" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- Other Banks: No interest rate caps or pricing limits of any kind" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "69" }, { "id": "FB.INC.INST.OB.LN.SL", "metatype": [ { "id": "Generalcomments", "value": "For OTHER BANKS, are there SOME INTEREST RATE CAPS OR PRICING LIMITS that apply to certain products or segments?" }, { "id": "IndicatorName", "value": "For other banks, are there some interest rate caps or pricing limits that apply to certain products or segments?" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- Other Banks: Some interest rate caps or pricing limits apply to certain products or consumer segments" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "69" }, { "id": "FB.INC.INST.OB.LT.NF", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER BANKS allowed to implement NON-FACE-TO-FACE CUSTOMER DUE DILIGENCE (CDD) under the risk-based approach to AML/CFT regulation?" }, { "id": "IndicatorName", "value": "Are other banks allowed to implement non-face-to-face customer due diligence (CDD) under the risk-based approach to AML/CFT regulation?" }, { "id": "Othernotes", "value": "VGSB_01" }, { "id": "Shortdefinition", "value": "What elements of a risk-based approach to AML/CFT regulation have been implemented? \n- Other Banks: Non-face-to-face customer due diligence (by agents and/or via mobile phone or other mobile device)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.OB.LT.NI", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER BANKS allowed to accept NON-STANDARD ID DOCUMENTS under the risk-based approach to AML/CFT regulation?" }, { "id": "IndicatorName", "value": "Are other banks allowed to accept non-standard id documents under the risk-based approach to AML/CFT regulation?" }, { "id": "Othernotes", "value": "VGSB_00" }, { "id": "Shortdefinition", "value": "What elements of a risk-based approach to AML/CFT regulation have been implemented? \n- Other Banks: Acceptance of non-standard identification documents" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.OB.LT.OE", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER BANKS allowed to implement OTHER ELEMENTS under the risk-based approach to AML/CFT regulation?" }, { "id": "IndicatorName", "value": "Are other banks allowed to implement other elements under the risk-based approach to AML/CFT regulation?" }, { "id": "Othernotes", "value": "VGSB_03" }, { "id": "Shortdefinition", "value": "What elements of a risk-based approach to AML/CFT regulation have been implemented?\n- Other Banks: Other" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.OB.LT.SM", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER BANKS allowed to implement SIMPLIFIED TRANSACTION MONITORING under the risk-based approach to AML/CFT regulation?" }, { "id": "IndicatorName", "value": "Are other banks allowed to implement simplified transaction monitoring under the risk-based approach to AML/CFT regulation?" }, { "id": "Othernotes", "value": "VGSB_02" }, { "id": "Shortdefinition", "value": "What elements of a risk-based approach to AML/CFT regulation have been implemented? \n- Other Banks: Allowing simplified transaction monitoring based on lower assessed risk" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.OB.TP.LI", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER BANKS liable for any ACTIONS OR OMISSIONS OF THEIR AGENTS?" }, { "id": "IndicatorName", "value": "Are other banks liable for any actions or omissions of their agents?" }, { "id": "Othernotes", "value": "VGFB_00" }, { "id": "Shortdefinition", "value": "If financial institutions are allowed to have agents, please specify which type of rules apply regulating the relationships between the financial institution, the agent, and the consumer: \n- Other Banks: Are there specific rules which indicate that financial service providers are liable for any actions or omissions of the agent?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.OD.AU.AR", "metatype": [ { "id": "Generalcomments", "value": "For OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs), is AUTHORIZATION of new or modified financial products ALWAYS EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For other deposit taking institutions (ODTIs), is authorization of new or modified financial products always explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- ODTIs" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.OD.AU.NR", "metatype": [ { "id": "Generalcomments", "value": "For OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs), is AUTHORIZATION of new or modified financial products NEVER EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For other deposit taking institutions (ODTIs), is authorization of new or modified financial products never explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- ODTIs" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.OD.AU.SR", "metatype": [ { "id": "Generalcomments", "value": "For OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs), is AUTHORIZATION of new or modified financial products ONLY IN SOME CASES EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For other deposit taking institutions (ODTIs), is authorization of new or modified financial products only in some cases explicitly required?" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- ODTIs" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "69" }, { "id": "FB.INC.INST.OD.CD.SE", "metatype": [ { "id": "Generalcomments", "value": "For OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs), are there SIMPLIFICATIONS or EXCEPTIONS to the documentation requirements for some types of applicants or deposit account products?" }, { "id": "IndicatorName", "value": "For other deposit taking institutions (ODTIs), are there simplifications or exceptions to the documentation requirements for some types of applicants or deposit account products?" }, { "id": "Othernotes", "value": "VGRA_03" }, { "id": "Shortdefinition", "value": "Are there simplifications or exceptions to the documentation requirements for certain types of applicants (e.g. low income) or deposit account products (e.g. small-value, low-risk transactions or basic accounts)?\n- ODTIs" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.OD.CRB.CR", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs) required to CHECK/REPORT TO CREDIT BUREAU for some/all loans?" }, { "id": "IndicatorName", "value": "Are other deposit taking institutions (ODTIs) required to check/report to credit bureau for some/all loans?" }, { "id": "Othernotes", "value": "VGHA_03" }, { "id": "Shortdefinition", "value": "Do financial institutions have access, or report, to a credit bureau or credit registry?  \n- ODTIs" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Credit Infrastructure" } ], "source_id": "69" }, { "id": "FB.INC.INST.OD.LN.AL", "metatype": [ { "id": "Generalcomments", "value": "For OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs), is all lending subject to INTEREST RATE CAPS OR PRICING LIMITS?" }, { "id": "IndicatorName", "value": "For other deposit taking institutions (ODTIs), is all lending subject to interest rate caps or pricing limits?" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- ODTIs: All lending is subject to interest rate caps or pricing limits" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "69" }, { "id": "FB.INC.INST.OD.LN.NL", "metatype": [ { "id": "Generalcomments", "value": "For OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs), are there NO INTEREST RATE CAPS OR PRICING LIMITS?" }, { "id": "IndicatorName", "value": "For other deposit taking institutions (ODTIs), are there no interest rate caps or pricing limits?" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- ODTIs: No interest rate caps or pricing limits of any kind" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "69" }, { "id": "FB.INC.INST.OD.LN.SL", "metatype": [ { "id": "Generalcomments", "value": "For OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs), are there SOME INTEREST RATE CAPS OR PRICING LIMITS that apply to certain products or segments?" }, { "id": "IndicatorName", "value": "For other deposit taking institutions (ODTIs), are there some interest rate caps or pricing limits that apply to certain products or segments?" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- ODTIs: Some interest rate caps or pricing limits apply to certain products or consumer segments" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "69" }, { "id": "FB.INC.INST.OD.LT.NF", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs) allowed to implement NON-FACE-TO-FACE CUSTOMER DUE DILIGENCE (CDD) under the risk-based approach to AML/CFT regulation?" }, { "id": "IndicatorName", "value": "Are other deposit taking institutions (ODTIs) allowed to implement non-face-to-face customer due diligence (CDD) under the risk-based approach to AML/CFT regulation?" }, { "id": "Othernotes", "value": "VGSD_01" }, { "id": "Shortdefinition", "value": "What elements of a risk-based approach to AML/CFT regulation have been implemented? \n- ODTIs: Non-face-to-face customer due diligence (by agents and/or via mobile phone or other mobile device)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.OD.LT.NI", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs) allowed to accept NON-STANDARD ID DOCUMENTS under the risk-based approach to AML/CFT regulation?" }, { "id": "IndicatorName", "value": "Are other deposit taking institutions (ODTIs) allowed to accept non-standard id documents under the risk-based approach to AML/CFT regulation?" }, { "id": "Othernotes", "value": "VGSD_00" }, { "id": "Shortdefinition", "value": "What elements of a risk-based approach to AML/CFT regulation have been implemented? \n- ODTIs: Acceptance of non-standard identification documents" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.OD.LT.OE", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs) allowed to implement OTHER ELEMENTS under the risk-based approach to AML/CFT regulation?" }, { "id": "IndicatorName", "value": "Are other deposit taking institutions (ODTIs) allowed to implement other elements under the risk-based approach to AML/CFT regulation?" }, { "id": "Othernotes", "value": "VGSD_03" }, { "id": "Shortdefinition", "value": "What elements of a risk-based approach to AML/CFT regulation have been implemented? \n- ODTIs: Other" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.OD.LT.SM", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs) allowed to implement SIMPLIFIED TRANSACTION MONITORING under the risk-based approach to AML/CFT regulation?" }, { "id": "IndicatorName", "value": "Are other deposit taking institutions (ODTIs) allowed to implement simplified transaction monitoring under the risk-based approach to AML/CFT regulation?" }, { "id": "Othernotes", "value": "VGSD_02" }, { "id": "Shortdefinition", "value": "What elements of a risk-based approach to AML/CFT regulation have been implemented? \n- ODTIs: Allowing simplified transaction monitoring based on lower assessed risk" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Simplified Customer Due Diligence" } ], "source_id": "69" }, { "id": "FB.INC.INST.OD.TP.LI", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs) liable for any ACTIONS OR OMISSIONS OF THEIR AGENTS?" }, { "id": "IndicatorName", "value": "Are other deposit taking institutions (ODTIs) liable for any actions or omissions of their agents?" }, { "id": "Othernotes", "value": "VGFD_00" }, { "id": "Shortdefinition", "value": "If financial institutions are allowed to have agents, please specify which type of rules apply regulating the relationships between the financial institution, the agent, and the consumer: \n- ODTIs: Are there specific rules which indicate that financial service providers are liable for any actions or omissions of the agent?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.CB.AG", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS permitted to ACT AS AN AGENT OF A FINANCIAL PROVIDER ?" }, { "id": "IndicatorName", "value": "Are commercial banks permitted to act as an agent of a financial provider?" }, { "id": "Othernotes", "value": "VGDA_02" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Commercial Banks: Act as an agent of a financial provider" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.CB.CA", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS permitted to PROVIDE CHECKING OR CURRENT ACCOUNTS ?" }, { "id": "IndicatorName", "value": "Are commercial banks permitted to provide checking or current accounts?" }, { "id": "Othernotes", "value": "VGDA_00" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Commercial Banks: Provide checking or current accounts" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.CB.EM", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS permitted to ISSUE E-MONEY (including PREPAID E-MONEY CARDS) ?" }, { "id": "IndicatorName", "value": "Are commercial banks permitted to issue e-money (including prepaid e-money cards) ?" }, { "id": "Othernotes", "value": "VGDA_06" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Commercial Banks: Issue e-money (including prepaid cards with an e-money function)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.CB.IN", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS permitted to DISTRIBUTE INSURANCE ?" }, { "id": "IndicatorName", "value": "Are commercial banks permitted to distribute insurance?" }, { "id": "Othernotes", "value": "VGDA_07" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Commercial Banks: Distribute insurance" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.CB.PC", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS permitted to ISSUE PAYMENT CARDS (CREDIT, DEBIT, and OTHER NON-PREPAID CARDS) ?" }, { "id": "IndicatorName", "value": "Are commercial banks permitted to issue payment cards (credit, debit, and other non-prepaid cards) ?" }, { "id": "Othernotes", "value": "VGDA_05" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Commercial Banks: Issue payment cards (credit cards, debit cards and other non-prepaid cards)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.CB.PN", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS permitted to DISTRIBUTE PENSION PRODUCTS ?" }, { "id": "IndicatorName", "value": "Are commercial banks permitted to distribute pension products?" }, { "id": "Othernotes", "value": "VGDA_08" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Commercial Banks: Distribute pension products" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.CB.TP", "metatype": [ { "id": "Generalcomments", "value": "Are COMMERCIAL BANKS permitted to CONTRACT WITH RETAIL AGENTS AS THIRD-PARTY DELIVERY CHANNELS ?" }, { "id": "IndicatorName", "value": "Are commercial banks permitted to contract with retail agents as third-party delivery channels?" }, { "id": "Othernotes", "value": "VGDA_01" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Commercial Banks: Contract with retail agents as third-party delivery channels" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.FC.AG", "metatype": [ { "id": "Generalcomments", "value": "Are FINANCIAL COOPERATIVES permitted to ACT AS AN AGENT OF A FINANCIAL PROVIDER ?" }, { "id": "IndicatorName", "value": "Are financial cooperatives permitted to act as an agent of a financial provider?" }, { "id": "Othernotes", "value": "VGDC_02" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Financial Cooperatives: Act as an agent of a financial provider" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.FC.CA", "metatype": [ { "id": "Generalcomments", "value": "Are FINANCIAL COOPERATIVES permitted to PROVIDE CHECKING OR CURRENT ACCOUNTS ?" }, { "id": "IndicatorName", "value": "Are financial cooperatives permitted to provide checking or current accounts?" }, { "id": "Othernotes", "value": "VGDC_00" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Financial Cooperatives: Provide checking or current accounts" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.FC.EM", "metatype": [ { "id": "Generalcomments", "value": "Are FINANCIAL COOPERATIVES permitted to ISSUE E-MONEY (including PREPAID E-MONEY CARDS) ?" }, { "id": "IndicatorName", "value": "Are financial cooperatives permitted to issue e-money (including prepaid e-money cards) ?" }, { "id": "Othernotes", "value": "VGDC_06" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Financial Cooperatives: Issue e-money (including prepaid cards with an e-money function)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.FC.IN", "metatype": [ { "id": "Generalcomments", "value": "Are FINANCIAL COOPERATIVES permitted to DISTRIBUTE INSURANCE ?" }, { "id": "IndicatorName", "value": "Are financial cooperatives permitted to distribute insurance?" }, { "id": "Othernotes", "value": "VGDC_07" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Financial Cooperatives: Distribute insurance" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.FC.PC", "metatype": [ { "id": "Generalcomments", "value": "Are FINANCIAL COOPERATIVES permitted to ISSUE PAYMENT CARDS (CREDIT, DEBIT, and OTHER NON-PREPAID CARDS) ?" }, { "id": "IndicatorName", "value": "Are financial cooperatives permitted to issue payment cards (credit, debit, and other non-prepaid cards) ?" }, { "id": "Othernotes", "value": "VGDC_05" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Financial Cooperatives: Issue payment cards (credit cards, debit cards and other non-prepaid cards)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.FC.PN", "metatype": [ { "id": "Generalcomments", "value": "Are FINANCIAL COOPERATIVES permitted to DISTRIBUTE PENSION PRODUCTS ?" }, { "id": "IndicatorName", "value": "Are financial cooperatives permitted to distribute pension products?" }, { "id": "Othernotes", "value": "VGDC_08" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Financial Cooperatives: Distribute pension products" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.FC.TP", "metatype": [ { "id": "Generalcomments", "value": "Are FINANCIAL COOPERATIVES permitted to CONTRACT WITH RETAIL AGENTS AS THIRD-PARTY DELIVERY CHANNELS ?" }, { "id": "IndicatorName", "value": "Are financial cooperatives permitted to contract with retail agents as third-party delivery channels?" }, { "id": "Othernotes", "value": "VGDC_01" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Financial Cooperatives: Contract with retail agents as third-party delivery channels" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.MC.AG", "metatype": [ { "id": "Generalcomments", "value": "Are MICROCREDIT INSTITUTIONS (MCIs) permitted to ACT AS AN AGENT OF A FINANCIAL PROVIDER ?" }, { "id": "IndicatorName", "value": "Are microcredit institutions (MCIs) permitted to act as an agent of a financial provider?" }, { "id": "Othernotes", "value": "VGDE_02" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- MCIs: Act as an agent of a financial provider" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.MC.CA", "metatype": [ { "id": "Generalcomments", "value": "Are MICROCREDIT INSTITUTIONS (MCIs) permitted to PROVIDE CHECKING OR CURRENT ACCOUNTS ?" }, { "id": "IndicatorName", "value": "Are microcredit institutions (MCIs) permitted to provide checking or current accounts?" }, { "id": "Othernotes", "value": "VGDE_00" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- MCIs: Provide checking or current accounts" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.MC.EM", "metatype": [ { "id": "Generalcomments", "value": "Are MICROCREDIT INSTITUTIONS (MCIs) permitted to ISSUE E-MONEY (including PREPAID E-MONEY CARDS) ?" }, { "id": "IndicatorName", "value": "Are microcredit institutions (MCIs) permitted to issue e-money (including prepaid e-money cards) ?" }, { "id": "Othernotes", "value": "VGDE_06" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- MCIs: Issue e-money (including prepaid cards with an e-money function)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.MC.IN", "metatype": [ { "id": "Generalcomments", "value": "Are MICROCREDIT INSTITUTIONS (MCIs) permitted to DISTRIBUTE INSURANCE ?" }, { "id": "IndicatorName", "value": "Are microcredit institutions (MCIs) permitted to distribute insurance?" }, { "id": "Othernotes", "value": "VGDE_07" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- MCIs: Distribute insurance" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.MC.PC", "metatype": [ { "id": "Generalcomments", "value": "Are MICROCREDIT INSTITUTIONS (MCIs) permitted to ISSUE PAYMENT CARDS (CREDIT, DEBIT, and OTHER NON-PREPAID CARDS) ?" }, { "id": "IndicatorName", "value": "Are microcredit institutions (MCIs) permitted to issue payment cards (credit, debit, and other non-prepaid cards) ?" }, { "id": "Othernotes", "value": "VGDE_05" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- MCIs: Issue payment cards (credit cards, debit cards and other non-prepaid cards)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.MC.PN", "metatype": [ { "id": "Generalcomments", "value": "Are MICROCREDIT INSTITUTIONS (MCIs) permitted to DISTRIBUTE PENSION PRODUCTS ?" }, { "id": "IndicatorName", "value": "Are microcredit institutions (MCIs) permitted to distribute pension products?" }, { "id": "Othernotes", "value": "VGDE_08" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- MCIs: Distribute pension products" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.MC.TP", "metatype": [ { "id": "Generalcomments", "value": "Are MICROCREDIT INSTITUTIONS (MCIs) permitted to CONTRACT WITH RETAIL AGENTS AS THIRD-PARTY DELIVERY CHANNELS ?" }, { "id": "IndicatorName", "value": "Are microcredit institutions (MCIs) permitted to contract with retail agents as third-party delivery channels?" }, { "id": "Othernotes", "value": "VGDE_01" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- MCIs: Contract with retail agents as third-party delivery channels" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.NB.AG", "metatype": [ { "id": "Generalcomments", "value": "Are NON-BANK E-MONEY ISSUERS (NBEIs) permitted to ACT AS AN AGENT OF A FINANCIAL PROVIDER ?" }, { "id": "IndicatorName", "value": "Are non-bank e-money issuers (NBEIs) permitted to act as an agent of a financial provider?" }, { "id": "Othernotes", "value": "VGDF_02" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- NBEIs: Act as an agent of a financial provider" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.NB.CA", "metatype": [ { "id": "Generalcomments", "value": "Are NON-BANK E-MONEY ISSUERS (NBEIs) permitted to PROVIDE CHECKING OR CURRENT ACCOUNTS ?" }, { "id": "IndicatorName", "value": "Are non-bank e-money issuers (NBEIs) permitted to provide checking or current accounts?" }, { "id": "Othernotes", "value": "VGDF_00" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- NBEIs: Provide checking or current accounts" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.NB.EM", "metatype": [ { "id": "Generalcomments", "value": "Are NON-BANK E-MONEY ISSUERS (NBEIs) permitted to ISSUE E-MONEY (including PREPAID E-MONEY CARDS) ?" }, { "id": "IndicatorName", "value": "Are non-bank e-money issuers (NBEIs) permitted to issue e-money (including prepaid e-money cards) ?" }, { "id": "Othernotes", "value": "VGDF_06" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- NBEIs: Issue e-money (including prepaid cards with an e-money function)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.NB.IN", "metatype": [ { "id": "Generalcomments", "value": "Are NON-BANK E-MONEY ISSUERS (NBEIs) permitted to DISTRIBUTE INSURANCE ?" }, { "id": "IndicatorName", "value": "Are non-bank e-money issuers (NBEIs) permitted to distribute insurance?" }, { "id": "Othernotes", "value": "VGDF_07" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- NBEIs: Distribute insurance" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.NB.PC", "metatype": [ { "id": "Generalcomments", "value": "Are NON-BANK E-MONEY ISSUERS (NBEIs) permitted to ISSUE PAYMENT CARDS (CREDIT, DEBIT, and OTHER NON-PREPAID CARDS) ?" }, { "id": "IndicatorName", "value": "Are non-bank e-money issuers (NBEIs) permitted to issue payment cards (credit, debit, and other non-prepaid cards) ?" }, { "id": "Othernotes", "value": "VGDF_05" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- NBEIs: Issue payment cards (credit cards, debit cards and other non-prepaid cards)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.NB.PN", "metatype": [ { "id": "Generalcomments", "value": "Are NON-BANK E-MONEY ISSUERS (NBEIs) permitted to DISTRIBUTE PENSION PRODUCTS ?" }, { "id": "IndicatorName", "value": "Are non-bank e-money issuers (NBEIs) permitted to distribute pension products?" }, { "id": "Othernotes", "value": "VGDF_08" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- NBEIs: Distribute pension products" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.NB.TP", "metatype": [ { "id": "Generalcomments", "value": "Are NON-BANK E-MONEY ISSUERS (NBEIs) permitted to CONTRACT WITH RETAIL AGENTS AS THIRD-PARTY DELIVERY CHANNELS ?" }, { "id": "IndicatorName", "value": "Are non-bank e-money issuers (NBEIs) permitted to contract with retail agents as third-party delivery channels?" }, { "id": "Othernotes", "value": "VGDF_01" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- NBEIs: Contract with retail agents as third-party delivery channels" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.OB.AG", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER BANKS permitted to ACT AS AN AGENT OF A FINANCIAL PROVIDER ?" }, { "id": "IndicatorName", "value": "Are other banks permitted to act as an agent of a financial provider?" }, { "id": "Othernotes", "value": "VGDB_02" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Other Banks: Act as an agent of a financial provider" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.OB.CA", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER BANKS permitted to PROVIDE CHECKING OR CURRENT ACCOUNTS ?" }, { "id": "IndicatorName", "value": "Are other banks permitted to provide checking or current accounts?" }, { "id": "Othernotes", "value": "VGDB_00" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Other Banks: Provide checking or current accounts" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.OB.EM", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER BANKS permitted to ISSUE E-MONEY (including PREPAID E-MONEY CARDS) ?" }, { "id": "IndicatorName", "value": "Are other banks permitted to issue e-money (including prepaid e-money cards) ?" }, { "id": "Othernotes", "value": "VGDB_06" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Other Banks: Issue e-money (including prepaid cards with an e-money function)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.OB.IN", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER BANKS permitted to DISTRIBUTE INSURANCE ?" }, { "id": "IndicatorName", "value": "Are other banks permitted to distribute insurance?" }, { "id": "Othernotes", "value": "VGDB_07" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Other Banks: Distribute insurance" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.OB.PC", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER BANKS permitted to ISSUE PAYMENT CARDS (CREDIT, DEBIT, and OTHER NON-PREPAID CARDS) ?" }, { "id": "IndicatorName", "value": "Are other banks permitted to issue payment cards (credit, debit, and other non-prepaid cards) ?" }, { "id": "Othernotes", "value": "VGDB_05" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Other Banks: Issue payment cards (credit cards, debit cards and other non-prepaid cards)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.OB.PN", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER BANKS permitted to DISTRIBUTE PENSION PRODUCTS ?" }, { "id": "IndicatorName", "value": "Are other banks permitted to distribute pension products?" }, { "id": "Othernotes", "value": "VGDB_08" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Other Banks: Distribute pension products" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.OB.TP", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER BANKS permitted to CONTRACT WITH RETAIL AGENTS AS THIRD-PARTY DELIVERY CHANNELS ?" }, { "id": "IndicatorName", "value": "Are other banks permitted to contract with retail agents as third-party delivery channels?" }, { "id": "Othernotes", "value": "VGDB_01" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- Other Banks: Contract with retail agents as third-party delivery channels" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.OD.AG", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs) permitted to ACT AS AN AGENT OF A FINANCIAL PROVIDER ?" }, { "id": "IndicatorName", "value": "Are other deposit taking institutions (ODTIs) permitted to act as an agent of a financial provider?" }, { "id": "Othernotes", "value": "VGDD_02" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- ODTIs: Act as an agent of a financial provider" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.OD.CA", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs) permitted to PROVIDE CHECKING OR CURRENT ACCOUNTS ?" }, { "id": "IndicatorName", "value": "Are other deposit taking institutions (ODTIs) permitted to provide checking or current accounts?" }, { "id": "Othernotes", "value": "VGDD_00" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- ODTIs: Provide checking or current accounts" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.OD.EM", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs) permitted to ISSUE E-MONEY (including PREPAID E-MONEY CARDS) ?" }, { "id": "IndicatorName", "value": "Are other deposit taking institutions (ODTIs) permitted to issue e-money (including prepaid e-money cards) ?" }, { "id": "Othernotes", "value": "VGDD_06" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- ODTIs: Issue e-money (including prepaid cards with an e-money function)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.OD.IN", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs) permitted to DISTRIBUTE INSURANCE ?" }, { "id": "IndicatorName", "value": "Are other deposit taking institutions (ODTIs) permitted to distribute insurance?" }, { "id": "Othernotes", "value": "VGDD_07" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- ODTIs: Distribute insurance" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.OD.PC", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs) permitted to ISSUE PAYMENT CARDS (CREDIT, DEBIT, and OTHER NON-PREPAID CARDS) ?" }, { "id": "IndicatorName", "value": "Are other deposit taking institutions (ODTIs) permitted to issue payment cards (credit, debit, and other non-prepaid cards) ?" }, { "id": "Othernotes", "value": "VGDD_05" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- ODTIs: Issue payment cards (credit cards, debit cards and other non-prepaid cards)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.OD.PN", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs) permitted to DISTRIBUTE PENSION PRODUCTS ?" }, { "id": "IndicatorName", "value": "Are other deposit taking institutions (ODTIs) permitted to distribute pension products?" }, { "id": "Othernotes", "value": "VGDD_08" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- ODTIs: Distribute pension products" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.PA.OD.TP", "metatype": [ { "id": "Generalcomments", "value": "Are OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs) permitted to CONTRACT WITH RETAIL AGENTS AS THIRD-PARTY DELIVERY CHANNELS ?" }, { "id": "IndicatorName", "value": "Are other deposit taking institutions (ODTIs) permitted to contract with retail agents as third-party delivery channels?" }, { "id": "Othernotes", "value": "VGDD_01" }, { "id": "Shortdefinition", "value": "Are the following financial institutions permitted to carry out the following activities? \n- ODTIs: Contract with retail agents as third-party delivery channels" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Permitted Activities" } ], "source_id": "69" }, { "id": "FB.INC.INST.SU.MN.NB", "metatype": [ { "id": "Generalcomments", "value": "Are any NON-BANK E-MONEY ISSUERS subsidiaries of mobile network operators?" }, { "id": "IndicatorName", "value": "Are any non-bank e-money issuers subsidiaries of mobile network operators?" }, { "id": "Othernotes", "value": "VGAB_00" }, { "id": "Shortdefinition", "value": "Are any NON-BANK E-MONEY ISSUERS subsidiaries of mobile network operators?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Financial Institutions" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.CB.ID", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to IDENTIFY AND/OR VERIFY THE IDENTITY OF THE CUSTOMER on behalf of COMMERCIAL BANKS?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to identify and/or verify the identity of the customer on behalf of commercial banks?" }, { "id": "Othernotes", "value": "VGEB_00" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities?\n- Commercial Banks: Identify and/or verify the identity of the customer" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.CB.LA", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to RECEIVE AND SUBMIT TO THE INSTITUTION A LOAN APPLICATION on behalf of COMMERCIAL BANKS?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to receive and submit to the institution a loan application on behalf of commercial banks?" }, { "id": "Othernotes", "value": "VGEB_02" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities?\n- Commercial Banks: Receive and submit to the institution a loan application" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.CB.OA", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to OPEN A CUSTOMER ACCOUNT on behalf of COMMERCIAL BANKS?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to open a customer account on behalf of commercial banks?" }, { "id": "Othernotes", "value": "VGEB_03" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities?\n- Commercial Banks: Open a customer account following the institution’s policies" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.CB.RD", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to RECEIVE DEPOSITS on behalf of COMMERCIAL BANKS?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to receive deposits on behalf of commercial banks?" }, { "id": "Othernotes", "value": "VGEB_05" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities?\n- Commercial Banks: Receive deposits" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.FC.ID", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to IDENTIFY AND/OR VERIFY THE IDENTITY OF THE CUSTOMER on behalf of FINANCIAL COOPERATIVES?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to identify and/or verify the identity of the customer on behalf of financial cooperatives?" }, { "id": "Othernotes", "value": "VGED_00" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- Financial Cooperatives: Identify and/or verify the identity of the customer" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.FC.LA", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to RECEIVE AND SUBMIT TO THE INSTITUTION A LOAN APPLICATION on behalf of FINANCIAL COOPERATIVES?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to receive and submit to the institution a loan application on behalf of financial cooperatives?" }, { "id": "Othernotes", "value": "VGED_02" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- Financial Cooperatives: Receive and submit to the institution a loan application" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.FC.OA", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to OPEN A CUSTOMER ACCOUNT on behalf of FINANCIAL COOPERATIVES?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to open a customer account on behalf of financial cooperatives?" }, { "id": "Othernotes", "value": "VGED_03" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- Financial Cooperatives: Open a customer account following the institution’s policies" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.FC.RD", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to RECEIVE DEPOSITS on behalf of FINANCIAL COOPERATIVES?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to receive deposits on behalf of financial cooperatives?" }, { "id": "Othernotes", "value": "VGED_05" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- Financial Cooperatives: Receive deposits" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.MC.ID", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to IDENTIFY AND/OR VERIFY THE IDENTITY OF THE CUSTOMER on behalf of MICROCREDIT INSTITUTIONS (MCIs)?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to identify and/or verify the identity of the customer on behalf of microcredit institutions (MCIs)?" }, { "id": "Othernotes", "value": "VGEF_00" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- MCIs: Identify and/or verify the identity of the customer" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.MC.LA", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to RECEIVE AND SUBMIT TO THE INSTITUTION A LOAN APPLICATION on behalf of MICROCREDIT INSTITUTIONS (MCIs)?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to receive and submit to the institution a loan application on behalf of microcredit institutions (MCIs)?" }, { "id": "Othernotes", "value": "VGEF_02" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- MCIs: Receive and submit to the institution a loan application" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.MC.OA", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to OPEN A CUSTOMER ACCOUNT on behalf of MICROCREDIT INSTITUTIONS (MCIs)?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to open a customer account on behalf of microcredit institutions (MCIs)?" }, { "id": "Othernotes", "value": "VGEF_03" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- MCIs: Open a customer account following the institution’s policies" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.MC.RD", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to RECEIVE DEPOSITS on behalf of MICROCREDIT INSTITUTIONS (MCIs)?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to receive deposits on behalf of microcredit institutions (MCIs)?" }, { "id": "Othernotes", "value": "VGEF_05" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- MCIs: Receive deposits" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.NB.ID", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to IDENTIFY AND/OR VERIFY THE IDENTITY OF THE CUSTOMER on behalf of NON-BANK E-MONEY ISSUERS (NBEIs)?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to identify and/or verify the identity of the customer on behalf of non-bank e-money issuers (NBEIs)?" }, { "id": "Othernotes", "value": "VGEG_00" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- NBEIs: Identify and/or verify the identity of the customer" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.NB.LA", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to RECEIVE AND SUBMIT TO THE INSTITUTION A LOAN APPLICATION on behalf of NON-BANK E-MONEY ISSUERS (NBEIs)?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to receive and submit to the institution a loan application on behalf of non-bank e-money issuers (NBEIs)?" }, { "id": "Othernotes", "value": "VGEG_02" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- NBEIs: Receive and submit to the institution a loan application" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.NB.OA", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to OPEN A CUSTOMER ACCOUNT on behalf of NON-BANK E-MONEY ISSUERS (NBEIs)?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to open a customer account on behalf of non-bank e-money issuers (NBEIs)?" }, { "id": "Othernotes", "value": "VGEG_03" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- NBEIs: Open a customer account following the institution’s policies" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.NB.RD", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to RECEIVE DEPOSITS on behalf of NON-BANK E-MONEY ISSUERS (NBEIs)?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to receive deposits on behalf of non-bank e-money issuers (NBEIs)?" }, { "id": "Othernotes", "value": "VGEG_05" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- NBEIs: Receive deposits" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.OB.ID", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to IDENTIFY AND/OR VERIFY THE IDENTITY OF THE CUSTOMER on behalf of OTHER BANKS?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to identify and/or verify the identity of the customer on behalf of other banks?" }, { "id": "Othernotes", "value": "VGEC_00" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities?\n- Other Banks: Identify and/or verify the identity of the customer" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.OB.LA", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to RECEIVE AND SUBMIT TO THE INSTITUTION A LOAN APPLICATION on behalf of OTHER BANKS?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to receive and submit to the institution a loan application on behalf of other banks?" }, { "id": "Othernotes", "value": "VGEC_02" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities?\n- Other Banks: Receive and submit to the institution a loan application" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.OB.OA", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to OPEN A CUSTOMER ACCOUNT on behalf of OTHER BANKS?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to open a customer account on behalf of other banks?" }, { "id": "Othernotes", "value": "VGEC_03" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities?\n- Other Banks: Open a customer account following the institution’s policies" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.OB.RD", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to RECEIVE DEPOSITS on behalf of OTHER BANKS?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to receive deposits on behalf of other banks?" }, { "id": "Othernotes", "value": "VGEC_05" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- Other Banks: Receive deposits" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.OD.ID", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to IDENTIFY AND/OR VERIFY THE IDENTITY OF THE CUSTOMER on behalf of OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs)?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to identify and/or verify the identity of the customer on behalf of other deposit taking institutions (ODTIs)?" }, { "id": "Othernotes", "value": "VGEE_00" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- ODTIs: Identify and/or verify the identity of the customer" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.OD.LA", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to RECEIVE AND SUBMIT TO THE INSTITUTION A LOAN APPLICATION on behalf of OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs)?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to receive and submit to the institution a loan application on behalf of other deposit taking institutions (ODTIs)?" }, { "id": "Othernotes", "value": "VGEE_02" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- ODTIs: Receive and submit to the institution a loan application" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.OD.OA", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to OPEN A CUSTOMER ACCOUNT on behalf of OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs)?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to open a customer account on behalf of other deposit taking institutions (ODTIs)?" }, { "id": "Othernotes", "value": "VGEE_03" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- ODTIs: Open a customer account following the institution’s policies" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.OD.RD", "metatype": [ { "id": "Generalcomments", "value": "Are third parties such as retail agents allowed to RECEIVE DEPOSITS on behalf of OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs)?" }, { "id": "IndicatorName", "value": "Are third parties such as retail agents allowed to receive deposits on behalf of other deposit taking institutions (ODTIs)?" }, { "id": "Othernotes", "value": "VGEE_05" }, { "id": "Shortdefinition", "value": "Are the following institutions allowed to outsource to third parties the following activities? \n- ODTIs: Receive deposits" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.INST.TP.RA", "metatype": [ { "id": "Generalcomments", "value": "Are AT LEAST SOME financial service providers permitted to use RETAIL AGENTS AS THIRD-PARTY DELIVERY CHANNELS?" }, { "id": "IndicatorName", "value": "Are at least some financial service providers permitted to use retail agents as third-party delivery channels?" }, { "id": "Othernotes", "value": "VGEA_00" }, { "id": "Shortdefinition", "value": "Are AT LEAST SOME financial service providers permitted to use RETAIL AGENTS AS THIRD-PARTY DELIVERY CHANNELS?" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Third-Party Agents" } ], "source_id": "69" }, { "id": "FB.INC.LEGL.DF.MC", "metatype": [ { "id": "Generalcomments", "value": "Is the term of MICROCREDIT explicitly defined in law or regulation?" }, { "id": "IndicatorName", "value": "Is the term of microcredit explicitly defined in law or regulation?" }, { "id": "Othernotes", "value": "VGGA_01" }, { "id": "Shortdefinition", "value": "Please note whether any of the following terms are explicitly defined in law or regulation:\n- Microcredit" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Microfinance Definitions" } ], "source_id": "69" }, { "id": "FB.INC.LEGL.DF.MF", "metatype": [ { "id": "Generalcomments", "value": "Is the term of MICROFINANCE explicitly defined in law or regulation?" }, { "id": "IndicatorName", "value": "Is the term of microfinance explicitly defined in law or regulation?" }, { "id": "Othernotes", "value": "VGGA_00" }, { "id": "Shortdefinition", "value": "Please note whether any of the following terms are explicitly defined in law or regulation:\n- Microfinance" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Microfinance Definitions" } ], "source_id": "69" }, { "id": "FB.INC.LEGL.DF.MS", "metatype": [ { "id": "Generalcomments", "value": "Is the term of MICROSAVINGS explicitly defined in law or regulation?" }, { "id": "IndicatorName", "value": "Is the term of microsavings explicitly defined in law or regulation?" }, { "id": "Othernotes", "value": "VGGA_02" }, { "id": "Shortdefinition", "value": "Please note whether any of the following terms are explicitly defined in law or regulation:\n- Microsavings" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Microfinance Definitions" } ], "source_id": "69" }, { "id": "FB.INC.NSTR.FC.DV", "metatype": [ { "id": "Generalcomments", "value": "Is a NATIONAL FINANCIAL CAPABILITY/LITERACY/EDUCATION STRATEGY (NFCS/NFLS/NFES) UNDER DEVELOPMENT?" }, { "id": "IndicatorName", "value": "Is a national financial capability/literacy/education strategy (NFCS/NFLS/NFES) under development?" }, { "id": "Othernotes", "value": "VGAG_01" }, { "id": "Shortdefinition", "value": "Does this country have any of the following national strategy documents to promote activities relevant to financial inclusion: \n- Financial Capability/Literacy/Education strategy: Under Development" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : National Strategies" } ], "source_id": "69" }, { "id": "FB.INC.NSTR.FC.LN", "metatype": [ { "id": "Generalcomments", "value": "Has a NATIONAL FINANCIAL CAPABILITY/LITERACY/EDUCATION STRATEGY (NFCS/NFLS/NFES) already been LAUNCHED?" }, { "id": "IndicatorName", "value": "Has a national financial capability/literacy/education strategy (NFCS/NFLS/NFES) already been launched?" }, { "id": "Othernotes", "value": "VGAG_00" }, { "id": "Shortdefinition", "value": "Does this country have any of the following national strategy documents to promote activities relevant to financial inclusion: \n- Financial Capability/Literacy/Education strategy: Launched" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : National Strategies" } ], "source_id": "69" }, { "id": "FB.INC.NSTR.FI.DV", "metatype": [ { "id": "Generalcomments", "value": "Is a NATIONAL FINANCIAL INCLUSION STRATEGY (NFIS) UNDER DEVELOPMENT?" }, { "id": "IndicatorName", "value": "Is a national financial inclusion strategy (NFIS) under development?" }, { "id": "Othernotes", "value": "VGAC_01" }, { "id": "Shortdefinition", "value": "Does this country have any of the following national strategy documents to promote activities relevant to financial inclusion: \n- National Financial Inclusion Strategy: Under Development" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : National Strategies" } ], "source_id": "69" }, { "id": "FB.INC.NSTR.FI.LN", "metatype": [ { "id": "Generalcomments", "value": "Has a NATIONAL FINANCIAL INCLUSION STRATEGY (NFIS) already been LAUNCHED?" }, { "id": "IndicatorName", "value": "Has a national financial inclusion strategy (NFIS) already been launched?" }, { "id": "Othernotes", "value": "VGAC_00" }, { "id": "Shortdefinition", "value": "Does this country have any of the following national strategy documents to promote activities relevant to financial inclusion: \n- National Financial Inclusion Strategy: Launched" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : National Strategies" } ], "source_id": "69" }, { "id": "FB.INC.NSTR.GF.FI.DV", "metatype": [ { "id": "Generalcomments", "value": "Is a GENERAL FINANCIAL SECTOR DEVELOPMENT STRATEGY WITH A FINANCIAL INCLUSION COMPONENT (GFSDS/FI) UNDER DEVELOPMENT?" }, { "id": "IndicatorName", "value": "Is a general financial sector development strategy with a financial inclusion component (GFSDS/FI) under development?" }, { "id": "Othernotes", "value": "VGAD_01" }, { "id": "Shortdefinition", "value": "Does this country have any of the following national strategy documents to promote activities relevant to financial inclusion: \n- General financial sector development strategy with a financial inclusion component: Under Development" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : National Strategies" } ], "source_id": "69" }, { "id": "FB.INC.NSTR.GF.FI.LN", "metatype": [ { "id": "Generalcomments", "value": "Has a GENERAL FINANCIAL SECTOR DEVELOPMENT STRATEGY WITH A FINANCIAL INCLUSION COMPONENT (GFSDS/FI) already been LAUNCHED?" }, { "id": "IndicatorName", "value": "Has a general financial sector development strategy with a financial inclusion component (GFSDS/FI) already been launched?" }, { "id": "Othernotes", "value": "VGAD_00" }, { "id": "Shortdefinition", "value": "Does this country have any of the following national strategy documents to promote activities relevant to financial inclusion: \n- General financial sector development strategy with a financial inclusion component: Launched" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : National Strategies" } ], "source_id": "69" }, { "id": "FB.INC.NSTR.MF.DV", "metatype": [ { "id": "Generalcomments", "value": "Is a NATIONAL MICROFINANCE STRATEGY (NMS) UNDER DEVELOPMENT?" }, { "id": "IndicatorName", "value": "Is a national microfinance strategy (NMS) under development?" }, { "id": "Othernotes", "value": "VGAF_01" }, { "id": "Shortdefinition", "value": "Does this country have any of the following national strategy documents to promote activities relevant to financial inclusion: \n- Microfinance strategy: Under Development" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : National Strategies" } ], "source_id": "69" }, { "id": "FB.INC.NSTR.MF.LN", "metatype": [ { "id": "Generalcomments", "value": "Has a NATIONAL MICROFINANCE STRATEGY (NMS) already been LAUNCHED?" }, { "id": "IndicatorName", "value": "Has a national microfinance strategy (NMS) already been launched?" }, { "id": "Othernotes", "value": "VGAF_00" }, { "id": "Shortdefinition", "value": "Does this country have any of the following national strategy documents to promote activities relevant to financial inclusion: \n- Microfinance strategy: Launched" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : National Strategies" } ], "source_id": "69" }, { "id": "FB.INC.NSTR.ND.FI.DV", "metatype": [ { "id": "Generalcomments", "value": "Is a NATIONAL DEVELOPMENT STRATEGY WITH A FINANCIAL INCLUSION COMPONENT (NDS/FI) UNDER DEVELOPMENT?" }, { "id": "IndicatorName", "value": "Is a national development strategy with a financial inclusion component (NDS/FI) under development?" }, { "id": "Othernotes", "value": "VGAE_01" }, { "id": "Shortdefinition", "value": "Does this country have any of the following national strategy documents to promote activities relevant to financial inclusion: \n- National development strategy with a financial inclusion component: Under Development" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : National Strategies" } ], "source_id": "69" }, { "id": "FB.INC.NSTR.ND.FI.LN", "metatype": [ { "id": "Generalcomments", "value": "Has a NATIONAL DEVELOPMENT STRATEGY WITH A FINANCIAL INCLUSION COMPONENT (NDS/FI) already been LAUNCHED?" }, { "id": "IndicatorName", "value": "Has a national development strategy with a financial inclusion component (NDS/FI) already been launched?" }, { "id": "Othernotes", "value": "VGAE_00" }, { "id": "Shortdefinition", "value": "Does this country have any of the following national strategy documents to promote activities relevant to financial inclusion: \n- National development strategy with a financial inclusion component: Launched" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : National Strategies" } ], "source_id": "69" }, { "id": "FB.INC.POLI.FI.DT.BP", "metatype": [ { "id": "Generalcomments", "value": "Are DEPOSIT-TAKING INSTITUTIONS required to OFFER BASIC FINANCIAL PRODUCTS to promote financial inclusion?" }, { "id": "IndicatorName", "value": "Are deposit-taking institutions required to offer basic financial products to promote financial inclusion?" }, { "id": "Othernotes", "value": "VGBA_03" }, { "id": "Shortdefinition", "value": "Which of the following programs or policies has this country implemented to promote financial inclusion?  \n- Deposit-taking institutions are required to offer basic financial products, such as a basic account (for the purpose of promoting financial inclusion)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Policies" } ], "source_id": "69" }, { "id": "FB.INC.POLI.FI.GT.FA", "metatype": [ { "id": "Generalcomments", "value": "Are recipients of government transfers ENCOURAGED OR MANDATED TO OPEN AN ACCOUNT to receive their funds?" }, { "id": "IndicatorName", "value": "Are recipients of government transfers encouraged or mandated to open an account to receive their funds?" }, { "id": "Othernotes", "value": "VGBA_04" }, { "id": "Shortdefinition", "value": "Which of the following programs or policies has this country implemented to promote financial inclusion?  \n- Encouraging (or mandating) recipients of government transfers to open an account to receive their funds (e.g. for pension payments, social program payments, tax refunds, etc.)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Policies" } ], "source_id": "69" }, { "id": "FB.INC.POLI.FI.PL", "metatype": [ { "id": "Generalcomments", "value": "Are PRIORITY LENDING policies in place to promote financial inclusion?" }, { "id": "IndicatorName", "value": "Are priority lending policies in place to promote financial inclusion?" }, { "id": "Othernotes", "value": "VGBA_01" }, { "id": "Shortdefinition", "value": "Which of the following programs or policies has this country implemented to promote financial inclusion?  \n- Priority lending: mandatory lending requirements targeting those with limited access to financial services e.g. poor people, SMEs, agricultural sector, etc." }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Policies" } ], "source_id": "69" }, { "id": "FB.INC.POLI.FI.RE", "metatype": [ { "id": "Generalcomments", "value": "Are REQUIREMENTS, EXCEPTIONS, TAX INCENTIVES, OR SUBSIDIES policies in place to promote financial inclusion?" }, { "id": "IndicatorName", "value": "Are requirements, exceptions, tax incentives, or subsidies policies in place to promote financial inclusion?" }, { "id": "Othernotes", "value": "VGBA_00" }, { "id": "Shortdefinition", "value": "Which of the following programs or policies has this country implemented to promote financial inclusion?  \n- Requirements, exceptions, tax incentives, or subsidies to promote opening of branches or outlets in underserved (e.g. poor or rural) areas" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Policies" } ], "source_id": "69" }, { "id": "FB.INC.POLI.FI.TI", "metatype": [ { "id": "Generalcomments", "value": "Are TAX INCENTIVE SAVINGS SCHEMES in place to promote financial inclusion?" }, { "id": "IndicatorName", "value": "Are tax incentive savings schemes in place to promote financial inclusion?" }, { "id": "Othernotes", "value": "VGBA_02" }, { "id": "Shortdefinition", "value": "Which of the following programs or policies has this country implemented to promote financial inclusion?  \n- Tax incentive savings schemes (such as tax incentives for retirement, education, or medical savings)" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Policies" } ], "source_id": "69" }, { "id": "FB.INC.SURV.AF.FR", "metatype": [ { "id": "Generalcomments", "value": "Has a SURVEY OF FIRMS including QUESTIONS ON FINANCIAL INCLUSION OR ACCESS TO FINANCE been conducted in the last 3 years?" }, { "id": "IndicatorName", "value": "Has a survey of firms including questions on financial inclusion or access to finance been conducted in the last 3 years?" }, { "id": "Othernotes", "value": "VGCA_01" }, { "id": "Shortdefinition", "value": "In order to monitor the level of access to financial services, has any agency conducted any of the following in the past 3 years? \n- Survey of firms including questions on financial inclusion / access to finance" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Surveys" } ], "source_id": "69" }, { "id": "FB.INC.SURV.AF.HH", "metatype": [ { "id": "Generalcomments", "value": "Has a SURVEY OF HOUSEHOLDS OR INDIVIDUALS including QUESTIONS ON FINANCIAL INCLUSION OR ACCESS TO FINANCE been conducted in the last 3 years?" }, { "id": "IndicatorName", "value": "Has a survey of households or individuals including questions on financial inclusion or access to finance been conducted in the last 3 years?" }, { "id": "Othernotes", "value": "VGCA_00" }, { "id": "Shortdefinition", "value": "In order to monitor the level of access to financial services, has any agency conducted any of the following in the past 3 years? \n- Survey of households or individuals including questions on financial inclusion" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Surveys" } ], "source_id": "69" }, { "id": "EF.EFM.PROD.XD", "metatype": [ { "id": "Developmentrelevance", "value": "Development encompasses many factors - economic, environmental, cultural, educational, and institutional - \nwhich are difficult to measure, compare, and assemble in a unified picture. The Economic Fitness approach assumes that such factors are summed up in the possible export competitiveness of countries, and algorithmically extracts this information directly from data." }, { "id": "IndicatorName", "value": "Economic Fitness Metric" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.\nIn order to define the countries-products network we perform several stages of data cleaning and regularization over the COMTRADE dataset. It is important to notice that some of these regularization procedures involve the use of the whole time-frame of the dataset." }, { "id": "Longdefinition", "value": "Economic Fitness (EF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis. Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries. Countries with low EF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries. The starting data is the COMTRADE list of products exported by each country. This data defines a bipartite network of countries and products. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedindicators", "value": "Universal Economic Fitness Metric (EF.EFM.UNIV.XD)" }, { "id": "Source", "value": "World Bank, Economic Fitness project. For more details, please visit\nThe Fitness and Complexity algorithm has been introduced in: https://www.nature.com/articles/srep00723 \nDetails about the cleaning procedure and the predictive performance of EF are described in: https://www.nature.com/articles/s41567-018-0204-y/\nand http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth \nThe convergence criterion of the Fitness and Complexity algorithm is discussed in: https://link.springer.com/article/10.1140/epjst/e2015-50118-1" }, { "id": "Statisticalconceptandmethodology", "value": "The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system. In this way, information about production capabilities may be extracted from trade in goods. The interaction among products traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth. A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems. Other data can then be added later in a controlled hierarchical framework (e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDPper capita-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data." }, { "id": "Topic", "value": "Economic Policy & Debt" }, { "id": "Unitofmeasure", "value": "Dimensionless" } ], "source_id": "70" }, { "id": "EF.EFM.UNIV.XD", "metatype": [ { "id": "Developmentrelevance", "value": "Development encompasses many factors - economic, environmental, cultural, educational, and institutional - \nwhich are difficult to measure, compare, and assemble in a unified picture. The Economic Fitness approach assumes that such factors are summed up in the possible export competitiveness of countries, and algorithmically extracts this information directly from data." }, { "id": "IndicatorName", "value": "Universal Economic Fitness Metric" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The trade data are necessary to define a coherent network for all countries and all traded sectors (products and services). This may have some limitations for countries in which the exported products and services are not a good proxy of their industrial competitiveness. While the fitness analysis usually refers to manufacturing only, in the Universal Fitness also services are included. However, being the corresponding database less granular (i.e., more aggregated), also the products database has been aggregated and this can mildly impact on the algorithm outputs. In the final, universal dataset the relative weight of products and services reflects the respective importance in the international trade flow. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. Finally, some countries have well known reporting issues in both products and services. For instance, some countries report only at some aggregation levels. Two notable examples are Ireland and China. In order to obtain a complete and more diversified database we adopt an interpolation procedure for such misreporting countries using the average declaration of similar countries and considering different aggregation levels. We can expect the final results to mildly change if export records will change in the future." }, { "id": "Longdefinition", "value": "The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to export complex goods and services on a globally competitive basis.  Countries with the highest levels of UEF have capabilities to produce a diverse portfolio of products and services, ability to upgrade into ever-increasing complex sectors, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries. Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the UN-COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedindicators", "value": "Economic Fitness Metric (EF.EFM.PROD.XD)" }, { "id": "Source", "value": "World Bank, Economic Fitness project. For more details please visit the following links.\nFor a detailed discussion of the data preprocessing, the computation of the universal fitness, and the economical implications: www.nature.com/articles/s41597-022-01732-5 \nand http://documents.worldbank.org/curated/en/309521529586431853/Integrating-services-in-the-economic-fitness-approach \nThe Fitness and Complexity algorithm has been introduced in: https://www.nature.com/articles/srep00723 \nThe convergence criterion of the Fitness and Complexity algorithm is discussed in: https://link.springer.com/article/10.1140/epjst/e2015-50118-1" }, { "id": "Statisticalconceptandmethodology", "value": "The literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods and, in the case of Universal Fitness, services.  The interaction among products and services traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.  A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework (e.g, science, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested, and consists in two coupled equations to be iterated up to convergence. The iterations are stopped when a rank-based criterion is met, that is, when we estimate that the next change in ranking will be in a very large number of iterations. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data. While the Economic Fitness is computed considering the RCA values, the Universal Economic Fitness is computed using the market shares and so while the former is intensive, the latter is extensive, that is more correlated with countries size. As a consequence, while the natural counterpart of the Economic Fitness is GDP per capita, the Universal Fitness is more comparable with GDP." }, { "id": "Topic", "value": "Economic Policy & Debt" }, { "id": "Unitofmeasure", "value": "Dimensionless" } ], "source_id": "70" }, { "id": "FB.CAP.INST.ST.MS.WB", "metatype": [ { "id": "Generalcomments", "value": "Does GOVERNMENT, INDUSTRY, OR NGOs participate in the multi-stakeholder structure to promote and coordinate financial education?" }, { "id": "IndicatorName", "value": "Does government, industry, or NGOs participate in the multi-stakeholder structure to promote and coordinate financial education?" }, { "id": "Othernotes", "value": "VHQC_00" }, { "id": "Shortdefinition", "value": "Who participates in the multi-stakeholder structure? \n- [1] Government authorities\n- [2] Industry (e.g. financial service providers)\n- [3] Government and industry only\n- [4] Government, industry, and NGOs" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "73" }, { "id": "FB.CAP.POLI.GL.WP", "metatype": [ { "id": "Generalcomments", "value": "Has the GOVERNMENT issued written guidelines DIRECTED TO ALL, SOME, OR NONE OF THE PROVIDERS OF FINANCIAL EDUCATION on content and/or methodology?" }, { "id": "IndicatorName", "value": "Has the government issued written guidelines directed to all, some, or none of the providers of financial education on content and/or methodology?" }, { "id": "Othernotes", "value": "VHUA_00" }, { "id": "Shortdefinition", "value": "Has the government issued written guidelines to providers of financial education on content and/or approaches to the provision of financial education? \n- Some: Yes, directed at a defined or limited set of providers of financial education (e.g. schools)\n- All: Yes, directed at all providers of financial education\n- None: No guidelines are issued" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "73" }, { "id": "FB.CAP.POLI.RE.WP", "metatype": [ { "id": "Generalcomments", "value": "Does the government explicitly require ALL, SOME, OR NONE OF THE FINANCIAL SERVICE PROVIDERS to offer financial education?" }, { "id": "IndicatorName", "value": "Does the government explicitly require all, some, or none of the financial service providers to offer financial education?" }, { "id": "Othernotes", "value": "VHUB_00" }, { "id": "Shortdefinition", "value": "Does the government explicitly require (i.e. via regulation, guidelines, or circular) financial service providers to offer financial education? \n- Some: Yes, directed at a defined or limited set of financial service providers (e.g. cooperatives, or financial service providers in a specific region)\n- All: Yes, directed at all financial service providers\n- None: No financial education requirements directed at financial service providers" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "73" }, { "id": "FB.CAP.POLI.RG.DC.WP", "metatype": [ { "id": "Generalcomments", "value": "Does the GOVERNMENT regularly collect data from ALL, SOME, OR NONE OF THE KNOWN PROVIDERS OF FINANCIAL EDUCATION on the reach of their programs?" }, { "id": "IndicatorName", "value": "Does the government regularly collect data from all, some, or none of the known providers of financial education on the reach of their programs?" }, { "id": "Othernotes", "value": "VHSA_00" }, { "id": "Shortdefinition", "value": "Does the government regularly collect data directly from providers of financial education programs on the reach (e.g. number of beneficiaries) of their programs?\n- Some: Yes, from a defined or limited set of providers of financial education\n- All: Yes, from all known providers of financial education\n- None: No data is collected" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Capability" } ], "source_id": "73" }, { "id": "FB.FCP.INST.ES.WH.EN", "metatype": [ { "id": "Generalcomments", "value": "WHEN was the financial consumer protection (FCP) unit in this country ESTABLISHED?" }, { "id": "IndicatorName", "value": "When was the financial consumer protection (FCP) unit in this country established?" }, { "id": "Othernotes", "value": "VGUF_00" }, { "id": "Shortdefinition", "value": "When was the financial consumer protection (FCP) unit or team established? Please specify when the separate unit(s) or team(s) designated to implement, oversee and/or enforce aspects of FCP law or regulation in your agency was established. If more than one unit or team exists, please respond in reference to the unit or team responsible for FCP supervision of commercial banks: \n- [1] Year the FCP unit or team established: before 2000\n- [2] Year the FCP unit or team established: between 2000 and 2010\n- [3] Year the FCP unit or team established: after 2010" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Institutional Arrangements" } ], "source_id": "73" }, { "id": "FB.FCP.INST.NS.HW.MY", "metatype": [ { "id": "Generalcomments", "value": "HOW MANY STAFF work in the financial consumer protection (FCP) unit in this country?" }, { "id": "IndicatorName", "value": "How many staff work in the financial consumer protection (FCP) unit in this country?" }, { "id": "Othernotes", "value": "VGUG_00" }, { "id": "Shortdefinition", "value": "How large is the financial consumer protection (FCP) unit or team in this country? Please specify the size of the separate unit(s) or team(s) designated to implement, oversee and/or enforce aspects of FCP law or regulation in your agency. If more than one unit or team exists, please respond in reference to the unit or team responsible for FCP supervision of commercial banks: \n- [1] Size of the FCP unit or team: less than 10 people\n- [2] Size of the FCP unit or team: 10-49 people\n- [3] Size of the FCP unit or team: 50-99 people\n- [4] Size of the FCP unit or team: more than 100 people" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Institutional Arrangements" } ], "source_id": "73" }, { "id": "FB.FCP.INST.ST.RS.WT", "metatype": [ { "id": "Generalcomments", "value": "What type of INSTITUTIONAL STRUCTURE for financial consumer protection (FCP) regulation and supervision is implemented in this country?" }, { "id": "IndicatorName", "value": "What type of institutional structure for financial consumer protection (FCP) regulation and supervision is implemented in this country?" }, { "id": "Othernotes", "value": "VGUC_00" }, { "id": "Shortdefinition", "value": "Please indicate which statement best describes the structure for financial consumer protection regulation and supervision (and more broadly market conduct) in your country? \n- [1] Integrated Single Agency Model: Financial consumer protection supervision responsibilities fall under a single agency that is responsible for all aspects of supervision (e.g., prudential and financial consumer protection) of all financial service providers operating within the jurisdiction.\n- [2] Integrated Multiple Agency Model (Sectoral): Financial consumer protection supervision responsibilities fall under multiple agencies that hold responsibility for all aspects of supervision (e.g., prudential and financial consumer protection) of financial service providers operating within specific financial sectors (banking, insurance, securities, etc.). Please list the name of relevant agencies and the sectors they oversee.\n- [3] Dedicated Market Conduct Agency Model: Financial consumer protection supervision responsibilities fall under a single agency dedicated to financial consumer protection supervision. Please provide the name of the agency.\n- [4] General Consumer Protection Agency Model that covers FCP: Financial consumer protection supervision responsibilities fall under an agency or agencies responsible for broader consumer protection supervision within the jurisdiction, including non-financial activities. Please provide the name of all relevant agencies.\n- [5] Shared financial and general consumer protection authority model - two institutions\n- [6] None" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Consumer Protection : Institutional Arrangements" } ], "source_id": "73" }, { "id": "FB.INC.EMNY.SP.IF.WH", "metatype": [ { "id": "Generalcomments", "value": "WHAT FRACTION OF CUSTOMERS' E-MONEY FUNDS is required to be SEPARATED FROM E-MONEY ISSUER'S FUNDS and KEPT AT HOW MANY prudentially regulated financial institutions?" }, { "id": "IndicatorName", "value": "What fraction of customers' e-money funds is required to be separated from e-money issuer's funds and kept at how many prudentially regulated financial institutions?" }, { "id": "Othernotes", "value": "VGLA_00" }, { "id": "Shortdefinition", "value": "What fraction of customers' e-money funds is required to be separated from e-money issuer's funds and kept at how many prudentially regulated financial institutions?\n- [1] 100% of customers' e-money funds; kept at a single prudentially regulated financial institution\n- [2] 100% of customers' e-money funds; kept at more than one prudentially regulated financial institution\n- [3] Only a fraction of customers' e-money funds; kept at one or more prudentially regulated financial institutions\n- [4] No requirement to separate customers' e-money funds" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Electronic Money" } ], "source_id": "73" }, { "id": "FB.INC.INST.CB.AU.WR", "metatype": [ { "id": "Generalcomments", "value": "For COMMERCIAL BANKS, is AUTHORIZATION of new or modified financial products EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For commercial banks, is authorization of new or modified financial products explicitly required?" }, { "id": "Othernotes", "value": "VGJA_00" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- Commercial Banks: Yes, No, or In some cases" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "73" }, { "id": "FB.INC.INST.CB.LN.WL", "metatype": [ { "id": "Generalcomments", "value": "For COMMERCIAL BANKS, is all, some, or no lending subject to INTEREST RATE CAPS OR PRICING LIMITS?" }, { "id": "IndicatorName", "value": "For commercial banks, is all, some, or no lending subject to interest rate caps or pricing limits?" }, { "id": "Othernotes", "value": "VGIA_00" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- Commercial Banks: All, some, or no interest rate caps or pricing limits apply to certain products or consumer segments" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "73" }, { "id": "FB.INC.INST.FC.AU.WR", "metatype": [ { "id": "Generalcomments", "value": "For FINANCIAL COOPERATIVES, is AUTHORIZATION of new or modified financial products EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For financial cooperatives, is authorization of new or modified financial products explicitly required?" }, { "id": "Othernotes", "value": "VGJA_02" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- Financial Cooperatives: Yes, No, or In some cases" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "73" }, { "id": "FB.INC.INST.FC.LN.WL", "metatype": [ { "id": "Generalcomments", "value": "For FINANCIAL COOPERATIVES, is all, some, or no lending subject to INTEREST RATE CAPS OR PRICING LIMITS?" }, { "id": "IndicatorName", "value": "For financial cooperatives, is all, some, or no lending subject to interest rate caps or pricing limits?" }, { "id": "Othernotes", "value": "VGIA_02" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- Financial Cooperatives: All, some, or no interest rate caps or pricing limits apply to certain products or consumer segments" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "73" }, { "id": "FB.INC.INST.MC.AU.WR", "metatype": [ { "id": "Generalcomments", "value": "For MICROCREDIT INSTITUTIONS (MCIs), is AUTHORIZATION of new or modified financial products EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For microcredit institutions (MCIs), is authorization of new or modified financial products explicitly required?" }, { "id": "Othernotes", "value": "VGJA_04" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- MCIs: Yes, No, or In some cases" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "73" }, { "id": "FB.INC.INST.MC.LN.WL", "metatype": [ { "id": "Generalcomments", "value": "For MICROCREDIT INSTITUTIONS (MCIs), is all, some, or no lending subject to INTEREST RATE CAPS OR PRICING LIMITS?" }, { "id": "IndicatorName", "value": "For microcredit institutions (MCIs), is all, some, or no lending subject to interest rate caps or pricing limits?" }, { "id": "Othernotes", "value": "VGIA_04" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- MCIs: All, some, or no interest rate caps or pricing limits apply to certain products or consumer segments" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "73" }, { "id": "FB.INC.INST.NB.AU.WR", "metatype": [ { "id": "Generalcomments", "value": "For NON-BANK E-MONEY ISSUERS (NBEIs), is AUTHORIZATION of new or modified financial products EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For non-bank e-money issuers (NBEIs), is authorization of new or modified financial products explicitly required?" }, { "id": "Othernotes", "value": "VGJA_05" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- NBEIs: Yes, No, or In some cases" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "73" }, { "id": "FB.INC.INST.OB.AU.WR", "metatype": [ { "id": "Generalcomments", "value": "For OTHER BANKS, is AUTHORIZATION of new or modified financial products EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For other banks, is authorization of new or modified financial products explicitly required?" }, { "id": "Othernotes", "value": "VGJA_01" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- Other Banks: Yes, No, or In some cases" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "73" }, { "id": "FB.INC.INST.OB.LN.WL", "metatype": [ { "id": "Generalcomments", "value": "For OTHER BANKS, is all, some, or no lending subject to INTEREST RATE CAPS OR PRICING LIMITS?" }, { "id": "IndicatorName", "value": "For other banks, is all, some, or no lending subject to interest rate caps or pricing limits?" }, { "id": "Othernotes", "value": "VGIA_01" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- Other Banks: All, some, or no interest rate caps or pricing limits apply to certain products or consumer segments" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "73" }, { "id": "FB.INC.INST.OD.AU.WR", "metatype": [ { "id": "Generalcomments", "value": "For OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs), is AUTHORIZATION of new or modified financial products EXPLICITLY REQUIRED?" }, { "id": "IndicatorName", "value": "For other deposit taking institutions (ODTIs), is authorization of new or modified financial products explicitly required?" }, { "id": "Othernotes", "value": "VGJA_03" }, { "id": "Shortdefinition", "value": "Does regulation explicitly require authorization of new or modified financial products? \n- ODTIs: Yes, No, or In some cases" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Product Authorization" } ], "source_id": "73" }, { "id": "FB.INC.INST.OD.LN.WL", "metatype": [ { "id": "Generalcomments", "value": "For OTHER DEPOSIT TAKING INSTITUTIONS (ODTIs), is all, some, or no lending subject to INTEREST RATE CAPS OR PRICING LIMITS?" }, { "id": "IndicatorName", "value": "For other deposit taking institutions (ODTIs), is all, some, or no lending subject to interest rate caps or pricing limits?" }, { "id": "Othernotes", "value": "VGIA_03" }, { "id": "Shortdefinition", "value": "Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread? Select one option for each type of financial institution. \n- ODTIs: All, some, or no interest rate caps or pricing limits apply to certain products or consumer segments" }, { "id": "Source", "value": "Global Financial Inclusion and Consumer Protection Survey 2017" }, { "id": "Topic", "value": "Financial Inclusion : Interest Rate Caps" } ], "source_id": "73" }, { "id": "AG.LND.AGRI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Agricultural land covers more than one-third of the world's land area, with arable land representing less than one-third of agricultural land (about 10 percent of the world's land area). Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\n\n\n\n\n\n\n\n\n\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\n\n\n\n\n\n\n\n\n\n\nFAO's agricultural land data contains a wide range of information on variables that are significant for: understanding the structure of a country's agricultural sector; making economic plans and policies for food security; deriving environmental indicators, including those related to investment in agriculture and data on gross crop area and net crop area which are useful for policy formulation and monitoring.\n\n\n\n\n\n\n\n\n\n\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Agricultural land (% of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through annual questionnaires and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage." }, { "id": "Longdefinition", "value": "Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Agriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\nAgricultural land is also sometimes classified as irrigated and non-irrigated land. In arid and semi-arid countries agriculture is often confined to irrigated land, with very little farming possible in non-irrigated areas. Land abandoned as a result of shifting cultivation is excluded from Arable land.\n\n\n\n\n\n\n\n\n\n\n\nData on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\n\n\n\n\n\n\n\n\n\n\nTotal land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Food Security" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "75" }, { "id": "AG.LND.FRLS.HA", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Generalcomments", "value": "In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height. It includes natural forests, but may also include plantations. Tree cover loss includes deforestation (the conversion of natural forest to other land uses) but also timber harvesting, fire, disease, or storm damage. Tree cover loss does not equate to deforestation." }, { "id": "IndicatorName", "value": "Tree Cover Loss" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Shows year-by-year tree cover loss, defined as stand level replacement of vegetation greater than 5 meters, within the selected area. The tree cover loss data set is a collaboration of the University of Maryland, Google, USGS, and NASA, and uses Landsat satellite images to map annual tree cover loss at a 30 × 30 meter resolution. Note that “tree cover loss” is not the same as “deforestation” – tree cover loss includes change in both natural and planted forest, and does not need to be human caused. The data from 2011 onward were produced with an updated methodology that may capture additional loss. Comparisons between the original 2001-2010 data and future years should be performed with caution." }, { "id": "Othernotes", "value": "Administrative boundaries: Global Administrative Areas database (GADM), version 3.6." }, { "id": "Otherweblinks", "value": "https://data.globalforestwatch.org/pages/data-policy" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "https://data.globalforestwatch.org/maps/gfw::tree-cover-loss-1/about" }, { "id": "Shortdefinition", "value": "Identifies areas of gross tree cover loss." }, { "id": "Source", "value": "Global Forest Watch. Tree Cover Loss (Hansen/UMD/Google/USGS/NASA)." }, { "id": "Topic", "value": "Environment: Natural capital endowment and management" }, { "id": "Unitofmeasure", "value": "Hectares" } ], "source_id": "75" }, { "id": "AG.LND.FRST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity.\n\n\n\n\n\n\n\n\n\n\n\nOn a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems. The decrease of forest area, .11 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions.\n\n\n\n\n\n\n\n\n\n\n\nDestruction of rainforests remains a significant environmental problem Much of what remains of the world's rainforests is in the Amazon basin, where the Amazon Rainforest covers approximately 4 million square kilometers. The regions with the highest tropical deforestation rate are in Central America and tropical Asia. FAO estimates that the decrease of primary forest area, 0.4 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions. Large-scale planting of trees is significantly reducing the net loss of forest area globally, and afforestation and natural expansion of forests in some countries and regions have reduced the net loss of forest area significantly at the global level.\n\n\n\n\n\n\n\n\n\n\n\nForests cover about 31 percent of total land area of the world; the world's total forest area is just over 4 billion hectares. On a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems.\n\n\n\n\n\n\n\n\n\n\n\nNational parks, game reserves, wilderness areas and other legally established protected areas cover more than 10 percent of the total forest area in most countries and regions. FAO estimates that around 10 million people are employed in forest management and conservation - but many more are directly dependent on forests for their livelihoods. Close to 1.2 billion hectares of forest are managed primarily for the production of wood and non-wood forest products. An additional 25 percent of forest area is designated for multiple uses - in most cases including the production of wood and non-wood forest products. The area designated primarily for productive purposes has decreased by more than 50 million hectares since 1990 as forests have been designated for other purposes." }, { "id": "IndicatorName", "value": "Forest area (% of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "FAO has been collecting and analyzing data on forest area since 1946. This is done at intervals of 5-10 years as part of the Global Forest Resources Assessment (FRA). FAO reports data for 229 countries and territories; for the remaining 56 small island states and territories where no information is provided, a report is prepared by FAO using existing information and a literature search. The data are aggregated at sub-regional, regional and global levels by the FRA team at FAO, and estimates are produced by straight summation.\n\n\n\n\n\n\n\nThe lag between the reference year and the actual production of data series as well as the frequency of data production varies between countries. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area.\n\n\n\n\n\n\n\nData includes areas with bamboo and palms; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters; plantations primarily used for forestry or protective purposes, such as rubber-wood plantations and cork oak stands. Data excludes tree stands in agricultural production systems, such as fruit plantations and agroforestry systems. Forest area also excludes trees in urban parks and gardens. The proportion of forest area to total land area is calculated and changes in the proportion are computed to identify trends." }, { "id": "Longdefinition", "value": "Forest area (% of land area) is the share of total land area that is under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens." }, { "id": "Othernotes", "value": "The world and regional aggregate series do not include data from countries that no longer exist." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "FAOSTAT, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/RL, publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 meters are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate.\nThe Food and Agriculture Organization (FAO) provides detail information on forest cover, and adjusted estimates of forest cover. The survey uses a uniform definition of forest. Although FAO provides a breakdown of forest cover between natural forest and plantation for developing countries, forest data used to derive this indictor data does not reflect that breakdown. Total land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area. \nThe indictor is derived by dividing total area under forest of a country by country's total land area, and multiplying by 100.\nStatistical concept(s): Forest - Forests are lands of more than 0.5 hectares, with a tree canopy cover of more than 10 percent, which are not primarily under agricultural or urban land use. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters in situ. Areas under reforestation which have yet to reach a crown density of 10 percent or tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, that are expected to regenerate. The term specifically includes: forest nurseries and seed orchards that constitute an integral part of the forest; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks and shelterbelts of trees with an area of more than 0.5 ha and width of more than 20 m; plantations primarily used for forestry purposes, including rubberwood plantations and cork oak stands. The term specifically excludes trees planted primarily for agricultural production, for example in fruit plantations and agroforestry systems." }, { "id": "Topic", "value": "Environment: Natural capital endowment and management" }, { "id": "Unitofmeasure", "value": "% (share) of land area" } ], "source_id": "75" }, { "id": "AG.PRD.FOOD.XD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "BasePeriod", "value": "2014-16" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. The category of food production includes commodities that are considered edible and that contain nutrients. Accordingly, coffee and tea are excluded along with inedible commodities because, although edible, they have practically no nutritive value.\n\n\n\n\n\n\n\n\n\n\n\nIt should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed.\n\n\n\n\n\n\n\n\n\n\n\nCrop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption)." }, { "id": "IndicatorName", "value": "Food production index (2014-2016 = 100)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Agricultural data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through the questionnaire and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage." }, { "id": "Longdefinition", "value": "Food production index covers food crops that are considered edible and that contain nutrients. Coffee and tea are excluded because, although edible, they have no nutritive value." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The agricultural production index is prepared by the Food and Agriculture Organization of the United Nations (FAO). The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 2014-2016. They are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. All the indices at the country, regional and world levels are calculated by the Laspeyres formula*. Production quantities of each commodity are weighted by 2014-2016 average international commodity prices and summed for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period 2014-2016. Since the FAO indices are based on the concept of agriculture as a single enterprise, amounts of seed and feed are subtracted from the production data to avoid double counting, once in the production data and once with the crops or livestock produced from them. Deductions for seed (in the case of eggs, for hatching) and for livestock and poultry feed apply to both domestically produced and imported commodities. They cover only primary agricultural products destined to animal feed (e.g. maize, potatoes, milk, etc.). Processed and semi-processed feed items such as bran, oilcakes, meals and molasses have been completely excluded from the calculations at all stages. It should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed. Indices which take into account deductions for feed and seed are referred to as ''net''. Indices calculated without any deductions for feed and seed are referred to as ''gross\". The \"international commodity prices\" are used in order to avoid the use of exchange rates for obtaining continental and world aggregates, and also to improve and facilitate international comparative analysis of productivity at the national level. These\" international prices,\" expressed in so-called \"international dollars,\" are derived using a Geary-Khamis formula** for the agricultural sector. This method assigns a single \"price\" to each commodity. For example, one metric ton of wheat has the same price regardless of the country where it was produced. The currency unit in which the prices are expressed has no influence on the indices published. The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. \n\n\n\n\n\n\n\n\n\n\n\n* A Laspeyres Index is known as a \"base-weighted\" or \"fixed-weighted\" index because the price increases are weighted by the quantities in the base period. The Consumer Price Index is an example of a Laspeyres Index. http://www.usna.edu/Users/econ/rbrady/312%20Materials/LaspeyresCalc.pdf\n\n\n\n\n\n** Geary-Khamis formula is an aggregation method in which category \"international prices\" (reflecting relative category values) and country purchasing power parities (PPPs), (depicting relative country price levels) are estimated simultaneously from a system of linear equations. http://stats.oecd.org/glossary/detail.asp?ID=5528\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Food Security" }, { "id": "Unitofmeasure", "value": "index (2014-2016=100)" } ], "source_id": "75" }, { "id": "CC.EST", "metatype": [ { "id": "IndicatorName", "value": "Control of Corruption: Estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Control of Corruption captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as \"capture\" of the state by elites and private interests. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org.The WGI are produced by Daniel Kaufmann (Natural Resource Governance Institute and Brookings Institution) and Aart Kraay (World Bank Development Research Group). Please cite Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2010). \"The Worldwide Governance Indicators: Methodology and Analytical Issues\". World Bank Policy Research Working Paper No. 5430 (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130). The WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent." }, { "id": "Topic", "value": "Governance: Stability & Rule of Law" } ], "source_id": "75" }, { "id": "EG.CFT.ACCS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "IndicatorName", "value": "Access to clean fuels and technologies for cooking (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Longdefinition", "value": "Access to clean fuels and technologies for cooking is the proportion of total population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Tracking SDG 7: The Energy Progress Report, International Energy Agency (IEA), note: License: Creative Commons Attribution—NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO);\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data for access to clean fuels and technologies for cooking are based on the World Health Organization’s (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Trends in the proportion of the population using each fuel type are estimated using a single multivariate hierarchical model, with urban and rural disaggregation. Estimates for overall ‘polluting’ fuels (unprocessed biomass, charcoal, coal, and kerosene) and ‘clean’ fuels (gaseous fuels, electricity, as well as an aggregation of any other clean fuels like alcohol) are produced by aggregating estimates of relevant fuel types. The model was used to derive clean fuel use estimates for 191 countries (ref. Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (2020), Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. J. R. Stat. Soc. C, 69: 815-839). Countries classified by the World Bank as high income (57 countries) in the 2022 fiscal year are assumed to have universal access to clean fuels and technologies for cooking." }, { "id": "Topic", "value": "Social: Access to Services" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "75" }, { "id": "EG.EGY.PRIM.PP.KD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "IndicatorName", "value": "Energy intensity level of primary energy (MJ/$2017 PPP GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Energy intensity level is only an imperfect proxy to energy efficiency indicator and it can be affected by a number of factors not necessarily linked to pure efficiency such as climate." }, { "id": "Longdefinition", "value": "Energy intensity level of primary energy is the ratio between energy supply and gross domestic product measured at purchasing power parity. Energy intensity is an indication of how much energy is used to produce one unit of economic output. Lower ratio indicates that less energy is used to produce one unit of output." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Tracking SDG 7: The Energy Progress Report, International Energy Agency (IEA), note: License: Creative Commons Attribution—NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO);\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is obtained by dividing total primary energy supply over gross domestic product measured in constant 2017 US dollars at purchasing power parity." }, { "id": "Topic", "value": "Environment: Energy use & security" }, { "id": "Unitofmeasure", "value": "MJ per 2017 USD PPP GDP" } ], "source_id": "75" }, { "id": "EG.ELC.ACCS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies.\n\n\n\n\n\n\n\n\n\n\n\nEnergy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indication of a country's energy poverty status.\n\n\n\n\n\n\n\n\n\n\n\nElectricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification.\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas." }, { "id": "IndicatorName", "value": "Access to electricity (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Longdefinition", "value": "Access to electricity is the percentage of population with access to electricity. Electrification data are collected from industry, national surveys and international sources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "SDG 7.1.1 Electrification Dataset, World Bank (WB), uri: https://trackingsdg7.esmap.org/downloads, note: Data is downloaded from ESMAP website. Data is released when a new Tracking SDG7 report is released., publisher: World Bank (WB), data accessed: 2024-05-16, date published: 2023" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The World Bank’s Global Electrification Database (GED) compiles nationally representative household survey data, and occasionally census data, from sources going back as far as 1990. The database also incorporates data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), Middle East and North Africa Poverty Database (MNAPOV) and the Europe and Central Asia Poverty Database (ECAPOV), which are based on similar surveys. At the time of this analysis, the GED contained 1,375 surveys for 149 countries in 1990-2021." }, { "id": "Topic", "value": "Social: Access to Services" }, { "id": "Unitofmeasure", "value": "% (share) of population" } ], "source_id": "75" }, { "id": "EG.ELC.COAL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average using total electricity production as weights" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Since the beginning of the 21st century, coal has been the fastest-growing global energy source; it currently provides about 40 percent of the world's electricity needs. Coal is the second source of primary energy in the world after oil, and the first source of electricity generation.. The last decade's growth in coal use has been driven by the economic growth of developing economies, mainly China. Irrespective of its economic benefits for the countries, the environmental impact of coal use, especially that coming from carbon dioxide emissions, is significant, and efforts are underway globally to build more efficient plants, to retrofit old plants and to decommission the oldest and least efficient coal plants.\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products." }, { "id": "Generalcomments", "value": "Electricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown. Restricted use: Please contact the International Energy Agency for third-party use of these data." }, { "id": "IndicatorName", "value": "Electricity production from coal sources (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "IEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "The share of electricity production from coal sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Coal refers to all coal and brown coal, both primary (including hard coal and lignite-brown coal) and derived fuels (including patent fuel, coke oven coke, gas coke, coke oven gas, and blast furnace gas). Peat is also included in this category." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), data accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Electricity production is total number of kilowatt-hours (kWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy." }, { "id": "Topic", "value": "Environment: Energy use & security" }, { "id": "Unitofmeasure", "value": "% (share) of total electricity production" } ], "source_id": "75" }, { "id": "EG.ELC.RNEW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Renewable energy sources are essential for reducing greenhouse gas emissions and combating climate change. They help decrease dependence on fossil fuels, enhancing energy security and price stability. The sector also drives economic growth by creating jobs and attracting investment. Technological advancements in renewables support innovation in storage, smart grids, and sustainable infrastructure. Additionally, they improve energy access in remote areas, promoting social and economic development worldwide." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data." }, { "id": "IndicatorName", "value": "Renewable electricity output (% of total electricity output)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Longdefinition", "value": "Renewable electricity is the share of electrity generated by renewable power plants in total electricity generated by all types of plants." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), data accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The share of renewable electricity output is calculated using the formula:\n\nRenewable Electricity Share(%)=(Electricity from Renewable Sources (MWh) / Total Electricity Output (MWh))×100\n\nWhere:\n\nElectricity from Renewable Sources = Total electricity generated from hydropower, wind, solar, biomass, geothermal, and ocean energy.\n\nTotal Electricity Output = Sum of electricity generated from all sources, including fossil fuels, nuclear, and renewables." }, { "id": "Topic", "value": "Environment: Energy use & security" }, { "id": "Unitofmeasure", "value": "% (share) of electricity output" } ], "source_id": "75" }, { "id": "EG.FEC.RNEW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "IndicatorName", "value": "Renewable energy consumption (% of total final energy consumption)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Longdefinition", "value": "Renewable energy consumption is the share of renewables energy in total final energy consumption." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), data accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The numerator includes the direct consumption of renewable energy sources plus the final consumption of gross electricity and heat estimated to have come from renewable sources, while the denominator is the total final energy consumption of all energy products." }, { "id": "Topic", "value": "Environment: Energy use & security" }, { "id": "Unitofmeasure", "value": "% (share) of final energy consumption" } ], "source_id": "75" }, { "id": "EG.IMP.CONS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Modern energy services are crucial to a country's economic development. Access to modern energy is essential for the provision of clean water, sanitation and healthcare and for the provision of reliable and efficient lighting, heating, cooking, mechanical power, and transport and telecommunications services.\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data." }, { "id": "IndicatorName", "value": "Energy imports, net (% of energy use)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Net energy imports are estimated as gross imports less gross exports, both measured in tons of oil equivalents (toe). A negative value indicates that the country is a net exporter. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), data accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\n\nA negative value in energy imports indicates that the country is a net exporter. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport." }, { "id": "Topic", "value": "Environment: Energy use & security" }, { "id": "Unitofmeasure", "value": "% (share) of energy use" } ], "source_id": "75" }, { "id": "EG.USE.COMM.FO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Fossil fuels are non-renewable resources because they take millions of years to form, and reserves are being depleted much faster than new ones are being made. In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\nTotal energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data." }, { "id": "IndicatorName", "value": "Fossil fuel energy consumption (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Fossil fuel comprises coal, oil, petroleum, and natural gas products." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), data accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\nData for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences. All forms of energy - primary energy and primary electricity - are converted into oil equivalents. A notional thermal efficiency of 33 percent is assumed for converting nuclear electricity into oil equivalents and 100 percent efficiency for converting hydroelectric power." }, { "id": "Topic", "value": "Environment: Energy use & security" }, { "id": "Unitofmeasure", "value": "% (share) of energy consumption" } ], "source_id": "75" }, { "id": "EG.USE.PCAP.KG.OE", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the International Energy Agency for third-party use of these data." }, { "id": "IndicatorName", "value": "Energy use (kg of oil equivalent per capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable." }, { "id": "Longdefinition", "value": "Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), data accessed: 2025-03-25" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Total energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. World Bank population estimates are used to calculate per capita data.\nEnergy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\n\n\n\n\n\n\n\n\n\n\n\nData for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences. All forms of energy - primary energy and primary electricity - are converted into oil equivalents. A notional thermal efficiency of 33 percent is assumed for converting nuclear electricity into oil equivalents and 100 percent efficiency for converting hydroelectric power.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Energy use & security" }, { "id": "Unitofmeasure", "value": "kg of oil equivalent per capita" } ], "source_id": "75" }, { "id": "EN.ATM.CO2E.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Carbon dioxide (CO2) is naturally occurring gas fixed by photosynthesis into organic matter. A byproduct of fossil fuel combustion and biomass burning, it is also emitted from land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth's radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1.\n\nBurning of carbon-based fuels since the industrial revolution has rapidly increased concentrations of atmospheric carbon dioxide, increasing the rate of global warming and causing anthropogenic climate change. It is also a major source of ocean acidification since it dissolves in water to form carbonic acid.\n\nThe addition of man-made greenhouse gases to the Atmosphere disturbs the earth's radiative balance. This is leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise and world agriculture. Emissions of CO2 are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production.\n\nThe carbon dioxide emissions of a country are only an indicator of one greenhouse gas. For a more complete idea of how a country influences climate change, gases such as methane and nitrous oxide should be taken into account. This is particularly important in agricultural economies.\n\nEmission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity. Emission intensities are also used to compare the environmental impact of different fuels or activities. The related terms - emission factor and carbon intensity - are often used interchangeably.\n\nThe environmental effects of carbon dioxide are of significant interest. Carbon dioxide (CO2) makes up the largest share of the greenhouse gases contributing to global warming and climate change. Converting all other greenhouse gases (methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6)) to carbon dioxide (or CO2) equivalents makes it possible to compare them and to determine their individual and total contributions to global warming. The Kyoto Protocol, an environmental agreement adopted in 1997 by many of the parties to the United Nations Framework Convention on Climate Change (UNFCCC), is working towards curbing CO2 emissions globally." }, { "id": "IndicatorName", "value": "CO2 emissions (metric tons per capita)" }, { "id": "License_Type", "value": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Longdefinition", "value": "Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Emissions data are sourced from Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions" }, { "id": "Statisticalconceptandmethodology", "value": "Carbon dioxide emissions, largely by-products of energy production and use, account for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Data for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as deforestation." }, { "id": "Topic", "value": "Environment: Emissions & pollution" } ], "source_id": "75" }, { "id": "EN.ATM.METH.PC", "metatype": [ { "id": "IndicatorName", "value": "Methane emissions (metric tons of CO2 equivalent per capita)" }, { "id": "License_Type", "value": "Data from CAIT carry a Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0)." }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Longdefinition", "value": "Methane emissions are those stemming from human activities such as agriculture and from industrial methane production." }, { "id": "Source", "value": "Emissions data are sourced from Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions" }, { "id": "Topic", "value": "Environment: Emissions & pollution" } ], "source_id": "75" }, { "id": "EN.ATM.NOXE.PC", "metatype": [ { "id": "IndicatorName", "value": "Nitrous oxide emissions (metric tons of CO2 equivalent per capita)" }, { "id": "License_Type", "value": "Data from CAIT carry a Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0)." }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by-nc/4.0/" }, { "id": "Longdefinition", "value": "Nitrous oxide emissions are emissions from agricultural biomass burning, industrial activities, and livestock management." }, { "id": "Source", "value": "Emissions data are sourced from Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions" }, { "id": "Topic", "value": "Environment: Emissions & pollution" } ], "source_id": "75" }, { "id": "EN.ATM.PM25.MC.M3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Air pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world’s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly." }, { "id": "IndicatorName", "value": "PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households." }, { "id": "Longdefinition", "value": "Population-weighted exposure to ambient PM2.5 pollution is defined as the average level of exposure of a nation's population to concentrations of suspended particles measuring less than 2.5 microns in aerodynamic diameter, which are capable of penetrating deep into the respiratory tract and causing severe health damage. Exposure is calculated by weighting mean annual concentrations of PM2.5 by population in both urban and rural areas." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Global Burden of Disease Study 2021 (GBD 2021) Air Pollution Exposure Estimates 1990-2021, Institute for Health Metrics and Evaluation (IHME), uri: https://ghdx.healthdata.org/record/ihme-data/gbd-2021-air-pollution-exposure-estimates-1990-2021, note: Need to create account to retrieve data., publisher: Institute for Health Metrics and Evaluation (IHME), data accessed: 2024-09-26, date published: 2024-06-06" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762–3772; GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Prüss-Üstün A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Exposure to concentrations of PM2.5 in both urban and rural areas is weighted by population and is aggregated at the national level." }, { "id": "Topic", "value": "Environment: Emissions & pollution" }, { "id": "Unitofmeasure", "value": "microgram per cubic meter" } ], "source_id": "75" }, { "id": "EN.CLC.CDDY.XD", "metatype": [ { "id": "IndicatorName", "value": "Cooling Degree Days" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "A cooling degree day (CDD) is a measurement designed to track energy use. It is the number of degrees that a day's average temperature is above 18°C (65°F). Daily degree days are accumulated to obtain annual values." }, { "id": "Othernotes", "value": "Refer to the Climate Knowledge Portal for important disclaimer information: https://climateknowledgeportal.worldbank.org/about#disclaimer. Based on RCP 8.5 scenario." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Cooling degree days can describe the overall need for cooling. For example, the highest temperature in a day was 32°C and the lowest temperature was 19°C. Then, the mean temperature for that day was (32+19)/2 =25.5. The result is above 18°C, so the cooling degree days are 25.5-18 = 7.5°C." }, { "id": "Topic", "value": "Environment: Environment/climate risk & resilience" }, { "id": "Unitofmeasure", "value": "Celsius" } ], "source_id": "75" }, { "id": "EN.CLC.CSTP.ZS", "metatype": [ { "id": "Generalcomments", "value": "Global OHI assessments require synthesizing highly heterogeneous information that is freely available from nearly one hundred sources. Data from each source are prepared and modeled using freely available coding and version control software. You may freely download and use any data, code, or instructional guides, but please see our Citation Policy." }, { "id": "IndicatorName", "value": "Coastal protection" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "https://ohi-science.org/ohi-global/download.html" }, { "id": "Source", "value": "Ocean Health Index. https://ohi-science.org/" }, { "id": "Topic", "value": "Environment: Natural capital endowment and management" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "75" }, { "id": "EN.CLC.GHGR.MT.CE", "metatype": [ { "id": "IndicatorName", "value": "GHG net emissions/removals by LUCF (Mt of CO2 equivalent)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GHG net emissions/removals by LUCF refers to changes in atmospheric levels of all greenhouse gases attributable to forest and land-use change activities, including but not limited to (1) emissions and removals of CO2 from decreases or increases in biomass stocks due to forest management, logging, fuelwood collection, etc.; (2) conversion of existing forests and natural grasslands to other land uses; (3) removal of CO2 from the abandonment of formerly managed lands (e.g. croplands and pastures); and (4) emissions and removals of CO2 in soil associated with land-use change and management. For Annex-I countries under the UNFCCC, these data are drawn from the annual GHG inventories submitted to the UNFCCC by each country; for non-Annex-I countries, data are drawn from the most recently submitted National Communication where available. Because of differences in reporting years and methodologies, these data are not generally considered comparable across countries. Data are in million metric tons." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Framework Convention on Climate Change." }, { "id": "Topic", "value": "Environment: Emissions & pollution" } ], "source_id": "75" }, { "id": "EN.CLC.HDDY.XD", "metatype": [ { "id": "IndicatorName", "value": "Heating Degree Days" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "A heating degree day (HDD) is a measurement designed to track energy use. It is the number of degrees that a day's average temperature is below 18°C (65°F). Daily degree days are accumulated to obtain annual values." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "Heating degree days can describe the overall need for heating. For example, the highest temperature in a day was 1°C and the lowest temperature was -4°C. Then, the mean temperature for that day was (1-4)/2 =-1.5. The result is below 18°C, so the heating degree days are 18-(-1.5) = 19.5°C." }, { "id": "Topic", "value": "Environment: Environment/climate risk & resilience" }, { "id": "Unitofmeasure", "value": "Celsius" } ], "source_id": "75" }, { "id": "EN.CLC.HEAT.XD", "metatype": [ { "id": "IndicatorName", "value": "Heat Index 35" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total count of days per year where the daily mean Heat Index rose above 35°C. A Heat Index is a measure of how hot it feels once humidity is factored in with air temperature." }, { "id": "Othernotes", "value": "Refer to the Climate Knowledge Portal for important disclaimer information: https://climateknowledgeportal.worldbank.org/about#disclaimer. Based on RCP 8.5 scenario." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org" }, { "id": "Statisticalconceptandmethodology", "value": "These days represent extreme heat, which may cause illnesses, including heat cramps, fainting, heat exhaustion, heat stroke, and death." }, { "id": "Topic", "value": "Environment: Environment/climate risk & resilience" } ], "source_id": "75" }, { "id": "EN.CLC.SPEI.XD", "metatype": [ { "id": "IndicatorName", "value": "Standardised Precipitation-Evapotranspiration Index" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The SPEI fulfills the requirements of a drought index since its multi-scalar character enables it to be used by different scientific disciplines to detect, monitor, and analyze droughts. Like the sc-PDSI and the SPI, the SPEI can measure drought severity according to its intensity and duration, and can identify the onset and end of drought episodes. The SPEI allows comparison of drought severity through time and space, since it can be calculated over a wide range of climates, as can the SPI." }, { "id": "Othernotes", "value": "The data presented in the Sovereign ESG Data Portal is the 12-month time-scale." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Global SPEI database (SPEIbase). https://spei.csic.es/database.html" }, { "id": "Statisticalconceptandmethodology", "value": "The Global SPEI database, SPEIbase, offers long-time, robust information about drought conditions at the global scale, with a 0.5 degrees spatial resolution and a monthly time resolution. It has a multi-scale character, providing SPEI time-scales between 1 and 48 months." }, { "id": "Topic", "value": "Environment: Environment/climate risk & resilience" } ], "source_id": "75" }, { "id": "EN.H2O.BDYS.ZS", "metatype": [ { "id": "Generalcomments", "value": "UN-Water maintains this website (the “Site”) as a courtesy to those who may choose to access it (“Users”). The information presented herein is for informative purposes only. UN-Water grants permission to Users to visit the Site and to download and copy the information, documents, and materials (collectively, “Materials”) from the Site for the User’s personal, non-commercial use, without any right to resell or redistribute them or to compile or create derivative works therefrom, subject to the terms and conditions outlined below, and also subject to more specific restrictions that may apply to specific Material within this Site." }, { "id": "IndicatorName", "value": "Proportion of bodies of water with good ambient water quality" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Otherweblinks", "value": "https://www.unwater.org/terms-use" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UN Water. https://www.unwater.org/" }, { "id": "Topic", "value": "Environment: Environment/climate risk & resilience" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "75" }, { "id": "EN.LND.LTMP.DC", "metatype": [ { "id": "Generalcomments", "value": "MODIS data and products acquired through the LP DAAC have no restrictions on subsequent use, sale, or redistribution." }, { "id": "IndicatorName", "value": "Land Surface Temperature" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "European Space Agency. https://climate.esa.int/en/projects/land-surface-temperature/" }, { "id": "Topic", "value": "Environment: Environment/climate risk & resilience" }, { "id": "Unitofmeasure", "value": "Celsius" } ], "source_id": "75" }, { "id": "EN.MAM.THRD.NO", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. The number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\n\n\n\n\n\n\n\n\n\n\nThreatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\n\n\n\n\n\n\n\n\n\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\n\n\n\n\n\n\n\n\n\n\nthe IUCN Red List covers a comprehensive assessment of the conservation status of the world's 5,488 mammal species, including global summary statistics, individual species accounts/threat category, range map, ecology information, and some other data. Mammal species are found spread across the globe, with the exception of the land mass of Antarctica. Nearly one-quarter of the world's mammal species are known to be globally threatened or extinct, 63 percent are known to not be threatened, and 15 percent have insufficient data to determine their threat status. Habitat loss, affecting over 2,000 mammal species, is the greatest threat globally. The second greatest threat is utilization which is affecting over 900 mammal species, mainly those in Asia.\n\n\n\n\n\n\n\n\n\n\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future." }, { "id": "IndicatorName", "value": "Mammal species, threatened" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Reporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\n\n\n\n\n\n\nSome parts of the world, such as the Andes, Central and West Africa, Angola, parts of South and Southeast Asia, and Melanesia, still have sparse information available of their mammal faunas. In addition, many species' names, especially in the tropics, actually represent complexes of several species that have not yet been resolved. The information on the relative importance of different threatening processes to mammal species is incomplete. IUCN codes all threats that appear to have an important impact, but not their relative importance for each species.\n\n\n\n\n\n\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\n\n\n\n\n\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List." }, { "id": "Longdefinition", "value": "Mammal species are mammals excluding whales and porpoises. Threatened species are the number of species classified by the IUCN as endangered, vulnerable, rare, indeterminate, out of danger, or insufficiently known." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "The IUCN Red List of Threatened Species, UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.iucnredlist.org/;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.iucnredlist.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threated species.\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Natural capital endowment and management" }, { "id": "Unitofmeasure", "value": "species" } ], "source_id": "75" }, { "id": "EN.POP.DNST", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries; in developing countries errors may be substantial because of limits in the transport, communications, and other resources required conducting and analyzing a full census.\n\n\n\n\n\n\n\n\n\n\n\nPopulation density is a measure of the intensity of land-use, and can be calculated for a block, city, county, state, country, continent or the entire world. Considering that over half of the Earth's land mass consists of areas inhospitable to human inhabitation, such as deserts and high mountains, and that population tends to cluster around seaports and fresh water sources, a simple number of population density by itself does not give any meaningful measurement of human population density.\n\n\n\n\n\n\n\n\n\n\n\nSeveral of the most densely populated territories in the world are city-states, microstates, or dependencies.[6][7] These territories share a relatively small area and a high urbanization level, with an economically specialized city population drawing also on rural resources outside the area, illustrating the difference between high population density and overpopulation." }, { "id": "IndicatorName", "value": "Population density (people per sq. km of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data are provided by the United Nations Population Division and other agencies. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\n\n\n\n\n\n\nThe quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data." }, { "id": "Longdefinition", "value": "Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "FAO population estimates, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO);\nWorld Bank population estimates, World Bank (WB), publisher: World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population density is midyear population divided by land area in square kilometers. This ratio can be calculated for any territorial unit for any point in time, depending on the source of the population data. Populationestimates are prepared by World Bank staff from variety of sources. They are based on the de facto definition of population and include all residents regardless of legal status or citizenship, within the physical boundaries of a country and under the jurisdiction of that country's political control. Refugees not permanently settled in the country of asylum are considered part of the population of their country of origin. Population numbers are either current census data or historical census data extrapolated through demographic methods. The count also excludes visitors from overseas.\nPopulation density is calculated by dividing midyear population by land area in a country. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship - except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes." }, { "id": "Topic", "value": "Environment: Environment/climate risk & resilience" }, { "id": "Unitofmeasure", "value": "people per square kilometer of land area" } ], "source_id": "75" }, { "id": "ER.H2O.FWST.ZS", "metatype": [ { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "The level of water stress can show the degree to which water resources are being exploited to meet the country's water demand. It measures a country's pressure on its water resources and therefore the challenge on the sustainability of its water use. It tracks progress in regard to “withdrawals and supply of freshwater to address water scarcity”, i.e. the environmental component of target 6.4. It also shows to what extent water resources are already used, and signals the importance of effective supply and demand management policies. It indicates the likelihood of increasing competition and conflict between different water uses and users in a situation of increasing water scarcity. Increased water stress, shown by an increase in the value of the indicator, has potentially negative effects on the sustainability of the natural resources and on economic development. On the other hand, low values of water stress indicate that water does not represent a particular challenge for economic development and sustainability." }, { "id": "IndicatorName", "value": "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Water withdrawal as a percentage of water resources is a good indicator of pressure on limited water resources, one of the most important natural resources. However, it only partially addresses the issues related to sustainable water management. Supplementary indicators that capture the multiple dimensions of water management would combine data on water demand management, behavioural changes with regard to water use and the availability of appropriate infrastructure, and measure progress in increasing the efficiency and sustainability of water use, in particular in relation to population and economic growth. They would also recognize the different climatic environments that affect water use in countries, in particular in agriculture, which is the main user of water. Sustainability assessment is also linked to the critical thresholds fixed for this indicator and there is no universal consensus on such threshold.\n\n\n\n\n\n\n\nTrends in water withdrawal show relatively slow patterns of change. Usually, three-five years are a minimum frequency to be able to detect significant changes, as it is unlikely that the indicator would show meaningful variations from one year to the other. Estimation of water withdrawal by sector is the main limitation to the computation of the indicator. Few countries actually publish water use data on a regular basis by sector. Renewable water resources include all surface water and groundwater resources that are available on a yearly basis without consideration of the capacity to harvest and use this resource. Exploitable water resources, which refer to the volume of surface water or groundwater that is available with an occurrence of 90% of the time, are considerably less than renewable water resources, but no universal method exists to assess such exploitable water resources. There is no universally agreed method for the computation of incoming freshwater flows originating outside of a country's borders. Nor is there any standard method to account for return flows, the part of the water withdrawn from its source and which flows back to the river system after use. In countries where return flow represents a substantial part of water withdrawal, the indicator tends to underestimate available water and therefore overestimate the level of water stress.\n\n\n\n\n\n\n\nOther limitations that affect the interpretation of the water stress indicator include: difficulty to obtain accurate, complete and up-to-date data; potentially large variation of sub-national data; lack of account of seasonal variations in water resources; lack of consideration to the distribution among water uses; lack of consideration of water quality and its suitability for use; and the indicator can be higher than 100 per cent when water withdrawal includes secondary freshwater (water withdrawn previously and returned to the system), non-renewable water (fossil groundwater), when annual groundwater withdrawal is higher than annual replenishment (over-abstraction) or when water withdrawal includes part or all of the water set aside for environmental water requirements. Some of these issues can be solved through disaggregation of the index at the level of hydrological units and by distinguishing between different use sectors. However, due to the complexity of water flows, both within a country and between countries, care should be taken not to double-count." }, { "id": "Longdefinition", "value": "The level of water stress: freshwater withdrawal as a proportion of available freshwater resources is the ratio between total freshwater withdrawn by all major sectors and total renewable freshwater resources, after taking into account environmental water requirements. Main sectors, as defined by ISIC standards, include agriculture; forestry and fishing; manufacturing; electricity industry; and services. This indicator is also known as water withdrawal intensity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), data accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Proportion of total renewable water resources withdrawn is the total volume of groundwater and surface water withdrawn from their sources for human use (in the agricultural, municipal and industrial sectors), expressed as a percentage of the total actual renewable water resources. The terms water resources and water withdrawal are understood as freshwater resources and freshwater withdrawal. Water withdrawal is estimated for the following three main sectors: agriculture, municipalities (including domestic water withdrawal) and industries, at country level and expressed in km3/year. The total actual renewable water resources for a country or region are defined as the sum of internal renewable water resources and the external renewable water resources, also expressed in km3/year. The indicator is computed by dividing total water withdrawal by total actual renewable water resources minus environmental requirements and expressed in percentage points.\n\n\n\n\n\n\n\n\n\n\n\nTotal freshwater withdrawal is the volume of freshwater extracted from its source (rivers, lakes, aquifers) for agriculture, industries and municipalities. It is estimated at the country level for the following three main sectors: agriculture, municipalities (including domestic water withdrawal) and industries. Freshwater withdrawal includes primary freshwater (not withdrawn before), secondary freshwater (previously withdrawn and returned to rivers and groundwater, such as discharged wastewater and agricultural drainage water) and fossil groundwater. It does not include non-conventional water, i.e. direct use of treated wastewater, direct use of agricultural drainage water and desalinated water. Total freshwater withdrawal is in general calculated as being the sum of total water withdrawal by sector minus direct use of wastewater, direct use of agricultural drainage water and use of desalinated water.\n\n\n\n\n\n\n\n\n\n\n\nTotal renewable freshwater resources are expressed as the sum of internal and external renewable water resources. The terms “water resources” and “water withdrawal” are understood here as freshwater resources and freshwater withdrawal. Internal renewable water resources are defined as the long-term average annual flow of rivers and recharge of groundwater for a given country generated from endogenous precipitation. External renewable water resources refer to the flows of water entering the country, taking into consideration the quantity of flows reserved to upstream and downstream countries through agreements or treaties.\n\n\n\n\n\n\n\n\n\n\n\nEnvironmental water requirements (Env.) are the quantities of water required to sustain freshwater and estuarine ecosystems. Water quality and also the resulting ecosystem services are excluded from this formulation which is confined to water volumes. This does not imply that quality and the support to societies which are dependent on environmental flows are not important and should not be taken care of. Methods of computation of Env. are extremely variable and range from global estimates to comprehensive assessments for river reaches. Water volumes can be expressed in the same units as the total freshwater withdrawal, and then as percentages of the available water resources." }, { "id": "Unitofmeasure", "value": "% (ratio)" } ], "source_id": "75" }, { "id": "ER.H2O.FWTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "While some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times)." }, { "id": "IndicatorName", "value": "Annual freshwater withdrawals, total (% of internal resources)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including withdrawals for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes. Data are for the most recent year available for 1987-2002." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), data accessed: 20240529" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Annual freshwater withdrawals are total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes." }, { "id": "Topic", "value": "Environment: Natural capital endowment and management" }, { "id": "Unitofmeasure", "value": "% (share) of internal resources" } ], "source_id": "75" }, { "id": "ER.PTD.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "The International Union for Conservation of Nature (IUCN) defines a protected area as \"a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values.\"\n\n\n\n\n\n\n\n\n\n\n\nTerrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use.\n\n\n\n\n\n\n\n\n\n\n\nMarine protected areas are areas of intertidal or subtidal terrain - and overlying water and associated flora and fauna and historical and cultural features - that have been reserved by law or other effective means to protect part or the entire enclosed environment. Sites protected under local or provincial law are excluded.\n\n\n\n\n\n\n\n\n\n\n\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n\n\n\n\n\n \n\n\n\n\n\nProtected areas remain the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. They provide the core of efforts to protect the world's threatened species and are increasingly recognized as essential providers of ecosystem services and biological resources. Some sites are owned and managed by governments, others by private individuals, companies, communities and faith groups.\n\n\n\n\n\n\n\n\n\n\n\nThe Sustainable Development Goals (SDGs) address concerns common to all economies. In recognition of the vulnerability of animal and plant species, SDGs include targets 14 and 15 to highlight the importance of marine and terrestorial protected areas. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity." }, { "id": "Generalcomments", "value": "Restricted use: Please contact the Protected Planet for third-party use of these data." }, { "id": "IndicatorName", "value": "Terrestrial and marine protected areas (% of total territorial area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "The data source for this indicator is the World Database on Protected Areas (WDPA), the most comprehensive global dataset on marine and terrestrial protected areas available. \n\n\n\n\n\n\n\nThe extent to which the land areas, including inland waters, and territorial waters of a country/territory are protected is useful for planning purpose to protect biodiversity. However, it is neither an indication of how well managed the terrestrial and marine protected areas are, nor confirmation that protection measures are effectively enforced. Further, the indicator does not provide information on non-designated or internationally designated protected areas that may also be important for conserving biodiversity. There are known data and knowledge gaps for some countries/regions due to difficulties in reporting national protected area data to the WDPA and/or determining whether a site conforms to the IUCN definition of a protected area.\n\n\n\n\n\n\n\nGaps and/or time lags in reporting national protected area data to the WDPA can however result in discrepancies, which are resolved in communication with data providers. The World Conservation Monitoring Centre (WCMC) compiles data on protected areas, numbers of certain species, and numbers of those species under threat from various sources. Because of differences in definitions, reporting practices, and reporting periods, cross-country comparability is limited.\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas." }, { "id": "Longdefinition", "value": "Terrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Marine protected areas are areas of intertidal or subtidal terrain--and overlying water and associated flora and fauna and historical and cultural features--that have been reserved by law or other effective means to protect part or all of the enclosed environment. Sites protected under local or provincial law are excluded." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM), UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, data accessed: 20240516, date published: 202405;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, data accessed: 20240516" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon).\nA GIS analysis is used to calculate terrestrial and marine protection. For this a global protected area layer is created by combining the polygons and points recorded in the WDPA. Circular buffers are created around points based on the known extent of protected areas for which no polygon is available. Annual protected area layers are created by dissolving the global protected area layer by the known year of establishment of protected areas recorded in the WDPA. The annual protected area layers are overlaid with country/territory boundaries, coastlines and buffered coastlines (delineating the territorial waters) to obtain the absolute coverage (in square kilometers) of protected areas by country/territory per year from 1990 to present. The total area of a country's/territory's terrestrial protected areas and marine protected areas in territorial waters is divided by the total area of its land areas (including inland waters) and territorial waters to obtain the relative coverage (percentage) of protected areas.\nStatistical concept(s):" }, { "id": "Topic", "value": "Environment: Natural capital endowment and management" }, { "id": "Unitofmeasure", "value": "% (share) of total territorial area" } ], "source_id": "75" }, { "id": "GB.XPD.RSDV.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Science, technology, and innovation constitute pivotal elements for achieving sustainable growth. Sustainable Development Goal (SDG) target 9.5 is dedicated to the enhancement of scientific research and the advancement of technological capabilities within industrial sectors, with a particular focus on low- and middle-income countries. Furthermore, this target encompasses the objective of augmenting the cadre of research and development personnel, as well as escalating expenditures in research." }, { "id": "IndicatorName", "value": "Research and development expenditure (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Estimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers. \n\n\n\nR&D totals from SNA accounts may differ from these estimates, due in part to the different treatments of software R&D in the totals." }, { "id": "Longdefinition", "value": "Gross domestic expenditures on research and development (R&D), expressed as a percent of GDP. They include both capital and current expenditures in the four main sectors: Business enterprise, Government, Higher education and Private non-profit. R&D covers basic research, applied research, and experimental development." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS), data accessed: 2023-04-23, date published: 2024-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by taking the number of researchers in a specified year, dividing it by the total population—referencing the mid-year population figure—and then multiplying the result by one million.\nThe calculation of this indicator is performed by dividing the total domestic intramural expenditure on research and development (R&D) for a specified year by the gross domestic product (GDP)—which is the aggregate of gross value added by all resident producers in the economy, inclusive of distributive trades and transport, along with product taxes and less any subsidies not included in product values—and then multiplying the quotient by 100. \nData are collected through national research and experimental development (R&D) surveys, either by the national statistical office or a line ministry (such as the Ministry for Science and Technology). The data compilers are the UNESCO Institute for Statistics (UIS), Organisation for Economic Co-operation and Development (OECD), Eurostat (Statistical Office of the European Union) and the Network on Science and Technology Indicators – Ibero-American and Inter-American (RICYT), African Science, Technology and Innovation (STI) Indicators Initiative (ASTII) of the African Union Development Agency-NEPAD (AUDA-NEPAD).\nStatistical concept(s): The gross domestic expenditure on R&D indicator consists of the total expenditure (current and capital) on R&D by all resident companies, research institutes, university and government laboratories, etc. It excludes R&D expenditures financed by domestic firms but performed abroad. \nThe OECD's Frascati Manual defines research and experimental development as \"creative work undertaken on a systemic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications.\" R&D covers basic research, applied research, and experimental development.\n(1) Basic research - Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view\n(2) Applied research - Applied research is also original investigation undertaken in order to acquire new knowledge; it is, however, directed primarily towards a specific practical aim or objective.\n(3) Experimental development - Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed.\nThe fields of science and technology used to classify R&D according to the Revised Fields of Science and Technology Classification are:\n1. Natural sciences;\n2. Engineering and technology;\n3. Medical and health sciences;\n4. Agricultural sciences;\n5. Social sciences;\n6. Humanities and the arts.\nThe data are obtained through statistical surveys which are regularly conducted at national level covering R&D performing entities in the private and public sectors." }, { "id": "Topic", "value": "Governance: Innovation" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "75" }, { "id": "GE.EST", "metatype": [ { "id": "IndicatorName", "value": "Government Effectiveness: Estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Government Effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org.The WGI are produced by Daniel Kaufmann (Natural Resource Governance Institute and Brookings Institution) and Aart Kraay (World Bank Development Research Group). Please cite Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2010). \"The Worldwide Governance Indicators: Methodology and Analytical Issues\". World Bank Policy Research Working Paper No. 5430 (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130). The WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent." }, { "id": "Topic", "value": "Governance: Government Effectiveness" } ], "source_id": "75" }, { "id": "IC.LGL.CRED.XQ", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "Access to finance can expand opportunities for all with higher levels of access and use of banking services associated with lower financing obstacles for people and businesses. A stable financial system that promotes efficient savings and investment is also crucial for a thriving democracy and market economy.\n\nThere are several aspects of access to financial services: availability, cost, and quality of services. The development and growth of credit markets depend on access to timely, reliable, and accurate data on borrowers' credit experiences. Access to credit can be improved by making it easy to create and enforce collateral agreements and by increasing information about potential borrowers' creditworthiness. Lenders look at a borrower's credit history and collateral. Where credit registries and effective collateral laws are absent - as in many developing countries - banks make fewer loans. Indicators that cover getting credit include the strength of legal rights index and the depth of credit information index.\n\nThe economic health of a country is measured not only in macroeconomic terms but also by other factors that shape daily economic activity such as laws, regulations, and institutional arrangements. The data measure business regulation, gauge regulatory outcomes, and measure the extent of legal protection of property, the flexibility of employment regulation, and the tax burden on businesses.\n\nThe fundamental premise of this data is that economic activity requires good rules and regulations that are efficient, accessible to all who need to use them, and simple to implement. Thus sometimes there is more emphasis on more regulation, such as stricter disclosure requirements in related-party transactions, and other times emphasis is on for simplified regulations, such as a one-stop shop for completing business startup formalities.\n\nEntrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. But where regulation is particularly onerous, levels of informality are higher, which comes at a cost: firms in the informal sector usually grow more slowly, have less access to credit, and employ fewer workers - and those workers remain outside the protections of labor law. The indicator can help policymakers understand the business environment in a country and - along with information from other sources such as the World Bank's Enterprise Surveys - provide insights into potential areas of reform." }, { "id": "Generalcomments", "value": "Data are presented for the survey year instead of publication year." }, { "id": "IndicatorName", "value": "Strength of legal rights index (0=weak to 12=strong)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Doing Business methodology has limitations that should be considered when interpreting the data. First, the data collected refer to businesses in the economy's largest city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies. These subnational studies point to significant differences in the speed of reform and the ease of doing business across cities in the same economy. Second, the data often focus on a specific business form - generally a limited liability company of a specified size - and may not represent regulation for other types of businesses such as sole proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent the full set of issues a business encounters. Fourth, the time measures involve an element of judgment by the expert respondents. When sources indicate different estimates, the Doing Business time indicators represent the median values of several responses given under the assumptions of the standardized case. Fifth, the methodology assumes that a business has full information on what is required and does not waste time when completing procedures." }, { "id": "Longdefinition", "value": "Strength of legal rights index measures the degree to which collateral and bankruptcy laws protect the rights of borrowers and lenders and thus facilitate lending. The index ranges from 0 to 12, with higher scores indicating that these laws are better designed to expand access to credit." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme" }, { "id": "Statisticalconceptandmethodology", "value": "Data are collected by the World Bank with a standardized survey that uses a simple business case to ensure comparability across economies and over time - with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys are administered through more than 9,000 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements.\n\nThe Doing Business project of the World Bank encompasses two types of data: data from readings of laws and regulations and data on time and motion indicators that measure efficiency in achieving a regulatory goal. Within the time and motion indicators cost estimates are recorded from official fee schedules where applicable. The data from surveys are subjected to numerous tests for robustness, which lead to revision or expansion of the information collected. Data starting in 2013 reflect the DB15-17 methodology change. For more information on methodology, see http://www.doingbusiness.org/Methodology/getting-credit#legalRights." }, { "id": "Topic", "value": "Governance: Human Rights" } ], "source_id": "75" }, { "id": "IP.JRN.ARTC.SC", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "A scientific journal is a periodical publication intended to further the progress of science, usually by reporting new research. Most journals are highly specialized, although some of the oldest journals such as Nature publish articles and scientific papers across a wide range of scientific fields. Scientific journals contain articles that have been peer reviewed. When a scientific journal describes experiments or calculations, they must supply enough details that an independent researcher could repeat the experiment or calculation to verify the results. Each such journal article becomes part of the permanent scientific record.\n\nSome journals, such as Nature, Science, Proceedings of the National Academy of Sciences of the United States of America (PNAS), and Physical Review Letters, have a reputation of publishing articles that mark a fundamental breakthrough in their respective fields." }, { "id": "IndicatorName", "value": "Scientific and technical journal articles" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Because the bibliometric database is constantly updated, the National Center for Science and Engineering Statistics (NCSES) does not recommend comparing bibliometric data across different editions of the Science and Engineering Indicators publication. For each edition of Indicators, NCSES uses a fixed snapshot of the database. This means that although trends are comparable, the exact number of articles, citations, and other data will vary across editions. For more information about comparing fixed versus dynamic journal data sets, see Schneider et al. (2019). Data before 2003 is sourced from earlier editions of the Science and Engineering Indicators report and may not be strictly comparable with 2003-2022 data.\n\nThe Scopus database is constructed from articles and conference proceedings with an English-language title and abstract; therefore, the database contains an unmeasurable bias because not all science and engineering (S&E) articles and conference proceedings meet the English language requirement (Elsevier 2020). (Source: https://ncses.nsf.gov/pubs/nsb202333/technical-appendix)" }, { "id": "Longdefinition", "value": "Article counts refer to publications from a selection of conference proceedings and peer-reviewed journals from Scopus in science and engineering fields, according to the National Center for Science and Engineering Statistics Taxonomy of Disciplines." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Science and Engineering Indicators, National Science Foundation (NSF), uri: https://ncses.nsf.gov/indicators" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Science and Engineering Indicators 2024 report “Publications Output: U.S. Trends and International Comparisons” uses a large database of publication records as a source of bibliometric data. Bibliometric data include each article’s title, author(s), authors’ institution(s), references, journal title, unique article-identifying information (journal volume, issue, and page numbers or digital object identifier), and year or date of publication. The PBS report uses Scopus, a bibliometric database owned by Elsevier and containing scientific literature with English titles and abstracts, to examine national and global scientific publication–related activity.? \n\nArticle counts refer to publications from a selection of conference proceedings and peer-reviewed journals in science and engineering fields from Scopus, according to the National Center for Science and Engineering Statistics Taxonomy of Disciplines: agricultural sciences, astronomy and astrophysics, biological and biomedical sciences, chemistry, computer and information sciences; engineering; geosciences, atmospheric sciences, and ocean sciences; health sciences; material sciences; mathematics and statistics; natural resources and conservation; physics; psychology; social sciences.\n\n\n\nStatistical concept(s): The number of journal articles is presented using fractional counting: a method of counting science and engineering publications in which credit for coauthored publications is divided among the collaborating institutions or regions, countries, or economies based on the proportion of their participating authors. Fractional counting allocates the publication count based on the proportion of the coauthors named on the article with institutional addresses from each region, country, or economy. Fractional counting enables the counts to sum up to the number of total articles. (Source: https://ncses.nsf.gov/pubs/nsb202333/glossary)" }, { "id": "Topic", "value": "Governance: Innovation" }, { "id": "Unitofmeasure", "value": "Fractional count" } ], "source_id": "75" }, { "id": "IP.PAT.RESD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "The Patent Cooperation Treaty (www.wipo.int/pct) provides a two phase system for filing patent. International applications under the treaty provide for a national patent grant only - there is no international patent. The national filing represents the applicant's seeking of patent protection for a given territory, whereas international filings, while representing a legal right, do not accurately reflect where patent protection is sought. Resident filings are those from residents of the country concerned. Nonresident filings are from applicants abroad. For regional offices applications from residents of any member state of the regional patent convention are considered nonresident filings. Some offices (notably the U.S. Patent and Trademark Office) use the residence of the inventor rather than the applicant to classify filings.\n\nPatent data are a great resource for the study of technical change in a country or region. Patent data provide a uniquely detailed source of information on inventive activity and the multiple dimensions of the inventive process (e.g. geographical location, technical and institutional origin, individuals and networks). Furthermore, patent data form a consistent basis for comparisons across time and across countries.\n\nPatent data can be used in the analysis of a wide array of topics related to technical change and patenting activity including industry-science linkages, patenting strategies by companies, internationalization of research, and indicators on the value of patents. Patent-based statistics reflect the inventive performance of countries, regions and firms, as well as other aspects of the dynamics of the innovation process such as co-operation in innovation or technology paths." }, { "id": "IndicatorName", "value": "Patent applications, residents" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "A patent is an exclusive right granted for a specified period (generally 20 years) for a new way of doing something or a new technical solution to a problem - an invention. The invention must be of practical use and display a characteristic unknown in the existing body of knowledge in its field. Most countries have systems to protect patentable inventions." }, { "id": "Longdefinition", "value": "Patent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention--a product or process that provides a new way of doing something or offers a new technical solution to a problem. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WIPO Patent Report: Statistics on Worldwide Patent Activity, World Intellectual Property Organization (WIPO), note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Resident patent applications are those for which the first-named applicant or assignee is a resident of the State or region concerned. In the case of regional offices such as the European Patent Office, a resident is an applicant from any of the member States of the regional patent convention.\n\nPatent data cover applications and grants classified by field of technology. International applications series distinguish four subcategories: a) patents taken out by residents of a country in that country; b) patents taken out in a country by non-residents of that country; c) total patents registered in the country or naming it; d) patents taken out outside a country by its residents. Data on patents granted only distinguish between patents awarded to residents and to non-residents. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years.\n\nPatent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention - a product or process that provides a new way of doing something or offers a new technical solution to a problem." }, { "id": "Topic", "value": "Governance: Innovation" } ], "source_id": "75" }, { "id": "IT.NET.USER.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nToday's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains." }, { "id": "Generalcomments", "value": "Please cite the International Telecommunication Union for third-party use of these data." }, { "id": "IndicatorName", "value": "Individuals using the Internet (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year." }, { "id": "Longdefinition", "value": "Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU), uri: https://datahub.itu.int/" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): The number of in-scope individuals using the Internet is calculated by aggregating the weighted responses. The proportion of individuals using the Internet is expressed as a percentage and is calculated by dividing the total number of in-scope individuals using the Internet by the total number of in-scope individuals, and then multiplying the result by 100." }, { "id": "Topic", "value": "Governance: Economic Environment" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "75" }, { "id": "NV.AGR.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness." }, { "id": "Generalcomments", "value": "Note: Data for OECD countries are based on ISIC, revision 4." }, { "id": "IndicatorName", "value": "Agriculture, forestry, and fishing, value added (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently \"netted out\" using equally crude and ad hoc approximations." }, { "id": "Longdefinition", "value": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period. Note: For VAB countries, gross value added at factor cost is used as the denominator." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Environment: Food Security" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "75" }, { "id": "NY.ADJ.DFOR.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, net forest depletion (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "A positive net depletion figure for forest resources implies that the harvest rate exceeds the rate of natural growth; this is not the same as deforestation, which represents a change in land use. In principle, there should be an addition to savings in countries where growth exceeds harvest, but empirical estimates suggest that most of this net growth is in forested areas that cannot currently be exploited economically. Because the depletion estimates reflect only timber values, they ignore all the external and nontimber benefits associated with standing forests." }, { "id": "Longdefinition", "value": "Net forest depletion is calculated as the product of unit resource rents and the excess of roundwood harvest over natural growth. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Environment: Natural capital endowment and management" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "75" }, { "id": "NY.ADJ.DRES.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "Adjusted savings, natural resources depletion (% of GNI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Net forest depletion is not the monetary value of deforestation. Roundwood and fuelwood production are different from deforestation, which represents a permanent change in land use and, thus, is not comparable. Areas logged out but intended for regeneration are not included in deforestation figures; rather, they are counted as producing timber depletion. Net forest depletion includes only timber values and does not include the loss of nontimber forest benefits and nonuse benefits.\n\n\n\nFor both energy and mineral depletion, unit resource rent is calculated as (unit world price - average cost) / unit world price. Marginal cost should be used instead of average cost in order to calculate the true opportunity cost of extraction; however, marginal cost is difficult to compute and data are not readily available. Unit prices refer to international rather than local prices to reflect the social cost of natural resources depletion. This differs from methodologies of national accounts, which may use local prices to measure energy or mineral GDP. This difference explains eventual discrepancies in the values for energy or mineral depletion, verses energy or mineral GDP." }, { "id": "Longdefinition", "value": "Natural resource depletion is the sum of net forest depletion, energy depletion, and mineral depletion. Net forest depletion is unit resource rents times the excess of roundwood harvest over natural growth. Energy depletion is the ratio of the value of the stock of energy resources to the remaining reserve lifetime (capped at 25 years). It covers coal, crude oil, and natural gas. Mineral depletion is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime (capped at 25 years). It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Environment: Natural capital endowment and management" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "75" }, { "id": "NY.GDP.MKTP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels." }, { "id": "IndicatorName", "value": "GDP (annual % growth)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Each industry's contribution to growth in the economy's output is measured by growth in the industry's value added. In principle, value added in constant prices can be estimated by measuring the quantity of goods and services produced in a period, valuing them at an agreed set of base year prices, and subtracting the cost of intermediate inputs, also in constant prices. This double-deflation method requires detailed information on the structure of prices of inputs and outputs.\n\n\n\nIn many industries, however, value added is extrapolated from the base year using single volume indexes of outputs or, less commonly, inputs. Particularly in the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult.\n\n\n\nMoreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in quality lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of inflation.\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\n\n\nRebasing of national accounts can alter the measured growth rate of an economy and lead to breaks in series that affect the consistency of data over time. When countries rebase their national accounts, they update the weights assigned to various components to better reflect current patterns of production or uses of output. The new base year should represent normal operation of the economy - it should be a year without major shocks or distortions. Some developing countries have not rebased their national accounts for many years. Using an old base year can be misleading because implicit price and volume weights become progressively less relevant and useful.\n\n\n\nTo obtain comparable series of constant price data for computing aggregates, the World Bank rescales GDP and value added by industrial origin to a common reference year. Because rescaling changes the implicit weights used in forming regional and income group aggregates, aggregate growth rates are not comparable with those from earlier editions with different base years. Rescaling may result in a discrepancy between the rescaled GDP and the sum of the rescaled components. To avoid distortions in the growth rates, the discrepancy is left unallocated. As a result, the weighted average of the growth rates of the components generally does not equal the GDP growth rate." }, { "id": "Longdefinition", "value": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed." }, { "id": "Topic", "value": "Governance: Economic Environment" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "75" }, { "id": "PV.EST", "metatype": [ { "id": "IndicatorName", "value": "Political Stability and Absence of Violence/Terrorism: Estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Political Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org.The WGI are produced by Daniel Kaufmann (Natural Resource Governance Institute and Brookings Institution) and Aart Kraay (World Bank Development Research Group). Please cite Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2010). \"The Worldwide Governance Indicators: Methodology and Analytical Issues\". World Bank Policy Research Working Paper No. 5430 (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130). The WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent." }, { "id": "Topic", "value": "Governance: Stability & Rule of Law" } ], "source_id": "75" }, { "id": "RL.EST", "metatype": [ { "id": "IndicatorName", "value": "Rule of Law: Estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Rule of Law captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org.The WGI are produced by Daniel Kaufmann (Natural Resource Governance Institute and Brookings Institution) and Aart Kraay (World Bank Development Research Group). Please cite Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2010). \"The Worldwide Governance Indicators: Methodology and Analytical Issues\". World Bank Policy Research Working Paper No. 5430 (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130). The WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent." }, { "id": "Topic", "value": "Governance: Stability & Rule of Law" } ], "source_id": "75" }, { "id": "RQ.EST", "metatype": [ { "id": "IndicatorName", "value": "Regulatory Quality: Estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Regulatory Quality captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org.The WGI are produced by Daniel Kaufmann (Natural Resource Governance Institute and Brookings Institution) and Aart Kraay (World Bank Development Research Group). Please cite Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2010). \"The Worldwide Governance Indicators: Methodology and Analytical Issues\". World Bank Policy Research Working Paper No. 5430 (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130). The WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent." }, { "id": "Topic", "value": "Governance: Government Effectiveness" } ], "source_id": "75" }, { "id": "SD.ESR.PERF.XQ", "metatype": [ { "id": "IndicatorName", "value": "Economic and Social Rights Performance Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Economic and social human rights ensure that all people have access to the basic goods, services, and opportunities necessary to survive and thrive." }, { "id": "Source", "value": "Human Rights Measurement Initiative. https://humanrightsmeasurement.org/" }, { "id": "Statisticalconceptandmethodology", "value": "For economic and social rights, HRMI draws on national statistics produced by governments and international agencies, and uses the award-winning SERF Index methodology to compare countries’ human rights outcomes with their income, to capture the concept of ‘progressive realisation’." }, { "id": "Topic", "value": "Governance: Human Rights" }, { "id": "Unitofmeasure", "value": "Standardized scores" } ], "source_id": "75" }, { "id": "SE.ADT.LITR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "IndicatorName", "value": "Literacy rate, adult total (% of people ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), data accessed: 2024-04-23, date published: 2024-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status. \n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations." }, { "id": "Topic", "value": "Social: Education & skills" }, { "id": "Unitofmeasure", "value": "% of people ages 15 and above" } ], "source_id": "75" }, { "id": "SE.ENR.PRSC.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women." }, { "id": "IndicatorName", "value": "School enrollment, primary and secondary (gross), gender parity index (GPI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Longdefinition", "value": "Gender parity index for gross enrollment ratio in primary and secondary education is the ratio of girls to boys enrolled at primary and secondary levels in public and private schools." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: This indicator is calculated by dividing female gross enrollment ratio in primary and secondary education by male gross enrollment ratio in primary and secondary education. \n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. \n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):" }, { "id": "Topic", "value": "Governance: Gender" } ], "source_id": "75" }, { "id": "SE.PRM.ENRR", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Primary education is fundamental to future educational success and opens pathways for continued advancement. This indicator measures the overall rate of participation in primary education, signifying the education system's ability to enroll students within a specific age cohort." }, { "id": "IndicatorName", "value": "School enrollment, primary (% gross)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced." }, { "id": "Longdefinition", "value": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), data accessed: 2024-04-23, date published: 2024-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Gross enrollment ratio for primary school is calculated by dividing the number of students enrolled in primary education regardless of age by the population of the age group which officially corresponds to primary education, and multiplying by 100. \nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system." }, { "id": "Topic", "value": "Social: Education & skills" }, { "id": "Unitofmeasure", "value": "% of population in the 5-year age group immediately following preprimary education" } ], "source_id": "75" }, { "id": "SE.XPD.TOTL.GB.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Median" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Investment in education serves as a driving force for economic growth, enhancement of productivity, and the promotion of individual and collective prosperity. This indicator is instrumental in evaluating the extent to which a government prioritizes education, whether over time or in relation to other nations. Furthermore, it reflects the government's dedication to the investment in human capital development." }, { "id": "IndicatorName", "value": "Government expenditure on education, total (% of government expenditure)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Data on government expenditure on education may refer to spending by the ministry of education only (excluding spending on educational activities by other ministries). In addition, definitions and methods of data on total general government expenditure may differ across countries." }, { "id": "Longdefinition", "value": "General government expenditure on education (current, capital, and transfers) is expressed as a percentage of total general government expenditure on all sectors (including health, education, social services, etc.). It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS), data accessed: 2024-04-23, date published: 2024-02" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Expenditure on education, total (% of government expenditure) is calculated by dividing total government expenditure on education by the total government expenditure on all sectors and multiplying by 100. Aggregate data are based on World Bank estimates.\nInformation regarding educational expenditures is obtained from national governments through their responses to the annual survey on formal education conducted by the UNESCO Institute for Statistics (UIS). The data provided for this questionnaire often originates from the annual financial reports of the Ministry of Finance or the Ministry of Education, or from the national accounts compiled by the National Statistical Office. Additionally, comprehensive data on general government expenditure across all sectors are sourced from the International Monetary Fund's (IMF) World Economic Outlook database, which undergoes an annual update.\nStatistical concept(s): A greater allocation of government funds towards education reflects a significant emphasis on educational priorities in comparison to other public sector investments. It is important to consider, however, that the capacity of governments to spend varies, resulting in differing budget sizes. Additionally, demographic factors such as the age distribution within a country can influence the proportion of spending on education versus other areas like health or social security." }, { "id": "Topic", "value": "Social: Education & skills" }, { "id": "Unitofmeasure", "value": "% of government expenditure" } ], "source_id": "75" }, { "id": "SG.GEN.PARL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Despite much progress in recent decades, gender inequalities remain pervasive in many dimensions of life - worldwide. But while disparities exist throughout the world, they are most prevalent in developing countries. Gender inequalities in the allocation of such resources as education, health care, nutrition, and political voice matter because of the strong association with well-being, productivity, and economic growth. These patterns of inequality begin at an early age, with boys routinely receiving a larger share of education and health spending than do girls, for example.\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen are vastly underrepresented in decision-making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.\n\n\n\n\n\n\n\n\n\n\n\n\n\nA strong and vibrant democracy is possible only when parliament is fully inclusive of the population it represents. Parliaments cannot consider themselves inclusive, however, until they can boast the full participation of women. This is not just about women's right to equality and their contribution to the conduct of public affairs, but also about using women's resources and potential to determine political and development priorities that benefit societies and the global community." }, { "id": "Generalcomments", "value": "Relevance to gender indicator: Women are vastly underrepresented in decision making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.\n\nThis is the Sustainable Development Goal indicator 5.5.1 (a). [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Proportion of seats held by women in national parliaments (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "The number of countries covered varies with suspensions or dissolutions of parliaments. There can be difficulties in obtaining information on by-election results and replacements due to death or resignation. These changes are ad hoc events which are more difficult to keep track of. By-elections, for instance, are often not announced internationally as general elections are. Parliaments vary considerably in their internal workings and procedures, however, generally legislate, oversee government and represent the electorate. In terms of measuring women's contribution to political decision making, this indicator may not be sufficient because some women may face obstacles in fully and efficiently carrying out their parliamentary mandate.\n\n\n\n\n\n\n\n\n\nThe data is compiled by the Inter-Parliamentary Union on the basis of information provided by National Parliaments. The percentages do not take into account the case of parliaments for which no data was available at that date. Information is available in all countries where a national legislature exists and therefore does not include parliaments that have been dissolved or suspended for an indefinite period." }, { "id": "Longdefinition", "value": "Women in parliaments are the percentage of parliamentary seats in a single or lower chamber held by women." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Monthly ranking of women in national parliaments, Inter-Parliamentary Union (IPU), uri: https://data.ipu.org/women-ranking/, note: For the year of 1998, the data is as of August 10, 1998., type: Excel, data accessed: 2024-03-21" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The proportion of seats held by women in national parliaments is the number of seats held by women members in single or lower chambers of national parliaments, expressed as a percentage of all occupied seats; it is derived by dividing the total number of seats occupied by women by the total number of seats in parliament.\nStatistical concept(s): This indicator assesses the extent to which women are provided with equal opportunities to participate in parliamentary decision-making processes. It applies to the sole chamber of unicameral national parliaments and the lower chamber in the case of bicameral systems. The upper chamber in bicameral parliaments is not included in this measure. Parliamentary seats are typically occupied by individuals who are victorious in general elections, though they can also be acquired through nomination, appointment, indirect election, member rotation, or by-elections. The term 'seats' refers to the total count of parliamentary mandates or the total number of parliament members." }, { "id": "Topic", "value": "Governance: Gender" }, { "id": "Unitofmeasure", "value": "% of single or lower chamber seats" } ], "source_id": "75" }, { "id": "SH.DTH.COMM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "IndicatorName", "value": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards." }, { "id": "Longdefinition", "value": "Cause of death refers to the share of all deaths for all ages by underlying causes. Communicable diseases and maternal, prenatal and nutrition conditions include infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates: Deaths by Cause, Age, Sex, by Country and by Region" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source." }, { "id": "Topic", "value": "Social: Health & Nutrition" } ], "source_id": "75" }, { "id": "SH.DYN.MORT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "Generalcomments", "value": "Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is the Sustainable Development Goal indicator 3.2.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Mortality rate, under-5 (per 1,000 live births)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work." }, { "id": "Longdefinition", "value": "Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org, publisher: UNICEF, WHO, World Bank, United Nations Population Division;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data." }, { "id": "Topic", "value": "Social: Health & Nutrition" }, { "id": "Unitofmeasure", "value": "Per 1000 live births" } ], "source_id": "75" }, { "id": "SH.H2O.SMDW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "Generalcomments", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\nThis is the Sustainable Development Goal indicator 6.1.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "People using safely managed drinking water services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "In order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality). Many countries lack data on one or more elements of safely managed drinking water. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability). Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), data accessed: 2023-07-25, date published: 2023-07-06;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed drinking water services are defined as the water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water)." }, { "id": "Topic", "value": "Social: Access to Services" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "75" }, { "id": "SH.MED.BEDS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Hospital beds are used to indicate the availability of inpatient services." }, { "id": "IndicatorName", "value": "Hospital beds (per 1,000 people)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Depending on the source and means of monitoring, data may not be exactly comparable across countries. For more information, see the original source." }, { "id": "Longdefinition", "value": "Hospital beds include inpatient beds available in public, private, general, and specialized hospitals and rehabilitation centers. In most cases beds for both acute and chronic care are included." }, { "id": "Notesfromoriginalsource", "value": "Depending on the source and means of monitoring, data may not be exactly comparable across countries. See listed source for country-specific details." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO data, supplemented by country data, World Health Organization (WHO)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data were compiled from the WHO Regional offices and country sources other (e.g. ministry of health, national statistical office) and modified to standardize the unit of measure of per 10 000 population by WHO.\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\nAvailability and use of health services, such as hospital beds per 1,000 people, reflect both demand- and supply-side factors. In the absence of a consistent definition this is a crude indicator of the extent of physical, financial, and other barriers to health care." }, { "id": "Topic", "value": "Social: Health & Nutrition" }, { "id": "Unitofmeasure", "value": "Per 1000 people" } ], "source_id": "75" }, { "id": "SH.STA.OWAD.ZS", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of overweight (% of adults)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Prevalence of overweight adults is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Prevalence of overweight adults is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters." }, { "id": "Source", "value": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)." }, { "id": "Topic", "value": "Social: Health & Nutrition" } ], "source_id": "75" }, { "id": "SH.STA.SMSS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "Generalcomments", "value": "Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\nThis is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "People using safely managed sanitation services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "There are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporality and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site). Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system. If no information is available, it is assumed that 50 percent is safely managed. Regional and income group estimates are made when data are available for at least 30 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), data accessed: 2023-07-25, date published: 2023-07-06;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed sanitation facilities are defined as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation)." }, { "id": "Topic", "value": "Social: Access to Services" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "75" }, { "id": "SI.DST.FRST.20", "metatype": [ { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "It measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust." }, { "id": "Generalcomments", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "IndicatorName", "value": "Income share held by lowest 20%" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "World Bank, Poverty and Inequality Platform: https://pip.worldbank.org/" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the bottom quintile, meaning the bottom 20% of people when ranked from lowest to highest income." }, { "id": "Topic", "value": "Social: Poverty & Inequality" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "75" }, { "id": "SI.POV.GINI", "metatype": [ { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group's vision of promoting shared prosperity includes a measure that tracks the number of economies with high inequality, defined as those with a Gini index greater than 0.4" }, { "id": "Generalcomments", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "IndicatorName", "value": "Gini index" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Gini coefficients are not unique. It is possible for two different Lorenz curves to give rise to the same Gini coefficient. Furthermore it is possible for the Gini coefficient of a developing country to rise (due to increasing inequality of income) while the number of people in absolute poverty decreases. This is because the Gini coefficient measures relative, not absolute, wealth.\n\n\n\n\n\nAnother limitation of the Gini coefficient is that it is not additive across groups, i.e. the total Gini of a society is not equal to the sum of the Gini's for its sub-groups. Thus, country-level Gini coefficients cannot be aggregated into regional or global Gini's, although a Gini coefficient can be computed for the aggregate.\n\n\n\n\n\nBecause the underlying household surveys differ in methods and types of welfare measures collected, data are not strictly comparable across countries or even across years within a country. Two sources of non-comparability should be noted for distributions of income in particular. First, the surveys can differ in many respects, including whether they use income or consumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much better welfare indicator, particularly in developing countries. Second, households differ in size (number of members) and in the extent of income sharing among members. And individuals differ in age and consumption needs. Differences among countries in these respects may bias comparisons of distribution. \n\n\n\n\n\nWorld Bank staff have made an effort to ensure that the data are as comparable as possible. Wherever possible, consumption has been used rather than income. Income distribution and Gini indexes for high-income economies are calculated directly from the Luxembourg Income Study database, using an estimation method consistent with that applied for developing countries." }, { "id": "Longdefinition", "value": "Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "World Bank, Poverty and Inequality Platform: https://pip.worldbank.org/" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.\nThe Gini index provides a convenient summary measure of the degree of inequality. Data on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\nStatistical concept(s): The Gini index is the average of all pairwise absolute differences between individual consumption or income, normalized by twice the mean. More intuitively, the Gini index is the average share of mean consumption or income that needs to be transferred between two randomly selected individuals to achieve equality. A Gini index of 1 represents perfect inequality, in which total consumption or income goes to one individual. A Gini index of 0 indicates represents perfect equality, in which all individuals have the same level of consumption or income." }, { "id": "Topic", "value": "Social: Poverty & Inequality" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "75" }, { "id": "SI.POV.NAHC", "metatype": [ { "id": "Aggregationmethod", "value": "Population-weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "The poverty rate as defined by national poverty lines reflects the share of the population that fails to meet the standard a country thinks is necessary to cover basic needs (typically in low- and middle-income countries) or afford a decent lifestyle (typically in high-income countries). SDG 1.2 aims to reduce by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions, by 2030." }, { "id": "Generalcomments", "value": "This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at national poverty lines (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Longdefinition", "value": "National poverty headcount ratio is the percentage of the population living below the national poverty line(s). National estimates are based on population-weighted subgroup estimates from household surveys. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "World Bank, Poverty and Inequality Platform: https://pip.worldbank.org/" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines., World Bank (WB), note: Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Poverty headcount ratio among the population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income. \nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies. \nAlmost all national poverty lines in developing economies are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years. \nThis series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year. For all other economies, the year reported is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which data collection started.\nStatistical concept(s): National poverty headcount ratio refers to the percentage of a population whose consumption or income per day falls short of the national poverty line. National poverty lines vary by country and over time. In low- and middle-income countries, national poverty lines tend to be absolute poverty lines, thus reflecting the estimated minimum amount of money needed to cover basic needs. In high-income countries, national poverty lines tend to be relative poverty lines, thus reflecting the typical amount of money needed for an individual to afford the typical standard of living and without any restraints to participating fully in the societies in which they live. National poverty lines tend to grow with economic growth, especially in high-income or upper-middle-income countries." }, { "id": "Topic", "value": "Social: Poverty & Inequality" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "75" }, { "id": "SI.SPR.PCAP.ZG", "metatype": [ { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "The World Bank Group has a goal of promoting shared prosperity within and across countries. Growth is good for the poor and growth in poor countries reflects in improvements in the World Bank’s new shared prosperity measure, the Global Prosperity Gap." }, { "id": "Generalcomments", "value": "The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes." }, { "id": "IndicatorName", "value": "Annualized average growth rate in per capita real survey mean consumption or income, total population (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution." }, { "id": "Longdefinition", "value": "The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2021 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "World Bank, Poverty and Inequality Platform: https://pip.worldbank.org/" }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The annualized growth rate in per capita real survey mean consumption of the total population is computed in the following steps. First, obtain the mean consumption or income levels of the total population of the survey distribution in two different periods. The two survey data sets should be comparable - that is, they use a similar method of sampling, collecting data, and constructing the welfare aggregate. Second, the rate of change in the survey mean values of the total population is annualized.\nStatistical concept(s): The annualized growth rate in per capita real survey mean consumption or income measures the rate of change in per capita consumption or income changes in a year." }, { "id": "Topic", "value": "Social: Poverty & Inequality" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "75" }, { "id": "SL.TLF.0714.ZS", "metatype": [ { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "In most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment." }, { "id": "IndicatorName", "value": "Children in employment, total (% of children ages 7-14)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Although efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point." }, { "id": "Longdefinition", "value": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14." }, { "id": "Topic", "value": "Social: Employment" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "75" }, { "id": "SL.TLF.ACTI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems." }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Labor force participation rate, total (% of total population ages 15-64) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Notesfromoriginalsource", "value": "Given the exceptional situation, including the scarcity of relevant data, the ILO modeled estimates and projections from 2020 onwards are subject to substantial uncertainty." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, data accessed: January 07, 2025" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Social: Employment" }, { "id": "Unitofmeasure", "value": "% of total population ages 15-64" } ], "source_id": "75" }, { "id": "SL.TLF.CACT.FM.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Estimates of women in the labor force and employment are generally lower than those of men and are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic. In many low-income countries women often work on farms or in other family enterprises without pay, and others work in or near their homes, mixing work and family activities during the day. In many high-income economies, women have been increasingly acquiring higher education that has led to better-compensated, longer-term careers rather than lower-skilled, shorter-term jobs. However, access to good- paying occupations for women remains unequal in many occupations and countries around the world. Labor force statistics by gender is important to monitor gender disparities in employment and unemployment patterns." }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates." }, { "id": "Longdefinition", "value": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period." }, { "id": "Notesfromoriginalsource", "value": "Given the exceptional situation, including the scarcity of relevant data, the ILO modeled estimates and projections from 2020 onwards are subject to substantial uncertainty." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100. The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed. \n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework. \n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave." }, { "id": "Topic", "value": "Governance: Gender" }, { "id": "Unitofmeasure", "value": "%" } ], "source_id": "75" }, { "id": "SL.UEM.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "DataQuality", "value": "Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings." }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all)." }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, total (% of total labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "While the unemployment rate may be considered the most informative labour market indicator, reflecting the general performance of the labour market and the economy as a whole, it should not be interpreted as a measure of economic hardship or of well-being. When based on the internationally-recommended standards, the unemployment rate simply reflects the proportion of the labour force that does not have a job but is available and actively looking for work. It says nothing about the economic resources of unemployed workers or their family members. Its use should, therefore, be limited to serving as a measurement of the utilization of labour and an indication of the failure to find work. Other measures, including income-related indicators, would be needed to evaluate economic hardship. An additional criticism of the aggregate unemployment measure is that it masks information on the composition of the jobless population and therefore misses out on the particularities of the education level, ethnic origin, socio-economic background, work experience, etc. of the unemployed. Moreover, the unemployment rate says nothing about the type of unemployment – whether it is cyclical and short-term or structural and long-term – which is a critical issue for policy makers in the development of their policy responses, especially given that structural unemployment cannot be addressed by boosting market demand only." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Notesfromoriginalsource", "value": "Given the exceptional situation, including the scarcity of relevant data, the ILO modeled estimates and projections from 2020 onwards are subject to substantial uncertainty." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, data accessed: January 07, 2025." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. \n\n\n\n\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit)." }, { "id": "Topic", "value": "Social: Employment" }, { "id": "Unitofmeasure", "value": "% of total labor force" } ], "source_id": "75" }, { "id": "SM.POP.NETM", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Movement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\n\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. In most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers.\n\n\n\nBut refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom." }, { "id": "IndicatorName", "value": "Net migration" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "International migration is the component of population change most difficult to measure and estimate reliably. Thus, the quality and quantity of the data used in the estimation and projection of net migration varies considerably by country. Furthermore, the movement of people across international boundaries, which is very often a response to changing socio-economic, political and environmental forces, is subject to a great deal of volatility. Refugee movements, for instance, may involve large numbers of people moving across boundaries in a short time. For these reasons, projections of future international migration levels are the least robust part of current population projections and reflect mainly a continuation of recent levels and trends in net migration." }, { "id": "Longdefinition", "value": "Net migration is the net total of migrants during the period, that is, the number of immigrants minus the number of emigrants, including both citizens and noncitizens." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: When there is insufficient data, net migration is derived through the difference between the overall population growth rate and the rate of natural increase (the difference between the birth rate and the death rate) during the same period. Such calculations are usually made for intercensal periods. The estimates are also derived from the data on foreign-born population - people who have residence in one country but were born in another country. When data on the foreign-born population are not available, data on foreign population - that is, people who are citizens of a country other than the country in which they reside - are used as estimates.\nStatistical concept(s): The United Nations Population Division provides data on net migration and migrant stock. Because data on migrant stock is difficult for countries to collect, the United Nations Population Division takes into account the past migration history of a country or area, the migration policy of a country, and the influx of refugees in recent periods when deriving estimates of net migration. The data to calculate these estimates come from a variety of sources, including border statistics, administrative records, surveys, and censuses." }, { "id": "Topic", "value": "Governance: Stability & Rule of Law" }, { "id": "Unitofmeasure", "value": "Unit" } ], "source_id": "75" }, { "id": "SN.ITK.DEFC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Good nutrition is the cornerstone for survival, health and development. Well-nourished children perform better in school, grow into healthy adults and in turn give their children a better start in life. Well-nourished women face fewer risks during pregnancy and childbirth, and their children set off on firmer developmental paths, both physically and mentally (UNICEF www.childinfo.org)." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 2.1.1[https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Prevalence of undernourishment (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "From a policy and program standpoint, this measure has its limits. First, food insecurity exists even where food availability is not a problem because of inadequate access of poor households to food. Second, food insecurity is an individual or household phenomenon, and the average food available to each person, even corrected for possible effects of low income, is not a good predictor of food insecurity among the population. And third, nutrition security is determined not only by food security but also by the quality of care of mothers and children and the quality of the household's health environment (Smith and Haddad 2000)." }, { "id": "Longdefinition", "value": "Prevalence of undernourishments is the percentage of the population whose habitual food consumption is insufficient to provide the dietary energy levels that are required to maintain a normal active and healthy life. Data showing as 2.5 may signify a prevalence of undernourishment below 2.5%." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization of the United Nations (FAO), uri: http://www.fao.org/faostat/en/#home" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Data on undernourishment are from the Food and Agriculture Organization (FAO) of the United Nations and measure food deprivation based on average food available for human consumption per person, the level of inequality in access to food, and the minimum calories required for an average person.\nStatistical concept(s): Data on undernourishment are from the Food and Agriculture Organization (FAO) of the United Nations and measure food deprivation based on average food available for human consumption per person, the level of inequality in access to food, and the minimum calories required for an average person." }, { "id": "Topic", "value": "Social: Health & Nutrition" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "75" }, { "id": "SP.DYN.LE00.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries." }, { "id": "IndicatorName", "value": "Life expectancy at birth, total (years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), uri: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices, note: Derived from male and female life expectancy at birth from sources such as statistical databases and publications from national statistical offices.;\nDemographic Statistics, Eurostat (ESTAT), note: Derived from male and female life expectancy at birth from sources such as Eurostat: Demographic Statistics." }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates, or derived from male and female life expectancy at birth.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years." }, { "id": "Topic", "value": "Social: Demography" }, { "id": "Unitofmeasure", "value": "Years" } ], "source_id": "75" }, { "id": "SP.DYN.TFRT.IN", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries." }, { "id": "Generalcomments", "value": "Relevance to gender indicator: it can indicate the status of women within households and a woman’s decision about the number and spacing of children." }, { "id": "IndicatorName", "value": "Fertility rate, total (births per woman)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data." }, { "id": "Longdefinition", "value": "Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National statistical offices;\nDemographic Statistics, Eurostat (ESTAT)" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Total fertility rate is the sum of the age-specific fertility rates (multiplied by five, if the age-specific fertility rates are for 5-year age groups).\nStatistical concept(s): Total fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without reliable vital registration systems fertility rates are generally based on extrapolations from trends observed in censuses or surveys from earlier years." }, { "id": "Topic", "value": "Social: Demography" }, { "id": "Unitofmeasure", "value": "Births per woman" } ], "source_id": "75" }, { "id": "SP.POP.65UP.TO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Developmentrelevance", "value": "Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population." }, { "id": "IndicatorName", "value": "Population ages 65 and above (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Limitationsandexceptions", "value": "Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source." }, { "id": "Longdefinition", "value": "Population ages 65 and above as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Population Prospects, United Nations (UN), publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates." }, { "id": "Topic", "value": "Social: Demography" }, { "id": "Unitofmeasure", "value": "Percentage" } ], "source_id": "75" }, { "id": "SP.UWT.TFRT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "WDI" }, { "id": "Generalcomments", "value": "Unmet need for contraception measures the capacity women have in achieving their desired family size and birth spacing. Many couples in developing countries want to limit or postpone childbearing but are not using effective contraception. These couples have an unmet need for contraception. Common reasons are lack of knowledge about contraceptive methods and concerns about possible side effects." }, { "id": "IndicatorName", "value": "Unmet need for contraception (% of married women ages 15-49)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://creativecommons.org/licenses/by/4.0/" }, { "id": "Longdefinition", "value": "Unmet need for contraception is the percentage of fertile, married women of reproductive age who do not want to become pregnant and are not using contraception." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division" }, { "id": "Statisticalconceptandmethodology", "value": "Methodology: Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\nMany couples in developing countries want to limit or postpone childbearing but are not using effective contraception. These couples have an unmet need for contraception. Common reasons are lack of knowledge about contraceptive methods and concerns about possible side effects. This indicator excludes women not exposed to the risk of unintended pregnancy because of menopause, infertility, or postpartum anovulation." }, { "id": "Topic", "value": "Governance: Gender" } ], "source_id": "75" }, { "id": "VA.EST", "metatype": [ { "id": "IndicatorName", "value": "Voice and Accountability: Estimate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Voice and Accountability captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org.The WGI are produced by Daniel Kaufmann (Natural Resource Governance Institute and Brookings Institution) and Aart Kraay (World Bank Development Research Group). Please cite Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2010). \"The Worldwide Governance Indicators: Methodology and Analytical Issues\". World Bank Policy Research Working Paper No. 5430 (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130). The WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent." }, { "id": "Topic", "value": "Governance: Human Rights" } ], "source_id": "75" }, { "id": "1000000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers individual consumption expenditure by households; individual consumption expenditure by nonprofit institutions serving households (NPISHs); individual consumption expenditure by government; collective consumption expenditure by government; gross capital formation; balance of exports and imports." } ], "source_id": "78" }, { "id": "1101000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Rice; Other cereals, flour and other cereal products; Bread; Other bakery products; Pasta products and couscous; Beef and veal; Pork; Lamb, mutton and goat; Poultry; Other meats and meat preparations; Fresh, chilled or frozen fish and seafood; Preserved or processed fish and seafood; Fresh milk; Preserved milk and other milk products; Cheese and curd; Eggs and egg-based products; Butter and margarine; Other edible oils and fats; Fresh or chilled fruit; Frozen, preserved or processed fruit and fruit-based products; Fresh or chilled vegetables, other than potatoes and other tuber vegetables; Fresh or chilled potatoes and other tuber vegetables; Frozen, preserved or processed vegetables and vegetable-based products; Sugar; Jams, marmalades and honey; Confectionery, chocolate and ice cream; Food products n.e.c.; Coffee, tea and cocoa; Mineral waters, soft drinks, fruit and vegetable juices." } ], "source_id": "78" }, { "id": "1101100", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Rice; Other cereals, flour and other cereal products; Bread; Other bakery products; Pasta products and couscous; Beef and veal; Pork; Lamb, mutton and goat; Poultry; Other meats and meat preparations; Fresh, chilled or frozen fish and seafood; Preserved or processed fish and seafood; Fresh milk; Preserved milk and other milk products; Cheese and curd; Eggs and egg-based products; Butter and margarine; Other edible oils and fats; Fresh or chilled fruit; Frozen, preserved or processed fruit and fruit-based products; Fresh or chilled vegetables, other than potatoes and other tuber vegetables; Fresh or chilled potatoes and other tuber vegetables; Frozen, preserved or processed vegetables and vegetable-based products; Sugar; Jams, marmalades and honey; Confectionery, chocolate and ice cream; Food products n.e.c." } ], "source_id": "78" }, { "id": "1101110", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Rice; Other cereals, flour and other cereal products; Bread; Other bakery products; Pasta products and couscous." } ], "source_id": "78" }, { "id": "1101120", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Beef and veal; Pork; Lamb, mutton and goat; Poultry; Other meats and meat preparations." } ], "source_id": "78" }, { "id": "1101130", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Fresh, chilled or frozen fish and seafood; Preserved or processed fish and seafood." } ], "source_id": "78" }, { "id": "1101140", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Fresh milk; Preserved milk and other milk products; Cheese and curd; Eggs and egg-based products." } ], "source_id": "78" }, { "id": "1101150", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Butter and margarine; Other edible oils and fats." } ], "source_id": "78" }, { "id": "1101160", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Fresh or chilled fruit; Frozen, preserved or processed fruit and fruit-based products." } ], "source_id": "78" }, { "id": "1101170", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Fresh or chilled vegetables, other than potatoes and other tuber vegetables; Fresh or chilled potatoes and other tuber vegetables; Frozen, preserved or processed vegetables and vegetable-based products." } ], "source_id": "78" }, { "id": "1101180", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Sugar; Jams, marmalades and honey; Confectionery, chocolate and ice cream." } ], "source_id": "78" }, { "id": "1101190", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Food products n.e.c." } ], "source_id": "78" }, { "id": "1101200", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Coffee, tea and cocoa; Mineral waters, soft drinks, fruit and vegetable juices." } ], "source_id": "78" }, { "id": "1102000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Spirits; Wine; Beer; Tobacco; Narcotics." } ], "source_id": "78" }, { "id": "1102100", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Spirits; Wine; Beer." } ], "source_id": "78" }, { "id": "1102200", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Tobacco." } ], "source_id": "78" }, { "id": "1103000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Clothing materials, other articles of clothing and clothing accessories; Garments; Cleaning, repair and hire of clothing; Shoes and other footwear; Repair and hire of footwear." } ], "source_id": "78" }, { "id": "1105000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Furniture and furnishings; Carpets and other floor coverings; Repair of furniture, furnishings and floor coverings; Household textiles; Major household appliances whether electric or not; Small electric household appliances; Repair of household appliances; Glassware, tableware and household utensils; Major tools and equipment; Small tools and miscellaneous accessories; Non-durable household goods; Domestic services; Household services." } ], "source_id": "78" }, { "id": "1107000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Motor cars; Motor cycles; Bicycles; Animal drawn vehicles; Fuels and lubricants for personal transport equipment; Maintenance and repair of personal transport equipment; Other services in respect of personal transport equipment; Passenger transport by railway; Passenger transport by road; Passenger transport by air; Passenger transport by sea and inland waterway; Combined passenger transport; Other purchased transport services." } ], "source_id": "78" }, { "id": "1107100", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Motor cars; Motor cycles; Bicycles; Animal drawn vehicles." } ], "source_id": "78" }, { "id": "1107300", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Passenger transport by railway; Passenger transport by road; Passenger transport by air; Passenger transport by sea and inland waterway; Combined passenger transport; Other purchased transport services." } ], "source_id": "78" }, { "id": "1108000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Postal services; Telephone and telefax equipment; Telephone and telefax services." } ], "source_id": "78" }, { "id": "1111000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Catering services; Accommodation services." } ], "source_id": "78" }, { "id": "1113000", "metatype": [ { "id": "IndicatorName", "value": "Zero national accounts expenditure value may represent that the value for this heading is allocated under other GDP expenditure headings based on the national implementing agency's best judgement. This ICP classification heading covers expenditures for Net purchases abroad." } ], "source_id": "78" }, { "id": "1300000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers individual consumption expenditures by government for Housing; Pharmaceutical products; Other medical products; Therapeutic appliances and equipment; Out-patient medical services; Out-patient dental services; Out-patient paramedical services; Hospital services; Compensation of employees - Ind. Hth. Govt; Intermediate consumption - Ind. Hth. Govt; Gross operating surplus - Ind. Hth. Govt; Net taxes on production - Ind. Hth. Govt; Receipts from sales - Ind. Hth. Govt; Recreation and culture; Education benefits and reimbursements; Compensation of employees - Ind. Edu. Govt; Intermediate consumption - Ind. Edu. Govt; Gross operating surplus - Ind. Edu. Govt; Net taxes on production - Ind. Edu. Govt; Receipt from sales - Ind. Edu. Govt; Social protection;" } ], "source_id": "78" }, { "id": "1400000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers collective consumption expenditures by government for Compensation of employees - Coll. Govt; Intermediate consumption - Coll. Govt; Gross operating surplus - Coll. Govt; Net taxes on production - Coll. Govt; Receipts from sales - Coll. Govt." } ], "source_id": "78" }, { "id": "1500000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Fabricated metal products, except machinery and equipment; Electrical and optical equipment; General purpose machinery; Special purpose machinery; Road transport equipment; Other transport equipment; Residential buildings; Non-residential buildings; Civil engineering works; Other products; Change in inventories; Acquisitions less disposals of valuables." } ], "source_id": "78" }, { "id": "1501000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Fabricated metal products, except machinery and equipment; Electrical and optical equipment; General purpose machinery; Special purpose machinery; Road transport equipment; Other transport equipment; Residential buildings; Non-residential buildings; Civil engineering works; Other products." } ], "source_id": "78" }, { "id": "1501100", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Fabricated metal products, except machinery and equipment; Electrical and optical equipment; General purpose machinery; Special purpose machinery; Road transport equipment; Other transport equipment." } ], "source_id": "78" }, { "id": "1501200", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Residential buildings; Non-residential buildings; Civil engineering works." } ], "source_id": "78" }, { "id": "1501300", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Other products." } ], "source_id": "78" }, { "id": "1502000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Change in inventories." } ], "source_id": "78" }, { "id": "1503000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Acquisitions less disposals of valuables." } ], "source_id": "78" }, { "id": "1600000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Exports of goods and services; Imports of goods and services." } ], "source_id": "78" }, { "id": "9020000", "metatype": [ { "id": "IndicatorName", "value": "The total value of the individual consumption expenditures of households, nonprofit institutions serving households (NPISHs), and government at purchasers’ prices." } ], "source_id": "78" }, { "id": "9060000", "metatype": [ { "id": "IndicatorName", "value": "Household expenditure on actual and imputed rentals for housing; maintenance and repair of the dwelling; water supply and services related to the dwelling; and electricity, gas, and other fuels plus expenditure by nonprofit institutions serving households (NPISHs) on housing plus general government expenditure on housing services provided to individuals." } ], "source_id": "78" }, { "id": "9080000", "metatype": [ { "id": "IndicatorName", "value": "Household expenditure on pharmaceuticals; medical products, appliances, and equipment; outpatient services; and hospital services plus expenditure of nonprofit institutions serving households (NPISHs) on health plus general government expenditure on health benefits and reimbursements, and the production of health services." } ], "source_id": "78" }, { "id": "9100000", "metatype": [ { "id": "IndicatorName", "value": "The total value of actual and imputed final consumption expenditures incurred by households and NPISHs on individual goods and services. It also includes expenditures on individual goods and services sold at prices that are not economically significant." } ], "source_id": "78" }, { "id": "9110000", "metatype": [ { "id": "IndicatorName", "value": "Household expenditure on audiovisual, photographic, and information processing equipment; other major durables for recreation and culture; other recreational items and equipment; gardens and pets; recreational and cultural services; newspapers, books, and stationery; and package holidays plus expenditure by nonprofit institutions serving households (NPISHs) on recreation and culture plus general government expenditure on recreation and culture" } ], "source_id": "78" }, { "id": "9120000", "metatype": [ { "id": "IndicatorName", "value": "Household expenditure on pre-primary, primary, secondary, postsecondary, and tertiary education plus expenditure of nonprofit institutions serving households (NPISHs) on education plus general government expenditure on education benefits and reimbursements and the production of education services." } ], "source_id": "78" }, { "id": "9140000", "metatype": [ { "id": "IndicatorName", "value": "Household expenditure on personal care, personal effects, social protection, insurance, and financial and other services plus expenditure by nonprofit institutions serving households (NPISHs) on social protection and other services plus general government expenditure on social protection." } ], "source_id": "78" }, { "id": "9250000", "metatype": [ { "id": "IndicatorName", "value": "Actual individual consumption at purchasers’ prices plus collective consumption expenditure by government at purchasers’ prices plus gross capital formation at purchasers’ prices." } ], "source_id": "78" }, { "id": "9260000", "metatype": [ { "id": "IndicatorName", "value": "The total value of actual and imputed final consumption expenditures incurred by households and NPISHs on individual goods and services, without housing related expenditures. It also includes expenditures on individual goods and services sold at prices that are not economically significant." } ], "source_id": "78" }, { "id": "9270000", "metatype": [ { "id": "IndicatorName", "value": "The total value of actual and imputed final consumption expenditures incurred by government on individual goods and services and final consumption expenditure of government on collective services." } ], "source_id": "78" }, { "id": "BM.GSR.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Imports of goods, services and primary income (current US$)" }, { "id": "Longdefinition", "value": "Imports of goods, services and income is the sum of goods (merchandise) imports, imports of (nonfactor) services and primary income (factor) payments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Imports of goods, services and primary income is the sum of goods (merchandise) imports, imports of (nonfactor) services and income (factor) payments. Data are in current U.S. dollars." }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" } ], "source_id": "81" }, { "id": "BN.CAB.XOKA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Current account balance (current US$)" }, { "id": "Longdefinition", "value": "Current account balance shows the difference between the sum of exports and income receivable and the sum of imports and income payable (exports and imports refer to both goods and services, while income refers to both primary and secondary income)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Current account balance shows the difference between the sum of exports and income receivable and the sum of imports and income payable (exports and imports refer to both goods and services, while income refers to both primary and secondary income)." }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Balances" } ], "source_id": "81" }, { "id": "BX.GRT.EXTA.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Grants, excluding technical cooperation (current US$)" }, { "id": "Longdefinition", "value": "Grants are defined as legally binding commitments that obligate a specific value of funds available for disbursement for which there is no repayment requirement. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Grants are defined as legally binding commitments that obligate a specific value of funds available for disbursement for which there is no repayment requirement. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "81" }, { "id": "BX.GRT.TECH.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Technical cooperation grants (current US$)" }, { "id": "Longdefinition", "value": "Technical cooperation grants include free-standing technical cooperation grants, which are intended to finance the transfer of technical and managerial skills or of technology for the purpose of building up general national capacity without reference to any specific investment projects; and investment-related technical cooperation grants, which are provided to strengthen the capacity to execute specific investment projects. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Technical cooperation grants include free-standing technical cooperation grants, which are intended to finance the transfer of technical and managerial skills or of technology for the purpose of building up general national capacity without reference to any specific investment projects; and investment-related technical cooperation grants, which are provided to strengthen the capacity to execute specific investment projects. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "81" }, { "id": "BX.GSR.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Exports of goods, services and primary income (current US$)" }, { "id": "Longdefinition", "value": "Exports of goods, services and income is the sum of goods (merchandise) exports, exports of (nonfactor) services and primary income (factor) receipts. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Exports of goods, services and primary income is the sum of goods (merchandise) exports, exports of (nonfactor) services and income (factor) receipts. Data are in current U.S. dollars." }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Goods, services & income" } ], "source_id": "81" }, { "id": "BX.KLT.DINV.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "Note: Data starting from 2005 are based the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "IndicatorName", "value": "Foreign direct investment, net inflows in reporting economy (DRS, current US$)" }, { "id": "Longdefinition", "value": "Foreign direct investment (net) shows the net change in foreign investment in the reporting country. Foreign direct investment is defined as investment that is made to acquire a lasting management interest (usually of 10 percent of voting stock) in an enterprise operating in a country other than that of the investor (defined according to residency), the investor's purpose being an effective voice in the management of the enterprise. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows net inflows in the reporting economy. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Foreign direct investment is net inflows of investment to acquire a lasting interest in or management control over an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvested earnings, other long-term capital, and short-term capital, as shown in the balance of payments. Data are in current U.S. dollars." }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments, supplemented by data from United Nations Conference on Trade and Development and official national sources. Data starting from 2005 are based the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "81" }, { "id": "BX.KLT.DREM.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Primary income on FDI (current US$)" }, { "id": "Longdefinition", "value": "Primary income on foreign direct investment covers payments of direct investment income (debit side), which consist of income on equity (dividends, branch profits, and reinvested earnings) and income on the intercompany debt (interest). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Primary income on foreign direct investment covers payments of direct investment income (debit side), which consist of income on equity (dividends, branch profits, and reinvested earnings) and income on the intercompany debt (interest). Data are in current U.S. dollars." }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "81" }, { "id": "BX.PEF.TOTL.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "Note: Data starting from 2005 are based the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "IndicatorName", "value": "Portfolio investment, equity (DRS, current US$)" }, { "id": "Longdefinition", "value": "Portfolio equity includes net inflows from equity securities other than those recorded as direct investment and including shares, stocks, depository receipts (American or global), and direct purchases of shares in local stock markets by foreign investors. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Portfolio equity includes net inflows from equity securities other than those recorded as direct investment and including shares, stocks, depository receipts (American or global), and direct purchases of shares in local stock markets by foreign investors. Data are in current U.S. dollars." }, { "id": "Source", "value": "International Monetary Fund, Balance of Payments Statistics Yearbook. Data starting from 2005 are based the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Capital & financial account" } ], "source_id": "81" }, { "id": "BX.TRF.PWKR.CD.DT", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "Note: Data starting from 2005 are based the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "IndicatorName", "value": "Personal transfers and compensation of employees, received (current US$)" }, { "id": "Longdefinition", "value": "Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual(BPM6): personal transfers and compensation of employees. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual (BPM6): personal transfers and compensation of employees. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank staff estimates based on IMF balance of payments data. Data starting from 2005 are based the sixth edition of the IMF's Balance of Payments Manual (BPM6)." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Current account: Transfers" } ], "source_id": "81" }, { "id": "DT.AMT.BLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves.Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves.Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.BLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.BLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.BLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.BLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.BLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves.Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves.Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.BLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "General goverment bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General goverment bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.BLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.BLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.BLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.DECB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, central bank (PPG) (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank long-term debt are aggregated. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank long-term debt are aggregated. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.DEGG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, general government sector (PPG) (AMT, current US$)" }, { "id": "Longdefinition", "value": "General government long-term debt are aggregated. General government debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government long-term debt are aggregated. General government debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.DEPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, public sector (PPG) (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector long-term debt are aggregated. Public sector debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector long-term debt are aggregated. Public sector debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "IMF repurchases (AMT, current US$)" }, { "id": "Longdefinition", "value": "IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.DLTF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, long-term + IMF (AMT, current US$)" }, { "id": "Longdefinition", "value": "Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. This item includes principal repayments on long-term debt and IMF repurchases. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. To maintain comparability between data on transactions with the IMF and data on long-term debt, use of IMF credit outstanding at the end of year (stock) is converted to dollars at the SDR exchange rate in effect at the end of year. Repurchases (flows) are converted at the average SDR exchange rate for the year in which transactions take place. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. This item includes principal repayments on long-term debt and IMF repurchases. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. To maintain comparability between data on transactions with the IMF and data on long-term debt, use of IMF credit outstanding at the end of year (stock) is converted to dollars at the SDR exchange rate in effect at the end of year. Repurchases (flows) are converted at the average SDR exchange rate for the year in which transactions take place. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, long-term (AMT, current US$)" }, { "id": "Longdefinition", "value": "Principal repayments on long-term debt are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal repayments on long-term debt are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.DOPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, other public sector (PPG) (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector long-term debt are aggregated. Other public sector debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector long-term debt are aggregated. Other public sector debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, private nonguaranteed (PNG) (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, public and publicly guaranteed (PPG) (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.MLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.MLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.MLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.MLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.MLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.MLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.MLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.MLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.MLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.MLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral concessional (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.OFFT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, official creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.OFFT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, official creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.OFFT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, official creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.OFFT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, official creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.OFFT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, official creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PBND.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bonds (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PBND.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bonds (AMT, current US$)" }, { "id": "Longdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PBND.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bonds (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PBND.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bonds (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from bonds that are either publicly issued or privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from bonds that are either publicly issued or privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PBND.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bonds (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PCBK.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, commercial banks (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PCBK.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, commercial banks (AMT, current US$)" }, { "id": "Longdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PCBK.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, commercial banks (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PCBK.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, commercial banks (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PCBK.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, commercial banks (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, bonds (AMT, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PROP.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, other private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, other private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PROP.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, other private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PROP.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, other private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PROP.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, other private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PROP.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, other private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PRPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal repayments on external debt, private guaranteed by public sector (PPG) (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector long-term debt are aggregated.Private sector guaranteed by Public Sector debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector long-term debt are aggregated.Private sector guaranteed by Public Sector debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PRVT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from private creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from private creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PRVT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "General government debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PRVT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PRVT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AMT.PRVT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, private creditors (AMT, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Amortization" } ], "source_id": "81" }, { "id": "DT.AXA.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal arrears, long-term DOD (current US$)" }, { "id": "Longdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.AXA.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal arrears, official creditors (current US$)" }, { "id": "Longdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.AXA.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal arrears, private creditors (current US$)" }, { "id": "Longdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal in arrears on long-term debt is defined as principal repayment due but not paid, on a cumulative basis. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.AXF.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal forgiven (current US$)" }, { "id": "Longdefinition", "value": "Principal forgiven is the amount of principal due or in arrears that was written off or forgiven in any given year. It includes debt forgiven within and outside Paris Club agreements, principal forgiven and principal arrears forgiven. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal forgiven is the amount of principal due or in arrears that was written off or forgiven in any given year. It includes debt forgiven within and outside Paris Club agreements, principal forgiven and principal arrears forgiven. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.AXR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal rescheduled (current US$)" }, { "id": "Longdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.AXR.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal rescheduled, official (current US$)" }, { "id": "Longdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.AXR.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Principal rescheduled, private (current US$)" }, { "id": "Longdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Principal rescheduled is the amount of principal due or in arrears that was rescheduled in any given year. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.COM.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, bilateral creditors (COM, current US$)" }, { "id": "Longdefinition", "value": "Bilateral commitments are the total amount of long-term loans for which contracts were signed in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral commitments are the total amount of long-term loans for which contracts were signed in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "81" }, { "id": "DT.COM.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, public and publicly guaranteed (COM, current US$)" }, { "id": "Longdefinition", "value": "Commitments are the total amount of long-term loans for which contracts were signed in the year specified; data for private nonguaranteed debt are not available. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Commitments are the total amount of long-term loans for which contracts were signed in the year specified; data for private nonguaranteed debt are not available. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "81" }, { "id": "DT.COM.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, IBRD (COM, current US$)" }, { "id": "Longdefinition", "value": "Commitments (IBRD) are the sum of new commitments on public and publicly guaranteed loans from the International Bank for Reconstruction and Development (IBRD). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Commitments (IBRD) are the sum of new commitments on public and publicly guaranteed loans from the International Bank for Reconstruction and Development (IBRD). Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "81" }, { "id": "DT.COM.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, IDA (COM, current US$)" }, { "id": "Longdefinition", "value": "Commitments (IDA) are the sum of new commitments on public and publicly guaranteed loans from the International Development Association (IDA). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Commitments (IDA) are the sum of new commitments on public and publicly guaranteed loans from the International Development Association (IDA). Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "81" }, { "id": "DT.COM.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, multilateral creditors (COM, current US$)" }, { "id": "Longdefinition", "value": "Multirateral commitments are the total amount of long-term loans for which contracts were signed in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Multirateral commitments are the total amount of long-term loans for which contracts were signed in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "81" }, { "id": "DT.COM.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, official creditors (COM, current US$)" }, { "id": "Longdefinition", "value": "Commitments are the amount of long-term loans for which contracts were signed in the year specified. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Commitments are the amount of long-term loans for which contracts were signed in the year specified. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "81" }, { "id": "DT.COM.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Commitments, private creditors (COM, current US$)" }, { "id": "Longdefinition", "value": "Commitments are the amount of long-term loans for which contracts were signed in the year specified; data for private nonguaranteed debt are not available. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Commitments are the amount of long-term loans for which contracts were signed in the year specified; data for private nonguaranteed debt are not available. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Commitments" } ], "source_id": "81" }, { "id": "DT.CUR.DMAK.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Deutsche mark (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Deutsche marks for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Deutsche marks for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "81" }, { "id": "DT.CUR.EURO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Euro (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Euros for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Euros for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "81" }, { "id": "DT.CUR.FFRC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, French franc (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in French francs for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in French francs for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "81" }, { "id": "DT.CUR.JYEN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Japanese yen (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Japanese yen for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Japanese yen for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "81" }, { "id": "DT.CUR.MULC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Multiple currencies (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in multiple currencies for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in multiple currencies for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "81" }, { "id": "DT.CUR.OTHC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, all other currencies (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in all other currencies not specified for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in all other currencies not specified for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "81" }, { "id": "DT.CUR.SDRW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, SDR (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in special drawing rights for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in special drawing rights for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "81" }, { "id": "DT.CUR.SWFR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Swiss franc (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Swiss francs for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in Swiss francs for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "81" }, { "id": "DT.CUR.UKPS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, Pound sterling (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in U.K. pound sterling for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in U.K. pound sterling for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "81" }, { "id": "DT.CUR.USDL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Currency composition of PPG debt, U.S. dollars (%)" }, { "id": "Longdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in U.S. dollars for the low- and middle-income countries." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The percentage of external long-term public and publicly-guaranteed debt contracted in U.S. dollars for the low- and middle-income countries." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Currency composition" } ], "source_id": "81" }, { "id": "DT.DFR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt forgiveness or reduction (current US$)" }, { "id": "Longdefinition", "value": "Debt forgiveness or reduction shows the change in debt stock due to debt forgiveness or reduction. It is derived by subtracting debt forgiven and debt stock reduction from debt buyback. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt forgiveness or reduction shows the change in debt stock due to debt forgiveness or reduction. It is derived by subtracting debt forgiven and debt stock reduction from debt buyback. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.DIS.BLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.BLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.BLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.BLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.BLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.BLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.BLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.BLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.BLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.BLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.DECB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, central bank (PPG) (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank long-term debt are aggregated. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank long-term debt are aggregated. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.DEGG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, general government sector (PPG) (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.DEPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, public sector (PPG) (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "IMF purchases (DIS, current US$)" }, { "id": "Longdefinition", "value": "IMF purchases are total drawings on the General Resources Account of the IMF during the year specified, excluding drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "IMF purchases are total drawings on the General Resources Account of the IMF during the year specified, excluding drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.DLTF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, long-term + IMF (DIS, current US$)" }, { "id": "Longdefinition", "value": "Disbursements are drawings by the borrower on loan commitments during the year specified. This item includes disbursements on long-term debt and IMF purchases. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. IMF purchases are total drawings on the General Resources Account of the IMF during the year specified, excluding drawings in the reserve tranche. To maintain comparability between data on transactions with the IMF and data on long-term debt, use of IMF credit outstanding at the end of year (stock) is converted to dollars at the SDR exchange rate in effect at the end of year. Purchases are converted at the average SDR exchange rate for the year in which transactions take place. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Disbursements are drawings by the borrower on loan commitments during the year specified. This item includes disbursements on long-term debt and IMF purchases. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. IMF purchases are total drawings on the General Resources Account of the IMF during the year specified, excluding drawings in the reserve tranche. To maintain comparability between data on transactions with the IMF and data on long-term debt, use of IMF credit outstanding at the end of year (stock) is converted to dollars at the SDR exchange rate in effect at the end of year. Purchases are converted at the average SDR exchange rate for the year in which transactions take place. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, long-term (DIS, current US$)" }, { "id": "Longdefinition", "value": "Disbursements on long-term debt are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Disbursements on long-term debt are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.DOPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, other public sector (PPG) (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, private nonguaranteed (PNG) (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, public and publicly guaranteed (PPG) (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.IDAG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "IDA grants (current US$)" }, { "id": "Longdefinition", "value": "IDA grants are net disbursements of grants from the International Development Association (IDA). Data are in current U.S. dollars. Regional allocations are included in aggregate data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "IDA grants are net disbursements of grants from the International Development Association (IDA). Data are in current U.S. dollars. Regional allocations are included in aggregate data." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "81" }, { "id": "DT.DIS.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.MLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.MLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.MLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.MLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.MLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.MLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.MLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "General goverment multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.MLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.MLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.MLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral concessional (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.OFFT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, official creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.OFFT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, official creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.OFFT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, official creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.OFFT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, official creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.OFFT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, official creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PBND.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bonds (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PBND.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bonds (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PBND.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bonds (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PBND.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bonds (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PBND.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bonds (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PCBK.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, commercial banks (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PCBK.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, commercial banks (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PCBK.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, commercial banks (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PCBK.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, commercial banks (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PCBK.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, commercial banks (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, bonds (DIS, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PROP.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, other private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, other private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PROP.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, other private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PROP.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, other private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PROP.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, other private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PROP.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, other private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PRPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Disbursements on external debt, private guaranteed by public sector (PPG) (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Disbursements are drawings by the borrower on loan commitments during the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PRVT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from private creditors.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from private creditors.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PRVT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PRVT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PRVT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DIS.PRVT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, private creditors (DIS, current US$)" }, { "id": "Longdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Disbursements are drawings by the borrower on loan commitments during the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Disbursements" } ], "source_id": "81" }, { "id": "DT.DOD.ALLC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Concessional external debt conveys information about the borrower's receipt of aid from official lenders at concessional terms as defined by the Development Assistance Committee (DAC) of the OECD. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Loans from major regional development banks--African Development Bank, Asian Development Bank, and the Inter-American Development Bank--and from the World Bank are classified as concessional according to each institution's classification and not according to the DAC definition, as was the practice in earlier reports. Long-term debt outstanding and disbursed is the total outstanding long-term debt at year end. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Concessional external debt conveys information about the borrower's receipt of aid from official lenders at concessional terms as defined by the Development Assistance Committee (DAC) of the OECD. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Loans from major regional development banks--African Development Bank, Asian Development Bank, and the Inter-American Development Bank--and from the World Bank are classified as concessional according to each institution's classification and not according to the DAC definition, as was the practice in earlier reports. Long-term debt outstanding and disbursed is the total outstanding long-term debt at year end. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.ALLC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Concessional debt (% of total external debt)" }, { "id": "Longdefinition", "value": "Concessional debt to total external debt stocks. Concessional debt is defined as loans with an original grant element of 35 percent or more." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Concessional debt to total external debt stocks. Concessional debt is defined as loans with an original grant element of 35 percent or more." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "81" }, { "id": "DT.DOD.BLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.BLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "General Government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General Government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.BLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Other Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.BLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.BLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.BLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.BLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.BLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.BLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.BLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Principal repayments are actual amounts of principal (amortization) paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.DECB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, central bank (PPG) (DOD, current US$)" }, { "id": "Longdefinition", "value": "Central Bank debt position at end of the reference period. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves.Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central Bank debt position at end of the reference period. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, total (DOD, current US$)" }, { "id": "Longdefinition", "value": "Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total external debt is debt owed to nonresidents repayable in currency, goods, or services. It is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, short-term debt, and use of IMF credit. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.DECT.CD.CG", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Total change in external debt stocks (current US$)" }, { "id": "Longdefinition", "value": "Total change in debt stocks shows the variation in debt stock between two consecutive years. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total change in debt stocks shows the variation in debt stock between two consecutive years. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.DOD.DECT.EX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "The denominator for this indicator in previous versions of Global Development Finance included workers' remittances. Workers' remittances are no longer included." }, { "id": "IndicatorName", "value": "External debt stocks (% of exports of goods, services and primary income)" }, { "id": "Longdefinition", "value": "Total external debt stocks to exports of goods, services and primary income." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total external debt stocks to exports of goods, services and primary income." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "81" }, { "id": "DT.DOD.DECT.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks (% of GNI)" }, { "id": "Longdefinition", "value": "Total external debt stocks to gross national income. Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total external debt stocks to gross national income." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "81" }, { "id": "DT.DOD.DECT.PC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Total external debt per capita (current US$)" }, { "id": "Longdefinition", "value": "Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total external debt is debt owed to nonresidents repayable in currency, goods, or services. It is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, short-term debt, and use of IMF credit. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.DEGG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, general government sector (PPG) (DOD, current US$)" }, { "id": "Longdefinition", "value": "General Government Sector comprises long-term external obligations of public debtors, including the national government of all levels, and political subdivisions (or an agency of either). Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General Government Sector comprises long-term external obligations of public debtors, including the national government of all levels, and political subdivisions (or an agency of either). Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.DEPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, public sector (PPG) (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt comprises long-term external obligations of public debtors, including the national government of all levels, political subdivisions (or an agency of either), autonomous public bodies such as Public Corporations, State Owned Enterprises, Development Banks and Other Mixed Enterprises. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt comprises long-term external obligations of public debtors, including the national government of all levels, political subdivisions (or an agency of either), autonomous public bodies such as Public Corporations, State Owned Enterprises, Development Banks and Other Mixed Enterprises. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Use of IMF credit (DOD, current US$)" }, { "id": "Longdefinition", "value": "Use of IMF Credit: Data related to the operations of the IMF are provided by the IMF Treasurer’s Department. They are converted from special drawing rights into dollars using end-of-period exchange rates for stocks and average-over-the-period exchange rates for flows. IMF trust fund operations under the Enhanced Structural Adjustment Facility, Extended Fund Facility, Poverty Reduction and Growth Facility, and Structural Adjustment Facility (Enhanced Structural Adjustment Facility in 1999) are presented together with all of the IMF’s special facilities (buffer stock, supplemental reserve, compensatory and contingency facilities, oil facilities, and other facilities). SDR allocations are also included in this category. According to the BPM6, SDR allocations are recorded as the incurrence of a debt liability of the member receiving them (because of a requirement to repay the allocation in certain circumstances, and also because interest accrues). This debt item is introduced for the first time this year with historical data starting in 1999." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Use of IMF credit denotes members’ drawings on the IMF other than amounts drawn against the country’s reserve tranche position. Use of IMF credit includes purchases and drawings under Stand-By, Extended, Structural Adjustment, Enhanced Structural Adjustment, and Systemic Transformation Facility Arrangements as well as Trust Fund loans. SDR allocations are also included in this category." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, long-term (DOD, current US$)" }, { "id": "Longdefinition", "value": "Long-term debt is debt that has an original or extended maturity of more than one year. It has three components: public, publicly guaranteed, and private nonguaranteed debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Long-term debt is debt that has an original or extended maturity of more than one year. It has three components: public, publicly guaranteed, and private nonguaranteed debt. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.DOPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, other public sector (PPG) (DOD, current US$)" }, { "id": "Longdefinition", "value": "Other Public Sector debt comprises long-term external obligations of public debtors, excluding general government. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other Public Sector debt comprises long-term external obligations of public debtors, excluding general government. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, private nonguaranteed (PNG) (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt comprises long-term external obligations of private debtors that are not guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed external debt comprises long-term external obligations of private debtors that are not guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, public and publicly guaranteed (PPG) (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt comprises long-term external obligations of public debtors, including the national government, Public Corporations, State Owned Enterprises, Development Banks and Other Mixed Enterprises, political subdivisions (or an agency of either), autonomous public bodies, and external obligations of private debtors that are guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt comprises long-term external obligations of public debtors, including the national government, Public Corporations, State Owned Enterprises, Development Banks and Other Mixed Enterprises, political subdivisions (or an agency of either), autonomous public bodies, and external obligations of private debtors that are guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.DSDR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Use of IMF credit, SDR allocations (DOD, current US$)" }, { "id": "Longdefinition", "value": "SDR allocations are also included in this category. According to the BPM6, SDR allocations are recorded as the incurrence of a debt liability of the member receiving them (because of a requirement to repay the allocation in certain circumstances, and also because interest accrues). This debt item is introduced for the first time this year with historical data starting in 1999." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDR allocations are also included in this category. According to the BPM6, SDR allocations are recorded as the incurrence of a debt liability of the member receiving them (because of a requirement to repay the allocation in certain circumstances, and also because interest accrues). This debt item is introduced for the first time this year with historical data starting in 1999." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.DSTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, short-term (DOD, current US$)" }, { "id": "Longdefinition", "value": "Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.DSTC.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Short-term debt (% of total external debt)" }, { "id": "Longdefinition", "value": "Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "81" }, { "id": "DT.DOD.MDRI.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "The aggregate figure for all developing countries is sourced from OECD and includes all OECD countries and regions." }, { "id": "IndicatorName", "value": "Debt forgiveness grants (current US$)" }, { "id": "Longdefinition", "value": "Debt forgiveness grants data cover both debt cancelled by agreement between debtor and creditor and a reduction in the net present value of non-ODA debt achieved by concessional rescheduling or refinancing. The data are on a disbursement basis and cover flows from all bilateral and multilateral donors. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt forgiveness grants data cover both debt cancelled by agreement between debtor and creditor and a reduction in the net present value of non-ODA debt achieved by concessional rescheduling or refinancing. The data are on a disbursement basis and cover flows from all bilateral and multilateral donors. Data are in current U.S. dollars." }, { "id": "Source", "value": "Development Assistance Committee of the Organisation for Economic Co-operation and Development." }, { "id": "Topic", "value": "Economic Policy & Debt: Official development assistance" } ], "source_id": "81" }, { "id": "DT.DOD.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.MLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.MLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.MLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.MLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.MLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral creditors are international financial institutions such as the World Bank, regional development banks, and other multilateral and intergovernmental agencies whose lending is administered on a multilateral basis. Funds administered by an international financial organization on behalf of a single donor government constitute bilateral loans (or grants). For lending by a number of multilateral creditors, the data presented in this publication are taken from the creditors’ records. Such creditors include the African Development Bank, the Asian Development Bank, the IDB, IBRD, and IDA. (IBRD and IDA are institutions of the World Bank.) Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral creditors are international financial institutions such as the World Bank, regional development banks, and other multilateral and intergovernmental agencies whose lending is administered on a multilateral basis. Funds administered by an international financial organization on behalf of a single donor government constitute bilateral loans (or grants). For lending by a number of multilateral creditors, the data presented in this publication are taken from the creditors’ records. Such creditors include the African Development Bank, the Asian Development Bank, the IDB, IBRD, and IDA. (IBRD and IDA are institutions of the World Bank.) Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.MLAT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Multilateral debt (% of total external debt)" }, { "id": "Longdefinition", "value": "Multilateral debt to total external debt stocks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Multilateral debt to total external debt stocks." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "81" }, { "id": "DT.DOD.MLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.MLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.MLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.MLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.MLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral concessional (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public sector guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.OFFT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, official creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.OFFT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, official creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. 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Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.PRVT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "General government debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.PRVT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.PRVT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.PRVT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, private creditors (DOD, current US$)" }, { "id": "Longdefinition", "value": "Private creditors include commercial banks, bondholders, and other private creditors. This line includes only publicly guaranteed creditors. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.PUBS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, long-term public sector (DOD, current US$)" }, { "id": "Longdefinition", "value": "Long-term public sector external debt conveys information about the distribution of long-term debt for DRS countries by type of debtor (central government, state and local government, central bank, public and mixed enterprises, and official development banks). Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Long-term public sector external debt conveys information about the distribution of long-term debt for DRS countries by type of debtor (central government, state and local government, central bank, public and mixed enterprises, and official development banks). Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.PVLX.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Present value of external debt (current US$)" }, { "id": "Longdefinition", "value": "Present value of debt is the discounted sum of total debt service payments due on public and publicly guaranteed long-term external debt over the life of existing loans. IMF Special Drawing Rights are excluded. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan.\nData are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Present value of debt is the discounted sum of total debt service payments due on public and publicly guaranteed long-term external debt over the life of existing loans. IMF Special Drawing Rights are excluded. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan.\nData are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.PVLX.EX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Present value of external debt (% of exports of goods, services and income)" }, { "id": "Longdefinition", "value": "Present value of debt is the discounted sum of total debt service payments due on public and publicly guaranteed long-term external debt over the life of existing loans. IMF Special Drawing Rights are excluded. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan.\nThe exports denominator is a three-year average." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Present value of debt is the discounted sum of total debt service payments due on public and publicly guaranteed long-term external debt over the life of existing loans. IMF Special Drawing Rights are excluded. The exports denominator is a three-year average." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "81" }, { "id": "DT.DOD.PVLX.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Present value of external debt (% of GNI)" }, { "id": "Longdefinition", "value": "Present value of debt is the discounted sum of total debt service payments due on public and publicly guaranteed long-term external debt over the life of existing loans. IMF Special Drawing Rights are excluded. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan.\nThe GNI denominator is a three-year average." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Present value of debt is the discounted sum of total debt service payments due on public and publicly guaranteed long-term external debt over the life of existing loans. The GNI denominator is a three-year average." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "81" }, { "id": "DT.DOD.RSDL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Residual, debt stock-flow reconciliation (current US$)" }, { "id": "Longdefinition", "value": "The residual difference, i.e. the change in stock not explained by any of the factors identified under debt stock-flow reconciliation, is calculated as the sum of identified accounts minus the change in stock. Where the latter is large it can, in some cases, serve as an illustration of the inconsistencies in the reported data. More often however, it can be explained by specific borrowing phenomenon in individual countries. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The residual difference, i.e. the change in stock not explained by any of the factors identified under debt stock-flow reconciliation, is calculated as the sum of identified accounts minus the change in stock. Where the latter is large it can, in some cases, serve as an illustration of the inconsistencies in the reported data. More often however, it can be explained by specific borrowing phenomenon in individual countries. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.DOD.VPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, variable rate public and publicly guaranteed debt (PPG) (DOD, current US$)" }, { "id": "Longdefinition", "value": "Debt stock contracted at variable interest rate for public and publicly guaranteed long-term external debt with interest rates that float with movements in a key market rate; for example, the Secured Overnight Financing Rate (SOFR) or the Euribor. This item conveys information about the borrower's exposure to changes in international interest rates. Public and publicly guaranteed long-term long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt stock contracted at variable interest rate for public and publicly guaranteed long-term external debt with interest rates that float with movements in a key market rate; for example, the Secured Overnight Financing Rate (SOFR) or the Euribor. This item conveys information about the borrower's exposure to changes in international interest rates. Public and publicly guaranteed long-term long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DOD.VTOT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "External debt stocks, variable rate (DOD, current US$)" }, { "id": "Longdefinition", "value": "Debt stock contracted at variable interest rate for long-term external debt with interest rates that float with movements in a key market rate; for example, the Secured Overnight Financing Rate (SOFR) or the Euribor. This item conveys information about the borrower's exposure to changes in international interest rates. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt stock contracted at variable interest rate for long-term external debt with interest rates that float with movements in a key market rate; for example, the Secured Overnight Financing Rate (SOFR) or the Euribor. This item conveys information about the borrower's exposure to changes in international interest rates. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt outstanding" } ], "source_id": "81" }, { "id": "DT.DSB.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt buyback (current US$)" }, { "id": "Longdefinition", "value": "Debt buyback is the repurchase by a debtor of its own debt, discounted or at par. In the event of a buyback of long-term debt, the face value of the debt bought back will be recorded as a decline in the long-term debt stock, and the cash amount received by creditors will be recorded as a principal repayment. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt buyback is the repurchase by a debtor of its own debt, discounted or at par. In the event of a buyback of long-term debt, the face value of the debt bought back will be recorded as a decline in the long-term debt stock, and the cash amount received by creditors will be recorded as a principal repayment. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.DSF.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt stock reduction (current US$)" }, { "id": "Longdefinition", "value": "Debt stock reductions show the amount that has been netted out of the stock of debt using debt conversion schemes such as buybacks and equity swaps or the discounted value of long-term bonds that were issued in exchange for outstanding debt. It includes the effect of any financial operation that will reduce the debt stock other than debt stock restructuring, repayment of principal and debt forgiven. In particular, debt stock reduction will include the face value of debt bought back, the face value of debt swapped for equity (or \"nature\" or \"development\"), any face value reduction that might result as the consequence of a bond exchange, and any face value reduction resulting from an exchange of debt for discount bonds. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt stock reductions show the amount that has been netted out of the stock of debt using debt conversion schemes such as buybacks and equity swaps or the discounted value of long-term bonds that were issued in exchange for outstanding debt. It includes the effect of any financial operation that will reduce the debt stock other than debt stock restructuring, repayment of principal and debt forgiven. In particular, debt stock reduction will include the face value of debt bought back, the face value of debt swapped for equity (or \"nature\" or \"development\"), any face value reduction that might result as the consequence of a bond exchange, and any face value reduction resulting from an exchange of debt for discount bonds. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.DXR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt stock rescheduled (current US$)" }, { "id": "Longdefinition", "value": "Debt stocks rescheduled is the amount of debt outstanding rescheduled in any given year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt stocks rescheduled is the amount of debt outstanding rescheduled in any given year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.GPA.DPPG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grace period on new external debt commitments (years)" }, { "id": "Longdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "81" }, { "id": "DT.GPA.OFFT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grace period on new external debt commitments, official (years)" }, { "id": "Longdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "81" }, { "id": "DT.GPA.PRVT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grace period on new external debt commitments, private (years)" }, { "id": "Longdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "81" }, { "id": "DT.GRE.DPPG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grant element on new external debt commitments (%)" }, { "id": "Longdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Data for private nonguaranteed debt are not available." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Data for private nonguaranteed debt are not available." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "81" }, { "id": "DT.GRE.OFFT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grant element on new external debt commitments, official (%)" }, { "id": "Longdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "81" }, { "id": "DT.GRE.PRVT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average grant element on new external debt commitments, private (%)" }, { "id": "Longdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "81" }, { "id": "DT.INR.DPPG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average interest on new external debt commitments (%)" }, { "id": "Longdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "81" }, { "id": "DT.INR.OFFT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average interest on new external debt commitments, official (%)" }, { "id": "Longdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "81" }, { "id": "DT.INR.PRVT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average interest on new external debt commitments, private (%)" }, { "id": "Longdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest represents the average interest rate on all new public and publicly guaranteed loans contracted during the year. To obtain the average, the interest rates for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "81" }, { "id": "DT.INT.BLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.BLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.BLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.BLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sectorbilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sectorbilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.BLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.BLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.BLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.BLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.BLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.BLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.DECB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, central bank (PPG) (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank long-term debt are aggregated. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank long-term debt are aggregated. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, total (INT, current US$)" }, { "id": "Longdefinition", "value": "Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. This item includes interest paid on long-term debt, IMF charges, and interest paid on short-term debt. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. This item includes interest paid on long-term debt, IMF charges, and interest paid on short-term debt. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.DECT.EX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "The denominator for this indicator in previous versions of Global Development Finance included workers' remittances. Workers' remittances are no longer included." }, { "id": "IndicatorName", "value": "Interest payments on external debt (% of exports of goods, services and primary income)" }, { "id": "Longdefinition", "value": "Total interest payments to exports of goods, services and primary income. Total interest payment is the sum of interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and charges to the IMF." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total interest payments to exports of goods, services and primary income. Total interest payment is the sum of interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and charges to the IMF." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "81" }, { "id": "DT.INT.DECT.GN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt (% of GNI)" }, { "id": "Longdefinition", "value": "Total interest payments to gross national income." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total interest payments to gross national income." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "81" }, { "id": "DT.INT.DEGG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, general government sector (PPG) (INT, current US$)" }, { "id": "Longdefinition", "value": "General government long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.DEPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, public sector (PPG) (INT, current US$)" }, { "id": "Longdefinition", "value": "Public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "IMF charges (INT, current US$)" }, { "id": "Longdefinition", "value": "IMF charges cover interest payments with respect to all uses of IMF resources, excluding those resulting from drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "IMF charges cover interest payments with respect to all uses of IMF resources, excluding those resulting from drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, long-term (INT, current US$)" }, { "id": "Longdefinition", "value": "Interest payments on long-term debt are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest payments on long-term debt are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.DOPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, other public sector (PPG) (INT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, private nonguaranteed (PNG) (INT, current US$)" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, public and publicly guaranteed (PPG) (INT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.DSTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest payments on external debt, short-term (INT, current US$)" }, { "id": "Longdefinition", "value": "Interest payments on short-term debt are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. This item includes interest paid on long-term debt, IMF charges, and interest paid on short-term debt. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest payments on short-term debt are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. This item includes interest paid on long-term debt, IMF charges, and interest paid on short-term debt. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (INT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (INT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.MLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves.Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.MLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.MLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.MLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.MLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral (INT, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.MLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.MLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.MLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.MLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.MLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral concessional (INT, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.OFFT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, official creditors (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (INT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.OFFT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, official creditors (INT, current US$)" }, { "id": "Longdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.OFFT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, official creditors (INT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.OFFT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, official creditors (INT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.OFFT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, official creditors (INT, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.PBND.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bonds (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (INT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.PBND.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bonds (INT, current US$)" }, { "id": "Longdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.PBND.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bonds (INT, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.PBND.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bonds (INT, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.PBND.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bonds (INT, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.PCBK.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, commercial banks (INT, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (INT, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.PCBK.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, commercial banks (INT, current US$)" }, { "id": "Longdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Interest" } ], "source_id": "81" }, { "id": "DT.INT.PCBK.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, commercial banks (INT, current US$)" }, { "id": "Longdefinition", "value": "Other oublic sector commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other oublic sector commercial bank loans from private banks and other private financial institutions. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. 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Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest in arrears on long-term debt is defined as interest payment due but not paid, on a cumulative basis. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.IXA.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest arrears, private creditors (current US$)" }, { "id": "Longdefinition", "value": "Interest in arrears on long-term debt is defined as interest payment due but not paid, on a cumulative basis. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest in arrears on long-term debt is defined as interest payment due but not paid, on a cumulative basis. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.IXF.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest forgiven (current US$)" }, { "id": "Longdefinition", "value": "Interest forgiven is the amount of interest due or in arrears that was written off or forgiven in any given year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest forgiven is the amount of interest due or in arrears that was written off or forgiven in any given year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.IXR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest rescheduled (capitalized) (current US$)" }, { "id": "Longdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. (Interest capitalized is the interest that became part of the stock of debt due to a rescheduling operation.) Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. (Interest capitalized is the interest that became part of the stock of debt due to a rescheduling operation.) Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.IXR.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest rescheduled, official (current US$)" }, { "id": "Longdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organizations include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organizations include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.IXR.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Interest rescheduled, private (current US$)" }, { "id": "Longdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Interest rescheduled is the amount of interest due or in arrears that was rescheduled in any given year. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.MAT.DPPG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average maturity on new external debt commitments (years)" }, { "id": "Longdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "81" }, { "id": "DT.MAT.OFFT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average maturity on new external debt commitments, official (years)" }, { "id": "Longdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "81" }, { "id": "DT.MAT.PRVT", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Average maturity on new external debt commitments, private (years)" }, { "id": "Longdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Maturity is the number of years to original maturity date, which is the sum of grace and repayment periods. Grace period for principal is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. The repayment period is the period from the first to last repayment of principal. To obtain the average, the maturity for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Terms" } ], "source_id": "81" }, { "id": "DT.NFL.BLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, bilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.BLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.BLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.BLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.BLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.BLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.BLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.BLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.BLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.BLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.DECB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, central bank (PPG) (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank long-term debt are aggregated. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank long-term debt are aggregated. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, total (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net flows on external debt are disbursements on long-term external debt and IMF purchases minus principal repayments on long-term external debt and IMF repurchases up to 1984. Beginning in 1985 this line includes the change in stock of short-term debt (including interest arrears for long-term debt). Thus, if the change in stock is positive, a disbursement is assumed to have taken place; if negative, a repayment is assumed to have taken place. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net flows on external debt are disbursements on long-term external debt and IMF purchases minus principal repayments on long-term external debt and IMF repurchases up to 1984. Beginning in 1985 this line includes the change in stock of short-term debt (including interest arrears for long-term debt). Thus, if the change in stock is positive, a disbursement is assumed to have taken place; if negative, a repayment is assumed to have taken place. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.DEGG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, general government sector (PPG) (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.DEPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, public sector (PPG) (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, long-term (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.DOPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, other public sector (PPG) (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, private nonguaranteed (PNG) (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, public and publicly guaranteed (PPG) (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.DSTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net flows on external debt, short-term (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.IMFC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, IMF concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IMF is the International Monetary Fund, which provides concessional lending through the Poverty Reduction and Growth Facility and the IMF Trust Fund. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IMF is the International Monetary Fund, which provides concessional lending through the Poverty Reduction and Growth Facility and the IMF Trust Fund. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.IMFN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, IMF nonconcessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IMF is the International Monetary Fund, which provides nonconcessional lending through the credit it provides to its members, mainly to meet balance of payments needs. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IMF is the International Monetary Fund, which provides nonconcessional lending through the credit it provides to its members, mainly to meet balance of payments needs. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, IBRD (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IBRD is the International Bank for Reconstruction and Development, the founding and largest member of the World Bank Group. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IBRD is the International Bank for Reconstruction and Development, the founding and largest member of the World Bank Group. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, IDA (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IDA is the International Development Association, the concessional loan window of the World Bank Group. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IDA is the International Development Association, the concessional loan window of the World Bank Group. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.MLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, multilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.MLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.MLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.MLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.MLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.MLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.MLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.MLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.MLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.MLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.MOTH.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, others (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Others is a residual category in the World Bank's Debtor Reporting System. It includes such institutions as the Caribbean Development Fund, Council of Europe, European Development Fund, Islamic Development Bank, Nordic Development Fund, and the like. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Others is a residual category in the World Bank's Debtor Reporting System. It includes such institutions as the Caribbean Development Fund, Council of Europe, European Development Fund, Islamic Development Bank, Nordic Development Fund, and the like. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.NEBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "EBRD, private nonguaranteed (NFL, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt privately placed from the European Bank for Reconstruction and Development (EBRD). Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt privately placed from the European Bank for Reconstruction and Development (EBRD). Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.NIFC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "IFC, private nonguaranteed (NFL, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt privately placed from the International Finance Corporation (IFC). Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt privately placed from the International Finance Corporation (IFC). Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.OFFT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, official creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.OFFT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, official creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.OFFT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, official creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.OFFT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, official creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.OFFT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, official creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.PBND.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bonds (NFL, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt in form of bonds.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.PBND.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bonds (NFL, current US$)" }, { "id": "Longdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. 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Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.PRVT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, private creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.PRVT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, private creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.PRVT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, private creditors (NFL, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.RDBC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, RDB concessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Concessional financial flows cover disbursements made through concessional lending facilities. Regional development banks are the African Development Bank, in Tunis, Tunisia, which serves all of Africa, including North Africa; the Asian Development Bank, in Manila, Philippines, which serves South and Central Asia and East Asia and Pacific; the European Bank for Reconstruction and Development, in London, United Kingdom, which serves Europe and Central Asia; and the Inter-American Development Bank, in Washington, D.C., which serves the Americas. Aggregates include amounts for economies not specified elsewhere. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Concessional financial flows cover disbursements made through concessional lending facilities. Regional development banks are the African Development Bank, in Tunis, Tunisia, which serves all of Africa, including North Africa; the Asian Development Bank, in Manila, Philippines, which serves South and Central Asia and East Asia and Pacific; the European Bank for Reconstruction and Development, in London, United Kingdom, which serves Europe and Central Asia; and the Inter-American Development Bank, in Washington, D.C., which serves the Americas. Aggregates include amounts for economies not specified elsewhere. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NFL.RDBN.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net financial flows, RDB nonconcessional (NFL, current US$)" }, { "id": "Longdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Nonconcessional financial flows cover all disbursements except those made through concessional lending facilities. Regional development banks are the African Development Bank, in Tunis, Tunisia, which serves all of Africa, including North Africa; the Asian Development Bank, in Manila, Philippines, which serves South and Central Asia and East Asia and Pacific; the European Bank for Reconstruction and Development, in London, United Kingdom, which serves Europe and Central Asia; and the Inter-American Development Bank, in Washington, D.C., which serves the Americas. Aggregates include amounts for economies not specified elsewhere. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Nonconcessional financial flows cover all disbursements except those made through concessional lending facilities. Regional development banks are the African Development Bank, in Tunis, Tunisia, which serves all of Africa, including North Africa; the Asian Development Bank, in Manila, Philippines, which serves South and Central Asia and East Asia and Pacific; the European Bank for Reconstruction and Development, in London, United Kingdom, which serves Europe and Central Asia; and the Inter-American Development Bank, in Washington, D.C., which serves the Americas. Aggregates include amounts for economies not specified elsewhere. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net flows" } ], "source_id": "81" }, { "id": "DT.NTR.BLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.BLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.BLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.BLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.BLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.BLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.BLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.BLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.BLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.BLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.DECB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, central bank (PPG) (NTR, current US$)" }, { "id": "Longdefinition", "value": "The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, total (NTR, current US$)" }, { "id": "Longdefinition", "value": "Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.DEGG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, general government sector (PPG) (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.DEPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, public sector (PPG) (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, long-term (NTR, current US$)" }, { "id": "Longdefinition", "value": "Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.DOPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, other public sector (PPG) (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, private nonguaranteed (PNG) (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, public and publicly guaranteed (PPG) (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.MLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.MLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.MLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.MLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.MLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.MLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.MLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.MLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.MLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.MLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral concessional (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.OFFT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, official creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.OFFT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, official creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.OFFT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, official creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.OFFT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, official creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.OFFT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, official creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PBND.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bonds (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt in form of bonds.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt in form of bonds.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PBND.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bonds (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PBND.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bonds (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PBND.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bonds (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PBND.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bonds (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from bonds that are either publicly issued or privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PCBK.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, commercial banks (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PCBK.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, commercial banks (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PCBK.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, commercial banks (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PCBK.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, commercial banks (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PCBK.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, commercial banks (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PCBK.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, commercial banks (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PNGB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, bonds (NTR, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term debt from bonds that are privately placed. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PNGC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PNG, commercial banks and other creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PROP.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, other private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank other private credits from manufacturers, exporters, and other suppliers of goods. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PROP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, other private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PROP.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, other private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PROP.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, other private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PROP.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, other private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sectorother private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sectorother private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PROP.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, other private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PRPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Net transfers on external debt, private guaranteed by public sector (PPG) (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PRVT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from private creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from private creditors.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PRVT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "General government debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PRVT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PRVT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.NTR.PRVT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, private creditors (NTR, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net transfers are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Net transfers" } ], "source_id": "81" }, { "id": "DT.TDS.BLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.BLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.BLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "General government debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.BLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.BLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.BLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.BLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank bilateral debt includes Central bank debt with bilateral creditors. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.BLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.BLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.BLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.BLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, bilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.BLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, bilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.DECB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, central bank (PPG) (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.DECT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, total (TDS, current US$)" }, { "id": "Longdefinition", "value": "Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.DECT.EX.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "Generalcomments", "value": "The denominator for this indicator in previous versions of Global Development Finance included workers' remittances. Workers' remittances are no longer included." }, { "id": "IndicatorName", "value": "Total debt service (% of exports of goods, services and primary income)" }, { "id": "Longdefinition", "value": "Total debt service to exports of goods, services and primary income. Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total debt service to exports of goods, services and primary income. Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt ratios & other items" } ], "source_id": "81" }, { "id": "DT.TDS.DEGG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, general government sector (PPG) (TDS, current US$)" }, { "id": "Longdefinition", "value": "General government debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.DEPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, public sector (PPG) (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.DIMF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "IMF repurchases and charges (TDS, current US$)" }, { "id": "Longdefinition", "value": "IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. IMF charges cover interest payments with respect to all uses of IMF resources, excluding those resulting from drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. IMF charges cover interest payments with respect to all uses of IMF resources, excluding those resulting from drawings in the reserve tranche. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.DLXF.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, long-term (TDS, current US$)" }, { "id": "Longdefinition", "value": "Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.DOPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, other public sector (PPG) (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.DPNG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, private nonguaranteed (PNG) (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private nonguaranteed debt service is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private nonguaranteed debt service is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Debt service on external debt, public and publicly guaranteed (PPG) (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.MIBR.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IBRD (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.MIDA.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, IDA (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.MLAT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves.Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.MLAT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Multilateral debt service (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.MLAT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.MLAT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.MLAT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.MLAT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.MLTC.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, multilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.MLTC.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, multilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.MLTC.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, multilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.MLTC.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, multilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.MLTC.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, multilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Private sector guaranteed by Public Sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.MLTC.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, multilateral concessional (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.OFFT.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, official creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, official creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.OFFT.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, official creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "General goverment debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General goverment debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). 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Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.OFFT.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, official creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Other public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.OFFT.PRVG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PRVG, official creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Private sector guaranteed by Public Sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). 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Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.OFFT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, official creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. 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Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.PBND.CB.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "CB, bonds (TDS, current US$)" }, { "id": "Longdefinition", "value": "Central bank debt in form of bonds.The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Central bank debt in form of bonds. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.PBND.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PPG, bonds (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.PBND.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, bonds (TDS, current US$)" }, { "id": "Longdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "General government debt from bonds that are either publicly issued or privately placed. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.PBND.OPS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "OPS, bonds (TDS, current US$)" }, { "id": "Longdefinition", "value": "Other public sector debt from bonds that are either publicly issued or privately placed. 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Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.PCBK.GG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GG, commercial banks (TDS, current US$)" }, { "id": "Longdefinition", "value": "General government commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. 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Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.PCBK.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, commercial banks (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector commercial bank loans from private banks and other private financial institutions. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. 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Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.PROP.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, other private creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. 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Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TDS.PRVT.PS.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "PS, private creditors (TDS, current US$)" }, { "id": "Longdefinition", "value": "Public sector guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public sector guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Debt service" } ], "source_id": "81" }, { "id": "DT.TXR.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Total amount of debt rescheduled (current US$)" }, { "id": "Longdefinition", "value": "Total amount of debt rescheduled includes the debt stock, principal, interest, charges and penalties rescheduled. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total amount of debt rescheduled includes the debt stock, principal, interest, charges and penalties rescheduled. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Arrears, reschedulings, etc." } ], "source_id": "81" }, { "id": "DT.UND.DPPG.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Undisbursed external debt, total (UND, current US$)" }, { "id": "Longdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Public and publicly guaranteed long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Undisbursed debt" } ], "source_id": "81" }, { "id": "DT.UND.OFFT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Undisbursed external debt, official creditors (UND, current US$)" }, { "id": "Longdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Undisbursed debt" } ], "source_id": "81" }, { "id": "DT.UND.PRVT.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Undisbursed external debt, private creditors (UND, current US$)" }, { "id": "Longdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Undisbursed debt is the total public and publicly guaranteed debt undrawn at year end; data for private nonguaranteed debt are not available. Debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Data are in current U.S. dollars." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: External debt: Undisbursed debt" } ], "source_id": "81" }, { "id": "FI.RES.TOTL.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Total reserves (includes gold, current US$)" }, { "id": "Longdefinition", "value": "Total reserves comprise holdings of monetary gold, special drawing rights, reserves of IMF members held by the IMF, and holdings of foreign exchange under the control of monetary authorities. The gold component of these reserves is valued at year-end (December 31) London prices. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total reserves comprise holdings of monetary gold, special drawing rights, reserves of IMF members held by the IMF, and holdings of foreign exchange under the control of monetary authorities. The gold component of these reserves is valued at year-end (December 31) London prices. Data are in current U.S. dollars." }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "81" }, { "id": "FI.RES.TOTL.DT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Total reserves (% of total external debt)" }, { "id": "Longdefinition", "value": "International reserves to total external debt stocks." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "International reserves to total external debt stocks." }, { "id": "Source", "value": "World Bank, International Debt Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "81" }, { "id": "FI.RES.TOTL.MO", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "Total reserves in months of imports" }, { "id": "Longdefinition", "value": "Total reserves comprise holdings of monetary gold, special drawing rights, reserves of IMF members held by the IMF, and holdings of foreign exchange under the control of monetary authorities. The gold component of these reserves is valued at year-end (December 31) London prices. This item shows reserves expressed in terms of the number of months of imports of goods and services they could pay for [Reserves/(Imports/12)]." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total reserves comprise holdings of monetary gold, special drawing rights, reserves of IMF members held by the IMF, and holdings of foreign exchange under the control of monetary authorities. The gold component of these reserves is valued at year-end (December 31) London prices. This item shows reserves expressed in terms of the number of months of imports of goods and services they could pay for [Reserves/(Imports/12)]." }, { "id": "Source", "value": "International Monetary Fund, International Financial Statistics." }, { "id": "Topic", "value": "Economic Policy & Debt: Balance of payments: Reserves & other items" } ], "source_id": "81" }, { "id": "NY.GNP.MKTP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "Dataset", "value": "International Debt Statistics" }, { "id": "IndicatorName", "value": "GNI (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Topic", "value": "Economic Policy & Debt: National accounts: US$ at current prices: Aggregate indicators" } ], "source_id": "81" }, { "id": "SP.POP.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Generalcomments", "value": "Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis." }, { "id": "IndicatorName", "value": "Population, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Limitationsandexceptions", "value": "Current population estimates for developing countries that lack (i) reliable recent census data, and (ii) pre- and post-census estimates for countries with census data, are provided by the United Nations Population Division and other agencies. \n\nThe cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in both the model and the data. In the UN estimates the five-year age group is the cohort unit and five-year period data are used; therefore interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\nBecause future trends cannot be known with certainty, population projections have a wide range of uncertainty." }, { "id": "Longdefinition", "value": "Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "(1) United Nations Population Division. World Population Prospects: 2019 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme." }, { "id": "Topic", "value": "Health: Population: Structure" } ], "source_id": "81" }, { "id": "SPI.D1.5.CHLD.MORT", "metatype": [ { "id": "Developmentrelevance", "value": "Child Mortality Metadata from UN IGME" }, { "id": "IndicatorName", "value": "Availability of Mortality rate, under-5 (per 1,000 live births) data meeting quality standards according to UN IGME (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Child Mortality Metadata from UN IGME" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Child Mortality Metadata from UN IGME" }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. At least three indicators that met UN IGME standards within past 5 years. 0.67 Points. Two indicators that met UN IGME standards within past 5 years. 0.33 Points. One indicators that met UN IGME standards within past 5 years 0 Points. None within past 5 years" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D1.5.DT.TDS.DPPF.XP.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Debt Reporting Metadata from World Bank" }, { "id": "IndicatorName", "value": "Quality of Debt service data according to World Bank" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Debt Reporting Metadata from World Bank" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Debt Reporting Metadata from World Bank" }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Points. Actual value. 0.67 Points. Preliminary value 0.33 Points. Estimated value. 0 Points. No value" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D1.5.LFP", "metatype": [ { "id": "Developmentrelevance", "value": "Labor force participation data for use by ILO" }, { "id": "IndicatorName", "value": "Labor force participation rate by sex and age (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Labor force participation data for use by ILO" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Labor force participation data for use by ILO" }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. Country has a labor force survey based estimate in past 5 years of labor force participation broken down by total, male, and female & estimated value from ILO is within 10 percentage points of value reported by national government. \n \n 0.5 Point. Country has labor force survey or is within 10 points of ILO, but not both \n \n 0 Points. Otherwise" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D1.5.POV", "metatype": [ { "id": "Developmentrelevance", "value": "Comparability data from World Bank's Povcalnet" }, { "id": "IndicatorName", "value": "Availability of Comparable Poverty headcount ratio at $1.90 a day (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Comparability data from World Bank's Povcalnet" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Comparability data from World Bank's Povcalnet" }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. Comparable data lasting at least two years within past 5 years. 0 Points. No comparable data within past 5 years" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D1.5.SAFE.MAN.WATER", "metatype": [ { "id": "Developmentrelevance", "value": "Availability of Safely Managed Drinking Water data for use by JMP" }, { "id": "IndicatorName", "value": "Safely Managed Drinking Water" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Availability of Safely Managed Drinking Water data for use by JMP" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Availability of Safely Managed Drinking Water data for use by JMP" }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. At least two estimates, with breakdowns for urban/rural areas, within an 8 year window \n \n 0.5 Points. At least two estimates, but not an urban/rural breakdown, within an 8 year window \n \n 0 Points. Otherwise" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D2.1.GDDS", "metatype": [ { "id": "Developmentrelevance", "value": "The Special Data Dissemination Standard (SDDS) and electronic General Data Dissemination Standard (e-GDDS) were established by the International Monetary Fund (IMF) for member countries that have or that might seek access to international capital markets, to guide them in providing their economic and financial data to the public. Although subscription is voluntary, the subscribing member needs to be committed to observing the standard and provide information about its data and data dissemination practices (metadata). The metadata are posted on the IMF’s SDDS and e-GDDS websites." }, { "id": "IndicatorName", "value": "SDDS/e-GDDS subscription" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The Special Data Dissemination Standard (SDDS) and electronic General Data Dissemination Standard (e-GDDS) were established by the International Monetary Fund (IMF) for member countries that have or that might seek access to international capital markets, to guide them in providing their economic and financial data to the public. Although subscription is voluntary, the subscribing member needs to be committed to observing the standard and provide information about its data and data dissemination practices (metadata). The metadata are posted on the IMF’s SDDS and e-GDDS websites." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The Special Data Dissemination Standard (SDDS) and electronic General Data Dissemination Standard (e-GDDS) were established by the International Monetary Fund (IMF) for member countries that have or that might seek access to international capital markets, to guide them in providing their economic and financial data to the public." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Point. Subscribing to IMF SDDS+ or SDDS standards 0.5 Points. Subscribing to IMF e-GDDS standards. 0 Points. Otherwise" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D2.2.Download.options", "metatype": [ { "id": "Developmentrelevance", "value": "In general, openness scores are based on the format and licensing of the datasets, the comprehensiveness of metadata, and what download options exist." }, { "id": "IndicatorName", "value": "Dimension 2.2: Online access - Download Options Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This openness element measures whether download options are available. ODIN looks for three download options: (1) bulk download (at the indicator level), (2) API, and (3) user-select download (custom downloads). Options 2 and 3 are interchangeable for scoring purposes." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "This openness element measures whether download options are available. ODIN looks for three download options: (1) bulk download (at the indicator level), (2) API, and (3) user-select download (custom downloads). Options 2 and 3 are interchangeable for scoring purposes." }, { "id": "Source", "value": "Open Data Watch" }, { "id": "Statisticalconceptandmethodology", "value": "Our source for this indicator is Open Data Watch. Scores range from 0-100. For more details, consult the ODIN technical documentation." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D2.2.Machine.readable", "metatype": [ { "id": "Developmentrelevance", "value": "In general, openness scores are based on the format and licensing of the datasets, the comprehensiveness of metadata, and what download options exist." }, { "id": "IndicatorName", "value": "Dimension 2.2: Online access - Machine Readability Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This openness element measures whether data are made available in machine readable formats. Machine readable file formats allow users to easily process data using a computer. Common machine readable formats include XLS, XLSX, CSV, and JSON files." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "This openness element measures whether data are made available in machine readable formats. Machine readable file formats allow users to easily process data using a computer. Common machine readable formats include XLS, XLSX, CSV, and JSON files." }, { "id": "Source", "value": "Open Data Watch" }, { "id": "Statisticalconceptandmethodology", "value": "Our source for this indicator is Open Data Watch. Scores range from 0-100. For more details, consult the ODIN technical documentation." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D2.2.Metadata.available", "metatype": [ { "id": "Developmentrelevance", "value": "In general, openness scores are based on the format and licensing of the datasets, the comprehensiveness of metadata, and what download options exist." }, { "id": "IndicatorName", "value": "Dimension 2.2: Online access - Metadata Available Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This openness element measures whether metadata are available for the published indicators. Metadata must be located in or near the data file or on a designated metadata section of the website. ODIN looks for three aspects of metadata: (1) definition of indicator; (2) date of upload; and (3) Source agency." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "This openness element measures whether metadata are available for the published indicators. Metadata must be located in or near the data file or on a designated metadata section of the website. ODIN looks for three aspects of metadata: (1) definition of indicator; (2) date of upload; and (3) Source agency." }, { "id": "Source", "value": "Open Data Watch" }, { "id": "Statisticalconceptandmethodology", "value": "Our source for this indicator is Open Data Watch. Scores range from 0-100. For more details, consult the ODIN technical documentation." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D2.2.Non.proprietary", "metatype": [ { "id": "Developmentrelevance", "value": "In general, openness scores are based on the format and licensing of the datasets, the comprehensiveness of metadata, and what download options exist." }, { "id": "IndicatorName", "value": "Dimension 2.2: Online access - Non-Proprietary format Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This openness element measures whether data are made available in nonproprietary formats. Nonproprietary file formats are important because they allow users to access data without requiring the use of a costly, proprietary software that may prevent some users from accessing the data. Common nonproprietary formats include PDF, HTML, XLSX, DOCX, CSV, and JSON files." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "This openness element measures whether data are made available in nonproprietary formats. Nonproprietary file formats are important because they allow users to access data without requiring the use of a costly, proprietary software that may prevent some users from accessing the data. Common nonproprietary formats include PDF, HTML, XLSX, DOCX, CSV, and JSON files." }, { "id": "Source", "value": "Open Data Watch" }, { "id": "Statisticalconceptandmethodology", "value": "Our source for this indicator is Open Data Watch. Scores range from 0-100. For more details, consult the ODIN technical documentation." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D2.2.Openness.subscore", "metatype": [ { "id": "Developmentrelevance", "value": "ODW Openness score" }, { "id": "IndicatorName", "value": "ODIN Open Data Openness score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "ODW Openness score" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "ODW Openness score" }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Our source for this indicator is Open Data Watch. Scores range from 0-100. For more details, consult the ODIN technical documentation." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D2.2.Terms.of.use", "metatype": [ { "id": "Developmentrelevance", "value": "In general, openness scores are based on the format and licensing of the datasets, the comprehensiveness of metadata, and what download options exist." }, { "id": "IndicatorName", "value": "Dimension 2.2: Online access - Terms of Use Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This openness element measures whether data are made available under an open license. Open licenses must allow the use, reuse, and sharing or adaption of data for commercial and noncommercial use without any obligation other than attribution, per the Open Definition. Licenses prohibiting commercial use or having two or more additional stipulations are classified as “Not Open”. Licenses with no more than one additional stipulation are classified as “Some Restrictions.” Licenses that do not explicitly state all allowed uses under the Open Definition and do not include restrictive language are also classified as “Some Restrictions.”" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "This openness element measures whether data are made available under an open license. Open licenses must allow the use, reuse, and sharing or adaption of data for commercial and noncommercial use without any obligation other than attribution, per the Open Definition. Licenses prohibiting commercial use or having two or more additional stipulations are classified as “Not Open”. Licenses with no more than one additional stipulation are classified as “Some Restrictions.” Licenses that do not explicitly state all allowed uses under the Open Definition and do not include restrictive language are also classified as “Some Restrictions.”" }, { "id": "Source", "value": "Open Data Watch" }, { "id": "Statisticalconceptandmethodology", "value": "Our source for this indicator is Open Data Watch. Scores range from 0-100. For more details, consult the ODIN technical documentation." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D2.4.NADA", "metatype": [ { "id": "Developmentrelevance", "value": "NADA/NSO websites. Statistical systems must be open and transparent about their methods and procedures and provide access to adequate metadata – detailed descriptions of the methods and procedures used to produce the data." }, { "id": "IndicatorName", "value": "NADA metadata" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "NADA/NSO websites. Statistical systems must be open and transparent about their methods and procedures and provide access to adequate metadata – detailed descriptions of the methods and procedures used to produce the data." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "NADA/NSO websites." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. Yes, available. 0 Points. No" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D3.1.POV", "metatype": [ { "id": "Developmentrelevance", "value": "SDG Goal 1 data availability. Source: UN Global SDG Indicators Database" }, { "id": "IndicatorName", "value": "GOAL 1: No Poverty (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDG Goal 1 data availability. Source: UN Global SDG Indicators Database" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDG Goal 1 data availability." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Fraction of Indicators in Goal 1 with value produced by countries statistical system" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D3.10.NEQL", "metatype": [ { "id": "Developmentrelevance", "value": "SDG Goal 10 data availability. Source: UN Global SDG Indicators Database" }, { "id": "IndicatorName", "value": "GOAL 10: Reduced Inequality (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDG Goal 10 data availability. Source: UN Global SDG Indicators Database" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDG Goal 10 data availability." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Fraction of Indicators in Goal 10 with value produced by countries statistical system" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D3.11.CITY", "metatype": [ { "id": "Developmentrelevance", "value": "SDG Goal 11 data availability. Source: UN Global SDG Indicators Database" }, { "id": "IndicatorName", "value": "GOAL 11: Sustainable Cities and Communities (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDG Goal 11 data availability. Source: UN Global SDG Indicators Database" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDG Goal 11 data availability." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Fraction of Indicators in Goal 11 with value produced by countries statistical system" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D3.12.CNSP", "metatype": [ { "id": "Developmentrelevance", "value": "SDG Goal 12 data availability. Source: UN Global SDG Indicators Database" }, { "id": "IndicatorName", "value": "GOAL 12: Responsible Consumption and Production (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDG Goal 12 data availability. Source: UN Global SDG Indicators Database" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDG Goal 12 data availability." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Fraction of Indicators in Goal 12 with value produced by countries statistical system" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D3.13.CLMT", "metatype": [ { "id": "Developmentrelevance", "value": "SDG Goal 13 data availability. Source: UN Global SDG Indicators Database" }, { "id": "IndicatorName", "value": "GOAL 13: Climate Action (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDG Goal 13 data availability. Source: UN Global SDG Indicators Database" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDG Goal 13 data availability." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Fraction of Indicators in Goal 13 with value produced by countries statistical system" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D3.15.LAND", "metatype": [ { "id": "Developmentrelevance", "value": "SDG Goal 15 data availability. Source: UN Global SDG Indicators Database" }, { "id": "IndicatorName", "value": "GOAL 15: Life on Land (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDG Goal 15 data availability. Source: UN Global SDG Indicators Database" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDG Goal 15 data availability." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Fraction of Indicators in Goal 15 with value produced by countries statistical system" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D3.16.INST", "metatype": [ { "id": "Developmentrelevance", "value": "SDG Goal 16 data availability. Source: UN Global SDG Indicators Database" }, { "id": "IndicatorName", "value": "GOAL 16: Peace and Justice Strong Institutions (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDG Goal 16 data availability. Source: UN Global SDG Indicators Database" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDG Goal 16 data availability." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Fraction of Indicators in Goal 16 with value produced by countries statistical system" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D3.17.PTNS", "metatype": [ { "id": "Developmentrelevance", "value": "SDG Goal 17 data availability. Source: UN Global SDG Indicators Database" }, { "id": "IndicatorName", "value": "GOAL 17: Partnerships to achieve the Goal (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDG Goal 17 data availability. Source: UN Global SDG Indicators Database" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDG Goal 17 data availability." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Fraction of Indicators in Goal 17 with value produced by countries statistical system" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D3.2.HNGR", "metatype": [ { "id": "Developmentrelevance", "value": "SDG Goal 2 data availability. Source: UN Global SDG Indicators Database" }, { "id": "IndicatorName", "value": "GOAL 2: Zero Hunger (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDG Goal 2 data availability. Source: UN Global SDG Indicators Database" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDG Goal 2 data availability." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Fraction of Indicators in Goal 2 with value produced by countries statistical system" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D3.3.HLTH", "metatype": [ { "id": "Developmentrelevance", "value": "SDG Goal 3 data availability. Source: UN Global SDG Indicators Database" }, { "id": "IndicatorName", "value": "GOAL 3: Good Health and Well-being (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDG Goal 3 data availability. Source: UN Global SDG Indicators Database" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDG Goal 3 data availability." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Fraction of Indicators in Goal 3 with value produced by countries statistical system" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D3.4.EDUC", "metatype": [ { "id": "Developmentrelevance", "value": "SDG Goal 4 data availability. Source: UN Global SDG Indicators Database" }, { "id": "IndicatorName", "value": "GOAL 4: Quality Education (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDG Goal 4 data availability. Source: UN Global SDG Indicators Database" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDG Goal 4 data availability." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Fraction of Indicators in Goal 4 with value produced by countries statistical system" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D3.5.GEND", "metatype": [ { "id": "Developmentrelevance", "value": "SDG Goal 5 data availability. Source: UN Global SDG Indicators Database" }, { "id": "IndicatorName", "value": "GOAL 5: Gender Equality (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDG Goal 5 data availability. Source: UN Global SDG Indicators Database" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDG Goal 5 data availability." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Fraction of Indicators in Goal 5 with value produced by countries statistical system" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D3.6.WTRS", "metatype": [ { "id": "Developmentrelevance", "value": "SDG Goal 6 data availability. Source: UN Global SDG Indicators Database" }, { "id": "IndicatorName", "value": "GOAL 6: Clean Water and Sanitation (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDG Goal 6 data availability. Source: UN Global SDG Indicators Database" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDG Goal 6 data availability." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Fraction of Indicators in Goal 6 with value produced by countries statistical system" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D3.7.ENRG", "metatype": [ { "id": "Developmentrelevance", "value": "SDG Goal 7 data availability. Source: UN Global SDG Indicators Database" }, { "id": "IndicatorName", "value": "GOAL 7: Affordable and Clean Energy (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDG Goal 7 data availability. Source: UN Global SDG Indicators Database" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDG Goal 7 data availability." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Fraction of Indicators in Goal 7 with value produced by countries statistical system" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D3.8.WORK", "metatype": [ { "id": "Developmentrelevance", "value": "SDG Goal 8 data availability. Source: UN Global SDG Indicators Database" }, { "id": "IndicatorName", "value": "GOAL 8: Decent Work and Economic Growth (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDG Goal 8 data availability. Source: UN Global SDG Indicators Database" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDG Goal 8 data availability." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Fraction of Indicators in Goal 8 with value produced by countries statistical system" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D3.9.INDY", "metatype": [ { "id": "Developmentrelevance", "value": "SDG Goal 9 data availability. Source: UN Global SDG Indicators Database" }, { "id": "IndicatorName", "value": "GOAL 9: Industry, Innovation and Infrastructure (5 year moving average)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDG Goal 9 data availability. Source: UN Global SDG Indicators Database" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDG Goal 9 data availability." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Fraction of Indicators in Goal 9 with value produced by countries statistical system" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D4.1.1.POPU", "metatype": [ { "id": "Developmentrelevance", "value": "Population censuses collect data on the size, distribution and composition of population and information on a broad range of social and economic characteristics of the population. It also provides sampling frames for household and other surveys. Housing censuses provide information on the supply of housing units, the structural characteristics and facilities, and health and the development of normal family living conditions. Data obtained as part of the population census, including data on homeless persons, are often used in the presentation and analysis of the results of the housing census. It is recommended that population and housing censuses be conducted at least every 10 years." }, { "id": "IndicatorName", "value": "Population & Housing census (Availability score over 20 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Population censuses collect data on the size, distribution and composition of population and information on a broad range of social and economic characteristics of the population. It also provides sampling frames for household and other surveys. Housing censuses provide information on the supply of housing units, the structural characteristics and facilities, and health and the development of normal family living conditions. Data obtained as part of the population census, including data on homeless persons, are often used in the presentation and analysis of the results of the housing census. It is recommended that population and housing censuses be conducted at least every 10 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Population censuses collect data on the size, distribution and composition of population and information on a broad range of social and economic characteristics of the population." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. Population census done within last 10 years. 0.5 Points. Population census done within last 20 years. 0 Points. Otherwise" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D4.1.2.AGRI", "metatype": [ { "id": "Developmentrelevance", "value": "Agriculture censuses collect information on agricultural activities, such as size of holding, land tenure, land use, employment and production, and provide basic structural data and sampling frames for agricultural surveys. Censuses of agriculture normally involves collecting key structural data by complete enumeration of all agricultural holdings, in combination with more detailed structural data using sampling methods. It is recommended that agricultural censuses be conducted at least every 10 years." }, { "id": "IndicatorName", "value": "Agriculture census (Availability score over 20 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Agriculture censuses collect information on agricultural activities, such as size of holding, land tenure, land use, employment and production, and provide basic structural data and sampling frames for agricultural surveys. Censuses of agriculture normally involves collecting key structural data by complete enumeration of all agricultural holdings, in combination with more detailed structural data using sampling methods. It is recommended that agricultural censuses be conducted at least every 10 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Agriculture censuses collect information on agricultural activities, such as size of holding, land tenure, land use, employment and production, and provide basic structural data and sampling frames for agricultural surveys." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. census done within last 10 years. 0.5 Points. census done within last 20 years. 0 Points. Otherwise" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D4.1.3.BIZZ", "metatype": [ { "id": "Developmentrelevance", "value": "Business/establishment censuses provide valuable information on all economic activities, number of employed and size of establishments in the economy. Business Register information is establishment-based and includes business location, organization type (e.g. subsidiary or parent), industry classification, and operating data (e.g., receipts and employment)." }, { "id": "IndicatorName", "value": "Business/establishment census (Availability score over 20 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Business/establishment censuses provide valuable information on all economic activities, number of employed and size of establishments in the economy. Business Register information is establishment-based and includes business location, organization type (e.g. subsidiary or parent), industry classification, and operating data (e.g., receipts and employment)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Business/establishment censuses provide valuable information on all economic activities, number of employed and size of establishments in the economy." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. census done within last 10 years. 0.5 Points. census done within last 20 years. 0 Points. Otherwise" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D4.1.4.HOUS", "metatype": [ { "id": "Developmentrelevance", "value": "These surveys collect data on household income (including income in kind), consumption and expenditure. They typically include income, expenditure, and consumption surveys, household budget surveys, integrated surveys. It is recommended that surveys on income and expenditure be conducted at least every 3 to 5 years." }, { "id": "IndicatorName", "value": "Household Survey on income, etc (Availability score over 10 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "These surveys collect data on household income (including income in kind), consumption and expenditure. They typically include income, expenditure, and consumption surveys, household budget surveys, integrated surveys. It is recommended that surveys on income and expenditure be conducted at least every 3 to 5 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "These surveys collect data on household income (including income in kind), consumption and expenditure." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. 3 or more surveys done within past 10 years. 0.6 Points. 2 surveys done within past 10 years. 0.3 Points. 1 survey done within past 10 years. 0 Points. None within past 10 years" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D4.1.5.AGSVY", "metatype": [ { "id": "Developmentrelevance", "value": "Agricultural surveys refer to surveys of agricultural holdings based on the sampling frames established by the agricultural census. These are surveys on agricultural land, production, crops and livestock, aquaculture, labor and cost, and time use. Some issues, such as gender and food security, are of interest to most agriculture surveys." }, { "id": "IndicatorName", "value": "Agriculture survey (Availability score over 10 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Agricultural surveys refer to surveys of agricultural holdings based on the sampling frames established by the agricultural census. These are surveys on agricultural land, production, crops and livestock, aquaculture, labor and cost, and time use. Some issues, such as gender and food security, are of interest to most agriculture surveys." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Agricultural surveys refer to surveys of agricultural holdings based on the sampling frames established by the agricultural census." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. 3 or more surveys done within past 10 years. 0.6 Points. 2 surveys done within past 10 years. 0.3 Points. 1 survey done within past 10 years. 0 Points. None within past 10 years" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D4.1.6.LABR", "metatype": [ { "id": "Developmentrelevance", "value": "Labor force survey is a standard household-based survey of work-related statistics at the national and sub-national employment or unemployment levels, rates or trends. The surveys also provide the characteristics of the employed or unemployed, including labor force status by age or gender, breakdowns between employees and the self-employed, public versus private sector employment, multiple job-holding, hiring, job creation, and duration of unemployment." }, { "id": "IndicatorName", "value": "Labor Force Survey (Availability score over 10 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Labor force survey is a standard household-based survey of work-related statistics at the national and sub-national employment or unemployment levels, rates or trends. The surveys also provide the characteristics of the employed or unemployed, including labor force status by age or gender, breakdowns between employees and the self-employed, public versus private sector employment, multiple job-holding, hiring, job creation, and duration of unemployment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Labor force survey is a standard household-based survey of work-related statistics at the national and sub-national employment or unemployment levels, rates or trends." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. 3 or more surveys done within past 10 years. 0.6 Points. 2 surveys done within past 10 years. 0.3 Points. 1 survey done within past 10 years. 0 Points. None within past 10 years" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D4.1.7.HLTH", "metatype": [ { "id": "Developmentrelevance", "value": "Health surveys collect information on various aspects of health of populations, such as health expenditure, access, utilization, and outcomes. They typically include Demographic and Health Surveys. It is recommended that health surveys be conducted at least every 3 to 5 years." }, { "id": "IndicatorName", "value": "Health/Demographic survey (Availability score over 10 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Health surveys collect information on various aspects of health of populations, such as health expenditure, access, utilization, and outcomes. They typically include Demographic and Health Surveys. It is recommended that health surveys be conducted at least every 3 to 5 years." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Health surveys collect information on various aspects of health of populations, such as health expenditure, access, utilization, and outcomes." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. 3 or more surveys done within past 10 years. 0.6 Points. 2 surveys done within past 10 years. 0.3 Points. 1 survey done within past 10 years. 0 Points. None within past 10 years" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D4.1.8.BZSVY", "metatype": [ { "id": "Developmentrelevance", "value": "The business/establishment survey provides information on employment, hours, and earnings of employees from a sample of business establishments including private and public, entities that are classified based on an establishment's principal activity from the business or establishment census. Establishment surveys include surveys of businesses, farms, and institutions. They may ask for information about the establishment itself and/or employee characteristics and demographics." }, { "id": "IndicatorName", "value": "Business/establishment survey (Availability score over 10 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The business/establishment survey provides information on employment, hours, and earnings of employees from a sample of business establishments including private and public, entities that are classified based on an establishment's principal activity from the business or establishment census. Establishment surveys include surveys of businesses, farms, and institutions. They may ask for information about the establishment itself and/or employee characteristics and demographics." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The business/establishment survey provides information on employment, hours, and earnings of employees from a sample of business establishments including private and public, entities that are classified based on an establishment's principal activity from the business or establishment census." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. 3 or more surveys done within past 10 years. 0.6 Points. 2 surveys done within past 10 years. 0.3 Points. 1 survey done within past 10 years. 0 Points. None within past 10 years" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D4.2.3.CRVS", "metatype": [ { "id": "Developmentrelevance", "value": "Civil Registration and Vital Statistics (CRVS) complete" }, { "id": "IndicatorName", "value": "CRVS (WDI)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Civil Registration and Vital Statistics (CRVS) complete" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Civil Registration and Vital Statistics (CRVS) complete" }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Birth registrations 90% complete and death registration 75% complete according to UNSD." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D4.3.GEO.first.admin.level", "metatype": [ { "id": "Developmentrelevance", "value": "Indicator data availability at sub-national levels" }, { "id": "IndicatorName", "value": "Geospatial data available at 1st Admin Level" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Indicator data availability at sub-national levels" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Indicator data availability at sub-national levels" }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Our source for this indicator is Open Data Watch. Indicator is whether data available at first administrative data level. Scores range from 0-100. For more details, consult the ODIN technical documentation." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D5.1.DILG", "metatype": [ { "id": "Developmentrelevance", "value": "Based on PARIS21 indicators on SDG 17.18.2 (national statistical legislation compliance with UN Fundamental Principles of Official Statistics), existence of National Statistical Council, national statistical strategy generation, national statistical plan. Also include some other legislative aspects that foster good use of statistics eg freedom of information, privacy/transparency, good governance (eg free and fair elections)." }, { "id": "IndicatorName", "value": "Legislation Indicator based on PARIS21 indicators on SDG 17.18.2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Based on PARIS21 indicators on SDG 17.18.2 (national statistical legislation compliance with UN Fundamental Principles of Official Statistics), existence of National Statistical Council, national statistical strategy generation, national statistical plan. Also include some other legislative aspects that foster good use of statistics eg freedom of information, privacy/transparency, good governance (eg free and fair elections)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Based on PARIS21 indicators on SDG 17.18.2 (national statistical legislation compliance with UN Fundamental Principles of Official Statistics), existence of National Statistical Council, national statistical strategy generation, national statistical plan." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Scores is 1 if the country has a national statistical legislation compliant with United Nations Fundamental Principles of Statistics. Scores of 0 or scores with missing values are treated the same (both given a score of zero)." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D5.2.1.SNAU", "metatype": [ { "id": "Developmentrelevance", "value": "The national accounts data are compiled using the concepts, definitions, framework, and methodology of the System of National Account 2008 (SNA2008) or European System of National and Regional Accounts (ESA 2010). The manual has evolved to meet the changing economic structure, to follow systematic accounting and ensure international compatibility." }, { "id": "IndicatorName", "value": "System of national accounts in use" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The national accounts data are compiled using the concepts, definitions, framework, and methodology of the System of National Account 2008 (SNA2008) or European System of National and Regional Accounts (ESA 2010). The manual has evolved to meet the changing economic structure, to follow systematic accounting and ensure international compatibility." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The national accounts data are compiled using the concepts, definitions, framework, and methodology of the System of National Account 2008 (SNA2008) or European System of National and Regional Accounts (ESA 2010)." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Scoring: 1 point for using SNA2008 or ESA 2010, 0.5 points for using SNA 1993 or ESA 1995. 0 points otherwise" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D5.2.10.GSBP", "metatype": [ { "id": "Developmentrelevance", "value": "The Generic Statistical Business Process Model (GSBPM) aims to describe statistics production in a general and process-oriented way. It is used both within and between statistical offices as a common basis for work with statistics production in different ways, such as quality, efficiency, standardization, and process-orientation. It is used for all types of surveys, and \"business\" is not related to \"business statistics\" but refers to the statistical office, simply expressed." }, { "id": "IndicatorName", "value": "Business process" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The Generic Statistical Business Process Model (GSBPM) aims to describe statistics production in a general and process-oriented way. It is used both within and between statistical offices as a common basis for work with statistics production in different ways, such as quality, efficiency, standardization, and process-orientation. It is used for all types of surveys, and \"business\" is not related to \"business statistics\" but refers to the statistical office, simply expressed." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The Generic Statistical Business Process Model (GSBPM) aims to describe statistics production in a general and process-oriented way." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. GSBPM is in use. 0 Points. Otherwise" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D5.2.2.NABY", "metatype": [ { "id": "Developmentrelevance", "value": "National accounts base year is the year used as the base period for constant price calculations in the country’s national accounts. It is recommended that the base year of constant price estimates be changed periodically to reflect changes in economic structure and relative prices." }, { "id": "IndicatorName", "value": "National Accounts base year" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "National accounts base year is the year used as the base period for constant price calculations in the country’s national accounts. It is recommended that the base year of constant price estimates be changed periodically to reflect changes in economic structure and relative prices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "National accounts base year is the year used as the base period for constant price calculations in the country’s national accounts." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 point for chained price, 0.5 for reference period within past 10 years. 0 points otherwise." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D5.2.3.CNIN", "metatype": [ { "id": "Developmentrelevance", "value": "The industrial production data are compiled using the International Standard Industrial Classification of All Economic Activities (ISIC) Rev.4 and Statistical Classification of Economic Activities in the European Community (NACE) Rev.2. ISIC Rev.4 is a standard classification of economic activities arranged so that entities can be classified per the activity they carry out using criteria such as input, output and use of the products produced, more emphasis has been given to the character of the production process in defining and delineating ISIC classes for international comparability. The manual and classification have changed to cover the complete scope of industrial production, employment, and GDP and other statistical areas." }, { "id": "IndicatorName", "value": "Classification of national industry" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "The industrial production data are compiled using the International Standard Industrial Classification of All Economic Activities (ISIC) Rev.4 and Statistical Classification of Economic Activities in the European Community (NACE) Rev.2. ISIC Rev.4 is a standard classification of economic activities arranged so that entities can be classified per the activity they carry out using criteria such as input, output and use of the products produced, more emphasis has been given to the character of the production process in defining and delineating ISIC classes for international comparability. The manual and classification have changed to cover the complete scope of industrial production, employment, and GDP and other statistical areas." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "The industrial production data are compiled using the International Standard Industrial Classification of All Economic Activities (ISIC) Rev.4 and Statistical Classification of Economic Activities in the European Community (NACE) Rev.2." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. Latest version is adopted (ISIC Rev 4, NACE Rev 2 or a compatible classification). 0.5 Points. Previous version is used (ISIC Rev 3, NACE Rev 1 or a compatible classification). 0 Points. Otherwise" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D5.2.4.CPIBY", "metatype": [ { "id": "Developmentrelevance", "value": "Consumer Price Index serves as indicators of inflation and reflects changes in the cost of acquiring a fixed basket of goods and services by the average consumer. Weights are usually derived from consumer expenditure surveys and the CPI base year refers to the year the weights were derived. It is recommended that the base year be changed periodically to reflect changes in expenditure structure." }, { "id": "IndicatorName", "value": "CPI base year" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Consumer Price Index serves as indicators of inflation and reflects changes in the cost of acquiring a fixed basket of goods and services by the average consumer. Weights are usually derived from consumer expenditure surveys and the CPI base year refers to the year the weights were derived. It is recommended that the base year be changed periodically to reflect changes in expenditure structure." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Consumer Price Index serves as indicators of inflation and reflects changes in the cost of acquiring a fixed basket of goods and services by the average consumer." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. Annual chain linking. 0.5 Points. Base year in last 10 years. 0 points. Otherwise" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D5.2.5.HOUS", "metatype": [ { "id": "Developmentrelevance", "value": "Classification of Individual Consumption According to Purpose (COICOP) is used in household budget surveys, consumer price indices and international comparisons of gross domestic product (GDP) and its component expenditures. Although COICOP is not strictly linked to any particular model of consumer behavior, the classification is designed to broadly reflect differences in income elasticities. It is an integral part of the SNA1993 and more detailed subdivision of the classes provide comparability between countries and between statistics in these different areas." }, { "id": "IndicatorName", "value": "Classification of household consumption" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Classification of Individual Consumption According to Purpose (COICOP) is used in household budget surveys, consumer price indices and international comparisons of gross domestic product (GDP) and its component expenditures. Although COICOP is not strictly linked to any particular model of consumer behavior, the classification is designed to broadly reflect differences in income elasticities. It is an integral part of the SNA1993 and more detailed subdivision of the classes provide comparability between countries and between statistics in these different areas." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Classification of Individual Consumption According to Purpose (COICOP) is used in household budget surveys, consumer price indices and international comparisons of gross domestic product (GDP) and its component expenditures." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. Follow Classification of Individual Consumption by Purpose (COICOP). 0 Points. Otherwise" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D5.2.6.EMPL", "metatype": [ { "id": "Developmentrelevance", "value": "Classification of status of employment refers to employment data that are compiled using the current international standard International Classification of Status in Employment (ISCE-93). It classifies jobs with respect to the type of explicit or implicit contract of employment between the job holder and the economic unit in which he or she is employed. Therefore, it aims to provide the basis for production of internationally comparable statistics on the employment relationship, including the distinction between salaried employment and self-employment." }, { "id": "IndicatorName", "value": "Classification of status of employment" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Classification of status of employment refers to employment data that are compiled using the current international standard International Classification of Status in Employment (ISCE-93). It classifies jobs with respect to the type of explicit or implicit contract of employment between the job holder and the economic unit in which he or she is employed. Therefore, it aims to provide the basis for production of internationally comparable statistics on the employment relationship, including the distinction between salaried employment and self-employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Classification of status of employment refers to employment data that are compiled using the current international standard International Classification of Status in Employment (ISCE-93)." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. Follow International Labour Organization, International Classification of Status in Employment (ICSE-93) or 2012 North American Industry Classification System (NAICS). 0 Points Otherwise." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D5.2.7.CGOV", "metatype": [ { "id": "Developmentrelevance", "value": "Government finance accounting status refers to the accounting basis for reporting central government financial data. For many countries’ government finance data, have been consolidated into one set of accounts capturing all the central government’s fiscal activities and following noncash recording basis. Budgetary central government accounts do not necessarily include all central government units, the picture they provide of central government activities is usually incomplete." }, { "id": "IndicatorName", "value": "Central government accounting status" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Government finance accounting status refers to the accounting basis for reporting central government financial data. For many countries’ government finance data, have been consolidated into one set of accounts capturing all the central government’s fiscal activities and following noncash recording basis. Budgetary central government accounts do not necessarily include all central government units, the picture they provide of central government activities is usually incomplete." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Government finance accounting status refers to the accounting basis for reporting central government financial data." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. Consolidated central government accounting follows noncash recording basis. 0.5 Points. Consolidated central government accounting follows cash recording basis. 0 Points. Otherwise" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D5.2.8.FINA", "metatype": [ { "id": "Developmentrelevance", "value": "Compilation of government finance statistics refers to the Government Finance Statistics Manual (GFSM) in use for compiling the data. It provides guidelines on the institutional structure of governments and the presentation of fiscal data in a format similar to business accounting with a balance sheet and income statement plus guidelines on the treatment of exchange rate and other valuation adjustments. The latest manual GFSM2014 is harmonized with the SNA2008." }, { "id": "IndicatorName", "value": "Compilation of government finance statistics" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Compilation of government finance statistics refers to the Government Finance Statistics Manual (GFSM) in use for compiling the data. It provides guidelines on the institutional structure of governments and the presentation of fiscal data in a format similar to business accounting with a balance sheet and income statement plus guidelines on the treatment of exchange rate and other valuation adjustments. The latest manual GFSM2014 is harmonized with the SNA2008." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Compilation of government finance statistics refers to the Government Finance Statistics Manual (GFSM) in use for compiling the data." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. Follow the latest Government Finance Statistical Manual (2014)/ ESA2010. 0.5 Points. Previous version is used (GFSM 2001). 0 Points. Otherwise" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D5.2.9.MONY", "metatype": [ { "id": "Developmentrelevance", "value": "Compilation of monetary and financial statistics refers to the Monetary and Financial Statistics Manual (MFSM) in use. It covers concepts, definitions, classifications of financial instruments and sectors, and accounting rules, and provides a comprehensive analytic framework for monetary and financial planning and policy determination. The Monetary and Finance Statistics: Compilation Guide (2008) provides detailed guidelines for the compilation of monetary and financial statistics in addition to MFSM." }, { "id": "IndicatorName", "value": "Compilation of monetary and financial statistics" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Compilation of monetary and financial statistics refers to the Monetary and Financial Statistics Manual (MFSM) in use. It covers concepts, definitions, classifications of financial instruments and sectors, and accounting rules, and provides a comprehensive analytic framework for monetary and financial planning and policy determination. The Monetary and Finance Statistics: Compilation Guide (2008) provides detailed guidelines for the compilation of monetary and financial statistics in addition to MFSM." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Compilation of monetary and financial statistics refers to the Monetary and Financial Statistics Manual (MFSM) in use." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "1 Point. Follow the latest Monetary and Finance Statistics Manual (2000) or Monetary and Finance Statistics: Compilation Guide (2008/2016). 0 Points. Otherwise" }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.D5.5.DIFI", "metatype": [ { "id": "Developmentrelevance", "value": "Indicator based on PARIS21 SDG indicators (SDG 17.18.3 (national statistical plan that is fully funded and under implementation) and SDG 17.19.1 (value of resources made available to strengthen statistical capacity)). Could also incorporate indicator of NSO budget as a percentage of GDP." }, { "id": "IndicatorName", "value": "Finance Indicator based on PARIS21 indicators on SDG 17.18.3 & SDG 17.19.1" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Indicator based on PARIS21 SDG indicators (SDG 17.18.3 (national statistical plan that is fully funded and under implementation) and SDG 17.19.1 (value of resources made available to strengthen statistical capacity)). Could also incorporate indicator of NSO budget as a percentage of GDP." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Indicator based on PARIS21 SDG indicators (SDG 17.18.3 (national statistical plan that is fully funded and under implementation) and SDG 17.19.1 (value of resources made available to strengthen statistical capacity))." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Scores is 1 if the country has a national statistical plan that is fully funded and under implementation. Scores of 0 or scores with missing values are treated the same (both given a score of zero)." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.DIM1.5.INDEX", "metatype": [ { "id": "Developmentrelevance", "value": "Five measures usefulness or reliability of country produced measures for international organizations. First, on comparability of poverty estimates for the World Bank reporting on international poverty (Source: Povcalnet). Second on usable surveys for statistics on child mortality for the UN Inter-agency Group for Child Mortality Estimation (Source: https://childmortality.org/). Third on accuracy of debt reporting as classified by the World Bank (Source: World Bank WDI metadata). Fourth, on availability of safely managed drinking water data for use by JMP. Fifth, on labor force participation data for use by ILO. We recognize that these data sources provide only partial coverage but consider that they do at least provide some indication of the performance of the national statistical system. With more complete data sources it would be possible to assess this further" }, { "id": "IndicatorName", "value": "Dimension 1.5: Data use by international organizations" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Five measures usefulness or reliability of country produced measures for international organizations. First, on comparability of poverty estimates for the World Bank reporting on international poverty (Source: Povcalnet). Second on usable surveys for statistics on child mortality for the UN Inter-agency Group for Child Mortality Estimation (Source: https://childmortality.org/). Third on accuracy of debt reporting as classified by the World Bank (Source: World Bank WDI metadata). Fourth, on availability of safely managed drinking water data for use by JMP. Fifth, on labor force participation data for use by ILO. We recognize that these data sources provide only partial coverage but consider that they do at least provide some indication of the performance of the national statistical system. With more complete data sources it would be possible to assess this further" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Five measures usefulness or reliability of country produced measures for international organizations." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Unweighted average of indicators in this dimension. Scores range from 0-1, with a score of 1 representing the best possible score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.DIM2.1.INDEX", "metatype": [ { "id": "Developmentrelevance", "value": "SDDS/e-GDDS subscription. This indicator is based on whether the country subscribes to IMF SDDS+, SDDS, or e-GDDS standards. The source is the IMF Dissemination Standards Bulletin Board. This is a good data source but we recognize that it is a proxy for the concept we are seeking to capture rather than a direct measurement." }, { "id": "IndicatorName", "value": "Dimension 2.1: Data Releases" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "SDDS/e-GDDS subscription. This indicator is based on whether the country subscribes to IMF SDDS+, SDDS, or e-GDDS standards. The source is the IMF Dissemination Standards Bulletin Board. This is a good data source but we recognize that it is a proxy for the concept we are seeking to capture rather than a direct measurement." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "SDDS/e-GDDS subscription." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Unweighted average of indicators in this dimension. Scores range from 0-1, with a score of 1 representing the best possible score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.DIM2.2.INDEX", "metatype": [ { "id": "Developmentrelevance", "value": "ODIN Open Data Openness score. This is a well-established data source with good country coverage. In using this indicator, it is important to describe carefully what is captured since the purpose of ODIN is different to the purpose of the SPI." }, { "id": "IndicatorName", "value": "Dimension 2.2: Online access" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "ODIN Open Data Openness score. This is a well-established data source with good country coverage. In using this indicator, it is important to describe carefully what is captured since the purpose of ODIN is different to the purpose of the SPI." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "ODIN Open Data Openness score." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Unweighted average of indicators in this dimension. Scores range from 0-1, with a score of 1 representing the best possible score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.DIM2.4.INDEX", "metatype": [ { "id": "Developmentrelevance", "value": "NADA metadata. This indicator checks whether NADA microdata cataloging is available for surveys produced by NSO. NADA is an open source microdata cataloging system, compliant with the Data Documentation Initiative (DDI) and Dublin Cores RDF metadata standards. Source: NSO websites." }, { "id": "IndicatorName", "value": "Dimension 2.4: Data services" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "NADA metadata. This indicator checks whether NADA microdata cataloging is available for surveys produced by NSO. NADA is an open source microdata cataloging system, compliant with the Data Documentation Initiative (DDI) and Dublin Cores RDF metadata standards. Source: NSO websites." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "NADA metadata." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Unweighted average of indicators in this dimension. Scores range from 0-1, with a score of 1 representing the best possible score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.DIM3.1.INDEX", "metatype": [ { "id": "Developmentrelevance", "value": "Average score for Goal 1-6 indicators. The primary data source is the UN SDG database. Whilst this is a database with comprehensive coverage that all countries have signed up to, it is clear that many (particularly developed countries) are not yet submitting their available national data. Scores for these countries are likely to represent an indicator of their willingness to submit national data rather than their performance in calculating the indicators. For OECD countries, we supplement the UN SDG database with comparable data submitted to the OECD following the methodology in Measuring Distance to the SDG Targets 2019: An Assessment of Where OECD Countries Stand (https://www.oecd.org/sdd/measuring-distance-to-the-sdg-targets-2019-a8caf3fa-en.htm)." }, { "id": "IndicatorName", "value": "Dimension 3.1: Social Statistics" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average score for Goal 1-6 indicators. The primary data source is the UN SDG database. Whilst this is a database with comprehensive coverage that all countries have signed up to, it is clear that many (particularly developed countries) are not yet submitting their available national data. Scores for these countries are likely to represent an indicator of their willingness to submit national data rather than their performance in calculating the indicators. For OECD countries, we supplement the UN SDG database with comparable data submitted to the OECD following the methodology in Measuring Distance to the SDG Targets 2019: An Assessment of Where OECD Countries Stand (https://www.oecd.org/sdd/measuring-distance-to-the-sdg-targets-2019-a8caf3fa-en.htm)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average score for Goal 1-6 indicators." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Unweighted average of indicators in this dimension. Scores range from 0-1, with a score of 1 representing the best possible score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.DIM3.2.INDEX", "metatype": [ { "id": "Developmentrelevance", "value": "Average score for Goal 7-12 indicators. See 3.1." }, { "id": "IndicatorName", "value": "Dimension 3.2: Economic Statistics" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average score for Goal 7-12 indicators. See 3.1." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average score for Goal 7-12 indicators." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Unweighted average of indicators in this dimension. Scores range from 0-1, with a score of 1 representing the best possible score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.DIM3.3.INDEX", "metatype": [ { "id": "Developmentrelevance", "value": "Average score for Goal 13-15 indicators. See 3.1." }, { "id": "IndicatorName", "value": "Dimension 3.3: Environmental Statistics" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average score for Goal 13-15 indicators. See 3.1." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average score for Goal 13-15 indicators." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Unweighted average of indicators in this dimension. Scores range from 0-1, with a score of 1 representing the best possible score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.DIM3.4.INDEX", "metatype": [ { "id": "Developmentrelevance", "value": "Average score for Goal 16-17 indicators. See 3.1." }, { "id": "IndicatorName", "value": "Dimension 3.4: Institutional Statistics" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average score for Goal 16-17 indicators. See 3.1." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average score for Goal 16-17 indicators." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Unweighted average of indicators in this dimension. Scores range from 0-1, with a score of 1 representing the best possible score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.DIM4.1.CEN.INDEX", "metatype": [ { "id": "Developmentrelevance", "value": "Average score Census and Survey Indicators indicators. Availability of recent censuses and surveys covering broad areas. The following censuses and surveys are considered: Population & Housing census, Agriculture census, Business/establishment census, Household Survey on income/ consumption/ expenditure/ budget/ Integrated Survey, Agriculture survey, Labor Force Survey, Health/Demographic survey, Business/establishment survey. Source: NSO websites, World Bank microdata library, ILO microdata library, IHSN microdata librar" }, { "id": "IndicatorName", "value": "Dimension 4.1: Censuses and Surveys - Censuses only" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average score Census and Survey Indicators indicators. Availability of recent censuses and surveys covering broad areas. The following censuses and surveys are considered: Population & Housing census, Agriculture census, Business/establishment census, Household Survey on income/ consumption/ expenditure/ budget/ Integrated Survey, Agriculture survey, Labor Force Survey, Health/Demographic survey, Business/establishment survey. Source: NSO websites, World Bank microdata library, ILO microdata library, IHSN microdata librar" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average score Census and Survey Indicators indicators." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Unweighted average of indicators in this dimension. Scores range from 0-1, with a score of 1 representing the best possible score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.DIM4.1.SVY.INDEX", "metatype": [ { "id": "Developmentrelevance", "value": "Average score Census and Survey Indicators indicators. Availability of recent censuses and surveys covering broad areas. The following censuses and surveys are considered: Population & Housing census, Agriculture census, Business/establishment census, Household Survey on income/ consumption/ expenditure/ budget/ Integrated Survey, Agriculture survey, Labor Force Survey, Health/Demographic survey, Business/establishment survey. Source: NSO websites, World Bank microdata library, ILO microdata library, IHSN microdata librar" }, { "id": "IndicatorName", "value": "Dimension 4.1: Censuses and Surveys - Surveys only" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average score Census and Survey Indicators indicators. Availability of recent censuses and surveys covering broad areas. The following censuses and surveys are considered: Population & Housing census, Agriculture census, Business/establishment census, Household Survey on income/ consumption/ expenditure/ budget/ Integrated Survey, Agriculture survey, Labor Force Survey, Health/Demographic survey, Business/establishment survey. Source: NSO websites, World Bank microdata library, ILO microdata library, IHSN microdata librar" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average score Census and Survey Indicators indicators." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Unweighted average of indicators in this dimension. Scores range from 0-1, with a score of 1 representing the best possible score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.DIM4.2.INDEX", "metatype": [ { "id": "Developmentrelevance", "value": "Average score for CRVS indicator. An ideal indicator would include a score based on the density of administrative data available in sectors of social protection, education, labor, and health. However, social protection, education, health, and labor admin data indicators not included because of lack of established methodology. While our team identified several promising sources for administrative data from the World Bank's ASPIRE team, WHO, UNESCO, and ILO, incomplete coverage across countries made us drop these indicators from our index. A major research and data collection effort is needed from all custodian agencies to fill in this information, so that a more comprehensive picture of administrative data availability can be produced." }, { "id": "IndicatorName", "value": "Dimension 4.2: Administrative Data" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average score for CRVS indicator. An ideal indicator would include a score based on the density of administrative data available in sectors of social protection, education, labor, and health. However, social protection, education, health, and labor admin data indicators not included because of lack of established methodology. While our team identified several promising sources for administrative data from the World Bank's ASPIRE team, WHO, UNESCO, and ILO, incomplete coverage across countries made us drop these indicators from our index. A major research and data collection effort is needed from all custodian agencies to fill in this information, so that a more comprehensive picture of administrative data availability can be produced." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average score for CRVS indicator." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Unweighted average of indicators in this dimension. Scores range from 0-1, with a score of 1 representing the best possible score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.DIM4.3.INDEX", "metatype": [ { "id": "Developmentrelevance", "value": "Geospatial data available at 1st Admin Level. We recognize that this data source provides only limited coverage but consider that it does at least provide some indication of the ability of the national statistical system to produce geospatial data. A major research and data collection effort is needed via GGIM to fill in this information, so that a more comprehensive picture of geospatial data capability at the national level can be produced. Until this is done, it we cannot even assess the scale of the data gaps in a comparable way." }, { "id": "IndicatorName", "value": "Dimension 4.3: Geospatial Data" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Geospatial data available at 1st Admin Level. We recognize that this data source provides only limited coverage but consider that it does at least provide some indication of the ability of the national statistical system to produce geospatial data. A major research and data collection effort is needed via GGIM to fill in this information, so that a more comprehensive picture of geospatial data capability at the national level can be produced. Until this is done, it we cannot even assess the scale of the data gaps in a comparable way." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Geospatial data available at 1st Admin Level." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Unweighted average of indicators in this dimension. Scores range from 0-1, with a score of 1 representing the best possible score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.DIM5.1.INDEX", "metatype": [ { "id": "Developmentrelevance", "value": "Included in dashboard, but not index because of insufficient country coverage. This indicator is based on PARIS21 indicators on SDG 17.18.2 (national statistical legislation compliance with UN Fundamental Principles of Official Statistics), existence of National Statistical Council, national statistical strategy generation, national statistical plan. Limited country coverage makes cross country comparison limited. A global database of statistical and data legislation and governance practice would be a valuable resource for capacity building in general not just for the SPI." }, { "id": "IndicatorName", "value": "Dimension 5.1: Legislation and governance" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Included in dashboard, but not index because of insufficient country coverage. This indicator is based on PARIS21 indicators on SDG 17.18.2 (national statistical legislation compliance with UN Fundamental Principles of Official Statistics), existence of National Statistical Council, national statistical strategy generation, national statistical plan. Limited country coverage makes cross country comparison limited. A global database of statistical and data legislation and governance practice would be a valuable resource for capacity building in general not just for the SPI." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Included in dashboard, but not index because of insufficient country coverage." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Unweighted average of indicators in this dimension. Scores range from 0-1, with a score of 1 representing the best possible score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.DIM5.2.INDEX", "metatype": [ { "id": "Developmentrelevance", "value": "Average score for Standards and Methods indicators. Internationally accepted and recommended methodology, classifications and standards provide the basis for national statistical offices (NSOs) on data integration, facilitating data exchange and providing the foundation for the preparation of relevant statistical indicators. The following methods and standards are considered: System of national accounts in use, National Accounts base year, Classification of national industry, CPI base year, Classification of household consumption, Classification of status of employment, Central government accounting status, Compilation of government finance statistics, Compilation of monetary and financial statistics, Business process. . Further work could improve the validity of this indicator and reduce the risk that countries may be incentivized to adopt only traditional standards and methods and neglect innovative solutions that may be more valid in the current context." }, { "id": "IndicatorName", "value": "Dimension 5.2: Standards and Methods" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Average score for Standards and Methods indicators. Internationally accepted and recommended methodology, classifications and standards provide the basis for national statistical offices (NSOs) on data integration, facilitating data exchange and providing the foundation for the preparation of relevant statistical indicators. The following methods and standards are considered: System of national accounts in use, National Accounts base year, Classification of national industry, CPI base year, Classification of household consumption, Classification of status of employment, Central government accounting status, Compilation of government finance statistics, Compilation of monetary and financial statistics, Business process. . Further work could improve the validity of this indicator and reduce the risk that countries may be incentivized to adopt only traditional standards and methods and neglect innovative solutions that may be more valid in the current context." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average score for Standards and Methods indicators." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Unweighted average of indicators in this dimension. Scores range from 0-1, with a score of 1 representing the best possible score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.DIM5.5.INDEX", "metatype": [ { "id": "Developmentrelevance", "value": "Included in dashboard, but not index because of insufficient country coverage and concerns that the indicator has biases that would lead to misleading incentives. The indicator is based on PARIS21 SDG indicators (SDG 17.18.3 (national statistical plan that is fully funded and under implementation)." }, { "id": "IndicatorName", "value": "Dimension 5.5: Finance" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Included in dashboard, but not index because of insufficient country coverage and concerns that the indicator has biases that would lead to misleading incentives. The indicator is based on PARIS21 SDG indicators (SDG 17.18.3 (national statistical plan that is fully funded and under implementation)." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Included in dashboard, but not index because of insufficient country coverage and concerns that the indicator has biases that would lead to misleading incentives." }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Unweighted average of indicators in this dimension. Scores range from 0-1, with a score of 1 representing the best possible score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.INDEX", "metatype": [ { "id": "Developmentrelevance", "value": "Overall Statistical Performance Indicators Index Score" }, { "id": "IndicatorName", "value": "SPI Overall Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Overall Statistical Performance Indicators Index Score" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Overall Statistical Performance Indicators Index Score" }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Weighted average of all statistical performance indicators. Scores range from 0-100 with 100 representing the best score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.INDEX.PIL1", "metatype": [ { "id": "Developmentrelevance", "value": "Indicators that capture demand side of statistical system" }, { "id": "IndicatorName", "value": "Pillar 1 - Data Use - Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Indicators that capture demand side of statistical system" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Indicators that capture demand side of statistical system" }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Weighted average of statistical performance indicators related to data use. Scores range from 0-100 with 100 representing the best score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.INDEX.PIL2", "metatype": [ { "id": "Developmentrelevance", "value": "Information on data releases, online access, and other data services" }, { "id": "IndicatorName", "value": "Pillar 2 - Data Services - Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Information on data releases, online access, and other data services" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Information on data releases, online access, and other data services" }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Weighted average of statistical performance indicators related to data services. Scores range from 0-100 with 100 representing the best score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.INDEX.PIL3", "metatype": [ { "id": "Developmentrelevance", "value": "Whether the country is able to produce relevant indicators, primarily related to SDGs" }, { "id": "IndicatorName", "value": "Pillar 3 - Data Products - Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Whether the country is able to produce relevant indicators, primarily related to SDGs" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Whether the country is able to produce relevant indicators, primarily related to SDGs" }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Weighted average of statistical performance indicators related to data products. Scores range from 0-100 with 100 representing the best score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.INDEX.PIL4", "metatype": [ { "id": "Developmentrelevance", "value": "Censuses and surveys, admin data, geospatial" }, { "id": "IndicatorName", "value": "Pillar 4 - Data Sources - Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Censuses and surveys, admin data, geospatial" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Censuses and surveys, admin data, geospatial" }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Weighted average of statistical performance indicators related to data sources. Scores range from 0-100 with 100 representing the best score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SPI.INDEX.PIL5", "metatype": [ { "id": "Developmentrelevance", "value": "Standards and methodology used in classification" }, { "id": "IndicatorName", "value": "Pillar 5 - Data Infrastructure - Score" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Standards and methodology used in classification" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Standards and methodology used in classification" }, { "id": "Source", "value": "Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)" }, { "id": "Statisticalconceptandmethodology", "value": "Weighted average of statistical performance indicators related to data infrastructure. Scores range from 0-100 with 100 representing the best score." }, { "id": "Topic", "value": "Statistical Performance" } ], "source_id": "83" }, { "id": "SE.GEPD.PRIM", "metatype": [ { "id": "IndicatorName", "value": "Learning Poverty Rate" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.GEPD.PRIM.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of 10 year old children who are out-of-school or in-school and not achieving basic proficiency on reading." }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.GEPD.PRIM.BMP", "metatype": [ { "id": "IndicatorName", "value": "Proficiency by End of Primary" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.GEPD.PRIM.BMP.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy and numeracy by end of primary, as reported by UIS" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.LPV.PRIM", "metatype": [ { "id": "IndicatorName", "value": "Learning poverty: Share of Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)" }, { "id": "Source", "value": "World Bank" } ], "source_id": "84" }, { "id": "SE.LPV.PRIM.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Share of Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)" }, { "id": "Source", "value": "World Bank" } ], "source_id": "84" }, { "id": "SE.PRM.ATTD", "metatype": [ { "id": "Generalcomments", "value": "Source Note: School Survey. Percent of 4th grade students who are present during an unannounced visit." }, { "id": "IndicatorName", "value": "Student Attendance" }, { "id": "Longdefinition", "value": "School Survey. Percent of 4th grade students who are present during an unannounced visit." }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ATTD.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of 4th grade students present during an unannounced visit" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ATTD.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of 4th grade students present during an unannounced visit - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ATTD.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of 4th grade students present during an unannounced visit - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ATTD.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of 4th grade students present during an unannounced visit - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ATTD.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of 4th grade students present during an unannounced visit - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BFIN", "metatype": [ { "id": "IndicatorName", "value": "Financing" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BFIN.1", "metatype": [ { "id": "IndicatorName", "value": "Financing score; where a score of 1 indicates low effectiveness and 5 indicates high effectiveness in terms of adequacy, efficiency, and equity." }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BFIN.2", "metatype": [ { "id": "IndicatorName", "value": "(Financing) - Adequacy expressed by the per child spending" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BFIN.3", "metatype": [ { "id": "IndicatorName", "value": "(Financing) Efficiency - Expressed by the score from the Public Expenditure and Financial Accountability (PEFA) assessment; where 0 is the lowest possible efficiency and 1 is the highest" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BFIN.4", "metatype": [ { "id": "IndicatorName", "value": "(Financing) Efficiency - Expressed by the relationship between financing and outcomes; where 0 is the lowest possible efficiency and 1 is the highest" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BFIN.5", "metatype": [ { "id": "IndicatorName", "value": "(Financing) - Equity" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BIMP", "metatype": [ { "id": "IndicatorName", "value": "Impartial Decision-Making" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BIMP.1", "metatype": [ { "id": "IndicatorName", "value": "Average score for Impartial Decision-Making; where a score of 1 indicates low effectiveness and 5 indicates high effectiveness" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BIMP.2", "metatype": [ { "id": "IndicatorName", "value": "(Impartial Decision-Making) average score for politicized personnel management" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BIMP.3", "metatype": [ { "id": "IndicatorName", "value": "(Impartial Decision-Making) average score for politicized policy-making" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BIMP.4", "metatype": [ { "id": "IndicatorName", "value": "(Impartial Decision-Making) average score for politicized policy implementation" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BIMP.5", "metatype": [ { "id": "IndicatorName", "value": "(Impartial Decision-Making) average score for employee unions as facilitators" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BMAC", "metatype": [ { "id": "IndicatorName", "value": "Mandates & Accountability" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BMAC.1", "metatype": [ { "id": "IndicatorName", "value": "Average score for Mandates & Accountability; where a score of 1 indicates low effectiveness and 5 indicates high effectiveness" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BMAC.2", "metatype": [ { "id": "IndicatorName", "value": "(Mandates & Accountability) Average score for coherence" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BMAC.3", "metatype": [ { "id": "IndicatorName", "value": "(Mandates & Accountability) Average score for transparency" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BMAC.4", "metatype": [ { "id": "IndicatorName", "value": "(Mandates & Accountability) Average score for accountability of public officials" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BNLG", "metatype": [ { "id": "IndicatorName", "value": "National Learning Goals" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BNLG.1", "metatype": [ { "id": "IndicatorName", "value": "Average score for National Learning Goals; where a score of 1 indicates low effectiveness and 5 indicates high effectiveness" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BNLG.2", "metatype": [ { "id": "IndicatorName", "value": "(National Learning Goals) Average score for targeting" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BNLG.3", "metatype": [ { "id": "IndicatorName", "value": "(National Learning Goals) Average score for monitoring" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BNLG.4", "metatype": [ { "id": "IndicatorName", "value": "(National Learning Goals) Average score for incentives" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BNLG.5", "metatype": [ { "id": "IndicatorName", "value": "(National Learning Goals) Average score for community engagement" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BQBR", "metatype": [ { "id": "IndicatorName", "value": "Characteristics of Bureaucracy" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BQBR.1", "metatype": [ { "id": "IndicatorName", "value": "Average score for Characteristics of Bureaucracy; where a score of 1 indicates low effectiveness and 5 indicates high effectiveness" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BQBR.2", "metatype": [ { "id": "IndicatorName", "value": "(Characteristics of Bureaucracy) average score for knowledge and skills" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BQBR.3", "metatype": [ { "id": "IndicatorName", "value": "(Characteristics of Bureaucracy) average score for work environment" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BQBR.4", "metatype": [ { "id": "IndicatorName", "value": "(Characteristics of Bureaucracy) average score for merit" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.BQBR.5", "metatype": [ { "id": "IndicatorName", "value": "(Characteristics of Bureaucracy) average score for motivation and attitudes" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.CONT", "metatype": [ { "id": "IndicatorName", "value": "Content Knowledge" }, { "id": "Longdefinition", "value": "School survey. Fraction correct on teacher assessment. In the future, we will align with SDG criteria for minimum proficiency. We have dropped the \"correct the letter\" exercise from the teacher assessment, due to cross,country comparability issues." }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.CONT.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subjects they teach" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.CONT.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subjects they teach - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.CONT.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subjects they teach - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.CONT.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subjects they teach - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.CONT.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subjects they teach - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.CONT.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of language" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.CONT.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of language - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.CONT.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of language - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.CONT.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of language - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.CONT.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of language - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.CONT.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of mathematics" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.CONT.3.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of mathematics - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.CONT.3.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of mathematics - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.CONT.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of mathematics - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.CONT.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers proficient in the subject of mathematics - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.EFFT", "metatype": [ { "id": "Generalcomments", "value": "Source Note: School survey. Percent of teachers present. Teacher is coded absent if they are: , not in school - in school but absent from the class." }, { "id": "IndicatorName", "value": "Teacher Presence" }, { "id": "Longdefinition", "value": "School survey. Percent of teachers absent. Teacher is coded absent if they are: , not in school - in school but absent from the class." }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.EFFT.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their classrooms, when they are scheduled to be teaching, during an announced visit" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.EFFT.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their classrooms, when they are scheduled to be teaching, during an announced visit - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.EFFT.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their classrooms, when they are scheduled to be teaching, during an announced visit - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.EFFT.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their classrooms, when they are scheduled to be teaching, during an announced visit - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.EFFT.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their classrooms, when they are scheduled to be teaching, during an announced visit - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.EFFT.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their schools during an announced visit" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.EFFT.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their schools during an announced visit - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.EFFT.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their schools during an announced visit - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.EFFT.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their schools during an announced visit - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.EFFT.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers present in their schools during an announced visit - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR", "metatype": [ { "id": "Generalcomments", "value": "Source Note: School survey. Total score starts at 1 and points added are the sum of whether a teacher has: , Had a classroom observation in past year - Had a discussion based on that observation that lasted longer than 30 min - Received actionable feedback from that observation - Teacher had a lesson plan and discussed it with another person" }, { "id": "IndicatorName", "value": "Instructional Leadership" }, { "id": "Longdefinition", "value": "School survey. Total score starts at 1 and points added are the sum of whether a teacher has: , Had a classroom observation in past year - Had a discussion based on that observation that lasted longer than 10 min - Received actionable feedback from that observation - Teacher had a lesson plan and discussed it with another person" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of instructional leadership" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of instructional leadership - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of instructional leadership - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of instructional leadership - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of instructional leadership - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having had their class observed" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having had their class observed - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having had their class observed - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having had their class observed - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having had their class observed - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the classroom observation happened recently" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.3.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the classroom observation happened recently - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.3.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the classroom observation happened recently - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the classroom observation happened recently - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the classroom observation happened recently - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having discussed the results of the classroom observation" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.4.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having discussed the results of the classroom observation - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.4.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having discussed the results of the classroom observation - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.4.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having discussed the results of the classroom observation - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.4.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having discussed the results of the classroom observation - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the discussion was over 30 minutes" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.5.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the discussion was over 30 minutes - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.5.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the discussion was over 30 minutes - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.5.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the discussion was over 30 minutes - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.5.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that the discussion was over 30 minutes - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they were provided with feedback in that discussion" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.6.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they were provided with feedback in that discussion - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.6.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they were provided with feedback in that discussion - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.6.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they were provided with feedback in that discussion - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.6.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they were provided with feedback in that discussion - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.7", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having lesson plans" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.7.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having lesson plans - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.7.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having lesson plans - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.7.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having lesson plans - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.7.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having lesson plans - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they had discussed their lesson plans with someone else (pricinpal, pedagogical coordinator, another teacher)" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.8.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they had discussed their lesson plans with someone else (pricinpal, pedagogical coordinator, another teacher) - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.8.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they had discussed their lesson plans with someone else (pricinpal, pedagogical coordinator, another teacher) - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.8.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they had discussed their lesson plans with someone else (pricinpal, pedagogical coordinator, another teacher) - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ILDR.8.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they had discussed their lesson plans with someone else (pricinpal, pedagogical coordinator, another teacher) - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.IMON", "metatype": [ { "id": "IndicatorName", "value": "Monitoring" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.IMON.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools that report there is someone monitoring that basic inputs are available to students" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.IMON.10", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Number of basic infrastructure features clearly articulated as needing to be monitored" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.IMON.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools that report that parents or community members are involved in the monitoring of availability of basic inputs" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.IMON.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools that report that there is an inventory to monitor availability of basic inputs" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.IMON.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools that report there is someone monitoring that basic infrastructure is available" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.IMON.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools that report that parents or community members are involved in the monitoring of availability of basic infrastructure" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.IMON.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools that report that there is an inventory to monitor availability of basic infrastructure" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.IMON.7", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is the responsibility of monitoring basic inputs clearly articulated in the policies?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.IMON.8", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Number of basic inputs clearly articulated as needing to be monitored" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.IMON.9", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is the responsibility of monitoring basic infrastructure clearly articulated in the policies?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.IMON.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Inputs & Infrastructure) - Monitoring" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.IMON.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Inputs & Infrastructure) - Monitoring" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR", "metatype": [ { "id": "Generalcomments", "value": "Source Note: School survey. Total score is the sum of whether a school has: , Access to adequate drinking water -Functional toilets that are separate for boys/girls, private, useable, and have hand washing facilities - Electricity - Internet - School is accessible for those with disabilities (road access, a school ramp for wheelchairs, an entrance wide enough for wheelchairs, ramps to classrooms where needed, accessible toilets, and disability screening for seeing, hearing, and learning disabilities with partial credit for having 1 or 2 or the 3).)" }, { "id": "IndicatorName", "value": "Basic Infrastructure" }, { "id": "Longdefinition", "value": "School survey. Total score is the sum of whether a school has: , Access to adequate drinking water -Functional toilets that are separate for boys/girls, private, useable, and have hand washing facilities - Electricity - Visibility in the classroom - School is accessible for those with disabilities (road access, a school ramp for wheelchairs, an entrance wide enough for wheelchairs, ramps to classrooms where needed, accessible toilets, and disability screening for seeing, hearing, and learning disabilities with partial credit for having 1 or 2 or the 3).)" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average number of infrastructure aspects present in schools" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average number of infrastructure aspects present in schools - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average number of infrastructure aspects present in schools - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with drinking water" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with drinking water - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with drinking water - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with functioning toilets" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with functioning toilets - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with functioning toilets - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to electricity" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.4.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to electricity - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.4.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to electricity - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to internet" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.5.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to internet - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.5.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to internet - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools accessible to children with special needs" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.6.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools accessible to children with special needs - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INFR.6.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools accessible to children with special needs - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INPT", "metatype": [ { "id": "Generalcomments", "value": "Source Note: School survey. Total score is the sum of whether a school has: , Functional blackboard - Pens, pencils, exercise books - Textbooks - Fraction of students in class with a desk - Used ICT in class and have access to ICT in the school." }, { "id": "IndicatorName", "value": "Basic Inputs" }, { "id": "Longdefinition", "value": "School survey. Total score is the sum of whether a school has: , Functional blackboard - Pens, pencils, exercise books
- Textbooks - Fraction of students in class with a desk - Used ICT in class and have access to ICT in the school." }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INPT.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average number of classroom inputs in classrooms" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INPT.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average number of classroom inputs in classrooms - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INPT.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average number of classroom inputs in classrooms - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INPT.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of classrooms with a functional blackboard and chalk" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INPT.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of classrooms with a functional blackboard and chalk - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INPT.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of classrooms with a functional blackboard and chalk - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INPT.3", "metatype": [ { "id": "IndicatorName", "value": "(De facto) Percent of classrooms equipped with pens/pencils, textbooks, and exercise books" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INPT.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De facto) Percent of classrooms equipped with pens/pencils, textbooks, and exercise books - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INPT.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De facto) Percent of classrooms equipped with pens/pencils, textbooks, and exercise books - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INPT.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of classrooms with basic classroom furniture" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INPT.4.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of classrooms with basic classroom furniture - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INPT.4.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of classrooms with basic classroom furniture - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INPT.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to EdTech" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INPT.5.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to EdTech - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.INPT.5.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools with access to EdTech - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD", "metatype": [ { "id": "IndicatorName", "value": "Standards" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy in place to require that students have access to the prescribed textbooks?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD.10", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if there is a policy in place to require that schools have access to drinking water?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD.11", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy in place to require that schools have functioning toilets?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD.12", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if there is a policy in place to require that schools have functioning toilets?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD.13", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy in place to require that schools are accessible to children with special needs?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD.14", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if there is there a policy in place to require that schools are accessible to children with special needs?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if there is a policy in place to require that students have access to the prescribed textbooks?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD.3", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a national connectivity program?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if there is a national connectivity program?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD.5", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy in place to require that students have access to PCs, laptops, tablets, and/or other computing devices?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if there is a policy in place to require that students have access to PCs, laptops, tablets, and/or other computing devices?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD.7", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy in place to require that schools have access to electricity?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if there is a policy in place to require that schools have access to electricity?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD.9", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy in place to require that schools have access to drinking water?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Inputs & Infrastructure) - Standards" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.ISTD.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Inputs & Infrastructure) - Standards" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP", "metatype": [ { "id": "Generalcomments", "value": "Source Note: School survey. Percent of sampled 1st grade students scoring at least 80% on GEPD Direct Assessment. In such assessment, total equal points (100) are allocated equally across the 4 domains measured. These include numeracy, literacy, socioemotional skills, and executive function. Within each domain, all questions are given an equal weight." }, { "id": "IndicatorName", "value": "Readiness for Learning" }, { "id": "Longdefinition", "value": "School survey. Fraction correct on the Early Childhood Assessment given to students in school." }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average developmental score for 1st Graders" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average developmental score for 1st Graders - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average developmental score for 1st Graders - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average developmental score for 1st Graders - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average developmental score for 1st Graders - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average numeracy score for 1st Graders" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average numeracy score for 1st Graders - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average numeracy score for 1st Graders - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average numeracy score for 1st Graders - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average numeracy score for 1st Graders - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average literacy score for 1st Graders" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.3.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average literacy score for 1st Graders - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.3.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average literacy score for 1st Graders - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average literacy score for 1st Graders - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average literacy score for 1st Graders - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average executive funcion score for 1st Graders" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.4.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average executive funcion score for 1st Graders - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.4.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average executive funcion score for 1st Graders - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.4.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average executive funcion score for 1st Graders - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.4.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average executive funcion score for 1st Graders - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average socioemotional score for 1st Graders" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.5.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average socioemotional score for 1st Graders - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.5.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average socioemotional score for 1st Graders - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.5.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average socioemotional score for 1st Graders - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCAP.5.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average socioemotional score for 1st Graders - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCBC", "metatype": [ { "id": "IndicatorName", "value": "Early Childhood Education" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCBC.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy that guarantees free education for some or all grades and ages included in pre-primary education (for children age 0-83 months)?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCBC.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children age 36-59 months who are attending an early childhood education programme" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCBC.3", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are there developmental standards established for early childhood care and education?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCBC.4", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) According to laws and regulations, are there requirement to become an early childhood educator, pre-primary teacher?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCBC.5", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) According to policy, are ECCE professionals working at public or private centers required to complete in-service training in ECCE service delivery?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCBC.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Learners) - Center-Based Care" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LCBC.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Learners) - Center-Based Care" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LERN", "metatype": [ { "id": "Generalcomments", "value": "Source Note: The fraction of students scoring at least 20/24 points on 4th grade language and 14/17 points on the math student assessment. Our team consulted several content experts to advise on how many of our math and language items a minimally proficient 4th grade student should be able to get correct to decide these thresholds." }, { "id": "IndicatorName", "value": "Proficiency on GEPD Assessment" }, { "id": "Longdefinition", "value": "The fraction correct on 4th grade student assessment." }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LERN.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy and numeracy according to GEPD School Survey" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LERN.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy and numeracy according to GEPD School Survey - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LERN.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy and numeracy according to GEPD School Survey - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LERN.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy and numeracy according to GEPD School Survey - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LERN.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy and numeracy according to GEPD School Survey - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LERN.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy according to GEPD School Survey" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LERN.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy according to GEPD School Survey - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LERN.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy according to GEPD School Survey - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LERN.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy according to GEPD School Survey - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LERN.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy according to GEPD School Survey - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LERN.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in numeracy according to GEPD School Survey" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LERN.3.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in numeracy according to GEPD School Survey - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LERN.3.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in numeracy according to GEPD School Survey - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LERN.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in numeracy according to GEPD School Survey - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LERN.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in numeracy according to GEPD School Survey - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LFCP", "metatype": [ { "id": "IndicatorName", "value": "Caregiver Financial Capacity" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LFCP.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are anti poverty interventions that focus on ECD publicly supported?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LFCP.2", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are cash transfers conditional on ECD services/enrollment publicly supported?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LFCP.3", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are cash transfers focused partially on ECD publicly supported?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LFCP.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Coverage of social protection programs" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LFCP.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Learners) - Caregiver Capacity – Financial Capacity" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LFCP.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Learners) - Caregiver Capacity – Financial Capacity" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LHTH", "metatype": [ { "id": "IndicatorName", "value": "Health Programs" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LHTH.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are young children required to receive a complete course of childhood immunizations?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LHTH.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children who at age 24-35 months had received all vaccinations recommended in the national immunization schedule" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LHTH.3", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy that assures access to healthcare for young children? Either by offering these services free or by subsidizing them" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LHTH.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children under 5 covered by health insurance" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LHTH.5", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are deworming pills funded and distributed by the government?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LHTH.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children age 6-59 months who received deworming medication" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LHTH.7", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy that guarantees pregnant women free antenatal visits and skilled delivery?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LHTH.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of women age 15-49 years with a live birth in the last 2 years whose most recent live birth was delivered in a health facility" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LHTH.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Learners) - Health" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LHTH.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Learners) - Health" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LNTN", "metatype": [ { "id": "IndicatorName", "value": "Nutrition Programs" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LNTN.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Does a national policy to encourage salt iodization exist?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LNTN.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of households with salt testing positive for any iodide among households" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LNTN.3", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Does a national policy exist to encourage iron fortification of staples like wheat, maize, or rice?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LNTN.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children age 6–23 months who had at least the minimum dietary diversity and the minimum meal frequency during the previous day" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LNTN.5", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Does a national policy exist to encourage breastfeeding?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LNTN.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children born in the five (three) years preceding the survey who were ever breastfed" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LNTN.7", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a publicly funded school feeding program?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LNTN.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of schools reporting having publicly funded school feeding program" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LNTN.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Learners) - Nutrition Programs" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LNTN.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Learners) - Nutrition Programs" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LSKC", "metatype": [ { "id": "IndicatorName", "value": "Caregiver Skills Capacity" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LSKC.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Does the government offer programs that aim to share good parenting practices with caregivers?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LSKC.2", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are any of the following publicly-supported delivery channels used to reach families in order to promote early childhood stimulation? Home visits, Group sessions, Community health programs, Health center waiting rooms, School-based groups, Mass media/Information campaigns" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LSKC.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children under age 5 who have three or more children’s books" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LSKC.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children age 24-59 months engaged in four or more activities to provide early stimulation and responsive care in the last 3 days with any adult in the household" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LSKC.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Learners) - Caregiver Capacity – Skills Capacity" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.LSKC.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Learners) - Caregiver Capacity – Skills Capacity" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.OPMN", "metatype": [ { "id": "Generalcomments", "value": "Source Note: School Survey. Principals/head teachers are given two vignettes: , One on solving the problem of a hypothetical leaky roof - One on solving a problem of inadequate numbers of textbooks. Each vignette is worth 2 points. The indicator will measure two things: presence of functions and quality of functions. In each vignette: - 0.5 points are awarded for someone specific having the responsibility to fix - 0.5 point is awarded if the school can fully fund the repair, 0.25 points is awarded if the school must get partial help from the community, and 0 points are awarded if the full cost must be born by the community - 1 point is awarded if the problem is fully resolved in a timely manner, with partial credit given if problem can only be partly resolved." }, { "id": "IndicatorName", "value": "Operational Management" }, { "id": "Longdefinition", "value": "School Survey. Principals/head teachers are given two vignettes: , One on solving the problem of a hypothetical leaky roof - One on solving a problem of inadequate numbers of textbooks. Each vignette is worth 2 points. The indicator will measure two things: presence of functions and quality of functions. In each vignette: - 0.5 points are awarded for someone specific having the responsibility to fix - 0.5 point is awarded if the school can fully fund the repair, 0.25 points is awarded if the school must get partial help from the community, and 0 points are awarded if the full cost must be born by the community - 1 point is awarded if the problem is fully resolved in a timely manner, with partial credit given if problem can only be partly resolved." }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.OPMN.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of core operational management functions" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.OPMN.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of core operational management functions - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.OPMN.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of core operational management functions - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.OPMN.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of core operational management functions - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.OPMN.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the presence and quality of core operational management functions - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.OPMN.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for infrastructure repair/maintenance" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.OPMN.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for infrastructure repair/maintenance - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.OPMN.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for infrastructure repair/maintenance - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.OPMN.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for infrastructure repair/maintenance - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.OPMN.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for infrastructure repair/maintenance - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.OPMN.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for ensuring availability of school inputs" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.OPMN.3.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for ensuring availability of school inputs - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.OPMN.3.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for ensuring availability of school inputs - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.OPMN.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for ensuring availability of school inputs - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.OPMN.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for ensuring availability of school inputs - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG", "metatype": [ { "id": "IndicatorName", "value": "Pedagogical Skills" }, { "id": "Longdefinition", "value": "School survey. Based on TEACH ratings from classroom observations" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good pedagogical skills (3 or above on Teach overall score)" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good pedagogical skills (3 or above on Teach overall score) - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good pedagogical skills (3 or above on Teach overall score) - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good pedagogical skills (3 or above on Teach overall score) - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good pedagogical skills (3 or above on Teach overall score) - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good classroom culture practices (3 or above on Teach Classroom Culture score)" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good classroom culture practices (3 or above on Teach Classroom Culture score) - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good classroom culture practices (3 or above on Teach Classroom Culture score) - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good classroom culture practices (3 or above on Teach Classroom Culture score) - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good classroom culture practices (3 or above on Teach Classroom Culture score) - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good instruction practices (3 or above on Teach Instruction score)" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.3.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good instruction practices (3 or above on Teach Instruction score) - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.3.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good instruction practices (3 or above on Teach Instruction score) - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good instruction practices (3 or above on Teach Instruction score) - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good instruction practices (3 or above on Teach Instruction score) - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good practices on socioemotional skills (3 or above on Teach Socioemotional Skills score)" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.4.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good practices on socioemotional skills (3 or above on Teach Socioemotional Skills score) - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.4.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good practices on socioemotional skills (3 or above on Teach Socioemotional Skills score) - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.4.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good practices on socioemotional skills (3 or above on Teach Socioemotional Skills score) - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PEDG.4.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers with good practices on socioemotional skills (3 or above on Teach Socioemotional Skills score) - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW", "metatype": [ { "id": "IndicatorName", "value": "School Knowledge" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals are familiar with certain key aspects of the day-to-day workings of the school" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals are familiar with certain key aspects of the day-to-day workings of the school - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals are familiar with certain key aspects of the day-to-day workings of the school - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals are familiar with certain key aspects of the day-to-day workings of the school - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals are familiar with certain key aspects of the day-to-day workings of the school - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' content knowledge" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' content knowledge - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' content knowledge - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' content knowledge - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' content knowledge - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' experience" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.3.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' experience - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.3.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' experience - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' experience - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with teachers' experience - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with availability of classroom inputs" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.4.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with availability of classroom inputs - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.4.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with availability of classroom inputs - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.4.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with availability of classroom inputs - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PKNW.4.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals familiar with availability of classroom inputs - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PMAN", "metatype": [ { "id": "IndicatorName", "value": "Management Practices" }, { "id": "Longdefinition", "value": "Score of 1,5 based on sum of following: - 1 Point. School Goals Exists - 1 Point. School goals are clear to school director, teachers, students, parents, and other members of community (partial credit available) - 1 Point. Specific goals related to improving student achievement ( improving test scores, improving pass rates, reducing drop out, reducing absenteeism, improving pedagogy, more resources for infrastructure, more resources for inputs) - 1 Point. School has defined system to measure goals (partial credit available)" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PMAN.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master two key managerial skills - problem-solving in the short-term, and goal-setting in the long term" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PMAN.1.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master two key managerial skills - problem-solving in the short-term, and goal-setting in the long term - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PMAN.1.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master two key managerial skills - problem-solving in the short-term, and goal-setting in the long term - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PMAN.1.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master two key managerial skills - problem-solving in the short-term, and goal-setting in the long term - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PMAN.1.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master two key managerial skills - problem-solving in the short-term, and goal-setting in the long term - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PMAN.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master problem-solving in the short-term" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PMAN.2.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master problem-solving in the short-term - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PMAN.2.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master problem-solving in the short-term - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PMAN.2.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master problem-solving in the short-term - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PMAN.2.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master problem-solving in the short-term - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PMAN.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master goal-setting in the long term" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PMAN.3.F", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master goal-setting in the long term - Female" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PMAN.3.M", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master goal-setting in the long term - Male" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PMAN.3.R", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master goal-setting in the long term - Rural" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PMAN.3.U", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average score for the extent to which principals master goal-setting in the long term - Urban" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PROE", "metatype": [ { "id": "IndicatorName", "value": "Proficiency by Grade 2/3" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.PROE.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of children proficient in literacy and numeracy by grade 2/3, as reported by UIS" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SATT", "metatype": [ { "id": "IndicatorName", "value": "Attraction" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SATT.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Do the national policies governing the education system portray the position of principal or head teacher as professionalized and distinct figure within schools?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SATT.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average principal salary as percent of GDP per capita" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SATT.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals reporting being satisfied or very satisfied with their social status in the community" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SATT.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (School Management) - Attraction" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SATT.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (School Management) - Attraction" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN", "metatype": [ { "id": "IndicatorName", "value": "Clarity of Functions" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if the policies governing schools assign responsibility for the implementation of the maintenance and expansion of school infrastructure?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN.10", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Do the policies governing schools assign the responsibility of student learning assessments?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN.11", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if the policies governing schools assign responsibility for the implementation of principal hiring and assignment?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN.12", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Do the policies governing schools assign the responsibility of principal hiring and assignment?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN.13", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if the policies governing schools assign responsibility for the implementation of principal supervision and training?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN.14", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Do the policies governing schools assign the responsibility of principal supervision and training?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN.2", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Do the policies governing schools assign the responsibility of maintenance and expansion of school infrastructure?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if the policies governing schools assign responsibility for the implementation of the procurement of materials?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN.4", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Do the policies governing schools assign the responsibility of procurement of materials?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if the policies governing schools assign responsibility for the implementation of teacher hiring and assignment?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN.6", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Do the policies governing schools assign the responsibility of teacher hiring and assignment?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN.7", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if the policies governing schools assign responsibility for the implementation of teacher supervision, training, and coaching?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN.8", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Do the policies governing schools assign the responsibility of teacher supervision, training, and coaching?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN.9", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Do you know if the policies governing schools assign responsibility for the implementation of student learning assessments?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (School Management) - Clarity of Functions" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SCFN.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (School Management) - Clarity of Functions" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SEVL", "metatype": [ { "id": "IndicatorName", "value": "Evaluation" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SEVL.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a policy that specifies the need to monitor principal or head teacher performance?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SEVL.2", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is the criteria to evaluate principals clear and includes multiple factors?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SEVL.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report having been evaluated during the last school year" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SEVL.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report having been evaluated on multiple factors" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SEVL.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report there would be consequences after two negative evaluations" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SEVL.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report there would be consequences after two positive evaluations" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SEVL.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (School Management) - Evaluation" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SEVL.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (School Management) - Evaluation" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD", "metatype": [ { "id": "IndicatorName", "value": "Selection & Deployment" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a systematic approach/rubric for the selection of principals?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.10", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the most important factor considered when selecting a principal is years of experience" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.11", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the most important factor considered when selecting a principal is quality of teaching" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.12", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the most important factor considered when selecting a principal is demonstrated management qualities" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.13", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the most important factor considered when selecting a principal is having a good relationship with the owner of the school" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.14", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the most important factor considered when selecting a principal is political affiliations" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.15", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the most important factor considered when selecting a principal is ethnic group" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.16", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the most important factor considered when selecting a principal is knowledge of the local community" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.2", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) How are the principals selected? Based on the requirements, is the selection system meritocratic?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the factors considered when selecting a principal include years of experience" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the factors considered when selecting a principal include quality of teaching" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the factors considered when selecting a principal include demonstrated management qualities" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the factors considered when selecting a principal include good relationship with the owner of the school" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.7", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the factors considered when selecting a principal include political affiliations" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the factors considered when selecting a principal include ethnic group" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.9", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report that the factors considered when selecting a principal include knowledge of the local community" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (School Management) - Selection & Deployment" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSLD.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (School Management) - Selection & Deployment" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSUP", "metatype": [ { "id": "IndicatorName", "value": "Support" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSUP.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are principals required to have training on how to manage a school?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSUP.10", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report having used the skills they gained at the last training they attended" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSUP.11", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average number of trainings that principals report having been offered to them in the past year" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSUP.2", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are principals required to have management training for new principals?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSUP.3", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are principals required to have in-service training?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSUP.4", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Are principals required to have mentoring/coaching by experienced principals?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSUP.5", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) How many times per year do principals have trainings?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSUP.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report ever having received formal training" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSUP.7", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report having received management training for new principals" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSUP.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report having received in-service training" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSUP.9", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of principals that report having received mentoring/coaching by experienced principals" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSUP.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (School Management) - Support" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.SSUP.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (School Management) - Support" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TATT", "metatype": [ { "id": "IndicatorName", "value": "Attraction" }, { "id": "Longdefinition", "value": "In the school survey, a number of De Facto questions on teacher attraction are asked. 0.8 points is awarded for each of the following: , 0.8 Points. Teacher satisfied with job - 0.8 Points. Teacher satisfied with status in community - 0.8 Points. Would better teachers be promoted faster? - 0.8 Points. Do teachers receive bonuses? - 0.8 Points. One minus the fraction of months in past year with a salary delay." }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TATT.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Average starting public-school teacher salary as percent of GDP per capita" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TATT.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting being satisfied or very satisfied with their social status in the community" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TATT.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting being satisfied or very satisfied with their job as teacher" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TATT.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having received financial bonuses in addition to their salaries" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TATT.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that there are incentives (financial or otherwise) for teachers to teach certain subjects/grades and/or in certain areas" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TATT.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that performance matters for promotions" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TATT.7", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Is there a well-established career path for teachers?" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TATT.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that report salary delays in the past 12 months" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TATT.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Teaching) - Attraction" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TATT.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Teaching) - Attraction" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TENR", "metatype": [ { "id": "IndicatorName", "value": "Net Adjusted Enrollment Rate" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TENR.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of primary school age children who are enrolled at primary education" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TEVL", "metatype": [ { "id": "IndicatorName", "value": "Evaluation" }, { "id": "Longdefinition", "value": "School survey. This policy lever measures whether there is a teacher evaluation system in place, and if so, the types of decisions that are made based on the evaluation results. Score is the sum of the following: , 1 Point. Was teacher formally evaluated in past school year? - 1 Point total. 0.2 points for each of the following: Evaluation included evaluation of attendance, knowledge of subject matter, pedagogical skills in the classroom, students' academic achievement, students' socio-emotional development - 1 Point. Consequences exist if teacher receives 2 or more negative evaluations - 1 Point. Rewards exist if teacher receives 2 or more positive evaluations" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TEVL.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Legislation assigns responsibility of evaluating the performance of teachers to a public authority (national, regional, local)" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TEVL.2", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Legislation assigns responsibility of evaluating the performance of teachers to the schools" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TEVL.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that report being evaluated in the past 12 months" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TEVL.4", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) The criteria to evaluate teachers is clear" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TEVL.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Number of criteria used to evaluate teachers" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TEVL.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that report there would be consequences after two negative evaluations" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TEVL.7", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that report there would be consequences after two positive evaluations" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TEVL.8", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) There are clear consequences for teachers who receive two or more negative evaluations" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TEVL.9", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) There are clear consequences for teachers who receive two or more positive evaluations" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TEVL.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Teaching) - Evaluation" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TEVL.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Teaching) - Evaluation" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TINM", "metatype": [ { "id": "IndicatorName", "value": "Intrinsic Motivation" }, { "id": "Longdefinition", "value": "School Survey. This lever measures whether teachers are intrinsically motivated to teach. The question(s) aim to address this phenomenon by measuring the level of intrinsic motivation among teachers as well as teacher values that may be relevant for ensuring that the teacher is motivated to focus on all children and not just some. Score is sum of the following , Max 1 point. Average response to battery of questions on whether teachers considers it acceptable to be absent in certain situations. Average response scores scaled to be continuous score between 0-1. - Max 1 point. Average response to battery of questions on whether teachers consider some students to be more deserving of attention. Average response scores scaled to be continuous score between 0-1. - Max 1 point. Average response to battery of questions on teachers growth mindset regarding students. Average response scores scaled to be continuous score between 0-1. - 1 point. Binary response to whether teacher said they became teacher, because teaching offered steady career. Teacher scored 0 points if they reported wanting to teach for a steady career." }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TINM.1", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"It is acceptable for a teacher to be absent if the assigned curriculum has been completed\"" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TINM.10", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"Students can change even their basic intelligence level considerably\"" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TINM.11", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers who state that intrinsic motivation was the main reason to become teachers" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TINM.12", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) New teachers are required to undergo a probationary period" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TINM.13", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) New teachers are required to undergo a probationary period" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TINM.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"It is acceptable for a teacher to be absent if students are left with work to do\"" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TINM.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"It is acceptable for a teacher to be absent if the teacher is doing something useful for the community\"" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TINM.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"Students deserve more attention if they attend school regularly\"" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TINM.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"Students deserve more attention if they come to school with materials\"" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TINM.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"Students deserve more attention if they are motivated to learn\"" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TINM.7", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"Students have a certain amount of intelligence and they really can’t do much to change it\"" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TINM.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"To be honest, students can’t really change how intelligent they are\"" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TINM.9", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that agree or strongly agrees with \"Students can always substantially change how intelligent they are\"" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TINM.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Teaching) - Intrinsic Motivation" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TINM.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Teaching) - Intrinsic Motivation" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TMNA", "metatype": [ { "id": "IndicatorName", "value": "Monitoring & Accountability" }, { "id": "Longdefinition", "value": "School Survey. This policy lever measures the extent to which teacher presence is being monitored, whether attendance is rewarded, and whether there are consequences for chronic absence. Score is the sum of the following: , 1 Point. Teachers evaluated by some authority on basis of absence. - 1 Point. Good attendance is rewarded. - 1 Point. There are consequences for chronic absence (more than 30% absence). - 1 Point. One minus the fraction of teachers that had to miss class because of any of the following: collect paycheck, school administrative procedure, errands or request of the school district office, other administrative tasks." }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TMNA.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Information on teacher presence/absenteeism is being collected on a regular basis" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TMNA.2", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Teachers receive monetary compensation for being present" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TMNA.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Teacher report receiving monetary compensation (aside from salary) for being present" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TMNA.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that report having been absent because of administrative processes" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TMNA.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that report that there would be consequences for being absent 40% of the time" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TMNA.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Teaching) - Monitoring & Accountability" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TMNA.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Teaching) - Monitoring & Accountability" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSDP", "metatype": [ { "id": "IndicatorName", "value": "Selection & Deployment" }, { "id": "Longdefinition", "value": "School Survey. The De Facto portion of the Teacher Selection and Deployment Indicator considers two issues: how teachers are selected into the profession and how teachers are assigned to positions (transferred) once in the profession. Research shows that degrees and years of experience explain in little variation in teacher quality, so more points are assigned for systems that also base hiring on content knowledge or pedagogical skill. 2 points are available for the way teachers are selected and 2 points are available for deployment. Selection , 0 Points. None of the below - 1 point. Teachers selected based on completion of coursework, educational qualifications, graduating from tertiary program (including specialized programs), selected based on experience - 2 points. Teacher recruited based on passing written content knowledge test, passed interview stage assessment, passed an assessment conducted by supervisor based on practical experience, conduct during mockup class. Deployment - 0 Points. None of the below - 1 point. Teachers deployed based on years of experience or job title hierarchy or other criteria - 2 points. Teacher deployed based on performance assessed by school authority, colleagues, or external evaluator, results of interview." }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSDP.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Requirements to enter into initial education programs" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSDP.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average quality of applicants accepted into initial education programs" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSDP.3", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Requirements to become a primary school teacher" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSDP.4", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Requirements to become a primary school teacher" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSDP.5", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Requirements to fulfill a transfer request" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSDP.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Requirements to fulfill a transfer request" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSDP.7", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Selectivity of teacher hiring process" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSDP.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Teaching) - Selection & Deployment" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSDP.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Teaching) - Selection & Deployment" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSUP", "metatype": [ { "id": "IndicatorName", "value": "Support" }, { "id": "Longdefinition", "value": "School survey. Our teaching support indicator asks teachers about participation and the experience with several types of formal/informal training: Pre,Service (Induction) Training: - 0.5 Points. Had a pre-service training - 0.5 Points. Teacher reported receiving usable skills from training Teacher practicum (teach a class with supervision) - 0.5 Points. Teacher participated in a practicum - 0.5 Points. Practicum lasted more than 3 months and teacher spent more than one hour per day teaching to students. In-Service Training: - 0.5 Points. Had an in-service training - 0.25 Points. In-service training lasted more than 2 total days - 0.125 Points. More than 25% of the in-service training was done in the classroom. - 0.125 Points. More than 50% of the in-service training was done in the classroom. Opportunities for teachers to come together to share ways of improving teaching:
- 1 Point if such opportunities exist." }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSUP.1", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Practicum required as part of pre-service training" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSUP.2", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent reporting they completed a practicum as part of pre-service training" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSUP.3", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting that they participated in an induction and/or mentorship program" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSUP.4", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Participation in professional development has professional implications for teachers" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSUP.5", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers reporting having attended in-service trainings in the past 12 months" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSUP.6", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average length of the trainings attended" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSUP.7", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average span of time (in weeks) of those trainings" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSUP.8", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Average percent of time spent inside the classrooms during the trainings" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSUP.9", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Percent of teachers that report having opportunities to come together with other teachers to discuss ways of improving teaching" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSUP.DF", "metatype": [ { "id": "IndicatorName", "value": "(De Facto) Policy Lever (Teaching) - Support" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "SE.PRM.TSUP.DJ", "metatype": [ { "id": "IndicatorName", "value": "(De Jure) Policy Lever (Teaching) - Support" }, { "id": "Source", "value": "World Bank, Global Education Policy Dashboard" } ], "source_id": "84" }, { "id": "JI.AGE.MPYR", "metatype": [ { "id": "IndicatorName", "value": "Average age of employers, aged 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of employer, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.MPYR.FE", "metatype": [ { "id": "IndicatorName", "value": "Average age of employers, aged 15-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of employer, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.MPYR.HE", "metatype": [ { "id": "IndicatorName", "value": "Average age of employers, aged 15-64, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of employer, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.MPYR.LE", "metatype": [ { "id": "IndicatorName", "value": "Average age of employers, aged 15-64, primary education and below" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of employer, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.MPYR.MA", "metatype": [ { "id": "IndicatorName", "value": "Average age of employers, aged 15-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of employer, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.MPYR.OL", "metatype": [ { "id": "IndicatorName", "value": "Average age of employers, aged 25-64" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of employer, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.MPYR.RU", "metatype": [ { "id": "IndicatorName", "value": "Average age of employers, aged 15-64, rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of employer, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.MPYR.UR", "metatype": [ { "id": "IndicatorName", "value": "Average age of employers, aged 15-64, urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of employer, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.MPYR.YG", "metatype": [ { "id": "IndicatorName", "value": "Average age of employers, aged 15-24" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of employer, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.SELF", "metatype": [ { "id": "IndicatorName", "value": "Average age of self-employed or unpaid workers, aged 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of self-employed or unpaid, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.SELF.FE", "metatype": [ { "id": "IndicatorName", "value": "Average age of self-employed or unpaid workers, aged 15-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of self-employed or unpaid, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.SELF.HE", "metatype": [ { "id": "IndicatorName", "value": "Average age of self-employed or unpaid workers, aged 15-64, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of self-employed or unpaid, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.SELF.LE", "metatype": [ { "id": "IndicatorName", "value": "Average age of self-employed or unpaid workers, aged 15-64, primary education and below" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of self-employed or unpaid, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.SELF.MA", "metatype": [ { "id": "IndicatorName", "value": "Average age of self-employed or unpaid workers, aged 15-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of self-employed or unpaid, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.SELF.OL", "metatype": [ { "id": "IndicatorName", "value": "Average age of self-employed or unpaid workers, aged 25-64" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of self-employed or unpaid, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.SELF.RU", "metatype": [ { "id": "IndicatorName", "value": "Average age of self-employed or unpaid workers, aged 15-64, rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of self-employed or unpaid, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.SELF.UR", "metatype": [ { "id": "IndicatorName", "value": "Average age of self-employed or unpaid workers, aged 15-64, urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of self-employed or unpaid, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.SELF.YG", "metatype": [ { "id": "IndicatorName", "value": "Average age of self-employed or unpaid workers, aged 15-24" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of self-employed or unpaid, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.TOTL", "metatype": [ { "id": "IndicatorName", "value": "Average age, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of the sample population. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.TOTL.FE", "metatype": [ { "id": "IndicatorName", "value": "Average age, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of the sample population. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.TOTL.HE", "metatype": [ { "id": "IndicatorName", "value": "Average age, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of the sample population. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.TOTL.LE", "metatype": [ { "id": "IndicatorName", "value": "Average age, primary education and below" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of the sample population. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.TOTL.MA", "metatype": [ { "id": "IndicatorName", "value": "Average age, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of the sample population. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.TOTL.OL", "metatype": [ { "id": "IndicatorName", "value": "Average age, aged 25-64" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of the sample population. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.TOTL.RU", "metatype": [ { "id": "IndicatorName", "value": "Average age, rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of the sample population. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.TOTL.UR", "metatype": [ { "id": "IndicatorName", "value": "Average age, urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of the sample population. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.TOTL.YG", "metatype": [ { "id": "IndicatorName", "value": "Average age, aged 15-24" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of the sample population. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WAGE", "metatype": [ { "id": "IndicatorName", "value": "Average age of wage workers, aged 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of wage workers, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WAGE.FE", "metatype": [ { "id": "IndicatorName", "value": "Average age of wage workers, aged 15-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of wage workers, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WAGE.HE", "metatype": [ { "id": "IndicatorName", "value": "Average age of wage workers, aged 15-64, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of wage workers, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WAGE.LE", "metatype": [ { "id": "IndicatorName", "value": "Average age of wage workers, aged 15-64, primary education and below" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of wage workers, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WAGE.MA", "metatype": [ { "id": "IndicatorName", "value": "Average age of wage workers, aged 15-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of wage workers, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WAGE.OL", "metatype": [ { "id": "IndicatorName", "value": "Average age of wage workers, aged 25-64" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of wage workers, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WAGE.RU", "metatype": [ { "id": "IndicatorName", "value": "Average age of wage workers, aged 15-64, rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of wage workers, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WAGE.UR", "metatype": [ { "id": "IndicatorName", "value": "Average age of wage workers, aged 15-64, urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of wage workers, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WAGE.YG", "metatype": [ { "id": "IndicatorName", "value": "Average age of wage workers, aged 15-24" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of wage workers, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WORK", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers, aged 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WORK.FE", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers, aged 15-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WORK.HE", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers, aged 15-64, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WORK.LE", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers, aged 15-64, primary education and below" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WORK.MA", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers, aged 15-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WORK.OL", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers, aged 25-64" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WORK.RU", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers, aged 15-64, rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WORK.UR", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers, aged 15-64, urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGE.WORK.YG", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers, aged 15-24" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.AGES", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the agricultural sector, aged 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers in agriculture between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.AGES.FE", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the agricultural sector, aged 15-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers in agriculture between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.AGES.HE", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the agricultural sector, aged 15-64, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers in agriculture between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.AGES.LE", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the agricultural sector, aged 15-64, primary education and below" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers in agriculture between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.AGES.MA", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the agricultural sector, aged 15-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers in agriculture between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.AGES.OL", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the agricultural sector, aged 25-64" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers in agriculture between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.AGES.RU", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the agricultural sector, aged 15-64, rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers in agriculture between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.AGES.UR", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the agricultural sector, aged 15-64, urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers in agriculture between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.AGES.YG", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the agricultural sector, aged 15-24" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers in agriculture between 15 and 64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in agriculture, aged 15-64, female (% of female workers in the agricultural sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in agriculture, aged 15-64, within all workers that work in the agricultural sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in agriculture, aged 15-64, above primary education (% of workers with high education in the agricultural sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in agriculture, aged 15-64, within all workers that work in the agricultural sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in agriculture, aged 15-64, primary education and below (% of workers with low education in the agricultural sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in agriculture, aged 15-64, within all workers that work in the agricultural sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in agriculture, aged 15-64, male (% of male workers in the agricultural sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in agriculture, aged 15-64, within all workers that work in the agricultural sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.MD.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the agricultural sector, aged 15-64, local currency values, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the agricultural sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.MD.FE.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the agricultural sector, aged 15-64, local currency values, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the agricultural sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.MD.HE.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the agricultural sector, aged 15-64, local currency values, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the agricultural sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.MD.LE.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the agricultural sector, aged 15-64, local currency values, primary education and below" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the agricultural sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.MD.MA.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the agricultural sector, aged 15-64, local currency values, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the agricultural sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.MD.OL.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the agricultural sector, aged 25-64, local currency values" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the agricultural sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.MD.RU.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the agricultural sector, aged 15-64, local currency values, rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the agricultural sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.MD.UR.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the agricultural sector, aged 15-64, local currency values, urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the agricultural sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.MD.YG.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the agricultural sector, aged 15-24, local currency values" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the agricultural sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in agriculture, aged 25-64 (% of workers aged 25-64 in the agricultural sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in agriculture, aged 15-64, within all workers that work in the agricultural sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in agriculture, aged 15-64, rural (% of rural workers in the agricultural sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in agriculture, aged 15-64, within all workers that work in the agricultural sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in agriculture, aged 15-64, urban (% of urban workers in the agricultural sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in agriculture, aged 15-64, within all workers that work in the agricultural sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in agriculture, aged 15-24 (% of workers aged 15-24 in the agricultural sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in agriculture, aged 15-64, within all workers that work in the agricultural sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.AGR.WAGE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in agriculture, aged 15-64, total (% of total workers in the agricultural sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in agriculture, aged 15-64, within all workers that work in the agricultural sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EDU.17UP", "metatype": [ { "id": "IndicatorName", "value": "Average number of completed years in formal education, aged 17 and above, total (% of total population aged 17 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average number of completed years in formal education for all individuals that are aged 17 or adulter. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EDU.17UP.FE", "metatype": [ { "id": "IndicatorName", "value": "Average number of completed years in formal education, aged 17 and above, female (% of female population aged 17 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average number of completed years in formal education for all individuals that are aged 17 or adulter. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EDU.17UP.HE", "metatype": [ { "id": "IndicatorName", "value": "Average number of completed years in formal education, aged 17 and above, above primary education (% of population aged 17 and above with high education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average number of completed years in formal education for all individuals that are aged 17 or adulter. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EDU.17UP.LE", "metatype": [ { "id": "IndicatorName", "value": "Average number of completed years in formal education, aged 17 and above, primary education and below (% of population aged 17 and above with low education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average number of completed years in formal education for all individuals that are aged 17 or adulter. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EDU.17UP.MA", "metatype": [ { "id": "IndicatorName", "value": "Average number of completed years in formal education, aged 17 and above, male (% of male population aged 17 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average number of completed years in formal education for all individuals that are aged 17 or adulter. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EDU.17UP.OL", "metatype": [ { "id": "IndicatorName", "value": "Average number of completed years in formal education, aged 25 and above (% of population aged 25 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average number of completed years in formal education for all individuals that are aged 17 or adulter. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EDU.17UP.RU", "metatype": [ { "id": "IndicatorName", "value": "Average number of completed years in formal education, aged 17 and above, rural (% of rural population aged 17 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average number of completed years in formal education for all individuals that are aged 17 or adulter. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EDU.17UP.UR", "metatype": [ { "id": "IndicatorName", "value": "Average number of completed years in formal education, aged 17 and above, urban (% of urban population aged 17 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average number of completed years in formal education for all individuals that are aged 17 or adulter. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EDU.17UP.YG", "metatype": [ { "id": "IndicatorName", "value": "Average number of completed years in formal education, aged 17-24 (% of population aged 17-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average number of completed years in formal education for all individuals that are aged 17 or adulter. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.1524.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth employment rate, aged 15-24, female (% of female youth labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed young individuals participating in the active labor force aged 15-24. Must add to 100 percent with share of employed young individuals participating in the active labor force aged 15-24. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.1524.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth employment rate, aged 15-24, above primary education (% of youth labor force with high education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed young individuals participating in the active labor force aged 15-24. Must add to 100 percent with share of employed young individuals participating in the active labor force aged 15-24. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.1524.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth employment rate, aged 15-24, primary education and below (% of youth labor force with low education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed young individuals participating in the active labor force aged 15-24. Must add to 100 percent with share of employed young individuals participating in the active labor force aged 15-24. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.1524.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth employment rate, aged 15-24, male (% of male youth labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed young individuals participating in the active labor force aged 15-24. Must add to 100 percent with share of employed young individuals participating in the active labor force aged 15-24. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.1524.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth employment rate, aged 15-24, rural (% of rural youth labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed young individuals participating in the active labor force aged 15-24. Must add to 100 percent with share of employed young individuals participating in the active labor force aged 15-24. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.1524.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth employment rate, aged 15-24, urban (% of urban youth labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed young individuals participating in the active labor force aged 15-24. Must add to 100 percent with share of employed young individuals participating in the active labor force aged 15-24. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.1524.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth employment rate, aged 15-24, total (% of total youth labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed young individuals participating in the active labor force aged 15-24. Must add to 100 percent with share of employed young individuals participating in the active labor force aged 15-24. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.1564.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment rate, aged 15-64, female (% of female labor force in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of unemployed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.1564.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment rate, aged 15-64, above primary education (% of labor force with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of unemployed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.1564.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment rate, aged 15-64, primary education and below (% of labor force with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of unemployed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.1564.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment rate, aged 15-64, male (% of male labor force in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of unemployed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.1564.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment rate, aged 25-64 (% of labor force aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of unemployed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.1564.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment rate, aged 15-64, rural (% of rural labor force in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of unemployed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.1564.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment rate, aged 15-64, urban (% of urban labor force in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of unemployed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.1564.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment rate, aged 15-24 (% of labor force aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of unemployed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.1564.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment rate, aged 15-64, total (% of total labor force in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of unemployed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.AGRI.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the agricultural sector, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in agriculture aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.AGRI.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the agricultural sector, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in agriculture aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.AGRI.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the agricultural sector, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in agriculture aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.AGRI.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the agricultural sector, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in agriculture aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.AGRI.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the agricultural sector, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in agriculture aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.AGRI.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the agricultural sector, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in agriculture aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.AGRI.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the agricultural sector, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in agriculture aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.AGRI.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the agricultural sector, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in agriculture aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.AGRI.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the agricultural sector, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in agriculture aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ARFC.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the armed forces occupation group, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the armed forces occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ARFC.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the armed forces occupation group, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the armed forces occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ARFC.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the armed forces occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the armed forces occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ARFC.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the armed forces occupation group, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the armed forces occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ARFC.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the armed forces occupation group, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the armed forces occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ARFC.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the armed forces occupation group, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the armed forces occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ARFC.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the armed forces occupation group, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the armed forces occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ARFC.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the armed forces occupation group, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the armed forces occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ARFC.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the armed forces occupation group, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the armed forces occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CLRK.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the clerks occupation group, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the clerks occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CLRK.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the clerks occupation group, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the clerks occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CLRK.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the clerks occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the clerks occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CLRK.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the clerks occupation group, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the clerks occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CLRK.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the clerks occupation group, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the clerks occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CLRK.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the clerks occupation group, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the clerks occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CLRK.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the clerks occupation group, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the clerks occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CLRK.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the clerks occupation group, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the clerks occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CLRK.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the clerks occupation group, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the clerks occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CNST.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the construction sector, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the construction sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CNST.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the construction sector, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the construction sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CNST.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the construction sector, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the construction sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CNST.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the construction sector, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the construction sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CNST.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the construction sector, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the construction sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CNST.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the construction sector, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the construction sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CNST.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the construction sector, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the construction sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CNST.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the construction sector, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the construction sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CNST.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the construction sector, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the construction sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.COME.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the commerce sector, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the commerce sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.COME.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the commerce sector, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the commerce sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.COME.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the commerce sector, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the commerce sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.COME.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the commerce sector, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the commerce sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.COME.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the commerce sector, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the commerce sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.COME.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the commerce sector, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the commerce sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.COME.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the commerce sector, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the commerce sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.COME.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the commerce sector, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the commerce sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.COME.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the commerce sector, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the commerce sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CONT.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with a work contract, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a work contract in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CONT.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with a work contract, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a work contract in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CONT.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with a work contract, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a work contract in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CONT.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with a work contract, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a work contract in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CONT.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with a work contract, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a work contract in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CONT.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with a work contract, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a work contract in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CONT.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with a work contract, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a work contract in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CONT.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with a work contract, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a work contract in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CONT.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with a work contract, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a work contract in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CRFT.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the craft workers occupation group, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the craft workers occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CRFT.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the craft workers occupation group, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the craft workers occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CRFT.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the craft workers occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the craft workers occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CRFT.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the craft workers occupation group, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the craft workers occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CRFT.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the craft workers occupation group, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the craft workers occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CRFT.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the craft workers occupation group, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the craft workers occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CRFT.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the craft workers occupation group, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the craft workers occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CRFT.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the craft workers occupation group, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the craft workers occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.CRFT.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the craft workers occupation group, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the craft workers occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEC.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the eletricity and public utilities sector, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in public utilities sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEC.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the eletricity and public utilities sector, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in public utilities sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEC.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the eletricity and public utilities sector, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in public utilities sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEC.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the eletricity and public utilities sector, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in public utilities sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEC.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the eletricity and public utilities sector, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in public utilities sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEC.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the eletricity and public utilities sector, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in public utilities sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEC.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the eletricity and public utilities sector, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in public utilities sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEC.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the eletricity and public utilities sector, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in public utilities sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEC.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the eletricity and public utilities sector, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in public utilities sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEM.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the elementary occupation group, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the elementary occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEM.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the elementary occupation group, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the elementary occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEM.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the elementary occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the elementary occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEM.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the elementary occupation group, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the elementary occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEM.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the elementary occupation group, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the elementary occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEM.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the elementary occupation group, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the elementary occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEM.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the elementary occupation group, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the elementary occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEM.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the elementary occupation group, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the elementary occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.ELEM.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the elementary occupation group, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the elementary occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.FABU.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the financial and business services sector, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the financial and business services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.FABU.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the financial and business services sector, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the financial and business services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.FABU.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the financial and business services sector, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the financial and business services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.FABU.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the financial and business services sector, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the financial and business services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.FABU.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the financial and business services sector, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the financial and business services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.FABU.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the financial and business services sector, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the financial and business services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.FABU.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the financial and business services sector, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the financial and business services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.FABU.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the financial and business services sector, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the financial and business services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.FABU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the financial and business services sector, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the financial and business services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.HINS.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with health insurance, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a health insurance in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.HINS.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with health insurance, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a health insurance in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.HINS.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with health insurance, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a health insurance in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.HINS.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with health insurance, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a health insurance in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.HINS.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with health insurance, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a health insurance in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.HINS.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with health insurance, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a health insurance in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.HINS.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with health insurance, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a health insurance in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.HINS.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with health insurance, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a health insurance in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.HINS.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with health insurance, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with a health insurance in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.IFRM.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Informal job workers, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in an informal job in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.IFRM.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Informal job workers, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in an informal job in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.IFRM.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Informal job workers, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in an informal job in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.IFRM.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Informal job workers, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in an informal job in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.IFRM.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Informal job workers, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in an informal job in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.IFRM.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Informal job workers, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in an informal job in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.IFRM.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Informal job workers, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in an informal job in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.IFRM.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Informal job workers, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in an informal job in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.IFRM.ZS", "metatype": [ { "id": "IndicatorName", "value": "Informal job workers, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in an informal job in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.INDU.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the industrial sector, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in industry sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.INDU.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the industrial sector, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in industry sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.INDU.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the industrial sector, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in industry sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.INDU.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the industrial sector, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in industry sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.INDU.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the industrial sector, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in industry sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.INDU.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the industrial sector, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in industry sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.INDU.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the industrial sector, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in industry sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.INDU.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the industrial sector, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in industry sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.INDU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the industrial sector, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in industry sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MACH.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the machine operators occupation group, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the machine operators occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MACH.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the machine operators occupation group, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the machine operators occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MACH.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the machine operators occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the machine operators occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MACH.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the machine operators occupation group, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the machine operators occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MACH.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the machine operators occupation group, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the machine operators occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MACH.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the machine operators occupation group, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the machine operators occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MACH.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the machine operators occupation group, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the machine operators occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MACH.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the machine operators occupation group, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the machine operators occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MACH.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the machine operators occupation group, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the machine operators occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MANF.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the manufacturing sector, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the manufacturing sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MANF.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the manufacturing sector, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the manufacturing sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MANF.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the manufacturing sector, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the manufacturing sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MANF.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the manufacturing sector, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the manufacturing sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MANF.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the manufacturing sector, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the manufacturing sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MANF.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the manufacturing sector, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the manufacturing sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MANF.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the manufacturing sector, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the manufacturing sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MANF.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the manufacturing sector, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the manufacturing sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MANF.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the manufacturing sector, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the manufacturing sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MINQ.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the mining sector, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the mining sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MINQ.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the mining sector, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the mining sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MINQ.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the mining sector, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the mining sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MINQ.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the mining sector, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the mining sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MINQ.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the mining sector, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the mining sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MINQ.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the mining sector, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the mining sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MINQ.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the mining sector, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the mining sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MINQ.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the mining sector, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the mining sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MINQ.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the mining sector, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the mining sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employers, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and unpaid. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employers, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and unpaid. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employers, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and unpaid. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employers, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and unpaid. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.NA.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural employers, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.NA.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural employers, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.NA.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural employers, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.NA.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural employers, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.NA.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural employers, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.NA.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural employers, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.NA.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural employers, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.NA.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural employers, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.NA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural employers, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employers, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and unpaid. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employers, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and unpaid. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employers, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and unpaid. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employers, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and unpaid. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.MPYR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employers, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employers within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and unpaid. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.NAGR.FE.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female in non-agricultural employment, aged 15-64, above primary education (% of employed female population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Within the group of employed women in working age (15-64)the share working in non-agricultural employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.NAGR.FE.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female in non-agricultural employment, aged 15-64, primary education and below (% of employed female population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Within the group of employed women in working age (15-64)the share working in non-agricultural employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.NAGR.FE.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female in non-agricultural employment, aged 25-64 (% of employed female population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Within the group of employed women in working age (15-64)the share working in non-agricultural employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.NAGR.FE.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female in non-agricultural employment, aged 15-64, rural (% of rural employed female population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Within the group of employed women in working age (15-64)the share working in non-agricultural employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.NAGR.FE.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female in non-agricultural employment, aged 15-64, urban (% of urban employed female population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Within the group of employed women in working age (15-64)the share working in non-agricultural employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.NAGR.FE.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female in non-agricultural employment, aged 15-24 (% of employed female population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Within the group of employed women in working age (15-64)the share working in non-agricultural employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.NAGR.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Female in non-agricultural employment, aged 15-64, total (% of total employed female population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Within the group of employed women in working age (15-64)the share working in non-agricultural employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.NAGR.YG.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth in non-agricultural employment, aged 15-24, female (% of female employed youth aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Within the group of employed youth aged 15-24 the share working in non-agricultural employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.NAGR.YG.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth in non-agricultural employment, aged 15-24, above primary education (% of employed youth with high education aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Within the group of employed youth aged 15-24 the share working in non-agricultural employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.NAGR.YG.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth in non-agricultural employment, aged 15-24, primary education and below (% of employed youth with low education aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Within the group of employed youth aged 15-24 the share working in non-agricultural employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.NAGR.YG.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth in non-agricultural employment, aged 15-24, male (% of male employed youth aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Within the group of employed youth aged 15-24 the share working in non-agricultural employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.NAGR.YG.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth in non-agricultural employment, aged 15-24, rural (% of rural employed youth aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Within the group of employed youth aged 15-24 the share working in non-agricultural employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.NAGR.YG.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth in non-agricultural employment, aged 15-24, urban (% of urban employed youth aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Within the group of employed youth aged 15-24 the share working in non-agricultural employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.NAGR.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth in non-agricultural employment, aged 15-24, total (% of total employed youth aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Within the group of employed youth aged 15-24 the share working in non-agricultural employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.OSRV.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the other services sector, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the other services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.OSRV.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the other services sector, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the other services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.OSRV.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the other services sector, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the other services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.OSRV.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the other services sector, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the other services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.OSRV.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the other services sector, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the other services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.OSRV.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the other services sector, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the other services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.OSRV.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the other services sector, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the other services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.OSRV.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the other services sector, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the other services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.OSRV.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the other services sector, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the other services sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PADM.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public administration sector, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the public administration sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PADM.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public administration sector, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the public administration sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PADM.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public administration sector, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the public administration sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PADM.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public administration sector, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the public administration sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PADM.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public administration sector, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the public administration sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PADM.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public administration sector, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the public administration sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PADM.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public administration sector, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the public administration sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PADM.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public administration sector, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the public administration sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PADM.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public administration sector, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the public administration sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PROF.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the professionals occupation group, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the professionals occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PROF.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the professionals occupation group, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the professionals occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PROF.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the professionals occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the professionals occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PROF.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the professionals occupation group, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the professionals occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PROF.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the professionals occupation group, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the professionals occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PROF.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the professionals occupation group, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the professionals occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PROF.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the professionals occupation group, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the professionals occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PROF.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the professionals occupation group, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the professionals occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PROF.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the professionals occupation group, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the professionals occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PUBS.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public sector, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals that work in the public sector in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PUBS.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public sector, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals that work in the public sector in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PUBS.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public sector, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals that work in the public sector in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PUBS.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public sector, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals that work in the public sector in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PUBS.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public sector, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals that work in the public sector in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PUBS.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public sector, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals that work in the public sector in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PUBS.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public sector, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals that work in the public sector in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PUBS.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public sector, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals that work in the public sector in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.PUBS.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the public sector, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals that work in the public sector in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Self-employed workers, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed within employed individuals in working age (15-64). Must add to 100 percent with wage employees, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Self-employed workers, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed within employed individuals in working age (15-64). Must add to 100 percent with wage employees, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Self-employed workers, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed within employed individuals in working age (15-64). Must add to 100 percent with wage employees, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Self-employed workers, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed within employed individuals in working age (15-64). Must add to 100 percent with wage employees, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.NA.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural self-employed workers, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.NA.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural self-employed workers, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.NA.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural self-employed workers, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.NA.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural self-employed workers, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.NA.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural self-employed workers, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.NA.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural self-employed workers, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.NA.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural self-employed workers, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.NA.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural self-employed workers, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.NA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural self-employed workers, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Self-employed workers, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed within employed individuals in working age (15-64). Must add to 100 percent with wage employees, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Self-employed workers, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed within employed individuals in working age (15-64). Must add to 100 percent with wage employees, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Self-employed workers, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed within employed individuals in working age (15-64). Must add to 100 percent with wage employees, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Self-employed workers, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed within employed individuals in working age (15-64). Must add to 100 percent with wage employees, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SELF.ZS", "metatype": [ { "id": "IndicatorName", "value": "Self-employed workers, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of self-employed within employed individuals in working age (15-64). Must add to 100 percent with wage employees, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SEOF.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the senior officials occupation group, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the senior officials occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SEOF.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the senior officials occupation group, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the senior officials occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SEOF.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the senior officials occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the senior officials occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SEOF.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the senior officials occupation group, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the senior officials occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SEOF.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the senior officials occupation group, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the senior officials occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SEOF.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the senior officials occupation group, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the senior officials occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SEOF.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the senior officials occupation group, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the senior officials occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SEOF.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the senior officials occupation group, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the senior officials occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SEOF.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the senior officials occupation group, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the senior officials occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SERV.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service sector, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in service sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SERV.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service sector, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in service sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SERV.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service sector, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in service sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SERV.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service sector, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in service sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SERV.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service sector, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in service sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SERV.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service sector, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in service sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SERV.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service sector, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in service sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SERV.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service sector, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in service sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SERV.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service sector, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in service sector aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SKAG.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the skilled agriculture occupation group, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the skilled agriculture occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SKAG.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the skilled agriculture occupation group, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the skilled agriculture occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SKAG.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the skilled agriculture occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the skilled agriculture occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SKAG.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the skilled agriculture occupation group, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the skilled agriculture occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SKAG.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the skilled agriculture occupation group, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the skilled agriculture occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SKAG.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the skilled agriculture occupation group, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the skilled agriculture occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SKAG.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the skilled agriculture occupation group, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the skilled agriculture occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SKAG.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the skilled agriculture occupation group, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the skilled agriculture occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SKAG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the skilled agriculture occupation group, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the skilled agriculture occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SSEC.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with social security, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with social security in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SSEC.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with social security, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with social security in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SSEC.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with social security, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with social security in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SSEC.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with social security, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with social security in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SSEC.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with social security, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with social security in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SSEC.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with social security, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with social security in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SSEC.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with social security, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with social security in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SSEC.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with social security, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with social security in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SSEC.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employed workers with social security, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals with social security in working age. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SVMK.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service and market sales occupation group, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the service and market sales occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SVMK.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service and market sales occupation group, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the service and market sales occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SVMK.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service and market sales occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the service and market sales occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SVMK.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service and market sales occupation group, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the service and market sales occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SVMK.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service and market sales occupation group, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the service and market sales occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SVMK.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service and market sales occupation group, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the service and market sales occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SVMK.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service and market sales occupation group, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the service and market sales occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SVMK.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service and market sales occupation group, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the service and market sales occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.SVMK.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the service and market sales occupation group, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the service and market sales occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TECH.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the technicians occupation group, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the technicians occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TECH.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the technicians occupation group, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the technicians occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TECH.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the technicians occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the technicians occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TECH.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the technicians occupation group, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the technicians occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TECH.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the technicians occupation group, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the technicians occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TECH.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the technicians occupation group, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the technicians occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TECH.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the technicians occupation group, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the technicians occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TECH.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the technicians occupation group, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the technicians occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TECH.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the technicians occupation group, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the technicians occupation group, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TOTL.SP.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment to population ratio, aged 15-64, female (% of female population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals within working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TOTL.SP.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment to population ratio, aged 15-64, above primary education (% of population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals within working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TOTL.SP.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment to population ratio, aged 15-64, primary education and below (% of population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals within working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TOTL.SP.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment to population ratio, aged 15-64, male (% of male population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals within working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TOTL.SP.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment to population ratio, aged 25-64 (% of population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals within working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TOTL.SP.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment to population ratio, aged 15-64, rural (% of rural population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals within working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TOTL.SP.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment to population ratio, aged 15-64, urban (% of urban population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals within working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TOTL.SP.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment to population ratio, aged 15-24 (% of population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals within working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TOTL.SP.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment to population ratio, aged 15-64, total (% of total population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals within working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TRCM.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the transport and communication sector, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the transport & communication sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TRCM.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the transport and communication sector, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the transport & communication sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TRCM.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the transport and communication sector, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the transport & communication sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TRCM.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the transport and communication sector, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the transport & communication sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TRCM.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the transport and communication sector, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the transport & communication sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TRCM.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the transport and communication sector, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the transport & communication sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TRCM.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the transport and communication sector, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the transport & communication sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TRCM.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the transport and communication sector, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the transport & communication sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.TRCM.ZS", "metatype": [ { "id": "IndicatorName", "value": "Employment in the transport and communication sector, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals working in the transport & communication sector, aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid workers, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid individuals within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid workers, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid individuals within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid workers, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid individuals within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid workers, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid individuals within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.NA.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural unpaid employment, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.NA.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural unpaid employment, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.NA.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural unpaid employment, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.NA.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural unpaid employment, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.NA.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural unpaid employment, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.NA.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural unpaid employment, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.NA.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural unpaid employment, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.NA.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural unpaid employment, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.NA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural unpaid employment, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid workers, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid individuals within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid workers, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid individuals within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid workers, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid individuals within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid workers, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid individuals within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UNPD.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid workers, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid individuals within employed individuals in working age (15-64). Must add to 100 percent with wage employees, self-employed and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UPSE.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid or self-employed workers, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid or self-employed individuals within employed individuals in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UPSE.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid or self-employed workers, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid or self-employed individuals within employed individuals in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UPSE.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid or self-employed workers, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid or self-employed individuals within employed individuals in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UPSE.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid or self-employed workers, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid or self-employed individuals within employed individuals in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UPSE.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid or self-employed workers, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid or self-employed individuals within employed individuals in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UPSE.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid or self-employed workers, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid or self-employed individuals within employed individuals in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UPSE.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid or self-employed workers, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid or self-employed individuals within employed individuals in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UPSE.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid or self-employed workers, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid or self-employed individuals within employed individuals in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.UPSE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unpaid or self-employed workers, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unpaid or self-employed individuals within employed individuals in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.1524.NA.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth in non-agricultural wage employment, aged 15-24, female (% of female employed youth aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young wage workers, aged 15-24, in non-agricultural sector employment within all young workers, aged 15-24, in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.1524.NA.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth in non-agricultural wage employment, aged 15-24, above primary education (% of employed youth with high education aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young wage workers, aged 15-24, in non-agricultural sector employment within all young workers, aged 15-24, in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.1524.NA.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth in non-agricultural wage employment, aged 15-24, primary education and below (% of employed youth with low education aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young wage workers, aged 15-24, in non-agricultural sector employment within all young workers, aged 15-24, in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.1524.NA.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth in non-agricultural wage employment, aged 15-24, male (% of male employed youth aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young wage workers, aged 15-24, in non-agricultural sector employment within all young workers, aged 15-24, in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.1524.NA.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth in non-agricultural wage employment, aged 15-24, rural (% of rural employed youth aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young wage workers, aged 15-24, in non-agricultural sector employment within all young workers, aged 15-24, in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.1524.NA.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth in non-agricultural wage employment, aged 15-24, urban (% of urban employed youth aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young wage workers, aged 15-24, in non-agricultural sector employment within all young workers, aged 15-24, in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.1524.NA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth in non-agricultural wage employment, aged 15-24, total (% of total employed youth aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young wage workers, aged 15-24, in non-agricultural sector employment within all young workers, aged 15-24, in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage workers, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage employed within employed individuals in working age (15-64). Must add to 100 percent with self-employed, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage workers, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage employed within employed individuals in working age (15-64). Must add to 100 percent with self-employed, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage workers, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage employed within employed individuals in working age (15-64). Must add to 100 percent with self-employed, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage workers, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage employed within employed individuals in working age (15-64). Must add to 100 percent with self-employed, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.NA.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural wage employment, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.NA.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural wage employment, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.NA.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural wage employment, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.NA.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural wage employment, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.NA.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural wage employment, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.NA.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural wage employment, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.NA.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural wage employment, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.NA.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural wage employment, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.NA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Non-agricultural wage employment, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in non-agricultural sector employment within all workers in employment. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage workers, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage employed within employed individuals in working age (15-64). Must add to 100 percent with self-employed, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage workers, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage employed within employed individuals in working age (15-64). Must add to 100 percent with self-employed, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage workers, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage employed within employed individuals in working age (15-64). Must add to 100 percent with self-employed, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage workers, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage employed within employed individuals in working age (15-64). Must add to 100 percent with self-employed, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.EMP.WAGE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage workers, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage employed within employed individuals in working age (15-64). Must add to 100 percent with self-employed, unpaid and employers. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.ENR.0616.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Enrollment rate, aged 6-16, female (% of female population aged 6-16)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young individuals, aged 6-16, currently enrolled in school. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.ENR.0616.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Enrollment rate, aged 6-16, above primary education (% of population with high education aged 6-16)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young individuals, aged 6-16, currently enrolled in school. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.ENR.0616.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Enrollment rate, aged 6-16, primary education and below (% of population with low education aged 6-16)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young individuals, aged 6-16, currently enrolled in school. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.ENR.0616.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Enrollment rate, aged 6-16, male (% of male population aged 6-16)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young individuals, aged 6-16, currently enrolled in school. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.ENR.0616.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Enrollment rate, aged 6-16, rural (% of rural population aged 6-16)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young individuals, aged 6-16, currently enrolled in school. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.ENR.0616.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Enrollment rate, aged 6-16, urban (% of urban population aged 6-16)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young individuals, aged 6-16, currently enrolled in school. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.ENR.0616.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Enrollment rate, aged 15-16 (% of population aged 15-16)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young individuals, aged 6-16, currently enrolled in school. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.ENR.0616.ZS", "metatype": [ { "id": "IndicatorName", "value": "Enrollment rate, aged 6-16, total (% of total population aged 6-16)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young individuals, aged 6-16, currently enrolled in school. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.AGES", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the industrial sector, aged 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the industry sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.AGES.FE", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the industrial sector, aged 15-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the industry sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.AGES.HE", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the industrial sector, aged 15-64, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the industry sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.AGES.LE", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the industrial sector, aged 15-64, primary education and below" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the industry sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.AGES.MA", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the industrial sector, aged 15-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the industry sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.AGES.OL", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the industrial sector, aged 25-64" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the industry sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.AGES.RU", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the industrial sector, aged 15-64, rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the industry sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.AGES.UR", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the industrial sector, aged 15-64, urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the industry sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.AGES.YG", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the industrial sector, aged 15-24" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the industry sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.WAGE.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in industry, aged 15-64, female (% of female workers in the industrial sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in industry, aged 15-64, within all workers that work in the industry sector. For the definition of the industry sector see the general definitions of terms. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.WAGE.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in industry, aged 15-64, above primary education (% of workers with high education in the industrial sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in industry, aged 15-64, within all workers that work in the industry sector. 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Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.WAGE.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in industry, aged 15-64, primary education and below (% of workers with low education in the industrial sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in industry, aged 15-64, within all workers that work in the industry sector. 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Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.WAGE.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in industry, aged 15-64, male (% of male workers in the industrial sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in industry, aged 15-64, within all workers that work in the industry sector. For the definition of the industry sector see the general definitions of terms. 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Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.WAGE.MD.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the industrial sector, aged 15-64, local currency values, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the industry sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. 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Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.WAGE.MD.FE.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the industrial sector, aged 15-64, local currency values, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the industry sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. 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Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.WAGE.MD.MA.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the industrial sector, aged 15-64, local currency values, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the industry sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. 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Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.WAGE.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in industry, aged 15-64, urban (% of urban workers in the industrial sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in industry, aged 15-64, within all workers that work in the industry sector. For the definition of the industry sector see the general definitions of terms. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.WAGE.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in industry, aged 15-24 (% of workers aged 15-24 in the industrial sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in industry, aged 15-64, within all workers that work in the industry sector. For the definition of the industry sector see the general definitions of terms. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.IND.WAGE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in industry, aged 15-64, total (% of total workers in the industrial sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in industry, aged 15-64, within all workers that work in the industry sector. For the definition of the industry sector see the general definitions of terms. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.JOB.MLTP.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Workers with more than one jobs in last week, aged 15-64, female (% of female population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of workers in employment within working age that had more than one job in the last week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.JOB.MLTP.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Workers with more than one jobs in last week, aged 15-64, above primary education (% of population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of workers in employment within working age that had more than one job in the last week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.JOB.MLTP.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Workers with more than one jobs in last week, aged 15-64, primary education and below (% of population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of workers in employment within working age that had more than one job in the last week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.JOB.MLTP.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Workers with more than one jobs in last week, aged 15-64, male (% of male population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of workers in employment within working age that had more than one job in the last week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.JOB.MLTP.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Workers with more than one jobs in last week, aged 25-64 (% of population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of workers in employment within working age that had more than one job in the last week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.JOB.MLTP.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Workers with more than one jobs in last week, aged 15-64, rural (% of rural population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of workers in employment within working age that had more than one job in the last week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.JOB.MLTP.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Workers with more than one jobs in last week, aged 15-64, urban (% of urban population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of workers in employment within working age that had more than one job in the last week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.JOB.MLTP.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Workers with more than one jobs in last week, aged 15-24 (% of population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of workers in employment within working age that had more than one job in the last week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.JOB.MLTP.ZS", "metatype": [ { "id": "IndicatorName", "value": "Workers with more than one jobs in last week, aged 15-64, total (% of total population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of workers in employment within working age that had more than one job in the last week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.0014.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 0-14, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of children within the total population that are aged between 0 to 14 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.0014.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 0-14, above primary education (% of population with high edcuation)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of children within the total population that are aged between 0 to 14 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.0014.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 0-14, primary education and below (% of population with low edcuation)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of children within the total population that are aged between 0 to 14 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.0014.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 0-14, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of children within the total population that are aged between 0 to 14 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.0014.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 0-14, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of children within the total population that are aged between 0 to 14 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.0014.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 0-14, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of children within the total population that are aged between 0 to 14 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.0014.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 0-14, total (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of children within the total population that are aged between 0 to 14 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.1524.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 15-24, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of youth within the total population that are aged between 15 to 24 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.1524.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 15-24, above primary education (% of population with high edcuation)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of youth within the total population that are aged between 15 to 24 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.1524.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 15-24, primary education and below (% of population with low edcuation)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of youth within the total population that are aged between 15 to 24 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.1524.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 15-24, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of youth within the total population that are aged between 15 to 24 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.1524.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 15-24, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of youth within the total population that are aged between 15 to 24 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.1524.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 15-24, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of youth within the total population that are aged between 15 to 24 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.1524.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 15-24, total (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of youth within the total population that are aged between 15 to 24 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.1564.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population, aged 15-64, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within the population that are in working age, defined as aged between 15 to 64 years. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.1564.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population, aged 15-64, above primary education (% of population with high education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within the population that are in working age, defined as aged between 15 to 64 years. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.1564.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population, aged 15-64, primary education and below (% of population with low education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within the population that are in working age, defined as aged between 15 to 64 years. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.1564.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population, aged 15-64, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within the population that are in working age, defined as aged between 15 to 64 years. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.1564.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population, aged 15-64, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within the population that are in working age, defined as aged between 15 to 64 years. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.1564.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population, aged 15-64, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within the population that are in working age, defined as aged between 15 to 64 years. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.1564.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population, aged 15-64, total (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within the population that are in working age, defined as aged between 15 to 64 years. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.2564.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 25-64, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of adults within the total population that are aged between 15 to 24 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.2564.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 25-64, above primary education (% of population with high edcuation)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of adults within the total population that are aged between 15 to 24 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.2564.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 25-64, primary education and below (% of population with low edcuation)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of adults within the total population that are aged between 15 to 24 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.2564.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 25-64, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of adults within the total population that are aged between 15 to 24 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.2564.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 25-64, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of adults within the total population that are aged between 15 to 24 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.2564.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 25-64, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of adults within the total population that are aged between 15 to 24 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.2564.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 25-64, total (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of adults within the total population that are aged between 15 to 24 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.65UP.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 65 and above, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of elderly within the total population that are adulter than 65 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.65UP.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 65 and above, above primary education (% of population with high edcuation)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of elderly within the total population that are adulter than 65 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.65UP.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 65 and above, primary education and below (% of population with low edcuation)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of elderly within the total population that are adulter than 65 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.65UP.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 65 and above, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of elderly within the total population that are adulter than 65 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.65UP.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 65 and above, rural (% of rural population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of elderly within the total population that are adulter than 65 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.65UP.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 65 and above, urban (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of elderly within the total population that are adulter than 65 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.65UP.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population aged 65 and above, total (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of elderly within the total population that are adulter than 65 years. The shares of children, youth, adult and elderly must add up to 100 percent. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.DPND", "metatype": [ { "id": "IndicatorName", "value": "Dependency rate, all compared to 15-64" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals younger than 15 years or adulter than 64 years compared to individuals in working age (15-64 years), calculated only for the total sample and not sub-groups. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.DPND.OL", "metatype": [ { "id": "IndicatorName", "value": "Old age dependency rate, adulter than 64 compared to 15-64" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals adulter than 64 years compared to individuals in working age (15-64 years), calculated only for the total sample and not sub-groups. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.DPND.YG", "metatype": [ { "id": "IndicatorName", "value": "Youth dependency rate, younger than 15 compared to 15-64" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals younger than 15 years compared to individuals in working age (15-64 years), calculated only for the total sample and not sub-groups. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.NEDU.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with no education, female (% of female population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have no education. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.NEDU.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with no education, primary education and below (% of population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have no education. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.NEDU.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with no education, male (% of male population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have no education. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.NEDU.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with no education, aged 25-64 (% of population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have no education. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.NEDU.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with no education, rural (% of rural population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have no education. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.NEDU.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with no education, urban (% of urban population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have no education. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.NEDU.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with no education, aged 15-24 (% of population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have no education. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.NEDU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with no education, total (% of total population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have no education. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.PRIM.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with primary education, female (% of female population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed primary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.PRIM.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with primary education, primary education and below (% of population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed primary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.PRIM.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with primary education, male (% of male population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed primary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.PRIM.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with primary education, aged 25-64 (% of population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed primary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.PRIM.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with primary education, rural (% of rural population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed primary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.PRIM.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with primary education, urban (% of urban population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed primary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.PRIM.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with primary education, aged 15-24 (% of population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed primary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.PRIM.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with primary education, total (% of total population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed primary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.SECO.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with secondary education, female (% of female population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed secondary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.SECO.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with secondary education, above primary education (% of population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed secondary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.SECO.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with secondary education, male (% of male population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed secondary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.SECO.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with secondary education, aged 25-64 (% of population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed secondary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.SECO.PO.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with post-secondary education, female (% of female population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed post-secondary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.SECO.PO.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with post-secondary education, above primary education (% of population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed post-secondary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.SECO.PO.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with post-secondary education, male (% of male population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed post-secondary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.SECO.PO.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with post-secondary education, aged 25-64 (% of population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed post-secondary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.SECO.PO.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with post-secondary education, rural (% of rural population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed post-secondary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.SECO.PO.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with post-secondary education, urban (% of urban population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed post-secondary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.SECO.PO.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with post-secondary education, aged 15-24 (% of population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed post-secondary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.SECO.PO.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with post-secondary education, total (% of total population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed secondary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.SECO.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with secondary education, rural (% of rural population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed secondary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.SECO.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with secondary education, urban (% of urban population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed secondary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.SECO.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with secondary education, aged 15-24 (% of population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed secondary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.SECO.ZS", "metatype": [ { "id": "IndicatorName", "value": "Working-age population with secondary education, total (% of total population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals that have passed secondary education levels but no higher education levels. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.TOTL", "metatype": [ { "id": "IndicatorName", "value": "Total sample population" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total number of inhabitants in the country. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.TOTL.FE", "metatype": [ { "id": "IndicatorName", "value": "Total sample population, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total number of inhabitants in the country. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.TOTL.HE", "metatype": [ { "id": "IndicatorName", "value": "Total sample population, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total number of inhabitants in the country. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.TOTL.LE", "metatype": [ { "id": "IndicatorName", "value": "Total sample population, primary education and below" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total number of inhabitants in the country. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.TOTL.MA", "metatype": [ { "id": "IndicatorName", "value": "Total sample population, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total number of inhabitants in the country. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.TOTL.OL", "metatype": [ { "id": "IndicatorName", "value": "Total sample population, aged 25-64" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total number of inhabitants in the country. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.TOTL.RU", "metatype": [ { "id": "IndicatorName", "value": "Total sample population, rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total number of inhabitants in the country. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.TOTL.UR", "metatype": [ { "id": "IndicatorName", "value": "Total sample population, urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total number of inhabitants in the country. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.TOTL.YG", "metatype": [ { "id": "IndicatorName", "value": "Total sample population, aged 15-24" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Total number of inhabitants in the country. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.URBN.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Urban population, female (% of female population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within the total population that are living in urban areas. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.URBN.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Urban population, above primary education (% of population with high education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within the total population that are living in urban areas. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.URBN.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Urban population, primary education and below (% of population with low education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within the total population that are living in urban areas. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.URBN.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Urban population, male (% of male population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within the total population that are living in urban areas. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.URBN.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Urban population, aged 25-64 (% of population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within the total population that are living in urban areas. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.URBN.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Urban population, aged 15-24 (% of population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within the total population that are living in urban areas. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.POP.URBN.ZS", "metatype": [ { "id": "IndicatorName", "value": "Urban population, total (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within the total population that are living in urban areas. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.AGES", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the service sector, aged 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the services sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.AGES.FE", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the service sector, aged 15-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the services sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.AGES.HE", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the service sector, aged 15-64, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the services sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.AGES.LE", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the service sector, aged 15-64, primary education and below" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the services sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.AGES.MA", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the service sector, aged 15-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the services sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.AGES.OL", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the service sector, aged 25-64" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the services sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.AGES.RU", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the service sector, aged 15-64, rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the services sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.AGES.UR", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the service sector, aged 15-64, urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the services sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.AGES.YG", "metatype": [ { "id": "IndicatorName", "value": "Average age of workers in the service sector, aged 15-24" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Average age of all workers, aged 15-64, in the services sector. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in services, aged 15-64, female (% of female workers in the service sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in services, aged 15-64, within all workers that work in the service sector. For the definition of the service sector see the general definitions of the term sector [services]. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in services, aged 15-64, above primary education (% of workers with high education in the service sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in services, aged 15-64, within all workers that work in the service sector. For the definition of the service sector see the general definitions of the term sector [services]. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in services, aged 15-64, primary education and below (% of workers with low education in the service sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in services, aged 15-64, within all workers that work in the service sector. For the definition of the service sector see the general definitions of the term sector [services]. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in services, aged 15-64, male (% of male workers in the service sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in services, aged 15-64, within all workers that work in the service sector. For the definition of the service sector see the general definitions of the term sector [services]. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.MD.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the service sector, aged 15-64, local currency values, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the service sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.MD.FE.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the service sector, aged 15-64, local currency values, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the service sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.MD.HE.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the service sector, aged 15-64, local currency values, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the service sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.MD.LE.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the service sector, aged 15-64, local currency values, primary education and below" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the service sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.MD.MA.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the service sector, aged 15-64, local currency values, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the service sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.MD.OL.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the service sector, aged 25-64, local currency values" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the service sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.MD.RU.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the service sector, aged 15-64, local currency values, rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the service sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.MD.UR.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the service sector, aged 15-64, local currency values, urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the service sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.MD.YG.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month in the service sector, aged 15-24, local currency values" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings in the service sector for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in services, aged 25-64 (% of workers aged 25-64 in the service sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in services, aged 15-64, within all workers that work in the service sector. For the definition of the service sector see the general definitions of the term sector [services]. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in services, aged 15-64, rural (% of rural workers in the service sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in services, aged 15-64, within all workers that work in the service sector. For the definition of the service sector see the general definitions of the term sector [services]. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in services, aged 15-64, urban (% of urban workers in the service sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in services, aged 15-64, within all workers that work in the service sector. For the definition of the service sector see the general definitions of the term sector [services]. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in services, aged 15-24 (% of workers aged 15-24 in the service sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in services, aged 15-64, within all workers that work in the service sector. For the definition of the service sector see the general definitions of the term sector [services]. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.SRV.WAGE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Wage employment in services, aged 15-64, total (% of total workers in the service sector)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of wage workers in services, aged 15-64, within all workers that work in the service sector. For the definition of the service sector see the general definitions of the term sector [services]. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.1564.WK.FE.TM", "metatype": [ { "id": "IndicatorName", "value": "Average weekly working hours, aged 15-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Mean of working hours for employed individuals aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.1564.WK.HE.TM", "metatype": [ { "id": "IndicatorName", "value": "Average weekly working hours, aged 15-64, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Mean of working hours for employed individuals aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.1564.WK.LE.TM", "metatype": [ { "id": "IndicatorName", "value": "Average weekly working hours, aged 15-64, primary education and below" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Mean of working hours for employed individuals aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.1564.WK.MA.TM", "metatype": [ { "id": "IndicatorName", "value": "Average weekly working hours, aged 15-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Mean of working hours for employed individuals aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.1564.WK.OL.TM", "metatype": [ { "id": "IndicatorName", "value": "Average weekly working hours, aged 25-64" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Mean of working hours for employed individuals aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.1564.WK.RU.TM", "metatype": [ { "id": "IndicatorName", "value": "Average weekly working hours, aged 15-64, rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Mean of working hours for employed individuals aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.1564.WK.TM", "metatype": [ { "id": "IndicatorName", "value": "Average weekly working hours, aged 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Mean of working hours for employed individuals aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.1564.WK.UR.TM", "metatype": [ { "id": "IndicatorName", "value": "Average weekly working hours, aged 15-64, urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Mean of working hours for employed individuals aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.1564.WK.YG.TM", "metatype": [ { "id": "IndicatorName", "value": "Average weekly working hours, aged 15-24" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Mean of working hours for employed individuals aged 15-64. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.35BL.TM.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Underemployment, less than 35 hours per week, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours less than 35 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.35BL.TM.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Underemployment, less than 35 hours per week, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours less than 35 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.35BL.TM.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Underemployment, less than 35 hours per week, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours less than 35 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.35BL.TM.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Underemployment, less than 35 hours per week, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours less than 35 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.35BL.TM.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Underemployment, less than 35 hours per week, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours less than 35 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.35BL.TM.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Underemployment, less than 35 hours per week, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours less than 35 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.35BL.TM.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Underemployment, less than 35 hours per week, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours less than 35 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.35BL.TM.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Underemployment, less than 35 hours per week, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours less than 35 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.35BL.TM.ZS", "metatype": [ { "id": "IndicatorName", "value": "Underemployment, less than 35 hours per week, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours less than 35 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.48UP.TM.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Excessive working hours, more than 48 hours per week, aged 15-64, female (% of female employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours higher than 48 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.48UP.TM.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Excessive working hours, more than 48 hours per week, aged 15-64, above primary education (% of employed population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours higher than 48 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.48UP.TM.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Excessive working hours, more than 48 hours per week, aged 15-64, primary education and below (% of employed population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours higher than 48 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.48UP.TM.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Excessive working hours, more than 48 hours per week, aged 15-64, male (% of male employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours higher than 48 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.48UP.TM.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Excessive working hours, more than 48 hours per week, aged 25-64 (% of employed population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours higher than 48 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.48UP.TM.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Excessive working hours, more than 48 hours per week, aged 15-64, rural (% of rural employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours higher than 48 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.48UP.TM.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Excessive working hours, more than 48 hours per week, aged 15-64, urban (% of urban employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours higher than 48 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.48UP.TM.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Excessive working hours, more than 48 hours per week, aged 15-24 (% of employed population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours higher than 48 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.48UP.TM.ZS", "metatype": [ { "id": "IndicatorName", "value": "Excessive working hours, more than 48 hours per week, aged 15-64, total (% of total employed population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of employed individuals aged 15-64 with working hours higher than 48 hours per week. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.ACTI.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Labor force participation rate, aged 15-64, female (% of female population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within all individuals in working age participating in the labor force. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.ACTI.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Labor force participation rate, aged 15-64, above primary education (% of population with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within all individuals in working age participating in the labor force. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.ACTI.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Labor force participation rate, aged 15-64, primary education and below (% of population with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within all individuals in working age participating in the labor force. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.ACTI.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Labor force participation rate, aged 15-64, male (% of male population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within all individuals in working age participating in the labor force. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.ACTI.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Labor force participation rate, aged 25-64 (% of population aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within all individuals in working age participating in the labor force. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.ACTI.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Labor force participation rate, aged 15-64, rural (% of rural population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within all individuals in working age participating in the labor force. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.ACTI.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Labor force participation rate, aged 15-64, urban (% of urban population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within all individuals in working age participating in the labor force. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.ACTI.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Labor force participation rate, aged 15-24 (% of population aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within all individuals in working age participating in the labor force. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.ACTI.ZS", "metatype": [ { "id": "IndicatorName", "value": "Labor force participation rate, aged 15-64, total (% of total population in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of individuals within all individuals in working age participating in the labor force. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.TOTL", "metatype": [ { "id": "IndicatorName", "value": "Total labor force in the sample, aged 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Number of individuals in the labor force and in working age (15-64 years). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.TOTL.FE", "metatype": [ { "id": "IndicatorName", "value": "Total labor force in the sample, aged 15-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Number of individuals in the labor force and in working age (15-64 years). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.TOTL.HE", "metatype": [ { "id": "IndicatorName", "value": "Total labor force in the sample, aged 15-64, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Number of individuals in the labor force and in working age (15-64 years). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.TOTL.LE", "metatype": [ { "id": "IndicatorName", "value": "Total labor force in the sample, aged 15-64, primary education and below" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Number of individuals in the labor force and in working age (15-64 years). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.TOTL.MA", "metatype": [ { "id": "IndicatorName", "value": "Total labor force in the sample, aged 15-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Number of individuals in the labor force and in working age (15-64 years). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.TOTL.OL", "metatype": [ { "id": "IndicatorName", "value": "Total labor force in the sample, aged 25-64" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Number of individuals in the labor force and in working age (15-64 years). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.TOTL.RU", "metatype": [ { "id": "IndicatorName", "value": "Total labor force in the sample, aged 15-64, rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Number of individuals in the labor force and in working age (15-64 years). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.TOTL.UR", "metatype": [ { "id": "IndicatorName", "value": "Total labor force in the sample, aged 15-64, urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Number of individuals in the labor force and in working age (15-64 years). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.TLF.TOTL.YG", "metatype": [ { "id": "IndicatorName", "value": "Total labor force in the sample, aged 15-24" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Number of individuals in the labor force and in working age (15-64 years). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.1524.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth unemployment rate, aged 15-24, female (% of female youth labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unemployed young individuals participating in the active labor force aged 15-24. Must add to 100 percent with share of employed young individuals participating in the active labor force aged 15-24. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.1524.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth unemployment rate, aged 15-24, above primary education (% of youth labor force with high education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unemployed young individuals participating in the active labor force aged 15-24. Must add to 100 percent with share of employed young individuals participating in the active labor force aged 15-24. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.1524.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth unemployment rate, aged 15-24, primary education and below (% of youth labor force with low education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unemployed young individuals participating in the active labor force aged 15-24. Must add to 100 percent with share of employed young individuals participating in the active labor force aged 15-24. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.1524.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth unemployment rate, aged 15-24, male (% of male youth labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unemployed young individuals participating in the active labor force aged 15-24. Must add to 100 percent with share of employed young individuals participating in the active labor force aged 15-24. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.1524.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth unemployment rate, aged 15-24, rural (% of rural youth labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unemployed young individuals participating in the active labor force aged 15-24. Must add to 100 percent with share of employed young individuals participating in the active labor force aged 15-24. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.1524.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth unemployment rate, aged 15-24, urban (% of urban youth labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unemployed young individuals participating in the active labor force aged 15-24. Must add to 100 percent with share of employed young individuals participating in the active labor force aged 15-24. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.1524.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth unemployment rate, aged 15-24, total (% of total youth labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unemployed young individuals participating in the active labor force aged 15-24. Must add to 100 percent with share of employed young individuals participating in the active labor force aged 15-24. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.1564.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unemployment rate, aged 15-64, female (% of female labor force in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unemployed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of employed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.1564.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unemployment rate, aged 15-64, above primary education (% of labor force with high education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unemployed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of employed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.1564.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unemployment rate, aged 15-64, primary education and below (% of labor force with low education in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unemployed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of employed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.1564.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unemployment rate, aged 15-64, male (% of male labor force in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unemployed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of employed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.1564.OL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unemployment rate, aged 25-64 (% of labor force aged 25-64)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unemployed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of employed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.1564.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unemployment rate, aged 15-64, rural (% of rural labor force in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unemployed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of employed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.1564.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unemployment rate, aged 15-64, urban (% of urban labor force in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unemployed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of employed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.1564.YG.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unemployment rate, aged 15-24 (% of labor force aged 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unemployed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of employed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.1564.ZS", "metatype": [ { "id": "IndicatorName", "value": "Unemployment rate, aged 15-64, total (% of total labor force in working age)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of unemployed individuals participating in the active labor force in working age (15-64). Must add to 100 percent with share of employed individuals participating in the active labor force in working age (15-64). The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.NEET.FE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth not in employment or education, aged 15-24, female (% of female youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young individuals within young individuals aged 15 to 24 that are neither participating in the labor force nor are in education. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.NEET.HE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth not in employment or education, aged 15-24, above primary education (% of youth population with high education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young individuals within young individuals aged 15 to 24 that are neither participating in the labor force nor are in education. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.NEET.LE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth not in employment or education, aged 15-24, primary education and below (% of youth population with low education)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young individuals within young individuals aged 15 to 24 that are neither participating in the labor force nor are in education. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.NEET.MA.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth not in employment or education, aged 15-24, male (% of male youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young individuals within young individuals aged 15 to 24 that are neither participating in the labor force nor are in education. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.NEET.RU.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth not in employment or education, aged 15-24, rural (% of rural youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young individuals within young individuals aged 15 to 24 that are neither participating in the labor force nor are in education. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.NEET.UR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth not in employment or education, aged 15-24, urban (% of urban youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young individuals within young individuals aged 15 to 24 that are neither participating in the labor force nor are in education. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.UEM.NEET.ZS", "metatype": [ { "id": "IndicatorName", "value": "Youth not in employment or education, aged 15-24, total (% of total youth population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Share of young individuals within young individuals aged 15 to 24 that are neither participating in the labor force nor are in education. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.GNDR", "metatype": [ { "id": "IndicatorName", "value": "Female to male gender wage gap, aged 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Female to male gender wage gap for wage workers aged 15-64. Reports the ratio of female wage workers earnings to male wage workers earnings. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.GNDR.HE", "metatype": [ { "id": "IndicatorName", "value": "Female to male gender wage gap, aged 15-64, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Female to male gender wage gap for wage workers aged 15-64. Reports the ratio of female wage workers earnings to male wage workers earnings. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.GNDR.LE", "metatype": [ { "id": "IndicatorName", "value": "Female to male gender wage gap, aged 15-64, primary education and below" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Female to male gender wage gap for wage workers aged 15-64. Reports the ratio of female wage workers earnings to male wage workers earnings. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.GNDR.OL", "metatype": [ { "id": "IndicatorName", "value": "Female to male gender wage gap, aged 25-64" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Female to male gender wage gap for wage workers aged 15-64. Reports the ratio of female wage workers earnings to male wage workers earnings. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.GNDR.RU", "metatype": [ { "id": "IndicatorName", "value": "Female to male gender wage gap, aged 15-64, rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Female to male gender wage gap for wage workers aged 15-64. Reports the ratio of female wage workers earnings to male wage workers earnings. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.GNDR.UR", "metatype": [ { "id": "IndicatorName", "value": "Female to male gender wage gap, aged 15-64, urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Female to male gender wage gap for wage workers aged 15-64. Reports the ratio of female wage workers earnings to male wage workers earnings. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.GNDR.YG", "metatype": [ { "id": "IndicatorName", "value": "Female to male gender wage gap, aged 15-24" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Female to male gender wage gap for wage workers aged 15-64. Reports the ratio of female wage workers earnings to male wage workers earnings. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.HOUR.MD.10", "metatype": [ { "id": "IndicatorName", "value": "Real median earnings per hour, aged 15-64, deflated to 2010 and PPP adjusted, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings for wage workers per hour aged 15-64, inflation corrected to 2010 values and adjusted for purchasing power parity using WDI provided exchange values. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.HOUR.MD.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per hour, aged 15-64, local currency values, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings for wage workers per hour aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.HOUR.MD.DF", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per hour, aged 15-64, deflated to 2010 local currency values, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings for wage workers per hour aged 15-64, inflation corrected to 2010 values. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.HOUR.MD.FE.10", "metatype": [ { "id": "IndicatorName", "value": "Real median earnings per hour, aged 15-64, deflated to 2010 and PPP adjusted, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings for wage workers per hour aged 15-64, inflation corrected to 2010 values and adjusted for purchasing power parity using WDI provided exchange values. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.HOUR.MD.FE.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per hour, aged 15-64, local currency values, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings for wage workers per hour aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.HOUR.MD.FE.DF", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per hour, aged 15-64, deflated to 2010 local currency values, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings for wage workers per hour aged 15-64, inflation corrected to 2010 values. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.HOUR.MD.HE.10", "metatype": [ { "id": "IndicatorName", "value": "Real median earnings per hour, aged 15-64, deflated to 2010 and PPP adjusted, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings for wage workers per hour aged 15-64, inflation corrected to 2010 values and adjusted for purchasing power parity using WDI provided exchange values. 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Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.HOUR.MD.HE.DF", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per hour, aged 15-64, deflated to 2010 local currency values, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings for wage workers per hour aged 15-64, inflation corrected to 2010 values. 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Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.MONT.MD.UR.DF", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month, aged 15-64, deflated to 2010 local currency values, urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings for wage workers per month aged 15-64, inflation corrected to 2010 values. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.MONT.MD.YG.10", "metatype": [ { "id": "IndicatorName", "value": "Real median earnings per month, aged 15-24, deflated to 2010 and PPP adjusted" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings for wage workers per month aged 15-64, inflation corrected to 2010 values and adjusted for purchasing power parity using WDI provided exchange values. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.MONT.MD.YG.CN", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month, aged 15-24, local currency values" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings for wage workers per month aged 15-64 reported in local currency values. Currency values are typically reported for the year when the survey was conducted. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.MONT.MD.YG.DF", "metatype": [ { "id": "IndicatorName", "value": "Median earnings per month, aged 15-24, deflated to 2010 local currency values" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Median earnings for wage workers per month aged 15-64, inflation corrected to 2010 values. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.PBPV", "metatype": [ { "id": "IndicatorName", "value": "Public to private wage gap, aged 15-64, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public to private wage gap for wage workers aged 15-64. Reports the ratio of public wage workers earnings to private wage workers earnings. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.PBPV.FE", "metatype": [ { "id": "IndicatorName", "value": "Public to private wage gap, aged 15-64, female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public to private wage gap for wage workers aged 15-64. Reports the ratio of public wage workers earnings to private wage workers earnings. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.PBPV.HE", "metatype": [ { "id": "IndicatorName", "value": "Public to private wage gap, aged 15-64, above primary education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public to private wage gap for wage workers aged 15-64. Reports the ratio of public wage workers earnings to private wage workers earnings. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.PBPV.LE", "metatype": [ { "id": "IndicatorName", "value": "Public to private wage gap, aged 15-64, primary education and below" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public to private wage gap for wage workers aged 15-64. Reports the ratio of public wage workers earnings to private wage workers earnings. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.PBPV.MA", "metatype": [ { "id": "IndicatorName", "value": "Public to private wage gap, aged 15-64, male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public to private wage gap for wage workers aged 15-64. Reports the ratio of public wage workers earnings to private wage workers earnings. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.PBPV.OL", "metatype": [ { "id": "IndicatorName", "value": "Public to private wage gap, aged 25-64" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public to private wage gap for wage workers aged 15-64. Reports the ratio of public wage workers earnings to private wage workers earnings. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.PBPV.RU", "metatype": [ { "id": "IndicatorName", "value": "Public to private wage gap, aged 15-64, rural" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public to private wage gap for wage workers aged 15-64. Reports the ratio of public wage workers earnings to private wage workers earnings. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.PBPV.UR", "metatype": [ { "id": "IndicatorName", "value": "Public to private wage gap, aged 15-64, urban" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public to private wage gap for wage workers aged 15-64. Reports the ratio of public wage workers earnings to private wage workers earnings. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "JI.WAG.PBPV.YG", "metatype": [ { "id": "IndicatorName", "value": "Public to private wage gap, aged 15-24" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Periodicity", "value": "Annual" }, { "id": "Shortdefinition", "value": "Public to private wage gap for wage workers aged 15-64. Reports the ratio of public wage workers earnings to private wage workers earnings. The sub-groups are: Urban shows the results for only the urban population, rural shows the results for the rural population, youth shows the results for the young population between 15 to 24 years, adult shows the results for the adults aged between 25 to 64 years, male shows the results for the male population and female shows the results for the female population. Low educated shows the results for those with primary education or less, and high educated shows the results for everyone who obtained a higher education." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank (https://datacatalog.worldbank.org/dataset/global-jobs-indicators-database)" }, { "id": "Topic", "value": "Social Protection & Labor" } ], "source_id": "86" }, { "id": "account.t.d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Account (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution (see definition for financial institution account) or report personally using a mobile money service in the .past 12 months (see definition for mobile money account)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "The percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution (see definition for financial institution account) or report personally using a mobile money service in the .past 12 months (see definition for mobile money account)." }, { "id": "Source", "value": "Global Findex database" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "account.t.d.7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Account, income, poorest 40% (% ages 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution (see definition for financial institution account) or report personally using a mobile money service in the past 12 months (see definition for mobile money account), income, poorest 40% (% age 15+)." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "The percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution (see definition for financial institution account) or report personally using a mobile money service in the past 12 months (see definition for mobile money account), income, poorest 40% (% age 15+)." }, { "id": "Source", "value": "Global Findex database" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "AG.LND.AGRI.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Agricultural land covers more than one-third of the world's land area, with arable land representing less than one-third of agricultural land (about 10 percent of the world's land area). Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\nFAO's agricultural land data contains a wide range of information on variables that are significant for: understanding the structure of a country's agricultural sector; making economic plans and policies for food security; deriving environmental indicators, including those related to investment in agriculture and data on gross crop area and net crop area which are useful for policy formulation and monitoring.\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used." }, { "id": "IndicatorName", "value": "Agricultural land (% of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through annual questionnaires and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage." }, { "id": "Longdefinition", "value": "Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, electronic files and web site." }, { "id": "Statisticalconceptandmethodology", "value": "Agriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\nAgricultural land is also sometimes classified as irrigated and non-irrigated land. In arid and semi-arid countries agriculture is often confined to irrigated land, with very little farming possible in non-irrigated areas. Land abandoned as a result of shifting cultivation is excluded from Arable land.\n\nData on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\nTotal land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of land area" } ], "source_id": "87" }, { "id": "AG.LND.ARBL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Agricultural land covers more than one-third of the world's land area. Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\nThere is significant geographic variation in the availability of land considered suitable for agriculture. Increasing population and demand from other sectors place growing pressure on available resources. According to FAO, the world's cultivated area has grown by 12 percent over the last 50 years. The global irrigated area has doubled over the same period, accounting for most of the net increase in cultivated land. Agriculture already uses 11 percent of the world's land surface for crop production. It also makes use of 70 percent of all water withdrawn from aquifers, streams and lakes. Agricultural policies have primarily benefitted farmers with productive land and access to water, bypassing the majority of small-scale producers who are still locked in a poverty trap of high vulnerability, land degradation and climatic uncertainty.\n\nLand resources are central to agriculture and rural development, and are intrinsically linked to global challenges of food insecurity and poverty, climate change adaptation and mitigation, as well as degradation and depletion of natural resources that affect the livelihoods of millions of rural people across the world.\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\nFAO's agricultural land data contains a wide range of information on variables that are significant for: understanding the structure of a country's agricultural sector; making economic plans and policies for food security; deriving environmental indicators, including those related to investment in agriculture and data on gross crop area and net crop area which are useful for policy formulation and monitoring." }, { "id": "IndicatorName", "value": "Arable land (% of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The Food and Agriculture Organization (FAO) tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\nThe data collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. Data on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\nTrue comparability of the data is limited, by variations in definitions, statistical methods, and quality of data. Countries use different definitions land use. The Food and Agriculture Organization of the United Nations (FAO), the primary compiler of the data, occasionally adjusts its definitions of land use categories and revises earlier data. Because the data reflect changes in reporting procedures as well as actual changes in land use, apparent trends should be interpreted cautiously.\n\nSatellite images show land use that differs from that of ground-based measures in area under cultivation and type of land use. Moreover, land use data in some countries (India is an example) are based on reporting systems designed for collecting tax revenue. With land taxes no longer a major source of government revenue, the quality and coverage of land use data have declined." }, { "id": "Longdefinition", "value": "Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, electronic files and web site." }, { "id": "Statisticalconceptandmethodology", "value": "Temporary fallow land refers to land left fallow for less than five years. The abandoned land resulting from shifting cultivation is not included in this category. Data for \"Arable land\" are not meant to indicate the amount of land that is potentially cultivable. Total land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area. The data collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of land area" } ], "source_id": "87" }, { "id": "AG.LND.FRST.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "As threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity.\n\nOn a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems. The decrease of forest area, .11 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions.\n\nDestruction of rainforests remains a significant environmental problem Much of what remains of the world's rainforests is in the Amazon basin, where the Amazon Rainforest covers approximately 4 million square kilometers. The regions with the highest tropical deforestation rate are in Central America and tropical Asia. FAO estimates that the decrease of primary forest area, 0.4 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions. Large-scale planting of trees is significantly reducing the net loss of forest area globally, and afforestation and natural expansion of forests in some countries and regions have reduced the net loss of forest area significantly at the global level.\n\nForests cover about 31 percent of total land area of the world; the world's total forest area is just over 4 billion hectares. On a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems.\n\nNational parks, game reserves, wilderness areas and other legally established protected areas cover more than 10 percent of the total forest area in most countries and regions. FAO estimates that around 10 million people are employed in forest management and conservation - but many more are directly dependent on forests for their livelihoods. Close to 1.2 billion hectares of forest are managed primarily for the production of wood and non-wood forest products. An additional 25 percent of forest area is designated for multiple uses - in most cases including the production of wood and non-wood forest products. The area designated primarily for productive purposes has decreased by more than 50 million hectares since 1990 as forests have been designated for other purposes." }, { "id": "IndicatorName", "value": "Forest area (% of land area)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "FAO has been collecting and analyzing data on forest area since 1946. This is done at intervals of 5-10 years as part of the Global Forest Resources Assessment (FRA). FAO reports data for 229 countries and territories; for the remaining 56 small island states and territories where no information is provided, a report is prepared by FAO using existing information and a literature search. The data are aggregated at sub-regional, regional and global levels by the FRA team at FAO, and estimates are produced by straight summation.\n\nThe lag between the reference year and the actual production of data series as well as the frequency of data production varies between countries. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area.\n\nData includes areas with bamboo and palms; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters; plantations primarily used for forestry or protective purposes, such as rubber-wood plantations and cork oak stands. Data excludes tree stands in agricultural production systems, such as fruit plantations and agroforestry systems. Forest area also excludes trees in urban parks and gardens. The proportion of forest area to total land area is calculated and changes in the proportion are computed to identify trends." }, { "id": "Longdefinition", "value": "Forest area is land under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens." }, { "id": "Othernotes", "value": "Areas of former states are included in the successor states." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, electronic files and web site." }, { "id": "Statisticalconceptandmethodology", "value": "Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 meters are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate.\n\nThe Food and Agriculture Organization (FAO) provides detail information on forest cover, and adjusted estimates of forest cover. The survey uses a uniform definition of forest. Although FAO provides a breakdown of forest cover between natural forest and plantation for developing countries, forest data used to derive this indictor data does not reflect that breakdown. Total land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area. The indictor is derived by dividing total area under forest of a country by country's total land area, and multiplying by 100." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ACLD.PRTS.NO", "metatype": [ { "id": "IndicatorName", "value": "Total protests and riots by event type since 2018" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total protests and riots reflects the aggregate of taken from ACLED. ACLED collects real-time data on the locations, dates, actors, fatalities, and types of all reported political violence and protest events across Africa, the Middle East, Latin America & the Caribbean, East Asia, South Asia, Southeast Asia, Central Asia & the Caucasus, Europe, and the United States of America." }, { "id": "Source", "value": "Armed Conflict Location & Event Data Project (ACLED). Available at: https://acleddata.com/#/dashboard" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "CC.ADPO.MAEX.AA", "metatype": [ { "id": "IndicatorName", "value": "Additional population exposed to annual coastal floods due to sea level rise, as a share of actual population (%) - Min. exposure, 2100" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Additional population exposed to annual coastal floods due to sea level rise, as a share of actual population, is reported as a percentage and is reflective of the population in the year 2100, determined by the minimum exposure scenario (RCP26)" }, { "id": "Source", "value": "Kulp, S.A., Strauss, B.H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat Commun 10, 4844 (2019). https://doi.org/10.1038/s41467-019-12808-z" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.ADPO.MAEX.BB", "metatype": [ { "id": "IndicatorName", "value": "Additional population exposed to annual coastal floods due to sea level rise, as a share of actual population (%) - Max exposure, 2100" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Additional population exposed to annual coastal floods due to sea level rise, as a share of actual population, is reported as a percentage and is reflective of the population in the year 2100, determined by the maximum exposure scenario (RCP85)" }, { "id": "Source", "value": "Kulp, S.A., Strauss, B.H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat Commun 10, 4844 (2019). https://doi.org/10.1038/s41467-019-12808-z" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.ADPO.MIEX.AA", "metatype": [ { "id": "IndicatorName", "value": "Additional population exposed to annual coastal floods due to sea level rise, as a share of actual population (%) - Min. exposure, 2050" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Additional population exposed to annual coastal floods due to sea level rise, as a share of actual population, is reported as a percentage and is reflective of the population in the year 2050, determined by the minimum exposure scenario (RCP26)" }, { "id": "Source", "value": "Kulp, S.A., Strauss, B.H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat Commun 10, 4844 (2019). https://doi.org/10.1038/s41467-019-12808-z" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.ADPO.MIEX.BB", "metatype": [ { "id": "IndicatorName", "value": "Additional population exposed to annual coastal floods due to sea level rise, as a share of actual population (%) - Max exposure, 2050" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Additional population exposed to annual coastal floods due to sea level rise, as a share of actual population, is reported as a percentage and is reflective of the population in the year 2050, determined by the maximum exposure scenario (RCP85)" }, { "id": "Source", "value": "Kulp, S.A., Strauss, B.H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat Commun 10, 4844 (2019). https://doi.org/10.1038/s41467-019-12808-z" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.AG.NTR.TOHA", "metatype": [ { "id": "IndicatorName", "value": "Nitrogen application (tons per ha)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data generated by taking Nutrient Nitrogen N (tonnes) [Fertilizers by Nutrient sheet] and dividing it by Area Harvested (ha) [Crops and Livestock Products sheet] from FAOSTAT. FAOSTAT data is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO license (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo). In addition to this license, some database specific terms of use are listed: Terms of Use of Datasets." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "tons per ha" } ], "source_id": "87" }, { "id": "CC.AVPB.PTPI.AI", "metatype": [ { "id": "IndicatorName", "value": "Additional people below $1.90 as % of total population by impact - All impacts" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Additional people below $1.90 as % of total population for all impacts, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones." }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "CC.AVPB.PTPI.AR", "metatype": [ { "id": "IndicatorName", "value": "Additional people below $1.90 as % of total population by impact - Agriculture Revenues" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Additional people below $1.90 as % of total population by impact, from climate change impacts relating to Agriculture Revenues, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones." }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "CC.AVPB.PTPI.DI", "metatype": [ { "id": "IndicatorName", "value": "Additional people below $1.90 as % of total population by impact - Disasters" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Additional people below $1.90 as % of total population by impact, from climate change impacts relating to Disasters, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones." }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "CC.AVPB.PTPI.FP", "metatype": [ { "id": "IndicatorName", "value": "Additional people below $1.90 as % of total population by impact - Food prices" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Additional people below $1.90 as % of total population by impact, from climate change impacts relating to Food Prices, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones." }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "CC.AVPB.PTPI.HE", "metatype": [ { "id": "IndicatorName", "value": "Additional people below $1.90 as % of total population by impact - Health" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Additional people below $1.90 as % of total population by impact, from climate change impacts relating to Health, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones." }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "CC.AVPB.PTPI.LP", "metatype": [ { "id": "IndicatorName", "value": "Additional people below $1.90 as % of total population by impact - Labor productivity" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Additional people below $1.90 as % of total population by impact, from climate change impacts relating to Labor Productivity, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones." }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "CC.AVPB.TPOP.AG", "metatype": [ { "id": "IndicatorName", "value": "Additional people below $4 as % of total population by impact, by 2030 - Agriculture" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Additional people below $4 as % of total population by impact, by 2030, from climate change impacts relating to Agriculture, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones." }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "CC.AVPB.TPOP.AI", "metatype": [ { "id": "IndicatorName", "value": "Additional people below $4 as % of total population by impact, by 2030 - All Impacts" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Additional people below $4 as % of total population by impact, by 2030, from all impacts, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones." }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "CC.AVPB.TPOP.DI", "metatype": [ { "id": "IndicatorName", "value": "Additional people below $4 as % of total population by impact, by 2030 - Disasters" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Additional people below $4 as % of total population by impact, by 2030, from climate change impacts relating to Disasters, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones." }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "CC.AVPB.TPOP.HE", "metatype": [ { "id": "IndicatorName", "value": "Additional people below $4 as % of total population by impact, by 2030 - Health" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Additional people below $4 as % of total population by impact, by 2030, from climate change impacts relating to Health, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones." }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "CC.AVPB.TPOP.TE", "metatype": [ { "id": "IndicatorName", "value": "Additional people below $4 as % of total population by impact, by 2030 - Temperature" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Additional people below $4 as % of total population by impact, by 2030, from climate change impacts relating to Temperature, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones." }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "CC.CHIC.BTFP.AG", "metatype": [ { "id": "IndicatorName", "value": "Change in income (%) for bottom 40% of the population by impact, by 2030 - Agriculture" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Change in income (%) for bottom 40% of the population by impact, by 2030, from climate change impacts relating to Agriculture, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones." }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.CHIC.BTFP.AI", "metatype": [ { "id": "IndicatorName", "value": "Change in income (%) for bottom 40% of the population by impact, by 2030 - All Impacts" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Change in income (%) for bottom 40% of the population for all impacts impact, by 2030, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones." }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.CHIC.BTFP.DI", "metatype": [ { "id": "IndicatorName", "value": "Change in income (%) for bottom 40% of the population by impact, by 2030 - Disasters" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Change in income (%) for bottom 40% of the population by impact, by 2030, from climate change impacts relating to Disasters, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones." }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.CHIC.BTFP.HE", "metatype": [ { "id": "IndicatorName", "value": "Change in income (%) for bottom 40% of the population by impact, by 2030 - Health" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Change in income (%) for bottom 40% of the population by impact, by 2030, from climate change impacts relating to Health, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones." }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.CHIC.BTFP.TE", "metatype": [ { "id": "IndicatorName", "value": "Change in income (%) for bottom 40% of the population by impact, by 2030 - Temperature" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Change in income (%) for bottom 40% of the population by impact, by 2030, from climate change impacts relating to Temperature, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones." }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.CHIC.CFPI.AG", "metatype": [ { "id": "IndicatorName", "value": "Change in income (%) for bottom 40% of the population by impact - Agriculture Revenues" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.CHIC.CFPI.AI", "metatype": [ { "id": "IndicatorName", "value": "Change in income (%) for bottom 40% of the population by impact - All impacts" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.CHIC.CFPI.DI", "metatype": [ { "id": "IndicatorName", "value": "Change in income (%) for bottom 40% of the population by impact - Disasters" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.CHIC.CFPI.FP", "metatype": [ { "id": "IndicatorName", "value": "Change in income (%) for bottom 40% of the population by impact - Food prices" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.CHIC.CFPI.HE", "metatype": [ { "id": "IndicatorName", "value": "Change in income (%) for bottom 40% of the population by impact - Health" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.CHIC.CFPI.LP", "metatype": [ { "id": "IndicatorName", "value": "Change in income (%) for bottom 40% of the population by impact - Labor productivity" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Source", "value": "Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.CO2.EMSE.BF", "metatype": [ { "id": "IndicatorName", "value": "CO2 emissions by sector (Mt CO2 eq) - Bunker Fuels" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Bunker fuels sector CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.CO2.EMSE.BL", "metatype": [ { "id": "IndicatorName", "value": "CO2 emissions by sector (Mt CO2 eq) - Building" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Building sector CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.CO2.EMSE.EH", "metatype": [ { "id": "IndicatorName", "value": "CO2 emissions by sector (Mt CO2 eq) - Electricity/Heat" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Electricity and heat sector CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.CO2.EMSE.EL", "metatype": [ { "id": "IndicatorName", "value": "CO2 emissions by sector (Mt CO2 eq) - Total excluding LUCF" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total CO2 emissions (excluding LUCF) are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.CO2.EMSE.EN", "metatype": [ { "id": "IndicatorName", "value": "CO2 emissions by sector (Mt CO2 eq) - Energy" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Energy sector CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.CO2.EMSE.FE", "metatype": [ { "id": "IndicatorName", "value": "CO2 emissions by sector (Mt CO2 eq) - Fugitive Emissions" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Fugitive sector CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.CO2.EMSE.IL", "metatype": [ { "id": "IndicatorName", "value": "CO2 emissions by sector (Mt CO2 eq) - Total including LUCF" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total CO2 emissions (including LUCF) are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.CO2.EMSE.IP", "metatype": [ { "id": "IndicatorName", "value": "CO2 emissions by sector (Mt CO2 eq) - Industrial Processes" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Industrial Processes sector CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.CO2.EMSE.LU", "metatype": [ { "id": "IndicatorName", "value": "CO2 emissions by sector (Mt CO2 eq) - Land-Use Change and Forestry" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Land-Use Change and Forestry sector CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.CO2.EMSE.MC", "metatype": [ { "id": "IndicatorName", "value": "CO2 emissions by sector (Mt CO2 eq) - Manufacturing/Construction" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Manufacturing and construction sector CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.CO2.EMSE.OF", "metatype": [ { "id": "IndicatorName", "value": "CO2 emissions by sector (Mt CO2 eq) - Other Fuel Combustion" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Other fuel combustion sector CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.CO2.EMSE.TR", "metatype": [ { "id": "IndicatorName", "value": "CO2 emissions by sector (Mt CO2 eq) - Transportation" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Transportation sector CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.COAL.EMIS.CH", "metatype": [ { "id": "IndicatorName", "value": "Operating coal mining GHG emissions (Mt CO2eq) - Methane" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Methane emissions from operating coal mining (Mt CO2eq) is generated from the Global Coal Mine Tracker (GCMT). The GCMT provides information on the world’s major coal mines: every operating mine producing 3 million tonnes per annum (mtpa) or greater, and every proposed mine with a capacity of 1 mtpa or greater. The map and underlying data are updated bi-annually, in January and July. With each update, coverage is expanded to include smaller mines. By January 2022, GCMT will have cataloged every coal mine producing 1 mtpa or greater. Each mine included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCMT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Mine Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-mine-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.COAL.EMIS.CO", "metatype": [ { "id": "IndicatorName", "value": "Operating coal mining GHG emissions (Mt CO2eq) - CO2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "CO2 emissions from operating coal mining (Mt CO2eq) is generated from the Global Coal Mine Tracker (GCMT). The GCMT provides information on the world’s major coal mines: every operating mine producing 3 million tonnes per annum (mtpa) or greater, and every proposed mine with a capacity of 1 mtpa or greater. The map and underlying data are updated bi-annually, in January and July. With each update, coverage is expanded to include smaller mines. By January 2022, GCMT will have cataloged every coal mine producing 1 mtpa or greater. Each mine included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCMT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Mine Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-mine-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.COAL.EMPR.CH", "metatype": [ { "id": "IndicatorName", "value": "Proposed coal mining GHG emissions (Mt CO2eq) - Methane" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Methane emissions from proposed coal mining (Mt CO2eq) is generated from the Global Coal Mine Tracker (GCMT). The GCMT provides information on the world’s major coal mines: every operating mine producing 3 million tonnes per annum (mtpa) or greater, and every proposed mine with a capacity of 1 mtpa or greater. The map and underlying data are updated bi-annually, in January and July. With each update, coverage is expanded to include smaller mines. By January 2022, GCMT will have cataloged every coal mine producing 1 mtpa or greater. Each mine included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCMT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Mine Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-mine-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.COAL.EMPR.CO", "metatype": [ { "id": "IndicatorName", "value": "Proposed coal mining GHG emissions (Mt CO2eq) - CO2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "CO2 emissions from proposed coal mining (Mt CO2eq) is generated from the Global Coal Mine Tracker (GCMT). The GCMT provides information on the world’s major coal mines: every operating mine producing 3 million tonnes per annum (mtpa) or greater, and every proposed mine with a capacity of 1 mtpa or greater. The map and underlying data are updated bi-annually, in January and July. With each update, coverage is expanded to include smaller mines. By January 2022, GCMT will have cataloged every coal mine producing 1 mtpa or greater. Each mine included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCMT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Mine Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-mine-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.COAL.PROD.OP", "metatype": [ { "id": "IndicatorName", "value": "Annual coal production (Mt per year) - Operating" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Annual coal production (Mt per year) is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt per year" } ], "source_id": "87" }, { "id": "CC.COAL.PROD.PR", "metatype": [ { "id": "IndicatorName", "value": "Annual coal production (Mt per year) - Proposed Projects (Total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Annual coal production (Mt per year) is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt per year" } ], "source_id": "87" }, { "id": "CC.CUF.STUN", "metatype": [ { "id": "IndicatorName", "value": "Children under 5 years of age who are stunted (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Children under 5 years of age who are stunted reflects modelled estimate from the Suite of Food Security Indicators on FAOSTAT. Suite of Food Security Indicators presents the core set of food security indicators. Following the recommendation of experts gathered in the Committee on World Food Security (CFS) Round Table on hunger measurement, hosted at FAO headquarters in September 2011, an initial set of indicators aiming to capture various aspects of food insecurity is presented on FAOSTAT. The choice of the indicators has been informed by expert judgment and the availability of data with sufficient coverage to enable comparisons across regions and over time. Many of these indicators are produced and published elsewhere by FAO and other international organizations. They are reported on FAOSTAT in a single database with the aim of building a wide food security information system. More indicators will be added to this set as more data will become available. Indicators are classified along the four dimensions of food security -- availability, access, utilization and stability. This work is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO license (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo). In addition to this license, some database specific terms of use are listed: Terms of Use of Datasets." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.DEN.AFO", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Direct emissions (N2O) - AFOLU" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.DEN.AGS", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Direct emissions (N2O) - Agricultural Soils" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.DEN.CRE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Direct emissions (N2O) - Crop Residues" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.DEN.EAL", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Direct emissions (N2O) - Emissions on agricultural land" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.DEN.FGE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Direct emissions (N2O) - Farm-gate emissions" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.DEN.IPA", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Direct emissions (N2O) - IPCC Agriculture" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.DEN.MLP", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Direct emissions (N2O) - Manure left on Pasture" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.DEN.SAS", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Direct emissions (N2O) - Manure applied to Soils" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.DEN.SFE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Direct emissions (N2O) - Synthetic Fertilizers" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.AFO", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - AFOLU" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.AGS", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Agricultural Soils" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.BCR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Burning - Crop residues" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.CRE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Crop Residues" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.DOC", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Drained organic soils (CO2)" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.DON", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Drained organic soils (N2O)" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.DOS", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Drained organic soils" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.EAL", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Emissions on agricultural land" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.EFE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Enteric Fermentation" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.FFR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Forest fires" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.FGE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Farm-gate emissions" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.FOR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Forestland" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.FOS", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Fires in organic soils" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.FTR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Fires in humid tropical forests" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.IPA", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - IPCC Agriculture" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.LUC", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Land Use change" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.LUL", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - LULUCF" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.MLP", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Manure left on Pasture" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.MMA", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Manure Management" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.NFC", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Net Forest conversion" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.OEU", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - On-farm energy use" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.RCA", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Rice Cultivation" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.SAS", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Manure applied to Soils" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.SFE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Synthetic Fertilizers" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EAR.SVF", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) (AR5) - Savanna fires" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECA.AFO", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from CH4 (AR5) - AFOLU" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECA.BCR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from CH4 (AR5) - Burning - Crop residues" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECA.EAL", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from CH4 (AR5) - Emissions on agricultural land" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECA.EFE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from CH4 (AR5) - Enteric Fermentation" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECA.FFR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from CH4 (AR5) - Forest fires" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECA.FGE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from CH4 (AR5) - Farm-gate emissions" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECA.FOS", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from CH4 (AR5) - Fires in organic soils" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECA.FTR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from CH4 (AR5) - Fires in humid tropical forests" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECA.GAEX.CA", "metatype": [ { "id": "IndicatorName", "value": "Capacity of Liquefied Natural Gas export terminals (Mt per year) - Cancelled" }, { "id": "Longdefinition", "value": "LNG terminal capacity is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt per year" } ], "source_id": "87" }, { "id": "CC.ECA.GAEX.ID", "metatype": [ { "id": "IndicatorName", "value": "Capacity of Liquefied Natural Gas export terminals (Mt per year) - In Development (Proposed + Construction)" }, { "id": "Longdefinition", "value": "LNG terminal capacity is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt per year" } ], "source_id": "87" }, { "id": "CC.ECA.GAEX.OP", "metatype": [ { "id": "IndicatorName", "value": "Capacity of Liquefied Natural Gas export terminals (Mt per year) - Operating" }, { "id": "Longdefinition", "value": "LNG terminal capacity is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt per year" } ], "source_id": "87" }, { "id": "CC.ECA.GAEX.SH", "metatype": [ { "id": "IndicatorName", "value": "Capacity of Liquefied Natural Gas export terminals (Mt per year) - Shelved" }, { "id": "Longdefinition", "value": "LNG terminal capacity is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt per year" } ], "source_id": "87" }, { "id": "CC.ECA.GAIM.CA", "metatype": [ { "id": "IndicatorName", "value": "Capacity of Liquefied Natural Gas import terminals (Mt per year) - Cancelled" }, { "id": "Longdefinition", "value": "LNG terminal capacity is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt per year" } ], "source_id": "87" }, { "id": "CC.ECA.GAIM.ID", "metatype": [ { "id": "IndicatorName", "value": "Capacity of Liquefied Natural Gas import terminals (Mt per year) - In Development (Proposed + Construction)" }, { "id": "Longdefinition", "value": "LNG terminal capacity is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt per year" } ], "source_id": "87" }, { "id": "CC.ECA.GAIM.OP", "metatype": [ { "id": "IndicatorName", "value": "Capacity of Liquefied Natural Gas import terminals (Mt per year) - Operating" }, { "id": "Longdefinition", "value": "LNG terminal capacity is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt per year" } ], "source_id": "87" }, { "id": "CC.ECA.GAIM.SH", "metatype": [ { "id": "IndicatorName", "value": "Capacity of Liquefied Natural Gas import terminals (Mt per year) - Shelved" }, { "id": "Longdefinition", "value": "LNG terminal capacity is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt per year" } ], "source_id": "87" }, { "id": "CC.ECA.IPA", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from CH4 (AR5) - IPCC Agriculture" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECA.LUC", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from CH4 (AR5) - Land Use change" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECA.LUL", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from CH4 (AR5) - LULUCF" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECA.MMA", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from CH4 (AR5) - Manure Management" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECA.OEU", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from CH4 (AR5) - On-farm energy use" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECA.RCA", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from CH4 (AR5) - Rice Cultivation" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECA.SVF", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from CH4 (AR5) - Savanna fires" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECH.AFO", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CH4) - AFOLU" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECH.BCR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CH4) - Burning - Crop residues" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECH.EAL", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CH4) - Emissions on agricultural land" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECH.EFE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CH4) - Enteric Fermentation" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECH.FFR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CH4) - Forest fires" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECH.FGE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CH4) - Farm-gate emissions" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECH.FOS", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CH4) - Fires in organic soils" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECH.FTR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CH4) - Fires in humid tropical forests" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECH.IPA", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CH4) - IPCC Agriculture" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECH.LUC", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CH4) - Land Use change" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECH.LUL", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CH4) - LULUCF" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECH.MMA", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CH4) - Manure Management" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECH.OEU", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CH4) - On-farm energy use" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECH.RCA", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CH4) - Rice Cultivation" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECH.SVF", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CH4) - Savanna fires" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECO.AFO", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2) - AFOLU" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECO.DOC", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2) - Drained organic soils (CO2)" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECO.EAL", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2) - Emissions on agricultural land" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECO.FGE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2) - Farm-gate emissions" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECO.FOR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2) - Forestland" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECO.FOS", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2) - Fires in organic soils" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECO.LUC", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2) - Land Use change" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECO.LUL", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2) - LULUCF" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECO.NFC", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2) - Net Forest conversion" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ECO.OEU", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2) - On-farm energy use" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.EG.CONS.COAL.PC", "metatype": [ { "id": "IndicatorName", "value": "Per capita daily coal consumption (barrels per capita per day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Per capita data has been generated by World Bank staff by taking Energy consumption data [U.S EIA] and applying country population data from the World Bank Group - World Development Indicators." }, { "id": "Source", "value": "U.S Energy Information Administration. Available at: https://www.eia.gov/opendata/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels per capita per day" } ], "source_id": "87" }, { "id": "CC.EG.CONS.GAS.PC", "metatype": [ { "id": "IndicatorName", "value": "Per capita daily gas consumption (barrels per capita per day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Per capita data has been generated by World Bank staff by taking Energy consumption data [U.S EIA] and applying country population data from the World Bank Group - World Development Indicators." }, { "id": "Source", "value": "U.S Energy Information Administration. Available at: https://www.eia.gov/opendata/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels per capita per day" } ], "source_id": "87" }, { "id": "CC.EG.CONS.OIL.PC", "metatype": [ { "id": "IndicatorName", "value": "Per capita daily oil consumption (barrels per capita per day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Per capita data has been generated by World Bank staff by taking Energy consumption data [U.S EIA] and applying country population data from the World Bank Group - World Development Indicators." }, { "id": "Source", "value": "U.S Energy Information Administration. Available at: https://www.eia.gov/opendata/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels per capita per day" } ], "source_id": "87" }, { "id": "CC.EG.EMIS.MAN", "metatype": [ { "id": "IndicatorName", "value": "CO2 emissions per dollar of manufacturing value added (kgCO2 per constant 2010 US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents CO2 emissions that can be attributed to each dollar generated from manufacturing value add. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.4.1: Carbon dioxide emissions per unit of manufacturing value added (kilogrammes of CO2 per constant 2015 United States dollars)." }, { "id": "Notesfromoriginalsource", "value": "This indicator presents CO2 emissions that can be attributed to each dollar generated from manufacturing value add. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.4.1: Carbon dioxide emissions per unit of manufacturing value added (kilogrammes of CO2 per constant 2015 United States dollars)." }, { "id": "Othernotes", "value": "This indicator presents CO2 emissions that can be attributed to each dollar generated from manufacturing value add. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.4.1: Carbon dioxide emissions per unit of manufacturing value added (kilogrammes of CO2 per constant 2015 United States dollars)." }, { "id": "Otherweblinks", "value": "This indicator presents CO2 emissions that can be attributed to each dollar generated from manufacturing value add. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.4.1: Carbon dioxide emissions per unit of manufacturing value added (kilogrammes of CO2 per constant 2015 United States dollars)." }, { "id": "Periodicity", "value": "This indicator presents CO2 emissions that can be attributed to each dollar generated from manufacturing value add. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.4.1: Carbon dioxide emissions per unit of manufacturing value added (kilogrammes of CO2 per constant 2015 United States dollars)." }, { "id": "Powercode", "value": "This indicator presents CO2 emissions that can be attributed to each dollar generated from manufacturing value add. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.4.1: Carbon dioxide emissions per unit of manufacturing value added (kilogrammes of CO2 per constant 2015 United States dollars)." }, { "id": "Previous_Indicator_Code", "value": "This indicator presents CO2 emissions that can be attributed to each dollar generated from manufacturing value add. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.4.1: Carbon dioxide emissions per unit of manufacturing value added (kilogrammes of CO2 per constant 2015 United States dollars)." }, { "id": "Previous_Indicator_Name", "value": "This indicator presents CO2 emissions that can be attributed to each dollar generated from manufacturing value add. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.4.1: Carbon dioxide emissions per unit of manufacturing value added (kilogrammes of CO2 per constant 2015 United States dollars)." }, { "id": "Referenceperiod", "value": "This indicator presents CO2 emissions that can be attributed to each dollar generated from manufacturing value add. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.4.1: Carbon dioxide emissions per unit of manufacturing value added (kilogrammes of CO2 per constant 2015 United States dollars)." }, { "id": "Relatedindicators", "value": "This indicator presents CO2 emissions that can be attributed to each dollar generated from manufacturing value add. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.4.1: Carbon dioxide emissions per unit of manufacturing value added (kilogrammes of CO2 per constant 2015 United States dollars)." }, { "id": "Relatedsourcelinks", "value": "This indicator presents CO2 emissions that can be attributed to each dollar generated from manufacturing value add. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.4.1: Carbon dioxide emissions per unit of manufacturing value added (kilogrammes of CO2 per constant 2015 United States dollars)." }, { "id": "Shortdefinition", "value": "This indicator presents CO2 emissions that can be attributed to each dollar generated from manufacturing value add. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.4.1: Carbon dioxide emissions per unit of manufacturing value added (kilogrammes of CO2 per constant 2015 United States dollars)." }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kgCO2 per constant 2010 US$" } ], "source_id": "87" }, { "id": "CC.EG.INTS.KW", "metatype": [ { "id": "IndicatorName", "value": "Energy intensity of the economy (kWh per 2011$PPP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Energy intensity is measured as primary energy consumption per unit of gross domestic product. This is measured in kilowatt-hours per 2011$ (PPP)." }, { "id": "Source", "value": "Our World in Data based on BP; World Bank; and Maddison Project Database" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kWh per 2011$PPP" } ], "source_id": "87" }, { "id": "CC.EG.SOLR.KW", "metatype": [ { "id": "IndicatorName", "value": "Average practical solar potential (kWh/kWp/day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Solar potential reflects analysis based on high-resolution datasets and GIS mask layers by Solargis's (https://solargis.com/) . These indicators have been generated as part of the Energy Sector Management Assistance Program [ESMAP]. Further information is available at: https://www.esmap.org/. Visualization of the data can be found at: https://globalsolaratlas.info/map." }, { "id": "Source", "value": "Global Photovoltaic Power Potential By Country, generated by Solaris, with ESMAP and World Bank Group contributions. Available at: https://datacatalog.worldbank.org/int/search/dataset/0038379" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kWh/kWp/day" } ], "source_id": "87" }, { "id": "CC.EG.STL.PROD", "metatype": [ { "id": "IndicatorName", "value": "Total steelmaking capacity (thousand tonnes per annum)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Steel making capacity is generated from the Global Steel Plant Tracker (GSPT). The GSPT provides information on global crude steel production plants, and includes information on every plant currently operating at a capacity of one million tonnes per year (mtpa) or more of crude steel. The GSPT is currently being expanded to include all plants meeting the one mtpa threshold that have been proposed since 2017 or retired or mothballed since 2020. Steel plants consist of multiple units, depending on the iron and steel production method used. Crude steel is typically produced through coal-based methods (blast furnace and basic oxygen furnace or open hearth furnace) or electricity-based production (electric arc furnace charged with scrap metal, pig iron, direct reduced iron, or a combination). The GSPT map and underlying data are updated annually in January. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GSPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Steel Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-steel-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "thousand tonnes per annum" } ], "source_id": "87" }, { "id": "CC.EG.SUBF.PC", "metatype": [ { "id": "IndicatorName", "value": "Fossil-fuel pre-tax subsidies (consumption and production) USD per capita" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the pre-tax subsidies (consumption and production) on fossil fuels as an index of USD per capita. Data is taken from the United Nations Sustainable Goals, representing Indicator 12.c.1: Fossil-fuel subsidies (consumption and production) per capita (constant United States dollars)." }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "USD per capita" } ], "source_id": "87" }, { "id": "CC.EG.WIND.PC", "metatype": [ { "id": "IndicatorName", "value": "Offshore wind potential - Per capita (kW/cap)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The offshore wind technical potential is an estimate of the amount of generation capacity that could be technically feasible, considering only wind speed and water depth. This is intended as an initial, high-level estimate and does not look at other technical, environmental, social, or economic constraints. These indicators have been generated as part of the Energy Sector Management Assistance Program [ESMAP]. Further information is available at: https://www.esmap.org/. Visualization of the data can be found at: https://globalwindatlas.info/" }, { "id": "Source", "value": "Global Offshore Wind Technical Potential, the World Bank Group. Available at: https://datacatalog.worldbank.org/int/search/dataset/0037787/Global-Offshore-Wind-Technical-Potential" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kW/cap" } ], "source_id": "87" }, { "id": "CC.EG.WIND.TOTL", "metatype": [ { "id": "IndicatorName", "value": "Offshore wind potential - Total (GW)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The offshore wind technical potential is an estimate of the amount of generation capacity that could be technically feasible, considering only wind speed and water depth. This is intended as an initial, high-level estimate and does not look at other technical, environmental, social, or economic constraints. These indicators have been generated as part of the Energy Sector Management Assistance Program [ESMAP]. Further information is available at: https://www.esmap.org/. Visualization of the data can be found at: https://globalwindatlas.info/" }, { "id": "Source", "value": "Global Offshore Wind Technical Potential, the World Bank Group. Available at: https://datacatalog.worldbank.org/int/search/dataset/0037787/Global-Offshore-Wind-Technical-Potential" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "GW" } ], "source_id": "87" }, { "id": "CC.ELEC.CON", "metatype": [ { "id": "IndicatorName", "value": "Electricity net consumption" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "Total electric power consumption = total net electricity generation + electricity imports - electricity exports - electricity transmission and distribution losses. Data are reported as net consumption, not gross consumption. Net consumption excludes the energy consumed by the generating units." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Billion kWh" } ], "source_id": "87" }, { "id": "CC.ELEC.GEN", "metatype": [ { "id": "IndicatorName", "value": "Electricity net generation" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "Generation data consist of both utility and non-utility sources from electricity and combined heat and power plants. Data are reported as net generation, not gross generation. The difference between gross and net generation is generally about 6% for fossil fuels stations, 1% for hydro stations, and 5% for all others. Fossil Fuels electricity generation consists of electricity generated from coal, petroleum, and natural gas. Solar electricity generation includes solar photovoltaic and solar thermal generation, and distributed solar generation where available. The term biomass and waste used here is similar to the term combustible renewables and waste. Electricity net generation excludes the energy consumed by the generating units and generation from hydroelectric pumped storage. Generation from hydroelectric pumped storage plants is included in total generation." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Billion kWh" } ], "source_id": "87" }, { "id": "CC.EN.ATM.COAL.CE", "metatype": [ { "id": "IndicatorName", "value": "Lifetime CO2 emissions from coal plants (Mt CO2)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Lifetime CO2 emissions from coal plants (Mt CO2) is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt CO2" } ], "source_id": "87" }, { "id": "CC.ENA.AFO", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - AFOLU" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.AGS", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - Agricultural Soils" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.BCR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - Burning - Crop residues" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.CRE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - Crop Residues" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.DON", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - Drained organic soils (N2O)" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.EAL", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - Emissions on agricultural land" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.FFR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - Forest fires" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.FGE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - Farm-gate emissions" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.FTR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - Fires in humid tropical forests" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.IPA", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - IPCC Agriculture" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.LUC", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - Land Use change" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.LUL", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - LULUCF" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.MLP", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - Manure left on Pasture" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.MMA", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - Manure Management" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.OEU", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - On-farm energy use" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.SAS", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - Manure applied to Soils" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.SFE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - Synthetic Fertilizers" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENA.SVF", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (CO2eq) from N2O (AR5) - Savanna fires" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.AFO", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - AFOLU" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.AGS", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - Agricultural Soils" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.BCR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - Burning - Crop residues" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.CRE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - Crop Residues" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.DON", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - Drained organic soils (N2O)" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.EAL", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - Emissions on agricultural land" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.FFR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - Forest fires" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.FGE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - Farm-gate emissions" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.FTR", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - Fires in humid tropical forests" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.IPA", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - IPCC Agriculture" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.LUC", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - Land Use change" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.LUL", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - LULUCF" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.MLP", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - Manure left on Pasture" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.MMA", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - Manure Management" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.OEU", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - On-farm energy use" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.SAS", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - Manure applied to Soils" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.SFE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - Synthetic Fertilizers" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENO.SVF", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Emissions (N2O) - Savanna fires" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.ENTX.ENE.ZS", "metatype": [ { "id": "IndicatorName", "value": "Total energy tax revenue (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Revenues from taxes raised on energy products (fossil fuels and electricity) including those used in transportation (petrol and diesel). This includes all CO2-related taxes." }, { "id": "Source", "value": "Policy Instrument for the Environment Data Base, OECD. Available at: https://www.oecd.org/env/indicators-modelling-outlooks/policy-instrument-database/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "87" }, { "id": "CC.ENTX.ENV.ZS", "metatype": [ { "id": "IndicatorName", "value": "Total environmental tax revenue (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total environmentally related tax revenue includes the revenue generated by energy, transport, pollution and resources tax bases." }, { "id": "Source", "value": "Policy Instrument for the Environment Data Base, OECD. Available at: https://www.oecd.org/env/indicators-modelling-outlooks/policy-instrument-database/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "87" }, { "id": "CC.ENV.GDS.CADV", "metatype": [ { "id": "IndicatorName", "value": "Comparative advantage in environmental goods (index)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Environmental goods include both goods connected to environmental protection—such as goods related to pollution management and resource management—and adapted goods—which are goods that have been specifically modified to be more “environmentally friendly” or “cleaner.” \n\nComparative advantage is a measure of the relative advantage or disadvantage a particular country has in a certain class of goods (in this case, environmental goods), and can be used to evaluate export potential in that class of goods. Environmental goods include both goods connected to environmental protection—such as goods related to pollution management and resource management—and adapted goods—which are goods that have been specifically modified to be more “environmentally friendly” or “cleaner.” A value greater than one indicates a relative advantage in environmental goods, while a value of less than one indicates a relative disadvantage." }, { "id": "Source", "value": "IMF Climate Change Dashboard. Data sources: UN Comtrade, DESA/UNSD; IMF staff calculations." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Index" } ], "source_id": "87" }, { "id": "CC.ENV.TRAD.EX", "metatype": [ { "id": "IndicatorName", "value": "Trade in environmental goods as share of total exports" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Environmental goods include both goods connected to environmental protection—such as goods related to pollution management and resource management—and adapted goods—which are goods that have been specifically modified to be more “environmentally friendly” or “cleaner.” \n\nA relatively high share of environmental goods imports indicates that a country purchases a significant share of environmental goods from other countries. A relatively high share of environmental goods exports indicates that a country produces and sells a significant share of environmental goods to other countries. A country’s environmental goods trade balance is the difference between its exports and imports of environmental goods." }, { "id": "Source", "value": "IMF Climate Change Dashboard. Data sources: UN Comtrade, DESA/UNSD; IMF staff calculations." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of total exports" } ], "source_id": "87" }, { "id": "CC.ENV.TRAD.IM", "metatype": [ { "id": "IndicatorName", "value": "Trade in environmental goods as share of total imports" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Environmental goods include both goods connected to environmental protection—such as goods related to pollution management and resource management—and adapted goods—which are goods that have been specifically modified to be more “environmentally friendly” or “cleaner.” \n\nA relatively high share of environmental goods imports indicates that a country purchases a significant share of environmental goods from other countries. A relatively high share of environmental goods exports indicates that a country produces and sells a significant share of environmental goods to other countries. A country’s environmental goods trade balance is the difference between its exports and imports of environmental goods." }, { "id": "Source", "value": "IMF Climate Change Dashboard. Data sources: UN Comtrade, DESA/UNSD; IMF staff calculations." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of total imports" } ], "source_id": "87" }, { "id": "CC.ESG.AGFE", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Agriculture - Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Females within Agriculture is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.AGMA", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Agriculture - Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Males within Agriculture is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.CMFE", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Commerce - Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Females within Commerce is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.CMMA", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Commerce - Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Males within Commerce is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.CNFE", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Construction - Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Females within Construction is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.CNMA", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Construction - Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Males within Construction is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.EUFE", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Electricity and utilities - Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Females within Electricity and Utilities is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.EUMA", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Electricity and utilities - Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Males within Electricity and Utilities is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.FBFE", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Financial and Business Services - Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Females within Financial and Business Services is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.FBMA", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Financial and Business Services - Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Males within Financial and Business Services is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.INFE", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Industry - Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Females within Industry is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.INMA", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Industry - Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Males within Industry is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.MAFE", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Manufacturing - Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Females within Manufacturing is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.MAMA", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Manufacturing - Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Males within Manufacturing is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.MIFE", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Mining - Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Females within Mining is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.MIMA", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Mining - Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Males within Mining is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.OSFE", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Other services - Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Females within Other services is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.OSMA", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Other services - Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Males within Other services is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.PAFE", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Public Administration - Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Females within Public Administration is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.PAMA", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Public Administration - Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Males within Public Administration is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.PSFE", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Public sector employment - Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Females within Public sector employment is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.PSMA", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Public sector employment - Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Males within Public sector employment is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.SEFE", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Services - Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Females within Services is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.SEMA", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Services - Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Males within Services is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.TCFE", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Transport & Communication - Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Females within Transport & Communication is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ESG.TCMA", "metatype": [ { "id": "IndicatorName", "value": "Employment by sector and gender (% of total) - Transport & Communication - Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Employment of Males within Transport & Communication is disaggregated from the Global Jobs Indicators Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.FGHG.SYS.CS", "metatype": [ { "id": "IndicatorName", "value": "Food GHG emissions by system stage (Share of total) - Consumption" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Food GHG emissions disaggregated by system stage are generated from EDGAR-FOOD, a global emission inventory of GHGs from the food systems. EDGAR-FOOD has been developed to aid the understanding of the activities underlying the energy demand and use, agriculture and land use change emissions associated with the production, distribution, consumption and disposal of food through the various stages and sectors of the composite global food system. These data were complemented with data from the FAOSTAT database on GHG emissions from land use related to agriculture (FAO, 2020). EDGAR-FOOD represents the first database consistently covering each stage of the food chain for all countries with yearly frequency for the period 1990-2015. Details regarding the methodology applied are available in Crippa et al. (2021)." }, { "id": "Source", "value": "Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F.N. and Leip, A. EDGAR-FOOD data. Figshare, doi:10.6084/m9.figshare.13476666 (2021)." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.FGHG.SYS.EO", "metatype": [ { "id": "IndicatorName", "value": "Food GHG emissions by system stage (Share of total) - End_of_Life" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Food GHG emissions disaggregated by system stage are generated from EDGAR-FOOD, a global emission inventory of GHGs from the food systems. EDGAR-FOOD has been developed to aid the understanding of the activities underlying the energy demand and use, agriculture and land use change emissions associated with the production, distribution, consumption and disposal of food through the various stages and sectors of the composite global food system. These data were complemented with data from the FAOSTAT database on GHG emissions from land use related to agriculture (FAO, 2020). EDGAR-FOOD represents the first database consistently covering each stage of the food chain for all countries with yearly frequency for the period 1990-2015. Details regarding the methodology applied are available in Crippa et al. (2021)." }, { "id": "Source", "value": "Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F.N. and Leip, A. EDGAR-FOOD data. Figshare, doi:10.6084/m9.figshare.13476666 (2021)." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.FGHG.SYS.LU", "metatype": [ { "id": "IndicatorName", "value": "Food GHG emissions by system stage (Share of total) - LULUC (Production)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Food GHG emissions disaggregated by system stage are generated from EDGAR-FOOD, a global emission inventory of GHGs from the food systems. EDGAR-FOOD has been developed to aid the understanding of the activities underlying the energy demand and use, agriculture and land use change emissions associated with the production, distribution, consumption and disposal of food through the various stages and sectors of the composite global food system. These data were complemented with data from the FAOSTAT database on GHG emissions from land use related to agriculture (FAO, 2020). EDGAR-FOOD represents the first database consistently covering each stage of the food chain for all countries with yearly frequency for the period 1990-2015. Details regarding the methodology applied are available in Crippa et al. (2021)." }, { "id": "Source", "value": "Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F.N. and Leip, A. EDGAR-FOOD data. Figshare, doi:10.6084/m9.figshare.13476666 (2021)." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.FGHG.SYS.PC", "metatype": [ { "id": "IndicatorName", "value": "Food GHG emissions by system stage (Share of total) - Packaging" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Food GHG emissions disaggregated by system stage are generated from EDGAR-FOOD, a global emission inventory of GHGs from the food systems. EDGAR-FOOD has been developed to aid the understanding of the activities underlying the energy demand and use, agriculture and land use change emissions associated with the production, distribution, consumption and disposal of food through the various stages and sectors of the composite global food system. These data were complemented with data from the FAOSTAT database on GHG emissions from land use related to agriculture (FAO, 2020). EDGAR-FOOD represents the first database consistently covering each stage of the food chain for all countries with yearly frequency for the period 1990-2015. Details regarding the methodology applied are available in Crippa et al. (2021)." }, { "id": "Source", "value": "Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F.N. and Leip, A. EDGAR-FOOD data. Figshare, doi:10.6084/m9.figshare.13476666 (2021)." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.FGHG.SYS.PD", "metatype": [ { "id": "IndicatorName", "value": "Food GHG emissions by system stage (Share of total) - Production" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Food GHG emissions disaggregated by system stage are generated from EDGAR-FOOD, a global emission inventory of GHGs from the food systems. EDGAR-FOOD has been developed to aid the understanding of the activities underlying the energy demand and use, agriculture and land use change emissions associated with the production, distribution, consumption and disposal of food through the various stages and sectors of the composite global food system. These data were complemented with data from the FAOSTAT database on GHG emissions from land use related to agriculture (FAO, 2020). EDGAR-FOOD represents the first database consistently covering each stage of the food chain for all countries with yearly frequency for the period 1990-2015. Details regarding the methodology applied are available in Crippa et al. (2021)." }, { "id": "Source", "value": "Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F.N. and Leip, A. EDGAR-FOOD data. Figshare, doi:10.6084/m9.figshare.13476666 (2021)." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.FGHG.SYS.PR", "metatype": [ { "id": "IndicatorName", "value": "Food GHG emissions by system stage (Share of total) - Processing" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Food GHG emissions disaggregated by system stage are generated from EDGAR-FOOD, a global emission inventory of GHGs from the food systems. EDGAR-FOOD has been developed to aid the understanding of the activities underlying the energy demand and use, agriculture and land use change emissions associated with the production, distribution, consumption and disposal of food through the various stages and sectors of the composite global food system. These data were complemented with data from the FAOSTAT database on GHG emissions from land use related to agriculture (FAO, 2020). EDGAR-FOOD represents the first database consistently covering each stage of the food chain for all countries with yearly frequency for the period 1990-2015. Details regarding the methodology applied are available in Crippa et al. (2021)." }, { "id": "Source", "value": "Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F.N. and Leip, A. EDGAR-FOOD data. Figshare, doi:10.6084/m9.figshare.13476666 (2021)." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.FGHG.SYS.RE", "metatype": [ { "id": "IndicatorName", "value": "Food GHG emissions by system stage (Share of total) - Retail" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Food GHG emissions disaggregated by system stage are generated from EDGAR-FOOD, a global emission inventory of GHGs from the food systems. EDGAR-FOOD has been developed to aid the understanding of the activities underlying the energy demand and use, agriculture and land use change emissions associated with the production, distribution, consumption and disposal of food through the various stages and sectors of the composite global food system. These data were complemented with data from the FAOSTAT database on GHG emissions from land use related to agriculture (FAO, 2020). EDGAR-FOOD represents the first database consistently covering each stage of the food chain for all countries with yearly frequency for the period 1990-2015. Details regarding the methodology applied are available in Crippa et al. (2021)." }, { "id": "Source", "value": "Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F.N. and Leip, A. EDGAR-FOOD data. Figshare, doi:10.6084/m9.figshare.13476666 (2021)." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.FGHG.SYS.TR", "metatype": [ { "id": "IndicatorName", "value": "Food GHG emissions by system stage (Share of total) - Transport" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Food GHG emissions disaggregated by system stage are generated from EDGAR-FOOD, a global emission inventory of GHGs from the food systems. EDGAR-FOOD has been developed to aid the understanding of the activities underlying the energy demand and use, agriculture and land use change emissions associated with the production, distribution, consumption and disposal of food through the various stages and sectors of the composite global food system. These data were complemented with data from the FAOSTAT database on GHG emissions from land use related to agriculture (FAO, 2020). EDGAR-FOOD represents the first database consistently covering each stage of the food chain for all countries with yearly frequency for the period 1990-2015. Details regarding the methodology applied are available in Crippa et al. (2021)." }, { "id": "Source", "value": "Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F.N. and Leip, A. EDGAR-FOOD data. Figshare, doi:10.6084/m9.figshare.13476666 (2021)." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.FLD.BELW.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population exposed to floods (share of population below 5.5$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Share of population that falls into the defined poverty threshold of $5.50 (consumption per day) that is exposed to floods within a country" }, { "id": "Source", "value": "Rentschler, Jun; Salhab, Melda. 2020. People in Harm's Way : Flood Exposure and Poverty in 189 Countries. Policy Research Working Paper;No. 9447. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/34655 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "87" }, { "id": "CC.FLD.TOTL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Population exposed to floods (share of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Share of total population that is exposed to floods within a country" }, { "id": "Source", "value": "Rentschler, Jun; Salhab, Melda. 2020. People in Harm's Way : Flood Exposure and Poverty in 189 Countries. Policy Research Working Paper;No. 9447. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/34655 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "87" }, { "id": "CC.FOOD.ANIM.ZS", "metatype": [ { "id": "IndicatorName", "value": "Share of animal products in food supply (kcal/capita/day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Animal products supply is taken from New Food Balances (domain), Food Supply (element) of the Food Balance Sheet (FAOSTAT). FAOSTAT Food Balance Sheet presents a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply and its utilization. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per caput food supplies are expressed in terms of quantity and - by applying appropriate food composition factors for all primary and processed products - also in terms of caloric value and protein and fat content. This work is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO license (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo). In addition to this license, some database specific terms of use are listed: Terms of Use of Datasets." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kcal/capita/day" } ], "source_id": "87" }, { "id": "CC.FOOD.VEGT.ZS", "metatype": [ { "id": "IndicatorName", "value": "Share of vegetal products in food supply (kcal/capita/day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Vegetal supply is taken from New Food Balances (domain), Food Supply (element) of the Food Balance Sheet (FAOSTAT). FAOSTAT Food Balance Sheet presents a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply and its utilization. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per caput food supplies are expressed in terms of quantity and - by applying appropriate food composition factors for all primary and processed products - also in terms of caloric value and protein and fat content. This work is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO license (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo). In addition to this license, some database specific terms of use are listed: Terms of Use of Datasets." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kcal/capita/day" } ], "source_id": "87" }, { "id": "CC.FRVOL.MOD.AI", "metatype": [ { "id": "IndicatorName", "value": "Freight volume by mode (billion tons-km) - Air transport" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the volume of freight attributable to air transport. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.1.2: Freight volume by mode of transport (tonne kilometres)" }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "billion tons-km" } ], "source_id": "87" }, { "id": "CC.FRVOL.MOD.IW", "metatype": [ { "id": "IndicatorName", "value": "Freight volume by mode (billion tons-km) - Inland waterway transport" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the volume of freight attributable to inland waterway transport. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.1.2: Freight volume by mode of transport (tonne kilometres)" }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "billion tons-km" } ], "source_id": "87" }, { "id": "CC.FRVOL.MOD.RA", "metatype": [ { "id": "IndicatorName", "value": "Freight volume by mode (billion tons-km) - Rail transport" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the volume of freight attributable to rail transport. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.1.2: Freight volume by mode of transport (tonne kilometres)" }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "billion tons-km" } ], "source_id": "87" }, { "id": "CC.FRVOL.MOD.RO", "metatype": [ { "id": "IndicatorName", "value": "Freight volume by mode (billion tons-km) - Road transport" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the volume of freight attributable to road transport. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.1.2: Freight volume by mode of transport (tonne kilometres)" }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "billion tons-km" } ], "source_id": "87" }, { "id": "CC.FSU.PECA", "metatype": [ { "id": "IndicatorName", "value": "Per capita food supply (kcal/cap/day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Per capita food supply is taken from New Food Balances (domain), Food Supply (element) of the Food Balance Sheet (FAOSTAT). FAOSTAT Food Balance Sheet presents a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply and its utilization. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per caput food supplies are expressed in terms of quantity and - by applying appropriate food composition factors for all primary and processed products - also in terms of caloric value and protein and fat content. This work is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO license (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo). In addition to this license, some database specific terms of use are listed: Terms of Use of Datasets." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "kcal/capita/day" } ], "source_id": "87" }, { "id": "CC.GAS.PPBC.CN", "metatype": [ { "id": "IndicatorName", "value": "Gas pipeline capacity by project status (barrels of oil equivalent per day) - Cancelled" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Gas pipeline capacity (barrels of oil equivalent per day), by capacity, is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Fossil Infrastructure Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-fossil-infrastructure-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels of oil equivalent per day" } ], "source_id": "87" }, { "id": "CC.GAS.PPBC.CO", "metatype": [ { "id": "IndicatorName", "value": "Gas pipeline capacity by project status (barrels of oil equivalent per day) - Construction" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Gas pipeline capacity (barrels of oil equivalent per day), by capacity, is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Fossil Infrastructure Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-fossil-infrastructure-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels of oil equivalent per day" } ], "source_id": "87" }, { "id": "CC.GAS.PPBC.ID", "metatype": [ { "id": "IndicatorName", "value": "Gas pipeline capacity by project status (barrels of oil equivalent per day) - In Development (Proposed + Construction)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Gas pipeline capacity (barrels of oil equivalent per day), by capacity, is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Fossil Infrastructure Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-fossil-infrastructure-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels of oil equivalent per day" } ], "source_id": "87" }, { "id": "CC.GAS.PPBC.MB", "metatype": [ { "id": "IndicatorName", "value": "Gas pipeline capacity by project status (barrels of oil equivalent per day) - Mothballed" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Gas pipeline capacity (barrels of oil equivalent per day), by capacity, is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Fossil Infrastructure Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-fossil-infrastructure-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels of oil equivalent per day" } ], "source_id": "87" }, { "id": "CC.GAS.PPBC.OP", "metatype": [ { "id": "IndicatorName", "value": "Gas pipeline capacity by project status (barrels of oil equivalent per day) - Operating" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Gas pipeline capacity (barrels of oil equivalent per day), by capacity, is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Fossil Infrastructure Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-fossil-infrastructure-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels of oil equivalent per day" } ], "source_id": "87" }, { "id": "CC.GAS.PPBC.PR", "metatype": [ { "id": "IndicatorName", "value": "Gas pipeline capacity by project status (barrels of oil equivalent per day) - Proposed" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Gas pipeline capacity (barrels of oil equivalent per day), by capacity, is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Fossil Infrastructure Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-fossil-infrastructure-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels of oil equivalent per day" } ], "source_id": "87" }, { "id": "CC.GAS.PPBC.RT", "metatype": [ { "id": "IndicatorName", "value": "Gas pipeline capacity by project status (barrels of oil equivalent per day) - Retired" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Gas pipeline capacity (barrels of oil equivalent per day), by capacity, is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Fossil Infrastructure Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-fossil-infrastructure-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels of oil equivalent per day" } ], "source_id": "87" }, { "id": "CC.GAS.PPBC.SH", "metatype": [ { "id": "IndicatorName", "value": "Gas pipeline capacity by project status (barrels of oil equivalent per day) - Shelved" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Gas pipeline capacity (barrels of oil equivalent per day), by capacity, is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Fossil Infrastructure Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-fossil-infrastructure-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels of oil equivalent per day" } ], "source_id": "87" }, { "id": "CC.GHG.EMSE.AG", "metatype": [ { "id": "IndicatorName", "value": "Total GHG emissions by sector (Mt CO2 eq) - Agriculture" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Agriculture sector greenhouse gas emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.EMSE.BF", "metatype": [ { "id": "IndicatorName", "value": "Total GHG emissions by sector (Mt CO2 eq) - Bunker Fuels" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Bunker fuels sector greenhouse gas emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.EMSE.BL", "metatype": [ { "id": "IndicatorName", "value": "Total GHG emissions by sector (Mt CO2 eq) - Building" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Building sector greenhouse gas emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.EMSE.EH", "metatype": [ { "id": "IndicatorName", "value": "Total GHG emissions by sector (Mt CO2 eq) - Electricity/Heat" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Electricity and heat sector greenhouse gas emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.EMSE.EL", "metatype": [ { "id": "IndicatorName", "value": "Total GHG emissions by sector (Mt CO2 eq) - Total excluding LUCF" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total greenhouse gas emissions (excluding LUCF) are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.EMSE.EN", "metatype": [ { "id": "IndicatorName", "value": "Total GHG emissions by sector (Mt CO2 eq) - Energy" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Energy sector greenhouse gas emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.EMSE.FE", "metatype": [ { "id": "IndicatorName", "value": "Total GHG emissions by sector (Mt CO2 eq) - Fugitive Emissions" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Fugitive sector greenhouse gas emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.EMSE.IL", "metatype": [ { "id": "IndicatorName", "value": "Total GHG emissions by sector (Mt CO2 eq) - Total including LUCF" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total greenhouse gas emissions (including LUCF) are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.EMSE.IP", "metatype": [ { "id": "IndicatorName", "value": "Total GHG emissions by sector (Mt CO2 eq) - Industrial Processes" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Industrial Processes sector greenhouse gas emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.EMSE.LU", "metatype": [ { "id": "IndicatorName", "value": "Total GHG emissions by sector (Mt CO2 eq) - Land-Use Change and Forestry" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Land-Use Change and Forestry sector greenhouse gas emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.EMSE.MC", "metatype": [ { "id": "IndicatorName", "value": "Total GHG emissions by sector (Mt CO2 eq) - Manufacturing/Construction" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Manufacturing and construction sector greenhouse gas emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.EMSE.OF", "metatype": [ { "id": "IndicatorName", "value": "Total GHG emissions by sector (Mt CO2 eq) - Other Fuel Combustion" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Other fuel combustion sector greenhouse gas emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.EMSE.TR", "metatype": [ { "id": "IndicatorName", "value": "Total GHG emissions by sector (Mt CO2 eq) - Transportation" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Transportation sector greenhouse gas emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.EMSE.WA", "metatype": [ { "id": "IndicatorName", "value": "Total GHG emissions by sector (Mt CO2 eq) - Waste" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Waste sector greenhouse gas emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.FSYS.CH", "metatype": [ { "id": "IndicatorName", "value": "GHG emissions from food systems, by gas (Mt CO2 eq) - Methane" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "GHG emissions from food systems disaggregated by gas are generated from EDGAR-FOOD, a global emission inventory of GHGs from the food systems. EDGAR-FOOD has been developed to aid the understanding of the activities underlying the energy demand and use, agriculture and land use change emissions associated with the production, distribution, consumption and disposal of food through the various stages and sectors of the composite global food system. These data were complemented with data from the FAOSTAT database on GHG emissions from land use related to agriculture (FAO, 2020). EDGAR-FOOD represents the first database consistently covering each stage of the food chain for all countries with yearly frequency for the period 1990-2015. Details regarding the methodology applied are available in Crippa et al. (2021)." }, { "id": "Source", "value": "Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F.N. and Leip, A. EDGAR-FOOD data. Figshare, doi:10.6084/m9.figshare.13476666 (2021)." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.FSYS.CO", "metatype": [ { "id": "IndicatorName", "value": "GHG emissions from food systems, by gas (Mt CO2 eq) - CO2" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "GHG emissions from food systems disaggregated by gas are generated from EDGAR-FOOD, a global emission inventory of GHGs from the food systems. EDGAR-FOOD has been developed to aid the understanding of the activities underlying the energy demand and use, agriculture and land use change emissions associated with the production, distribution, consumption and disposal of food through the various stages and sectors of the composite global food system. These data were complemented with data from the FAOSTAT database on GHG emissions from land use related to agriculture (FAO, 2020). EDGAR-FOOD represents the first database consistently covering each stage of the food chain for all countries with yearly frequency for the period 1990-2015. Details regarding the methodology applied are available in Crippa et al. (2021)." }, { "id": "Source", "value": "Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F.N. and Leip, A. EDGAR-FOOD data. Figshare, doi:10.6084/m9.figshare.13476666 (2021)." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.FSYS.FG", "metatype": [ { "id": "IndicatorName", "value": "GHG emissions from food systems, by gas (Mt CO2 eq) - F-gases" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "GHG emissions from food systems disaggregated by gas are generated from EDGAR-FOOD, a global emission inventory of GHGs from the food systems. EDGAR-FOOD has been developed to aid the understanding of the activities underlying the energy demand and use, agriculture and land use change emissions associated with the production, distribution, consumption and disposal of food through the various stages and sectors of the composite global food system. These data were complemented with data from the FAOSTAT database on GHG emissions from land use related to agriculture (FAO, 2020). EDGAR-FOOD represents the first database consistently covering each stage of the food chain for all countries with yearly frequency for the period 1990-2015. Details regarding the methodology applied are available in Crippa et al. (2021)." }, { "id": "Source", "value": "Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F.N. and Leip, A. EDGAR-FOOD data. Figshare, doi:10.6084/m9.figshare.13476666 (2021)." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.FSYS.NO", "metatype": [ { "id": "IndicatorName", "value": "GHG emissions from food systems, by gas (Mt CO2 eq) - N2O" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "GHG emissions from food systems disaggregated by gas are generated from EDGAR-FOOD, a global emission inventory of GHGs from the food systems. EDGAR-FOOD has been developed to aid the understanding of the activities underlying the energy demand and use, agriculture and land use change emissions associated with the production, distribution, consumption and disposal of food through the various stages and sectors of the composite global food system. These data were complemented with data from the FAOSTAT database on GHG emissions from land use related to agriculture (FAO, 2020). EDGAR-FOOD represents the first database consistently covering each stage of the food chain for all countries with yearly frequency for the period 1990-2015. Details regarding the methodology applied are available in Crippa et al. (2021)." }, { "id": "Source", "value": "Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F.N. and Leip, A. EDGAR-FOOD data. Figshare, doi:10.6084/m9.figshare.13476666 (2021)." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.GHG.GRPE", "metatype": [ { "id": "IndicatorName", "value": "GHG growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Greenhouse gas emissions growth is generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "annual %" } ], "source_id": "87" }, { "id": "CC.GHG.MEMG.EI", "metatype": [ { "id": "IndicatorName", "value": "Macro drivers of GHG emissions growth in the period 2012-2018 - Emission Intensity of GDP (contribution to total growth, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data reflects the impact of the emissions intensity of GDP (as a macro-driver) to total emission growth (excluding LUCF) across the period 2012-2018. This data has been calculated by World Bank staff using greenhouse gas emissions data from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.GHG.MEMG.GC", "metatype": [ { "id": "IndicatorName", "value": "Macro drivers of GHG emissions growth in the period 2012-2018 - GDP per capita (contribution to total growth, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data reflects the impact of GDP-per capita (as a macro-driver) to total emission growth (excluding LUCF) across the period 2012-2018. This data has been calculated by World Bank staff using greenhouse gas emissions data from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.GHG.MEMG.PO", "metatype": [ { "id": "IndicatorName", "value": "Macro drivers of GHG emissions growth in the period 2012-2018 - Population (contribution to total growth, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data reflects the impact of population (as a macro-driver) to total emission growth (excluding LUCF) across the period 2012-2018. This data has been calculated by World Bank staff using greenhouse gas emissions data from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.GHG.PECA", "metatype": [ { "id": "IndicatorName", "value": "Per capita GHG emissions (tons/capita)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Per capita greenhouse gas emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "tons/capita" } ], "source_id": "87" }, { "id": "CC.GHG.SDEG.AG", "metatype": [ { "id": "IndicatorName", "value": "Sectoral drivers of GHG emissions growth in the period 2012-2018 - Agriculture (contribution to total growth, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data reflects the aggregate contribution of the Agriculture sector to total emission growth across the period 2012-2018. This data has been calculated by World Bank staff using greenhouse gas emissions data from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.GHG.SDEG.BU", "metatype": [ { "id": "IndicatorName", "value": "Sectoral drivers of GHG emissions growth in the period 2012-2018 - Building (contribution to total growth, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data reflects the aggregate contribution of the Building sector to total emission growth across the period 2012-2018. This data has been calculated by World Bank staff using greenhouse gas emissions data from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.GHG.SDEG.EH", "metatype": [ { "id": "IndicatorName", "value": "Sectoral drivers of GHG emissions growth in the period 2012-2018 - Electricity/Heat (contribution to total growth, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data reflects the aggregate contribution of the Electricity and Heat sector to total emission growth across the period 2012-2018. This data has been calculated by World Bank staff using greenhouse gas emissions data from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.GHG.SDEG.FE", "metatype": [ { "id": "IndicatorName", "value": "Sectoral drivers of GHG emissions growth in the period 2012-2018 - Fugitive Emissions (contribution to total growth, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data reflects the aggregate contribution of the Fugitive Emissions sector to total emission growth across the period 2012-2018. This data has been calculated by World Bank staff using greenhouse gas emissions data from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.GHG.SDEG.IP", "metatype": [ { "id": "IndicatorName", "value": "Sectoral drivers of GHG emissions growth in the period 2012-2018 - Industrial Processes (contribution to total growth, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data reflects the aggregate contribution of the Industrial Processes sector to total emission growth across the period 2012-2018. This data has been calculated by World Bank staff using greenhouse gas emissions data from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.GHG.SDEG.LU", "metatype": [ { "id": "IndicatorName", "value": "Sectoral drivers of GHG emissions growth in the period 2012-2018 - Land-Use Change and Forestry (contribution to total growth, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data reflects the aggregate contribution of the Land-Use Change and Forestry sector to total emission growth across the period 2012-2018. This data has been calculated by World Bank staff using greenhouse gas emissions data from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.GHG.SDEG.MC", "metatype": [ { "id": "IndicatorName", "value": "Sectoral drivers of GHG emissions growth in the period 2012-2018 - Manufacturing/Construction (contribution to total growth, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data reflects the aggregate contribution of the Manufacturing and Construction sector to total emission growth across the period 2012-2018. This data has been calculated by World Bank staff using greenhouse gas emissions data from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.GHG.SDEG.OF", "metatype": [ { "id": "IndicatorName", "value": "Sectoral drivers of GHG emissions growth in the period 2012-2018 - Other Fuel Combustion (contribution to total growth, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data reflects the aggregate contribution of the Other Fuel Combustion sector to total emission growth across the period 2012-2018. This data has been calculated by World Bank staff using greenhouse gas emissions data from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.GHG.SDEG.TR", "metatype": [ { "id": "IndicatorName", "value": "Sectoral drivers of GHG emissions growth in the period 2012-2018 - Transportation (contribution to total growth, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data reflects the aggregate contribution of the Transportation sector to total emission growth across the period 2012-2018. This data has been calculated by World Bank staff using greenhouse gas emissions data from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.GHG.SDEG.WA", "metatype": [ { "id": "IndicatorName", "value": "Sectoral drivers of GHG emissions growth in the period 2012-2018 - Waste (contribution to total growth, %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data reflects the aggregate contribution of the Waste sector to total emission growth across the period 2012-2018. This data has been calculated by World Bank staff using greenhouse gas emissions data from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.GNBD.ISS.BD", "metatype": [ { "id": "IndicatorName", "value": "Total sovereign green bonds issuance (billion US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Sovereign green bond issuance is disaggregated from Green Bond Issuances by Type of Issuer data. Green Bonds are created to fund projects that have positive environmental and/or climate benefits. Note: While green finance indicators may cover more than climate finance (i.e. to include environmental issues at large) they do provide a good proxy for the greening of the finance system in the country. Countries that rank well on these indicators are more likely to mobilize green finance to facilitate their climate transition." }, { "id": "Source", "value": "IMF Climate Change Dashboard. Sources: Refinitiv; Country authorities; IMF staff calculations." }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "billion US$" } ], "source_id": "87" }, { "id": "CC.IEN.AFO", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Indirect emissions (N2O) - AFOLU" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.IEN.AGS", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Indirect emissions (N2O) - Agricultural Soils" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.IEN.CRE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Indirect emissions (N2O) - Crop Residues" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.IEN.EAL", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Indirect emissions (N2O) - Emissions on agricultural land" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.IEN.FGE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Indirect emissions (N2O) - Farm-gate emissions" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.IEN.IPA", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Indirect emissions (N2O) - IPCC Agriculture" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.IEN.MLP", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Indirect emissions (N2O) - Manure left on Pasture" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.IEN.SAS", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Indirect emissions (N2O) - Manure applied to Soils" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.IEN.SFE", "metatype": [ { "id": "IndicatorName", "value": "Emission Totals - Indirect emissions (N2O) - Synthetic Fertilizers" }, { "id": "License_Type", "value": "CC BY-3.0" }, { "id": "Longdefinition", "value": "The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually.\n\nThe FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014)." }, { "id": "Source", "value": "FAO, FAOSTAT Climate Change, Emissions Totals, http://www.fao.org/faostat/en/#data/GT." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "kilotonnes" } ], "source_id": "87" }, { "id": "CC.INCP.ALRS", "metatype": [ { "id": "IndicatorName", "value": "Annual investment needs for coastal protection, by risk strategy (% of GDP) - low risk tolerance" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data reflects the cost of a risk taking strategy that adopts an ambititious low-risk strategy. This is discussed in Policy Note 5 of the Beyond the Gap report." }, { "id": "Source", "value": "Rozenberg, Julie; Fay, Marianne. 2019. Beyond the Gap : How Countries Can Afford the Infrastructure They Need while Protecting the Planet. Sustainable Infrastructure;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/31291 License: CC BY 3.0 IGO" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "87" }, { "id": "CC.INCP.KRGC", "metatype": [ { "id": "IndicatorName", "value": "Annual investment needs for coastal protection, by risk strategy (% of GDP) - constant relative flood risk" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data reflects the cost of a risk taking strategy that keeps the risk relative to GDP constant. This is discussed in Policy Note 5 of the Beyond the Gap report." }, { "id": "Source", "value": "Rozenberg, Julie; Fay, Marianne. 2019. Beyond the Gap : How Countries Can Afford the Infrastructure They Need while Protecting the Planet. Sustainable Infrastructure;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/31291 License: CC BY 3.0 IGO" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "87" }, { "id": "CC.INCP.SPMC", "metatype": [ { "id": "IndicatorName", "value": "Annual investment needs for coastal protection, by risk strategy (% of GDP) - optimal protection" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Data reflects the level of spending in protection that minimizes the cost of floods, including the prevention of expenditure and damages. This is discussed in Policy Note 5 of the Beyond the Gap report." }, { "id": "Source", "value": "Rozenberg, Julie; Fay, Marianne. 2019. Beyond the Gap : How Countries Can Afford the Infrastructure They Need while Protecting the Planet. Sustainable Infrastructure;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/31291 License: CC BY 3.0 IGO" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "87" }, { "id": "CC.ISG.FFFE", "metatype": [ { "id": "IndicatorName", "value": "Informal employment by sector and gender (% of total) - Agriculture, forestry and fishing - Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the share of agriculture (female) employment which is classified as informal employment in the total economy. Data is taken from the United Nations Sustainable Goals, representing Indicator 8.3.1: Proportion of informal employment in total employment, by sector and sex." }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ISG.FFMA", "metatype": [ { "id": "IndicatorName", "value": "Informal employment by sector and gender (% of total) - Agriculture, forestry and fishing - Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the share of agriculture (male) employment which is classified as informal employment in the total economy. Data is taken from the United Nations Sustainable Goals, representing Indicator 8.3.1: Proportion of informal employment in total employment, by sector and sex." }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ISG.NAFE", "metatype": [ { "id": "IndicatorName", "value": "Informal employment by sector and gender (% of total) - Non-agriculture - Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the share of non-agriculture (female) employment which is classified as informal employment in the total economy. Data is taken from the United Nations Sustainable Goals, representing Indicator 8.3.1: Proportion of informal employment in total employment, by sector and sex." }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ISG.NAMA", "metatype": [ { "id": "IndicatorName", "value": "Informal employment by sector and gender (% of total) - Non-agriculture - Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the share of non-agriculture (male) employment which is classified as informal employment in the total economy. Data is taken from the United Nations Sustainable Goals, representing Indicator 8.3.1: Proportion of informal employment in total employment, by sector and sex." }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ISG.NBFE", "metatype": [ { "id": "IndicatorName", "value": "Informal employment by sector and gender (% of total) - No breakdown - Female" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the share of employment that had no-breakdown as either non-agricultural or agricultural (female), which is classified as informal employment in the total economy. Data is taken from the United Nations Sustainable Goals, representing Indicator 8.3.1: Proportion of informal employment in total employment, by sector and sex." }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.ISG.NBMA", "metatype": [ { "id": "IndicatorName", "value": "Informal employment by sector and gender (% of total) - No breakdown - Male" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the share of employment that had no-breakdown as either non-agricultural or agricultural (male), which is classified as informal employment in the total economy. Data is taken from the United Nations Sustainable Goals, representing Indicator 8.3.1: Proportion of informal employment in total employment, by sector and sex." }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.KBA.MRN.ZS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of freshwater key biodiversity areas (KBAs) covered by protected areas (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the proportion of freshwater key biodiversity areas (KBAs) covered by protected areas. Data is taken from the United Nations Sustainable Goals, representing Indicator 15.1.2: Average proportion of Freshwater Key Biodiversity Areas (KBAs) covered by protected areas (percent)." }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.KBA.TERR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Proportion of terrestrial key biodiversity areas (KBAs) covered by protected areas (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the proportion of terrestrial key biodiversity areas (KBAs) covered by protected areas. Data is taken from the United Nations Sustainable Goals, representing Indicator 15.1.2: Average proportion of Terrestrial Key Biodiversity Areas (KBAs) covered by protected areas (percent)." }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.NCO.GHG.AG", "metatype": [ { "id": "IndicatorName", "value": "Non-CO2 GHG emissions by sector (Mt CO2 eq) - Agriculture" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Agriculture sector non-CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.NCO.GHG.EL", "metatype": [ { "id": "IndicatorName", "value": "Non-CO2 GHG emissions by sector (Mt CO2 eq) - Total excluding LUCF" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total non-CO2 emissions (excluding LUCF) are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.NCO.GHG.EN", "metatype": [ { "id": "IndicatorName", "value": "Non-CO2 GHG emissions by sector (Mt CO2 eq) - Energy" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Energy sector non-CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.NCO.GHG.FE", "metatype": [ { "id": "IndicatorName", "value": "Non-CO2 GHG emissions by sector (Mt CO2 eq) - Fugitive Emissions" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Fugitive sector non-CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.NCO.GHG.IL", "metatype": [ { "id": "IndicatorName", "value": "Non-CO2 GHG emissions by sector (Mt CO2 eq) - Total including LUCF" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total non-CO2 emissions (including LUCF) are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.NCO.GHG.IP", "metatype": [ { "id": "IndicatorName", "value": "Non-CO2 GHG emissions by sector (Mt CO2 eq) - Industrial Processes" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Industrial Processes sector non-CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.NCO.GHG.LU", "metatype": [ { "id": "IndicatorName", "value": "Non-CO2 GHG emissions by sector (Mt CO2 eq) - Land-Use Change and Forestry" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Land-Use Change and Forestry sector non-CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.NCO.GHG.OF", "metatype": [ { "id": "IndicatorName", "value": "Non-CO2 GHG emissions by sector (Mt CO2 eq) - Other Fuel Combustion" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Other fuel combustion sector non-CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.NCO.GHG.WA", "metatype": [ { "id": "IndicatorName", "value": "Non-CO2 GHG emissions by sector (Mt CO2 eq) - Waste" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Waste sector non-CO2 emissions are generated from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020." }, { "id": "Source", "value": "Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Mt CO2 eq" } ], "source_id": "87" }, { "id": "CC.NEFO.IMCR.BA", "metatype": [ { "id": "IndicatorName", "value": "Net food imports by crop - Barley" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Net food imports are generated from FAOSTAT, taking Export level (tonnes) from the Import level (tonnes) [both under Trade: Crops and Livestock Products]. Net food imports are a proxy for the country's vulnerability to global food prices." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Tonnes" } ], "source_id": "87" }, { "id": "CC.NEFO.IMCR.BP", "metatype": [ { "id": "IndicatorName", "value": "Net food imports by crop - Rice, paddy" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Net food imports are generated from FAOSTAT, taking Export level (tonnes) from the Import level (tonnes) [both under Trade: Crops and Livestock Products]. Net food imports are a proxy for the country's vulnerability to global food prices." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Tonnes" } ], "source_id": "87" }, { "id": "CC.NEFO.IMCR.MA", "metatype": [ { "id": "IndicatorName", "value": "Net food imports by crop - Maize" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Net food imports are generated from FAOSTAT, taking Export level (tonnes) from the Import level (tonnes) [both under Trade: Crops and Livestock Products]. Net food imports are a proxy for the country's vulnerability to global food prices." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Tonnes" } ], "source_id": "87" }, { "id": "CC.NEFO.IMCR.RB", "metatype": [ { "id": "IndicatorName", "value": "Net food imports by crop - Rice, broken" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Net food imports are generated from FAOSTAT, taking Export level (tonnes) from the Import level (tonnes) [both under Trade: Crops and Livestock Products]. Net food imports are a proxy for the country's vulnerability to global food prices." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Tonnes" } ], "source_id": "87" }, { "id": "CC.NEFO.IMCR.RE", "metatype": [ { "id": "IndicatorName", "value": "Net food imports by crop - Rice, paddy (rice milled equivalent)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Net food imports are generated from FAOSTAT, taking Export level (tonnes) from the Import level (tonnes) [both under Trade: Crops and Livestock Products]. Net food imports are a proxy for the country's vulnerability to global food prices." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Tonnes" } ], "source_id": "87" }, { "id": "CC.NEFO.IMCR.RH", "metatype": [ { "id": "IndicatorName", "value": "Net food imports by crop - Rice, husked" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Net food imports are generated from FAOSTAT, taking Export level (tonnes) from the Import level (tonnes) [both under Trade: Crops and Livestock Products]. Net food imports are a proxy for the country's vulnerability to global food prices." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Tonnes" } ], "source_id": "87" }, { "id": "CC.NEFO.IMCR.RI", "metatype": [ { "id": "IndicatorName", "value": "Net food imports by crop - Rice, milled/husked" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Net food imports are generated from FAOSTAT, taking Export level (tonnes) from the Import level (tonnes) [both under Trade: Crops and Livestock Products]. Net food imports are a proxy for the country's vulnerability to global food prices." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Tonnes" } ], "source_id": "87" }, { "id": "CC.NEFO.IMCR.RM", "metatype": [ { "id": "IndicatorName", "value": "Net food imports by crop - Rice, milled" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Net food imports are generated from FAOSTAT, taking Export level (tonnes) from the Import level (tonnes) [both under Trade: Crops and Livestock Products]. Net food imports are a proxy for the country's vulnerability to global food prices." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Tonnes" } ], "source_id": "87" }, { "id": "CC.NEFO.IMCR.SB", "metatype": [ { "id": "IndicatorName", "value": "Net food imports by crop - Soybeans" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Net food imports are generated from FAOSTAT, taking Export level (tonnes) from the Import level (tonnes) [both under Trade: Crops and Livestock Products]. Net food imports are a proxy for the country's vulnerability to global food prices." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Tonnes" } ], "source_id": "87" }, { "id": "CC.NEFO.IMCR.WH", "metatype": [ { "id": "IndicatorName", "value": "Net food imports by crop - Wheat" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Net food imports are generated from FAOSTAT, taking Export level (tonnes) from the Import level (tonnes) [both under Trade: Crops and Livestock Products]. Net food imports are a proxy for the country's vulnerability to global food prices." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Tonnes" } ], "source_id": "87" }, { "id": "CC.OIL.PSBC.CN", "metatype": [ { "id": "IndicatorName", "value": "Oil pipeline capacity by project status (barrels of oil equivalent per day) - Cancelled" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Oil pipeline capacity (barrels of oil equivalent per day), by capacity, is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Fossil Infrastructure Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-fossil-infrastructure-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels of oil equivalent per day" } ], "source_id": "87" }, { "id": "CC.OIL.PSBC.CO", "metatype": [ { "id": "IndicatorName", "value": "Oil pipeline capacity by project status (barrels of oil equivalent per day) - Construction" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Oil pipeline capacity (barrels of oil equivalent per day), by capacity, is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Fossil Infrastructure Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-fossil-infrastructure-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels of oil equivalent per day" } ], "source_id": "87" }, { "id": "CC.OIL.PSBC.ID", "metatype": [ { "id": "IndicatorName", "value": "Oil pipeline capacity by project status (barrels of oil equivalent per day) - In Development (Proposed + Construction)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Oil pipeline capacity (barrels of oil equivalent per day), by capacity, is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Fossil Infrastructure Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-fossil-infrastructure-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels of oil equivalent per day" } ], "source_id": "87" }, { "id": "CC.OIL.PSBC.OP", "metatype": [ { "id": "IndicatorName", "value": "Oil pipeline capacity by project status (barrels of oil equivalent per day) - Operating" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Oil pipeline capacity (barrels of oil equivalent per day), by capacity, is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Fossil Infrastructure Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-fossil-infrastructure-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels of oil equivalent per day" } ], "source_id": "87" }, { "id": "CC.OIL.PSBC.PR", "metatype": [ { "id": "IndicatorName", "value": "Oil pipeline capacity by project status (barrels of oil equivalent per day) - Proposed" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Oil pipeline capacity (barrels of oil equivalent per day), by capacity, is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Fossil Infrastructure Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-fossil-infrastructure-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels of oil equivalent per day" } ], "source_id": "87" }, { "id": "CC.OIL.PSBC.RT", "metatype": [ { "id": "IndicatorName", "value": "Oil pipeline capacity by project status (barrels of oil equivalent per day) - Retired" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Oil pipeline capacity (barrels of oil equivalent per day), by capacity, is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Fossil Infrastructure Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-fossil-infrastructure-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels of oil equivalent per day" } ], "source_id": "87" }, { "id": "CC.OIL.PSBC.SH", "metatype": [ { "id": "IndicatorName", "value": "Oil pipeline capacity by project status (barrels of oil equivalent per day) - Shelved" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Oil pipeline capacity (barrels of oil equivalent per day), by capacity, is generated from the Global Fossil Infrastructure Tracker (GFIT). The GFIT is an information resource on fossil fuel infrastructure projects and their development. Currently, the GFIT includes all global LNG import terminals and export terminals, and all global oil and gas transmission pipelines over a pre-determined size threshold. The Tracker organizes information in both map and table format. The interactive map format allows users to geographically visualize pipeline routes and terminal locations, while the table format allows users to access additional data points on each project. Both the map and table provide menu-based data filtering options as well as links to further information in project-specific wiki pages housed on GEM.wiki. GFIT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Fossil Infrastructure Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-fossil-infrastructure-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "barrels of oil equivalent per day" } ], "source_id": "87" }, { "id": "CC.OPCO.AG.CA1", "metatype": [ { "id": "IndicatorName", "value": "Operating coal power capacity by plant age (MW) - Coal plant age (0-9 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Operating coal power capacity, by plant age, is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.OPCO.AG.CA2", "metatype": [ { "id": "IndicatorName", "value": "Operating coal power capacity by plant age (MW) - Coal plant age (10-19 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Operating coal power capacity, by plant age, is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.OPCO.AG.CA3", "metatype": [ { "id": "IndicatorName", "value": "Operating coal power capacity by plant age (MW) - Coal plant age (20-29 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Operating coal power capacity, by plant age, is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.OPCO.AG.CA4", "metatype": [ { "id": "IndicatorName", "value": "Operating coal power capacity by plant age (MW) - Coal plant age (30-39 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Operating coal power capacity, by plant age, is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.OPCO.AG.CA5", "metatype": [ { "id": "IndicatorName", "value": "Operating coal power capacity by plant age (MW) - Coal plant age (40-49 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Operating coal power capacity, by plant age, is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.OPCO.AG.CA6", "metatype": [ { "id": "IndicatorName", "value": "Operating coal power capacity by plant age (MW) - Coal plant age (50-plus years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Operating coal power capacity, by plant age, is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.OPCO.PLTY.CC", "metatype": [ { "id": "IndicatorName", "value": "Operating coal power capacity by plant type (MW) - Plant Type (Subcritical/CCS)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Operating coal power capacity, by plant age, is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.OPCO.PLTY.CF", "metatype": [ { "id": "IndicatorName", "value": "Operating coal power capacity by plant type (MW) - Plant Type (CFB)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Operating coal power capacity, by plant age, is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.OPCO.PLTY.IG", "metatype": [ { "id": "IndicatorName", "value": "Operating coal power capacity by plant type (MW) - Plant Type (IGCC)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Operating coal power capacity, by plant age, is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.OPCO.PLTY.SC", "metatype": [ { "id": "IndicatorName", "value": "Operating coal power capacity by plant type (MW) - Plant Type (Supercritical)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Operating coal power capacity, by plant age, is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.OPCO.PLTY.SU", "metatype": [ { "id": "IndicatorName", "value": "Operating coal power capacity by plant type (MW) - Plant Type (Subcritical)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Operating coal power capacity, by plant age, is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.OPCO.PLTY.UK", "metatype": [ { "id": "IndicatorName", "value": "Operating coal power capacity by plant type (MW) - Plant Type (Unknown)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Operating coal power capacity, by plant age, is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.OPCO.PLTY.US", "metatype": [ { "id": "IndicatorName", "value": "Operating coal power capacity by plant type (MW) - Plant Type (Ultra-super)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Operating coal power capacity, by plant age, is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.PRNX.CAT1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of people who believe it is somewhat very likely that they could use the right to use their property of part of it against their will in the next 5 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Indicator reflects percentage of people who believe it is somewhat very likely that they could lose the right to use their property of part of it against their will in the next 5 years" }, { "id": "Source", "value": "Prindex. Available at: https://www.prindex.net/data/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "CC.PRNX.CAT2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of people who believe it is very unlikely or unlikely that they could use the right to use their property of part of it against their will in the next 5 years" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Indicator reflects percentage of people who believe it is very unlikely or unlikely that they could lose the right to use their property of part of it against their will in the next 5 years" }, { "id": "Source", "value": "Prindex. Available at: https://www.prindex.net/data/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "CC.PRNX.CAT3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of people who say they have formal documents, legally-binding documents that demonstrate their right to live in or use any of their properties" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Indicator reflects percentage of people who say they have formal documents, legally-binding documents that demonstrate their right to live in or use any of their properties" }, { "id": "Source", "value": "Prindex. Available at: https://www.prindex.net/data/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "CC.PRNX.CAT4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of people who say they have informal documents that demonstrate their right to live in or use any of their properties" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Indicator reflects percentage of people who say they have informal documents that demonstrate their right to live in or use any of their properties" }, { "id": "Source", "value": "Prindex. Available at: https://www.prindex.net/data/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "CC.PRNX.CAT5.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of people who say they have no documents or informal documents that demonstrate their right to live in or use any of their properties" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Indicator reflects percentage of people who say they have no documents or informal documents that demonstrate their right to live in or use any of their properties" }, { "id": "Source", "value": "Prindex. Available at: https://www.prindex.net/data/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "CC.PRO.ANI", "metatype": [ { "id": "IndicatorName", "value": "Average supply of protein of animal origin (g/cap/day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Average supply of protein of animal origin reflects a 3-year average from the Suite of Food Security Indicators on FAOSTAT. Suite of Food Security Indicators presents the core set of food security indicators. Following the recommendation of experts gathered in the Committee on World Food Security (CFS) Round Table on hunger measurement, hosted at FAO headquarters in September 2011, an initial set of indicators aiming to capture various aspects of food insecurity is presented on FAOSTAT. The choice of the indicators has been informed by expert judgment and the availability of data with sufficient coverage to enable comparisons across regions and over time. Many of these indicators are produced and published elsewhere by FAO and other international organizations. They are reported on FAOSTAT in a single database with the aim of building a wide food security information system. More indicators will be added to this set as more data will become available. Indicators are classified along the four dimensions of food security -- availability, access, utilization and stability. This work is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO license (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo). In addition to this license, some database specific terms of use are listed: Terms of Use of Datasets." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "g/cap/day" } ], "source_id": "87" }, { "id": "CC.PSVOL.MOD.AI", "metatype": [ { "id": "IndicatorName", "value": "Passenger volume by mode (billion tons-km) - Air transport" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the passenger volume attributable to air transport. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.1.2: Passenger volume (passenger kilometres) by mode of transport." }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "billion tons-km" } ], "source_id": "87" }, { "id": "CC.PSVOL.MOD.RA", "metatype": [ { "id": "IndicatorName", "value": "Passenger volume by mode (billion tons-km) - Rail transport" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the passenger volume attributable to rail transport. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.1.2: Passenger volume (passenger kilometres) by mode of transport." }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "billion tons-km" } ], "source_id": "87" }, { "id": "CC.PSVOL.MOD.RO", "metatype": [ { "id": "IndicatorName", "value": "Passenger volume by mode (billion tons-km) - Road transport" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the passenger volume attributable to road transport. Data is taken from the United Nations Sustainable Goals, representing Indicator 9.1.2: Passenger volume (passenger kilometres) by mode of transport." }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "billion tons-km" } ], "source_id": "87" }, { "id": "CC.PUN.TOTL", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of undernourishment (3-year average) (% of total)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Prevalence of undernourishment relfects a 3-year average from the Suite of Food Security Indicators on FAOSTAT. Suite of Food Security Indicators presents the core set of food security indicators. Following the recommendation of experts gathered in the Committee on World Food Security (CFS) Round Table on hunger measurement, hosted at FAO headquarters in September 2011, an initial set of indicators aiming to capture various aspects of food insecurity is presented on FAOSTAT. The choice of the indicators has been informed by expert judgment and the availability of data with sufficient coverage to enable comparisons across regions and over time. Many of these indicators are produced and published elsewhere by FAO and other international organizations. They are reported on FAOSTAT in a single database with the aim of building a wide food security information system. More indicators will be added to this set as more data will become available. Indicators are classified along the four dimensions of food security -- availability, access, utilization and stability. This work is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO license (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo). In addition to this license, some database specific terms of use are listed: Terms of Use of Datasets." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "% of total" } ], "source_id": "87" }, { "id": "CC.RIC.PECA", "metatype": [ { "id": "IndicatorName", "value": "Rice supply quantity (g/capita/day)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Rice supply is taken from crops processed (domain), food supply quantity (element), rice milled (item) of the Crop statistics (FAOSTAT). FAOSTAT Crop statistics are recorded for 173 products, covering the following categories: Crops Primary, Fibre Crops Primary, Cereals, Coarse Grain, Citrus Fruit, Fruit, Jute Jute-like Fibres, Oilcakes Equivalent, Oil crops Primary, Pulses, Roots and Tubers, Treenuts and Vegetables and Melons. Data are expressed in terms of area harvested, production quantity and yield. The objective is to comprehensively cover production of all primary crops for all countries and regions in the world.Cereals: Area and production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed or silage or used for grazing are therefore excluded. Area data relate to harvested area. Some countries report sown or cultivated area only. This work is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO license (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo). In addition to this license, some database specific terms of use are listed: Terms of Use of Datasets." }, { "id": "Source", "value": "FAOSTAT. Available at: http://www.fao.org/faostat/en/#data" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "g/cap/day" } ], "source_id": "87" }, { "id": "CC.RISK.AST.ZS", "metatype": [ { "id": "IndicatorName", "value": "Risk to asset (average annual losses as % of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Risk to asset indicator is intended to be viewed with risk to wellbeing indicator (both reported as average annual losses as % of GDP). The Unbreakable report which the indicators are taken from moves beyond asset and production losses and shifts its attention to how natural disasters affect people’s well-being. Disasters are far greater threats to well-being than traditional estimates suggest. This approach provides a more nuanced view of natural disasters than usual reporting, and a perspective that takes fuller account of poor people’s vulnerabilities." }, { "id": "Source", "value": "Hallegatte, Stephane; Vogt-Schilb, Adrien; Bangalore, Mook; Rozenberg, Julie. 2017. Unbreakable : Building the Resilience of the Poor in the Face of Natural Disasters. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/25335 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "87" }, { "id": "CC.RISK.WELL.ZS", "metatype": [ { "id": "IndicatorName", "value": "Risk to wellbeing (average annual losses as % of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Risk to wellbeing indicator is intended to be viewed with risk to asset indicator (both reported as average annual losses as % of GDP). The Unbreakable report which the indicators are taken from moves beyond asset and production losses and shifts its attention to how natural disasters affect people’s well-being. Disasters are far greater threats to well-being than traditional estimates suggest. This approach provides a more nuanced view of natural disasters than usual reporting, and a perspective that takes fuller account of poor people’s vulnerabilities." }, { "id": "Source", "value": "Hallegatte, Stephane; Vogt-Schilb, Adrien; Bangalore, Mook; Rozenberg, Julie. 2017. Unbreakable : Building the Resilience of the Poor in the Face of Natural Disasters. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/25335 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "87" }, { "id": "CC.SE.CAT1.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of population with No Education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Education status as a percentage of the total population is disaggregated from the Global Jobs Indicatos Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "87" }, { "id": "CC.SE.CAT2.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of population with Primary Education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Education status as a percentage of the total population is disaggregated from the Global Jobs Indicatos Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "87" }, { "id": "CC.SE.CAT3.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of population with Secondary Education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Education status as a percentage of the total population is disaggregated from the Global Jobs Indicatos Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "87" }, { "id": "CC.SE.CAT4.ZS", "metatype": [ { "id": "IndicatorName", "value": "Percentage of population with Post Secondary Education" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Education status as a percentage of the total population is disaggregated from the Global Jobs Indicatos Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.SE.NYRS.AVG", "metatype": [ { "id": "IndicatorName", "value": "Mean number of years of education completed, aged 17 and older" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Mean number of years of education completed (aged 17+) is disaggregated from the Global Jobs Indicatos Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group." }, { "id": "Source", "value": "Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number of years" } ], "source_id": "87" }, { "id": "CC.SH.AIRP.AIR", "metatype": [ { "id": "IndicatorName", "value": "Mortality rate attributable to household air pollution (deaths per 100 000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the mortality rate attributable to household air pollution as a factor of deaths per 100 000 population. Data is taken from the United Nations Sustainable Goals, representing Indicator 3.9.1: Crude death rate attributed to ambient air pollution (deaths per 100 000 population)." }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Deaths per 100 000 population" } ], "source_id": "87" }, { "id": "CC.SH.AIRP.AMB", "metatype": [ { "id": "IndicatorName", "value": "Mortality rate attributable to ambient air pollution (deaths per 100 000 population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator presents the mortality rate attributable to ambient air pollution as a factor of deaths per 100 000 population. Data is taken from the United Nations Sustainable Goals, representing Indicator 3.9.1: Crude death rate attributed to household air pollution (deaths per 100 000 population)." }, { "id": "Source", "value": "UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Deaths per 100 000 population" } ], "source_id": "87" }, { "id": "CC.SP.COV.ZS", "metatype": [ { "id": "IndicatorName", "value": "Share of population covered by at least one social protection benefit" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator conveys the share of the population effectively covered by a social protection system, including social protection floors. It also provides the coverage rates of the main components of social protection: child and maternity benefits, support for persons without a job, persons with disabilities, victims of work injuries and older persons. For more information, refer to the concepts and definitions page. Alternate source for this information is UN open data hub, SDG Indicator 1.3.1: Proportion of population covered by social protection floors/systems (%) | Annual" }, { "id": "Source", "value": "International Labour Organization - ILOSTAT database, https://ilostat.ilo.org/data" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "87" }, { "id": "CC.SP.EXP.ZS", "metatype": [ { "id": "IndicatorName", "value": "Public social protection expenditure (%of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Source", "value": "The Atlas of Social Protection: Indicators of Resilience and Equity, World Bank Group. Available at: https://datacatalog.worldbank.org/int/search/dataset/0037942" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "87" }, { "id": "CC.TCFD.COMP.CB", "metatype": [ { "id": "IndicatorName", "value": "Number of companies that are TCFD compliant by sector - Central Bank" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "TCFD compliant company data is sourced from the Task Force on Climate-Related Financial Disclosures and aggregated into sector brackets. The Task Force on Climate-related Financial Disclosures (TCFD) provides a framework for companies and other organizations to develop more effective climate related financial disclosures through their existing reporting processes." }, { "id": "Source", "value": "Task Force on Climate-Related Financial Disclosures. Available at: https://www.fsb-tcfd.org/supporters/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "CC.TCFD.COMP.CD", "metatype": [ { "id": "IndicatorName", "value": "Number of companies that are TCFD compliant by sector - Consumer Discretionary" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "TCFD compliant company data is sourced from the Task Force on Climate-Related Financial Disclosures and aggregated into sector brackets. The Task Force on Climate-related Financial Disclosures (TCFD) provides a framework for companies and other organizations to develop more effective climate related financial disclosures through their existing reporting processes." }, { "id": "Source", "value": "Task Force on Climate-Related Financial Disclosures. Available at: https://www.fsb-tcfd.org/supporters/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "CC.TCFD.COMP.CM", "metatype": [ { "id": "IndicatorName", "value": "Number of companies that are TCFD compliant by sector - Communication Services" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "TCFD compliant company data is sourced from the Task Force on Climate-Related Financial Disclosures and aggregated into sector brackets. The Task Force on Climate-related Financial Disclosures (TCFD) provides a framework for companies and other organizations to develop more effective climate related financial disclosures through their existing reporting processes." }, { "id": "Source", "value": "Task Force on Climate-Related Financial Disclosures. Available at: https://www.fsb-tcfd.org/supporters/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "CC.TCFD.COMP.CS", "metatype": [ { "id": "IndicatorName", "value": "Number of companies that are TCFD compliant by sector - Consumer Staples" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "TCFD compliant company data is sourced from the Task Force on Climate-Related Financial Disclosures and aggregated into sector brackets. The Task Force on Climate-related Financial Disclosures (TCFD) provides a framework for companies and other organizations to develop more effective climate related financial disclosures through their existing reporting processes." }, { "id": "Source", "value": "Task Force on Climate-Related Financial Disclosures. Available at: https://www.fsb-tcfd.org/supporters/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "CC.TCFD.COMP.EN", "metatype": [ { "id": "IndicatorName", "value": "Number of companies that are TCFD compliant by sector - Energy" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "TCFD compliant company data is sourced from the Task Force on Climate-Related Financial Disclosures and aggregated into sector brackets. The Task Force on Climate-related Financial Disclosures (TCFD) provides a framework for companies and other organizations to develop more effective climate related financial disclosures through their existing reporting processes." }, { "id": "Source", "value": "Task Force on Climate-Related Financial Disclosures. Available at: https://www.fsb-tcfd.org/supporters/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "CC.TCFD.COMP.FI", "metatype": [ { "id": "IndicatorName", "value": "Number of companies that are TCFD compliant by sector - Financials" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "TCFD compliant company data is sourced from the Task Force on Climate-Related Financial Disclosures and aggregated into sector brackets. The Task Force on Climate-related Financial Disclosures (TCFD) provides a framework for companies and other organizations to develop more effective climate related financial disclosures through their existing reporting processes." }, { "id": "Source", "value": "Task Force on Climate-Related Financial Disclosures. Available at: https://www.fsb-tcfd.org/supporters/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "CC.TCFD.COMP.GO", "metatype": [ { "id": "IndicatorName", "value": "Number of companies that are TCFD compliant by sector - Government" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "TCFD compliant company data is sourced from the Task Force on Climate-Related Financial Disclosures and aggregated into sector brackets. The Task Force on Climate-related Financial Disclosures (TCFD) provides a framework for companies and other organizations to develop more effective climate related financial disclosures through their existing reporting processes." }, { "id": "Source", "value": "Task Force on Climate-Related Financial Disclosures. Available at: https://www.fsb-tcfd.org/supporters/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "CC.TCFD.COMP.HC", "metatype": [ { "id": "IndicatorName", "value": "Number of companies that are TCFD compliant by sector - Health Care" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "TCFD compliant company data is sourced from the Task Force on Climate-Related Financial Disclosures and aggregated into sector brackets. The Task Force on Climate-related Financial Disclosures (TCFD) provides a framework for companies and other organizations to develop more effective climate related financial disclosures through their existing reporting processes." }, { "id": "Source", "value": "Task Force on Climate-Related Financial Disclosures. Available at: https://www.fsb-tcfd.org/supporters/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "CC.TCFD.COMP.IN", "metatype": [ { "id": "IndicatorName", "value": "Number of companies that are TCFD compliant by sector - Industrials" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "TCFD compliant company data is sourced from the Task Force on Climate-Related Financial Disclosures and aggregated into sector brackets. The Task Force on Climate-related Financial Disclosures (TCFD) provides a framework for companies and other organizations to develop more effective climate related financial disclosures through their existing reporting processes." }, { "id": "Source", "value": "Task Force on Climate-Related Financial Disclosures. Available at: https://www.fsb-tcfd.org/supporters/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "CC.TCFD.COMP.IT", "metatype": [ { "id": "IndicatorName", "value": "Number of companies that are TCFD compliant by sector - Information Technology" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "TCFD compliant company data is sourced from the Task Force on Climate-Related Financial Disclosures and aggregated into sector brackets. The Task Force on Climate-related Financial Disclosures (TCFD) provides a framework for companies and other organizations to develop more effective climate related financial disclosures through their existing reporting processes." }, { "id": "Source", "value": "Task Force on Climate-Related Financial Disclosures. Available at: https://www.fsb-tcfd.org/supporters/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "CC.TCFD.COMP.MT", "metatype": [ { "id": "IndicatorName", "value": "Number of companies that are TCFD compliant by sector - Materials" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "TCFD compliant company data is sourced from the Task Force on Climate-Related Financial Disclosures and aggregated into sector brackets. The Task Force on Climate-related Financial Disclosures (TCFD) provides a framework for companies and other organizations to develop more effective climate related financial disclosures through their existing reporting processes." }, { "id": "Source", "value": "Task Force on Climate-Related Financial Disclosures. Available at: https://www.fsb-tcfd.org/supporters/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "CC.TCFD.COMP.OT", "metatype": [ { "id": "IndicatorName", "value": "Number of companies that are TCFD compliant by sector - Other" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "TCFD compliant company data is sourced from the Task Force on Climate-Related Financial Disclosures and aggregated into sector brackets. The Task Force on Climate-related Financial Disclosures (TCFD) provides a framework for companies and other organizations to develop more effective climate related financial disclosures through their existing reporting processes." }, { "id": "Source", "value": "Task Force on Climate-Related Financial Disclosures. Available at: https://www.fsb-tcfd.org/supporters/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "CC.TCFD.COMP.RE", "metatype": [ { "id": "IndicatorName", "value": "Number of companies that are TCFD compliant by sector - Real Estate" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "TCFD compliant company data is sourced from the Task Force on Climate-Related Financial Disclosures and aggregated into sector brackets. The Task Force on Climate-related Financial Disclosures (TCFD) provides a framework for companies and other organizations to develop more effective climate related financial disclosures through their existing reporting processes." }, { "id": "Source", "value": "Task Force on Climate-Related Financial Disclosures. Available at: https://www.fsb-tcfd.org/supporters/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "CC.TCFD.COMP.TR", "metatype": [ { "id": "IndicatorName", "value": "Number of companies that are TCFD compliant by sector - Transportation" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "TCFD compliant company data is sourced from the Task Force on Climate-Related Financial Disclosures and aggregated into sector brackets. The Task Force on Climate-related Financial Disclosures (TCFD) provides a framework for companies and other organizations to develop more effective climate related financial disclosures through their existing reporting processes." }, { "id": "Source", "value": "Task Force on Climate-Related Financial Disclosures. Available at: https://www.fsb-tcfd.org/supporters/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "CC.TCFD.COMP.UT", "metatype": [ { "id": "IndicatorName", "value": "Number of companies that are TCFD compliant by sector - Utilities" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "TCFD compliant company data is sourced from the Task Force on Climate-Related Financial Disclosures and aggregated into sector brackets. The Task Force on Climate-related Financial Disclosures (TCFD) provides a framework for companies and other organizations to develop more effective climate related financial disclosures through their existing reporting processes." }, { "id": "Source", "value": "Task Force on Climate-Related Financial Disclosures. Available at: https://www.fsb-tcfd.org/supporters/" }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "CC.THHZ.RANK.CF", "metatype": [ { "id": "IndicatorName", "value": "Natural hazard levels - Coastal flood" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Natural Hazard levels from Coastal Flood are generated from ThinkHazard! data available from the Climate Change Knowledge Portal. ThinkHazard! provides a general view of the hazards, for a given location, that should be considered in project design and implementation to promote disaster and climate resilience. Teams interested in historical and future climate, vulnerabilities and impacts should utilize the CCKP resource [available at: https://climateknowledgeportal.worldbank.org/]. This resource allows users to explore data across 40+ indicators, at different aggregations (national and sub-national), with annual and seasonal calculations." }, { "id": "Source", "value": "ThinkHazard! data [https://thinkhazard.org/en/] taken from the Climate Change Knowledge Portal [available at: https://climateknowledgeportal.worldbank.org/]" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Hazard level" } ], "source_id": "87" }, { "id": "CC.THHZ.RANK.CY", "metatype": [ { "id": "IndicatorName", "value": "Natural hazard levels - Cyclone" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Natural Hazard levels from Cyclones are generated from ThinkHazard! data available from the Climate Change Knowledge Portal. ThinkHazard! provides a general view of the hazards, for a given location, that should be considered in project design and implementation to promote disaster and climate resilience. Teams interested in historical and future climate, vulnerabilities and impacts should utilize the CCKP resource [available at: https://climateknowledgeportal.worldbank.org/]. This resource allows users to explore data across 40+ indicators, at different aggregations (national and sub-national), with annual and seasonal calculations." }, { "id": "Source", "value": "ThinkHazard! data [https://thinkhazard.org/en/] taken from the Climate Change Knowledge Portal [available at: https://climateknowledgeportal.worldbank.org/]" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Hazard level" } ], "source_id": "87" }, { "id": "CC.THHZ.RANK.EH", "metatype": [ { "id": "IndicatorName", "value": "Natural hazard levels - Extreme heat" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Natural Hazard levels from Extreme Heat are generated from ThinkHazard! data available from the Climate Change Knowledge Portal. ThinkHazard! provides a general view of the hazards, for a given location, that should be considered in project design and implementation to promote disaster and climate resilience. Teams interested in historical and future climate, vulnerabilities and impacts should utilize the CCKP resource [available at: https://climateknowledgeportal.worldbank.org/]. This resource allows users to explore data across 40+ indicators, at different aggregations (national and sub-national), with annual and seasonal calculations." }, { "id": "Source", "value": "ThinkHazard! data [https://thinkhazard.org/en/] taken from the Climate Change Knowledge Portal [available at: https://climateknowledgeportal.worldbank.org/]" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Hazard level" } ], "source_id": "87" }, { "id": "CC.THHZ.RANK.EQ", "metatype": [ { "id": "IndicatorName", "value": "Natural hazard levels - Earthquake" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Natural Hazard levels from Earthquakes are generated from ThinkHazard! data available from the Climate Change Knowledge Portal. ThinkHazard! provides a general view of the hazards, for a given location, that should be considered in project design and implementation to promote disaster and climate resilience. Teams interested in historical and future climate, vulnerabilities and impacts should utilize the CCKP resource [available at: https://climateknowledgeportal.worldbank.org/]. This resource allows users to explore data across 40+ indicators, at different aggregations (national and sub-national), with annual and seasonal calculations." }, { "id": "Source", "value": "ThinkHazard! data [https://thinkhazard.org/en/] taken from the Climate Change Knowledge Portal [available at: https://climateknowledgeportal.worldbank.org/]" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Hazard level" } ], "source_id": "87" }, { "id": "CC.THHZ.RANK.LS", "metatype": [ { "id": "IndicatorName", "value": "Natural hazard levels - Landslide" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Natural Hazard levels from Landslides are generated from ThinkHazard! data available from the Climate Change Knowledge Portal. ThinkHazard! provides a general view of the hazards, for a given location, that should be considered in project design and implementation to promote disaster and climate resilience. Teams interested in historical and future climate, vulnerabilities and impacts should utilize the CCKP resource [available at: https://climateknowledgeportal.worldbank.org/]. This resource allows users to explore data across 40+ indicators, at different aggregations (national and sub-national), with annual and seasonal calculations." }, { "id": "Source", "value": "ThinkHazard! data [https://thinkhazard.org/en/] taken from the Climate Change Knowledge Portal [available at: https://climateknowledgeportal.worldbank.org/]" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Hazard level" } ], "source_id": "87" }, { "id": "CC.THHZ.RANK.RF", "metatype": [ { "id": "IndicatorName", "value": "Natural hazard levels - River flood" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Natural Hazard levels from River Flood are generated from ThinkHazard! data available from the Climate Change Knowledge Portal. ThinkHazard! provides a general view of the hazards, for a given location, that should be considered in project design and implementation to promote disaster and climate resilience. Teams interested in historical and future climate, vulnerabilities and impacts should utilize the CCKP resource [available at: https://climateknowledgeportal.worldbank.org/]. This resource allows users to explore data across 40+ indicators, at different aggregations (national and sub-national), with annual and seasonal calculations." }, { "id": "Source", "value": "ThinkHazard! data [https://thinkhazard.org/en/] taken from the Climate Change Knowledge Portal [available at: https://climateknowledgeportal.worldbank.org/]" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Hazard level" } ], "source_id": "87" }, { "id": "CC.THHZ.RANK.TS", "metatype": [ { "id": "IndicatorName", "value": "Natural hazard levels - Tsunami" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Natural Hazard levels from Tsunamis are generated from ThinkHazard! data available from the Climate Change Knowledge Portal. ThinkHazard! provides a general view of the hazards, for a given location, that should be considered in project design and implementation to promote disaster and climate resilience. Teams interested in historical and future climate, vulnerabilities and impacts should utilize the CCKP resource [available at: https://climateknowledgeportal.worldbank.org/]. This resource allows users to explore data across 40+ indicators, at different aggregations (national and sub-national), with annual and seasonal calculations." }, { "id": "Source", "value": "ThinkHazard! data [https://thinkhazard.org/en/] taken from the Climate Change Knowledge Portal [available at: https://climateknowledgeportal.worldbank.org/]" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Hazard level" } ], "source_id": "87" }, { "id": "CC.THHZ.RANK.UF", "metatype": [ { "id": "IndicatorName", "value": "Natural hazard levels - Urban flood" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Natural Hazard levels from Urban Flood are generated from ThinkHazard! data available from the Climate Change Knowledge Portal. ThinkHazard! provides a general view of the hazards, for a given location, that should be considered in project design and implementation to promote disaster and climate resilience. Teams interested in historical and future climate, vulnerabilities and impacts should utilize the CCKP resource [available at: https://climateknowledgeportal.worldbank.org/]. This resource allows users to explore data across 40+ indicators, at different aggregations (national and sub-national), with annual and seasonal calculations." }, { "id": "Source", "value": "ThinkHazard! data [https://thinkhazard.org/en/] taken from the Climate Change Knowledge Portal [available at: https://climateknowledgeportal.worldbank.org/]" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Hazard level" } ], "source_id": "87" }, { "id": "CC.THHZ.RANK.WF", "metatype": [ { "id": "IndicatorName", "value": "Natural hazard levels - Wildfire" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Natural Hazard levels from Wildfire are generated from ThinkHazard! data available from the Climate Change Knowledge Portal. ThinkHazard! provides a general view of the hazards, for a given location, that should be considered in project design and implementation to promote disaster and climate resilience. Teams interested in historical and future climate, vulnerabilities and impacts should utilize the CCKP resource [available at: https://climateknowledgeportal.worldbank.org/]. This resource allows users to explore data across 40+ indicators, at different aggregations (national and sub-national), with annual and seasonal calculations." }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Hazard level" } ], "source_id": "87" }, { "id": "CC.TNET.CYC.ZS", "metatype": [ { "id": "IndicatorName", "value": "Share of transport network exposed to cyclones (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Share of transport network that is exposed to cyclones, represented as a percentage of the total transport network within a given country. This metric measures the fraction of the transport network that has a significant risk of experiencing an intense cyclone (the threshold is country dependent, based on the expected resistance of the infrastructure in the country)." }, { "id": "Source", "value": "Hallegatte, Stephane; Rentschler, Jun; Rozenberg, Julie. 2019. Lifelines : The Resilient Infrastructure Opportunity. Sustainable Infrastructure;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/31805 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.TNET.EAR.ZS", "metatype": [ { "id": "IndicatorName", "value": "Share of transport network exposed to earthquakes (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Share of transport network that is exposed to earthquakes, represented as a percentage of the total transport network within a given country. This metric measures the fraction of the transport network that has a significant risk of experiencing an intense earthquake (the threshold is country dependent, based on the expected resistance of the infrastructure in the country)." }, { "id": "Source", "value": "Hallegatte, Stephane; Rentschler, Jun; Rozenberg, Julie. 2019. Lifelines : The Resilient Infrastructure Opportunity. Sustainable Infrastructure;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/31805 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.TNET.FLD.ZS", "metatype": [ { "id": "IndicatorName", "value": "Share of transport network exposed to floods (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Share of transport network that is exposed to floods, represented as a percentage of the total transport network within a given country. This metric measures the fraction of the transport network that has a significant risk of experiencing an intense flood (the threshold is country dependent, based on the expected resistance of the infrastructure in the country)." }, { "id": "Source", "value": "Hallegatte, Stephane; Rentschler, Jun; Rozenberg, Julie. 2019. Lifelines : The Resilient Infrastructure Opportunity. Sustainable Infrastructure;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/31805 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.TNET.INV.ZS", "metatype": [ { "id": "IndicatorName", "value": "Annual investments needed to make transport infrastructure more resilient by 2030 (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The annual investments, shown as a percentage of a country's GDP, that are needed to make transport infrastructure more resilient by the year 2030. The Lifelines report, Appendix A, lists 70 interventions to make infrastructure systems, including transport networks, more resilient. These interventions have been used to estimate the investment needs for this metric." }, { "id": "Source", "value": "Hallegatte, Stephane; Rentschler, Jun; Rozenberg, Julie. 2019. Lifelines : The Resilient Infrastructure Opportunity. Sustainable Infrastructure;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/31805 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "87" }, { "id": "CC.TNET.NAT.ZS", "metatype": [ { "id": "IndicatorName", "value": "Share of transport network exposed to natural disasters (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Share of transport network that is exposed to natural disasters, represented as a percentage of the total transport network within a given country. This metric measures the fraction of the transport network that has a significant risk of experiencing an intense natural disaster (the threshold is country dependent, based on the expected resistance of the infrastructure in the country). In this instance natural disaster total reflects the results of earthquakes, floods, and cyclones." }, { "id": "Source", "value": "Hallegatte, Stephane; Rentschler, Jun; Rozenberg, Julie. 2019. Lifelines : The Resilient Infrastructure Opportunity. Sustainable Infrastructure;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/31805 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.TNET.REP.FAC", "metatype": [ { "id": "IndicatorName", "value": "Resilient infrastructure would reduce annual repair cost by a factor" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "The Lifelines report, Appendix A, lists 70 interventions to make infrastructure systems, including transport networks, more resilient. These interventions have been used to estimate the annual investments needed to make transport infrastructure more resilient by 2030 (% of GDP). This result was used to derive the factor reduction in annual repair costs." }, { "id": "Source", "value": "Hallegatte, Stephane; Rentschler, Jun; Rozenberg, Julie. 2019. Lifelines : The Resilient Infrastructure Opportunity. Sustainable Infrastructure;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/31805 License: CC BY 3.0 IGO." }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Value factor" } ], "source_id": "87" }, { "id": "CC.TOT.GHG.GR", "metatype": [ { "id": "IndicatorName", "value": "Total GHG emissions growth in the period 2012-2018 (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Topic", "value": "Country profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "CC.TOTL.COCA.AN", "metatype": [ { "id": "IndicatorName", "value": "Total coal capacity by plant status (MW) - Announced" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total coal capacity by plant status is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.TOTL.COCA.CA", "metatype": [ { "id": "IndicatorName", "value": "Total coal capacity by plant status (MW) - Cancelled" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total coal capacity by plant status is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.TOTL.COCA.CO", "metatype": [ { "id": "IndicatorName", "value": "Total coal capacity by plant status (MW) - Construction" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total coal capacity by plant status is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.TOTL.COCA.MB", "metatype": [ { "id": "IndicatorName", "value": "Total coal capacity by plant status (MW) - Mothballed" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total coal capacity by plant status is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.TOTL.COCA.OP", "metatype": [ { "id": "IndicatorName", "value": "Total coal capacity by plant status (MW) - Operating" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total coal capacity by plant status is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.TOTL.COCA.PE", "metatype": [ { "id": "IndicatorName", "value": "Total coal capacity by plant status (MW) - Permitted" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total coal capacity by plant status is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.TOTL.COCA.PP", "metatype": [ { "id": "IndicatorName", "value": "Total coal capacity by plant status (MW) - Pre-permit" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total coal capacity by plant status is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.TOTL.COCA.RE", "metatype": [ { "id": "IndicatorName", "value": "Total coal capacity by plant status (MW) - Retired" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total coal capacity by plant status is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "CC.TOTL.COCA.SH", "metatype": [ { "id": "IndicatorName", "value": "Total coal capacity by plant status (MW) - Shelved" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "Total coal capacity by plant status is generated from the Global Coal Plant Tracker (GCPT). The GCPT provides information on coal-fired power units from around the world generating 30 megawatts and above. The GCPT catalogues every operating coal-fired generating unit, every new unit proposed since 2010, and every unit retired since 2000. Units often consist of a boiler and turbine, and several units may make up one coal-fired power station. The map and underlying data is updated bi-annually, in January and July. Each plant included in the tracker is linked to a wiki page on GEM.wiki, which provides additional details. GCPT was accessed in July 2021 to generate this data." }, { "id": "Source", "value": "Global Coal Plant Tracker, Global Energy Monitor. Available at: https://globalenergymonitor.org/projects/global-coal-plant-tracker/" }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "MW" } ], "source_id": "87" }, { "id": "EG.ELC.ACCS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies.\n\nEnergy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indication of a country's energy poverty status.\n\nElectricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification.\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas." }, { "id": "IndicatorName", "value": "Access to electricity (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Access to electricity is the percentage of population with access to electricity. Electrification data are collected from industry, national surveys and international sources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank Global Electrification Database from \"Tracking SDG 7: The Energy Progress Report\" led jointly by the custodian agencies: the International Energy Agency (IEA), the International Renewable Energy Agency (IRENA), the United Nations Statistics Division (UNSD), the World Bank and the World Health Organization (WHO)." }, { "id": "Statisticalconceptandmethodology", "value": "Data for access to electricity are collected among different sources: mostly data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Given the low frequency and the regional distribution of some surveys, a number of countries have gaps in available data. To develop the historical evolution and starting point of electrification rates, a simple modeling approach was adopted to fill in the missing data points - around 1990, around 2000, and around 2010. Therefore, a country can have a continuum of zero to three data points. There are 42 countries with zero data point and the weighted regional average was used as an estimate for electrification in each of the data periods. 170 countries have between one and three data points and missing data are estimated by using a model with region, country, and time variables. The model keeps the original observation if data is available for any of the time periods. This modeling approach allowed the estimation of electrification rates for 212 countries over these three time periods (Indicated as \"Estimate\"). Notation \"Assumption\" refers to the assumption of universal access in countries classified as developed by the United Nations. Data begins from the year in which the first survey data is available for each country." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "EN.POP.SLUM.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "Population living in slums (% of urban population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, housing durability, and security of tenure, as adopted in the Millennium Development Goal Target 7.D. The successor, the Sustainable Development Goal 11.1.1, considers inadequate housing (housing affordability) to complement the above definition of slums/informal settlements." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Human Settlements Programme (UN-HABITAT)" }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "% of urban population" } ], "source_id": "87" }, { "id": "ER.H2O.INTR.PC", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "UNESCO estimates that in developing countries in Asia, Africa and Latin America, public water withdrawal represents just 50-100 liters (13 to 26 gallons) per person per day. In regions with insufficient water resources, this figure may be as low as 20-60 (5 to 15 gallons) liters per day. People in developed countries on average consume about 10 times more water daily than those in developing countries.\n\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations." }, { "id": "IndicatorName", "value": "Renewable internal freshwater resources per capita (cubic meters)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "A common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems." }, { "id": "Longdefinition", "value": "Renewable internal freshwater resources flows refer to internal renewable resources (internal river flows and groundwater from rainfall) in the country. Renewable internal freshwater resources per capita are calculated using the World Bank's population estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Food and Agriculture Organization, AQUASTAT data." }, { "id": "Statisticalconceptandmethodology", "value": "Renewable water resources (internal and external) include average annual flow of rivers and recharge of aquifers generated from endogenous precipitation, and those water resources that are not generated in the country, such as inflows from upstream countries (groundwater and surface water), and part of the water of border lakes and/or rivers. Non-renewable water includes groundwater bodies (deep aquifers) that have a negligible rate of recharge on the human time-scale. While renewable water resources are expressed in flows, non-renewable water resources have to be expressed in quantity (stock). Runoff from glaciers where the mass balance is negative is considered non-renewable. Renewable internal freshwater resources per capita are calculated using the World Bank's population estimates. The unit of calculation is m3/year per inhabitant. Internal renewable freshwater resources per capita are calculated using the World Bank's population estimates.\n\nTotal actual renewable water resources correspond to the maximum theoretical yearly amount of water actually available for a country at a given moment. The unit of calculation is km3/year or 109 m3/year. Calculation Criteria is [Water resources: total renewable (actual)] = [Surface water: total renewable (actual)] + [Groundwater: total renewable (actual)] - [Overlap between surface water and groundwater].*\n\nFresh water is naturally occurring water on the Earth's surface. It is a renewable but limited natural resource. Fresh water can only be renewed through the process of the water cycle, where water from seas, lakes, forests, land, rivers, and dams evaporates, forms clouds, and returns as precipitation. However, if more fresh water is consumed through human activities than is restored by nature, the result is that the quantity of fresh water available in lakes, rivers, dams and underground waters can be reduced which can cause serious damage to the surrounding environment.\n\n* http://www.fao.org/nr/water/aquastat/data/glossary/search.html?termId=4188&submitBtn=s&cls=yes" }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "Cubic meters" } ], "source_id": "87" }, { "id": "fin18.t.d", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Saved any money in the past year (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report personally saving or setting aside any money for any reason and using any mode of saving in the past 12 months." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "The percentage of respondents who report personally saving or setting aside any money for any reason and using any mode of saving in the past 12 months." }, { "id": "Source", "value": "Global Findex database" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "g20.t.made", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Dataset", "value": "Global Findex" }, { "id": "IndicatorName", "value": "Made digital payments in the past year (% age 15+)" }, { "id": "Longdefinition", "value": "The percentage of respondents who report using mobile money, a debit or credit card, or a mobile phone to make a payment from an account, or report using the internet to pay bills or to buy something online, in the past 12 months. It also includes respondents who report paying bills or sending remittances directly from a financial institution account or through a mobile money account in the past 12 months" }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Shortdefinition", "value": "The percentage of respondents who report using mobile money, a debit or credit card, or a mobile phone to make a payment from an account, or report using the internet to pay bills or to buy something online, in the past 12 months. It also includes respondents who report paying bills or sending remittances directly from a financial institution account or through a mobile money account in the past 12 months" }, { "id": "Source", "value": "Global Findex database" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "GC.DOD.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Central government debt, total (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Debt is the entire stock of direct government fixed-term contractual obligations to others outstanding on a particular date. It includes domestic and foreign liabilities such as currency and money deposits, securities other than shares, and loans. It is the gross amount of government liabilities reduced by the amount of equity and financial derivatives held by the government. Because debt is a stock rather than a flow, it is measured as of a given date, usually the last day of the fiscal year." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Government Finance Statistics Yearbook and data files, and World Bank and OECD GDP estimates." }, { "id": "Statisticalconceptandmethodology", "value": "The IMF's Government Finance Statistics Manual 2014, harmonized with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, revenues, and expenses, not just those represented by cash transactions. It accounts for all changes in stocks, so stock data at the end of an accounting period equal stock data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks.\n\nGovernment finance statistics are reported in local currency. Many countries report government finance data by fiscal year; see country metadata for information on fiscal year end by country." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "87" }, { "id": "GC.TAX.TOTL.GD.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "IndicatorName", "value": "Tax revenue (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries." }, { "id": "Longdefinition", "value": "Tax revenue refers to compulsory transfers to the central government for public purposes. Certain compulsory transfers such as fines, penalties, and most social security contributions are excluded. Refunds and corrections of erroneously collected tax revenue are treated as negative revenue." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Monetary Fund, Government Finance Statistics Yearbook and data files, and World Bank and OECD GDP estimates." }, { "id": "Statisticalconceptandmethodology", "value": "The IMF's Government Finance Statistics Manual 2014, harmonized with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, revenues, and expenses, not just those represented by cash transactions. It accounts for all changes in stocks, so stock data at the end of an accounting period equal stock data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks.\n\nGovernment finance statistics are reported in local currency. Many countries report government finance data by fiscal year; see country metadata for information on fiscal year end by country." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "87" }, { "id": "GE.EST", "metatype": [ { "id": "IndicatorName", "value": "Government Effectiveness: Estimate" }, { "id": "Longdefinition", "value": "Government Effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org.The WGI are produced by Daniel Kaufmann (Natural Resource Governance Institute and Brookings Institution) and Aart Kraay (World Bank Development Research Group). Please cite Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2010). \"The Worldwide Governance Indicators: Methodology and Analytical Issues\". World Bank Policy Research Working Paper No. 5430 (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130). The WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent." }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "87" }, { "id": "IC.ELC.OUTG", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "Firms evaluating investment options, governments interested in improving business conditions, and economists seeking to explain economic performance have all grappled with defining and measuring the business environment. The firm-level data from Enterprise Surveys provide a useful tool for benchmarking economies across a large number of indicators measured at the firm level.\n\nInternational trade can be beneficial for firms in terms of less expensive inputs for manufacturing and new markets for exporting finished products and services. Time spent waiting for imports and exports to clear customs can be costly for firms and deter them from engaging in trade or making them uncompetitive globally." }, { "id": "IndicatorName", "value": "Power outages in firms in a typical month (number)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Longdefinition", "value": "Power outages are the average number of power outages that establishments experience in a typical month." }, { "id": "Notesfromoriginalsource", "value": "All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Firm-level surveys have been conducted since the 1990's by different units within the World Bank. Since 2005-06, most data collection efforts have been centralized within the Enterprise Analysis Unit. Surveys implemented by the Enterprise Analysis Unit follow the Global Methodology.\n\nPrivate contractors conduct the Enterprise Surveys on behalf of the World Bank. Due to sensitive survey questions addressing business-government relations and bribery-related topics, private contractors, rather than any government agency or an organization/institution associated with government, are hired by the World Bank to collect the data.\n\nConfidentiality of the survey respondents and the sensitive information they provide is necessary to ensure the greatest degree of survey participation, integrity and confidence in the quality of the data. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but confidentiality is never compromised.\n\nThe Enterprise Survey is answered by business owners and top managers. Sometimes the survey respondent calls company accountants and human resource managers into the interview to answer questions in the sales and labor sections of the survey. Typically 1200-1800 interviews are conducted in larger economies, 360 interviews are conducted in medium-sized economies, and for smaller economies, 150 interviews take place.\n\nThe manufacturing and services sectors are the primary business sectors of interest. This corresponds to firms classified with ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies with 5 or more employees are targeted for interview. Services firms include construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government/state ownership are not eligible to participate in an Enterprise Survey. Occasionally, for a few surveyed countries, other sectors are included in the companies surveyed such as education or health-related businesses. In each country, businesses in the cities/regions of major economic activity are interviewed.\n\nIn some countries, other surveys, which depart from the usual Enterprise Survey methodology, are conducted. Examples include 1) Informal Surveys- surveys of informal (unregistered) enterprises, 2) Micro Surveys- surveys fielded to registered firms with less than five employees, and 3) Financial Crisis Assessment Surveys- short surveys administered by telephone to assess the effects of the global financial crisis of 2008-09.\n\nThe Enterprise Surveys Unit uses two instruments: the Manufacturing Questionnaire and the Services Questionnaire. Although many questions overlap, some are only applicable to one type of business. For example, retail firms are not asked about production and nonproduction workers.\n\nThe standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.\n\nOver 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "IC.ELC.OUTG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "Firms evaluating investment options, governments interested in improving business conditions, and economists seeking to explain economic performance have all grappled with defining and measuring the business environment. The firm-level data from Enterprise Surveys provide a useful tool for benchmarking economies across a large number of indicators measured at the firm level.\n\nInformality is associated with business operations without registration. The informal sector in an economy may be a source of unfair competition to formal firms and also deprive governments of potential tax revenue and diminish a government's capacity for regulatory oversight.\n\nInformality can be defined along different dimensions such as operating without registration, income tax evasion, labor tax evasion, or operating outside the legal framework of an economy. Firms may show different degrees of informality along these dimensions which may also overlap.\n\nA large informal sector has serious consequences for the formal private sector, and may pose unfair competition for formal firms. It is an approximation to the prevalence of informality in the private economy." }, { "id": "IndicatorName", "value": "Firms experiencing electrical outages (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Longdefinition", "value": "Percent of firms experiencing electrical outages during the previous fiscal year." }, { "id": "Notesfromoriginalsource", "value": "All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Firm-level surveys have been conducted since the 1990's by different units within the World Bank. Since 2005-06, most data collection efforts have been centralized within the Enterprise Analysis Unit. Surveys implemented by the Enterprise Analysis Unit follow the Global Methodology.\n\nPrivate contractors conduct the Enterprise Surveys on behalf of the World Bank. Due to sensitive survey questions addressing business-government relations and bribery-related topics, private contractors, rather than any government agency or an organization/institution associated with government, are hired by the World Bank to collect the data.\n\nConfidentiality of the survey respondents and the sensitive information they provide is necessary to ensure the greatest degree of survey participation, integrity and confidence in the quality of the data. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but confidentiality is never compromised.\n\nThe Enterprise Survey is answered by business owners and top managers. Sometimes the survey respondent calls company accountants and human resource managers into the interview to answer questions in the sales and labor sections of the survey. Typically 1200-1800 interviews are conducted in larger economies, 360 interviews are conducted in medium-sized economies, and for smaller economies, 150 interviews take place.\n\nThe manufacturing and services sectors are the primary business sectors of interest. This corresponds to firms classified with ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies with 5 or more employees are targeted for interview. Services firms include construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government/state ownership are not eligible to participate in an Enterprise Survey. Occasionally, for a few surveyed countries, other sectors are included in the companies surveyed such as education or health-related businesses. In each country, businesses in the cities/regions of major economic activity are interviewed.\n\nIn some countries, other surveys, which depart from the usual Enterprise Survey methodology, are conducted. Examples include 1) Informal Surveys- surveys of informal (unregistered) enterprises, 2) Micro Surveys- surveys fielded to registered firms with less than five employees, and 3) Financial Crisis Assessment Surveys- short surveys administered by telephone to assess the effects of the global financial crisis of 2008-09.\n\nThe Enterprise Surveys Unit uses two instruments: the Manufacturing Questionnaire and the Services Questionnaire. Although many questions overlap, some are only applicable to one type of business. For example, retail firms are not asked about production and nonproduction workers.\n\nThe standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.\n\nOver 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of firms" } ], "source_id": "87" }, { "id": "IC.FRM.FEMO.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "Firms evaluating investment options, governments interested in improving business conditions, and economists seeking to explain economic performance have all grappled with defining and measuring the business environment. The firm-level data from Enterprise Surveys provide a useful tool for benchmarking economies across a large number of indicators measured at the firm level.\n\nFemale participation in firm ownership and in management measures women's integration as decision makers. Benchmarking female participation in firm ownership, management, and the workforce is important to achieving gender equality promotion and empowerment of women. The gender topic provides information about women's entrepreneurship and economic participation in the labor force." }, { "id": "IndicatorName", "value": "Firms with female participation in ownership (% of firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Longdefinition", "value": "Firms with female participation in ownership are the percentage of firms with a woman among the principal owners." }, { "id": "Notesfromoriginalsource", "value": "All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Firm-level surveys have been conducted since the 1990's by different units within the World Bank. Since 2005-06, most data collection efforts have been centralized within the Enterprise Analysis Unit. Surveys implemented by the Enterprise Analysis Unit follow the Global Methodology.\n\nPrivate contractors conduct the Enterprise Surveys on behalf of the World Bank. Due to sensitive survey questions addressing business-government relations and bribery-related topics, private contractors, rather than any government agency or an organization/institution associated with government, are hired by the World Bank to collect the data.\n\nConfidentiality of the survey respondents and the sensitive information they provide is necessary to ensure the greatest degree of survey participation, integrity and confidence in the quality of the data. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but confidentiality is never compromised.\n\nThe Enterprise Survey is answered by business owners and top managers. Sometimes the survey respondent calls company accountants and human resource managers into the interview to answer questions in the sales and labor sections of the survey. Typically 1200-1800 interviews are conducted in larger economies, 360 interviews are conducted in medium-sized economies, and for smaller economies, 150 interviews take place.\n\nThe manufacturing and services sectors are the primary business sectors of interest. This corresponds to firms classified with ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies with 5 or more employees are targeted for interview. Services firms include construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government/state ownership are not eligible to participate in an Enterprise Survey. Occasionally, for a few surveyed countries, other sectors are included in the companies surveyed such as education or health-related businesses. In each country, businesses in the cities/regions of major economic activity are interviewed.\n\nIn some countries, other surveys, which depart from the usual Enterprise Survey methodology, are conducted. Examples include 1) Informal Surveys- surveys of informal (unregistered) enterprises, 2) Micro Surveys- surveys fielded to registered firms with less than five employees, and 3) Financial Crisis Assessment Surveys- short surveys administered by telephone to assess the effects of the global financial crisis of 2008-09.\n\nThe Enterprise Surveys Unit uses two instruments: the Manufacturing Questionnaire and the Services Questionnaire. Although many questions overlap, some are only applicable to one type of business. For example, retail firms are not asked about production and nonproduction workers.\n\nThe standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.\n\nOver 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews." }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "% of firms" } ], "source_id": "87" }, { "id": "IC.FRM.OUTG.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Unweighted average" }, { "id": "Developmentrelevance", "value": "Firms evaluating investment options, governments interested in improving business conditions, and economists seeking to explain economic performance have all grappled with defining and measuring the business environment. The firm-level data from Enterprise Surveys provide a useful tool for benchmarking economies across a large number of indicators measured at the firm level.\n\nThe reliability and availability of infrastructure benefit households and support development. Firms with access to modern and efficient infrastructure - telecommunications, electricity, and transport - can be more productive.\n\nA strong infrastructure enhances the competitiveness of an economy and generates a business environment conducive to firm growth and development. Good infrastructure efficiently connects firms to their customers and suppliers, and enables the use of modern production technologies. Conversely, deficiencies in infrastructure, such as loss of electricity on regular basis, create barriers to productive opportunities and increase costs for all firms, from micro enterprises to large multinational corporations." }, { "id": "IndicatorName", "value": "Value lost due to electrical outages (% of sales for affected firms)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates' precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).\n\nThe strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation. Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity.\n\nIdeally the survey sample frame is derived from the universe of eligible firms obtained from the country’s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank “manually” constructs a list of eligible firms after 1) partitioning a country’s cities of major economic activity into clusters and blocks, 2) randomly selecting a subset of blocks which will then be enumerated. In surveys conducted since 2005-06, survey documentation which explains the source of the sample frame and any special circumstances encountered during survey fieldwork are included with the collected datasets.\n\nObtaining panel data, i.e. interviews with the same firms across multiple years, is a priority in current Enterprise Surveys. When conducting a new Enterprise Survey in a country where data was previously collected, maximal effort is expended to re-interview as many firms (from the prior survey) as possible. For these panel firms, sampling weights can be adjusted to take into account the resulting altered probabilities of inclusion in the sample frame." }, { "id": "Longdefinition", "value": "Average losses due to electrical outages, as percentage of total annual sales. The value represents average losses for all firms which reported outages (please see indicator IC.ELC.OUTG.ZS)." }, { "id": "Notesfromoriginalsource", "value": "All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/)." }, { "id": "Statisticalconceptandmethodology", "value": "Firm-level surveys have been conducted since the 1990's by different units within the World Bank. Since 2005-06, most data collection efforts have been centralized within the Enterprise Analysis Unit. Surveys implemented by the Enterprise Analysis Unit follow the Global Methodology.\n\nPrivate contractors conduct the Enterprise Surveys on behalf of the World Bank. Due to sensitive survey questions addressing business-government relations and bribery-related topics, private contractors, rather than any government agency or an organization/institution associated with government, are hired by the World Bank to collect the data.\n\nConfidentiality of the survey respondents and the sensitive information they provide is necessary to ensure the greatest degree of survey participation, integrity and confidence in the quality of the data. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but confidentiality is never compromised.\n\nThe Enterprise Survey is answered by business owners and top managers. Sometimes the survey respondent calls company accountants and human resource managers into the interview to answer questions in the sales and labor sections of the survey. Typically 1200-1800 interviews are conducted in larger economies, 360 interviews are conducted in medium-sized economies, and for smaller economies, 150 interviews take place.\n\nThe manufacturing and services sectors are the primary business sectors of interest. This corresponds to firms classified with ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies with 5 or more employees are targeted for interview. Services firms include construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government/state ownership are not eligible to participate in an Enterprise Survey. Occasionally, for a few surveyed countries, other sectors are included in the companies surveyed such as education or health-related businesses. In each country, businesses in the cities/regions of major economic activity are interviewed.\n\nIn some countries, other surveys, which depart from the usual Enterprise Survey methodology, are conducted. Examples include 1) Informal Surveys- surveys of informal (unregistered) enterprises, 2) Micro Surveys- surveys fielded to registered firms with less than five employees, and 3) Financial Crisis Assessment Surveys- short surveys administered by telephone to assess the effects of the global financial crisis of 2008-09.\n\nThe Enterprise Surveys Unit uses two instruments: the Manufacturing Questionnaire and the Services Questionnaire. Although many questions overlap, some are only applicable to one type of business. For example, retail firms are not asked about production and nonproduction workers.\n\nThe standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.\n\nOver 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews." }, { "id": "Topic", "value": "Mitigation" }, { "id": "Unitofmeasure", "value": "% of sales for affected firms" } ], "source_id": "87" }, { "id": "NY.GDP.COAL.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Coal rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Coal rents are the difference between the value of both hard and soft coal production at world prices and their total costs of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "87" }, { "id": "NY.GDP.MINR.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Mineral rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Mineral rents are the difference between the value of production for a stock of minerals at world prices and their total costs of production. Minerals included in the calculation are tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "87" }, { "id": "NY.GDP.MKTP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Gap-filled total" }, { "id": "IndicatorName", "value": "GDP (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Gross domestic product (GDP), though widely tracked, may not always be the most relevant summary of aggregated economic performance for all economies, especially when production occurs at the expense of consuming capital stock.\n\nWhile GDP estimates based on the production approach are generally more reliable than estimates compiled from the income or expenditure side, different countries use different definitions, methods, and reporting standards. World Bank staff review the quality of national accounts data and sometimes make adjustments to improve consistency with international guidelines. Nevertheless, significant discrepancies remain between international standards and actual practice. Many statistical offices, especially those in developing countries, face severe limitations in the resources, time, training, and budgets required to produce reliable and comprehensive series of national accounts statistics.\n\nAmong the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money." }, { "id": "Longdefinition", "value": "GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars. Dollar figures for GDP are converted from domestic currencies using single year official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Statisticalconceptandmethodology", "value": "Gross domestic product (GDP) represents the sum of value added by all its producers. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers but excluding sales or value added taxes). Both valuations exclude transport charges that are invoiced separately by producers. Total GDP is measured at purchaser prices. Value added by industry is normally measured at basic prices." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "87" }, { "id": "NY.GDP.MKTP.KD.ZG", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "An economy's growth is measured by the change in the volume of its output or in the real incomes of its residents. The 2008 United Nations System of National Accounts (2008 SNA) offers three plausible indicators for calculating growth: the volume of gross domestic product (GDP), real gross domestic income, and real gross national income. The volume of GDP is the sum of value added, measured at constant prices, by households, government, and industries operating in the economy. GDP accounts for all domestic production, regardless of whether the income accrues to domestic or foreign institutions." }, { "id": "IndicatorName", "value": "GDP growth (annual %)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Each industry's contribution to growth in the economy's output is measured by growth in the industry's value added. In principle, value added in constant prices can be estimated by measuring the quantity of goods and services produced in a period, valuing them at an agreed set of base year prices, and subtracting the cost of intermediate inputs, also in constant prices. This double-deflation method requires detailed information on the structure of prices of inputs and outputs.\n\nIn many industries, however, value added is extrapolated from the base year using single volume indexes of outputs or, less commonly, inputs. Particularly in the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult.\n\nMoreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in quality lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of inflation.\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\nRebasing of national accounts can alter the measured growth rate of an economy and lead to breaks in series that affect the consistency of data over time. When countries rebase their national accounts, they update the weights assigned to various components to better reflect current patterns of production or uses of output. The new base year should represent normal operation of the economy - it should be a year without major shocks or distortions. Some developing countries have not rebased their national accounts for many years. Using an old base year can be misleading because implicit price and volume weights become progressively less relevant and useful.\n\nTo obtain comparable series of constant price data for computing aggregates, the World Bank rescales GDP and value added by industrial origin to a common reference year. Because rescaling changes the implicit weights used in forming regional and income group aggregates, aggregate growth rates are not comparable with those from earlier editions with different base years. Rescaling may result in a discrepancy between the rescaled GDP and the sum of the rescaled components. To avoid distortions in the growth rates, the discrepancy is left unallocated. As a result, the weighted average of the growth rates of the components generally does not equal the GDP growth rate." }, { "id": "Longdefinition", "value": "Annual percentage growth rate of GDP at market prices based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Statisticalconceptandmethodology", "value": "Gross domestic product (GDP) represents the sum of value added by all its producers. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers but excluding sales or value added taxes). Both valuations exclude transport charges that are invoiced separately by producers. Total GDP is measured at purchaser prices. Value added by industry is normally measured at basic prices. When value added is measured at producer prices.\n\nGrowth rates of GDP and its components are calculated using the least squares method and constant price data in the local currency. Constant price in U.S. dollar series are used to calculate regional and income group growth rates. Local currency series are converted to constant U.S. dollars using an exchange rate in the common reference year." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "annual %" } ], "source_id": "87" }, { "id": "NY.GDP.NGAS.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Natural gas rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Natural gas rents are the difference between the value of natural gas production at regional prices and total costs of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "87" }, { "id": "NY.GDP.PCAP.CD", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "GDP per capita (current US$)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank national accounts data, and OECD National Accounts data files." }, { "id": "Statisticalconceptandmethodology", "value": "For more information, see the metadata for current U.S. dollar GDP (NY.GDP.MKTP.CD) and total population (SP.POP.TOTL)." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "Current US$" } ], "source_id": "87" }, { "id": "NY.GDP.PETR.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Oil rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Oil rents are the difference between the value of crude oil production at regional prices and total costs of production." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "87" }, { "id": "NY.GDP.TOTL.RT.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future." }, { "id": "IndicatorName", "value": "Total natural resources rents (% of GDP)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Total natural resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations." }, { "id": "Statisticalconceptandmethodology", "value": "The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP)." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "% of GDP" } ], "source_id": "87" }, { "id": "per_allsp.avt_pop_preT_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Aggregated indicators are calculated using simple and weighted averages of country-level social protection indicators across categories (regions, country income groups and lending categories) using latest survey year available within 2010-2019. Weights are based on countries’ populations from the World Development Indicators (WDI) for the corresponding year of the indicator. “2010-2019 S” refers to simple averages and “2010-2019 W” refers to weighted averages." }, { "id": "IndicatorName", "value": "Average per capita transfer -All Social Protection and Labor (preT)" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Average transfer amount of Social Protection and Labor programs among program beneficiaries (per capita, daily $ppp)" }, { "id": "Source", "value": "ASPIRE" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Per capita, daily $ppp" } ], "source_id": "87" }, { "id": "per_allsp.cov_pop_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Aggregated indicators are calculated using simple and weighted averages of country-level social protection indicators across categories (regions, country income groups and lending categories) using latest survey year available within 2010-2019. Weights are based on countries’ populations from the World Development Indicators (WDI) for the corresponding year of the indicator. “2010-2019 S” refers to simple averages and “2010-2019 W” refers to weighted averages." }, { "id": "IndicatorName", "value": "Coverage (%) -All Social Protection and Labor" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Percentage of population participating in Social Protection and Labor programs (includes direct and indirect beneficiaries)" }, { "id": "Source", "value": "ASPIRE" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "per_allsp.cov_q1_tot", "metatype": [ { "id": "Aggregationmethod", "value": "Aggregated indicators are calculated using simple and weighted averages of country-level social protection indicators across categories (regions, country income groups and lending categories) using latest survey year available within 2010-2019. Weights are based on countries’ populations from the World Development Indicators (WDI) for the corresponding year of the indicator. “2010-2019 S” refers to simple averages and “2010-2019 W” refers to weighted averages." }, { "id": "IndicatorName", "value": "Coverage in 1st quintile (poorest) (%) -All Social Protection and Labor" }, { "id": "Limitationsandexceptions", "value": "When interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations." }, { "id": "Longdefinition", "value": "Percentage of population participating in Social Protection and Labor programs (includes direct and indirect beneficiaries)" }, { "id": "Source", "value": "ASPIRE" }, { "id": "Topic", "value": "Adaptation" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "PV.EST", "metatype": [ { "id": "IndicatorName", "value": "Political Stability and Absence of Violence/Terrorism: Estimate" }, { "id": "Longdefinition", "value": "Political Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org.The WGI are produced by Daniel Kaufmann (Natural Resource Governance Institute and Brookings Institution) and Aart Kraay (World Bank Development Research Group). Please cite Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2010). \"The Worldwide Governance Indicators: Methodology and Analytical Issues\". World Bank Policy Research Working Paper No. 5430 (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130). The WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent." }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "87" }, { "id": "RL.EST", "metatype": [ { "id": "IndicatorName", "value": "Rule of Law: Estimate" }, { "id": "Longdefinition", "value": "Rule of Law captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org.The WGI are produced by Daniel Kaufmann (Natural Resource Governance Institute and Brookings Institution) and Aart Kraay (World Bank Development Research Group). Please cite Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2010). \"The Worldwide Governance Indicators: Methodology and Analytical Issues\". World Bank Policy Research Working Paper No. 5430 (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130). The WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent." }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "87" }, { "id": "RQ.EST", "metatype": [ { "id": "IndicatorName", "value": "Regulatory Quality: Estimate" }, { "id": "Longdefinition", "value": "Regulatory Quality captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org.The WGI are produced by Daniel Kaufmann (Natural Resource Governance Institute and Brookings Institution) and Aart Kraay (World Bank Development Research Group). Please cite Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2010). \"The Worldwide Governance Indicators: Methodology and Analytical Issues\". World Bank Policy Research Working Paper No. 5430 (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130). The WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent." }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "87" }, { "id": "SE.ADT.LITR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role." }, { "id": "Generalcomments", "value": "For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022)." }, { "id": "IndicatorName", "value": "Literacy rate, adult total (% of people ages 15 and above)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously." }, { "id": "Longdefinition", "value": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org." }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "% of people ages 15 and above" } ], "source_id": "87" }, { "id": "SG.GEN.PARL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Despite much progress in recent decades, gender inequalities remain pervasive in many dimensions of life - worldwide. But while disparities exist throughout the world, they are most prevalent in developing countries. Gender inequalities in the allocation of such resources as education, health care, nutrition, and political voice matter because of the strong association with well-being, productivity, and economic growth. These patterns of inequality begin at an early age, with boys routinely receiving a larger share of education and health spending than do girls, for example.\n\nWomen are vastly underrepresented in decision-making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.\n\nA strong and vibrant democracy is possible only when parliament is fully inclusive of the population it represents. Parliaments cannot consider themselves inclusive, however, until they can boast the full participation of women. This is not just about women's right to equality and their contribution to the conduct of public affairs, but also about using women's resources and potential to determine political and development priorities that benefit societies and the global community." }, { "id": "Generalcomments", "value": "Relevance to gender indicator: Women are vastly underrepresented in decision making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.\n\nThis is the Sustainable Development Goal indicator 5.5.1 (a). [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "Proportion of seats held by women in national parliaments (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The number of countries covered varies with suspensions or dissolutions of parliaments. There can be difficulties in obtaining information on by-election results and replacements due to death or resignation. These changes are ad hoc events which are more difficult to keep track of. By-elections, for instance, are often not announced internationally as general elections are. Parliaments vary considerably in their internal workings and procedures, however, generally legislate, oversee government and represent the electorate. In terms of measuring women's contribution to political decision making, this indicator may not be sufficient because some women may face obstacles in fully and efficiently carrying out their parliamentary mandate.\n\nThe data is compiled by the Inter-Parliamentary Union on the basis of information provided by National Parliaments. The percentages do not take into account the case of parliaments for which no data was available at that date. Information is available in all countries where a national legislature exists and therefore does not include parliaments that have been dissolved or suspended for an indefinite period." }, { "id": "Longdefinition", "value": "Women in parliaments are the percentage of parliamentary seats in a single or lower chamber held by women." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Inter-Parliamentary Union (IPU) (www.ipu.org). For the year of 1998, the data is as of August 10, 1998." }, { "id": "Statisticalconceptandmethodology", "value": "The proportion of seats held by women in national parliaments is the number of seats held by women members in single or lower chambers of national parliaments, expressed as a percentage of all occupied seats; it is derived by dividing the total number of seats occupied by women by the total number of seats in parliament.\n\nNational parliaments can be bicameral or unicameral. This indicator covers the single chamber in unicameral parliaments and the lower chamber in bicameral parliaments. It does not cover the upper chamber of bicameral parliaments. Seats are usually won by members in general parliamentary elections. Seats may also be filled by nomination, appointment, indirect election, rotation of members and by-election. Seats refer to the number of parliamentary mandates, or the number of members of parliament." }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "SH.H2O.BASW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. \n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. \n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. \n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings." }, { "id": "IndicatorName", "value": "People using at least basic drinking water services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic drinking water service as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "87" }, { "id": "SH.STA.BASS.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. \n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. \n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. \n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. \n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired." }, { "id": "IndicatorName", "value": "People using at least basic sanitation services (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "National, regional and income group estimates are made when data are available for at least 50 percent of the population." }, { "id": "Longdefinition", "value": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines basic sanitation facilities as improved sanitation facilities that are not shared with other households. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "87" }, { "id": "SH.STA.HYGN.ZS", "metatype": [ { "id": "Developmentrelevance", "value": "Hygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior." }, { "id": "Generalcomments", "value": "This is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/]." }, { "id": "IndicatorName", "value": "People with basic handwashing facilities including soap and water (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy. Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal." }, { "id": "Longdefinition", "value": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org)." }, { "id": "Statisticalconceptandmethodology", "value": "Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys. WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "87" }, { "id": "SI.POV.GINI", "metatype": [ { "id": "Developmentrelevance", "value": "The World Bank Group's goal of promoting shared prosperity has been defined as fostering income growth of the bottom 40 per cent of the welfare distribution in every country. Gini coefficients are important background information for shared prosperity." }, { "id": "Generalcomments", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "IndicatorName", "value": "Gini index" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Gini coefficients are not unique. It is possible for two different Lorenz curves to give rise to the same Gini coefficient. Furthermore it is possible for the Gini coefficient of a developing country to rise (due to increasing inequality of income) while the number of people in absolute poverty decreases. This is because the Gini coefficient measures relative, not absolute, wealth.\n\nAnother limitation of the Gini coefficient is that it is not additive across groups, i.e. the total Gini of a society is not equal to the sum of the Gini's for its sub-groups. Thus, country-level Gini coefficients cannot be aggregated into regional or global Gini's, although a Gini coefficient can be computed for the aggregate.\n\nBecause the underlying household surveys differ in methods and types of welfare measures collected, data are not strictly comparable across countries or even across years within a country. Two sources of non-comparability should be noted for distributions of income in particular. First, the surveys can differ in many respects, including whether they use income or consumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much better welfare indicator, particularly in developing countries. Second, households differ in size (number of members) and in the extent of income sharing among members. And individuals differ in age and consumption needs. Differences among countries in these respects may bias comparisons of distribution. \n\nWorld Bank staff have made an effort to ensure that the data are as comparable as possible. Wherever possible, consumption has been used rather than income. Income distribution and Gini indexes for high-income economies are calculated directly from the Luxembourg Income Study database, using an estimation method consistent with that applied for developing countries." }, { "id": "Longdefinition", "value": "Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "World Bank, Poverty and Inequality Platform: https://pip.worldbank.org/" }, { "id": "Shortdefinition", "value": "The Gini index measures the extent to which the distribution of income or consumption among individuals or households within an economy deviates from a perfectly equal distribution. A Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality." }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org." }, { "id": "Statisticalconceptandmethodology", "value": "The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.\n\nThe Gini index provides a convenient summary measure of the degree of inequality. Data on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. \n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "87" }, { "id": "SI.POV.UMIC", "metatype": [ { "id": "Developmentrelevance", "value": "The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The World Bank produced its first global poverty estimates for developing countries for World Development Report 1990: Poverty (World Bank 1990) using household survey data for 22 countries (Ravallion, Datt, and van de Walle 1991). Since then there has been considerable expansion in the number of countries that field household income and expenditure surveys." }, { "id": "Generalcomments", "value": "The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org)." }, { "id": "IndicatorName", "value": "Poverty headcount ratio at $6.85 a day (2017 PPP) (% of population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. \n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary." }, { "id": "Longdefinition", "value": "Poverty headcount ratio at $6.85 a day is the percentage of the population living on less than $6.85 a day at 2017 international prices." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedsourcelinks", "value": "World Bank, Poverty and Inequality Platform: https://pip.worldbank.org/" }, { "id": "Shortdefinition", "value": "Poverty headcount ratio at $6.85 a day is the percentage of the population living on less than $6.85 a day at 2017 international prices." }, { "id": "Source", "value": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org." }, { "id": "Statisticalconceptandmethodology", "value": "International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $2.15 as the international poverty line using the 2017 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $3.65 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $6.85 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.\n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2017 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $2.15 a day in 2017 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "% of population" } ], "source_id": "87" }, { "id": "SL.TLF.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "IndicatorName", "value": "Labor force, female (% of total labor force)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Statisticalconceptandmethodology", "value": "The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave. \n\nData are generated with World Bank population estimates and ILO estimates on labor force participation rate. The ILO estimates are harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "% of total labor force" } ], "source_id": "87" }, { "id": "SL.UEM.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Paradoxically, low unemployment rates can disguise substantial poverty in a country, while high unemployment rates can occur in countries with a high level of economic development and low rates of poverty. In countries without unemployment or welfare benefits people eke out a living in vulnerable employment. In countries with well-developed safety nets workers can afford to wait for suitable or desirable jobs. But high and sustained unemployment indicates serious inefficiencies in resource allocation.\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\nUnemployment is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.5.2]" }, { "id": "Generalcomments", "value": "National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates." }, { "id": "IndicatorName", "value": "Unemployment, total (% of total labor force) (modeled ILO estimate)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men. \n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment." }, { "id": "Longdefinition", "value": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "International Labour Organization, ILOSTAT database. Data as of June 2022." }, { "id": "Statisticalconceptandmethodology", "value": "The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or who have voluntarily left work. Persons who did not look for work but have an arrangements for a future job are also counted as unemployed. \n\nSome unemployment is unavoidable. At any time some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population.\n\nThe series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "% of total labor force" } ], "source_id": "87" }, { "id": "SP.POP.TOTL", "metatype": [ { "id": "Aggregationmethod", "value": "Sum" }, { "id": "Developmentrelevance", "value": "Increases in human population, whether as a result of immigration or more births than deaths, can impact natural resources and social infrastructure. This can place pressure on a country's sustainability. A significant growth in population will negatively impact the availability of land for agricultural production, and will aggravate demand for food, energy, water, social services, and infrastructure. On the other hand, decreasing population size - a result of fewer births than deaths, and people moving out of a country - can impact a government's commitment to maintain services and infrastructure." }, { "id": "Generalcomments", "value": "Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis." }, { "id": "IndicatorName", "value": "Population, total" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Current population estimates for developing countries that lack (i) reliable recent census data, and (ii) pre- and post-census estimates for countries with census data, are provided by the United Nations Population Division and other agencies. \n\nThe cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in both the model and the data. In the UN estimates the five-year age group is the cohort unit and five-year period data are used; therefore interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\nBecause future trends cannot be known with certainty, population projections have a wide range of uncertainty." }, { "id": "Longdefinition", "value": "Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "(1) United Nations Population Division. World Population Prospects: 2019 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme." }, { "id": "Statisticalconceptandmethodology", "value": "Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models.\n\nErrors and undercounting occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census.\n\nThe quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data.\n\nThe currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census.\n\nThe United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems.\n\nInternational migration is the only other factor besides birth and death rates that directly determines a country's population growth. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect.\n\nPopulation projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "Number" } ], "source_id": "87" }, { "id": "SP.URB.TOTL.IN.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted average" }, { "id": "Developmentrelevance", "value": "Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment." }, { "id": "IndicatorName", "value": "Urban population (% of total population)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Aggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. \n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution." }, { "id": "Longdefinition", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "United Nations Population Division. World Urbanization Prospects: 2018 Revision." }, { "id": "Statisticalconceptandmethodology", "value": "Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects.\n\nPercentages urban are the numbers of persons residing in an area defined as ''urban'' per 100 total population. They are calculated by the Statistics Division of the United Nations Department of Economic and Social Affairs. Particular caution should be used in interpreting the figures for percentage urban for different countries.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen." }, { "id": "Topic", "value": "Country Profile" }, { "id": "Unitofmeasure", "value": "% of total population" } ], "source_id": "87" }, { "id": "VA.EST", "metatype": [ { "id": "IndicatorName", "value": "Voice and Accountability: Estimate" }, { "id": "Longdefinition", "value": "Voice and Accountability captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org.The WGI are produced by Daniel Kaufmann (Natural Resource Governance Institute and Brookings Institution) and Aart Kraay (World Bank Development Research Group). Please cite Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2010). \"The Worldwide Governance Indicators: Methodology and Analytical Issues\". World Bank Policy Research Working Paper No. 5430 (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130). The WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent." }, { "id": "Topic", "value": "Managing the transition" }, { "id": "Unitofmeasure", "value": "Score" } ], "source_id": "87" }, { "id": "CoCA_fexp", "metatype": [ { "id": "IndicatorName", "value": "Affordability of an energy sufficient diet: ratio of cost to food expenditures" }, { "id": "Longdefinition", "value": "The ratio of the cost of an energy sufficient diet to total food expenditure per capita per day from national accounts. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); and Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoCA_headcount", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of unaffordability of an energy sufficient diet" }, { "id": "Longdefinition", "value": "The indicator estimates the percentage of individuals in a population whose disposable income, net of the amount needed to acquire basic non-food goods and services, is lower than the average cost of the least-expensive energy sufficient diet in a country. The expenditures for basic non-food needs are calculated as average non-food expenditure shares of low-income consumers multipled by internaitonal poverty lines set by the World Bank. The non-food expenditure share is 37% and 44% in low-income and lower-middle-income countries for the second quintile of consumers, and 54% in upper-middle-and-high-income countries for the first quintile of consumers, according to household surveys compiled by the World Bank. The international poverty lines are $2.15/day for low-income countries, $3.65/day for lower-middle-income countries, $6.85/day for upper-middle-income countries, and $24.36/day for high-income countries, in 2017PPP$. Countries' income classifications follow the calendar year of 2021 standard (fiscal year of 2023 of the World Bank), which is the base year of the latest ICP cycle. Income data are provided by the World Bank’s Poverty and Inequality Platform. A value of zero indicates a null or a small number rounded down at the current precision level. Data available for 2021.\n\nNational income distribution data are provided by the World Bank’s Poverty and Inequality Platform and are based on 2017 purchasing power parities (PPPs), updated in fall 2024. Latest data of India are also featured." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "88" }, { "id": "CoCA_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of an energy sufficient diet in local currency unit" }, { "id": "Longdefinition", "value": "Cost of the least expensive starchy staple for energy balance, in LCU/person/day, for a representative person within energy balance at 2330 kcal/day. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); and Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoCA_pov", "metatype": [ { "id": "IndicatorName", "value": "Affordability of an energy sufficient diet: ratio of cost to the food poverty line" }, { "id": "Longdefinition", "value": "The ratio of the cost of an energy sufficient diet to the food poverty lines, defined as $1.35/day for low-income countries (63% of the international poverty line of $2.15/day in 2017 PPP$), $2.04/day for lower-middle-income countries (56% of the international poverty line of $3.65/day in 2017 PPP$), $3.15/day for upper-middle-income countries (46% of the international poverty line of $6.85/day in 2017 PPP$), and $11.2/day (46% of the international poverty line of $24.36/day in 2017 PPP$). The percentages (63%, 56%, and 46%) represent the average food expenditure shares in the second quintile of consumers in low-income and lower-middle-income countries, and the first quintile of consumers in upper-middle-and-high-income countries, according to household surveys compiled by the World Bank. Countries' income classifications follow the calendar year of 2021 standard (fiscal year of 2023 of the World Bank), which is the base year of the latest ICP cycle. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "88" }, { "id": "CoCA_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of an energy sufficient diet in PPP dollars" }, { "id": "Longdefinition", "value": "Cost of the least expensive starchy staple for energy balance, in 2021 PPP$/person/day, for a representative person within energy balance at 2330 kcal/day. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); and Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoCA_unafford_n", "metatype": [ { "id": "IndicatorName", "value": "Number of people unable to afford an energy sufficient diet" }, { "id": "Longdefinition", "value": "The indicator estimates the total number of people who cannot afford an energy sufficient diet in a given country and year. The indicator is computed by multiplying the percentage of the population in a country unable to afford an energy sufficient diet by population data from the World Population Prospect by the United Nations Department of Economic and Social Affairs (UN DESA). A value of zero indicates a null or a small number rounded down at the current precision level. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "88" }, { "id": "CoHD_asf_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of animal source foods in local currency unit" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available animal source foods to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in LCU/person/day. Animal-source foods are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_asf_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of animal source foods in PPP dollars" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available animal source foods to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in 2021 PPP$/person/day. Animal-source foods are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_asf_prop", "metatype": [ { "id": "IndicatorName", "value": "Cost share for animal-sourced foods in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Share of costs for the least expensive animal-source foods to meet daily recommendations in food-based dietary guidelines (FBDG), as a proportion of the total cost of a healthy diet. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_asf_ss", "metatype": [ { "id": "IndicatorName", "value": "Cost of animal-sourced foods relative to the starchy staples in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Cost of the least expensive animal-source foods as a multiple of the least expensive starchy staples to meet daily recommendations in food-based dietary guidelines (FBDG). Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_CoCA", "metatype": [ { "id": "IndicatorName", "value": "Cost of a healthy diet relative to the cost of sufficient energy from starchy staples" }, { "id": "Longdefinition", "value": "Ratio between the cost of a healthy diet (CoHD) that meets requirements for energy and food-based dietary guidelines (FBDG) and the cost of caloric adequacy (CoCA) that uses only starchy staples to meet energy requirements. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); and Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_f_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of fruits in local currency unit" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available fruits to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in LCU/person/day. Fruits are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_f_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of fruits in PPP dollars" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available fruits to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in 2021 PPP$/person/day. Fruits are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_f_prop", "metatype": [ { "id": "IndicatorName", "value": "Cost share for fruits in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Share of costs for the least expensive fruits to meet daily recommendations in food-based dietary guidelines (FBDG), as a proportion of the total cost of a healthy diet. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_f_ss", "metatype": [ { "id": "IndicatorName", "value": "Cost of fruits relative to the starchy staples in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Cost of the least expensive fruits as a multiple of the least expensive starchy staples to meet daily recommendations in food-based dietary guidelines (FBDG). Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_fexp", "metatype": [ { "id": "IndicatorName", "value": "Affordability of a healthy diet: ratio of cost to food expenditures" }, { "id": "Longdefinition", "value": "The ratio of the cost of a healthy diet to total food expenditure per capita per day from national accounts. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_headcount", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of unaffordability of a healthy diet" }, { "id": "Longdefinition", "value": "The indicator estimates the percentage of individuals in a population whose disposable income, net of the amount needed to acquire basic non-food goods and services, is lower than the average cost of the least-expensive healthy diet in a country. The expenditures for basic non-food needs are calculated as average non-food expenditure shares of low-income consumers multiplied by international poverty lines set by the World Bank. The non-food expenditure share is 37% and 44% in low-income and lower-middle-income countries for the second quintile of consumers, and 54% in upper-middle-and-high-income countries for the first quintile of consumers, according to recent household surveys for 71 countries compiled by the World Bank. The international poverty lines are $2.15/day for low-income countries, $3.65/day for lower-middle-income countries, $6.85/day for upper-middle-income countries, and $24.36/day for high-income countries, in 2017PPP$. Countries' income classifications follow the calendar year of 2021 standard (fiscal year of 2023 of the World Bank), which is the base year of the latest ICP cycle. Income data are provided by the World Bank’s Poverty and Inequality Platform. A value of zero indicates a null or a small number rounded down at the current precision level. Data available from 2017 to 2024.\n\nNational income distribution data are provided by the World Bank’s Poverty and Inequality Platform and are based on 2017 purchasing power parities (PPPs), updated in fall 2024. Latest data of India are also featured." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); Bai, Y., Conti, V., Herforth, A., Cafiero, C., Ebel, A., Rissanen, M.O., Rosero Moncayo, J. & Masters, W.A. 2024. Methods for monitoring the cost of a healthy diet based on price data from the International Comparison Program. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3037en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "88" }, { "id": "CoHD_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of a healthy diet in local currency unit" }, { "id": "Longdefinition", "value": "The cost of a healthy diet indicator is the cost of purchasing the least expensive locally available foods to meet requirements for energy and food-based dietary guidelines (FBDG) for a representative person within energy balance at 2330 kcal/day. The cost of a healthy diet is in local currency unit. Data are available for the period from 2017 to 2024.\n\nFor Bahamas, Guyana, Haiti, Iran, Myanmar, Sint Maarten (Dutch Part), and Turks and Caicos Islands, retail food prices were not available from the ICP 2021. As a result, the CoHD for these countries is calculated using data from the ICP 2017." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); and Bai, Y., Conti, V., Herforth, A., Cafiero, C., Ebel, A., Rissanen, M.O., Rosero Moncayo, J. & Masters, W.A. 2024. Methods for monitoring the cost of a healthy diet based on price data from the International Comparison Program. FAO Statistics Division Working Paper. Rome, FAO. (forthcoming)" } ], "source_id": "88" }, { "id": "CoHD_lns_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of legumes, nuts and seeds in local currency unit" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available legumes, nuts and seeds to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in LCU/person/day. Legumes, nuts and seeds are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_lns_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of legumes, nuts and seeds in PPP dollars" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available legumes, nuts and seeds to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in 2021 PPP$/person/day. Legumes, nuts and seeds are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_lns_prop", "metatype": [ { "id": "IndicatorName", "value": "Cost share for legumes, nuts and seeds in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Share of costs for the least expensive legumes, nuts or seeds to meet daily recommendations in food-based dietary guidelines (FBDG), as a proportion of the total cost of a healthy diet. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_lns_ss", "metatype": [ { "id": "IndicatorName", "value": "Cost of legumes, nuts and seeds relative to the starchy staples in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Cost of the least expensive legumes, nuts and seeds as a multiple of the least expensive starchy staples to meet daily recommendations in food-based dietary guidelines (FBDG). Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_of_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of fats and oils in local currency unit" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available fats or oils to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in LCU/person/day. Fats and oils are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_of_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of fats and oils in PPP dollars" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available fats or oils to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in 2021 PPP$/person/day. Fats and oils are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_of_prop", "metatype": [ { "id": "IndicatorName", "value": "Cost share for oils and fats in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Share of costs for the least expensive oils or fats to meet daily recommendations in food-based dietary guidelines (FBDG), as a proportion of the total cost of a healthy diet. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_of_ss", "metatype": [ { "id": "IndicatorName", "value": "Cost of oils and fats relative to the starchy staples in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Cost of the least expensive oils and fats as a multiple of the least expensive starchy staples to meet daily recommendations in food-based dietary guidelines (FBDG). Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_pov", "metatype": [ { "id": "IndicatorName", "value": "Affordability of a healthy diet: ratio of cost to the food poverty line" }, { "id": "Longdefinition", "value": "The ratio of the cost of a healthy diet to the food poverty lines, defined as $1.35/day for low-income countries (63% of the international poverty line of $2.15/day in 2017 PPP$), $2.04/day for lower-middle-income countries (56% of the international poverty line of $3.65/day in 2017 PPP$), $3.15/day for upper-middle-income countries (46% of the international poverty line of $6.85/day in 2017 PPP$), and $11.2/day (46% of the international poverty line of $24.36/day in 2017 PPP$). The percentages (63%, 56%, and 46%) represent the average food expenditure shares in the second quintile of consumers in low-income and lower-middle-income countries, and the first quintile of consumers in upper-middle-and-high-income countries, according to household surveys compiled by the World Bank. Countries' income classifications follow the calendar year of 2021 standard (fiscal year of 2023 of the World Bank), which is the base year of the latest ICP cycle. Data available from 2017 to 2024." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); Bai, Y., Conti, V., Herforth, A., Cafiero, C., Ebel, A., Rissanen, M.O., Rosero Moncayo, J. & Masters, W.A. 2024. Methods for monitoring the cost of a healthy diet based on price data from the International Comparison Program. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3037en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "88" }, { "id": "CoHD_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of a healthy diet in PPP dollars" }, { "id": "Longdefinition", "value": "The cost of a healthy diet indicator is the cost of purchasing the least expensive locally available foods to meet requirements for energy and food-based dietary guidelines (FBDG) for a representative person within energy balance at 2330 kcal/day. The cost of a healthy diet is converted to international dollars using purchasing power parity (PPP). Data are available for the period from 2017 and 2024. PPP conversion factors are imputed by the FAO for years not available in the WDI database. PPPs are imputed in one or multiple years for the following countries: Angola, Argentina, Aruba, Bermuda, British Virgin Islands, Cayman Islands, Curaçao, Democratic Republic of the Congo, Dominica, Eswatini, Kazakhstan, Lebanon, Liberia, Myanmar, Seychelles, Sint Maarten (Dutch part), Suriname, Tajikistan and Zimbabwe.\n\nFor Bahamas, Guyana, Haiti, Iran, Myanmar, Sint Maarten (Dutch Part), and Turks and Caicos Islands, retail food prices were not available from the ICP 2021. As a result, the CoHD for these countries is calculated using data from the ICP 2017." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); and Bai, Y., Conti, V., Herforth, A., Cafiero, C., Ebel, A., Rissanen, M.O., Rosero Moncayo, J. & Masters, W.A. 2024. Methods for monitoring the cost of a healthy diet based on price data from the International Comparison Program. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3037en)" } ], "source_id": "88" }, { "id": "CoHD_ss_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of starchy staples in local currency unit" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available starchy staples to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in LCU/person/day. Starchy staples are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_ss_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of starchy staples in PPP dollars" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available starchy staples to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in 2021 PPP$/person/day. Starchy staples are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_ss_prop", "metatype": [ { "id": "IndicatorName", "value": "Cost share for starchy staples in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Share of costs for the least expensive starchy staples to meet daily recommendations in food-based dietary guidelines (FBDG), as a proportion of the total cost of a healthy diet. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_unafford_n", "metatype": [ { "id": "IndicatorName", "value": "Number of people unable to afford a healthy diet" }, { "id": "Longdefinition", "value": "The indicator expresses the total number of people who cannot afford a healthy diet in a given country and year. The indicator is computed by multiplying the percentage of the population in a country unable to afford a healthy diet by population data from the World Population Prospect by the United Nations Department of Economic and Social Affairs (UN DESA). A value of zero indicates a null or a small number rounded down at the current precision level. Data available from 2017 to 2024." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); Bai, Y., Conti, V., Herforth, A., Cafiero, C., Ebel, A., Rissanen, M.O., Rosero Moncayo, J. & Masters, W.A. 2024. Methods for monitoring the cost of a healthy diet based on price data from the International Comparison Program. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3037en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "88" }, { "id": "CoHD_v_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of vegetables in local currency unit" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available vegetables to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in LCU/person/day. Vegetables are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_v_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of vegetables in PPP dollars" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available vegetables to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in 2021 PPP$/person/day. Vegetables are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_v_prop", "metatype": [ { "id": "IndicatorName", "value": "Cost share for vegetables in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Share of costs for the least expensive vegetables to meet daily recommendations in food-based dietary guidelines (FBDG), as a proportion of the total cost of a healthy diet. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoHD_v_ss", "metatype": [ { "id": "IndicatorName", "value": "Cost of vegetables relative to the starchy staples in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Cost of the least expensive vegetables as a multiple of the least expensive starchy staples to meet daily recommendations in food-based dietary guidelines (FBDG). Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoNA_fexp", "metatype": [ { "id": "IndicatorName", "value": "Affordability of a nutrient adequate diet: ratio of cost to food expenditures" }, { "id": "Longdefinition", "value": "The ratio of the cost of a nutrient adequate diet to total food expenditure per capita per day from national accounts. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); and Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoNA_headcount", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of unaffordability of a nutrient adequate diet" }, { "id": "Longdefinition", "value": "The indicator estimates the percentage of individuals in a population whose disposable income, net of the amount needed to acquire basic non-food goods and services, is lower than the average cost of the least-expensive nutrient adequate diet in a country. The expenditures for basic non-food needs are calculated as average non-food expenditure shares of low-income consumers multiplied by international poverty lines set by the World Bank. The non-food expenditure share is 37% and 44% in low-income and lower-middle-income countries for the second quintile of consumers, and 54% in upper-middle-and-high-income countries for the first quintile of consumers, according to household surveys compiled by the World Bank. The international poverty lines are $2.15/day for low-income countries, $3.65/day for lower-middle-income countries, $6.85/day for upper-middle-income countries, and $24.36/day for high-income countries, in 2017PPP$. Countries' income classifications follow the calendar year of 2021 (fiscal year of 2023 of the World Bank) as the base year of the latest ICP cycle. Income data are provided by the World Bank’s Poverty and Inequality Platform. A value of zero indicates a null or a small number rounded down at the current precision level. Data available for 2021.\n\nNational income distribution data are provided by the World Bank’s Poverty and Inequality Platform and are based on 2017 purchasing power parities (PPPs), updated in fall 2024. Latest data of India are also featured." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "88" }, { "id": "CoNA_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of a nutrient adequate diet in local currency unit" }, { "id": "Longdefinition", "value": "Cost of the least expensive locally-available foods for nutrient adequacy, in LCU/person/day, for a representative person within upper and lower bounds for 23 essential macro- and micronutrients plus energy balance at 2330 kcal/day. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); and Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoNA_pov", "metatype": [ { "id": "IndicatorName", "value": "Affordability of a nutrient adequate diet: ratio of cost to the food poverty line" }, { "id": "Longdefinition", "value": "The ratio of the cost of a nutrient adequate diet to the food poverty lines, defined as $1.35/day for low-income countries (63% of the international poverty line of $2.15/day in 2017 PPP$), $2.04/day for lower-middle-income countries (56% of the international poverty line of $3.65/day in 2017 PPP$), $3.15/day for upper-middle-income countries (46% of the international poverty line of $6.85/day in 2017 PPP$), and $11.2/day (46% of the international poverty line of $24.36/day in 2017 PPP$). The percentages (63%, 56%, and 46%) represent the average food expenditure shares in the second quintile of consumers in low-income and lower-middle-income countries, and the first quintile of consumers in upper-middle-and-high-income countries, according to household surveys compiled by the World Bank. Countries' income classifications follow the calendar year of 2021 standard (fiscal year of 2023 of the World Bank), which is the base year of the latest ICP cycle. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "88" }, { "id": "CoNA_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of a nutrient adequate diet in PPP dollars" }, { "id": "Longdefinition", "value": "Cost of the least expensive locally-available foods for nutrient adequacy, in 2021 PPP$/person/day, for a representative person within upper and lower bounds for 23 essential macro- and micronutrients plus energy balance at 2330 kcal/day. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); and Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "88" }, { "id": "CoNA_unafford_n", "metatype": [ { "id": "IndicatorName", "value": "Number of people unable to afford a nutrient adequate diet" }, { "id": "Longdefinition", "value": "The indicator estimates the total number of people who cannot afford a nutrient adequate diet in a given country and year. The indicator is computed by multiplying the percentage of the population in a country unable to afford a nutrient adequate diet by population data from the World Population Prospect by the United Nations Department of Economic and Social Affairs (UN DESA). A value of zero indicates a null or a small number rounded down at the current precision level. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "88" }, { "id": "NY.GDP.PCAP.PP.CD", "metatype": [ { "id": "IndicatorName", "value": "GDP per capita, PPP (current international $)" }, { "id": "Longdefinition", "value": "This indicator provides values for gross domestic product (GDP) expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" } ], "source_id": "88" }, { "id": "NY.GNP.PCAP.PP.CD", "metatype": [ { "id": "IndicatorName", "value": "GNI per capita, PPP (current international $)" }, { "id": "Longdefinition", "value": "This indicator provides values for gross national income (GNI) per person expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. \n\nGross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components." }, { "id": "Source", "value": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF)" } ], "source_id": "88" }, { "id": "Pop", "metatype": [ { "id": "IndicatorName", "value": "Population" }, { "id": "Longdefinition", "value": "Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates." }, { "id": "Source", "value": "Population data are sounced from the United Nations Department of Economic and Social Affairs (UN DESA) 2024 revision of the World Population Prospects." } ], "source_id": "88" }, { "id": "birth_reg", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life – from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Children under age 5 whose births are registered, total (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Percentage of children under age 5 who were registered at the moment of the survey. The numerator of this indicator includes children reported to have a birth certificate, regardless of whether or not it was seen by the interviewer, and those without a birth certificate whose mother or caregiver says the birth has been registered, total." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF. https://data.unicef.org/topic/child-protection/birth-registration/" }, { "id": "Statisticalconceptandmethodology", "value": "Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Identification: Birth Registration" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "birth_reg_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life – from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Children under age 5 whose births are registered, female (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Percentage of children under age 5 who were registered at the moment of the survey. The numerator of this indicator includes children reported to have a birth certificate, regardless of whether or not it was seen by the interviewer, and those without a birth certificate whose mother or caregiver says the birth has been registered, women." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF. https://data.unicef.org/topic/child-protection/birth-registration/" }, { "id": "Statisticalconceptandmethodology", "value": "Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Identification: Birth Registration" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "birth_reg_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "Society typically first acknowledges a child’s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person’s life – from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to “By 2030, provide legal identity for all, including birth registration”." }, { "id": "IndicatorName", "value": "Children under age 5 whose births are registered, male (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "Data on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9)." }, { "id": "Longdefinition", "value": "Percentage of children under age 5 who were registered at the moment of the survey. The numerator of this indicator includes children reported to have a birth certificate, regardless of whether or not it was seen by the interviewer, and those without a birth certificate whose mother or caregiver says the birth has been registered, men." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Source", "value": "UNICEF. https://data.unicef.org/topic/child-protection/birth-registration/" }, { "id": "Statisticalconceptandmethodology", "value": "Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD)." }, { "id": "Topic", "value": "Identification: Birth Registration" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "diff_elections_s", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Challenge: Participating in elections, total (% of population ages 15+ without an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents without an ID who report that one limitation that they experience because they lack an ID is the inability to participate in elections. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, total sample." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "diff_finance_s", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Challenge: Using financial services, total (% of population ages 15+ without an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents without an ID who report that one limitation that they experience because they lack an ID is the inability to open a bank account. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, total sample." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "diff_gov_s", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Challenge: Receiving financial support, total (% of population ages 15+ without an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents without an ID who report that one limitation that they experience because they lack an ID is the inability to receive financial support from the government. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, total sample." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "diff_job_s", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Challenge: Applying for a job, total (% of population ages 15+ without an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents without an ID who report that one limitation that they experience because they lack an ID is the inability apply for a job. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, total sample." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "diff_med_s", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Challenge: Receiving medical care, total (% of population ages 15+ without an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents without an ID who report that one limitation that they experience because they lack an ID is the inability to use financial services. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, total sample." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "diff_sim_s", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Challenge: Obtaining a SIM card, total (% of population ages 15+ without an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents without an ID who report that one limitation that they experience because they lack an ID is the inability to obtain a SIM card. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, total sample." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "has_eid", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID ownership, total (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, total sample." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "has_eid_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID ownership, female (% of female population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample women." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "has_eid_10", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID ownership, urban (% of urban population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample urban." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "has_eid_11", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID ownership, out of the workforce (% of population ages 15+ out of the workforce)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample out of workforce." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "has_eid_12", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID ownership, in the workforce (% of population ages 15+ in the workforce)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample in workforce." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "has_eid_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID ownership, male (% of male population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample men." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "has_eid_3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID ownership, ages 15-24 (% of population ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample below 25 years of age." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "has_eid_4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID ownership, ages 25+ (% population ages 25+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample 25 years of age and above." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "has_eid_5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID ownership, primary education or less (% of population ages 15+ with primary education or less)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample primary education or less." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "has_eid_6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID ownership, secondary education or more (% of population ages 15+ with secondary education or more)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample secondary education or more." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "has_eid_7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID ownership, poorest 40% (% of population ages 15+ in the poorest 40%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample lower 40% income category." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "has_eid_8", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID ownership, richest 60% (% of population ages 15+ in the richest 60%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample upper 60% income category." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "has_eid_9", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID ownership, rural (% of rural population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample rural." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "ID.DIG.AUTH", "metatype": [ { "id": "Developmentrelevance", "value": "Enabling digital identity verification and authentication for in-person transactions can have benefits for streamlining service delivery and reducing certain types of fraud. Digital verification of identities or certain attributes can reduce transaction costs and enhance the convenience and speed of many transactions by providing higher levels of assurance or trust. This, in turn, has the potential to simplify services and transactions, making them faster and easier (e.g., by reducing the number of physical documents to be presented) and to decrease the cost of onboarding for people, government, and businesses." }, { "id": "IndicatorName", "value": "Digital ID Verification (1=yes; 0= no)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Identities and/or identity information (e.g., name, date of birth, etc.) can be verified or authenticated using digital—rather than manual—means in the context of in-person transactions." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Relatedindicators", "value": "ID ownership (% age 15+) (ID.OWN.TOTL.ZS)" }, { "id": "Source", "value": "(i) Data provided by ID authorities in response to questionnaires, as part of the ID4D Global Dataset data collection; and (ii) Desk research relying on official sources, such as government websites, press releases, reports from UN agencies, and other development partners. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "The “digital ID verification” indicator is coded as a “Yes” when there is at least one public and/or private service provider that can use the ID system to verify and/or authenticate a person’s information or identity digitally. Therefore, it should be interpreted as a lower bound for the availability and use of digital identity for in-person transactions within a country. This broad-based approach was adopted, given data limitations and the diversity of verification and authentication mechanisms and modalities. For more details, see: https://documents1.worldbank.org/curated/en/099020824141510923/pdf/P176341192f2c50e11bc5619be95c4fb2ed.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "1=yes; 0= no" } ], "source_id": "89" }, { "id": "ID.DIG.AUTH.RM", "metatype": [ { "id": "Developmentrelevance", "value": "Enabling digital identity verification and authentication for in-person transactions can have benefits for streamlining service delivery and reducing certain types of fraud. Digital verification of identities or certain attributes can reduce transaction costs and enhance the convenience and speed of many transactions by providing higher levels of assurance or trust. This, in turn, has the potential to simplify services and transactions, making them faster and easier (e.g., by reducing the number of physical documents to be presented) and to decrease the cost of onboarding for people, government, and businesses." }, { "id": "IndicatorName", "value": "Online Digital Identity (1=yes; 0= no)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Identities or identity information, such as name or date of birth, can be verified or authenticated using digital rather than manual methods in in-person transactions. “1=yes” means a country has an online digital identity system or credential, which may be part of or separate from the foundational ID system. “0=no” means a country does not have an online digital identity system or credential." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database." }, { "id": "Statisticalconceptandmethodology", "value": "The “digital ID verification” indicator is coded as a “Yes” when there is at least one public and/or private service provider that can use the ID system to verify and/or authenticate a person’s information or identity digitally. Therefore, it should be interpreted as a lower bound for the availability and use of digital identity for in-person transactions within a country. This broad-based approach was adopted, given data limitations and the diversity of verification and authentication mechanisms and modalities. For more details, see: https://documents1.worldbank.org/curated/en/099020824141510923/pdf/P176341192f2c50e11bc5619be95c4fb2ed.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "1=yes; 0= no" } ], "source_id": "89" }, { "id": "ID.DIG.RECS", "metatype": [ { "id": "Developmentrelevance", "value": "Storing records in a digital format (i.e., in a database), rather than using an analog, paper-based system (i.e., ledgers or books), is an important first step in digitalization. Typically, having digital data is a pre-requisite for digital verification and authentication services" }, { "id": "IndicatorName", "value": "Digital Data Use (1=yes; 0= no)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Longdefinition", "value": "Records are stored in a digital format, rather than in paper records or ledgers." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Relatedindicators", "value": "ID ownership (% age 15+) (ID.OWN.TOTL.ZS)" }, { "id": "Source", "value": "(i) Data provided by ID authorities in response to questionnaires, as part of the ID4D Global Dataset data collection; and (ii) Desk research relying on official sources, such as government websites, press releases, reports from UN agencies, and other development partners. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "The digital data indicator was coded as “Yes” if the country’s foundational ID system uses electronic storage for new records. For countries where the implementation of a digitalized system/database for identity records was in the pilot stage or otherwise not yet fully operationalized in the year of data collection, the indicator was coded as “No.” The digital data indicator is coded as a “Yes” for countries where newly collected data is stored digitally, but where some portion of older records may still be stored in paper ledgers or similar analog formats. Where ID and civil registration systems are separate, this indicator codes the ID system only.\nFor more details, see: https://documents1.worldbank.org/curated/en/099020824141510923/pdf/P176341192f2c50e11bc5619be95c4fb2ed.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "1=yes; 0= no" } ], "source_id": "89" }, { "id": "ID.OWN.1524.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account or activate a SIM card. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "ID ownership, ages 15-24 (% of population ages 15-24)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics. For a full list of exclusions, see The Global Findex Database 2021 report and methodology: https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Longdefinition", "value": "The percentage of adult respondents age 15 and above who report owning a primary foundational ID (national ID or similar credential). If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample below 25 years of age." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database." }, { "id": "Statisticalconceptandmethodology", "value": "ID ownership is calculated based on a survey conducted on representative samples of the non-institutionalized civilian population over age 15. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s primary foundational ID, using the actual term for the foundational ID in the local language. A foundational ID system is primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. The name of the credentials referenced in the survey is included in the 2021 ID4D Global ID Coverage Estimates report, available at: https://id4d.worldbank.org/global-dataset. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "ID.OWN.25UP.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account or activate a SIM card. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "ID ownership, ages 25+ (% of population ages 25+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics. For a full list of exclusions, see The Global Findex Database 2021 report and methodology: https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Longdefinition", "value": "The percentage of adult respondents age 15 and above who report owning a primary foundational ID (national ID or similar credential). If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample 25 years of age and above." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database." }, { "id": "Statisticalconceptandmethodology", "value": "ID ownership is calculated based on a survey conducted on representative samples of the non-institutionalized civilian population over age 15. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s primary foundational ID, using the actual term for the foundational ID in the local language. A foundational ID system is primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. The name of the credentials referenced in the survey is included in the 2021 ID4D Global ID Coverage Estimates report, available at: https://id4d.worldbank.org/global-dataset. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "ID.OWN.BRTH.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "A birth certificate serves as a legal proof of identity for children and is an important document for securing their recognition before the law and safeguarding their rights. Without a birth certificate, children may be unable to access basic services, such as routine vaccines and healthcare." }, { "id": "IndicatorName", "value": "Birth certification (%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "The primary source for data on birth certification comes from nationally representative surveys. Research suggest that birth certification rates reported by survey respondents (typically the mother or other primary caregiver) may be higher that actual birth certification as respondents may confuse documents such as the birth notification form with a birth certificate." }, { "id": "Longdefinition", "value": "The percentage of children under age 5 whose births were registered at the time of the survey and whose birth certificate was seen by the interviewer or whose mother or caretaker says that a birth certificate has been issued for the child." }, { "id": "Periodicity", "value": "Annual" }, { "id": "Relatedindicators", "value": "Children under age 5 whose births are registered, total (%) (birth_reg)" }, { "id": "Source", "value": "UNICEF Global Databases, 2022" }, { "id": "Statisticalconceptandmethodology", "value": "The primary source for data on birth certification are nationally representative household surveys such as the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). The DHS and MICS employ slightly different question structures for this topic, but both ask a caregiver to report whether a child has been registered and whether a birth certificate was issued for that child. This creates three categories of response: “birth registered, with a certificate”, “birth registered, no certificate”, and “birth not registered”. The DHS combines all three outcomes as responses for one question, whereas the MICS question is multi-step and also probes further by asking the respondent to show the birth certificate if they can (in an attempt to mitigate for confusion with other documents)." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "ID.OWN.TOTL.40.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account or activate a SIM card. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "ID ownership, poorest 40% (% of population ages 15+ in the poorest 40%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics. For a full list of exclusions, see The Global Findex Database 2021 report and methodology: https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Longdefinition", "value": "The percentage of adult respondents age 15 and above who report owning a primary foundational ID (national ID or similar credential). If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample lower 40% income group." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database." }, { "id": "Statisticalconceptandmethodology", "value": "ID ownership is calculated based on a survey conducted on representative samples of the non-institutionalized civilian population over age 15. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s primary foundational ID, using the actual term for the foundational ID in the local language. A foundational ID system is primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. The name of the credentials referenced in the survey is included in the 2021 ID4D Global ID Coverage Estimates report, available at: https://id4d.worldbank.org/global-dataset. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "ID.OWN.TOTL.60.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account or activate a SIM card. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "ID ownership, richest 60% (% of population ages 15+ in the richest 60%)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics. For a full list of exclusions, see The Global Findex Database 2021 report and methodology: https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Longdefinition", "value": "The percentage of adult respondents age 15 and above who report owning a primary foundational ID (national ID or similar credential). If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample upper 60% income group." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database." }, { "id": "Statisticalconceptandmethodology", "value": "ID ownership is calculated based on a survey conducted on representative samples of the non-institutionalized civilian population over age 15. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s primary foundational ID, using the actual term for the foundational ID in the local language. A foundational ID system is primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. The name of the credentials referenced in the survey is included in the 2021 ID4D Global ID Coverage Estimates report, available at: https://id4d.worldbank.org/global-dataset. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "ID.OWN.TOTL.FE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account or activate a SIM card. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "ID ownership, female (% of female population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics. For a full list of exclusions, see The Global Findex Database 2021 report and methodology: https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Longdefinition", "value": "The percentage of adult respondents age 15 and above who report owning a primary foundational ID (national ID or similar credential). If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample women." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database." }, { "id": "Statisticalconceptandmethodology", "value": "ID ownership is calculated based on a survey conducted on representative samples of the non-institutionalized civilian population over age 15. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s primary foundational ID, using the actual term for the foundational ID in the local language. A foundational ID system is primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. The name of the credentials referenced in the survey is included in the 2021 ID4D Global ID Coverage Estimates report, available at: https://id4d.worldbank.org/global-dataset. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "ID.OWN.TOTL.MA.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account or activate a SIM card. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "ID ownership, male (% of male population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics. For a full list of exclusions, see The Global Findex Database 2021 report and methodology: https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Longdefinition", "value": "The percentage of adult respondents age 15 and above who report owning a primary foundational ID (national ID or similar credential). If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample men." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database." }, { "id": "Statisticalconceptandmethodology", "value": "ID ownership is calculated based on a survey conducted on representative samples of the non-institutionalized civilian population over age 15. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s primary foundational ID, using the actual term for the foundational ID in the local language. A foundational ID system is primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. The name of the credentials referenced in the survey is included in the 2021 ID4D Global ID Coverage Estimates report, available at: https://id4d.worldbank.org/global-dataset. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "ID.OWN.TOTL.OW.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account or activate a SIM card. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "ID ownership, out of the workforce (% of population ages 15+ out of the workforce)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics. For a full list of exclusions, see The Global Findex Database 2021 report and methodology: https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Longdefinition", "value": "The percentage of adult respondents age 15 and above who report owning a primary foundational ID (national ID or similar credential). If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample out of workforce." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database." }, { "id": "Statisticalconceptandmethodology", "value": "ID ownership is calculated based on a survey conducted on representative samples of the non-institutionalized civilian population over age 15. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s primary foundational ID, using the actual term for the foundational ID in the local language. A foundational ID system is primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. The name of the credentials referenced in the survey is included in the 2021 ID4D Global ID Coverage Estimates report, available at: https://id4d.worldbank.org/global-dataset. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "ID.OWN.TOTL.PR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account or activate a SIM card. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "ID ownership, primary education or less (% of population ages 15+ with primary education or less)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics. For a full list of exclusions, see The Global Findex Database 2021 report and methodology: https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Longdefinition", "value": "The percentage of adult respondents age 15 and above who report owning a primary foundational ID (national ID or similar credential). If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample primary education or less." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database." }, { "id": "Statisticalconceptandmethodology", "value": "ID ownership is calculated based on a survey conducted on representative samples of the non-institutionalized civilian population over age 15. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s primary foundational ID, using the actual term for the foundational ID in the local language. A foundational ID system is primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. The name of the credentials referenced in the survey is included in the 2021 ID4D Global ID Coverage Estimates report, available at: https://id4d.worldbank.org/global-dataset. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "ID.OWN.TOTL.RU.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account or activate a SIM card. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "ID ownership, rural (% of rural population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics. For a full list of exclusions, see The Global Findex Database 2021 report and methodology: https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Longdefinition", "value": "The percentage of adult respondents age 15 and above who report owning a primary foundational ID (national ID or similar credential). If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample rural." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database." }, { "id": "Statisticalconceptandmethodology", "value": "ID ownership is calculated based on a survey conducted on representative samples of the non-institutionalized civilian population over age 15. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s primary foundational ID, using the actual term for the foundational ID in the local language. A foundational ID system is primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. The name of the credentials referenced in the survey is included in the 2021 ID4D Global ID Coverage Estimates report, available at: https://id4d.worldbank.org/global-dataset. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "ID.OWN.TOTL.SE.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account or activate a SIM card. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "ID ownership, secondary education or more (% of population ages 15+ with secondary education or more)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics. For a full list of exclusions, see The Global Findex Database 2021 report and methodology: https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Longdefinition", "value": "The percentage of adult respondents age 15 and above who report owning a primary foundational ID (national ID or similar credential). If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample secondary education or more." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database." }, { "id": "Statisticalconceptandmethodology", "value": "ID ownership is calculated based on a survey conducted on representative samples of the non-institutionalized civilian population over age 15. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s primary foundational ID, using the actual term for the foundational ID in the local language. A foundational ID system is primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. The name of the credentials referenced in the survey is included in the 2021 ID4D Global ID Coverage Estimates report, available at: https://id4d.worldbank.org/global-dataset. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "ID.OWN.TOTL.UR.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account or activate a SIM card. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "ID ownership, urban (% of urban population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics. For a full list of exclusions, see The Global Findex Database 2021 report and methodology: https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Longdefinition", "value": "The percentage of adult respondents age 15 and above who report owning a primary foundational ID (national ID or similar credential). If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample urban." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database." }, { "id": "Statisticalconceptandmethodology", "value": "ID ownership is calculated based on a survey conducted on representative samples of the non-institutionalized civilian population over age 15. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s primary foundational ID, using the actual term for the foundational ID in the local language. A foundational ID system is primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. The name of the credentials referenced in the survey is included in the 2021 ID4D Global ID Coverage Estimates report, available at: https://id4d.worldbank.org/global-dataset. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "ID.OWN.TOTL.WF.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account or activate a SIM card. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "ID ownership, in the workforce (% of population ages 15+ in the workforce)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics. For a full list of exclusions, see The Global Findex Database 2021 report and methodology: https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Longdefinition", "value": "The percentage of adult respondents age 15 and above who report owning a primary foundational ID (national ID or similar credential). If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample in workforce." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database." }, { "id": "Statisticalconceptandmethodology", "value": "ID ownership is calculated based on a survey conducted on representative samples of the non-institutionalized civilian population over age 15. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s primary foundational ID, using the actual term for the foundational ID in the local language. A foundational ID system is primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. The name of the credentials referenced in the survey is included in the 2021 ID4D Global ID Coverage Estimates report, available at: https://id4d.worldbank.org/global-dataset. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "ID.OWN.TOTL.ZS", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account or activate a SIM card. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "ID ownership, total (% of population ages 15+)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics. For a full list of exclusions, see The Global Findex Database 2021 report and methodology: https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Longdefinition", "value": "The percentage of adult respondents age 15 and above who report owning a primary foundational ID (national ID or similar credential). If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, total sample." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database." }, { "id": "Statisticalconceptandmethodology", "value": "ID ownership is calculated based on a survey conducted on representative samples of the non-institutionalized civilian population over age 15. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s primary foundational ID, using the actual term for the foundational ID in the local language. A foundational ID system is primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. The name of the credentials referenced in the survey is included in the 2021 ID4D Global ID Coverage Estimates report, available at: https://id4d.worldbank.org/global-dataset. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "id_misuse_s", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Foundational ID has been used without permission for illegal purposes, total (% of population ages 15+ with an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who own a foundational ID and who reported that their foundational ID has been used without their permission for illegal purposes. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, total sample." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "id_misuse_s_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Foundational ID has been used without permission for illegal purposes, female (% of female population ages 15+ with an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who own a foundational ID and who reported that their foundational ID has been used without their permission for illegal purposes. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample women." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "id_misuse_s_10", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Foundational ID has been used without permission for illegal purposes, urban (% of urban population ages 15+ with an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who own a foundational ID and who reported that their foundational ID has been used without their permission for illegal purposes. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample urban." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "id_misuse_s_11", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Foundational ID has been used without permission for illegal purposes, out of the workforce (% of population ages 15+ out of the workforce with an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who own a foundational ID and who reported that their foundational ID has been used without their permission for illegal purposes. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample out of workforce." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "id_misuse_s_12", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Foundational ID has been used without permission for illegal purposes, in the workforce (% of population ages 15+ in the workforce with an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who own a foundational ID and who reported that their foundational ID has been used without their permission for illegal purposes. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample in workforce." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "id_misuse_s_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Foundational ID has been used without permission for illegal purposes, male (% of male population ages 15+ with an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who own a foundational ID and who reported that their foundational ID has been used without their permission for illegal purposes. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample men." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "id_misuse_s_3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Foundational ID has been used without permission for illegal purposes, ages 15-24 (% of population ages 15-24 with an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who own a foundational ID and who reported that their foundational ID has been used without their permission for illegal purposes. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample below 25 years of age." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "id_misuse_s_4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Foundational ID has been used without permission for illegal purposes, ages 25+ (% of population ages 25+ with an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who own a foundational ID and who reported that their foundational ID has been used without their permission for illegal purposes. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample 25 years of age and above." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "id_misuse_s_5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Foundational ID has been used without permission for illegal purposes, primary education or less (% of population ages 15+ with primary education or less and an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who own a foundational ID and who reported that their foundational ID has been used without their permission for illegal purposes. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample primary education or less." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "id_misuse_s_6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Foundational ID has been used without permission for illegal purposes, secondary education or more (% of population ages 15+ with secondary education or more and an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who own a foundational ID and who reported that their foundational ID has been used without their permission for illegal purposes. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample secondary education or more." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "id_misuse_s_7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Foundational ID has been used without permission for illegal purposes, poorest 40% (% of population ages 15+ in the poorest 40% with an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who own a foundational ID and who reported that their foundational ID has been used without their permission for illegal purposes. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample lower 40% income group." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "id_misuse_s_8", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Foundational ID has been used without permission for illegal purposes, richest 60% (% of population ages 15+ in the richest 60% with an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who own a foundational ID and who reported that their foundational ID has been used without their permission for illegal purposes. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample upper 60% income group." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "id_misuse_s_9", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Foundational ID has been used without permission for illegal purposes, rural (% of rural population ages 15+ with an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who own a foundational ID and who reported that their foundational ID has been used without their permission for illegal purposes. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample rural." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "noid_expensive_s", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Reason no ID: Too expensive, total (% of population ages 15+ without an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents without an ID who report that one of the reasons they do not own an ID is because getting an ID is too expensive. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, total sample." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "noid_far_s", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Reason no ID: Travel too far, total (% of population ages 15+ without an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents without an ID who report that one of the reasons they do not own an ID is because they have to travel too far to a registration center to get an ID. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, total sample." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "noid_nodocs_s", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Reason no ID: No documents, total (% of population ages 15+ without an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents without an ID who report that one of the reasons they do not own an ID is because they lack necessary documents. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, total sample." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "noid_noneed_s", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Reason no ID: Do not need an ID, total (% of population ages 15+ without an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents without an ID who report that one of the reasons they do not own an ID is because they do not need one. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, total sample." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "noid_other_s", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Reason no ID: Have another ID, total (% of population ages 15+ without an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents without an ID who report that one of the reasons they do not own an ID is becasue they own another ID. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, total sample." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "noid_uncomfortable_s", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Reason no ID: Uncomfortable sharing info, total (% of population ages 15+ without an ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents without an ID who report that one of the reasons they do not own an ID is because they feel uncomfortable sharing their information. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, total sample." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "used_eid_s", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID has been used on phone or computer to confirm identity online, total (% of population ages 15+ who own an online digital ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID and also report having used their online digital ID to confirm their identity online. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, total sample." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "used_eid_s_1", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID has been used on phone or computer to confirm identity online, female (% of female population ages 15+ who own an online digital ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID and also report having used their online digital ID to confirm their identity online. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample women." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "used_eid_s_10", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID has been used on phone or computer to confirm identity online, urban (% of urban population ages 15+ who own an online digital ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID and also report having used their online digital ID to confirm their identity online. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample urban." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "used_eid_s_11", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID has been used on phone or computer to confirm identity online, out of the workforce (% of population ages 15+ out of the workforce who own an online digital ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID and also report having used their online digital ID to confirm their identity online. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample out of workforce." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "used_eid_s_12", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID has been used on phone or computer to confirm identity online, in the workforce (% of population ages 15+ in the workforce who own an online digital ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID and also report having used their online digital ID to confirm their identity online. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample in workforce." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "used_eid_s_2", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID has been used on phone or computer to confirm identity online, male (% of male population ages 15+ who own an online digital ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID and also report having used their online digital ID to confirm their identity online. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample men." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "used_eid_s_3", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID has been used on phone or computer to confirm identity online, ages 15-24 (% of population ages 15-24 who own an online digital ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID and also report having used their online digital ID to confirm their identity online. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample below 25 years of age." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "used_eid_s_4", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID has been used on phone or computer to confirm identity online, ages 25+ (% of population ages 25+ who own an online digital ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID and also report having used their online digital ID to confirm their identity online. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample 25 years of age and above." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "used_eid_s_5", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID has been used on phone or computer to confirm identity online, primary education or less (% of population ages 15+ with primary education or less who own an online digital ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID and also report having used their online digital ID to confirm their identity online. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample primary education or less." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "used_eid_s_6", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID has been used on phone or computer to confirm identity online, secondary education or more (% of population ages 15+ with secondary education or more who own an online digital ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID and also report having used their online digital ID to confirm their identity online. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample secondary education or more." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "used_eid_s_7", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID has been used on phone or computer to confirm identity online, poorest 40% (% of population ages 15+ in the poorest 40% who own an online digital ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID and also report having used their online digital ID to confirm their identity online. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample lower 40% income group." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "used_eid_s_8", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID has been used on phone or computer to confirm identity online, richest 60% (% of population ages 15+ in the richest 60% who own an online digital ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID and also report having used their online digital ID to confirm their identity online. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample upper 60% income group." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "used_eid_s_9", "metatype": [ { "id": "Aggregationmethod", "value": "Weighted Average" }, { "id": "Developmentrelevance", "value": "An official proof of identity is essential for participation in economic, social, and political life and for the fulfillment of rights. Without an ID, people can find it difficult or impossible to access social assistance, healthcare, education, open a bank account, activate a SIM card, or participate in the digital economies. Countries with (large) gaps in ID ownership will also face added challenges in effectively designing and delivering public services. Providing “legal identity to all, including birth registration” is a target (16.9) under the Sustainable Development Goals." }, { "id": "IndicatorName", "value": "Online digital ID has been used on phone or computer to confirm identity online, rural (% of rural population ages 15+ who own an online digital ID)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "License_URL", "value": "https://datacatalog.worldbank.org/public-licenses#cc-by" }, { "id": "Limitationsandexceptions", "value": "In some economies, surveys may not have been able to reach certain areas due to remoteness or conflict. However, in most cases, the samples are nationally representative and weighted against select demographics." }, { "id": "Longdefinition", "value": "The percentage of adult respondents who report owning an online digital ID and also report having used their online digital ID to confirm their identity online. If the minimum age for obtaining an ID is above 15, observations below the minimum age are excluded, sub-sample rural." }, { "id": "Periodicity", "value": "Triennial" }, { "id": "Source", "value": "Global Findex ID4D Database. https://id4d.worldbank.org/" }, { "id": "Statisticalconceptandmethodology", "value": "Values calculated based on surveys conducted on representative samples of the non-institutionalized civilian population over age 15 that are eligible for the countries foundational IDs. Data points reflect weighted averages. The data is collected in collaboration between the ID4D Initiative and Global Findex, and carried out by Gallup, Inc., as part of its Gallup World Poll. The survey asks respondents whether they personally own the economy’s foundational or online digital ID (if available), using the actual term in the local language. Identification systems are primarily created to manage identity information for the general population and provide credentials that serve as proof of identity for a wide variety of public and private sector transactions and services. For additional details on the survey methodology, see https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf." }, { "id": "Topic", "value": "Identification: Identification" }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "89" }, { "id": "1000000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers individual consumption expenditure by households; individual consumption expenditure by nonprofit institutions serving households (NPISHs); individual consumption expenditure by government; collective consumption expenditure by government; gross capital formation; balance of exports and imports." } ], "source_id": "90" }, { "id": "1101000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Rice; Other cereals, flour and other cereal products; Bread; Other bakery products; Pasta products and couscous; Beef and veal; Pork; Lamb, mutton and goat; Poultry; Other meats and meat preparations; Fresh, chilled or frozen fish and seafood; Preserved or processed fish and seafood; Fresh milk; Preserved milk and other milk products; Cheese and curd; Eggs and egg-based products; Butter and margarine; Other edible oils and fats; Fresh or chilled fruit; Frozen, preserved or processed fruit and fruit-based products; Fresh or chilled vegetables, other than potatoes and other tuber vegetables; Fresh or chilled potatoes and other tuber vegetables; Frozen, preserved or processed vegetables and vegetable-based products; Sugar; Jams, marmalades and honey; Confectionery, chocolate and ice cream; Food products n.e.c.; Coffee, tea and cocoa; Mineral waters, soft drinks, fruit and vegetable juices." } ], "source_id": "90" }, { "id": "1101100", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Rice; Other cereals, flour and other cereal products; Bread; Other bakery products; Pasta products and couscous; Beef and veal; Pork; Lamb, mutton and goat; Poultry; Other meats and meat preparations; Fresh, chilled or frozen fish and seafood; Preserved or processed fish and seafood; Fresh milk; Preserved milk and other milk products; Cheese and curd; Eggs and egg-based products; Butter and margarine; Other edible oils and fats; Fresh or chilled fruit; Frozen, preserved or processed fruit and fruit-based products; Fresh or chilled vegetables, other than potatoes and other tuber vegetables; Fresh or chilled potatoes and other tuber vegetables; Frozen, preserved or processed vegetables and vegetable-based products; Sugar; Jams, marmalades and honey; Confectionery, chocolate and ice cream; Food products n.e.c." } ], "source_id": "90" }, { "id": "1101110", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Rice; Other cereals, flour and other cereal products; Bread; Other bakery products; Pasta products and couscous." } ], "source_id": "90" }, { "id": "1101120", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Beef and veal; Pork; Lamb, mutton and goat; Poultry; Other meats and meat preparations." } ], "source_id": "90" }, { "id": "1101130", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Fresh, chilled or frozen fish and seafood; Preserved or processed fish and seafood." } ], "source_id": "90" }, { "id": "1101140", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Fresh milk; Preserved milk and other milk products; Cheese and curd; Eggs and egg-based products." } ], "source_id": "90" }, { "id": "1101150", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Butter and margarine; Other edible oils and fats." } ], "source_id": "90" }, { "id": "1101160", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Fresh or chilled fruit; Frozen, preserved or processed fruit and fruit-based products." } ], "source_id": "90" }, { "id": "1101170", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Fresh or chilled vegetables, other than potatoes and other tuber vegetables; Fresh or chilled potatoes and other tuber vegetables; Frozen, preserved or processed vegetables and vegetable-based products." } ], "source_id": "90" }, { "id": "1101180", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Sugar; Jams, marmalades and honey; Confectionery, chocolate and ice cream." } ], "source_id": "90" }, { "id": "1101190", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Food products n.e.c." } ], "source_id": "90" }, { "id": "1101200", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Coffee, tea and cocoa; Mineral waters, soft drinks, fruit and vegetable juices." } ], "source_id": "90" }, { "id": "1102000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Spirits; Wine; Beer; Tobacco; Narcotics." } ], "source_id": "90" }, { "id": "1102100", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Spirits; Wine; Beer." } ], "source_id": "90" }, { "id": "1102200", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Tobacco." } ], "source_id": "90" }, { "id": "1103000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Clothing materials, other articles of clothing and clothing accessories; Garments; Cleaning, repair and hire of clothing; Shoes and other footwear; Repair and hire of footwear." } ], "source_id": "90" }, { "id": "1105000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Furniture and furnishings; Carpets and other floor coverings; Repair of furniture, furnishings and floor coverings; Household textiles; Major household appliances whether electric or not; Small electric household appliances; Repair of household appliances; Glassware, tableware and household utensils; Major tools and equipment; Small tools and miscellaneous accessories; Non-durable household goods; Domestic services; Household services." } ], "source_id": "90" }, { "id": "1107000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Motor cars; Motor cycles; Bicycles; Animal drawn vehicles; Fuels and lubricants for personal transport equipment; Maintenance and repair of personal transport equipment; Other services in respect of personal transport equipment; Passenger transport by railway; Passenger transport by road; Passenger transport by air; Passenger transport by sea and inland waterway; Combined passenger transport; Other purchased transport services." } ], "source_id": "90" }, { "id": "1107100", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Motor cars; Motor cycles; Bicycles; Animal drawn vehicles." } ], "source_id": "90" }, { "id": "1107300", "metatype": [ { "id": "IndicatorName", "value": "National accounts expenditure value is the best possible estimate provided by the national implementing agency. This ICP classification heading covers expenditures for Passenger transport by railway; Passenger transport by road; Passenger transport by air; Passenger transport by sea and inland waterway; Combined passenger transport; Other purchased transport services." } ], "source_id": "90" }, { "id": "1108000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Postal services; Telephone and telefax equipment; Telephone and telefax services." } ], "source_id": "90" }, { "id": "1111000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Catering services; Accommodation services." } ], "source_id": "90" }, { "id": "1113000", "metatype": [ { "id": "IndicatorName", "value": "Zero national accounts expenditure value may represent that the value for this heading is allocated under other GDP expenditure headings based on the national implementing agency's best judgement. This ICP classification heading covers expenditures for Net purchases abroad." } ], "source_id": "90" }, { "id": "1300000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers individual consumption expenditures by government for Housing; Pharmaceutical products; Other medical products; Therapeutic appliances and equipment; Out-patient medical services; Out-patient dental services; Out-patient paramedical services; Hospital services; Compensation of employees - Ind. Hth. Govt; Intermediate consumption - Ind. Hth. Govt; Gross operating surplus - Ind. Hth. Govt; Net taxes on production - Ind. Hth. Govt; Receipts from sales - Ind. Hth. Govt; Recreation and culture; Education benefits and reimbursements; Compensation of employees - Ind. Edu. Govt; Intermediate consumption - Ind. Edu. Govt; Gross operating surplus - Ind. Edu. Govt; Net taxes on production - Ind. Edu. Govt; Receipt from sales - Ind. Edu. Govt; Social protection;" } ], "source_id": "90" }, { "id": "1400000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers collective consumption expenditures by government for Compensation of employees - Coll. Govt; Intermediate consumption - Coll. Govt; Gross operating surplus - Coll. Govt; Net taxes on production - Coll. Govt; Receipts from sales - Coll. Govt." } ], "source_id": "90" }, { "id": "1500000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Fabricated metal products, except machinery and equipment; Electrical and optical equipment; General purpose machinery; Special purpose machinery; Road transport equipment; Other transport equipment; Residential buildings; Non-residential buildings; Civil engineering works; Other products; Change in inventories; Acquisitions less disposals of valuables." } ], "source_id": "90" }, { "id": "1501000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Fabricated metal products, except machinery and equipment; Electrical and optical equipment; General purpose machinery; Special purpose machinery; Road transport equipment; Other transport equipment; Residential buildings; Non-residential buildings; Civil engineering works; Other products." } ], "source_id": "90" }, { "id": "1501100", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Fabricated metal products, except machinery and equipment; Electrical and optical equipment; General purpose machinery; Special purpose machinery; Road transport equipment; Other transport equipment." } ], "source_id": "90" }, { "id": "1501200", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Residential buildings; Non-residential buildings; Civil engineering works." } ], "source_id": "90" }, { "id": "1501300", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Other products." } ], "source_id": "90" }, { "id": "1502000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Change in inventories." } ], "source_id": "90" }, { "id": "1503000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Acquisitions less disposals of valuables." } ], "source_id": "90" }, { "id": "1600000", "metatype": [ { "id": "IndicatorName", "value": "This ICP classification heading covers expenditures for Exports of goods and services; Imports of goods and services." } ], "source_id": "90" }, { "id": "9020000", "metatype": [ { "id": "IndicatorName", "value": "The total value of the individual consumption expenditures of households, nonprofit institutions serving households (NPISHs), and government at purchasers’ prices." } ], "source_id": "90" }, { "id": "9060000", "metatype": [ { "id": "IndicatorName", "value": "Household expenditure on actual and imputed rentals for housing; maintenance and repair of the dwelling; water supply and services related to the dwelling; and electricity, gas, and other fuels plus expenditure by nonprofit institutions serving households (NPISHs) on housing plus general government expenditure on housing services provided to individuals." } ], "source_id": "90" }, { "id": "9080000", "metatype": [ { "id": "IndicatorName", "value": "Household expenditure on pharmaceuticals; medical products, appliances, and equipment; outpatient services; and hospital services plus expenditure of nonprofit institutions serving households (NPISHs) on health plus general government expenditure on health benefits and reimbursements, and the production of health services." } ], "source_id": "90" }, { "id": "9100000", "metatype": [ { "id": "IndicatorName", "value": "The total value of actual and imputed final consumption expenditures incurred by households and NPISHs on individual goods and services. It also includes expenditures on individual goods and services sold at prices that are not economically significant." } ], "source_id": "90" }, { "id": "9110000", "metatype": [ { "id": "IndicatorName", "value": "Household expenditure on audiovisual, photographic, and information processing equipment; other major durables for recreation and culture; other recreational items and equipment; gardens and pets; recreational and cultural services; newspapers, books, and stationery; and package holidays plus expenditure by nonprofit institutions serving households (NPISHs) on recreation and culture plus general government expenditure on recreation and culture" } ], "source_id": "90" }, { "id": "9120000", "metatype": [ { "id": "IndicatorName", "value": "Household expenditure on pre-primary, primary, secondary, postsecondary, and tertiary education plus expenditure of nonprofit institutions serving households (NPISHs) on education plus general government expenditure on education benefits and reimbursements and the production of education services." } ], "source_id": "90" }, { "id": "9140000", "metatype": [ { "id": "IndicatorName", "value": "Household expenditure on personal care, personal effects, social protection, insurance, and financial and other services plus expenditure by nonprofit institutions serving households (NPISHs) on social protection and other services plus general government expenditure on social protection." } ], "source_id": "90" }, { "id": "9250000", "metatype": [ { "id": "IndicatorName", "value": "Actual individual consumption at purchasers’ prices plus collective consumption expenditure by government at purchasers’ prices plus gross capital formation at purchasers’ prices." } ], "source_id": "90" }, { "id": "9260000", "metatype": [ { "id": "IndicatorName", "value": "The total value of actual and imputed final consumption expenditures incurred by households and NPISHs on individual goods and services, without housing related expenditures. It also includes expenditures on individual goods and services sold at prices that are not economically significant." } ], "source_id": "90" }, { "id": "9270000", "metatype": [ { "id": "IndicatorName", "value": "The total value of actual and imputed final consumption expenditures incurred by government on individual goods and services and final consumption expenditure of government on collective goods and services." } ], "source_id": "90" }, { "id": "9280000", "metatype": [ { "id": "IndicatorName", "value": "The total value of actual and imputed final consumption expenditures incurred by households, NPISHs, and government on individual goods and services and final consumption expenditure of government on collective goods and services." } ], "source_id": "90" }, { "id": "ad_hsng_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with adequate housing (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities i.e. with at least a lot of functional difficulty, aged 15 to 29 years who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with disabilities refers to those who report 'a lot of difficulty', or 'unable to do' in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with adequate housing (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities i.e. with at least a lot of functional difficulty, aged 30 to 44 years who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with disabilities refers to those who report 'a lot of difficulty', or 'unable to do' in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with adequate housing (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities i.e. with at least a lot of functional difficulty, aged 45 to 64 years who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with disabilities refers to those who report 'a lot of difficulty', or 'unable to do' in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with adequate housing (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities i.e. with at least a lot of functional difficulty, aged 65 years or older who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with disabilities refers to those who report 'a lot of difficulty', or 'unable to do' in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with adequate housing (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities i.e. with at least a lot of functional difficulty, aged 15+ who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with disabilities refers to those who report 'a lot of difficulty', or 'unable to do' in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with adequate housing (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities i.e. with at least a lot of functional difficulty, aged 15+ who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with disabilities refers to those who report 'a lot of difficulty', or 'unable to do' in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with adequate housing (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities i.e. with at least a lot of functional difficulty, aged 15+ who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with disabilities refers to those who report 'a lot of difficulty', or 'unable to do' in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with adequate housing (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities i.e. with at least a lot of functional difficulty, aged 15+ living in rural areas, who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with disabilities refers to those who report 'a lot of difficulty', or 'unable to do' in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with adequate housing (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities i.e. with at least a lot of functional difficulty, aged 15+ living in urban areas, who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with disabilities refers to those who report 'a lot of difficulty', or 'unable to do' in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with adequate housing (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with any degree of functional difficulty, aged 15 to 29 years who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with any degree of functional difficulty refers to those who report any difficulty (some difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with adequate housing (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with any degree of functional difficulty, aged 30 to 44 years who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with any degree of functional difficulty refers to those who report any difficulty (some difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with adequate housing (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with any degree of functional difficulty, aged 45 to 64 years who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks).Persons with any degree of functional difficulty refers to those who report any difficulty (some difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with adequate housing (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with any degree of functional difficulty, aged 65 or older who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with any degree of functional difficulty refers to those who report any difficulty (some difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with adequate housing (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with any degree of functional difficulty, aged 15+ who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with any degree of functional difficulty refers to those who report any difficulty (some difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with adequate housing (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty in cognition (concentrating/remembering) who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with any degree of functional difficulty in the cognitition function refers to those who report any difficulty (some difficulty, a lot of difficulty, or unable to do) in this domain." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with adequate housing (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty in communication who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with any degree of functional difficulty in communication refers to those who report any difficulty (some difficulty, a lot of difficulty, or unable to do) in this domain." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with adequate housing (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty, aged 15+ who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with any degree of functional difficulty refers to those who report any difficulty (some difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with adequate housing (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty in hearing who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with any degree of functional difficulty in hearing refers to those who report any difficulty (some difficulty, a lot of difficulty, or unable to do) in this domain." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with adequate housing (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty, who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with any degree of functional difficulty refers to those who report any difficulty (some difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with adequate housing (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty in mobility who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with any degree of functional difficulty in the mobility function refers to those who report any difficulty (some difficulty, a lot of difficulty, or unable to do) in this domain." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with adequate housing (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with any degree of functional difficulty, aged 15+ living in rural areas who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with any degree of functional difficulty refers to those who report any difficulty (some difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with adequate housing (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty in seeing who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with any degree of functional difficulty in seeing refers to those who report any difficulty (some difficulty, a lot of difficulty, or unable to do) in this domain." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with adequate housing (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty with selfcare who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with any degree of functional difficulty in the self care function refers to those who report any difficulty (some difficulty, a lot of difficulty, or unable to do) in this domain." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with adequate housing (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with any degree of functional difficulty, aged 15+ living in urban areas who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with any degree of functional difficulty refers to those who report any difficulty (some difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with adequate housing (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no degree of functional difficulty, aged 15+ who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with no degree of functional difficulty refers to those who report no functional difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with adequate housing (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no degree of functional difficulty, aged 15+ who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with no degree of functional difficulty refers to those who report no functional difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with adequate housing (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no degree of functional difficulty, aged 15+ who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with no degree of functional difficulty refers to those who report no functional difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with adequate housing (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 years or older with no degree of functional difficulty, aged 15+ who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with no degree of functional difficulty refers to those who report no functional difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with adequate housing (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with no degree of functional difficulty, aged 15+ who live in the households with adequate housing. Adequate housing is defined as having appropriate quality materials for floors (e.g., cement or tile), roofs (e.g., metal sheets or concrete), and walls (e.g., bricks or cement blocks). Persons with no degree of functional difficulty refers to those who report no functional difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with adequate housing (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no degree of functional difficulty, who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with no degree of functional difficulty refers to those who report no functional difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with adequate housing (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males , aged 15+, with no degree of functional difficulty, who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with no degree of functional difficulty refers to those who report no functional difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with adequate housing (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas, with no degree of functional difficulty, who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with no degree of functional difficulty refers to those who report no functional difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with adequate housing (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+, living in urban areas, with no degree of functional difficulty, who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with no degree of functional difficulty refers to those who report no functional difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with adequate housing (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with none or some degree of functional difficulty who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with adequate housing (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with none or some degree of functional difficulty who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with adequate housing (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with none or some degree of functional difficulty who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with adequate housing (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with none or some degree of functional difficulty who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with adequate housing (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with adequate housing (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty, who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with none or some degree of functional difficulty who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with adequate housing (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with none or some degree of functional difficulty who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with adequate housing (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with none or some degree of functional difficulty who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with adequate housing (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with none or some degree of functional difficulty who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with adequate housing (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with adequate housing (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with adequate housing (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with some degree of functional difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with adequate housing (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with some degree of functional difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with adequate housing (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of functional difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with some degree of functional difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with adequate housing (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with some degree of functional difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with adequate housing (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with some degree of functional difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with adequate housing (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with some degree of functional difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ad_hsng_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with adequate housing (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who live in the households with adequate housing. Adequate housing refers to a household living in a dwelling with appropiate quality materials for floor, roof and wall materials. Persons with some degree of functional difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ad_hsng_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with disabilities i.e. at least a lot of functional difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with disabilities i.e. at least a lot of functional difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with disabilities i.e. at least a lot of functional difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with disabilities i.e. at least a lot of functional difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with disabilities i.e. at least a lot of functional difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with disabilities i.e. at least a lot of functional difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with disabilities i.e. at least a lot of functional difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_cogn_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with cognitive disabilities i.e. at least a lot of functional difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported a lot of difficulty or being unable to perform in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_cognition_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_cogn_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with cognitive disabilities i.e. at least a lot of functional difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported a lot of difficulty or being unable to perform in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_cognition_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_cogn_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with cognitive disabilities i.e. at least a lot of functional difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported a lot of difficulty or being unable to perform in cognition. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_cognition_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_cogn_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with cognitive disabilities i.e. at least a lot of functional difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported a lot of difficulty or being unable to perform in cognition. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_cognition_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_cogn_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with cognitive disabilities i.e. at least a lot of functional difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported a lot of difficulty or being unable to perform in cognition. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_cognition_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_cogn_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with cognitive disabilities i.e. at least a lot of functional difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported a lot of difficulty or being unable to perform in cognition. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_cognition_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with cognitive disabilities i.e. at least a lot of functional difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported a lot of difficulty or being unable to perform in cognition. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_cogn_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with cognitive disabilities i.e. at least a lot of functional difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15+ who reported a lot of difficulty or being unable to perform in cognition. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_cognition_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_cogn_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with cognitive disabilities i.e. at least a lot of functional difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15+ who reported a lot of difficulty or being unable to perform in cognition. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_cognition_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_cogn_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with cognitive disabilities i.e. at least a lot of functional difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+, living in rural areas, who reported a lot of difficulty or being unable to perform in cognition. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_cognition_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_cogn_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with cognitive disabilities i.e. at least a lot of functional difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+, living in urban areas, who reported a lot of difficulty or being unable to perform in cognition. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_cognition_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_comm_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with communication disabilities i.e. at least a lot of functional difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported a lot of difficulty or being unable to perform in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_communicating_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_comm_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with communication disabilities i.e. at least a lot of functional difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported a lot of difficulty or being unable to perform in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_communicating_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_comm_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with communication disabilities i.e. at least a lot of functional difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported a lot of difficulty or being unable to perform in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_communicating_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_comm_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with communication disabilities i.e. at least a lot of functional difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported a lot of difficulty or being unable to perform in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_communicating_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_comm_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with communication disabilities i.e. at least a lot of functional difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported a lot of difficulty or being unable to perform in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_communicating_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_comm_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with communication disabilities i.e. at least a lot of functional difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported a lot of difficulty or being unable to perform in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_communicating_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with communication disabilities i.e. at least a lot of functional difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported a lot of difficulty or being unable to perform in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_comm_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with communication disabilities i.e. at least a lot of functional difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15+ who reported a lot of difficulty or being unable to perform in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_communicating_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_comm_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with communication disabilities i.e. at least a lot of functional difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15+ who reported a lot of difficulty or being unable to perform in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_communicating_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_comm_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with communication disabilities i.e. at least a lot of functional difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ living in rural areas who reported a lot of difficulty or being unable to perform in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_communicating_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_comm_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with communication disabilities i.e. at least a lot of functional difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ living in urban areas who reported a lot of difficulty or being unable to perform in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_communicating_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with disabilities i.e. at least a lot of functional difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15+ who reported a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_hearing_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with hearing disabilities i.e. at least a lot of functional difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported a lot of difficulty or being unable to perform in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_hearing_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_hearing_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with hearing disabilities i.e. at least a lot of functional difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported a lot of difficulty or being unable to perform in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_hearing_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_hearing_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with hearing disabilities i.e. at least a lot of functional difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported a lot of difficulty or being unable to perform in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_hearing_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_hearing_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with hearing disabilities i.e. at least a lot of functional difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported a lot of difficulty or being unable to perform in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_hearing_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_hearing_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with hearing disabilities i.e. at least a lot of functional difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported a lot of difficulty or being unable to perform in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_hearing_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_hearing_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with hearing disabilities i.e. at least a lot of functional difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported a lot of difficulty or being unable to perform in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_hearing_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with hearing disabilities i.e. at least a lot of functional difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported a lot of difficulty or being unable to perform in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_hearing_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with hearing disabilities i.e. at least a lot of functional difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15+ who reported a lot of difficulty or being unable to perform in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_hearing_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_hearing_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with hearing disabilities i.e. at least a lot of functional difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15+ who reported a lot of difficulty or being unable to perform in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_hearing_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_hearing_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with hearing disabilities i.e. at least a lot of functional difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported a lot of difficulty or being unable to perform in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_hearing_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_hearing_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with hearing disabilities i.e. at least a lot of functional difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported a lot of difficulty or being unable to perform in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_hearing_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with disabilities i.e. at least a lot of functional difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who reported a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_mobile_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with mobility disabilities i.e. at least a lot of functional difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported a lot of difficulty or being unable to perform in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_mobile_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_mobile_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with mobility disabilities i.e. at least a lot of functional difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported a lot of difficulty or being unable to perform in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_mobile_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_mobile_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with mobility disabilities i.e. at least a lot of functional difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported a lot of difficulty or being unable to perform in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_mobile_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_mobile_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with mobility disabilities i.e. at least a lot of functional difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported a lot of difficulty or being unable to perform in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_mobile_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_mobile_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with mobility disabilities i.e. at least a lot of functional difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported a lot of difficulty or being unable to perform in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_mobile_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_mobile_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with mobility disabilities i.e. at least a lot of functional difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported a lot of difficulty or being unable to perform in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_mobile_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with mobility disabilities i.e. at least a lot of functional difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported a lot of difficulty or being unable to perform in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_mobile_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with mobility disabilities i.e. at least a lot of functional difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, who reported a lot of difficulty or being unable to perform in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_mobile_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_mobile_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with mobility disabilities i.e. at least a lot of functional difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who reported a lot of difficulty or being unable to perform in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_mobile_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_mobile_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with mobility disabilities i.e. at least a lot of functional difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported a lot of difficulty or being unable to perform in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_mobile_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_mobile_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with mobility disabilities i.e. at least a lot of functional difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported a lot of difficulty or being unable to perform in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_mobile_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with disabilities i.e. at least a lot of functional difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_seeing_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with visual disabilities i.e. at least a lot of functional difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported a lot of difficulty or being unable to perform in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_seeing_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_seeing_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with visual disabilities i.e. at least a lot of functional difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported a lot of difficulty or being unable to perform in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_seeing_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_seeing_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with visual disabilities i.e. at least a lot of functional difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported a lot of difficulty or being unable to perform in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_seeing_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_seeing_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with visual disabilities i.e. at least a lot of functional difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported a lot of difficulty or being unable to perform in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_seeing_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_seeing_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with visual disabilities i.e. at least a lot of functional difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported a lot of difficulty or being unable to perform in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_seeing_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_seeing_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with visual disabilities i.e. at least a lot of functional difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported a lot of difficulty or being unable to perform in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_seeing_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with visual disabilities i.e. at least a lot of functional difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported a lot of difficulty or being unable to perform in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_seeing_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with visual disabilities i.e. at least a lot of functional difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, who reported a lot of difficulty or being unable to perform in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_seeing_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_seeing_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with visual disabilities i.e. at least a lot of functional difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who reported a lot of difficulty or being unable to perform in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_seeing_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_seeing_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with visual disabilities i.e. at least a lot of functional difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported a lot of difficulty or being unable to perform in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_seeing_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_seeing_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with visual disabilities i.e. at least a lot of functional difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported a lot of difficulty or being unable to perform in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_seeing_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_selfcare_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with selfcare disabilities i.e. at least a lot of functional difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported a lot of difficulty or being unable to perform in the selfcare function. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_selfcare_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_selfcare_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with selfcare disabilities i.e. at least a lot of functional difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported a lot of difficulty or being unable to perform in the selfcare function. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_selfcare_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_selfcare_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with selfcare disabilities i.e. at least a lot of functional difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported a lot of difficulty or being unable to perform in the selfcare function. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_selfcare_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_selfcare_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with selfcare disabilities i.e. at least a lot of functional difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported a lot of difficulty or being unable to perform in the selfcare function. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_selfcare_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_selfcare_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with selfcare disabilities i.e. at least a lot of functional difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported a lot of difficulty or being unable to perform in the selfcare function. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_selfcare_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_selfcare_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with selfcare disabilities i.e. at least a lot of functional difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported a lot of difficulty or being unable to perform in the selfcare function. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_selfcare_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with selfcare disabilities i.e. at least a lot of functional difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported a lot of difficulty or being unable to perform in the self care function. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_selfcare_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with selfcare disabilities i.e. at least a lot of functional difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, who reported a lot of difficulty or being unable to perform in the selfcare function. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_selfcare_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_selfcare_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with selfcare disabilities i.e. at least a lot of functional difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who reported a lot of difficulty or being unable to perform in the selfcare function. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_selfcare_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_selfcare_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with selfcare disabilities i.e. at least a lot of functional difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported a lot of difficulty or being unable to perform in the selfcare function. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_selfcare_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_selfcare_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with selfcare disabilities i.e. at least a lot of functional difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported a lot of difficulty or being unable to perform in the selfcare function. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_selfcare_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with disabilities i.e. at least a lot of functional difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with any degree of functional difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with any degree of functional difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with any degree of functional difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with any degree of functional difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with any degree of functional difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with any degree of functional difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with any degree of functional difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, who reported any degree of difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_cogn_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with any degree of cognitive difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_cognition_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_cogn_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with any degree of cognitive difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_cognition_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_cogn_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with any degree of cognitive difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_cognition_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_cogn_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with any degree of cognitive difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_cognition_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_cogn_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with any degree of cognitive difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_cognition_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_cogn_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with any degree of cognitive difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_cognition_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with any degree of cognitive difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported any degree of difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_cogn_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with any degree of cognitive difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15+ who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_cognition_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_cogn_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with any degree of cognitive difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15+ who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_cognition_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_cogn_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with any degree of cognitive difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_cognition_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_cogn_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with any degree of cognitive difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_cognition_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_comm_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with any degree of communication difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_communicating_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_comm_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with any degree of communication difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_communicating_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_comm_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with any degree of communication difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_communicating_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_comm_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with any degree of communication difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_communicating_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_comm_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with any degree of communication difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_communicating_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_comm_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with any degree of communication difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_communicating_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with any degree of communication difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported any degree of difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_comm_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with any degree of communication difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15+ who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_communicating_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_comm_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with any degree of communication difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15+ who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_communicating_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_comm_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with any degree of communication difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_communicating_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_comm_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with any degree of communication difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_communicating_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with any degree of functional difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_hearing_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with any degree of hearing difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_hearing_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_hearing_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with any degree of hearing difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_hearing_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_hearing_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with any degree of hearing difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_hearing_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_hearing_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with any degree of hearing difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_hearing_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_hearing_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with any degree of hearing difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_hearing_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_hearing_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with any degree of hearing difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_hearing_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with any degree of hearing difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported any degree of difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_hearing_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with any degree of hearing difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_hearing_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_hearing_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with any degree of hearing difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_hearing_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_hearing_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with any degree of hearing difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_hearing_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_hearing_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with any degree of hearing difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_hearing_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with any degree of functional difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_mobile_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with any degree of mobility difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_mobile_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_mobile_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with any degree of mobility difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_mobile_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_mobile_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with any degree of mobility difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_mobile_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_mobile_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with any degree of mobility difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_mobile_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_mobile_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with any degree of mobility difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_mobile_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_mobile_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with any degree of mobility difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_mobile_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with any degree of mobility difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported any degree of difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in the mobility function. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_mobile_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with any degree of mobility difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_mobile_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_mobile_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with any degree of mobility difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_mobile_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_mobile_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with any degree of mobility difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_mobile_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_mobile_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with any degree of mobility difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_mobile_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with any degree of functional difficulties (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_seeing_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with any degree of seeing difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_seeing_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_seeing_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with any degree of seeing difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_seeing_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_seeing_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with any degree of seeing difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_seeing_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_seeing_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with any degree of seeing difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_seeing_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_seeing_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with any degree of seeing difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_seeing_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_seeing_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with any degree of seeing difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_seeing_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with any degree of seeing difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported any degree of difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_seeing_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with any degree of seeing difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15+ who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_seeing_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_seeing_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with any degree of seeing difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15+ who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_seeing_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_seeing_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with any degree of seeing difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_seeing_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_seeing_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with any degree of seeing difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_seeing_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_selfcare_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with any degree of selfcare difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_selfcare_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_selfcare_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with any degree of selfcare difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_selfcare_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_selfcare_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with any degree of selfcare difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_selfcare_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_selfcare_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with any degree of selfcare difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_selfcare_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_selfcare_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with any degree of selfcare difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_selfcare_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_selfcare_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with any degree of selfcare difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_selfcare_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with any degree of selfcare difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported any degree of difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in the self care function. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_selfcare_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with any degree of selfcare difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_selfcare_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_selfcare_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with any degree of selfcare difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_selfcare_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_selfcare_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with any degree of selfcare difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_selfcare_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_selfcare_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with any degree of selfcare difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_selfcare_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with any degree of functional difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with some degree of functional difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with some degree of functional difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with some degree of functional difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with some degree of functional difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with some degree of functional difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with some degree of functional difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with some degree of functional difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported some degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_cogn_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with some degree of cognitive difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported some degree of functional difficulty in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_cognition_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_cogn_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with some degree of cognitive difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported some degree of functional difficulty in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_cognition_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_cogn_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with some degree of cognitive difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported some degree of functional difficulty in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_cognition_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_cogn_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with some degree of cognitive difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported some degree of functional difficulty in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_cognition_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_cogn_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with some degree of cognitive difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported some degree of functional difficulty in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_cognition_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_cogn_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with some degree of cognitive difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported some degree of functional difficulty in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_cognition_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with some degree of cognitive difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported some degree of difficulty in the cognition function. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_cogn_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with some degree of cognitive difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, who reported some degree of functional difficulty in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_cognition_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_cogn_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with some degree of cognitive difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who reported some degree of functional difficulty in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_cognition_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_cogn_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with some degree of cognitive difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported some degree of functional difficulty in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_cognition_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_cogn_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with some degree of cognitive difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported some degree of functional difficulty in cognition (concentrating/remembering). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_cognition_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_comm_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with some degree of communication difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported some degree of functional difficulty in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_communicating_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_comm_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with some degree of communication difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported some degree of functional difficulty in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_communicating_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_comm_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with some degree of communication difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported some degree of functional difficulty in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_communicating_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_comm_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with some degree of communication difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported some degree of functional difficulty in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_communicating_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_comm_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with some degree of communication difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported some degree of functional difficulty in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_communicating_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_comm_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with some degree of communication difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported some degree of functional difficulty in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_communicating_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with some degree of communication difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported some degree of difficulty in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_comm_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with some degree of communication difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, who reported some degree of functional difficulty in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_communicating_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_comm_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with some degree of communication difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who reported some degree of functional difficulty in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_communicating_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_comm_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with some degree of communication difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported some degree of functional difficulty in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_communicating_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_comm_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with some degree of communication difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported some degree of functional difficulty in communication. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_communicating_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with some degree of functional difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, who reported some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_hearing_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with some degree of hearing difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported some degree of functional difficulty in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_hearing_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_hearing_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with some degree of hearing difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported some degree of functional difficulty in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_hearing_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_hearing_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with some degree of hearing difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported some degree of functional difficulty in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_hearing_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_hearing_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with some degree of hearing difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported some degree of functional difficulty in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_hearing_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_hearing_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with some degree of hearing difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported some degree of functional difficulty in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_hearing_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_hearing_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with some degree of hearing difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported some degree of functional difficulty in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_hearing_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with some degree of hearing difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported some degree of functional difficulty in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_hearing_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with some degree of hearing difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, who reported some degree of functional difficulty in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_hearing_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_hearing_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with some degree of hearing difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who reported some degree of functional difficulty in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_hearing_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_hearing_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with some degree of hearing difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported some degree of functional difficulty in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_hearing_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_hearing_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with some degree of hearing difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported some degree of functional difficulty in hearing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_hearing_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with some degree of functional difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who reported some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_mobile_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with some degree of mobility difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported some degree of functional difficulty in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_mobile_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_mobile_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with some degree of mobility difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported some degree of functional difficulty in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_mobile_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_mobile_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with some degree of mobility difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported some degree of functional difficulty in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_mobile_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_mobile_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with some degree of mobility difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported some degree of functional difficulty in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_mobile_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_mobile_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with some degree of mobility difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported some degree of functional difficulty in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_mobile_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_mobile_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with some degree of mobility difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported some degree of functional difficulty in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_mobile_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with some degree of mobility difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported some degree of difficulty in the mobility function. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_mobile_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with some degree of mobility difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, who reported some degree of functional difficulty in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_mobile_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_mobile_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with some degree of mobility difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who reported some degree of functional difficulty in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_mobile_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_mobile_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with some degree of mobility difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported some degree of functional difficulty in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_mobile_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_mobile_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with some degree of mobility difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported some degree of functional difficulty in mobility (walking/climbing stairs). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_mobile_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with some degree of functional difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_seeing_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with some degree of seeing difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported some degree of functional difficulty in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_seeing_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_seeing_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with some degree of seeing difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported some degree of functional difficulty in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_seeing_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_seeing_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with some degree of seeing difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported some degree of functional difficulty in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_seeing_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_seeing_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with some degree of seeing difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported some degree of functional difficulty in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_seeing_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_seeing_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with some degree of seeing difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported some degree of functional difficulty in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_seeing_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_seeing_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with some degree of seeing difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported some degree of functional difficulty in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_seeing_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with some degree of seeing difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported some degree of difficulty in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_seeing_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with some degree of seeing difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, who reported some degree of functional difficulty in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_seeing_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_seeing_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with some degree of seeing difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who reported some degree of functional difficulty in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_seeing_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_seeing_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with some degree of seeing difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported some degree of functional difficulty in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_seeing_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_seeing_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with some degree of seeing difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported some degree of functional difficulty in seeing. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_seeing_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_selfcare_1524", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 15 to 24 years with some degree of selfcare difficulty (% of persons aged 15 to 24 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who reported some degree of functional difficulty with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_selfcare_15to24" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_selfcare_2534", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 25 to 34 years with some degree of selfcare difficulty (% of persons aged 25 to 34 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 25 to 34 years who reported some degree of functional difficulty with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_selfcare_25to34" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_selfcare_3544", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 35 to 44 years with some degree of selfcare difficulty (% of persons aged 35 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 35 to 44 years who reported some degree of functional difficulty with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_selfcare_35to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_selfcare_4554", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 45 to 54 years with some degree of selfcare difficulty (% of persons aged 45 to 54 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 54 years who reported some degree of functional difficulty with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_selfcare_45to54" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_selfcare_5564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 55 to 64 years with some degree of selfcare difficulty (% of persons aged 55 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 55 to 64 years who reported some degree of functional difficulty with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_selfcare_55to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_selfcare_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons aged 65 or older with some degree of selfcare difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who reported some degree of functional difficulty with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_selfcare_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons with some degree of selfcare difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported some degree of difficulty in the self care function. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_selfcare_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of female persons with some degree of selfcare difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, who reported some degree of functional difficulty with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_selfcare_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_selfcare_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of male persons with some degree of selfcare difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who reported some degree of functional difficulty with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_selfcare_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_selfcare_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in rural areas with some degree of selfcare difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who reported some degree of functional difficulty with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_selfcare_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_selfcare_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with some degree of selfcare difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported some degree of functional difficulty with selfcare. This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_selfcare_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "adj_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Adjusted prevalence of persons living in urban areas with some degree of functional difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who reported some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This estimate controls for age and sex differences across countries by first calculating prevalence rates for each sex-age group (12 groups). These rates are then weighted according to the proportion of each age-sex group in the total population aged 15+ in each country, using UN population data." }, { "id": "Previous_Indicator_Code", "value": "adj_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 15 to 29 years with disabilities i.e. at least a lot of functional difficulty (% of persons aged 15 to 29 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 30 to 44 years with disabilities i.e. at least a lot of functional difficulty (% of persons aged 30 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 45 to 64 years with disabilities i.e. at least a lot of functional difficulty (% of persons aged 45 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 65 or older with disabilities i.e. at least a lot of functional difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons with disabilities i.e. at least a lot of functional difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). These rates have not been adjusted." }, { "id": "Previous_Indicator_Code", "value": "al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of female persons with disabilities i.e. at least a lot of functional difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of male persons with disabilities i.e. at least a lot of functional difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in rural areas with disabilities i.e. at least a lot of functional difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in urban areas with disabilities i.e. at least a lot of functional difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with at least primary education (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with at least primary education (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with at least primary education (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with at least primary education (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least primary education (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with at least primary education (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15+ who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with at least primary education (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15+ who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with at least primary education (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with at least primary education (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with at least primary education (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with at least primary education (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with at least primary education (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with at least primary education (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least primary education (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least primary education (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition." }, { "id": "Previous_Indicator_Code", "value": "al_prim_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least primary education (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication." }, { "id": "Previous_Indicator_Code", "value": "al_prim_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with at least primary education (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15+ who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least primary education (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing." }, { "id": "Previous_Indicator_Code", "value": "al_prim_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with at least primary education (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15+ who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least primary education (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with at least primary education (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least primary education (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who completed primary school (Persons who have completed primary school but did not complete secondary school are included in this category)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least primary education (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who completed primary school (Persons who have completed primary school but did not complete secondary school are included in this category)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with at least primary education (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who (i) completed primary school (persons who have completed primary school but did not complete secondary school are included in this category); and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with at least primary education (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with at least primary education (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with at least primary education (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with at least primary education (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least primary education (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty who completed primary school (Persons who have completed primary school but did not complete secondary school are included in this category). Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with at least primary education (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with at least primary education (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with at least primary education (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with at least primary education (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with at least primary education (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with at least primary education (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with at least primary education (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with at least primary education (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least primary education (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with at least primary education (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15+ with none or some degree of functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with at least primary education (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15+ with none or some degree of functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with at least primary education (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with at least primary education (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with at least primary education (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with at least primary education (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with at least primary education (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with at least primary education (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least primary education (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of disability who completed primary school (Persons who have completed primary school but did not complete secondary school are included in this category). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with at least primary education (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with at least primary education (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with at least primary education (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_prim_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with at least primary education (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who completed primary school (persons who have completed primary school but did not complete secondary school are included in this category). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_prim_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with at least secondary education (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years who (i) have completed secondary school, whether or not they also attended tertiary school and who (ii) who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with at least secondary education (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years who (i) have completed secondary school, whether or not they also attended tertiary school and who (ii) who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with at least secondary education (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years who (i) have completed secondary school, whether or not they also attended tertiary school and who (ii) who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with at least secondary education (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who (i) have completed secondary school, whether or not they also attended tertiary school and who (ii) who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least secondary education (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ who have completed secondary school, whether or not they also attended tertiary school. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with at least secondary education (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities who have completed secondary school, whether or not they also attended tertiary school. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with at least secondary education (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities who have completed secondary school, whether or not they also attended tertiary school. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with at least secondary education (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in rural areas who have completed secondary school, whether or not they also attended tertiary school. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with at least secondary education (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas who have completed secondary school, whether or not they also attended tertiary school. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with at least secondary education (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years who (i) have completed secondary school, whether or not they also attended tertiary school; and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with at least secondary education (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years who (i) have completed secondary school, whether or not they also attended tertiary school; and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with at least secondary education (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years who (i) have completed secondary school, whether or not they also attended tertiary school; and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with at least secondary education (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who (i) have completed secondary school, whether or not they also attended tertiary school; and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least secondary education (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) have completed secondary school, whether or not they also attended tertiary school; and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least secondary education (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition who have completed secondary school, whether or not they also attended tertiary school." }, { "id": "Previous_Indicator_Code", "value": "al_seco_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least secondary education (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who have completed secondary school, whether or not they also attended tertiary school." }, { "id": "Previous_Indicator_Code", "value": "al_seco_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with at least secondary education (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, who (i) have completed secondary school, whether or not they also attended tertiary school; and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least secondary education (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who have completed secondary school, whether or not they also attended tertiary school." }, { "id": "Previous_Indicator_Code", "value": "al_seco_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with at least secondary education (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who (i) have completed secondary school, whether or not they also attended tertiary school; and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least secondary education (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who have completed secondary school, whether or not they also attended tertiary school." }, { "id": "Previous_Indicator_Code", "value": "al_seco_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with at least secondary education (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who (i) have completed secondary school, whether or not they also attended tertiary school; and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least secondary education (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who have completed secondary school, whether or not they also attended tertiary school." }, { "id": "Previous_Indicator_Code", "value": "al_seco_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least secondary education (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who have completed secondary school, whether or not they also attended tertiary school." }, { "id": "Previous_Indicator_Code", "value": "al_seco_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with at least secondary education (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who (i) have completed secondary school, whether or not they also attended tertiary school; and who (ii) report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with at least secondary education (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty with no functional difficulty who completed secondary school, whether or not they also attended tertiary school. Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with at least secondary education (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty with no functional difficulty who completed secondary school, whether or not they also attended tertiary school. Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with at least secondary education (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty with no functional difficulty who completed secondary school, whether or not they also attended tertiary school. Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with at least secondary education (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty who with no functional difficulty who completed secondary school, whether or not they also attended tertiary school. Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least secondary education (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional diffculty who have completed secondary school, whether or not they also attended tertiary school. Persons with no functional difficulty are those who report no functional difficulty at all in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with at least secondary education (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty who completed secondary school, whether or not they also attended tertiary school. Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with at least secondary education (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty who completed secondary school, whether or not they also attended tertiary school. Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with at least secondary education (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty who completed secondary school, whether or not they also attended tertiary school. Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with at least secondary education (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty who completed secondary school, whether or not they also attended tertiary school. Persons with no functional difficulty are those who report no difficulty at all in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with at least secondary education (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with at least secondary education (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with at least secondary education (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with at least secondary education (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least secondary education (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with at least secondary education (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with at least secondary education (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with at least secondary education (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with at least secondary education (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with at least secondary education (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with at least secondary education (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with at least secondary education (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with at least secondary education (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with at least secondary education (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of functional difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with some degree of functional difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with at least secondary education (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with at least secondary education (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with at least secondary education (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "al_seco_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with at least secondary education (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who have completed secondary school, whether or not they also attended tertiary school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "al_seco_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 15 to 29 years with any degree of functional difficulties (% of persons aged 15 to 29 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 30 to 44 years any degree of functional difficulties (% of persons aged 30 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 45 to 64 years any degree of functional difficulties (% of persons aged 45 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 65 or older any degree of functional difficulties (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons with any degree of functional difficulties (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_cogn_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 15 to 29 years with any degree of cognitive difficulty (% of persons aged 15 to 29 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_cognition_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_cogn_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 30 to 44 years with any degree of cognitive difficulty (% of persons aged 30 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_cognition_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_cogn_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 45 to 64 years with any degree of cognitive difficulty (% of persons aged 45 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_cognition_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_cogn_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 65 or older with any degree of cognitive difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_cognition_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons with any degree of cognitive difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_cogn_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of female persons with any degree of cognitive difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_cognition_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_cogn_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of male persons with any degree of cognitive difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_cognition_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_cogn_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in rural areas with any degree of cognitive difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_cognition_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_cogn_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in urban areas with any degree of cognitive difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_cognition_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_comm_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 15 to 29 years with any degree of communication difficulty (% of persons aged 15 to 29 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_communicating_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_comm_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 30 to 44 years with any degree of communication difficulty (% of persons aged 30 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_communicating_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_comm_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 45 to 64 years with any degree of communication difficulty (% of persons aged 45 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_communicating_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_comm_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 65 or older with any degree of communication difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_communicating_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons with any degree of communication difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_comm_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of female persons with any degree of communication difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_communicating_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_comm_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of male persons with any degree of communication difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_communicating_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_comm_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in rural areas with any degree of communication difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_communicating_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_comm_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in urban areas with any degree of communication difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_communicating_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of female persons with any degree of functional difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_hearing_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 15 to 29 years with any degree of hearing difficulty (% of persons aged 15 to 29 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_hearing_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_hearing_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 30 to 44 years with any degree of hearing difficulty (% of persons aged 30 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_hearing_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_hearing_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 45 to 64 years with any degree of hearing difficulty (% of persons aged 45 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_hearing_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_hearing_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 65 or older with any degree of hearing difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_hearing_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons with any degree of hearing difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_hearing_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of female persons with any degree of hearing difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_hearing_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_hearing_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of male persons with any degree of hearing difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_hearing_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_hearing_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in rural areas with any degree of hearing difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_hearing_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_hearing_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in urban areas with any degree of hearing difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_hearing_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of male persons with any degree of functional difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_mobile_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 15 to 29 years with any degree of mobility difficulty (% of persons aged 15 to 29 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_mobile_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_mobile_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 30 to 44 years with any degree of mobility difficulty (% of persons aged 30 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_mobile_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_mobile_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 45 to 64 years with any degree of mobility difficulty (% of persons aged 45 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_mobile_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_mobile_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 65 or older with any degree of mobility difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_mobile_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons with any degree of mobility difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_mobile_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of female persons with any degree of mobility difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_mobile_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_mobile_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of male persons with any degree of mobility difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_mobile_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_mobile_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in rural areas with any degree of mobility difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_mobile_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_mobile_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in urban areas with any degree of mobility difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_mobile_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in rural areas with any degree of functional difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_seeing_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 15 to 29 years with any degree of seeing difficulty (% of persons aged 15 to 29 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_seeing_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_seeing_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 30 to 44 years with any degree of seeing difficulty (% of persons aged 30 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_seeing_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_seeing_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 45 to 64 years with any degree of seeing difficulty (% of persons aged 45 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_seeing_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_seeing_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 65 or older with any degree of seeing difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_seeing_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons with any degree of seeing difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_seeing_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of female persons with any degree of seeing difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_seeing_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_seeing_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of male persons with any degree of seeing difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_seeing_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_seeing_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in rural areas with any degree of seeing difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_seeing_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_seeing_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in urban areas with any degree of seeing difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_seeing_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_selfcare_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 15 to 29 years with any degree of selfcare difficulty (% of persons aged 15 to 29 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_selfcare_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_selfcare_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 30 to 44 years with any degree of selfcare difficulty (% of persons aged 30 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_selfcare_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_selfcare_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 45 to 64 years with any degree of selfcare difficulty (% of persons aged 45 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_selfcare_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_selfcare_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 65 or older with any degree of selfcare difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_selfcare_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons with any degree of selfcare difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_selfcare_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of female persons with any degree of selfcare difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_selfcare_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_selfcare_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of male persons with any degree of selfcare difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_selfcare_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_selfcare_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in rural areas with any degree of selfcare difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_selfcare_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_selfcare_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in urban areas with any degree of selfcare difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_selfcare_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in urban areas with any degree of functional difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women with disabilities aged 15-29 who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women with disabilities aged 30-44 who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with disabilities who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in rural areas with disabilities who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in urban areas with disabilities who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-29 with any degree of functional difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 30-44 with any degree of functional difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with any degree of functional difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual." }, { "id": "Previous_Indicator_Code", "value": "avlc_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual." }, { "id": "Previous_Indicator_Code", "value": "avlc_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual." }, { "id": "Previous_Indicator_Code", "value": "avlc_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual." }, { "id": "Previous_Indicator_Code", "value": "avlc_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in rural areas with any degree of functional difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual." }, { "id": "Previous_Indicator_Code", "value": "avlc_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual." }, { "id": "Previous_Indicator_Code", "value": "avlc_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in urban areas with any degree of functional difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-29 with no functional difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 30-44 with no functional difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with no functional difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in rural areas with no functional difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in urban areas with no functional difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-29 with none or some degree of functional difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 30-44 with none or some degree of functional difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with none or some degree of functional difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in rural areas with none or some degree of functional difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in urban areas with none or some degree of functional difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-29 with some degree of difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 30-44 with some degree of difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with some degree of difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in rural areas with some degree of difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "avlc_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women subjected to violence in the last 12 months (% of ever-partnered women living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in urban areas with some degree of difficulty who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "avlc_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with clean cooking fuel (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15 to 29 years who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with clean cooking fuel (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 30 to 44 years who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with clean cooking fuel (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 45 to 64 years who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with clean cooking fuel (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 65 or older who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with clean cooking fuel (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with clean cooking fuel (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with clean cooking fuel (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males with disabilities, aged 15+, who live in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with clean cooking fuel (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in rural areas who live in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with clean cooking fuel (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with clean cooking fuel (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with clean cooking fuel (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with clean cooking fuel (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with clean cooking fuel (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with clean cooking fuel (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with clean cooking fuel (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with clean cooking fuel (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with clean cooking fuel (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty who live in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with clean cooking fuel (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with clean cooking fuel (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with clean cooking fuel (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with clean cooking fuel (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with clean cooking fuel (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with clean cooking fuel (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with clean cooking fuel (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with clean cooking fuel (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with clean cooking fuel (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with clean cooking fuel (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with clean cooking fuel (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with clean cooking fuel (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with clean cooking fuel (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with clean cooking fuel (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with clean cooking fuel (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with clean cooking fuel (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with clean cooking fuel (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with clean cooking fuel (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with clean cooking fuel (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with clean cooking fuel (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with clean cooking fuel (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with clean cooking fuel (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with clean cooking fuel (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with clean cooking fuel (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with clean cooking fuel (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with clean cooking fuel (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with clean cooking fuel (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with clean cooking fuel (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with clean cooking fuel (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with clean cooking fuel (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with clean cooking fuel (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with clean cooking fuel (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with clean cooking fuel (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cfuel_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with clean cooking fuel (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who lives in a household with clean cooking fuel, which includes electricity and gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung, charcoal, and coal). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cfuel_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years using a computer (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15 to 29 years who use computer for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years using a computer (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 30 to 44 years who use computer for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years using a computer (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 45 to 64 years who use computer for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older using a computer (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 65 or older who use computer for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using a computer (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ who use computer for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons using a computer (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities who use computer for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons using a computer (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities who use computer for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas using a computer (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities , aged 15+, living in rural areas who use computer for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas using a computer (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas who use computer for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years using a computer (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty who use computer for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years using a computer (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty who use computer for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years using a computer (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty who use computer for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older using a computer (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty who use computer for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using a computer (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty who use computer for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using a computer (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who use computer for the past three months." }, { "id": "Previous_Indicator_Code", "value": "cmpu_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using a computer (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who use computer for the past three months." }, { "id": "Previous_Indicator_Code", "value": "cmpu_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons using a computer (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty who use computer for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using a computer (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who use computer for the past three months." }, { "id": "Previous_Indicator_Code", "value": "cmpu_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons using a computer (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty who use computer for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using a computer (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who use computer for the past three months." }, { "id": "Previous_Indicator_Code", "value": "cmpu_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas using a computer (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty who use computer for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using a computer (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who use computer for the past three months." }, { "id": "Previous_Indicator_Code", "value": "cmpu_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using a computer (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who use computer for the past three months." }, { "id": "Previous_Indicator_Code", "value": "cmpu_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas using a computer (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty who use computer for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years using a computer (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty who use computer for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years using a computer (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty who use computer for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years using a computer (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty who use computer for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older using a computer (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty who use computer for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using a computer (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty who use computer for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons using a computer (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty who use computer for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons using a computer (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty who use computer for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas using a computer (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty who use computer for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas using a computer (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty who use computer for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years using a computer (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who use computer for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years using a computer (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who use computer for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years using a computer (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who use computer for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older using a computer (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty who use computer for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using a computer (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty who use computer for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons using a computer (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty who use computer for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons using a computer (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty who use computer for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas using a computer (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who use computer for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas using a computer (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who use computer for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years using a computer (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who use computer for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years using a computer (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who use computer for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years using a computer (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who use computer for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older using a computer (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty who use computer for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using a computer (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of difficulty who use computer for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons using a computer (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who use computer for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons using a computer (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who use computer for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas using a computer (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who use computer for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "cmpu_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas using a computer (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who use computer for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "cmpu_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years who have ever attended school (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15 to 29 years who have ever been to school. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years who have ever attended school (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 30 to 44 years who have ever been to school. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years who have ever attended school (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 45 to 64 years who have ever been to school. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older who have ever attended school (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 65 or older who have ever been to school. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons who have ever attended school (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ who have ever been to school. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons who have ever attended school (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities who have ever been to school. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons who have ever attended school (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities who have ever been to school. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas who have ever attended school (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in rural areas who have ever been to school. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas who have ever attended school (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas who have ever been to school. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years who have ever attended school (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty who have ever been to school. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years who have ever attended school (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty who have ever been to school. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years who have ever attended school (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty who have ever been to school. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older who have ever attended school (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty who have ever been to school. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons who have ever attended school (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty who have ever been to school. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons who have ever attended school (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who have ever been to school." }, { "id": "Previous_Indicator_Code", "value": "eatn_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons who have ever attended school (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who have ever been to school." }, { "id": "Previous_Indicator_Code", "value": "eatn_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons who have ever attended school (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty who have ever been to school. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons who have ever attended school (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who have ever been to school." }, { "id": "Previous_Indicator_Code", "value": "eatn_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons who have ever attended school (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty who have ever been to school. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons who have ever attended school (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who have ever been to school." }, { "id": "Previous_Indicator_Code", "value": "eatn_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas who have ever attended school (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty who have ever been to school. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons who have ever attended school (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who have ever been to school." }, { "id": "Previous_Indicator_Code", "value": "eatn_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons who have ever attended school (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who have ever been to school." }, { "id": "Previous_Indicator_Code", "value": "eatn_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas who have ever attended school (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty who have ever been to school. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years who have ever attended school (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty who have ever been to school. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years who have ever attended school (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty who have ever been to school. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years who have ever attended school (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty who have ever been to school. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older who have ever attended school (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty who have ever been to school. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons who have ever attended school (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty who have ever been to school. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons who have ever attended school (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty who have ever been to school. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons who have ever attended school (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty who have ever been to school. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas who have ever attended school (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty who have ever been to school. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas who have ever attended school (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty who have ever been to school. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years who have ever attended school (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who have ever been to school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years who have ever attended school (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who have ever been to school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years who have ever attended school (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who have ever been to school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older who have ever attended school (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty who have ever been to school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons who have ever attended school (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty who have ever been to school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons who have ever attended school (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty who have ever been to school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons who have ever attended school (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty who have ever been to school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas who have ever attended school (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who have ever been to school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas who have ever attended school (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who have ever been to school. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years who have ever attended school (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who have ever been to school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years who have ever attended school (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who have ever been to school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years who have ever attended school (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who have ever been to school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older who have ever attended school (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty who have ever been to school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons who have ever attended school (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of difficulty who have ever been to school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons who have ever attended school (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who have ever been to school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons who have ever attended school (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who have ever been to school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas who have ever attended school (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who have ever been to school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "eatn_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas who have ever attended school (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who have ever been to school. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "eatn_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with electricity (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15 to 29 years who live in households with electricity. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with electricity (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 30 to 44 years who live in households with electricity. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with electricity (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 45 to 64 years who live in households with electricity. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with electricity (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 65 or older who live in households with electricity. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with electricity (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ who live in households with electricity. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with electricity (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities who live in households with electricity. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with electricity (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities who live in households with electricity. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with electricity (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in rural areas who live in households with electricity. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with electricity (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas who live in households with electricity. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with electricity (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty who live in households with electricity. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with electricity (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty who live in households with electricity. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with electricity (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty who live in households with electricity. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with electricity (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty who live in households with electricity. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with electricity (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty who live in households with electricity. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with electricity (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who live in households with electricity." }, { "id": "Previous_Indicator_Code", "value": "elc_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with electricity (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who live in households with electricity." }, { "id": "Previous_Indicator_Code", "value": "elc_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with electricity (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty who live in households with electricity. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with electricity (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who live in households with electricity." }, { "id": "Previous_Indicator_Code", "value": "elc_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with electricity (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty who live in households with electricity. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with electricity (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who live in households with electricity." }, { "id": "Previous_Indicator_Code", "value": "elc_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with electricity (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty who live in households with electricity. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with electricity (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who live in households with electricity." }, { "id": "Previous_Indicator_Code", "value": "elc_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with electricity (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who live in households with electricity." }, { "id": "Previous_Indicator_Code", "value": "elc_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with electricity (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty who live in households with electricity. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with electricity (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty who live in households with electricity. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with electricity (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty who live in households with electricity. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with electricity (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty who live in households with electricity. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with electricity (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty who live in households with electricity. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with electricity (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty who live in households with electricity. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with electricity (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty who live in households with electricity. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with electricity (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty who live in households with electricity. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with electricity (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty who live in households with electricity. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with electricity (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty who live in households with electricity. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with electricity (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who live in households with electricity. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with electricity (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who live in households with electricity. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with electricity (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who live in households with electricity. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with electricity (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty who live in households with electricity. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with electricity (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty who live in households with electricity. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with electricity (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty who live in households with electricity. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with electricity (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty who live in households with electricity. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with electricity (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who live in households with electricity. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with electricity (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who live in households with electricity. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with electricity (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who live in households with electricity. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with electricity (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who live in households with electricity. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with electricity (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who live in households with electricity. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with electricity (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty who live in households with electricity. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with electricity (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of difficulty who live in households with electricity. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with electricity (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who live in households with electricity. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with electricity (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who live in households with electricity. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with electricity (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who live in households with electricity. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "elc_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with electricity (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who live in households with electricity. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "elc_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15 to 29 years who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 30 to 44 years who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 45 to 64 years who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 65 or older who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in rural areas who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid)." }, { "id": "Previous_Indicator_Code", "value": "empl_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid)." }, { "id": "Previous_Indicator_Code", "value": "empl_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid)." }, { "id": "Previous_Indicator_Code", "value": "empl_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid)." }, { "id": "Previous_Indicator_Code", "value": "empl_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid)." }, { "id": "Previous_Indicator_Code", "value": "empl_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid)." }, { "id": "Previous_Indicator_Code", "value": "empl_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "empl_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Employment to population ratio (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid). Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "empl_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in food insecure households (% persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15 to 29 years living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in food insecure households (% persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 30 to 44 years living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in food insecure households (% persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 45 to 64 years living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in food insecure households (% persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 65 or older living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in food insecure households (% persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in food insecure households (% female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in food insecure households (% male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in food insecure households (% persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in rural areas living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in food insecure households (% persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in food insecure households (% persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in food insecure households (% persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in food insecure households (% persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in food insecure households (% persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in food insecure households (% persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in food insecure households (% persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household." }, { "id": "Previous_Indicator_Code", "value": "fdis_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in food insecure households (% persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household." }, { "id": "Previous_Indicator_Code", "value": "fdis_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in food insecure households (% female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in food insecure households (% persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household." }, { "id": "Previous_Indicator_Code", "value": "fdis_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in food insecure households (% male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in food insecure households (% persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household." }, { "id": "Previous_Indicator_Code", "value": "fdis_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in food insecure households (% persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in food insecure households (% persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household." }, { "id": "Previous_Indicator_Code", "value": "fdis_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in food insecure households (% persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household." }, { "id": "Previous_Indicator_Code", "value": "fdis_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in food insecure households (% persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in food insecure households (% persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in food insecure households (% persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in food insecure households (% persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in food insecure households (% persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in food insecure households (% persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in food insecure households (% female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in food insecure households (% male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in food insecure households (% persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in food insecure households (% persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in food insecure households (% persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in food insecure households (% persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in food insecure households (% persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in food insecure households (% persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in food insecure households (% persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in food insecure households (% female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in food insecure households (% male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in food insecure households (% persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in food insecure households (% persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in food insecure households (% persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in food insecure households (% persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in food insecure households (% persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in food insecure households (% persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in food insecure households (% persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in food insecure households (% female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in food insecure households (% male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in food insecure households (% persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fdis_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in food insecure households (% persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty living in households where, recently (in the past week, month, or 12 months), they worried about not having enough food or faced a situation where there was not enough food to feed the household. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fdis_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women with disabilities aged 15-29 who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women with disabilities aged 30-44 who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women with disabilities aged 15-44 who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women with disabilities aged 15-44 living in rural areas who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women with disabilities aged 15-44 living in urban areas who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-29 with any degree of functional difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 30-44 with any degree of functional difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with any degree of functional difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods." }, { "id": "Previous_Indicator_Code", "value": "fplm_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods." }, { "id": "Previous_Indicator_Code", "value": "fplm_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods." }, { "id": "Previous_Indicator_Code", "value": "fplm_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods." }, { "id": "Previous_Indicator_Code", "value": "fplm_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in rural areas with any degree of functional difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods." }, { "id": "Previous_Indicator_Code", "value": "fplm_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods." }, { "id": "Previous_Indicator_Code", "value": "fplm_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in urban areas with any degree of functional difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-29 with no functional difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with no functional difficulty are those who report no disability at all in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 30-44 with no functional difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with no functional difficulty are those who report no disability at all in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with no functional difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with no functional difficulty are those who report no disability at all in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in rural areas with no functional difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with no functional difficulty are those who report no disability at all in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in urban areas with no functional difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with no functional difficulty are those who report no disability at all in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-29 with none or some degree of functional difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 30-44 with none or some degree of functional difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with none or some degree of functional difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in rural areas with none or some degree of functional difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in urban areas with none or some degree of functional difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-29 with some degree of difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 30-44 with some degree of difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 with some degree of difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in rural areas with some degree of difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "fplm_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women with family planning needs met (% of women living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-44 living in urban areas with some degree of difficulty who self-report that they have their family planning needs met, i.e. they want and have access to modern contraceptive methods. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "fplm_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households using improved drinking water sources (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15 to 29 years who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households using improved drinking water sources (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 30 to 44 years who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households using improved drinking water sources (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 45 to 64 years who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households using improved drinking water sources (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 65 or older who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved drinking water sources (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households using improved drinking water sources (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households using improved drinking water sources (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households using improved drinking water sources (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in rural areas who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households using improved drinking water sources (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households using improved drinking water sources (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households using improved drinking water sources (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households using improved drinking water sources (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households using improved drinking water sources (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved drinking water sources (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved drinking water sources (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water." }, { "id": "Previous_Indicator_Code", "value": "h2o_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved drinking water sources (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water." }, { "id": "Previous_Indicator_Code", "value": "h2o_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households using improved drinking water sources (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved drinking water sources (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water." }, { "id": "Previous_Indicator_Code", "value": "h2o_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households using improved drinking water sources (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved drinking water sources (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water." }, { "id": "Previous_Indicator_Code", "value": "h2o_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households using improved drinking water sources (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved drinking water sources (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water." }, { "id": "Previous_Indicator_Code", "value": "h2o_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved drinking water sources (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water." }, { "id": "Previous_Indicator_Code", "value": "h2o_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households using improved drinking water sources (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households using improved drinking water sources (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households using improved drinking water sources (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households using improved drinking water sources (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households using improved drinking water sources (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved drinking water sources (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households using improved drinking water sources (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households using improved drinking water sources (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households using improved drinking water sources (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households using improved drinking water sources (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households using improved drinking water sources (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households using improved drinking water sources (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households using improved drinking water sources (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households using improved drinking water sources (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved drinking water sources (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households using improved drinking water sources (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households using improved drinking water sources (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households using improved drinking water sources (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households using improved drinking water sources (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households using improved drinking water sources (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households using improved drinking water sources (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households using improved drinking water sources (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households using improved drinking water sources (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved drinking water sources (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households using improved drinking water sources (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households using improved drinking water sources (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households using improved drinking water sources (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "h2o_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households using improved drinking water sources (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who live in households using improved drinking water sources. Improved drinking-water sources are defined as those that are likely to be protected from outside contamination, and from faecal matter in particular. Improved water sources include household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection. Unimproved water sources include unprotected wells, unprotected springs, surface water (e.g. river, dam or lake), vendor-provided water, bottled water (unless water for other uses is available from an improved source) and tanker truck-provided water. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "h2o_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households with a mobility phone (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15 to 29 years who live in the households with a mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households with a mobility phone (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 30 to 44 years who live in the households with a mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households with a mobility phone (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 45 to 64 years who live in the households with a mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households with a mobility phone (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 65 or older who live in the households with a mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households with a mobility phone (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ who live in the households with a mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households with a mobility phone (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities who live in the households with a mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households with a mobility phone (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities who live in the households with a mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households with a mobility phone (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in rural areas who live in the households with a mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households with a mobility phone (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas who live in the households with a mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households with a mobility phone (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty who live in the households with a mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households with a mobility phone (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty who live in the households with a mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households with a mobility phone (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty who live in the households with a mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households with a mobility phone (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty who live in the households with a mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households with a mobility phone (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty who live in the households with a mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households with a mobility phone (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who live in the households with a mobile phone." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households with a mobility phone (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who live in the households with a mobile phone." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households with a mobility phone (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty who live in the households with a mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households with a mobility phone (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who live in the households with a mobile phone." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households with a mobility phone (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty who live in the households with a mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households with a mobility phone (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who live in the households with a mobile phone." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households with a mobility phone (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty who live in the households with a mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households with a mobility phone (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who live in the households with a mobile phone." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households with a mobility phone (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who live in the households with a mobile phone." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households with a mobility phone (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty who live in the households with a mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households with a mobility phone (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty who live in the households with a mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households with a mobility phone (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty who live in the households with a mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households with a mobility phone (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty who live in the households with a mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households with a mobility phone (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty who live in the households with a mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households with a mobility phone (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty who live in the households with a mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households with a mobility phone (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty who live in the households with a mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households with a mobility phone (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty who live in the households with a mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households with a mobility phone (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty who live in the households with a mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households with a mobility phone (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty who live in the households with a mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households with a mobility phone (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who live in the households with a mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households with a mobility phone (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who live in the households with a mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households with a mobility phone (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who live in the households with a mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households with a mobility phone (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty who live in the households with a mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households with a mobility phone (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty who live in the households with a mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households with a mobility phone (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty who live in the households with a mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households with a mobility phone (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty who live in the households with a mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households with a mobility phone (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who live in the households with a mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households with a mobility phone (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who live in the households with a mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households with a mobility phone (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who live in the households with a mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households with a mobility phone (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who live in the households with a mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households with a mobility phone (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who live in the households with a mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households with a mobility phone (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty who live in the households with a mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households with a mobility phone (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of difficulty who live in the households with a mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households with a mobility phone (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who live in the households with a mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households with a mobility phone (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who live in the households with a mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households with a mobility phone (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who live in the households with a mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_cel_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households with a mobility phone (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who live in the households with a mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_cel_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_prev_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of households that have persons with disability i.e. at least a lot of functional difficulty (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of households who have at least an adult household member with disability. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_prev_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_prev_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of households in rural areas that have persons with disability i.e. at least a lot of functional difficulty (% of households in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of households in rural areas who have at least an adult household member with disability. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_prev_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_prev_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of households in urban areas that have persons with disability i.e. at least a lot of functional difficulty (% of households in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of households in urban areas who have at least an adult household member with disability. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_prev_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_prev_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of households that have persons with any degree of functional difficulty (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of households who have at least an adult household member with any degree of functional difficulty. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_prev_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_prev_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of households in rural areas that have persons with any degree of functional difficulty (% of households in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of households in rural areas who have at least an adult household member with any degree of functional difficulty. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_prev_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_prev_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of households in urban areas that have persons with any degree of functional difficulty (% of households in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of households in urban areas who have at least an adult household member with any degree of functional difficulty. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_prev_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_prev_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of households that have persons with some degree of functional difficulty (% of households)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of households who have at least an adult household member with some degree of functional difficulty. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_prev_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_prev_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of households in rural areas that have persons with some degree of functional difficulty (% of households in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of households in rural areas who have at least an adult household member with some degree of functional difficulty. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_prev_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hh_prev_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of households in urban areas that have persons with some degree of functional difficulty (% of households in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of households in urban areas who have at least an adult household member with some degree of functional difficulty. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hh_prev_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with health insurance (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15 to 29 years who have health insurance. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with health insurance (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 30 to 44 years who have health insurance. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with health insurance (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 45 to 64 years who have health insurance. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with health insurance (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 65 or older who have health insurance. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with health insurance (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ who have health insurance. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with health insurance (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities who have health insurance. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with health insurance (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities who have health insurance. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with health insurance (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in rural areas who have health insurance. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with health insurance (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas who have health insurance. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with health insurance (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty who have health insurance. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with health insurance (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty who have health insurance. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with health insurance (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty who have health insurance. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with health insurance (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty who have health insurance. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with health insurance (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty who have health insurance. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with health insurance (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who have health insurance." }, { "id": "Previous_Indicator_Code", "value": "htin_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with health insurance (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who have health insurance." }, { "id": "Previous_Indicator_Code", "value": "htin_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with health insurance (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty who have health insurance. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with health insurance (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who have health insurance." }, { "id": "Previous_Indicator_Code", "value": "htin_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with health insurance (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty who have health insurance. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with health insurance (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who have health insurance." }, { "id": "Previous_Indicator_Code", "value": "htin_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with health insurance (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty who have health insurance. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with health insurance (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who have health insurance." }, { "id": "Previous_Indicator_Code", "value": "htin_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with health insurance (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who have health insurance." }, { "id": "Previous_Indicator_Code", "value": "htin_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with health insurance (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty who have health insurance. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with health insurance (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty who have health insurance. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with health insurance (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty who have health insurance. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with health insurance (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty who have health insurance. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with health insurance (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty who have health insurance. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with health insurance (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty who have health insurance. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with health insurance (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty who have health insurance. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with health insurance (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty who have health insurance. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with health insurance (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty who have health insurance. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with health insurance (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty who have health insurance. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with health insurance (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who have health insurance. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with health insurance (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who have health insurance. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with health insurance (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who have health insurance. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with health insurance (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty who have health insurance. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with health insurance (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty who have health insurance. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with health insurance (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty who have health insurance. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with health insurance (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty who have health insurance. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with health insurance (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who have health insurance. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with health insurance (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who have health insurance. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years with health insurance (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who have health insurance. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years with health insurance (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who have health insurance. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years with health insurance (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who have health insurance. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older with health insurance (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty who have health insurance. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons with health insurance (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of difficulty who have health insurance. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons with health insurance (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who have health insurance. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons with health insurance (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who have health insurance. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas with health insurance (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who have health insurance. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "htin_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas with health insurance (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who have health insurance. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "htin_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with disabilities aged 15 to 29 years. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with disabilities aged 30 to 44 years. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with disabilities aged 45 to 64 years. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with disabilities aged 65 or older. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with disabilities. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have females with disabilities. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have males with disabilities. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with disabilities, aged 15+, living in rural areas. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with disabilities, aged 15+, living in urban areas. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons aged 15 to 29 years with any degree of functional difficulty. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons aged 30 to 44 years with any degree of functional difficulty. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons aged 45 to 64 years with any degree of functional difficulty. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons aged 65 or older with any degree of functional difficulty. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with any degree of functional difficulty. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication." }, { "id": "Previous_Indicator_Code", "value": "hxpd_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have females with any degree of functional difficulty. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing." }, { "id": "Previous_Indicator_Code", "value": "hxpd_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have males with any degree of functional difficulty. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons, aged 15+, living in rural areas with any degree of functional difficulty. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing." }, { "id": "Previous_Indicator_Code", "value": "hxpd_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare." }, { "id": "Previous_Indicator_Code", "value": "hxpd_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons, aged 15+, living in urban areas with any degree of functional difficulty. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons aged 15 to 29 years with no functional difficulty. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons aged 30 to 44 years with no functional difficulty. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons aged 45 to 64 years with no functional difficulty. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons aged 65 or older with no functional difficulty. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with no functional difficulty. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have females with no functional difficulty. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have males with no functional difficulty. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons, aged 15+, living in rural areas with no functional difficulty. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons, aged 15+, living in urban areas with no functional difficulty. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons aged 15 to 29 years with none or some degree of functional difficulty. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons aged 30 to 44 years with none or some degree of functional difficulty. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons aged 45 to 64 years with none or some degree of functional difficulty. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons aged 65 or older with none or some degree of functional difficulty. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with none or some degree of functional difficulty. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have females with none or some degree of functional difficulty. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have males with none or some degree of functional difficulty. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons, aged 15+, living in rural areas with none or some degree of functional difficulty. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons, aged 15+, living in urban areas with none or some degree of functional difficulty. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons aged 15 to 29 years with some degree of difficulty. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons aged 30 to 44 years with some degree of difficulty. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons aged 45 to 64 years with some degree of difficulty. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons aged 65 or older with some degree of difficulty. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons with some degree of difficulty. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have females with some degree of difficulty. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have males with some degree of difficulty. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons, aged 15+, living in rural areas with some degree of difficulty. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "hxpd_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Household health expenditures (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the household total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines). Households included are those who have persons, aged 15+, living in urban areas with some degree of difficulty. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "hxpd_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in informal work (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15 to 29 years who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in informal work (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 30 to 44 years who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in informal work (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 45 to 64 years who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in informal work (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 65 or older who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in informal work (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in informal work (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in informal work (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in informal work (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in rural areas who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in informal work (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in informal work (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in informal work (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in informal work (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in informal work (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in informal work (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in informal work (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers." }, { "id": "Previous_Indicator_Code", "value": "ifrm_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in informal work (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers." }, { "id": "Previous_Indicator_Code", "value": "ifrm_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in informal work (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in informal work (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers." }, { "id": "Previous_Indicator_Code", "value": "ifrm_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in informal work (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in informal work (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers." }, { "id": "Previous_Indicator_Code", "value": "ifrm_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in informal work (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in informal work (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers." }, { "id": "Previous_Indicator_Code", "value": "ifrm_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in informal work (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers." }, { "id": "Previous_Indicator_Code", "value": "ifrm_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in informal work (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in informal work (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in informal work (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in informal work (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in informal work (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in informal work (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in informal work (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in informal work (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in informal work (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in informal work (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in informal work (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in informal work (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in informal work (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in informal work (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in informal work (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in informal work (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in informal work (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in informal work (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in informal work (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in informal work (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in informal work (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in informal work (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in informal work (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in informal work (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in informal work (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in informal work (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in informal work (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ifrm_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in informal work (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ifrm_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15 to 29 years who can read and write in any language. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 30 to 44 years who can read and write in any language. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 45 to 64 years who can read and write in any language. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 65 or older who can read and write in any language. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ who can read and write in any language. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities who can read and write in any language. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities who can read and write in any language. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in rural areas who can read and write in any language. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas who can read and write in any language. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty who can read and write in any language. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty who can read and write in any language. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty who can read and write in any language. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty who can read and write in any language. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty who can read and write in any language. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who can read and write in any language." }, { "id": "Previous_Indicator_Code", "value": "litr_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who can read and write in any language." }, { "id": "Previous_Indicator_Code", "value": "litr_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty who can read and write in any language. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who can read and write in any language." }, { "id": "Previous_Indicator_Code", "value": "litr_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty who can read and write in any language. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who can read and write in any language." }, { "id": "Previous_Indicator_Code", "value": "litr_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty who can read and write in any language. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who can read and write in any language." }, { "id": "Previous_Indicator_Code", "value": "litr_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who can read and write in any language." }, { "id": "Previous_Indicator_Code", "value": "litr_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty who can read and write in any language. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty who can read and write in any language. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty who can read and write in any language. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty who can read and write in any language. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty who can read and write in any language. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty who can read and write in any language. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty who can read and write in any language. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty who can read and write in any language. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty who can read and write in any language. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty who can read and write in any language. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who can read and write in any language. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who can read and write in any language. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who can read and write in any language. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty who can read and write in any language. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty who can read and write in any language. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty who can read and write in any language. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty who can read and write in any language. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who can read and write in any language. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who can read and write in any language. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who can read and write in any language. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who can read and write in any language. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who can read and write in any language. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty who can read and write in any language. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of difficulty who can read and write in any language. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who can read and write in any language. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who can read and write in any language. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who can read and write in any language. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "litr_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Literacy rate (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who can read and write in any language. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "litr_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women with disabilities aged 15-29 who hold managerial positions. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women with disabilities aged 30-44 who hold managerial positions. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women with disabilities aged 45-64 who hold managerial positions. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 65 years or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women with disabilities aged 65 or older who hold managerial positions. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ with disabilities who hold managerial positions. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women with disabilities aged 15+ living in rural areas who hold managerial positions. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women with disabilities aged 15+ living in urban areas who hold managerial positions. Women with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-29 with any degree of functional difficulty who hold managerial positions. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 30-44 with any degree of functional difficulty who hold managerial positions. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 45-64 with any degree of functional difficulty who hold managerial positions. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 65 years or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 65 or older with any degree of functional difficulty who hold managerial positions. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ with any degree of functional difficulty who hold managerial positions. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who hold managerial positions." }, { "id": "Previous_Indicator_Code", "value": "mgrw_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who hold managerial positions." }, { "id": "Previous_Indicator_Code", "value": "mgrw_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who hold managerial positions." }, { "id": "Previous_Indicator_Code", "value": "mgrw_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who hold managerial positions." }, { "id": "Previous_Indicator_Code", "value": "mgrw_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ living in rural areas with any degree of functional difficulty who hold managerial positions. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who hold managerial positions." }, { "id": "Previous_Indicator_Code", "value": "mgrw_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ years with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who hold managerial positions." }, { "id": "Previous_Indicator_Code", "value": "mgrw_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ living in urban areas with any degree of functional difficulty who hold managerial positions. Women with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-29 with no functional difficulty who hold managerial positions. Women with no functional difficulty are those who report no disability at all in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 30-44 with no functional difficulty who hold managerial positions. Women with no functional difficulty are those who report no disability at all in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 45-64 with no functional difficulty who hold managerial positions. Women with no functional difficulty are those who report no disability at all in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 65 years or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 65 or older with no functional difficulty who hold managerial positions. Women with no functional difficulty are those who report no disability at all in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ with no functional difficulty who hold managerial positions. Women with no functional difficulty are those who report no disability at all in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ living in rural areas with no functional difficulty who hold managerial positions. Women with no functional difficulty are those who report no disability at all in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ living in urban areas with no functional difficulty who hold managerial positions. Women with no functional difficulty are those who report no disability at all in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-29 with none or some degree of functional difficulty who hold managerial positions. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 30-44 with none or some degree of functional difficulty who hold managerial positions. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 45-64 with none or some degree of functional difficulty who hold managerial positions. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 65 years or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 65 or older with none or some degree of functional difficulty who hold managerial positions. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ with none or some degree of functional difficulty who hold managerial positions. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ living in rural areas with none or some degree of functional difficulty who hold managerial positions. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ living in urban areas with none or some degree of functional difficulty who hold managerial positions. Women with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15-29 with some degree of difficulty who hold managerial positions. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 30-44 with some degree of difficulty who hold managerial positions. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 45-64 with some degree of difficulty who hold managerial positions. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women aged 65 years or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 65 or older with some degree of difficulty who hold managerial positions. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ with some degree of difficulty who hold managerial positions. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ living in rural areas with some degree of difficulty who hold managerial positions. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mgrw_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Women in managerial positions (% of women living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of women aged 15+ living in urban areas with some degree of difficulty who hold managerial positions. Women with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mgrw_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15 to 29 years working in the manufacturing sector. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 30 to 44 years working in the manufacturing sector. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 45 to 64 years working in the manufacturing sector. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 65 or older working in the manufacturing sector. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ working in the manufacturing sector. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities working in the manufacturing sector. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities working in the manufacturing sector. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in rural areas working in the manufacturing sector. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas working in the manufacturing sector. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty working in the manufacturing sector. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty working in the manufacturing sector. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty working in the manufacturing sector. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty working in the manufacturing sector. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty working in the manufacturing sector. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) working in the manufacturing sector." }, { "id": "Previous_Indicator_Code", "value": "mnfw_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication working in the manufacturing sector." }, { "id": "Previous_Indicator_Code", "value": "mnfw_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty working in the manufacturing sector. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing working in the manufacturing sector." }, { "id": "Previous_Indicator_Code", "value": "mnfw_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty working in the manufacturing sector. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) working in the manufacturing sector." }, { "id": "Previous_Indicator_Code", "value": "mnfw_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty working in the manufacturing sector. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing working in the manufacturing sector." }, { "id": "Previous_Indicator_Code", "value": "mnfw_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare working in the manufacturing sector." }, { "id": "Previous_Indicator_Code", "value": "mnfw_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty working in the manufacturing sector. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty working in the manufacturing sector. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty working in the manufacturing sector. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty working in the manufacturing sector. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty working in the manufacturing sector. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty working in the manufacturing sector. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty working in the manufacturing sector. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty working in the manufacturing sector. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty working in the manufacturing sector. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty working in the manufacturing sector. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty working in the manufacturing sector. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty working in the manufacturing sector. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty working in the manufacturing sector. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty working in the manufacturing sector. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty working in the manufacturing sector. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty working in the manufacturing sector. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty working in the manufacturing sector. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty working in the manufacturing sector. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty working in the manufacturing sector. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty working in the manufacturing sector. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty working in the manufacturing sector. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty working in the manufacturing sector. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty working in the manufacturing sector. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of difficulty working in the manufacturing sector. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty working in the manufacturing sector. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty working in the manufacturing sector. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty working in the manufacturing sector. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "mnfw_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Working individuals in manufacturing (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty working in the manufacturing sector. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "mnfw_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years using the Internet (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15 to 29 years who used the Internet for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years using the Internet (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 30 to 44 years who used the Internet for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years using the Internet (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 45 to 64 years who used the Internet for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older using the Internet (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 65 or older who used the Internet for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using the Internet (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ who used the Internet for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons using the Internet (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities who used the Internet for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons using the Internet (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities who used the Internet for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas using the Internet (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in rural areas who used the Internet for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas using the Internet (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas who used the Internet for the past three months. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years using the Internet (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty who used the Internet for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years using the Internet (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty who used the Internet for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years using the Internet (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty who used the Internet for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older using the Internet (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty who used the Internet for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using the Internet (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty who used the Internet for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using the Internet (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who used the Internet for the past three months." }, { "id": "Previous_Indicator_Code", "value": "netu_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using the Internet (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who used the Internet for the past three months." }, { "id": "Previous_Indicator_Code", "value": "netu_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons using the Internet (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty who used the Internet for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using the Internet (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who used the Internet for the past three months." }, { "id": "Previous_Indicator_Code", "value": "netu_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons using the Internet (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty who used the Internet for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using the Internet (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who used the Internet for the past three months." }, { "id": "Previous_Indicator_Code", "value": "netu_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas using the Internet (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty who used the Internet for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using the Internet (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who used the Internet for the past three months." }, { "id": "Previous_Indicator_Code", "value": "netu_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using the Internet (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who used the Internet for the past three months." }, { "id": "Previous_Indicator_Code", "value": "netu_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas using the Internet (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty who used the Internet for the past three months. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years using the Internet (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty who used the Internet for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years using the Internet (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty who used the Internet for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years using the Internet (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty who used the Internet for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older using the Internet (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty who used the Internet for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using the Internet (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty who used the Internet for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons using the Internet (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty who used the Internet for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons using the Internet (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty who used the Internet for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas using the Internet (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty who used the Internet for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas using the Internet (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty who used the Internet for the past three months. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years using the Internet (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who used the Internet for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years using the Internet (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who used the Internet for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years using the Internet (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who used the Internet for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older using the Internet (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty who used the Internet for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using the Internet (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty who used the Internet for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons using the Internet (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty who used the Internet for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons using the Internet (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty who used the Internet for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas using the Internet (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who used the Internet for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas using the Internet (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who used the Internet for the past three months. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years using the Internet (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who used the Internet for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years using the Internet (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who used the Internet for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years using the Internet (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who used the Internet for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older using the Internet (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty who used the Internet for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons using the Internet (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of difficulty who used the Internet for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons using the Internet (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who used the Internet for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons using the Internet (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who used the Internet for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas using the Internet (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who used the Internet for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "netu_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas using the Internet (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who used the Internet for the past three months. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "netu_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years owing a mobility phone (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15 to 29 years who have their own mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years owing a mobility phone (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 30 to 44 years who have their own mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years owing a mobility phone (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 45 to 64 years who have their own mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older owing a mobility phone (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 65 or older who have their own mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons owing a mobility phone (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ who have their own mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons owing a mobility phone (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities who have their own mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons owing a mobility phone (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities who have their own mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas owing a mobility phone (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in rural areas who have their own mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas owing a mobility phone (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas who have their own mobile phone. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years owing a mobility phone (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty who have their own mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years owing a mobility phone (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty who have their own mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years owing a mobility phone (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty who have their own mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older owing a mobility phone (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty who have their own mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons owing a mobility phone (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty who have their own mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons owing a mobility phone (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who have their own mobile phone." }, { "id": "Previous_Indicator_Code", "value": "ocel_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons owing a mobility phone (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who have their own mobile phone." }, { "id": "Previous_Indicator_Code", "value": "ocel_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons owing a mobility phone (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty who have their own mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons owing a mobility phone (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who have their own mobile phone." }, { "id": "Previous_Indicator_Code", "value": "ocel_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons owing a mobility phone (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty who have their own mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons owing a mobility phone (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who have their own mobile phone." }, { "id": "Previous_Indicator_Code", "value": "ocel_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas owing a mobility phone (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty who have their own mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons owing a mobility phone (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who have their own mobile phone." }, { "id": "Previous_Indicator_Code", "value": "ocel_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons owing a mobility phone (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who have their own mobile phone." }, { "id": "Previous_Indicator_Code", "value": "ocel_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas owing a mobility phone (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty who have their own mobile phone. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years owing a mobility phone (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty who have their own mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years owing a mobility phone (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty who have their own mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years owing a mobility phone (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty who have their own mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older owing a mobility phone (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty who have their own mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons owing a mobility phone (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty who have their own mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons owing a mobility phone (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty who have their own mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons owing a mobility phone (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty who have their own mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas owing a mobility phone (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty who have their own mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas owing a mobility phone (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty who have their own mobile phone. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years owing a mobility phone (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who have their own mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years owing a mobility phone (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who have their own mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years owing a mobility phone (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who have their own mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older owing a mobility phone (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty who have their own mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons owing a mobility phone (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty who have their own mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons owing a mobility phone (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty who have their own mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons owing a mobility phone (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty who have their own mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas owing a mobility phone (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who have their own mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas owing a mobility phone (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who have their own mobile phone. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years owing a mobility phone (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who have their own mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years owing a mobility phone (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who have their own mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years owing a mobility phone (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who have their own mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older owing a mobility phone (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty who have their own mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons owing a mobility phone (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of difficulty who have their own mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons owing a mobility phone (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who have their own mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons owing a mobility phone (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who have their own mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas owing a mobility phone (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who have their own mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ocel_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas owing a mobility phone (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who have their own mobile phone. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ocel_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households receiving social protection (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15 to 29 years who live in households that received any aid through social protection programs. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households receiving social protection (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 30 to 44 years who live in households that received any aid through social protection programs. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households receiving social protection (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 45 to 64 years who live in households that received any aid through social protection programs. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households receiving social protection (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 65 or older who live in households that received any aid through social protection programs. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households receiving social protection (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ who live in households that received any aid through social protection programs. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households receiving social protection (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities who live in households that received any aid through social protection programs. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households receiving social protection (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities who live in households that received any aid through social protection programs. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households receiving social protection (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in rural areas who live in households that received any aid through social protection programs. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households receiving social protection (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas who live in households that received any aid through social protection programs. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households receiving social protection (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty who live in households that received any aid through social protection programs. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households receiving social protection (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty who live in households that received any aid through social protection programs. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households receiving social protection (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty who live in households that received any aid through social protection programs. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households receiving social protection (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty who live in households that received any aid through social protection programs. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households receiving social protection (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty who live in households that received any aid through social protection programs. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households receiving social protection (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who live in households that received any aid through social protection programs." }, { "id": "Previous_Indicator_Code", "value": "prot_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households receiving social protection (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who live in households that received any aid through social protection programs." }, { "id": "Previous_Indicator_Code", "value": "prot_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households receiving social protection (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty who live in households that received any aid through social protection programs. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households receiving social protection (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who live in households that received any aid through social protection programs." }, { "id": "Previous_Indicator_Code", "value": "prot_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households receiving social protection (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty who live in households that received any aid through social protection programs. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households receiving social protection (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who live in households that received any aid through social protection programs." }, { "id": "Previous_Indicator_Code", "value": "prot_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households receiving social protection (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty who live in households that received any aid through social protection programs. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households receiving social protection (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who live in households that received any aid through social protection programs." }, { "id": "Previous_Indicator_Code", "value": "prot_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households receiving social protection (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who live in households that received any aid through social protection programs." }, { "id": "Previous_Indicator_Code", "value": "prot_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households receiving social protection (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty who live in households that received any aid through social protection programs. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households receiving social protection (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty who live in households that received any aid through social protection programs. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households receiving social protection (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty who live in households that received any aid through social protection programs. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households receiving social protection (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty who live in households that received any aid through social protection programs. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households receiving social protection (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty who live in households that received any aid through social protection programs. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households receiving social protection (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty who live in households that received any aid through social protection programs. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households receiving social protection (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty who live in households that received any aid through social protection programs. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households receiving social protection (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty who live in households that received any aid through social protection programs. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households receiving social protection (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty who live in households that received any aid through social protection programs. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households receiving social protection (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty who live in households that received any aid through social protection programs. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households receiving social protection (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who live in households that received any aid through social protection programs. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households receiving social protection (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who live in households that received any aid through social protection programs. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households receiving social protection (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who live in households that received any aid through social protection programs. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households receiving social protection (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with none or some degree of functional difficulty who live in households that received any aid through social protection programs. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households receiving social protection (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty who live in households that received any aid through social protection programs. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households receiving social protection (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty who live in households that received any aid through social protection programs. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households receiving social protection (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty who live in households that received any aid through social protection programs. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households receiving social protection (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who live in households that received any aid through social protection programs. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households receiving social protection (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who live in households that received any aid through social protection programs. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households receiving social protection (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who live in households that received any aid through social protection programs. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households receiving social protection (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who live in households that received any aid through social protection programs. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households receiving social protection (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who live in households that received any aid through social protection programs. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households receiving social protection (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with some degree of difficulty who live in households that received any aid through social protection programs. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households receiving social protection (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of difficulty who live in households that received any aid through social protection programs. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households receiving social protection (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who live in households that received any aid through social protection programs. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households receiving social protection (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who live in households that received any aid through social protection programs. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households receiving social protection (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who live in households that received any aid through social protection programs. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "prot_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households receiving social protection (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who live in households that received any aid through social protection programs. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "prot_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households using improved sanitation services (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15 to 29 years who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households using improved sanitation services (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 30 to 44 years who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households using improved sanitation services (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 45 to 64 years who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households using improved sanitation services (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 65 or older who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved sanitation services (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities aged 15+ who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households using improved sanitation services (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with disabilities who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households using improved sanitation services (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with disabilities who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households using improved sanitation services (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in rural areas who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households using improved sanitation services (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons with disabilities, aged 15+, living in urban areas who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households using improved sanitation services (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with any degree of functional difficulty who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households using improved sanitation services (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with any degree of functional difficulty who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households using improved sanitation services (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with any degree of functional difficulty who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households using improved sanitation services (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with any degree of functional difficulty who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved sanitation services (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved sanitation services (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved." }, { "id": "Previous_Indicator_Code", "value": "san_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved sanitation services (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved." }, { "id": "Previous_Indicator_Code", "value": "san_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households using improved sanitation services (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with any degree of functional difficulty who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved sanitation services (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved." }, { "id": "Previous_Indicator_Code", "value": "san_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households using improved sanitation services (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with any degree of functional difficulty who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved sanitation services (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved." }, { "id": "Previous_Indicator_Code", "value": "san_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households using improved sanitation services (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with any degree of functional difficulty who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved sanitation services (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved." }, { "id": "Previous_Indicator_Code", "value": "san_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved sanitation services (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved." }, { "id": "Previous_Indicator_Code", "value": "san_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households using improved sanitation services (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with any degree of functional difficulty who live in the households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households using improved sanitation services (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with no functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households using improved sanitation services (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with no functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households using improved sanitation services (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with no functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households using improved sanitation services (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65 or older with no functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved sanitation services (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with no functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households using improved sanitation services (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with no functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households using improved sanitation services (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with no functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households using improved sanitation services (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with no functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households using improved sanitation services (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with no functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households using improved sanitation services (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with none or some degree of functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households using improved sanitation services (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with none or some degree of functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households using improved sanitation services (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with none or some degree of functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households using improved sanitation services (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65+ with none or some degree of functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved sanitation services (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with none or some degree of functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households using improved sanitation services (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with none or some degree of functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households using improved sanitation services (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with none or some degree of functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households using improved sanitation services (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with none or some degree of functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households using improved sanitation services (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with none or some degree of functional difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households using improved sanitation services (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years with some degree of difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households using improved sanitation services (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years with some degree of difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households using improved sanitation services (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years with some degree of difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households using improved sanitation services (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65+ with some degree of difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households using improved sanitation services (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ with some degree of difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households using improved sanitation services (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, with some degree of difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households using improved sanitation services (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, with some degree of difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households using improved sanitation services (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas with some degree of difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "san_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households using improved sanitation services (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas with some degree of difficulty who live in households using improved sanitation facility. Improved sanitation includes flush or pour-flush to piped sewer system, septic tank pit latrines, ventilated-improved pit latrines, or pit latrines with slab or composting toilets. Shared or public-use sanitation facilities are not considered to be improved. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "san_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 15 to 29 years with disabilities are considered. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 30 to 44 years with disabilities are considered. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 45 to 64 years with disabilities are considered. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 65 or older with disabilities are considered. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only Persons with disabilities are considered. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only females with disabilities are considered. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only males with disabilities are considered. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons, aged 15+, living in rural areas with disabilities are considered. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons, aged 15+, living in urban areas with disabilities are considered. Persons with disabilities are those who report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 15 to 29 years with any degree of functional difficulty are considered. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 30 to 44 years with any degree of functional difficulty are considered. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 45 to 64 years with any degree of functional difficulty are considered. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 65 or older with any degree of functional difficulty are considered. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons with any degree of functional difficulty are considered. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only Persons with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in cognition (concentrating/remembering) are considered." }, { "id": "Previous_Indicator_Code", "value": "saso_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only Persons with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in communication are considered." }, { "id": "Previous_Indicator_Code", "value": "saso_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only females with any degree of functional difficulty are considered. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only Persons with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in hearing are considered." }, { "id": "Previous_Indicator_Code", "value": "saso_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only males with any degree of functional difficulty are considered. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only Persons with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in mobility (walking/climbing stairs) are considered." }, { "id": "Previous_Indicator_Code", "value": "saso_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons, aged 15+, living in rural areas with any degree of functional difficulty are considered. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only Persons with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in seeing are considered." }, { "id": "Previous_Indicator_Code", "value": "saso_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only Persons with any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) with selfcare are considered." }, { "id": "Previous_Indicator_Code", "value": "saso_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons, aged 15+, living in urban areas with any degree of functional difficulty are considered. Persons with any degree of functional difficulty are those who report any degree of functional difficulty (some degree of functional difficulty, a lot of difficulty, or unable to do) in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 15 to 29 years with no functional difficulty are considered. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 30 to 44 years with no functional difficulty are considered. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 45 to 64 years with no functional difficulty are considered. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 65 or older with no functional difficulty are considered. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only Persons with no functional difficulty are considered. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only females with no functional difficulty are considered. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only males with no functional difficulty are considered. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons, aged 15+, living in rural areas with no functional difficulty are considered. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons, aged 15+, living in urban areas with no functional difficulty are considered. Persons with no functional difficulty are those who report no difficulty in all of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 15 to 29 years with none or some degree of functional difficulty are considered. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 30 to 44 years with none or some degree of functional difficulty are considered. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 45 to 64 years with none or some degree of functional difficulty are considered. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 65 or older with none or some degree of functional difficulty are considered. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only Persons with none or some degree of functional difficulty are considered. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only females with none or some degree of functional difficulty are considered. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only males with none or some degree of functional difficulty are considered. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons, aged 15+, living in rural areas with none or some degree of functional difficulty are considered. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons, aged 15+, living in urban areas with none or some degree of functional difficulty are considered. Persons with none or some degree of functional difficulty are those who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 15 to 29 years with some degree of difficulty are considered. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 30 to 44 years with some degree of difficulty are considered. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 45 to 64 years with some degree of difficulty are considered. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons aged 65 or older with some degree of difficulty are considered. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only Persons with some degree of difficulty are considered. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only females with some degree of difficulty are considered. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only males with some degree of difficulty are considered. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons, aged 15+, living in rural areas with some degree of difficulty are considered. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "saso_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Assets owned by individual's household (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of the following assets that the individual’s household owns:  a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer. Only persons, aged 15+, living in urban areas with some degree of difficulty are considered. Persons with some degree of difficulty are those who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "saso_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_al_alot_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households that experienced a shock recently (% of persons aged 15 to 29 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_al_alot_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_al_alot_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households that experienced a shock recently (% of persons aged 30 to 44 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_al_alot_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_al_alot_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households that experienced a shock recently (% of persons aged 45 to 64 years with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_al_alot_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_al_alot_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households that experienced a shock recently (% of persons aged 65 or older with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_al_alot_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households that experienced a shock recently (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households that experienced a shock recently (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households that experienced a shock recently (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households that experienced a shock recently (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ living in rural areas who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households that experienced a shock recently (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15+ living in urban areas who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_any_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households that experienced a shock recently (% of persons aged 15 to 29 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 years who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report any degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_any_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_any_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households that experienced a shock recently (% of persons aged 30 to 44 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 years who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report any degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_any_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_any_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households that experienced a shock recently (% of persons aged 45 to 64 years with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 years who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report any degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_any_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_any_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households that experienced a shock recently (% of persons aged 65 or older with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report any degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_any_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households that experienced a shock recently (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report any degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households that experienced a shock recently (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report any degree of difficulty with cognition." }, { "id": "Previous_Indicator_Code", "value": "shck_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households that experienced a shock recently (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report any degree of difficulty with communication." }, { "id": "Previous_Indicator_Code", "value": "shck_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households that experienced a shock recently (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report any degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households that experienced a shock recently (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report any degree of difficulty with hearing." }, { "id": "Previous_Indicator_Code", "value": "shck_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households that experienced a shock recently (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report any degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households that experienced a shock recently (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report any degree of difficulty with mobility." }, { "id": "Previous_Indicator_Code", "value": "shck_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households that experienced a shock recently (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ living in rural areas who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report any degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households that experienced a shock recently (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report any degree of difficulty with seeing." }, { "id": "Previous_Indicator_Code", "value": "shck_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households that experienced a shock recently (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report any degree of difficulty with self care." }, { "id": "Previous_Indicator_Code", "value": "shck_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households that experienced a shock recently (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ living in urban areas who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report any degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_none_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households that experienced a shock recently (% of persons aged 15 to 29 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report no degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_none_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_none_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households that experienced a shock recently (% of persons aged 30 to 44 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report no degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_none_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_none_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households that experienced a shock recently (% of persons aged 45 to 64 years with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report no degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_none_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_none_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households that experienced a shock recently (% of persons aged 65 or older with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report no degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_none_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households that experienced a shock recently (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report no degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households that experienced a shock recently (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report no degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households that experienced a shock recently (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report no degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households that experienced a shock recently (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ living in rural areas who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report no degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households that experienced a shock recently (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ living in urban areas who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) report no degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_nosome_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households that experienced a shock recently (% of persons aged 15 to 29 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_nosome_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_nosome_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households that experienced a shock recently (% of persons aged 30 to 44 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_nosome_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_nosome_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households that experienced a shock recently (% of persons aged 45 to 64 years with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_nosome_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_nosome_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households that experienced a shock recently (% of persons aged 65 or older with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_nosome_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households that experienced a shock recently (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households that experienced a shock recently (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households that experienced a shock recently (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households that experienced a shock recently (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ living in rural areas who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households that experienced a shock recently (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ living in urban areas who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 15 to 29 years in households that experienced a shock recently (% of persons aged 15 to 29 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 30 to 44 years in households that experienced a shock recently (% of persons aged 30 to 44 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 45 to 64 years in households that experienced a shock recently (% of persons aged 45 to 64 years with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons aged 65 or older in households that experienced a shock recently (% of persons aged 65 or older with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons in households that experienced a shock recently (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Female persons in households that experienced a shock recently (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Male persons in households that experienced a shock recently (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15+ who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in rural areas in households that experienced a shock recently (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ living in rural areas who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "shck_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Persons living in urban areas in households that experienced a shock recently (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ living in urban areas who (i) live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months; AND who (ii) who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "shck_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "some_dfcl_1529", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 15 to 29 years with some degree of functional difficulty (% of persons aged 15 to 29 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 29 who reported some degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This rate has not been adjusted." }, { "id": "Previous_Indicator_Code", "value": "some_dfcl_15to29" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "some_dfcl_3044", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 30 to 44 years with some degree of functional difficulty (% of persons aged 30 to 44 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 30 to 44 who reported some degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This rate has not been adjusted." }, { "id": "Previous_Indicator_Code", "value": "some_dfcl_30to44" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "some_dfcl_4564", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 45 to 64 years with some degree of functional difficulty (% of persons aged 45 to 64 years)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 45 to 64 who reported some degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This rate has not been adjusted." }, { "id": "Previous_Indicator_Code", "value": "some_dfcl_45to64" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "some_dfcl_65up", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons aged 65 or older with some degree of functional difficulty (% of persons aged 65 or older)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 65+ who reported some degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This rate has not been adjusted." }, { "id": "Previous_Indicator_Code", "value": "some_dfcl_65more" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons with some degree of functional difficulty (% of persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15+ who reported some degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication). This rate has not been adjusted." }, { "id": "Previous_Indicator_Code", "value": "some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of female persons with some degree of functional difficulty (% of female persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females, aged 15+, report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of male persons with some degree of functional difficulty (% of male persons)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males, aged 15+, report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in rural areas with some degree of functional difficulty (% of persons living in rural areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in rural areas who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Prevalence of persons living in urban areas with some degree of functional difficulty (% of persons living in urban areas)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons, aged 15+, living in urban areas who report some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_al_alot_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_al_alot_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_al_alot_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of female persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_al_alot_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_al_alot_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of male persons with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_al_alot_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_al_alot_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons living in rural areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years living in rural areas (i) who are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_al_alot_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_al_alot_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons living in urban areas with disability i.e. at least a lot of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years living in urban areas who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) report a lot of difficulty or unable to do in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_al_alot_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_any_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported any degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_any_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_any_dfcl_cogn_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons with any degree of cognitive difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported any degree of difficulty with cognition." }, { "id": "Previous_Indicator_Code", "value": "ytil_any_dfcl_cognition_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_any_dfcl_comm_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons with any degree of communication difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported any degree of difficulty with communication." }, { "id": "Previous_Indicator_Code", "value": "ytil_any_dfcl_communicating_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_any_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of female persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported any degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_any_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_any_dfcl_hearing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons with any degree of hearing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported any degree of difficulty with hearing." }, { "id": "Previous_Indicator_Code", "value": "ytil_any_dfcl_hearing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_any_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of male persons with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported any degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_any_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_any_dfcl_mobile_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons with any degree of mobility difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported any degree of difficulty with mobility." }, { "id": "Previous_Indicator_Code", "value": "ytil_any_dfcl_mobile_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_any_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons living in rural areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years living in rural areas who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported any degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_any_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_any_dfcl_seeing_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons with any degree of seeing difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported any degree of difficulty with seeing." }, { "id": "Previous_Indicator_Code", "value": "ytil_any_dfcl_seeing_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_any_dfcl_selfcare_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons with any degree of selfcare difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported any degree of difficulty with self care." }, { "id": "Previous_Indicator_Code", "value": "ytil_any_dfcl_selfcare_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_any_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons living in urban areas with any degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years living in urban areas who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported any degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_any_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_none_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported no degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_none_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_none_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of female persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported no degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_none_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_none_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of male persons with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported no degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_none_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_none_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons living in rural areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons living in rural areas aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported no degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_none_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_none_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons living in urban areas with no functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons living in urban areas aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported no degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_none_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_nosome_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons living in urban areas aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training.; AND who (ii) who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_nosome_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_nosome_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of female persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training.; AND who (ii) who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_nosome_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_nosome_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of male persons with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of males aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training.; AND who (ii) who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_nosome_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_nosome_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons living in rural areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons living in rural areas aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training.; AND who (ii) who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_nosome_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_nosome_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons living in urban areas with none or some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons living in urban areas aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training.; AND who (ii) who report none or some degree of functional difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_nosome_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_some_dfcl_all", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported some degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_some_dfcl_all" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_some_dfcl_fem", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of female persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported some degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_some_dfcl_fem" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_some_dfcl_male", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of male persons with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of females aged 15 to 24 years who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported some degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_some_dfcl_male" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_some_dfcl_rur", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons living in rural areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years living in rural areas who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported some degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_some_dfcl_rur" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "ytil_some_dfcl_urb", "metatype": [ { "id": "Dataset", "value": "Disability Data Hub" }, { "id": "IndicatorName", "value": "Youth idle rate (% of persons living in urban areas with some degree of functional difficulty)" }, { "id": "License_Type", "value": "CC BY-4.0" }, { "id": "Longdefinition", "value": "This indicator refers to the proportion of persons aged 15 to 24 years living in urban areas who (i) are neither enrolled in school or any educational or training program, nor employed. As information on training was not consistently available, it does not reflect whether youth might be in training. ; AND who (ii) who reported some degree of difficulty in any of the six domains (seeing, hearing, mobility, cognition, self-care, communication)." }, { "id": "Previous_Indicator_Code", "value": "ytil_some_dfcl_urb" }, { "id": "Source", "value": "World Bank (2025). Disability Data Hub." }, { "id": "Unitofmeasure", "value": "Percent" } ], "source_id": "92" }, { "id": "CoCA_fexp", "metatype": [ { "id": "IndicatorName", "value": "Affordability of an energy sufficient diet: ratio of cost to food expenditures" }, { "id": "Longdefinition", "value": "The ratio of the cost of an energy sufficient diet to total food expenditure per capita per day from national accounts. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); and Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoCA_headcount", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of unaffordability of an energy sufficient diet" }, { "id": "Longdefinition", "value": "The indicator estimates the percentage of individuals in a population whose disposable income, net of the amount needed to acquire basic non-food goods and services, is lower than the average cost of the least-expensive energy sufficient diet in a country. The expenditures for basic non-food needs are calculated as average non-food expenditure shares of low-income consumers multipled by internaitonal poverty lines set by the World Bank. The non-food expenditure share is 37% and 44% in low-income and lower-middle-income countries for the second quintile of consumers, and 54% in upper-middle-and-high-income countries for the first quintile of consumers, according to household surveys compiled by the World Bank. The international poverty lines are $2.15/day for low-income countries, $3.65/day for lower-middle-income countries, $6.85/day for upper-middle-income countries, and $24.36/day for high-income countries, in 2017PPP$. Countries' income classifications follow the calendar year of 2021 standard (fiscal year of 2023 of the World Bank), which is the base year of the latest ICP cycle. Income data are provided by the World Bank’s Poverty and Inequality Platform. A value of zero indicates a null or a small number rounded down at the current precision level. Data available for 2021.\n\nNational income distribution data are provided by the World Bank’s Poverty and Inequality Platform and are based on 2017 purchasing power parities (PPPs), updated in fall 2024. Latest data of India are also featured." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "93" }, { "id": "CoCA_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of an energy sufficient diet in local currency unit" }, { "id": "Longdefinition", "value": "Cost of the least expensive starchy staple for energy balance, in LCU/person/day, for a representative person within energy balance at 2330 kcal/day. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); and Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoCA_pov", "metatype": [ { "id": "IndicatorName", "value": "Affordability of an energy sufficient diet: ratio of cost to the food poverty line" }, { "id": "Longdefinition", "value": "The ratio of the cost of an energy sufficient diet to the food poverty lines, defined as $1.35/day for low-income countries (63% of the international poverty line of $2.15/day in 2017 PPP$), $2.04/day for lower-middle-income countries (56% of the international poverty line of $3.65/day in 2017 PPP$), $3.15/day for upper-middle-income countries (46% of the international poverty line of $6.85/day in 2017 PPP$), and $11.2/day (46% of the international poverty line of $24.36/day in 2017 PPP$). The percentages (63%, 56%, and 46%) represent the average food expenditure shares in the second quintile of consumers in low-income and lower-middle-income countries, and the first quintile of consumers in upper-middle-and-high-income countries, according to household surveys compiled by the World Bank. Countries' income classifications follow the calendar year of 2021 standard (fiscal year of 2023 of the World Bank), which is the base year of the latest ICP cycle. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "93" }, { "id": "CoCA_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of an energy sufficient diet in PPP dollars" }, { "id": "Longdefinition", "value": "Cost of the least expensive starchy staple for energy balance, in 2021 PPP$/person/day, for a representative person within energy balance at 2330 kcal/day. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); and Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoCA_unafford_n", "metatype": [ { "id": "IndicatorName", "value": "Number of people unable to afford an energy sufficient diet" }, { "id": "Longdefinition", "value": "The indicator estimates the total number of people who cannot afford an energy sufficient diet in a given country and year. The indicator is computed by multiplying the percentage of the population in a country unable to afford an energy sufficient diet by population data from the World Population Prospect by the United Nations Department of Economic and Social Affairs (UN DESA). A value of zero indicates a null or a small number rounded down at the current precision level. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "93" }, { "id": "CoHD_asf_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of animal source foods in local currency unit" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available animal source foods to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in LCU/person/day. Animal-source foods are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_asf_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of animal source foods in PPP dollars" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available animal source foods to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in 2021 PPP$/person/day. Animal-source foods are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_asf_prop", "metatype": [ { "id": "IndicatorName", "value": "Cost share for animal-sourced foods in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Share of costs for the least expensive animal-source foods to meet daily recommendations in food-based dietary guidelines (FBDG), as a proportion of the total cost of a healthy diet. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_asf_ss", "metatype": [ { "id": "IndicatorName", "value": "Cost of animal-sourced foods relative to the starchy staples in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Cost of the least expensive animal-source foods as a multiple of the least expensive starchy staples to meet daily recommendations in food-based dietary guidelines (FBDG). Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_CoCA", "metatype": [ { "id": "IndicatorName", "value": "Cost of a healthy diet relative to the cost of sufficient energy from starchy staples" }, { "id": "Longdefinition", "value": "Ratio between the cost of a healthy diet (CoHD) that meets requirements for energy and food-based dietary guidelines (FBDG) and the cost of caloric adequacy (CoCA) that uses only starchy staples to meet energy requirements. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); and Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_f_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of fruits in local currency unit" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available fruits to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in LCU/person/day. Fruits are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_f_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of fruits in PPP dollars" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available fruits to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in 2021 PPP$/person/day. Fruits are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_f_prop", "metatype": [ { "id": "IndicatorName", "value": "Cost share for fruits in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Share of costs for the least expensive fruits to meet daily recommendations in food-based dietary guidelines (FBDG), as a proportion of the total cost of a healthy diet. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_f_ss", "metatype": [ { "id": "IndicatorName", "value": "Cost of fruits relative to the starchy staples in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Cost of the least expensive fruits as a multiple of the least expensive starchy staples to meet daily recommendations in food-based dietary guidelines (FBDG). Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_fexp", "metatype": [ { "id": "IndicatorName", "value": "Affordability of a healthy diet: ratio of cost to food expenditures" }, { "id": "Longdefinition", "value": "The ratio of the cost of a healthy diet to total food expenditure per capita per day from national accounts. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_headcount", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of unaffordability of a healthy diet" }, { "id": "Longdefinition", "value": "The indicator estimates the percentage of individuals in a population whose disposable income, net of the amount needed to acquire basic non-food goods and services, is lower than the average cost of the least-expensive healthy diet in a country. The expenditures for basic non-food needs are calculated as average non-food expenditure shares of low-income consumers multiplied by international poverty lines set by the World Bank. The non-food expenditure share is 37% and 44% in low-income and lower-middle-income countries for the second quintile of consumers, and 54% in upper-middle-and-high-income countries for the first quintile of consumers, according to recent household surveys for 71 countries compiled by the World Bank. The international poverty lines are $2.15/day for low-income countries, $3.65/day for lower-middle-income countries, $6.85/day for upper-middle-income countries, and $24.36/day for high-income countries, in 2017PPP$. Countries' income classifications follow the calendar year of 2021 standard (fiscal year of 2023 of the World Bank), which is the base year of the latest ICP cycle. Income data are provided by the World Bank’s Poverty and Inequality Platform. A value of zero indicates a null or a small number rounded down at the current precision level. Data available from 2017 to 2024.\n\nNational income distribution data are provided by the World Bank’s Poverty and Inequality Platform and are based on 2017 purchasing power parities (PPPs), updated in fall 2024. Latest data of India are also featured." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); Bai, Y., Conti, V., Herforth, A., Cafiero, C., Ebel, A., Rissanen, M.O., Rosero Moncayo, J. & Masters, W.A. 2024. Methods for monitoring the cost of a healthy diet based on price data from the International Comparison Program. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3037en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "93" }, { "id": "CoHD_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of a healthy diet in local currency unit" }, { "id": "Longdefinition", "value": "The cost of a healthy diet indicator is the cost of purchasing the least expensive locally available foods to meet requirements for energy and food-based dietary guidelines (FBDG) for a representative person within energy balance at 2330 kcal/day. The cost of a healthy diet is in local currency unit. Data are available for the period from 2017 to 2024.\n\nFor Bahamas, Guyana, Haiti, Iran, Myanmar, Sint Maarten (Dutch Part), and Turks and Caicos Islands, retail food prices were not available from the ICP 2021. As a result, the CoHD for these countries is calculated using data from the ICP 2017." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); and Bai, Y., Conti, V., Herforth, A., Cafiero, C., Ebel, A., Rissanen, M.O., Rosero Moncayo, J. & Masters, W.A. 2024. Methods for monitoring the cost of a healthy diet based on price data from the International Comparison Program. FAO Statistics Division Working Paper. Rome, FAO. (forthcoming)" } ], "source_id": "93" }, { "id": "CoHD_lns_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of legumes, nuts and seeds in local currency unit" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available legumes, nuts and seeds to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in LCU/person/day. Legumes, nuts and seeds are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_lns_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of legumes, nuts and seeds in PPP dollars" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available legumes, nuts and seeds to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in 2021 PPP$/person/day. Legumes, nuts and seeds are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_lns_prop", "metatype": [ { "id": "IndicatorName", "value": "Cost share for legumes, nuts and seeds in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Share of costs for the least expensive legumes, nuts or seeds to meet daily recommendations in food-based dietary guidelines (FBDG), as a proportion of the total cost of a healthy diet. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_lns_ss", "metatype": [ { "id": "IndicatorName", "value": "Cost of legumes, nuts and seeds relative to the starchy staples in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Cost of the least expensive legumes, nuts and seeds as a multiple of the least expensive starchy staples to meet daily recommendations in food-based dietary guidelines (FBDG). Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_of_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of fats and oils in local currency unit" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available fats or oils to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in LCU/person/day. Fats and oils are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_of_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of fats and oils in PPP dollars" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available fats or oils to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in 2021 PPP$/person/day. Fats and oils are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_of_prop", "metatype": [ { "id": "IndicatorName", "value": "Cost share for oils and fats in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Share of costs for the least expensive oils or fats to meet daily recommendations in food-based dietary guidelines (FBDG), as a proportion of the total cost of a healthy diet. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_of_ss", "metatype": [ { "id": "IndicatorName", "value": "Cost of oils and fats relative to the starchy staples in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Cost of the least expensive oils and fats as a multiple of the least expensive starchy staples to meet daily recommendations in food-based dietary guidelines (FBDG). Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_pov", "metatype": [ { "id": "IndicatorName", "value": "Affordability of a healthy diet: ratio of cost to the food poverty line" }, { "id": "Longdefinition", "value": "The ratio of the cost of a healthy diet to the food poverty lines, defined as $1.35/day for low-income countries (63% of the international poverty line of $2.15/day in 2017 PPP$), $2.04/day for lower-middle-income countries (56% of the international poverty line of $3.65/day in 2017 PPP$), $3.15/day for upper-middle-income countries (46% of the international poverty line of $6.85/day in 2017 PPP$), and $11.2/day (46% of the international poverty line of $24.36/day in 2017 PPP$). The percentages (63%, 56%, and 46%) represent the average food expenditure shares in the second quintile of consumers in low-income and lower-middle-income countries, and the first quintile of consumers in upper-middle-and-high-income countries, according to household surveys compiled by the World Bank. Countries' income classifications follow the calendar year of 2021 standard (fiscal year of 2023 of the World Bank), which is the base year of the latest ICP cycle. Data available from 2017 to 2024." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); Bai, Y., Conti, V., Herforth, A., Cafiero, C., Ebel, A., Rissanen, M.O., Rosero Moncayo, J. & Masters, W.A. 2024. Methods for monitoring the cost of a healthy diet based on price data from the International Comparison Program. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3037en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "93" }, { "id": "CoHD_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of a healthy diet in PPP dollars" }, { "id": "Longdefinition", "value": "The cost of a healthy diet indicator is the cost of purchasing the least expensive locally available foods to meet requirements for energy and food-based dietary guidelines (FBDG) for a representative person within energy balance at 2330 kcal/day. The cost of a healthy diet is converted to international dollars using purchasing power parity (PPP). Data are available for the period from 2017 and 2024. PPP conversion factors are imputed by the FAO for years not available in the WDI database. PPPs are imputed in one or multiple years for the following countries: Angola, Argentina, Aruba, Bermuda, British Virgin Islands, Cayman Islands, Curaçao, Democratic Republic of the Congo, Dominica, Eswatini, Kazakhstan, Lebanon, Liberia, Myanmar, Seychelles, Sint Maarten (Dutch part), Suriname, Tajikistan and Zimbabwe.\n\nFor Bahamas, Guyana, Haiti, Iran, Myanmar, Sint Maarten (Dutch Part), and Turks and Caicos Islands, retail food prices were not available from the ICP 2021. As a result, the CoHD for these countries is calculated using data from the ICP 2017." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); and Bai, Y., Conti, V., Herforth, A., Cafiero, C., Ebel, A., Rissanen, M.O., Rosero Moncayo, J. & Masters, W.A. 2024. Methods for monitoring the cost of a healthy diet based on price data from the International Comparison Program. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3037en)" } ], "source_id": "93" }, { "id": "CoHD_ss_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of starchy staples in local currency unit" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available starchy staples to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in LCU/person/day. Starchy staples are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_ss_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of starchy staples in PPP dollars" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available starchy staples to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in 2021 PPP$/person/day. Starchy staples are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_ss_prop", "metatype": [ { "id": "IndicatorName", "value": "Cost share for starchy staples in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Share of costs for the least expensive starchy staples to meet daily recommendations in food-based dietary guidelines (FBDG), as a proportion of the total cost of a healthy diet. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_unafford_n", "metatype": [ { "id": "IndicatorName", "value": "Number of people unable to afford a healthy diet" }, { "id": "Longdefinition", "value": "The indicator expresses the total number of people who cannot afford a healthy diet in a given country and year. The indicator is computed by multiplying the percentage of the population in a country unable to afford a healthy diet by population data from the World Population Prospect by the United Nations Department of Economic and Social Affairs (UN DESA). A value of zero indicates a null or a small number rounded down at the current precision level. Data available from 2017 to 2024." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); Bai, Y., Conti, V., Herforth, A., Cafiero, C., Ebel, A., Rissanen, M.O., Rosero Moncayo, J. & Masters, W.A. 2024. Methods for monitoring the cost of a healthy diet based on price data from the International Comparison Program. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3037en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "93" }, { "id": "CoHD_v_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of vegetables in local currency unit" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available vegetables to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in LCU/person/day. Vegetables are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_v_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of vegetables in PPP dollars" }, { "id": "Longdefinition", "value": "The cost of purchasing the least expensive locally available vegetables to meet daily intake levels recommended in food-based dietary guidelines (FBDG), in 2021 PPP$/person/day. Vegetables are one of the six food groups within the Healthy Diet Basket. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_v_prop", "metatype": [ { "id": "IndicatorName", "value": "Cost share for vegetables in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Share of costs for the least expensive vegetables to meet daily recommendations in food-based dietary guidelines (FBDG), as a proportion of the total cost of a healthy diet. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoHD_v_ss", "metatype": [ { "id": "IndicatorName", "value": "Cost of vegetables relative to the starchy staples in a least-cost healthy diet" }, { "id": "Longdefinition", "value": "Cost of the least expensive vegetables as a multiple of the least expensive starchy staples to meet daily recommendations in food-based dietary guidelines (FBDG). Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoNA_fexp", "metatype": [ { "id": "IndicatorName", "value": "Affordability of a nutrient adequate diet: ratio of cost to food expenditures" }, { "id": "Longdefinition", "value": "The ratio of the cost of a nutrient adequate diet to total food expenditure per capita per day from national accounts. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); and Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoNA_headcount", "metatype": [ { "id": "IndicatorName", "value": "Prevalence of unaffordability of a nutrient adequate diet" }, { "id": "Longdefinition", "value": "The indicator estimates the percentage of individuals in a population whose disposable income, net of the amount needed to acquire basic non-food goods and services, is lower than the average cost of the least-expensive nutrient adequate diet in a country. The expenditures for basic non-food needs are calculated as average non-food expenditure shares of low-income consumers multiplied by international poverty lines set by the World Bank. The non-food expenditure share is 37% and 44% in low-income and lower-middle-income countries for the second quintile of consumers, and 54% in upper-middle-and-high-income countries for the first quintile of consumers, according to household surveys compiled by the World Bank. The international poverty lines are $2.15/day for low-income countries, $3.65/day for lower-middle-income countries, $6.85/day for upper-middle-income countries, and $24.36/day for high-income countries, in 2017PPP$. Countries' income classifications follow the calendar year of 2021 (fiscal year of 2023 of the World Bank) as the base year of the latest ICP cycle. Income data are provided by the World Bank’s Poverty and Inequality Platform. A value of zero indicates a null or a small number rounded down at the current precision level. Data available for 2021.\n\nNational income distribution data are provided by the World Bank’s Poverty and Inequality Platform and are based on 2017 purchasing power parities (PPPs), updated in fall 2024. Latest data of India are also featured." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "93" }, { "id": "CoNA_LCU", "metatype": [ { "id": "IndicatorName", "value": "Cost of a nutrient adequate diet in local currency unit" }, { "id": "Longdefinition", "value": "Cost of the least expensive locally-available foods for nutrient adequacy, in LCU/person/day, for a representative person within upper and lower bounds for 23 essential macro- and micronutrients plus energy balance at 2330 kcal/day. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); and Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoNA_pov", "metatype": [ { "id": "IndicatorName", "value": "Affordability of a nutrient adequate diet: ratio of cost to the food poverty line" }, { "id": "Longdefinition", "value": "The ratio of the cost of a nutrient adequate diet to the food poverty lines, defined as $1.35/day for low-income countries (63% of the international poverty line of $2.15/day in 2017 PPP$), $2.04/day for lower-middle-income countries (56% of the international poverty line of $3.65/day in 2017 PPP$), $3.15/day for upper-middle-income countries (46% of the international poverty line of $6.85/day in 2017 PPP$), and $11.2/day (46% of the international poverty line of $24.36/day in 2017 PPP$). The percentages (63%, 56%, and 46%) represent the average food expenditure shares in the second quintile of consumers in low-income and lower-middle-income countries, and the first quintile of consumers in upper-middle-and-high-income countries, according to household surveys compiled by the World Bank. Countries' income classifications follow the calendar year of 2021 standard (fiscal year of 2023 of the World Bank), which is the base year of the latest ICP cycle. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "93" }, { "id": "CoNA_PPP", "metatype": [ { "id": "IndicatorName", "value": "Cost of a nutrient adequate diet in PPP dollars" }, { "id": "Longdefinition", "value": "Cost of the least expensive locally-available foods for nutrient adequacy, in 2021 PPP$/person/day, for a representative person within upper and lower bounds for 23 essential macro- and micronutrients plus energy balance at 2330 kcal/day. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); and Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en)" } ], "source_id": "93" }, { "id": "CoNA_unafford_n", "metatype": [ { "id": "IndicatorName", "value": "Number of people unable to afford a nutrient adequate diet" }, { "id": "Longdefinition", "value": "The indicator estimates the total number of people who cannot afford a nutrient adequate diet in a given country and year. The indicator is computed by multiplying the percentage of the population in a country unable to afford a nutrient adequate diet by population data from the World Population Prospect by the United Nations Department of Economic and Social Affairs (UN DESA). A value of zero indicates a null or a small number rounded down at the current precision level. Data available for 2021." }, { "id": "Source", "value": "Adapted from Herforth, A., Bai, Y., Venkat, A., Mahrt, K., Ebel, A. & Masters, W.A. 2020. Cost and affordability of healthy diets across and within countries. Background paper for The State of Food Security and Nutrition in the World 2020. FAO Agricultural Development Economics Technical Study No. 9. Rome, FAO. (https://doi.org/10.4060/cb2431en); Herforth, A., Venkat, A., Bai, Y., Costlow, L., Holleman, C. & Masters, W.A. 2022. Methods and options to monitor the cost and affordability of a healthy diet globally. Background paper for The State of Food Security and Nutrition in the World 2022. FAO Agricultural Development Economics Working Paper 22-03. Rome, FAO. (https://doi.org/10.4060/cc1169en); and Bai, Y., Herforth, A., Cafiero C., Conti V., Rissanen, M.O., Masters, W.A & Rosero Moncayo, J. 2024 Methods for monitoring the affordability of a healthy diet. FAO Statistics Division Working Paper. Rome, FAO. (https://doi.org/10.4060/cd3703en)" } ], "source_id": "93" }, { "id": "Pop", "metatype": [ { "id": "IndicatorName", "value": "Population" }, { "id": "Longdefinition", "value": "Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates." }, { "id": "Source", "value": "Population data are sounced from the United Nations Department of Economic and Social Affairs (UN DESA) 2024 revision of the World Population Prospects." } ], "source_id": "93" } ]